diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/!!LINK!! Download Archexteriors Vol 18 Torrent 33.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/!!LINK!! Download Archexteriors Vol 18 Torrent 33.md
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-<h1>Download Archexteriors Vol 18 Torrent 33: A Guide for 3D Artists</h1>
-<p>If you are a 3D artist who is looking for some high-quality and realistic architectural templates for your projects, you might be interested in downloading Archexteriors Vol 18 torrent 33. This is a collection of ten fully modeled and textured 3D exteriors with complete lighting and three cameras setups for every scene, created by Evermotion, a leading company in the field of 3D modeling and rendering. In this article, we will show you what Archexteriors Vol 18 is, what are its features and benefits, how to download it using torrent, how to use it in your 3D projects, and some FAQs that you might have. Let's get started!</p>
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- <h2>What is Archexteriors Vol 18?</h2>
-<p>Archexteriors Vol 18 is a collection of architectural templates that consists of ten fully modeled and textured 3D exteriors with complete lighting and three cameras setups for every scene. It is part of the Archexteriors series by Evermotion, which offers various collections of outdoor environments for different purposes and styles. You can find more information about Archexteriors Vol 18 on its official website or on Trinity3D, where you can also purchase it for $60.</p>
- <h2>What are the features and benefits of Archexteriors Vol 18?</h2>
-<p>Archexteriors Vol 18 has many features and benefits that make it a great choice for any 3D artist who wants to create stunning outdoor scenes. Here are some of them:</p>
-<ul>
-<li><b>Ten fully modeled and textured 3D exteriors with complete lighting and three cameras setups for every scene.</b> This means that you don't have to worry about modeling, texturing, lighting, or setting up cameras for your scenes. You just need to put your building model in the scene and click "render". You can also adjust the lighting and camera settings according to your preferences.</li>
-<li><b>Scenes perfect for villas, houses, and small and medium buildings, mostly with natural surroundings.</b> This means that you can use Archexteriors Vol 18 for various types of projects, whether you want to create a cozy villa in the countryside, a modern house in the suburbs, or a small office building in the city. The scenes have natural surroundings such as trees, grass, flowers, rocks, water, etc., which add realism and beauty to your scenes.</li>
-<li><b>Prepared for V-Ray 2.0 with 3ds Max 2010.</b> This means that you can use Archexteriors Vol 18 with one of the most popular rendering engines and software in the industry. V-Ray is known for its realistic and fast rendering, and 3ds Max is a powerful and versatile 3D software that allows you to model, animate, and edit your scenes. You can also use Archexteriors Vol 18 with other software and engines, but you might need to convert the files or tweak the settings.</li>
-<li><b>Vegetation in the scenes is a V-Ray proxy and only usable with V-Ray and 3ds Max.</b> This means that the trees, grass, flowers, and other plants in the scenes are not actual geometry, but proxies that are loaded from external files during rendering. This reduces the memory usage and improves the performance of your scenes. However, this also means that you can only use the vegetation with V-Ray and 3ds Max, and not with other software or engines.</li>
-</ul>
- <h2>How to download Archexteriors Vol 18 torrent 33?</h2>
-<p>If you want to download Archexteriors Vol 18 torrent 33, you need to follow these steps:</p>
-<ol>
-<li><b>Find a reliable torrent website that offers Archexteriors Vol 18 torrent 33.</b> There are many torrent websites on the internet, but not all of them are trustworthy or safe. Some of them might have fake or malicious files, or expose you to legal risks. Therefore, you need to do some research and find a reputable torrent website that has Archexteriors Vol 18 torrent 33 available. Some examples of popular torrent websites are The Pirate Bay, RARBG, and 1337x. However, we do not endorse or recommend any of these websites, and you should use them at your own risk.</li>
-<li><b>Download a torrent client that can handle Archexteriors Vol 18 torrent 33.</b> A torrent client is a software that allows you to download and upload files using the BitTorrent protocol. You need a torrent client to download Archexteriors Vol 18 torrent 33 from the torrent website. There are many torrent clients to choose from, but some of the most popular ones are uTorrent, BitTorrent, and qBittorrent. Again, we do not endorse or recommend any of these software, and you should use them at your own discretion.</li>
-<li><b>Open the torrent file or magnet link of Archexteriors Vol 18 torrent 33 with your torrent client.</b> Once you have found a reliable torrent website and downloaded a torrent client, you can proceed to download Archexteriors Vol 18 torrent 33. You can either download the torrent file, which is a small file that contains information about the files you want to download, or use the magnet link, which is a URL that does the same thing without requiring a file. You can then open the torrent file or magnet link with your torrent client, and it will start downloading Archexteriors Vol 18 torrent 33 to your computer.</li>
-<li><b>Wait for the download to finish and verify the files.</b> Depending on the size of Archexteriors Vol 18 torrent 33, your internet speed, and the number of seeders and leechers (people who have or want the files), the download might take some time. You can check the progress and status of your download on your torrent client. Once the download is complete, you should verify the files to make sure they are not corrupted or infected. You can use a file manager or an antivirus software to do this.</li>
-<li><b>Extract the files and install Archexteriors Vol 18 on your computer.</b> After verifying the files, you need to extract them from the compressed folder they are in. You can use a software like WinRAR or 7-Zip to do this. Then, you need to install Archexteriors Vol 18 on your computer by following the instructions provided by Evermotion. You might need to enter a license key or activate the product online.</li>
-</ol>
- <h2>How to use Archexteriors Vol 18 in your 3D projects?</h2>
-<p>Now that you have downloaded and installed Archexteriors Vol 18 on your computer, you can start using it in your 3D projects. Here are some steps on how to do that:</p>
-<p></p>
-<ol>
-<li><b>Open your 3D software and import one of the scenes from Archexteriors Vol 18.</b> You can use any 3D software that supports V-Ray and 3ds Max files, such as Blender, Maya, or SketchUp. However, for optimal results, we recommend using V-Ray with 3ds Max. To import one of the scenes from Archexteriors Vol 18, you need to go to File > Import > Merge and select one of the . max files from the Archexteriors Vol 18 folder. You will see a list of objects and materials that you can merge into your scene. You can select all of them or only the ones you need.</li>
-<li><b>Adjust the scale, position, and orientation of the scene to fit your project.</b> Depending on the size and dimensions of your project, you might need to adjust the scale, position, and orientation of the scene from Archexteriors Vol 18. You can use the tools and commands in your 3D software to do this. For example, in 3ds Max, you can use the Scale, Move, and Rotate tools, or the Transform Type-In dialog box.</li>
-<li><b>Replace the placeholder building model with your own building model.</b> The scenes from Archexteriors Vol 18 come with a placeholder building model that you can replace with your own building model. To do this, you need to delete or hide the placeholder model, and import or merge your own model into the scene. You can then adjust the scale, position, and orientation of your model to match the scene. You can also apply materials and textures to your model if needed.</li>
-<li><b>Customize the lighting and camera settings of the scene according to your preferences.</b> The scenes from Archexteriors Vol 18 come with complete lighting and three cameras setups for every scene. However, you can customize them according to your preferences. You can change the intensity, color, direction, and type of the lights, or add new lights if needed. You can also change the focal length, aperture, exposure, and angle of the cameras, or add new cameras if needed. You can use the tools and commands in your 3D software to do this. For example, in 3ds Max, you can use the Light Lister and Camera Lister dialogs.</li>
-<li><b>Render the scene and save the image file.</b> Once you are satisfied with your scene, you can render it using V-Ray or any other rendering engine that supports V-Ray materials and proxies. You can adjust the render settings according to your desired quality and speed. You can then save the image file in any format that you want. You can use the tools and commands in your 3D software to do this. For example, in 3ds Max, you can use the Render Setup dialog and the Save Image dialog.</li>
-</ol>
- <h2>How to optimize the performance and quality of Archexteriors Vol 18 scenes?</h2>
-<p>Archexteriors Vol 18 scenes are designed to be realistic and detailed, but they can also be demanding on your computer resources and rendering time. Therefore, you might want to optimize them for better performance and quality. Here are some tips and tricks on how to do that:</p>
-<ul>
-<li><b>Use proxies for vegetation and other complex objects.</b> As mentioned before, the vegetation in Archexteriors Vol 18 scenes is a V-Ray proxy that reduces memory usage and improves performance. You can also use proxies for other complex objects that are not essential for your scene, such as cars, furniture, or people. You can create proxies using V-Ray or other software that supports proxy creation. For example, in 3ds Max, you can use the V-Ray Proxy Exporter tool.</li>
-<li><b>Use instancing for repeated objects.</b> Instancing is a technique that allows you to create multiple copies of an object without increasing memory usage or rendering time. You can use instancing for repeated objects that are identical or similar in your scene, such as trees, grass blades, or bricks. You can create instances using V-Ray or other software that supports instancing. For example, in 3ds Max, you can use the Instance tool or the V-Ray Scatter tool.</li>
-<li><b>Use low-poly models for distant objects.</b> Low-poly models are models that have fewer polygons and less detail than high-poly models. You can use low-poly models for distant objects that are not visible or important for your scene, such as buildings or mountains in the background. This will reduce memory usage and rendering time without affecting quality. You can create low-poly models using V-Ray or other software that supports polygon reduction. For example , in 3ds Max, you can use the ProOptimizer modifier or the V-Ray Mesh Exporter tool.</li>
-<li><b>Use adaptive subdivision for displacement maps.</b> Displacement maps are textures that add depth and detail to the surface of an object by displacing its geometry. However, they can also increase memory usage and rendering time if they are applied to the whole object uniformly. You can use adaptive subdivision for displacement maps, which means that the object is subdivided only where the displacement map requires more detail. This will reduce memory usage and rendering time without affecting quality. You can use adaptive subdivision for displacement maps using V-Ray or other software that supports it. For example, in 3ds Max, you can use the V-Ray Displacement modifier or the V-Ray Material.</li>
-<li><b>Use render elements for post-processing.</b> Render elements are separate images that contain information about different aspects of your scene, such as lighting, shadows, reflections, or colors. You can use render elements for post-processing, which means that you can edit and adjust your scene after rendering without having to re-render it. This will save you time and allow you to fine-tune your scene to your liking. You can use render elements for post-processing using V-Ray or other software that supports it. For example, in 3ds Max, you can use the Render Elements dialog or the V-Ray Frame Buffer.</li>
-</ul>
- <h2>How to create stunning projects with Archexteriors Vol 18?</h2>
-<p>Archexteriors Vol 18 is a great tool for creating stunning projects with realistic and detailed outdoor scenes. However, you can also enhance your projects with some creativity and imagination. Here are some examples of projects made with Archexteriors Vol 18 that might inspire you:</p>
-<ul>
-<li><b>A cozy villa in the countryside.</b> This project uses scene 1 from Archexteriors Vol 18, which features a villa surrounded by trees and flowers. The project adds some personal touches to the scene, such as a swimming pool, a barbecue grill, and some outdoor furniture. The project also uses warm and soft colors for the lighting and materials, creating a cozy and inviting atmosphere.</li>
-<li><b>A modern house in the suburbs.</b> This project uses scene 5 from Archexteriors Vol 18, which features a house with a minimalist design and a geometric shape. The project modifies the scene by changing the color and texture of the house, adding some windows and doors, and placing some cars and bikes in the driveway. The project also uses cool and bright colors for the lighting and materials, creating a modern and sleek look.</li>
-<li><b>A small office building in the city.</b> This project uses scene 8 from Archexteriors Vol 18, which features a building with a brick facade and a metal roof. The project transforms the scene by adding some signs and logos to the building, placing some people and plants on the balcony, and inserting some skyscrapers in the background. The project also uses neutral and realistic colors for the lighting and materials, creating a professional and urban feel.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Archexteriors Vol 18 is a collection of architectural templates that consists of ten fully modeled and textured 3D exteriors with complete lighting and three cameras setups for every scene. It is a great choice for any 3D artist who wants to create stunning outdoor scenes for villas, houses, and small and medium buildings, mostly with natural surroundings. It is prepared for V-Ray 2.0 with 3ds Max 2010, but it can also be used with other software and engines. It can be downloaded using torrent from various websites, but you need to be careful about the reliability and safety of these websites. It can be used in your 3D projects by importing, customizing, and rendering the scenes in your 3D software. It can also be optimized for better performance and quality by using proxies, instancing, low-poly models, adaptive subdivision, and render elements. It can also be enhanced with some creativity and imagination by adding your own building models, changing the lighting and camera settings, or modifying the materials and textures.</p>
- <h2>FAQs</h2>
-<p>Here are some FAQs that you might have about Archexteriors Vol 18:</p>
-<ol>
-<li><b>Is Archexteriors Vol 18 compatible with other versions of V-Ray or 3ds Max?</b> Yes, Archexteriors Vol 18 is compatible with other versions of V-Ray or 3ds Max, but you might need to convert or tweak some files or settings to make them work properly.</li>
-<li><b>Can I use Archexteriors Vol 18 for commercial purposes?</b> Yes, you can use Archexteriors Vol 18 for commercial purposes as long as you have purchased a license from Evermotion or Trinity 3D, and you have followed their terms and conditions. You can find more information about the license agreement on their websites .</li>
-<li><b>Can I modify or edit Archexteriors Vol 18 scenes?</b> Yes, you can modify or edit Archexteriors Vol 18 scenes as much as you want, as long as you do not resell or redistribute them. You can change the lighting, camera, material, texture, or geometry of the scenes, or add your own objects or models to them.</li>
-<li><b>Can I use Archexteriors Vol 18 with other Archexteriors collections?</b> Yes, you can use Archexteriors Vol 18 with other Archexteriors collections, as long as they are compatible with V-Ray and 3ds Max. You can mix and match different scenes from different collections, or use elements from one collection in another scene. However, you might need to adjust the scale, position, orientation, lighting, camera, material, texture, or geometry of the scenes or elements to make them fit together.</li>
-<li><b>Can I get support or help for Archexteriors Vol 18?</b> Yes, you can get support or help for Archexteriors Vol 18 from Evermotion or Trinity3D, depending on where you purchased it from. You can contact them via email, phone, or online chat. You can also find some tutorials, tips, and FAQs on their websites  or on their YouTube channels .</li>
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- <p>The third step is to install the APK file and extract the OBB file to the Android/obb folder on your device. To do this, you need to locate the downloaded APK file on your device using a file manager app and tap on it to start the installation process. Follow the instructions on the screen to complete the installation. Then, you need to locate the downloaded OBB file on your device using a file manager app and extract it using a zip extractor app. You will get a folder named com.bus.simulator2023. Copy this folder and paste it into the Android/obb folder on your device.</p>
- <h3>Step 4: Launch the game and enjoy</h3>
- <p>The final step is to launch the game and enjoy it. To do this, you need to go to your app drawer and tap on the Bus Simulator 2023 icon to start the game. You will see a loading screen and then a main menu with different options. You can choose your preferred mode, bus, map and settings and start driving your bus in realistic environments.</p>
- <h2>How to Download and Install Bus Simulator 2023 on PC Windows</h2>
- <p>If you want to play Bus Simulator 2023 on your PC Windows, you need to follow these steps:</p>
- <h3>Step 1: Download and install an Android emulator on your PC</h3>
- <p>The first step is to download and install an Android emulator on your PC. An Android emulator is a software that allows you to run Android apps and games on your PC Windows. There are many Android emulators available for PC Windows, such as BlueStacks[^3 ^), NoxPlayer, MEmu, LDPlayer 9, etc. You can choose any emulator that suits your PC specifications and preferences. You can download and install an emulator from its official website or from a trusted source. Follow the instructions on the screen to complete the installation.</p>
- <h3>Step 2: Download the APK and OBB files from a trusted source</h3>
- <p>The second step is to download the APK and OBB files of Bus Simulator 2023 from a trusted source. You can use the same link as mentioned above for Android devices, or you can search for another source that offers these files for free. Make sure that the source is reliable and safe, and that the files are compatible with your emulator.</p>
- <h3>Step 3: Install the APK file and copy the OBB file to the emulator's Android/obb folder</h3>
- <p>The third step is to install the APK file and copy the OBB file to the emulator's Android/obb folder on your PC. To do this, you need to open your emulator and locate the downloaded APK file using the built-in file manager or browser. Tap on it to start the installation process. Follow the instructions on the screen to complete the installation. Then, you need to locate the downloaded OBB file on your PC using a file manager or browser. Extract it using a zip extractor app. You will get a folder named com.bus.simulator2023. Copy this folder and paste it into the emulator's Android/obb folder on your PC.</p>
- <h3>Step 4: Launch the game and enjoy</h3>
- <p>The final step is to launch the game and enjoy it. To do this, you need to go to your emulator's app drawer and tap on the Bus Simulator 2023 icon to start the game. You will see a loading screen and then a main menu with different options. You can choose your preferred mode, bus, map and settings and start driving your bus in realistic environments.</p>
- <h2>Bus Simulator 2023 Review: Pros and Cons</h2>
- <p>Now that you know how to download and install Bus Simulator 2023 APK + OBB on your devices, you might be wondering how good is the game itself. Well, like any other game, Bus Simulator 2023 has its pros and cons, which we will discuss below.</p>
- <h3>Pros</h3>
- <p>Some of the pros of Bus Simulator 2023 are:</p>
- <ul>
-<li><strong>Realistic graphics and physics:</strong> The game boasts of stunning graphics that make you feel like you are driving a real bus in a real city. The game also uses a 1:1 physics engine that simulates realistic bus behavior, such as braking, steering, acceleration, etc.</li>
-<li><strong>Variety of buses and maps:</strong> The game offers a wide range of buses that you can drive, such as diesel, hybrid, electric, articulated, coach and school buses. Each bus has its own characteristics, such as speed, fuel consumption, capacity, etc. You can also drive your bus in various maps from around the world, such as United States of America (San Francisco and Texas), South America (Buenos Aires), Europe (Germany, Spain, Prague, St. Petersburg), Dubai, Shanghai and more.</li>
-<li><strong>Career, Free-ride and Multiplayer modes:</strong> The game lets you play in different modes depending on your preference. In career mode, you have to complete all the routes assigned to you by your bus company. In free-ride mode, you can drive your bus anywhere you want without any restrictions. In multiplayer mode, you can join or create online sessions with other players and cooperate or compete with them.</li>
-<li><strong>Customizable buses and interiors:</strong> The game lets you customize your bus with paint, accessories, body parts, air conditioning, flags, decals and many more. You can also change the interior design of your bus with different seats, steering wheels, dashboards, etc.</li>
-<li><strong>Intelligent traffic system and passengers:</strong> The game features an intelligent traffic system that creates realistic traffic situations, such as traffic jams, accidents, roadworks, etc. You also have to interact with your passengers, who have different personalities, moods and requests. You have to follow the traffic rules, respect the speed limits, use the indicators, etc. You also have to deal with different weather conditions, such as rain, snow, fog, etc.</li>
-</ul>
- <h3>Cons</h3>
- <p>Some of the cons of Bus Simulator 2023 are:</p>
- <ul>
-<li><strong>Buggy AI behavior and glitches:</strong> The game suffers from some bugs and glitches that affect the gameplay and immersion. For example, some AI vehicles may drive erratically or crash into you or other objects. Some passengers may get stuck in the bus or disappear. Some textures may not load properly or flicker.</li>
-<li><strong>Ugly and overwhelming UI:</strong> The game has a very cluttered and unappealing user interface that makes it hard to navigate and access the options. The game also has too many pop-ups and notifications that distract you from the driving experience.</li>
-<li><strong>An acquired taste for some gamers:</strong> The game is not for everyone, as it requires a lot of patience, concentration and skill to drive a bus in realistic scenarios. Some gamers may find the game boring, repetitive or frustrating, especially if they are looking for a more action-packed or casual game.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Bus Simulator 2023 is a bus driving game that offers a realistic and immersive experience of driving a bus in various locations around the world. The game has many features, such as realistic graphics and physics, variety of buses and maps, career, free-ride and multiplayer modes, customizable buses and interiors, intelligent traffic system and passengers, etc. However, the game also has some drawbacks, such as buggy AI behavior and glitches, ugly and overwhelming UI, and an acquired taste for some gamers. Overall, Bus Simulator 2023 is a game that can appeal to bus enthusiasts and simulation fans, but may not be suitable for everyone.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Bus Simulator 2023:</p>
- <ol>
-<li><strong>Is Bus Simulator 2023 free to play?</strong></li>
-<p>Yes, Bus Simulator 2023 is free to play on Android devices. However, the game may contain ads and in-app purchases that can enhance your gameplay or unlock more features.</p>
-<li><strong>Is Bus Simulator 2023 available on iOS devices?</strong></li>
-<p>No, Bus Simulator 2023 is not available on iOS devices at the moment. The game is only compatible with Android devices and PC Windows.</p>
-<li><strong>How can I play Bus Simulator 2023 with a controller?</strong></li>
-<p>You can play Bus Simulator 2023 with a controller by connecting your controller to your device via Bluetooth or USB. You can also use an emulator on your PC Windows to play the game with a controller.</p>
-<li><strong>How can I update Bus Simulator 2023 to the latest version?</strong></li>
-<p>You can update Bus Simulator 2023 to the latest version by downloading and installing the latest APK file from a trusted source. You can also check for updates from within the game or from the Google Play Store.</p>
-<li><strong>How can I contact the developers of Bus Simulator 2023?</strong></li>
-<p>You can contact the developers of Bus Simulator 2023 by sending them an email at support@bussimulator2023.com or by visiting their official website at www.bussimulator2023.com.</p>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Candy Crush Saga APK The Most Downloaded Game on Google Play.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Candy Crush Saga APK The Most Downloaded Game on Google Play.md
deleted file mode 100644
index aa5b342f8110b099f59d956b65b7d746cb7cc0e4..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Candy Crush Saga APK The Most Downloaded Game on Google Play.md	
+++ /dev/null
@@ -1,117 +0,0 @@
-
-<h1>Candy Crush Saga APK: How to Download and Play the Sweetest Game Ever</h1>
- <p>If you are looking for a fun and addictive game that will keep you entertained for hours, you might want to try Candy Crush Saga. This game is one of the most popular and successful puzzle games ever created, with millions of players around the world. But what if you want to play it on your Android device without using Google Play Store? In this article, we will show you how to download and install Candy Crush Saga APK, as well as how to play it and enjoy its features.</p>
- <h2>What is Candy Crush Saga?</h2>
- <p>Candy Crush Saga is a game that splendidly tackles the match-3 genre. It was developed by King, a leading mobile game developer, and released in 2012. Since then, it has become a global phenomenon, with over a billion downloads and hundreds of levels to complete.</p>
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- <h3>The gameplay of Candy Crush Saga</h3>
- <p>The gameplay of Candy Crush Saga is simple but challenging. You have to match three or more candies of the same color in a row or column to clear them from the board. You can also create special candies by matching four or more candies in different shapes, such as striped, wrapped, or color bomb candies. These special candies can help you clear more candies and score more points.</p>
- <p>Each level has a different objective and a limited number of moves or time. You have to achieve the objective before running out of moves or time, or else you will lose a life. You can also earn stars based on your score, which can unlock new episodes and features. Some levels also have obstacles, such as chocolate, jelly, licorice, or blockers, that make the game more difficult.</p>
- <h3>The features of Candy Crush Saga</h3>
- <p>Candy Crush Saga has many features that make it an enjoyable and rewarding game. Some of these features are:</p>
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- <ul>
-<li>Thousands of levels to play, with new ones added every week.</li>
-<li>Various game modes, such as target score, jelly, ingredients, timed, order, mixed mode, and more.</li>
-<li>Daily rewards, such as free spins, boosters, lives, and gold bars.</li>
-<li>Social features, such as connecting with Facebook friends, sending and receiving lives and gifts, competing on leaderboards, and joining teams.</li>
-<li>Fun events and challenges, such as sugar drops, candy order, treasure hunt, sweet streaks, and more.</li>
-<li>Beautiful graphics and sound effects that create a sweet and colorful atmosphere.</li>
-</ul>
- <h2>Why download Candy Crush Saga APK?</h2>
- <p>Candy Crush Saga is available on Google Play Store for free, but there are some reasons why you might want to download its APK file instead. APK stands for Android Package Kit, which is a file format that contains all the elements needed to install an app on an Android device. By downloading an APK file, you can enjoy some benefits that are not possible with the official version.</p>
- <h3>The benefits of downloading Candy Crush Saga APK</h3>
- <p>Some of the benefits of downloading Candy Crush Saga APK are:</p>
- <ul>
-<li>You can play the game on any Android device that supports it, even if it is not compatible with Google Play Store or does not have enough storage space.</li>
-<li>You can access the latest version of the game before it is released on Google Play Store, which means you can enjoy new levels and features sooner than others.</li>
-<li>You can modify the game according to your preferences, such as unlocking all levels, getting unlimited lives, gold bars, boosters, or removing ads.</li>
-</ul>
- <h3>The risks of downloading Candy Crush Saga APK</h3>
- <p>However, downloading Candy Crush Saga APK also comes with some risks that you should be aware of. Some of these risks are:</p>
- <ul>
-<li>You might download a fake or malicious APK file that can harm your device or steal your personal information.</li>
-<li>You might violate the terms and conditions of the game and get banned from playing it or accessing its online features.</li>
-<li>You might lose your progress or data if the APK file is not compatible with your device or the official version of the game.</li>
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- <p>Therefore, you should always download Candy Crush Saga APK from a trusted and reputable source, and scan it with an antivirus software before installing it. You should also backup your data and progress before using the APK file, and use it at your own risk.</p>
- <h2>How to download and install Candy Crush Saga APK?</h2>
- <p>If you have decided to download and install Candy Crush Saga APK, you will need to follow some simple steps. Here is a guide on how to do it:</p>
- <h3>Step 1: Find a reliable source for the APK file</h3>
- <p>The first step is to find a website that offers the APK file for Candy Crush Saga. You can search for it on Google or use a dedicated APK website, such as APKPure, APKMirror, or Uptodown. Make sure that the website is safe and secure, and that the APK file is updated and verified. You can also check the reviews and ratings of the APK file from other users.</p>
- <h3>Step 2: Enable unknown sources on your device</h3>
- <p>The next step is to enable unknown sources on your device, which will allow you to install apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You might also need to grant permission to your browser or file manager to install apps from unknown sources.</p>
- <h3>Step 3: Download and install the APK file</h3>
- <p>The final step is to download and install the APK file on your device. To do this, go to the website where you found the APK file and tap on the download button. Once the download is complete, open the file and tap on install. Wait for the installation process to finish and then launch the game.</p>
- <h2>How to play Candy Crush Saga APK?</h2>
- <p>Playing Candy Crush Saga APK is similar to playing the official version of the game. You can log in with your Facebook account or play as a guest. You can also sync your progress and data with the official version if you have it installed on your device. However, you might encounter some issues or errors while playing the game, such as crashing, freezing, or lagging. If this happens, you can try clearing the cache and data of the game, updating the game, or reinstalling it.</p>
- <h3>Tips and tricks for playing Candy Crush Saga APK</h3>
- <p>If you want to master Candy Crush Saga APK and complete all the levels, you might need some tips and tricks to help you out. Here are some of them:</p>
- <ul>
-<li>Plan your moves ahead and look for possible matches before making a move.</li>
-<li>Try to create as many special candies as possible and combine them for more effects.</li>
-<li>Use boosters wisely and save them for difficult levels.</li>
-<li>Pay attention to the objective and the board layout of each level.</li>
-<li>Take advantage of the daily rewards and events to get more lives, gold bars, boosters, and other goodies.</li>
-<li>Join a team or invite your friends to play with you and get more support and fun.</li>
-</ul>
- <h3>How to update Candy Crush Saga APK</h3>
- <p>To keep playing Candy Crush Saga APK without any problems, you should always update it to the latest version. To do this, you can either check for updates on the website where you downloaded the APK file, or use an app updater tool, such as ApkUpdater or ApkTrack. These tools will notify you when there is a new version available and let you download and install it easily.</p>
- <h2>Conclusion</h2>
- <p>Candy Crush Saga is a game that will make you fall in love with its sweet and colorful world. It is a game that will challenge your mind and skills with its thousands of levels and modes. It is a game that will connect you with millions of other players who share your passion for candy crushing. And it is a game that you can play on your Android device without using Google Play Store by downloading its APK file.</p>
- <p>In this article, we have shown you what Candy Crush Saga is, why you might want to download its APK file, how to download and install it, how to play it, and some tips and tricks to help you succeed. We hope that this article has been helpful and informative for you, and that you will enjoy playing Candy Crush Saga APK on your device.</p>
- <p>Before we end this article, here are some frequently asked questions that you might have about Candy Crush Saga APK:</p>
- <h4>FAQs</h4>
- <ol>
-<li>Is Candy Crush Saga APK safe to download and install?</li>
-<p>Yes, Candy Crush Saga APK is safe to download and install, as long as you get it from a reliable and reputable source. You should also scan the APK file with an antivirus software before installing it, and backup your data and progress before using it.</p>
-<li>Is Candy Crush Saga APK free to play?</li>
-<p>Yes, Candy Crush Saga APK is free to play, but it also offers in-app purchases that can enhance your gaming experience. You can buy extra lives, gold bars, boosters, or other items with real money. However, you can also play the game without spending any money, as there are many ways to get free rewards and bonuses.</p>
-<li>How can I contact the developer of Candy Crush Saga APK?</li>
-<p>If you have any questions, feedback, or issues regarding Candy Crush Saga APK, you can contact the developer of the game by visiting their website, https://king.com/, or by sending an email to candycrush.techhelp@king.com. You can also follow them on their social media accounts, such as Facebook, Twitter, Instagram, or YouTube.</p>
-<li>Can I play Candy Crush Saga APK offline?</li>
-<p>Yes, you can play Candy Crush Saga APK offline, but you will not be able to access some of its online features, such as connecting with Facebook friends, joining teams, competing on leaderboards, or participating in events and challenges. You will also need an internet connection to update the game or sync your progress and data with the official version.</p>
-<li>Can I play Candy Crush Saga APK on other devices?</li>
-<p>Yes, you can play Candy Crush Saga APK on other devices that support Android operating system, such as tablets or smart TVs. However, you might need to adjust the settings or resolution of the game to fit your device's screen size and performance.</p>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download FIFA 16 Ultimate Team Mod APK and Experience the Most Realistic Football Ever.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download FIFA 16 Ultimate Team Mod APK and Experience the Most Realistic Football Ever.md
deleted file mode 100644
index ee443c610de4e61d9732cb71466a7adcaadd0b88..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download FIFA 16 Ultimate Team Mod APK and Experience the Most Realistic Football Ever.md	
+++ /dev/null
@@ -1,139 +0,0 @@
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-<li>Select the file and tap on the extract option.</li>
-<li>Choose a destination folder where you want to extract the file. You can create a new folder or use an existing one.</li>
-<li>Wait for the extraction process to complete.</li>
-</ol>
-<p>After extracting the file, you will get a folder named "com.ea.gp.fifaworld". This folder contains the data of the game.</p>
- <h4>Step 3: Install the apk file</h4>
-<p>The third step is to install the FIFA 16 Ultimate Team mod apk file on your device. To do this, follow these steps:</p>
-<ol>
-<li>Open the file manager app and locate the FIFA 16 Ultimate Team mod apk file that you downloaded.</li>
-<li>Select the file and tap on it to start the installation process.</li>
-<li>You may get a warning message that says "This type of file can harm your device". Ignore it and tap on "OK".</li>
-<li>You may also get a prompt that says "For your security, your phone is not allowed to install unknown apps from this source". Tap on "Settings" and enable the option "Allow from this source".</li>
-<li>Go back to the installation screen and tap on "Install".</li>
-<li>Wait for the installation process to complete.</li>
-</ol>
-<p>After installing the apk file, you will see an icon of FIFA 16 Ultimate Team on your device's home screen or app drawer.</p>
- <h4>Step 4: Move the obb file</h4>
-<p>The fourth step is to move the FIFA 16 Ultimate Team obb folder that you extracted to the right location on your device. To do this, follow these steps:</p>
-<ol>
-<li>Open the file manager app and locate the FIFA 16 Ultimate Team obb folder that you extracted. It should be named "com.ea.gp.fifaworld".</li>
-<li>Select the folder and tap on the cut or move option.</li>
-<li>Navigate to the following path on your device: Internal Storage > Android > obb. If you don't see an obb folder, create one.</li>
-<li>Paste or move the FIFA 16 Ultimate Team obb folder in the obb folder.</li>
-</ol>
-<p>This step is important because it will allow the game to access its data and run properly.</p>
- <h4>Step 5: Launch the game and enjoy</h4>
-<p>The final step is to launch FIFA 16 Ultimate Team mod apk on your device and enjoy playing with unlimited coins, points, players, items, packs, etc. To do this, follow these steps:</p>
-<ol>
-<li>Tap on the FIFA 16 Ultimate Team icon on your device's home screen or app drawer.</li>
-<li>You may get a message that says "Download failed because you may not have purchased this app". Ignore it and tap on "OK".</li>
-<li>The game will start loading and verifying its data. Wait for it to finish.</li>
-<li>You may also get a message that says "You need an internet connection for first time verification". Make sure you have a stable internet connection and tap on "Retry".</li>
-<li>The game will launch and ask you to choose your language and accept some terms and conditions. Do so accordingly.</li>
-<li>You will then see the main menu of FIFA 16 Ultimate Team mod apk. You can choose any mode or option you want and start playing.</li>
-</ol>
- <h2>Conclusion</h 2>Conclusion</h2>
-<p>In this article, we have shown you how to download and install FIFA 16 Ultimate Team mod apk on your Android device. This mod apk will give you unlimited coins, points, players, items, packs, etc. and unlock all the features and modes of the game. You can enjoy playing with your favorite players and teams and compete in various leagues, tournaments, and events. FIFA 16 Ultimate Team mod apk is one of the best soccer games for Android devices and you should definitely try it out.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about FIFA 16 Ultimate Team mod apk:</p>
-<ul>
-<li>Q: Is FIFA 16 Ultimate Team mod apk safe to use?</li>
-<li>A: Yes, FIFA 16 Ultimate Team mod apk is safe to use as long as you download it from a reliable source and follow the steps given in this article. However, you should always be careful when installing apps from unknown sources and scan them for viruses or malware before installing them.</li>
-<li>Q: Will FIFA 16 Ultimate Team mod apk work on any Android device?</li>
-<li>A: FIFA 16 Ultimate Team mod apk should work on most Android devices that have version 4.4 or higher and at least 1.5 GB of free storage space. However, some devices may not be compatible or may experience some issues due to different specifications or settings. If you encounter any problems, you can try changing some options in the game settings or contact the developer for support.</li>
-<li>Q: Do I need to root my device to use FIFA 16 Ultimate Team mod apk?</li>
-<li>A: No, you do not need to root your device to use FIFA 16 Ultimate Team mod apk. You just need to enable the permission to install apps from unknown sources in your device settings.</li>
-<li>Q: Can I play FIFA 16 Ultimate Team mod apk online with other players?</li>
-<li>A: Yes, you can play FIFA 16 Ultimate Team mod apk online with other players as long as you have a stable internet connection. However, you may face some issues or bans if the game detects that you are using a modded version. To avoid this, you can use a VPN app or play offline mode.</li>
-<li>Q: Can I update FIFA 16 Ultimate Team mod apk to the latest version?</li>
-<li>A: Yes, you can update FIFA 16 Ultimate Team mod apk to the latest version by downloading and installing the new mod apk file from the same source. However, you may lose your progress or data if you do not backup your game before updating. To backup your game, you can use a cloud service or a backup app.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy the New Features of Lokicraft 1.18 0 APK on Your Android Device.md b/spaces/1phancelerku/anime-remove-background/Enjoy the New Features of Lokicraft 1.18 0 APK on Your Android Device.md
deleted file mode 100644
index df6528a9c13653add8026bba930e4c358cabc6c5..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Enjoy the New Features of Lokicraft 1.18 0 APK on Your Android Device.md	
+++ /dev/null
@@ -1,125 +0,0 @@
-
-<h1>Lokicraft 1.18 0 APK: Everything You Need to Know</h1>
-<p>If you are a fan of sandbox games, you might have heard of Lokicraft, a game inspired by the popular Minecraft. In this article, we will tell you everything you need to know about Lokicraft 1.18 0 APK, the latest version of the game that has some exciting new features and improvements. We will also show you how to download and install it on your Android device, and why you should give it a try.</p>
-<h2>lokicraft 1.18 0 apk</h2><br /><p><b><b>Download File</b> &#10003;&#10003;&#10003; <a href="https://jinyurl.com/2uNTZS">https://jinyurl.com/2uNTZS</a></b></p><br /><br />
- <h2>What is Lokicraft?</h2>
-<p>Lokicraft is a sandbox game that allows you to create your own world using various blocks and materials. You can explore, build, craft, and survive in different environments, such as forests, deserts, mountains, and caves. You can also play with other players online or offline, and share your creations with them.</p>
- <h3>A sandbox game inspired by Minecraft</h3>
-<p>Lokicraft is clearly influenced by Minecraft, one of the most popular and successful games of all time. The graphics, gameplay, and mechanics of Lokicraft are very similar to those of Minecraft, but with some differences and variations. For example, Lokicraft has more types of blocks and items than Minecraft, and some of them have unique functions and effects. Lokicraft also has more animals and creatures than Minecraft, some of which are friendly and some of which are hostile.</p>
- <h3>Features and gameplay of Lokicraft</h3>
-<p>Lokicraft has two main modes: creative mode and survival mode. In creative mode, you have unlimited resources and can build anything you want without any restrictions or dangers. In survival mode, you have to gather resources, craft tools and weapons, and protect yourself from enemies and environmental hazards. You also have to manage your hunger and health levels.</p>
-<p>Lokicraft has many features that make it fun and engaging to play. Some of them are:</p>
-<ul>
-<li>Over 100 types of blocks and items to use in your constructions</li>
-<li>Over 50 types of animals and creatures to encounter in your adventures</li>
-<li>Multiple biomes and terrains to explore and customize</li>
-<li>Day and night cycle and weather effects</li>
-<li>Multiplayer mode to play with other players online or offline</li>
-<li>World editor to create your own maps and scenarios</li>
-<li>Achievements and leaderboards to track your progress and compete with others</li>
-</ul>
- <h2>What is new in Lokicraft 1.18 0 APK?</h2>
-<p>Lokicraft 1.18 0 APK is the latest version of the game that was released in June 2023. It has some new features and improvements that make it more enjoyable and immersive than ever. Here are some of them:</p>
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- <h3>New biomes and blocks</h3>
-<p>Lokicraft 1.18 0 APK introduces two new biomes: the swamp biome and the snow biome. The swamp biome is a wetland area with water, mud, grass, vines, mushrooms, frogs, snakes, crocodiles, and other swampy creatures. The snow biome is a frozen area with snow, ice, pine trees, polar bears, penguins, snowmen, and other snowy creatures.</p>
-<p>Lokicraft 1.18 0 APK also adds some new blocks and items that are related to these biomes. For example, you can find mud blocks, vine blocks, mushroom blocks, crocodile eggs, snow blocks, ice blocks, pine cones, and snowballs in these biomes. You can use them to craft new items and decorations, such as mushroom soup, vine ladders, mud bricks, ice sculptures, snow forts, and snowballs.</p>
- <h3>Improved graphics and performance</h3>
-<p>Lokicraft 1.18 0 APK also improves the graphics and performance of the game. The game now has more realistic lighting and shadows, smoother animations, and higher resolution textures. The game also runs faster and smoother on most devices, and has less bugs and glitches.</p>
- <h3>How to download and install Lokicraft 1.18 0 APK</h3>
-<p>If you want to download and install Lokicraft 1.18 0 APK on your Android device, you can follow these simple steps:</p>
-<ol>
-<li>Go to the official website of Lokicraft or any trusted third-party source that provides the APK file.</li>
-<li>Download the APK file to your device.</li>
-<li>Enable the installation of apps from unknown sources in your device settings.</li>
-<li>Locate the APK file in your device storage and tap on it to install it.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
-<p>Note: You may need to uninstall the previous version of Lokicraft before installing the new one.</p>
- <h2>Why should you play Lokicraft 1.18 0 APK?</h2>
-<p>Lokicraft 1.18 0 APK is a great game for anyone who loves sandbox games, creativity, and adventure. Here are some reasons why you should play it:</p>
- <h3>Pros and cons of Lokicraft</h3>
-<p>Lokicraft has many pros and cons that make it different from other sandbox games. Some of the pros are:</p>
-<ul>
-<li>It is free to play and does not require any registration or subscription.</li>
-<li>It has a lot of content and variety to keep you entertained for hours.</li>
-<li>It has a simple and intuitive interface and controls that are easy to learn and use.</li>
-<li>It has a friendly and supportive community of players and developers.</li>
-</ul>
-<p>Some of the cons are:</p>
-<ul>
-<li>It is not an official or licensed product of Minecraft or Mojang Studios.</li>
-<li>It may have some compatibility issues with some devices or operating systems.</li>
-<li>It may have some bugs or errors that need to be fixed or updated.</li>
-<li>It may have some ads or in-app purchases that may affect your gaming experience.</li>
-</ul>
- <h3>Comparison with Minecraft</h3>
-<p>Lokicraft is often compared with Minecraft, as they are both sandbox games that share many similarities. However, they also have some differences that make them unique and appealing to different types of players. Here are some of the main differences between Lokicraft and Minecraft:</p>
- | Feature | Lokicraft | Minecraft | | --- | --- | --- | | Graphics | Pixelated and colorful | Pixelated and realistic | | Blocks | More types and functions | Less types and functions | | Items | More types and variations | Less types and variations | | Animals | More types and behaviors | Less types and behaviors | | Biomes | More types and diversity | Less types and diversity | | Modes | Creative mode and survival mode | Creative mode, survival mode, adventure mode, hardcore mode, spectator mode | | Multiplayer | Online mode and offline mode | Online mode only | | World editor | Available for all players | Available for PC players only | | Achievements | Available for all players | Available for PC players only | | Price | Free to play | Paid to play | <h3>Tips and tricks for playing Lokicraft</h3>
-<p>If you want to have more fun and success in playing Lokicraft, you can follow these tips and tricks:</p>
-<ul>
-<li>Use the world editor to create your own maps and scenarios. You can customize the terrain, the weather, the time, the difficulty, the enemies, the items, and more. You can also share your maps with other players or download their maps.</li>
-<li>Use the multiplayer mode to play with other players online or offline. You can join or create servers, chat with other players, cooperate or compete with them, trade items with them, or just have fun with them.</li>
-<li>Use the achievements to track your progress and challenge yourself. You can earn achievements by completing certain tasks or goals in the game. You can also compare your achievements with other players or try to unlock them all.</li>
-<li>Use the resources wisely in survival mode. You have to gather resources, craft tools and weapons, and protect yourself from enemies and environmental hazards. You also have to manage your hunger [user](# and health levels. You can find resources in different biomes, such as wood, stone, coal, iron, gold, diamond, and more. You can also farm crops, breed animals, and fish for food. You can craft tools and weapons using a crafting table, and craft armor and other items using a furnace. You can also use a chest to store your items.</li>
-<li>Use the creative mode to unleash your imagination and creativity. You have unlimited resources and can build anything you want without any restrictions or dangers. You can also fly around the map and see your creations from different angles. You can use different blocks and items to create buildings, sculptures, machines, art, and more.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Lokicraft 1.18 0 APK is a sandbox game that lets you create your own world using blocks and materials. It is inspired by Minecraft, but has its own features and gameplay that make it unique and fun. It has two main modes: creative mode and survival mode. It also has a world editor, a multiplayer mode, and achievements. It has new biomes and blocks, improved graphics and performance, and is free to play.</p>
- <h3>Summary of the main points</h3>
-<p>To summarize, here are the main points of this article:</p>
-<ul>
-<li>Lokicraft is a sandbox game that allows you to create your own world using various blocks and materials.</li>
-<li>Lokicraft is influenced by Minecraft, but has more types of blocks, items, animals, and biomes than Minecraft.</li>
-<li>Lokicraft has two main modes: creative mode and survival mode. In creative mode, you have unlimited resources and can build anything you want. In survival mode, you have to gather resources, craft tools and weapons, and protect yourself from enemies and environmental hazards.</li>
-<li>Lokicraft 1.18 0 APK is the latest version of the game that has some new features and improvements. It introduces two new biomes: the swamp biome and the snow biome. It also improves the graphics and performance of the game.</li>
-<li>Lokicraft 1.18 0 APK is free to play and easy to download and install on your Android device.</li>
-<li>Lokicraft is a fun and engaging game for anyone who loves sandbox games, creativity, and adventure.</li>
-</ul>
- <h3>Call to action for the readers</h3>
-<p>If you are interested in playing Lokicraft 1.18 0 APK, you can download it from the official website of Lokicraft or any trusted third-party source that provides the APK file. You can also follow Lokicraft on social media platforms to get the latest news and updates about the game. You can also share your feedback and suggestions with the developers and other players.</p>
-<p>So what are you waiting for? Download Lokicraft 1.18 0 APK today and start creating your own world!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Lokicraft 1.18 0 APK:</p>
- <h4>Q: Is Lokicraft 1.18 0 APK safe to download?</h4>
-<p>A: Yes, Lokicraft 1.18 0 APK is safe to download as long as you get it from a reliable source that does not contain any viruses or malware. However, you should always be careful when downloading any APK file from unknown sources, as they may harm your device or compromise your privacy.</p>
- <h4>Q: Is Lokicraft 1.18 0 APK compatible with my device?</h4>
-<p>A: Lokicraft 1.18 0 APK is compatible with most Android devices that have Android 4.4 or higher versions installed. However, some devices may have some compatibility issues or performance problems depending on their specifications or operating systems.</p>
- <h4>Q: How can I update Lokicraft 1.18 0 APK?</h4>
-<p>A: You can update Lokicraft 1.18 0 APK by downloading the latest version of the game from the official website of Lokicraft or any trusted third-party source that provides the APK file. You may need to uninstall the previous version of Lokicraft before installing the new one.</p>
- <h4>Q: How can I contact the developers of Lokicraft?</h4>
-<p>A: You can contact the developers of Lokicraft by sending them an email at lokicraft@gmail.com or by visiting their website at www.lokicraft.com. You can also follow them on Facebook, Twitter, Instagram, YouTube, or TikTok to get the latest news and updates about the game.</p>
- <h4>Q: How can I support the developers of Lokicraft?</h4>
-<p>A: You can support the developers of Lokicraft by making a donation to them via PayPal or by purchasing some of their in-app products or services. You can also support them by rating and reviewing their game on the Google Play Store or any other platform where you downloaded it. You can also share their game with your friends and family and invite them to play with you.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/docs/install.md b/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/docs/install.md
deleted file mode 100644
index 6314a40441285e9236438e468caf8b71a407531a..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/docs/install.md
+++ /dev/null
@@ -1,51 +0,0 @@
-## v1.8.0 
-### Linux and Windows  
-```shell
-# CUDA 11.0
-pip --default-timeout=100 install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 10.2
-pip --default-timeout=100 install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0
-
-# CPU only
-pip --default-timeout=100 install torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
-
-```
-
-
-## v1.7.1  
-### Linux and Windows  
-```shell
-# CUDA 11.0
-pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 10.2
-pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
-
-# CUDA 10.1
-pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 9.2
-pip install torch==1.7.1+cu92 torchvision==0.8.2+cu92 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CPU only
-pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-
-## v1.6.0  
-
-### Linux and Windows
-```shell
-# CUDA 10.2
-pip install torch==1.6.0 torchvision==0.7.0
-
-# CUDA 10.1
-pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CUDA 9.2
-pip install torch==1.6.0+cu92 torchvision==0.7.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
-
-# CPU only
-pip install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
-```
\ No newline at end of file
diff --git a/spaces/4Taps/SadTalker/src/face3d/util/preprocess.py b/spaces/4Taps/SadTalker/src/face3d/util/preprocess.py
deleted file mode 100644
index b77a3a4058c208e5ba8cb1cfbb563954a5f7a3e2..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/face3d/util/preprocess.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""This script contains the image preprocessing code for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-from scipy.io import loadmat
-from PIL import Image
-import cv2
-import os
-from skimage import transform as trans
-import torch
-import warnings
-warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning) 
-warnings.filterwarnings("ignore", category=FutureWarning) 
-
-
-# calculating least square problem for image alignment
-def POS(xp, x):
-    npts = xp.shape[1]
-
-    A = np.zeros([2*npts, 8])
-
-    A[0:2*npts-1:2, 0:3] = x.transpose()
-    A[0:2*npts-1:2, 3] = 1
-
-    A[1:2*npts:2, 4:7] = x.transpose()
-    A[1:2*npts:2, 7] = 1
-
-    b = np.reshape(xp.transpose(), [2*npts, 1])
-
-    k, _, _, _ = np.linalg.lstsq(A, b)
-
-    R1 = k[0:3]
-    R2 = k[4:7]
-    sTx = k[3]
-    sTy = k[7]
-    s = (np.linalg.norm(R1) + np.linalg.norm(R2))/2
-    t = np.stack([sTx, sTy], axis=0)
-
-    return t, s
-    
-# resize and crop images for face reconstruction
-def resize_n_crop_img(img, lm, t, s, target_size=224., mask=None):
-    w0, h0 = img.size
-    w = (w0*s).astype(np.int32)
-    h = (h0*s).astype(np.int32)
-    left = (w/2 - target_size/2 + float((t[0] - w0/2)*s)).astype(np.int32)
-    right = left + target_size
-    up = (h/2 - target_size/2 + float((h0/2 - t[1])*s)).astype(np.int32)
-    below = up + target_size
-
-    img = img.resize((w, h), resample=Image.BICUBIC)
-    img = img.crop((left, up, right, below))
-
-    if mask is not None:
-        mask = mask.resize((w, h), resample=Image.BICUBIC)
-        mask = mask.crop((left, up, right, below))
-
-    lm = np.stack([lm[:, 0] - t[0] + w0/2, lm[:, 1] -
-                  t[1] + h0/2], axis=1)*s
-    lm = lm - np.reshape(
-            np.array([(w/2 - target_size/2), (h/2-target_size/2)]), [1, 2])
-
-    return img, lm, mask
-
-# utils for face reconstruction
-def extract_5p(lm):
-    lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1
-    lm5p = np.stack([lm[lm_idx[0], :], np.mean(lm[lm_idx[[1, 2]], :], 0), np.mean(
-        lm[lm_idx[[3, 4]], :], 0), lm[lm_idx[5], :], lm[lm_idx[6], :]], axis=0)
-    lm5p = lm5p[[1, 2, 0, 3, 4], :]
-    return lm5p
-
-# utils for face reconstruction
-def align_img(img, lm, lm3D, mask=None, target_size=224., rescale_factor=102.):
-    """
-    Return:
-        transparams        --numpy.array  (raw_W, raw_H, scale, tx, ty)
-        img_new            --PIL.Image  (target_size, target_size, 3)
-        lm_new             --numpy.array  (68, 2), y direction is opposite to v direction
-        mask_new           --PIL.Image  (target_size, target_size)
-    
-    Parameters:
-        img                --PIL.Image  (raw_H, raw_W, 3)
-        lm                 --numpy.array  (68, 2), y direction is opposite to v direction
-        lm3D               --numpy.array  (5, 3)
-        mask               --PIL.Image  (raw_H, raw_W, 3)
-    """
-
-    w0, h0 = img.size
-    if lm.shape[0] != 5:
-        lm5p = extract_5p(lm)
-    else:
-        lm5p = lm
-
-    # calculate translation and scale factors using 5 facial landmarks and standard landmarks of a 3D face
-    t, s = POS(lm5p.transpose(), lm3D.transpose())
-    s = rescale_factor/s
-
-    # processing the image
-    img_new, lm_new, mask_new = resize_n_crop_img(img, lm, t, s, target_size=target_size, mask=mask)
-    trans_params = np.array([w0, h0, s, t[0], t[1]])
-
-    return trans_params, img_new, lm_new, mask_new
diff --git a/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/dataset_VQ.py b/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/dataset_VQ.py
deleted file mode 100644
index 2342de946f2cbdf64729a5145168df1bdda54fa0..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/VQ-Trans/dataset/dataset_VQ.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import torch
-from torch.utils import data
-import numpy as np
-from os.path import join as pjoin
-import random
-import codecs as cs
-from tqdm import tqdm
-
-
-
-class VQMotionDataset(data.Dataset):
-    def __init__(self, dataset_name, window_size = 64, unit_length = 4):
-        self.window_size = window_size
-        self.unit_length = unit_length
-        self.dataset_name = dataset_name
-
-        if dataset_name == 't2m':
-            self.data_root = './dataset/HumanML3D'
-            self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
-            self.text_dir = pjoin(self.data_root, 'texts')
-            self.joints_num = 22
-            self.max_motion_length = 196
-            self.meta_dir = 'checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta'
-
-        elif dataset_name == 'kit':
-            self.data_root = './dataset/KIT-ML'
-            self.motion_dir = pjoin(self.data_root, 'new_joint_vecs')
-            self.text_dir = pjoin(self.data_root, 'texts')
-            self.joints_num = 21
-
-            self.max_motion_length = 196
-            self.meta_dir = 'checkpoints/kit/VQVAEV3_CB1024_CMT_H1024_NRES3/meta'
-        
-        joints_num = self.joints_num
-
-        mean = np.load(pjoin(self.meta_dir, 'mean.npy'))
-        std = np.load(pjoin(self.meta_dir, 'std.npy'))
-
-        split_file = pjoin(self.data_root, 'train.txt')
-
-        self.data = []
-        self.lengths = []
-        id_list = []
-        with cs.open(split_file, 'r') as f:
-            for line in f.readlines():
-                id_list.append(line.strip())
-
-        for name in tqdm(id_list):
-            try:
-                motion = np.load(pjoin(self.motion_dir, name + '.npy'))
-                if motion.shape[0] < self.window_size:
-                    continue
-                self.lengths.append(motion.shape[0] - self.window_size)
-                self.data.append(motion)
-            except:
-                # Some motion may not exist in KIT dataset
-                pass
-
-            
-        self.mean = mean
-        self.std = std
-        print("Total number of motions {}".format(len(self.data)))
-
-    def inv_transform(self, data):
-        return data * self.std + self.mean
-    
-    def compute_sampling_prob(self) : 
-        
-        prob = np.array(self.lengths, dtype=np.float32)
-        prob /= np.sum(prob)
-        return prob
-    
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, item):
-        motion = self.data[item]
-        
-        idx = random.randint(0, len(motion) - self.window_size)
-
-        motion = motion[idx:idx+self.window_size]
-        "Z Normalization"
-        motion = (motion - self.mean) / self.std
-
-        return motion
-
-def DATALoader(dataset_name,
-               batch_size,
-               num_workers = 8,
-               window_size = 64,
-               unit_length = 4):
-    
-    trainSet = VQMotionDataset(dataset_name, window_size=window_size, unit_length=unit_length)
-    prob = trainSet.compute_sampling_prob()
-    sampler = torch.utils.data.WeightedRandomSampler(prob, num_samples = len(trainSet) * 1000, replacement=True)
-    train_loader = torch.utils.data.DataLoader(trainSet,
-                                              batch_size,
-                                              shuffle=True,
-                                              #sampler=sampler,
-                                              num_workers=num_workers,
-                                              #collate_fn=collate_fn,
-                                              drop_last = True)
-    
-    return train_loader
-
-def cycle(iterable):
-    while True:
-        for x in iterable:
-            yield x
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/models/diffusion/classifier.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/models/diffusion/classifier.py
deleted file mode 100644
index 67e98b9d8ffb96a150b517497ace0a242d7163ef..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/models/diffusion/classifier.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import os
-import torch
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-from torch.nn import functional as F
-from torch.optim import AdamW
-from torch.optim.lr_scheduler import LambdaLR
-from copy import deepcopy
-from einops import rearrange
-from glob import glob
-from natsort import natsorted
-
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
-from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-
-__models__ = {
-    'class_label': EncoderUNetModel,
-    'segmentation': UNetModel
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class NoisyLatentImageClassifier(pl.LightningModule):
-
-    def __init__(self,
-                 diffusion_path,
-                 num_classes,
-                 ckpt_path=None,
-                 pool='attention',
-                 label_key=None,
-                 diffusion_ckpt_path=None,
-                 scheduler_config=None,
-                 weight_decay=1.e-2,
-                 log_steps=10,
-                 monitor='val/loss',
-                 *args,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.num_classes = num_classes
-        # get latest config of diffusion model
-        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
-        self.diffusion_config = OmegaConf.load(diffusion_config).model
-        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
-        self.load_diffusion()
-
-        self.monitor = monitor
-        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
-        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
-        self.log_steps = log_steps
-
-        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
-            else self.diffusion_model.cond_stage_key
-
-        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
-
-        if self.label_key not in __models__:
-            raise NotImplementedError()
-
-        self.load_classifier(ckpt_path, pool)
-
-        self.scheduler_config = scheduler_config
-        self.use_scheduler = self.scheduler_config is not None
-        self.weight_decay = weight_decay
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def load_diffusion(self):
-        model = instantiate_from_config(self.diffusion_config)
-        self.diffusion_model = model.eval()
-        self.diffusion_model.train = disabled_train
-        for param in self.diffusion_model.parameters():
-            param.requires_grad = False
-
-    def load_classifier(self, ckpt_path, pool):
-        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
-        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
-        model_config.out_channels = self.num_classes
-        if self.label_key == 'class_label':
-            model_config.pool = pool
-
-        self.model = __models__[self.label_key](**model_config)
-        if ckpt_path is not None:
-            print('#####################################################################')
-            print(f'load from ckpt "{ckpt_path}"')
-            print('#####################################################################')
-            self.init_from_ckpt(ckpt_path)
-
-    @torch.no_grad()
-    def get_x_noisy(self, x, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x))
-        continuous_sqrt_alpha_cumprod = None
-        if self.diffusion_model.use_continuous_noise:
-            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
-            # todo: make sure t+1 is correct here
-
-        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
-                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
-
-    def forward(self, x_noisy, t, *args, **kwargs):
-        return self.model(x_noisy, t)
-
-    @torch.no_grad()
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    @torch.no_grad()
-    def get_conditioning(self, batch, k=None):
-        if k is None:
-            k = self.label_key
-        assert k is not None, 'Needs to provide label key'
-
-        targets = batch[k].to(self.device)
-
-        if self.label_key == 'segmentation':
-            targets = rearrange(targets, 'b h w c -> b c h w')
-            for down in range(self.numd):
-                h, w = targets.shape[-2:]
-                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
-
-            # targets = rearrange(targets,'b c h w -> b h w c')
-
-        return targets
-
-    def compute_top_k(self, logits, labels, k, reduction="mean"):
-        _, top_ks = torch.topk(logits, k, dim=1)
-        if reduction == "mean":
-            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
-        elif reduction == "none":
-            return (top_ks == labels[:, None]).float().sum(dim=-1)
-
-    def on_train_epoch_start(self):
-        # save some memory
-        self.diffusion_model.model.to('cpu')
-
-    @torch.no_grad()
-    def write_logs(self, loss, logits, targets):
-        log_prefix = 'train' if self.training else 'val'
-        log = {}
-        log[f"{log_prefix}/loss"] = loss.mean()
-        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
-            logits, targets, k=1, reduction="mean"
-        )
-        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
-            logits, targets, k=5, reduction="mean"
-        )
-
-        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
-        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
-        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
-        lr = self.optimizers().param_groups[0]['lr']
-        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
-
-    def shared_step(self, batch, t=None):
-        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
-        targets = self.get_conditioning(batch)
-        if targets.dim() == 4:
-            targets = targets.argmax(dim=1)
-        if t is None:
-            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
-        else:
-            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
-        x_noisy = self.get_x_noisy(x, t)
-        logits = self(x_noisy, t)
-
-        loss = F.cross_entropy(logits, targets, reduction='none')
-
-        self.write_logs(loss.detach(), logits.detach(), targets.detach())
-
-        loss = loss.mean()
-        return loss, logits, x_noisy, targets
-
-    def training_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-        return loss
-
-    def reset_noise_accs(self):
-        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
-                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
-
-    def on_validation_start(self):
-        self.reset_noise_accs()
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-
-        for t in self.noisy_acc:
-            _, logits, _, targets = self.shared_step(batch, t)
-            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
-            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
-
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
-
-        if self.use_scheduler:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [optimizer], scheduler
-
-        return optimizer
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, *args, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.diffusion_model.first_stage_key)
-        log['inputs'] = x
-
-        y = self.get_conditioning(batch)
-
-        if self.label_key == 'class_label':
-            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-            log['labels'] = y
-
-        if ismap(y):
-            log['labels'] = self.diffusion_model.to_rgb(y)
-
-            for step in range(self.log_steps):
-                current_time = step * self.log_time_interval
-
-                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
-
-                log[f'inputs@t{current_time}'] = x_noisy
-
-                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
-                pred = rearrange(pred, 'b h w c -> b c h w')
-
-                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
-
-        for key in log:
-            log[key] = log[key][:N]
-
-        return log
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/CLAP/CLAPWrapper.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/CLAP/CLAPWrapper.py
deleted file mode 100644
index b26af847dcfdd314d10aa2c795362deac1e1fac7..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/CLAP/CLAPWrapper.py
+++ /dev/null
@@ -1,257 +0,0 @@
-import random
-import torchaudio
-from torch._six import string_classes
-import collections
-import re
-import torch.nn.functional as F
-import numpy as np
-from transformers import AutoTokenizer
-from ldm.modules.encoders.CLAP.utils import read_config_as_args
-from ldm.modules.encoders.CLAP.clap import CLAP
-import math
-import torchaudio.transforms as T
-import os
-import torch
-from importlib_resources import files
-
-
-class CLAPWrapper():
-    """
-    A class for interfacing CLAP model.  
-    """
-
-    def __init__(self, model_fp, device):
-        self.np_str_obj_array_pattern = re.compile(r'[SaUO]')
-        self.file_path = os.path.realpath(__file__)
-        self.default_collate_err_msg_format = (
-            "default_collate: batch must contain tensors, numpy arrays, numbers, "
-            "dicts or lists; found {}")
-        self.config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
-        self.model_fp = model_fp
-        self.device = device
-        self.clap, self.tokenizer, self.args = self.load_clap()
-
-    def load_clap(self):
-        r"""Load CLAP model with args from config file"""
-
-        args = read_config_as_args(self.config_as_str, is_config_str=True)
-
-        if 'bert' in args.text_model:
-            self.token_keys = ['input_ids', 'token_type_ids', 'attention_mask']
-        else:
-            self.token_keys = ['input_ids', 'attention_mask']
-
-        clap = CLAP(
-            audioenc_name=args.audioenc_name,
-            sample_rate=args.sampling_rate,
-            window_size=args.window_size,
-            hop_size=args.hop_size,
-            mel_bins=args.mel_bins,
-            fmin=args.fmin,
-            fmax=args.fmax,
-            classes_num=args.num_classes,
-            out_emb=args.out_emb,
-            text_model=args.text_model,
-            transformer_embed_dim=args.transformer_embed_dim,
-            d_proj=args.d_proj
-        )
-
-        # Load pretrained weights for model
-        model_state_dict = torch.load(self.model_fp, map_location=torch.device('cpu'))['model']
-        clap.load_state_dict(model_state_dict)
-
-        clap.eval()  # set clap in eval mode
-        tokenizer = AutoTokenizer.from_pretrained(args.text_model)
-
-        clap = clap.to(self.device)
-        tokenizer = tokenizer.to(self.device)
-
-        return clap, tokenizer, args
-
-    def default_collate(self, batch):
-        r"""Puts each data field into a tensor with outer dimension batch size"""
-        elem = batch[0]
-        elem_type = type(elem)
-        if isinstance(elem, torch.Tensor):
-            out = None
-            if torch.utils.data.get_worker_info() is not None:
-                # If we're in a background process, concatenate directly into a
-                # shared memory tensor to avoid an extra copy
-                numel = sum([x.numel() for x in batch])
-                storage = elem.storage()._new_shared(numel)
-                out = elem.new(storage)
-            return torch.stack(batch, 0, out=out)
-        elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
-                and elem_type.__name__ != 'string_':
-            if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
-                # array of string classes and object
-                if self.np_str_obj_array_pattern.search(elem.dtype.str) is not None:
-                    raise TypeError(
-                        self.default_collate_err_msg_format.format(elem.dtype))
-
-                return self.default_collate([torch.as_tensor(b) for b in batch])
-            elif elem.shape == ():  # scalars
-                return torch.as_tensor(batch)
-        elif isinstance(elem, float):
-            return torch.tensor(batch, dtype=torch.float64)
-        elif isinstance(elem, int):
-            return torch.tensor(batch)
-        elif isinstance(elem, string_classes):
-            return batch
-        elif isinstance(elem, collections.abc.Mapping):
-            return {key: self.default_collate([d[key] for d in batch]) for key in elem}
-        elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
-            return elem_type(*(self.default_collate(samples) for samples in zip(*batch)))
-        elif isinstance(elem, collections.abc.Sequence):
-            # check to make sure that the elements in batch have consistent size
-            it = iter(batch)
-            elem_size = len(next(it))
-            if not all(len(elem) == elem_size for elem in it):
-                raise RuntimeError(
-                    'each element in list of batch should be of equal size')
-            transposed = zip(*batch)
-            return [self.default_collate(samples) for samples in transposed]
-
-        raise TypeError(self.default_collate_err_msg_format.format(elem_type))
-
-    def load_audio_into_tensor(self, audio_path, audio_duration, resample=False):
-        r"""Loads audio file and returns raw audio."""
-        # Randomly sample a segment of audio_duration from the clip or pad to match duration
-        audio_time_series, sample_rate = torchaudio.load(audio_path)
-        resample_rate = self.args.sampling_rate
-        if resample:
-            resampler = T.Resample(sample_rate, resample_rate)
-            audio_time_series = resampler(audio_time_series)
-        audio_time_series = audio_time_series.reshape(-1)
-
-        # audio_time_series is shorter than predefined audio duration,
-        # so audio_time_series is extended
-        if audio_duration*sample_rate >= audio_time_series.shape[0]:
-            repeat_factor = int(np.ceil((audio_duration*sample_rate) /
-                                        audio_time_series.shape[0]))
-            # Repeat audio_time_series by repeat_factor to match audio_duration
-            audio_time_series = audio_time_series.repeat(repeat_factor)
-            # remove excess part of audio_time_series
-            audio_time_series = audio_time_series[0:audio_duration*sample_rate]
-        else:
-            # audio_time_series is longer than predefined audio duration,
-            # so audio_time_series is trimmed
-            start_index = random.randrange(
-                audio_time_series.shape[0] - audio_duration*sample_rate)
-            audio_time_series = audio_time_series[start_index:start_index +
-                                                  audio_duration*sample_rate]
-        return torch.FloatTensor(audio_time_series)
-
-    def preprocess_audio(self, audio_files, resample):
-        r"""Load list of audio files and return raw audio"""
-        audio_tensors = []
-        for audio_file in audio_files:
-            audio_tensor = self.load_audio_into_tensor(
-                audio_file, self.args.duration, resample)
-            audio_tensor = audio_tensor.reshape(1, -1).to(self.device)
-            audio_tensors.append(audio_tensor)
-        return self.default_collate(audio_tensors)
-
-    def preprocess_text(self, text_queries, text_len=100):
-        r"""Load list of class labels and return tokenized text"""
-        device = next(self.clap.parameters()).device
-        tokenized_texts = []
-        for ttext in text_queries:
-            tok = self.tokenizer.encode_plus(
-                text=ttext, add_special_tokens=True, max_length=text_len, pad_to_max_length=True, return_tensors="pt")
-            for key in self.token_keys:
-                tok[key] = tok[key].reshape(-1).to(device)
-            tokenized_texts.append(tok)
-        return self.default_collate(tokenized_texts)
-
-    def get_text_embeddings(self, class_labels):
-        r"""Load list of class labels and return text embeddings"""
-        preprocessed_text = self.preprocess_text(class_labels)
-        text_embeddings = self._get_text_embeddings(preprocessed_text)
-        text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True)
-        return text_embeddings
-
-    def get_audio_embeddings(self, audio_files, resample):
-        r"""Load list of audio files and return a audio embeddings"""
-        preprocessed_audio = self.preprocess_audio(audio_files, resample)
-        audio_embeddings = self._get_audio_embeddings(preprocessed_audio)
-        audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True)
-        return audio_embeddings
-
-    def _get_text_embeddings(self, preprocessed_text):
-        r"""Load preprocessed text and return text embeddings"""
-        with torch.no_grad():
-            text_embeddings = self.clap.caption_encoder(preprocessed_text)
-            text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True)
-            return text_embeddings
-
-    def _get_audio_embeddings(self, preprocessed_audio):
-        r"""Load preprocessed audio and return a audio embeddings"""
-        with torch.no_grad():
-            preprocessed_audio = preprocessed_audio.reshape(
-                preprocessed_audio.shape[0], preprocessed_audio.shape[2])
-            #Append [0] the audio emebdding, [1] has output class probabilities
-            audio_embeddings = self.clap.audio_encoder(preprocessed_audio)[0]
-            audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True)
-            return audio_embeddings
-    
-    def compute_similarity(self, audio_embeddings, text_embeddings):
-        r"""Compute similarity between text and audio embeddings"""
-        logit_scale = self.clap.logit_scale.exp()
-        similarity = logit_scale*text_embeddings @ audio_embeddings.T
-        return similarity.T
-
-    def _generic_batch_inference(self, func, *args):
-        r"""Process audio and/or text per batch"""
-        input_tmp = args[0]
-        batch_size = args[-1]
-        # args[0] has audio_files, args[1] has class_labels
-        inputs = [args[0], args[1]] if len(args) == 3 else [args[0]]
-        args0_len = len(args[0])
-        # compute text_embeddings once for all the audio_files batches
-        if len(inputs) == 2:
-            text_embeddings = self.get_text_embeddings(args[1])
-            inputs = [args[0], args[1], text_embeddings]
-        dataset_idx = 0
-        for _ in range(math.ceil(args0_len/batch_size)):
-            next_batch_idx = dataset_idx + batch_size
-            # batch size is bigger than available audio/text items
-            if next_batch_idx >= args0_len:
-                inputs[0] = input_tmp[dataset_idx:]
-                return func(*tuple(inputs))
-            else:
-                inputs[0] = input_tmp[dataset_idx:next_batch_idx]
-                yield func(*tuple(inputs))
-            dataset_idx = next_batch_idx
-
-    def get_audio_embeddings_per_batch(self, audio_files, batch_size):
-        r"""Load preprocessed audio and return a audio embeddings per batch"""
-        return self._generic_batch_inference(self.get_audio_embeddings, audio_files, batch_size)
-
-    def get_text_embeddings_per_batch(self, class_labels, batch_size):
-        r"""Load preprocessed text and return text embeddings per batch"""
-        return self._generic_batch_inference(self.get_text_embeddings, class_labels, batch_size)
-
-    def classify_audio_files_per_batch(self, audio_files, class_labels, batch_size):
-        r"""Compute classification probabilities for each audio recording in a batch and each class label"""
-        return self._generic_batch_inference(self.classify_audio_files, audio_files, class_labels, batch_size)
-
-if __name__ == '__main__':
-
-    # Load and initialize CLAP
-    weights_path = "/home1/huangrongjie/Project/Diffusion/LatentDiffusion/CLAP/CLAP_weights_2022.pth"
-    clap_model = CLAPWrapper(weights_path, use_cuda=False)
-
-    y = ["A woman talks nearby as water pours", "Multiple clanging and clanking sounds"]
-    x = ['/home2/huangjiawei/data/audiocaps/train/Yr1nicOVtvkQ.wav', '/home2/huangjiawei/data/audiocaps/train/YUDGBjjwyaqE.wav']
-
-    # Computing text embeddings
-    text_embeddings = clap_model.get_text_embeddings(y)
-
-    import ipdb
-    ipdb.set_trace()
-
-    # Computing audio embeddings
-    audio_embeddings = clap_model.get_audio_embeddings(x, resample=True)
-    similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)
-
diff --git a/spaces/AIKey/facetofacechat/style.css b/spaces/AIKey/facetofacechat/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/AIKey/facetofacechat/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/AIatUIUC/CodeLATS/generators/model.py b/spaces/AIatUIUC/CodeLATS/generators/model.py
deleted file mode 100644
index c2c6465b21c8a04847a8ab8535f33061516e7f9b..0000000000000000000000000000000000000000
--- a/spaces/AIatUIUC/CodeLATS/generators/model.py
+++ /dev/null
@@ -1,120 +0,0 @@
-from typing import List, Union, Optional, Literal
-import dataclasses
-
-from tenacity import (
-    retry,
-    stop_after_attempt,  # type: ignore
-    wait_random_exponential,  # type: ignore
-)
-import openai
-
-MessageRole = Literal["system", "user", "assistant"]
-
-
-@dataclasses.dataclass()
-class Message():
-    role: MessageRole
-    content: str
-
-
-def message_to_str(message: Message) -> str:
-    return f"{message.role}: {message.content}"
-
-
-def messages_to_str(messages: List[Message]) -> str:
-    return "\n".join([message_to_str(message) for message in messages])
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def gpt_completion(
-        model: str,
-        prompt: str,
-        max_tokens: int = 1024,
-        stop_strs: Optional[List[str]] = None,
-        temperature: float = 0.0,
-        num_comps=1,
-) -> Union[List[str], str]:
-    response = openai.Completion.create(
-        model=model,
-        prompt=prompt,
-        temperature=temperature,
-        max_tokens=max_tokens,
-        top_p=1,
-        frequency_penalty=0.0,
-        presence_penalty=0.0,
-        stop=stop_strs,
-        n=num_comps,
-    )
-    if num_comps == 1:
-        return response.choices[0].text  # type: ignore
-
-    return [choice.text for choice in response.choices]  # type: ignore
-
-
-@retry(wait=wait_random_exponential(min=1, max=180), stop=stop_after_attempt(6))
-def gpt_chat(
-    model: str,
-    messages: List,
-    max_tokens: int = 1024,
-    temperature: float = 0.0,
-    num_comps=1,
-) -> Union[List[str], str]:
-    try:
-        response = openai.ChatCompletion.create(
-            model=model,
-            messages=[dataclasses.asdict(message) for message in messages],
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=1,
-            frequency_penalty=0.0,
-            presence_penalty=0.0,
-            n=num_comps,
-        )
-        if num_comps == 1:
-            return response.choices[0].message.content  # type: ignore
-        return [choice.message.content for choice in response.choices]  # type: ignore
-
-    except Exception as e:
-        print(f"An error occurred while calling OpenAI: {e}")
-        raise
-
-class ModelBase():
-    def __init__(self, name: str):
-        self.name = name
-        self.is_chat = False
-
-    def __repr__(self) -> str:
-        return f'{self.name}'
-
-    def generate_chat(self, messages: List[Message], max_tokens: int = 1024, temperature: float = 0.2, num_comps: int = 1) -> Union[List[str], str]:
-        raise NotImplementedError
-
-    def generate(self, prompt: str, max_tokens: int = 1024, stop_strs: Optional[List[str]] = None, temperature: float = 0.0, num_comps=1) -> Union[List[str], str]:
-        raise NotImplementedError
-
-
-class GPTChat(ModelBase):
-    def __init__(self, model_name: str):
-        self.name = model_name
-        self.is_chat = True
-
-    def generate_chat(self, messages: List[Message], max_tokens: int = 1024, temperature: float = 0.2, num_comps: int = 1) -> Union[List[str], str]:
-        return gpt_chat(self.name, messages, max_tokens, temperature, num_comps)
-
-
-class GPT4(GPTChat):
-    def __init__(self):
-        super().__init__("gpt-4")
-
-
-class GPT35(GPTChat):
-    def __init__(self):
-        super().__init__("gpt-3.5-turbo")
-
-
-class GPTDavinci(ModelBase):
-    def __init__(self, model_name: str):
-        self.name = model_name
-
-    def generate(self, prompt: str, max_tokens: int = 1024, stop_strs: Optional[List[str]] = None, temperature: float = 0, num_comps=1) -> Union[List[str], str]:
-        return gpt_completion(self.name, prompt, max_tokens, stop_strs, temperature, num_comps)
\ No newline at end of file
diff --git a/spaces/ARTeLab/ARTeLab-SummIT/style.css b/spaces/ARTeLab/ARTeLab-SummIT/style.css
deleted file mode 100644
index 466c5241e568ca1659988d0aff1954369db026c6..0000000000000000000000000000000000000000
--- a/spaces/ARTeLab/ARTeLab-SummIT/style.css
+++ /dev/null
@@ -1,38 +0,0 @@
-body {
-    background-color: #eee;
-}
-/*.fullScreenFrame > div {*/
-/*    display: flex;*/
-/*    justify-content: center;*/
-/*}*/
-/*.stButton>button {*/
-/*    color: #4F8BF9;*/
-/*    border-radius: 50%;*/
-/*    height: 3em;*/
-/*    width: 3em;*/
-/*}*/
-
-.stTextInput>div>div>input {
-    color: #4F8BF9;
-}
-.stTextArea>div>div>input {
-    color: #4F8BF9;
-    min-height: 500px;
-}
-
-
-/*.st-cj {*/
-/*    min-height: 500px;*/
-/*    spellcheck="false";*/
-/*    color: #4F8BF9;*/
-/*}*/
-/*.st-ch {*/
-/*    min-height: 500px;*/
-/*    spellcheck="false";*/
-/*    color: #4F8BF9;*/
-/*}*/
-/*.st-bb {*/
-/*    min-height: 500px;*/
-/*    spellcheck="false";*/
-/*    color: #4F8BF9;*/
-/*}*/
\ No newline at end of file
diff --git a/spaces/Abhilashvj/planogram-compliance/utils/loggers/clearml/README.md b/spaces/Abhilashvj/planogram-compliance/utils/loggers/clearml/README.md
deleted file mode 100644
index 3cf4c268583fc69df9ae3b58ea2566ed871a896c..0000000000000000000000000000000000000000
--- a/spaces/Abhilashvj/planogram-compliance/utils/loggers/clearml/README.md
+++ /dev/null
@@ -1,230 +0,0 @@
-# ClearML Integration
-
-<img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">
-
-## About ClearML
-
-[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
-
-🔨 Track every YOLOv5 training run in the <b>experiment manager</b>
-
-🔧 Version and easily access your custom training data with the integrated ClearML <b>Data Versioning Tool</b>
-
-🔦 <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent
-
-🔬 Get the very best mAP using ClearML <b>Hyperparameter Optimization</b>
-
-🔭 Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
-
-<br />
-And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
-<br />
-<br />
-
-![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif)
-
-
-<br />
-<br />
-
-## 🦾 Setting Things Up
-
-To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
-
-Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
-
-1. Install the `clearml` python package:
-
-    ```bash
-    pip install clearml
-    ```
-
-1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
-
-    ```bash
-    clearml-init
-    ```
-
-That's it! You're done 😎
-
-<br />
-
-## 🚀 Training YOLOv5 With ClearML
-
-To enable ClearML experiment tracking, simply install the ClearML pip package.
-
-```bash
-pip install clearml>=1.2.0
-```
-
-This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
-
-If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`.
-PLEASE NOTE: ClearML uses `/` as a delimter for subprojects, so be careful when using `/` in your project name!
-
-```bash
-python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
-```
-
-or with custom project and task name:
-```bash
-python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
-```
-
-This will capture:
-- Source code + uncommitted changes
-- Installed packages
-- (Hyper)parameters
-- Model files (use `--save-period n` to save a checkpoint every n epochs)
-- Console output
-- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
-- General info such as machine details, runtime, creation date etc.
-- All produced plots such as label correlogram and confusion matrix
-- Images with bounding boxes per epoch
-- Mosaic per epoch
-- Validation images per epoch
-- ...
-
-That's a lot right? 🤯
-Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
-
-There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
-
-<br />
-
-## 🔗 Dataset Version Management
-
-Versioning your data separately from your code is generally a good idea and makes it easy to aqcuire the latest version too. This repository supports supplying a dataset version ID and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
-
-![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif)
-
-### Prepare Your Dataset
-
-The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure:
-
-```
-..
-|_ yolov5
-|_ datasets
-    |_ coco128
-        |_ images
-        |_ labels
-        |_ LICENSE
-        |_ README.txt
-```
-But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
-
-Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls.
-
-Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
-
-```
-..
-|_ yolov5
-|_ datasets
-    |_ coco128
-        |_ images
-        |_ labels
-        |_ coco128.yaml  # <---- HERE!
-        |_ LICENSE
-        |_ README.txt
-```
-
-### Upload Your Dataset
-
-To get this dataset into ClearML as a versionned dataset, go to the dataset root folder and run the following command:
-```bash
-cd coco128
-clearml-data sync --project YOLOv5 --name coco128 --folder .
-```
-
-The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
-```bash
-# Optionally add --parent <parent_dataset_id> if you want to base
-# this version on another dataset version, so no duplicate files are uploaded!
-clearml-data create --name coco128 --project YOLOv5
-clearml-data add --files .
-clearml-data close
-```
-
-### Run Training Using A ClearML Dataset
-
-Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
-
-```bash
-python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
-```
-
-<br />
-
-## 👀 Hyperparameter Optimization
-
-Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
-
-Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does!
-
-To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters.
-
-You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead.
-
-```bash
-# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch
-pip install optuna
-python utils/loggers/clearml/hpo.py
-```
-
-![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png)
-
-## 🤯 Remote Execution (advanced)
-
-Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site or you have some budget to use cloud GPUs.
-This is where the ClearML Agent comes into play. Check out what the agent can do here:
-
-- [YouTube video](https://youtu.be/MX3BrXnaULs)
-- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
-
-In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
-
-You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
-```bash
-clearml-agent daemon --queue <queues_to_listen_to> [--docker]
-```
-
-### Cloning, Editing And Enqueuing
-
-With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too!
-
-🪄 Clone the experiment by right clicking it
-
-🎯 Edit the hyperparameters to what you wish them to be
-
-⏳ Enqueue the task to any of the queues by right clicking it
-
-![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif)
-
-### Executing A Task Remotely
-
-Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on!
-
-To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instatiated:
-```python
-# ...
-# Loggers
-data_dict = None
-if RANK in {-1, 0}:
-    loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
-    if loggers.clearml:
-        loggers.clearml.task.execute_remotely(queue='my_queue')  # <------ ADD THIS LINE
-        # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
-        data_dict = loggers.clearml.data_dict
-# ...
-```
-When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead!
-
-### Autoscaling workers
-
-ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines and you stop paying!
-
-Check out the autoscalers getting started video below.
-
-[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E)
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/utils/trimSuffix.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/utils/trimSuffix.ts
deleted file mode 100644
index 729107942ebaa2d7e1281dd77f8e52e8b135a5ad..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/utils/trimSuffix.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-export function trimSuffix(input: string, end: string): string {
-	if (input.endsWith(end)) {
-		return input.slice(0, input.length - end.length);
-	}
-	return input;
-}
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/login/callback/updateUser.spec.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/login/callback/updateUser.spec.ts
deleted file mode 100644
index 336da17dd996d62cf0ea0e7cf24ca3249a3c9e05..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/login/callback/updateUser.spec.ts
+++ /dev/null
@@ -1,143 +0,0 @@
-import { assert, it, describe, afterEach, vi, expect } from "vitest";
-import type { Cookies } from "@sveltejs/kit";
-import { collections } from "$lib/server/database";
-import { updateUser } from "./updateUser";
-import { DEFAULT_SETTINGS } from "$lib/types/Settings";
-import { defaultModel } from "$lib/server/models";
-
-const userData = {
-	preferred_username: "new-username",
-	name: "name",
-	picture: "https://example.com/avatar.png",
-	sub: "1234567890",
-};
-
-const locals = {
-	userId: "1234567890",
-	sessionId: "1234567890",
-};
-
-// @ts-expect-error SvelteKit cookies dumb mock
-const cookiesMock: Cookies = {
-	set: vi.fn(),
-};
-
-const insertRandomUser = async () => {
-	/*const res = await collections.users.insertOne({
-		_id: new ObjectId(),
-		createdAt: new Date(),
-		updatedAt: new Date(),
-		username: "base-username",
-		name: userData.name,
-		avatarUrl: userData.picture,
-		hfUserId: userData.sub,
-		sessionId: locals.sessionId,
-	});
-
-	return res.insertedId;*/
-};
-
-const insertRandomConversations = async (count: number) => {
-	/*const res = await collections.conversations.insertMany(
-		new Array(count).fill(0).map(() => ({
-			_id: new ObjectId(),
-			title: "random title",
-			messages: [],
-			model: defaultModel.id,
-			createdAt: new Date(),
-			updatedAt: new Date(),
-			sessionId: locals.sessionId,
-		}))
-	);
-
-	return res.insertedIds;*/
-};
-
-describe("login", () => {
-	it("should update user if existing", async () => {
-		/*await insertRandomUser();
-
-		await updateUser({ userData, locals, cookies: cookiesMock });
-
-		const existingUser = await collections.users.findOne({ hfUserId: userData.sub });
-
-		assert.equal(existingUser?.name, userData.name);
-
-		expect(cookiesMock.set).toBeCalledTimes(1);*/
-	});
-
-	it("should migrate pre-existing conversations for new user", async () => {
-		/*const insertedId = await insertRandomUser();
-
-		await insertRandomConversations(2);
-
-		await updateUser({ userData, locals, cookies: cookiesMock });
-
-		const conversationCount = await collections.conversations.countDocuments({
-			userId: insertedId,
-			sessionId: { $exists: false },
-		});
-
-		assert.equal(conversationCount, 2);
-
-		await collections.conversations.deleteMany({ userId: insertedId });*/
-	});
-
-	it("should create default settings for new user", async () => {
-		/*await updateUser({ userData, locals, cookies: cookiesMock });
-
-		const user = await collections.users.findOne({ sessionId: locals.sessionId });
-
-		assert.exists(user);
-
-		const settings = await collections.settings.findOne({ userId: user?._id });
-
-		expect(settings).toMatchObject({
-			userId: user?._id,
-			updatedAt: expect.any(Date),
-			createdAt: expect.any(Date),
-			ethicsModalAcceptedAt: expect.any(Date),
-			...DEFAULT_SETTINGS,
-		});
-
-		await collections.settings.deleteOne({ userId: user?._id });*/
-	});
-
-	it("should migrate pre-existing settings for pre-existing user", async () => {
-		/*const { insertedId } = await collections.settings.insertOne({
-			sessionId: locals.sessionId,
-			ethicsModalAcceptedAt: new Date(),
-			updatedAt: new Date(),
-			createdAt: new Date(),
-			...DEFAULT_SETTINGS,
-			shareConversationsWithModelAuthors: false,
-		});
-
-		await updateUser({ userData, locals, cookies: cookiesMock });
-
-		const settings = await collections.settings.findOne({
-			_id: insertedId,
-			sessionId: { $exists: false },
-		});
-
-		assert.exists(settings);
-
-		const user = await collections.users.findOne({ hfUserId: userData.sub });
-
-		expect(settings).toMatchObject({
-			userId: user?._id,
-			updatedAt: expect.any(Date),
-			createdAt: expect.any(Date),
-			ethicsModalAcceptedAt: expect.any(Date),
-			...DEFAULT_SETTINGS,
-			shareConversationsWithModelAuthors: false,
-		});
-
-		await collections.settings.deleteOne({ userId: user?._id });*/
-	});
-});
-
-afterEach(async () => {
-	/*await collections.users.deleteMany({ hfUserId: userData.sub });
-	vi.clearAllMocks();*/
-});
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/needs_auth/__init__.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/needs_auth/__init__.py
deleted file mode 100644
index 815194c4c76b97d56aa7d5356cd2324ea6ab5093..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/needs_auth/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .Bard          import Bard
-from .Raycast       import Raycast
-from .Theb          import Theb
-from .HuggingChat   import HuggingChat
-from .OpenaiChat    import OpenaiChat
-from .OpenAssistant import OpenAssistant
\ No newline at end of file
diff --git a/spaces/Aditya9790/yolo7-object-tracking/scripts/get_coco.sh b/spaces/Aditya9790/yolo7-object-tracking/scripts/get_coco.sh
deleted file mode 100644
index 524f8dd9e2cae992a4047476520a7e4e1402e6de..0000000000000000000000000000000000000000
--- a/spaces/Aditya9790/yolo7-object-tracking/scripts/get_coco.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/bin/bash
-# COCO 2017 dataset http://cocodataset.org
-# Download command: bash ./scripts/get_coco.sh
-
-# Download/unzip labels
-d='./' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco2017labels-segments.zip' # or 'coco2017labels.zip', 68 MB
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
-
-# Download/unzip images
-d='./coco/images' # unzip directory
-url=http://images.cocodataset.org/zips/
-f1='train2017.zip' # 19G, 118k images
-f2='val2017.zip'   # 1G, 5k images
-f3='test2017.zip'  # 7G, 41k images (optional)
-for f in $f1 $f2 $f3; do
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
-done
-wait # finish background tasks
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/decision_maker/horizontal_tool.py b/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/decision_maker/horizontal_tool.py
deleted file mode 100644
index 5cea85eab38b8cea3fcb05c509a44f61d1040c86..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/decision_maker/horizontal_tool.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from __future__ import annotations
-import json
-import asyncio
-from copy import deepcopy
-from colorama import Fore
-from itertools import cycle
-
-from typing import TYPE_CHECKING, List
-
-from . import decision_maker_registry
-from .base import BaseDecisionMaker
-from agentverse.logging import logger
-from agentverse.message import SolverMessage, Message
-
-if TYPE_CHECKING:
-    from agentverse.agents.base import BaseAgent
-    from agentverse.message import CriticMessage
-
-
-@decision_maker_registry.register("horizontal-tool")
-class HorizontalToolDecisionMaker(BaseDecisionMaker):
-    """
-    Discuss in a horizontal manner.
-    """
-
-    name: str = "horizontal_tool"
-    tools: List[dict] = []
-    tool_names: List[str] = []
-    tool_config: str = None
-
-    def __init__(self, *args, **kwargs):
-        assert kwargs.get("tool_config", None) is not None
-        with open(kwargs.get("tool_config"), "r") as f:
-            tools_dict = json.load(f)
-        tools = tools_dict["tools_json"]
-        tool_names = [t["name"] for t in tools]
-        super().__init__(tools=tools, tool_names=tool_names, *args, **kwargs)
-
-    # def step(
-    async def astep(
-        self,
-        agents: List[BaseAgent],
-        task_description: str,
-        previous_plan: str = "No solution yet.",
-        advice: str = "No advice yet.",
-        **kwargs,
-    ) -> List[str]:
-        agents[0].memory.reset()
-        if advice != "No advice yet.":
-            self.broadcast_messages(
-                agents[1:], [Message(content=advice, sender="Evaluator")]
-            )
-        all_roles = "\n".join(
-            [f"{agent.name}: {agent.role_description}" for agent in agents[1:]]
-        )
-        end_flag = False
-        discussion_cnt = 0
-        for agent in cycle(agents[1:]):
-            discussion_cnt += 1
-            review: CriticMessage = await agent.astep(
-                previous_plan, advice, task_description, all_roles
-            )
-            if review.content.strip().endswith("[END]"):
-                review.content = review.content.strip().replace("[END]", "")
-                if discussion_cnt >= len(agents) - 1:
-                    # Force all the agents to speak at least once.
-                    end_flag = True
-            if review.content != "":
-                self.broadcast_messages(agents, [review])
-
-            logger.info("", "Reviews:", Fore.YELLOW)
-            logger.info(
-                "",
-                f"[{review.sender}]: {review.content}",
-                Fore.YELLOW,
-            )
-            if end_flag:
-                break
-
-        result: SolverMessage = agents[0].step(previous_plan, advice, task_description)
-        result_list = []
-        for res in result.content:
-            res_tmp = deepcopy(result)
-            res_tmp.content = " - ".join(res)
-            result_list.append(res_tmp)
-        return result_list
-
-    def reset(self):
-        pass
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/DelayCallMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/DelayCallMethods.js
deleted file mode 100644
index be82ac0bcbcd804a2099a8a0da5e32944dc42601..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/DelayCallMethods.js
+++ /dev/null
@@ -1,17 +0,0 @@
-import PostUpdateDelayCall from '../../../../plugins/utils/time/PostUpdateDelayCall.js';
-
-export default {
-    delayCall(delay, callback, scope) {
-        // Invoke callback under scene's 'postupdate' event
-        this.timer = PostUpdateDelayCall(this, delay, callback, scope);
-        return this;
-    },
-
-    removeDelayCall() {
-        if (this.timer) {
-            this.timer.remove(false);
-            this.timer = undefined;
-        }
-        return this;
-    }
-}
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetChildrenHeight.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetChildrenHeight.js
deleted file mode 100644
index bcc82329d2e14d50c1e4ca16f55d9f63f1bfa0ac..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetChildrenHeight.js
+++ /dev/null
@@ -1,24 +0,0 @@
-import { GetDisplayHeight } from '../../../plugins/utils/size/GetDisplaySize.js';
-
-var GetChildrenHeight = function () {
-    if (this.rexSizer.hidden) {
-        return 0;
-    }
-
-    var result = 0;
-    var children = this.sizerChildren;
-    var child, padding, childHeight;
-    for (var key in children) {
-        child = children[key];
-        childHeight = (child.isRexSizer) ?
-            Math.max(child.minHeight, child.childrenHeight) :
-            (child.minHeight !== undefined) ? child.minHeight : GetDisplayHeight(child);
-
-        padding = child.rexSizer.padding;
-        childHeight += (padding.top + padding.bottom);
-        result = Math.max(childHeight, result);
-    }
-    return result + this.space.top + this.space.bottom;
-}
-
-export default GetChildrenHeight;
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/alt_diffusion.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/alt_diffusion.md
deleted file mode 100644
index ed8db52f9a51198260c4f0d1927b29f7e3913f8a..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/alt_diffusion.md
+++ /dev/null
@@ -1,47 +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.
--->
-
-# AltDiffusion
-
-AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://huggingface.co/papers/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu.
-
-The abstract from the paper is:
-
-*In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.*
-
-## Tips
-
-`AltDiffusion` is conceptually the same as [Stable Diffusion](./stable_diffusion/overview).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## AltDiffusionPipeline
-
-[[autodoc]] AltDiffusionPipeline
-	- all
-	- __call__
-
-## AltDiffusionImg2ImgPipeline
-
-[[autodoc]] AltDiffusionImg2ImgPipeline
-	- all
-	- __call__
-
-## AltDiffusionPipelineOutput
-
-[[autodoc]] pipelines.alt_diffusion.AltDiffusionPipelineOutput
-	- all
-	- __call__
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md
deleted file mode 100644
index d44e9f507830e8c8afa026a404cfb0f093b8edb9..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md
+++ /dev/null
@@ -1,139 +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.
--->
-
-# Stable Diffusion 2
-
-Stable Diffusion 2 is a text-to-image _latent diffusion_ model built upon the work of the original [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release), and it was led by Robin Rombach and Katherine Crowson from [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/).
-
-*The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels. 
-These models are trained on an aesthetic subset of the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using [LAION’s NSFW filter](https://openreview.net/forum?id=M3Y74vmsMcY).*
-
-For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer to the official [announcement post](https://stability.ai/blog/stable-diffusion-v2-release).
-
-The architecture of Stable Diffusion 2 is more or less identical to the original [Stable Diffusion model](./text2img) so check out it's API documentation for how to use Stable Diffusion 2. We recommend using the [`DPMSolverMultistepScheduler`] as it's currently the fastest scheduler.
-
-Stable Diffusion 2 is available for tasks like text-to-image, inpainting, super-resolution, and depth-to-image:
-
-| Task                    | Repository                                                                                                    |
-|-------------------------|---------------------------------------------------------------------------------------------------------------|
-| text-to-image (512x512) | [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base)             |
-| text-to-image (768x768) | [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2)                       |
-| inpainting              | [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) |
-| super-resolution        | [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler)               |
-| depth-to-image          | [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth)           |
-
-Here are some examples for how to use Stable Diffusion 2 for each task:
-
-<Tip>
-
-Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! 
-
-If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
-
-</Tip>
-
-## Text-to-image
-
-```py
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-import torch
-
-repo_id = "stabilityai/stable-diffusion-2-base"
-pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
-
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
-
-prompt = "High quality photo of an astronaut riding a horse in space"
-image = pipe(prompt, num_inference_steps=25).images[0]
-image.save("astronaut.png")
-```
-
-## Inpainting
-
-```py
-import PIL
-import requests
-import torch
-from io import BytesIO
-
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-
-
-def download_image(url):
-    response = requests.get(url)
-    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
-
-
-img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-
-init_image = download_image(img_url).resize((512, 512))
-mask_image = download_image(mask_url).resize((512, 512))
-
-repo_id = "stabilityai/stable-diffusion-2-inpainting"
-pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
-
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
-
-prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
-image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0]
-
-image.save("yellow_cat.png")
-```
-
-## Super-resolution
-
-```py
-import requests
-from PIL import Image
-from io import BytesIO
-from diffusers import StableDiffusionUpscalePipeline
-import torch
-
-# load model and scheduler
-model_id = "stabilityai/stable-diffusion-x4-upscaler"
-pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipeline = pipeline.to("cuda")
-
-# let's download an  image
-url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
-response = requests.get(url)
-low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
-low_res_img = low_res_img.resize((128, 128))
-prompt = "a white cat"
-upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
-upscaled_image.save("upsampled_cat.png")
-```
-
-## Depth-to-image
-
-```py
-import torch
-import requests
-from PIL import Image
-
-from diffusers import StableDiffusionDepth2ImgPipeline
-
-pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-depth",
-    torch_dtype=torch.float16,
-).to("cuda")
-
-
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-init_image = Image.open(requests.get(url, stream=True).raw)
-prompt = "two tigers"
-n_propmt = "bad, deformed, ugly, bad anotomy"
-image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0]
-```
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py
deleted file mode 100644
index c02a3117af6e44680edcfdfbb98d5e97022f57d6..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py
+++ /dev/null
@@ -1,363 +0,0 @@
-# Copyright 2023 Open AI and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPTextModelWithProjection, CLIPTokenizer
-
-from ...models import PriorTransformer
-from ...schedulers import HeunDiscreteScheduler
-from ...utils import (
-    BaseOutput,
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-from .renderer import ShapERenderer
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import DiffusionPipeline
-        >>> from diffusers.utils import export_to_gif
-
-        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        >>> repo = "openai/shap-e"
-        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
-        >>> pipe = pipe.to(device)
-
-        >>> guidance_scale = 15.0
-        >>> prompt = "a shark"
-
-        >>> images = pipe(
-        ...     prompt,
-        ...     guidance_scale=guidance_scale,
-        ...     num_inference_steps=64,
-        ...     frame_size=256,
-        ... ).images
-
-        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")
-        ```
-"""
-
-
-@dataclass
-class ShapEPipelineOutput(BaseOutput):
-    """
-    Output class for [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`].
-
-    Args:
-        images (`torch.FloatTensor`)
-            A list of images for 3D rendering.
-    """
-
-    images: Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]
-
-
-class ShapEPipeline(DiffusionPipeline):
-    """
-    Pipeline for generating latent representation of a 3D asset and rendering with NeRF method with Shap-E.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        prior ([`PriorTransformer`]):
-            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            Frozen text-encoder.
-        tokenizer (`CLIPTokenizer`):
-             A [`~transformers.CLIPTokenizer`] to tokenize text.
-        scheduler ([`HeunDiscreteScheduler`]):
-            A scheduler to be used in combination with `prior` to generate image embedding.
-        shap_e_renderer ([`ShapERenderer`]):
-            Shap-E renderer projects the generated latents into parameters of a MLP that's used to create 3D objects
-            with the NeRF rendering method.
-    """
-
-    def __init__(
-        self,
-        prior: PriorTransformer,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        scheduler: HeunDiscreteScheduler,
-        shap_e_renderer: ShapERenderer,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            prior=prior,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            shap_e_renderer=shap_e_renderer,
-        )
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offload all models to CPU to reduce memory usage with a low impact on performance. 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 using `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}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.prior, self.shap_e_renderer]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        if self.safety_checker is not None:
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-    ):
-        len(prompt) if isinstance(prompt, list) else 1
-
-        # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
-        self.tokenizer.pad_token_id = 0
-        # get prompt text embeddings
-        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}"
-            )
-
-        text_encoder_output = self.text_encoder(text_input_ids.to(device))
-        prompt_embeds = text_encoder_output.text_embeds
-
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        # in Shap-E it normalize the prompt_embeds and then later rescale it
-        prompt_embeds = prompt_embeds / torch.linalg.norm(prompt_embeds, dim=-1, keepdim=True)
-
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
-
-            # 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])
-
-        # Rescale the features to have unit variance
-        prompt_embeds = math.sqrt(prompt_embeds.shape[1]) * prompt_embeds
-
-        return prompt_embeds
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: str,
-        num_images_per_prompt: int = 1,
-        num_inference_steps: int = 25,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        guidance_scale: float = 4.0,
-        frame_size: int = 64,
-        output_type: Optional[str] = "pil",  # pil, np, latent, mesh
-        return_dict: bool = True,
-    ):
-        """
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-                usually at the expense of lower image quality.
-            frame_size (`int`, *optional*, default to 64):
-                The width and height of each image frame of the generated 3D output.
-            output_type (`str`, *optional*, defaults to `"pt"`):
-                The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
-                (`np.array`),`"latent"` (`torch.Tensor`), mesh ([`MeshDecoderOutput`]).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] instead of a plain
-                tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images.
-        """
-
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        device = self._execution_device
-
-        batch_size = batch_size * num_images_per_prompt
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-        prompt_embeds = self._encode_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance)
-
-        # prior
-
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        num_embeddings = self.prior.config.num_embeddings
-        embedding_dim = self.prior.config.embedding_dim
-
-        latents = self.prepare_latents(
-            (batch_size, num_embeddings * embedding_dim),
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-            self.scheduler,
-        )
-
-        # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
-        latents = latents.reshape(latents.shape[0], num_embeddings, embedding_dim)
-
-        for i, t in enumerate(self.progress_bar(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
-            scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            noise_pred = self.prior(
-                scaled_model_input,
-                timestep=t,
-                proj_embedding=prompt_embeds,
-            ).predicted_image_embedding
-
-            # remove the variance
-            noise_pred, _ = noise_pred.split(
-                scaled_model_input.shape[2], dim=2
-            )  # batch_size, num_embeddings, embedding_dim
-
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
-
-            latents = self.scheduler.step(
-                noise_pred,
-                timestep=t,
-                sample=latents,
-            ).prev_sample
-
-        if output_type not in ["np", "pil", "latent", "mesh"]:
-            raise ValueError(
-                f"Only the output types `pil`, `np`, `latent` and `mesh` are supported not output_type={output_type}"
-            )
-
-        if output_type == "latent":
-            return ShapEPipelineOutput(images=latents)
-
-        images = []
-        if output_type == "mesh":
-            for i, latent in enumerate(latents):
-                mesh = self.shap_e_renderer.decode_to_mesh(
-                    latent[None, :],
-                    device,
-                )
-                images.append(mesh)
-
-        else:
-            # np, pil
-            for i, latent in enumerate(latents):
-                image = self.shap_e_renderer.decode_to_image(
-                    latent[None, :],
-                    device,
-                    size=frame_size,
-                )
-                images.append(image)
-
-            images = torch.stack(images)
-
-            images = images.cpu().numpy()
-
-            if output_type == "pil":
-                images = [self.numpy_to_pil(image) for image in images]
-
-        # 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 (images,)
-
-        return ShapEPipelineOutput(images=images)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/spectrogram_diffusion/continous_encoder.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/spectrogram_diffusion/continous_encoder.py
deleted file mode 100644
index 556136d4023df32e4df2477523463829a0722db4..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/spectrogram_diffusion/continous_encoder.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright 2022 The Music Spectrogram Diffusion Authors.
-# 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.
-
-import torch
-import torch.nn as nn
-from transformers.modeling_utils import ModuleUtilsMixin
-from transformers.models.t5.modeling_t5 import (
-    T5Block,
-    T5Config,
-    T5LayerNorm,
-)
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...models import ModelMixin
-
-
-class SpectrogramContEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):
-    @register_to_config
-    def __init__(
-        self,
-        input_dims: int,
-        targets_context_length: int,
-        d_model: int,
-        dropout_rate: float,
-        num_layers: int,
-        num_heads: int,
-        d_kv: int,
-        d_ff: int,
-        feed_forward_proj: str,
-        is_decoder: bool = False,
-    ):
-        super().__init__()
-
-        self.input_proj = nn.Linear(input_dims, d_model, bias=False)
-
-        self.position_encoding = nn.Embedding(targets_context_length, d_model)
-        self.position_encoding.weight.requires_grad = False
-
-        self.dropout_pre = nn.Dropout(p=dropout_rate)
-
-        t5config = T5Config(
-            d_model=d_model,
-            num_heads=num_heads,
-            d_kv=d_kv,
-            d_ff=d_ff,
-            feed_forward_proj=feed_forward_proj,
-            dropout_rate=dropout_rate,
-            is_decoder=is_decoder,
-            is_encoder_decoder=False,
-        )
-        self.encoders = nn.ModuleList()
-        for lyr_num in range(num_layers):
-            lyr = T5Block(t5config)
-            self.encoders.append(lyr)
-
-        self.layer_norm = T5LayerNorm(d_model)
-        self.dropout_post = nn.Dropout(p=dropout_rate)
-
-    def forward(self, encoder_inputs, encoder_inputs_mask):
-        x = self.input_proj(encoder_inputs)
-
-        # terminal relative positional encodings
-        max_positions = encoder_inputs.shape[1]
-        input_positions = torch.arange(max_positions, device=encoder_inputs.device)
-
-        seq_lens = encoder_inputs_mask.sum(-1)
-        input_positions = torch.roll(input_positions.unsqueeze(0), tuple(seq_lens.tolist()), dims=0)
-        x += self.position_encoding(input_positions)
-
-        x = self.dropout_pre(x)
-
-        # inverted the attention mask
-        input_shape = encoder_inputs.size()
-        extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape)
-
-        for lyr in self.encoders:
-            x = lyr(x, extended_attention_mask)[0]
-        x = self.layer_norm(x)
-
-        return self.dropout_post(x), encoder_inputs_mask
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 3503c76935e294c881130b309999d32f13df8839..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/bootstrap/bootstrap.min.js b/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/bootstrap/bootstrap.min.js
deleted file mode 100644
index cc0a25561dfb616832227c9e2b2c1b1bbe69bc05..0000000000000000000000000000000000000000
--- a/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/bootstrap/bootstrap.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
-  * Bootstrap v5.1.3 (https://getbootstrap.com/)
-  * Copyright 2011-2021 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
-  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
-  */
-!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t="transitionend",e=t=>{let e=t.getAttribute("data-bs-target");if(!e||"#"===e){let i=t.getAttribute("href");if(!i||!i.includes("#")&&!i.startsWith("."))return null;i.includes("#")&&!i.startsWith("#")&&(i=`#${i.split("#")[1]}`),e=i&&"#"!==i?i.trim():null}return e},i=t=>{const i=e(t);return i&&document.querySelector(i)?i:null},n=t=>{const i=e(t);return i?document.querySelector(i):null},s=e=>{e.dispatchEvent(new Event(t))},o=t=>!(!t||"object"!=typeof t)&&(void 0!==t.jquery&&(t=t[0]),void 0!==t.nodeType),r=t=>o(t)?t.jquery?t[0]:t:"string"==typeof t&&t.length>0?document.querySelector(t):null,a=(t,e,i)=>{Object.keys(i).forEach((n=>{const s=i[n],r=e[n],a=r&&o(r)?"element":null==(l=r)?`${l}`:{}.toString.call(l).match(/\s([a-z]+)/i)[1].toLowerCase();var 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" 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this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new 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t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
-//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/util/visualizer.py b/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/util/visualizer.py
deleted file mode 100644
index 63ce19ec5bbc01915ffae0cb550e2a3d0c4d9059..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/util/visualizer.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import numpy as np
-import os
-import sys
-import ntpath
-import time
-from . import util, html
-from subprocess import Popen, PIPE
-
-if sys.version_info[0] == 2:
-    VisdomExceptionBase = Exception
-else:
-    VisdomExceptionBase = ConnectionError
-
-
-def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
-    """Save examples to the disk.
-
-    Parameters:
-        webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
-        visuals (OrderedDict)    -- an ordered dictionary that stores (name, examples (either tensor or numpy) ) pairs
-        image_path (str)         -- the string is used to create image paths
-        aspect_ratio (float)     -- the aspect ratio of saved examples
-        width (int)              -- the examples will be resized to width x width
-
-    This function will save examples stored in 'visuals' to the HTML file specified by 'webpage'.
-    """
-    image_dir = webpage.get_image_dir()
-    short_path = ntpath.basename(image_path[0])
-    name = os.path.splitext(short_path)[0]
-
-    webpage.add_header(name)
-    ims, txts, links = [], [], []
-
-    for label, im_data in visuals.items():
-        im = util.tensor2im(im_data)
-        image_name = '%s/%s.png' % (label, name)
-        os.makedirs(os.path.join(image_dir, label), exist_ok=True)
-        save_path = os.path.join(image_dir, image_name)
-        util.save_image(im, save_path)
-        ims.append(image_name)
-        txts.append(label)
-        links.append(image_name)
-    webpage.add_images(ims, txts, links, width=width)
-
-
-class Visualizer():
-    """This class includes several functions that can display/save examples and print/save logging information.
-
-    It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with examples.
-    """
-
-    def __init__(self, opt):
-        """Initialize the Visualizer class
-
-        Parameters:
-            opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        Step 1: Cache the training/test options
-        Step 2: connect to a visdom server
-        Step 3: create an HTML object for saveing HTML filters
-        Step 4: create a logging file to store training losses
-        """
-        self.opt = opt  # cache the option
-        if opt.display_id is None:
-            self.display_id = np.random.randint(100000) * 10  # just a random display id
-        else:
-            self.display_id = opt.display_id
-        self.use_html = opt.isTrain and not opt.no_html
-        self.win_size = opt.display_winsize
-        self.name = opt.name
-        self.port = opt.display_port
-        self.saved = False
-        if self.display_id > 0:  # connect to a visdom server given <display_port> and <display_server>
-            import visdom
-            self.plot_data = {}
-            self.ncols = opt.display_ncols
-            self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env)
-            if not self.vis.check_connection():
-                self.create_visdom_connections()
-
-        if self.use_html:  # create an HTML object at <checkpoints_dir>/web/; examples will be saved under <checkpoints_dir>/web/examples/
-            self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
-            self.img_dir = os.path.join(self.web_dir, 'examples')
-            print('create web directory %s...' % self.web_dir)
-            util.mkdirs([self.web_dir, self.img_dir])
-        # create a logging file to store training losses
-        self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
-        with open(self.log_name, "a") as log_file:
-            now = time.strftime("%c")
-            log_file.write('================ Training Loss (%s) ================\n' % now)
-
-    def reset(self):
-        """Reset the self.saved status"""
-        self.saved = False
-
-    def create_visdom_connections(self):
-        """If the program could not connect to Visdom server, this function will start a new server at port < self.port > """
-        cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port
-        print('\n\nCould not connect to Visdom server. \n Trying to start a server....')
-        print('Command: %s' % cmd)
-        Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
-
-    def display_current_results(self, visuals, epoch, save_result):
-        """Display current results on visdom; save current results to an HTML file.
-
-        Parameters:
-            visuals (OrderedDict) - - dictionary of examples to display or save
-            epoch (int) - - the current epoch
-            save_result (bool) - - if save the current results to an HTML file
-        """
-        if self.display_id > 0:  # show examples in the browser using visdom
-            ncols = self.ncols
-            if ncols > 0:        # show all the examples in one visdom panel
-                ncols = min(ncols, len(visuals))
-                h, w = next(iter(visuals.values())).shape[:2]
-                table_css = """<style>
-                        table {border-collapse: separate; border-spacing: 4px; white-space: nowrap; text-align: center}
-                        table td {width: % dpx; height: % dpx; padding: 4px; outline: 4px solid black}
-                        </style>""" % (w, h)  # create a table css
-                # create a table of examples.
-                title = self.name
-                label_html = ''
-                label_html_row = ''
-                images = []
-                idx = 0
-                for label, image in visuals.items():
-                    image_numpy = util.tensor2im(image)
-                    label_html_row += '<td>%s</td>' % label
-                    images.append(image_numpy.transpose([2, 0, 1]))
-                    idx += 1
-                    if idx % ncols == 0:
-                        label_html += '<tr>%s</tr>' % label_html_row
-                        label_html_row = ''
-                white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255
-                while idx % ncols != 0:
-                    images.append(white_image)
-                    label_html_row += '<td></td>'
-                    idx += 1
-                if label_html_row != '':
-                    label_html += '<tr>%s</tr>' % label_html_row
-                try:
-                    self.vis.images(images, nrow=ncols, win=self.display_id + 1,
-                                    padding=2, opts=dict(title=title + ' examples'))
-                    label_html = '<table>%s</table>' % label_html
-                    self.vis.text(table_css + label_html, win=self.display_id + 2,
-                                  opts=dict(title=title + ' labels'))
-                except VisdomExceptionBase:
-                    self.create_visdom_connections()
-
-            else:     # show each image in a separate visdom panel;
-                idx = 1
-                try:
-                    for label, image in visuals.items():
-                        image_numpy = util.tensor2im(image)
-                        self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label),
-                                       win=self.display_id + idx)
-                        idx += 1
-                except VisdomExceptionBase:
-                    self.create_visdom_connections()
-
-        if self.use_html and (save_result or not self.saved):  # save examples to an HTML file if they haven't been saved.
-            self.saved = True
-            # save examples to the disk
-            for label, image in visuals.items():
-                image_numpy = util.tensor2im(image)
-                img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
-                util.save_image(image_numpy, img_path)
-
-            # update website
-            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=1)
-            for n in range(epoch, 0, -1):
-                webpage.add_header('epoch [%d]' % n)
-                ims, txts, links = [], [], []
-
-                for label, image_numpy in visuals.items():
-                    img_path = 'epoch%.3d_%s.png' % (n, label)
-                    ims.append(img_path)
-                    txts.append(label)
-                    links.append(img_path)
-                webpage.add_images(ims, txts, links, width=self.win_size)
-            webpage.save()
-
-    def plot_current_losses(self, epoch, counter_ratio, losses):
-        """display the current losses on visdom display: dictionary of error labels and values
-
-        Parameters:
-            epoch (int)           -- current epoch
-            counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1
-            losses (OrderedDict)  -- training losses stored in the format of (name, float) pairs
-        """
-        if len(losses) == 0:
-            return
-
-        plot_name = '_'.join(list(losses.keys()))
-
-        if plot_name not in self.plot_data:
-            self.plot_data[plot_name] = {'X': [], 'Y': [], 'legend': list(losses.keys())}
-
-        plot_data = self.plot_data[plot_name]
-        plot_id = list(self.plot_data.keys()).index(plot_name)
-
-        plot_data['X'].append(epoch + counter_ratio)
-        plot_data['Y'].append([losses[k] for k in plot_data['legend']])
-        try:
-            self.vis.line(
-                X=np.stack([np.array(plot_data['X'])] * len(plot_data['legend']), 1),
-                Y=np.array(plot_data['Y']),
-                opts={
-                    'title': self.name,
-                    'legend': plot_data['legend'],
-                    'xlabel': 'epoch',
-                    'ylabel': 'loss'},
-                win=self.display_id - plot_id)
-        except VisdomExceptionBase:
-            self.create_visdom_connections()
-
-    # losses: same format as |losses| of plot_current_losses
-    def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
-        """print current losses on console; also save the losses to the disk
-
-        Parameters:
-            epoch (int) -- current epoch
-            iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
-            losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
-            t_comp (float) -- computational time per data point (normalized by batch_size)
-            t_data (float) -- data loading time per data point (normalized by batch_size)
-        """
-        message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
-        for k, v in losses.items():
-            message += '%s: %.3f ' % (k, v)
-
-        print(message)  # print the message
-        with open(self.log_name, "a") as log_file:
-            log_file.write('%s\n' % message)  # save the message
diff --git a/spaces/Awiny/Image2Paragraph/models/segment_models/edit_anything_model.py b/spaces/Awiny/Image2Paragraph/models/segment_models/edit_anything_model.py
deleted file mode 100644
index afeb3b337572cc17e442465c4de2f72e41decd4e..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/segment_models/edit_anything_model.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import cv2
-import torch
-import mmcv
-import numpy as np
-from PIL import Image
-from utils.util import resize_long_edge
-from concurrent.futures import ThreadPoolExecutor
-import time
-
-class EditAnything:
-    def __init__(self, image_caption_model):
-        self.device = image_caption_model.device
-        self.data_type = image_caption_model.data_type
-        self.image_caption_model = image_caption_model
-
-    def region_classify_w_blip2(self, images):
-        inputs = self.image_caption_model.processor(images=images, return_tensors="pt").to(self.device, self.data_type)
-        generated_ids = self.image_caption_model.model.generate(**inputs)
-        generated_texts = self.image_caption_model.processor.batch_decode(generated_ids, skip_special_tokens=True)
-        return [text.strip() for text in generated_texts]
-
-    def process_ann(self, ann, image, target_size=(224, 224)):
-        start_time = time.time()
-        m = ann['segmentation']
-        m_3c = m[:, :, np.newaxis]
-        m_3c = np.concatenate((m_3c, m_3c, m_3c), axis=2)
-        bbox = ann['bbox']
-        region = mmcv.imcrop(image * m_3c, np.array([bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]), scale=1)
-        resized_region = mmcv.imresize(region, target_size)
-        end_time = time.time()
-        print("process_ann took {:.2f} seconds".format(end_time - start_time))
-        return resized_region, ann
-
-    def region_level_semantic_api(self, image, anns, topk=5):
-        """
-        rank regions by area, and classify each region with blip2, parallel processing for speed up
-        Args:
-            image: numpy array
-            topk: int
-        Returns:
-            topk_region_w_class_label: list of dict with key 'class_label'
-        """
-        start_time = time.time()
-        if len(anns) == 0:
-            return []
-        sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
-        topk_anns = sorted_anns[:min(topk, len(sorted_anns))]
-        with ThreadPoolExecutor() as executor:
-            regions_and_anns = list(executor.map(lambda ann: self.process_ann(ann, image), topk_anns))
-        regions = [region for region, _ in regions_and_anns]
-        region_class_labels = self.region_classify_w_blip2(regions)
-        for (region, ann), class_label in zip(regions_and_anns, region_class_labels):
-            ann['class_name'] = class_label
-        end_time = time.time()
-        print("region_level_semantic_api took {:.2f} seconds".format(end_time - start_time))
-
-        return [ann for _, ann in regions_and_anns]
-
-    def semantic_class_w_mask(self, img_src, anns):
-        image = Image.open(img_src)
-        image = resize_long_edge(image, 384)
-        return self.region_level_semantic_api(image, anns)
\ No newline at end of file
diff --git a/spaces/Bart92/RVC_HF/demucs/train.py b/spaces/Bart92/RVC_HF/demucs/train.py
deleted file mode 100644
index 6bd221279dc986a6df1a8d7b4d4444bb822a1cb3..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/demucs/train.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-import tqdm
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-
-from .utils import apply_model, average_metric, center_trim
-
-
-def train_model(epoch,
-                dataset,
-                model,
-                criterion,
-                optimizer,
-                augment,
-                quantizer=None,
-                diffq=0,
-                repeat=1,
-                device="cpu",
-                seed=None,
-                workers=4,
-                world_size=1,
-                batch_size=16):
-
-    if world_size > 1:
-        sampler = DistributedSampler(dataset)
-        sampler_epoch = epoch * repeat
-        if seed is not None:
-            sampler_epoch += seed * 1000
-        sampler.set_epoch(sampler_epoch)
-        batch_size //= world_size
-        loader = DataLoader(dataset, batch_size=batch_size, sampler=sampler, num_workers=workers)
-    else:
-        loader = DataLoader(dataset, batch_size=batch_size, num_workers=workers, shuffle=True)
-    current_loss = 0
-    model_size = 0
-    for repetition in range(repeat):
-        tq = tqdm.tqdm(loader,
-                       ncols=120,
-                       desc=f"[{epoch:03d}] train ({repetition + 1}/{repeat})",
-                       leave=False,
-                       file=sys.stdout,
-                       unit=" batch")
-        total_loss = 0
-        for idx, sources in enumerate(tq):
-            if len(sources) < batch_size:
-                # skip uncomplete batch for augment.Remix to work properly
-                continue
-            sources = sources.to(device)
-            sources = augment(sources)
-            mix = sources.sum(dim=1)
-
-            estimates = model(mix)
-            sources = center_trim(sources, estimates)
-            loss = criterion(estimates, sources)
-            model_size = 0
-            if quantizer is not None:
-                model_size = quantizer.model_size()
-
-            train_loss = loss + diffq * model_size
-            train_loss.backward()
-            grad_norm = 0
-            for p in model.parameters():
-                if p.grad is not None:
-                    grad_norm += p.grad.data.norm()**2
-            grad_norm = grad_norm**0.5
-            optimizer.step()
-            optimizer.zero_grad()
-
-            if quantizer is not None:
-                model_size = model_size.item()
-
-            total_loss += loss.item()
-            current_loss = total_loss / (1 + idx)
-            tq.set_postfix(loss=f"{current_loss:.4f}", ms=f"{model_size:.2f}",
-                           grad=f"{grad_norm:.5f}")
-
-            # free some space before next round
-            del sources, mix, estimates, loss, train_loss
-
-        if world_size > 1:
-            sampler.epoch += 1
-
-    if world_size > 1:
-        current_loss = average_metric(current_loss)
-    return current_loss, model_size
-
-
-def validate_model(epoch,
-                   dataset,
-                   model,
-                   criterion,
-                   device="cpu",
-                   rank=0,
-                   world_size=1,
-                   shifts=0,
-                   overlap=0.25,
-                   split=False):
-    indexes = range(rank, len(dataset), world_size)
-    tq = tqdm.tqdm(indexes,
-                   ncols=120,
-                   desc=f"[{epoch:03d}] valid",
-                   leave=False,
-                   file=sys.stdout,
-                   unit=" track")
-    current_loss = 0
-    for index in tq:
-        streams = dataset[index]
-        # first five minutes to avoid OOM on --upsample models
-        streams = streams[..., :15_000_000]
-        streams = streams.to(device)
-        sources = streams[1:]
-        mix = streams[0]
-        estimates = apply_model(model, mix, shifts=shifts, split=split, overlap=overlap)
-        loss = criterion(estimates, sources)
-        current_loss += loss.item() / len(indexes)
-        del estimates, streams, sources
-
-    if world_size > 1:
-        current_loss = average_metric(current_loss, len(indexes))
-    return current_loss
diff --git a/spaces/Benson/text-generation/Examples/Amor.ly App Descargar Apk.md b/spaces/Benson/text-generation/Examples/Amor.ly App Descargar Apk.md
deleted file mode 100644
index 8b47e77f22a1178e4336bc6882a9549fd3ab417e..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Amor.ly App Descargar Apk.md	
+++ /dev/null
@@ -1,62 +0,0 @@
-
-<h1>Love.ly App: Una forma creativa de capturar y compartir tu historia de vídeo</h1>
-<p>¿Te encanta hacer videos y compartirlos con tus amigos y seguidores? ¿Quieres expresarte a través de streaming de video en vivo y conectarte con gente de todo el mundo? ¿Desea vender sus productos o servicios a través de chat de vídeo en vivo y aumentar sus ventas? Si respondiste sí a cualquiera de estas preguntas, entonces deberías revisar la aplicación Love.ly, una forma creativa de capturar y compartir tu historia de video. </p>
-<h2>¿Qué es la aplicación Love.ly? </h2>
-<p>La aplicación Love.ly es una aplicación de videollamada gratuita que te permite hacer videollamadas en vivo y chatear con personas al azar, hacer nuevos amigos e incluso vender tus productos o servicios a través de streaming de video en vivo. También puedes mostrar tus emociones a tus streamers favoritos enviándoles regalos virtuales geniales y disfrutar de las ventajas exclusivas a medida que subes de nivel. La aplicación Love.ly está disponible para dispositivos iOS y Android, y puedes descargarla desde la App Store o Google Play Store.</p>
-<h2>amor.ly app descargar apk</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash;>>> <a href="https://bltlly.com/2v6LQL">https://bltlly.com/2v6LQL</a></b></p><br /><br />
-<h3>Características de la aplicación Love.ly</h3>
-<p>Algunas de las características de la aplicación Love.ly son:</p>
-<ul>
-<li>Puedes hacer videollamadas gratis en cualquier momento y en cualquier lugar con cualquier persona. </li>
-<li>Puedes conectarte con personas de diferentes países y culturas y aprender algo nuevo. </li>
-<li>Puedes crear tu propia historia de video y compartirla con tus amigos y seguidores. </li>
-<li> Puede vender sus productos o servicios a través de chat de vídeo en vivo y obtener comentarios instantáneos de sus clientes. </li>
-<li>Puedes enviar y recibir regalos y monedas virtuales y usarlas para desbloquear más funciones y recompensas. </li>
-<li>Puedes unirte o crear chats de video grupales con tus amigos y divertirte juntos. </li>
-</ul>
-<h3>Cómo descargar e instalar la aplicación Love.ly en tu dispositivo</h3>
-<p>Para descargar e instalar la aplicación Love.ly en tu dispositivo, sigue estos sencillos pasos:</p>
-<ol>
-<li>Ir a la App Store o Google Play Store en su dispositivo. </li>
-
-<li> Toque en el icono de la aplicación y luego toque en "Instalar" o "Obtener" botón. </li>
-<li>Espere a que la aplicación se descargue e instale en su dispositivo. </li>
-<li>Abra la aplicación y regístrese con su correo electrónico, número de teléfono o cuenta de redes sociales. </li>
-<li>Empieza a hacer videollamadas y disfruta de la aplicación. </li>
-</ol>
-<h2>¿Por qué debería usar la aplicación Love.ly? </h2>
-<p>Hay muchas razones por las que deberías usar la aplicación Love.ly, como:</p>
-<h3>Beneficios de usar la aplicación Love.ly</h3>
-<ul>
-<li>Puedes expresarte creativa y auténticamente a través de streaming de video en vivo. </li>
-<li>Puedes conocer gente nueva, hacer nuevos amigos y expandir tu red social. </li>
-<li> Puede aumentar sus ventas, promover su marca, y hacer crecer su negocio a través de chat de vídeo en vivo. </li>
-<li>Puedes divertirte, relajarte y entretenerte viendo o uniéndote a transmisiones de video en vivo. </li>
-<li>Puedes ganar monedas, regalos y recompensas usando la aplicación regularmente. </li>
-</ul>
-<h3>Consejos y trucos para usar la aplicación Love.ly</h3>
-<ul>
-<li>Usa una buena cámara, micrófono e iluminación para mejorar la calidad de tu video. </li>
-<li>Elija un título pegadizo, descripción y miniatura para su flujo de vídeo para atraer a más espectadores. </li>
-<li>Interactúa con tus espectadores, responde a sus preguntas, agradéceles por sus regalos y pídeles que te sigan. </li>
-<li>Sé respetuoso, educado y amigable con todos en la aplicación. Reporta cualquier comportamiento o contenido inapropiado. </li>
-<li>Explora diferentes categorías, temas y hashtags en la aplicación para encontrar flujos interesantes o personas para ver o chatear con. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <p>En este artículo, hemos discutido qué es la aplicación Love.ly, qué características tiene, cómo descargarla e instalarla en tu dispositivo, por qué deberías usarla y algunos consejos y trucos para usarla. Esperamos que este artículo te haya ayudado a aprender más sobre la aplicación Love.ly y te haya inspirado a probarla. Si tiene alguna pregunta o comentario, no dude en contactarnos o dejar un comentario a continuación. </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre la aplicación Love.ly:</p>
-<p></p>
- <h4>Q: ¿Es segura la aplicación Love.ly? </h4>
-<p>A: Sí, la aplicación Love.ly es segura. Utiliza el cifrado y la protección de la privacidad para garantizar que su información personal y sus datos no se filtren o se utilicen indebidamente. También puedes bloquear o reportar a cualquier usuario que te moleste en la aplicación. </p>
- <h4>Q: ¿Cómo puedo ganar monedas y regalos en la aplicación Love.ly? </h4>
-<p>A: Puede ganar monedas y regalos en la aplicación Love.ly viendo o uniéndose a transmisiones de video en vivo, enviando o recibiendo regalos virtuales, invitando a sus amigos a unirse a la aplicación, completando tareas diarias y participando en eventos y actividades. Puedes usar las monedas y los regalos para desbloquear más funciones y recompensas en la aplicación. </p>
- <h4>Q: ¿Cómo puedo convertirme en un streamer en la aplicación Love.ly? </h4>
-<p>A: Puedes convertirte en un streamer en la aplicación Love.ly creando tu propia historia de video y compartiéndola con tus amigos y seguidores. También puede solicitar convertirse en un streamer verificado en la aplicación mediante el cumplimiento de ciertos requisitos y criterios. Como streamer, puede ganar dinero recibiendo regalos de sus espectadores, vendiendo sus productos o servicios a través del chat de video en vivo y uniéndose al programa de socios. </p>
- <h4>Q: ¿Cuáles son las categorías y temas en la aplicación Love.ly? </h4>
-<p>A: Hay varias categorías y temas en la aplicación Love.ly que puede explorar, como música, danza, belleza, moda, juegos, deportes, viajes, educación, estilo de vida, comedia, arte y más. También puedes buscar hashtags o palabras clave específicas para encontrar flujos o personas que coincidan con tus intereses. </p>
-
-<p>A: Puede ponerse en contacto con el servicio de atención al cliente de la aplicación Love.ly enviando un correo electrónico a support@love.ly.com o rellenando el formulario de comentarios en la aplicación. También puede visitar el sitio web oficial de la aplicación Love.ly en www.love.ly.com para obtener más información y actualizaciones. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/BetterAPI/BetterChat/src/lib/stores/errors.ts b/spaces/BetterAPI/BetterChat/src/lib/stores/errors.ts
deleted file mode 100644
index c7dd124ff03c1845237213b6c22ec7afefcd18e8..0000000000000000000000000000000000000000
--- a/spaces/BetterAPI/BetterChat/src/lib/stores/errors.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { writable } from "svelte/store";
-
-export const ERROR_MESSAGES = {
-	default: "Oops, something went wrong.",
-};
-
-export const error = writable<string | null>(null);
diff --git a/spaces/BetterAPI/BetterChat_new/src/lib/stores/pendingMessage.ts b/spaces/BetterAPI/BetterChat_new/src/lib/stores/pendingMessage.ts
deleted file mode 100644
index f28d7aaf9995f9848f6c7988503c20a08d81d97c..0000000000000000000000000000000000000000
--- a/spaces/BetterAPI/BetterChat_new/src/lib/stores/pendingMessage.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import { writable } from "svelte/store";
-
-export const pendingMessage = writable<string>("");
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/metadata/pkg_resources.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/metadata/pkg_resources.py
deleted file mode 100644
index f330ef12a2c5ea0a4adbecbeea389741479d5eb4..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/metadata/pkg_resources.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import email.message
-import email.parser
-import logging
-import os
-import zipfile
-from typing import Collection, Iterable, Iterator, List, Mapping, NamedTuple, Optional
-
-from pip._vendor import pkg_resources
-from pip._vendor.packaging.requirements import Requirement
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.exceptions import InvalidWheel, NoneMetadataError, UnsupportedWheel
-from pip._internal.utils.egg_link import egg_link_path_from_location
-from pip._internal.utils.misc import display_path, normalize_path
-from pip._internal.utils.wheel import parse_wheel, read_wheel_metadata_file
-
-from .base import (
-    BaseDistribution,
-    BaseEntryPoint,
-    BaseEnvironment,
-    DistributionVersion,
-    InfoPath,
-    Wheel,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class EntryPoint(NamedTuple):
-    name: str
-    value: str
-    group: str
-
-
-class InMemoryMetadata:
-    """IMetadataProvider that reads metadata files from a dictionary.
-
-    This also maps metadata decoding exceptions to our internal exception type.
-    """
-
-    def __init__(self, metadata: Mapping[str, bytes], wheel_name: str) -> None:
-        self._metadata = metadata
-        self._wheel_name = wheel_name
-
-    def has_metadata(self, name: str) -> bool:
-        return name in self._metadata
-
-    def get_metadata(self, name: str) -> str:
-        try:
-            return self._metadata[name].decode()
-        except UnicodeDecodeError as e:
-            # Augment the default error with the origin of the file.
-            raise UnsupportedWheel(
-                f"Error decoding metadata for {self._wheel_name}: {e} in {name} file"
-            )
-
-    def get_metadata_lines(self, name: str) -> Iterable[str]:
-        return pkg_resources.yield_lines(self.get_metadata(name))
-
-    def metadata_isdir(self, name: str) -> bool:
-        return False
-
-    def metadata_listdir(self, name: str) -> List[str]:
-        return []
-
-    def run_script(self, script_name: str, namespace: str) -> None:
-        pass
-
-
-class Distribution(BaseDistribution):
-    def __init__(self, dist: pkg_resources.Distribution) -> None:
-        self._dist = dist
-
-    @classmethod
-    def from_directory(cls, directory: str) -> BaseDistribution:
-        dist_dir = directory.rstrip(os.sep)
-
-        # Build a PathMetadata object, from path to metadata. :wink:
-        base_dir, dist_dir_name = os.path.split(dist_dir)
-        metadata = pkg_resources.PathMetadata(base_dir, dist_dir)
-
-        # Determine the correct Distribution object type.
-        if dist_dir.endswith(".egg-info"):
-            dist_cls = pkg_resources.Distribution
-            dist_name = os.path.splitext(dist_dir_name)[0]
-        else:
-            assert dist_dir.endswith(".dist-info")
-            dist_cls = pkg_resources.DistInfoDistribution
-            dist_name = os.path.splitext(dist_dir_name)[0].split("-")[0]
-
-        dist = dist_cls(base_dir, project_name=dist_name, metadata=metadata)
-        return cls(dist)
-
-    @classmethod
-    def from_metadata_file_contents(
-        cls,
-        metadata_contents: bytes,
-        filename: str,
-        project_name: str,
-    ) -> BaseDistribution:
-        metadata_dict = {
-            "METADATA": metadata_contents,
-        }
-        dist = pkg_resources.DistInfoDistribution(
-            location=filename,
-            metadata=InMemoryMetadata(metadata_dict, filename),
-            project_name=project_name,
-        )
-        return cls(dist)
-
-    @classmethod
-    def from_wheel(cls, wheel: Wheel, name: str) -> BaseDistribution:
-        try:
-            with wheel.as_zipfile() as zf:
-                info_dir, _ = parse_wheel(zf, name)
-                metadata_dict = {
-                    path.split("/", 1)[-1]: read_wheel_metadata_file(zf, path)
-                    for path in zf.namelist()
-                    if path.startswith(f"{info_dir}/")
-                }
-        except zipfile.BadZipFile as e:
-            raise InvalidWheel(wheel.location, name) from e
-        except UnsupportedWheel as e:
-            raise UnsupportedWheel(f"{name} has an invalid wheel, {e}")
-        dist = pkg_resources.DistInfoDistribution(
-            location=wheel.location,
-            metadata=InMemoryMetadata(metadata_dict, wheel.location),
-            project_name=name,
-        )
-        return cls(dist)
-
-    @property
-    def location(self) -> Optional[str]:
-        return self._dist.location
-
-    @property
-    def installed_location(self) -> Optional[str]:
-        egg_link = egg_link_path_from_location(self.raw_name)
-        if egg_link:
-            location = egg_link
-        elif self.location:
-            location = self.location
-        else:
-            return None
-        return normalize_path(location)
-
-    @property
-    def info_location(self) -> Optional[str]:
-        return self._dist.egg_info
-
-    @property
-    def installed_by_distutils(self) -> bool:
-        # A distutils-installed distribution is provided by FileMetadata. This
-        # provider has a "path" attribute not present anywhere else. Not the
-        # best introspection logic, but pip has been doing this for a long time.
-        try:
-            return bool(self._dist._provider.path)
-        except AttributeError:
-            return False
-
-    @property
-    def canonical_name(self) -> NormalizedName:
-        return canonicalize_name(self._dist.project_name)
-
-    @property
-    def version(self) -> DistributionVersion:
-        return parse_version(self._dist.version)
-
-    def is_file(self, path: InfoPath) -> bool:
-        return self._dist.has_metadata(str(path))
-
-    def iter_distutils_script_names(self) -> Iterator[str]:
-        yield from self._dist.metadata_listdir("scripts")
-
-    def read_text(self, path: InfoPath) -> str:
-        name = str(path)
-        if not self._dist.has_metadata(name):
-            raise FileNotFoundError(name)
-        content = self._dist.get_metadata(name)
-        if content is None:
-            raise NoneMetadataError(self, name)
-        return content
-
-    def iter_entry_points(self) -> Iterable[BaseEntryPoint]:
-        for group, entries in self._dist.get_entry_map().items():
-            for name, entry_point in entries.items():
-                name, _, value = str(entry_point).partition("=")
-                yield EntryPoint(name=name.strip(), value=value.strip(), group=group)
-
-    def _metadata_impl(self) -> email.message.Message:
-        """
-        :raises NoneMetadataError: if the distribution reports `has_metadata()`
-            True but `get_metadata()` returns None.
-        """
-        if isinstance(self._dist, pkg_resources.DistInfoDistribution):
-            metadata_name = "METADATA"
-        else:
-            metadata_name = "PKG-INFO"
-        try:
-            metadata = self.read_text(metadata_name)
-        except FileNotFoundError:
-            if self.location:
-                displaying_path = display_path(self.location)
-            else:
-                displaying_path = repr(self.location)
-            logger.warning("No metadata found in %s", displaying_path)
-            metadata = ""
-        feed_parser = email.parser.FeedParser()
-        feed_parser.feed(metadata)
-        return feed_parser.close()
-
-    def iter_dependencies(self, extras: Collection[str] = ()) -> Iterable[Requirement]:
-        if extras:  # pkg_resources raises on invalid extras, so we sanitize.
-            extras = frozenset(extras).intersection(self._dist.extras)
-        return self._dist.requires(extras)
-
-    def iter_provided_extras(self) -> Iterable[str]:
-        return self._dist.extras
-
-
-class Environment(BaseEnvironment):
-    def __init__(self, ws: pkg_resources.WorkingSet) -> None:
-        self._ws = ws
-
-    @classmethod
-    def default(cls) -> BaseEnvironment:
-        return cls(pkg_resources.working_set)
-
-    @classmethod
-    def from_paths(cls, paths: Optional[List[str]]) -> BaseEnvironment:
-        return cls(pkg_resources.WorkingSet(paths))
-
-    def _iter_distributions(self) -> Iterator[BaseDistribution]:
-        for dist in self._ws:
-            yield Distribution(dist)
-
-    def _search_distribution(self, name: str) -> Optional[BaseDistribution]:
-        """Find a distribution matching the ``name`` in the environment.
-
-        This searches from *all* distributions available in the environment, to
-        match the behavior of ``pkg_resources.get_distribution()``.
-        """
-        canonical_name = canonicalize_name(name)
-        for dist in self.iter_all_distributions():
-            if dist.canonical_name == canonical_name:
-                return dist
-        return None
-
-    def get_distribution(self, name: str) -> Optional[BaseDistribution]:
-        # Search the distribution by looking through the working set.
-        dist = self._search_distribution(name)
-        if dist:
-            return dist
-
-        # If distribution could not be found, call working_set.require to
-        # update the working set, and try to find the distribution again.
-        # This might happen for e.g. when you install a package twice, once
-        # using setup.py develop and again using setup.py install. Now when
-        # running pip uninstall twice, the package gets removed from the
-        # working set in the first uninstall, so we have to populate the
-        # working set again so that pip knows about it and the packages gets
-        # picked up and is successfully uninstalled the second time too.
-        try:
-            # We didn't pass in any version specifiers, so this can never
-            # raise pkg_resources.VersionConflict.
-            self._ws.require(name)
-        except pkg_resources.DistributionNotFound:
-            return None
-        return self._search_distribution(name)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/build_clib.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/build_clib.py
deleted file mode 100644
index 67ce2444ea69a0bbdfab0bda8c2aa14951187096..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/build_clib.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import distutils.command.build_clib as orig
-from distutils.errors import DistutilsSetupError
-from distutils import log
-from setuptools.dep_util import newer_pairwise_group
-
-
-class build_clib(orig.build_clib):
-    """
-    Override the default build_clib behaviour to do the following:
-
-    1. Implement a rudimentary timestamp-based dependency system
-       so 'compile()' doesn't run every time.
-    2. Add more keys to the 'build_info' dictionary:
-        * obj_deps - specify dependencies for each object compiled.
-                     this should be a dictionary mapping a key
-                     with the source filename to a list of
-                     dependencies. Use an empty string for global
-                     dependencies.
-        * cflags   - specify a list of additional flags to pass to
-                     the compiler.
-    """
-
-    def build_libraries(self, libraries):
-        for (lib_name, build_info) in libraries:
-            sources = build_info.get('sources')
-            if sources is None or not isinstance(sources, (list, tuple)):
-                raise DistutilsSetupError(
-                    "in 'libraries' option (library '%s'), "
-                    "'sources' must be present and must be "
-                    "a list of source filenames" % lib_name)
-            sources = list(sources)
-
-            log.info("building '%s' library", lib_name)
-
-            # Make sure everything is the correct type.
-            # obj_deps should be a dictionary of keys as sources
-            # and a list/tuple of files that are its dependencies.
-            obj_deps = build_info.get('obj_deps', dict())
-            if not isinstance(obj_deps, dict):
-                raise DistutilsSetupError(
-                    "in 'libraries' option (library '%s'), "
-                    "'obj_deps' must be a dictionary of "
-                    "type 'source: list'" % lib_name)
-            dependencies = []
-
-            # Get the global dependencies that are specified by the '' key.
-            # These will go into every source's dependency list.
-            global_deps = obj_deps.get('', list())
-            if not isinstance(global_deps, (list, tuple)):
-                raise DistutilsSetupError(
-                    "in 'libraries' option (library '%s'), "
-                    "'obj_deps' must be a dictionary of "
-                    "type 'source: list'" % lib_name)
-
-            # Build the list to be used by newer_pairwise_group
-            # each source will be auto-added to its dependencies.
-            for source in sources:
-                src_deps = [source]
-                src_deps.extend(global_deps)
-                extra_deps = obj_deps.get(source, list())
-                if not isinstance(extra_deps, (list, tuple)):
-                    raise DistutilsSetupError(
-                        "in 'libraries' option (library '%s'), "
-                        "'obj_deps' must be a dictionary of "
-                        "type 'source: list'" % lib_name)
-                src_deps.extend(extra_deps)
-                dependencies.append(src_deps)
-
-            expected_objects = self.compiler.object_filenames(
-                sources,
-                output_dir=self.build_temp,
-            )
-
-            if (
-                newer_pairwise_group(dependencies, expected_objects)
-                != ([], [])
-            ):
-                # First, compile the source code to object files in the library
-                # directory.  (This should probably change to putting object
-                # files in a temporary build directory.)
-                macros = build_info.get('macros')
-                include_dirs = build_info.get('include_dirs')
-                cflags = build_info.get('cflags')
-                self.compiler.compile(
-                    sources,
-                    output_dir=self.build_temp,
-                    macros=macros,
-                    include_dirs=include_dirs,
-                    extra_postargs=cflags,
-                    debug=self.debug
-                )
-
-            # Now "link" the object files together into a static library.
-            # (On Unix at least, this isn't really linking -- it just
-            # builds an archive.  Whatever.)
-            self.compiler.create_static_lib(
-                expected_objects,
-                lib_name,
-                output_dir=self.build_clib,
-                debug=self.debug
-            )
diff --git a/spaces/CVPR/LIVE/pybind11/include/pybind11/detail/descr.h b/spaces/CVPR/LIVE/pybind11/include/pybind11/detail/descr.h
deleted file mode 100644
index 92720cd56277e73a27da3bac85c3c2ae6a3589ac..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/include/pybind11/detail/descr.h
+++ /dev/null
@@ -1,100 +0,0 @@
-/*
-    pybind11/detail/descr.h: Helper type for concatenating type signatures at compile time
-
-    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#pragma once
-
-#include "common.h"
-
-PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
-PYBIND11_NAMESPACE_BEGIN(detail)
-
-#if !defined(_MSC_VER)
-#  define PYBIND11_DESCR_CONSTEXPR static constexpr
-#else
-#  define PYBIND11_DESCR_CONSTEXPR const
-#endif
-
-/* Concatenate type signatures at compile time */
-template <size_t N, typename... Ts>
-struct descr {
-    char text[N + 1];
-
-    constexpr descr() : text{'\0'} { }
-    constexpr descr(char const (&s)[N+1]) : descr(s, make_index_sequence<N>()) { }
-
-    template <size_t... Is>
-    constexpr descr(char const (&s)[N+1], index_sequence<Is...>) : text{s[Is]..., '\0'} { }
-
-    template <typename... Chars>
-    constexpr descr(char c, Chars... cs) : text{c, static_cast<char>(cs)..., '\0'} { }
-
-    static constexpr std::array<const std::type_info *, sizeof...(Ts) + 1> types() {
-        return {{&typeid(Ts)..., nullptr}};
-    }
-};
-
-template <size_t N1, size_t N2, typename... Ts1, typename... Ts2, size_t... Is1, size_t... Is2>
-constexpr descr<N1 + N2, Ts1..., Ts2...> plus_impl(const descr<N1, Ts1...> &a, const descr<N2, Ts2...> &b,
-                                                   index_sequence<Is1...>, index_sequence<Is2...>) {
-    return {a.text[Is1]..., b.text[Is2]...};
-}
-
-template <size_t N1, size_t N2, typename... Ts1, typename... Ts2>
-constexpr descr<N1 + N2, Ts1..., Ts2...> operator+(const descr<N1, Ts1...> &a, const descr<N2, Ts2...> &b) {
-    return plus_impl(a, b, make_index_sequence<N1>(), make_index_sequence<N2>());
-}
-
-template <size_t N>
-constexpr descr<N - 1> _(char const(&text)[N]) { return descr<N - 1>(text); }
-constexpr descr<0> _(char const(&)[1]) { return {}; }
-
-template <size_t Rem, size_t... Digits> struct int_to_str : int_to_str<Rem/10, Rem%10, Digits...> { };
-template <size_t...Digits> struct int_to_str<0, Digits...> {
-    static constexpr auto digits = descr<sizeof...(Digits)>(('0' + Digits)...);
-};
-
-// Ternary description (like std::conditional)
-template <bool B, size_t N1, size_t N2>
-constexpr enable_if_t<B, descr<N1 - 1>> _(char const(&text1)[N1], char const(&)[N2]) {
-    return _(text1);
-}
-template <bool B, size_t N1, size_t N2>
-constexpr enable_if_t<!B, descr<N2 - 1>> _(char const(&)[N1], char const(&text2)[N2]) {
-    return _(text2);
-}
-
-template <bool B, typename T1, typename T2>
-constexpr enable_if_t<B, T1> _(const T1 &d, const T2 &) { return d; }
-template <bool B, typename T1, typename T2>
-constexpr enable_if_t<!B, T2> _(const T1 &, const T2 &d) { return d; }
-
-template <size_t Size> auto constexpr _() -> decltype(int_to_str<Size / 10, Size % 10>::digits) {
-    return int_to_str<Size / 10, Size % 10>::digits;
-}
-
-template <typename Type> constexpr descr<1, Type> _() { return {'%'}; }
-
-constexpr descr<0> concat() { return {}; }
-
-template <size_t N, typename... Ts>
-constexpr descr<N, Ts...> concat(const descr<N, Ts...> &descr) { return descr; }
-
-template <size_t N, typename... Ts, typename... Args>
-constexpr auto concat(const descr<N, Ts...> &d, const Args &...args)
-    -> decltype(std::declval<descr<N + 2, Ts...>>() + concat(args...)) {
-    return d + _(", ") + concat(args...);
-}
-
-template <size_t N, typename... Ts>
-constexpr descr<N + 2, Ts...> type_descr(const descr<N, Ts...> &descr) {
-    return _("{") + descr + _("}");
-}
-
-PYBIND11_NAMESPACE_END(detail)
-PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/iter_swap.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/iter_swap.h
deleted file mode 100644
index d9da52a6274c151e8602f41b72a0a9dafed13c26..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/iter_swap.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the iter_swap.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch iter_swap
-
-#include <thrust/system/detail/sequential/iter_swap.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/iter_swap.h>
-#include <thrust/system/cuda/detail/iter_swap.h>
-#include <thrust/system/omp/detail/iter_swap.h>
-#include <thrust/system/tbb/detail/iter_swap.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_ITER_SWAP_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/iter_swap.h>
-#include __THRUST_HOST_SYSTEM_ITER_SWAP_HEADER
-#undef __THRUST_HOST_SYSTEM_ITER_SWAP_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_ITER_SWAP_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/iter_swap.h>
-#include __THRUST_DEVICE_SYSTEM_ITER_SWAP_HEADER
-#undef __THRUST_DEVICE_SYSTEM_ITER_SWAP_HEADER
-
diff --git a/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/normalization/main.py b/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/normalization/main.py
deleted file mode 100644
index f56becc972000202a0e794e2b427072f21d15052..0000000000000000000000000000000000000000
--- a/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/normalization/main.py
+++ /dev/null
@@ -1,43 +0,0 @@
-
-import ast
-import pandas as pd
-
-from normalization.hand_normalization import normalize_hands_full
-from normalization.body_normalization import normalize_body_full
-
-
-# Load the dataset
-df = pd.read_csv("/Users/matyasbohacek/Documents/WLASL_test_15fps.csv", encoding="utf-8")
-
-# Retrieve metadata
-video_size_heights = df["video_size_height"].to_list()
-video_size_widths = df["video_size_width"].to_list()
-
-# Delete redundant (non-related) properties
-del df["video_size_height"]
-del df["video_size_width"]
-
-# Temporarily remove other relevant metadata
-labels = df["labels"].to_list()
-video_fps = df["video_fps"].to_list()
-del df["labels"]
-del df["video_fps"]
-
-# Convert the strings into lists
-convert = lambda x: ast.literal_eval(str(x))
-for column in df.columns:
-    df[column] = df[column].apply(convert)
-
-# Perform the normalizations
-df = normalize_hands_full(df)
-df, invalid_row_indexes = normalize_body_full(df)
-
-# Clear lists of items from deleted rows
-# labels = [t for i, t in enumerate(labels) if i not in invalid_row_indexes]
-# video_fps = [t for i, t in enumerate(video_fps) if i not in invalid_row_indexes]
-
-# Return the metadata back to the dataset
-df["labels"] = labels
-df["video_fps"] = video_fps
-
-df.to_csv("/Users/matyasbohacek/Desktop/WLASL_test_15fps_normalized.csv", encoding="utf-8", index=False)
diff --git a/spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/th.py b/spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/th.py
deleted file mode 100644
index ca6ef9385e3b5c0a439579d3fd7aa73b5dc62758..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/th.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import torch
-from torch.autograd import Variable
-import numpy as np
-import collections
-
-__all__ = ['as_variable', 'as_numpy', 'mark_volatile']
-
-def as_variable(obj):
-    if isinstance(obj, Variable):
-        return obj
-    if isinstance(obj, collections.Sequence):
-        return [as_variable(v) for v in obj]
-    elif isinstance(obj, collections.Mapping):
-        return {k: as_variable(v) for k, v in obj.items()}
-    else:
-        return Variable(obj)
-
-def as_numpy(obj):
-    if isinstance(obj, collections.Sequence):
-        return [as_numpy(v) for v in obj]
-    elif isinstance(obj, collections.Mapping):
-        return {k: as_numpy(v) for k, v in obj.items()}
-    elif isinstance(obj, Variable):
-        return obj.data.cpu().numpy()
-    elif torch.is_tensor(obj):
-        return obj.cpu().numpy()
-    else:
-        return np.array(obj)
-
-def mark_volatile(obj):
-    if torch.is_tensor(obj):
-        obj = Variable(obj)
-    if isinstance(obj, Variable):
-        obj.no_grad = True
-        return obj
-    elif isinstance(obj, collections.Mapping):
-        return {k: mark_volatile(o) for k, o in obj.items()}
-    elif isinstance(obj, collections.Sequence):
-        return [mark_volatile(o) for o in obj]
-    else:
-        return obj
diff --git "a/spaces/CikeyQI/Yunzai/Yunzai/plugins/example/\350\277\233\347\276\244\351\200\200\347\276\244\351\200\232\347\237\245.js" "b/spaces/CikeyQI/Yunzai/Yunzai/plugins/example/\350\277\233\347\276\244\351\200\200\347\276\244\351\200\232\347\237\245.js"
deleted file mode 100644
index e80ec3041a27cb990620efc792d804dfa175df48..0000000000000000000000000000000000000000
--- "a/spaces/CikeyQI/Yunzai/Yunzai/plugins/example/\350\277\233\347\276\244\351\200\200\347\276\244\351\200\232\347\237\245.js"
+++ /dev/null
@@ -1,64 +0,0 @@
-import plugin from '../../lib/plugins/plugin.js'
-export class newcomer extends plugin {
-  constructor() {
-    super({
-      name: '欢迎新人',
-      dsc: '新人入群欢迎',
-      /** https://oicqjs.github.io/oicq/#events */
-      event: 'notice.group.increase',
-      priority: 5000
-    })
-  }
-
-  /** 接受到消息都会执行一次 */
-  async accept() {
-    if (this.e.user_id == this.e.self_id) return
-
-    /** 定义入群欢迎内容 */
-    let msg = '欢迎新人！'
-    /** 冷却cd 30s */
-    let cd = 30
-
-    /** cd */
-    let key = `Yz:newcomers:${this.e.group_id}`
-    if (await redis.get(key)) return
-    redis.set(key, '1', { EX: cd })
-
-    /** 回复 */
-    await this.reply([
-      segment.at(this.e.user_id),
-      // segment.image(),
-      msg
-    ])
-  }
-}
-
-export class outNotice extends plugin {
-  constructor() {
-    super({
-      name: '退群通知',
-      dsc: 'xx退群了',
-      event: 'notice.group.decrease'
-    })
-
-    /** 退群提示词 */
-    this.tips = '退群了'
-  }
-
-  async accept() {
-    if (this.e.user_id == this.e.self_id) return
-
-    let name, msg
-    if (this.e.member) {
-      name = this.e.member.card || this.e.member.nickname
-    }
-
-    if (name) {
-      msg = `${name}(${this.e.user_id}) ${this.tips}`
-    } else {
-      msg = `${this.e.user_id} ${this.tips}`
-    }
-    logger.mark(`[退出通知]${this.e.logText} ${msg}`)
-    await this.reply(msg)
-  }
-}
\ No newline at end of file
diff --git a/spaces/Cippppy/RegressionVisualization/app.py b/spaces/Cippppy/RegressionVisualization/app.py
deleted file mode 100644
index 346449baa8c8a9a07b20a3e079eed134b1cbb276..0000000000000000000000000000000000000000
--- a/spaces/Cippppy/RegressionVisualization/app.py
+++ /dev/null
@@ -1,271 +0,0 @@
-## CHOOSE BETWEEN ALTAIR & MATPLOTLIB
-
-import gradio as gr
-import altair as alt
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import time
-
-def make_plot(plot_type, a, epoch, progress=gr.Progress()):
-    if plot_type == "log":
-        return logReg(a=a, epoch=epoch, progress=progress)
-    elif plot_type == "lin":
-        return linReg(a=a,epoch=epoch, progress=progress)
-        
-
-# a = learning rate
-# epoch = number of training iterations
-def logReg(a, epoch, progress):
-    #### generate random data-set ####
-    progress(0.2, desc="Generating Data")
-    time.sleep(1)
-    #np.random.seed(0) # set random seed (optional)
-
-    ## set mean and covariance of our datasets
-    mean1 = [20,35]
-    cov1 = [[100,100],[-100,100]] 
-    mean2 = [60,70] 
-    cov2 = [[100,100],[100,-100]] 
-
-    ## concatenate values to set x values for datasets
-    x1, x2 = np.random.multivariate_normal(mean1, cov1, 100).T
-    x_1, x_2 = np.random.multivariate_normal(mean2, cov2, 100).T
-    x1 = (np.concatenate((x1, x_1), axis=0))/10
-    x2 = (np.concatenate((x2, x_2), axis=0))/10
-
-    ## set y values of datasets
-    y1 = np.zeros(100) # y[0:100] is zero dataset (dataset we want our decision boundary to be above)
-    y2 = np.ones(100) # y[101:200] is one dataset (dataset we want our decision boundary to be below)
-    y = np.concatenate((y1, y2), axis=0) # combine datasets into one term
-
-    w = np.matrix([(np.random.rand())/100,(np.random.rand())+0.0001/100]) # begin weights at random starting point
-    b = np.matrix([np.random.rand()]) # begin bias term at random starting point
-    wb = np.concatenate((b, w), axis=1) # combine w and b into one weight term
-    print('f = b + x1*w1 + x2*w2')
-    print('Starting weights:', 'f = ', wb[0,0],'+ x1', wb[0,1], '+ x2' , wb[0,2])
-
-    loss = np.empty([epoch]) # term to store all loss terms for plotting
-    iterat = np.empty([epoch]) # term to store all epoch numbers to be plotted vs loss
-    for n in range (epoch):
-        iterat[n] = n
-
-    progress(0.5, desc="Finding Loss & Regression")
-    time.sleep(1.5)
-    
-    for p in range (epoch):
-        L, J = np.matrix([[0.0, 0.0, 0.0]]), 0.0 # reset gradient (∂J(w)/∂w) and loss for each epoch
-        #### Code the equations to solve for the loss and to update 
-        #### the weights and biases for each epoch below. 
-        
-        #### Hint: you will need to use the for loop below to create a summation to solve 
-        #### for wb and J (loss) for each epoch. xj has been given as a starting point.
-        for i in range(len(x1)):
-            xj = np.matrix([1,x1[i],x2[i]])
-            
-            # y_hat = (y_hat or h_w(x) expression)
-            y_hat = 1 / (1 + np.exp(-(wb * xj.T)))
-            # J = (cost function, also referred to as L)
-            J = -((y[i] * np.log(y_hat)) + ((1 - y[i])*np.log(1 - y_hat)))
-            # d_J = (∂J(w)/∂w function, equation can be solved with information on slide 27)
-            d_J = ((y_hat) - y[i]) * xj
-            # wb = (weight updating equation)
-            wb = wb - a * (d_J)
-
-        loss[p] = J
-        if ((p % 100) == 0):
-            print('loss:', J,'  Gradient (∂J(w)/∂w) [[b, w1, w2]]:',L[0])
-    print('Updated weights:', 'f = ', wb[0,0],'+ x1', wb[0,1], '+ x2' , wb[0,2])
-    equation = "f = {w1} + {w2}x1 + {w3}x2".format(w1 = wb[0,0], w2 = wb[0,1], w3 = wb[0,2])
-
-## Plot decision boundary and data
-
-    progress(0.8, desc="Plotting Data")
-    time.sleep(1.5)
-    
-    scatterData1 = pd.DataFrame({'x': x1[1:100],
-                                'y': x2[1:100]})
-    scatterFig1 = alt.Chart(scatterData1).mark_point().encode(
-                                                            x='x:Q',
-                                                            y='y:Q'
-                                                        ).properties(
-                                                            title="Decision Boundary"
-                                                        )
-    scatterData2 = pd.DataFrame({'x': x1[101:200],
-                                'y': x2[101:200]})
-    scatterFig2 = alt.Chart(scatterData2).mark_point(color='green').encode(
-                                                            x='x:Q',
-                                                            y='y:Q',
-                                                        ).properties(
-                                                            title="Decision Boundary"
-                                                        )
-    
-    y2 = np.array(np.array(-(x1*wb[0,1] + wb[0,0])/wb[0,2],dtype=float))
-                
-    trendLine = pd.DataFrame({'x': x1.flatten(),
-                            'y': y2.flatten() })
-    trendLineFig = alt.Chart(trendLine).mark_line().encode(
-                                                            x='x:Q',
-                                                            y='y:Q'
-                                                        ).properties(
-                                                            title="Decision Boundary"
-                                                        )
-    
-    finalFig = scatterFig1 + scatterFig2 + trendLineFig
-    
-    lossData = pd.DataFrame({'Number of Iterations': iterat[100:],
-                            'Loss Value': loss[100:] })
-    lossFig = alt.Chart(lossData).mark_line().encode(
-                                                    x='Number of Iterations:Q',
-                                                    y='Loss Value:Q'
-                                                ).properties(
-                                                    title='Plot of loss values over number of iterations'
-                                                )
-    
-    plt.figure()
-    plt.plot(x1[1:100],x2[1:100],'x', x1[101:200], x2[101:200],'x') # plot random data points
-    plt.plot(x1, -(x1*wb[0,1] + wb[0,0])/wb[0,2] , linestyle = 'solid') # plot decision boundary
-    plt.axis('equal')
-    plt.xlabel('x1')
-    plt.ylabel('x2')
-    plt.title('Decision Boundary')
-    plt.savefig("plt1.png")
-
-    ## Plot training loss v epoch
-    plt.figure()
-    plt.plot(iterat[100:],loss[100:],'x')
-    plt.xlabel('Epoch')
-    plt.ylabel('Loss')
-    plt.title('Training Loss v Epoch')
-    plt.savefig("plt2.png")
-    
-    return [finalFig.interactive(),lossFig.interactive(),"plt1.png","plt2.png",str(loss[len(loss)-1]),str(equation)]
-
-# a = learning rate step size
-# epoch = number of training iterations
-def linReg(a, epoch, progress):
-    # generate random data-set
-    progress(0.2, desc="Generating Data")
-    time.sleep(1)
-    # np.random.seed(0) # choose random seed (optional)
-    x = np.random.rand(100, 1)
-    y = 2 + 3 * x + np.random.rand(100, 1)
-
-    # J = 0 # initialize J, this can be deleted once J is defined in the loop
-    w = np.matrix([np.random.rand(),np.random.rand()]) # slope and y-intercept
-    ite = epoch # number of training iterations
-
-    jList = []
-    numIte = []
-
-    # Write Linear Regression Code to Solve for w (slope and y-intercept) Here ##
-    progress(0.5, desc="Finding Loss & Regression")
-    time.sleep(1.5)
-    
-    for p in range (ite):
-        for i in range(len(x)):
-            # Calculate w and J here
-            x_vec = np.matrix([x[i][0],1]) # Option 1 | Setting up a vector for x (x_vec[j] corresponds to w[j])
-            h = w * x_vec.T ## Hint: you may need to transpose x or w by adding .T to the end of the variable
-            w = w - a * (h - y[i]) * x_vec
-            J = (1/2) * (((h - y[i])) ** 2)
-            J = J.item()
-            
-        jList.append(J)
-        numIte.append(p)
-        print('Loss:', J)
-
-    ## if done correctly the line should be in line with the data points ##
-
-    print('f = ', w[0,0],'x + ', w[0,1])
-    equation = "f = {w1}x + {w2}".format(w1 = w[0,0], w2 = w[0,1])
-    
-    progress(0.8, desc="Plotting Data")
-    time.sleep(1.5)
-    y2 = np.array(np.array((w[0,1]+(w[0,0] * x)),dtype=float)).T
-    
-    scatterData = pd.DataFrame({'x': x.flatten(),
-                                'y': y.flatten()})
-    scatterFig = alt.Chart(scatterData).mark_point().encode(
-                                                            x='x:Q',
-                                                            y='y:Q'
-                                                        ).properties(
-                                                            title='Plot of random data values with linear regression line'
-                                                        )
-    
-    trendLine = pd.DataFrame({'x': x.flatten(),
-                            'y': y2.flatten() })
-    trendLineFig = alt.Chart(trendLine).mark_line().encode(
-                                                            x='x:Q',
-                                                            y='y:Q'
-                                                        )
-    
-    finalFig = scatterFig + trendLineFig
-    
-    lossData = pd.DataFrame({'Number of Iterations': range(1,len(jList)+1),
-                            'Loss Value': jList })
-    lossFig = alt.Chart(lossData).mark_line().encode(
-                                                    x='Number of Iterations:Q',
-                                                    y='Loss Value:Q'
-                                                ).properties(
-                                                    title='Plot of loss values over number of iterations'
-                                                )
-    
-    # plot
-    plt.figure(1)
-    plt.scatter(x,y,s=ite)
-    plt.plot(x, w[0,1] + (w[0,0] * x), linestyle='solid')
-    plt.xlabel('x')
-    plt.ylabel('y')
-    plt.title('Plot of random data values with linear regression line')
-    plt.savefig("plt1.png")
-    
-    plt.figure(2)
-    plt.plot(jList)
-    plt.xlabel('Number of Iterations')
-    plt.ylabel('Loss Value')
-    plt.title('Plot of loss values over number of iterations')
-    plt.savefig("plt2.png")
-    
-    return [finalFig.interactive(),lossFig.interactive(),"plt1.png","plt2.png",str(jList[len(jList)-1]),str(equation)]
-
-with gr.Blocks(title="Regression Visualization") as demo:
-    gr.Markdown(
-    """
-    # Regression Visualization for Machine Learning
-    Choose your variables below to create a linear or logistic regression model!
-    """)
-    with gr.Row():
-        pack = gr.Radio(label="Plot Package",info="Choose 'MatPlot' for MatPlotLib, Choose 'Altair' for Altair",
-                      choices=['MatPlot','Altair'], value='Altair')
-        bType = gr.Radio(label="Regression Type",info="Choose 'log' for logistic, Choose 'lin' for linear",
-                      choices=['log','lin'], value='log')
-        l_rate = gr.Number(value=0.01,label="Learning Rate",info="Enter a value in the range 0.0 - 1.0")
-        epochs = gr.Number(value=100,label="Number of Epochs (Number of Training Iterations)",info="Enter an integer larger than 0",precision=0)
-    bStart = gr.Button(label="Start")
-    with gr.Row() as alt_row:
-        altPlot1 = gr.Plot()
-        altPlot2 = gr.Plot()
-    with gr.Row(visible=False) as mat_row:
-        matPlot1 = gr.Image(type='filepath',label="Regression Graph",height=600,width=600)
-        matPlot2 = gr.Image(type='filepath',label="Regression Graph",height=600,width=600)
-    loss = gr.Textbox(label="Final Loss Value")
-    equ = gr.Textbox(label="Equation for Plotted Line")
-    def changeComp(package):
-        if package == "Altair":
-            return {
-                alt_row: gr.Row.update(visible=True),
-                mat_row: gr.Row.update(visible=False)
-            }
-        else:
-            return {
-                alt_row: gr.Row.update(visible=False),
-                mat_row: gr.Row.update(visible=True)
-            }
-        
-    pack.input(changeComp, show_progress=True, inputs=[pack], outputs=[alt_row, mat_row])
-    bStart.click(make_plot, show_progress=True, inputs=[bType,l_rate,epochs], outputs=[altPlot1,altPlot2, matPlot1, matPlot2, loss, equ])
-    demo.load()
-
-if __name__== "__main__" :
-    demo.queue().launch() 
\ No newline at end of file
diff --git a/spaces/ClearLove443/Robby-chatbot/README.md b/spaces/ClearLove443/Robby-chatbot/README.md
deleted file mode 100644
index 956055a94f999f81f26356e764d8a25e1bf4e38d..0000000000000000000000000000000000000000
--- a/spaces/ClearLove443/Robby-chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Robby Chatbot
-emoji: 🌍
-colorFrom: pink
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Covert1107/sd-diffusers-webui/README.md b/spaces/Covert1107/sd-diffusers-webui/README.md
deleted file mode 100644
index aba04433b7d51c3e29848dcf22487da1fe814c1d..0000000000000000000000000000000000000000
--- a/spaces/Covert1107/sd-diffusers-webui/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Sd Diffusers Webui
-emoji: 🐳
-colorFrom: purple
-colorTo: gray
-sdk: docker
-sdk_version: 3.9
-pinned: false
-license: openrail
-app_port: 7860
-duplicated_from: nyanko7/sd-diffusers-webui
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/FitsImagePlugin.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/FitsImagePlugin.py
deleted file mode 100644
index 1359aeb1282ee78e38f40fc25b4a50b621db4043..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/FitsImagePlugin.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# FITS file handling
-#
-# Copyright (c) 1998-2003 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import math
-
-from . import Image, ImageFile
-
-
-def _accept(prefix):
-    return prefix[:6] == b"SIMPLE"
-
-
-class FitsImageFile(ImageFile.ImageFile):
-    format = "FITS"
-    format_description = "FITS"
-
-    def _open(self):
-        headers = {}
-        while True:
-            header = self.fp.read(80)
-            if not header:
-                msg = "Truncated FITS file"
-                raise OSError(msg)
-            keyword = header[:8].strip()
-            if keyword == b"END":
-                break
-            value = header[8:].split(b"/")[0].strip()
-            if value.startswith(b"="):
-                value = value[1:].strip()
-            if not headers and (not _accept(keyword) or value != b"T"):
-                msg = "Not a FITS file"
-                raise SyntaxError(msg)
-            headers[keyword] = value
-
-        naxis = int(headers[b"NAXIS"])
-        if naxis == 0:
-            msg = "No image data"
-            raise ValueError(msg)
-        elif naxis == 1:
-            self._size = 1, int(headers[b"NAXIS1"])
-        else:
-            self._size = int(headers[b"NAXIS1"]), int(headers[b"NAXIS2"])
-
-        number_of_bits = int(headers[b"BITPIX"])
-        if number_of_bits == 8:
-            self.mode = "L"
-        elif number_of_bits == 16:
-            self.mode = "I"
-            # rawmode = "I;16S"
-        elif number_of_bits == 32:
-            self.mode = "I"
-        elif number_of_bits in (-32, -64):
-            self.mode = "F"
-            # rawmode = "F" if number_of_bits == -32 else "F;64F"
-
-        offset = math.ceil(self.fp.tell() / 2880) * 2880
-        self.tile = [("raw", (0, 0) + self.size, offset, (self.mode, 0, -1))]
-
-
-# --------------------------------------------------------------------
-# Registry
-
-Image.register_open(FitsImageFile.format, FitsImageFile, _accept)
-
-Image.register_extensions(FitsImageFile.format, [".fit", ".fits"])
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/arrayTools.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/arrayTools.py
deleted file mode 100644
index 5fb01a838ae8769809b4f8ab28cb69ea5e84a3dc..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/arrayTools.py
+++ /dev/null
@@ -1,422 +0,0 @@
-"""Routines for calculating bounding boxes, point in rectangle calculations and
-so on.
-"""
-
-from fontTools.misc.roundTools import otRound
-from fontTools.misc.vector import Vector as _Vector
-import math
-import warnings
-
-
-def calcBounds(array):
-    """Calculate the bounding rectangle of a 2D points array.
-
-    Args:
-        array: A sequence of 2D tuples.
-
-    Returns:
-        A four-item tuple representing the bounding rectangle ``(xMin, yMin, xMax, yMax)``.
-    """
-    if not array:
-        return 0, 0, 0, 0
-    xs = [x for x, y in array]
-    ys = [y for x, y in array]
-    return min(xs), min(ys), max(xs), max(ys)
-
-
-def calcIntBounds(array, round=otRound):
-    """Calculate the integer bounding rectangle of a 2D points array.
-
-    Values are rounded to closest integer towards ``+Infinity`` using the
-    :func:`fontTools.misc.fixedTools.otRound` function by default, unless
-    an optional ``round`` function is passed.
-
-    Args:
-        array: A sequence of 2D tuples.
-        round: A rounding function of type ``f(x: float) -> int``.
-
-    Returns:
-        A four-item tuple of integers representing the bounding rectangle:
-        ``(xMin, yMin, xMax, yMax)``.
-    """
-    return tuple(round(v) for v in calcBounds(array))
-
-
-def updateBounds(bounds, p, min=min, max=max):
-    """Add a point to a bounding rectangle.
-
-    Args:
-        bounds: A bounding rectangle expressed as a tuple
-            ``(xMin, yMin, xMax, yMax)``.
-        p: A 2D tuple representing a point.
-        min,max: functions to compute the minimum and maximum.
-
-    Returns:
-        The updated bounding rectangle ``(xMin, yMin, xMax, yMax)``.
-    """
-    (x, y) = p
-    xMin, yMin, xMax, yMax = bounds
-    return min(xMin, x), min(yMin, y), max(xMax, x), max(yMax, y)
-
-
-def pointInRect(p, rect):
-    """Test if a point is inside a bounding rectangle.
-
-    Args:
-        p: A 2D tuple representing a point.
-        rect: A bounding rectangle expressed as a tuple
-            ``(xMin, yMin, xMax, yMax)``.
-
-    Returns:
-        ``True`` if the point is inside the rectangle, ``False`` otherwise.
-    """
-    (x, y) = p
-    xMin, yMin, xMax, yMax = rect
-    return (xMin <= x <= xMax) and (yMin <= y <= yMax)
-
-
-def pointsInRect(array, rect):
-    """Determine which points are inside a bounding rectangle.
-
-    Args:
-        array: A sequence of 2D tuples.
-        rect: A bounding rectangle expressed as a tuple
-            ``(xMin, yMin, xMax, yMax)``.
-
-    Returns:
-        A list containing the points inside the rectangle.
-    """
-    if len(array) < 1:
-        return []
-    xMin, yMin, xMax, yMax = rect
-    return [(xMin <= x <= xMax) and (yMin <= y <= yMax) for x, y in array]
-
-
-def vectorLength(vector):
-    """Calculate the length of the given vector.
-
-    Args:
-        vector: A 2D tuple.
-
-    Returns:
-        The Euclidean length of the vector.
-    """
-    x, y = vector
-    return math.sqrt(x**2 + y**2)
-
-
-def asInt16(array):
-    """Round a list of floats to 16-bit signed integers.
-
-    Args:
-        array: List of float values.
-
-    Returns:
-        A list of rounded integers.
-    """
-    return [int(math.floor(i + 0.5)) for i in array]
-
-
-def normRect(rect):
-    """Normalize a bounding box rectangle.
-
-    This function "turns the rectangle the right way up", so that the following
-    holds::
-
-        xMin <= xMax and yMin <= yMax
-
-    Args:
-        rect: A bounding rectangle expressed as a tuple
-            ``(xMin, yMin, xMax, yMax)``.
-
-    Returns:
-        A normalized bounding rectangle.
-    """
-    (xMin, yMin, xMax, yMax) = rect
-    return min(xMin, xMax), min(yMin, yMax), max(xMin, xMax), max(yMin, yMax)
-
-
-def scaleRect(rect, x, y):
-    """Scale a bounding box rectangle.
-
-    Args:
-        rect: A bounding rectangle expressed as a tuple
-            ``(xMin, yMin, xMax, yMax)``.
-        x: Factor to scale the rectangle along the X axis.
-        Y: Factor to scale the rectangle along the Y axis.
-
-    Returns:
-        A scaled bounding rectangle.
-    """
-    (xMin, yMin, xMax, yMax) = rect
-    return xMin * x, yMin * y, xMax * x, yMax * y
-
-
-def offsetRect(rect, dx, dy):
-    """Offset a bounding box rectangle.
-
-    Args:
-        rect: A bounding rectangle expressed as a tuple
-            ``(xMin, yMin, xMax, yMax)``.
-        dx: Amount to offset the rectangle along the X axis.
-        dY: Amount to offset the rectangle along the Y axis.
-
-    Returns:
-        An offset bounding rectangle.
-    """
-    (xMin, yMin, xMax, yMax) = rect
-    return xMin + dx, yMin + dy, xMax + dx, yMax + dy
-
-
-def insetRect(rect, dx, dy):
-    """Inset a bounding box rectangle on all sides.
-
-    Args:
-        rect: A bounding rectangle expressed as a tuple
-            ``(xMin, yMin, xMax, yMax)``.
-        dx: Amount to inset the rectangle along the X axis.
-        dY: Amount to inset the rectangle along the Y axis.
-
-    Returns:
-        An inset bounding rectangle.
-    """
-    (xMin, yMin, xMax, yMax) = rect
-    return xMin + dx, yMin + dy, xMax - dx, yMax - dy
-
-
-def sectRect(rect1, rect2):
-    """Test for rectangle-rectangle intersection.
-
-    Args:
-        rect1: First bounding rectangle, expressed as tuples
-            ``(xMin, yMin, xMax, yMax)``.
-        rect2: Second bounding rectangle.
-
-    Returns:
-        A boolean and a rectangle.
-        If the input rectangles intersect, returns ``True`` and the intersecting
-        rectangle. Returns ``False`` and ``(0, 0, 0, 0)`` if the input
-        rectangles don't intersect.
-    """
-    (xMin1, yMin1, xMax1, yMax1) = rect1
-    (xMin2, yMin2, xMax2, yMax2) = rect2
-    xMin, yMin, xMax, yMax = (
-        max(xMin1, xMin2),
-        max(yMin1, yMin2),
-        min(xMax1, xMax2),
-        min(yMax1, yMax2),
-    )
-    if xMin >= xMax or yMin >= yMax:
-        return False, (0, 0, 0, 0)
-    return True, (xMin, yMin, xMax, yMax)
-
-
-def unionRect(rect1, rect2):
-    """Determine union of bounding rectangles.
-
-    Args:
-        rect1: First bounding rectangle, expressed as tuples
-            ``(xMin, yMin, xMax, yMax)``.
-        rect2: Second bounding rectangle.
-
-    Returns:
-        The smallest rectangle in which both input rectangles are fully
-        enclosed.
-    """
-    (xMin1, yMin1, xMax1, yMax1) = rect1
-    (xMin2, yMin2, xMax2, yMax2) = rect2
-    xMin, yMin, xMax, yMax = (
-        min(xMin1, xMin2),
-        min(yMin1, yMin2),
-        max(xMax1, xMax2),
-        max(yMax1, yMax2),
-    )
-    return (xMin, yMin, xMax, yMax)
-
-
-def rectCenter(rect):
-    """Determine rectangle center.
-
-    Args:
-        rect: Bounding rectangle, expressed as tuples
-            ``(xMin, yMin, xMax, yMax)``.
-
-    Returns:
-        A 2D tuple representing the point at the center of the rectangle.
-    """
-    (xMin, yMin, xMax, yMax) = rect
-    return (xMin + xMax) / 2, (yMin + yMax) / 2
-
-
-def rectArea(rect):
-    """Determine rectangle area.
-
-    Args:
-        rect: Bounding rectangle, expressed as tuples
-            ``(xMin, yMin, xMax, yMax)``.
-
-    Returns:
-        The area of the rectangle.
-    """
-    (xMin, yMin, xMax, yMax) = rect
-    return (yMax - yMin) * (xMax - xMin)
-
-
-def intRect(rect):
-    """Round a rectangle to integer values.
-
-    Guarantees that the resulting rectangle is NOT smaller than the original.
-
-    Args:
-        rect: Bounding rectangle, expressed as tuples
-            ``(xMin, yMin, xMax, yMax)``.
-
-    Returns:
-        A rounded bounding rectangle.
-    """
-    (xMin, yMin, xMax, yMax) = rect
-    xMin = int(math.floor(xMin))
-    yMin = int(math.floor(yMin))
-    xMax = int(math.ceil(xMax))
-    yMax = int(math.ceil(yMax))
-    return (xMin, yMin, xMax, yMax)
-
-
-def quantizeRect(rect, factor=1):
-    """
-    >>> bounds = (72.3, -218.4, 1201.3, 919.1)
-    >>> quantizeRect(bounds)
-    (72, -219, 1202, 920)
-    >>> quantizeRect(bounds, factor=10)
-    (70, -220, 1210, 920)
-    >>> quantizeRect(bounds, factor=100)
-    (0, -300, 1300, 1000)
-    """
-    if factor < 1:
-        raise ValueError(f"Expected quantization factor >= 1, found: {factor!r}")
-    xMin, yMin, xMax, yMax = normRect(rect)
-    return (
-        int(math.floor(xMin / factor) * factor),
-        int(math.floor(yMin / factor) * factor),
-        int(math.ceil(xMax / factor) * factor),
-        int(math.ceil(yMax / factor) * factor),
-    )
-
-
-class Vector(_Vector):
-    def __init__(self, *args, **kwargs):
-        warnings.warn(
-            "fontTools.misc.arrayTools.Vector has been deprecated, please use "
-            "fontTools.misc.vector.Vector instead.",
-            DeprecationWarning,
-        )
-
-
-def pairwise(iterable, reverse=False):
-    """Iterate over current and next items in iterable.
-
-    Args:
-        iterable: An iterable
-        reverse: If true, iterate in reverse order.
-
-    Returns:
-        A iterable yielding two elements per iteration.
-
-    Example:
-
-        >>> tuple(pairwise([]))
-        ()
-        >>> tuple(pairwise([], reverse=True))
-        ()
-        >>> tuple(pairwise([0]))
-        ((0, 0),)
-        >>> tuple(pairwise([0], reverse=True))
-        ((0, 0),)
-        >>> tuple(pairwise([0, 1]))
-        ((0, 1), (1, 0))
-        >>> tuple(pairwise([0, 1], reverse=True))
-        ((1, 0), (0, 1))
-        >>> tuple(pairwise([0, 1, 2]))
-        ((0, 1), (1, 2), (2, 0))
-        >>> tuple(pairwise([0, 1, 2], reverse=True))
-        ((2, 1), (1, 0), (0, 2))
-        >>> tuple(pairwise(['a', 'b', 'c', 'd']))
-        (('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'a'))
-        >>> tuple(pairwise(['a', 'b', 'c', 'd'], reverse=True))
-        (('d', 'c'), ('c', 'b'), ('b', 'a'), ('a', 'd'))
-    """
-    if not iterable:
-        return
-    if reverse:
-        it = reversed(iterable)
-    else:
-        it = iter(iterable)
-    first = next(it, None)
-    a = first
-    for b in it:
-        yield (a, b)
-        a = b
-    yield (a, first)
-
-
-def _test():
-    """
-    >>> import math
-    >>> calcBounds([])
-    (0, 0, 0, 0)
-    >>> calcBounds([(0, 40), (0, 100), (50, 50), (80, 10)])
-    (0, 10, 80, 100)
-    >>> updateBounds((0, 0, 0, 0), (100, 100))
-    (0, 0, 100, 100)
-    >>> pointInRect((50, 50), (0, 0, 100, 100))
-    True
-    >>> pointInRect((0, 0), (0, 0, 100, 100))
-    True
-    >>> pointInRect((100, 100), (0, 0, 100, 100))
-    True
-    >>> not pointInRect((101, 100), (0, 0, 100, 100))
-    True
-    >>> list(pointsInRect([(50, 50), (0, 0), (100, 100), (101, 100)], (0, 0, 100, 100)))
-    [True, True, True, False]
-    >>> vectorLength((3, 4))
-    5.0
-    >>> vectorLength((1, 1)) == math.sqrt(2)
-    True
-    >>> list(asInt16([0, 0.1, 0.5, 0.9]))
-    [0, 0, 1, 1]
-    >>> normRect((0, 10, 100, 200))
-    (0, 10, 100, 200)
-    >>> normRect((100, 200, 0, 10))
-    (0, 10, 100, 200)
-    >>> scaleRect((10, 20, 50, 150), 1.5, 2)
-    (15.0, 40, 75.0, 300)
-    >>> offsetRect((10, 20, 30, 40), 5, 6)
-    (15, 26, 35, 46)
-    >>> insetRect((10, 20, 50, 60), 5, 10)
-    (15, 30, 45, 50)
-    >>> insetRect((10, 20, 50, 60), -5, -10)
-    (5, 10, 55, 70)
-    >>> intersects, rect = sectRect((0, 10, 20, 30), (0, 40, 20, 50))
-    >>> not intersects
-    True
-    >>> intersects, rect = sectRect((0, 10, 20, 30), (5, 20, 35, 50))
-    >>> intersects
-    1
-    >>> rect
-    (5, 20, 20, 30)
-    >>> unionRect((0, 10, 20, 30), (0, 40, 20, 50))
-    (0, 10, 20, 50)
-    >>> rectCenter((0, 0, 100, 200))
-    (50.0, 100.0)
-    >>> rectCenter((0, 0, 100, 199.0))
-    (50.0, 99.5)
-    >>> intRect((0.9, 2.9, 3.1, 4.1))
-    (0, 2, 4, 5)
-    """
-
-
-if __name__ == "__main__":
-    import sys
-    import doctest
-
-    sys.exit(doctest.testmod().failed)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/qu2cu/qu2cu.c b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/qu2cu/qu2cu.c
deleted file mode 100644
index 16f262624e082735e6d8c2c6dcb008a785f9ebb6..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/qu2cu/qu2cu.c
+++ /dev/null
@@ -1,13186 +0,0 @@
-/* Generated by Cython 0.29.36 */
-
-/* BEGIN: Cython Metadata
-{
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-        "define_macros": [
-            [
-                "CYTHON_TRACE_NOGIL",
-                "1"
-            ]
-        ],
-        "name": "fontTools.qu2cu.qu2cu",
-        "sources": [
-            "Lib/fontTools/qu2cu/qu2cu.py"
-        ]
-    },
-    "module_name": "fontTools.qu2cu.qu2cu"
-}
-END: Cython Metadata */
-
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-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#else
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_PYSTON 0
-  #define CYTHON_COMPILING_IN_CPYTHON 1
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #if PY_VERSION_HEX < 0x02070000
-    #undef CYTHON_USE_PYTYPE_LOOKUP
-    #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)
-    #define CYTHON_USE_PYTYPE_LOOKUP 1
-  #endif
-  #if PY_MAJOR_VERSION < 3
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #if PY_VERSION_HEX < 0x02070000
-    #undef CYTHON_USE_PYLONG_INTERNALS
-    #define CYTHON_USE_PYLONG_INTERNALS 0
-  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)
-    #define CYTHON_USE_PYLONG_INTERNALS (PY_VERSION_HEX < 0x030C00A5)
-  #endif
-  #ifndef CYTHON_USE_PYLIST_INTERNALS
-    #define CYTHON_USE_PYLIST_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
-    #undef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #elif !defined(CYTHON_USE_UNICODE_WRITER)
-    #define CYTHON_USE_UNICODE_WRITER 1
-  #endif
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #if PY_VERSION_HEX >= 0x030B00A4
-    #undef CYTHON_FAST_THREAD_STATE
-    #define CYTHON_FAST_THREAD_STATE 0
-  #elif !defined(CYTHON_FAST_THREAD_STATE)
-    #define CYTHON_FAST_THREAD_STATE 1
-  #endif
-  #ifndef CYTHON_FAST_PYCALL
-    #define CYTHON_FAST_PYCALL (PY_VERSION_HEX < 0x030A0000)
-  #endif
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000)
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
-  #endif
-  #ifndef CYTHON_USE_DICT_VERSIONS
-    #define CYTHON_USE_DICT_VERSIONS ((PY_VERSION_HEX >= 0x030600B1) && (PY_VERSION_HEX < 0x030C00A5))
-  #endif
-  #if PY_VERSION_HEX >= 0x030B00A4
-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3)
-  #endif
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
-  #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-           typedef unsigned char     uint8_t;
-           typedef unsigned int      uint32_t;
-        #else
-           typedef unsigned __int8   uint8_t;
-           typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-#else
-   #include <stdint.h>
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus) && __cplusplus >= 201103L
-    #if __has_cpp_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH [[fallthrough]]
-    #elif __has_cpp_attribute(clang::fallthrough)
-      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-    #elif __has_cpp_attribute(gnu::fallthrough)
-      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__ ) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-  #define __Pyx_DefaultClassType PyClass_Type
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject* co=NULL;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(0))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto cleanup_code_too;
-        if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here
-        if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too;
-        Py_XDECREF((PyObject*)co);
-        co = (PyCodeObject*)call_result;
-        call_result = NULL;
-        if (0) {
-            cleanup_code_too:
-            Py_XDECREF((PyObject*)co);
-            co = NULL;
-        }
-        end:
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(call_result);
-        Py_XDECREF(empty);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return co;
-    }
-#else
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-  #define __Pyx_DefaultClassType PyType_Type
-#endif
-#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o)
-#else
-  #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o)
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_FAST_PYCCALL
-#define __Pyx_PyFastCFunction_Check(func)\
-    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))
-#else
-#define __Pyx_PyFastCFunction_Check(func) 0
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
-  #define PyObject_Realloc(p)  PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1
-  #define PyMem_RawMalloc(n)           PyMem_Malloc(n)
-  #define PyMem_RawRealloc(p, n)       PyMem_Realloc(p, n)
-  #define PyMem_RawFree(p)             PyMem_Free(p)
-#endif
-#if CYTHON_COMPILING_IN_PYSTON
-  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
-#else
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
-#endif
-#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#elif PY_VERSION_HEX >= 0x03060000
-  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
-#elif PY_VERSION_HEX >= 0x03000000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#else
-  #define __Pyx_PyThreadState_Current _PyThreadState_Current
-#endif
-#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
-#include "pythread.h"
-#define Py_tss_NEEDS_INIT 0
-typedef int Py_tss_t;
-static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
-  *key = PyThread_create_key();
-  return 0;
-}
-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
-  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
-  *key = Py_tss_NEEDS_INIT;
-  return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
-  PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
-  return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
-  PyThread_delete_key(*key);
-  *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
-  return PyThread_set_key_value(*key, value);
-}
-static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
-  return PyThread_get_key_value(*key);
-}
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
-#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
-#else
-#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
-#endif
-#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
-#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
-#else
-#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)
-#endif
-#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
-  #define CYTHON_PEP393_ENABLED 1
-  #if PY_VERSION_HEX >= 0x030C0000
-    #define __Pyx_PyUnicode_READY(op)       (0)
-  #else
-    #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
-                                                0 : _PyUnicode_Ready((PyObject *)(op)))
-  #endif
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
-  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
-  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
-  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
-  #if PY_VERSION_HEX >= 0x030C0000
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))
-  #else
-    #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length))
-    #else
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
-    #endif
-  #endif
-#else
-  #define CYTHON_PEP393_ENABLED 0
-  #define PyUnicode_1BYTE_KIND  1
-  #define PyUnicode_2BYTE_KIND  2
-  #define PyUnicode_4BYTE_KIND  4
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
-  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
-#else
-  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
-      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
-  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
-  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
-  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
-#endif
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-  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
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-  #define PyIntObject                  PyLongObject
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-  #define PyInt_FromLong               PyLong_FromLong
-  #define PyInt_FromSize_t             PyLong_FromSize_t
-  #define PyInt_FromSsize_t            PyLong_FromSsize_t
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-#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
-  #ifndef PyUnicode_InternFromString
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-  typedef long Py_hash_t;
-  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsHash_t
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-  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
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-  #if PY_VERSION_HEX >= 0x030500B1
-    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
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-  #define __Pyx_PyType_AsAsync(obj) NULL
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-#ifndef __Pyx_PyAsyncMethodsStruct
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-
-#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS)
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-  float value;
-  memset(&value, 0xFF, sizeof(value));
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-#define __Pyx_truncl trunc
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-#define __Pyx_truncl truncl
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-#define __PYX_ERR(f_index, lineno, Ln_error) \
-    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
-
-#ifndef __PYX_EXTERN_C
-  #ifdef __cplusplus
-    #define __PYX_EXTERN_C extern "C"
-  #else
-    #define __PYX_EXTERN_C extern
-  #endif
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-
-#define __PYX_HAVE__fontTools__qu2cu__qu2cu
-#define __PYX_HAVE_API__fontTools__qu2cu__qu2cu
-/* Early includes */
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-#include <omp.h>
-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
-                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_uchar_cast(c) ((unsigned char)c)
-#define __Pyx_long_cast(x) ((long)x)
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
-    (sizeof(type) < sizeof(Py_ssize_t))  ||\
-    (sizeof(type) > sizeof(Py_ssize_t) &&\
-          likely(v < (type)PY_SSIZE_T_MAX ||\
-                 v == (type)PY_SSIZE_T_MAX)  &&\
-          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
-                                v == (type)PY_SSIZE_T_MIN)))  ||\
-    (sizeof(type) == sizeof(Py_ssize_t) &&\
-          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
-                               v == (type)PY_SSIZE_T_MAX)))  )
-static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
-    return (size_t) i < (size_t) limit;
-}
-#if defined (__cplusplus) && __cplusplus >= 201103L
-    #include <cstdlib>
-    #define __Pyx_sst_abs(value) std::abs(value)
-#elif SIZEOF_INT >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
-    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
-    #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
-    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
-    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-static PyObject *__pyx_m = NULL;
-static PyObject *__pyx_d;
-static PyObject *__pyx_b;
-static PyObject *__pyx_cython_runtime = NULL;
-static PyObject *__pyx_empty_tuple;
-static PyObject *__pyx_empty_bytes;
-static PyObject *__pyx_empty_unicode;
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm= __FILE__;
-static const char *__pyx_filename;
-
-/* Header.proto */
-#if !defined(CYTHON_CCOMPLEX)
-  #if defined(__cplusplus)
-    #define CYTHON_CCOMPLEX 1
-  #elif defined(_Complex_I)
-    #define CYTHON_CCOMPLEX 1
-  #else
-    #define CYTHON_CCOMPLEX 0
-  #endif
-#endif
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #include <complex>
-  #else
-    #include <complex.h>
-  #endif
-#endif
-#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
-  #undef _Complex_I
-  #define _Complex_I 1.0fj
-#endif
-
-
-static const char *__pyx_f[] = {
-  "Lib/fontTools/qu2cu/qu2cu.py",
-};
-/* Declarations.proto */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    typedef ::std::complex< double > __pyx_t_double_complex;
-  #else
-    typedef double _Complex __pyx_t_double_complex;
-  #endif
-#else
-    typedef struct { double real, imag; } __pyx_t_double_complex;
-#endif
-static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
-
-
-/*--- Type declarations ---*/
-struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__quadratic_to_curves;
-struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr;
-struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_2_spline_to_curves;
-struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_3_genexpr;
-
-/* "fontTools/qu2cu/qu2cu.py":185
- *     is_complex=cython.int,
- * )
- * def quadratic_to_curves(             # <<<<<<<<<<<<<<
- *     quads: List[List[Point]],
- *     max_err: float = 0.5,
- */
-struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__quadratic_to_curves {
-  PyObject_HEAD
-  PyObject *__pyx_8genexpr3__pyx_v_curve;
-};
-
-
-/* "fontTools/qu2cu/qu2cu.py":238
- * 
- *     if not is_complex:
- *         curves = [tuple((c.real, c.imag) for c in curve) for curve in curves]             # <<<<<<<<<<<<<<
- *     return curves
- * 
- */
-struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr {
-  PyObject_HEAD
-  struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__quadratic_to_curves *__pyx_outer_scope;
-  PyObject *__pyx_v_c;
-  int __pyx_v_cost;
-  int __pyx_v_is_complex;
-  PyObject *__pyx_t_0;
-  Py_ssize_t __pyx_t_1;
-  PyObject *(*__pyx_t_2)(PyObject *);
-};
-
-
-/* "fontTools/qu2cu/qu2cu.py":268
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- * )
- * def spline_to_curves(q, costs, tolerance=0.5, all_cubic=False):             # <<<<<<<<<<<<<<
- *     """
- *     q: quadratic spline with alternating on-curve / off-curve points.
- */
-struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_2_spline_to_curves {
-  PyObject_HEAD
-  PyObject *__pyx_v_orig;
-  PyObject *__pyx_v_reconst;
-};
-
-
-/* "fontTools/qu2cu/qu2cu.py":343
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- *                 p0, p1, p2, p3 = tuple(v - u for v, u in zip(reconst, orig))             # <<<<<<<<<<<<<<
- * 
- *                 if not cubic_farthest_fit_inside(p0, p1, p2, p3, tolerance):
- */
-struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_3_genexpr {
-  PyObject_HEAD
-  struct __pyx_obj_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_2_spline_to_curves *__pyx_outer_scope;
-  int __pyx_v_all_cubic;
-  int __pyx_v_count;
-  double __pyx_v_err;
-  double __pyx_v_error;
-  int __pyx_v_i;
-  int __pyx_v_i_sol_count;
-  double __pyx_v_i_sol_error;
-  int __pyx_v_is_cubic;
-  int __pyx_v_j;
-  int __pyx_v_j_sol_count;
-  double __pyx_v_j_sol_error;
-  int __pyx_v_k;
-  __pyx_t_double_complex __pyx_v_p0;
-  __pyx_t_double_complex __pyx_v_p1;
-  __pyx_t_double_complex __pyx_v_p2;
-  __pyx_t_double_complex __pyx_v_p3;
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-/* PyErrExceptionMatches.proto */
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-#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
-#else
-#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* pyfrozenset_new.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it);
-
-/* PySetContains.proto */
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq);
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
-
-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
-
-/* IncludeStringH.proto */
-#include <string.h>
-
-/* PyObject_GenericGetAttrNoDict.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* FetchCommonType.proto */
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
-
-/* CythonFunctionShared.proto */
-#define __Pyx_CyFunction_USED 1
-#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
-#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
-#define __Pyx_CYFUNCTION_CCLASS        0x04
-#define __Pyx_CyFunction_GetClosure(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_closure)
-#define __Pyx_CyFunction_GetClassObj(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_classobj)
-#define __Pyx_CyFunction_Defaults(type, f)\
-    ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
-#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
-    ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
-typedef struct {
-    PyCFunctionObject func;
-#if PY_VERSION_HEX < 0x030500A0
-    PyObject *func_weakreflist;
-#endif
-    PyObject *func_dict;
-    PyObject *func_name;
-    PyObject *func_qualname;
-    PyObject *func_doc;
-    PyObject *func_globals;
-    PyObject *func_code;
-    PyObject *func_closure;
-    PyObject *func_classobj;
-    void *defaults;
-    int defaults_pyobjects;
-    size_t defaults_size;  // used by FusedFunction for copying defaults
-    int flags;
-    PyObject *defaults_tuple;
-    PyObject *defaults_kwdict;
-    PyObject *(*defaults_getter)(PyObject *);
-    PyObject *func_annotations;
-} __pyx_CyFunctionObject;
-static PyTypeObject *__pyx_CyFunctionType = 0;
-#define __Pyx_CyFunction_Check(obj)  (__Pyx_TypeCheck(obj, __pyx_CyFunctionType))
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *self,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
-                                                         size_t size,
-                                                         int pyobjects);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
-                                                            PyObject *tuple);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
-                                                             PyObject *dict);
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
-                                                              PyObject *dict);
-static int __pyx_CyFunction_init(void);
-
-/* CythonFunction.proto */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-
-/* ObjectGetItem.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key);
-#else
-#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
-#endif
-
-/* BytesEquals.proto */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* UnicodeEquals.proto */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* PyErrFetchRestore.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
-#define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
-#define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
-#define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
-#define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
-#else
-#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
-#endif
-#else
-#define __Pyx_PyErr_Clear() PyErr_Clear()
-#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
-#define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)
-#define __Pyx_ErrRestoreInState(tstate, type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetchInState(tstate, type, value, tb)  PyErr_Fetch(type, value, tb)
-#define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)
-#endif
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-/* FromPy.proto */
-static __pyx_t_double_complex __Pyx_PyComplex_As___pyx_t_double_complex(PyObject*);
-
-/* GCCDiagnostics.proto */
-#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-#define __Pyx_HAS_GCC_DIAGNOSTIC
-#endif
-
-/* RealImag.proto */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #define __Pyx_CREAL(z) ((z).real())
-    #define __Pyx_CIMAG(z) ((z).imag())
-  #else
-    #define __Pyx_CREAL(z) (__real__(z))
-    #define __Pyx_CIMAG(z) (__imag__(z))
-  #endif
-#else
-    #define __Pyx_CREAL(z) ((z).real)
-    #define __Pyx_CIMAG(z) ((z).imag)
-#endif
-#if defined(__cplusplus) && CYTHON_CCOMPLEX\
-        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
-    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
-    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
-#else
-    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
-    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_double(a, b) ((a)-(b))
-    #define __Pyx_c_prod_double(a, b) ((a)*(b))
-    #define __Pyx_c_quot_double(a, b) ((a)/(b))
-    #define __Pyx_c_neg_double(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
-    #define __Pyx_c_conj_double(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (::std::abs(z))
-        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_double(z) ((z)==0)
-    #define __Pyx_c_conj_double(z)    (conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (cabs(z))
-        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    #endif
-#endif
-
-/* ToPy.proto */
-#define __pyx_PyComplex_FromComplex(z)\
-        PyComplex_FromDoubles((double)__Pyx_CREAL(z),\
-                              (double)__Pyx_CIMAG(z))
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-/* RaiseException.proto */
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* PyObjectCallMethod1.proto */
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg);
-
-/* CoroutineBase.proto */
-typedef PyObject *(*__pyx_coroutine_body_t)(PyObject *, PyThreadState *, PyObject *);
-#if CYTHON_USE_EXC_INFO_STACK
-#define __Pyx_ExcInfoStruct  _PyErr_StackItem
-#else
-typedef struct {
-    PyObject *exc_type;
-    PyObject *exc_value;
-    PyObject *exc_traceback;
-} __Pyx_ExcInfoStruct;
-#endif
-typedef struct {
-    PyObject_HEAD
-    __pyx_coroutine_body_t body;
-    PyObject *closure;
-    __Pyx_ExcInfoStruct gi_exc_state;
-    PyObject *gi_weakreflist;
-    PyObject *classobj;
-    PyObject *yieldfrom;
-    PyObject *gi_name;
-    PyObject *gi_qualname;
-    PyObject *gi_modulename;
-    PyObject *gi_code;
-    PyObject *gi_frame;
-    int resume_label;
-    char is_running;
-} __pyx_CoroutineObject;
-static __pyx_CoroutineObject *__Pyx__Coroutine_New(
-    PyTypeObject *type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-    PyObject *name, PyObject *qualname, PyObject *module_name);
-static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
-            __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name);
-static CYTHON_INLINE void __Pyx_Coroutine_ExceptionClear(__Pyx_ExcInfoStruct *self);
-static int __Pyx_Coroutine_clear(PyObject *self);
-static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value);
-static PyObject *__Pyx_Coroutine_Close(PyObject *self);
-static PyObject *__Pyx_Coroutine_Throw(PyObject *gen, PyObject *args);
-#if CYTHON_USE_EXC_INFO_STACK
-#define __Pyx_Coroutine_SwapException(self)
-#define __Pyx_Coroutine_ResetAndClearException(self)  __Pyx_Coroutine_ExceptionClear(&(self)->gi_exc_state)
-#else
-#define __Pyx_Coroutine_SwapException(self) {\
-    __Pyx_ExceptionSwap(&(self)->gi_exc_state.exc_type, &(self)->gi_exc_state.exc_value, &(self)->gi_exc_state.exc_traceback);\
-    __Pyx_Coroutine_ResetFrameBackpointer(&(self)->gi_exc_state);\
-    }
-#define __Pyx_Coroutine_ResetAndClearException(self) {\
-    __Pyx_ExceptionReset((self)->gi_exc_state.exc_type, (self)->gi_exc_state.exc_value, (self)->gi_exc_state.exc_traceback);\
-    (self)->gi_exc_state.exc_type = (self)->gi_exc_state.exc_value = (self)->gi_exc_state.exc_traceback = NULL;\
-    }
-#endif
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyGen_FetchStopIterationValue(pvalue)\
-    __Pyx_PyGen__FetchStopIterationValue(__pyx_tstate, pvalue)
-#else
-#define __Pyx_PyGen_FetchStopIterationValue(pvalue)\
-    __Pyx_PyGen__FetchStopIterationValue(__Pyx_PyThreadState_Current, pvalue)
-#endif
-static int __Pyx_PyGen__FetchStopIterationValue(PyThreadState *tstate, PyObject **pvalue);
-static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__Pyx_ExcInfoStruct *exc_state);
-
-/* PatchModuleWithCoroutine.proto */
-static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code);
-
-/* PatchGeneratorABC.proto */
-static int __Pyx_patch_abc(void);
-
-/* Generator.proto */
-#define __Pyx_Generator_USED
-static PyTypeObject *__pyx_GeneratorType = 0;
-#define __Pyx_Generator_CheckExact(obj) (Py_TYPE(obj) == __pyx_GeneratorType)
-#define __Pyx_Generator_New(body, code, closure, name, qualname, module_name)\
-    __Pyx__Coroutine_New(__pyx_GeneratorType, body, code, closure, name, qualname, module_name)
-static PyObject *__Pyx_Generator_Next(PyObject *self);
-static int __pyx_Generator_init(void);
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-
-/* Module declarations from 'cython' */
-
-/* Module declarations from 'fontTools.qu2cu.qu2cu' */
-static PyTypeObject *__pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__quadratic_to_curves = 0;
-static PyTypeObject *__pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr = 0;
-static PyTypeObject *__pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_2_spline_to_curves = 0;
-static PyTypeObject *__pyx_ptype_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_3_genexpr = 0;
-static int __pyx_f_9fontTools_5qu2cu_5qu2cu_cubic_farthest_fit_inside(__pyx_t_double_complex, __pyx_t_double_complex, __pyx_t_double_complex, __pyx_t_double_complex, double); /*proto*/
-static PyObject *__pyx_f_9fontTools_5qu2cu_5qu2cu_merge_curves(PyObject *, int, int); /*proto*/
-#define __Pyx_MODULE_NAME "fontTools.qu2cu.qu2cu"
-extern int __pyx_module_is_main_fontTools__qu2cu__qu2cu;
-int __pyx_module_is_main_fontTools__qu2cu__qu2cu = 0;
-
-/* Implementation of 'fontTools.qu2cu.qu2cu' */
-static PyObject *__pyx_builtin_AttributeError;
-static PyObject *__pyx_builtin_ImportError;
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_ZeroDivisionError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_reversed;
-static PyObject *__pyx_builtin_zip;
-static PyObject *__pyx_builtin_print;
-static const char __pyx_k_i[] = "i";
-static const char __pyx_k_j[] = "j";
-static const char __pyx_k_k[] = "k";
-static const char __pyx_k_p[] = "p";
-static const char __pyx_k_q[] = "q";
-static const char __pyx_k_u[] = "u";
-static const char __pyx_k_v[] = "v";
-static const char __pyx_k_x[] = "x";
-static const char __pyx_k_y[] = "y";
-static const char __pyx_k_on[] = "on";
-static const char __pyx_k_p0[] = "p0";
-static const char __pyx_k_p1[] = "p1";
-static const char __pyx_k_p2[] = "p2";
-static const char __pyx_k_p3[] = "p3";
-static const char __pyx_k_qq[] = "qq";
-static const char __pyx_k_ts[] = "ts";
-static const char __pyx_k_all[] = "__all__";
-static const char __pyx_k_err[] = "err";
-static const char __pyx_k_pop[] = "pop";
-static const char __pyx_k_zip[] = "zip";
-static const char __pyx_k_List[] = "List";
-static const char __pyx_k_args[] = "args";
-static const char __pyx_k_cost[] = "cost";
-static const char __pyx_k_imag[] = "imag";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_math[] = "math";
-static const char __pyx_k_name[] = "__name__";
-static const char __pyx_k_off1[] = "off1";
-static const char __pyx_k_off2[] = "off2";
-static const char __pyx_k_orig[] = "orig";
-static const char __pyx_k_real[] = "real";
-static const char __pyx_k_send[] = "send";
-static const char __pyx_k_sols[] = "sols";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_Point[] = "Point";
-static const char __pyx_k_Tuple[] = "Tuple";
-static const char __pyx_k_Union[] = "Union";
-static const char __pyx_k_close[] = "close";
-static const char __pyx_k_costs[] = "costs";
-static const char __pyx_k_count[] = "count";
-static const char __pyx_k_cubic[] = "cubic";
-static const char __pyx_k_curve[] = "curve";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_float[] = "float";
-static const char __pyx_k_i_sol[] = "i_sol";
-static const char __pyx_k_print[] = "print";
-static const char __pyx_k_quads[] = "quads";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_throw[] = "throw";
-static const char __pyx_k_curves[] = "curves";
-static const char __pyx_k_cython[] = "cython";
-static const char __pyx_k_forced[] = "forced";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_main_2[] = "main";
-static const char __pyx_k_p1_2_3[] = "p1_2_3";
-static const char __pyx_k_return[] = "return";
-static const char __pyx_k_splits[] = "splits";
-static const char __pyx_k_typing[] = "typing";
-static const char __pyx_k_genexpr[] = "genexpr";
-static const char __pyx_k_max_err[] = "max_err";
-static const char __pyx_k_reconst[] = "reconst";
-static const char __pyx_k_COMPILED[] = "COMPILED";
-static const char __pyx_k_Solution[] = "Solution";
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-static PyObject *__pyx_n_s_quadratic_to_curves_locals_genex;
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-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_2add_implicit_on_curves(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_p); /* proto */
-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_19quadratic_to_curves_8genexpr3_genexpr(PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_4quadratic_to_curves(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_quads, double __pyx_v_max_err, PyObject *__pyx_v_all_cubic); /* proto */
-static PyObject *__pyx_pf_9fontTools_5qu2cu_5qu2cu_16spline_to_curves_genexpr(PyObject *__pyx_self); /* proto */
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-static PyObject *__pyx_tp_new_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct__quadratic_to_curves(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
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-  /* "fontTools/qu2cu/qu2cu.py":80
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-
-static PyObject *__pyx_tp_new_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_1_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
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-static int __pyx_tp_traverse_9fontTools_5qu2cu_5qu2cu___pyx_scope_struct_3_genexpr(PyObject *o, visitproc v, void *a) {
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-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-        if (likely(m && m->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
-                Py_ssize_t l = m->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return m->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* py_abs */
-#if CYTHON_USE_PYLONG_INTERNALS
-static PyObject *__Pyx_PyLong_AbsNeg(PyObject *n) {
-    if (likely(Py_SIZE(n) == -1)) {
-        return PyLong_FromLong(((PyLongObject*)n)->ob_digit[0]);
-    }
-#if CYTHON_COMPILING_IN_CPYTHON
-    {
-        PyObject *copy = _PyLong_Copy((PyLongObject*)n);
-        if (likely(copy)) {
-            __Pyx_SET_SIZE(copy, -Py_SIZE(copy));
-        }
-        return copy;
-    }
-#else
-    return PyNumber_Negative(n);
-#endif
-}
-#endif
-
-/* SliceTupleAndList */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE void __Pyx_crop_slice(Py_ssize_t* _start, Py_ssize_t* _stop, Py_ssize_t* _length) {
-    Py_ssize_t start = *_start, stop = *_stop, length = *_length;
-    if (start < 0) {
-        start += length;
-        if (start < 0)
-            start = 0;
-    }
-    if (stop < 0)
-        stop += length;
-    else if (stop > length)
-        stop = length;
-    *_length = stop - start;
-    *_start = start;
-    *_stop = stop;
-}
-static CYTHON_INLINE void __Pyx_copy_object_array(PyObject** CYTHON_RESTRICT src, PyObject** CYTHON_RESTRICT dest, Py_ssize_t length) {
-    PyObject *v;
-    Py_ssize_t i;
-    for (i = 0; i < length; i++) {
-        v = dest[i] = src[i];
-        Py_INCREF(v);
-    }
-}
-static CYTHON_INLINE PyObject* __Pyx_PyList_GetSlice(
-            PyObject* src, Py_ssize_t start, Py_ssize_t stop) {
-    PyObject* dest;
-    Py_ssize_t length = PyList_GET_SIZE(src);
-    __Pyx_crop_slice(&start, &stop, &length);
-    if (unlikely(length <= 0))
-        return PyList_New(0);
-    dest = PyList_New(length);
-    if (unlikely(!dest))
-        return NULL;
-    __Pyx_copy_object_array(
-        ((PyListObject*)src)->ob_item + start,
-        ((PyListObject*)dest)->ob_item,
-        length);
-    return dest;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyTuple_GetSlice(
-            PyObject* src, Py_ssize_t start, Py_ssize_t stop) {
-    PyObject* dest;
-    Py_ssize_t length = PyTuple_GET_SIZE(src);
-    __Pyx_crop_slice(&start, &stop, &length);
-    if (unlikely(length <= 0))
-        return PyTuple_New(0);
-    dest = PyTuple_New(length);
-    if (unlikely(!dest))
-        return NULL;
-    __Pyx_copy_object_array(
-        ((PyTupleObject*)src)->ob_item + start,
-        ((PyTupleObject*)dest)->ob_item,
-        length);
-    return dest;
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractCObj(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {
-    (void)inplace;
-    (void)zerodivision_check;
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op2))) {
-        const long a = intval;
-        long x;
-        long b = PyInt_AS_LONG(op2);
-            x = (long)((unsigned long)a - b);
-            if (likely((x^a) >= 0 || (x^~b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op2))) {
-        const long a = intval;
-        long b, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG lla = intval;
-        PY_LONG_LONG llb, llx;
-#endif
-        const digit* digits = ((PyLongObject*)op2)->ob_digit;
-        const Py_ssize_t size = Py_SIZE(op2);
-        if (likely(__Pyx_sst_abs(size) <= 1)) {
-            b = likely(size) ? digits[0] : 0;
-            if (size == -1) b = -b;
-        } else {
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        b = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        b = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        b = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        b = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        b = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        llb = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        b = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        llb = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
-            }
-        }
-                x = a - b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla - llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op2)) {
-        const long a = intval;
-        double b = PyFloat_AS_DOUBLE(op2);
-            double result;
-            PyFPE_START_PROTECT("subtract", return NULL)
-            result = ((double)a) - (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2);
-}
-#endif
-
-/* None */
-static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname) {
-    PyErr_Format(PyExc_NameError, "free variable '%s' referenced before assignment in enclosing scope", varname);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* IterFinish */
-static CYTHON_INLINE int __Pyx_IterFinish(void) {
-#if CYTHON_FAST_THREAD_STATE
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject* exc_type = tstate->curexc_type;
-    if (unlikely(exc_type)) {
-        if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) {
-            PyObject *exc_value, *exc_tb;
-            exc_value = tstate->curexc_value;
-            exc_tb = tstate->curexc_traceback;
-            tstate->curexc_type = 0;
-            tstate->curexc_value = 0;
-            tstate->curexc_traceback = 0;
-            Py_DECREF(exc_type);
-            Py_XDECREF(exc_value);
-            Py_XDECREF(exc_tb);
-            return 0;
-        } else {
-            return -1;
-        }
-    }
-    return 0;
-#else
-    if (unlikely(PyErr_Occurred())) {
-        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
-            PyErr_Clear();
-            return 0;
-        } else {
-            return -1;
-        }
-    }
-    return 0;
-#endif
-}
-
-/* UnpackItemEndCheck */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
-    if (unlikely(retval)) {
-        Py_DECREF(retval);
-        __Pyx_RaiseTooManyValuesError(expected);
-        return -1;
-    }
-    return __Pyx_IterFinish();
-}
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* PyCFunctionFastCall */
-#if CYTHON_FAST_PYCCALL
-static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
-    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
-    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
-    PyObject *self = PyCFunction_GET_SELF(func);
-    int flags = PyCFunction_GET_FLAGS(func);
-    assert(PyCFunction_Check(func));
-    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));
-    assert(nargs >= 0);
-    assert(nargs == 0 || args != NULL);
-    /* _PyCFunction_FastCallDict() must not be called with an exception set,
-       because it may clear it (directly or indirectly) and so the
-       caller loses its exception */
-    assert(!PyErr_Occurred());
-    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
-        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);
-    } else {
-        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);
-    }
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-#if 1 || PY_VERSION_HEX < 0x030600B1
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
-    PyObject *args, *result = NULL;
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyFunction_FastCall(function, args, 2);
-    }
-    #endif
-    #if CYTHON_FAST_PYCCALL
-    if (__Pyx_PyFastCFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyCFunction_FastCall(function, args, 2);
-    }
-    #endif
-    args = PyTuple_New(2);
-    if (unlikely(!args)) goto done;
-    Py_INCREF(arg1);
-    PyTuple_SET_ITEM(args, 0, arg1);
-    Py_INCREF(arg2);
-    PyTuple_SET_ITEM(args, 1, arg2);
-    Py_INCREF(function);
-    result = __Pyx_PyObject_Call(function, args, NULL);
-    Py_DECREF(args);
-    Py_DECREF(function);
-done:
-    return result;
-}
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCallOneArg */
-#if CYTHON_COMPILING_IN_CPYTHON
-static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_New(1);
-    if (unlikely(!args)) return NULL;
-    Py_INCREF(arg);
-    PyTuple_SET_ITEM(args, 0, arg);
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-#if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCall(func, &arg, 1);
-    }
-#endif
-    if (likely(PyCFunction_Check(func))) {
-        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-            return __Pyx_PyObject_CallMethO(func, arg);
-#if CYTHON_FAST_PYCCALL
-        } else if (__Pyx_PyFastCFunction_Check(func)) {
-            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
-#endif
-        }
-    }
-    return __Pyx__PyObject_CallOneArg(func, arg);
-}
-#else
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_Pack(1, arg);
-    if (unlikely(!args)) return NULL;
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-#endif
-
-/* SliceObject */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj,
-        Py_ssize_t cstart, Py_ssize_t cstop,
-        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
-        int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
-#if CYTHON_USE_TYPE_SLOTS
-    PyMappingMethods* mp;
-#if PY_MAJOR_VERSION < 3
-    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
-    if (likely(ms && ms->sq_slice)) {
-        if (!has_cstart) {
-            if (_py_start && (*_py_start != Py_None)) {
-                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
-                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstart = 0;
-        }
-        if (!has_cstop) {
-            if (_py_stop && (*_py_stop != Py_None)) {
-                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
-                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-            } else
-                cstop = PY_SSIZE_T_MAX;
-        }
-        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
-            Py_ssize_t l = ms->sq_length(obj);
-            if (likely(l >= 0)) {
-                if (cstop < 0) {
-                    cstop += l;
-                    if (cstop < 0) cstop = 0;
-                }
-                if (cstart < 0) {
-                    cstart += l;
-                    if (cstart < 0) cstart = 0;
-                }
-            } else {
-                if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                    goto bad;
-                PyErr_Clear();
-            }
-        }
-        return ms->sq_slice(obj, cstart, cstop);
-    }
-#endif
-    mp = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(mp && mp->mp_subscript))
-#endif
-    {
-        PyObject* result;
-        PyObject *py_slice, *py_start, *py_stop;
-        if (_py_slice) {
-            py_slice = *_py_slice;
-        } else {
-            PyObject* owned_start = NULL;
-            PyObject* owned_stop = NULL;
-            if (_py_start) {
-                py_start = *_py_start;
-            } else {
-                if (has_cstart) {
-                    owned_start = py_start = PyInt_FromSsize_t(cstart);
-                    if (unlikely(!py_start)) goto bad;
-                } else
-                    py_start = Py_None;
-            }
-            if (_py_stop) {
-                py_stop = *_py_stop;
-            } else {
-                if (has_cstop) {
-                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
-                    if (unlikely(!py_stop)) {
-                        Py_XDECREF(owned_start);
-                        goto bad;
-                    }
-                } else
-                    py_stop = Py_None;
-            }
-            py_slice = PySlice_New(py_start, py_stop, Py_None);
-            Py_XDECREF(owned_start);
-            Py_XDECREF(owned_stop);
-            if (unlikely(!py_slice)) goto bad;
-        }
-#if CYTHON_USE_TYPE_SLOTS
-        result = mp->mp_subscript(obj, py_slice);
-#else
-        result = PyObject_GetItem(obj, py_slice);
-#endif
-        if (!_py_slice) {
-            Py_DECREF(py_slice);
-        }
-        return result;
-    }
-    PyErr_Format(PyExc_TypeError,
-        "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name);
-bad:
-    return NULL;
-}
-
-/* PyObjectCallNoArg */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-#if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCall(func, NULL, 0);
-    }
-#endif
-#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG)
-    if (likely(PyCFunction_Check(func) || __Pyx_CyFunction_Check(func)))
-#else
-    if (likely(PyCFunction_Check(func)))
-#endif
-    {
-        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
-            return __Pyx_PyObject_CallMethO(func, NULL);
-        }
-    }
-    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
-}
-#endif
-
-/* PyObjectGetMethod */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {
-    PyObject *attr;
-#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyObject *descr;
-    descrgetfunc f = NULL;
-    PyObject **dictptr, *dict;
-    int meth_found = 0;
-    assert (*method == NULL);
-    if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {
-        attr = __Pyx_PyObject_GetAttrStr(obj, name);
-        goto try_unpack;
-    }
-    if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {
-        return 0;
-    }
-    descr = _PyType_Lookup(tp, name);
-    if (likely(descr != NULL)) {
-        Py_INCREF(descr);
-#if PY_MAJOR_VERSION >= 3
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || (Py_TYPE(descr) == &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr) || (Py_TYPE(descr) == &PyMethodDescr_Type)))
-        #endif
-#else
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr)))
-        #endif
-#endif
-        {
-            meth_found = 1;
-        } else {
-            f = Py_TYPE(descr)->tp_descr_get;
-            if (f != NULL && PyDescr_IsData(descr)) {
-                attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-                Py_DECREF(descr);
-                goto try_unpack;
-            }
-        }
-    }
-    dictptr = _PyObject_GetDictPtr(obj);
-    if (dictptr != NULL && (dict = *dictptr) != NULL) {
-        Py_INCREF(dict);
-        attr = __Pyx_PyDict_GetItemStr(dict, name);
-        if (attr != NULL) {
-            Py_INCREF(attr);
-            Py_DECREF(dict);
-            Py_XDECREF(descr);
-            goto try_unpack;
-        }
-        Py_DECREF(dict);
-    }
-    if (meth_found) {
-        *method = descr;
-        return 1;
-    }
-    if (f != NULL) {
-        attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-        Py_DECREF(descr);
-        goto try_unpack;
-    }
-    if (descr != NULL) {
-        *method = descr;
-        return 0;
-    }
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'%.50s' object has no attribute '%U'",
-                 tp->tp_name, name);
-#else
-                 "'%.50s' object has no attribute '%.400s'",
-                 tp->tp_name, PyString_AS_STRING(name));
-#endif
-    return 0;
-#else
-    attr = __Pyx_PyObject_GetAttrStr(obj, name);
-    goto try_unpack;
-#endif
-try_unpack:
-#if CYTHON_UNPACK_METHODS
-    if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {
-        PyObject *function = PyMethod_GET_FUNCTION(attr);
-        Py_INCREF(function);
-        Py_DECREF(attr);
-        *method = function;
-        return 1;
-    }
-#endif
-    *method = attr;
-    return 0;
-}
-
-/* PyObjectCallMethod0 */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) {
-    PyObject *method = NULL, *result = NULL;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_CallOneArg(method, obj);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) goto bad;
-    result = __Pyx_PyObject_CallNoArg(method);
-    Py_DECREF(method);
-bad:
-    return result;
-}
-
-/* UnpackUnboundCMethod */
-static int __Pyx_TryUnpackUnboundCMethod(__Pyx_CachedCFunction* target) {
-    PyObject *method;
-    method = __Pyx_PyObject_GetAttrStr(target->type, *target->method_name);
-    if (unlikely(!method))
-        return -1;
-    target->method = method;
-#if CYTHON_COMPILING_IN_CPYTHON
-    #if PY_MAJOR_VERSION >= 3
-    if (likely(__Pyx_TypeCheck(method, &PyMethodDescr_Type)))
-    #endif
-    {
-        PyMethodDescrObject *descr = (PyMethodDescrObject*) method;
-        target->func = descr->d_method->ml_meth;
-        target->flag = descr->d_method->ml_flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_STACKLESS);
-    }
-#endif
-    return 0;
-}
-
-/* CallUnboundCMethod0 */
-static PyObject* __Pyx__CallUnboundCMethod0(__Pyx_CachedCFunction* cfunc, PyObject* self) {
-    PyObject *args, *result = NULL;
-    if (unlikely(!cfunc->method) && unlikely(__Pyx_TryUnpackUnboundCMethod(cfunc) < 0)) return NULL;
-#if CYTHON_ASSUME_SAFE_MACROS
-    args = PyTuple_New(1);
-    if (unlikely(!args)) goto bad;
-    Py_INCREF(self);
-    PyTuple_SET_ITEM(args, 0, self);
-#else
-    args = PyTuple_Pack(1, self);
-    if (unlikely(!args)) goto bad;
-#endif
-    result = __Pyx_PyObject_Call(cfunc->method, args, NULL);
-    Py_DECREF(args);
-bad:
-    return result;
-}
-
-/* pop */
-static CYTHON_INLINE PyObject* __Pyx__PyObject_Pop(PyObject* L) {
-    if (Py_TYPE(L) == &PySet_Type) {
-        return PySet_Pop(L);
-    }
-    return __Pyx_PyObject_CallMethod0(L, __pyx_n_s_pop);
-}
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE PyObject* __Pyx_PyList_Pop(PyObject* L) {
-    if (likely(PyList_GET_SIZE(L) > (((PyListObject*)L)->allocated >> 1))) {
-        __Pyx_SET_SIZE(L, Py_SIZE(L) - 1);
-        return PyList_GET_ITEM(L, PyList_GET_SIZE(L));
-    }
-    return __Pyx_CallUnboundCMethod0(&__pyx_umethod_PyList_Type_pop, L);
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {
-    (void)inplace;
-    (void)zerodivision_check;
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long x;
-        long a = PyInt_AS_LONG(op1);
-            x = (long)((unsigned long)a + b);
-            if (likely((x^a) >= 0 || (x^b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_add(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        const long b = intval;
-        long a, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG llb = intval;
-        PY_LONG_LONG lla, llx;
-#endif
-        const digit* digits = ((PyLongObject*)op1)->ob_digit;
-        const Py_ssize_t size = Py_SIZE(op1);
-        if (likely(__Pyx_sst_abs(size) <= 1)) {
-            a = likely(size) ? digits[0] : 0;
-            if (size == -1) a = -a;
-        } else {
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
-            }
-        }
-                x = a + b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla + llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-        double a = PyFloat_AS_DOUBLE(op1);
-            double result;
-            PyFPE_START_PROTECT("add", return NULL)
-            result = ((double)a) + (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
-}
-#endif
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    #endif
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-#endif
-
-/* PyErrExceptionMatches */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
-    PyObject *exc_type = tstate->curexc_type;
-    if (exc_type == err) return 1;
-    if (unlikely(!exc_type)) return 0;
-    if (unlikely(PyTuple_Check(err)))
-        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
-    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* pyfrozenset_new */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it) {
-    if (it) {
-        PyObject* result;
-#if CYTHON_COMPILING_IN_PYPY
-        PyObject* args;
-        args = PyTuple_Pack(1, it);
-        if (unlikely(!args))
-            return NULL;
-        result = PyObject_Call((PyObject*)&PyFrozenSet_Type, args, NULL);
-        Py_DECREF(args);
-        return result;
-#else
-        if (PyFrozenSet_CheckExact(it)) {
-            Py_INCREF(it);
-            return it;
-        }
-        result = PyFrozenSet_New(it);
-        if (unlikely(!result))
-            return NULL;
-        if ((PY_VERSION_HEX >= 0x031000A1) || likely(PySet_GET_SIZE(result)))
-            return result;
-        Py_DECREF(result);
-#endif
-    }
-#if CYTHON_USE_TYPE_SLOTS
-    return PyFrozenSet_Type.tp_new(&PyFrozenSet_Type, __pyx_empty_tuple, NULL);
-#else
-    return PyObject_Call((PyObject*)&PyFrozenSet_Type, __pyx_empty_tuple, NULL);
-#endif
-}
-
-/* PySetContains */
-static int __Pyx_PySet_ContainsUnhashable(PyObject *set, PyObject *key) {
-    int result = -1;
-    if (PySet_Check(key) && PyErr_ExceptionMatches(PyExc_TypeError)) {
-        PyObject *tmpkey;
-        PyErr_Clear();
-        tmpkey = __Pyx_PyFrozenSet_New(key);
-        if (tmpkey != NULL) {
-            result = PySet_Contains(set, tmpkey);
-            Py_DECREF(tmpkey);
-        }
-    }
-    return result;
-}
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq) {
-    int result = PySet_Contains(set, key);
-    if (unlikely(result < 0)) {
-        result = __Pyx_PySet_ContainsUnhashable(set, key);
-    }
-    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *empty_list = 0;
-    PyObject *module = 0;
-    PyObject *global_dict = 0;
-    PyObject *empty_dict = 0;
-    PyObject *list;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (!py_import)
-        goto bad;
-    #endif
-    if (from_list)
-        list = from_list;
-    else {
-        empty_list = PyList_New(0);
-        if (!empty_list)
-            goto bad;
-        list = empty_list;
-    }
-    global_dict = PyModule_GetDict(__pyx_m);
-    if (!global_dict)
-        goto bad;
-    empty_dict = PyDict_New();
-    if (!empty_dict)
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                module = PyImport_ImportModuleLevelObject(
-                    name, global_dict, empty_dict, list, 1);
-                if (!module) {
-                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (!py_level)
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, global_dict, empty_dict, list, level);
-            #endif
-        }
-    }
-bad:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    Py_XDECREF(empty_list);
-    Py_XDECREF(empty_dict);
-    return module;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'%.50s' object has no attribute '%U'",
-                 tp->tp_name, attr_name);
-#else
-                 "'%.50s' object has no attribute '%.400s'",
-                 tp->tp_name, PyString_AS_STRING(attr_name));
-#endif
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* FetchCommonType */
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* fake_module;
-    PyTypeObject* cached_type = NULL;
-    fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI);
-    if (!fake_module) return NULL;
-    Py_INCREF(fake_module);
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name);
-    if (cached_type) {
-        if (!PyType_Check((PyObject*)cached_type)) {
-            PyErr_Format(PyExc_TypeError,
-                "Shared Cython type %.200s is not a type object",
-                type->tp_name);
-            goto bad;
-        }
-        if (cached_type->tp_basicsize != type->tp_basicsize) {
-            PyErr_Format(PyExc_TypeError,
-                "Shared Cython type %.200s has the wrong size, try recompiling",
-                type->tp_name);
-            goto bad;
-        }
-    } else {
-        if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-        PyErr_Clear();
-        if (PyType_Ready(type) < 0) goto bad;
-        if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0)
-            goto bad;
-        Py_INCREF(type);
-        cached_type = type;
-    }
-done:
-    Py_DECREF(fake_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-
-/* CythonFunctionShared */
-#include <structmember.h>
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure)
-{
-    if (unlikely(op->func_doc == NULL)) {
-        if (op->func.m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
-            op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc);
-#else
-            op->func_doc = PyString_FromString(op->func.m_ml->ml_doc);
-#endif
-            if (unlikely(op->func_doc == NULL))
-                return NULL;
-        } else {
-            Py_INCREF(Py_None);
-            return Py_None;
-        }
-    }
-    Py_INCREF(op->func_doc);
-    return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp = op->func_doc;
-    if (value == NULL) {
-        value = Py_None;
-    }
-    Py_INCREF(value);
-    op->func_doc = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
-        op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name);
-#else
-        op->func_name = PyString_InternFromString(op->func.m_ml->ml_name);
-#endif
-        if (unlikely(op->func_name == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_name);
-    return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp;
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    tmp = op->func_name;
-    Py_INCREF(value);
-    op->func_name = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    Py_INCREF(op->func_qualname);
-    return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp;
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    tmp = op->func_qualname;
-    Py_INCREF(value);
-    op->func_qualname = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure)
-{
-    PyObject *self;
-    self = m->func_closure;
-    if (self == NULL)
-        self = Py_None;
-    Py_INCREF(self);
-    return self;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    if (unlikely(op->func_dict == NULL)) {
-        op->func_dict = PyDict_New();
-        if (unlikely(op->func_dict == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_dict);
-    return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp;
-    if (unlikely(value == NULL)) {
-        PyErr_SetString(PyExc_TypeError,
-               "function's dictionary may not be deleted");
-        return -1;
-    }
-    if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-               "setting function's dictionary to a non-dict");
-        return -1;
-    }
-    tmp = op->func_dict;
-    Py_INCREF(value);
-    op->func_dict = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    Py_INCREF(op->func_globals);
-    return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context)
-{
-    PyObject* result = (op->func_code) ? op->func_code : Py_None;
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
-    int result = 0;
-    PyObject *res = op->defaults_getter((PyObject *) op);
-    if (unlikely(!res))
-        return -1;
-    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
-    Py_INCREF(op->defaults_tuple);
-    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
-    Py_INCREF(op->defaults_kwdict);
-    #else
-    op->defaults_tuple = PySequence_ITEM(res, 0);
-    if (unlikely(!op->defaults_tuple)) result = -1;
-    else {
-        op->defaults_kwdict = PySequence_ITEM(res, 1);
-        if (unlikely(!op->defaults_kwdict)) result = -1;
-    }
-    #endif
-    Py_DECREF(res);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) {
-    PyObject* tmp;
-    if (!value) {
-        value = Py_None;
-    } else if (value != Py_None && !PyTuple_Check(value)) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__defaults__ must be set to a tuple object");
-        return -1;
-    }
-    Py_INCREF(value);
-    tmp = op->defaults_tuple;
-    op->defaults_tuple = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) {
-    PyObject* result = op->defaults_tuple;
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
-            result = op->defaults_tuple;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) {
-    PyObject* tmp;
-    if (!value) {
-        value = Py_None;
-    } else if (value != Py_None && !PyDict_Check(value)) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__kwdefaults__ must be set to a dict object");
-        return -1;
-    }
-    Py_INCREF(value);
-    tmp = op->defaults_kwdict;
-    op->defaults_kwdict = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) {
-    PyObject* result = op->defaults_kwdict;
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
-            result = op->defaults_kwdict;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) {
-    PyObject* tmp;
-    if (!value || value == Py_None) {
-        value = NULL;
-    } else if (!PyDict_Check(value)) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__annotations__ must be set to a dict object");
-        return -1;
-    }
-    Py_XINCREF(value);
-    tmp = op->func_annotations;
-    op->func_annotations = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) {
-    PyObject* result = op->func_annotations;
-    if (unlikely(!result)) {
-        result = PyDict_New();
-        if (unlikely(!result)) return NULL;
-        op->func_annotations = result;
-    }
-    Py_INCREF(result);
-    return result;
-}
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
-    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
-    {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0},
-    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
-    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
-    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0},
-    {0, 0, 0,  0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args)
-{
-#if PY_MAJOR_VERSION >= 3
-    Py_INCREF(m->func_qualname);
-    return m->func_qualname;
-#else
-    return PyString_FromString(m->func.m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
-    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
-    {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
-                                       PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    if (unlikely(op == NULL))
-        return NULL;
-    op->flags = flags;
-    __Pyx_CyFunction_weakreflist(op) = NULL;
-    op->func.m_ml = ml;
-    op->func.m_self = (PyObject *) op;
-    Py_XINCREF(closure);
-    op->func_closure = closure;
-    Py_XINCREF(module);
-    op->func.m_module = module;
-    op->func_dict = NULL;
-    op->func_name = NULL;
-    Py_INCREF(qualname);
-    op->func_qualname = qualname;
-    op->func_doc = NULL;
-    op->func_classobj = NULL;
-    op->func_globals = globals;
-    Py_INCREF(op->func_globals);
-    Py_XINCREF(code);
-    op->func_code = code;
-    op->defaults_pyobjects = 0;
-    op->defaults_size = 0;
-    op->defaults = NULL;
-    op->defaults_tuple = NULL;
-    op->defaults_kwdict = NULL;
-    op->defaults_getter = NULL;
-    op->func_annotations = NULL;
-    return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
-    Py_CLEAR(m->func_closure);
-    Py_CLEAR(m->func.m_module);
-    Py_CLEAR(m->func_dict);
-    Py_CLEAR(m->func_name);
-    Py_CLEAR(m->func_qualname);
-    Py_CLEAR(m->func_doc);
-    Py_CLEAR(m->func_globals);
-    Py_CLEAR(m->func_code);
-    Py_CLEAR(m->func_classobj);
-    Py_CLEAR(m->defaults_tuple);
-    Py_CLEAR(m->defaults_kwdict);
-    Py_CLEAR(m->func_annotations);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_XDECREF(pydefaults[i]);
-        PyObject_Free(m->defaults);
-        m->defaults = NULL;
-    }
-    return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    if (__Pyx_CyFunction_weakreflist(m) != NULL)
-        PyObject_ClearWeakRefs((PyObject *) m);
-    __Pyx_CyFunction_clear(m);
-    PyObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    PyObject_GC_UnTrack(m);
-    __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
-    Py_VISIT(m->func_closure);
-    Py_VISIT(m->func.m_module);
-    Py_VISIT(m->func_dict);
-    Py_VISIT(m->func_name);
-    Py_VISIT(m->func_qualname);
-    Py_VISIT(m->func_doc);
-    Py_VISIT(m->func_globals);
-    Py_VISIT(m->func_code);
-    Py_VISIT(m->func_classobj);
-    Py_VISIT(m->defaults_tuple);
-    Py_VISIT(m->defaults_kwdict);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_VISIT(pydefaults[i]);
-    }
-    return 0;
-}
-static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type)
-{
-#if PY_MAJOR_VERSION < 3
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) {
-        Py_INCREF(func);
-        return func;
-    }
-    if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) {
-        if (type == NULL)
-            type = (PyObject *)(Py_TYPE(obj));
-        return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type)));
-    }
-    if (obj == Py_None)
-        obj = NULL;
-#endif
-    return __Pyx_PyMethod_New(func, obj, type);
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromFormat("<cyfunction %U at %p>",
-                                op->func_qualname, (void *)op);
-#else
-    return PyString_FromFormat("<cyfunction %s at %p>",
-                               PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
-    PyCFunctionObject* f = (PyCFunctionObject*)func;
-    PyCFunction meth = f->m_ml->ml_meth;
-    Py_ssize_t size;
-    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
-    case METH_VARARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0))
-            return (*meth)(self, arg);
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
-    case METH_NOARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 0))
-                return (*meth)(self, NULL);
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    case METH_O:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 1)) {
-                PyObject *result, *arg0;
-                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                arg0 = PyTuple_GET_ITEM(arg, 0);
-                #else
-                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
-                #endif
-                result = (*meth)(self, arg0);
-                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-                Py_DECREF(arg0);
-                #endif
-                return result;
-            }
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags in "
-                        "__Pyx_CyFunction_Call. METH_OLDARGS is no "
-                        "longer supported!");
-        return NULL;
-    }
-    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
-                 f->m_ml->ml_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
-    PyObject *result;
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        Py_ssize_t argc;
-        PyObject *new_args;
-        PyObject *self;
-        argc = PyTuple_GET_SIZE(args);
-        new_args = PyTuple_GetSlice(args, 1, argc);
-        if (unlikely(!new_args))
-            return NULL;
-        self = PyTuple_GetItem(args, 0);
-        if (unlikely(!self)) {
-            Py_DECREF(new_args);
-#if PY_MAJOR_VERSION > 2
-            PyErr_Format(PyExc_TypeError,
-                         "unbound method %.200S() needs an argument",
-                         cyfunc->func_qualname);
-#else
-            PyErr_SetString(PyExc_TypeError,
-                            "unbound method needs an argument");
-#endif
-            return NULL;
-        }
-        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
-        Py_DECREF(new_args);
-    } else {
-        result = __Pyx_CyFunction_Call(func, args, kw);
-    }
-    return result;
-}
-static PyTypeObject __pyx_CyFunctionType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-    (destructor) __Pyx_CyFunction_dealloc,
-    0,
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    (reprfunc) __Pyx_CyFunction_repr,
-    0,
-    0,
-    0,
-    0,
-    __Pyx_CyFunction_CallAsMethod,
-    0,
-    0,
-    0,
-    0,
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC,
-    0,
-    (traverseproc) __Pyx_CyFunction_traverse,
-    (inquiry) __Pyx_CyFunction_clear,
-    0,
-#if PY_VERSION_HEX < 0x030500A0
-    offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
-    offsetof(PyCFunctionObject, m_weakreflist),
-#endif
-    0,
-    0,
-    __pyx_CyFunction_methods,
-    __pyx_CyFunction_members,
-    __pyx_CyFunction_getsets,
-    0,
-    0,
-    __Pyx_CyFunction_descr_get,
-    0,
-    offsetof(__pyx_CyFunctionObject, func_dict),
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-static int __pyx_CyFunction_init(void) {
-    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-    if (unlikely(__pyx_CyFunctionType == NULL)) {
-        return -1;
-    }
-    return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults = PyObject_Malloc(size);
-    if (unlikely(!m->defaults))
-        return PyErr_NoMemory();
-    memset(m->defaults, 0, size);
-    m->defaults_pyobjects = pyobjects;
-    m->defaults_size = size;
-    return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_tuple = tuple;
-    Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_kwdict = dict;
-    Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->func_annotations = dict;
-    Py_INCREF(dict);
-}
-
-/* CythonFunction */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
-                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyObject *op = __Pyx_CyFunction_Init(
-        PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
-        ml, flags, qualname, closure, module, globals, code
-    );
-    if (likely(op)) {
-        PyObject_GC_Track(op);
-    }
-    return op;
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
-    PyObject *runerr = NULL;
-    Py_ssize_t key_value;
-    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
-    if (unlikely(!(m && m->sq_item))) {
-        PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
-        return NULL;
-    }
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
-    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(m && m->mp_subscript)) {
-        return m->mp_subscript(obj, key);
-    }
-    return __Pyx_PyObject_GetIndex(obj, key);
-}
-#endif
-
-/* BytesEquals */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result;
-#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000)
-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* PyErrFetchRestore */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    tmp_type = tstate->curexc_type;
-    tmp_value = tstate->curexc_value;
-    tmp_tb = tstate->curexc_traceback;
-    tstate->curexc_type = type;
-    tstate->curexc_value = value;
-    tstate->curexc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    *type = tstate->curexc_type;
-    *value = tstate->curexc_value;
-    *tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-}
-#endif
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject *ptype, *pvalue, *ptraceback;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) {
-            /* If the code object creation fails, then we should clear the
-               fetched exception references and propagate the new exception */
-            Py_XDECREF(ptype);
-            Py_XDECREF(pvalue);
-            Py_XDECREF(ptraceback);
-            goto bad;
-        }
-        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-
-/* FromPy */
-static __pyx_t_double_complex __Pyx_PyComplex_As___pyx_t_double_complex(PyObject* o) {
-    Py_complex cval;
-#if !CYTHON_COMPILING_IN_PYPY
-    if (PyComplex_CheckExact(o))
-        cval = ((PyComplexObject *)o)->cval;
-    else
-#endif
-        cval = PyComplex_AsCComplex(o);
-    return __pyx_t_double_complex_from_parts(
-               (double)cval.real,
-               (double)cval.imag);
-}
-
-/* CIntFromPyVerify */
-#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* Declarations */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return ::std::complex< double >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return x + y*(__pyx_t_double_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      __pyx_t_double_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-#if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabs(b.real) >= fabs(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                double r = b.imag / b.real;
-                double s = (double)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_double_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            double r = b.real / b.imag;
-            double s = (double)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_double_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            double denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_double_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrt(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypot(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-            __pyx_t_double_complex z;
-            double r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    double denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_double(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if ((b.imag == 0) && (a.real >= 0)) {
-                    z.real = pow(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_double(a);
-                theta = atan2(a.imag, a.real);
-            }
-            lnr = log(r);
-            z_r = exp(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cos(z_theta);
-            z.imag = z_r * sin(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* CIntFromPy */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(int) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
-                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
-                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
-                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(int) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntToPy */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(long) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
-                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
-                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
-                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(long) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(long) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = a->tp_base;
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
-    if (!res) {
-        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-    return res;
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
-                        CYTHON_UNUSED PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-#if CYTHON_FAST_THREAD_STATE
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#else
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-    #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* PyObjectCallMethod1 */
-static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) {
-    PyObject *result = __Pyx_PyObject_CallOneArg(method, arg);
-    Py_DECREF(method);
-    return result;
-}
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {
-    PyObject *method = NULL, *result;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_Call2Args(method, obj, arg);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) return NULL;
-    return __Pyx__PyObject_CallMethod1(method, arg);
-}
-
-/* CoroutineBase */
-#include <structmember.h>
-#include <frameobject.h>
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#define __Pyx_Coroutine_Undelegate(gen) Py_CLEAR((gen)->yieldfrom)
-static int __Pyx_PyGen__FetchStopIterationValue(CYTHON_UNUSED PyThreadState *__pyx_tstate, PyObject **pvalue) {
-    PyObject *et, *ev, *tb;
-    PyObject *value = NULL;
-    __Pyx_ErrFetch(&et, &ev, &tb);
-    if (!et) {
-        Py_XDECREF(tb);
-        Py_XDECREF(ev);
-        Py_INCREF(Py_None);
-        *pvalue = Py_None;
-        return 0;
-    }
-    if (likely(et == PyExc_StopIteration)) {
-        if (!ev) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#if PY_VERSION_HEX >= 0x030300A0
-        else if (Py_TYPE(ev) == (PyTypeObject*)PyExc_StopIteration) {
-            value = ((PyStopIterationObject *)ev)->value;
-            Py_INCREF(value);
-            Py_DECREF(ev);
-        }
-#endif
-        else if (unlikely(PyTuple_Check(ev))) {
-            if (PyTuple_GET_SIZE(ev) >= 1) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                value = PyTuple_GET_ITEM(ev, 0);
-                Py_INCREF(value);
-#else
-                value = PySequence_ITEM(ev, 0);
-#endif
-            } else {
-                Py_INCREF(Py_None);
-                value = Py_None;
-            }
-            Py_DECREF(ev);
-        }
-        else if (!__Pyx_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration)) {
-            value = ev;
-        }
-        if (likely(value)) {
-            Py_XDECREF(tb);
-            Py_DECREF(et);
-            *pvalue = value;
-            return 0;
-        }
-    } else if (!__Pyx_PyErr_GivenExceptionMatches(et, PyExc_StopIteration)) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    PyErr_NormalizeException(&et, &ev, &tb);
-    if (unlikely(!PyObject_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration))) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    Py_XDECREF(tb);
-    Py_DECREF(et);
-#if PY_VERSION_HEX >= 0x030300A0
-    value = ((PyStopIterationObject *)ev)->value;
-    Py_INCREF(value);
-    Py_DECREF(ev);
-#else
-    {
-        PyObject* args = __Pyx_PyObject_GetAttrStr(ev, __pyx_n_s_args);
-        Py_DECREF(ev);
-        if (likely(args)) {
-            value = PySequence_GetItem(args, 0);
-            Py_DECREF(args);
-        }
-        if (unlikely(!value)) {
-            __Pyx_ErrRestore(NULL, NULL, NULL);
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-    }
-#endif
-    *pvalue = value;
-    return 0;
-}
-static CYTHON_INLINE
-void __Pyx_Coroutine_ExceptionClear(__Pyx_ExcInfoStruct *exc_state) {
-    PyObject *t, *v, *tb;
-    t = exc_state->exc_type;
-    v = exc_state->exc_value;
-    tb = exc_state->exc_traceback;
-    exc_state->exc_type = NULL;
-    exc_state->exc_value = NULL;
-    exc_state->exc_traceback = NULL;
-    Py_XDECREF(t);
-    Py_XDECREF(v);
-    Py_XDECREF(tb);
-}
-#define __Pyx_Coroutine_AlreadyRunningError(gen)  (__Pyx__Coroutine_AlreadyRunningError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyRunningError(CYTHON_UNUSED __pyx_CoroutineObject *gen) {
-    const char *msg;
-    if ((0)) {
-    #ifdef __Pyx_Coroutine_USED
-    } else if (__Pyx_Coroutine_Check((PyObject*)gen)) {
-        msg = "coroutine already executing";
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    } else if (__Pyx_AsyncGen_CheckExact((PyObject*)gen)) {
-        msg = "async generator already executing";
-    #endif
-    } else {
-        msg = "generator already executing";
-    }
-    PyErr_SetString(PyExc_ValueError, msg);
-}
-#define __Pyx_Coroutine_NotStartedError(gen)  (__Pyx__Coroutine_NotStartedError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_NotStartedError(CYTHON_UNUSED PyObject *gen) {
-    const char *msg;
-    if ((0)) {
-    #ifdef __Pyx_Coroutine_USED
-    } else if (__Pyx_Coroutine_Check(gen)) {
-        msg = "can't send non-None value to a just-started coroutine";
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    } else if (__Pyx_AsyncGen_CheckExact(gen)) {
-        msg = "can't send non-None value to a just-started async generator";
-    #endif
-    } else {
-        msg = "can't send non-None value to a just-started generator";
-    }
-    PyErr_SetString(PyExc_TypeError, msg);
-}
-#define __Pyx_Coroutine_AlreadyTerminatedError(gen, value, closing)  (__Pyx__Coroutine_AlreadyTerminatedError(gen, value, closing), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyTerminatedError(CYTHON_UNUSED PyObject *gen, PyObject *value, CYTHON_UNUSED int closing) {
-    #ifdef __Pyx_Coroutine_USED
-    if (!closing && __Pyx_Coroutine_Check(gen)) {
-        PyErr_SetString(PyExc_RuntimeError, "cannot reuse already awaited coroutine");
-    } else
-    #endif
-    if (value) {
-        #ifdef __Pyx_AsyncGen_USED
-        if (__Pyx_AsyncGen_CheckExact(gen))
-            PyErr_SetNone(__Pyx_PyExc_StopAsyncIteration);
-        else
-        #endif
-        PyErr_SetNone(PyExc_StopIteration);
-    }
-}
-static
-PyObject *__Pyx_Coroutine_SendEx(__pyx_CoroutineObject *self, PyObject *value, int closing) {
-    __Pyx_PyThreadState_declare
-    PyThreadState *tstate;
-    __Pyx_ExcInfoStruct *exc_state;
-    PyObject *retval;
-    assert(!self->is_running);
-    if (unlikely(self->resume_label == 0)) {
-        if (unlikely(value && value != Py_None)) {
-            return __Pyx_Coroutine_NotStartedError((PyObject*)self);
-        }
-    }
-    if (unlikely(self->resume_label == -1)) {
-        return __Pyx_Coroutine_AlreadyTerminatedError((PyObject*)self, value, closing);
-    }
-#if CYTHON_FAST_THREAD_STATE
-    __Pyx_PyThreadState_assign
-    tstate = __pyx_tstate;
-#else
-    tstate = __Pyx_PyThreadState_Current;
-#endif
-    exc_state = &self->gi_exc_state;
-    if (exc_state->exc_type) {
-        #if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_PYSTON
-        #else
-        if (exc_state->exc_traceback) {
-            PyTracebackObject *tb = (PyTracebackObject *) exc_state->exc_traceback;
-            PyFrameObject *f = tb->tb_frame;
-            assert(f->f_back == NULL);
-            #if PY_VERSION_HEX >= 0x030B00A1
-            f->f_back = PyThreadState_GetFrame(tstate);
-            #else
-            Py_XINCREF(tstate->frame);
-            f->f_back = tstate->frame;
-            #endif
-        }
-        #endif
-    }
-#if CYTHON_USE_EXC_INFO_STACK
-    exc_state->previous_item = tstate->exc_info;
-    tstate->exc_info = exc_state;
-#else
-    if (exc_state->exc_type) {
-        __Pyx_ExceptionSwap(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
-    } else {
-        __Pyx_Coroutine_ExceptionClear(exc_state);
-        __Pyx_ExceptionSave(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
-    }
-#endif
-    self->is_running = 1;
-    retval = self->body((PyObject *) self, tstate, value);
-    self->is_running = 0;
-#if CYTHON_USE_EXC_INFO_STACK
-    exc_state = &self->gi_exc_state;
-    tstate->exc_info = exc_state->previous_item;
-    exc_state->previous_item = NULL;
-    __Pyx_Coroutine_ResetFrameBackpointer(exc_state);
-#endif
-    return retval;
-}
-static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__Pyx_ExcInfoStruct *exc_state) {
-    PyObject *exc_tb = exc_state->exc_traceback;
-    if (likely(exc_tb)) {
-#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_PYSTON
-#else
-        PyTracebackObject *tb = (PyTracebackObject *) exc_tb;
-        PyFrameObject *f = tb->tb_frame;
-        Py_CLEAR(f->f_back);
-#endif
-    }
-}
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_MethodReturn(CYTHON_UNUSED PyObject* gen, PyObject *retval) {
-    if (unlikely(!retval)) {
-        __Pyx_PyThreadState_declare
-        __Pyx_PyThreadState_assign
-        if (!__Pyx_PyErr_Occurred()) {
-            PyObject *exc = PyExc_StopIteration;
-            #ifdef __Pyx_AsyncGen_USED
-            if (__Pyx_AsyncGen_CheckExact(gen))
-                exc = __Pyx_PyExc_StopAsyncIteration;
-            #endif
-            __Pyx_PyErr_SetNone(exc);
-        }
-    }
-    return retval;
-}
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-static CYTHON_INLINE
-PyObject *__Pyx_PyGen_Send(PyGenObject *gen, PyObject *arg) {
-#if PY_VERSION_HEX <= 0x030A00A1
-    return _PyGen_Send(gen, arg);
-#else
-    PyObject *result;
-    if (PyIter_Send((PyObject*)gen, arg ? arg : Py_None, &result) == PYGEN_RETURN) {
-        if (PyAsyncGen_CheckExact(gen)) {
-            assert(result == Py_None);
-            PyErr_SetNone(PyExc_StopAsyncIteration);
-        }
-        else if (result == Py_None) {
-            PyErr_SetNone(PyExc_StopIteration);
-        }
-        else {
-            _PyGen_SetStopIterationValue(result);
-        }
-        Py_CLEAR(result);
-    }
-    return result;
-#endif
-}
-#endif
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_FinishDelegation(__pyx_CoroutineObject *gen) {
-    PyObject *ret;
-    PyObject *val = NULL;
-    __Pyx_Coroutine_Undelegate(gen);
-    __Pyx_PyGen__FetchStopIterationValue(__Pyx_PyThreadState_Current, &val);
-    ret = __Pyx_Coroutine_SendEx(gen, val, 0);
-    Py_XDECREF(val);
-    return ret;
-}
-static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value) {
-    PyObject *retval;
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        #ifdef __Pyx_Generator_USED
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, value);
-        } else
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-        if (__Pyx_Coroutine_Check(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, value);
-        } else
-        #endif
-        #ifdef __Pyx_AsyncGen_USED
-        if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
-            ret = __Pyx_async_gen_asend_send(yf, value);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyGen_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03050000 && defined(PyCoro_CheckExact) && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyCoro_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
-        } else
-        #endif
-        {
-            if (value == Py_None)
-                ret = Py_TYPE(yf)->tp_iternext(yf);
-            else
-                ret = __Pyx_PyObject_CallMethod1(yf, __pyx_n_s_send, value);
-        }
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        retval = __Pyx_Coroutine_FinishDelegation(gen);
-    } else {
-        retval = __Pyx_Coroutine_SendEx(gen, value, 0);
-    }
-    return __Pyx_Coroutine_MethodReturn(self, retval);
-}
-static int __Pyx_Coroutine_CloseIter(__pyx_CoroutineObject *gen, PyObject *yf) {
-    PyObject *retval = NULL;
-    int err = 0;
-    #ifdef __Pyx_Generator_USED
-    if (__Pyx_Generator_CheckExact(yf)) {
-        retval = __Pyx_Coroutine_Close(yf);
-        if (!retval)
-            return -1;
-    } else
-    #endif
-    #ifdef __Pyx_Coroutine_USED
-    if (__Pyx_Coroutine_Check(yf)) {
-        retval = __Pyx_Coroutine_Close(yf);
-        if (!retval)
-            return -1;
-    } else
-    if (__Pyx_CoroutineAwait_CheckExact(yf)) {
-        retval = __Pyx_CoroutineAwait_Close((__pyx_CoroutineAwaitObject*)yf, NULL);
-        if (!retval)
-            return -1;
-    } else
-    #endif
-    #ifdef __Pyx_AsyncGen_USED
-    if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
-        retval = __Pyx_async_gen_asend_close(yf, NULL);
-    } else
-    if (__pyx_PyAsyncGenAThrow_CheckExact(yf)) {
-        retval = __Pyx_async_gen_athrow_close(yf, NULL);
-    } else
-    #endif
-    {
-        PyObject *meth;
-        gen->is_running = 1;
-        meth = __Pyx_PyObject_GetAttrStr(yf, __pyx_n_s_close);
-        if (unlikely(!meth)) {
-            if (!PyErr_ExceptionMatches(PyExc_AttributeError)) {
-                PyErr_WriteUnraisable(yf);
-            }
-            PyErr_Clear();
-        } else {
-            retval = PyObject_CallFunction(meth, NULL);
-            Py_DECREF(meth);
-            if (!retval)
-                err = -1;
-        }
-        gen->is_running = 0;
-    }
-    Py_XDECREF(retval);
-    return err;
-}
-static PyObject *__Pyx_Generator_Next(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        #ifdef __Pyx_Generator_USED
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Generator_Next(yf);
-        } else
-        #endif
-        #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-        if (PyGen_CheckExact(yf)) {
-            ret = __Pyx_PyGen_Send((PyGenObject*)yf, NULL);
-        } else
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-        if (__Pyx_Coroutine_Check(yf)) {
-            ret = __Pyx_Coroutine_Send(yf, Py_None);
-        } else
-        #endif
-            ret = Py_TYPE(yf)->tp_iternext(yf);
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        return __Pyx_Coroutine_FinishDelegation(gen);
-    }
-    return __Pyx_Coroutine_SendEx(gen, Py_None, 0);
-}
-static PyObject *__Pyx_Coroutine_Close_Method(PyObject *self, CYTHON_UNUSED PyObject *arg) {
-    return __Pyx_Coroutine_Close(self);
-}
-static PyObject *__Pyx_Coroutine_Close(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject *retval, *raised_exception;
-    PyObject *yf = gen->yieldfrom;
-    int err = 0;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        Py_INCREF(yf);
-        err = __Pyx_Coroutine_CloseIter(gen, yf);
-        __Pyx_Coroutine_Undelegate(gen);
-        Py_DECREF(yf);
-    }
-    if (err == 0)
-        PyErr_SetNone(PyExc_GeneratorExit);
-    retval = __Pyx_Coroutine_SendEx(gen, NULL, 1);
-    if (unlikely(retval)) {
-        const char *msg;
-        Py_DECREF(retval);
-        if ((0)) {
-        #ifdef __Pyx_Coroutine_USED
-        } else if (__Pyx_Coroutine_Check(self)) {
-            msg = "coroutine ignored GeneratorExit";
-        #endif
-        #ifdef __Pyx_AsyncGen_USED
-        } else if (__Pyx_AsyncGen_CheckExact(self)) {
-#if PY_VERSION_HEX < 0x03060000
-            msg = "async generator ignored GeneratorExit - might require Python 3.6+ finalisation (PEP 525)";
-#else
-            msg = "async generator ignored GeneratorExit";
-#endif
-        #endif
-        } else {
-            msg = "generator ignored GeneratorExit";
-        }
-        PyErr_SetString(PyExc_RuntimeError, msg);
-        return NULL;
-    }
-    raised_exception = PyErr_Occurred();
-    if (likely(!raised_exception || __Pyx_PyErr_GivenExceptionMatches2(raised_exception, PyExc_GeneratorExit, PyExc_StopIteration))) {
-        if (raised_exception) PyErr_Clear();
-        Py_INCREF(Py_None);
-        return Py_None;
-    }
-    return NULL;
-}
-static PyObject *__Pyx__Coroutine_Throw(PyObject *self, PyObject *typ, PyObject *val, PyObject *tb,
-                                        PyObject *args, int close_on_genexit) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(gen->is_running))
-        return __Pyx_Coroutine_AlreadyRunningError(gen);
-    if (yf) {
-        PyObject *ret;
-        Py_INCREF(yf);
-        if (__Pyx_PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit) && close_on_genexit) {
-            int err = __Pyx_Coroutine_CloseIter(gen, yf);
-            Py_DECREF(yf);
-            __Pyx_Coroutine_Undelegate(gen);
-            if (err < 0)
-                return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-            goto throw_here;
-        }
-        gen->is_running = 1;
-        if (0
-        #ifdef __Pyx_Generator_USED
-            || __Pyx_Generator_CheckExact(yf)
-        #endif
-        #ifdef __Pyx_Coroutine_USED
-            || __Pyx_Coroutine_Check(yf)
-        #endif
-            ) {
-            ret = __Pyx__Coroutine_Throw(yf, typ, val, tb, args, close_on_genexit);
-        #ifdef __Pyx_Coroutine_USED
-        } else if (__Pyx_CoroutineAwait_CheckExact(yf)) {
-            ret = __Pyx__Coroutine_Throw(((__pyx_CoroutineAwaitObject*)yf)->coroutine, typ, val, tb, args, close_on_genexit);
-        #endif
-        } else {
-            PyObject *meth = __Pyx_PyObject_GetAttrStr(yf, __pyx_n_s_throw);
-            if (unlikely(!meth)) {
-                Py_DECREF(yf);
-                if (!PyErr_ExceptionMatches(PyExc_AttributeError)) {
-                    gen->is_running = 0;
-                    return NULL;
-                }
-                PyErr_Clear();
-                __Pyx_Coroutine_Undelegate(gen);
-                gen->is_running = 0;
-                goto throw_here;
-            }
-            if (likely(args)) {
-                ret = PyObject_CallObject(meth, args);
-            } else {
-                ret = PyObject_CallFunctionObjArgs(meth, typ, val, tb, NULL);
-            }
-            Py_DECREF(meth);
-        }
-        gen->is_running = 0;
-        Py_DECREF(yf);
-        if (!ret) {
-            ret = __Pyx_Coroutine_FinishDelegation(gen);
-        }
-        return __Pyx_Coroutine_MethodReturn(self, ret);
-    }
-throw_here:
-    __Pyx_Raise(typ, val, tb, NULL);
-    return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-}
-static PyObject *__Pyx_Coroutine_Throw(PyObject *self, PyObject *args) {
-    PyObject *typ;
-    PyObject *val = NULL;
-    PyObject *tb = NULL;
-    if (!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb))
-        return NULL;
-    return __Pyx__Coroutine_Throw(self, typ, val, tb, args, 1);
-}
-static CYTHON_INLINE int __Pyx_Coroutine_traverse_excstate(__Pyx_ExcInfoStruct *exc_state, visitproc visit, void *arg) {
-    Py_VISIT(exc_state->exc_type);
-    Py_VISIT(exc_state->exc_value);
-    Py_VISIT(exc_state->exc_traceback);
-    return 0;
-}
-static int __Pyx_Coroutine_traverse(__pyx_CoroutineObject *gen, visitproc visit, void *arg) {
-    Py_VISIT(gen->closure);
-    Py_VISIT(gen->classobj);
-    Py_VISIT(gen->yieldfrom);
-    return __Pyx_Coroutine_traverse_excstate(&gen->gi_exc_state, visit, arg);
-}
-static int __Pyx_Coroutine_clear(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    Py_CLEAR(gen->closure);
-    Py_CLEAR(gen->classobj);
-    Py_CLEAR(gen->yieldfrom);
-    __Pyx_Coroutine_ExceptionClear(&gen->gi_exc_state);
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        Py_CLEAR(((__pyx_PyAsyncGenObject*)gen)->ag_finalizer);
-    }
-#endif
-    Py_CLEAR(gen->gi_code);
-    Py_CLEAR(gen->gi_frame);
-    Py_CLEAR(gen->gi_name);
-    Py_CLEAR(gen->gi_qualname);
-    Py_CLEAR(gen->gi_modulename);
-    return 0;
-}
-static void __Pyx_Coroutine_dealloc(PyObject *self) {
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    PyObject_GC_UnTrack(gen);
-    if (gen->gi_weakreflist != NULL)
-        PyObject_ClearWeakRefs(self);
-    if (gen->resume_label >= 0) {
-        PyObject_GC_Track(self);
-#if PY_VERSION_HEX >= 0x030400a1 && CYTHON_USE_TP_FINALIZE
-        if (PyObject_CallFinalizerFromDealloc(self))
-#else
-        Py_TYPE(gen)->tp_del(self);
-        if (Py_REFCNT(self) > 0)
-#endif
-        {
-            return;
-        }
-        PyObject_GC_UnTrack(self);
-    }
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        /* We have to handle this case for asynchronous generators
-           right here, because this code has to be between UNTRACK
-           and GC_Del. */
-        Py_CLEAR(((__pyx_PyAsyncGenObject*)self)->ag_finalizer);
-    }
-#endif
-    __Pyx_Coroutine_clear(self);
-    PyObject_GC_Del(gen);
-}
-static void __Pyx_Coroutine_del(PyObject *self) {
-    PyObject *error_type, *error_value, *error_traceback;
-    __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
-    __Pyx_PyThreadState_declare
-    if (gen->resume_label < 0) {
-        return;
-    }
-#if !CYTHON_USE_TP_FINALIZE
-    assert(self->ob_refcnt == 0);
-    __Pyx_SET_REFCNT(self, 1);
-#endif
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&error_type, &error_value, &error_traceback);
-#ifdef __Pyx_AsyncGen_USED
-    if (__Pyx_AsyncGen_CheckExact(self)) {
-        __pyx_PyAsyncGenObject *agen = (__pyx_PyAsyncGenObject*)self;
-        PyObject *finalizer = agen->ag_finalizer;
-        if (finalizer && !agen->ag_closed) {
-            PyObject *res = __Pyx_PyObject_CallOneArg(finalizer, self);
-            if (unlikely(!res)) {
-                PyErr_WriteUnraisable(self);
-            } else {
-                Py_DECREF(res);
-            }
-            __Pyx_ErrRestore(error_type, error_value, error_traceback);
-            return;
-        }
-    }
-#endif
-    if (unlikely(gen->resume_label == 0 && !error_value)) {
-#ifdef __Pyx_Coroutine_USED
-#ifdef __Pyx_Generator_USED
-    if (!__Pyx_Generator_CheckExact(self))
-#endif
-        {
-        PyObject_GC_UnTrack(self);
-#if PY_MAJOR_VERSION >= 3  || defined(PyErr_WarnFormat)
-        if (unlikely(PyErr_WarnFormat(PyExc_RuntimeWarning, 1, "coroutine '%.50S' was never awaited", gen->gi_qualname) < 0))
-            PyErr_WriteUnraisable(self);
-#else
-        {PyObject *msg;
-        char *cmsg;
-        #if CYTHON_COMPILING_IN_PYPY
-        msg = NULL;
-        cmsg = (char*) "coroutine was never awaited";
-        #else
-        char *cname;
-        PyObject *qualname;
-        qualname = gen->gi_qualname;
-        cname = PyString_AS_STRING(qualname);
-        msg = PyString_FromFormat("coroutine '%.50s' was never awaited", cname);
-        if (unlikely(!msg)) {
-            PyErr_Clear();
-            cmsg = (char*) "coroutine was never awaited";
-        } else {
-            cmsg = PyString_AS_STRING(msg);
-        }
-        #endif
-        if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, cmsg, 1) < 0))
-            PyErr_WriteUnraisable(self);
-        Py_XDECREF(msg);}
-#endif
-        PyObject_GC_Track(self);
-        }
-#endif
-    } else {
-        PyObject *res = __Pyx_Coroutine_Close(self);
-        if (unlikely(!res)) {
-            if (PyErr_Occurred())
-                PyErr_WriteUnraisable(self);
-        } else {
-            Py_DECREF(res);
-        }
-    }
-    __Pyx_ErrRestore(error_type, error_value, error_traceback);
-#if !CYTHON_USE_TP_FINALIZE
-    assert(Py_REFCNT(self) > 0);
-    if (--self->ob_refcnt == 0) {
-        return;
-    }
-    {
-        Py_ssize_t refcnt = Py_REFCNT(self);
-        _Py_NewReference(self);
-        __Pyx_SET_REFCNT(self, refcnt);
-    }
-#if CYTHON_COMPILING_IN_CPYTHON
-    assert(PyType_IS_GC(Py_TYPE(self)) &&
-           _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED);
-    _Py_DEC_REFTOTAL;
-#endif
-#ifdef COUNT_ALLOCS
-    --Py_TYPE(self)->tp_frees;
-    --Py_TYPE(self)->tp_allocs;
-#endif
-#endif
-}
-static PyObject *
-__Pyx_Coroutine_get_name(__pyx_CoroutineObject *self, CYTHON_UNUSED void *context)
-{
-    PyObject *name = self->gi_name;
-    if (unlikely(!name)) name = Py_None;
-    Py_INCREF(name);
-    return name;
-}
-static int
-__Pyx_Coroutine_set_name(__pyx_CoroutineObject *self, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp;
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    tmp = self->gi_name;
-    Py_INCREF(value);
-    self->gi_name = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_qualname(__pyx_CoroutineObject *self, CYTHON_UNUSED void *context)
-{
-    PyObject *name = self->gi_qualname;
-    if (unlikely(!name)) name = Py_None;
-    Py_INCREF(name);
-    return name;
-}
-static int
-__Pyx_Coroutine_set_qualname(__pyx_CoroutineObject *self, PyObject *value, CYTHON_UNUSED void *context)
-{
-    PyObject *tmp;
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    tmp = self->gi_qualname;
-    Py_INCREF(value);
-    self->gi_qualname = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_frame(__pyx_CoroutineObject *self, CYTHON_UNUSED void *context)
-{
-    PyObject *frame = self->gi_frame;
-    if (!frame) {
-        if (unlikely(!self->gi_code)) {
-            Py_RETURN_NONE;
-        }
-        frame = (PyObject *) PyFrame_New(
-            PyThreadState_Get(),            /*PyThreadState *tstate,*/
-            (PyCodeObject*) self->gi_code,  /*PyCodeObject *code,*/
-            __pyx_d,                 /*PyObject *globals,*/
-            0                               /*PyObject *locals*/
-        );
-        if (unlikely(!frame))
-            return NULL;
-        self->gi_frame = frame;
-    }
-    Py_INCREF(frame);
-    return frame;
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_New(
-            PyTypeObject* type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name) {
-    __pyx_CoroutineObject *gen = PyObject_GC_New(__pyx_CoroutineObject, type);
-    if (unlikely(!gen))
-        return NULL;
-    return __Pyx__Coroutine_NewInit(gen, body, code, closure, name, qualname, module_name);
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
-            __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
-            PyObject *name, PyObject *qualname, PyObject *module_name) {
-    gen->body = body;
-    gen->closure = closure;
-    Py_XINCREF(closure);
-    gen->is_running = 0;
-    gen->resume_label = 0;
-    gen->classobj = NULL;
-    gen->yieldfrom = NULL;
-    gen->gi_exc_state.exc_type = NULL;
-    gen->gi_exc_state.exc_value = NULL;
-    gen->gi_exc_state.exc_traceback = NULL;
-#if CYTHON_USE_EXC_INFO_STACK
-    gen->gi_exc_state.previous_item = NULL;
-#endif
-    gen->gi_weakreflist = NULL;
-    Py_XINCREF(qualname);
-    gen->gi_qualname = qualname;
-    Py_XINCREF(name);
-    gen->gi_name = name;
-    Py_XINCREF(module_name);
-    gen->gi_modulename = module_name;
-    Py_XINCREF(code);
-    gen->gi_code = code;
-    gen->gi_frame = NULL;
-    PyObject_GC_Track(gen);
-    return gen;
-}
-
-/* PatchModuleWithCoroutine */
-static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-    int result;
-    PyObject *globals, *result_obj;
-    globals = PyDict_New();  if (unlikely(!globals)) goto ignore;
-    result = PyDict_SetItemString(globals, "_cython_coroutine_type",
-    #ifdef __Pyx_Coroutine_USED
-        (PyObject*)__pyx_CoroutineType);
-    #else
-        Py_None);
-    #endif
-    if (unlikely(result < 0)) goto ignore;
-    result = PyDict_SetItemString(globals, "_cython_generator_type",
-    #ifdef __Pyx_Generator_USED
-        (PyObject*)__pyx_GeneratorType);
-    #else
-        Py_None);
-    #endif
-    if (unlikely(result < 0)) goto ignore;
-    if (unlikely(PyDict_SetItemString(globals, "_module", module) < 0)) goto ignore;
-    if (unlikely(PyDict_SetItemString(globals, "__builtins__", __pyx_b) < 0)) goto ignore;
-    result_obj = PyRun_String(py_code, Py_file_input, globals, globals);
-    if (unlikely(!result_obj)) goto ignore;
-    Py_DECREF(result_obj);
-    Py_DECREF(globals);
-    return module;
-ignore:
-    Py_XDECREF(globals);
-    PyErr_WriteUnraisable(module);
-    if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, "Cython module failed to patch module with custom type", 1) < 0)) {
-        Py_DECREF(module);
-        module = NULL;
-    }
-#else
-    py_code++;
-#endif
-    return module;
-}
-
-/* PatchGeneratorABC */
-#ifndef CYTHON_REGISTER_ABCS
-#define CYTHON_REGISTER_ABCS 1
-#endif
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-static PyObject* __Pyx_patch_abc_module(PyObject *module);
-static PyObject* __Pyx_patch_abc_module(PyObject *module) {
-    module = __Pyx_Coroutine_patch_module(
-        module, ""
-"if _cython_generator_type is not None:\n"
-"    try: Generator = _module.Generator\n"
-"    except AttributeError: pass\n"
-"    else: Generator.register(_cython_generator_type)\n"
-"if _cython_coroutine_type is not None:\n"
-"    try: Coroutine = _module.Coroutine\n"
-"    except AttributeError: pass\n"
-"    else: Coroutine.register(_cython_coroutine_type)\n"
-    );
-    return module;
-}
-#endif
-static int __Pyx_patch_abc(void) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-    static int abc_patched = 0;
-    if (CYTHON_REGISTER_ABCS && !abc_patched) {
-        PyObject *module;
-        module = PyImport_ImportModule((PY_MAJOR_VERSION >= 3) ? "collections.abc" : "collections");
-        if (!module) {
-            PyErr_WriteUnraisable(NULL);
-            if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning,
-                    ((PY_MAJOR_VERSION >= 3) ?
-                        "Cython module failed to register with collections.abc module" :
-                        "Cython module failed to register with collections module"), 1) < 0)) {
-                return -1;
-            }
-        } else {
-            module = __Pyx_patch_abc_module(module);
-            abc_patched = 1;
-            if (unlikely(!module))
-                return -1;
-            Py_DECREF(module);
-        }
-        module = PyImport_ImportModule("backports_abc");
-        if (module) {
-            module = __Pyx_patch_abc_module(module);
-            Py_XDECREF(module);
-        }
-        if (!module) {
-            PyErr_Clear();
-        }
-    }
-#else
-    if ((0)) __Pyx_Coroutine_patch_module(NULL, NULL);
-#endif
-    return 0;
-}
-
-/* Generator */
-static PyMethodDef __pyx_Generator_methods[] = {
-    {"send", (PyCFunction) __Pyx_Coroutine_Send, METH_O,
-     (char*) PyDoc_STR("send(arg) -> send 'arg' into generator,\nreturn next yielded value or raise StopIteration.")},
-    {"throw", (PyCFunction) __Pyx_Coroutine_Throw, METH_VARARGS,
-     (char*) PyDoc_STR("throw(typ[,val[,tb]]) -> raise exception in generator,\nreturn next yielded value or raise StopIteration.")},
-    {"close", (PyCFunction) __Pyx_Coroutine_Close_Method, METH_NOARGS,
-     (char*) PyDoc_STR("close() -> raise GeneratorExit inside generator.")},
-    {0, 0, 0, 0}
-};
-static PyMemberDef __pyx_Generator_memberlist[] = {
-    {(char *) "gi_running", T_BOOL, offsetof(__pyx_CoroutineObject, is_running), READONLY, NULL},
-    {(char*) "gi_yieldfrom", T_OBJECT, offsetof(__pyx_CoroutineObject, yieldfrom), READONLY,
-     (char*) PyDoc_STR("object being iterated by 'yield from', or None")},
-    {(char*) "gi_code", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_code), READONLY, NULL},
-    {0, 0, 0, 0, 0}
-};
-static PyGetSetDef __pyx_Generator_getsets[] = {
-    {(char *) "__name__", (getter)__Pyx_Coroutine_get_name, (setter)__Pyx_Coroutine_set_name,
-     (char*) PyDoc_STR("name of the generator"), 0},
-    {(char *) "__qualname__", (getter)__Pyx_Coroutine_get_qualname, (setter)__Pyx_Coroutine_set_qualname,
-     (char*) PyDoc_STR("qualified name of the generator"), 0},
-    {(char *) "gi_frame", (getter)__Pyx_Coroutine_get_frame, NULL,
-     (char*) PyDoc_STR("Frame of the generator"), 0},
-    {0, 0, 0, 0, 0}
-};
-static PyTypeObject __pyx_GeneratorType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    "generator",
-    sizeof(__pyx_CoroutineObject),
-    0,
-    (destructor) __Pyx_Coroutine_dealloc,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
-    0,
-    (traverseproc) __Pyx_Coroutine_traverse,
-    0,
-    0,
-    offsetof(__pyx_CoroutineObject, gi_weakreflist),
-    0,
-    (iternextfunc) __Pyx_Generator_Next,
-    __pyx_Generator_methods,
-    __pyx_Generator_memberlist,
-    __pyx_Generator_getsets,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if CYTHON_USE_TP_FINALIZE
-    0,
-#else
-    __Pyx_Coroutine_del,
-#endif
-    0,
-#if CYTHON_USE_TP_FINALIZE
-    __Pyx_Coroutine_del,
-#elif PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-static int __pyx_Generator_init(void) {
-    __pyx_GeneratorType_type.tp_getattro = __Pyx_PyObject_GenericGetAttrNoDict;
-    __pyx_GeneratorType_type.tp_iter = PyObject_SelfIter;
-    __pyx_GeneratorType = __Pyx_FetchCommonType(&__pyx_GeneratorType_type);
-    if (unlikely(!__pyx_GeneratorType)) {
-        return -1;
-    }
-    return 0;
-}
-
-/* CheckBinaryVersion */
-static int __Pyx_check_binary_version(void) {
-    char ctversion[5];
-    int same=1, i, found_dot;
-    const char* rt_from_call = Py_GetVersion();
-    PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    found_dot = 0;
-    for (i = 0; i < 4; i++) {
-        if (!ctversion[i]) {
-            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');
-            break;
-        }
-        if (rt_from_call[i] != ctversion[i]) {
-            same = 0;
-            break;
-        }
-    }
-    if (!same) {
-        char rtversion[5] = {'\0'};
-        char message[200];
-        for (i=0; i<4; ++i) {
-            if (rt_from_call[i] == '.') {
-                if (found_dot) break;
-                found_dot = 1;
-            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {
-                break;
-            }
-            rtversion[i] = rt_from_call[i];
-        }
-        PyOS_snprintf(message, sizeof(message),
-                      "compiletime version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION < 3
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        #else
-        if (t->is_unicode | t->is_str) {
-            if (t->intern) {
-                *t->p = PyUnicode_InternFromString(t->s);
-            } else if (t->encoding) {
-                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
-            } else {
-                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
-            }
-        } else {
-            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
-        }
-        #endif
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type %.200s).  "
-                "The ability to return an instance of a strict subclass of int "
-                "is deprecated, and may be removed in a future version of Python.",
-                Py_TYPE(result)->tp_name)) {
-            Py_DECREF(result);
-            return NULL;
-        }
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type %.200s)",
-                 type_name, type_name, Py_TYPE(result)->tp_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    const digit* digits = ((PyLongObject*)b)->ob_digit;
-    const Py_ssize_t size = Py_SIZE(b);
-    if (likely(__Pyx_sst_abs(size) <= 1)) {
-        ival = likely(size) ? digits[0] : 0;
-        if (size == -1) ival = -ival;
-        return ival;
-    } else {
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {
-  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {
-    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);
-#if PY_MAJOR_VERSION < 3
-  } else if (likely(PyInt_CheckExact(o))) {
-    return PyInt_AS_LONG(o);
-#endif
-  } else {
-    Py_ssize_t ival;
-    PyObject *x;
-    x = PyNumber_Index(o);
-    if (!x) return -1;
-    ival = PyInt_AsLong(x);
-    Py_DECREF(x);
-    return ival;
-  }
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-#endif /* Py_PYTHON_H */
diff --git a/spaces/DakMak/gradio-start/index.html b/spaces/DakMak/gradio-start/index.html
deleted file mode 100644
index e519b94973ea2a63a6d0219b7f193bc6decf158f..0000000000000000000000000000000000000000
--- a/spaces/DakMak/gradio-start/index.html
+++ /dev/null
@@ -1,2 +0,0 @@
-	
-<img src="qr-img.png" alt="qr-1234.png">
\ No newline at end of file
diff --git a/spaces/Daniele/forma-locutionis/README.md b/spaces/Daniele/forma-locutionis/README.md
deleted file mode 100644
index f00251f2ca5f5b4c6857446e7ecf909ea727efce..0000000000000000000000000000000000000000
--- a/spaces/Daniele/forma-locutionis/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Dante Thoughts
-emoji: 🌌 ⌛ 📔
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Detomo/ai-comic-generation/src/lib/utils.ts b/spaces/Detomo/ai-comic-generation/src/lib/utils.ts
deleted file mode 100644
index ec79801fe9cdd7711f6dbef26678a134c634a8be..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/src/lib/utils.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { type ClassValue, clsx } from "clsx"
-import { twMerge } from "tailwind-merge"
- 
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
diff --git a/spaces/Devap001/top-5_movies_recommendation/README.md b/spaces/Devap001/top-5_movies_recommendation/README.md
deleted file mode 100644
index 7dc344f56518619dad2de9117775de459d1d5da3..0000000000000000000000000000000000000000
--- a/spaces/Devap001/top-5_movies_recommendation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Top-5 Movies Recommendation
-emoji: 🏢
-colorFrom: purple
-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/Dragneel/Recon/app.py b/spaces/Dragneel/Recon/app.py
deleted file mode 100644
index b09688da251eb5c3b781bb9683807d7bf59c5d8d..0000000000000000000000000000000000000000
--- a/spaces/Dragneel/Recon/app.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import streamlit as st
-import random
-import pickle
-from sentiment import get_sentiment
-
-# Load the data
-novel_list = pickle.load(open('data/novel_list.pkl', 'rb'))
-novel_list['english_publisher'] = novel_list['english_publisher'].fillna('unknown')
-name_list = novel_list['name'].values
-
-def recommend(novel, slider_start):
-    try:
-        similarity = pickle.load(open('data/similarity.pkl', 'rb'))
-        novel_index = novel_list[novel_list['name'] == novel].index[0]
-        distances = similarity[novel_index]
-        new_novel_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[slider_start:slider_start+9]
-    except IndexError:
-        return None
-
-    recommend_novel = [{'name': novel_list.iloc[i[0]]['name'], 'image_url': novel_list.iloc[i[0]]['image_url'], 'english_publisher': novel_list.iloc[i[0]]['english_publisher']} for i in new_novel_list]
-    return recommend_novel
-
-def main():
-    st.title("📚 Novel Recommender System")
-
-    # Input fields and buttons
-    selected_novel_name = st.text_input("🔎 Choose a Novel to get Recommendations", "Mother of Learning")
-    slider_value = st.slider("Slider", 1, 100, 1)
-
-    col1, col2, col3 = st.columns(3)  # Create three columns to place buttons side by side
-    with col1:
-        btn_recommend = st.button("💡 Recommend")
-    with col2:
-        btn_random = st.button("🎲 Random")
-    with col3:
-        btn_analysis = st.button("Analysis")
-
-    if btn_recommend:
-        recommendations = recommend(selected_novel_name, slider_value)
-        if recommendations:
-            for i in range(0, len(recommendations), 3):  # Process 3 recommendations at a time
-                cols = st.columns(3)
-                for j in range(3):
-                    if i + j < len(recommendations):
-                        novel = recommendations[i + j]
-                        with cols[j]:
-                            st.image(novel["image_url"], use_column_width=True)
-                            st.write(novel["name"])
-        else:
-            st.warning("Novel not found in our database. Please try another one.")
-    
-    if btn_random:
-        random_novels = random.sample(list(name_list), 9)
-        for i in range(0, len(random_novels), 3):
-            cols = st.columns(3)
-            for j in range(3):
-                if i + j < len(random_novels):
-                    novel_name = random_novels[i + j]
-                    novel_img = novel_list[novel_list['name'] == novel_name]['image_url'].values[0]
-                    with cols[j]:
-                        st.image(novel_img, use_column_width=True)
-                        st.write(novel_name)
-
-    if btn_analysis:
-        try:
-            positive, negative, wordcloud = get_sentiment(selected_novel_name)
-            st.write(f"😊 {positive}% Positive")
-            st.write(f"☹️ {negative}% Negative")
-            print(wordcloud)
-           
-           
-
-            st.image(wordcloud)
-
-        except Exception as e:
-            st.error("An error occurred during sentiment analysis.")
-    
-if __name__ == "__main__":
-    main()
diff --git a/spaces/EnD-Diffusers/Photography-Test/app.py b/spaces/EnD-Diffusers/Photography-Test/app.py
deleted file mode 100644
index 52a948655132770976dfa9789d3370b15666f5d5..0000000000000000000000000000000000000000
--- a/spaces/EnD-Diffusers/Photography-Test/app.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import gradio as gr
-
-API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None)
-
-article = """---
-This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator)."""
-
-gr.Interface.load(
-    name="models/Duskfallcrew/photography-and-landscapes",
-    title="""Photography And Landscapes""",
-    description="""Demo for <a href="https://huggingface.co/Duskfallcrew/photography-and-landscapes">Photography And Landscapes</a> Stable Diffusion model.""",
-    article=article,
-    api_key=API_KEY,
-    ).queue(concurrency_count=20).launch()
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/evaluate_topic.py b/spaces/EuroPython2022/clickbaitonator/fudge/evaluate_topic.py
deleted file mode 100644
index ac927bf94d63aa9e0a1ccba3300c8c087d35b34e..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/evaluate_topic.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import os
-import random
-import time
-import pickle
-import math
-from argparse import ArgumentParser
-from collections import defaultdict
-import string
-import csv
-
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline, set_seed, GPT2Tokenizer, GPT2Model
-
-from data import Dataset
-from model import Model
-from util import save_checkpoint, ProgressMeter, AverageMeter, num_params, pad_mask
-from predict_topic import predict
-from constants import *
-
-
-def main(args):
-    with open(args.dataset_info, 'rb') as rf:
-        dataset_info = pickle.load(rf)
-    gpt_tokenizer = AutoTokenizer.from_pretrained(args.model_string)
-    gpt_tokenizer.add_special_tokens({'pad_token': PAD_TOKEN})
-    gpt_pad_id = gpt_tokenizer.encode(PAD_TOKEN)[0]
-    gpt_model = AutoModelWithLMHead.from_pretrained(args.model_string).to(args.device)
-    gpt_model.eval()
-
-    checkpoint = torch.load(args.ckpt, map_location=args.device)
-    model_args = checkpoint['args']
-    conditioning_model = Model(model_args, gpt_pad_id, len(dataset_info.index2word)) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
-    conditioning_model.load_state_dict(checkpoint['state_dict'])
-    conditioning_model = conditioning_model.to(args.device)
-    conditioning_model.eval()
-    if args.verbose:
-        print("=> loaded checkpoint '{}' (epoch {})"
-                .format(args.ckpt, checkpoint['epoch']))
-        print('num params', num_params(conditioning_model))
-
-    input_texts, conditions, categories = [], [], []
-
-    if args.condition_file is not None:
-        with open(args.condition_file, 'r') as rf:
-            for line in rf:
-                input_texts.append(line.strip().split('\t')[0])
-                conditions.append(line.strip().split('\t')[1])
-                categories.append(None)
-                for cw in conditions[-1].split():
-                    assert cw in dataset_info.word2index
-    else:
-        prefixes = []
-        with open(args.prefix_file, 'r') as rf:
-            for line in rf:
-                prefixes.append(line.strip())
-        condition_wordlists = []
-        for root, _, files in os.walk(args.wordlist_dir):
-            for fname in files:
-                words = []
-                with open(os.path.join(root, fname), 'r') as rf:
-                    for line in rf:
-                        word = line.strip()
-                        if word in dataset_info.word2index:
-                            words.append(word)
-                        else:
-                            if args.verbose:
-                                print('word not found:', word)
-                condition_wordlists.append((' '.join(words), fname.split('.')[0]))
-        for p in prefixes:
-            for c, category in condition_wordlists:
-                input_texts.append(p)
-                conditions.append(c)
-                categories.append(category)
-    
-    all_cr = []
-    pair_num = 0
-    for input_text, condition_words, category in tqdm(zip(input_texts, conditions, categories), total=len(conditions)):
-        predict_function = predict
-        condition_results = []
-        for i in range(0, args.sample_size, args.max_sample_batch):
-            num_samples = min(args.max_sample_batch, args.sample_size - i)
-            condition_results += predict_function(gpt_model, 
-                            gpt_tokenizer, 
-                            conditioning_model, 
-                            [input_text for _ in range(num_samples)],
-                            condition_words,
-                            dataset_info, 
-                            args.precondition_topk,
-                            args.topk, 
-                            args.length_cutoff,
-                            condition_lambda=args.condition_lambda,
-                            device=args.device)
-        all_cr.append((input_text, category, condition_results))
-        pair_num += 1
-        if args.max_pairs > 0 and pair_num >= args.max_pairs:
-            break
-    with open(args.log_file, 'w') as wf:
-        writer = csv.DictWriter(wf, fieldnames=['category', 'input_text', 'generation'])
-        writer.writeheader()
-        for cr_group in all_cr:
-            for cr in cr_group[2]:
-                writer.writerow({'category': cr_group[1], 'input_text': cr_group[0], 'generation': cr})
-
-
-if __name__=='__main__':
-    parser = ArgumentParser()
-
-    # DATA
-    parser.add_argument('--ckpt', type=str, required=True)
-    parser.add_argument('--log_file', type=str, required=True, help='file to write outputs to (csv format)')
-    parser.add_argument('--dataset_info', type=str, required=True, help='saved dataset info')
-    parser.add_argument('--model_string', type=str, default='gpt2-medium')
-
-    parser.add_argument('--condition_file', type=str, default=None, help='file of inputs and conditions')
-    parser.add_argument('--prefix_file', type=str, default=None, help='prefix set')
-    parser.add_argument('--wordlist_dir', type=str, default=None, help='dir of bow wordlists for categories')
-    parser.add_argument('--sample_size', type=int, default=3, help='samples per input text-condition pair')
-    parser.add_argument('--max_sample_batch', type=int, default=3, help='max samples at a time')
-    parser.add_argument('--max_pairs', type=int, default=-1, help='max input-condition pairs, for debugging quickly')
-
-    parser.add_argument('--precondition_topk', type=int, default=200, help='consider top k outputs from gpt at each step before conditioning and re-pruning')
-    parser.add_argument('--topk', type=int, default=10, help='consider top k outputs from gpt at each step')
-    parser.add_argument('--condition_lambda', type=float, default=1.0, help='lambda weight on conditioning model')
-    parser.add_argument('--length_cutoff', type=int, default=80, help='max length')
-
-    parser.add_argument('--seed', type=int, default=1, help='random seed')
-    parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
-    parser.add_argument('--debug', action='store_true', default=False)
-    parser.add_argument('--verbose', action='store_true', default=False)
-
-    args = parser.parse_args()
-
-    assert (args.condition_file is not None) != (args.prefix_file is not None and args.wordlist_dir is not None) # one of two interfaces for specifying
-
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-
-    main(args)
\ No newline at end of file
diff --git a/spaces/EyanAn/vits-uma-genshin-honkai/commons.py b/spaces/EyanAn/vits-uma-genshin-honkai/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/EyanAn/vits-uma-genshin-honkai/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/FebryanS/Wakaranai/app.py b/spaces/FebryanS/Wakaranai/app.py
deleted file mode 100644
index 708d50e2572cde2fd35ad82a7552883e3f8b1695..0000000000000000000000000000000000000000
--- a/spaces/FebryanS/Wakaranai/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-import requests
-import random
-
-def yandere(popular: bool, tags, limit=1):
-    if popular is True:
-        params = {
-            'period': tags,
-            'api_version': 2,
-            'include_tags': 1
-        }
-        pop = requests.get("https://yande.re/post/popular_recent.json?", params=params).json()
-        return pop
-    elif popular is False:
-        params = {
-            'api_version': 2,
-            'tags': tags,
-            'limit': limit,
-            'include_tags': 1
-        }
-        res = requests.get("https://yande.re/post.json", params=params).json()
-        return res
-
-iface = gr.Interface(
-    fn=yandere,
-    inputs= [
-        "checkbox",
-        "text",
-        "number"
-    ],
-    outputs= "list"
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Felladrin/MiniSearch/src/components/ResponseView.tsx b/spaces/Felladrin/MiniSearch/src/components/ResponseView.tsx
deleted file mode 100644
index 98545d8760d76edb0bf1457782871aa6e8ed9dc9..0000000000000000000000000000000000000000
--- a/spaces/Felladrin/MiniSearch/src/components/ResponseView.tsx
+++ /dev/null
@@ -1,58 +0,0 @@
-import Markdown from "markdown-to-jsx";
-import { SearchResults, searchQueryKey } from "../modules/search";
-import { getDisableAiResponseSetting } from "../modules/pubSub";
-import { SearchResultsList } from "./SearchResultsList";
-import { Tooltip } from "react-tooltip";
-
-export function ResponseView({
-  prompt,
-  response,
-  searchResults,
-  urlsDescriptions,
-}: {
-  prompt: string;
-  response: string;
-  searchResults: SearchResults;
-  urlsDescriptions: Record<string, string>;
-}) {
-  return (
-    <>
-      <Tooltip
-        id="query-tooltip"
-        place="bottom-start"
-        variant="info"
-        opacity="1"
-        float
-      />
-      <blockquote
-        style={{ cursor: "pointer" }}
-        onClick={() => {
-          localStorage.setItem(searchQueryKey, prompt);
-          window.location.href = window.location.origin;
-        }}
-        data-tooltip-id="query-tooltip"
-        data-tooltip-content="Click to edit the query"
-      >
-        <Markdown>{prompt}</Markdown>
-      </blockquote>
-      {!getDisableAiResponseSetting() && (
-        <>
-          <div>
-            <Markdown>{response}</Markdown>
-          </div>
-          <hr />
-        </>
-      )}
-      <div>
-        {searchResults.length > 0 ? (
-          <SearchResultsList
-            searchResults={searchResults}
-            urlsDescriptions={urlsDescriptions}
-          />
-        ) : (
-          "Searching the Web..."
-        )}
-      </div>
-    </>
-  );
-}
diff --git a/spaces/FrancisLi/advance_autotrain/Dockerfile b/spaces/FrancisLi/advance_autotrain/Dockerfile
deleted file mode 100644
index 94ee76a4f45af463ab7f945633c9258172f9cc80..0000000000000000000000000000000000000000
--- a/spaces/FrancisLi/advance_autotrain/Dockerfile
+++ /dev/null
@@ -1,2 +0,0 @@
-FROM huggingface/autotrain-advanced:latest
-CMD autotrain app --port 7860
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/cleaners.py b/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/cleaners.py
deleted file mode 100644
index 263df9c0f7c185290600454abfff464e7f774576..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/cleaners.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import re
-from text.japanese import japanese_to_romaji_with_accent, japanese_to_ipa, japanese_to_ipa2, japanese_to_ipa3
-from text.korean import latin_to_hangul, number_to_hangul, divide_hangul, korean_to_lazy_ipa, korean_to_ipa
-from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2
-from text.sanskrit import devanagari_to_ipa
-from text.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2
-from text.thai import num_to_thai, latin_to_thai
-# from text.shanghainese import shanghainese_to_ipa
-# from text.cantonese import cantonese_to_ipa
-# from text.ngu_dialect import ngu_dialect_to_ipa
-
-
-def japanese_cleaners(text):
-    text = japanese_to_romaji_with_accent(text)
-    text = re.sub(r'([A-Za-z])$', r'\1.', text)
-    return text
-
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
-    '''Pipeline for Korean text'''
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text = divide_hangul(text)
-    text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
-    return text
-
-
-# def chinese_cleaners(text):
-#     '''Pipeline for Chinese text'''
-#     text = number_to_chinese(text)
-#     text = chinese_to_bopomofo(text)
-#     text = latin_to_bopomofo(text)
-#     text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-#     return text
-
-def chinese_cleaners(text):
-    from pypinyin import Style, pinyin
-    text = text.replace("[ZH]", "")
-    phones = [phone[0] for phone in pinyin(text, style=Style.TONE3)]
-    return ' '.join(phones)
-
-
-def zh_ja_mixture_cleaners(text):
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_romaji(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
-        x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def sanskrit_cleaners(text):
-    text = text.replace('॥', '।').replace('ॐ', 'ओम्')
-    text = re.sub(r'([^।])$', r'\1।', text)
-    return text
-
-
-def cjks_cleaners(text):
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[SA\](.*?)\[SA\]',
-                  lambda x: devanagari_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners(text):
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
-        'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
-        'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
-        'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners2(text):
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def thai_cleaners(text):
-    text = num_to_thai(text)
-    text = latin_to_thai(text)
-    return text
-
-
-# def shanghainese_cleaners(text):
-#     text = shanghainese_to_ipa(text)
-#     text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-#     return text
-
-
-# def chinese_dialect_cleaners(text):
-#     text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-#                   lambda x: chinese_to_ipa2(x.group(1))+' ', text)
-#     text = re.sub(r'\[JA\](.*?)\[JA\]',
-#                   lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
-#     text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
-#                   '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
-#     text = re.sub(r'\[GD\](.*?)\[GD\]',
-#                   lambda x: cantonese_to_ipa(x.group(1))+' ', text)
-#     text = re.sub(r'\[EN\](.*?)\[EN\]',
-#                   lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
-#     text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
-#         1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
-#     text = re.sub(r'\s+$', '', text)
-#     text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-#     return text
diff --git a/spaces/Frorozcol/financIA/app.py b/spaces/Frorozcol/financIA/app.py
deleted file mode 100644
index 96d7d32558efae1bbc8608a0e44c3317726718ca..0000000000000000000000000000000000000000
--- a/spaces/Frorozcol/financIA/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import streamlit as st
-from src import get_predict
-def main():
-    st.title("FinacIA")
-    st.write("Este es un DEMO del proyecto FinacIA, una aplicación que permite analizar noticias financieras y predecir el sentimiento del mercado")
-    st.write("En las páginas posteriores se encuentra la documentación del proyecto, el entrenamiento del modelo, el análisis exploratorio de los datos y tambien la arquitectura del proyecto")
-    
-    st.write("Ingresa el titulo de una noticia y te diremos si es positiva, negativa o neutral")
-    
-    texto = st.text_input("Ingresa un texto")
-    if not texto:
-        texto = "Una de cada diez empresas cerrará y la mitad entrará en pérdidas"
-    resultado = get_predict(texto)
-    st.write("Texto:", texto)
-    st.write("Resultado:", resultado)
-    
-    st.subheader("Creadores:")
-    st.write("👽 Alejandro Bedoya Taborda")
-    st.write("🕵 Alejandro Noriega Soto")
-    st.write("👨‍🏭 Cristian Camilo Henao Rojas")
-    st.write("💂 Fredy Alberto Orozco Loaiza")
-    st.write("👨‍⚖ Ronald Gabriel Palencia")
-if __name__ == '__main__':
-    main()
diff --git a/spaces/GaenKoki/voicevox/voicevox_engine/metas/__init__.py b/spaces/GaenKoki/voicevox/voicevox_engine/metas/__init__.py
deleted file mode 100644
index 4907fdf38d604dc7949dd361812938afd9db0abb..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/voicevox_engine/metas/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from . import Metas, MetasStore
-
-__all__ = [
-    "Metas",
-    "MetasStore",
-]
diff --git a/spaces/Gaurav261/medical_image_classification/README.md b/spaces/Gaurav261/medical_image_classification/README.md
deleted file mode 100644
index 6132eeb7a955e3979a7a083048826c4eea365f14..0000000000000000000000000000000000000000
--- a/spaces/Gaurav261/medical_image_classification/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Medical Image Classification
-emoji: 📊
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GeekedReals/jonatasgrosman-wav2vec2-large-xlsr-53-english/README.md b/spaces/GeekedReals/jonatasgrosman-wav2vec2-large-xlsr-53-english/README.md
deleted file mode 100644
index 7edc261253e49a5a37af9bb4ce1e87f8f9b899b8..0000000000000000000000000000000000000000
--- a/spaces/GeekedReals/jonatasgrosman-wav2vec2-large-xlsr-53-english/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Jonatasgrosman Wav2vec2 Large Xlsr 53 English
-emoji: 🐢
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/streams/one_stream_attention_lang_fusion.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/streams/one_stream_attention_lang_fusion.py
deleted file mode 100644
index 0e433a6d60d933ae259ee84ef2d0371bdfb3a20e..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/streams/one_stream_attention_lang_fusion.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""Attention module."""
-
-import cliport.models as models
-from cliport.models.streams.two_stream_attention_lang_fusion import TwoStreamAttentionLangFusion
-
-
-class OneStreamAttentionLangFusion(TwoStreamAttentionLangFusion):
-    """Attention (a.k.a Pick) module with language features fused at the bottleneck."""
-
-    def __init__(self, stream_fcn, in_shape, n_rotations, preprocess, cfg, device):
-        self.fusion_type = cfg['train']['attn_stream_fusion_type']
-        super().__init__(stream_fcn, in_shape, n_rotations, preprocess, cfg, device)
-
-    def _build_nets(self):
-        stream_one_fcn, _ = self.stream_fcn
-        stream_one_model = models.names[stream_one_fcn]
-
-        self.attn_stream_one = stream_one_model(self.in_shape, 1, self.cfg, self.device, self.preprocess)
-        print(f"Attn FCN: {stream_one_fcn}")
-
-    def attend(self, x, l):
-        x = self.attn_stream_one(x, l)
-        return x
\ No newline at end of file
diff --git a/spaces/Gen-Sim/Gen-Sim/misc/prepare_finetune_gpt.py b/spaces/Gen-Sim/Gen-Sim/misc/prepare_finetune_gpt.py
deleted file mode 100644
index 784923bf4380983904d1db86d8a4c4ff363e86ad..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/misc/prepare_finetune_gpt.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import cv2
-import numpy as np
-import IPython
-import os
-
-import openai
-import pandas as pd
-import json
-import subprocess
-
-
-# create dataset by loading the python file
-def format_prompt(task_name):
-    instruction_text = open('misc/finetune_instructions_prompt.txt').read()
-    instruction_text = instruction_text.replace("TASK_NAME_TEMPLATE", task_name)
-    prompt_text = "\n Instructions: "  + instruction_text + "\n\n###\n\n"
-    return prompt_text
-
-def format_completion(task_name, descriptions, code):
-    completion_text = f" \nDescriptions: \n  ```{task_name}: {descriptions} \n\n###\n\n"
-    completion_text += "Implementation: \n  ```python\n" + code  + "<|endoftext|>"
-    return completion_text
-
-# test if using the finetuned model can generate better task coed than the base model
-# https://platform.openai.com/docs/guides/fine-tuning
-data_path = 'prompts/data'
-def load_offline_memory():
-    """get the current task descriptions, assets, and code"""
-    base_task_path = os.path.join(data_path, "base_tasks.json")
-    base_asset_path = os.path.join(data_path, "base_assets.json")
-    base_task_code_path = os.path.join(data_path, "base_task_codes.json")
-
-    base_tasks = json.load(open(base_task_path))
-    base_assets = json.load(open(base_asset_path))
-    base_task_codes = json.load(open(base_task_code_path))
-
-    generated_task_path = os.path.join(data_path, "generated_tasks.json")
-    generated_asset_path = os.path.join(data_path, "generated_assets.json")
-    generated_task_code_path = os.path.join(data_path, "generated_task_codes.json")
-
-    # print("original base task num:", len(base_tasks))
-    base_tasks.update(json.load(open(generated_task_path)))
-    # base_assets.update(json.load(open(generated_asset_path)))
-
-    for task in json.load(open(generated_task_code_path)):
-        if task not in base_task_codes:
-          base_task_codes.append(task)
-
-        # print("current base task num:", len(base_tasks))
-    return base_tasks, base_assets, base_task_codes
-
-
-code_buffer = {}
-base_tasks, base_assets, base_task_codes = load_offline_memory()
-TOTAL_DATASET_TOKENS = 0
-
-added_tasks = []
-df = pd.DataFrame()
-for task_file in base_task_codes:
-    ## TODO(lirui): consider adding more structure here.
-    task_name = task_file[:-3].replace("_", "-")
-    if task_name in added_tasks:
-        continue
-
-    if task_name not in base_tasks:
-        print(f"{task_name} missing")
-        continue
-
-    added_tasks.append(task_name)
-    task_description = base_tasks[task_name]
-
-    if os.path.exists("cliport/tasks/" + task_file):
-        task_code = open("cliport/tasks/" + task_file).read()
-
-        # the generated cliport task path
-    elif os.path.exists("cliport/generated_tasks/" + task_file):
-        task_code = open("cliport/generated_tasks/" + task_file).read()
-
-    prompt = format_prompt(task_name)
-    completion = format_completion(task_name, task_description, task_code)
-
-    # rough estimates
-    TOTAL_DATASET_TOKENS += len(prompt) / 4
-    TOTAL_DATASET_TOKENS += len(completion) / 4
-    new_row = { 'prompt': prompt,
-                'completion': completion}
-    new_row = pd.DataFrame([new_row])
-    df = pd.concat([df, new_row], axis=0, ignore_index=True)
-
-df.to_csv("misc/finetune_data.csv", index=False)
-print("======================================")
-print("estimate number of tokens:", TOTAL_DATASET_TOKENS)
-print("estimate price for davinci:", TOTAL_DATASET_TOKENS / 1000 * 0.03)
-print("total number of instructions:", len(df))
-print("======================================")
-# actual finetuning
-
-## prepared_data.csv --> prepared_data_prepared.json
-subprocess.run('openai tools fine_tunes.prepare_data --file misc/finetune_data.csv --quiet'.split())
-
-print("now you can run \n openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix 'GenSim'")
-# Model    Training    Usage
-# Ada $0.0004 / 1K tokens $0.0016 / 1K tokens
-# Curie   $0.0030 / 1K tokens $0.0120 / 1K tokens
-# Davinci $0.0300 / 1K tokens $0.1200 / 1K tokens
-
-# ## Start fine-tuning
-# openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim"
-# subprocess.run('openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim"'.split())
-
-
-# Tracking Finetune Status
-# openai api fine_tunes.follow -i
-# openai api fine_tunes.get -i
-# openai wandb sync
\ No newline at end of file
diff --git a/spaces/Geonmo/nllb-translation-demo/app.py b/spaces/Geonmo/nllb-translation-demo/app.py
deleted file mode 100644
index d0e56d0ea7c003b0eda096331214e4c4cf2a208a..0000000000000000000000000000000000000000
--- a/spaces/Geonmo/nllb-translation-demo/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import os
-import torch
-import gradio as gr
-import time
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-from flores200_codes import flores_codes
-
-
-def load_models():
-    # build model and tokenizer
-    model_name_dict = {'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M',
-                  #'nllb-1.3B': 'facebook/nllb-200-1.3B',
-                  #'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
-                  #'nllb-3.3B': 'facebook/nllb-200-3.3B',
-                  }
-
-    model_dict = {}
-
-    for call_name, real_name in model_name_dict.items():
-        print('\tLoading model: %s' % call_name)
-        model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
-        tokenizer = AutoTokenizer.from_pretrained(real_name)
-        model_dict[call_name+'_model'] = model
-        model_dict[call_name+'_tokenizer'] = tokenizer
-
-    return model_dict
-
-
-def translation(source, target, text):
-    if len(model_dict) == 2:
-        model_name = 'nllb-distilled-600M'
-
-    start_time = time.time()
-    source = flores_codes[source]
-    target = flores_codes[target]
-
-    model = model_dict[model_name + '_model']
-    tokenizer = model_dict[model_name + '_tokenizer']
-
-    translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
-    output = translator(text, max_length=400)
-
-    end_time = time.time()
-
-    output = output[0]['translation_text']
-    result = {'inference_time': end_time - start_time,
-              'source': source,
-              'target': target,
-              'result': output}
-    return result
-
-
-if __name__ == '__main__':
-    print('\tinit models')
-
-    global model_dict
-
-    model_dict = load_models()
-    
-    # define gradio demo
-    lang_codes = list(flores_codes.keys())
-    #inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
-    inputs = [gr.inputs.Dropdown(lang_codes, default='English', label='Source'),
-              gr.inputs.Dropdown(lang_codes, default='Korean', label='Target'),
-              gr.inputs.Textbox(lines=5, label="Input text"),
-              ]
-
-    outputs = gr.outputs.JSON()
-
-    title = "NLLB distilled 600M demo"
-
-    demo_status = "Demo is running on CPU"
-    description = f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
-    examples = [
-    ['English', 'Korean', 'Hi. nice to meet you']
-    ]
-
-    gr.Interface(translation,
-                 inputs,
-                 outputs,
-                 title=title,
-                 description=description,
-                 ).launch()
-
-
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rpn/README.md b/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rpn/README.md
deleted file mode 100644
index 2b0c6de8f2508ca3f1bdd3d4a203a71dfcffce3a..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rpn/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Cascade RPN
-
-[ALGORITHM]
-
-We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720).
-
-```
-@inproceedings{vu2019cascade,
-  title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution},
-  author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D},
-  booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
-  year={2019}
-}
-```
-
-## Benchmark
-
-### Region proposal performance
-
-| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 |                Download                |
-|:------:|:--------:|:-----:|:--------:|:-------------------:|:--------------:|:-------:|:--------------------------------------:|
-|  CRPN  | R-50-FPN | caffe |     -    |          -          |        -       |   72.0  | [model](https://drive.google.com/file/d/1qxVdOnCgK-ee7_z0x6mvAir_glMu2Ihi/view?usp=sharing) |
-
-### Detection performance
-
-|     Method    |   Proposal  | Backbone |  Style  | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                   Download                   |
-|:-------------:|:-----------:|:--------:|:-------:|:--------:|:--------:|:-------------------:|:--------------:|:------:|:--------------------------------------------:|
-|   Fast R-CNN  | Cascade RPN | R-50-FPN |  caffe  |    1x    |    -     |          -          |        -       |  39.9  | [model](https://drive.google.com/file/d/1NmbnuY5VHi8I9FE8xnp5uNvh2i-t-6_L/view?usp=sharing) |
-|  Faster R-CNN | Cascade RPN | R-50-FPN |  caffe  |    1x    |    -     |          -          |        -       |  40.4  | [model](https://drive.google.com/file/d/1dS3Q66qXMJpcuuQgDNkLp669E5w1UMuZ/view?usp=sharing) |
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py
deleted file mode 100644
index d2edab113649c38cac3c7dc3ff425462f7c40ffd..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
deleted file mode 100644
index 4f7150ca718e2ead46eb63e74b6be06f50aa0fce..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 23])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/models/__init__.py b/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/models/__init__.py
deleted file mode 100644
index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/MusicGen_Plus/audiocraft/models/__init__.py
+++ /dev/null
@@ -1,10 +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 .musicgen import MusicGen
-from .lm import LMModel
-from .encodec import CompressionModel, EncodecModel
diff --git a/spaces/GroveStreet/GTA_SOVITS/vencoder/hubert/__init__.py b/spaces/GroveStreet/GTA_SOVITS/vencoder/hubert/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Hallucinate/demo/ldm/models/diffusion/__init__.py b/spaces/Hallucinate/demo/ldm/models/diffusion/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/transformer_utils.py b/spaces/HaloMaster/chinesesummary/fengshen/models/transformer_utils.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh
deleted file mode 100644
index ca3591b3db1715f136773d62e4b9b9ede97d436c..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh
+++ /dev/null
@@ -1,225 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-#echo 'Cloning Moses github repository (for tokenization scripts)...'
-#git clone https://github.com/moses-smt/mosesdecoder.git
-
-if [ -z $WORKDIR_ROOT ] ;
-then
-        echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
-        exit
-fi
-
- 
-
-data_root=${WORKDIR_ROOT}/iwsltv2
-DESTDIR=${WORKDIR_ROOT}/ML50/raw
-
-
-langs="ar_AR it_IT nl_XX ko_KR vi_VN"
-echo "data_root: $data_root"
-
-download_path=${data_root}/downloads
-raw=${DESTDIR}
-tmp=${data_root}/tmp
-orig=${data_root}/orig
- 
-mkdir -p $download_path $orig $raw $tmp
-#######################
-download_iwslt(){
-    iwslt_key=$1
-    src=$2
-    tgt=$3
-    save_prefix=$4
-    pushd ${download_path}
-    if [[ ! -f ${save_prefix}$src-$tgt.tgz ]]; then
-        wget https://wit3.fbk.eu/archive/${iwslt_key}/texts/$src/$tgt/$src-$tgt.tgz -O ${save_prefix}$src-$tgt.tgz
-        [ $? -eq 0 ] && return 0
-    fi         
-    popd
-}
-
-extract_iwslt(){
-    src=$1
-    tgt=$2
-    prefix=$3
-    pushd $orig                
-    tar zxvf ${download_path}/${prefix}$src-${tgt}.tgz
-    popd 
-}
-
-generate_train(){
-    lsrc=$1
-    ltgt=$2
-    src=${lsrc:0:2}    
-    tgt=${ltgt:0:2}
-    for ll in $lsrc $ltgt; do
-        l=${ll:0:2}
-        f="$orig/*/train.tags.$src-$tgt.$l"
-        f_raw=$raw/train.$lsrc-$ltgt.$ll
-        cat $f \
-        | grep -v '<url>' \
-        | grep -v '<talkid>' \
-        | grep -v '<keywords>' \
-        | grep -v '<speaker>' \
-        | grep -v '<reviewer' \
-        | grep -v '<translator' \
-        | grep -v '<doc' \
-        | grep -v '</doc>' \
-        | sed -e 's/<title>//g' \
-        | sed -e 's/<\/title>//g' \
-        | sed -e 's/<description>//g' \
-        | sed -e 's/<\/description>//g' \
-        | sed 's/^\s*//g' \
-        | sed 's/\s*$//g' \
-        > $f_raw
-        [ $? -eq 0 ] && echo "extracted $f to $f_raw"
-    done
-    return 0        
-}
-
-convert_valid_test(){
-    src=$1
-    tgt=$2
-    for l in $src $tgt; do
-        echo "lang: ${l}"
-        for o in `ls $orig/*/IWSLT*.TED*.$src-$tgt.$l.xml`; do
-            fname=${o##*/}
-            f=$tmp/${fname%.*}
-            echo "$o => $f"
-            grep '<seg id' $o \
-            | sed -e 's/<seg id="[0-9]*">\s*//g' \
-            | sed -e 's/\s*<\/seg>\s*//g' \
-            | sed -e "s/\’/\'/g" \
-            > $f
-            echo ""
-        done
-    done    
-}
-
-generate_subset(){
-    lsrc=$1
-    ltgt=$2
-    src=${lsrc:0:2}
-    tgt=${ltgt:0:2}
-    subset=$3
-    prefix=$4
-    for ll in $lsrc $ltgt; do
-        l=${ll:0:2}
-        f=$tmp/$prefix.${src}-${tgt}.$l
-        if [[ -f $f ]]; then        
-            cp $f $raw/$subset.${lsrc}-$ltgt.${ll}
-        fi
-    done      
-}
-#################
-
-echo "downloading iwslt training and dev data"
-# using multilingual for it, nl 
-download_iwslt "2017-01-trnmted" DeEnItNlRo DeEnItNlRo
-download_iwslt "2017-01-trnted" ar en
-download_iwslt "2017-01-trnted" en ar
-download_iwslt "2017-01-trnted" ko en
-download_iwslt "2017-01-trnted" en ko
-download_iwslt "2015-01" vi en   
-download_iwslt "2015-01" en vi   
-
-echo "donwloading iwslt test data"
-download_iwslt "2017-01-mted-test" it en "test."
-download_iwslt "2017-01-mted-test" en it "test."
-download_iwslt "2017-01-mted-test" nl en "test."
-download_iwslt "2017-01-mted-test" en nl "test."
-
-download_iwslt "2017-01-ted-test" ar en "test."
-download_iwslt "2017-01-ted-test" en ar "test."
-download_iwslt "2017-01-ted-test" ko en "test."
-download_iwslt "2017-01-ted-test" en ko "test."
-download_iwslt "2015-01-test" vi en "test."
-download_iwslt "2015-01-test" en vi "test."
-
-echo "extract training data tar balls"
-extract_iwslt  DeEnItNlRo DeEnItNlRo
-extract_iwslt  ar en
-extract_iwslt  en ar
-extract_iwslt  ko en
-extract_iwslt  en ko
-extract_iwslt  vi en   
-extract_iwslt  en vi   
-
-
-echo "extracting iwslt test data"
-for lang in $langs; do
-    l=${lang:0:2}
-    extract_iwslt $l en "test."
-    extract_iwslt en $l "test."
-done
-
-echo "convert dev and test data"
-for lang in $langs; do
-    s_lang=${lang:0:2}
-    convert_valid_test $s_lang en  
-    convert_valid_test en $s_lang
-done
-
-
-
-echo "creating training data into $raw"
-for lang in $langs; do
-    generate_train $lang en_XX
-    generate_train en_XX $lang
-done
-
-echo "creating iwslt dev data into raw"
-generate_subset en_XX vi_VN valid "IWSLT15.TED.tst2013"
-generate_subset vi_VN en_XX valid "IWSLT15.TED.tst2013"
-
-generate_subset en_XX ar_AR valid "IWSLT17.TED.tst2016"
-generate_subset ar_AR en_XX valid "IWSLT17.TED.tst2016"
-generate_subset en_XX ko_KR valid "IWSLT17.TED.tst2016"
-generate_subset ko_KR en_XX valid "IWSLT17.TED.tst2016"
-
-
-generate_subset en_XX it_IT valid "IWSLT17.TED.tst2010"
-generate_subset it_IT en_XX valid "IWSLT17.TED.tst2010"
-generate_subset en_XX nl_XX valid "IWSLT17.TED.tst2010"
-generate_subset nl_XX en_XX valid "IWSLT17.TED.tst2010"
-
-echo "creating iswslt test data into raw"
-generate_subset en_XX vi_VN test "IWSLT15.TED.tst2015"
-generate_subset vi_VN en_XX test "IWSLT15.TED.tst2015"
-
-generate_subset en_XX ar_AR test "IWSLT17.TED.tst2017"
-generate_subset ar_AR en_XX test "IWSLT17.TED.tst2017"
-generate_subset en_XX ko_KR test "IWSLT17.TED.tst2017"
-generate_subset ko_KR en_XX test "IWSLT17.TED.tst2017"
-
-generate_subset en_XX it_IT test "IWSLT17.TED.tst2017.mltlng"
-generate_subset it_IT en_XX test "IWSLT17.TED.tst2017.mltlng"
-generate_subset en_XX nl_XX test "IWSLT17.TED.tst2017.mltlng"
-generate_subset nl_XX en_XX test "IWSLT17.TED.tst2017.mltlng"
-
-# normalze iwslt directions into x-en
-pushd $raw
-for lang in $langs; do
-    for split in test valid; do
-        x_en_f1=$split.$lang-en_XX.en_XX
-        x_en_f2=$split.$lang-en_XX.${lang}
-
-        en_x_f1=$split.en_XX-$lang.en_XX
-        en_x_f2=$split.en_XX-$lang.${lang}        
-
-        if [ -f $en_x_f1 ] && [ ! -f $x_en_f1 ]; then
-            echo "cp $en_x_f1 $x_en_f1"
-            cp $en_x_f1 $x_en_f1
-        fi
-        if [ -f $x_en_f2 ] && [ ! -f $x_en_f2 ]; then
-            echo "cp $en_x_f2 $x_en_f2"
-            cp $en_x_f2 $x_en_f2
-        fi        
-    done
-done
-popd
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/simultaneous_translation/utils/p_choose_strategy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/simultaneous_translation/utils/p_choose_strategy.py
deleted file mode 100644
index 724c6912a62d48fc61988cac1434a4f5c8754521..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/simultaneous_translation/utils/p_choose_strategy.py
+++ /dev/null
@@ -1,126 +0,0 @@
-from typing import Optional, Dict
-from torch import Tensor
-import torch
-
-
-def waitk_p_choose(
-    tgt_len: int,
-    src_len: int,
-    bsz: int,
-    waitk_lagging: int,
-    key_padding_mask: Optional[Tensor] = None,
-    incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None
-):
-
-    max_src_len = src_len
-    if incremental_state is not None:
-        # Retrieve target length from incremental states
-        # For inference the length of query is always 1
-        max_tgt_len = incremental_state["steps"]["tgt"]
-        assert max_tgt_len is not None
-        max_tgt_len = int(max_tgt_len)
-    else:
-        max_tgt_len = tgt_len
-
-    if max_src_len < waitk_lagging:
-        if incremental_state is not None:
-            max_tgt_len = 1
-        return torch.zeros(
-            bsz, max_tgt_len, max_src_len
-        )
-
-    # Assuming the p_choose looks like this for wait k=3
-    # src_len = 6, max_tgt_len = 5
-    #   [0, 0, 1, 0, 0, 0, 0]
-    #   [0, 0, 0, 1, 0, 0, 0]
-    #   [0, 0, 0, 0, 1, 0, 0]
-    #   [0, 0, 0, 0, 0, 1, 0]
-    #   [0, 0, 0, 0, 0, 0, 1]
-    # linearize the p_choose matrix:
-    # [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0...]
-    # The indices of linearized matrix that equals 1 is
-    # 2 + 6 * 0
-    # 3 + 6 * 1
-    # ...
-    # n + src_len * n + k - 1 = n * (src_len + 1) + k - 1
-    # n from 0 to max_tgt_len - 1
-    #
-    # First, generate the indices (activate_indices_offset: bsz, max_tgt_len)
-    # Second, scatter a zeros tensor (bsz, max_tgt_len * src_len)
-    # with activate_indices_offset
-    # Third, resize the tensor to (bsz, max_tgt_len, src_len)
-
-    activate_indices_offset = (
-        (
-            torch.arange(max_tgt_len) * (max_src_len + 1)
-            + waitk_lagging - 1
-        )
-        .unsqueeze(0)
-        .expand(bsz, max_tgt_len)
-        .long()
-    )
-
-    if key_padding_mask is not None:
-        if key_padding_mask[:, 0].any():
-            # Left padding
-            activate_indices_offset += (
-                key_padding_mask.sum(dim=1, keepdim=True)
-            )
-
-    # Need to clamp the indices that are too large
-    activate_indices_offset = (
-        activate_indices_offset
-        .clamp(
-            0,
-            min(
-                [
-                    max_tgt_len,
-                    max_src_len - waitk_lagging + 1
-                ]
-            ) * max_src_len - 1
-        )
-    )
-
-    p_choose = torch.zeros(bsz, max_tgt_len * max_src_len)
-
-    p_choose = p_choose.scatter(
-        1,
-        activate_indices_offset,
-        1.0
-    ).view(bsz, max_tgt_len, max_src_len)
-
-    if key_padding_mask is not None:
-        p_choose = p_choose.to(key_padding_mask)
-        p_choose = p_choose.masked_fill(key_padding_mask.unsqueeze(1), 0)
-
-    if incremental_state is not None:
-        p_choose = p_choose[:, -1:]
-
-    return p_choose.float()
-
-
-def learnable_p_choose(
-    energy,
-    noise_mean: float = 0.0,
-    noise_var: float = 0.0,
-    training: bool = True
-):
-    """
-    Calculating step wise prob for reading and writing
-    1 to read, 0 to write
-    energy: bsz, tgt_len, src_len
-    """
-
-    noise = 0
-    if training:
-        # add noise here to encourage discretness
-        noise = (
-            torch.normal(noise_mean, noise_var, energy.size())
-            .type_as(energy)
-            .to(energy.device)
-        )
-
-    p_choose = torch.sigmoid(energy + noise)
-
-    # p_choose: bsz * self.num_heads, tgt_len, src_len
-    return p_choose
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/id_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/id_dataset.py
deleted file mode 100644
index 3e4d7969cf2a26e852b466f165a6fadabae3b35f..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/id_dataset.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 torch
-
-from . import FairseqDataset
-
-
-class IdDataset(FairseqDataset):
-    def __getitem__(self, index):
-        return index
-
-    def __len__(self):
-        return 0
-
-    def collater(self, samples):
-        return torch.tensor(samples)
diff --git a/spaces/Harsh502s/Anime-Recommender/README.md b/spaces/Harsh502s/Anime-Recommender/README.md
deleted file mode 100644
index 5cc22b70918059cb7f141f2c5b77fc0b37cb6e35..0000000000000000000000000000000000000000
--- a/spaces/Harsh502s/Anime-Recommender/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anime Recommender
-emoji: 📈
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/README.md b/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/README.md
deleted file mode 100644
index bbad28a86e6c8a3f38f44a55a3ae392b9853be70..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Vakyansh Malayalam TTS
-emoji: 🏃
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/utils/hifi/prepare_iitm_data_hifi.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/utils/hifi/prepare_iitm_data_hifi.py
deleted file mode 100644
index 1e1de2e28735143aeef8ddb10bc5a4672c02564b..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/utils/hifi/prepare_iitm_data_hifi.py
+++ /dev/null
@@ -1,64 +0,0 @@
-
-import glob
-import random
-import sys
-import os
-import argparse
-
-
-
-
-def process_data(args):
-    
-    path = args.input_path
-    valid_files = args.valid_files
-    test_files = args.test_files
-    dest_path = args.dest_path
-
-    list_paths = path.split(',')
-
-    valid_set = []
-    training_set = []
-    test_set = []
-
-    for local_path in list_paths:
-        files = glob.glob(local_path+'/*.wav')
-        print(f"Total files: {len(files)}")
-
-        valid_set_local = random.sample(files, valid_files)
-
-        test_set_local = random.sample(valid_set_local, test_files)
-        valid_set.extend(list(set(valid_set_local) - set(test_set_local)))
-        test_set.extend(test_set_local)
-
-        print(len(valid_set_local))
-
-        training_set_local = set(files) - set(valid_set_local)
-        print(len(training_set_local))
-        training_set.extend(training_set_local)
-
-
-    valid_set = random.sample(valid_set, len(valid_set))
-    test_set = random.sample(test_set, len(test_set))
-    training_set = random.sample(training_set, len(training_set))
-
-    with open(os.path.join(dest_path , 'valid.txt'), mode = 'w+') as file:
-        file.write("\n".join(list(valid_set)))
-
-    with open(os.path.join(dest_path , 'train.txt'), mode = 'w+') as file:
-        file.write("\n".join(list(training_set)))
-
-    with open(os.path.join(dest_path , 'test.txt'), mode = 'w+') as file:
-        file.write("\n".join(list(test_set)))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-i','--input-path',type=str,help='path to input wav files')
-    parser.add_argument('-v','--valid-files',type=int,help='number of valid files')
-    parser.add_argument('-t','--test-files',type=int,help='number of test files')
-    parser.add_argument('-d','--dest-path',type=str,help='destination path to output filelists')
-
-    args = parser.parse_args()
-
-    process_data(args)
\ No newline at end of file
diff --git a/spaces/Harveenchadha/en_to_indic_translation/indic_nlp_library/indicnlp/transliterate/__init__.py b/spaces/Harveenchadha/en_to_indic_translation/indic_nlp_library/indicnlp/transliterate/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Hila/RobustViT/ViT/helpers.py b/spaces/Hila/RobustViT/ViT/helpers.py
deleted file mode 100644
index e2840ea741a5dad1473c14fba1edfd68d91a12d9..0000000000000000000000000000000000000000
--- a/spaces/Hila/RobustViT/ViT/helpers.py
+++ /dev/null
@@ -1,295 +0,0 @@
-""" Model creation / weight loading / state_dict helpers
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import logging
-import os
-import math
-from collections import OrderedDict
-from copy import deepcopy
-from typing import Callable
-
-import torch
-import torch.nn as nn
-import torch.utils.model_zoo as model_zoo
-
-_logger = logging.getLogger(__name__)
-
-
-def load_state_dict(checkpoint_path, use_ema=False):
-    if checkpoint_path and os.path.isfile(checkpoint_path):
-        checkpoint = torch.load(checkpoint_path, map_location='cpu')
-        state_dict_key = 'state_dict'
-        if isinstance(checkpoint, dict):
-            if use_ema and 'state_dict_ema' in checkpoint:
-                state_dict_key = 'state_dict_ema'
-        if state_dict_key and state_dict_key in checkpoint:
-            new_state_dict = OrderedDict()
-            for k, v in checkpoint[state_dict_key].items():
-                # strip `module.` prefix
-                name = k[7:] if k.startswith('module') else k
-                new_state_dict[name] = v
-            state_dict = new_state_dict
-        else:
-            state_dict = checkpoint
-        _logger.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path))
-        return state_dict
-    else:
-        _logger.error("No checkpoint found at '{}'".format(checkpoint_path))
-        raise FileNotFoundError()
-
-
-def load_checkpoint(model, checkpoint_path, use_ema=False, strict=True):
-    state_dict = load_state_dict(checkpoint_path, use_ema)
-    model.load_state_dict(state_dict, strict=strict)
-
-
-def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scaler=None, log_info=True):
-    resume_epoch = None
-    if os.path.isfile(checkpoint_path):
-        checkpoint = torch.load(checkpoint_path, map_location='cpu')
-        if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
-            if log_info:
-                _logger.info('Restoring model state from checkpoint...')
-            new_state_dict = OrderedDict()
-            for k, v in checkpoint['state_dict'].items():
-                name = k[7:] if k.startswith('module') else k
-                new_state_dict[name] = v
-            model.load_state_dict(new_state_dict)
-
-            if optimizer is not None and 'optimizer' in checkpoint:
-                if log_info:
-                    _logger.info('Restoring optimizer state from checkpoint...')
-                optimizer.load_state_dict(checkpoint['optimizer'])
-
-            if loss_scaler is not None and loss_scaler.state_dict_key in checkpoint:
-                if log_info:
-                    _logger.info('Restoring AMP loss scaler state from checkpoint...')
-                loss_scaler.load_state_dict(checkpoint[loss_scaler.state_dict_key])
-
-            if 'epoch' in checkpoint:
-                resume_epoch = checkpoint['epoch']
-                if 'version' in checkpoint and checkpoint['version'] > 1:
-                    resume_epoch += 1  # start at the next epoch, old checkpoints incremented before save
-
-            if log_info:
-                _logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, checkpoint['epoch']))
-        else:
-            model.load_state_dict(checkpoint)
-            if log_info:
-                _logger.info("Loaded checkpoint '{}'".format(checkpoint_path))
-        return resume_epoch
-    else:
-        _logger.error("No checkpoint found at '{}'".format(checkpoint_path))
-        raise FileNotFoundError()
-
-
-def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=None, strict=True):
-    if cfg is None:
-        cfg = getattr(model, 'default_cfg')
-    if cfg is None or 'url' not in cfg or not cfg['url']:
-        _logger.warning("Pretrained model URL is invalid, using random initialization.")
-        return
-
-    state_dict = model_zoo.load_url(cfg['url'], progress=False, map_location='cpu')
-
-    if filter_fn is not None:
-        state_dict = filter_fn(state_dict)
-
-    if in_chans == 1:
-        conv1_name = cfg['first_conv']
-        _logger.info('Converting first conv (%s) pretrained weights from 3 to 1 channel' % conv1_name)
-        conv1_weight = state_dict[conv1_name + '.weight']
-        # Some weights are in torch.half, ensure it's float for sum on CPU
-        conv1_type = conv1_weight.dtype
-        conv1_weight = conv1_weight.float()
-        O, I, J, K = conv1_weight.shape
-        if I > 3:
-            assert conv1_weight.shape[1] % 3 == 0
-            # For models with space2depth stems
-            conv1_weight = conv1_weight.reshape(O, I // 3, 3, J, K)
-            conv1_weight = conv1_weight.sum(dim=2, keepdim=False)
-        else:
-            conv1_weight = conv1_weight.sum(dim=1, keepdim=True)
-        conv1_weight = conv1_weight.to(conv1_type)
-        state_dict[conv1_name + '.weight'] = conv1_weight
-    elif in_chans != 3:
-        conv1_name = cfg['first_conv']
-        conv1_weight = state_dict[conv1_name + '.weight']
-        conv1_type = conv1_weight.dtype
-        conv1_weight = conv1_weight.float()
-        O, I, J, K = conv1_weight.shape
-        if I != 3:
-            _logger.warning('Deleting first conv (%s) from pretrained weights.' % conv1_name)
-            del state_dict[conv1_name + '.weight']
-            strict = False
-        else:
-            # NOTE this strategy should be better than random init, but there could be other combinations of
-            # the original RGB input layer weights that'd work better for specific cases.
-            _logger.info('Repeating first conv (%s) weights in channel dim.' % conv1_name)
-            repeat = int(math.ceil(in_chans / 3))
-            conv1_weight = conv1_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :]
-            conv1_weight *= (3 / float(in_chans))
-            conv1_weight = conv1_weight.to(conv1_type)
-            state_dict[conv1_name + '.weight'] = conv1_weight
-
-    classifier_name = cfg['classifier']
-    if num_classes == 1000 and cfg['num_classes'] == 1001:
-        # special case for imagenet trained models with extra background class in pretrained weights
-        classifier_weight = state_dict[classifier_name + '.weight']
-        state_dict[classifier_name + '.weight'] = classifier_weight[1:]
-        classifier_bias = state_dict[classifier_name + '.bias']
-        state_dict[classifier_name + '.bias'] = classifier_bias[1:]
-    elif num_classes != cfg['num_classes']:
-        # completely discard fully connected for all other differences between pretrained and created model
-        del state_dict[classifier_name + '.weight']
-        del state_dict[classifier_name + '.bias']
-        strict = False
-
-    model.load_state_dict(state_dict, strict=strict)
-
-
-def extract_layer(model, layer):
-    layer = layer.split('.')
-    module = model
-    if hasattr(model, 'module') and layer[0] != 'module':
-        module = model.module
-    if not hasattr(model, 'module') and layer[0] == 'module':
-        layer = layer[1:]
-    for l in layer:
-        if hasattr(module, l):
-            if not l.isdigit():
-                module = getattr(module, l)
-            else:
-                module = module[int(l)]
-        else:
-            return module
-    return module
-
-
-def set_layer(model, layer, val):
-    layer = layer.split('.')
-    module = model
-    if hasattr(model, 'module') and layer[0] != 'module':
-        module = model.module
-    lst_index = 0
-    module2 = module
-    for l in layer:
-        if hasattr(module2, l):
-            if not l.isdigit():
-                module2 = getattr(module2, l)
-            else:
-                module2 = module2[int(l)]
-            lst_index += 1
-    lst_index -= 1
-    for l in layer[:lst_index]:
-        if not l.isdigit():
-            module = getattr(module, l)
-        else:
-            module = module[int(l)]
-    l = layer[lst_index]
-    setattr(module, l, val)
-
-
-def adapt_model_from_string(parent_module, model_string):
-    separator = '***'
-    state_dict = {}
-    lst_shape = model_string.split(separator)
-    for k in lst_shape:
-        k = k.split(':')
-        key = k[0]
-        shape = k[1][1:-1].split(',')
-        if shape[0] != '':
-            state_dict[key] = [int(i) for i in shape]
-
-    new_module = deepcopy(parent_module)
-    for n, m in parent_module.named_modules():
-        old_module = extract_layer(parent_module, n)
-        if isinstance(old_module, nn.Conv2d) or isinstance(old_module, Conv2dSame):
-            if isinstance(old_module, Conv2dSame):
-                conv = Conv2dSame
-            else:
-                conv = nn.Conv2d
-            s = state_dict[n + '.weight']
-            in_channels = s[1]
-            out_channels = s[0]
-            g = 1
-            if old_module.groups > 1:
-                in_channels = out_channels
-                g = in_channels
-            new_conv = conv(
-                in_channels=in_channels, out_channels=out_channels, kernel_size=old_module.kernel_size,
-                bias=old_module.bias is not None, padding=old_module.padding, dilation=old_module.dilation,
-                groups=g, stride=old_module.stride)
-            set_layer(new_module, n, new_conv)
-        if isinstance(old_module, nn.BatchNorm2d):
-            new_bn = nn.BatchNorm2d(
-                num_features=state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
-                affine=old_module.affine, track_running_stats=True)
-            set_layer(new_module, n, new_bn)
-        if isinstance(old_module, nn.Linear):
-            # FIXME extra checks to ensure this is actually the FC classifier layer and not a diff Linear layer?
-            num_features = state_dict[n + '.weight'][1]
-            new_fc = nn.Linear(
-                in_features=num_features, out_features=old_module.out_features, bias=old_module.bias is not None)
-            set_layer(new_module, n, new_fc)
-            if hasattr(new_module, 'num_features'):
-                new_module.num_features = num_features
-    new_module.eval()
-    parent_module.eval()
-
-    return new_module
-
-
-def adapt_model_from_file(parent_module, model_variant):
-    adapt_file = os.path.join(os.path.dirname(__file__), 'pruned', model_variant + '.txt')
-    with open(adapt_file, 'r') as f:
-        return adapt_model_from_string(parent_module, f.read().strip())
-
-
-def build_model_with_cfg(
-        model_cls: Callable,
-        variant: str,
-        pretrained: bool,
-        default_cfg: dict,
-        model_cfg: dict = None,
-        feature_cfg: dict = None,
-        pretrained_strict: bool = True,
-        pretrained_filter_fn: Callable = None,
-        **kwargs):
-    pruned = kwargs.pop('pruned', False)
-    features = False
-    feature_cfg = feature_cfg or {}
-
-    if kwargs.pop('features_only', False):
-        features = True
-        feature_cfg.setdefault('out_indices', (0, 1, 2, 3, 4))
-        if 'out_indices' in kwargs:
-            feature_cfg['out_indices'] = kwargs.pop('out_indices')
-
-    model = model_cls(**kwargs) if model_cfg is None else model_cls(cfg=model_cfg, **kwargs)
-    model.default_cfg = deepcopy(default_cfg)
-
-    if pruned:
-        model = adapt_model_from_file(model, variant)
-
-    if pretrained:
-        load_pretrained(
-            model,
-            num_classes=kwargs.get('num_classes', 0),
-            in_chans=kwargs.get('in_chans', 3),
-            filter_fn=pretrained_filter_fn, strict=pretrained_strict)
-
-    if features:
-        feature_cls = FeatureListNet
-        if 'feature_cls' in feature_cfg:
-            feature_cls = feature_cfg.pop('feature_cls')
-            if isinstance(feature_cls, str):
-                feature_cls = feature_cls.lower()
-                if 'hook' in feature_cls:
-                    feature_cls = FeatureHookNet
-                else:
-                    assert False, f'Unknown feature class {feature_cls}'
-        model = feature_cls(model, **feature_cfg)
-
-    return model
\ No newline at end of file
diff --git a/spaces/Hina4867/bingo/src/components/tone-selector.tsx b/spaces/Hina4867/bingo/src/components/tone-selector.tsx
deleted file mode 100644
index 5c6e464c91f564b895acd121f0a4a79ed9c5c356..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/tone-selector.tsx
+++ /dev/null
@@ -1,43 +0,0 @@
-import React from 'react'
-import { BingConversationStyle } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-
-type ToneItem = {
-  type: BingConversationStyle,
-  name: string
-}
-
-const ToneList: ToneItem[] = [
-  { name: '有创造力', type: BingConversationStyle.Creative },
-  { name: '更平衡', type: BingConversationStyle.Balanced },
-  { name: '更精确', type: BingConversationStyle.Precise }
-]
-
-interface ToneSelectorProps {
-  type: BingConversationStyle | ''
-  onChange?: (type: BingConversationStyle) => void
-}
-
-export function ToneSelector({ type, onChange }: ToneSelectorProps) {
-  return (
-    <div className="fieldset">
-      <div className="legend">
-        选择对话样式
-      </div>
-      <div className="options-list-container">
-        <ul id="tone-options" className="options">
-          {
-            ToneList.map(tone => (
-              <li className="option" key={tone.name} onClick={() => onChange?.(tone.type)}>
-                <button className={cn(`tone-${type.toLowerCase()}`, { selected: tone.type === type}) } aria-pressed="true" >
-                  <span className="caption-2-strong label-modifier">更</span>
-                  <span className="body-1-strong label">{tone.name}</span>
-                </button>
-              </li>
-            ))
-          }
-        </ul>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/Hina4867/bingo/tailwind.config.js b/spaces/Hina4867/bingo/tailwind.config.js
deleted file mode 100644
index 03da3c3c45be6983b9f5ffa6df5f1fd0870e9636..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/tailwind.config.js
+++ /dev/null
@@ -1,48 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/ui/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  "darkMode": "class",
-  theme: {
-    extend: {
-      colors: {
-        'primary-blue': 'rgb(var(--color-primary-blue) / <alpha-value>)',
-        secondary: 'rgb(var(--color-secondary) / <alpha-value>)',
-        'primary-background': 'rgb(var(--primary-background) / <alpha-value>)',
-        'primary-text': 'rgb(var(--primary-text) / <alpha-value>)',
-        'secondary-text': 'rgb(var(--secondary-text) / <alpha-value>)',
-        'light-text': 'rgb(var(--light-text) / <alpha-value>)',
-        'primary-border': 'rgb(var(--primary-border) / <alpha-value>)',
-      },
-      keyframes: {
-        slideDownAndFade: {
-          from: { opacity: 0, transform: 'translateY(-2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideLeftAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-        slideUpAndFade: {
-          from: { opacity: 0, transform: 'translateY(2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideRightAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-      },
-      animation: {
-        slideDownAndFade: 'slideDownAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideLeftAndFade: 'slideLeftAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideUpAndFade: 'slideUpAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideRightAndFade: 'slideRightAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-      },
-    },
-  },
-  plugins: [require('@headlessui/tailwindcss'), require('tailwind-scrollbar')],
-}
diff --git a/spaces/Hobe/bing/Dockerfile b/spaces/Hobe/bing/Dockerfile
deleted file mode 100644
index 139c333a3bba5ac3680d42b6f356824207f05255..0000000000000000000000000000000000000000
--- a/spaces/Hobe/bing/Dockerfile
+++ /dev/null
@@ -1,33 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，并且清除缓存🧹
-RUN apk --no-cache add git && \
-    git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app && \
-    apk del git
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 编译 go 项目
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像🪞
-FROM alpine
-
-# 设置工作目录💼
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件👔
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# （可选）设置环境变量✍️
-ENV Go_Proxy_BingAI_USER_TOKEN_1="G4hJ9k544565uhjjhjlkjh6356223p3EaYc0FvIjHmLzXeRfAq"
-
-# 端口
-EXPOSE 8080
-
-# 容器运行✅
-CMD ["/workspace/app/go-proxy-bingai"]
diff --git a/spaces/HugoDzz/spaceship_drift/build/_app/immutable/chunks/index.0d3f7c7a.js b/spaces/HugoDzz/spaceship_drift/build/_app/immutable/chunks/index.0d3f7c7a.js
deleted file mode 100644
index 201a9e8eabdf4ba1fc43634ee314891dd098b062..0000000000000000000000000000000000000000
--- a/spaces/HugoDzz/spaceship_drift/build/_app/immutable/chunks/index.0d3f7c7a.js
+++ /dev/null
@@ -1 +0,0 @@
-function $(){}function F(t,e){for(const n in e)t[n]=e[n];return t}function T(t){return t()}function M(){return Object.create(null)}function g(t){t.forEach(T)}function B(t){return typeof t=="function"}function at(t,e){return t!=t?e==e:t!==e||t&&typeof t=="object"||typeof t=="function"}let x;function st(t,e){return x||(x=document.createElement("a")),x.href=e,t===x.href}function H(t){return Object.keys(t).length===0}function I(t,...e){if(t==null)return $;const n=t.subscribe(...e);return n.unsubscribe?()=>n.unsubscribe():n}function ft(t,e,n){t.$$.on_destroy.push(I(e,n))}function dt(t,e,n,i){if(t){const r=L(t,e,n,i);return t[0](r)}}function L(t,e,n,i){return t[1]&&i?F(n.ctx.slice(),t[1](i(e))):n.ctx}function _t(t,e,n,i){if(t[2]&&i){const r=t[2](i(n));if(e.dirty===void 0)return r;if(typeof r=="object"){const a=[],l=Math.max(e.dirty.length,r.length);for(let o=0;o<l;o+=1)a[o]=e.dirty[o]|r[o];return a}return e.dirty|r}return e.dirty}function ht(t,e,n,i,r,a){if(r){const 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diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/scripts/binarize_manifest.sh b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/scripts/binarize_manifest.sh
deleted file mode 100644
index 6f201bdb524fad51a69d8c45889eaa1578efc62d..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/scripts/binarize_manifest.sh
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env bash
-
-# usage: bash binarize_manifest <dest_dir> <train_split> <valid_split>
-
-DEST_DIR=$1
-TRAIN_SPLIT=$2
-VALID_SPLIT=$3
-FAIRSEQ_ROOT=$4
-
-mkdir -p $DEST_DIR
-
-# split file path and lengths into separate files
-cut -f1 $TRAIN_SPLIT.tsv > $DEST_DIR/train_fnames.txt
-cut -f1 $VALID_SPLIT.tsv > $DEST_DIR/valid_fnames.txt
-cut -f2 $TRAIN_SPLIT.tsv > $DEST_DIR/train.lengths
-cut -f2 $VALID_SPLIT.tsv > $DEST_DIR/valid.lengths
-
-# copy root directory
-head -1 $TRAIN_SPLIT.tsv > $DEST_DIR/train.root
-head -1 $VALID_SPLIT.tsv > $DEST_DIR/valid.root
-
-# remove root directory
-sed -i '1d' $DEST_DIR/train_fnames.txt
-sed -i '1d' $DEST_DIR/valid_fnames.txt
-sed -i '1d' $DEST_DIR/train.lengths
-sed -i '1d' $DEST_DIR/valid.lengths
-
-# insert spaces between characters
-sed -i -e 's/\(.\)/\1 /g' $DEST_DIR/train_fnames.txt
-sed -i -e 's/\(.\)/\1 /g' $DEST_DIR/valid_fnames.txt
-
-# run preprocessor
-PYTHONPATH=$FAIRSEQ_ROOT python $FAIRSEQ_ROOT/fairseq_cli/preprocess.py --dataset-impl mmap --trainpref $DEST_DIR/train_fnames.txt --validpref $DEST_DIR/valid_fnames.txt --workers 60 --only-source --destdir $DEST_DIR
diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py
deleted file mode 100644
index 27792ebda842057e33fed3dc53dd9d8a594d0483..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/models/wav2vec_u.py
+++ /dev/null
@@ -1,637 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-from enum import Enum, auto
-import math
-import numpy as np
-from typing import Tuple, List, Optional, Dict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import autograd
-
-from fairseq import checkpoint_utils, utils
-from fairseq.dataclass import FairseqDataclass
-from fairseq.models import BaseFairseqModel, register_model
-from fairseq.modules import (
-    SamePad,
-    TransposeLast,
-)
-
-
-class SegmentationType(Enum):
-    NONE = auto()
-    RANDOM = auto()
-    UNIFORM_RANDOM = auto()
-    UNIFORM_RANDOM_JOIN = auto()
-    JOIN = auto()
-
-
-@dataclass
-class SegmentationConfig(FairseqDataclass):
-    type: SegmentationType = SegmentationType.NONE
-    subsample_rate: float = 0.25
-    mean_pool: bool = True
-    mean_pool_join: bool = False
-    remove_zeros: bool = False
-
-
-@dataclass
-class Wav2vec_UConfig(FairseqDataclass):
-
-    discriminator_kernel: int = 3
-    discriminator_dilation: int = 1
-    discriminator_dim: int = 256
-    discriminator_causal: bool = True
-    discriminator_linear_emb: bool = False
-    discriminator_depth: int = 1
-    discriminator_max_pool: bool = False
-    discriminator_act_after_linear: bool = False
-    discriminator_dropout: float = 0.0
-    discriminator_spectral_norm: bool = False
-    discriminator_weight_norm: bool = False
-
-    generator_kernel: int = 4
-    generator_dilation: int = 1
-    generator_stride: int = 1
-    generator_bias: bool = False
-    generator_dropout: float = 0.0
-
-    blank_weight: float = 0
-    blank_mode: str = "add"
-    blank_is_sil: bool = False
-    no_softmax: bool = False
-
-    smoothness_weight: float = 0.0
-    smoothing: float = 0.0
-    smoothing_one_sided: bool = False
-    gradient_penalty: float = 0.0
-    probabilistic_grad_penalty_slicing: bool = False
-    code_penalty: float = 0.0
-    gumbel: bool = False
-    hard_gumbel: bool = True
-    temp: Tuple[float, float, float] = (2, 0.1, 0.99995)
-    input_dim: int = 128
-
-    segmentation: SegmentationConfig = SegmentationConfig()
-
-
-class Segmenter(nn.Module):
-    cfg: SegmentationConfig
-
-    def __init__(self, cfg: SegmentationConfig):
-        super().__init__()
-        self.cfg = cfg
-        self.subsample_rate = cfg.subsample_rate
-
-    def pre_segment(self, dense_x, dense_padding_mask):
-        return dense_x, dense_padding_mask
-
-    def logit_segment(self, logits, padding_mask):
-        return logits, padding_mask
-
-
-class RandomSegmenter(Segmenter):
-    def pre_segment(self, dense_x, dense_padding_mask):
-        target_num = math.ceil(dense_x.size(1) * self.subsample_rate)
-        ones = torch.ones(dense_x.shape[:-1], device=dense_x.device)
-        indices, _ = ones.multinomial(target_num).sort(dim=-1)
-        indices_ld = indices.unsqueeze(-1).expand(-1, -1, dense_x.size(-1))
-        dense_x = dense_x.gather(1, indices_ld)
-        dense_padding_mask = dense_padding_mask.gather(1, index=indices)
-        return dense_x, dense_padding_mask
-
-
-class UniformRandomSegmenter(Segmenter):
-    def pre_segment(self, dense_x, dense_padding_mask):
-        bsz, tsz, fsz = dense_x.shape
-
-        target_num = math.ceil(tsz * self.subsample_rate)
-
-        rem = tsz % target_num
-
-        if rem > 0:
-            dense_x = F.pad(dense_x, [0, 0, 0, target_num - rem])
-            dense_padding_mask = F.pad(
-                dense_padding_mask, [0, target_num - rem], value=True
-            )
-
-        dense_x = dense_x.view(bsz, target_num, -1, fsz)
-        dense_padding_mask = dense_padding_mask.view(bsz, target_num, -1)
-
-        if self.cfg.mean_pool:
-            dense_x = dense_x.mean(dim=-2)
-            dense_padding_mask = dense_padding_mask.all(dim=-1)
-        else:
-            ones = torch.ones((bsz, dense_x.size(2)), device=dense_x.device)
-            indices = ones.multinomial(1)
-            indices = indices.unsqueeze(-1).expand(-1, target_num, -1)
-            indices_ld = indices.unsqueeze(-1).expand(-1, -1, -1, fsz)
-            dense_x = dense_x.gather(2, indices_ld).reshape(bsz, -1, fsz)
-            dense_padding_mask = dense_padding_mask.gather(2, index=indices).reshape(
-                bsz, -1
-            )
-        return dense_x, dense_padding_mask
-
-
-class JoinSegmenter(Segmenter):
-    def logit_segment(self, logits, padding_mask):
-        preds = logits.argmax(dim=-1)
-
-        if padding_mask.any():
-            preds[padding_mask] = -1  # mark pad
-        uniques = []
-
-        bsz, tsz, csz = logits.shape
-
-        for p in preds:
-            uniques.append(
-                p.cpu().unique_consecutive(return_inverse=True, return_counts=True)
-            )
-
-        new_tsz = max(u[0].numel() for u in uniques)
-        new_logits = logits.new_zeros(bsz, new_tsz, csz)
-        new_pad = padding_mask.new_zeros(bsz, new_tsz)
-
-        for b in range(bsz):
-            u, idx, c = uniques[b]
-            keep = u != -1
-
-            if self.cfg.remove_zeros:
-                keep.logical_and_(u != 0)
-
-            if self.training and not self.cfg.mean_pool_join:
-                u[0] = 0
-                u[1:] = c.cumsum(0)[:-1]
-                m = c > 1
-                r = torch.rand(m.sum())
-                o = (c[m] * r).long()
-                u[m] += o
-                new_logits[b, : u.numel()] = logits[b, u]
-            else:
-                new_logits[b].index_add_(
-                    dim=0, index=idx.to(new_logits.device), source=logits[b]
-                )
-                new_logits[b, : c.numel()] /= c.unsqueeze(-1).to(new_logits.device)
-
-            new_sz = keep.sum()
-            if not keep.all():
-                kept_logits = new_logits[b, : c.numel()][keep]
-                new_logits[b, :new_sz] = kept_logits
-
-            if new_sz < new_tsz:
-                pad = new_tsz - new_sz
-                new_logits[b, -pad:] = 0
-                new_pad[b, -pad:] = True
-
-        return new_logits, new_pad
-
-
-class UniformRandomJoinSegmenter(UniformRandomSegmenter, JoinSegmenter):
-    pass
-
-
-SEGMENT_FACTORY = {
-    SegmentationType.NONE: Segmenter,
-    SegmentationType.RANDOM: RandomSegmenter,
-    SegmentationType.UNIFORM_RANDOM: UniformRandomSegmenter,
-    SegmentationType.UNIFORM_RANDOM_JOIN: UniformRandomJoinSegmenter,
-    SegmentationType.JOIN: JoinSegmenter,
-}
-
-
-class Discriminator(nn.Module):
-    def __init__(self, dim, cfg: Wav2vec_UConfig):
-        super().__init__()
-
-        inner_dim = cfg.discriminator_dim
-        kernel = cfg.discriminator_kernel
-        dilation = cfg.discriminator_dilation
-        self.max_pool = cfg.discriminator_max_pool
-
-        if cfg.discriminator_causal:
-            padding = kernel - 1
-        else:
-            padding = kernel // 2
-
-        def make_conv(in_d, out_d, k, p=0, has_dilation=True):
-            conv = nn.Conv1d(
-                in_d,
-                out_d,
-                kernel_size=k,
-                padding=p,
-                dilation=dilation if has_dilation else 1,
-            )
-            if cfg.discriminator_spectral_norm:
-                conv = nn.utils.spectral_norm(conv)
-            elif cfg.discriminator_weight_norm:
-                conv = nn.utils.weight_norm(conv)
-            return conv
-
-        inner_net = [
-            nn.Sequential(
-                make_conv(inner_dim, inner_dim, kernel, padding),
-                SamePad(kernel_size=kernel, causal=cfg.discriminator_causal),
-                nn.Dropout(cfg.discriminator_dropout),
-                nn.GELU(),
-            )
-            for _ in range(cfg.discriminator_depth - 1)
-        ] + [
-            make_conv(inner_dim, 1, kernel, padding, has_dilation=False),
-            SamePad(kernel_size=kernel, causal=cfg.discriminator_causal),
-        ]
-
-        if cfg.discriminator_linear_emb:
-            emb_net = [make_conv(dim, inner_dim, 1)]
-        else:
-            emb_net = [
-                make_conv(dim, inner_dim, kernel, padding),
-                SamePad(kernel_size=kernel, causal=cfg.discriminator_causal),
-            ]
-
-        if cfg.discriminator_act_after_linear:
-            emb_net.append(nn.GELU())
-
-        self.net = nn.Sequential(
-            *emb_net,
-            nn.Dropout(cfg.discriminator_dropout),
-            *inner_net,
-        )
-
-    def forward(self, x, padding_mask):
-        x = x.transpose(1, 2)  # BTC -> BCT
-        x = self.net(x)
-        x = x.transpose(1, 2)
-        x_sz = x.size(1)
-        if padding_mask is not None and padding_mask.any() and padding_mask.dim() > 1:
-            padding_mask = padding_mask[:, : x.size(1)]
-            x[padding_mask] = float("-inf") if self.max_pool else 0
-            x_sz = x_sz - padding_mask.sum(dim=-1)
-        x = x.squeeze(-1)
-        if self.max_pool:
-            x, _ = x.max(dim=-1)
-        else:
-            x = x.sum(dim=-1)
-            x = x / x_sz
-        return x
-
-
-class Generator(nn.Module):
-    def __init__(self, input_dim, output_dim, cfg: Wav2vec_UConfig):
-        super().__init__()
-
-        self.cfg = cfg
-        self.output_dim = output_dim
-        self.stride = cfg.generator_stride
-        self.dropout = nn.Dropout(cfg.generator_dropout)
-
-        padding = cfg.generator_kernel // 2
-        self.proj = nn.Sequential(
-            TransposeLast(),
-            nn.Conv1d(
-                input_dim,
-                output_dim,
-                kernel_size=cfg.generator_kernel,
-                stride=cfg.generator_stride,
-                dilation=cfg.generator_dilation,
-                padding=padding,
-                bias=cfg.generator_bias,
-            ),
-            TransposeLast(),
-        )
-
-    def forward(self, dense_x, tokens, dense_padding_mask):
-        dense_x = self.dropout(dense_x)
-
-        dense_x = self.proj(dense_x)
-        if self.stride > 1:
-            dense_padding_mask = dense_padding_mask[:, :: self.stride]
-
-        if dense_padding_mask.size(1) != dense_x.size(1):
-            new_padding = dense_padding_mask.new_zeros(dense_x.shape[:-1])
-            diff = new_padding.size(1) - dense_padding_mask.size(1)
-            assert (
-                diff > 0
-            ), f"{new_padding.shape}, {dense_padding_mask.shape}, {dense_x.shape}, {diff}"
-            if diff > 0:
-                new_padding[:, diff:] = dense_padding_mask
-            else:
-                assert diff < 0
-                new_padding = dense_padding_mask[:, :diff]
-
-            dense_padding_mask = new_padding
-
-        result = {}
-
-        token_x = None
-        if tokens is not None:
-            token_x = dense_x.new_zeros(tokens.numel(), self.output_dim)
-            token_x.scatter_(1, tokens.view(-1, 1).long(), 1)
-            token_x = token_x.view(tokens.shape + (self.output_dim,))
-
-        result["dense_x"] = dense_x
-        result["token_x"] = token_x
-        result["dense_padding_mask"] = dense_padding_mask
-
-        return result
-
-
-@register_model("wav2vec_u", dataclass=Wav2vec_UConfig)
-class Wav2vec_U(BaseFairseqModel):
-    def calc_gradient_penalty(self, real_data, fake_data):
-
-        b_size = min(real_data.size(0), fake_data.size(0))
-        t_size = min(real_data.size(1), fake_data.size(1))
-
-        if self.cfg.probabilistic_grad_penalty_slicing:
-
-            def get_slice(data, dim, target_size):
-
-                size = data.size(dim)
-                diff = size - target_size
-                if diff <= 0:
-                    return data
-
-                start = np.random.randint(0, diff + 1)
-                return data.narrow(dim=dim, start=start, length=target_size)
-
-            real_data = get_slice(real_data, 0, b_size)
-            real_data = get_slice(real_data, 1, t_size)
-            fake_data = get_slice(fake_data, 0, b_size)
-            fake_data = get_slice(fake_data, 1, t_size)
-
-        else:
-            real_data = real_data[:b_size, :t_size]
-            fake_data = fake_data[:b_size, :t_size]
-
-        alpha = torch.rand(real_data.size(0), 1, 1)
-        alpha = alpha.expand(real_data.size())
-        alpha = alpha.to(real_data.device)
-
-        interpolates = alpha * real_data + ((1 - alpha) * fake_data)
-
-        disc_interpolates = self.discriminator(interpolates, None)
-
-        gradients = autograd.grad(
-            outputs=disc_interpolates,
-            inputs=interpolates,
-            grad_outputs=torch.ones(disc_interpolates.size(), device=real_data.device),
-            create_graph=True,
-            retain_graph=True,
-            only_inputs=True,
-        )[0]
-
-        gradient_penalty = (gradients.norm(2, dim=1) - 1) ** 2
-        return gradient_penalty
-
-    def set_num_updates(self, num_updates):
-        super().set_num_updates(num_updates)
-        self.update_num = num_updates
-        self.curr_temp = max(
-            self.max_temp * self.temp_decay ** num_updates, self.min_temp
-        )
-
-    def discrim_step(self, num_updates):
-        return num_updates % 2 == 1
-
-    def get_groups_for_update(self, num_updates):
-        return "discriminator" if self.discrim_step(num_updates) else "generator"
-
-    def __init__(self, cfg: Wav2vec_UConfig, target_dict):
-        super().__init__()
-
-        self.cfg = cfg
-        self.zero_index = target_dict.index("<SIL>") if "<SIL>" in target_dict else 0
-        self.smoothness_weight = cfg.smoothness_weight
-
-        output_size = len(target_dict)
-        self.pad = target_dict.pad()
-        self.eos = target_dict.eos()
-        self.smoothing = cfg.smoothing
-        self.smoothing_one_sided = cfg.smoothing_one_sided
-        self.no_softmax = cfg.no_softmax
-        self.gumbel = cfg.gumbel
-        self.hard_gumbel = cfg.hard_gumbel
-        self.last_acc = None
-
-        self.gradient_penalty = cfg.gradient_penalty
-        self.code_penalty = cfg.code_penalty
-        self.blank_weight = cfg.blank_weight
-        self.blank_mode = cfg.blank_mode
-        self.blank_index = target_dict.index("<SIL>") if cfg.blank_is_sil else 0
-        assert self.blank_index != target_dict.unk()
-
-        self.discriminator = Discriminator(output_size, cfg)
-        for p in self.discriminator.parameters():
-            p.param_group = "discriminator"
-
-        self.pca_A = self.pca_b = None
-        d = cfg.input_dim
-
-        self.segmenter = SEGMENT_FACTORY[cfg.segmentation.type](cfg.segmentation)
-
-        self.generator = Generator(d, output_size, cfg)
-
-        for p in self.generator.parameters():
-            p.param_group = "generator"
-
-        for p in self.segmenter.parameters():
-            p.param_group = "generator"
-
-        self.max_temp, self.min_temp, self.temp_decay = cfg.temp
-        self.curr_temp = self.max_temp
-        self.update_num = 0
-
-    @classmethod
-    def build_model(cls, cfg, task):
-        return cls(cfg, task.target_dictionary)
-
-    def get_logits(
-        self,
-        net_output: Optional[Dict[str, List[Optional[torch.Tensor]]]],
-        normalize: bool = False,
-    ):
-        logits = net_output["logits"]
-
-        if self.blank_weight != 0:
-            if self.blank_mode == "add":
-                logits[..., self.blank_index] += self.blank_weight
-            elif self.blank_mode == "set":
-                logits[..., self.blank_index] = self.blank_weight
-            else:
-                raise Exception(f"invalid blank mode {self.blank_mode}")
-
-        padding = net_output["padding_mask"]
-        if padding.any():
-            logits[padding] = float("-inf")
-            logits[padding][..., self.blank_index] = float("inf")
-
-        if normalize:
-            logits = utils.log_softmax(logits.float(), dim=-1)
-
-        return logits.transpose(0, 1)
-
-    def get_normalized_probs(
-        self,
-        net_output: Tuple[
-            torch.Tensor, Optional[Dict[str, List[Optional[torch.Tensor]]]]
-        ],
-        log_probs: bool,
-        sample: Optional[Dict[str, torch.Tensor]] = None,
-    ):
-        logits = self.get_logits(net_output)
-
-        probs = super().get_normalized_probs(logits, log_probs, sample)
-        # BTC -> TBC for ctc
-        probs = probs.transpose(0, 1)
-        return probs
-
-    def normalize(self, dense_x):
-
-        bsz, tsz, csz = dense_x.shape
-
-        if dense_x.numel() == 0:
-            raise Exception(dense_x.shape)
-        _, k = dense_x.max(-1)
-        hard_x = (
-            dense_x.new_zeros(bsz * tsz, csz)
-            .scatter_(-1, k.view(-1, 1), 1.0)
-            .view(-1, csz)
-        )
-        hard_probs = torch.mean(hard_x.float(), dim=0)
-        code_perplexity = torch.exp(
-            -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1)
-        )
-
-        avg_probs = torch.softmax(dense_x.reshape(-1, csz).float(), dim=-1).mean(dim=0)
-        prob_perplexity = torch.exp(
-            -torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1)
-        )
-
-        if not self.no_softmax:
-            if self.training and self.gumbel:
-                dense_x = F.gumbel_softmax(
-                    dense_x.float(), tau=self.curr_temp, hard=self.hard_gumbel
-                ).type_as(dense_x)
-            else:
-                dense_x = dense_x.softmax(-1)
-
-        return dense_x, code_perplexity, prob_perplexity
-
-    def forward(
-        self,
-        features,
-        padding_mask,
-        random_label=None,
-        dense_x_only=False,
-        segment=True,
-    ):
-        if segment:
-            features, padding_mask = self.segmenter.pre_segment(features, padding_mask)
-
-        orig_size = features.size(0) * features.size(1) - padding_mask.sum()
-
-        gen_result = self.generator(features, random_label, padding_mask)
-
-        orig_dense_x, token_x = gen_result["dense_x"], gen_result["token_x"]
-        orig_dense_padding_mask = gen_result["dense_padding_mask"]
-
-        if segment:
-            dense_x, dense_padding_mask = self.segmenter.logit_segment(
-                orig_dense_x, orig_dense_padding_mask
-            )
-        else:
-            dense_x = orig_dense_x
-            dense_padding_mask = orig_dense_padding_mask
-
-        dense_logits = dense_x
-        prob_perplexity = None
-        code_perplexity = None
-
-        if not (self.no_softmax and dense_x_only):
-            dense_x, code_perplexity, prob_perplexity = self.normalize(dense_logits)
-
-        if dense_x_only or self.discriminator is None:
-            return {
-                "logits": dense_x,
-                "padding_mask": dense_padding_mask,
-            }
-
-        token_padding_mask = random_label == self.pad
-
-        dense_y = self.discriminator(dense_x, dense_padding_mask)
-        token_y = self.discriminator(token_x, token_padding_mask)
-
-        sample_size = features.size(0)
-
-        d_step = self.discrim_step(self.update_num)
-
-        fake_smooth = self.smoothing
-        real_smooth = self.smoothing
-        if self.smoothing_one_sided:
-            fake_smooth = 0
-
-        zero_loss = None
-        smoothness_loss = None
-        code_pen = None
-
-        if d_step:
-            loss_dense = F.binary_cross_entropy_with_logits(
-                dense_y,
-                dense_y.new_ones(dense_y.shape) - fake_smooth,
-                reduction="sum",
-            )
-            loss_token = F.binary_cross_entropy_with_logits(
-                token_y,
-                token_y.new_zeros(token_y.shape) + real_smooth,
-                reduction="sum",
-            )
-            if self.training and self.gradient_penalty > 0:
-                grad_pen = self.calc_gradient_penalty(token_x, dense_x)
-                grad_pen = grad_pen.sum() * self.gradient_penalty
-            else:
-                grad_pen = None
-        else:
-            grad_pen = None
-            loss_token = None
-            loss_dense = F.binary_cross_entropy_with_logits(
-                dense_y,
-                dense_y.new_zeros(dense_y.shape) + fake_smooth,
-                reduction="sum",
-            )
-            num_vars = dense_x.size(-1)
-            if prob_perplexity is not None:
-                code_pen = (num_vars - prob_perplexity) / num_vars
-                code_pen = code_pen * sample_size * self.code_penalty
-
-            if self.smoothness_weight > 0:
-                smoothness_loss = F.mse_loss(
-                    dense_logits[:, :-1], dense_logits[:, 1:], reduction="none"
-                )
-                smoothness_loss[dense_padding_mask[:, 1:]] = 0
-                smoothness_loss = (
-                    smoothness_loss.mean() * sample_size * self.smoothness_weight
-                )
-
-        result = {
-            "losses": {
-                "grad_pen": grad_pen,
-                "code_pen": code_pen,
-                "smoothness": smoothness_loss,
-            },
-            "temp": self.curr_temp,
-            "code_ppl": code_perplexity,
-            "prob_ppl": prob_perplexity,
-            "d_steps": int(d_step),
-            "sample_size": sample_size,
-        }
-
-        suff = "_d" if d_step else "_g"
-        result["losses"]["dense" + suff] = loss_dense
-        result["losses"]["token" + suff] = loss_token
-
-        return result
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/modules/kmeans_attention.py b/spaces/ICML2022/OFA/fairseq/fairseq/modules/kmeans_attention.py
deleted file mode 100644
index 11a7debcf2ac025fb02ba5e672987f87dbbc49a4..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/modules/kmeans_attention.py
+++ /dev/null
@@ -1,609 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-from inspect import isfunction
-from operator import mul
-from functools import reduce, wraps
-
-from aml.multimodal_video.utils.einops.lib import rearrange, repeat
-from aml.multimodal_video.utils.einops.lib.layers.torch import Rearrange
-
-from fairseq.modules.local_attention import LocalAttention
-
-# constants
-
-TOKEN_SELF_ATTN_VALUE = -5e4
-KMEAN_INIT_ITERS = 10
-
-# helper functions
-
-
-def exists(val):
-    return val is not None
-
-
-def identity(x, *args, **kwargs):
-    return x
-
-
-def default(x, d):
-    if not exists(x):
-        return d if not isfunction(d) else d()
-    return x
-
-
-def cast_tuple(x):
-    return x if isinstance(x, tuple) else (x,)
-
-
-def cache_fn(f):
-    cache = None
-
-    @wraps(f)
-    def cached_fn(*args, **kwargs):
-        nonlocal cache
-        if exists(cache):
-            return cache
-        cache = f(*args, **kwargs)
-        return cache
-    return cached_fn
-
-
-def to(t):
-    return {'device': t.device, 'dtype': t.dtype}
-
-
-def find_modules(nn_module, type):
-    return [module for module in nn_module.modules() if isinstance(module, type)]
-
-
-def is_empty(t):
-    return t.nelement() == 0
-
-
-def max_neg_value(tensor):
-    return -torch.finfo(tensor.dtype).max
-
-
-def batched_index_select(values, indices):
-    last_dim = values.shape[-1]
-    return values.gather(2, expand_dim(indices, -1, last_dim))
-
-
-def merge_dims(ind_from, ind_to, tensor):
-    shape = list(tensor.shape)
-    arr_slice = slice(ind_from, ind_to + 1)
-    shape[arr_slice] = [reduce(mul, shape[arr_slice])]
-    return tensor.reshape(*shape)
-
-
-def expand_dim(t, dim, k):
-    t = t.unsqueeze(dim)
-    expand_shape = [-1] * len(t.shape)
-    expand_shape[dim] = k
-    return t.expand(*expand_shape)
-
-
-def scatter_mean(src, t, index, dim, eps=1e-5):
-    numer = src.scatter_add(dim, index, t)
-    denom = src.scatter_add(dim, index, torch.ones_like(t))
-    return numer / (denom + eps)
-
-
-def split_at_index(dim, index, t):
-    pre_slices = (slice(None),) * dim
-    l = (*pre_slices, slice(None, index))
-    r = (*pre_slices, slice(index, None))
-    return t[l], t[r]
-
-
-def reshape_dim(t, dim, split_dims):
-    shape = list(t.shape)
-    num_dims = len(shape)
-    dim = (dim + num_dims) % num_dims
-    shape[dim:dim+1] = split_dims
-    return t.reshape(shape)
-
-
-def ema(old, new, decay):
-    if not exists(old):
-        return new
-    return old * decay + new * (1 - decay)
-
-
-def ema_inplace(moving_avg, new, decay):
-    if is_empty(moving_avg):
-        moving_avg.data.copy_(new)
-        return
-    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
-
-# helper classes
-
-
-def map_first_tuple_or_el(x, fn):
-    if isinstance(x, tuple):
-        return (fn(x[0]),) + x[1:]
-    return fn(x)
-
-
-class Chunk(nn.Module):
-    def __init__(self, chunks, fn, along_dim=-1):
-        super().__init__()
-        self.dim = along_dim
-        self.chunks = chunks
-        self.fn = fn
-
-    def forward(self, x, **kwargs):
-        if self.chunks <= 1:
-            return self.fn(x, **kwargs)
-        chunks = x.chunk(self.chunks, dim=self.dim)
-        return torch.cat([self.fn(c, **kwargs) for c in chunks], dim=self.dim)
-
-
-class PreNorm(nn.ModuleList):
-    def __init__(self, norm_class, dim, fn):
-        super().__init__()
-        self.norm = norm_class(dim)
-        self.fn = fn
-
-    def forward(self, x, **kwargs):
-        x = self.norm(x)
-        return self.fn(x, **kwargs)
-
-
-class ReZero(nn.Module):
-    def __init__(self, fn):
-        super().__init__()
-        self.residual_weight = nn.Parameter(torch.zeros(1))
-        self.fn = fn
-
-    def forward(self, x, **kwargs):
-        x = self.fn(x, **kwargs)
-        return map_first_tuple_or_el(x, lambda t: t * self.residual_weight)
-
-
-class ScaleNorm(nn.Module):
-    def __init__(self, dim, eps=1e-5):
-        super().__init__()
-        self.g = nn.Parameter(torch.ones(1))
-        self.eps = eps
-
-    def forward(self, x):
-        def norm(t):
-            n = torch.norm(t, dim=-1, keepdim=True).clamp(min=self.eps)
-            return t / n * self.g
-        return map_first_tuple_or_el(x, norm)
-
-
-class ProjectInOut(nn.Module):
-    def __init__(self, fn, dim_in, dim_out, project_out=True):
-        super().__init__()
-        self.fn = fn
-        self.project_in = nn.Linear(dim_in, dim_out)
-        self.project_out = nn.Linear(dim_out, dim_in) if project_out else identity
-
-    def forward(self, x, **kwargs):
-        x = self.project_in(x)
-        x, loss = self.fn(x, **kwargs)
-        x = self.project_out(x)
-        return x, loss
-
-
-class MatrixMultiply(nn.Module):
-    def __init__(self, tensor, transpose=False):
-        super().__init__()
-        self.tensor = tensor
-        self.transpose = transpose
-
-    def forward(self, x):
-        tensor = self.tensor
-        if self.transpose:
-            tensor = tensor.t()
-        return x @ tensor
-
-# positional embeddings
-
-
-class DepthWiseConv1d(nn.Module):
-    def __init__(self, dim_in, dim_out, kernel_size, stride=1, bias=True, causal=False):
-        super().__init__()
-        self.padding = ((kernel_size - 1), 0) if causal else (kernel_size // 2, kernel_size // 2)
-
-        self.net = nn.Sequential(
-            nn.Conv1d(dim_in, dim_in, kernel_size=kernel_size, groups=dim_in, stride=stride, bias=bias),
-            nn.Conv1d(dim_in, dim_out, 1, bias=bias)
-        )
-
-    def forward(self, x):
-        x = F.pad(x, self.padding, value=0.)
-        return self.net(x)
-
-
-class FixedPositionalEmbedding(nn.Module):
-    def __init__(self, dim, max_seq_len):
-        super().__init__()
-        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        position = torch.arange(0, max_seq_len, dtype=torch.float)
-        sinusoid_inp = torch.einsum("i,j->ij", position, inv_freq)
-        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
-        self.register_buffer('emb', emb)
-
-    def forward(self, x):
-        return self.emb[None, :x.shape[1], :].to(x)
-
-
-def rotate_every_two(x):
-    x = rearrange(x, '... (d j) -> ... d j', j=2)
-    x1, x2 = x.unbind(dim=-1)
-    x = torch.stack((-x2, x1), dim=-1)
-    return rearrange(x, '... d j -> ... (d j)')
-
-
-def apply_rotary_pos_emb(q, k, sinu_pos):
-    sinu_pos = rearrange(sinu_pos, '() n (j d) -> n j d', j=2)
-    sin, cos = sinu_pos.unbind(dim=-2)
-    sin, cos = map(lambda t: repeat(t, 'b n -> b (n j)', j=2), (sin, cos))
-    q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
-    return q, k
-
-# kmeans related function and class
-
-
-def update_kmeans_on_backwards(module):
-    module.kmean_modules = find_modules(module, Kmeans)
-
-    def hook(_, grad_in, grad_out):
-        for m in module.kmean_modules:
-            m.update()
-
-    return module.register_backward_hook(hook)
-
-
-def similarity(x, means):
-    return torch.einsum('bhld,hcd->bhlc', x, means)
-
-
-def dists_and_buckets(x, means):
-    dists = similarity(x, means)
-    _, buckets = torch.max(dists, dim=-1)
-    return dists, buckets
-
-
-def batched_bincount(index, num_classes, dim=-1):
-    shape = list(index.shape)
-    shape[dim] = num_classes
-    out = index.new_zeros(shape)
-    out.scatter_add_(dim, index, torch.ones_like(index, dtype=index.dtype))
-    return out
-
-
-def kmeans_iter(x, means, buckets=None):
-    b, h, _, d, dtype, num_clusters = *x.shape, x.dtype, means.shape[1]
-
-    if not exists(buckets):
-        _, buckets = dists_and_buckets(x, means)
-
-    bins = batched_bincount(buckets, num_clusters).sum(0, keepdim=True)
-    zero_mask = bins.long() == 0
-
-    means_ = buckets.new_zeros(b, h, num_clusters, d, dtype=dtype)
-    means_.scatter_add_(-2, expand_dim(buckets, -1, d), x)
-    means_ = F.normalize(means_.sum(0, keepdim=True), dim=-1).type(dtype)
-
-    means = torch.where(zero_mask.unsqueeze(-1), means, means_)
-    means = means.squeeze(0)
-    return means
-
-
-def distribution(dists, window_size):
-    _, topk_indices = dists.topk(k=window_size, dim=-2)
-    indices = topk_indices.transpose(-2, -1)
-    return indices.reshape(*indices.size()[:2], -1)
-
-
-class Kmeans(nn.Module):
-    def __init__(self, num_heads, head_dim, num_clusters, ema_decay=0.999, commitment=1e-4):
-        super().__init__()
-        self.commitment = commitment
-        self.ema_decay = ema_decay
-
-        self.register_buffer('means', torch.randn(num_heads, num_clusters, head_dim))
-        self.register_buffer('initted', torch.tensor(False))
-        self.num_new_means = 0
-        self.new_means = None
-
-    @torch.no_grad()
-    def init(self, x):
-        if self.initted:
-            return
-        _, h, _, d, device, _ = *x.shape, x.device, x.dtype
-
-        num_clusters = self.means.shape[1]
-
-        means = x.transpose(0, 1).contiguous().view(h, -1, d)
-        num_samples = means.shape[1]
-
-        if num_samples >= num_clusters:
-            indices = torch.randperm(num_samples, device=device)[:num_clusters]
-        else:
-            indices = torch.randint(0, num_samples, (num_clusters,), device=device)
-
-        means = means[:, indices]
-
-        for _ in range(KMEAN_INIT_ITERS):
-            means = kmeans_iter(x, means)
-
-        self.num_new_means = 0
-        self.means.data.copy_(means)
-        self.initted.data.copy_(torch.tensor(True))
-
-    @torch.no_grad()
-    def update(self, new_means=None):
-        new_means = default(new_means, self.new_means)
-        assert exists(new_means), 'new kmeans has not been supplied'
-        ema_inplace(self.means, new_means, self.ema_decay)
-
-        del self.new_means
-        self.new_means = None
-        self.num_new_means = 0
-
-    def forward(self, x, update_means=False):
-        self.init(x)
-
-        b, dtype = x.shape[0], x.dtype
-        means = self.means.type(dtype)
-        x = F.normalize(x, 2, dim=-1).type(dtype)
-
-        with torch.no_grad():
-            dists, buckets = dists_and_buckets(x, means)
-
-        routed_means = batched_index_select(expand_dim(means, 0, b), buckets)
-        loss = F.mse_loss(x, routed_means) * self.commitment
-
-        if update_means:
-            with torch.no_grad():
-                means = kmeans_iter(x, means, buckets)
-            self.new_means = ema(self.new_means, means, self.num_new_means / (self.num_new_means + 1))
-            self.num_new_means += 1
-
-        return dists, loss
-
-# kmeans attention class
-
-
-class KmeansAttention(nn.Module):
-    def __init__(self, num_clusters, window_size, num_heads, head_dim, causal=False, dropout=0., ema_decay=0.999, commitment=1e-4, context_window_size=None, receives_context=False, num_mem_kv=0, shared_qk=False):
-        super().__init__()
-        self.num_heads = num_heads
-        self.num_clusters = num_clusters
-        self.head_dim = head_dim
-
-        self.window_size = window_size
-        self.context_window_size = default(context_window_size, window_size)
-        self.causal = causal
-
-        self.shared_qk = shared_qk
-        self.receives_context = receives_context
-        self.kmeans = Kmeans(num_heads, head_dim, num_clusters, ema_decay, commitment)
-        self.dropout = nn.Dropout(dropout)
-
-        self.num_mem_kv = max(num_mem_kv, 1 if causal and not shared_qk else 0)
-        self.mem_key = nn.Parameter(torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim))
-        self.mem_value = nn.Parameter(torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim))
-
-    def forward(self, q, k, v, query_mask=None, key_mask=None, **kwargs):
-        b, h, t, d, kv_t, wsz, c_wsz, nc, device, dtype = *q.shape, k.shape[2], self.window_size, self.context_window_size, self.num_clusters, q.device, q.dtype
-        is_reverse = kwargs.pop('_reverse', False)
-
-        out = torch.zeros_like(q, dtype=dtype)
-
-        update_kmeans = self.training and not is_reverse
-
-        key_mask = default(key_mask, query_mask) if not self.receives_context else key_mask
-        kv_wsz = wsz if not self.receives_context else c_wsz
-
-        wsz = min(wsz, t)
-        kv_wsz = min(kv_wsz, kv_t)
-
-        if not self.shared_qk or self.receives_context:
-            dists, aux_loss = self.kmeans(torch.cat((q, k), dim=2), update_kmeans)
-            q_dists, k_dists = split_at_index(2, t, dists)
-            indices = distribution(q_dists, wsz)
-            kv_indices = distribution(k_dists, kv_wsz)
-        else:
-            dists, aux_loss = self.kmeans(q, update_kmeans)
-            k = F.normalize(k, dim=-1).to(q)
-            indices = distribution(dists, wsz)
-            kv_indices = indices
-
-        q = batched_index_select(q, indices)
-        k = batched_index_select(k, kv_indices)
-        v = batched_index_select(v, kv_indices)
-
-        reshape_with_window = lambda x: x.reshape(b, h, nc, -1, d)
-        q, k, v = map(reshape_with_window, (q, k, v))
-
-        m_k, m_v = map(lambda x: expand_dim(x, 0, b).to(q), (self.mem_key, self.mem_value))
-        k, v = map(lambda x: torch.cat(x, dim=3), ((m_k, k), (m_v, v)))
-
-        dots = torch.einsum('bhnid,bhnjd->bhnij', q, k) * (d ** -0.5)
-
-        mask_value = max_neg_value(dots)
-
-        if exists(query_mask) or exists(key_mask):
-            query_mask = default(query_mask, lambda: torch.ones((b, t), device=device).bool())
-            key_mask = default(key_mask, lambda: torch.ones((b, kv_t), device=device).bool())
-
-            q_mask = expand_dim(query_mask, 1, h).gather(2, indices)
-            kv_mask = expand_dim(key_mask, 1, h).gather(2, kv_indices)
-            q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (q_mask, kv_mask))
-            mask = q_mask[:, :, :, :, None] * kv_mask[:, :, :, None, :]
-            mask = F.pad(mask, (self.num_mem_kv, 0), value=1)
-            dots.masked_fill_(~mask, mask_value)
-            del mask
-
-        if self.causal:
-            q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices))
-            mask = q_mask[:, :, :, :, None] >= kv_mask[:, :, :, None, :]
-            mask = F.pad(mask, (self.num_mem_kv, 0), value=1)
-            dots.masked_fill_(~mask, mask_value)
-            del mask
-
-        if self.shared_qk:
-            q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices))
-            mask = q_mask[:, :, :, :, None] == kv_mask[:, :, :, None, :]
-            mask = F.pad(mask, (self.num_mem_kv, 0), value=0)
-            dots.masked_fill_(mask, TOKEN_SELF_ATTN_VALUE)
-            del mask
-
-        dots = dots.softmax(dim=-1)
-        dots = self.dropout(dots)
-
-        bo = torch.einsum('bhcij,bhcjd->bhcid', dots, v)
-        so = torch.reshape(bo, (b, h, -1, bo.shape[-1])).type(dtype)
-        out = scatter_mean(out, so, indices.unsqueeze(-1).expand_as(so), -2)
-        return out, aux_loss
-
-# feedforward
-
-
-class GELU_(nn.Module):
-    def forward(self, x):
-        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
-
-
-GELU = nn.GELU if hasattr(nn, 'GELU') else GELU_
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, mult=4, dropout=0., activation=None, glu=False):
-        super().__init__()
-        activation = default(activation, GELU)
-
-        self.glu = glu
-        self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1))
-        self.act = activation()
-        self.dropout = nn.Dropout(dropout)
-        self.w2 = nn.Linear(dim * mult, dim)
-
-    def forward(self, x, **kwargs):
-        if not self.glu:
-            x = self.w1(x)
-            x = self.act(x)
-        else:
-            x, v = self.w1(x).chunk(2, dim=-1)
-            x = self.act(x) * v
-
-        x = self.dropout(x)
-        x = self.w2(x)
-        return x
-
-# self attention
-
-
-class SelfAttention(nn.Module):
-    def __init__(self, dim, max_seq_len, heads, local_attn_heads, window_size, dim_head=None, local_attn_window_size=None, local_attn_radius_blocks=1, causal=False, attn_dropout=0., dropout=0., kmeans_ema_decay=0.999, commitment_factor=1e-4, receives_context=False, context_window_size=None, rel_pos_emb=True, num_mem_kv=0, shared_qk=False, conv_query_kernel=9):
-        super().__init__()
-        assert dim_head or (dim % heads) == 0, 'hidden dimension must be divisible by number of heads'
-        assert (max_seq_len % window_size) == 0, 'maximum sequence length must be divisible by the target window size'
-        assert local_attn_heads <= heads, 'number of local attention heads must be less than total heads'
-        assert not (receives_context and local_attn_heads > 0), 'local attention cannot be used for self attention with context'
-        assert not (receives_context and causal), 'contextual attention layer cannot be causal'
-
-        local_attn_window_size = default(local_attn_window_size, window_size)
-        context_window_size = default(context_window_size, window_size)
-
-        self.shared_qk = shared_qk
-        self.receives_context = receives_context
-        self.heads = heads
-        self.local_attn_heads = local_attn_heads
-        self.global_attn_heads = heads - local_attn_heads
-
-        self.causal = causal
-        self.window_size = window_size
-
-        dim_head = default(dim_head, dim // heads)
-        dim_heads = dim_head * heads
-        self.dim_head = dim_head
-
-        num_clusters = max_seq_len // window_size
-
-        # local
-
-        local_dim_heads = dim_head * self.local_attn_heads
-
-        if self.local_attn_heads > 0:
-            rel_pos_emb_config = (dim_head, local_attn_heads) if rel_pos_emb else None
-            self.local_attn = LocalAttention(dim=dim_head, window_size=local_attn_window_size, causal=causal, dropout=attn_dropout, rel_pos_emb_config=rel_pos_emb_config, look_backward=local_attn_radius_blocks, look_forward=0 if causal else local_attn_radius_blocks)
-            self.local_to_qkv = nn.Linear(dim, 3 * local_dim_heads)
-
-        # global
-
-        global_dim_heads = dim_head * self.global_attn_heads
-
-        if self.global_attn_heads > 0:
-            self.global_attn = KmeansAttention(num_clusters, window_size, self.global_attn_heads, dim_head, causal=causal, dropout=attn_dropout, ema_decay=kmeans_ema_decay, commitment=commitment_factor, receives_context=receives_context, num_mem_kv=num_mem_kv, shared_qk=shared_qk)
-
-        self.to_q = nn.Sequential(
-            Rearrange('b n c -> b c n'),
-            DepthWiseConv1d(dim, global_dim_heads, conv_query_kernel, causal=causal),
-            Rearrange('b c n -> b n c')
-        )
-
-        self.to_v = nn.Linear(dim, global_dim_heads, bias=False)
-
-        if not self.shared_qk:
-            self.to_k = nn.Linear(dim, global_dim_heads, bias=False)
-
-        # out
-
-        self.to_out = nn.Linear(dim_heads, dim, bias=False)
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, query, key, value, context=None, key_padding_mask=None, context_mask=None, pos_emb=None, **kwargs):
-        assert not (self.receives_context and not exists(context)), 'context must be passed if self attention is set to receive context'
-        input_mask = key_padding_mask
-        x = query.transpose(0, 1)
-        b, t, _, h, dh = *x.shape, self.heads, self.dim_head
-        has_local, has_global = map(lambda x: x > 0, (self.local_attn_heads, self.global_attn_heads))
-
-        split_heads = lambda v: reshape_dim(v, -1, (-1, dh)).transpose(1, 2).contiguous()
-
-        if has_local:
-            local_qkv = self.local_to_qkv(x).chunk(3, dim=-1)
-            lq, lk, lv = map(split_heads, local_qkv)
-
-        if has_global:
-            kv_input = x if not self.receives_context else context
-
-            q, v = self.to_q(x), self.to_v(kv_input)
-
-            if not self.shared_qk:
-                k = self.to_k(kv_input)
-            else:
-                k = self.to_q(kv_input) if self.receives_context else q
-
-            q, k, v = map(split_heads, (q, k, v))
-
-        out = []
-        total_loss = torch.tensor(0., requires_grad=True, **to(x))
-
-        if has_local:
-            local_out = self.local_attn(lq, lk, lv, input_mask=input_mask)
-            out.append(local_out)
-
-        if has_global:
-            if not self.receives_context and exists(pos_emb):
-                q, k = apply_rotary_pos_emb(q, k, pos_emb)
-
-            global_out, loss = self.global_attn(q, k, v, query_mask=input_mask, key_mask=context_mask)
-            total_loss = total_loss + loss
-
-            out.append(global_out)
-
-        out = torch.cat(out, dim=1)
-        out = out.reshape(b, h, t, -1).transpose(1, 2).reshape(b, t, -1)
-        out = self.dropout(out.transpose(0, 1))
-        # out = self.to_out(out)
-        return out, total_loss
diff --git a/spaces/ICML2022/resefa/models/stylegan2_discriminator.py b/spaces/ICML2022/resefa/models/stylegan2_discriminator.py
deleted file mode 100644
index 1802d44b68d0801290dcd691f09f605c3cfc9cbd..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/models/stylegan2_discriminator.py
+++ /dev/null
@@ -1,729 +0,0 @@
-# python3.7
-"""Contains the implementation of discriminator described in StyleGAN2.
-
-Compared to that of StyleGAN, the discriminator in StyleGAN2 mainly adds skip
-connections, increases model size and disables progressive growth. This script
-ONLY supports config F in the original paper.
-
-Paper: https://arxiv.org/pdf/1912.04958.pdf
-
-Official TensorFlow implementation: https://github.com/NVlabs/stylegan2
-"""
-
-import numpy as np
-
-import torch
-import torch.nn as nn
-
-from third_party.stylegan2_official_ops import bias_act
-from third_party.stylegan2_official_ops import upfirdn2d
-from third_party.stylegan2_official_ops import conv2d_gradfix
-
-__all__ = ['StyleGAN2Discriminator']
-
-# Resolutions allowed.
-_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
-
-# Architectures allowed.
-_ARCHITECTURES_ALLOWED = ['resnet', 'skip', 'origin']
-
-# pylint: disable=missing-function-docstring
-
-class StyleGAN2Discriminator(nn.Module):
-    """Defines the discriminator network in StyleGAN2.
-
-    NOTE: The discriminator takes images with `RGB` channel order and pixel
-    range [-1, 1] as inputs.
-
-    Settings for the backbone:
-
-    (1) resolution: The resolution of the input image. (default: -1)
-    (2) init_res: Smallest resolution of the convolutional backbone.
-        (default: 4)
-    (3) image_channels: Number of channels of the input image. (default: 3)
-    (4) architecture: Type of architecture. Support `origin`, `skip`, and
-        `resnet`. (default: `resnet`)
-    (5) use_wscale: Whether to use weight scaling. (default: True)
-    (6) wscale_gain: The factor to control weight scaling. (default: 1.0)
-    (7) lr_mul: Learning rate multiplier for backbone. (default: 1.0)
-    (8) mbstd_groups: Group size for the minibatch standard deviation layer.
-        `0` means disable. (default: 4)
-    (9) mbstd_channels: Number of new channels (appended to the original feature
-        map) after the minibatch standard deviation layer. (default: 1)
-    (10) fmaps_base: Factor to control number of feature maps for each layer.
-         (default: 32 << 10)
-    (11) fmaps_max: Maximum number of feature maps in each layer. (default: 512)
-    (12) filter_kernel: Kernel used for filtering (e.g., downsampling).
-         (default: (1, 3, 3, 1))
-    (13) conv_clamp: A threshold to clamp the output of convolution layers to
-         avoid overflow under FP16 training. (default: None)
-    (14) eps: A small value to avoid divide overflow. (default: 1e-8)
-
-    Settings for conditional model:
-
-    (1) label_dim: Dimension of the additional label for conditional generation.
-        In one-hot conditioning case, it is equal to the number of classes. If
-        set to 0, conditioning training will be disabled. (default: 0)
-    (2) embedding_dim: Dimension of the embedding space, if needed.
-        (default: 512)
-    (3) embedding_bias: Whether to add bias to embedding learning.
-        (default: True)
-    (4) embedding_use_wscale: Whether to use weight scaling for embedding
-        learning. (default: True)
-    (5) embedding_lr_mul: Learning rate multiplier for the embedding learning.
-        (default: 1.0)
-    (6) normalize_embedding: Whether to normalize the embedding. (default: True)
-    (7) mapping_layers: Number of layers of the additional mapping network after
-        embedding. (default: 0)
-    (8) mapping_fmaps: Number of hidden channels of the additional mapping
-        network after embedding. (default: 512)
-    (9) mapping_use_wscale: Whether to use weight scaling for the additional
-        mapping network. (default: True)
-    (10) mapping_lr_mul: Learning rate multiplier for the additional mapping
-         network after embedding. (default: 0.1)
-
-    Runtime settings:
-
-    (1) fp16_res: Layers at resolution higher than (or equal to) this field will
-        use `float16` precision for computation. This is merely used for
-        acceleration. If set as `None`, all layers will use `float32` by
-        default. (default: None)
-    (2) impl: Implementation mode of some particular ops, e.g., `filtering`,
-        `bias_act`, etc. `cuda` means using the official CUDA implementation
-        from StyleGAN2, while `ref` means using the native PyTorch ops.
-        (default: `cuda`)
-    """
-
-    def __init__(self,
-                 # Settings for backbone.
-                 resolution=-1,
-                 init_res=4,
-                 image_channels=3,
-                 architecture='resnet',
-                 use_wscale=True,
-                 wscale_gain=1.0,
-                 lr_mul=1.0,
-                 mbstd_groups=4,
-                 mbstd_channels=1,
-                 fmaps_base=32 << 10,
-                 fmaps_max=512,
-                 filter_kernel=(1, 3, 3, 1),
-                 conv_clamp=None,
-                 eps=1e-8,
-                 # Settings for conditional model.
-                 label_dim=0,
-                 embedding_dim=512,
-                 embedding_bias=True,
-                 embedding_use_wscale=True,
-                 embedding_lr_mul=1.0,
-                 normalize_embedding=True,
-                 mapping_layers=0,
-                 mapping_fmaps=512,
-                 mapping_use_wscale=True,
-                 mapping_lr_mul=0.1):
-        """Initializes with basic settings.
-
-        Raises:
-            ValueError: If the `resolution` is not supported, or `architecture`
-                is not supported.
-        """
-        super().__init__()
-
-        if resolution not in _RESOLUTIONS_ALLOWED:
-            raise ValueError(f'Invalid resolution: `{resolution}`!\n'
-                             f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.')
-        architecture = architecture.lower()
-        if architecture not in _ARCHITECTURES_ALLOWED:
-            raise ValueError(f'Invalid architecture: `{architecture}`!\n'
-                             f'Architectures allowed: '
-                             f'{_ARCHITECTURES_ALLOWED}.')
-
-        self.init_res = init_res
-        self.init_res_log2 = int(np.log2(init_res))
-        self.resolution = resolution
-        self.final_res_log2 = int(np.log2(resolution))
-        self.image_channels = image_channels
-        self.architecture = architecture
-        self.use_wscale = use_wscale
-        self.wscale_gain = wscale_gain
-        self.lr_mul = lr_mul
-        self.mbstd_groups = mbstd_groups
-        self.mbstd_channels = mbstd_channels
-        self.fmaps_base = fmaps_base
-        self.fmaps_max = fmaps_max
-        self.filter_kernel = filter_kernel
-        self.conv_clamp = conv_clamp
-        self.eps = eps
-
-        self.label_dim = label_dim
-        self.embedding_dim = embedding_dim
-        self.embedding_bias = embedding_bias
-        self.embedding_use_wscale = embedding_use_wscale
-        self.embedding_lr_mul = embedding_lr_mul
-        self.normalize_embedding = normalize_embedding
-        self.mapping_layers = mapping_layers
-        self.mapping_fmaps = mapping_fmaps
-        self.mapping_use_wscale = mapping_use_wscale
-        self.mapping_lr_mul = mapping_lr_mul
-
-        self.pth_to_tf_var_mapping = {}
-
-        # Embedding for conditional discrimination.
-        self.use_embedding = label_dim > 0 and embedding_dim > 0
-        if self.use_embedding:
-            self.embedding = DenseLayer(in_channels=label_dim,
-                                        out_channels=embedding_dim,
-                                        add_bias=embedding_bias,
-                                        init_bias=0.0,
-                                        use_wscale=embedding_use_wscale,
-                                        wscale_gain=wscale_gain,
-                                        lr_mul=embedding_lr_mul,
-                                        activation_type='linear')
-            self.pth_to_tf_var_mapping['embedding.weight'] = 'LabelEmbed/weight'
-            if self.embedding_bias:
-                self.pth_to_tf_var_mapping['embedding.bias'] = 'LabelEmbed/bias'
-
-            if self.normalize_embedding:
-                self.norm = PixelNormLayer(dim=1, eps=eps)
-
-            for i in range(mapping_layers):
-                in_channels = (embedding_dim if i == 0 else mapping_fmaps)
-                out_channels = (embedding_dim if i == (mapping_layers - 1) else
-                                mapping_fmaps)
-                layer_name = f'mapping{i}'
-                self.add_module(layer_name,
-                                DenseLayer(in_channels=in_channels,
-                                           out_channels=out_channels,
-                                           add_bias=True,
-                                           init_bias=0.0,
-                                           use_wscale=mapping_use_wscale,
-                                           wscale_gain=wscale_gain,
-                                           lr_mul=mapping_lr_mul,
-                                           activation_type='lrelu'))
-                self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                    f'Mapping{i}/weight')
-                self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
-                    f'Mapping{i}/bias')
-
-        # Convolutional backbone.
-        for res_log2 in range(self.final_res_log2, self.init_res_log2 - 1, -1):
-            res = 2 ** res_log2
-            in_channels = self.get_nf(res)
-            out_channels = self.get_nf(res // 2)
-            block_idx = self.final_res_log2 - res_log2
-
-            # Input convolution layer for each resolution (if needed).
-            if res_log2 == self.final_res_log2 or self.architecture == 'skip':
-                layer_name = f'input{block_idx}'
-                self.add_module(layer_name,
-                                ConvLayer(in_channels=image_channels,
-                                          out_channels=in_channels,
-                                          kernel_size=1,
-                                          add_bias=True,
-                                          scale_factor=1,
-                                          filter_kernel=None,
-                                          use_wscale=use_wscale,
-                                          wscale_gain=wscale_gain,
-                                          lr_mul=lr_mul,
-                                          activation_type='lrelu',
-                                          conv_clamp=conv_clamp))
-                self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                    f'{res}x{res}/FromRGB/weight')
-                self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
-                    f'{res}x{res}/FromRGB/bias')
-
-            # Convolution block for each resolution (except the last one).
-            if res != self.init_res:
-                # First layer (kernel 3x3) without downsampling.
-                layer_name = f'layer{2 * block_idx}'
-                self.add_module(layer_name,
-                                ConvLayer(in_channels=in_channels,
-                                          out_channels=in_channels,
-                                          kernel_size=3,
-                                          add_bias=True,
-                                          scale_factor=1,
-                                          filter_kernel=None,
-                                          use_wscale=use_wscale,
-                                          wscale_gain=wscale_gain,
-                                          lr_mul=lr_mul,
-                                          activation_type='lrelu',
-                                          conv_clamp=conv_clamp))
-                self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                    f'{res}x{res}/Conv0/weight')
-                self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
-                    f'{res}x{res}/Conv0/bias')
-
-                # Second layer (kernel 3x3) with downsampling
-                layer_name = f'layer{2 * block_idx + 1}'
-                self.add_module(layer_name,
-                                ConvLayer(in_channels=in_channels,
-                                          out_channels=out_channels,
-                                          kernel_size=3,
-                                          add_bias=True,
-                                          scale_factor=2,
-                                          filter_kernel=filter_kernel,
-                                          use_wscale=use_wscale,
-                                          wscale_gain=wscale_gain,
-                                          lr_mul=lr_mul,
-                                          activation_type='lrelu',
-                                          conv_clamp=conv_clamp))
-                self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                    f'{res}x{res}/Conv1_down/weight')
-                self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
-                    f'{res}x{res}/Conv1_down/bias')
-
-                # Residual branch (kernel 1x1) with downsampling, without bias,
-                # with linear activation.
-                if self.architecture == 'resnet':
-                    layer_name = f'residual{block_idx}'
-                    self.add_module(layer_name,
-                                    ConvLayer(in_channels=in_channels,
-                                              out_channels=out_channels,
-                                              kernel_size=1,
-                                              add_bias=False,
-                                              scale_factor=2,
-                                              filter_kernel=filter_kernel,
-                                              use_wscale=use_wscale,
-                                              wscale_gain=wscale_gain,
-                                              lr_mul=lr_mul,
-                                              activation_type='linear',
-                                              conv_clamp=None))
-                    self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                        f'{res}x{res}/Skip/weight')
-
-            # Convolution block for last resolution.
-            else:
-                self.mbstd = MiniBatchSTDLayer(
-                    groups=mbstd_groups, new_channels=mbstd_channels, eps=eps)
-
-                # First layer (kernel 3x3) without downsampling.
-                layer_name = f'layer{2 * block_idx}'
-                self.add_module(
-                    layer_name,
-                    ConvLayer(in_channels=in_channels + mbstd_channels,
-                              out_channels=in_channels,
-                              kernel_size=3,
-                              add_bias=True,
-                              scale_factor=1,
-                              filter_kernel=None,
-                              use_wscale=use_wscale,
-                              wscale_gain=wscale_gain,
-                              lr_mul=lr_mul,
-                              activation_type='lrelu',
-                              conv_clamp=conv_clamp))
-                self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                    f'{res}x{res}/Conv/weight')
-                self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
-                    f'{res}x{res}/Conv/bias')
-
-                # Second layer, as a fully-connected layer.
-                layer_name = f'layer{2 * block_idx + 1}'
-                self.add_module(layer_name,
-                                DenseLayer(in_channels=in_channels * res * res,
-                                           out_channels=in_channels,
-                                           add_bias=True,
-                                           init_bias=0.0,
-                                           use_wscale=use_wscale,
-                                           wscale_gain=wscale_gain,
-                                           lr_mul=lr_mul,
-                                           activation_type='lrelu'))
-                self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                    f'{res}x{res}/Dense0/weight')
-                self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
-                    f'{res}x{res}/Dense0/bias')
-
-                # Final dense layer to output score.
-                self.output = DenseLayer(in_channels=in_channels,
-                                         out_channels=(embedding_dim
-                                                       if self.use_embedding
-                                                       else max(label_dim, 1)),
-                                         add_bias=True,
-                                         init_bias=0.0,
-                                         use_wscale=use_wscale,
-                                         wscale_gain=wscale_gain,
-                                         lr_mul=lr_mul,
-                                         activation_type='linear')
-                self.pth_to_tf_var_mapping['output.weight'] = 'Output/weight'
-                self.pth_to_tf_var_mapping['output.bias'] = 'Output/bias'
-
-        # Used for downsampling input image for `skip` architecture.
-        if self.architecture == 'skip':
-            self.register_buffer(
-                'filter', upfirdn2d.setup_filter(filter_kernel))
-
-    def get_nf(self, res):
-        """Gets number of feature maps according to the given resolution."""
-        return min(self.fmaps_base // res, self.fmaps_max)
-
-    def forward(self, image, label=None, fp16_res=None, impl='cuda'):
-        # Check shape.
-        expected_shape = (self.image_channels, self.resolution, self.resolution)
-        if image.ndim != 4 or image.shape[1:] != expected_shape:
-            raise ValueError(f'The input tensor should be with shape '
-                             f'[batch_size, channel, height, width], where '
-                             f'`channel` equals to {self.image_channels}, '
-                             f'`height`, `width` equal to {self.resolution}!\n'
-                             f'But `{image.shape}` is received!')
-        if self.label_dim > 0:
-            if label is None:
-                raise ValueError(f'Model requires an additional label '
-                                 f'(with dimension {self.label_dim}) as input, '
-                                 f'but no label is received!')
-            batch_size = image.shape[0]
-            if label.ndim != 2 or label.shape != (batch_size, self.label_dim):
-                raise ValueError(f'Input label should be with shape '
-                                 f'[batch_size, label_dim], where '
-                                 f'`batch_size` equals to that of '
-                                 f'images ({image.shape[0]}) and '
-                                 f'`label_dim` equals to {self.label_dim}!\n'
-                                 f'But `{label.shape}` is received!')
-            label = label.to(dtype=torch.float32)
-            if self.use_embedding:
-                embed = self.embedding(label, impl=impl)
-                if self.normalize_embedding:
-                    embed = self.norm(embed)
-                for i in range(self.mapping_layers):
-                    embed = getattr(self, f'mapping{i}')(embed, impl=impl)
-
-        # Cast to `torch.float16` if needed.
-        if fp16_res is not None and self.resolution >= fp16_res:
-            image = image.to(torch.float16)
-
-        x = self.input0(image, impl=impl)
-
-        for res_log2 in range(self.final_res_log2, self.init_res_log2, -1):
-            res = 2 ** res_log2
-            # Cast to `torch.float16` if needed.
-            if fp16_res is not None and res >= fp16_res:
-                x = x.to(torch.float16)
-            else:
-                x = x.to(torch.float32)
-
-            idx = self.final_res_log2 - res_log2  # Block index
-
-            if self.architecture == 'skip' and idx > 0:
-                image = upfirdn2d.downsample2d(image, self.filter, impl=impl)
-                # Cast to `torch.float16` if needed.
-                if fp16_res is not None and res >= fp16_res:
-                    image = image.to(torch.float16)
-                else:
-                    image = image.to(torch.float32)
-                y = getattr(self, f'input{idx}')(image, impl=impl)
-                x = x + y
-
-            if self.architecture == 'resnet':
-                residual = getattr(self, f'residual{idx}')(
-                    x, runtime_gain=np.sqrt(0.5), impl=impl)
-                x = getattr(self, f'layer{2 * idx}')(x, impl=impl)
-                x = getattr(self, f'layer{2 * idx + 1}')(
-                    x, runtime_gain=np.sqrt(0.5), impl=impl)
-                x = x + residual
-            else:
-                x = getattr(self, f'layer{2 * idx}')(x, impl=impl)
-                x = getattr(self, f'layer{2 * idx + 1}')(x, impl=impl)
-
-        # Final output.
-        idx += 1
-        if fp16_res is not None:  # Always use FP32 for the last block.
-            x = x.to(torch.float32)
-        if self.architecture == 'skip':
-            image = upfirdn2d.downsample2d(image, self.filter, impl=impl)
-            if fp16_res is not None:  # Always use FP32 for the last block.
-                image = image.to(torch.float32)
-            y = getattr(self, f'input{idx}')(image, impl=impl)
-            x = x + y
-        x = self.mbstd(x)
-        x = getattr(self, f'layer{2 * idx}')(x, impl=impl)
-        x = getattr(self, f'layer{2 * idx + 1}')(x, impl=impl)
-        x = self.output(x, impl=impl)
-
-        if self.use_embedding:
-            x = (x * embed).sum(dim=1, keepdim=True)
-            x = x / np.sqrt(self.embedding_dim)
-        elif self.label_dim > 0:
-            x = (x * label).sum(dim=1, keepdim=True)
-
-        results = {
-            'score': x,
-            'label': label
-        }
-        if self.use_embedding:
-            results['embedding'] = embed
-        return results
-
-
-class PixelNormLayer(nn.Module):
-    """Implements pixel-wise feature vector normalization layer."""
-
-    def __init__(self, dim, eps):
-        super().__init__()
-        self.dim = dim
-        self.eps = eps
-
-    def extra_repr(self):
-        return f'dim={self.dim}, epsilon={self.eps}'
-
-    def forward(self, x):
-        scale = (x.square().mean(dim=self.dim, keepdim=True) + self.eps).rsqrt()
-        return x * scale
-
-
-class MiniBatchSTDLayer(nn.Module):
-    """Implements the minibatch standard deviation layer."""
-
-    def __init__(self, groups, new_channels, eps):
-        super().__init__()
-        self.groups = groups
-        self.new_channels = new_channels
-        self.eps = eps
-
-    def extra_repr(self):
-        return (f'groups={self.groups}, '
-                f'new_channels={self.new_channels}, '
-                f'epsilon={self.eps}')
-
-    def forward(self, x):
-        if self.groups <= 1 or self.new_channels < 1:
-            return x
-
-        dtype = x.dtype
-
-        N, C, H, W = x.shape
-        G = min(self.groups, N)  # Number of groups.
-        nC = self.new_channels  # Number of channel groups.
-        c = C // nC             # Channels per channel group.
-
-        y = x.reshape(G, -1, nC, c, H, W)  # [GnFcHW]
-        y = y - y.mean(dim=0)              # [GnFcHW]
-        y = y.square().mean(dim=0)         # [nFcHW]
-        y = (y + self.eps).sqrt()          # [nFcHW]
-        y = y.mean(dim=(2, 3, 4))          # [nF]
-        y = y.reshape(-1, nC, 1, 1)        # [nF11]
-        y = y.repeat(G, 1, H, W)           # [NFHW]
-        x = torch.cat((x, y), dim=1)       # [N(C+F)HW]
-
-        assert x.dtype == dtype
-        return x
-
-
-class ConvLayer(nn.Module):
-    """Implements the convolutional layer.
-
-    If downsampling is needed (i.e., `scale_factor = 2`), the feature map will
-    be filtered with `filter_kernel` first.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 add_bias,
-                 scale_factor,
-                 filter_kernel,
-                 use_wscale,
-                 wscale_gain,
-                 lr_mul,
-                 activation_type,
-                 conv_clamp):
-        """Initializes with layer settings.
-
-        Args:
-            in_channels: Number of channels of the input tensor.
-            out_channels: Number of channels of the output tensor.
-            kernel_size: Size of the convolutional kernels.
-            add_bias: Whether to add bias onto the convolutional result.
-            scale_factor: Scale factor for downsampling. `1` means skip
-                downsampling.
-            filter_kernel: Kernel used for filtering.
-            use_wscale: Whether to use weight scaling.
-            wscale_gain: Gain factor for weight scaling.
-            lr_mul: Learning multiplier for both weight and bias.
-            activation_type: Type of activation.
-            conv_clamp: A threshold to clamp the output of convolution layers to
-                avoid overflow under FP16 training.
-        """
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.add_bias = add_bias
-        self.scale_factor = scale_factor
-        self.filter_kernel = filter_kernel
-        self.use_wscale = use_wscale
-        self.wscale_gain = wscale_gain
-        self.lr_mul = lr_mul
-        self.activation_type = activation_type
-        self.conv_clamp = conv_clamp
-
-        weight_shape = (out_channels, in_channels, kernel_size, kernel_size)
-        fan_in = kernel_size * kernel_size * in_channels
-        wscale = wscale_gain / np.sqrt(fan_in)
-        if use_wscale:
-            self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul)
-            self.wscale = wscale * lr_mul
-        else:
-            self.weight = nn.Parameter(
-                torch.randn(*weight_shape) * wscale / lr_mul)
-            self.wscale = lr_mul
-
-        if add_bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels))
-            self.bscale = lr_mul
-        else:
-            self.bias = None
-        self.act_gain = bias_act.activation_funcs[activation_type].def_gain
-
-        if scale_factor > 1:
-            assert filter_kernel is not None
-            self.register_buffer(
-                'filter', upfirdn2d.setup_filter(filter_kernel))
-            fh, fw = self.filter.shape
-            self.filter_padding = (
-                kernel_size // 2 + (fw - scale_factor + 1) // 2,
-                kernel_size // 2 + (fw - scale_factor) // 2,
-                kernel_size // 2 + (fh - scale_factor + 1) // 2,
-                kernel_size // 2 + (fh - scale_factor) // 2)
-
-    def extra_repr(self):
-        return (f'in_ch={self.in_channels}, '
-                f'out_ch={self.out_channels}, '
-                f'ksize={self.kernel_size}, '
-                f'wscale_gain={self.wscale_gain:.3f}, '
-                f'bias={self.add_bias}, '
-                f'lr_mul={self.lr_mul:.3f}, '
-                f'downsample={self.scale_factor}, '
-                f'downsample_filter={self.filter_kernel}, '
-                f'act={self.activation_type}, '
-                f'clamp={self.conv_clamp}')
-
-    def forward(self, x, runtime_gain=1.0, impl='cuda'):
-        dtype = x.dtype
-
-        weight = self.weight
-        if self.wscale != 1.0:
-            weight = weight * self.wscale
-        bias = None
-        if self.bias is not None:
-            bias = self.bias.to(dtype)
-            if self.bscale != 1.0:
-                bias = bias * self.bscale
-
-        if self.scale_factor == 1:  # Native convolution without downsampling.
-            padding = self.kernel_size // 2
-            x = conv2d_gradfix.conv2d(
-                x, weight.to(dtype), stride=1, padding=padding, impl=impl)
-        else:  # Convolution with downsampling.
-            down = self.scale_factor
-            f = self.filter
-            padding = self.filter_padding
-            # When kernel size = 1, use filtering function for downsampling.
-            if self.kernel_size == 1:
-                x = upfirdn2d.upfirdn2d(
-                    x, f, down=down, padding=padding, impl=impl)
-                x = conv2d_gradfix.conv2d(
-                    x, weight.to(dtype), stride=1, padding=0, impl=impl)
-            # When kernel size != 1, use stride convolution for downsampling.
-            else:
-                x = upfirdn2d.upfirdn2d(
-                    x, f, down=1, padding=padding, impl=impl)
-                x = conv2d_gradfix.conv2d(
-                    x, weight.to(dtype), stride=down, padding=0, impl=impl)
-
-        act_gain = self.act_gain * runtime_gain
-        act_clamp = None
-        if self.conv_clamp is not None:
-            act_clamp = self.conv_clamp * runtime_gain
-        x = bias_act.bias_act(x, bias,
-                              act=self.activation_type,
-                              gain=act_gain,
-                              clamp=act_clamp,
-                              impl=impl)
-
-        assert x.dtype == dtype
-        return x
-
-
-class DenseLayer(nn.Module):
-    """Implements the dense layer."""
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 add_bias,
-                 init_bias,
-                 use_wscale,
-                 wscale_gain,
-                 lr_mul,
-                 activation_type):
-        """Initializes with layer settings.
-
-        Args:
-            in_channels: Number of channels of the input tensor.
-            out_channels: Number of channels of the output tensor.
-            add_bias: Whether to add bias onto the fully-connected result.
-            init_bias: The initial bias value before training.
-            use_wscale: Whether to use weight scaling.
-            wscale_gain: Gain factor for weight scaling.
-            lr_mul: Learning multiplier for both weight and bias.
-            activation_type: Type of activation.
-        """
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.add_bias = add_bias
-        self.init_bias = init_bias
-        self.use_wscale = use_wscale
-        self.wscale_gain = wscale_gain
-        self.lr_mul = lr_mul
-        self.activation_type = activation_type
-
-        weight_shape = (out_channels, in_channels)
-        wscale = wscale_gain / np.sqrt(in_channels)
-        if use_wscale:
-            self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul)
-            self.wscale = wscale * lr_mul
-        else:
-            self.weight = nn.Parameter(
-                torch.randn(*weight_shape) * wscale / lr_mul)
-            self.wscale = lr_mul
-
-        if add_bias:
-            init_bias = np.float32(init_bias) / lr_mul
-            self.bias = nn.Parameter(torch.full([out_channels], init_bias))
-            self.bscale = lr_mul
-        else:
-            self.bias = None
-
-    def extra_repr(self):
-        return (f'in_ch={self.in_channels}, '
-                f'out_ch={self.out_channels}, '
-                f'wscale_gain={self.wscale_gain:.3f}, '
-                f'bias={self.add_bias}, '
-                f'init_bias={self.init_bias}, '
-                f'lr_mul={self.lr_mul:.3f}, '
-                f'act={self.activation_type}')
-
-    def forward(self, x, impl='cuda'):
-        dtype = x.dtype
-
-        if x.ndim != 2:
-            x = x.flatten(start_dim=1)
-
-        weight = self.weight.to(dtype) * self.wscale
-        bias = None
-        if self.bias is not None:
-            bias = self.bias.to(dtype)
-            if self.bscale != 1.0:
-                bias = bias * self.bscale
-
-        # Fast pass for linear activation.
-        if self.activation_type == 'linear' and bias is not None:
-            x = torch.addmm(bias.unsqueeze(0), x, weight.t())
-        else:
-            x = x.matmul(weight.t())
-            x = bias_act.bias_act(x, bias, act=self.activation_type, impl=impl)
-
-        assert x.dtype == dtype
-        return x
-
-# pylint: enable=missing-function-docstring
diff --git a/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/build_sam.py b/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/build_sam.py
deleted file mode 100644
index 07abfca24e96eced7f13bdefd3212ce1b77b8999..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/build_sam.py
+++ /dev/null
@@ -1,107 +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 functools import partial
-
-from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer
-
-
-def build_sam_vit_h(checkpoint=None):
-    return _build_sam(
-        encoder_embed_dim=1280,
-        encoder_depth=32,
-        encoder_num_heads=16,
-        encoder_global_attn_indexes=[7, 15, 23, 31],
-        checkpoint=checkpoint,
-    )
-
-
-build_sam = build_sam_vit_h
-
-
-def build_sam_vit_l(checkpoint=None):
-    return _build_sam(
-        encoder_embed_dim=1024,
-        encoder_depth=24,
-        encoder_num_heads=16,
-        encoder_global_attn_indexes=[5, 11, 17, 23],
-        checkpoint=checkpoint,
-    )
-
-
-def build_sam_vit_b(checkpoint=None):
-    return _build_sam(
-        encoder_embed_dim=768,
-        encoder_depth=12,
-        encoder_num_heads=12,
-        encoder_global_attn_indexes=[2, 5, 8, 11],
-        checkpoint=checkpoint,
-    )
-
-
-sam_model_registry = {
-    "default": build_sam,
-    "vit_h": build_sam,
-    "vit_l": build_sam_vit_l,
-    "vit_b": build_sam_vit_b,
-}
-
-
-def _build_sam(
-    encoder_embed_dim,
-    encoder_depth,
-    encoder_num_heads,
-    encoder_global_attn_indexes,
-    checkpoint=None,
-):
-    prompt_embed_dim = 256
-    image_size = 1024
-    vit_patch_size = 16
-    image_embedding_size = image_size // vit_patch_size
-    sam = Sam(
-        image_encoder=ImageEncoderViT(
-            depth=encoder_depth,
-            embed_dim=encoder_embed_dim,
-            img_size=image_size,
-            mlp_ratio=4,
-            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
-            num_heads=encoder_num_heads,
-            patch_size=vit_patch_size,
-            qkv_bias=True,
-            use_rel_pos=True,
-            global_attn_indexes=encoder_global_attn_indexes,
-            window_size=14,
-            out_chans=prompt_embed_dim,
-        ),
-        prompt_encoder=PromptEncoder(
-            embed_dim=prompt_embed_dim,
-            image_embedding_size=(image_embedding_size, image_embedding_size),
-            input_image_size=(image_size, image_size),
-            mask_in_chans=16,
-        ),
-        mask_decoder=MaskDecoder(
-            num_multimask_outputs=3,
-            transformer=TwoWayTransformer(
-                depth=2,
-                embedding_dim=prompt_embed_dim,
-                mlp_dim=2048,
-                num_heads=8,
-            ),
-            transformer_dim=prompt_embed_dim,
-            iou_head_depth=3,
-            iou_head_hidden_dim=256,
-        ),
-        pixel_mean=[123.675, 116.28, 103.53],
-        pixel_std=[58.395, 57.12, 57.375],
-    )
-    sam.eval()
-    if checkpoint is not None:
-        with open(checkpoint, "rb") as f:
-            state_dict = torch.load(f)
-        sam.load_state_dict(state_dict)
-    return sam
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/llama_v2.h b/spaces/Illumotion/Koboldcpp/otherarch/llama_v2.h
deleted file mode 100644
index 2b1cfc725b8a73b7879f07f00410fbdcc5753bfe..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/llama_v2.h
+++ /dev/null
@@ -1,263 +0,0 @@
-#ifndef LLAMA_V2_H
-#define LLAMA_V2_H
-
-#include <stddef.h>
-#include <stdint.h>
-#include <stdbool.h>
-
-#ifdef LLAMA_V2_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_V2_BUILD
-#            define LLAMA_V2_API __declspec(dllexport)
-#        else
-#            define LLAMA_V2_API __declspec(dllimport)
-#        endif
-#    else
-#        define LLAMA_V2_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define LLAMA_V2_API
-#endif
-
-#define LLAMA_V2_FILE_VERSION           3
-#define LLAMA_V2_FILE_MAGIC             'ggjt'
-#define LLAMA_V2_FILE_MAGIC_UNVERSIONED 'ggml'
-#define LLAMA_V2_SESSION_MAGIC          'ggsn'
-#define LLAMA_V2_SESSION_VERSION        1
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-    //
-    // C interface
-    //
-    // TODO: show sample usage
-    //
-
-    struct llama_v2_context;
-
-    typedef int llama_v2_token;
-
-    typedef struct llama_v2_token_data {
-        llama_v2_token id;  // token id
-        float logit; // log-odds of the token
-        float p;     // probability of the token
-    } llama_v2_token_data;
-
-    typedef struct llama_v2_token_data_array {
-        llama_v2_token_data * data;
-        size_t size;
-        bool sorted;
-    } llama_v2_token_data_array;
-
-    typedef void (*llama_v2_progress_callback)(float progress, void *ctx);
-
-    struct llama_v2_context_params {
-        int n_ctx;        // text context
-        int n_gpu_layers; // number of layers to store in VRAM
-        int seed;         // RNG seed, -1 for random
-
-        bool f16_kv;     // use fp16 for KV cache
-        bool logits_all; // the llama_v2_eval() call computes all logits, not just the last one
-        bool vocab_only; // only load the vocabulary, no weights
-        bool use_mmap;   // use mmap if possible
-        bool use_mlock;  // force system to keep model in RAM
-        bool embedding;  // embedding mode only
-
-        // called with a progress value between 0 and 1, pass NULL to disable
-        llama_v2_progress_callback progress_callback;
-        // context pointer passed to the progress callback
-        void * progress_callback_user_data;
-    };
-
-    // model file types
-    enum llama_v2_ftype {
-        LLAMA_V2_FTYPE_ALL_F32     = 0,
-        LLAMA_V2_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
-        LLAMA_V2_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
-        LLAMA_V2_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
-        LLAMA_V2_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
-        LLAMA_V2_FTYPE_MOSTLY_Q4_2 = 5,  // except 1d tensors
-        LLAMA_V2_FTYPE_MOSTLY_Q4_3 = 6,  // except 1d tensors
-        LLAMA_V2_FTYPE_MOSTLY_Q8_0 = 7,  // except 1d tensors
-        LLAMA_V2_FTYPE_MOSTLY_Q5_0 = 8,  // except 1d tensors
-        LLAMA_V2_FTYPE_MOSTLY_Q5_1 = 9,  // except 1d tensors
-    };
-
-    LLAMA_V2_API struct llama_v2_context_params llama_v2_context_default_params();
-
-    LLAMA_V2_API bool llama_v2_mmap_supported();
-    LLAMA_V2_API bool llama_v2_mlock_supported();
-
-    // Various functions for loading a ggml llama model.
-    // Allocate (almost) all memory needed for the model.
-    // Return NULL on failure
-    LLAMA_V2_API struct llama_v2_context * llama_v2_init_from_file(
-                             const char * path_model,
-            struct llama_v2_context_params   params);
-
-    // Frees all allocated memory
-    LLAMA_V2_API void llama_v2_free(struct llama_v2_context * ctx);
-
-    // TODO: not great API - very likely to change
-    // Returns 0 on success
-    // nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
-    LLAMA_V2_API int llama_v2_model_quantize(
-            const char * fname_inp,
-            const char * fname_out,
-      enum llama_v2_ftype   ftype,
-            int          nthread);
-
-    // Apply a LoRA adapter to a loaded model
-    // path_base_model is the path to a higher quality model to use as a base for
-    // the layers modified by the adapter. Can be NULL to use the current loaded model.
-    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
-    // will be applied on top of the previous one
-    // Returns 0 on success
-    LLAMA_V2_API int llama_v2_apply_lora_from_file(
-            struct llama_v2_context * ctx,
-                      const char * path_lora,
-                      const char * path_base_model,
-                             int   n_threads);
-
-    // Returns the number of tokens in the KV cache
-    LLAMA_V2_API int llama_v2_get_kv_cache_token_count(const struct llama_v2_context * ctx);
-
-    // Sets the current rng seed.
-    LLAMA_V2_API void llama_v2_set_rng_seed(struct llama_v2_context * ctx, int seed);
-
-    // Returns the maximum size in bytes of the state (rng, logits, embedding
-    // and kv_cache) - will often be smaller after compacting tokens
-    LLAMA_V2_API size_t llama_v2_get_state_size(const struct llama_v2_context * ctx);
-
-    // Copies the state to the specified destination address.
-    // Destination needs to have allocated enough memory.
-    // Returns the number of bytes copied
-    LLAMA_V2_API size_t llama_v2_copy_state_data(struct llama_v2_context * ctx, uint8_t * dst);
-
-    // Set the state reading from the specified address
-    // Returns the number of bytes read
-    LLAMA_V2_API size_t llama_v2_set_state_data(struct llama_v2_context * ctx, const uint8_t * src);
-
-    // Save/load session file
-    LLAMA_V2_API bool llama_v2_load_session_file(struct llama_v2_context * ctx, const char * path_session, llama_v2_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
-    LLAMA_V2_API bool llama_v2_save_session_file(struct llama_v2_context * ctx, const char * path_session, const llama_v2_token * tokens, size_t n_token_count);
-
-    // Run the llama inference to obtain the logits and probabilities for the next token.
-    // tokens + n_tokens is the provided batch of new tokens to process
-    // n_past is the number of tokens to use from previous eval calls
-    // Returns 0 on success
-    LLAMA_V2_API int llama_v2_eval(
-            struct llama_v2_context * ctx,
-               const llama_v2_token * tokens,
-                             int   n_tokens,
-                             int   n_past,
-                             int   n_threads);
-
-    // Convert the provided text into tokens.
-    // The tokens pointer must be large enough to hold the resulting tokens.
-    // Returns the number of tokens on success, no more than n_max_tokens
-    // Returns a negative number on failure - the number of tokens that would have been returned
-    // TODO: not sure if correct
-    LLAMA_V2_API int llama_v2_tokenize(
-            struct llama_v2_context * ctx,
-                      const char * text,
-                     llama_v2_token * tokens,
-                             int   n_max_tokens,
-                            bool   add_bos);
-
-    
-    std::vector<llama_v2_token> legacy_llama_v2_tokenize(struct llama_v2_context * ctx, const std::string & text, bool add_bos);
-
-    LLAMA_V2_API int llama_v2_n_vocab(const struct llama_v2_context * ctx);
-    LLAMA_V2_API int llama_v2_n_ctx  (const struct llama_v2_context * ctx);
-    LLAMA_V2_API int llama_v2_n_embd (const struct llama_v2_context * ctx);
-
-    // Token logits obtained from the last call to llama_v2_eval()
-    // The logits for the last token are stored in the last row
-    // Can be mutated in order to change the probabilities of the next token
-    // Rows: n_tokens
-    // Cols: n_vocab
-    LLAMA_V2_API float * llama_v2_get_logits(struct llama_v2_context * ctx);
-
-    // Get the embeddings for the input
-    // shape: [n_embd] (1-dimensional)
-    LLAMA_V2_API float * llama_v2_get_embeddings(struct llama_v2_context * ctx);
-
-    // Token Id -> String. Uses the vocabulary in the provided context
-    LLAMA_V2_API const char * llama_v2_token_to_str(const struct llama_v2_context * ctx, llama_v2_token token);
-
-    // Special tokens
-    LLAMA_V2_API llama_v2_token llama_v2_token_bos();
-    LLAMA_V2_API llama_v2_token llama_v2_token_eos();
-    LLAMA_V2_API llama_v2_token llama_v2_token_nl();
-
-    // Sampling functions
-
-    /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
-    LLAMA_V2_API void llama_v2_sample_repetition_penalty(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, const llama_v2_token * last_tokens, size_t last_tokens_size, float penalty);
-
-    /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
-    LLAMA_V2_API void llama_v2_sample_frequency_and_presence_penalties(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, const llama_v2_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
-
-    /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
-    LLAMA_V2_API void llama_v2_sample_softmax(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates);
-
-    /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-    LLAMA_V2_API void llama_v2_sample_top_k(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, int k, size_t min_keep);
-
-    /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-    LLAMA_V2_API void llama_v2_sample_top_p(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, float p, size_t min_keep);
-
-    /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
-    LLAMA_V2_API void llama_v2_sample_tail_free(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, float z, size_t min_keep);
-
-    /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
-    LLAMA_V2_API void llama_v2_sample_typical(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, float p, size_t min_keep);
-    LLAMA_V2_API void llama_v2_sample_temperature(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, float temp);
-
-    /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
-    /// @param candidates A vector of `llama_v2_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
-    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
-    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
-    /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
-    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
-    LLAMA_V2_API llama_v2_token llama_v2_sample_token_mirostat(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, float tau, float eta, int m, float * mu);
-
-    /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
-    /// @param candidates A vector of `llama_v2_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
-    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
-    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
-    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
-    LLAMA_V2_API llama_v2_token llama_v2_sample_token_mirostat_v2(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates, float tau, float eta, float * mu);
-
-    /// @details Selects the token with the highest probability.
-    LLAMA_V2_API llama_v2_token llama_v2_sample_token_greedy(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates);
-
-    /// @details Randomly selects a token from the candidates based on their probabilities.
-    LLAMA_V2_API llama_v2_token llama_v2_sample_token(struct llama_v2_context * ctx, llama_v2_token_data_array * candidates);
-
-    // Performance information
-    LLAMA_V2_API void llama_v2_print_timings(struct llama_v2_context * ctx);
-    LLAMA_V2_API void llama_v2_reset_timings(struct llama_v2_context * ctx);
-
-    // Print system information
-    LLAMA_V2_API const char * llama_v2_print_system_info(void);
-
-#ifdef __cplusplus
-}
-#endif
-
-// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
-#ifdef LLAMA_V2_API_INTERNAL
-
-#include <vector>
-#include <string>
-struct ggml_v2_tensor;
-
-std::vector<std::pair<std::string, struct ggml_v2_tensor *>>& llama_v2_internal_get_tensor_map(struct llama_v2_context * ctx);
-
-#endif
-
-#endif // LLAMA_V2_H
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/utils/data/distributed.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/utils/data/distributed.py
deleted file mode 100644
index c3d890e28fd2b9e044bdd9494de4a43ad2471eed..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/utils/data/distributed.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import math
-import torch
-from .sampler import Sampler
-from torch.distributed import get_world_size, get_rank
-
-
-class DistributedSampler(Sampler):
-    """Sampler that restricts data loading to a subset of the dataset.
-
-    It is especially useful in conjunction with
-    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
-    process can pass a DistributedSampler instance as a DataLoader sampler,
-    and load a subset of the original dataset that is exclusive to it.
-
-    .. note::
-        Dataset is assumed to be of constant size.
-
-    Arguments:
-        dataset: Dataset used for sampling.
-        num_replicas (optional): Number of processes participating in
-            distributed training.
-        rank (optional): Rank of the current process within num_replicas.
-    """
-
-    def __init__(self, dataset, num_replicas=None, rank=None):
-        if num_replicas is None:
-            num_replicas = get_world_size()
-        if rank is None:
-            rank = get_rank()
-        self.dataset = dataset
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.epoch = 0
-        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
-        self.total_size = self.num_samples * self.num_replicas
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-        indices = list(torch.randperm(len(self.dataset), generator=g))
-
-        # add extra samples to make it evenly divisible
-        indices += indices[:(self.total_size - len(indices))]
-        assert len(indices) == self.total_size
-
-        # subsample
-        offset = self.num_samples * self.rank
-        indices = indices[offset:offset + self.num_samples]
-        assert len(indices) == self.num_samples
-
-        return iter(indices)
-
-    def __len__(self):
-        return self.num_samples
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch
diff --git a/spaces/JUNGU/Image-to-Story-Ko-multiplot/app.py b/spaces/JUNGU/Image-to-Story-Ko-multiplot/app.py
deleted file mode 100644
index 2177d432520e9b4dd84946910354bb3e07fcb27b..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/Image-to-Story-Ko-multiplot/app.py
+++ /dev/null
@@ -1,132 +0,0 @@
-from fpdf import FPDF
-import gradio as gr
-from urllib.parse import quote
-import os
-import openai
-from gradio_client import Client
-import requests
-from PIL import Image
-from io import BytesIO
-
-
-def generate_image_url(keywords):
-    truncated_keywords = keywords[:20]
-    return f"https://image.pollinations.ai/prompt/{quote(truncated_keywords)}"
-
-def download_image(image_url):
-    response = requests.get(image_url)
-    img = Image.open(BytesIO(response.content))
-    local_image_path = f"/tmp/{image_url.split('/')[-1]}.png"
-    img.save(local_image_path)
-    return local_image_path
-    
-class PDF(FPDF):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.add_font('NanumGothic', '', '/home/user/app/NanumGothic.ttf', uni=True)
-        self.set_font('/home/user/app/NanumGothic.ttf', '', 12)
-        
-    def chapter_title(self, num, label):
-        self.set_font('/home/user/app/NanumGothic.ttf', '', 15)
-        self.cell(0, 10, f"Chapter {num} : {label}", 0, 1, 'C')
-        self.ln(10)
-
-    def chapter_body(self, body, image_url):
-        self.set_font('/home/user/app/NanumGothic.ttf', '', 12)
-        self.multi_cell(0, 10, body)
-        self.image(image_url, x=10, w=100)
-        self.ln(60)
-
-    def add_chapter(self, num, title, body, image_url):
-        local_image_path = download_image(image_url)
-        self.add_page()
-        self.chapter_title(num, title)
-        self.chapter_body(body, local_image_path)
-
-def save_as_pdf():
-    global chapters
-    pdf_path = "/mnt/data/chapters.pdf"
-    pdf = PDF()
-    for i, chapter in enumerate(chapters, start=1):
-        pdf.add_chapter(i, f"Chapter {i}", chapter["story"], chapter["image"])
-    pdf.output(pdf_path)
-    return pdf_path
-
-def create_markdown_table():
-    global chapters
-    markdown_table = "| 챕터 | 이야기 | 그림 |\n|-------|-------|------|\n"
-    for i, chapter in enumerate(chapters, start=1):
-        markdown_table += f"| Chapter {i} | {chapter['story']} | ![Chapter {i} Image]({chapter['image']}) |\n"
-    return markdown_table
-
-OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
-clipi_client = Client("https://fffiloni-clip-interrogator-2.hf.space/")
-
-chapter_num = 1
-story_intro = ""
-current_story = ""
-current_image_url = ""
-chapters = []
-
-def next_chapter(audience, keyword, protagonist):
-    global chapter_num, current_story, current_image_url, chapters
-    current_image_url = generate_image_url(current_story)
-    gr.Info(f'Chapter {chapter_num}를 생성하고 있습니다...')
-    chapter_prompt = f"{story_intro}\n\nKeyword: {keyword}\nProtagonist: {protagonist}\n\n![Chapter {chapter_num} Image]({current_image_url})\n\nChapter {chapter_num} 내용을 만들어줘."
-    chat_completion = openai.ChatCompletion.create(model="gpt-3.5-turbo-16k", messages=[{"role": "user", "content": chapter_prompt}])
-    current_story = chat_completion.choices[0].message.content
-    chapters.append({"story": current_story, "image": current_image_url})
-    chapter_num += 1
-    return current_story, current_image_url, create_markdown_table()
-
-def infer(image_input, audience, keyword, protagonist):
-    global story_intro, current_story, current_image_url, chapter_num, chapters
-    chapter_num = 1
-    chapters = []
-    gr.Info('Calling CLIP Interrogator, 이미지를 해석하고 있습니다...')
-    clipi_result = clipi_client.predict(image_input, "best", 4, api_name="/clipi2")[0]
-    story_intro = f"""
-    # Illustrated Tales
-    ## Created by [Sigkawat Pengnoo](https://flowgpt.com/prompt/qzv2D3OvHkzkfSE4rQCqv) at FlowGPT
-    Keyword: {keyword}
-    Protagonist: {protagonist}
-    한국어로 답변해줘.
-    STORY : "{{ {clipi_result} }}"
-    Let's begin with Chapter 1!
-    """
-    current_story = clipi_result
-    return next_chapter(audience, keyword, protagonist)
-
-css = """
-#col-container {max-width: 910px; margin-left: auto; margin-right: auto;}
-a {text-decoration-line: underline; font-weight: 600;}
-"""
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(
-            """
-            <h1 style="text-align: center">Illustrated Tales - Korean</h1>
-            <p style="text-align: center">이미지를 업로드하세요, ChatGPT를 통해 한국어로 이야기와 그림을 만들어 줍니다!</p>
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                image_in = gr.Image(label="이미지 입력", type="filepath", elem_id="image-in", height=420)
-                audience = gr.Radio(label="대상", choices=["Children", "Adult"], value="Children")
-                keyword_in = gr.Textbox(label="핵심 키워드")
-                protagonist_in = gr.Textbox(label="주인공")
-                submit_btn = gr.Button('이야기와 그림을 만들어 주세요')
-                next_chapter_btn = gr.Button('다음 이야기')
-                save_pdf_btn = gr.Button('PDF로 저장하기')
-            with gr.Column(elem_id="component-10"):
-                chapter_story = gr.Markdown(label="이야기", elem_id="chapter_story")
-                chapter_image = gr.Image(label="그림", elem_id="chapter_image")
-
-        table_markdown = gr.Markdown(label="이야기와 그림 정리", elem_id="table_markdown", width="100%")
-
-    submit_btn.click(fn=infer, inputs=[image_in, audience, keyword_in, protagonist_in], outputs=[chapter_story, chapter_image, table_markdown])
-    next_chapter_btn.click(fn=lambda: next_chapter(audience=audience.value, keyword=keyword_in.value, protagonist=protagonist_in.value), outputs=[chapter_story, chapter_image, table_markdown])
-    save_pdf_btn.click(fn=save_as_pdf, outputs=gr.File(label="PDF 다운로드"))
-
-    demo.queue(max_size=12).launch()
-
diff --git a/spaces/Jaehan/zero-shot-classification-2/app.py b/spaces/Jaehan/zero-shot-classification-2/app.py
deleted file mode 100644
index bb54bd25a274cfa0efe4443c246f89c33dc9ddfe..0000000000000000000000000000000000000000
--- a/spaces/Jaehan/zero-shot-classification-2/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from transformers import BartForSequenceClassification, BartTokenizer
-import gradio as grad
-
-model_name = "facebook/bart-large-mnli"
-bart_tokenizer = BartTokenizer.from_pretrained(model_name)
-model = BartForSequenceClassification.from_pretrained(model_name)
-
-def classify(text, label):
-    token_ids = bart_tokenizer.encode(text, label, return_tensors="pt")
-    token_logits = model(token_ids)[0]
-    entail_contra_token_logits = token_logits[:, [0, 2]]
-    probabilities = entail_contra_token_logits.softmax(dim=1)
-    response = probabilities[:, 1].item() * 100
-    return response
-
-in_text = grad.Textbox(lines=1, label="English", placeholder="Text to be classified")
-in_labels = grad.Textbox(lines=1, label="Label", placeholder="Input a label")
-out = grad.Textbox(lines=1, label="Probability of label being true is ")
-
-grad.Interface(classify, inputs=[in_text, in_labels], outputs=[out]).launch()
\ No newline at end of file
diff --git a/spaces/Jamel887/Rv-percobaan887/app.py b/spaces/Jamel887/Rv-percobaan887/app.py
deleted file mode 100644
index c78eb66470a6c6adff0b6f8a21617014a90b822e..0000000000000000000000000000000000000000
--- a/spaces/Jamel887/Rv-percobaan887/app.py
+++ /dev/null
@@ -1,503 +0,0 @@
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"
-
-audio_mode = []
-f0method_mode = []
-f0method_info = ""
-if limitation is True:
-    audio_mode = ["Upload audio", "TTS Audio"]
-    f0method_mode = ["pm", "crepe", "harvest"]
-    f0method_info = "PM is fast, Crepe or harvest is good but it was extremely slow (Default: PM)"
-else:
-    audio_mode = ["Upload audio", "Youtube", "TTS Audio"]
-    f0method_mode = ["pm", "crepe", "harvest"]
-    f0method_info = "PM is fast, Crepe or harvest is good but it was extremely slow (Default: PM))"
-def create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, file_index):
-    def vc_fn(
-        vc_audio_mode,
-        vc_input, 
-        vc_upload,
-        tts_text,
-        tts_voice,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        filter_radius,
-        resample_sr,
-        rms_mix_rate,
-        protect,
-    ):
-        try:
-            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
-                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
-            elif vc_audio_mode == "Upload audio":
-                if vc_upload is None:
-                    return "You need to upload an audio", None
-                sampling_rate, audio = vc_upload
-                duration = audio.shape[0] / sampling_rate
-                if duration > 360 and limitation:
-                    return "Please upload an audio file that is less than 1 minute.", None
-                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                if len(audio.shape) > 1:
-                    audio = librosa.to_mono(audio.transpose(1, 0))
-                if sampling_rate != 16000:
-                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            elif vc_audio_mode == "TTS Audio":
-                if len(tts_text) > 600 and limitation:
-                    return "Text is too long", None
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-                vc_input = "tts.mp3"
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                vc_input,
-                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,
-            )
-            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            print(f"{model_title} | {info}")
-            return info, (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return vc_fn
-
-def load_model():
-    categories = []
-    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
-        folder_info = json.load(f)
-    for category_name, category_info in folder_info.items():
-        if not category_info['enable']:
-            continue
-        category_title = category_info['title']
-        category_folder = category_info['folder_path']
-        models = []
-        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
-            models_info = json.load(f)
-        for character_name, info in models_info.items():
-            if not info['enable']:
-                continue
-            model_title = info['title']
-            model_name = info['model_path']
-            model_author = info.get("author", None)
-            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
-            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
-            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
-            tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-            if_f0 = cpt.get("f0", 1)
-            version = cpt.get("version", "v1")
-            if version == "v1":
-                if if_f0 == 1:
-                    net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-                else:
-                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-                model_version = "V1"
-            elif version == "v2":
-                if if_f0 == 1:
-                    net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-                else:
-                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-                model_version = "V2"
-            del net_g.enc_q
-            print(net_g.load_state_dict(cpt["weight"], strict=False))
-            net_g.eval().to(config.device)
-            if config.is_half:
-                net_g = net_g.half()
-            else:
-                net_g = net_g.float()
-            vc = VC(tgt_sr, config)
-            print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
-            models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, model_index)))
-        categories.append([category_title, category_folder, models])
-    return categories
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
-    if url != "":
-        if not os.path.exists("dl_audio"):
-            os.mkdir("dl_audio")
-        if audio_provider == "Youtube":
-            ydl_opts = {
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'wav',
-            }],
-            "outtmpl": 'dl_audio/youtube_audio',
-            }
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                ydl.download([url])
-            audio_path = "dl_audio/youtube_audio.wav"
-        else:
-            # Spotify doesnt work.
-            # Need to find other solution soon.
-            ''' 
-            command = f"spotdl download {url} --output dl_audio/.wav"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            audio_path = "dl_audio/spotify_audio.wav"
-            '''
-        if split_model == "htdemucs":
-            command = f"demucs --two-stems=vocals {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
-        else:
-            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
-    else:
-        raise gr.Error("URL Required!")
-        return None, None, None, None
-
-def combine_vocal_and_inst(audio_data, audio_volume, split_model):
-    if not os.path.exists("output/result"):
-        os.mkdir("output/result")
-    vocal_path = "output/result/output.wav"
-    output_path = "output/result/combine.mp3"
-    if split_model == "htdemucs":
-        inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
-    else:
-        inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
-    with wave.open(vocal_path, "w") as wave_file:
-        wave_file.setnchannels(1) 
-        wave_file.setsampwidth(2)
-        wave_file.setframerate(audio_data[0])
-        wave_file.writeframes(audio_data[1].tobytes())
-    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
-    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-    print(result.stdout.decode())
-    return output_path
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-def change_audio_mode(vc_audio_mode):
-    if vc_audio_mode == "Input path":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=True),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "Upload audio":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Audio.update(visible=True),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "Youtube":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=True),
-            gr.Textbox.update(visible=True),
-            gr.Dropdown.update(visible=True),
-            gr.Button.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Button.update(visible=True),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "TTS Audio":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=True),
-            gr.Dropdown.update(visible=True)
-        )
-    else:
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Audio.update(visible=True),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-
-if __name__ == '__main__':
-    load_hubert()
-    categories = load_model()
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    with gr.Blocks(theme=gr.themes.Base()) as app:
-        gr.Markdown(
-            "# <center> Hololive RVC Models\n"
-            "### <center> will update every hololive ai model that i can find or make.\n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/aziib/hololive-rvc-models-v2/blob/main/hololive_rvc_models_v2.ipynb)\n\n"
-            "[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/megaaziib)\n\n"
-        )
-        for (folder_title, folder, models) in categories:
-            with gr.TabItem(folder_title):
-                with gr.Tabs():
-                    if not models:
-                        gr.Markdown("# <center> No Model Loaded.")
-                        gr.Markdown("## <center> Please add model or fix your model path.")
-                        continue
-                    for (name, title, author, cover, model_version, vc_fn) in models:
-                        with gr.TabItem(name):
-                            with gr.Row():
-                                gr.Markdown(
-                                    '<div align="center">'
-                                    f'<div>{title}</div>\n'+
-                                    f'<div>RVC {model_version} Model</div>\n'+
-                                    (f'<div>Model author: {author}</div>' if author else "")+
-                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
-                                    '</div>'
-                                )
-                            with gr.Row():
-                                with gr.Column():
-                                    vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
-                                    # Input and Upload
-                                    vc_input = gr.Textbox(label="Input audio path", visible=False)
-                                    vc_upload = gr.Audio(label="Upload audio file", visible=True, interactive=True)
-                                    # Youtube
-                                    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
-                                    vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
-                                    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-                                    vc_split = gr.Button("Split Audio", variant="primary", visible=False)
-                                    vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
-                                    vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
-                                    vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
-                                    # TTS
-                                    tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
-                                    tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                                with gr.Column():
-                                    vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-                                    f0method0 = gr.Radio(
-                                        label="Pitch extraction algorithm",
-                                        info=f0method_info,
-                                        choices=f0method_mode,
-                                        value="pm",
-                                        interactive=True
-                                    )
-                                    index_rate1 = gr.Slider(
-                                        minimum=0,
-                                        maximum=1,
-                                        label="Retrieval feature ratio",
-                                        info="Accents controling. Too high prob gonna sounds too robotic (Default: 0.4)",
-                                        value=0.4,
-                                        interactive=True,
-                                    )
-                                    filter_radius0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=7,
-                                        label="Apply Median Filtering",
-                                        info="The value represents the filter radius and can reduce breathiness.",
-                                        value=1,
-                                        step=1,
-                                        interactive=True,
-                                    )
-                                    resample_sr0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=48000,
-                                        label="Resample the output audio",
-                                        info="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="Volume Envelope",
-                                        info="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,
-                                    )
-                                    protect0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=0.5,
-                                        label="Voice Protection",
-                                        info="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=0.23,
-                                        step=0.01,
-                                        interactive=True,
-                                    )
-                                with gr.Column():
-                                    vc_log = gr.Textbox(label="Output Information", interactive=False)
-                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
-                                    vc_convert = gr.Button("Convert", variant="primary")
-                                    vc_volume = gr.Slider(
-                                        minimum=0,
-                                        maximum=10,
-                                        label="Vocal volume",
-                                        value=4,
-                                        interactive=True,
-                                        step=1,
-                                        info="Adjust vocal volume (Default: 4}",
-                                        visible=False
-                                    )
-                                    vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
-                                    vc_combine =  gr.Button("Combine",variant="primary", visible=False)
-                        vc_convert.click(
-                            fn=vc_fn, 
-                            inputs=[
-                                vc_audio_mode,
-                                vc_input, 
-                                vc_upload,
-                                tts_text,
-                                tts_voice,
-                                vc_transform0,
-                                f0method0,
-                                index_rate1,
-                                filter_radius0,
-                                resample_sr0,
-                                rms_mix_rate0,
-                                protect0,
-                            ], 
-                            outputs=[vc_log ,vc_output]
-                        )
-                        vc_split.click(
-                            fn=cut_vocal_and_inst, 
-                            inputs=[vc_link, vc_download_audio, vc_split_model], 
-                            outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
-                        )
-                        vc_combine.click(
-                            fn=combine_vocal_and_inst,
-                            inputs=[vc_output, vc_volume, vc_split_model],
-                            outputs=[vc_combined_output]
-                        )
-                        vc_audio_mode.change(
-                            fn=change_audio_mode,
-                            inputs=[vc_audio_mode],
-                            outputs=[
-                                vc_input, 
-                                vc_upload,
-                                vc_download_audio,
-                                vc_link,
-                                vc_split_model,
-                                vc_split,
-                                vc_vocal_preview,
-                                vc_inst_preview,
-                                vc_audio_preview,
-                                vc_volume,
-                                vc_combined_output,
-                                vc_combine,
-                                tts_text,
-                                tts_voice
-                            ]
-                        )
-if limitation is True:
-    app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
-else:
-    app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=True)
\ No newline at end of file
diff --git a/spaces/Jikiwi/sovits-models/inference/infer_tool.py b/spaces/Jikiwi/sovits-models/inference/infer_tool.py
deleted file mode 100644
index fed81f5abb6f2f525af616171ee9838ae341cb5f..0000000000000000000000000000000000000000
--- a/spaces/Jikiwi/sovits-models/inference/infer_tool.py
+++ /dev/null
@@ -1,324 +0,0 @@
-import hashlib
-import io
-import json
-import logging
-import os
-import time
-from pathlib import Path
-from inference import slicer
-
-import librosa
-import numpy as np
-# import onnxruntime
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-
-import cluster
-from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def read_temp(file_name):
-    if not os.path.exists(file_name):
-        with open(file_name, "w") as f:
-            f.write(json.dumps({"info": "temp_dict"}))
-        return {}
-    else:
-        try:
-            with open(file_name, "r") as f:
-                data = f.read()
-            data_dict = json.loads(data)
-            if os.path.getsize(file_name) > 50 * 1024 * 1024:
-                f_name = file_name.replace("\\", "/").split("/")[-1]
-                print(f"clean {f_name}")
-                for wav_hash in list(data_dict.keys()):
-                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
-                        del data_dict[wav_hash]
-        except Exception as e:
-            print(e)
-            print(f"{file_name} error,auto rebuild file")
-            data_dict = {"info": "temp_dict"}
-        return data_dict
-
-
-def write_temp(file_name, data):
-    with open(file_name, "w") as f:
-        f.write(json.dumps(data))
-
-
-def timeit(func):
-    def run(*args, **kwargs):
-        t = time.time()
-        res = func(*args, **kwargs)
-        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
-        return res
-
-    return run
-
-
-def format_wav(audio_path):
-    if Path(audio_path).suffix == '.wav':
-        return
-    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
-    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
-
-
-def get_end_file(dir_path, end):
-    file_lists = []
-    for root, dirs, files in os.walk(dir_path):
-        files = [f for f in files if f[0] != '.']
-        dirs[:] = [d for d in dirs if d[0] != '.']
-        for f_file in files:
-            if f_file.endswith(end):
-                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
-    return file_lists
-
-
-def get_md5(content):
-    return hashlib.new("md5", content).hexdigest()
-
-def fill_a_to_b(a, b):
-    if len(a) < len(b):
-        for _ in range(0, len(b) - len(a)):
-            a.append(a[0])
-
-def mkdir(paths: list):
-    for path in paths:
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-def pad_array(arr, target_length):
-    current_length = arr.shape[0]
-    if current_length >= target_length:
-        return arr
-    else:
-        pad_width = target_length - current_length
-        pad_left = pad_width // 2
-        pad_right = pad_width - pad_left
-        padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
-        return padded_arr
-    
-def split_list_by_n(list_collection, n, pre=0):
-    for i in range(0, len(list_collection), n):
-        yield list_collection[i-pre if i-pre>=0 else i: i + n]
-
-
-class F0FilterException(Exception):
-    pass
-
-class Svc(object):
-    def __init__(self, net_g_path, config_path,
-                 device=None,
-                 cluster_model_path="logs/44k/kmeans_10000.pt"):
-        self.net_g_path = net_g_path
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.net_g_ms = None
-        self.hps_ms = utils.get_hparams_from_file(config_path)
-        self.target_sample = self.hps_ms.data.sampling_rate
-        self.hop_size = self.hps_ms.data.hop_length
-        self.spk2id = self.hps_ms.spk
-        # 加载hubert
-        self.hubert_model = utils.get_hubert_model().to(self.dev)
-        self.load_model()
-        if os.path.exists(cluster_model_path):
-            self.cluster_model = cluster.get_cluster_model(cluster_model_path)
-
-    def load_model(self):
-        # 获取模型配置
-        self.net_g_ms = SynthesizerTrn(
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            **self.hps_ms.model)
-        _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            _ = self.net_g_ms.half().eval().to(self.dev)
-        else:
-            _ = self.net_g_ms.eval().to(self.dev)
-
-
-
-    def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,F0_mean_pooling):
-
-        wav, sr = librosa.load(in_path, sr=self.target_sample)
-
-        if F0_mean_pooling == True:
-            f0, uv = utils.compute_f0_uv_torchcrepe(torch.FloatTensor(wav), sampling_rate=self.target_sample, hop_length=self.hop_size,device=self.dev)
-            if f0_filter and sum(f0) == 0:
-                raise F0FilterException("未检测到人声")
-            f0 = torch.FloatTensor(list(f0))
-            uv = torch.FloatTensor(list(uv))
-        if F0_mean_pooling == False:
-            f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
-            if f0_filter and sum(f0) == 0:
-                raise F0FilterException("未检测到人声")
-            f0, uv = utils.interpolate_f0(f0)
-            f0 = torch.FloatTensor(f0)
-            uv = torch.FloatTensor(uv)
-
-        f0 = f0 * 2 ** (tran / 12)
-        f0 = f0.unsqueeze(0).to(self.dev)
-        uv = uv.unsqueeze(0).to(self.dev)
-
-        wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
-        wav16k = torch.from_numpy(wav16k).to(self.dev)
-        c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
-
-        if cluster_infer_ratio !=0:
-            cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
-            cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
-            c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
-
-        c = c.unsqueeze(0)
-        return c, f0, uv
-
-    def infer(self, speaker, tran, raw_path,
-              cluster_infer_ratio=0,
-              auto_predict_f0=False,
-              noice_scale=0.4,
-              f0_filter=False,
-              F0_mean_pooling=False
-              ):
-
-        speaker_id = self.spk2id.__dict__.get(speaker)
-        if not speaker_id and type(speaker) is int:
-            if len(self.spk2id.__dict__) >= speaker:
-                speaker_id = speaker
-        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
-        c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            c = c.half()
-        with torch.no_grad():
-            start = time.time()
-            audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
-            use_time = time.time() - start
-            print("vits use time:{}".format(use_time))
-        return audio, audio.shape[-1]
-
-    def clear_empty(self):
-        # 清理显存
-        torch.cuda.empty_cache()
-
-    def slice_inference(self,
-                        raw_audio_path,
-                        spk,
-                        tran,
-                        slice_db,
-                        cluster_infer_ratio,
-                        auto_predict_f0,
-                        noice_scale,
-                        pad_seconds=0.5,
-                        clip_seconds=0,
-                        lg_num=0,
-                        lgr_num =0.75,
-                        F0_mean_pooling = False
-                        ):
-        wav_path = raw_audio_path
-        chunks = slicer.cut(wav_path, db_thresh=slice_db)
-        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
-        per_size = int(clip_seconds*audio_sr)
-        lg_size = int(lg_num*audio_sr)
-        lg_size_r = int(lg_size*lgr_num)
-        lg_size_c_l = (lg_size-lg_size_r)//2
-        lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
-        lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
-        
-        audio = []
-        for (slice_tag, data) in audio_data:
-            print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-            # padd
-            length = int(np.ceil(len(data) / audio_sr * self.target_sample))
-            if slice_tag:
-                print('jump empty segment')
-                _audio = np.zeros(length)
-                audio.extend(list(pad_array(_audio, length)))
-                continue
-            if per_size != 0:
-                datas = split_list_by_n(data, per_size,lg_size)
-            else:
-                datas = [data]
-            for k,dat in enumerate(datas):
-                per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
-                if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
-                # padd
-                pad_len = int(audio_sr * pad_seconds)
-                dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
-                raw_path = io.BytesIO()
-                soundfile.write(raw_path, dat, audio_sr, format="wav")
-                raw_path.seek(0)
-                out_audio, out_sr = self.infer(spk, tran, raw_path,
-                                                    cluster_infer_ratio=cluster_infer_ratio,
-                                                    auto_predict_f0=auto_predict_f0,
-                                                    noice_scale=noice_scale,
-                                                    F0_mean_pooling = F0_mean_pooling
-                                                    )
-                _audio = out_audio.cpu().numpy()
-                pad_len = int(self.target_sample * pad_seconds)
-                _audio = _audio[pad_len:-pad_len]
-                _audio = pad_array(_audio, per_length)
-                if lg_size!=0 and k!=0:
-                    lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
-                    lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r]  if lgr_num != 1 else _audio[0:lg_size]
-                    lg_pre = lg1*(1-lg)+lg2*lg
-                    audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
-                    audio.extend(lg_pre)
-                    _audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
-                audio.extend(list(_audio))
-        return np.array(audio)
-
-class RealTimeVC:
-    def __init__(self):
-        self.last_chunk = None
-        self.last_o = None
-        self.chunk_len = 16000  # 区块长度
-        self.pre_len = 3840  # 交叉淡化长度，640的倍数
-
-    """输入输出都是1维numpy 音频波形数组"""
-
-    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
-                cluster_infer_ratio=0,
-                auto_predict_f0=False,
-                noice_scale=0.4,
-                f0_filter=False):
-
-        import maad
-        audio, sr = torchaudio.load(input_wav_path)
-        audio = audio.cpu().numpy()[0]
-        temp_wav = io.BytesIO()
-        if self.last_chunk is None:
-            input_wav_path.seek(0)
-
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
-                                        cluster_infer_ratio=cluster_infer_ratio,
-                                        auto_predict_f0=auto_predict_f0,
-                                        noice_scale=noice_scale,
-                                        f0_filter=f0_filter)
-
-            audio = audio.cpu().numpy()
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return audio[-self.chunk_len:]
-        else:
-            audio = np.concatenate([self.last_chunk, audio])
-            soundfile.write(temp_wav, audio, sr, format="wav")
-            temp_wav.seek(0)
-
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
-                                        cluster_infer_ratio=cluster_infer_ratio,
-                                        auto_predict_f0=auto_predict_f0,
-                                        noice_scale=noice_scale,
-                                        f0_filter=f0_filter)
-
-            audio = audio.cpu().numpy()
-            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return ret[self.chunk_len:2 * self.chunk_len]
diff --git a/spaces/Juancho/forest_fire_detector/app.py b/spaces/Juancho/forest_fire_detector/app.py
deleted file mode 100644
index 529001c4b7c712a32158989b9778fc1377846e41..0000000000000000000000000000000000000000
--- a/spaces/Juancho/forest_fire_detector/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-
-learn = load_learner('model_convnext_small_in22k_version_1.pkl')
-categories = ("fire", "nofire")
-
-def classify_image(img):
-    pred, idx, prob = learn.predict(img)
-    return dict(zip(categories, map(float, prob)))
-
-img = gr.inputs.Image(shape=(250, 250))
-label = gr.outputs.Label()
-
-gr.Interface(
-    fn=classify_image, 
-    inputs="image", 
-    outputs="label",
-    enable_queue=True,
-    examples=["after-a-bushfire.jpg", "fire_1.jpg", "forest_1.jpg", "sebastian-unrau-sp-p7uuT0tw-unsplash.jpg"],
-    interpretation='default').launch()
\ No newline at end of file
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/densecl_hook.py b/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/densecl_hook.py
deleted file mode 100644
index 8c7e17d3419cbc2a540d3aecd81e223eed670df2..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/densecl_hook.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional, Sequence
-
-from mmengine.hooks import Hook
-
-from mmpretrain.registry import HOOKS
-from mmpretrain.utils import get_ori_model
-
-
-@HOOKS.register_module()
-class DenseCLHook(Hook):
-    """Hook for DenseCL.
-
-    This hook includes ``loss_lambda`` warmup in DenseCL.
-    Borrowed from the authors' code: `<https://github.com/WXinlong/DenseCL>`_.
-
-    Args:
-        start_iters (int): The number of warmup iterations to set
-            ``loss_lambda=0``. Defaults to 1000.
-    """
-
-    def __init__(self, start_iters: int = 1000) -> None:
-        self.start_iters = start_iters
-
-    def before_train(self, runner) -> None:
-        """Obtain ``loss_lambda`` from algorithm."""
-        assert hasattr(get_ori_model(runner.model), 'loss_lambda'), \
-            "The runner must have attribute \"loss_lambda\" in DenseCL."
-        self.loss_lambda = get_ori_model(runner.model).loss_lambda
-
-    def before_train_iter(self,
-                          runner,
-                          batch_idx: int,
-                          data_batch: Optional[Sequence[dict]] = None) -> None:
-        """Adjust ``loss_lambda`` every train iter."""
-        assert hasattr(get_ori_model(runner.model), 'loss_lambda'), \
-            "The runner must have attribute \"loss_lambda\" in DenseCL."
-        cur_iter = runner.iter
-        if cur_iter >= self.start_iters:
-            get_ori_model(runner.model).loss_lambda = self.loss_lambda
-        else:
-            get_ori_model(runner.model).loss_lambda = 0.
diff --git a/spaces/LanguageBind/LanguageBind/data/process_video.py b/spaces/LanguageBind/LanguageBind/data/process_video.py
deleted file mode 100644
index 1b7171be0318349003b9cd20c3503fbdf24db36d..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/data/process_video.py
+++ /dev/null
@@ -1,161 +0,0 @@
-
-import io
-import logging
-import os
-
-import cv2
-import numpy as np
-import torch
-import decord
-import torchvision.transforms
-from PIL import Image
-from decord import VideoReader, cpu
-
-try:
-    from petrel_client.client import Client
-    petrel_backend_imported = True
-except (ImportError, ModuleNotFoundError):
-    petrel_backend_imported = False
-
-
-from pytorchvideo.data.encoded_video import EncodedVideo
-from torchvision.transforms import Compose, Lambda, ToTensor
-from torchvision.transforms._transforms_video import NormalizeVideo, RandomCropVideo, RandomHorizontalFlipVideo
-from pytorchvideo.transforms import ApplyTransformToKey, ShortSideScale, UniformTemporalSubsample
-import sys
-sys.path.append('../')
-from open_clip import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
-from os.path import join as opj
-
-
-def get_video_loader(use_petrel_backend: bool = True,
-                     enable_mc: bool = True,
-                     conf_path: str = None):
-    if petrel_backend_imported and use_petrel_backend:
-        _client = Client(conf_path=conf_path, enable_mc=enable_mc)
-    else:
-        _client = None
-
-    def _loader(video_path):
-        if _client is not None and 's3:' in video_path:
-            video_path = io.BytesIO(_client.get(video_path))
-
-        vr = VideoReader(video_path, num_threads=1, ctx=cpu(0))
-        return vr
-
-    return _loader
-
-
-decord.bridge.set_bridge('torch')
-# video_loader = get_video_loader()
-
-
-def get_video_transform(args):
-    if args.video_decode_backend == 'pytorchvideo':
-        transform = ApplyTransformToKey(
-            key="video",
-            transform=Compose(
-                [
-                    UniformTemporalSubsample(args.num_frames),
-                    Lambda(lambda x: x / 255.0),
-                    NormalizeVideo(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
-                    ShortSideScale(size=224),
-                    RandomCropVideo(size=224),
-                    RandomHorizontalFlipVideo(p=0.5),
-                ]
-            ),
-        )
-
-    elif args.video_decode_backend == 'decord':
-
-        transform = Compose(
-            [
-                # UniformTemporalSubsample(num_frames),
-                Lambda(lambda x: x / 255.0),
-                NormalizeVideo(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
-                ShortSideScale(size=224),
-                RandomCropVideo(size=224),
-                RandomHorizontalFlipVideo(p=0.5),
-            ]
-        )
-
-    elif args.video_decode_backend == 'opencv':
-        transform = Compose(
-            [
-                # UniformTemporalSubsample(num_frames),
-                Lambda(lambda x: x / 255.0),
-                NormalizeVideo(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
-                ShortSideScale(size=224),
-                RandomCropVideo(size=224),
-                RandomHorizontalFlipVideo(p=0.5),
-            ]
-        )
-
-    elif args.video_decode_backend == 'imgs':
-        transform = Compose(
-            [
-                # UniformTemporalSubsample(num_frames),
-                # Lambda(lambda x: x / 255.0),
-                NormalizeVideo(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
-                ShortSideScale(size=224),
-                RandomCropVideo(size=224),
-                RandomHorizontalFlipVideo(p=0.5),
-            ]
-        )
-    else:
-        raise NameError('video_decode_backend should specify in (pytorchvideo, decord, opencv, imgs)')
-    return transform
-
-def load_and_transform_video(
-    video_path,
-    transform,
-    video_decode_backend='opencv',
-    clip_start_sec=0.0,
-    clip_end_sec=None,
-    num_frames=8,
-):
-    if video_decode_backend == 'pytorchvideo':
-        #  decord pyav
-        video = EncodedVideo.from_path(video_path, decoder="decord", decode_audio=False)
-        duration = video.duration
-        start_sec = clip_start_sec  # secs
-        end_sec = clip_end_sec if clip_end_sec is not None else duration  # secs
-        video_data = video.get_clip(start_sec=start_sec, end_sec=end_sec)
-        video_outputs = transform(video_data)
-
-    elif video_decode_backend == 'decord':
-        decord_vr = VideoReader(video_path, ctx=cpu(0))
-        duration = len(decord_vr)
-        frame_id_list = np.linspace(0, duration-1, num_frames, dtype=int)
-        video_data = decord_vr.get_batch(frame_id_list)
-        video_data = video_data.permute(3, 0, 1, 2)  # (T, H, W, C) -> (C, T, H, W)
-        video_outputs = transform(video_data)
-
-    elif video_decode_backend == 'opencv':
-        cv2_vr = cv2.VideoCapture(video_path)
-        duration = int(cv2_vr.get(cv2.CAP_PROP_FRAME_COUNT))
-        frame_id_list = np.linspace(0, duration-1, num_frames, dtype=int)
-
-        video_data = []
-        for frame_idx in frame_id_list:
-            cv2_vr.set(1, frame_idx)
-            _, frame = cv2_vr.read()
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            video_data.append(torch.from_numpy(frame).permute(2, 0, 1))
-        cv2_vr.release()
-        video_data = torch.stack(video_data, dim=1)
-        video_outputs = transform(video_data)
-
-    elif video_decode_backend == 'imgs':
-        resize256_folder = video_path.replace('.mp4', '_resize256_folder')
-        video_data = [ToTensor()(Image.open(opj(resize256_folder, f'{i}.jpg'))) for i in range(8)]
-        video_data = torch.stack(video_data, dim=1)
-        # print(video_data.shape, video_data.max(), video_data.min())
-        video_outputs = transform(video_data)
-
-    else:
-        raise NameError('video_decode_backend should specify in (pytorchvideo, decord, opencv, imgs)')
-    return {'pixel_values': video_outputs}
-
-if __name__ == '__main__':
-    load_and_transform_video(r"D:\ONE-PEACE-main\lb_test\zHSOYcZblvY.mp4")
\ No newline at end of file
diff --git a/spaces/LaynzKunz/RCVAICOVER/src/infer_pack/models_onnx_moess.py b/spaces/LaynzKunz/RCVAICOVER/src/infer_pack/models_onnx_moess.py
deleted file mode 100644
index 12efb0629a2e3d0d746a34f467254536c2bdbe5f..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/RCVAICOVER/src/infer_pack/models_onnx_moess.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(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,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/indicators/dpo.py b/spaces/Lianjd/stock_dashboard/backtrader/indicators/dpo.py
deleted file mode 100644
index e0c871b3a3fbe463caf3b70c9ab61eae8e78600a..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/indicators/dpo.py
+++ /dev/null
@@ -1,68 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-# Python 2/3 compatibility imports
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-from . import Indicator, MovAv
-
-
-class DetrendedPriceOscillator(Indicator):
-    '''
-    Defined by Joe DiNapoli in his book *"Trading with DiNapoli levels"*
-
-    It measures the price variations against a Moving Average (the trend)
-    and therefore removes the "trend" factor from the price.
-
-    Formula:
-      - movav = MovingAverage(close, period)
-      - dpo = close - movav(shifted period / 2 + 1)
-
-    See:
-      - http://en.wikipedia.org/wiki/Detrended_price_oscillator
-    '''
-    # Named alias for invocation
-    alias = ('DPO',)
-
-    # Named output lines
-    lines = ('dpo',)
-
-    # Accepted parameters (and defaults) -
-    # MovAvg also parameter to allow experimentation
-    params = (('period', 20), ('movav', MovAv.Simple))
-
-    # Emphasize central 0.0 line in plot
-    plotinfo = dict(plothlines=[0.0])
-
-    # Indicator information after the name (in brackets)
-    def _plotlabel(self):
-        plabels = [self.p.period]
-        plabels += [self.p.movav] * self.p.notdefault('movav')
-        return plabels
-
-    def __init__(self):
-        # Create the Moving Average
-        ma = self.p.movav(self.data, period=self.p.period)
-
-        # Calculate value (look back period/2 + 1 in MA) and bind to 'dpo' line
-        self.lines.dpo = self.data - ma(-self.p.period // 2 + 1)
-
-        super(DetrendedPriceOscillator, self).__init__()
diff --git a/spaces/Lookimi/TuberTranscript/README.md b/spaces/Lookimi/TuberTranscript/README.md
deleted file mode 100644
index bf075ce07ec4c21f5f6678da8e7af993ffc52f58..0000000000000000000000000000000000000000
--- a/spaces/Lookimi/TuberTranscript/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TuberTranscript
-emoji: 🏢
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.17.0
-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/LuxOAI/zenFace-Recognition-SDK/app.py b/spaces/LuxOAI/zenFace-Recognition-SDK/app.py
deleted file mode 100644
index 5c0ef30dd74b75cec6b9e356c5f7d4dbb62af6eb..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/zenFace-Recognition-SDK/app.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import sys
-sys.path.append('.')
-
-from flask import Flask, request, jsonify
-from time import gmtime, strftime
-import os
-import base64
-import json
-import cv2
-import numpy as np
-
-from facewrapper.facewrapper import ttv_version
-from facewrapper.facewrapper import ttv_get_hwid
-from facewrapper.facewrapper import ttv_init
-from facewrapper.facewrapper import ttv_init_offline
-from facewrapper.facewrapper import ttv_extract_feature
-from facewrapper.facewrapper import ttv_compare_feature
-
-app = Flask(__name__) 
-
-app.config['SITE'] = "http://0.0.0.0:8000/"
-app.config['DEBUG'] = False
-
-licenseKey = os.environ.get("LICENSE_KEY")
-licensePath = "license.txt"
-modelFolder = os.path.abspath(os.path.dirname(__file__)) + '/facewrapper/dict'
-
-version = ttv_version()
-print("version: ", version.decode('utf-8'))
-
-ret = ttv_init(modelFolder.encode('utf-8'), licenseKey.encode('utf-8'))
-if ret != 0:
-    print(f"online init failed: {ret}");
-
-    hwid = ttv_get_hwid()
-    print("hwid: ", hwid.decode('utf-8'))
-
-    ret = ttv_init_offline(modelFolder.encode('utf-8'), licensePath.encode('utf-8'))
-    if ret != 0:
-        print(f"offline init failed: {ret}")
-        exit(-1)
-    else:
-        print(f"offline init ok")
-
-else:
-    print(f"online init ok")
-
-@app.route('/api/compare_face', methods=['POST'])
-def compare_face():
-    file1 = request.files['image1']
-    image1 = cv2.imdecode(np.fromstring(file1.read(), np.uint8), cv2.IMREAD_COLOR)
-    if image1 is None:
-        result = "image1: is null!"
-        status = "ok"
-        response = jsonify({"status": status, "data": {"result": result}})
-        response.status_code = 200
-        response.headers["Content-Type"] = "application/json; charset=utf-8"
-        return response
-
-    file2 = request.files['image2']
-    image2 = cv2.imdecode(np.fromstring(file2.read(), np.uint8), cv2.IMREAD_COLOR)
-    if image2 is None:
-        result = "image2: is null!"
-        status = "ok"
-        response = jsonify({"status": status, "data": {"result": result}})
-        response.status_code = 200
-        response.headers["Content-Type"] = "application/json; charset=utf-8"
-        return response
-    
-    faceRect1 = np.zeros([4], dtype=np.int32)
-    feature1 = np.zeros([2048], dtype=np.uint8)
-    featureSize1 = np.zeros([1], dtype=np.int32)
-
-    ret = ttv_extract_feature(image1, image1.shape[1], image1.shape[0], faceRect1, feature1, featureSize1)
-    if ret <= 0:
-        if ret == -1:
-            result = "license error!"
-        elif ret == -2:
-            result = "init error!"
-        elif ret == 0:
-            result = "image1: no face detected!"
-
-        status = "ok"
-        response = jsonify({"status": status, "data": {"result": result}})
-        response.status_code = 200
-        response.headers["Content-Type"] = "application/json; charset=utf-8"
-        return response
-
-    faceRect2 = np.zeros([4], dtype=np.int32)
-    feature2 = np.zeros([2048], dtype=np.uint8)
-    featureSize2 = np.zeros([1], dtype=np.int32)
-
-    ret = ttv_extract_feature(image2, image2.shape[1], image2.shape[0], faceRect2, feature2, featureSize2)
-    if ret <= 0:
-        if ret == -1:
-            result = "license error!"
-        elif ret == -2:
-            result = "init error!"
-        elif ret == 0:
-            result = "image2: no face detected!"
-
-        status = "ok"
-        response = jsonify({"status": status, "data": {"result": result}})
-        response.status_code = 200
-        response.headers["Content-Type"] = "application/json; charset=utf-8"
-        return response
-
-    similarity = ttv_compare_feature(feature1, feature2)
-    if similarity > 0.7:
-        result = "same"
-    else:
-        result = "different"
-  
-    status = "ok"
-    response = jsonify(
-    {
-        "status": status, 
-        "data": {
-            "result": result, 
-            "similarity": float(similarity), 
-            "face1": {"x1": int(faceRect1[0]), "y1": int(faceRect1[1]), "x2": int(faceRect1[2]), "y2" : int(faceRect1[3])}, 
-            "face2": {"x1": int(faceRect2[0]), "y1": int(faceRect2[1]), "x2": int(faceRect2[2]), "y2" : int(faceRect2[3])}, 
-            }
-    })
-
-    response.status_code = 200
-    response.headers["Content-Type"] = "application/json; charset=utf-8"
-    return response
-
-
-@app.route('/api/compare_face_base64', methods=['POST'])
-def coompare_face_base64():
-    content = request.get_json()
-    imageBase641 = content['image1']
-    image1 = cv2.imdecode(np.frombuffer(base64.b64decode(imageBase641), dtype=np.uint8), cv2.IMREAD_COLOR)
-
-    if image1 is None:
-        result = "image1: is null!"
-        status = "ok"
-        response = jsonify({"status": status, "data": {"result": result}})
-        response.status_code = 200
-        response.headers["Content-Type"] = "application/json; charset=utf-8"
-        return response
-
-    imageBase642 = content['image2']
-    image2 = cv2.imdecode(np.frombuffer(base64.b64decode(imageBase642), dtype=np.uint8), cv2.IMREAD_COLOR)
-
-    if image2 is None:
-        result = "image2: is null!"
-        status = "ok"
-        response = jsonify({"status": status, "data": {"result": result}})
-        response.status_code = 200
-        response.headers["Content-Type"] = "application/json; charset=utf-8"
-        return response
-
-    faceRect1 = np.zeros([4], dtype=np.int32)
-    feature1 = np.zeros([2048], dtype=np.uint8)
-    featureSize1 = np.zeros([1], dtype=np.int32)
-
-    ret = ttv_extract_feature(image1, image1.shape[1], image1.shape[0], faceRect1, feature1, featureSize1)
-    if ret <= 0:
-        if ret == -1:
-            result = "license error!"
-        elif ret == -2:
-            result = "init error!"
-        elif ret == 0:
-            result = "image1: no face detected!"
-
-        status = "ok"
-        response = jsonify({"status": status, "data": {"result": result}})
-        response.status_code = 200
-        response.headers["Content-Type"] = "application/json; charset=utf-8"
-        return response
-
-    faceRect2 = np.zeros([4], dtype=np.int32)
-    feature2 = np.zeros([2048], dtype=np.uint8)
-    featureSize2 = np.zeros([1], dtype=np.int32)
-
-    ret = ttv_extract_feature(image2, image2.shape[1], image2.shape[0], faceRect2, feature2, featureSize2)
-    if ret <= 0:
-        if ret == -1:
-            result = "license error!"
-        elif ret == -2:
-            result = "init error!"
-        elif ret == 0:
-            result = "image2: no face detected!"
-
-        status = "ok"
-        response = jsonify({"status": status, "data": {"result": result}})
-        response.status_code = 200
-        response.headers["Content-Type"] = "application/json; charset=utf-8"
-        return response
-
-    similarity = ttv_compare_feature(feature1, feature2)
-    if similarity > 0.7:
-        result = "same"
-    else:
-        result = "different"
-  
-    status = "ok"
-    response = jsonify(
-    {
-        "status": status, 
-        "data": {
-            "result": result, 
-            "similarity": float(similarity), 
-            "face1": {"x1": int(faceRect1[0]), "y1": int(faceRect1[1]), "x2": int(faceRect1[2]), "y2" : int(faceRect1[3])}, 
-            "face2": {"x1": int(faceRect2[0]), "y1": int(faceRect2[1]), "x2": int(faceRect2[2]), "y2" : int(faceRect2[3])}, 
-            }
-    })
-    response.status_code = 200
-    response.headers["Content-Type"] = "application/json; charset=utf-8"
-    return response
-
-if __name__ == '__main__':
-    port = int(os.environ.get("PORT", 8000))
-    app.run(host='0.0.0.0', port=port)
diff --git a/spaces/Lyra121/finetuned_diffusion/README.md b/spaces/Lyra121/finetuned_diffusion/README.md
deleted file mode 100644
index bca1a00cd251d1c13fc3fe72baad06e256245d3e..0000000000000000000000000000000000000000
--- a/spaces/Lyra121/finetuned_diffusion/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Finetuned Diffusion
-emoji: 🪄🖼️
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-license: mit
-duplicated_from: anzorq/finetuned_diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mahiruoshi/BangDream-Bert-VITS2/webui.py b/spaces/Mahiruoshi/BangDream-Bert-VITS2/webui.py
deleted file mode 100644
index d9dbd2ca721fe178703a063ffa6c25e85f461bb8..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/BangDream-Bert-VITS2/webui.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# flake8: noqa: E402
-
-import sys, os
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(
-    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
-)
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-import numpy as np
-
-net_g = None
-
-if sys.platform == "darwin" and torch.backends.mps.is_available():
-    device = "mps"
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-else:
-    device = "cuda"
-
-
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str, device)
-    del word2ph
-    assert bert.shape[-1] == len(phone), phone
-
-    if language_str == "ZH":
-        bert = bert
-        ja_bert = torch.zeros(768, len(phone))
-    elif language_str == "JP":
-        ja_bert = bert
-        bert = torch.zeros(1024, len(phone))
-    else:
-        bert = torch.zeros(1024, len(phone))
-        ja_bert = torch.zeros(768, len(phone))
-
-    assert bert.shape[-1] == len(
-        phone
-    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, ja_bert, phone, tone, language
-
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
-    global net_g
-    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
-    with torch.no_grad():
-        x_tst = phones.to(device).unsqueeze(0)
-        tones = tones.to(device).unsqueeze(0)
-        lang_ids = lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        ja_bert = ja_bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        audio = (
-            net_g.infer(
-                x_tst,
-                x_tst_lengths,
-                speakers,
-                tones,
-                lang_ids,
-                bert,
-                ja_bert,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-            )[0][0, 0]
-            .data.cpu()
-            .float()
-            .numpy()
-        )
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        torch.cuda.empty_cache()
-        return audio
-
-
-def tts_fn(
-    text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language
-):
-    slices = text.split("|")
-    audio_list = []
-    with torch.no_grad():
-        for slice in slices:
-            audio = infer(
-                slice,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-                sid=speaker,
-                language=language,
-            )
-            audio_list.append(audio)
-            silence = np.zeros(hps.data.sampling_rate)  # 生成1秒的静音
-            audio_list.append(silence)  # 将静音添加到列表中
-    audio_concat = np.concatenate(audio_list)
-    return "Success", (hps.data.sampling_rate, audio_concat)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-m", "--model", default="./logs/as/G_8000.pth", help="path of your model"
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        default="./configs/config.json",
-        help="path of your config file",
-    )
-    parser.add_argument(
-        "--share", default=False, help="make link public", action="store_true"
-    )
-    parser.add_argument(
-        "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
-    )
-
-    args = parser.parse_args()
-    if args.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(args.config)
-
-    device = (
-        "cuda:0"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if sys.platform == "darwin" and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model,
-    ).to(device)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)
-
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    languages = ["ZH", "JP"]
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                text = gr.TextArea(
-                    label="Text",
-                    placeholder="Input Text Here",
-                    value="吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。",
-                )
-                speaker = gr.Dropdown(
-                    choices=speakers, value=speakers[0], label="Speaker"
-                )
-                sdp_ratio = gr.Slider(
-                    minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"
-                )
-                noise_scale = gr.Slider(
-                    minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale"
-                )
-                noise_scale_w = gr.Slider(
-                    minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W"
-                )
-                length_scale = gr.Slider(
-                    minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale"
-                )
-                language = gr.Dropdown(
-                    choices=languages, value=languages[0], label="Language"
-                )
-                btn = gr.Button("Generate!", variant="primary")
-            with gr.Column():
-                text_output = gr.Textbox(label="Message")
-                audio_output = gr.Audio(label="Output Audio")
-
-        btn.click(
-            tts_fn,
-            inputs=[
-                text,
-                speaker,
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                language,
-            ],
-            outputs=[text_output, audio_output],
-        )
-
-    webbrowser.open("http://127.0.0.1:7860")
-    app.launch(share=args.share)
diff --git a/spaces/Manjushri/MusicGen/audiocraft/models/__init__.py b/spaces/Manjushri/MusicGen/audiocraft/models/__init__.py
deleted file mode 100644
index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/audiocraft/models/__init__.py
+++ /dev/null
@@ -1,10 +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 .musicgen import MusicGen
-from .lm import LMModel
-from .encodec import CompressionModel, EncodecModel
diff --git a/spaces/Manjushri/MusicGen/tests/__init__.py b/spaces/Manjushri/MusicGen/tests/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/tests/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/MarcyWu/text_generator/README.md b/spaces/MarcyWu/text_generator/README.md
deleted file mode 100644
index 550f93bb5dc60ef8a77941040b44bb69710cac82..0000000000000000000000000000000000000000
--- a/spaces/MarcyWu/text_generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Generator
-emoji: 🏃
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MathFabian/p2_m5_hugging/app.py b/spaces/MathFabian/p2_m5_hugging/app.py
deleted file mode 100644
index a31e94cd55326a87852ba0aea2b062d9597adcdc..0000000000000000000000000000000000000000
--- a/spaces/MathFabian/p2_m5_hugging/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import cv2
-import numpy as np
-import gradio as gr
-from ultralytics import YOLO
-
-def predict(path:str):
-    model = YOLO("yolov8l.yaml")
-    model = YOLO("best.pt")
-    imagen = cv2.imread(path)
-    results = model.predict(source=imagen)
-
-    for r in results:
-        output_rgb = cv2.cvtColor(r.plot(), cv2.COLOR_BGR2RGB)
-        return output_rgb
-
-gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="filepath", label="Input"),
-    outputs=gr.Image(type="numpy", label="Output")
-).launch(debug=False)
\ No newline at end of file
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py
deleted file mode 100644
index b37c79bed4ef9fd8913715e62dbe3fc5cafdc3aa..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import pickle
-
-from .base import BaseFileHandler
-
-
-class PickleHandler(BaseFileHandler):
-
-    str_like = False
-
-    def load_from_fileobj(self, file, **kwargs):
-        return pickle.load(file, **kwargs)
-
-    def load_from_path(self, filepath, **kwargs):
-        return super(PickleHandler, self).load_from_path(
-            filepath, mode='rb', **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault('protocol', 2)
-        return pickle.dumps(obj, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault('protocol', 2)
-        pickle.dump(obj, file, **kwargs)
-
-    def dump_to_path(self, obj, filepath, **kwargs):
-        super(PickleHandler, self).dump_to_path(
-            obj, filepath, mode='wb', **kwargs)
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/distributions/distributions.py b/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/distributions/distributions.py
deleted file mode 100644
index f2b8ef901130efc171aa69742ca0244d94d3f2e9..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/distributions/distributions.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import torch
-import numpy as np
-
-
-class AbstractDistribution:
-    def sample(self):
-        raise NotImplementedError()
-
-    def mode(self):
-        raise NotImplementedError()
-
-
-class DiracDistribution(AbstractDistribution):
-    def __init__(self, value):
-        self.value = value
-
-    def sample(self):
-        return self.value
-
-    def mode(self):
-        return self.value
-
-
-class DiagonalGaussianDistribution(object):
-    def __init__(self, parameters, deterministic=False):
-        self.parameters = parameters
-        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
-        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
-        self.deterministic = deterministic
-        self.std = torch.exp(0.5 * self.logvar)
-        self.var = torch.exp(self.logvar)
-        if self.deterministic:
-            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
-
-    def sample(self):
-        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
-        return x
-
-    def kl(self, other=None):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        else:
-            if other is None:
-                return 0.5 * torch.sum(torch.pow(self.mean, 2)
-                                       + self.var - 1.0 - self.logvar,
-                                       dim=[1, 2, 3])
-            else:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
-                    dim=[1, 2, 3])
-
-    def nll(self, sample, dims=[1,2,3]):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
-            dim=dims)
-
-    def mode(self):
-        return self.mean
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
-    Compute the KL divergence between two gaussians.
-    Shapes are automatically broadcasted, so batches can be compared to
-    scalars, among other use cases.
-    """
-    tensor = None
-    for obj in (mean1, logvar1, mean2, logvar2):
-        if isinstance(obj, torch.Tensor):
-            tensor = obj
-            break
-    assert tensor is not None, "at least one argument must be a Tensor"
-
-    # Force variances to be Tensors. Broadcasting helps convert scalars to
-    # Tensors, but it does not work for torch.exp().
-    logvar1, logvar2 = [
-        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
-        for x in (logvar1, logvar2)
-    ]
-
-    return 0.5 * (
-        -1.0
-        + logvar2
-        - logvar1
-        + torch.exp(logvar1 - logvar2)
-        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
-    )
diff --git a/spaces/MiloSobral/PortiloopDemo/portiloop/src/processing.py b/spaces/MiloSobral/PortiloopDemo/portiloop/src/processing.py
deleted file mode 100644
index b5ba836032441a5222a97cb565765626de878b60..0000000000000000000000000000000000000000
--- a/spaces/MiloSobral/PortiloopDemo/portiloop/src/processing.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import numpy as np
-from scipy.signal import firwin
-
-
-def filter_24(value):
-    return (value * 4.5) / (2**23 - 1) / 24.0 * 1e6  # 23 because 1 bit is lost for sign
-
-def filter_2scomplement_np(value):
-    return np.where((value & (1 << 23)) != 0, value - (1 << 24), value)
-
-
-def int_to_float(value):
-    """
-    Convert the int value out of the ADS into a value in microvolts 
-    """
-    return filter_24(filter_2scomplement_np(value))
-
-
-def shift_numpy(arr, num, fill_value=np.nan):
-    result = np.empty_like(arr)
-    if num > 0:
-        result[:num] = fill_value
-        result[num:] = arr[:-num]
-    elif num < 0:
-        result[num:] = fill_value
-        result[:num] = arr[-num:]
-    else:
-        result[:] = arr
-    return result
-
-
-class FIR:
-    def __init__(self, nb_channels, coefficients, buffer=None):
-        
-        self.coefficients = np.expand_dims(np.array(coefficients), axis=1)
-        self.taps = len(self.coefficients)
-        self.nb_channels = nb_channels
-        self.buffer = np.array(buffer) if buffer is not None else np.zeros((self.taps, self.nb_channels))
-    
-    def filter(self, x):
-        self.buffer = shift_numpy(self.buffer, 1, x)
-        filtered = np.sum(self.buffer * self.coefficients, axis=0)
-        return filtered
-
-    
-class FilterPipeline:
-    def __init__(self,
-                 nb_channels,
-                 sampling_rate,
-                 power_line_fq=60,
-                 use_custom_fir=False,
-                 custom_fir_order=20,
-                 custom_fir_cutoff=30,
-                 alpha_avg=0.1,
-                 alpha_std=0.001,
-                 epsilon=0.000001,
-                 filter_args=[]):
-        if len(filter_args) > 0:
-            use_fir, use_notch, use_std = filter_args
-        else:
-            use_fir=True,
-            use_notch=True,
-            use_std=True
-        self.use_fir = use_fir
-        self.use_notch = use_notch
-        self.use_std = use_std
-        self.nb_channels = nb_channels
-        assert power_line_fq in [50, 60], f"The only supported power line frequencies are 50 Hz and 60 Hz"
-        if power_line_fq == 60:
-            self.notch_coeff1 = -0.12478308884588535
-            self.notch_coeff2 = 0.98729186796473023
-            self.notch_coeff3 = 0.99364593398236511
-            self.notch_coeff4 = -0.12478308884588535
-            self.notch_coeff5 = 0.99364593398236511
-        else:
-            self.notch_coeff1 = -0.61410695998423581
-            self.notch_coeff2 =  0.98729186796473023
-            self.notch_coeff3 = 0.99364593398236511
-            self.notch_coeff4 = -0.61410695998423581
-            self.notch_coeff5 = 0.99364593398236511
-        self.dfs = [np.zeros(self.nb_channels), np.zeros(self.nb_channels)]
-        
-        self.moving_average = None
-        self.moving_variance = np.zeros(self.nb_channels)
-        self.ALPHA_AVG = alpha_avg
-        self.ALPHA_STD = alpha_std
-        self.EPSILON = epsilon
-        
-        if use_custom_fir:
-            self.fir_coef = firwin(numtaps=custom_fir_order+1, cutoff=custom_fir_cutoff, fs=sampling_rate)
-        else:
-            self.fir_coef = [
-                0.001623780150148094927192721215192250384,
-                0.014988684599373741992978104065059596905,
-                0.021287595318265635502275046064823982306,
-                0.007349500393709578957568417933998716762,
-                -0.025127515717112181709014251396183681209,
-                -0.052210507359822452833064687638398027048,
-                -0.039273839505489904766477593511808663607,
-                0.033021568427940004020193498490698402748,
-                0.147606943281569008563636202779889572412,
-                0.254000252034505602516389899392379447818,
-                0.297330876398883392486283128164359368384,
-                0.254000252034505602516389899392379447818,
-                0.147606943281569008563636202779889572412,
-                0.033021568427940004020193498490698402748,
-                -0.039273839505489904766477593511808663607,
-                -0.052210507359822452833064687638398027048,
-                -0.025127515717112181709014251396183681209,
-                0.007349500393709578957568417933998716762,
-                0.021287595318265635502275046064823982306,
-                0.014988684599373741992978104065059596905,
-                0.001623780150148094927192721215192250384]
-        self.fir = FIR(self.nb_channels, self.fir_coef)
-        
-    def filter(self, value):
-        """
-        value: a numpy array of shape (data series, channels)
-        """
-        for i, x in enumerate(value):  # loop over the data series
-            # FIR:
-            if self.use_fir:
-                x = self.fir.filter(x)
-            # notch:
-            if self.use_notch:
-                denAccum = (x - self.notch_coeff1 * self.dfs[0]) - self.notch_coeff2 * self.dfs[1]
-                x = (self.notch_coeff3 * denAccum + self.notch_coeff4 * self.dfs[0]) + self.notch_coeff5 * self.dfs[1]
-                self.dfs[1] = self.dfs[0]
-                self.dfs[0] = denAccum
-            # standardization:
-            if self.use_std:
-                if self.moving_average is not None:
-                    delta = x - self.moving_average
-                    self.moving_average = self.moving_average + self.ALPHA_AVG * delta
-                    self.moving_variance = (1 - self.ALPHA_STD) * (self.moving_variance + self.ALPHA_STD * delta**2)
-                    moving_std = np.sqrt(self.moving_variance)
-                    x = (x - self.moving_average) / (moving_std + self.EPSILON)
-                else:
-                    self.moving_average = x
-            value[i] = x
-        return value
\ No newline at end of file
diff --git a/spaces/MirageML/fantasy-sword/README.md b/spaces/MirageML/fantasy-sword/README.md
deleted file mode 100644
index 99802bfe43997cd5a21091faceaae85260837a3d..0000000000000000000000000000000000000000
--- a/spaces/MirageML/fantasy-sword/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Fantasy Sword
-emoji: 💻
-colorFrom: pink
-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/Miuzarte/SUI-svc-3.0/vdecoder/hifigan/nvSTFT.py b/spaces/Miuzarte/SUI-svc-3.0/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index ec90bb1e07a23d5e406e843f65ee088560166952..0000000000000000000000000000000000000000
--- a/spaces/Miuzarte/SUI-svc-3.0/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
-    sampling_rate = None
-    try:
-        data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
-    except Exception as ex:
-        print(f"'{full_path}' failed to load.\nException:")
-        print(ex)
-        if return_empty_on_exception:
-            return [], sampling_rate or target_sr or 48000
-        else:
-            raise Exception(ex)
-    
-    if len(data.shape) > 1:
-        data = data[:, 0]
-        assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-    
-    if np.issubdtype(data.dtype, np.integer): # if audio data is type int
-        max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
-    else: # if audio data is type fp32
-        max_mag = max(np.amax(data), -np.amin(data))
-        max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-    
-    data = torch.FloatTensor(data.astype(np.float32))/max_mag
-    
-    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
-        return [], sampling_rate or target_sr or 48000
-    if target_sr is not None and sampling_rate != target_sr:
-        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
-        sampling_rate = target_sr
-    
-    return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-class STFT():
-    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
-        self.target_sr = sr
-        
-        self.n_mels     = n_mels
-        self.n_fft      = n_fft
-        self.win_size   = win_size
-        self.hop_length = hop_length
-        self.fmin     = fmin
-        self.fmax     = fmax
-        self.clip_val = clip_val
-        self.mel_basis = {}
-        self.hann_window = {}
-    
-    def get_mel(self, y, center=False):
-        sampling_rate = self.target_sr
-        n_mels     = self.n_mels
-        n_fft      = self.n_fft
-        win_size   = self.win_size
-        hop_length = self.hop_length
-        fmin       = self.fmin
-        fmax       = self.fmax
-        clip_val   = self.clip_val
-        
-        if torch.min(y) < -1.:
-            print('min value is ', torch.min(y))
-        if torch.max(y) > 1.:
-            print('max value is ', torch.max(y))
-        
-        if fmax not in self.mel_basis:
-            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
-            self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
-            self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-        
-        y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
-        y = y.squeeze(1)
-        
-        spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
-                          center=center, pad_mode='reflect', normalized=False, onesided=True)
-        # print(111,spec)
-        spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
-        # print(222,spec)
-        spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
-        # print(333,spec)
-        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
-        # print(444,spec)
-        return spec
-    
-    def __call__(self, audiopath):
-        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
-        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
-        return spect
-
-stft = STFT()
diff --git a/spaces/Mohammed-Khalil/Chat_with_Youtube_Videos/app.py b/spaces/Mohammed-Khalil/Chat_with_Youtube_Videos/app.py
deleted file mode 100644
index ac3424404bf5b50d19733caaa5ff8f167a0dcd00..0000000000000000000000000000000000000000
--- a/spaces/Mohammed-Khalil/Chat_with_Youtube_Videos/app.py
+++ /dev/null
@@ -1,266 +0,0 @@
-
-import streamlit as st
-hide_streamlit_style = """
-            <style>
-            #MainMenu  {visibility: hidden;}
-            footer  {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-def paid_version():
-    import os
-    import argparse
-    import shutil
-    from langchain.document_loaders import YoutubeLoader
-    from langchain.text_splitter import RecursiveCharacterTextSplitter
-    from langchain.vectorstores import Chroma
-    from langchain.embeddings import OpenAIEmbeddings
-    from langchain.chains import RetrievalQA
-    from langchain.llms import OpenAI
-    import streamlit as st
-    from langchain.chat_models import ChatOpenAI
-    from urllib.parse import urlparse, parse_qs
-    def extract_video_id(youtube_url):
-        try:
-            parsed_url = urlparse(youtube_url)
-            query_params = parse_qs(parsed_url.query)
-            video_id = query_params.get('v', [None])[0]
-
-            return video_id
-        except Exception as e:
-            print(f"Error extracting video ID: {e}")
-            return None
-    def set_openAi_api_key(api_key: str):
-                st.session_state["OPENAI_API_KEY"] = api_key
-                os.environ['OPENAI_API_KEY'] = api_key
-    def openai_api_insert_component():
-                with st.sidebar:
-                    st.markdown(
-                        """
-                        ## Quick Guide 🚀
-                        1. Get started by adding your [OpenAI API key](https://platform.openai.com/account/api-keys) below🔑
-                        2. Easily input the video url
-                        3. Engage with the content - ask questions, seek answers💬
-                        """
-                    )
-
-                    api_key_input = st.text_input("Input your OpenAI API Key",
-                                                type="password",
-                                                placeholder="Format: sk-...",
-                                                help="You can get your API key from https://platform.openai.com/account/api-keys.")
-
-
-                    if api_key_input == "" or api_key_input is None:
-                            st.sidebar.caption("👆 :red[Please set your OpenAI API Key here]")
-
-
-                    st.caption(":green[Your API is not stored anywhere. It is only used to generate answers to your questions.]")
-
-                    set_openAi_api_key(api_key_input)
-
-    def launchpaidversion():
-            openai_api_insert_component()
-            os.environ['OPENAI_API_KEY'] = st.session_state['OPENAI_API_KEY']
-            st.title('MKG: Your Chat with Youtube Assistant')
-            
-
-            videourl = st.text_input("Insert The video URL")
-            query = st.text_input("Ask any question about the video")
-            if st.button("Submit Question", type="primary"):
-                video_id = extract_video_id(videourl)
-                loader = YoutubeLoader(video_id)
-                documents = loader.load()
-
-                text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-                documents = text_splitter.split_documents(documents)
-
-                shutil.rmtree('./data')
-                vectordb = Chroma.from_documents(
-                    documents,
-                    embedding=OpenAIEmbeddings(),
-                    persist_directory='./data'
-            )
-                vectordb.persist()
-
-                qa_chain = RetrievalQA.from_chain_type(
-                    llm=ChatOpenAI(model_name='gpt-3.5-turbo'),
-                    retriever=vectordb.as_retriever(),
-                    return_source_documents=True,
-                    verbose=False
-                )
-                response = qa_chain(query)
-                st.write(response)
-
-
-    launchpaidversion()
-def free_version():
-          import torch
-          import os
-          import argparse
-          import shutil
-          from langchain.document_loaders import YoutubeLoader
-          from langchain.text_splitter import RecursiveCharacterTextSplitter
-          from langchain.vectorstores import Chroma
-          from langchain.embeddings import OpenAIEmbeddings
-          from langchain.chains import RetrievalQA
-          from langchain.llms import OpenAI
-          import streamlit as st
-          from langchain.chat_models import ChatOpenAI
-          from langchain import HuggingFaceHub
-          from langchain.embeddings import HuggingFaceInstructEmbeddings
-          from urllib.parse import urlparse, parse_qs
-          from langchain.embeddings import HuggingFaceBgeEmbeddings
-          from transformers import pipeline
-          import textwrap
-          import time
-          from deep_translator import GoogleTranslator
-          from langdetect import detect
-
-
-          def typewriter(text: str, speed: float):
-                      container = st.empty()
-                      displayed_text = ""
-
-                      for char in text:
-                          displayed_text += char
-                          container.markdown(displayed_text)
-                          time.sleep(1/speed)
-          def wrap_text_preserve_newlines(text, width=110):
-                      # Split the input text into lines based on newline characters
-                      lines = text.split('\n')
-
-                      # Wrap each line individually
-                      wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
-
-                      # Join the wrapped lines back together using newline characters
-                      wrapped_text = '\n'.join(wrapped_lines)
-                      return wrapped_text
-          def process_llm_response(llm_originalresponse2):
-                      #result_text = wrap_text_preserve_newlines(llm_originalresponse2["result"])
-                      typewriter(llm_originalresponse2["result"], speed=40)
-
-          def extract_video_id(youtube_url):
-              try:
-                  parsed_url = urlparse(youtube_url)
-                  query_params = parse_qs(parsed_url.query)
-                  video_id = query_params.get('v', [None])[0]
-
-                  return video_id
-              except Exception as e:
-                  print(f"Error extracting video ID: {e}")
-                  return None
-          def set_openAi_api_key(api_key: str):
-                      st.session_state["OPENAI_API_KEY"] = api_key
-                      os.environ['OPENAI_API_KEY'] = api_key
-          def openai_api_insert_component():
-                      with st.sidebar:
-                          st.markdown(
-                              """
-                              ## Quick Guide 🚀
-                              1. Get started by adding your [OpenAI API key](https://platform.openai.com/account/api-keys) below🔑
-                              2. Easily input the video url
-                              3. Engage with the content - ask questions, seek answers💬
-                              """
-                          )
-
-                          api_key_input = st.text_input("Input your OpenAI API Key",
-                                                      type="password",
-                                                      placeholder="Format: sk-...",
-                                                      help="You can get your API key from https://platform.openai.com/account/api-keys.")
-
-
-                          if api_key_input == "" or api_key_input is None:
-                                  st.sidebar.caption("👆 :red[Please set your OpenAI API Key here]")
-
-
-                          st.caption(":green[Your API is not stored anywhere. It is only used to generate answers to your questions.]")
-
-                          set_openAi_api_key(api_key_input)
-
-          def launchfreeversion():
-                  HUGGINGFACE_API_TOKEN = os.environ['access_code']
-                  model_name = "BAAI/bge-base-en"
-                  encode_kwargs = {'normalize_embeddings': True}
-
-                  st.title('MKG: Your Chat with Youtube Assistant')
-
-                  videourl = st.text_input("Insert The video URL",  placeholder="Format should be like: https://www.youtube.com/watch?v=pSLeYvld8Mk")
-                  query = st.text_input("Ask any question about the video",help="Suggested queries: Summarize the key points of this video - What is this video about - Ask about a specific thing in the video ")
-                  st.warning("⚠️ Please Keep in mind that the accuracy of the response relies on the :red[Video's quality] and the :red[prompt's Quality]. Occasionally, the response may not be entirely accurate. Consider using the response as a reference rather than a definitive answer.")
-
-                  if st.button("Submit Question", type="primary"):
-                    with st.spinner('Processing the Video...'):
-                        video_id = extract_video_id(videourl)
-                        loader = YoutubeLoader(video_id)
-                        documents = loader.load()
-
-                        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-                        documents = text_splitter.split_documents(documents)
-                        
-                        
-                        vectordb = Chroma.from_documents(
-                        documents,
-                        #embedding = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
-                                                                                              # model_kwargs={"device": "cuda"})
-                        embedding= HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs={'device': 'cuda' if torch.cuda.is_available() else 'cpu'}, encode_kwargs=encode_kwargs)
-                    )
-
-                        repo_id = "tiiuae/falcon-7b-instruct"
-                        qa_chain = RetrievalQA.from_chain_type(
-
-                        llm=HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACE_API_TOKEN,
-                                        repo_id=repo_id,
-                                        model_kwargs={"temperature":0.1, "max_new_tokens":1000}),
-                            retriever=vectordb.as_retriever(),
-                            return_source_documents=False,
-                            verbose=False
-                        )
-                    with st.spinner('Generating Answer...'):
-                          llm_response = qa_chain(query)
-                          #llm_originalresponse2=llm_response['result']
-                          process_llm_response(llm_response)
-          launchfreeversion()
-
-
-def intro():
-            st.markdown("""
-            # MKG: Your Chat with Youtube Assistant 🎬🤖
-
-            Welcome to MKG-Assistant, where AI meets Youtube! 🚀🔍
-
-            ## Base Models
-
-            Q&A-Assistant is built on OpenAI's GPT 3.5 for the premium version and Falcon 7B instruct Model for the free version to enhance your websites browsing experience. Whether you're a student, researcher, or professional, we're here to simplify your interactions with the web. 💡📚
-
-            ## How to Get Started
-
-            1.Enter the Video URL.
-            2. Enter your API key.(Only if you chose the premium version. Key is not needed in the free version)
-            3. Ask questions using everyday language.
-            4. Get detailed, AI-generated answers.
-
-            5. Enjoy a smarter way to Interact with Youtube!
-
-
-
-            ## It is Time to Dive in!
-
-
-            """)
-page_names_to_funcs = {
-    "Main Page": intro,
-    "Open Source Edition (Free version)": free_version,
-    "Premium edition (Requires Open AI API Key )": paid_version
-
-}
-
-
-
-
-
-
-#test
-demo_name = st.sidebar.selectbox("Choose a version", page_names_to_funcs.keys())
-page_names_to_funcs[demo_name]()
-st.sidebar.markdown('<a href="https://www.linkedin.com/in/mohammed-khalil-ghali-11305119b/"> Connect on LinkedIn <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/linkedin/linkedin-original.svg" alt="LinkedIn" width="30" height="30"></a>', unsafe_allow_html=True)
-st.sidebar.markdown('<a href="https://github.com/khalil-ghali"> Check out my GitHub <img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/github/github-original.svg" alt="GitHub" width="30" height="30"></a>', unsafe_allow_html=True)
\ No newline at end of file
diff --git a/spaces/MountLiteraSwd/mount_ai_school1/README.md b/spaces/MountLiteraSwd/mount_ai_school1/README.md
deleted file mode 100644
index 8d33ad73a264ddca61332814ae0b69a60b18a566..0000000000000000000000000000000000000000
--- a/spaces/MountLiteraSwd/mount_ai_school1/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Mount Ai School
-emoji: 💻
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
-duplicated_from: MountLiteraSwd/mount_ai_school
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NMEX/rvc-hoyo-game/infer_pack/models_onnx_moess.py b/spaces/NMEX/rvc-hoyo-game/infer_pack/models_onnx_moess.py
deleted file mode 100644
index 12efb0629a2e3d0d746a34f467254536c2bdbe5f..0000000000000000000000000000000000000000
--- a/spaces/NMEX/rvc-hoyo-game/infer_pack/models_onnx_moess.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(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,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/Nultx/VITS-TTS/ONNXVITS_inference.py b/spaces/Nultx/VITS-TTS/ONNXVITS_inference.py
deleted file mode 100644
index 258b618cd338322365dfa25bec468a0a3f70ccd1..0000000000000000000000000000000000000000
--- a/spaces/Nultx/VITS-TTS/ONNXVITS_inference.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import IPython.display as ipd
-import torch
-import commons
-import utils
-import ONNXVITS_infer
-from text import text_to_sequence
-
-def get_text(text, hps):
-    text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-hps = utils.get_hparams_from_file("../vits/pretrained_models/uma87.json")
-
-net_g = ONNXVITS_infer.SynthesizerTrn(
-    len(hps.symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("../vits/pretrained_models/uma_1153000.pth", net_g)
-
-text1 = get_text("おはようございます。", hps)
-stn_tst = text1
-with torch.no_grad():
-    x_tst = stn_tst.unsqueeze(0)
-    x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-    sid = torch.LongTensor([0])
-    audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
-print(audio)
\ No newline at end of file
diff --git a/spaces/OAOA/DifFace/basicsr/archs/__init__.py b/spaces/OAOA/DifFace/basicsr/archs/__init__.py
deleted file mode 100644
index af6bcbd97bb3e4914c3c91dc53e0708bcac66075..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/archs/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import importlib
-from copy import deepcopy
-from os import path as osp
-
-from basicsr.utils import get_root_logger, scandir
-from basicsr.utils.registry import ARCH_REGISTRY
-
-__all__ = ['build_network']
-
-# automatically scan and import arch modules for registry
-# scan all the files under the 'archs' folder and collect files ending with '_arch.py'
-arch_folder = osp.dirname(osp.abspath(__file__))
-arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
-# import all the arch modules
-_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames]
-
-
-def build_network(opt):
-    opt = deepcopy(opt)
-    network_type = opt.pop('type')
-    net = ARCH_REGISTRY.get(network_type)(**opt)
-    logger = get_root_logger()
-    logger.info(f'Network [{net.__class__.__name__}] is created.')
-    return net
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md
deleted file mode 100644
index 90741f42b0b070f2a91b63c8badb817c6aa24230..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md
+++ /dev/null
@@ -1,87 +0,0 @@
-# ASR-based evaluation
-
-Overall, the life cycle of the ASR-based evaluation for an ULM contains the following steps:
- 1. Training an ULM and sampling from it [[description]](./../../ulm)
- 2. Running UTS on the sampled unit sequences [[description]](./../../unit2speech)
- 3. Pre-processing for the ASR (down-sampling to 16 KHz, aligning length of the generated audio with ground-truth utterances)
- 4. Running ASR
- 5. Calculation of the post-ASR evaluation metrics
-
-Here we assume that you have already went throught the first two steps and focus on the rest.
-
-## Preprocessing
-### Down-sampling to 16KHz
-The bulk conversion can be done by running
-```bash
- python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py $UTS_OUTPUT $UTS_OUTPUT_DOWNSAMPLE
- ```
- where `$UTS_OUTPUT` specifies the directory with the generated audio and `$UTS_OUTPUT_DOWNSAMPLE` is the directory where downsampled audio would be saved.
-
- ### Matching by length
-This step is somewhat optional. However, if you want to compare the fluency and diversity of a generated speech utterance to that of the ground-truth speech with the same prefix, it is a good idea to force them to be of the same length.
-```bash
-python $FAIRSEQ_ROOT/examples/textless_nlp/asr_metrics/cut_as.py \
-    --samples_dir=$UTS_OUTPUT_DOWNSAMPLE --out_dir=$UTS_OUTPUT_DOWNSAMPLE_CUT \
-    --prompts_description=data/ground_truth_continuation_dev.json
-```
-
-Here `ground_truth_continuation_dev.json` is a json file with ground-truth text from LibriSpeech dev-clean, associated with some meta-data (assuming the evaluation is done on dev-clean). This file can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_dev.json). A similar file for the test-clean is [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_test.json). These files are used for the evaluation and contain texts for audio sequences that are at least 6s long.
-
-## Running ASR
-We use a pre-trained wav2vec model to run the ASR step. We firstly need to prepare manifest files which, roughly, tell the ASR system which files we want to transcribe. You can find more details and download the `960h_scratch.pt` checkpoint
-[[here]](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md)). To run ASR, you would also need to
-install KenLM, Flashlight decoder, and download the KenLM 4-gram English language model.
-
-```bash
- python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py  \
-    $UTS_OUTPUT_DOWNSAMPLE_CUT --valid-percent 0.0  --dest $MANIFEST_DIR --ext wav
-```
-where `$UTS_OUTPUT_DOWNSAMPLE_CUT` speficies the directory with the preprocessed UTS outputs and `$MANIFEST_DIR` is the output directory.
-
-We will be running an out-of-the-box evaluation script which requires ground-truth transcripts to measure quality metrics. We are only
-interested in the transcripts (and we don't have ground-truth outputs for when our ULM generated!), hence we will just generate
-some dummy transcripts instead:
-```bash
-cp $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dict.ltr.txt $MANIFEST_DIR
-python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dummy_asr_data.py  --tsv=$MANIFEST_DIR/train.tsv \
- --output-dir=$MANIFEST_DIR
-```
-
-Now we are ready for running ASR:
-```
-mkdir -p asr
-python $FAIRSEQ_ROOT/examples/speech_recognition/infer.py  \
-    $MANIFEST_DIR \
-    --task audio_pretraining --nbest 1 --path 960h_scratch.pt \
-    --gen-subset=train --results-path $PATH_TO_ASR_OUTPUT \
-    --w2l-decoder kenlm --lm-model 4-gram.bin \
-    --lexicon librispeech/lexicon_ltr.lst --word-score -1 \
-    --sil-weight 0 --lm-weight 2 --criterion ctc --labels ltr --max-tokens 300000 --remove-bpe letter
-```
-where `lexicon_ltr.lst` is the LibriSpeech lexicon and `$PATH_TO_ASR_OUTPUT` is the output directory (can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/lexicon_ltr.lst)).
-
-## Evaluation metrics
-We run evaluation on the 1_000 shortest sequences that are at least 6s long. To filter those from the ASR transcript, we additionally provide each metric script with the paths to the manifest and `ground_truth_continuation_*` files.
-
-### Perplexity (PPX)
-To get a PPX metric estimate on an ASR transcript, you need to run the following command:
-```bash
-python ppx.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt --cut-tail\
-  --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-where `--cut-tail` tells the script to ignore the last token on each line (ASR puts the sequence ID there).
-
-### Self- and Auto-BLEU
-```bash
-python self_bleu.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt  --cut-tail \
-  --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-
-### Continuation-BLEU
-```bash
-python continuation_eval.py --asr-transcript $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt \
-   --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-
-### AUC
-Based on the metrics calculated above, we can estimate the AUC of the perplexity/diversity trade-off. We provide an illustration in a [Colab notebook](https://colab.research.google.com/drive/1pVPfOVax_PU3MkYdHRSsa-SI8GBUldNt?usp=sharing).
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/ofa.py b/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/ofa.py
deleted file mode 100644
index 01abdf64706d9555a42fa4cd7a7f38fb6649c53e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/ofa.py
+++ /dev/null
@@ -1,410 +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.
-"""
-OFA
-"""
-from typing import Optional
-
-import logging
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.models import register_model, register_model_architecture
-from fairseq.modules.transformer_sentence_encoder import init_bert_params
-
-from .unify_transformer import TransformerModel
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("ofa")
-class OFAModel(TransformerModel):
-    __jit_unused_properties__ = ["supported_targets"]
-
-    def __init__(self, args, encoder, decoder):
-        super().__init__(args, encoder, decoder)
-
-        # We follow BERT's random weight initialization
-        self.apply(init_bert_params)
-
-        self.classification_heads = nn.ModuleDict()
-        if hasattr(self.encoder, "dictionary"):
-            self.eos: int = self.encoder.dictionary.eos()
-
-    @staticmethod
-    def add_args(parser):
-        super(OFAModel, OFAModel).add_args(parser)
-        parser.add_argument(
-            "--pooler-dropout",
-            type=float,
-            metavar="D",
-            help="dropout probability in the masked_lm pooler layers",
-        )
-        parser.add_argument(
-            "--pooler-classifier",
-            type=str,
-            choices=['mlp', 'linear'],
-            help="type of pooler classifier",
-        )
-        parser.add_argument(
-            "--pooler-activation-fn",
-            choices=utils.get_available_activation_fns(),
-            help="activation function to use for pooler layer",
-        )
-        parser.add_argument(
-            "--spectral-norm-classification-head",
-            action="store_true",
-            help="Apply spectral normalization on the classification head",
-        )
-
-    @property
-    def supported_targets(self):
-        return {"self"}
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens,
-        patch_images: Optional[torch.Tensor] = None,
-        patch_images_2: Optional[torch.Tensor] = None,
-        patch_masks: Optional[torch.Tensor] = None,
-        code_masks: Optional[torch.Tensor] = None,
-        sample_patch_num: Optional[int] = None,
-        features_only: bool = False,
-        classification_head_name: Optional[str] = None,
-        token_embeddings: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        if classification_head_name is not None:
-            features_only = True
-
-        encoder_out = self.encoder(
-            src_tokens,
-            src_lengths=src_lengths,
-            patch_images=patch_images,
-            patch_masks=patch_masks,
-            patch_images_2=patch_images_2,
-            token_embeddings=token_embeddings,
-            return_all_hiddens=return_all_hiddens,
-            sample_patch_num=sample_patch_num
-        )
-        x, extra = self.decoder(
-            prev_output_tokens,
-            code_masks=code_masks,
-            encoder_out=encoder_out,
-            features_only=features_only,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-            src_lengths=src_lengths,
-            return_all_hiddens=return_all_hiddens,
-        )
-
-        pad = self.encoder.padding_idx
-        if classification_head_name is not None:
-            prev_lengths = prev_output_tokens.ne(pad).sum(1)
-            gather_index = prev_lengths[:, None, None].expand(x.size(0), 1, x.size(2)) - 1
-            sentence_representation = x.gather(1, gather_index).squeeze()
-            if self.classification_heads[classification_head_name].use_two_images:
-                hidden_size = sentence_representation.size(1)
-                sentence_representation = sentence_representation.view(-1, hidden_size * 2)
-            for k, head in self.classification_heads.items():
-                # for torch script only supports iteration
-                if k == classification_head_name:
-                    x = head(sentence_representation)
-                    break
-
-        return x, extra
-
-    def register_embedding_tokens(self, ans2label_dict, src_dict, bpe):
-        """Register embedding tokens"""
-        logger.info("Registering embedding tokens")
-        self.ans_tensor_list = []
-        for i in range(len(ans2label_dict)):
-            ans = src_dict[-len(ans2label_dict)+i]
-            ans = ans[5:-1].replace('_', ' ')
-            ans_tensor = src_dict.encode_line(
-                line=bpe.encode(' {}'.format(ans.lower())),
-                add_if_not_exist=False,
-                append_eos=False
-            ).long()
-            self.ans_tensor_list.append(ans_tensor)
-
-    def register_classification_head(
-        self, name, num_classes=None, inner_dim=None, use_two_images=False, **kwargs
-    ):
-        """Register a classification head."""
-        logger.info("Registering classification head: {0}".format(name))
-        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] = OFAClassificationHead(
-            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,
-            pooler_classifier=self.args.pooler_classifier,
-            use_two_images=use_two_images,
-            do_spectral_norm=getattr(
-                self.args, "spectral_norm_classification_head", False
-            ),
-        )
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        super().upgrade_state_dict_named(state_dict, name)
-
-        prefix = name + "." if name != "" else ""
-        current_head_names = (
-            []
-            if not hasattr(self, "classification_heads")
-            else self.classification_heads.keys()
-        )
-
-        # Handle new classification heads present in the state dict.
-        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]
-
-        def truncate_emb(key):
-            if key in state_dict:
-                state_dict[key] = state_dict[key][:-1, :]
-
-        # When finetuning on translation task, remove last row of
-        # embedding matrix that corresponds to mask_idx token.
-        loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0)
-        if (
-            loaded_dict_size == len(self.encoder.dictionary) + 1
-            and "<mask>" not in self.encoder.dictionary
-        ):
-            truncate_emb("encoder.embed_tokens.weight")
-            truncate_emb("decoder.embed_tokens.weight")
-            truncate_emb("encoder.output_projection.weight")
-            truncate_emb("decoder.output_projection.weight")
-
-        if loaded_dict_size < len(self.encoder.dictionary):
-            num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size
-            embed_dim = state_dict["encoder.embed_tokens.weight"].size(1)
-
-            new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim)
-            if getattr(self, "ans_tensor_list", None):
-                assert len(new_lang_embed_to_add) == len(self.ans_tensor_list)
-                for i, ans_tensor in enumerate(self.ans_tensor_list):
-                    ans_embed = F.embedding(ans_tensor, state_dict["encoder.embed_tokens.weight"])
-                    ans_embed = ans_embed.sum(0) / ans_embed.size(0)
-                    new_lang_embed_to_add[i] = ans_embed
-            else:
-                nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_lang_embed_to_add = new_lang_embed_to_add.to(
-                dtype=state_dict["encoder.embed_tokens.weight"].dtype,
-            )
-
-            state_dict["encoder.embed_tokens.weight"] = torch.cat(
-                [state_dict["encoder.embed_tokens.weight"], new_lang_embed_to_add]
-            )
-            state_dict["decoder.embed_tokens.weight"] = torch.cat(
-                [state_dict["decoder.embed_tokens.weight"], new_lang_embed_to_add]
-            )
-            state_dict["decoder.output_projection.weight"] = torch.cat(
-                [state_dict["decoder.output_projection.weight"], new_lang_embed_to_add]
-            )
-
-        # 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 OFAClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(
-        self,
-        input_dim,
-        inner_dim,
-        num_classes,
-        activation_fn,
-        pooler_dropout,
-        pooler_classifier,
-        use_two_images=False,
-        do_spectral_norm=False,
-    ):
-        super().__init__()
-        self.pooler_classifier = pooler_classifier
-        self.use_two_images = use_two_images
-        input_dim = input_dim * 2 if use_two_images else input_dim
-        if pooler_classifier == "mlp":
-            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 = nn.Linear(inner_dim, num_classes)
-        elif pooler_classifier == "linear":
-            self.dropout = nn.Dropout(p=pooler_dropout)
-            self.out_proj = nn.Linear(input_dim, num_classes)
-        else:
-            raise NotImplementedError
-
-        if do_spectral_norm:
-            self.out_proj = torch.nn.utils.spectral_norm(self.out_proj)
-
-    def forward(self, features, **kwargs):
-        if self.pooler_classifier == 'mlp':
-            x = features
-            x = self.dropout(x)
-            x = self.dense(x)
-            x = self.activation_fn(x)
-            x = self.dropout(x)
-            x = self.out_proj(x)
-        elif self.pooler_classifier == 'linear':
-            x = features
-            x = self.dropout(x)
-            x = self.out_proj(x)
-        else:
-            raise NotImplementedError
-        return x
-
-
-@register_model_architecture("ofa", "ofa_large")
-def ofa_large_architecture(args):
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
-    args.encoder_layers = getattr(args, "encoder_layers", 12)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 12)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.relu_dropout = getattr(args, "relu_dropout", 0.0)
-    args.dropout = getattr(args, "dropout", 0.0)
-    args.max_target_positions = getattr(args, "max_target_positions", 1024)
-    args.max_source_positions = getattr(args, "max_source_positions", 1024)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", True
-    )
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
-
-    args.activation_fn = getattr(args, "activation_fn", "gelu")
-    args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
-    args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
-    args.pooler_classifier = getattr(args, "pooler_classifier", "mlp")
-
-    args.resnet_drop_path_rate = getattr(args, "resnet_drop_path_rate", 0.0)
-    args.encoder_drop_path_rate = getattr(args, "encoder_drop_path_rate", 0.0)
-    args.decoder_drop_path_rate = getattr(args, "decoder_drop_path_rate", 0.0)
-
-    args.resnet_type = getattr(args, "resnet_type", "resnet152")
-    args.token_bucket_size = getattr(args, "token_bucket_size", 256)
-    args.image_bucket_size = getattr(args, "image_bucket_size", 42)
-
-    args.freeze_encoder_embedding = getattr(args, "freeze_encoder_embedding", False)
-    args.freeze_decoder_embedding = getattr(args, "freeze_decoder_embedding", False)
-    args.add_type_embedding = getattr(args, "add_type_embedding", True)
-    args.attn_scale_factor = getattr(args, "attn_scale_factor", 2)
-
-    args.code_image_size = getattr(args, "code_image_size", 128)
-    args.patch_layernorm_embedding = getattr(args, "patch_layernorm_embedding", True)
-    args.code_layernorm_embedding = getattr(args, "code_layernorm_embedding", True)
-    args.entangle_position_embedding = getattr(args, "entangle_position_embedding", False)
-    args.disable_entangle = getattr(args, "disable_entangle", False)
-    args.sync_bn = getattr(args, "sync_bn", False)
-
-    args.scale_attn = getattr(args, "scale_attn", False)
-    args.scale_fc = getattr(args, "scale_fc", False)
-    args.scale_heads = getattr(args, "scale_heads", False)
-    args.scale_resids = getattr(args, "scale_resids", False)
-
-
-@register_model_architecture("ofa", "ofa_base")
-def ofa_base_architecture(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12)
-    args.resnet_type = getattr(args, "resnet_type", "resnet101")
-    ofa_large_architecture(args)
-
-
-@register_model_architecture("ofa", "ofa_huge")
-def ofa_huge_architecture(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1280)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1280)
-    args.encoder_layers = getattr(args, "encoder_layers", 24)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    args.decoder_layers = getattr(args, "decoder_layers", 12)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
-    args.resnet_type = getattr(args, "resnet_type", "resnet152")
-    ofa_large_architecture(args)
-
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/model_parallel/criterions/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/model_parallel/criterions/__init__.py
deleted file mode 100644
index 5fae7bd4c2cfa7b4f64ad62dd9b9082f59f0e50d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/model_parallel/criterions/__init__.py
+++ /dev/null
@@ -1,14 +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 criterions/ directory
-for file in sorted(os.listdir(os.path.dirname(__file__))):
-    if file.endswith(".py") and not file.startswith("_"):
-        module = file[: file.find(".py")]
-        importlib.import_module("fairseq.model_parallel.criterions." + module)
diff --git a/spaces/OOlajide/common-nlp-tasks/app.py b/spaces/OOlajide/common-nlp-tasks/app.py
deleted file mode 100644
index 244a124dbe798ee834127e4b1b84abf339f11674..0000000000000000000000000000000000000000
--- a/spaces/OOlajide/common-nlp-tasks/app.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-st.set_page_config(page_title="Common NLP Tasks")
-st.title("Common NLP Tasks")
-st.subheader(":point_left: Use the menu on the left to select a NLP task (click on > if closed).")
-"""
-[![](https://img.shields.io/github/followers/OOlajide?label=OOlajide&style=social)](https://gitHub.com/OOlajide)
-[![](https://img.shields.io/twitter/follow/sageOlamide?label=@sageOlamide&style=social)](https://twitter.com/sageOlamide)
-"""
-expander = st.sidebar.expander("About")
-expander.write("This web app allows you to perform common Natural Language Processing tasks, select a task below to get started.")
-
-st.sidebar.header("What will you like to do?")
-option = st.sidebar.radio("", ["Text summarization", "Extractive question answering", "Text generation"])
-
-@st.cache(show_spinner=False, allow_output_mutation=True)
-def question_model():
-    model_name = "deepset/tinyroberta-squad2"
-    question_answerer = pipeline(model=model_name, tokenizer=model_name, task="question-answering")
-    return question_answerer
-
-@st.cache(show_spinner=False, allow_output_mutation=True)
-def summarization_model():
-    model_name = "google/pegasus-xsum"
-    summarizer = pipeline(model=model_name, tokenizer=model_name, task="summarization")
-    return summarizer
-
-@st.cache(show_spinner=False, allow_output_mutation=True)
-def generation_model():
-    model_name = "distilgpt2"
-    generator = pipeline(model=model_name, tokenizer=model_name, task="text-generation")
-    return generator
-
-if option == "Extractive question answering":
-    st.markdown("<h2 style='text-align: center; color:grey;'>Extractive Question Answering</h2>", unsafe_allow_html=True)
-    st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is extractive question answering about?<b></h3>", unsafe_allow_html=True)
-    st.write("Extractive question answering is a Natural Language Processing task where text is provided for a model so that the model can refer to it and make predictions about where the answer to a question is.")
-    st.markdown('___')
-    source = st.radio("How would you like to start? Choose an option below", ["I want to input some text", "I want to upload a file"])
-    sample_question = "What did the shepherd boy do to amuse himself?"
-    if source == "I want to input some text":
-        with open("sample.txt", "r") as text_file:
-            sample_text = text_file.read()
-        context = st.text_area("Use the example below or input your own text in English (10,000 characters max)", value=sample_text, max_chars=10000, height=330)
-        question = st.text_input(label="Use the question below or enter your own question", value=sample_question)
-        button = st.button("Get answer")
-        if button:
-            with st.spinner(text="Loading question model..."):
-                question_answerer = question_model()
-            with st.spinner(text="Getting answer..."):
-                answer = question_answerer(context=context, question=question)
-                answer = answer["answer"]
-                st.text(answer)
-    elif source == "I want to upload a file":
-        uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"])
-        if uploaded_file is not None:
-            raw_text = str(uploaded_file.read(),"utf-8")
-            context = st.text_area("", value=raw_text, height=330)
-            question = st.text_input(label="Enter your question", value=sample_question)
-            button = st.button("Get answer")
-            if button:
-                with st.spinner(text="Loading summarization model..."):
-                    question_answerer = question_model()
-                with st.spinner(text="Getting answer..."):
-                    answer = question_answerer(context=context, question=question)
-                    answer = answer["answer"]
-                    st.text(answer)
-
-elif option == "Text summarization":
-    st.markdown("<h2 style='text-align: center; color:grey;'>Text Summarization</h2>", unsafe_allow_html=True)
-    st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is text summarization about?<b></h3>", unsafe_allow_html=True)
-    st.write("Text summarization is producing a shorter version of a given text while preserving its important information.")
-    st.markdown('___')
-    source = st.radio("How would you like to start? Choose an option below", ["I want to input some text", "I want to upload a file"])
-    if source == "I want to input some text":
-        with open("sample.txt", "r") as text_file:
-            sample_text = text_file.read()
-        text = st.text_area("Input a text in English (10,000 characters max) or use the example below", value=sample_text, max_chars=10000, height=330)
-        button = st.button("Get summary")
-        if button:
-            with st.spinner(text="Loading summarization model..."):
-                summarizer = summarization_model()
-            with st.spinner(text="Summarizing text..."):
-                summary = summarizer(text, max_length=130, min_length=30)
-                st.text(summary[0]["summary_text"])
-
-    elif source == "I want to upload a file":
-        uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"])
-        if uploaded_file is not None:
-            raw_text = str(uploaded_file.read(),"utf-8")
-            text = st.text_area("", value=raw_text, height=330)
-            button = st.button("Get summary")
-            if button:
-                with st.spinner(text="Loading summarization model..."):
-                    summarizer = summarization_model()
-                with st.spinner(text="Summarizing text..."):
-                    summary = summarizer(text, max_length=130, min_length=30)
-                    st.text(summary[0]["summary_text"])
-                
-elif option == "Text generation":
-    st.markdown("<h2 style='text-align: center; color:grey;'>Text Generation</h2>", unsafe_allow_html=True)
-    st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is text generation about?<b></h3>", unsafe_allow_html=True)
-    st.write("Text generation is the task of generating text with the goal of appearing indistinguishable to human-written text.")
-    st.markdown('___')
-    text = st.text_input(label="Enter one line of text and let the NLP model generate the rest for you")
-    button = st.button("Generate text")
-    if button:
-        with st.spinner(text="Loading text generation model..."):
-            generator = generation_model()
-        with st.spinner(text="Generating text..."):
-            generated_text = generator(text, max_length=50)
-            st.text(generated_text[0]["generated_text"])
\ No newline at end of file
diff --git a/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/index.css b/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/index.css
deleted file mode 100644
index 86601e8611ce6c443c58e58b587848d0956b0f5a..0000000000000000000000000000000000000000
--- a/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/index.css
+++ /dev/null
@@ -1,78 +0,0 @@
-:root {
-    font-family: Inter, Avenir, Helvetica, Arial, sans-serif;
-    font-size: 16px;
-    line-height: 24px;
-    font-weight: 400;
-    color-scheme: light dark;
-    color: rgb(255 255 255 / 87%);
-    background-color: #242424;
-    font-synthesis: none;
-    text-rendering: optimizelegibility;
-    -webkit-font-smoothing: antialiased;
-    -moz-osx-font-smoothing: grayscale;
-    text-size-adjust: 100%;
-}
-
-a {
-    font-weight: 500;
-    color: #646cff;
-    text-decoration: inherit;
-}
-
-a:hover {
-    color: #535bf2;
-}
-
-body {
-    margin: 0;
-    padding: 0 32px;
-}
-
-h1 {
-    font-size: 3.2em;
-    line-height: 1.1;
-}
-
-select {
-    border: 2px solid gray;
-    border-radius: 4px;
-    padding: 4px 8px;
-}
-
-button {
-    border-radius: 8px;
-    border: 1px solid transparent;
-    padding: 0.6em 1.2em;
-    font-size: 1em;
-    font-weight: 500;
-    font-family: inherit;
-    background-color: #1a1a1a;
-    cursor: pointer;
-    transition: border-color 0.25s;
-    word-break: keep-all;
-    outline: none;
-}
-
-button:hover {
-    border-color: #646cff;
-}
-
-button:focus,
-button:focus-visible {
-    outline: 4px auto -webkit-focus-ring-color;
-}
-
-@media (prefers-color-scheme: light) {
-    :root {
-        color: #213547;
-        background-color: #fff;
-    }
-
-    a:hover {
-        color: #747bff;
-    }
-
-    button {
-        background-color: #f9f9f9;
-    }
-}
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/mask_head.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/mask_head.py
deleted file mode 100644
index 5ac5c4b9aaa34653d6c50e512a5a4300da450c7f..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/modeling/roi_heads/mask_head.py
+++ /dev/null
@@ -1,292 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import List
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm
-from detectron2.structures import Instances
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.registry import Registry
-
-__all__ = [
-    "BaseMaskRCNNHead",
-    "MaskRCNNConvUpsampleHead",
-    "build_mask_head",
-    "ROI_MASK_HEAD_REGISTRY",
-]
-
-
-ROI_MASK_HEAD_REGISTRY = Registry("ROI_MASK_HEAD")
-ROI_MASK_HEAD_REGISTRY.__doc__ = """
-Registry for mask heads, which predicts instance masks given
-per-region features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-@torch.jit.unused
-def mask_rcnn_loss(pred_mask_logits: torch.Tensor, instances: List[Instances], vis_period: int = 0):
-    """
-    Compute the mask prediction loss defined in the Mask R-CNN paper.
-
-    Args:
-        pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask)
-            for class-specific or class-agnostic, where B is the total number of predicted masks
-            in all images, C is the number of foreground classes, and Hmask, Wmask are the height
-            and width of the mask predictions. The values are logits.
-        instances (list[Instances]): A list of N Instances, where N is the number of images
-            in the batch. These instances are in 1:1
-            correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask,
-            ...) associated with each instance are stored in fields.
-        vis_period (int): the period (in steps) to dump visualization.
-
-    Returns:
-        mask_loss (Tensor): A scalar tensor containing the loss.
-    """
-    cls_agnostic_mask = pred_mask_logits.size(1) == 1
-    total_num_masks = pred_mask_logits.size(0)
-    mask_side_len = pred_mask_logits.size(2)
-    assert pred_mask_logits.size(2) == pred_mask_logits.size(3), "Mask prediction must be square!"
-
-    gt_classes = []
-    gt_masks = []
-    for instances_per_image in instances:
-        if len(instances_per_image) == 0:
-            continue
-        if not cls_agnostic_mask:
-            gt_classes_per_image = instances_per_image.gt_classes.to(dtype=torch.int64)
-            gt_classes.append(gt_classes_per_image)
-
-        gt_masks_per_image = instances_per_image.gt_masks.crop_and_resize(
-            instances_per_image.proposal_boxes.tensor, mask_side_len
-        ).to(device=pred_mask_logits.device)
-        # A tensor of shape (N, M, M), N=#instances in the image; M=mask_side_len
-        gt_masks.append(gt_masks_per_image)
-
-    if len(gt_masks) == 0:
-        return pred_mask_logits.sum() * 0
-
-    gt_masks = cat(gt_masks, dim=0)
-
-    if cls_agnostic_mask:
-        pred_mask_logits = pred_mask_logits[:, 0]
-    else:
-        indices = torch.arange(total_num_masks)
-        gt_classes = cat(gt_classes, dim=0)
-        pred_mask_logits = pred_mask_logits[indices, gt_classes]
-
-    if gt_masks.dtype == torch.bool:
-        gt_masks_bool = gt_masks
-    else:
-        # Here we allow gt_masks to be float as well (depend on the implementation of rasterize())
-        gt_masks_bool = gt_masks > 0.5
-    gt_masks = gt_masks.to(dtype=torch.float32)
-
-    # Log the training accuracy (using gt classes and 0.5 threshold)
-    mask_incorrect = (pred_mask_logits > 0.0) != gt_masks_bool
-    mask_accuracy = 1 - (mask_incorrect.sum().item() / max(mask_incorrect.numel(), 1.0))
-    num_positive = gt_masks_bool.sum().item()
-    false_positive = (mask_incorrect & ~gt_masks_bool).sum().item() / max(
-        gt_masks_bool.numel() - num_positive, 1.0
-    )
-    false_negative = (mask_incorrect & gt_masks_bool).sum().item() / max(num_positive, 1.0)
-
-    storage = get_event_storage()
-    storage.put_scalar("mask_rcnn/accuracy", mask_accuracy)
-    storage.put_scalar("mask_rcnn/false_positive", false_positive)
-    storage.put_scalar("mask_rcnn/false_negative", false_negative)
-    if vis_period > 0 and storage.iter % vis_period == 0:
-        pred_masks = pred_mask_logits.sigmoid()
-        vis_masks = torch.cat([pred_masks, gt_masks], axis=2)
-        name = "Left: mask prediction;   Right: mask GT"
-        for idx, vis_mask in enumerate(vis_masks):
-            vis_mask = torch.stack([vis_mask] * 3, axis=0)
-            storage.put_image(name + f" ({idx})", vis_mask)
-
-    mask_loss = F.binary_cross_entropy_with_logits(pred_mask_logits, gt_masks, reduction="mean")
-    return mask_loss
-
-
-def mask_rcnn_inference(pred_mask_logits: torch.Tensor, pred_instances: List[Instances]):
-    """
-    Convert pred_mask_logits to estimated foreground probability masks while also
-    extracting only the masks for the predicted classes in pred_instances. For each
-    predicted box, the mask of the same class is attached to the instance by adding a
-    new "pred_masks" field to pred_instances.
-
-    Args:
-        pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask)
-            for class-specific or class-agnostic, where B is the total number of predicted masks
-            in all images, C is the number of foreground classes, and Hmask, Wmask are the height
-            and width of the mask predictions. The values are logits.
-        pred_instances (list[Instances]): A list of N Instances, where N is the number of images
-            in the batch. Each Instances must have field "pred_classes".
-
-    Returns:
-        None. pred_instances will contain an extra "pred_masks" field storing a mask of size (Hmask,
-            Wmask) for predicted class. Note that the masks are returned as a soft (non-quantized)
-            masks the resolution predicted by the network; post-processing steps, such as resizing
-            the predicted masks to the original image resolution and/or binarizing them, is left
-            to the caller.
-    """
-    cls_agnostic_mask = pred_mask_logits.size(1) == 1
-
-    if cls_agnostic_mask:
-        mask_probs_pred = pred_mask_logits.sigmoid()
-    else:
-        # Select masks corresponding to the predicted classes
-        num_masks = pred_mask_logits.shape[0]
-        class_pred = cat([i.pred_classes for i in pred_instances])
-        indices = torch.arange(num_masks, device=class_pred.device)
-        mask_probs_pred = pred_mask_logits[indices, class_pred][:, None].sigmoid()
-    # mask_probs_pred.shape: (B, 1, Hmask, Wmask)
-
-    num_boxes_per_image = [len(i) for i in pred_instances]
-    mask_probs_pred = mask_probs_pred.split(num_boxes_per_image, dim=0)
-
-    for prob, instances in zip(mask_probs_pred, pred_instances):
-        instances.pred_masks = prob  # (1, Hmask, Wmask)
-
-
-class BaseMaskRCNNHead(nn.Module):
-    """
-    Implement the basic Mask R-CNN losses and inference logic described in :paper:`Mask R-CNN`
-    """
-
-    @configurable
-    def __init__(self, *, loss_weight: float = 1.0, vis_period: int = 0):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            loss_weight (float): multiplier of the loss
-            vis_period (int): visualization period
-        """
-        super().__init__()
-        self.vis_period = vis_period
-        self.loss_weight = loss_weight
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        return {"vis_period": cfg.VIS_PERIOD}
-
-    def forward(self, x, instances: List[Instances]):
-        """
-        Args:
-            x: input region feature(s) provided by :class:`ROIHeads`.
-            instances (list[Instances]): contains the boxes & labels corresponding
-                to the input features.
-                Exact format is up to its caller to decide.
-                Typically, this is the foreground instances in training, with
-                "proposal_boxes" field and other gt annotations.
-                In inference, it contains boxes that are already predicted.
-
-        Returns:
-            A dict of losses in training. The predicted "instances" in inference.
-        """
-        x = self.layers(x)
-        if self.training:
-            return {"loss_mask": mask_rcnn_loss(x, instances, self.vis_period) * self.loss_weight}
-        else:
-            mask_rcnn_inference(x, instances)
-            return instances
-
-    def layers(self, x):
-        """
-        Neural network layers that makes predictions from input features.
-        """
-        raise NotImplementedError
-
-
-# To get torchscript support, we make the head a subclass of `nn.Sequential`.
-# Therefore, to add new layers in this head class, please make sure they are
-# added in the order they will be used in forward().
-@ROI_MASK_HEAD_REGISTRY.register()
-class MaskRCNNConvUpsampleHead(BaseMaskRCNNHead, nn.Sequential):
-    """
-    A mask head with several conv layers, plus an upsample layer (with `ConvTranspose2d`).
-    Predictions are made with a final 1x1 conv layer.
-    """
-
-    @configurable
-    def __init__(self, input_shape: ShapeSpec, *, num_classes, conv_dims, conv_norm="", **kwargs):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            input_shape (ShapeSpec): shape of the input feature
-            num_classes (int): the number of foreground classes (i.e. background is not
-                included). 1 if using class agnostic prediction.
-            conv_dims (list[int]): a list of N>0 integers representing the output dimensions
-                of N-1 conv layers and the last upsample layer.
-            conv_norm (str or callable): normalization for the conv layers.
-                See :func:`detectron2.layers.get_norm` for supported types.
-        """
-        super().__init__(**kwargs)
-        assert len(conv_dims) >= 1, "conv_dims have to be non-empty!"
-
-        self.conv_norm_relus = []
-
-        cur_channels = input_shape.channels
-        for k, conv_dim in enumerate(conv_dims[:-1]):
-            conv = Conv2d(
-                cur_channels,
-                conv_dim,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=not conv_norm,
-                norm=get_norm(conv_norm, conv_dim),
-                activation=nn.ReLU(),
-            )
-            self.add_module("mask_fcn{}".format(k + 1), conv)
-            self.conv_norm_relus.append(conv)
-            cur_channels = conv_dim
-
-        self.deconv = ConvTranspose2d(
-            cur_channels, conv_dims[-1], kernel_size=2, stride=2, padding=0
-        )
-        self.add_module("deconv_relu", nn.ReLU())
-        cur_channels = conv_dims[-1]
-
-        self.predictor = Conv2d(cur_channels, num_classes, kernel_size=1, stride=1, padding=0)
-
-        for layer in self.conv_norm_relus + [self.deconv]:
-            weight_init.c2_msra_fill(layer)
-        # use normal distribution initialization for mask prediction layer
-        nn.init.normal_(self.predictor.weight, std=0.001)
-        if self.predictor.bias is not None:
-            nn.init.constant_(self.predictor.bias, 0)
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        ret = super().from_config(cfg, input_shape)
-        conv_dim = cfg.MODEL.ROI_MASK_HEAD.CONV_DIM
-        num_conv = cfg.MODEL.ROI_MASK_HEAD.NUM_CONV
-        ret.update(
-            conv_dims=[conv_dim] * (num_conv + 1),  # +1 for ConvTranspose
-            conv_norm=cfg.MODEL.ROI_MASK_HEAD.NORM,
-            input_shape=input_shape,
-        )
-        if cfg.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK:
-            ret["num_classes"] = 1
-        else:
-            ret["num_classes"] = cfg.MODEL.ROI_HEADS.NUM_CLASSES
-        return ret
-
-    def layers(self, x):
-        for layer in self:
-            x = layer(x)
-        return x
-
-
-def build_mask_head(cfg, input_shape):
-    """
-    Build a mask head defined by `cfg.MODEL.ROI_MASK_HEAD.NAME`.
-    """
-    name = cfg.MODEL.ROI_MASK_HEAD.NAME
-    return ROI_MASK_HEAD_REGISTRY.get(name)(cfg, input_shape)
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/loading.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/loading.py
deleted file mode 100644
index d3692ae91f19b9c7ccf6023168788ff42c9e93e3..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/loading.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os.path as osp
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class LoadImageFromFile(object):
-    """Load an image from file.
-
-    Required keys are "img_prefix" and "img_info" (a dict that must contain the
-    key "filename"). Added or updated keys are "filename", "img", "img_shape",
-    "ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`),
-    "scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1).
-
-    Args:
-        to_float32 (bool): Whether to convert the loaded image to a float32
-            numpy array. If set to False, the loaded image is an uint8 array.
-            Defaults to False.
-        color_type (str): The flag argument for :func:`mmcv.imfrombytes`.
-            Defaults to 'color'.
-        file_client_args (dict): Arguments to instantiate a FileClient.
-            See :class:`mmcv.fileio.FileClient` for details.
-            Defaults to ``dict(backend='disk')``.
-        imdecode_backend (str): Backend for :func:`mmcv.imdecode`. Default:
-            'cv2'
-    """
-
-    def __init__(self,
-                 to_float32=False,
-                 color_type='color',
-                 file_client_args=dict(backend='disk'),
-                 imdecode_backend='cv2'):
-        self.to_float32 = to_float32
-        self.color_type = color_type
-        self.file_client_args = file_client_args.copy()
-        self.file_client = None
-        self.imdecode_backend = imdecode_backend
-
-    def __call__(self, results):
-        """Call functions to load image and get image meta information.
-
-        Args:
-            results (dict): Result dict from :obj:`mmseg.CustomDataset`.
-
-        Returns:
-            dict: The dict contains loaded image and meta information.
-        """
-
-        if self.file_client is None:
-            self.file_client = mmcv.FileClient(**self.file_client_args)
-
-        if results.get('img_prefix') is not None:
-            filename = osp.join(results['img_prefix'],
-                                results['img_info']['filename'])
-        else:
-            filename = results['img_info']['filename']
-        img_bytes = self.file_client.get(filename)
-        img = mmcv.imfrombytes(
-            img_bytes, flag=self.color_type, backend=self.imdecode_backend)
-        if self.to_float32:
-            img = img.astype(np.float32)
-
-        results['filename'] = filename
-        results['ori_filename'] = results['img_info']['filename']
-        results['img'] = img
-        results['img_shape'] = img.shape
-        results['ori_shape'] = img.shape
-        # Set initial values for default meta_keys
-        results['pad_shape'] = img.shape
-        results['scale_factor'] = 1.0
-        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
-        results['img_norm_cfg'] = dict(
-            mean=np.zeros(num_channels, dtype=np.float32),
-            std=np.ones(num_channels, dtype=np.float32),
-            to_rgb=False)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(to_float32={self.to_float32},'
-        repr_str += f"color_type='{self.color_type}',"
-        repr_str += f"imdecode_backend='{self.imdecode_backend}')"
-        return repr_str
-
-
-@PIPELINES.register_module()
-class LoadAnnotations(object):
-    """Load annotations for semantic segmentation.
-
-    Args:
-        reduce_zero_label (bool): Whether reduce all label value by 1.
-            Usually used for datasets where 0 is background label.
-            Default: False.
-        file_client_args (dict): Arguments to instantiate a FileClient.
-            See :class:`mmcv.fileio.FileClient` for details.
-            Defaults to ``dict(backend='disk')``.
-        imdecode_backend (str): Backend for :func:`mmcv.imdecode`. Default:
-            'pillow'
-    """
-
-    def __init__(self,
-                 reduce_zero_label=False,
-                 file_client_args=dict(backend='disk'),
-                 imdecode_backend='pillow'):
-        self.reduce_zero_label = reduce_zero_label
-        self.file_client_args = file_client_args.copy()
-        self.file_client = None
-        self.imdecode_backend = imdecode_backend
-
-    def __call__(self, results):
-        """Call function to load multiple types annotations.
-
-        Args:
-            results (dict): Result dict from :obj:`mmseg.CustomDataset`.
-
-        Returns:
-            dict: The dict contains loaded semantic segmentation annotations.
-        """
-
-        if self.file_client is None:
-            self.file_client = mmcv.FileClient(**self.file_client_args)
-
-        if results.get('seg_prefix', None) is not None:
-            filename = osp.join(results['seg_prefix'],
-                                results['ann_info']['seg_map'])
-        else:
-            filename = results['ann_info']['seg_map']
-        img_bytes = self.file_client.get(filename)
-        gt_semantic_seg = mmcv.imfrombytes(
-            img_bytes, flag='unchanged',
-            backend=self.imdecode_backend).squeeze().astype(np.uint8)
-        # modify if custom classes
-        if results.get('label_map', None) is not None:
-            for old_id, new_id in results['label_map'].items():
-                gt_semantic_seg[gt_semantic_seg == old_id] = new_id
-        # reduce zero_label
-        if self.reduce_zero_label:
-            # avoid using underflow conversion
-            gt_semantic_seg[gt_semantic_seg == 0] = 255
-            gt_semantic_seg = gt_semantic_seg - 1
-            gt_semantic_seg[gt_semantic_seg == 254] = 255
-        results['gt_semantic_seg'] = gt_semantic_seg
-        results['seg_fields'].append('gt_semantic_seg')
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(reduce_zero_label={self.reduce_zero_label},'
-        repr_str += f"imdecode_backend='{self.imdecode_backend}')"
-        return repr_str
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/epoch_based_runner.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/epoch_based_runner.py
deleted file mode 100644
index 766a9ce6afdf09cd11b1b15005f5132583011348..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/epoch_based_runner.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import platform
-import shutil
-import time
-import warnings
-
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-from .base_runner import BaseRunner
-from .builder import RUNNERS
-from .checkpoint import save_checkpoint
-from .utils import get_host_info
-
-
-@RUNNERS.register_module()
-class EpochBasedRunner(BaseRunner):
-    """Epoch-based Runner.
-
-    This runner train models epoch by epoch.
-    """
-
-    def run_iter(self, data_batch, train_mode, **kwargs):
-        if self.batch_processor is not None:
-            outputs = self.batch_processor(
-                self.model, data_batch, train_mode=train_mode, **kwargs)
-        elif train_mode:
-            outputs = self.model.train_step(data_batch, self.optimizer,
-                                            **kwargs)
-        else:
-            outputs = self.model.val_step(data_batch, self.optimizer, **kwargs)
-        if not isinstance(outputs, dict):
-            raise TypeError('"batch_processor()" or "model.train_step()"'
-                            'and "model.val_step()" must return a dict')
-        if 'log_vars' in outputs:
-            self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
-        self.outputs = outputs
-
-    def train(self, data_loader, **kwargs):
-        self.model.train()
-        self.mode = 'train'
-        self.data_loader = data_loader
-        self._max_iters = self._max_epochs * len(self.data_loader)
-        self.call_hook('before_train_epoch')
-        time.sleep(2)  # Prevent possible deadlock during epoch transition
-        for i, data_batch in enumerate(self.data_loader):
-            self._inner_iter = i
-            self.call_hook('before_train_iter')
-            self.run_iter(data_batch, train_mode=True, **kwargs)
-            self.call_hook('after_train_iter')
-            self._iter += 1
-
-        self.call_hook('after_train_epoch')
-        self._epoch += 1
-
-    @torch.no_grad()
-    def val(self, data_loader, **kwargs):
-        self.model.eval()
-        self.mode = 'val'
-        self.data_loader = data_loader
-        self.call_hook('before_val_epoch')
-        time.sleep(2)  # Prevent possible deadlock during epoch transition
-        for i, data_batch in enumerate(self.data_loader):
-            self._inner_iter = i
-            self.call_hook('before_val_iter')
-            self.run_iter(data_batch, train_mode=False)
-            self.call_hook('after_val_iter')
-
-        self.call_hook('after_val_epoch')
-
-    def run(self, data_loaders, workflow, max_epochs=None, **kwargs):
-        """Start running.
-
-        Args:
-            data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
-                and validation.
-            workflow (list[tuple]): A list of (phase, epochs) to specify the
-                running order and epochs. E.g, [('train', 2), ('val', 1)] means
-                running 2 epochs for training and 1 epoch for validation,
-                iteratively.
-        """
-        assert isinstance(data_loaders, list)
-        assert mmcv.is_list_of(workflow, tuple)
-        assert len(data_loaders) == len(workflow)
-        if max_epochs is not None:
-            warnings.warn(
-                'setting max_epochs in run is deprecated, '
-                'please set max_epochs in runner_config', DeprecationWarning)
-            self._max_epochs = max_epochs
-
-        assert self._max_epochs is not None, (
-            'max_epochs must be specified during instantiation')
-
-        for i, flow in enumerate(workflow):
-            mode, epochs = flow
-            if mode == 'train':
-                self._max_iters = self._max_epochs * len(data_loaders[i])
-                break
-
-        work_dir = self.work_dir if self.work_dir is not None else 'NONE'
-        self.logger.info('Start running, host: %s, work_dir: %s',
-                         get_host_info(), work_dir)
-        self.logger.info('Hooks will be executed in the following order:\n%s',
-                         self.get_hook_info())
-        self.logger.info('workflow: %s, max: %d epochs', workflow,
-                         self._max_epochs)
-        self.call_hook('before_run')
-
-        while self.epoch < self._max_epochs:
-            for i, flow in enumerate(workflow):
-                mode, epochs = flow
-                if isinstance(mode, str):  # self.train()
-                    if not hasattr(self, mode):
-                        raise ValueError(
-                            f'runner has no method named "{mode}" to run an '
-                            'epoch')
-                    epoch_runner = getattr(self, mode)
-                else:
-                    raise TypeError(
-                        'mode in workflow must be a str, but got {}'.format(
-                            type(mode)))
-
-                for _ in range(epochs):
-                    if mode == 'train' and self.epoch >= self._max_epochs:
-                        break
-                    epoch_runner(data_loaders[i], **kwargs)
-
-        time.sleep(1)  # wait for some hooks like loggers to finish
-        self.call_hook('after_run')
-
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl='epoch_{}.pth',
-                        save_optimizer=True,
-                        meta=None,
-                        create_symlink=True):
-        """Save the checkpoint.
-
-        Args:
-            out_dir (str): The directory that checkpoints are saved.
-            filename_tmpl (str, optional): The checkpoint filename template,
-                which contains a placeholder for the epoch number.
-                Defaults to 'epoch_{}.pth'.
-            save_optimizer (bool, optional): Whether to save the optimizer to
-                the checkpoint. Defaults to True.
-            meta (dict, optional): The meta information to be saved in the
-                checkpoint. Defaults to None.
-            create_symlink (bool, optional): Whether to create a symlink
-                "latest.pth" to point to the latest checkpoint.
-                Defaults to True.
-        """
-        if meta is None:
-            meta = {}
-        elif not isinstance(meta, dict):
-            raise TypeError(
-                f'meta should be a dict or None, but got {type(meta)}')
-        if self.meta is not None:
-            meta.update(self.meta)
-            # Note: meta.update(self.meta) should be done before
-            # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
-            # there will be problems with resumed checkpoints.
-            # More details in https://github.com/open-mmlab/mmcv/pull/1108
-        meta.update(epoch=self.epoch + 1, iter=self.iter)
-
-        filename = filename_tmpl.format(self.epoch + 1)
-        filepath = osp.join(out_dir, filename)
-        optimizer = self.optimizer if save_optimizer else None
-        save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
-        # in some environments, `os.symlink` is not supported, you may need to
-        # set `create_symlink` to False
-        if create_symlink:
-            dst_file = osp.join(out_dir, 'latest.pth')
-            if platform.system() != 'Windows':
-                mmcv.symlink(filename, dst_file)
-            else:
-                shutil.copy(filepath, dst_file)
-
-
-@RUNNERS.register_module()
-class Runner(EpochBasedRunner):
-    """Deprecated name of EpochBasedRunner."""
-
-    def __init__(self, *args, **kwargs):
-        warnings.warn(
-            'Runner was deprecated, please use EpochBasedRunner instead')
-        super().__init__(*args, **kwargs)
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/background.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/background.py
deleted file mode 100644
index 65c5fcbbff0f5fc9c3ecaac2257a875cf597fbd8..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/background.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import os
-import os.path
-import json
-from PIL import Image
-
-import torch
-import torchvision
-import torch.utils.data as data
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-
-class Background(data.Dataset):
-    """ Background
-
-    Args:
-        root (string): Root directory where images are downloaded to.
-        annFile (string): Path to json annotation file.
-        transform (callable, optional): A function/transform that  takes in an PIL image
-            and returns a transformed version. E.g, ``transforms.ToTensor``
-    """
-
-    def __init__(self, ann_file, root, remove_images_without_annotations=None, transforms=None):
-        self.root = root
-
-        with open(ann_file, 'r') as f:
-            self.ids = json.load(f)['images']
-        self.transform = transforms
-
-    def __getitem__(self, index):
-        """
-        Args:
-            index (int): Index
-
-        Returns:
-            tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
-        """
-        im_info = self.ids[index]
-        path = im_info['file_name']
-        fp = os.path.join(self.root, path)
-
-        img = Image.open(fp).convert('RGB')
-        if self.transform is not None:
-            img, _ = self.transform(img, None)
-        null_target = BoxList(torch.zeros((0,4)), (img.shape[-1], img.shape[-2]))
-        null_target.add_field('labels', torch.zeros(0))
-
-        return img, null_target, index
-
-    def __len__(self):
-        return len(self.ids)
-
-    def get_img_info(self, index):
-        im_info = self.ids[index]
-        return im_info
\ No newline at end of file
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/blocks.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/blocks.py
deleted file mode 100644
index 73f4feb0e83d058b6326d43de067af4adaf7f63b..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/backbone/blocks.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import torch.nn as nn
-from .ops import *
-
-
-class stem(nn.Module):
-    num_layer = 1
-
-    def __init__(self, conv, inplanes, planes, stride=1, norm_layer=nn.BatchNorm2d):
-        super(stem, self).__init__()
-
-        self.conv1 = conv(inplanes, planes, stride)
-        self.bn1 = norm_layer(planes)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        return out
-
-
-class basic(nn.Module):
-    expansion = 1
-    num_layer = 2
-
-    def __init__(self, conv, inplanes, planes, stride=1, midplanes=None, norm_layer=nn.BatchNorm2d):
-        super(basic, self).__init__()
-        midplanes = planes if midplanes is None else midplanes
-        self.conv1 = conv(inplanes, midplanes, stride)
-        self.bn1 = norm_layer(midplanes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv(midplanes, planes)
-        self.bn2 = norm_layer(planes)
-        if stride!=1 or inplanes!=planes*self.expansion:
-            self.downsample = nn.Sequential(
-                conv1x1(inplanes, planes, stride),
-                norm_layer(planes),
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x):
-        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):
-    expansion = 4
-    num_layer = 3
-
-    def __init__(self, conv, inplanes, planes, stride=1, midplanes=None, norm_layer=nn.BatchNorm2d):
-        super(bottleneck, self).__init__()
-        midplanes = planes if midplanes is None else midplanes
-        self.conv1 = conv1x1(inplanes, midplanes)
-        self.bn1 = norm_layer(midplanes)
-        self.conv2 = conv(midplanes, midplanes, stride)
-        self.bn2 = norm_layer(midplanes)
-        self.conv3 = conv1x1(midplanes, planes * self.expansion)
-        self.bn3 = norm_layer(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        if stride!=1 or inplanes!=planes*self.expansion:
-            self.downsample = nn.Sequential(
-                conv1x1(inplanes, planes*self.expansion, stride),
-                norm_layer(planes*self.expansion),
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x):
-        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 invert(nn.Module):
-    def __init__(self, conv, inp, oup, stride=1, expand_ratio=1, norm_layer=nn.BatchNorm2d):
-        super(invert, self).__init__()
-        self.stride = stride
-        assert stride in [1, 2]
-
-        hidden_dim = round(inp * expand_ratio)
-        self.use_res_connect = self.stride == 1 and inp == oup
-
-        if expand_ratio == 1:
-            self.conv = nn.Sequential(
-                # dw
-                conv(hidden_dim, hidden_dim, stride),
-                norm_layer(hidden_dim),
-                nn.ReLU6(inplace=True),
-                # pw-linear
-                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
-                norm_layer(oup),
-            )
-        else:
-            self.conv = nn.Sequential(
-                # pw
-                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
-                norm_layer(hidden_dim),
-                nn.ReLU6(inplace=True),
-                # dw
-                conv(hidden_dim, hidden_dim, stride),
-                norm_layer(hidden_dim),
-                nn.ReLU6(inplace=True),
-                # pw-linear
-                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
-                norm_layer(oup),
-            )
-
-    def forward(self, x):
-        if self.use_res_connect:
-            return x + self.conv(x)
-        else:
-            return self.conv(x)
-
-
-invert2 = lambda op, inp, outp, stride, **kwargs: invert(op, inp, outp, stride, expand_ratio=2, **kwargs)
-invert3 = lambda op, inp, outp, stride, **kwargs: invert(op, inp, outp, stride, expand_ratio=3, **kwargs)
-invert4 = lambda op, inp, outp, stride, **kwargs: invert(op, inp, outp, stride, expand_ratio=4, **kwargs)
-invert6 = lambda op, inp, outp, stride, **kwargs: invert(op, inp, outp, stride, expand_ratio=6, **kwargs)
-
-
-def channel_shuffle(x, groups):
-    batchsize, num_channels, height, width = x.data.size()
-    channels_per_group = num_channels // groups
-    # reshape
-    x = x.view(batchsize, groups, channels_per_group, height, width)
-    x = torch.transpose(x, 1, 2).contiguous()
-    # flatten
-    x = x.view(batchsize, -1, height, width)
-    return x
-
-
-class shuffle(nn.Module):
-    expansion = 1
-    num_layer = 3
-
-    def __init__(self, conv, inplanes, outplanes, stride=1, midplanes=None, norm_layer=nn.BatchNorm2d):
-        super(shuffle, self).__init__()
-        inplanes = inplanes // 2 if stride == 1 else inplanes
-        midplanes = outplanes // 2 if midplanes is None else midplanes
-        rightoutplanes = outplanes - inplanes
-        if stride == 2:
-            self.left_branch = nn.Sequential(
-                # dw
-                conv(inplanes, inplanes, stride),
-                norm_layer(inplanes),
-                # pw-linear
-                conv1x1(inplanes, inplanes),
-                norm_layer(inplanes),
-                nn.ReLU(inplace=True),
-            )
-
-        self.right_branch = nn.Sequential(
-            # pw
-            conv1x1(inplanes, midplanes),
-            norm_layer(midplanes),
-            nn.ReLU(inplace=True),
-            # dw
-            conv(midplanes, midplanes, stride),
-            norm_layer(midplanes),
-            # pw-linear
-            conv1x1(midplanes, rightoutplanes),
-            norm_layer(rightoutplanes),
-            nn.ReLU(inplace=True),
-        )
-
-        self.reduce = stride==2
-
-    def forward(self, x):
-        if self.reduce:
-            out = torch.cat((self.left_branch(x), self.right_branch(x)), 1)
-        else:
-            x1 = x[:, :(x.shape[1]//2), :, :]
-            x2 = x[:, (x.shape[1]//2):, :, :]
-            out = torch.cat((x1, self.right_branch(x2)), 1)
-
-        return channel_shuffle(out, 2)
-
-
-class shufflex(nn.Module):
-    expansion = 1
-    num_layer = 3
-
-    def __init__(self, conv, inplanes, outplanes, stride=1, midplanes=None, norm_layer=nn.BatchNorm2d):
-        super(shufflex, self).__init__()
-        inplanes = inplanes // 2 if stride == 1 else inplanes
-        midplanes = outplanes // 2 if midplanes is None else midplanes
-        rightoutplanes = outplanes - inplanes
-        if stride==2:
-            self.left_branch = nn.Sequential(
-                # dw
-                conv(inplanes, inplanes, stride),
-                norm_layer(inplanes),
-                # pw-linear
-                conv1x1(inplanes, inplanes),
-                norm_layer(inplanes),
-                nn.ReLU(inplace=True),
-            )
-
-        self.right_branch = nn.Sequential(
-            # dw
-            conv(inplanes, inplanes, stride),
-            norm_layer(inplanes),
-            # pw-linear
-            conv1x1(inplanes, midplanes),
-            norm_layer(midplanes),
-            nn.ReLU(inplace=True),
-            # dw
-            conv(midplanes, midplanes, 1),
-            norm_layer(midplanes),
-            # pw-linear
-            conv1x1(midplanes, midplanes),
-            norm_layer(midplanes),
-            nn.ReLU(inplace=True),
-            # dw
-            conv(midplanes, midplanes, 1),
-            norm_layer(midplanes),
-            # pw-linear
-            conv1x1(midplanes, rightoutplanes),
-            norm_layer(rightoutplanes),
-            nn.ReLU(inplace=True),
-        )
-
-        self.reduce = stride==2
-
-    def forward(self, x):
-        if self.reduce:
-            out = torch.cat((self.left_branch(x), self.right_branch(x)), 1)
-        else:
-            x1 = x[:, :(x.shape[1] // 2), :, :]
-            x2 = x[:, (x.shape[1] // 2):, :, :]
-            out = torch.cat((x1, self.right_branch(x2)), 1)
-
-        return channel_shuffle(out, 2)
\ No newline at end of file
diff --git a/spaces/Plachta/VALL-E-X/utils/generation.py b/spaces/Plachta/VALL-E-X/utils/generation.py
deleted file mode 100644
index b0952dba48146ba3be2f3f1133b9f0640ad30f3c..0000000000000000000000000000000000000000
--- a/spaces/Plachta/VALL-E-X/utils/generation.py
+++ /dev/null
@@ -1,257 +0,0 @@
-import os
-import torch
-import gdown
-import logging
-import psutil
-import langid
-langid.set_languages(['en', 'zh', 'ja'])
-
-import pathlib
-import platform
-if platform.system().lower() == 'windows':
-    temp = pathlib.PosixPath
-    pathlib.PosixPath = pathlib.WindowsPath
-elif platform.system().lower() == 'linux':
-    temp = pathlib.WindowsPath
-    pathlib.WindowsPath = pathlib.PosixPath
-
-import numpy as np
-from data.tokenizer import (
-    AudioTokenizer,
-    tokenize_audio,
-)
-from data.collation import get_text_token_collater
-from models.vallex import VALLE
-from utils.g2p import PhonemeBpeTokenizer
-from utils.sentence_cutter import split_text_into_sentences
-
-from macros import *
-
-device = torch.device("cpu")
-if torch.cuda.is_available():
-    device = torch.device("cuda", 0)
-
-url = 'https://drive.google.com/file/d/10gdQWvP-K_e1undkvv0p2b7SU6I4Egyl/view?usp=sharing'
-
-checkpoints_dir = "./checkpoints/"
-
-model_checkpoint_name = "vallex-checkpoint.pt"
-
-model = None
-
-codec = None
-
-text_tokenizer = PhonemeBpeTokenizer(tokenizer_path="./utils/g2p/bpe_69.json")
-text_collater = get_text_token_collater()
-
-def preload_models():
-    global model, codec
-    if not os.path.exists(checkpoints_dir): os.mkdir(checkpoints_dir)
-    if not os.path.exists(os.path.join(checkpoints_dir, model_checkpoint_name)):
-        gdown.download(id="10gdQWvP-K_e1undkvv0p2b7SU6I4Egyl", output=os.path.join(checkpoints_dir, model_checkpoint_name), quiet=False)
-    # VALL-E
-    model = VALLE(
-        N_DIM,
-        NUM_HEAD,
-        NUM_LAYERS,
-        norm_first=True,
-        add_prenet=False,
-        prefix_mode=PREFIX_MODE,
-        share_embedding=True,
-        nar_scale_factor=1.0,
-        prepend_bos=True,
-        num_quantizers=NUM_QUANTIZERS,
-    ).to(device)
-    checkpoint = torch.load(os.path.join(checkpoints_dir, model_checkpoint_name), map_location='cpu')
-    missing_keys, unexpected_keys = model.load_state_dict(
-        checkpoint["model"], strict=True
-    )
-    assert not missing_keys
-    model.eval()
-
-    # Encodec
-    codec = AudioTokenizer(device)
-
-@torch.no_grad()
-def generate_audio(text, prompt=None, language='auto', accent='no-accent'):
-    global model, codec, text_tokenizer, text_collater
-    text = text.replace("\n", "").strip(" ")
-    # detect language
-    if language == "auto":
-        language = langid.classify(text)[0]
-    lang_token = lang2token[language]
-    lang = token2lang[lang_token]
-    text = lang_token + text + lang_token
-
-    # load prompt
-    if prompt is not None:
-        prompt_path = prompt
-        if not os.path.exists(prompt_path):
-            prompt_path = "./presets/" + prompt + ".npz"
-        if not os.path.exists(prompt_path):
-            prompt_path = "./customs/" + prompt + ".npz"
-        if not os.path.exists(prompt_path):
-            raise ValueError(f"Cannot find prompt {prompt}")
-        prompt_data = np.load(prompt_path)
-        audio_prompts = prompt_data['audio_tokens']
-        text_prompts = prompt_data['text_tokens']
-        lang_pr = prompt_data['lang_code']
-        lang_pr = code2lang[int(lang_pr)]
-
-        # numpy to tensor
-        audio_prompts = torch.tensor(audio_prompts).type(torch.int32).to(device)
-        text_prompts = torch.tensor(text_prompts).type(torch.int32)
-    else:
-        audio_prompts = torch.zeros([1, 0, NUM_QUANTIZERS]).type(torch.int32).to(device)
-        text_prompts = torch.zeros([1, 0]).type(torch.int32)
-        lang_pr = lang if lang != 'mix' else 'en'
-
-    enroll_x_lens = text_prompts.shape[-1]
-    logging.info(f"synthesize text: {text}")
-    phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip())
-    text_tokens, text_tokens_lens = text_collater(
-        [
-            phone_tokens
-        ]
-    )
-    text_tokens = torch.cat([text_prompts, text_tokens], dim=-1)
-    text_tokens_lens += enroll_x_lens
-    # accent control
-    lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]]
-    encoded_frames = model.inference(
-        text_tokens.to(device),
-        text_tokens_lens.to(device),
-        audio_prompts,
-        enroll_x_lens=enroll_x_lens,
-        top_k=-100,
-        temperature=1,
-        prompt_language=lang_pr,
-        text_language=langs if accent == "no-accent" else lang,
-    )
-    samples = codec.decode(
-        [(encoded_frames.transpose(2, 1), None)]
-    )
-
-    return samples[0][0].cpu().numpy()
-
-@torch.no_grad()
-def generate_audio_from_long_text(text, prompt=None, language='auto', accent='no-accent', mode='sliding-window'):
-    """
-    For long audio generation, two modes are available.
-    fixed-prompt: This mode will keep using the same prompt the user has provided, and generate audio sentence by sentence.
-    sliding-window: This mode will use the last sentence as the prompt for the next sentence, but has some concern on speaker maintenance.
-    """
-    global model, codec, text_tokenizer, text_collater
-    if prompt is None or prompt == "":
-        mode = 'sliding-window'  # If no prompt is given, use sliding-window mode
-    sentences = split_text_into_sentences(text)
-    # detect language
-    if language == "auto":
-        language = langid.classify(text)[0]
-
-    # if initial prompt is given, encode it
-    if prompt is not None and prompt != "":
-        prompt_path = prompt
-        if not os.path.exists(prompt_path):
-            prompt_path = "./presets/" + prompt + ".npz"
-        if not os.path.exists(prompt_path):
-            prompt_path = "./customs/" + prompt + ".npz"
-        if not os.path.exists(prompt_path):
-            raise ValueError(f"Cannot find prompt {prompt}")
-        prompt_data = np.load(prompt_path)
-        audio_prompts = prompt_data['audio_tokens']
-        text_prompts = prompt_data['text_tokens']
-        lang_pr = prompt_data['lang_code']
-        lang_pr = code2lang[int(lang_pr)]
-
-        # numpy to tensor
-        audio_prompts = torch.tensor(audio_prompts).type(torch.int32).to(device)
-        text_prompts = torch.tensor(text_prompts).type(torch.int32)
-    else:
-        audio_prompts = torch.zeros([1, 0, NUM_QUANTIZERS]).type(torch.int32).to(device)
-        text_prompts = torch.zeros([1, 0]).type(torch.int32)
-        lang_pr = language if language != 'mix' else 'en'
-    if mode == 'fixed-prompt':
-        complete_tokens = torch.zeros([1, NUM_QUANTIZERS, 0]).type(torch.LongTensor).to(device)
-        for text in sentences:
-            text = text.replace("\n", "").strip(" ")
-            if text == "":
-                continue
-            lang_token = lang2token[language]
-            lang = token2lang[lang_token]
-            text = lang_token + text + lang_token
-
-            enroll_x_lens = text_prompts.shape[-1]
-            logging.info(f"synthesize text: {text}")
-            phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip())
-            text_tokens, text_tokens_lens = text_collater(
-                [
-                    phone_tokens
-                ]
-            )
-            text_tokens = torch.cat([text_prompts, text_tokens], dim=-1)
-            text_tokens_lens += enroll_x_lens
-            # accent control
-            lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]]
-            encoded_frames = model.inference(
-                text_tokens.to(device),
-                text_tokens_lens.to(device),
-                audio_prompts,
-                enroll_x_lens=enroll_x_lens,
-                top_k=-100,
-                temperature=1,
-                prompt_language=lang_pr,
-                text_language=langs if accent == "no-accent" else lang,
-            )
-            complete_tokens = torch.cat([complete_tokens, encoded_frames.transpose(2, 1)], dim=-1)
-        samples = codec.decode(
-            [(complete_tokens, None)]
-        )
-        return samples[0][0].cpu().numpy()
-    elif mode == "sliding-window":
-        complete_tokens = torch.zeros([1, NUM_QUANTIZERS, 0]).type(torch.LongTensor).to(device)
-        original_audio_prompts = audio_prompts
-        original_text_prompts = text_prompts
-        for text in sentences:
-            text = text.replace("\n", "").strip(" ")
-            if text == "":
-                continue
-            lang_token = lang2token[language]
-            lang = token2lang[lang_token]
-            text = lang_token + text + lang_token
-
-            enroll_x_lens = text_prompts.shape[-1]
-            logging.info(f"synthesize text: {text}")
-            phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip())
-            text_tokens, text_tokens_lens = text_collater(
-                [
-                    phone_tokens
-                ]
-            )
-            text_tokens = torch.cat([text_prompts, text_tokens], dim=-1)
-            text_tokens_lens += enroll_x_lens
-            # accent control
-            lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]]
-            encoded_frames = model.inference(
-                text_tokens.to(device),
-                text_tokens_lens.to(device),
-                audio_prompts,
-                enroll_x_lens=enroll_x_lens,
-                top_k=-100,
-                temperature=1,
-                prompt_language=lang_pr,
-                text_language=langs if accent == "no-accent" else lang,
-            )
-            complete_tokens = torch.cat([complete_tokens, encoded_frames.transpose(2, 1)], dim=-1)
-            if torch.rand(1) < 0.5:
-                audio_prompts = encoded_frames[:, :, -NUM_QUANTIZERS:]
-                text_prompts = text_tokens[:, enroll_x_lens:]
-            else:
-                audio_prompts = original_audio_prompts
-                text_prompts = original_text_prompts
-        samples = codec.decode(
-            [(complete_tokens, None)]
-        )
-        return samples[0][0].cpu().numpy()
-    else:
-        raise ValueError(f"No such mode {mode}")
diff --git a/spaces/Poupeto/RVC_Ryu7ztv/app.py b/spaces/Poupeto/RVC_Ryu7ztv/app.py
deleted file mode 100644
index 78860b1b60857b61f273cb10e5642dec9b01d83e..0000000000000000000000000000000000000000
--- a/spaces/Poupeto/RVC_Ryu7ztv/app.py
+++ /dev/null
@@ -1,514 +0,0 @@
-
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"
-
-audio_mode = []
-f0method_mode = []
-f0method_info = ""
-if limitation is True:
-    audio_mode = ["Upload audio", "TTS Audio"]
-    f0method_mode = ["pm", "harvest"]
-    f0method_info = "PM is fast, Harvest is good but extremely slow. (Default: PM)"
-else:
-    audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
-    f0method_mode = ["pm", "harvest", "crepe"]
-    f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)"
-
-def create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, file_index):
-    def vc_fn(
-        vc_audio_mode,
-        vc_input, 
-        vc_upload,
-        tts_text,
-        tts_voice,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        filter_radius,
-        resample_sr,
-        rms_mix_rate,
-        protect,
-    ):
-        try:
-            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
-                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
-            elif vc_audio_mode == "Upload audio":
-                if vc_upload is None:
-                    return "You need to upload an audio", None
-                sampling_rate, audio = vc_upload
-                duration = audio.shape[0] / sampling_rate
-                if duration > 240 and limitation:
-                    return "max 4 min, pour plus faites tourner en local ou demander moi sur discord", None
-                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                if len(audio.shape) > 1:
-                    audio = librosa.to_mono(audio.transpose(1, 0))
-                if sampling_rate != 16000:
-                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            elif vc_audio_mode == "TTS Audio":
-                if len(tts_text) > 100 and limitation:
-                    return "Text is too long", None
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-                vc_input = "tts.mp3"
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                vc_input,
-                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,
-            )
-            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            print(f"{model_title} | {info}")
-            return info, (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, None
-    return vc_fn
-
-def load_model():
-    categories = []
-    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
-        folder_info = json.load(f)
-    for category_name, category_info in folder_info.items():
-        if not category_info['enable']:
-            continue
-        category_title = category_info['title']
-        category_folder = category_info['folder_path']
-        description = category_info['description']
-        models = []
-        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
-            models_info = json.load(f)
-        for character_name, info in models_info.items():
-            if not info['enable']:
-                continue
-            model_title = info['title']
-            model_name = info['model_path']
-            model_author = info.get("author", None)
-            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
-            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
-            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
-            tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-            if_f0 = cpt.get("f0", 1)
-            version = cpt.get("version", "v1")
-            if version == "v1":
-                if if_f0 == 1:
-                    net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-                else:
-                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-                model_version = "V1"
-            elif version == "v2":
-                if if_f0 == 1:
-                    net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-                else:
-                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-                model_version = "V2"
-            del net_g.enc_q
-            print(net_g.load_state_dict(cpt["weight"], strict=False))
-            net_g.eval().to(config.device)
-            if config.is_half:
-                net_g = net_g.half()
-            else:
-                net_g = net_g.float()
-            vc = VC(tgt_sr, config)
-            print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
-            models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, model_index)))
-        categories.append([category_title, category_folder, description, models])
-    return categories
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
-    if url != "":
-        if not os.path.exists("dl_audio"):
-            os.mkdir("dl_audio")
-        if audio_provider == "Youtube":
-            ydl_opts = {
-                'noplaylist': True,
-                'format': 'bestaudio/best',
-                'postprocessors': [{
-                    'key': 'FFmpegExtractAudio',
-                    'preferredcodec': 'wav',
-                }],
-                "outtmpl": 'dl_audio/youtube_audio',
-            }
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                ydl.download([url])
-            audio_path = "dl_audio/youtube_audio.wav"
-        if split_model == "htdemucs":
-            command = f"demucs --two-stems=vocals {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
-        else:
-            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
-    else:
-        raise gr.Error("URL Required!")
-        return None, None, None, None
-
-def combine_vocal_and_inst(audio_data, audio_volume, split_model):
-    if not os.path.exists("output/result"):
-        os.mkdir("output/result")
-    vocal_path = "output/result/output.wav"
-    output_path = "output/result/combine.mp3"
-    if split_model == "htdemucs":
-        inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
-    else:
-        inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
-    with wave.open(vocal_path, "w") as wave_file:
-        wave_file.setnchannels(1) 
-        wave_file.setsampwidth(2)
-        wave_file.setframerate(audio_data[0])
-        wave_file.writeframes(audio_data[1].tobytes())
-    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
-    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-    print(result.stdout.decode())
-    return output_path
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-def change_audio_mode(vc_audio_mode):
-    if vc_audio_mode == "Input path":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=True),
-            gr.Checkbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "Upload audio":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Checkbox.update(visible=True),
-            gr.Audio.update(visible=True),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "Youtube":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Checkbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=True),
-            gr.Textbox.update(visible=True),
-            gr.Dropdown.update(visible=True),
-            gr.Button.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Button.update(visible=True),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "TTS Audio":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Checkbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=True),
-            gr.Dropdown.update(visible=True)
-        )
-    else:
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Checkbox.update(visible=True),
-            gr.Audio.update(visible=True),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-
-def use_microphone(microphone):
-    if microphone == True:
-        return gr.Audio.update(source="microphone")
-    else:
-        return gr.Audio.update(source="upload")
-
-if __name__ == '__main__':
-    load_hubert()
-    categories = load_model()
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "<div align='center'>\n\n"+
-            "# RVC Ryu7ztv\n\n"
-        )
-        for (folder_title, folder, description, models) in categories:
-            with gr.TabItem(folder_title):
-                if description:
-                    gr.Markdown(f"### <center> {description}")
-                with gr.Tabs():
-                    if not models:
-                        gr.Markdown("# <center> No Model Loaded.")
-                        gr.Markdown("## <center> Please add model or fix your model path.")
-                        continue
-                    for (name, title, author, cover, model_version, vc_fn) in models:
-                        with gr.TabItem(name):
-                            with gr.Row():
-                                gr.Markdown(
-                                    '<div align="center">'
-                                    f'<div>{title}</div>\n'+
-                                    f'<div>RVC {model_version} Model</div>\n'+
-                                    (f'<div>Model author: {author}</div>' if author else "")+
-                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
-                                    '</div>'
-                                )
-                            with gr.Row():
-                                with gr.Column():
-                                    vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
-                                    # Input
-                                    vc_input = gr.Textbox(label="Input audio path", visible=False)
-                                    # Upload
-                                    vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
-                                    vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
-                                    # Youtube
-                                    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
-                                    vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
-                                    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-                                    vc_split = gr.Button("Split Audio", variant="primary", visible=False)
-                                    vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
-                                    vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
-                                    vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
-                                    # TTS
-                                    tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
-                                    tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                                with gr.Column():
-                                    vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-                                    f0method0 = gr.Radio(
-                                        label="Pitch extraction algorithm",
-                                        info=f0method_info,
-                                        choices=f0method_mode,
-                                        value="pm",
-                                        interactive=True
-                                    )
-                                    index_rate1 = gr.Slider(
-                                        minimum=0,
-                                        maximum=1,
-                                        label="Retrieval feature ratio",
-                                        info="(Default: 0.7)",
-                                        value=0.7,
-                                        interactive=True,
-                                    )
-                                    filter_radius0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=7,
-                                        label="Apply Median Filtering",
-                                        info="The value represents the filter radius and can reduce breathiness.",
-                                        value=3,
-                                        step=1,
-                                        interactive=True,
-                                    )
-                                    resample_sr0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=48000,
-                                        label="Resample the output audio",
-                                        info="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="Volume Envelope",
-                                        info="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,
-                                    )
-                                    protect0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=0.5,
-                                        label="Voice Protection",
-                                        info="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=0.5,
-                                        step=0.01,
-                                        interactive=True,
-                                    )
-                                with gr.Column():
-                                    vc_log = gr.Textbox(label="Output Information", interactive=False)
-                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
-                                    vc_convert = gr.Button("Convert", variant="primary")
-                                    vc_volume = gr.Slider(
-                                        minimum=0,
-                                        maximum=10,
-                                        label="Vocal volume",
-                                        value=4,
-                                        interactive=True,
-                                        step=1,
-                                        info="Adjust vocal volume (Default: 4}",
-                                        visible=False
-                                    )
-                                    vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
-                                    vc_combine =  gr.Button("Combine",variant="primary", visible=False)
-                        vc_convert.click(
-                            fn=vc_fn, 
-                            inputs=[
-                                vc_audio_mode,
-                                vc_input,
-                                vc_upload,
-                                tts_text,
-                                tts_voice,
-                                vc_transform0,
-                                f0method0,
-                                index_rate1,
-                                filter_radius0,
-                                resample_sr0,
-                                rms_mix_rate0,
-                                protect0,
-                            ], 
-                            outputs=[vc_log ,vc_output]
-                        )
-                        vc_split.click(
-                            fn=cut_vocal_and_inst, 
-                            inputs=[vc_link, vc_download_audio, vc_split_model], 
-                            outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
-                        )
-                        vc_combine.click(
-                            fn=combine_vocal_and_inst,
-                            inputs=[vc_output, vc_volume, vc_split_model],
-                            outputs=[vc_combined_output]
-                        )
-                        vc_microphone_mode.change(
-                            fn=use_microphone,
-                            inputs=vc_microphone_mode,
-                            outputs=vc_upload
-                        )
-                        vc_audio_mode.change(
-                            fn=change_audio_mode,
-                            inputs=[vc_audio_mode],
-                            outputs=[
-                                vc_input,
-                                vc_microphone_mode,
-                                vc_upload,
-                                vc_download_audio,
-                                vc_link,
-                                vc_split_model,
-                                vc_split,
-                                vc_vocal_preview,
-                                vc_inst_preview,
-                                vc_audio_preview,
-                                vc_volume,
-                                vc_combined_output,
-                                vc_combine,
-                                tts_text,
-                                tts_voice
-                            ]
-                        )
-        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/data/music_dataset.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/data/music_dataset.py
deleted file mode 100644
index 4e28796939f9cde2b23a2c4bf43fd7ba5fa26b2d..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/data/music_dataset.py
+++ /dev/null
@@ -1,270 +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.
-"""Dataset of music tracks with rich metadata.
-"""
-from dataclasses import dataclass, field, fields, replace
-import gzip
-import json
-import logging
-from pathlib import Path
-import random
-import typing as tp
-
-import torch
-
-from .info_audio_dataset import (
-    InfoAudioDataset,
-    AudioInfo,
-    get_keyword_list,
-    get_keyword,
-    get_string
-)
-from ..modules.conditioners import (
-    ConditioningAttributes,
-    JointEmbedCondition,
-    WavCondition,
-)
-from ..utils.utils import warn_once
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class MusicInfo(AudioInfo):
-    """Segment info augmented with music metadata.
-    """
-    # music-specific metadata
-    title: tp.Optional[str] = None
-    artist: tp.Optional[str] = None  # anonymized artist id, used to ensure no overlap between splits
-    key: tp.Optional[str] = None
-    bpm: tp.Optional[float] = None
-    genre: tp.Optional[str] = None
-    moods: tp.Optional[list] = None
-    keywords: tp.Optional[list] = None
-    description: tp.Optional[str] = None
-    name: tp.Optional[str] = None
-    instrument: tp.Optional[str] = None
-    # original wav accompanying the metadata
-    self_wav: tp.Optional[WavCondition] = None
-    # dict mapping attributes names to tuple of wav, text and metadata
-    joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)
-
-    @property
-    def has_music_meta(self) -> bool:
-        return self.name is not None
-
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        out = ConditioningAttributes()
-        for _field in fields(self):
-            key, value = _field.name, getattr(self, _field.name)
-            if key == 'self_wav':
-                out.wav[key] = value
-            elif key == 'joint_embed':
-                for embed_attribute, embed_cond in value.items():
-                    out.joint_embed[embed_attribute] = embed_cond
-            else:
-                if isinstance(value, list):
-                    value = ' '.join(value)
-                out.text[key] = value
-        return out
-
-    @staticmethod
-    def attribute_getter(attribute):
-        if attribute == 'bpm':
-            preprocess_func = get_bpm
-        elif attribute == 'key':
-            preprocess_func = get_musical_key
-        elif attribute in ['moods', 'keywords']:
-            preprocess_func = get_keyword_list
-        elif attribute in ['genre', 'name', 'instrument']:
-            preprocess_func = get_keyword
-        elif attribute in ['title', 'artist', 'description']:
-            preprocess_func = get_string
-        else:
-            preprocess_func = None
-        return preprocess_func
-
-    @classmethod
-    def from_dict(cls, dictionary: dict, fields_required: bool = False):
-        _dictionary: tp.Dict[str, tp.Any] = {}
-
-        # allow a subset of attributes to not be loaded from the dictionary
-        # these attributes may be populated later
-        post_init_attributes = ['self_wav', 'joint_embed']
-        optional_fields = ['keywords']
-
-        for _field in fields(cls):
-            if _field.name in post_init_attributes:
-                continue
-            elif _field.name not in dictionary:
-                if fields_required and _field.name not in optional_fields:
-                    raise KeyError(f"Unexpected missing key: {_field.name}")
-            else:
-                preprocess_func: tp.Optional[tp.Callable] = cls.attribute_getter(_field.name)
-                value = dictionary[_field.name]
-                if preprocess_func:
-                    value = preprocess_func(value)
-                _dictionary[_field.name] = value
-        return cls(**_dictionary)
-
-
-def augment_music_info_description(music_info: MusicInfo, merge_text_p: float = 0.,
-                                   drop_desc_p: float = 0., drop_other_p: float = 0.) -> MusicInfo:
-    """Augment MusicInfo description with additional metadata fields and potential dropout.
-    Additional textual attributes are added given probability 'merge_text_conditions_p' and
-    the original textual description is dropped from the augmented description given probability drop_desc_p.
-
-    Args:
-        music_info (MusicInfo): The music metadata to augment.
-        merge_text_p (float): Probability of merging additional metadata to the description.
-            If provided value is 0, then no merging is performed.
-        drop_desc_p (float): Probability of dropping the original description on text merge.
-            if provided value is 0, then no drop out is performed.
-        drop_other_p (float): Probability of dropping the other fields used for text augmentation.
-    Returns:
-        MusicInfo: The MusicInfo with augmented textual description.
-    """
-    def is_valid_field(field_name: str, field_value: tp.Any) -> bool:
-        valid_field_name = field_name in ['key', 'bpm', 'genre', 'moods', 'instrument', 'keywords']
-        valid_field_value = field_value is not None and isinstance(field_value, (int, float, str, list))
-        keep_field = random.uniform(0, 1) < drop_other_p
-        return valid_field_name and valid_field_value and keep_field
-
-    def process_value(v: tp.Any) -> str:
-        if isinstance(v, (int, float, str)):
-            return str(v)
-        if isinstance(v, list):
-            return ", ".join(v)
-        else:
-            raise ValueError(f"Unknown type for text value! ({type(v), v})")
-
-    description = music_info.description
-
-    metadata_text = ""
-    if random.uniform(0, 1) < merge_text_p:
-        meta_pairs = [f'{_field.name}: {process_value(getattr(music_info, _field.name))}'
-                      for _field in fields(music_info) if is_valid_field(_field.name, getattr(music_info, _field.name))]
-        random.shuffle(meta_pairs)
-        metadata_text = ". ".join(meta_pairs)
-        description = description if not random.uniform(0, 1) < drop_desc_p else None
-        logger.debug(f"Applying text augmentation on MMI info. description: {description}, metadata: {metadata_text}")
-
-    if description is None:
-        description = metadata_text if len(metadata_text) > 1 else None
-    else:
-        description = ". ".join([description.rstrip('.'), metadata_text])
-    description = description.strip() if description else None
-
-    music_info = replace(music_info)
-    music_info.description = description
-    return music_info
-
-
-class Paraphraser:
-    def __init__(self, paraphrase_source: tp.Union[str, Path], paraphrase_p: float = 0.):
-        self.paraphrase_p = paraphrase_p
-        open_fn = gzip.open if str(paraphrase_source).lower().endswith('.gz') else open
-        with open_fn(paraphrase_source, 'rb') as f:  # type: ignore
-            self.paraphrase_source = json.loads(f.read())
-        logger.info(f"loaded paraphrasing source from: {paraphrase_source}")
-
-    def sample_paraphrase(self, audio_path: str, description: str):
-        if random.random() >= self.paraphrase_p:
-            return description
-        info_path = Path(audio_path).with_suffix('.json')
-        if info_path not in self.paraphrase_source:
-            warn_once(logger, f"{info_path} not in paraphrase source!")
-            return description
-        new_desc = random.choice(self.paraphrase_source[info_path])
-        logger.debug(f"{description} -> {new_desc}")
-        return new_desc
-
-
-class MusicDataset(InfoAudioDataset):
-    """Music dataset is an AudioDataset with music-related metadata.
-
-    Args:
-        info_fields_required (bool): Whether to enforce having required fields.
-        merge_text_p (float): Probability of merging additional metadata to the description.
-        drop_desc_p (float): Probability of dropping the original description on text merge.
-        drop_other_p (float): Probability of dropping the other fields used for text augmentation.
-        joint_embed_attributes (list[str]): A list of attributes for which joint embedding metadata is returned.
-        paraphrase_source (str, optional): Path to the .json or .json.gz file containing the
-            paraphrases for the description. The json should be a dict with keys are the
-            original info path (e.g. track_path.json) and each value is a list of possible
-            paraphrased.
-        paraphrase_p (float): probability of taking a paraphrase.
-
-    See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
-    """
-    def __init__(self, *args, info_fields_required: bool = True,
-                 merge_text_p: float = 0., drop_desc_p: float = 0., drop_other_p: float = 0.,
-                 joint_embed_attributes: tp.List[str] = [],
-                 paraphrase_source: tp.Optional[str] = None, paraphrase_p: float = 0,
-                 **kwargs):
-        kwargs['return_info'] = True  # We require the info for each song of the dataset.
-        super().__init__(*args, **kwargs)
-        self.info_fields_required = info_fields_required
-        self.merge_text_p = merge_text_p
-        self.drop_desc_p = drop_desc_p
-        self.drop_other_p = drop_other_p
-        self.joint_embed_attributes = joint_embed_attributes
-        self.paraphraser = None
-        if paraphrase_source is not None:
-            self.paraphraser = Paraphraser(paraphrase_source, paraphrase_p)
-
-    def __getitem__(self, index):
-        wav, info = super().__getitem__(index)
-        info_data = info.to_dict()
-        music_info_path = Path(info.meta.path).with_suffix('.json')
-
-        if Path(music_info_path).exists():
-            with open(music_info_path, 'r') as json_file:
-                music_data = json.load(json_file)
-                music_data.update(info_data)
-                music_info = MusicInfo.from_dict(music_data, fields_required=self.info_fields_required)
-            if self.paraphraser is not None:
-                music_info.description = self.paraphraser.sample(music_info.meta.path, music_info.description)
-            if self.merge_text_p:
-                music_info = augment_music_info_description(
-                    music_info, self.merge_text_p, self.drop_desc_p, self.drop_other_p)
-        else:
-            music_info = MusicInfo.from_dict(info_data, fields_required=False)
-
-        music_info.self_wav = WavCondition(
-            wav=wav[None], length=torch.tensor([info.n_frames]),
-            sample_rate=[info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-
-        for att in self.joint_embed_attributes:
-            att_value = getattr(music_info, att)
-            joint_embed_cond = JointEmbedCondition(
-                wav[None], [att_value], torch.tensor([info.n_frames]),
-                sample_rate=[info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-            music_info.joint_embed[att] = joint_embed_cond
-
-        return wav, music_info
-
-
-def get_musical_key(value: tp.Optional[str]) -> tp.Optional[str]:
-    """Preprocess key keywords, discarding them if there are multiple key defined."""
-    if value is None or (not isinstance(value, str)) or len(value) == 0 or value == 'None':
-        return None
-    elif ',' in value:
-        # For now, we discard when multiple keys are defined separated with comas
-        return None
-    else:
-        return value.strip().lower()
-
-
-def get_bpm(value: tp.Optional[str]) -> tp.Optional[float]:
-    """Preprocess to a float."""
-    if value is None:
-        return None
-    try:
-        return float(value)
-    except ValueError:
-        return None
diff --git a/spaces/RMXK/RVC_HFF/README.md b/spaces/RMXK/RVC_HFF/README.md
deleted file mode 100644
index 9d8914cd05791e4f8db6267eb2a5fe2133e22e58..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: RVC Inference HF
-emoji: 👀
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/RamV/ChatRobo_II/README.md b/spaces/RamV/ChatRobo_II/README.md
deleted file mode 100644
index 1ea6a0e725c0ccb28b543c7a03ef2fdb63b9c739..0000000000000000000000000000000000000000
--- a/spaces/RamV/ChatRobo_II/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ChatRobo II
-emoji: 🐠
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/connection.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/connection.py
deleted file mode 100644
index 10fb36c4e350d8ca6f65e4036a60c48a9b3216fc..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/connection.py
+++ /dev/null
@@ -1,567 +0,0 @@
-from __future__ import absolute_import
-
-import datetime
-import logging
-import os
-import re
-import socket
-import warnings
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-from .packages import six
-from .packages.six.moves.http_client import HTTPConnection as _HTTPConnection
-from .packages.six.moves.http_client import HTTPException  # noqa: F401
-from .util.proxy import create_proxy_ssl_context
-
-try:  # Compiled with SSL?
-    import ssl
-
-    BaseSSLError = ssl.SSLError
-except (ImportError, AttributeError):  # Platform-specific: No SSL.
-    ssl = None
-
-    class BaseSSLError(BaseException):
-        pass
-
-
-try:
-    # Python 3: not a no-op, we're adding this to the namespace so it can be imported.
-    ConnectionError = ConnectionError
-except NameError:
-    # Python 2
-    class ConnectionError(Exception):
-        pass
-
-
-try:  # Python 3:
-    # Not a no-op, we're adding this to the namespace so it can be imported.
-    BrokenPipeError = BrokenPipeError
-except NameError:  # Python 2:
-
-    class BrokenPipeError(Exception):
-        pass
-
-
-from ._collections import HTTPHeaderDict  # noqa (historical, removed in v2)
-from ._version import __version__
-from .exceptions import (
-    ConnectTimeoutError,
-    NewConnectionError,
-    SubjectAltNameWarning,
-    SystemTimeWarning,
-)
-from .util import SKIP_HEADER, SKIPPABLE_HEADERS, connection
-from .util.ssl_ import (
-    assert_fingerprint,
-    create_urllib3_context,
-    is_ipaddress,
-    resolve_cert_reqs,
-    resolve_ssl_version,
-    ssl_wrap_socket,
-)
-from .util.ssl_match_hostname import CertificateError, match_hostname
-
-log = logging.getLogger(__name__)
-
-port_by_scheme = {"http": 80, "https": 443}
-
-# When it comes time to update this value as a part of regular maintenance
-# (ie test_recent_date is failing) update it to ~6 months before the current date.
-RECENT_DATE = datetime.date(2022, 1, 1)
-
-_CONTAINS_CONTROL_CHAR_RE = re.compile(r"[^-!#$%&'*+.^_`|~0-9a-zA-Z]")
-
-
-class HTTPConnection(_HTTPConnection, object):
-    """
-    Based on :class:`http.client.HTTPConnection` but provides an extra constructor
-    backwards-compatibility layer between older and newer Pythons.
-
-    Additional keyword parameters are used to configure attributes of the connection.
-    Accepted parameters include:
-
-    - ``strict``: See the documentation on :class:`urllib3.connectionpool.HTTPConnectionPool`
-    - ``source_address``: Set the source address for the current connection.
-    - ``socket_options``: Set specific options on the underlying socket. If not specified, then
-      defaults are loaded from ``HTTPConnection.default_socket_options`` which includes disabling
-      Nagle's algorithm (sets TCP_NODELAY to 1) unless the connection is behind a proxy.
-
-      For example, if you wish to enable TCP Keep Alive in addition to the defaults,
-      you might pass:
-
-      .. code-block:: python
-
-         HTTPConnection.default_socket_options + [
-             (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),
-         ]
-
-      Or you may want to disable the defaults by passing an empty list (e.g., ``[]``).
-    """
-
-    default_port = port_by_scheme["http"]
-
-    #: Disable Nagle's algorithm by default.
-    #: ``[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]``
-    default_socket_options = [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
-
-    #: Whether this connection verifies the host's certificate.
-    is_verified = False
-
-    #: Whether this proxy connection (if used) verifies the proxy host's
-    #: certificate.
-    proxy_is_verified = None
-
-    def __init__(self, *args, **kw):
-        if not six.PY2:
-            kw.pop("strict", None)
-
-        # Pre-set source_address.
-        self.source_address = kw.get("source_address")
-
-        #: The socket options provided by the user. If no options are
-        #: provided, we use the default options.
-        self.socket_options = kw.pop("socket_options", self.default_socket_options)
-
-        # Proxy options provided by the user.
-        self.proxy = kw.pop("proxy", None)
-        self.proxy_config = kw.pop("proxy_config", None)
-
-        _HTTPConnection.__init__(self, *args, **kw)
-
-    @property
-    def host(self):
-        """
-        Getter method to remove any trailing dots that indicate the hostname is an FQDN.
-
-        In general, SSL certificates don't include the trailing dot indicating a
-        fully-qualified domain name, and thus, they don't validate properly when
-        checked against a domain name that includes the dot. In addition, some
-        servers may not expect to receive the trailing dot when provided.
-
-        However, the hostname with trailing dot is critical to DNS resolution; doing a
-        lookup with the trailing dot will properly only resolve the appropriate FQDN,
-        whereas a lookup without a trailing dot will search the system's search domain
-        list. Thus, it's important to keep the original host around for use only in
-        those cases where it's appropriate (i.e., when doing DNS lookup to establish the
-        actual TCP connection across which we're going to send HTTP requests).
-        """
-        return self._dns_host.rstrip(".")
-
-    @host.setter
-    def host(self, value):
-        """
-        Setter for the `host` property.
-
-        We assume that only urllib3 uses the _dns_host attribute; httplib itself
-        only uses `host`, and it seems reasonable that other libraries follow suit.
-        """
-        self._dns_host = value
-
-    def _new_conn(self):
-        """Establish a socket connection and set nodelay settings on it.
-
-        :return: New socket connection.
-        """
-        extra_kw = {}
-        if self.source_address:
-            extra_kw["source_address"] = self.source_address
-
-        if self.socket_options:
-            extra_kw["socket_options"] = self.socket_options
-
-        try:
-            conn = connection.create_connection(
-                (self._dns_host, self.port), self.timeout, **extra_kw
-            )
-
-        except SocketTimeout:
-            raise ConnectTimeoutError(
-                self,
-                "Connection to %s timed out. (connect timeout=%s)"
-                % (self.host, self.timeout),
-            )
-
-        except SocketError as e:
-            raise NewConnectionError(
-                self, "Failed to establish a new connection: %s" % e
-            )
-
-        return conn
-
-    def _is_using_tunnel(self):
-        # Google App Engine's httplib does not define _tunnel_host
-        return getattr(self, "_tunnel_host", None)
-
-    def _prepare_conn(self, conn):
-        self.sock = conn
-        if self._is_using_tunnel():
-            # TODO: Fix tunnel so it doesn't depend on self.sock state.
-            self._tunnel()
-            # Mark this connection as not reusable
-            self.auto_open = 0
-
-    def connect(self):
-        conn = self._new_conn()
-        self._prepare_conn(conn)
-
-    def putrequest(self, method, url, *args, **kwargs):
-        """ """
-        # Empty docstring because the indentation of CPython's implementation
-        # is broken but we don't want this method in our documentation.
-        match = _CONTAINS_CONTROL_CHAR_RE.search(method)
-        if match:
-            raise ValueError(
-                "Method cannot contain non-token characters %r (found at least %r)"
-                % (method, match.group())
-            )
-
-        return _HTTPConnection.putrequest(self, method, url, *args, **kwargs)
-
-    def putheader(self, header, *values):
-        """ """
-        if not any(isinstance(v, str) and v == SKIP_HEADER for v in values):
-            _HTTPConnection.putheader(self, header, *values)
-        elif six.ensure_str(header.lower()) not in SKIPPABLE_HEADERS:
-            raise ValueError(
-                "urllib3.util.SKIP_HEADER only supports '%s'"
-                % ("', '".join(map(str.title, sorted(SKIPPABLE_HEADERS))),)
-            )
-
-    def request(self, method, url, body=None, headers=None):
-        if headers is None:
-            headers = {}
-        else:
-            # Avoid modifying the headers passed into .request()
-            headers = headers.copy()
-        if "user-agent" not in (six.ensure_str(k.lower()) for k in headers):
-            headers["User-Agent"] = _get_default_user_agent()
-        super(HTTPConnection, self).request(method, url, body=body, headers=headers)
-
-    def request_chunked(self, method, url, body=None, headers=None):
-        """
-        Alternative to the common request method, which sends the
-        body with chunked encoding and not as one block
-        """
-        headers = headers or {}
-        header_keys = set([six.ensure_str(k.lower()) for k in headers])
-        skip_accept_encoding = "accept-encoding" in header_keys
-        skip_host = "host" in header_keys
-        self.putrequest(
-            method, url, skip_accept_encoding=skip_accept_encoding, skip_host=skip_host
-        )
-        if "user-agent" not in header_keys:
-            self.putheader("User-Agent", _get_default_user_agent())
-        for header, value in headers.items():
-            self.putheader(header, value)
-        if "transfer-encoding" not in header_keys:
-            self.putheader("Transfer-Encoding", "chunked")
-        self.endheaders()
-
-        if body is not None:
-            stringish_types = six.string_types + (bytes,)
-            if isinstance(body, stringish_types):
-                body = (body,)
-            for chunk in body:
-                if not chunk:
-                    continue
-                if not isinstance(chunk, bytes):
-                    chunk = chunk.encode("utf8")
-                len_str = hex(len(chunk))[2:]
-                to_send = bytearray(len_str.encode())
-                to_send += b"\r\n"
-                to_send += chunk
-                to_send += b"\r\n"
-                self.send(to_send)
-
-        # After the if clause, to always have a closed body
-        self.send(b"0\r\n\r\n")
-
-
-class HTTPSConnection(HTTPConnection):
-    """
-    Many of the parameters to this constructor are passed to the underlying SSL
-    socket by means of :py:func:`urllib3.util.ssl_wrap_socket`.
-    """
-
-    default_port = port_by_scheme["https"]
-
-    cert_reqs = None
-    ca_certs = None
-    ca_cert_dir = None
-    ca_cert_data = None
-    ssl_version = None
-    assert_fingerprint = None
-    tls_in_tls_required = False
-
-    def __init__(
-        self,
-        host,
-        port=None,
-        key_file=None,
-        cert_file=None,
-        key_password=None,
-        strict=None,
-        timeout=socket._GLOBAL_DEFAULT_TIMEOUT,
-        ssl_context=None,
-        server_hostname=None,
-        **kw
-    ):
-
-        HTTPConnection.__init__(self, host, port, strict=strict, timeout=timeout, **kw)
-
-        self.key_file = key_file
-        self.cert_file = cert_file
-        self.key_password = key_password
-        self.ssl_context = ssl_context
-        self.server_hostname = server_hostname
-
-        # Required property for Google AppEngine 1.9.0 which otherwise causes
-        # HTTPS requests to go out as HTTP. (See Issue #356)
-        self._protocol = "https"
-
-    def set_cert(
-        self,
-        key_file=None,
-        cert_file=None,
-        cert_reqs=None,
-        key_password=None,
-        ca_certs=None,
-        assert_hostname=None,
-        assert_fingerprint=None,
-        ca_cert_dir=None,
-        ca_cert_data=None,
-    ):
-        """
-        This method should only be called once, before the connection is used.
-        """
-        # If cert_reqs is not provided we'll assume CERT_REQUIRED unless we also
-        # have an SSLContext object in which case we'll use its verify_mode.
-        if cert_reqs is None:
-            if self.ssl_context is not None:
-                cert_reqs = self.ssl_context.verify_mode
-            else:
-                cert_reqs = resolve_cert_reqs(None)
-
-        self.key_file = key_file
-        self.cert_file = cert_file
-        self.cert_reqs = cert_reqs
-        self.key_password = key_password
-        self.assert_hostname = assert_hostname
-        self.assert_fingerprint = assert_fingerprint
-        self.ca_certs = ca_certs and os.path.expanduser(ca_certs)
-        self.ca_cert_dir = ca_cert_dir and os.path.expanduser(ca_cert_dir)
-        self.ca_cert_data = ca_cert_data
-
-    def connect(self):
-        # Add certificate verification
-        self.sock = conn = self._new_conn()
-        hostname = self.host
-        tls_in_tls = False
-
-        if self._is_using_tunnel():
-            if self.tls_in_tls_required:
-                self.sock = conn = self._connect_tls_proxy(hostname, conn)
-                tls_in_tls = True
-
-            # Calls self._set_hostport(), so self.host is
-            # self._tunnel_host below.
-            self._tunnel()
-            # Mark this connection as not reusable
-            self.auto_open = 0
-
-            # Override the host with the one we're requesting data from.
-            hostname = self._tunnel_host
-
-        server_hostname = hostname
-        if self.server_hostname is not None:
-            server_hostname = self.server_hostname
-
-        is_time_off = datetime.date.today() < RECENT_DATE
-        if is_time_off:
-            warnings.warn(
-                (
-                    "System time is way off (before {0}). This will probably "
-                    "lead to SSL verification errors"
-                ).format(RECENT_DATE),
-                SystemTimeWarning,
-            )
-
-        # Wrap socket using verification with the root certs in
-        # trusted_root_certs
-        default_ssl_context = False
-        if self.ssl_context is None:
-            default_ssl_context = True
-            self.ssl_context = create_urllib3_context(
-                ssl_version=resolve_ssl_version(self.ssl_version),
-                cert_reqs=resolve_cert_reqs(self.cert_reqs),
-            )
-
-        context = self.ssl_context
-        context.verify_mode = resolve_cert_reqs(self.cert_reqs)
-
-        # Try to load OS default certs if none are given.
-        # Works well on Windows (requires Python3.4+)
-        if (
-            not self.ca_certs
-            and not self.ca_cert_dir
-            and not self.ca_cert_data
-            and default_ssl_context
-            and hasattr(context, "load_default_certs")
-        ):
-            context.load_default_certs()
-
-        self.sock = ssl_wrap_socket(
-            sock=conn,
-            keyfile=self.key_file,
-            certfile=self.cert_file,
-            key_password=self.key_password,
-            ca_certs=self.ca_certs,
-            ca_cert_dir=self.ca_cert_dir,
-            ca_cert_data=self.ca_cert_data,
-            server_hostname=server_hostname,
-            ssl_context=context,
-            tls_in_tls=tls_in_tls,
-        )
-
-        # If we're using all defaults and the connection
-        # is TLSv1 or TLSv1.1 we throw a DeprecationWarning
-        # for the host.
-        if (
-            default_ssl_context
-            and self.ssl_version is None
-            and hasattr(self.sock, "version")
-            and self.sock.version() in {"TLSv1", "TLSv1.1"}
-        ):
-            warnings.warn(
-                "Negotiating TLSv1/TLSv1.1 by default is deprecated "
-                "and will be disabled in urllib3 v2.0.0. Connecting to "
-                "'%s' with '%s' can be enabled by explicitly opting-in "
-                "with 'ssl_version'" % (self.host, self.sock.version()),
-                DeprecationWarning,
-            )
-
-        if self.assert_fingerprint:
-            assert_fingerprint(
-                self.sock.getpeercert(binary_form=True), self.assert_fingerprint
-            )
-        elif (
-            context.verify_mode != ssl.CERT_NONE
-            and not getattr(context, "check_hostname", False)
-            and self.assert_hostname is not False
-        ):
-            # While urllib3 attempts to always turn off hostname matching from
-            # the TLS library, this cannot always be done. So we check whether
-            # the TLS Library still thinks it's matching hostnames.
-            cert = self.sock.getpeercert()
-            if not cert.get("subjectAltName", ()):
-                warnings.warn(
-                    (
-                        "Certificate for {0} has no `subjectAltName`, falling back to check for a "
-                        "`commonName` for now. This feature is being removed by major browsers and "
-                        "deprecated by RFC 2818. (See https://github.com/urllib3/urllib3/issues/497 "
-                        "for details.)".format(hostname)
-                    ),
-                    SubjectAltNameWarning,
-                )
-            _match_hostname(cert, self.assert_hostname or server_hostname)
-
-        self.is_verified = (
-            context.verify_mode == ssl.CERT_REQUIRED
-            or self.assert_fingerprint is not None
-        )
-
-    def _connect_tls_proxy(self, hostname, conn):
-        """
-        Establish a TLS connection to the proxy using the provided SSL context.
-        """
-        proxy_config = self.proxy_config
-        ssl_context = proxy_config.ssl_context
-        if ssl_context:
-            # If the user provided a proxy context, we assume CA and client
-            # certificates have already been set
-            return ssl_wrap_socket(
-                sock=conn,
-                server_hostname=hostname,
-                ssl_context=ssl_context,
-            )
-
-        ssl_context = create_proxy_ssl_context(
-            self.ssl_version,
-            self.cert_reqs,
-            self.ca_certs,
-            self.ca_cert_dir,
-            self.ca_cert_data,
-        )
-
-        # If no cert was provided, use only the default options for server
-        # certificate validation
-        socket = ssl_wrap_socket(
-            sock=conn,
-            ca_certs=self.ca_certs,
-            ca_cert_dir=self.ca_cert_dir,
-            ca_cert_data=self.ca_cert_data,
-            server_hostname=hostname,
-            ssl_context=ssl_context,
-        )
-
-        if ssl_context.verify_mode != ssl.CERT_NONE and not getattr(
-            ssl_context, "check_hostname", False
-        ):
-            # While urllib3 attempts to always turn off hostname matching from
-            # the TLS library, this cannot always be done. So we check whether
-            # the TLS Library still thinks it's matching hostnames.
-            cert = socket.getpeercert()
-            if not cert.get("subjectAltName", ()):
-                warnings.warn(
-                    (
-                        "Certificate for {0} has no `subjectAltName`, falling back to check for a "
-                        "`commonName` for now. This feature is being removed by major browsers and "
-                        "deprecated by RFC 2818. (See https://github.com/urllib3/urllib3/issues/497 "
-                        "for details.)".format(hostname)
-                    ),
-                    SubjectAltNameWarning,
-                )
-            _match_hostname(cert, hostname)
-
-        self.proxy_is_verified = ssl_context.verify_mode == ssl.CERT_REQUIRED
-        return socket
-
-
-def _match_hostname(cert, asserted_hostname):
-    # Our upstream implementation of ssl.match_hostname()
-    # only applies this normalization to IP addresses so it doesn't
-    # match DNS SANs so we do the same thing!
-    stripped_hostname = asserted_hostname.strip("u[]")
-    if is_ipaddress(stripped_hostname):
-        asserted_hostname = stripped_hostname
-
-    try:
-        match_hostname(cert, asserted_hostname)
-    except CertificateError as e:
-        log.warning(
-            "Certificate did not match expected hostname: %s. Certificate: %s",
-            asserted_hostname,
-            cert,
-        )
-        # Add cert to exception and reraise so client code can inspect
-        # the cert when catching the exception, if they want to
-        e._peer_cert = cert
-        raise
-
-
-def _get_default_user_agent():
-    return "python-urllib3/%s" % __version__
-
-
-class DummyConnection(object):
-    """Used to detect a failed ConnectionCls import."""
-
-    pass
-
-
-if not ssl:
-    HTTPSConnection = DummyConnection  # noqa: F811
-
-
-VerifiedHTTPSConnection = HTTPSConnection
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/semantic_version/base.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/semantic_version/base.py
deleted file mode 100644
index 777c27ac463f34996d0281fb7a68e5f6c7fb9a9c..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/semantic_version/base.py
+++ /dev/null
@@ -1,1449 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) The python-semanticversion project
-# This code is distributed under the two-clause BSD License.
-
-import functools
-import re
-import warnings
-
-
-def _has_leading_zero(value):
-    return (value
-            and value[0] == '0'
-            and value.isdigit()
-            and value != '0')
-
-
-class MaxIdentifier(object):
-    __slots__ = []
-
-    def __repr__(self):
-        return 'MaxIdentifier()'
-
-    def __eq__(self, other):
-        return isinstance(other, self.__class__)
-
-
-@functools.total_ordering
-class NumericIdentifier(object):
-    __slots__ = ['value']
-
-    def __init__(self, value):
-        self.value = int(value)
-
-    def __repr__(self):
-        return 'NumericIdentifier(%r)' % self.value
-
-    def __eq__(self, other):
-        if isinstance(other, NumericIdentifier):
-            return self.value == other.value
-        return NotImplemented
-
-    def __lt__(self, other):
-        if isinstance(other, MaxIdentifier):
-            return True
-        elif isinstance(other, AlphaIdentifier):
-            return True
-        elif isinstance(other, NumericIdentifier):
-            return self.value < other.value
-        else:
-            return NotImplemented
-
-
-@functools.total_ordering
-class AlphaIdentifier(object):
-    __slots__ = ['value']
-
-    def __init__(self, value):
-        self.value = value.encode('ascii')
-
-    def __repr__(self):
-        return 'AlphaIdentifier(%r)' % self.value
-
-    def __eq__(self, other):
-        if isinstance(other, AlphaIdentifier):
-            return self.value == other.value
-        return NotImplemented
-
-    def __lt__(self, other):
-        if isinstance(other, MaxIdentifier):
-            return True
-        elif isinstance(other, NumericIdentifier):
-            return False
-        elif isinstance(other, AlphaIdentifier):
-            return self.value < other.value
-        else:
-            return NotImplemented
-
-
-class Version(object):
-
-    version_re = re.compile(r'^(\d+)\.(\d+)\.(\d+)(?:-([0-9a-zA-Z.-]+))?(?:\+([0-9a-zA-Z.-]+))?$')
-    partial_version_re = re.compile(r'^(\d+)(?:\.(\d+)(?:\.(\d+))?)?(?:-([0-9a-zA-Z.-]*))?(?:\+([0-9a-zA-Z.-]*))?$')
-
-    def __init__(
-            self,
-            version_string=None,
-            major=None,
-            minor=None,
-            patch=None,
-            prerelease=None,
-            build=None,
-            partial=False):
-        if partial:
-            warnings.warn(
-                "Partial versions will be removed in 3.0; use SimpleSpec('1.x.x') instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-        has_text = version_string is not None
-        has_parts = not (major is minor is patch is prerelease is build is None)
-        if not has_text ^ has_parts:
-            raise ValueError("Call either Version('1.2.3') or Version(major=1, ...).")
-
-        if has_text:
-            major, minor, patch, prerelease, build = self.parse(version_string, partial)
-        else:
-            # Convenience: allow to omit prerelease/build.
-            prerelease = tuple(prerelease or ())
-            if not partial:
-                build = tuple(build or ())
-            self._validate_kwargs(major, minor, patch, prerelease, build, partial)
-
-        self.major = major
-        self.minor = minor
-        self.patch = patch
-        self.prerelease = prerelease
-        self.build = build
-
-        self.partial = partial
-
-        # Cached precedence keys
-        # _cmp_precedence_key is used for semver-precedence comparison
-        self._cmp_precedence_key = self._build_precedence_key(with_build=False)
-        # _sort_precedence_key is used for self.precedence_key, esp. for sorted(...)
-        self._sort_precedence_key = self._build_precedence_key(with_build=True)
-
-    @classmethod
-    def _coerce(cls, value, allow_none=False):
-        if value is None and allow_none:
-            return value
-        return int(value)
-
-    def next_major(self):
-        if self.prerelease and self.minor == self.patch == 0:
-            return Version(
-                major=self.major,
-                minor=0,
-                patch=0,
-                partial=self.partial,
-            )
-        else:
-            return Version(
-                major=self.major + 1,
-                minor=0,
-                patch=0,
-                partial=self.partial,
-            )
-
-    def next_minor(self):
-        if self.prerelease and self.patch == 0:
-            return Version(
-                major=self.major,
-                minor=self.minor,
-                patch=0,
-                partial=self.partial,
-            )
-        else:
-            return Version(
-                major=self.major,
-                minor=self.minor + 1,
-                patch=0,
-                partial=self.partial,
-            )
-
-    def next_patch(self):
-        if self.prerelease:
-            return Version(
-                major=self.major,
-                minor=self.minor,
-                patch=self.patch,
-                partial=self.partial,
-            )
-        else:
-            return Version(
-                major=self.major,
-                minor=self.minor,
-                patch=self.patch + 1,
-                partial=self.partial,
-            )
-
-    def truncate(self, level='patch'):
-        """Return a new Version object, truncated up to the selected level."""
-        if level == 'build':
-            return self
-        elif level == 'prerelease':
-            return Version(
-                major=self.major,
-                minor=self.minor,
-                patch=self.patch,
-                prerelease=self.prerelease,
-                partial=self.partial,
-            )
-        elif level == 'patch':
-            return Version(
-                major=self.major,
-                minor=self.minor,
-                patch=self.patch,
-                partial=self.partial,
-            )
-        elif level == 'minor':
-            return Version(
-                major=self.major,
-                minor=self.minor,
-                patch=None if self.partial else 0,
-                partial=self.partial,
-            )
-        elif level == 'major':
-            return Version(
-                major=self.major,
-                minor=None if self.partial else 0,
-                patch=None if self.partial else 0,
-                partial=self.partial,
-            )
-        else:
-            raise ValueError("Invalid truncation level `%s`." % level)
-
-    @classmethod
-    def coerce(cls, version_string, partial=False):
-        """Coerce an arbitrary version string into a semver-compatible one.
-
-        The rule is:
-        - If not enough components, fill minor/patch with zeroes; unless
-          partial=True
-        - If more than 3 dot-separated components, extra components are "build"
-          data. If some "build" data already appeared, append it to the
-          extra components
-
-        Examples:
-            >>> Version.coerce('0.1')
-            Version(0, 1, 0)
-            >>> Version.coerce('0.1.2.3')
-            Version(0, 1, 2, (), ('3',))
-            >>> Version.coerce('0.1.2.3+4')
-            Version(0, 1, 2, (), ('3', '4'))
-            >>> Version.coerce('0.1+2-3+4_5')
-            Version(0, 1, 0, (), ('2-3', '4-5'))
-        """
-        base_re = re.compile(r'^\d+(?:\.\d+(?:\.\d+)?)?')
-
-        match = base_re.match(version_string)
-        if not match:
-            raise ValueError(
-                "Version string lacks a numerical component: %r"
-                % version_string
-            )
-
-        version = version_string[:match.end()]
-        if not partial:
-            # We need a not-partial version.
-            while version.count('.') < 2:
-                version += '.0'
-
-        # Strip leading zeros in components
-        # Version is of the form nn, nn.pp or nn.pp.qq
-        version = '.'.join(
-            # If the part was '0', we end up with an empty string.
-            part.lstrip('0') or '0'
-            for part in version.split('.')
-        )
-
-        if match.end() == len(version_string):
-            return Version(version, partial=partial)
-
-        rest = version_string[match.end():]
-
-        # Cleanup the 'rest'
-        rest = re.sub(r'[^a-zA-Z0-9+.-]', '-', rest)
-
-        if rest[0] == '+':
-            # A 'build' component
-            prerelease = ''
-            build = rest[1:]
-        elif rest[0] == '.':
-            # An extra version component, probably 'build'
-            prerelease = ''
-            build = rest[1:]
-        elif rest[0] == '-':
-            rest = rest[1:]
-            if '+' in rest:
-                prerelease, build = rest.split('+', 1)
-            else:
-                prerelease, build = rest, ''
-        elif '+' in rest:
-            prerelease, build = rest.split('+', 1)
-        else:
-            prerelease, build = rest, ''
-
-        build = build.replace('+', '.')
-
-        if prerelease:
-            version = '%s-%s' % (version, prerelease)
-        if build:
-            version = '%s+%s' % (version, build)
-
-        return cls(version, partial=partial)
-
-    @classmethod
-    def parse(cls, version_string, partial=False, coerce=False):
-        """Parse a version string into a tuple of components:
-           (major, minor, patch, prerelease, build).
-
-        Args:
-            version_string (str), the version string to parse
-            partial (bool), whether to accept incomplete input
-            coerce (bool), whether to try to map the passed in string into a
-                valid Version.
-        """
-        if not version_string:
-            raise ValueError('Invalid empty version string: %r' % version_string)
-
-        if partial:
-            version_re = cls.partial_version_re
-        else:
-            version_re = cls.version_re
-
-        match = version_re.match(version_string)
-        if not match:
-            raise ValueError('Invalid version string: %r' % version_string)
-
-        major, minor, patch, prerelease, build = match.groups()
-
-        if _has_leading_zero(major):
-            raise ValueError("Invalid leading zero in major: %r" % version_string)
-        if _has_leading_zero(minor):
-            raise ValueError("Invalid leading zero in minor: %r" % version_string)
-        if _has_leading_zero(patch):
-            raise ValueError("Invalid leading zero in patch: %r" % version_string)
-
-        major = int(major)
-        minor = cls._coerce(minor, partial)
-        patch = cls._coerce(patch, partial)
-
-        if prerelease is None:
-            if partial and (build is None):
-                # No build info, strip here
-                return (major, minor, patch, None, None)
-            else:
-                prerelease = ()
-        elif prerelease == '':
-            prerelease = ()
-        else:
-            prerelease = tuple(prerelease.split('.'))
-            cls._validate_identifiers(prerelease, allow_leading_zeroes=False)
-
-        if build is None:
-            if partial:
-                build = None
-            else:
-                build = ()
-        elif build == '':
-            build = ()
-        else:
-            build = tuple(build.split('.'))
-            cls._validate_identifiers(build, allow_leading_zeroes=True)
-
-        return (major, minor, patch, prerelease, build)
-
-    @classmethod
-    def _validate_identifiers(cls, identifiers, allow_leading_zeroes=False):
-        for item in identifiers:
-            if not item:
-                raise ValueError(
-                    "Invalid empty identifier %r in %r"
-                    % (item, '.'.join(identifiers))
-                )
-
-            if item[0] == '0' and item.isdigit() and item != '0' and not allow_leading_zeroes:
-                raise ValueError("Invalid leading zero in identifier %r" % item)
-
-    @classmethod
-    def _validate_kwargs(cls, major, minor, patch, prerelease, build, partial):
-        if (
-                major != int(major)
-                or minor != cls._coerce(minor, partial)
-                or patch != cls._coerce(patch, partial)
-                or prerelease is None and not partial
-                or build is None and not partial
-        ):
-            raise ValueError(
-                "Invalid kwargs to Version(major=%r, minor=%r, patch=%r, "
-                "prerelease=%r, build=%r, partial=%r" % (
-                    major, minor, patch, prerelease, build, partial
-                ))
-        if prerelease is not None:
-            cls._validate_identifiers(prerelease, allow_leading_zeroes=False)
-        if build is not None:
-            cls._validate_identifiers(build, allow_leading_zeroes=True)
-
-    def __iter__(self):
-        return iter((self.major, self.minor, self.patch, self.prerelease, self.build))
-
-    def __str__(self):
-        version = '%d' % self.major
-        if self.minor is not None:
-            version = '%s.%d' % (version, self.minor)
-        if self.patch is not None:
-            version = '%s.%d' % (version, self.patch)
-
-        if self.prerelease or (self.partial and self.prerelease == () and self.build is None):
-            version = '%s-%s' % (version, '.'.join(self.prerelease))
-        if self.build or (self.partial and self.build == ()):
-            version = '%s+%s' % (version, '.'.join(self.build))
-        return version
-
-    def __repr__(self):
-        return '%s(%r%s)' % (
-            self.__class__.__name__,
-            str(self),
-            ', partial=True' if self.partial else '',
-        )
-
-    def __hash__(self):
-        # We don't include 'partial', since this is strictly equivalent to having
-        # at least a field being `None`.
-        return hash((self.major, self.minor, self.patch, self.prerelease, self.build))
-
-    def _build_precedence_key(self, with_build=False):
-        """Build a precedence key.
-
-        The "build" component should only be used when sorting an iterable
-        of versions.
-        """
-        if self.prerelease:
-            prerelease_key = tuple(
-                NumericIdentifier(part) if part.isdigit() else AlphaIdentifier(part)
-                for part in self.prerelease
-            )
-        else:
-            prerelease_key = (
-                MaxIdentifier(),
-            )
-
-        if not with_build:
-            return (
-                self.major,
-                self.minor,
-                self.patch,
-                prerelease_key,
-            )
-
-        build_key = tuple(
-            NumericIdentifier(part) if part.isdigit() else AlphaIdentifier(part)
-            for part in self.build or ()
-        )
-
-        return (
-            self.major,
-            self.minor,
-            self.patch,
-            prerelease_key,
-            build_key,
-        )
-
-    @property
-    def precedence_key(self):
-        return self._sort_precedence_key
-
-    def __cmp__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        if self < other:
-            return -1
-        elif self > other:
-            return 1
-        elif self == other:
-            return 0
-        else:
-            return NotImplemented
-
-    def __eq__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return (
-            self.major == other.major
-            and self.minor == other.minor
-            and self.patch == other.patch
-            and (self.prerelease or ()) == (other.prerelease or ())
-            and (self.build or ()) == (other.build or ())
-        )
-
-    def __ne__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return tuple(self) != tuple(other)
-
-    def __lt__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return self._cmp_precedence_key < other._cmp_precedence_key
-
-    def __le__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return self._cmp_precedence_key <= other._cmp_precedence_key
-
-    def __gt__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return self._cmp_precedence_key > other._cmp_precedence_key
-
-    def __ge__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return self._cmp_precedence_key >= other._cmp_precedence_key
-
-
-class SpecItem(object):
-    """A requirement specification."""
-
-    KIND_ANY = '*'
-    KIND_LT = '<'
-    KIND_LTE = '<='
-    KIND_EQUAL = '=='
-    KIND_SHORTEQ = '='
-    KIND_EMPTY = ''
-    KIND_GTE = '>='
-    KIND_GT = '>'
-    KIND_NEQ = '!='
-    KIND_CARET = '^'
-    KIND_TILDE = '~'
-    KIND_COMPATIBLE = '~='
-
-    # Map a kind alias to its full version
-    KIND_ALIASES = {
-        KIND_SHORTEQ: KIND_EQUAL,
-        KIND_EMPTY: KIND_EQUAL,
-    }
-
-    re_spec = re.compile(r'^(<|<=||=|==|>=|>|!=|\^|~|~=)(\d.*)$')
-
-    def __init__(self, requirement_string, _warn=True):
-        if _warn:
-            warnings.warn(
-                "The `SpecItem` class will be removed in 3.0.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-        kind, spec = self.parse(requirement_string)
-        self.kind = kind
-        self.spec = spec
-        self._clause = Spec(requirement_string).clause
-
-    @classmethod
-    def parse(cls, requirement_string):
-        if not requirement_string:
-            raise ValueError("Invalid empty requirement specification: %r" % requirement_string)
-
-        # Special case: the 'any' version spec.
-        if requirement_string == '*':
-            return (cls.KIND_ANY, '')
-
-        match = cls.re_spec.match(requirement_string)
-        if not match:
-            raise ValueError("Invalid requirement specification: %r" % requirement_string)
-
-        kind, version = match.groups()
-        if kind in cls.KIND_ALIASES:
-            kind = cls.KIND_ALIASES[kind]
-
-        spec = Version(version, partial=True)
-        if spec.build is not None and kind not in (cls.KIND_EQUAL, cls.KIND_NEQ):
-            raise ValueError(
-                "Invalid requirement specification %r: build numbers have no ordering."
-                % requirement_string
-            )
-        return (kind, spec)
-
-    @classmethod
-    def from_matcher(cls, matcher):
-        if matcher == Always():
-            return cls('*', _warn=False)
-        elif matcher == Never():
-            return cls('<0.0.0-', _warn=False)
-        elif isinstance(matcher, Range):
-            return cls('%s%s' % (matcher.operator, matcher.target), _warn=False)
-
-    def match(self, version):
-        return self._clause.match(version)
-
-    def __str__(self):
-        return '%s%s' % (self.kind, self.spec)
-
-    def __repr__(self):
-        return '<SpecItem: %s %r>' % (self.kind, self.spec)
-
-    def __eq__(self, other):
-        if not isinstance(other, SpecItem):
-            return NotImplemented
-        return self.kind == other.kind and self.spec == other.spec
-
-    def __hash__(self):
-        return hash((self.kind, self.spec))
-
-
-def compare(v1, v2):
-    return Version(v1).__cmp__(Version(v2))
-
-
-def match(spec, version):
-    return Spec(spec).match(Version(version))
-
-
-def validate(version_string):
-    """Validates a version string againt the SemVer specification."""
-    try:
-        Version.parse(version_string)
-        return True
-    except ValueError:
-        return False
-
-
-DEFAULT_SYNTAX = 'simple'
-
-
-class BaseSpec(object):
-    """A specification of compatible versions.
-
-    Usage:
-    >>> Spec('>=1.0.0', syntax='npm')
-
-    A version matches a specification if it matches any
-    of the clauses of that specification.
-
-    Internally, a Spec is AnyOf(
-        AllOf(Matcher, Matcher, Matcher),
-        AllOf(...),
-    )
-    """
-    SYNTAXES = {}
-
-    @classmethod
-    def register_syntax(cls, subclass):
-        syntax = subclass.SYNTAX
-        if syntax is None:
-            raise ValueError("A Spec needs its SYNTAX field to be set.")
-        elif syntax in cls.SYNTAXES:
-            raise ValueError(
-                "Duplicate syntax for %s: %r, %r"
-                % (syntax, cls.SYNTAXES[syntax], subclass)
-            )
-        cls.SYNTAXES[syntax] = subclass
-        return subclass
-
-    def __init__(self, expression):
-        super(BaseSpec, self).__init__()
-        self.expression = expression
-        self.clause = self._parse_to_clause(expression)
-
-    @classmethod
-    def parse(cls, expression, syntax=DEFAULT_SYNTAX):
-        """Convert a syntax-specific expression into a BaseSpec instance."""
-        return cls.SYNTAXES[syntax](expression)
-
-    @classmethod
-    def _parse_to_clause(cls, expression):
-        """Converts an expression to a clause."""
-        raise NotImplementedError()
-
-    def filter(self, versions):
-        """Filter an iterable of versions satisfying the Spec."""
-        for version in versions:
-            if self.match(version):
-                yield version
-
-    def match(self, version):
-        """Check whether a Version satisfies the Spec."""
-        return self.clause.match(version)
-
-    def select(self, versions):
-        """Select the best compatible version among an iterable of options."""
-        options = list(self.filter(versions))
-        if options:
-            return max(options)
-        return None
-
-    def __contains__(self, version):
-        """Whether `version in self`."""
-        if isinstance(version, Version):
-            return self.match(version)
-        return False
-
-    def __eq__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-
-        return self.clause == other.clause
-
-    def __hash__(self):
-        return hash(self.clause)
-
-    def __str__(self):
-        return self.expression
-
-    def __repr__(self):
-        return '<%s: %r>' % (self.__class__.__name__, self.expression)
-
-
-class Clause(object):
-    __slots__ = []
-
-    def match(self, version):
-        raise NotImplementedError()
-
-    def __and__(self, other):
-        raise NotImplementedError()
-
-    def __or__(self, other):
-        raise NotImplementedError()
-
-    def __eq__(self, other):
-        raise NotImplementedError()
-
-    def prettyprint(self, indent='\t'):
-        """Pretty-print the clause.
-        """
-        return '\n'.join(self._pretty()).replace('\t', indent)
-
-    def _pretty(self):
-        """Actual pretty-printing logic.
-
-        Yields:
-            A list of string. Indentation is performed with \t.
-        """
-        yield repr(self)
-
-    def __ne__(self, other):
-        return not self == other
-
-    def simplify(self):
-        return self
-
-
-class AnyOf(Clause):
-    __slots__ = ['clauses']
-
-    def __init__(self, *clauses):
-        super(AnyOf, self).__init__()
-        self.clauses = frozenset(clauses)
-
-    def match(self, version):
-        return any(c.match(version) for c in self.clauses)
-
-    def simplify(self):
-        subclauses = set()
-        for clause in self.clauses:
-            simplified = clause.simplify()
-            if isinstance(simplified, AnyOf):
-                subclauses |= simplified.clauses
-            elif simplified == Never():
-                continue
-            else:
-                subclauses.add(simplified)
-        if len(subclauses) == 1:
-            return subclauses.pop()
-        return AnyOf(*subclauses)
-
-    def __hash__(self):
-        return hash((AnyOf, self.clauses))
-
-    def __iter__(self):
-        return iter(self.clauses)
-
-    def __eq__(self, other):
-        return isinstance(other, self.__class__) and self.clauses == other.clauses
-
-    def __and__(self, other):
-        if isinstance(other, AllOf):
-            return other & self
-        elif isinstance(other, Matcher) or isinstance(other, AnyOf):
-            return AllOf(self, other)
-        else:
-            return NotImplemented
-
-    def __or__(self, other):
-        if isinstance(other, AnyOf):
-            clauses = list(self.clauses | other.clauses)
-        elif isinstance(other, Matcher) or isinstance(other, AllOf):
-            clauses = list(self.clauses | set([other]))
-        else:
-            return NotImplemented
-        return AnyOf(*clauses)
-
-    def __repr__(self):
-        return 'AnyOf(%s)' % ', '.join(sorted(repr(c) for c in self.clauses))
-
-    def _pretty(self):
-        yield 'AnyOF('
-        for clause in self.clauses:
-            lines = list(clause._pretty())
-            for line in lines[:-1]:
-                yield '\t' + line
-            yield '\t' + lines[-1] + ','
-        yield ')'
-
-
-class AllOf(Clause):
-    __slots__ = ['clauses']
-
-    def __init__(self, *clauses):
-        super(AllOf, self).__init__()
-        self.clauses = frozenset(clauses)
-
-    def match(self, version):
-        return all(clause.match(version) for clause in self.clauses)
-
-    def simplify(self):
-        subclauses = set()
-        for clause in self.clauses:
-            simplified = clause.simplify()
-            if isinstance(simplified, AllOf):
-                subclauses |= simplified.clauses
-            elif simplified == Always():
-                continue
-            else:
-                subclauses.add(simplified)
-        if len(subclauses) == 1:
-            return subclauses.pop()
-        return AllOf(*subclauses)
-
-    def __hash__(self):
-        return hash((AllOf, self.clauses))
-
-    def __iter__(self):
-        return iter(self.clauses)
-
-    def __eq__(self, other):
-        return isinstance(other, self.__class__) and self.clauses == other.clauses
-
-    def __and__(self, other):
-        if isinstance(other, Matcher) or isinstance(other, AnyOf):
-            clauses = list(self.clauses | set([other]))
-        elif isinstance(other, AllOf):
-            clauses = list(self.clauses | other.clauses)
-        else:
-            return NotImplemented
-        return AllOf(*clauses)
-
-    def __or__(self, other):
-        if isinstance(other, AnyOf):
-            return other | self
-        elif isinstance(other, Matcher):
-            return AnyOf(self, AllOf(other))
-        elif isinstance(other, AllOf):
-            return AnyOf(self, other)
-        else:
-            return NotImplemented
-
-    def __repr__(self):
-        return 'AllOf(%s)' % ', '.join(sorted(repr(c) for c in self.clauses))
-
-    def _pretty(self):
-        yield 'AllOF('
-        for clause in self.clauses:
-            lines = list(clause._pretty())
-            for line in lines[:-1]:
-                yield '\t' + line
-            yield '\t' + lines[-1] + ','
-        yield ')'
-
-
-class Matcher(Clause):
-    __slots__ = []
-
-    def __and__(self, other):
-        if isinstance(other, AllOf):
-            return other & self
-        elif isinstance(other, Matcher) or isinstance(other, AnyOf):
-            return AllOf(self, other)
-        else:
-            return NotImplemented
-
-    def __or__(self, other):
-        if isinstance(other, AnyOf):
-            return other | self
-        elif isinstance(other, Matcher) or isinstance(other, AllOf):
-            return AnyOf(self, other)
-        else:
-            return NotImplemented
-
-
-class Never(Matcher):
-    __slots__ = []
-
-    def match(self, version):
-        return False
-
-    def __hash__(self):
-        return hash((Never,))
-
-    def __eq__(self, other):
-        return isinstance(other, self.__class__)
-
-    def __and__(self, other):
-        return self
-
-    def __or__(self, other):
-        return other
-
-    def __repr__(self):
-        return 'Never()'
-
-
-class Always(Matcher):
-    __slots__ = []
-
-    def match(self, version):
-        return True
-
-    def __hash__(self):
-        return hash((Always,))
-
-    def __eq__(self, other):
-        return isinstance(other, self.__class__)
-
-    def __and__(self, other):
-        return other
-
-    def __or__(self, other):
-        return self
-
-    def __repr__(self):
-        return 'Always()'
-
-
-class Range(Matcher):
-    OP_EQ = '=='
-    OP_GT = '>'
-    OP_GTE = '>='
-    OP_LT = '<'
-    OP_LTE = '<='
-    OP_NEQ = '!='
-
-    # <1.2.3 matches 1.2.3-a1
-    PRERELEASE_ALWAYS = 'always'
-    # <1.2.3 does not match 1.2.3-a1
-    PRERELEASE_NATURAL = 'natural'
-    # 1.2.3-a1 is only considered if target == 1.2.3-xxx
-    PRERELEASE_SAMEPATCH = 'same-patch'
-
-    # 1.2.3 matches 1.2.3+*
-    BUILD_IMPLICIT = 'implicit'
-    # 1.2.3 matches only 1.2.3, not 1.2.3+4
-    BUILD_STRICT = 'strict'
-
-    __slots__ = ['operator', 'target', 'prerelease_policy', 'build_policy']
-
-    def __init__(self, operator, target, prerelease_policy=PRERELEASE_NATURAL, build_policy=BUILD_IMPLICIT):
-        super(Range, self).__init__()
-        if target.build and operator not in (self.OP_EQ, self.OP_NEQ):
-            raise ValueError(
-                "Invalid range %s%s: build numbers have no ordering."
-                % (operator, target))
-        self.operator = operator
-        self.target = target
-        self.prerelease_policy = prerelease_policy
-        self.build_policy = self.BUILD_STRICT if target.build else build_policy
-
-    def match(self, version):
-        if self.build_policy != self.BUILD_STRICT:
-            version = version.truncate('prerelease')
-
-        if version.prerelease:
-            same_patch = self.target.truncate() == version.truncate()
-
-            if self.prerelease_policy == self.PRERELEASE_SAMEPATCH and not same_patch:
-                return False
-
-        if self.operator == self.OP_EQ:
-            if self.build_policy == self.BUILD_STRICT:
-                return (
-                    self.target.truncate('prerelease') == version.truncate('prerelease')
-                    and version.build == self.target.build
-                )
-            return version == self.target
-        elif self.operator == self.OP_GT:
-            return version > self.target
-        elif self.operator == self.OP_GTE:
-            return version >= self.target
-        elif self.operator == self.OP_LT:
-            if (
-                version.prerelease
-                and self.prerelease_policy == self.PRERELEASE_NATURAL
-                and version.truncate() == self.target.truncate()
-                and not self.target.prerelease
-            ):
-                return False
-            return version < self.target
-        elif self.operator == self.OP_LTE:
-            return version <= self.target
-        else:
-            assert self.operator == self.OP_NEQ
-            if self.build_policy == self.BUILD_STRICT:
-                return not (
-                    self.target.truncate('prerelease') == version.truncate('prerelease')
-                    and version.build == self.target.build
-                )
-
-            if (
-                version.prerelease
-                and self.prerelease_policy == self.PRERELEASE_NATURAL
-                and version.truncate() == self.target.truncate()
-                and not self.target.prerelease
-            ):
-                return False
-            return version != self.target
-
-    def __hash__(self):
-        return hash((Range, self.operator, self.target, self.prerelease_policy))
-
-    def __eq__(self, other):
-        return (
-            isinstance(other, self.__class__)
-            and self.operator == other.operator
-            and self.target == other.target
-            and self.prerelease_policy == other.prerelease_policy
-        )
-
-    def __str__(self):
-        return '%s%s' % (self.operator, self.target)
-
-    def __repr__(self):
-        policy_part = (
-            '' if self.prerelease_policy == self.PRERELEASE_NATURAL
-            else ', prerelease_policy=%r' % self.prerelease_policy
-        ) + (
-            '' if self.build_policy == self.BUILD_IMPLICIT
-            else ', build_policy=%r' % self.build_policy
-        )
-        return 'Range(%r, %r%s)' % (
-            self.operator,
-            self.target,
-            policy_part,
-        )
-
-
-@BaseSpec.register_syntax
-class SimpleSpec(BaseSpec):
-
-    SYNTAX = 'simple'
-
-    @classmethod
-    def _parse_to_clause(cls, expression):
-        return cls.Parser.parse(expression)
-
-    class Parser:
-        NUMBER = r'\*|0|[1-9][0-9]*'
-        NAIVE_SPEC = re.compile(r"""^
-            (?P<op><|<=||=|==|>=|>|!=|\^|~|~=)
-            (?P<major>{nb})(?:\.(?P<minor>{nb})(?:\.(?P<patch>{nb}))?)?
-            (?:-(?P<prerel>[a-z0-9A-Z.-]*))?
-            (?:\+(?P<build>[a-z0-9A-Z.-]*))?
-            $
-            """.format(nb=NUMBER),
-            re.VERBOSE,
-        )
-
-        @classmethod
-        def parse(cls, expression):
-            blocks = expression.split(',')
-            clause = Always()
-            for block in blocks:
-                if not cls.NAIVE_SPEC.match(block):
-                    raise ValueError("Invalid simple block %r" % block)
-                clause &= cls.parse_block(block)
-
-            return clause
-
-        PREFIX_CARET = '^'
-        PREFIX_TILDE = '~'
-        PREFIX_COMPATIBLE = '~='
-        PREFIX_EQ = '=='
-        PREFIX_NEQ = '!='
-        PREFIX_GT = '>'
-        PREFIX_GTE = '>='
-        PREFIX_LT = '<'
-        PREFIX_LTE = '<='
-
-        PREFIX_ALIASES = {
-            '=': PREFIX_EQ,
-            '': PREFIX_EQ,
-        }
-
-        EMPTY_VALUES = ['*', 'x', 'X', None]
-
-        @classmethod
-        def parse_block(cls, expr):
-            if not cls.NAIVE_SPEC.match(expr):
-                raise ValueError("Invalid simple spec component: %r" % expr)
-            prefix, major_t, minor_t, patch_t, prerel, build = cls.NAIVE_SPEC.match(expr).groups()
-            prefix = cls.PREFIX_ALIASES.get(prefix, prefix)
-
-            major = None if major_t in cls.EMPTY_VALUES else int(major_t)
-            minor = None if minor_t in cls.EMPTY_VALUES else int(minor_t)
-            patch = None if patch_t in cls.EMPTY_VALUES else int(patch_t)
-
-            if major is None:  # '*'
-                target = Version(major=0, minor=0, patch=0)
-                if prefix not in (cls.PREFIX_EQ, cls.PREFIX_GTE):
-                    raise ValueError("Invalid simple spec: %r" % expr)
-            elif minor is None:
-                target = Version(major=major, minor=0, patch=0)
-            elif patch is None:
-                target = Version(major=major, minor=minor, patch=0)
-            else:
-                target = Version(
-                    major=major,
-                    minor=minor,
-                    patch=patch,
-                    prerelease=prerel.split('.') if prerel else (),
-                    build=build.split('.') if build else (),
-                )
-
-            if (major is None or minor is None or patch is None) and (prerel or build):
-                raise ValueError("Invalid simple spec: %r" % expr)
-
-            if build is not None and prefix not in (cls.PREFIX_EQ, cls.PREFIX_NEQ):
-                raise ValueError("Invalid simple spec: %r" % expr)
-
-            if prefix == cls.PREFIX_CARET:
-                # Accept anything with the same most-significant digit
-                if target.major:
-                    high = target.next_major()
-                elif target.minor:
-                    high = target.next_minor()
-                else:
-                    high = target.next_patch()
-                return Range(Range.OP_GTE, target) & Range(Range.OP_LT, high)
-
-            elif prefix == cls.PREFIX_TILDE:
-                assert major is not None
-                # Accept any higher patch in the same minor
-                # Might go higher if the initial version was a partial
-                if minor is None:
-                    high = target.next_major()
-                else:
-                    high = target.next_minor()
-                return Range(Range.OP_GTE, target) & Range(Range.OP_LT, high)
-
-            elif prefix == cls.PREFIX_COMPATIBLE:
-                assert major is not None
-                # ~1 is 1.0.0..2.0.0; ~=2.2 is 2.2.0..3.0.0; ~=1.4.5 is 1.4.5..1.5.0
-                if minor is None or patch is None:
-                    # We got a partial version
-                    high = target.next_major()
-                else:
-                    high = target.next_minor()
-                return Range(Range.OP_GTE, target) & Range(Range.OP_LT, high)
-
-            elif prefix == cls.PREFIX_EQ:
-                if major is None:
-                    return Range(Range.OP_GTE, target)
-                elif minor is None:
-                    return Range(Range.OP_GTE, target) & Range(Range.OP_LT, target.next_major())
-                elif patch is None:
-                    return Range(Range.OP_GTE, target) & Range(Range.OP_LT, target.next_minor())
-                elif build == '':
-                    return Range(Range.OP_EQ, target, build_policy=Range.BUILD_STRICT)
-                else:
-                    return Range(Range.OP_EQ, target)
-
-            elif prefix == cls.PREFIX_NEQ:
-                assert major is not None
-                if minor is None:
-                    # !=1.x => <1.0.0 || >=2.0.0
-                    return Range(Range.OP_LT, target) | Range(Range.OP_GTE, target.next_major())
-                elif patch is None:
-                    # !=1.2.x => <1.2.0 || >=1.3.0
-                    return Range(Range.OP_LT, target) | Range(Range.OP_GTE, target.next_minor())
-                elif prerel == '':
-                    # !=1.2.3-
-                    return Range(Range.OP_NEQ, target, prerelease_policy=Range.PRERELEASE_ALWAYS)
-                elif build == '':
-                    # !=1.2.3+ or !=1.2.3-a2+
-                    return Range(Range.OP_NEQ, target, build_policy=Range.BUILD_STRICT)
-                else:
-                    return Range(Range.OP_NEQ, target)
-
-            elif prefix == cls.PREFIX_GT:
-                assert major is not None
-                if minor is None:
-                    # >1.x => >=2.0
-                    return Range(Range.OP_GTE, target.next_major())
-                elif patch is None:
-                    return Range(Range.OP_GTE, target.next_minor())
-                else:
-                    return Range(Range.OP_GT, target)
-
-            elif prefix == cls.PREFIX_GTE:
-                return Range(Range.OP_GTE, target)
-
-            elif prefix == cls.PREFIX_LT:
-                assert major is not None
-                if prerel == '':
-                    # <1.2.3-
-                    return Range(Range.OP_LT, target, prerelease_policy=Range.PRERELEASE_ALWAYS)
-                return Range(Range.OP_LT, target)
-
-            else:
-                assert prefix == cls.PREFIX_LTE
-                assert major is not None
-                if minor is None:
-                    # <=1.x => <2.0
-                    return Range(Range.OP_LT, target.next_major())
-                elif patch is None:
-                    return Range(Range.OP_LT, target.next_minor())
-                else:
-                    return Range(Range.OP_LTE, target)
-
-
-class LegacySpec(SimpleSpec):
-    def __init__(self, *expressions):
-        warnings.warn(
-            "The Spec() class will be removed in 3.1; use SimpleSpec() instead.",
-            PendingDeprecationWarning,
-            stacklevel=2,
-        )
-
-        if len(expressions) > 1:
-            warnings.warn(
-                "Passing 2+ arguments to SimpleSpec will be removed in 3.0; concatenate them with ',' instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-        expression = ','.join(expressions)
-        super(LegacySpec, self).__init__(expression)
-
-    @property
-    def specs(self):
-        return list(self)
-
-    def __iter__(self):
-        warnings.warn(
-            "Iterating over the components of a SimpleSpec object will be removed in 3.0.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        try:
-            clauses = list(self.clause)
-        except TypeError:  # Not an iterable
-            clauses = [self.clause]
-        for clause in clauses:
-            yield SpecItem.from_matcher(clause)
-
-
-Spec = LegacySpec
-
-
-@BaseSpec.register_syntax
-class NpmSpec(BaseSpec):
-    SYNTAX = 'npm'
-
-    @classmethod
-    def _parse_to_clause(cls, expression):
-        return cls.Parser.parse(expression)
-
-    class Parser:
-        JOINER = '||'
-        HYPHEN = ' - '
-
-        NUMBER = r'x|X|\*|0|[1-9][0-9]*'
-        PART = r'[a-zA-Z0-9.-]*'
-        NPM_SPEC_BLOCK = re.compile(r"""
-            ^(?:v)?                     # Strip optional initial v
-            (?P<op><|<=|>=|>|=|\^|~|)   # Operator, can be empty
-            (?P<major>{nb})(?:\.(?P<minor>{nb})(?:\.(?P<patch>{nb}))?)?
-            (?:-(?P<prerel>{part}))?    # Optional re-release
-            (?:\+(?P<build>{part}))?    # Optional build
-            $""".format(nb=NUMBER, part=PART),
-            re.VERBOSE,
-        )
-
-        @classmethod
-        def range(cls, operator, target):
-            return Range(operator, target, prerelease_policy=Range.PRERELEASE_SAMEPATCH)
-
-        @classmethod
-        def parse(cls, expression):
-            result = Never()
-            groups = expression.split(cls.JOINER)
-            for group in groups:
-                group = group.strip()
-                if not group:
-                    group = '>=0.0.0'
-
-                subclauses = []
-                if cls.HYPHEN in group:
-                    low, high = group.split(cls.HYPHEN, 2)
-                    subclauses = cls.parse_simple('>=' + low) + cls.parse_simple('<=' + high)
-
-                else:
-                    blocks = group.split(' ')
-                    for block in blocks:
-                        if not cls.NPM_SPEC_BLOCK.match(block):
-                            raise ValueError("Invalid NPM block in %r: %r" % (expression, block))
-
-                        subclauses.extend(cls.parse_simple(block))
-
-                prerelease_clauses = []
-                non_prerel_clauses = []
-                for clause in subclauses:
-                    if clause.target.prerelease:
-                        if clause.operator in (Range.OP_GT, Range.OP_GTE):
-                            prerelease_clauses.append(Range(
-                                operator=Range.OP_LT,
-                                target=Version(
-                                    major=clause.target.major,
-                                    minor=clause.target.minor,
-                                    patch=clause.target.patch + 1,
-                                ),
-                                prerelease_policy=Range.PRERELEASE_ALWAYS,
-                            ))
-                        elif clause.operator in (Range.OP_LT, Range.OP_LTE):
-                            prerelease_clauses.append(Range(
-                                operator=Range.OP_GTE,
-                                target=Version(
-                                    major=clause.target.major,
-                                    minor=clause.target.minor,
-                                    patch=0,
-                                    prerelease=(),
-                                ),
-                                prerelease_policy=Range.PRERELEASE_ALWAYS,
-                            ))
-                        prerelease_clauses.append(clause)
-                        non_prerel_clauses.append(cls.range(
-                            operator=clause.operator,
-                            target=clause.target.truncate(),
-                        ))
-                    else:
-                        non_prerel_clauses.append(clause)
-                if prerelease_clauses:
-                    result |= AllOf(*prerelease_clauses)
-                result |= AllOf(*non_prerel_clauses)
-
-            return result
-
-        PREFIX_CARET = '^'
-        PREFIX_TILDE = '~'
-        PREFIX_EQ = '='
-        PREFIX_GT = '>'
-        PREFIX_GTE = '>='
-        PREFIX_LT = '<'
-        PREFIX_LTE = '<='
-
-        PREFIX_ALIASES = {
-            '': PREFIX_EQ,
-        }
-
-        PREFIX_TO_OPERATOR = {
-            PREFIX_EQ: Range.OP_EQ,
-            PREFIX_LT: Range.OP_LT,
-            PREFIX_LTE: Range.OP_LTE,
-            PREFIX_GTE: Range.OP_GTE,
-            PREFIX_GT: Range.OP_GT,
-        }
-
-        EMPTY_VALUES = ['*', 'x', 'X', None]
-
-        @classmethod
-        def parse_simple(cls, simple):
-            match = cls.NPM_SPEC_BLOCK.match(simple)
-
-            prefix, major_t, minor_t, patch_t, prerel, build = match.groups()
-
-            prefix = cls.PREFIX_ALIASES.get(prefix, prefix)
-            major = None if major_t in cls.EMPTY_VALUES else int(major_t)
-            minor = None if minor_t in cls.EMPTY_VALUES else int(minor_t)
-            patch = None if patch_t in cls.EMPTY_VALUES else int(patch_t)
-
-            if build is not None and prefix not in [cls.PREFIX_EQ]:
-                # Ignore the 'build' part when not comparing to a specific part.
-                build = None
-
-            if major is None:  # '*', 'x', 'X'
-                target = Version(major=0, minor=0, patch=0)
-                if prefix not in [cls.PREFIX_EQ, cls.PREFIX_GTE]:
-                    raise ValueError("Invalid expression %r" % simple)
-                prefix = cls.PREFIX_GTE
-            elif minor is None:
-                target = Version(major=major, minor=0, patch=0)
-            elif patch is None:
-                target = Version(major=major, minor=minor, patch=0)
-            else:
-                target = Version(
-                    major=major,
-                    minor=minor,
-                    patch=patch,
-                    prerelease=prerel.split('.') if prerel else (),
-                    build=build.split('.') if build else (),
-                )
-
-            if (major is None or minor is None or patch is None) and (prerel or build):
-                raise ValueError("Invalid NPM spec: %r" % simple)
-
-            if prefix == cls.PREFIX_CARET:
-                if target.major:  # ^1.2.4 => >=1.2.4 <2.0.0 ; ^1.x => >=1.0.0 <2.0.0
-                    high = target.truncate().next_major()
-                elif target.minor:  # ^0.1.2 => >=0.1.2 <0.2.0
-                    high = target.truncate().next_minor()
-                elif minor is None:  # ^0.x => >=0.0.0 <1.0.0
-                    high = target.truncate().next_major()
-                elif patch is None:  # ^0.2.x => >=0.2.0 <0.3.0
-                    high = target.truncate().next_minor()
-                else:  # ^0.0.1 => >=0.0.1 <0.0.2
-                    high = target.truncate().next_patch()
-                return [cls.range(Range.OP_GTE, target), cls.range(Range.OP_LT, high)]
-
-            elif prefix == cls.PREFIX_TILDE:
-                assert major is not None
-                if minor is None:  # ~1.x => >=1.0.0 <2.0.0
-                    high = target.next_major()
-                else:  # ~1.2.x => >=1.2.0 <1.3.0; ~1.2.3 => >=1.2.3 <1.3.0
-                    high = target.next_minor()
-                return [cls.range(Range.OP_GTE, target), cls.range(Range.OP_LT, high)]
-
-            elif prefix == cls.PREFIX_EQ:
-                if major is None:
-                    return [cls.range(Range.OP_GTE, target)]
-                elif minor is None:
-                    return [cls.range(Range.OP_GTE, target), cls.range(Range.OP_LT, target.next_major())]
-                elif patch is None:
-                    return [cls.range(Range.OP_GTE, target), cls.range(Range.OP_LT, target.next_minor())]
-                else:
-                    return [cls.range(Range.OP_EQ, target)]
-
-            elif prefix == cls.PREFIX_GT:
-                assert major is not None
-                if minor is None:  # >1.x
-                    return [cls.range(Range.OP_GTE, target.next_major())]
-                elif patch is None:  # >1.2.x => >=1.3.0
-                    return [cls.range(Range.OP_GTE, target.next_minor())]
-                else:
-                    return [cls.range(Range.OP_GT, target)]
-
-            elif prefix == cls.PREFIX_GTE:
-                return [cls.range(Range.OP_GTE, target)]
-
-            elif prefix == cls.PREFIX_LT:
-                assert major is not None
-                return [cls.range(Range.OP_LT, target)]
-
-            else:
-                assert prefix == cls.PREFIX_LTE
-                assert major is not None
-                if minor is None:  # <=1.x => <2.0.0
-                    return [cls.range(Range.OP_LT, target.next_major())]
-                elif patch is None:  # <=1.2.x => <1.3.0
-                    return [cls.range(Range.OP_LT, target.next_minor())]
-                else:
-                    return [cls.range(Range.OP_LTE, target)]
diff --git a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/losses/robust_loss.py b/spaces/Realcat/image-matching-webui/third_party/Roma/roma/losses/robust_loss.py
deleted file mode 100644
index cd9fd5bbc9c2d01bb6dd40823e350b588bd598b3..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/losses/robust_loss.py
+++ /dev/null
@@ -1,222 +0,0 @@
-from einops.einops import rearrange
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from roma.utils.utils import get_gt_warp
-import wandb
-import roma
-import math
-
-
-class RobustLosses(nn.Module):
-    def __init__(
-        self,
-        robust=False,
-        center_coords=False,
-        scale_normalize=False,
-        ce_weight=0.01,
-        local_loss=True,
-        local_dist=4.0,
-        local_largest_scale=8,
-        smooth_mask=False,
-        depth_interpolation_mode="bilinear",
-        mask_depth_loss=False,
-        relative_depth_error_threshold=0.05,
-        alpha=1.0,
-        c=1e-3,
-    ):
-        super().__init__()
-        self.robust = robust  # measured in pixels
-        self.center_coords = center_coords
-        self.scale_normalize = scale_normalize
-        self.ce_weight = ce_weight
-        self.local_loss = local_loss
-        self.local_dist = local_dist
-        self.local_largest_scale = local_largest_scale
-        self.smooth_mask = smooth_mask
-        self.depth_interpolation_mode = depth_interpolation_mode
-        self.mask_depth_loss = mask_depth_loss
-        self.relative_depth_error_threshold = relative_depth_error_threshold
-        self.avg_overlap = dict()
-        self.alpha = alpha
-        self.c = c
-
-    def gm_cls_loss(self, x2, prob, scale_gm_cls, gm_certainty, scale):
-        with torch.no_grad():
-            B, C, H, W = scale_gm_cls.shape
-            device = x2.device
-            cls_res = round(math.sqrt(C))
-            G = torch.meshgrid(
-                *[
-                    torch.linspace(
-                        -1 + 1 / cls_res, 1 - 1 / cls_res, steps=cls_res, device=device
-                    )
-                    for _ in range(2)
-                ]
-            )
-            G = torch.stack((G[1], G[0]), dim=-1).reshape(C, 2)
-            GT = (
-                (G[None, :, None, None, :] - x2[:, None])
-                .norm(dim=-1)
-                .min(dim=1)
-                .indices
-            )
-        cls_loss = F.cross_entropy(scale_gm_cls, GT, reduction="none")[prob > 0.99]
-        if not torch.any(cls_loss):
-            cls_loss = certainty_loss * 0.0  # Prevent issues where prob is 0 everywhere
-
-        certainty_loss = F.binary_cross_entropy_with_logits(gm_certainty[:, 0], prob)
-        losses = {
-            f"gm_certainty_loss_{scale}": certainty_loss.mean(),
-            f"gm_cls_loss_{scale}": cls_loss.mean(),
-        }
-        wandb.log(losses, step=roma.GLOBAL_STEP)
-        return losses
-
-    def delta_cls_loss(
-        self, x2, prob, flow_pre_delta, delta_cls, certainty, scale, offset_scale
-    ):
-        with torch.no_grad():
-            B, C, H, W = delta_cls.shape
-            device = x2.device
-            cls_res = round(math.sqrt(C))
-            G = torch.meshgrid(
-                *[
-                    torch.linspace(
-                        -1 + 1 / cls_res, 1 - 1 / cls_res, steps=cls_res, device=device
-                    )
-                    for _ in range(2)
-                ]
-            )
-            G = torch.stack((G[1], G[0]), dim=-1).reshape(C, 2) * offset_scale
-            GT = (
-                (G[None, :, None, None, :] + flow_pre_delta[:, None] - x2[:, None])
-                .norm(dim=-1)
-                .min(dim=1)
-                .indices
-            )
-        cls_loss = F.cross_entropy(delta_cls, GT, reduction="none")[prob > 0.99]
-        if not torch.any(cls_loss):
-            cls_loss = certainty_loss * 0.0  # Prevent issues where prob is 0 everywhere
-        certainty_loss = F.binary_cross_entropy_with_logits(certainty[:, 0], prob)
-        losses = {
-            f"delta_certainty_loss_{scale}": certainty_loss.mean(),
-            f"delta_cls_loss_{scale}": cls_loss.mean(),
-        }
-        wandb.log(losses, step=roma.GLOBAL_STEP)
-        return losses
-
-    def regression_loss(self, x2, prob, flow, certainty, scale, eps=1e-8, mode="delta"):
-        epe = (flow.permute(0, 2, 3, 1) - x2).norm(dim=-1)
-        if scale == 1:
-            pck_05 = (epe[prob > 0.99] < 0.5 * (2 / 512)).float().mean()
-            wandb.log({"train_pck_05": pck_05}, step=roma.GLOBAL_STEP)
-
-        ce_loss = F.binary_cross_entropy_with_logits(certainty[:, 0], prob)
-        a = self.alpha
-        cs = self.c * scale
-        x = epe[prob > 0.99]
-        reg_loss = cs**a * ((x / (cs)) ** 2 + 1**2) ** (a / 2)
-        if not torch.any(reg_loss):
-            reg_loss = ce_loss * 0.0  # Prevent issues where prob is 0 everywhere
-        losses = {
-            f"{mode}_certainty_loss_{scale}": ce_loss.mean(),
-            f"{mode}_regression_loss_{scale}": reg_loss.mean(),
-        }
-        wandb.log(losses, step=roma.GLOBAL_STEP)
-        return losses
-
-    def forward(self, corresps, batch):
-        scales = list(corresps.keys())
-        tot_loss = 0.0
-        # scale_weights due to differences in scale for regression gradients and classification gradients
-        scale_weights = {1: 1, 2: 1, 4: 1, 8: 1, 16: 1}
-        for scale in scales:
-            scale_corresps = corresps[scale]
-            (
-                scale_certainty,
-                flow_pre_delta,
-                delta_cls,
-                offset_scale,
-                scale_gm_cls,
-                scale_gm_certainty,
-                flow,
-                scale_gm_flow,
-            ) = (
-                scale_corresps["certainty"],
-                scale_corresps["flow_pre_delta"],
-                scale_corresps.get("delta_cls"),
-                scale_corresps.get("offset_scale"),
-                scale_corresps.get("gm_cls"),
-                scale_corresps.get("gm_certainty"),
-                scale_corresps["flow"],
-                scale_corresps.get("gm_flow"),
-            )
-            flow_pre_delta = rearrange(flow_pre_delta, "b d h w -> b h w d")
-            b, h, w, d = flow_pre_delta.shape
-            gt_warp, gt_prob = get_gt_warp(
-                batch["im_A_depth"],
-                batch["im_B_depth"],
-                batch["T_1to2"],
-                batch["K1"],
-                batch["K2"],
-                H=h,
-                W=w,
-            )
-            x2 = gt_warp.float()
-            prob = gt_prob
-
-            if self.local_largest_scale >= scale:
-                prob = prob * (
-                    F.interpolate(prev_epe[:, None], size=(h, w), mode="nearest-exact")[
-                        :, 0
-                    ]
-                    < (2 / 512) * (self.local_dist[scale] * scale)
-                )
-
-            if scale_gm_cls is not None:
-                gm_cls_losses = self.gm_cls_loss(
-                    x2, prob, scale_gm_cls, scale_gm_certainty, scale
-                )
-                gm_loss = (
-                    self.ce_weight * gm_cls_losses[f"gm_certainty_loss_{scale}"]
-                    + gm_cls_losses[f"gm_cls_loss_{scale}"]
-                )
-                tot_loss = tot_loss + scale_weights[scale] * gm_loss
-            elif scale_gm_flow is not None:
-                gm_flow_losses = self.regression_loss(
-                    x2, prob, scale_gm_flow, scale_gm_certainty, scale, mode="gm"
-                )
-                gm_loss = (
-                    self.ce_weight * gm_flow_losses[f"gm_certainty_loss_{scale}"]
-                    + gm_flow_losses[f"gm_regression_loss_{scale}"]
-                )
-                tot_loss = tot_loss + scale_weights[scale] * gm_loss
-
-            if delta_cls is not None:
-                delta_cls_losses = self.delta_cls_loss(
-                    x2,
-                    prob,
-                    flow_pre_delta,
-                    delta_cls,
-                    scale_certainty,
-                    scale,
-                    offset_scale,
-                )
-                delta_cls_loss = (
-                    self.ce_weight * delta_cls_losses[f"delta_certainty_loss_{scale}"]
-                    + delta_cls_losses[f"delta_cls_loss_{scale}"]
-                )
-                tot_loss = tot_loss + scale_weights[scale] * delta_cls_loss
-            else:
-                delta_regression_losses = self.regression_loss(
-                    x2, prob, flow, scale_certainty, scale
-                )
-                reg_loss = (
-                    self.ce_weight
-                    * delta_regression_losses[f"delta_certainty_loss_{scale}"]
-                    + delta_regression_losses[f"delta_regression_loss_{scale}"]
-                )
-                tot_loss = tot_loss + scale_weights[scale] * reg_loss
-            prev_epe = (flow.permute(0, 2, 3, 1) - x2).norm(dim=-1).detach()
-        return tot_loss
diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/viz/methods/__init__.py b/spaces/Realcat/image-matching-webui/third_party/TopicFM/viz/methods/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SShaik/SS-02-H5-AR-VR-IOT/index.html b/spaces/SShaik/SS-02-H5-AR-VR-IOT/index.html
deleted file mode 100644
index f64aad6580cd12cbdbb0bcc0321ed7a6486d2a19..0000000000000000000000000000000000000000
--- a/spaces/SShaik/SS-02-H5-AR-VR-IOT/index.html
+++ /dev/null
@@ -1,66 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <title>Dynamic Lights - A-Frame</title>
-    <meta name="description" content="Dynamic Lights - A-Frame">
-    <script src="https://aframe.io/releases/1.0.4/aframe.min.js"></script>
-    <script src="https://unpkg.com/aframe-randomizer-components@3.0.2/dist/aframe-randomizer-components.min.js"></script>
-    <script src="https://unpkg.com/aframe-entity-generator-component@3.0.1/dist/aframe-entity-generator-component.min.js"></script>
-    <script>
-      AFRAME.registerComponent('random-material', {
-        init: function () {
-          this.el.setAttribute('material', {
-            color: this.getRandomColor(),
-            metalness: Math.random(),
-            roughness: Math.random()
-          });
-        },
-        getRandomColor: function () {
-          var letters = '0123456789ABCDEF'.split('');
-          var color = '#';
-          for (var i = 0; i < 6; i++) {
-            color += letters[Math.floor(Math.random() * 16)];
-          }
-          return color;
-        }
-      });
-      AFRAME.registerComponent('random-torus-knot', {
-        init: function () {
-          this.el.setAttribute('geometry', {
-            primitive: 'torusKnot',
-            radius: Math.random() * 10,
-            radiusTubular: Math.random() * .75,
-            p: Math.round(Math.random() * 10),
-            q: Math.round(Math.random() * 10)
-          });
-        }
-      });
-    </script>
-  </head>
-  <body>
-    <a-scene background="color: #111">
-      <a-assets>
-        <a-mixin id="light"
-                 geometry="primitive: sphere; radius: 1.5"
-                 material="color: #FFF; shader: flat"
-                 light="color: #DDDDFF; distance: 120; intensity: 2; type: point"></a-mixin>
-        <a-mixin id="torusKnot"
-                 random-torus-knot
-                 random-material
-                 random-position="min: -60 -60 -80; max: 60 60 40"></a-mixin>
-      </a-assets>
-
-      <!-- Use entity-generator component to generate 120 entities with the torusKnot mixin. -->
-      <a-entity entity-generator="mixin: torusKnot; num: 120"></a-entity>
-
-      <!-- Lights. -->
-      <a-entity animation="property: rotation; to: 0 0 360; dur: 4000; easing: linear; loop: true">
-        <a-entity mixin="light" position="30 0 0"></a-entity>
-      </a-entity>
-
-      <a-entity animation="property: rotation; to: 360 0 0; dur: 4000; easing: linear; loop: true">
-        <a-entity mixin="light" position="0 0 40"></a-entity>
-      </a-entity>
-    </a-scene>
-  </body>
-</html>
\ No newline at end of file
diff --git a/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/attentions.py b/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/Sapphire-356/Video2MC/common/model.py b/spaces/Sapphire-356/Video2MC/common/model.py
deleted file mode 100644
index 8be5a30e9847cdf7a93403c1a5f8a4e497946829..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/common/model.py
+++ /dev/null
@@ -1,200 +0,0 @@
-# Copyright (c) 2018-present, Facebook, Inc.
-# 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.nn as nn
-
-
-class TemporalModelBase(nn.Module):
-    """
-    Do not instantiate this class.
-    """
-
-    def __init__(self, num_joints_in, in_features, num_joints_out,
-                 filter_widths, causal, dropout, channels):
-        super().__init__()
-
-        # Validate input
-        for fw in filter_widths:
-            assert fw % 2 != 0, 'Only odd filter widths are supported'
-
-        self.num_joints_in = num_joints_in
-        self.in_features = in_features
-        self.num_joints_out = num_joints_out
-        self.filter_widths = filter_widths
-
-        self.drop = nn.Dropout(dropout)
-        self.relu = nn.ReLU(inplace=True)
-
-        self.pad = [filter_widths[0] // 2]
-        self.expand_bn = nn.BatchNorm1d(channels, momentum=0.1)
-        self.shrink = nn.Conv1d(channels, num_joints_out * 3, 1)
-
-    def set_bn_momentum(self, momentum):
-        self.expand_bn.momentum = momentum
-        for bn in self.layers_bn:
-            bn.momentum = momentum
-
-    def receptive_field(self):
-        """
-        Return the total receptive field of this model as # of frames.
-        """
-        frames = 0
-        for f in self.pad:
-            frames += f
-        return 1 + 2 * frames
-
-    def total_causal_shift(self):
-        """
-        Return the asymmetric offset for sequence padding.
-        The returned value is typically 0 if causal convolutions are disabled,
-        otherwise it is half the receptive field.
-        """
-        frames = self.causal_shift[0]
-        next_dilation = self.filter_widths[0]
-        for i in range(1, len(self.filter_widths)):
-            frames += self.causal_shift[i] * next_dilation
-            next_dilation *= self.filter_widths[i]
-        return frames
-
-    def forward(self, x):
-        assert len(x.shape) == 4
-        assert x.shape[-2] == self.num_joints_in
-        assert x.shape[-1] == self.in_features
-
-        sz = x.shape[:3]
-        x = x.view(x.shape[0], x.shape[1], -1)
-        x = x.permute(0, 2, 1)
-
-        x = self._forward_blocks(x)
-
-        x = x.permute(0, 2, 1)
-        x = x.view(sz[0], -1, self.num_joints_out, 3)
-
-        return x
-
-
-class TemporalModel(TemporalModelBase):
-    """
-    Reference 3D pose estimation model with temporal convolutions.
-    This implementation can be used for all use-cases.
-    """
-
-    def __init__(self, num_joints_in, in_features, num_joints_out,
-                 filter_widths, causal=False, dropout=0.25, channels=1024, dense=False):
-        """
-        Initialize this model.
-
-        Arguments:
-        num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
-        in_features -- number of input features for each joint (typically 2 for 2D input)
-        num_joints_out -- number of output joints (can be different than input)
-        filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
-        causal -- use causal convolutions instead of symmetric convolutions (for real-time applications)
-        dropout -- dropout probability
-        channels -- number of convolution channels
-        dense -- use regular dense convolutions instead of dilated convolutions (ablation experiment)
-        """
-        super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
-
-        self.expand_conv = nn.Conv1d(num_joints_in * in_features, channels, filter_widths[0], bias=False)
-
-        layers_conv = []
-        layers_bn = []
-
-        self.causal_shift = [(filter_widths[0]) // 2 if causal else 0]
-        next_dilation = filter_widths[0]
-        for i in range(1, len(filter_widths)):
-            self.pad.append((filter_widths[i] - 1) * next_dilation // 2)
-            self.causal_shift.append((filter_widths[i] // 2 * next_dilation) if causal else 0)
-
-            layers_conv.append(nn.Conv1d(channels, channels,
-                                         filter_widths[i] if not dense else (2 * self.pad[-1] + 1),
-                                         dilation=next_dilation if not dense else 1,
-                                         bias=False))
-            layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
-            layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
-            layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
-
-            next_dilation *= filter_widths[i]
-
-        self.layers_conv = nn.ModuleList(layers_conv)
-        self.layers_bn = nn.ModuleList(layers_bn)
-
-    def _forward_blocks(self, x):
-        x = self.drop(self.relu(self.expand_bn(self.expand_conv(x))))
-
-        for i in range(len(self.pad) - 1):
-            pad = self.pad[i + 1]
-            shift = self.causal_shift[i + 1]
-            # clip
-            res = x[:, :, pad + shift: x.shape[2] - pad + shift]
-
-            x = self.drop(self.relu(self.layers_bn[2 * i](self.layers_conv[2 * i](x))))
-            x = res + self.drop(self.relu(self.layers_bn[2 * i + 1](self.layers_conv[2 * i + 1](x))))
-
-        x = self.shrink(x)
-        return x
-
-
-class TemporalModelOptimized1f(TemporalModelBase):
-    """
-    3D pose estimation model optimized for single-frame batching, i.e.
-    where batches have input length = receptive field, and output length = 1.
-    This scenario is only used for training when stride == 1.
-
-    This implementation replaces dilated convolutions with strided convolutions
-    to avoid generating unused intermediate results. The weights are interchangeable
-    with the reference implementation.
-    """
-
-    def __init__(self, num_joints_in, in_features, num_joints_out,
-                 filter_widths, causal=False, dropout=0.25, channels=1024):
-        """
-        Initialize this model.
-
-        Arguments:
-        num_joints_in -- number of input joints (e.g. 17 for Human3.6M)
-        in_features -- number of input features for each joint (typically 2 for 2D input)
-        num_joints_out -- number of output joints (can be different than input)
-        filter_widths -- list of convolution widths, which also determines the # of blocks and receptive field
-        causal -- use causal convolutions instead of symmetric convolutions (for real-time applications)
-        dropout -- dropout probability
-        channels -- number of convolution channels
-        """
-        super().__init__(num_joints_in, in_features, num_joints_out, filter_widths, causal, dropout, channels)
-
-        self.expand_conv = nn.Conv1d(num_joints_in * in_features, channels, filter_widths[0], stride=filter_widths[0], bias=False)
-
-        layers_conv = []
-        layers_bn = []
-
-        self.causal_shift = [(filter_widths[0] // 2) if causal else 0]
-        next_dilation = filter_widths[0]
-        for i in range(1, len(filter_widths)):
-            self.pad.append((filter_widths[i] - 1) * next_dilation // 2)
-            self.causal_shift.append((filter_widths[i] // 2) if causal else 0)
-
-            layers_conv.append(nn.Conv1d(channels, channels, filter_widths[i], stride=filter_widths[i], bias=False))
-            layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
-            layers_conv.append(nn.Conv1d(channels, channels, 1, dilation=1, bias=False))
-            layers_bn.append(nn.BatchNorm1d(channels, momentum=0.1))
-            next_dilation *= filter_widths[i]
-
-        self.layers_conv = nn.ModuleList(layers_conv)
-        self.layers_bn = nn.ModuleList(layers_bn)
-
-    def _forward_blocks(self, x):
-        x = self.drop(self.relu(self.expand_bn(self.expand_conv(x))))
-
-        for i in range(len(self.pad) - 1):
-            res = x[:, :, self.causal_shift[i + 1] + self.filter_widths[i + 1] // 2:: self.filter_widths[i + 1]]
-
-            x = self.drop(self.relu(self.layers_bn[2 * i](self.layers_conv[2 * i](x))))
-            x = res + self.drop(self.relu(self.layers_bn[2 * i + 1](self.layers_conv[2 * i + 1](x))))
-
-        x = self.shrink(x)
-        return x
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/openpose/main.py b/spaces/Sapphire-356/Video2MC/joints_detectors/openpose/main.py
deleted file mode 100644
index a2f33174c1ced8b53bd46e05c4253415aec71a63..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/openpose/main.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-import sys
-
-import cv2
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-sys.path.insert(0, dir_path)
-import ipdb;
-
-pdb = ipdb.set_trace
-import argparse
-from tqdm import tqdm
-from utils import convert
-import numpy as np
-
-sys.path.remove(dir_path)
-
-try:
-    from openpose import pyopenpose as op
-except ImportError as e:
-    print('Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?')
-    raise e
-
-# Flags
-parser = argparse.ArgumentParser()
-parser.add_argument("--image_path", default="../../examples/media/COCO_val2014_000000000192.jpg",
-                    help="Process an image. Read all standard formats (jpg, png, bmp, etc.).")
-args = parser.parse_known_args()
-
-params = dict()
-cur_dir = os.path.dirname(os.path.abspath(__file__))
-params["model_folder"] = cur_dir + "/models/"
-params['tracking'] = 5
-params['number_people_max'] = 1
-
-
-#  params['num_gpu'] = 1
-#  params['num_gpu_start'] = 1
-#  import ipdb;ipdb.set_trace()
-
-
-def load_model():
-    try:
-        opWrapper = op.WrapperPython()
-        opWrapper.configure(params)
-        opWrapper.start()
-    except Exception as e:
-        print(e)
-        sys.exit(-1)
-
-    return opWrapper
-
-
-def test_video(model, video_name=0):
-    opWrapper = model
-
-    cam = cv2.VideoCapture(video_name)
-    # warm up
-    for i in range(5):
-        datum = op.Datum()
-        _, imageToProcess = cam.read()
-        datum.cvInputData = imageToProcess
-        opWrapper.emplaceAndPop([datum])
-
-    for i in tqdm(range(2000)):
-        datum = op.Datum()
-        _, imageToProcess = cam.read()
-        datum.cvInputData = imageToProcess
-        opWrapper.emplaceAndPop([datum])
-
-    # Display Image
-    #  print("Body keypoints: \n" + str(datum.poseKeypoints))
-    #  cv2.imshow("OpenPose 1.4.0 - Tutorial Python API", datum.cvOutputData)
-    #  cv2.waitKey(10)
-    #  cv2.destroyAllWindows()
-
-
-def generate_kpts(video_name):
-    kpt_results = []
-
-    cap = cv2.VideoCapture(video_name)
-    length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    opWrapper = load_model()
-    for i in tqdm(range(length)):
-
-        try:
-            datum = op.Datum()
-            _, imageToProcess = cap.read()
-            datum.cvInputData = imageToProcess
-            opWrapper.emplaceAndPop([datum])
-            results = datum.poseKeypoints
-
-            assert len(results) == 1, 'videopose3D only support one pserson restruction'
-            # 25 to 17
-            kpts = convert(results[0])
-            kpt_results.append(kpts)
-        except Exception as e:
-            print(e)
-
-    # pose processes
-    result = np.array(kpt_results)
-
-    #  # save
-    #  name = '/home/xyliu/experiments/VideoPose3D/data/tmp.npz'
-    #  kpts = result.astype(np.float32)
-    #  print('kpts npz save in ', name)
-    #  np.savez_compressed(name, kpts=kpts)
-    return result
-
-
-def generate_frame_kpt(frame, opWrapper):
-    '''
-    提供frame and model
-    '''
-    datum = op.Datum()
-    datum.cvInputData = frame
-    opWrapper.emplaceAndPop([datum])
-    re = datum.poseKeypoints
-    assert len(re) == 1, 'videopose3D only support one pserson restruction'
-    kpt = convert(re[0])
-
-    return kpt
-
-
-if __name__ == "__main__":
-    generate_kpts(os.environ.get('VIDEO_PATH') + 'dance.mp4')
diff --git a/spaces/ShoaibMajidDar/PDF-chatbot/apikey.py b/spaces/ShoaibMajidDar/PDF-chatbot/apikey.py
deleted file mode 100644
index bf74cec5441e15062e94ed8e04c1171abf76745c..0000000000000000000000000000000000000000
--- a/spaces/ShoaibMajidDar/PDF-chatbot/apikey.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import streamlit as st
-
-def get_apikey():
-    OPENAI_API_KEY = st.text_input(":blue[Enter Your OPENAI API-KEY :]", 
-                placeholder="Paste your OpenAI API key here  (sk-...)",
-                type="password",
-                )
-    return OPENAI_API_KEY
\ No newline at end of file
diff --git a/spaces/Sphila/Sphila-Diffusion/app.py b/spaces/Sphila/Sphila-Diffusion/app.py
deleted file mode 100644
index 1cd1cd730c7434c36281db2aa86bcc324adc7566..0000000000000000000000000000000000000000
--- a/spaces/Sphila/Sphila-Diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/prompthero/openjourney").launch()
diff --git a/spaces/SrikanthPhalgun/Cifar10_ERAV1_GradCam_Demo/app.py b/spaces/SrikanthPhalgun/Cifar10_ERAV1_GradCam_Demo/app.py
deleted file mode 100644
index 957c08b3d43079f2aa180934015ec29326d15626..0000000000000000000000000000000000000000
--- a/spaces/SrikanthPhalgun/Cifar10_ERAV1_GradCam_Demo/app.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import torch
-import torch.optim as optim
-from torchvision import datasets, transforms
-import matplotlib.pyplot as plt
-#%matplotlib inline
-import numpy as np
-import pytorch_lightning as pl
-from models.custom_resnet import CustomRes_Network
-from models.lit_model import LitModule
-from Cifar_datamodule import *
-from transformation import *
-from torchsummary import summary
-from utils import *
-from torch.optim.lr_scheduler import StepLR
-from torch_lr_finder import LRFinder
-from torch.optim.lr_scheduler import OneCycleLR
-import pytorch_lightning as pl
-import math
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from pytorch_grad_cam import GradCAM
-from pytorch_grad_cam.utils.image import show_cam_on_image,scale_cam_image
-from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
-import warnings
-warnings.filterwarnings('ignore')
-import gradio as gr
-from PIL import Image
-
-datamodule = CIFAR10DataModule(batch_size=1,num_workers=2, pin_memory=True)
-torch_model = CustomRes_Network()
-inference_model = LitModule(torch_model,num_classes = 10 , learning_rate = 0.001)
-
-inference_model.load_state_dict(torch.load("Model_Artifacts/custom_trained_resnet_model.pth", map_location=torch.device('cpu')), strict=False)
-inference_model.eval()
-
-classes = ('plane', 'car', 'bird', 'cat', 'deer',
-           'dog', 'frog', 'horse', 'ship', 'truck')
-
-
-new_line = '\n'
-wrong_img = pd.read_csv('wrongprediction.csv')
-wrong_img_no = len(wrong_img)
-           
-
-def inference_up_img(input_img,see_gradcam= True,target_layer_number = -1,transparency = 0.85,top_classes=3):
-    org_img = input_img
-    # model inference
-    transform = transforms.ToTensor()
-    input_img = transform(input_img)
-    input_img = input_img.unsqueeze(0)
-    outputs = inference_model.net(input_img)
-    softmax = torch.nn.Softmax(dim=0)
-    o = softmax(outputs.flatten())
-    confidences = {classes[i]: float(o[i]) for i in range(10)}
-    sorted_confidences = dict(sorted(confidences.items(), key=lambda x:x[1], reverse=True))
-    _, prediction = torch.max(outputs, 1)
-
-    # gradcam
-    if see_gradcam:
-      target_layers = [inference_model.net.layer2[target_layer_number]]
-      cam = GradCAM(model=inference_model.net, target_layers=target_layers, use_cuda=False)
-      grayscale_cam = cam(input_tensor=input_img, targets=None)
-      grayscale_cam = grayscale_cam[0, :]
-      visualization = show_cam_on_image(org_img/255.0, grayscale_cam, use_rgb=True, image_weight=transparency)
-    else:
-      visualization = org_img
-
-    # top n classes only
-    sorted_confidences = {k: sorted_confidences[k] for k in list(sorted_confidences)[:top_classes]}
-    return sorted_confidences, visualization
-
-def misclass_fn(misclassified_check,num_misclassified=1,see_gradcam=True,num_gradcam=1,gradcam_layer=-2,gradcam_opa= 0.50):
-  img_gallery = []
-
-  if misclassified_check:
-    for i in range(int(num_misclassified)):
-      org_img   =  np.asarray(Image.open(f'Misclassified_images/{i+1}.jpg'))
-      input_img = org_img
-      actual = classes[wrong_img.loc[i].at["actual"]]
-      predicted = classes[wrong_img.loc[i].at["predicted"]]  
-      img_gallery.append((org_img,f"Actual:{actual}{new_line}Predicted:{predicted}"))  
-
-      if see_gradcam:
-        transform = transforms.ToTensor()
-        input_img = transform(input_img)
-        input_img = input_img.unsqueeze(0)
-        target_layers = [inference_model.net.layer2[gradcam_layer]]
-        cam = GradCAM(model=inference_model.net, target_layers=target_layers, use_cuda=False)
-        grayscale_cam = cam(input_tensor=input_img, targets=None)
-        grayscale_cam = grayscale_cam[0, :]
-        visualization = show_cam_on_image(org_img/255.0, grayscale_cam, use_rgb=True, image_weight=gradcam_opa)
-        img_gallery.append((visualization,f"Actual:{actual}{new_line}Predicted:{predicted}"))
-      else:
-          pass
-        #img_gallery.append((org_img,f"Actual:{actual}{new_line}Predicted:{predicted}"))
-
-  elif see_gradcam:
-    for i in range(int(num_gradcam)):
-      org_img   =  np.asarray(Image.open(f'Misclassified_images/{i+1}.jpg'))
-      input_img = org_img
-      actual = classes[wrong_img.loc[i].at["actual"]]
-      predicted = classes[wrong_img.loc[i].at["predicted"]]  
-      transform = transforms.ToTensor()
-      input_img = transform(input_img)
-      input_img = input_img.unsqueeze(0)
-      target_layers = [inference_model.net.layer2[gradcam_layer]]
-      cam = GradCAM(model=inference_model.net, target_layers=target_layers, use_cuda=False)
-      grayscale_cam = cam(input_tensor=input_img, targets=None)
-      grayscale_cam = grayscale_cam[0, :]
-      visualization = show_cam_on_image(org_img/255.0, grayscale_cam, use_rgb=True, image_weight=gradcam_opa)
-      img_gallery.append((visualization,f"Actual:{actual}{new_line}Predicted:{predicted}"))
-
-
-  return img_gallery
-
-examples = [["Images/cat.jpg"], ["Images/plane.jpg"],["Images/dog.jpg"],["Images/truck.jpg"],["Images/bird.jpg"],["Images/ship.jpg"],["Images/horse.jpg"],["Images/frog.jpg"],["Images/deer.jpg"],["Images/car.jpg"]]
-
-with gr.Blocks() as demo:
-  gr.Markdown("Explore Custom ResNet model for CIFAR10.")
-  with gr.Tab("Upload your own image"):
-    with gr.Row():
-      image_input   = gr.Image(shape=(32, 32), label="Input Image")
-      image_label   = gr.Label()
-    with gr.Row():
-      with gr.Column():
-        gradcam_check = gr.Checkbox(label="Gradcam")
-        gradcam_layer = gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?")
-        gradcam_opa = gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM")
-        top_classes = gr.Slider(1, 10, value=3, step=1, label="How many top classes?")
-      image_output  = gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)
-    with gr.Row():
-      examples = gr.Examples(examples=examples, 
-                             inputs=[image_input,gradcam_check,gradcam_layer,gradcam_opa,top_classes,image_label],
-                             outputs=[image_output],
-                             fn=inference_up_img, cache_examples=False)
-    with gr.Row():
-      tab_1_button = gr.Button("Submit")
-      tab_1_cl_button = gr.ClearButton([image_input,gradcam_check,gradcam_layer,gradcam_opa,top_classes,image_label,image_output])
-      
-  with gr.Tab("Explore Misclassified/Gradcam Images"):
-    with gr.Row():
-      with gr.Column():
-        misclassified_check = gr.Checkbox(label="Misclassified")
-        num_misclassified = gr.Number(value=2,minimum=1,maximum=10,label="Total Misclassified Images")
-        gradcam_check1 = gr.Checkbox(label="Gradcam")
-        num_gradcam = gr.Number(value=2,minimum=1,maximum=10,label="Total Gradcam Images")
-        gradcam_layer1 = gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?")
-        gradcam_opa1 = gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM")
-      image_gallery_output = gr.Gallery(label="Output Images", show_label=False, elem_id="gallery").style(columns=[2], rows=[5], object_fit="contain", height="auto")
-    with gr.Row():
-      tab_2_button = gr.Button("Submit")
-      tab_2_cl_button = gr.ClearButton([misclassified_check,num_misclassified,gradcam_check1,num_gradcam,gradcam_layer1,gradcam_opa1,image_gallery_output])
-      
-  tab_1_button.click(inference_up_img, inputs=[image_input,gradcam_check,gradcam_layer,gradcam_opa,top_classes], outputs=[image_label,image_output])
-  tab_2_button.click(misclass_fn, inputs=[misclassified_check,num_misclassified,gradcam_check1,num_gradcam,gradcam_layer1,gradcam_opa1], outputs=[image_gallery_output])
-demo.launch(debug=True)
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/oinspect.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/oinspect.py
deleted file mode 100644
index ef6a0d02d7f66c73c8cd4b4daf2ebd12aa434e27..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/oinspect.py
+++ /dev/null
@@ -1,1171 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Tools for inspecting Python objects.
-
-Uses syntax highlighting for presenting the various information elements.
-
-Similar in spirit to the inspect module, but all calls take a name argument to
-reference the name under which an object is being read.
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-__all__ = ['Inspector','InspectColors']
-
-# stdlib modules
-from dataclasses import dataclass
-from inspect import signature
-from textwrap import dedent
-import ast
-import html
-import inspect
-import io as stdlib_io
-import linecache
-import os
-import sys
-import types
-import warnings
-
-from typing import Any, Optional, Dict, Union, List, Tuple
-
-if sys.version_info <= (3, 10):
-    from typing_extensions import TypeAlias
-else:
-    from typing import TypeAlias
-
-# IPython's own
-from IPython.core import page
-from IPython.lib.pretty import pretty
-from IPython.testing.skipdoctest import skip_doctest
-from IPython.utils import PyColorize
-from IPython.utils import openpy
-from IPython.utils.dir2 import safe_hasattr
-from IPython.utils.path import compress_user
-from IPython.utils.text import indent
-from IPython.utils.wildcard import list_namespace
-from IPython.utils.wildcard import typestr2type
-from IPython.utils.coloransi import TermColors, ColorScheme, ColorSchemeTable
-from IPython.utils.py3compat import cast_unicode
-from IPython.utils.colorable import Colorable
-from IPython.utils.decorators import undoc
-
-from pygments import highlight
-from pygments.lexers import PythonLexer
-from pygments.formatters import HtmlFormatter
-
-HOOK_NAME = "__custom_documentations__"
-
-
-UnformattedBundle: TypeAlias = Dict[str, List[Tuple[str, str]]]  # List of (title, body)
-Bundle: TypeAlias = Dict[str, str]
-
-
-@dataclass
-class OInfo:
-    ismagic: bool
-    isalias: bool
-    found: bool
-    namespace: Optional[str]
-    parent: Any
-    obj: Any
-
-    def get(self, field):
-        """Get a field from the object for backward compatibility with before 8.12
-
-        see https://github.com/h5py/h5py/issues/2253
-        """
-        # We need to deprecate this at some point, but the warning will show in completion.
-        # Let's comment this for now and uncomment end of 2023 ish
-        #        warnings.warn(
-        #            f"OInfo dataclass with fields access since IPython 8.12 please use OInfo.{field} instead."
-        #            "OInfo used to be a dict but a dataclass provide static fields verification with mypy."
-        #            "This warning and backward compatibility `get()` method were added in 8.13.",
-        #            DeprecationWarning,
-        #            stacklevel=2,
-        #        )
-        return getattr(self, field)
-
-
-def pylight(code):
-    return highlight(code, PythonLexer(), HtmlFormatter(noclasses=True))
-
-# builtin docstrings to ignore
-_func_call_docstring = types.FunctionType.__call__.__doc__
-_object_init_docstring = object.__init__.__doc__
-_builtin_type_docstrings = {
-    inspect.getdoc(t) for t in (types.ModuleType, types.MethodType,
-                                types.FunctionType, property)
-}
-
-_builtin_func_type = type(all)
-_builtin_meth_type = type(str.upper)  # Bound methods have the same type as builtin functions
-#****************************************************************************
-# Builtin color schemes
-
-Colors = TermColors  # just a shorthand
-
-InspectColors = PyColorize.ANSICodeColors
-
-#****************************************************************************
-# Auxiliary functions and objects
-
-# See the messaging spec for the definition of all these fields.  This list
-# effectively defines the order of display
-info_fields = ['type_name', 'base_class', 'string_form', 'namespace',
-               'length', 'file', 'definition', 'docstring', 'source',
-               'init_definition', 'class_docstring', 'init_docstring',
-               'call_def', 'call_docstring',
-               # These won't be printed but will be used to determine how to
-               # format the object
-               'ismagic', 'isalias', 'isclass', 'found', 'name'
-               ]
-
-
-def object_info(**kw):
-    """Make an object info dict with all fields present."""
-    infodict = {k:None for k in info_fields}
-    infodict.update(kw)
-    return infodict
-
-
-def get_encoding(obj):
-    """Get encoding for python source file defining obj
-
-    Returns None if obj is not defined in a sourcefile.
-    """
-    ofile = find_file(obj)
-    # run contents of file through pager starting at line where the object
-    # is defined, as long as the file isn't binary and is actually on the
-    # filesystem.
-    if ofile is None:
-        return None
-    elif ofile.endswith(('.so', '.dll', '.pyd')):
-        return None
-    elif not os.path.isfile(ofile):
-        return None
-    else:
-        # Print only text files, not extension binaries.  Note that
-        # getsourcelines returns lineno with 1-offset and page() uses
-        # 0-offset, so we must adjust.
-        with stdlib_io.open(ofile, 'rb') as buffer:   # Tweaked to use io.open for Python 2
-            encoding, lines = openpy.detect_encoding(buffer.readline)
-        return encoding
-
-def getdoc(obj) -> Union[str,None]:
-    """Stable wrapper around inspect.getdoc.
-
-    This can't crash because of attribute problems.
-
-    It also attempts to call a getdoc() method on the given object.  This
-    allows objects which provide their docstrings via non-standard mechanisms
-    (like Pyro proxies) to still be inspected by ipython's ? system.
-    """
-    # Allow objects to offer customized documentation via a getdoc method:
-    try:
-        ds = obj.getdoc()
-    except Exception:
-        pass
-    else:
-        if isinstance(ds, str):
-            return inspect.cleandoc(ds)
-    docstr = inspect.getdoc(obj)
-    return docstr
-
-
-def getsource(obj, oname='') -> Union[str,None]:
-    """Wrapper around inspect.getsource.
-
-    This can be modified by other projects to provide customized source
-    extraction.
-
-    Parameters
-    ----------
-    obj : object
-        an object whose source code we will attempt to extract
-    oname : str
-        (optional) a name under which the object is known
-
-    Returns
-    -------
-    src : unicode or None
-
-    """
-
-    if isinstance(obj, property):
-        sources = []
-        for attrname in ['fget', 'fset', 'fdel']:
-            fn = getattr(obj, attrname)
-            if fn is not None:
-                encoding = get_encoding(fn)
-                oname_prefix = ('%s.' % oname) if oname else ''
-                sources.append(''.join(('# ', oname_prefix, attrname)))
-                if inspect.isfunction(fn):
-                    _src = getsource(fn)
-                    if _src:
-                        # assert _src is not None, "please mypy"
-                        sources.append(dedent(_src))
-                else:
-                    # Default str/repr only prints function name,
-                    # pretty.pretty prints module name too.
-                    sources.append(
-                        '%s%s = %s\n' % (oname_prefix, attrname, pretty(fn))
-                    )
-        if sources:
-            return '\n'.join(sources)
-        else:
-            return None
-
-    else:
-        # Get source for non-property objects.
-
-        obj = _get_wrapped(obj)
-
-        try:
-            src = inspect.getsource(obj)
-        except TypeError:
-            # The object itself provided no meaningful source, try looking for
-            # its class definition instead.
-            try:
-                src = inspect.getsource(obj.__class__)
-            except (OSError, TypeError):
-                return None
-        except OSError:
-            return None
-
-        return src
-
-
-def is_simple_callable(obj):
-    """True if obj is a function ()"""
-    return (inspect.isfunction(obj) or inspect.ismethod(obj) or \
-            isinstance(obj, _builtin_func_type) or isinstance(obj, _builtin_meth_type))
-
-@undoc
-def getargspec(obj):
-    """Wrapper around :func:`inspect.getfullargspec`
-
-    In addition to functions and methods, this can also handle objects with a
-    ``__call__`` attribute.
-
-    DEPRECATED: Deprecated since 7.10. Do not use, will be removed.
-    """
-
-    warnings.warn('`getargspec` function is deprecated as of IPython 7.10'
-                  'and will be removed in future versions.', DeprecationWarning, stacklevel=2)
-
-    if safe_hasattr(obj, '__call__') and not is_simple_callable(obj):
-        obj = obj.__call__
-
-    return inspect.getfullargspec(obj)
-
-@undoc
-def format_argspec(argspec):
-    """Format argspect, convenience wrapper around inspect's.
-
-    This takes a dict instead of ordered arguments and calls
-    inspect.format_argspec with the arguments in the necessary order.
-
-    DEPRECATED (since 7.10): Do not use; will be removed in future versions.
-    """
-    
-    warnings.warn('`format_argspec` function is deprecated as of IPython 7.10'
-                  'and will be removed in future versions.', DeprecationWarning, stacklevel=2)
-
-
-    return inspect.formatargspec(argspec['args'], argspec['varargs'],
-                                 argspec['varkw'], argspec['defaults'])
-
-@undoc
-def call_tip(oinfo, format_call=True):
-    """DEPRECATED since 6.0. Extract call tip data from an oinfo dict."""
-    warnings.warn(
-        "`call_tip` function is deprecated as of IPython 6.0"
-        "and will be removed in future versions.",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    # Get call definition
-    argspec = oinfo.get('argspec')
-    if argspec is None:
-        call_line = None
-    else:
-        # Callable objects will have 'self' as their first argument, prune
-        # it out if it's there for clarity (since users do *not* pass an
-        # extra first argument explicitly).
-        try:
-            has_self = argspec['args'][0] == 'self'
-        except (KeyError, IndexError):
-            pass
-        else:
-            if has_self:
-                argspec['args'] = argspec['args'][1:]
-
-        call_line = oinfo['name']+format_argspec(argspec)
-
-    # Now get docstring.
-    # The priority is: call docstring, constructor docstring, main one.
-    doc = oinfo.get('call_docstring')
-    if doc is None:
-        doc = oinfo.get('init_docstring')
-    if doc is None:
-        doc = oinfo.get('docstring','')
-
-    return call_line, doc
-
-
-def _get_wrapped(obj):
-    """Get the original object if wrapped in one or more @decorators
-
-    Some objects automatically construct similar objects on any unrecognised
-    attribute access (e.g. unittest.mock.call). To protect against infinite loops,
-    this will arbitrarily cut off after 100 levels of obj.__wrapped__
-    attribute access. --TK, Jan 2016
-    """
-    orig_obj = obj
-    i = 0
-    while safe_hasattr(obj, '__wrapped__'):
-        obj = obj.__wrapped__
-        i += 1
-        if i > 100:
-            # __wrapped__ is probably a lie, so return the thing we started with
-            return orig_obj
-    return obj
-
-def find_file(obj) -> str:
-    """Find the absolute path to the file where an object was defined.
-
-    This is essentially a robust wrapper around `inspect.getabsfile`.
-
-    Returns None if no file can be found.
-
-    Parameters
-    ----------
-    obj : any Python object
-
-    Returns
-    -------
-    fname : str
-        The absolute path to the file where the object was defined.
-    """
-    obj = _get_wrapped(obj)
-
-    fname = None
-    try:
-        fname = inspect.getabsfile(obj)
-    except TypeError:
-        # For an instance, the file that matters is where its class was
-        # declared.
-        try:
-            fname = inspect.getabsfile(obj.__class__)
-        except (OSError, TypeError):
-            # Can happen for builtins
-            pass
-    except OSError:
-        pass
-
-    return cast_unicode(fname)
-
-
-def find_source_lines(obj):
-    """Find the line number in a file where an object was defined.
-
-    This is essentially a robust wrapper around `inspect.getsourcelines`.
-
-    Returns None if no file can be found.
-
-    Parameters
-    ----------
-    obj : any Python object
-
-    Returns
-    -------
-    lineno : int
-        The line number where the object definition starts.
-    """
-    obj = _get_wrapped(obj)
-
-    try:
-        lineno = inspect.getsourcelines(obj)[1]
-    except TypeError:
-        # For instances, try the class object like getsource() does
-        try:
-            lineno = inspect.getsourcelines(obj.__class__)[1]
-        except (OSError, TypeError):
-            return None
-    except OSError:
-        return None
-
-    return lineno
-
-class Inspector(Colorable):
-
-    def __init__(self, color_table=InspectColors,
-                 code_color_table=PyColorize.ANSICodeColors,
-                 scheme=None,
-                 str_detail_level=0,
-                 parent=None, config=None):
-        super(Inspector, self).__init__(parent=parent, config=config)
-        self.color_table = color_table
-        self.parser = PyColorize.Parser(out='str', parent=self, style=scheme)
-        self.format = self.parser.format
-        self.str_detail_level = str_detail_level
-        self.set_active_scheme(scheme)
-
-    def _getdef(self,obj,oname='') -> Union[str,None]:
-        """Return the call signature for any callable object.
-
-        If any exception is generated, None is returned instead and the
-        exception is suppressed."""
-        try:
-            return _render_signature(signature(obj), oname)
-        except:
-            return None
-
-    def __head(self,h) -> str:
-        """Return a header string with proper colors."""
-        return '%s%s%s' % (self.color_table.active_colors.header,h,
-                           self.color_table.active_colors.normal)
-
-    def set_active_scheme(self, scheme):
-        if scheme is not None:
-            self.color_table.set_active_scheme(scheme)
-            self.parser.color_table.set_active_scheme(scheme)
-
-    def noinfo(self, msg, oname):
-        """Generic message when no information is found."""
-        print('No %s found' % msg, end=' ')
-        if oname:
-            print('for %s' % oname)
-        else:
-            print()
-
-    def pdef(self, obj, oname=''):
-        """Print the call signature for any callable object.
-
-        If the object is a class, print the constructor information."""
-
-        if not callable(obj):
-            print('Object is not callable.')
-            return
-
-        header = ''
-
-        if inspect.isclass(obj):
-            header = self.__head('Class constructor information:\n')
-
-
-        output = self._getdef(obj,oname)
-        if output is None:
-            self.noinfo('definition header',oname)
-        else:
-            print(header,self.format(output), end=' ')
-
-    # In Python 3, all classes are new-style, so they all have __init__.
-    @skip_doctest
-    def pdoc(self, obj, oname='', formatter=None):
-        """Print the docstring for any object.
-
-        Optional:
-        -formatter: a function to run the docstring through for specially
-        formatted docstrings.
-
-        Examples
-        --------
-        In [1]: class NoInit:
-           ...:     pass
-
-        In [2]: class NoDoc:
-           ...:     def __init__(self):
-           ...:         pass
-
-        In [3]: %pdoc NoDoc
-        No documentation found for NoDoc
-
-        In [4]: %pdoc NoInit
-        No documentation found for NoInit
-
-        In [5]: obj = NoInit()
-
-        In [6]: %pdoc obj
-        No documentation found for obj
-
-        In [5]: obj2 = NoDoc()
-
-        In [6]: %pdoc obj2
-        No documentation found for obj2
-        """
-
-        head = self.__head  # For convenience
-        lines = []
-        ds = getdoc(obj)
-        if formatter:
-            ds = formatter(ds).get('plain/text', ds)
-        if ds:
-            lines.append(head("Class docstring:"))
-            lines.append(indent(ds))
-        if inspect.isclass(obj) and hasattr(obj, '__init__'):
-            init_ds = getdoc(obj.__init__)
-            if init_ds is not None:
-                lines.append(head("Init docstring:"))
-                lines.append(indent(init_ds))
-        elif hasattr(obj,'__call__'):
-            call_ds = getdoc(obj.__call__)
-            if call_ds:
-                lines.append(head("Call docstring:"))
-                lines.append(indent(call_ds))
-
-        if not lines:
-            self.noinfo('documentation',oname)
-        else:
-            page.page('\n'.join(lines))
-
-    def psource(self, obj, oname=''):
-        """Print the source code for an object."""
-
-        # Flush the source cache because inspect can return out-of-date source
-        linecache.checkcache()
-        try:
-            src = getsource(obj, oname=oname)
-        except Exception:
-            src = None
-
-        if src is None:
-            self.noinfo('source', oname)
-        else:
-            page.page(self.format(src))
-
-    def pfile(self, obj, oname=''):
-        """Show the whole file where an object was defined."""
-
-        lineno = find_source_lines(obj)
-        if lineno is None:
-            self.noinfo('file', oname)
-            return
-
-        ofile = find_file(obj)
-        # run contents of file through pager starting at line where the object
-        # is defined, as long as the file isn't binary and is actually on the
-        # filesystem.
-        if ofile.endswith(('.so', '.dll', '.pyd')):
-            print('File %r is binary, not printing.' % ofile)
-        elif not os.path.isfile(ofile):
-            print('File %r does not exist, not printing.' % ofile)
-        else:
-            # Print only text files, not extension binaries.  Note that
-            # getsourcelines returns lineno with 1-offset and page() uses
-            # 0-offset, so we must adjust.
-            page.page(self.format(openpy.read_py_file(ofile, skip_encoding_cookie=False)), lineno - 1)
-
-
-    def _mime_format(self, text:str, formatter=None) -> dict:
-        """Return a mime bundle representation of the input text.
-
-        - if `formatter` is None, the returned mime bundle has
-           a ``text/plain`` field, with the input text.
-           a ``text/html`` field with a ``<pre>`` tag containing the input text.
-
-        - if ``formatter`` is not None, it must be a callable transforming the
-          input text into a mime bundle. Default values for ``text/plain`` and
-          ``text/html`` representations are the ones described above.
-
-        Note:
-
-        Formatters returning strings are supported but this behavior is deprecated.
-
-        """
-        defaults = {
-            "text/plain": text,
-            "text/html": f"<pre>{html.escape(text)}</pre>",
-        }
-
-        if formatter is None:
-            return defaults
-        else:
-            formatted = formatter(text)
-
-            if not isinstance(formatted, dict):
-                # Handle the deprecated behavior of a formatter returning
-                # a string instead of a mime bundle.
-                return {"text/plain": formatted, "text/html": f"<pre>{formatted}</pre>"}
-
-            else:
-                return dict(defaults, **formatted)
-
-    def format_mime(self, bundle: UnformattedBundle) -> Bundle:
-        """Format a mimebundle being created by _make_info_unformatted into a real mimebundle"""
-        # Format text/plain mimetype
-        assert isinstance(bundle["text/plain"], list)
-        for item in bundle["text/plain"]:
-            assert isinstance(item, tuple)
-
-        new_b: Bundle = {}
-        lines = []
-        _len = max(len(h) for h, _ in bundle["text/plain"])
-
-        for head, body in bundle["text/plain"]:
-            body = body.strip("\n")
-            delim = "\n" if "\n" in body else " "
-            lines.append(
-                f"{self.__head(head+':')}{(_len - len(head))*' '}{delim}{body}"
-            )
-
-        new_b["text/plain"] = "\n".join(lines)
-
-        if "text/html" in bundle:
-            assert isinstance(bundle["text/html"], list)
-            for item in bundle["text/html"]:
-                assert isinstance(item, tuple)
-            # Format the text/html mimetype
-            if isinstance(bundle["text/html"], (list, tuple)):
-                # bundle['text/html'] is a list of (head, formatted body) pairs
-                new_b["text/html"] = "\n".join(
-                    (f"<h1>{head}</h1>\n{body}" for (head, body) in bundle["text/html"])
-                )
-
-        for k in bundle.keys():
-            if k in ("text/html", "text/plain"):
-                continue
-            else:
-                new_b = bundle[k]  # type:ignore
-        return new_b
-
-    def _append_info_field(
-        self,
-        bundle: UnformattedBundle,
-        title: str,
-        key: str,
-        info,
-        omit_sections,
-        formatter,
-    ):
-        """Append an info value to the unformatted mimebundle being constructed by _make_info_unformatted"""
-        if title in omit_sections or key in omit_sections:
-            return
-        field = info[key]
-        if field is not None:
-            formatted_field = self._mime_format(field, formatter)
-            bundle["text/plain"].append((title, formatted_field["text/plain"]))
-            bundle["text/html"].append((title, formatted_field["text/html"]))
-
-    def _make_info_unformatted(
-        self, obj, info, formatter, detail_level, omit_sections
-    ) -> UnformattedBundle:
-        """Assemble the mimebundle as unformatted lists of information"""
-        bundle: UnformattedBundle = {
-            "text/plain": [],
-            "text/html": [],
-        }
-
-        # A convenience function to simplify calls below
-        def append_field(
-            bundle: UnformattedBundle, title: str, key: str, formatter=None
-        ):
-            self._append_info_field(
-                bundle,
-                title=title,
-                key=key,
-                info=info,
-                omit_sections=omit_sections,
-                formatter=formatter,
-            )
-
-        def code_formatter(text) -> Bundle:
-            return {
-                'text/plain': self.format(text),
-                'text/html': pylight(text)
-            }
-
-        if info["isalias"]:
-            append_field(bundle, "Repr", "string_form")
-
-        elif info['ismagic']:
-            if detail_level > 0:
-                append_field(bundle, "Source", "source", code_formatter)
-            else:
-                append_field(bundle, "Docstring", "docstring", formatter)
-            append_field(bundle, "File", "file")
-
-        elif info['isclass'] or is_simple_callable(obj):
-            # Functions, methods, classes
-            append_field(bundle, "Signature", "definition", code_formatter)
-            append_field(bundle, "Init signature", "init_definition", code_formatter)
-            append_field(bundle, "Docstring", "docstring", formatter)
-            if detail_level > 0 and info["source"]:
-                append_field(bundle, "Source", "source", code_formatter)
-            else:
-                append_field(bundle, "Init docstring", "init_docstring", formatter)
-
-            append_field(bundle, "File", "file")
-            append_field(bundle, "Type", "type_name")
-            append_field(bundle, "Subclasses", "subclasses")
-
-        else:
-            # General Python objects
-            append_field(bundle, "Signature", "definition", code_formatter)
-            append_field(bundle, "Call signature", "call_def", code_formatter)
-            append_field(bundle, "Type", "type_name")
-            append_field(bundle, "String form", "string_form")
-
-            # Namespace
-            if info["namespace"] != "Interactive":
-                append_field(bundle, "Namespace", "namespace")
-
-            append_field(bundle, "Length", "length")
-            append_field(bundle, "File", "file")
-
-            # Source or docstring, depending on detail level and whether
-            # source found.
-            if detail_level > 0 and info["source"]:
-                append_field(bundle, "Source", "source", code_formatter)
-            else:
-                append_field(bundle, "Docstring", "docstring", formatter)
-
-            append_field(bundle, "Class docstring", "class_docstring", formatter)
-            append_field(bundle, "Init docstring", "init_docstring", formatter)
-            append_field(bundle, "Call docstring", "call_docstring", formatter)
-        return bundle
-
-
-    def _get_info(
-        self,
-        obj: Any,
-        oname: str = "",
-        formatter=None,
-        info: Optional[OInfo] = None,
-        detail_level=0,
-        omit_sections=(),
-    ) -> Bundle:
-        """Retrieve an info dict and format it.
-
-        Parameters
-        ----------
-        obj : any
-            Object to inspect and return info from
-        oname : str (default: ''):
-            Name of the variable pointing to `obj`.
-        formatter : callable
-        info
-            already computed information
-        detail_level : integer
-            Granularity of detail level, if set to 1, give more information.
-        omit_sections : container[str]
-            Titles or keys to omit from output (can be set, tuple, etc., anything supporting `in`)
-        """
-
-        info_dict = self.info(obj, oname=oname, info=info, detail_level=detail_level)
-        bundle = self._make_info_unformatted(
-            obj,
-            info_dict,
-            formatter,
-            detail_level=detail_level,
-            omit_sections=omit_sections,
-        )
-        return self.format_mime(bundle)
-
-    def pinfo(
-        self,
-        obj,
-        oname="",
-        formatter=None,
-        info: Optional[OInfo] = None,
-        detail_level=0,
-        enable_html_pager=True,
-        omit_sections=(),
-    ):
-        """Show detailed information about an object.
-
-        Optional arguments:
-
-        - oname: name of the variable pointing to the object.
-
-        - formatter: callable (optional)
-              A special formatter for docstrings.
-
-              The formatter is a callable that takes a string as an input
-              and returns either a formatted string or a mime type bundle
-              in the form of a dictionary.
-
-              Although the support of custom formatter returning a string
-              instead of a mime type bundle is deprecated.
-
-        - info: a structure with some information fields which may have been
-          precomputed already.
-
-        - detail_level: if set to 1, more information is given.
-
-        - omit_sections: set of section keys and titles to omit
-        """
-        assert info is not None
-        info_b: Bundle = self._get_info(
-            obj, oname, formatter, info, detail_level, omit_sections=omit_sections
-        )
-        if not enable_html_pager:
-            del info_b["text/html"]
-        page.page(info_b)
-
-    def _info(self, obj, oname="", info=None, detail_level=0):
-        """
-        Inspector.info() was likely improperly marked as deprecated
-        while only a parameter was deprecated. We "un-deprecate" it.
-        """
-
-        warnings.warn(
-            "The `Inspector.info()` method has been un-deprecated as of 8.0 "
-            "and the `formatter=` keyword removed. `Inspector._info` is now "
-            "an alias, and you can just call `.info()` directly.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.info(obj, oname=oname, info=info, detail_level=detail_level)
-
-    def info(self, obj, oname="", info=None, detail_level=0) -> Dict[str, Any]:
-        """Compute a dict with detailed information about an object.
-
-        Parameters
-        ----------
-        obj : any
-            An object to find information about
-        oname : str (default: '')
-            Name of the variable pointing to `obj`.
-        info : (default: None)
-            A struct (dict like with attr access) with some information fields
-            which may have been precomputed already.
-        detail_level : int (default:0)
-            If set to 1, more information is given.
-
-        Returns
-        -------
-        An object info dict with known fields from `info_fields`. Keys are
-        strings, values are string or None.
-        """
-
-        if info is None:
-            ismagic = False
-            isalias = False
-            ospace = ''
-        else:
-            ismagic = info.ismagic
-            isalias = info.isalias
-            ospace = info.namespace
-
-        # Get docstring, special-casing aliases:
-        att_name = oname.split(".")[-1]
-        parents_docs = None
-        prelude = ""
-        if info and info.parent is not None and hasattr(info.parent, HOOK_NAME):
-            parents_docs_dict = getattr(info.parent, HOOK_NAME)
-            parents_docs = parents_docs_dict.get(att_name, None)
-        out = dict(
-            name=oname, found=True, isalias=isalias, ismagic=ismagic, subclasses=None
-        )
-
-        if parents_docs:
-            ds = parents_docs
-        elif isalias:
-            if not callable(obj):
-                try:
-                    ds = "Alias to the system command:\n  %s" % obj[1]
-                except:
-                    ds = "Alias: " + str(obj)
-            else:
-                ds = "Alias to " + str(obj)
-                if obj.__doc__:
-                    ds += "\nDocstring:\n" + obj.__doc__
-        else:
-            ds_or_None = getdoc(obj)
-            if ds_or_None is None:
-                ds = '<no docstring>'
-            else:
-                ds = ds_or_None
-
-        ds = prelude + ds
-
-        # store output in a dict, we initialize it here and fill it as we go
-
-        string_max = 200 # max size of strings to show (snipped if longer)
-        shalf = int((string_max - 5) / 2)
-
-        if ismagic:
-            out['type_name'] = 'Magic function'
-        elif isalias:
-            out['type_name'] = 'System alias'
-        else:
-            out['type_name'] = type(obj).__name__
-
-        try:
-            bclass = obj.__class__
-            out['base_class'] = str(bclass)
-        except:
-            pass
-
-        # String form, but snip if too long in ? form (full in ??)
-        if detail_level >= self.str_detail_level:
-            try:
-                ostr = str(obj)
-                str_head = 'string_form'
-                if not detail_level and len(ostr)>string_max:
-                    ostr = ostr[:shalf] + ' <...> ' + ostr[-shalf:]
-                    ostr = ("\n" + " " * len(str_head.expandtabs())).\
-                            join(q.strip() for q in ostr.split("\n"))
-                out[str_head] = ostr
-            except:
-                pass
-
-        if ospace:
-            out['namespace'] = ospace
-
-        # Length (for strings and lists)
-        try:
-            out['length'] = str(len(obj))
-        except Exception:
-            pass
-
-        # Filename where object was defined
-        binary_file = False
-        fname = find_file(obj)
-        if fname is None:
-            # if anything goes wrong, we don't want to show source, so it's as
-            # if the file was binary
-            binary_file = True
-        else:
-            if fname.endswith(('.so', '.dll', '.pyd')):
-                binary_file = True
-            elif fname.endswith('<string>'):
-                fname = 'Dynamically generated function. No source code available.'
-            out['file'] = compress_user(fname)
-
-        # Original source code for a callable, class or property.
-        if detail_level:
-            # Flush the source cache because inspect can return out-of-date
-            # source
-            linecache.checkcache()
-            try:
-                if isinstance(obj, property) or not binary_file:
-                    src = getsource(obj, oname)
-                    if src is not None:
-                        src = src.rstrip()
-                    out['source'] = src
-
-            except Exception:
-                pass
-
-        # Add docstring only if no source is to be shown (avoid repetitions).
-        if ds and not self._source_contains_docstring(out.get('source'), ds):
-            out['docstring'] = ds
-
-        # Constructor docstring for classes
-        if inspect.isclass(obj):
-            out['isclass'] = True
-
-            # get the init signature:
-            try:
-                init_def = self._getdef(obj, oname)
-            except AttributeError:
-                init_def = None
-
-            # get the __init__ docstring
-            try:
-                obj_init = obj.__init__
-            except AttributeError:
-                init_ds = None
-            else:
-                if init_def is None:
-                    # Get signature from init if top-level sig failed.
-                    # Can happen for built-in types (list, etc.).
-                    try:
-                        init_def = self._getdef(obj_init, oname)
-                    except AttributeError:
-                        pass
-                init_ds = getdoc(obj_init)
-                # Skip Python's auto-generated docstrings
-                if init_ds == _object_init_docstring:
-                    init_ds = None
-
-            if init_def:
-                out['init_definition'] = init_def
-
-            if init_ds:
-                out['init_docstring'] = init_ds
-
-            names = [sub.__name__ for sub in type.__subclasses__(obj)]
-            if len(names) < 10:
-                all_names = ', '.join(names)
-            else:
-                all_names = ', '.join(names[:10]+['...'])
-            out['subclasses'] = all_names
-        # and class docstring for instances:
-        else:
-            # reconstruct the function definition and print it:
-            defln = self._getdef(obj, oname)
-            if defln:
-                out['definition'] = defln
-
-            # First, check whether the instance docstring is identical to the
-            # class one, and print it separately if they don't coincide.  In
-            # most cases they will, but it's nice to print all the info for
-            # objects which use instance-customized docstrings.
-            if ds:
-                try:
-                    cls = getattr(obj,'__class__')
-                except:
-                    class_ds = None
-                else:
-                    class_ds = getdoc(cls)
-                # Skip Python's auto-generated docstrings
-                if class_ds in _builtin_type_docstrings:
-                    class_ds = None
-                if class_ds and ds != class_ds:
-                    out['class_docstring'] = class_ds
-
-            # Next, try to show constructor docstrings
-            try:
-                init_ds = getdoc(obj.__init__)
-                # Skip Python's auto-generated docstrings
-                if init_ds == _object_init_docstring:
-                    init_ds = None
-            except AttributeError:
-                init_ds = None
-            if init_ds:
-                out['init_docstring'] = init_ds
-
-            # Call form docstring for callable instances
-            if safe_hasattr(obj, '__call__') and not is_simple_callable(obj):
-                call_def = self._getdef(obj.__call__, oname)
-                if call_def and (call_def != out.get('definition')):
-                    # it may never be the case that call def and definition differ,
-                    # but don't include the same signature twice
-                    out['call_def'] = call_def
-                call_ds = getdoc(obj.__call__)
-                # Skip Python's auto-generated docstrings
-                if call_ds == _func_call_docstring:
-                    call_ds = None
-                if call_ds:
-                    out['call_docstring'] = call_ds
-
-        return object_info(**out)
-
-    @staticmethod
-    def _source_contains_docstring(src, doc):
-        """
-        Check whether the source *src* contains the docstring *doc*.
-
-        This is is helper function to skip displaying the docstring if the
-        source already contains it, avoiding repetition of information.
-        """
-        try:
-            (def_node,) = ast.parse(dedent(src)).body
-            return ast.get_docstring(def_node) == doc  # type: ignore[arg-type]
-        except Exception:
-            # The source can become invalid or even non-existent (because it
-            # is re-fetched from the source file) so the above code fail in
-            # arbitrary ways.
-            return False
-
-    def psearch(self,pattern,ns_table,ns_search=[],
-                ignore_case=False,show_all=False, *, list_types=False):
-        """Search namespaces with wildcards for objects.
-
-        Arguments:
-
-        - pattern: string containing shell-like wildcards to use in namespace
-          searches and optionally a type specification to narrow the search to
-          objects of that type.
-
-        - ns_table: dict of name->namespaces for search.
-
-        Optional arguments:
-
-          - ns_search: list of namespace names to include in search.
-
-          - ignore_case(False): make the search case-insensitive.
-
-          - show_all(False): show all names, including those starting with
-            underscores.
-
-          - list_types(False): list all available object types for object matching.
-        """
-        #print 'ps pattern:<%r>' % pattern # dbg
-
-        # defaults
-        type_pattern = 'all'
-        filter = ''
-
-        # list all object types
-        if list_types:
-            page.page('\n'.join(sorted(typestr2type)))
-            return
-
-        cmds = pattern.split()
-        len_cmds  =  len(cmds)
-        if len_cmds == 1:
-            # Only filter pattern given
-            filter = cmds[0]
-        elif len_cmds == 2:
-            # Both filter and type specified
-            filter,type_pattern = cmds
-        else:
-            raise ValueError('invalid argument string for psearch: <%s>' %
-                             pattern)
-
-        # filter search namespaces
-        for name in ns_search:
-            if name not in ns_table:
-                raise ValueError('invalid namespace <%s>. Valid names: %s' %
-                                 (name,ns_table.keys()))
-
-        #print 'type_pattern:',type_pattern # dbg
-        search_result, namespaces_seen = set(), set()
-        for ns_name in ns_search:
-            ns = ns_table[ns_name]
-            # Normally, locals and globals are the same, so we just check one.
-            if id(ns) in namespaces_seen:
-                continue
-            namespaces_seen.add(id(ns))
-            tmp_res = list_namespace(ns, type_pattern, filter,
-                                    ignore_case=ignore_case, show_all=show_all)
-            search_result.update(tmp_res)
-
-        page.page('\n'.join(sorted(search_result)))
-
-
-def _render_signature(obj_signature, obj_name) -> str:
-    """
-    This was mostly taken from inspect.Signature.__str__.
-    Look there for the comments.
-    The only change is to add linebreaks when this gets too long.
-    """
-    result = []
-    pos_only = False
-    kw_only = True
-    for param in obj_signature.parameters.values():
-        if param.kind == inspect.Parameter.POSITIONAL_ONLY:
-            pos_only = True
-        elif pos_only:
-            result.append('/')
-            pos_only = False
-
-        if param.kind == inspect.Parameter.VAR_POSITIONAL:
-            kw_only = False
-        elif param.kind == inspect.Parameter.KEYWORD_ONLY and kw_only:
-            result.append('*')
-            kw_only = False
-
-        result.append(str(param))
-
-    if pos_only:
-        result.append('/')
-
-    # add up name, parameters, braces (2), and commas
-    if len(obj_name) + sum(len(r) + 2 for r in result) > 75:
-        # This doesn’t fit behind “Signature: ” in an inspect window.
-        rendered = '{}(\n{})'.format(obj_name, ''.join(
-            '    {},\n'.format(r) for r in result)
-        )
-    else:
-        rendered = '{}({})'.format(obj_name, ', '.join(result))
-
-    if obj_signature.return_annotation is not inspect._empty:
-        anno = inspect.formatannotation(obj_signature.return_annotation)
-        rendered += ' -> {}'.format(anno)
-
-    return rendered
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/tests/test_help.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/tests/test_help.py
deleted file mode 100644
index 038f560b2fb2addf7d95f45b358a7d4e0864e943..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/tests/test_help.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""Test help output of various IPython entry points"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-import pytest
-import IPython.testing.tools as tt
-
-
-def test_ipython_help():
-    tt.help_all_output_test()
-
-def test_profile_help():
-    tt.help_all_output_test("profile")
-
-def test_profile_list_help():
-    tt.help_all_output_test("profile list")
-
-def test_profile_create_help():
-    tt.help_all_output_test("profile create")
-
-def test_locate_help():
-    tt.help_all_output_test("locate")
-
-def test_locate_profile_help():
-    tt.help_all_output_test("locate profile")
-
-def test_trust_help():
-    pytest.importorskip("nbformat")
-    tt.help_all_output_test("trust")
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/db/migrations/00002-migration-2.sqlite.sql b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/db/migrations/00002-migration-2.sqlite.sql
deleted file mode 100644
index 01e4b222af541efb9022d2eeb69e39239faecb34..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/test/db/migrations/00002-migration-2.sqlite.sql
+++ /dev/null
@@ -1,3 +0,0 @@
-CREATE TABLE table2 (
-    name TEXT PRIMARY KEY
-);
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/contourpy/util/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/contourpy/util/__init__.py
deleted file mode 100644
index fe33fcef1e18d2a4b92287e434cf6b1257e4274f..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/contourpy/util/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from __future__ import annotations
-
-from contourpy.util._build_config import build_config
-
-__all__ = ["build_config"]
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_extension_api.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_extension_api.py
deleted file mode 100644
index 8c5a441b13feee85eedf1f9b7c228fa195c44333..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_extension_api.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import abc
-from typing import Any
-
-
-# borrowed from from six
-def _with_metaclass(meta, *bases):
-    """Create a base class with a metaclass."""
-
-    class metaclass(meta):
-
-        def __new__(cls, name, this_bases, d):
-            return meta(name, bases, d)
-
-    return type.__new__(metaclass, 'temporary_class', (), {})
-
-
-# =======================================================================================================================
-# AbstractResolver
-# =======================================================================================================================
-class _AbstractResolver(_with_metaclass(abc.ABCMeta)):
-    """
-    This class exists only for documentation purposes to explain how to create a resolver.
-
-    Some examples on how to resolve things:
-    - list: get_dictionary could return a dict with index->item and use the index to resolve it later
-    - set: get_dictionary could return a dict with id(object)->object and reiterate in that array to resolve it later
-    - arbitrary instance: get_dictionary could return dict with attr_name->attr and use getattr to resolve it later
-    """
-
-    @abc.abstractmethod
-    def resolve(self, var, attribute):
-        """
-        In this method, we'll resolve some child item given the string representation of the item in the key
-        representing the previously asked dictionary.
-
-        :param var: this is the actual variable to be resolved.
-        :param attribute: this is the string representation of a key previously returned in get_dictionary.
-        """
-        raise NotImplementedError
-
-    @abc.abstractmethod
-    def get_dictionary(self, var):
-        """
-        :param var: this is the variable that should have its children gotten.
-
-        :return: a dictionary where each pair key, value should be shown to the user as children items
-        in the variables view for the given var.
-        """
-        raise NotImplementedError
-
-
-class _AbstractProvider(_with_metaclass(abc.ABCMeta)):
-
-    @abc.abstractmethod
-    def can_provide(self, type_object, type_name):
-        raise NotImplementedError
-
-# =======================================================================================================================
-# API CLASSES:
-# =======================================================================================================================
-
-
-class TypeResolveProvider(_AbstractResolver, _AbstractProvider):
-    """
-    Implement this in an extension to provide a custom resolver, see _AbstractResolver
-    """
-
-
-class StrPresentationProvider(_AbstractProvider):
-    """
-    Implement this in an extension to provide a str presentation for a type
-    """
-
-    def get_str_in_context(self, val: Any, context: str):
-        '''
-        :param val:
-            This is the object for which we want a string representation.
-
-        :param context:
-            This is the context in which the variable is being requested. Valid values:
-                "watch",
-                "repl",
-                "hover",
-                "clipboard"
-
-        :note: this method is not required (if it's not available, get_str is called directly,
-               so, it's only needed if the string representation needs to be converted based on
-               the context).
-        '''
-        return self.get_str(val)
-
-    @abc.abstractmethod
-    def get_str(self, val):
-        raise NotImplementedError
-
-
-class DebuggerEventHandler(_with_metaclass(abc.ABCMeta)):
-    """
-    Implement this to receive lifecycle events from the debugger
-    """
-
-    def on_debugger_modules_loaded(self, **kwargs):
-        """
-        This method invoked after all debugger modules are loaded. Useful for importing and/or patching debugger
-        modules at a safe time
-        :param kwargs: This is intended to be flexible dict passed from the debugger.
-        Currently passes the debugger version
-        """
diff --git a/spaces/Surn/UnlimitedMusicGen/audiocraft/modules/codebooks_patterns.py b/spaces/Surn/UnlimitedMusicGen/audiocraft/modules/codebooks_patterns.py
deleted file mode 100644
index c5b35cbea8cff84aa56116dbdd860fc72a913a13..0000000000000000000000000000000000000000
--- a/spaces/Surn/UnlimitedMusicGen/audiocraft/modules/codebooks_patterns.py
+++ /dev/null
@@ -1,539 +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 collections import namedtuple
-from dataclasses import dataclass
-from functools import lru_cache
-import logging
-import typing as tp
-
-from abc import ABC, abstractmethod
-import torch
-
-LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
-PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Pattern:
-    """Base implementation of a pattern over a sequence with multiple codebooks.
-
-    The codebook pattern consists in a layout, defining for each sequence step
-    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
-    The first item of the pattern is always an empty list in order to properly insert a special token
-    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
-    and ``timesteps`` the number of timesteps corresponding to the original sequence.
-
-    The pattern provides convenient methods to build and revert interleaved sequences from it:
-    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
-        to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
-        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
-        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
-        is returned along with a mask indicating valid tokens.
-    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
-        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
-        to fill and specify invalid positions if needed.
-    See the dedicated methods for more details.
-    """
-    # Pattern layout, for each sequence step, we have a list of coordinates
-    # corresponding to the original codebook timestep and position.
-    # The first list is always an empty list in order to properly insert
-    # a special token to start with.
-    layout: PatternLayout
-    timesteps: int
-    n_q: int
-
-    def __post_init__(self):
-        assert len(self.layout) > 0
-        assert self.layout[0] == []
-        self._validate_layout()
-        self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
-        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
-        logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
-
-    def _validate_layout(self):
-        """Runs checks on the layout to ensure a valid pattern is defined.
-        A pattern is considered invalid if:
-            - Multiple timesteps for a same codebook are defined in the same sequence step
-            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
-              (this would mean that we have future timesteps before past timesteps).
-        """
-        q_timesteps = {q: 0 for q in range(self.n_q)}
-        for s, seq_coords in enumerate(self.layout):
-            if len(seq_coords) > 0:
-                qs = set()
-                for coord in seq_coords:
-                    qs.add(coord.q)
-                    last_q_timestep = q_timesteps[coord.q]
-                    assert coord.t >= last_q_timestep, \
-                        f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
-                    q_timesteps[coord.q] = coord.t
-                # each sequence step contains at max 1 coordinate per codebook
-                assert len(qs) == len(seq_coords), \
-                    f"Multiple entries for a same codebook are found at step {s}"
-
-    @property
-    def num_sequence_steps(self):
-        return len(self.layout) - 1
-
-    @property
-    def max_delay(self):
-        max_t_in_seq_coords = 0
-        for seq_coords in self.layout[1:]:
-            for coords in seq_coords:
-                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
-        return max_t_in_seq_coords - self.timesteps
-
-    @property
-    def valid_layout(self):
-        valid_step = len(self.layout) - self.max_delay
-        return self.layout[:valid_step]
-
-    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
-        """Get codebook coordinates in the layout that corresponds to the specified timestep t
-        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
-        and the actual codebook coordinates.
-        """
-        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
-        if q is not None:
-            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
-        coords = []
-        for s, seq_codes in enumerate(self.layout):
-            for code in seq_codes:
-                if code.t == t and (q is None or code.q == q):
-                    coords.append((s, code))
-        return coords
-
-    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
-        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
-
-    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
-        steps_with_timesteps = self.get_steps_with_timestep(t, q)
-        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
-
-    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
-                                                device: tp.Union[torch.device, str] = 'cpu'):
-        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
-
-        Args:
-            timesteps (int): Maximum number of timesteps steps to consider.
-            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
-            device (Union[torch.device, str]): Device for created tensors.
-        Returns:
-            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
-        """
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
-        # use the proper layout based on whether we limit ourselves to valid steps only or not,
-        # note that using the valid_layout will result in a truncated sequence up to the valid steps
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
-        # which will correspond to the index: n_q * timesteps
-        indexes[:] = n_q * timesteps
-        # iterate over the pattern and fill scattered indexes and mask
-        for s, sequence_coords in enumerate(ref_layout):
-            for coords in sequence_coords:
-                if coords.t < timesteps:
-                    indexes[coords.q, s] = coords.t + coords.q * timesteps
-                    mask[coords.q, s] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Build sequence corresponding to the pattern from the input tensor z.
-        The sequence is built using up to sequence_steps if specified, and non-pattern
-        coordinates are filled with the special token.
-
-        Args:
-            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
-            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
-                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
-        """
-        B, K, T = z.shape
-        indexes, mask = self._build_pattern_sequence_scatter_indexes(
-            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
-        )
-        z = z.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
-        values = z[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
-                                                 keep_only_valid_steps: bool = False,
-                                                 is_model_output: bool = False,
-                                                 device: tp.Union[torch.device, str] = 'cpu'):
-        """Builds scatter indexes required to retrieve the original multi-codebook sequence
-        from interleaving pattern.
-
-        Args:
-            sequence_steps (int): Sequence steps.
-            n_q (int): Number of codebooks.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
-            device (Union[torch.device, str]): Device for created tensors.
-        Returns:
-            torch.Tensor: Indexes for reconstructing the output, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
-        timesteps = self.timesteps
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert sequence_steps <= len(ref_layout), \
-            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
-
-        # ensure we take the appropriate indexes to keep the model output from the first special token as well
-        if is_model_output:
-            ref_layout = ref_layout[1:]
-
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        indexes[:] = n_q * sequence_steps
-        for s, sequence_codes in enumerate(ref_layout):
-            if s < sequence_steps:
-                for code in sequence_codes:
-                    if code.t < timesteps:
-                        indexes[code.q, code.t] = s + code.q * sequence_steps
-                        mask[code.q, code.t] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
-        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
-        are filled with the special token.
-
-        Args:
-            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
-            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
-                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        B, K, S = s.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
-        )
-        s = s.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
-        values = s[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
-        """Revert model logits obtained on a sequence built from the pattern
-        back to a tensor matching the original sequence.
-
-        This method is similar to ``revert_pattern_sequence`` with the following specificities:
-        1. It is designed to work with the extra cardinality dimension
-        2. We return the logits for the first sequence item that matches the special_token and
-        which matching target in the original sequence is the first item of the sequence,
-        while we skip the last logits as there is no matching target
-        """
-        B, card, K, S = logits.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
-        )
-        logits = logits.reshape(B, card, -1)
-        # we append the special token as the last index of our flattened z tensor
-        logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1)  # [B, card, K x S]
-        values = logits[:, :, indexes.view(-1)]
-        values = values.view(B, card, K, indexes.shape[-1])
-        return values, indexes, mask
-
-
-class CodebooksPatternProvider(ABC):
-    """Abstraction around providing pattern for interleaving codebooks.
-
-    The CodebooksPatternProvider abstraction allows to implement various strategies to
-    define interleaving pattern of sequences composed of multiple codebooks. For a given
-    number of codebooks `n_q`, the pattern provider can generate a specified pattern
-    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
-    can be used to construct a new sequence from the original codes respecting the specified
-    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
-    being a tuple with the original timestep and codebook to build the new sequence.
-    Note that all patterns must start with an empty list that is then used to insert a first
-    sequence step of special tokens in the newly generated sequence.
-
-    Args:
-        n_q (int): number of codebooks.
-        cached (bool): if True, patterns for a given length are cached. In general
-            that should be true for efficiency reason to avoid synchronization points.
-    """
-    def __init__(self, n_q: int, cached: bool = True):
-        assert n_q > 0
-        self.n_q = n_q
-        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore
-
-    @abstractmethod
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern with specific interleaving between codebooks.
-
-        Args:
-            timesteps (int): Total numer of timesteps.
-        """
-        raise NotImplementedError()
-
-
-class DelayedPatternProvider(CodebooksPatternProvider):
-    """Provider for delayed pattern across delayed codebooks.
-    Codebooks are delayed in the sequence and sequence steps will contain codebooks
-    from different timesteps.
-
-    Example:
-        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
-        [[1, 2, 3, 4],
-        [1, 2, 3, 4],
-        [1, 2, 3, 4]]
-        The resulting sequence obtained from the returned pattern is:
-        [[S, 1, 2, 3, 4],
-        [S, S, 1, 2, 3],
-        [S, S, S, 1, 2]]
-        (with S being a special token)
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (Optional[List[int]]): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-        flatten_first (int): Flatten the first N timesteps.
-        empty_initial (int): Prepend with N empty list of coordinates.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
-                 flatten_first: int = 0, empty_initial: int = 0):
-        super().__init__(n_q)
-        if delays is None:
-            delays = list(range(n_q))
-        self.delays = delays
-        self.flatten_first = flatten_first
-        self.empty_initial = empty_initial
-        assert len(self.delays) == self.n_q
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        max_delay = max(self.delays)
-        if self.empty_initial:
-            out += [[] for _ in range(self.empty_initial)]
-        if self.flatten_first:
-            for t in range(min(timesteps, self.flatten_first)):
-                for q in range(self.n_q):
-                    out.append([LayoutCoord(t, q)])
-        for t in range(self.flatten_first, timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= self.flatten_first:
-                    v.append(LayoutCoord(t_for_q, q))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class ParallelPatternProvider(DelayedPatternProvider):
-    """Provider for parallel pattern across codebooks.
-    This pattern provider is a special case of the delayed pattern with actually no delay,
-    hence delays=repeat(0, n_q).
-
-    Args:
-        n_q (int): Number of codebooks.
-    """
-    def __init__(self, n_q: int):
-        super().__init__(n_q, [0] * n_q)
-
-
-class UnrolledPatternProvider(CodebooksPatternProvider):
-    """Provider for unrolling codebooks pattern.
-    This pattern provider enables to represent the codebook flattened completely or only to some extend
-    while also specifying a given delay between the flattened codebooks representation, allowing to
-    unroll the codebooks in the sequence.
-
-    Example:
-        1. Flattening of the codebooks.
-        By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
-        taking n_q = 3 and timesteps = 4:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
-        for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
-        taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
-        allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
-        same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
-        and delays = [0, 3, 3]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, S, 1, S, 2, S, 3, S, 4],
-         [S, S, S, 1, S, 2, S, 3, S, 4],
-         [1, 2, 3, S, 4, S, 5, S, 6, S]]
-
-    Args:
-        n_q (int): Number of codebooks.
-        flattening (Optional[List[int]]): Flattening schema over the codebooks. If not defined,
-            the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
-            have n_q extra steps for each timestep.
-        delays (Optional[List[int]]): Delay for each of the codebooks. If not defined,
-            no delay is added and therefore will default to [0] * ``n_q``.
-            Note that two codebooks that will be flattened to the same inner step
-            should have the same delay, otherwise the pattern is considered as invalid.
-    """
-    FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
-
-    def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
-                 delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if flattening is None:
-            flattening = list(range(n_q))
-        if delays is None:
-            delays = [0] * n_q
-        assert len(flattening) == n_q
-        assert len(delays) == n_q
-        assert sorted(flattening) == flattening
-        assert sorted(delays) == delays
-        self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
-        self.max_delay = max(delays)
-
-    def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
-        """Build a flattened codebooks representation as a dictionary of inner step
-        and the actual codebook indices corresponding to the flattened codebook. For convenience, we
-        also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
-        """
-        flattened_codebooks: dict = {}
-        for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
-            if inner_step not in flattened_codebooks:
-                flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
-            else:
-                flat_codebook = flattened_codebooks[inner_step]
-                assert flat_codebook.delay == delay, (
-                    "Delay and flattening between codebooks is inconsistent: ",
-                    "two codebooks flattened to the same position should have the same delay."
-                )
-                flat_codebook.codebooks.append(q)
-            flattened_codebooks[inner_step] = flat_codebook
-        return flattened_codebooks
-
-    @property
-    def _num_inner_steps(self):
-        """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
-        """
-        return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
-
-    def num_virtual_steps(self, timesteps: int) -> int:
-        return timesteps * self._num_inner_steps + 1
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern for delay across codebooks.
-
-        Args:
-            timesteps (int): Total numer of timesteps.
-        """
-        # the PatternLayout is built as a tuple of sequence position and list of coordinates
-        # so that it can be reordered properly given the required delay between codebooks of given timesteps
-        indexed_out: list = [(-1, [])]
-        max_timesteps = timesteps + self.max_delay
-        for t in range(max_timesteps):
-            # for each timestep, we unroll the flattened codebooks,
-            # emitting the sequence step with the corresponding delay
-            for step in range(self._num_inner_steps):
-                if step in self._flattened_codebooks:
-                    # we have codebooks at this virtual step to emit
-                    step_codebooks = self._flattened_codebooks[step]
-                    t_for_q = t + step_codebooks.delay
-                    coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
-                    if t_for_q < max_timesteps and t < max_timesteps:
-                        indexed_out.append((t_for_q, coords))
-                else:
-                    # there is no codebook in this virtual step so we emit an empty list
-                    indexed_out.append((t, []))
-        out = [coords for _, coords in sorted(indexed_out)]
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class VALLEPattern(CodebooksPatternProvider):
-    """Almost VALL-E style pattern. We futher allow some delays for the
-    codebooks other than the first one.
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (Optional[List[int]]): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if delays is None:
-            delays = [0] * (n_q - 1)
-        self.delays = delays
-        assert len(self.delays) == self.n_q - 1
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for t in range(timesteps):
-            out.append([LayoutCoord(t, 0)])
-        max_delay = max(self.delays)
-        for t in range(timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= 0:
-                    v.append(LayoutCoord(t_for_q, q + 1))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class MusicLMPattern(CodebooksPatternProvider):
-    """Almost MusicLM style pattern. This is equivalent to full flattening
-    but in a different order.
-
-    Args:
-        n_q (int): Number of codebooks.
-        group_by (int): Number of codebooks to group together.
-    """
-    def __init__(self, n_q: int, group_by: int = 2):
-        super().__init__(n_q)
-        self.group_by = group_by
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for offset in range(0, self.n_q, self.group_by):
-            for t in range(timesteps):
-                for q in range(offset, offset + self.group_by):
-                    out.append([LayoutCoord(t, q)])
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
diff --git a/spaces/TIMBOVILL/RVC-Noobie/lib/infer_pack/modules/F0Predictor/__init__.py b/spaces/TIMBOVILL/RVC-Noobie/lib/infer_pack/modules/F0Predictor/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/predictors/biased_predictor.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/predictors/biased_predictor.py
deleted file mode 100644
index 1a4d4076d5bf25c287b928573959d4bf2e9409f8..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/predictors/biased_predictor.py
+++ /dev/null
@@ -1,568 +0,0 @@
-from dataclasses import dataclass
-from functools import partial
-from typing import Callable, List, Optional, Tuple, Union
-
-
-from einops import repeat
-from mmcv import Config
-import pytorch_lightning as pl
-import torch
-
-from risk_biased.models.cvae_params import CVAEParams
-from risk_biased.models.biased_cvae_model import (
-    cvae_factory,
-)
-
-from risk_biased.utils.cost import TTCCostTorch, TTCCostParams
-from risk_biased.utils.risk import get_risk_estimator
-from risk_biased.utils.risk import get_risk_level_sampler
-
-
-@dataclass
-class LitTrajectoryPredictorParams:
-    """
-    cvae_params: CVAEParams class defining the necessary parameters for the CVAE model
-    risk distribution: dict of string and values defining the risk distribution to use
-    risk_estimator: dict of string and values defining the risk estimator to use
-    kl_weight: float defining the weight of the KL term in the loss function
-    kl_threshold: float defining the threshold to apply when computing kl divergence (avoid posterior collapse)
-    risk_weight: float defining the weight of the risk term in the loss function
-    n_mc_samples_risk: int defining the number of Monte Carlo samples to use when estimating the risk
-    n_mc_samples_biased: int defining the number of Monte Carlo samples to use when estimating the expected biased cost
-    dt: float defining the duration between two consecutive time steps
-    learning_rate: float defining the learning rate for the optimizer
-    use_risk_constraint: bool defining whether to use the risk constrained optimization procedure
-    risk_constraint_update_every_n_epoch: int defining the number of epochs between two risk weight updates
-    risk_constraint_weight_update_factor: float defining the factor by which the risk weight is multiplied at each update
-    risk_constraint_weight_maximum: float defining the maximum value of the risk weight
-    num_samples_min_fde: int defining the number of samples to use when estimating the minimum FDE
-    condition_on_ego_future: bool defining whether to condition the biasing on the ego future trajectory (else on the ego past)
-
-    """
-
-    cvae_params: CVAEParams
-    risk_distribution: dict
-    risk_estimator: dict
-    kl_weight: float
-    kl_threshold: float
-    risk_weight: float
-    n_mc_samples_risk: int
-    n_mc_samples_biased: int
-    dt: float
-    learning_rate: float
-    use_risk_constraint: bool
-    risk_constraint_update_every_n_epoch: int
-    risk_constraint_weight_update_factor: float
-    risk_constraint_weight_maximum: float
-    num_samples_min_fde: int
-    condition_on_ego_future: bool
-
-    @staticmethod
-    def from_config(cfg: Config):
-        cvae_params = CVAEParams.from_config(cfg)
-        return LitTrajectoryPredictorParams(
-            risk_distribution=cfg.risk_distribution,
-            risk_estimator=cfg.risk_estimator,
-            kl_weight=cfg.kl_weight,
-            kl_threshold=cfg.kl_threshold,
-            risk_weight=cfg.risk_weight,
-            n_mc_samples_risk=cfg.n_mc_samples_risk,
-            n_mc_samples_biased=cfg.n_mc_samples_biased,
-            dt=cfg.dt,
-            learning_rate=cfg.learning_rate,
-            cvae_params=cvae_params,
-            use_risk_constraint=cfg.use_risk_constraint,
-            risk_constraint_update_every_n_epoch=cfg.risk_constraint_update_every_n_epoch,
-            risk_constraint_weight_update_factor=cfg.risk_constraint_weight_update_factor,
-            risk_constraint_weight_maximum=cfg.risk_constraint_weight_maximum,
-            num_samples_min_fde=cfg.num_samples_min_fde,
-            condition_on_ego_future=cfg.condition_on_ego_future,
-        )
-
-
-class LitTrajectoryPredictor(pl.LightningModule):
-    """Pytorch Lightning Module for Trajectory Prediction with the biased cvae model
-
-    Args:
-        params : dataclass object containing the necessary parameters
-        cost_params: dataclass object defining the TTC cost function
-        unnormalizer: function that takes in a trajectory and an offset and that outputs the
-                      unnormalized trajectory
-    """
-
-    def __init__(
-        self,
-        params: LitTrajectoryPredictorParams,
-        cost_params: TTCCostParams,
-        unnormalizer: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
-    ) -> None:
-        super().__init__()
-        model = cvae_factory(
-            params.cvae_params,
-            cost_function=TTCCostTorch(cost_params),
-            risk_estimator=get_risk_estimator(params.risk_estimator),
-            training_mode="cvae",
-        )
-        self.model = model
-        self.params = params
-        self._unnormalize_trajectory = unnormalizer
-        self.set_training_mode("cvae")
-
-        self.learning_rate = params.learning_rate
-        self.num_samples_min_fde = params.num_samples_min_fde
-
-        self.dynamic_state_dim = params.cvae_params.dynamic_state_dim
-        self.dt = params.cvae_params.dt
-
-        self.use_risk_constraint = params.use_risk_constraint
-        self.risk_weight = params.risk_weight
-        self.risk_weight_ratio = params.risk_weight / params.kl_weight
-        self.kl_weight = params.kl_weight
-        if self.use_risk_constraint:
-            self.risk_constraint_update_every_n_epoch = (
-                params.risk_constraint_update_every_n_epoch
-            )
-            self.risk_constraint_weight_update_factor = (
-                params.risk_constraint_weight_update_factor
-            )
-            self.risk_constraint_weight_maximum = params.risk_constraint_weight_maximum
-
-        self._risk_sampler = get_risk_level_sampler(params.risk_distribution)
-
-    def set_training_mode(self, training_mode: str):
-        self.model.set_training_mode(training_mode)
-        self.partial_get_loss = partial(
-            self.model.get_loss,
-            kl_threshold=self.params.kl_threshold,
-            n_samples_risk=self.params.n_mc_samples_risk,
-            n_samples_biased=self.params.n_mc_samples_biased,
-            dt=self.params.dt,
-            unnormalizer=self._unnormalize_trajectory,
-        )
-
-    def _get_loss(
-        self,
-        x: torch.Tensor,
-        mask_x: torch.Tensor,
-        map: torch.Tensor,
-        mask_map: torch.Tensor,
-        y: torch.Tensor,
-        mask_y: torch.Tensor,
-        mask_loss: torch.Tensor,
-        x_ego: torch.Tensor,
-        y_ego: torch.Tensor,
-        offset: Optional[torch.Tensor] = None,
-        risk_level: Optional[torch.Tensor] = None,
-    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor, ...]], dict]:
-        """Compute loss based on trajectory history x and future y
-
-        Args:
-            x: (batch_size, num_agents, num_steps, state_dim) tensor of history
-            mask_x: (batch_size, num_agents, num_steps) tensor of bool mask
-            map: (batch_size, num_objects, object_sequence_length, map_feature_dim) tensor of encoded map objects
-            mask_map: (batch_size, num_objects, object_sequence_length) tensor True where map features are good False where it is padding
-            y: (batch_size, num_agents, num_steps_future, state_dim) tensor of future trajectory.
-            mask_y: (batch_size, num_agents, num_steps_future) tensor of bool mask.
-            mask_loss: (batch_size, num_agents, num_steps_future) tensor of bool mask set to True where the loss
-            should be computed and to False where it shouldn't
-            offset : (batch_size, num_agents, state_dim) offset position from ego
-            risk_level : (batch_size, num_agents) tensor of risk levels desired for future trajectories
-
-        Returns:
-            Union[torch.Tensor, Tuple[torch.Tensor, ...]]: (1,) loss tensor or tuple of
-            loss tensors
-            dict: dict that contains values to be logged
-        """
-        return self.partial_get_loss(
-            x=x,
-            mask_x=mask_x,
-            map=map,
-            mask_map=mask_map,
-            y=y,
-            mask_y=mask_y,
-            mask_loss=mask_loss,
-            offset=offset,
-            risk_level=risk_level,
-            x_ego=x_ego,
-            y_ego=y_ego,
-            risk_weight=self.risk_weight,
-            kl_weight=self.kl_weight,
-        )
-
-    def log_with_prefix(
-        self,
-        log_dict: dict,
-        prefix: Optional[str] = None,
-        on_step: Optional[bool] = None,
-        on_epoch: Optional[bool] = None,
-    ) -> None:
-        """log entries in log_dict while optinally adding "<prefix>/" to its keys
-
-        Args:
-            log_dict: dict that contains values to be logged
-            prefix: prefix to be added to keys
-            on_step: if True logs at this step. None auto-logs at the training_step but not
-            validation/test_step
-            on_epoch: if True logs epoch accumulated metrics. None auto-logs at the val/test
-            step but not training_step
-        """
-        if prefix is None:
-            prefix = ""
-        else:
-            prefix += "/"
-
-        for (metric, value) in log_dict.items():
-            metric = prefix + metric
-            self.log(metric, value, on_step=on_step, on_epoch=on_epoch)
-
-    def configure_optimizers(
-        self,
-    ) -> Union[torch.optim.Optimizer, List[torch.optim.Optimizer]]:
-        """Configure optimizer for PyTorch-Lightning
-
-        Returns:
-            torch.optim.Optimizer: optimizer to be used for training
-        """
-        if isinstance(self.model.get_parameters(), list):
-            self._optimizers = [
-                torch.optim.Adam(params, lr=self.learning_rate)
-                for params in self.model.get_parameters()
-            ]
-        else:
-            self._optimizers = [
-                torch.optim.Adam(self.model.get_parameters(), lr=self.learning_rate)
-            ]
-        return self._optimizers
-
-    def training_step(
-        self,
-        batch: Tuple[
-            torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor
-        ],
-        batch_idx: int,
-    ) -> dict:
-        """Training step definition for PyTorch-Lightning
-
-        Args:
-            batch : [(batch_size, num_agents, num_steps, state_dim),           # past trajectories of all agents in the scene
-                     (batch_size, num_agents, num_steps),                      # mask past False where past trajectories are padding data
-                     (batch_size, num_agents, num_steps_future, state_dim),    # future trajectory
-                     (batch_size, num_agents, num_steps_future),               # mask future False where future trajectories are padding data
-                     (batch_size, num_agents, num_steps_future),               # mask loss False where future trajectories are not to be predicted
-                     (batch_size, num_objects, object_seq_len, state_dim),     # map object sequences in the scene
-                     (batch_size, num_objects, object_seq_len),                # mask map False where map objects are padding data
-                     (batch_size, num_agents, state_dim),                      # position offset of all agents relative to ego at present time
-                     (batch_size, 1, num_steps, state_dim),                    # ego past trajectory
-                     (batch_size, 1, num_steps_future, state_dim)]             # ego future trajectory
-            batch_idx : batch_idx to be used by PyTorch-Lightning
-
-        Returns:
-            dict: dict of outputs containing loss
-        """
-        x, mask_x, y, mask_y, mask_loss, map, mask_map, offset, x_ego, y_ego = batch
-        risk_level = repeat(
-            self._risk_sampler.sample(x.shape[0], x.device),
-            "b -> b num_agents",
-            num_agents=x.shape[1],
-        )
-        loss, log_dict = self._get_loss(
-            x=x,
-            mask_x=mask_x,
-            map=map,
-            mask_map=mask_map,
-            y=y,
-            mask_y=mask_y,
-            mask_loss=mask_loss,
-            offset=offset,
-            risk_level=risk_level,
-            x_ego=x_ego,
-            y_ego=y_ego,
-        )
-        if isinstance(loss, tuple):
-            loss = sum(loss)
-        self.log_with_prefix(log_dict, prefix="train", on_step=True, on_epoch=False)
-
-        return {"loss": loss}
-
-    def training_epoch_end(self, outputs: List[dict]) -> None:
-        """Called at the end of the training epoch with the outputs of all training steps
-
-        Args:
-            outputs: list of outputs of all training steps in the current epoch
-        """
-        if self.use_risk_constraint:
-            if (
-                self.model.training_mode == "bias"
-                and (self.trainer.current_epoch + 1)
-                % self.risk_constraint_update_every_n_epoch
-                == 0
-            ):
-                self.risk_weight_ratio *= self.risk_constraint_weight_update_factor
-                if self.risk_weight_ratio < self.risk_constraint_weight_maximum:
-                    sum_weight = self.risk_weight + self.kl_weight
-                    self.risk_weight = (
-                        sum_weight
-                        * self.risk_weight_ratio
-                        / (1 + self.risk_weight_ratio)
-                    )
-                    self.kl_weight = sum_weight / (1 + self.risk_weight_ratio)
-                # self.risk_weight *= self.risk_constraint_weight_update_factor
-                # if self.risk_weight > self.risk_constraint_weight_maximum:
-                #     self.risk_weight = self.risk_constraint_weight_maximum
-
-    def _get_risk_tensor(
-        self,
-        batch_size: int,
-        num_agents: int,
-        device: torch.device,
-        risk_level: Optional[torch.Tensor] = None,
-    ):
-        """This function is used to reformat different possible formattings of risk_level input arguments into a tensor of shape (batch_size).
-            If given a tensor the same tensor is returned.
-            If given a float value, a tensor of this value is returned.
-            If given None, a tensor filled with random samples is returned.
-
-        Args:
-            batch_size : desired batch size
-            device : device on which we want to store risk
-            risk_level : The risk level as a tensor, a float value or None
-
-        Returns:
-            _type_: _description_
-        """
-        if risk_level is not None:
-            if isinstance(risk_level, float):
-                risk_level = (
-                    torch.ones(batch_size, num_agents, device=device) * risk_level
-                )
-            else:
-                risk_level = risk_level.to(device)
-        else:
-            risk_level = None
-
-        return risk_level
-
-    def validation_step(
-        self,
-        batch: Tuple[
-            torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor
-        ],
-        batch_idx: int,
-        risk_level: float = 1.0,
-    ) -> dict:
-        """Validation step definition for PyTorch-Lightning
-
-        Args:
-            batch : [(batch_size, num_agents, num_steps, state_dim),           # past trajectories of all agents in the scene
-                     (batch_size, num_agents, num_steps),                      # mask past False where past trajectories are padding data
-                     (batch_size, num_agents, num_steps_future, state_dim),    # future trajectory
-                     (batch_size, num_agents, num_steps_future),               # mask future False where future trajectories are padding data
-                     (batch_size, num_agents, num_steps_future),               # mask loss False where future trajectories are not to be predicted
-                     (batch_size, num_objects, object_seq_len, state_dim),     # map object sequences in the scene
-                     (batch_size, num_objects, object_seq_len),                # mask map False where map objects are padding data
-                     (batch_size, num_agents, state_dim),                      # position offset of all agents relative to ego at present time
-                     (batch_size, 1, num_steps, state_dim),                    # ego past trajectory
-                     (batch_size, 1, num_steps_future, state_dim)]             # ego future trajectory
-            batch_idx : batch_idx to be used by PyTorch-Lightning
-            risk_level : optional desired risk level
-
-        Returns:
-            dict: dict of outputs containing loss
-        """
-        x, mask_x, y, mask_y, mask_loss, map, mask_map, offset, x_ego, y_ego = batch
-
-        risk_level = self._get_risk_tensor(
-            x.shape[0], x.shape[1], x.device, risk_level=risk_level
-        )
-        self.model.eval()
-        log_dict_accuracy = self.model.get_prediction_accuracy(
-            x=x,
-            mask_x=mask_x,
-            map=map,
-            mask_map=mask_map,
-            y=y,
-            mask_loss=mask_loss,
-            offset=offset,
-            x_ego=x_ego,
-            y_ego=y_ego,
-            unnormalizer=self._unnormalize_trajectory,
-            risk_level=risk_level,
-            num_samples_min_fde=self.num_samples_min_fde,
-        )
-
-        loss, log_dict_loss = self._get_loss(
-            x=x,
-            mask_x=mask_x,
-            map=map,
-            mask_map=mask_map,
-            y=y,
-            mask_y=mask_y,
-            mask_loss=mask_loss,
-            offset=offset,
-            risk_level=risk_level,
-            x_ego=x_ego,
-            y_ego=y_ego,
-        )
-
-        if isinstance(loss, tuple):
-            loss = sum(loss)
-
-        self.log_with_prefix(
-            dict(log_dict_accuracy, **log_dict_loss),
-            prefix="val",
-            on_step=False,
-            on_epoch=True,
-        )
-        self.model.train()
-        return {"loss": loss}
-
-    def test_step(
-        self,
-        batch: Tuple[
-            torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor
-        ],
-        batch_idx: int,
-        risk_level: Optional[torch.Tensor] = None,
-    ) -> dict:
-        """Test step definition for PyTorch-Lightning
-
-        Args:
-            batch : [(batch_size, num_agents, num_steps, state_dim),           # past trajectories of all agents in the scene
-                     (batch_size, num_agents, num_steps),                      # mask past False where past trajectories are padding data
-                     (batch_size, num_agents, num_steps_future, state_dim),    # future trajectory
-                     (batch_size, num_agents, num_steps_future),               # mask future False where future trajectories are padding data
-                     (batch_size, num_agents, num_steps_future),               # mask loss False where future trajectories are not to be predicted
-                     (batch_size, num_objects, object_seq_len, state_dim),     # map object sequences in the scene
-                     (batch_size, num_objects, object_seq_len),                # mask map False where map objects are padding data
-                     (batch_size, num_agents, state_dim),                      # position offset of all agents relative to ego at present time
-                     (batch_size, 1, num_steps, state_dim),                    # ego past trajectory
-                     (batch_size, 1, num_steps_future, state_dim)]             # ego future trajectory
-            batch_idx : batch_idx to be used by PyTorch-Lightning
-            risk_level : optional desired risk level
-
-        Returns:
-            dict: dict of outputs containing loss
-        """
-        x, mask_x, y, mask_y, mask_loss, map, mask_map, offset, x_ego, y_ego = batch
-        risk_level = self._get_risk_tensor(
-            x.shape[0], x.shape[1], x.device, risk_level=risk_level
-        )
-        loss, log_dict = self._get_loss(
-            x=x,
-            mask_x=mask_x,
-            map=map,
-            mask_map=mask_map,
-            y=y,
-            mask_y=mask_y,
-            mask_loss=mask_loss,
-            offset=offset,
-            risk_level=risk_level,
-            x_ego=x_ego,
-            y_ego=y_ego,
-        )
-        if isinstance(loss, tuple):
-            loss = sum(loss)
-        self.log_with_prefix(log_dict, prefix="test", on_step=False, on_epoch=True)
-        return {"loss": loss}
-
-    def predict_step(
-        self,
-        batch: Tuple[torch.Tensor, torch.Tensor],
-        batch_idx: int = 0,
-        risk_level: Optional[torch.Tensor] = None,
-        n_samples: int = 0,
-        return_weights: bool = False,
-    ) -> torch.Tensor:
-        """Predict step definition for PyTorch-Lightning
-
-        Args:
-            batch: [(batch_size, num_agents, num_steps, state_dim),           # past trajectories of all agents in the scene
-                    (batch_size, num_agents, num_steps),                      # mask past False where past trajectories are padding data
-                    (batch_size, num_objects, object_seq_len, state_dim),     # map object sequences in the scene
-                    (batch_size, num_objects, object_seq_len),                # mask map False where map objects are padding data
-                    (batch_size, num_agents, state_dim),                      # position offset of all agents relative to ego at present time
-                    (batch_size, 1, num_steps, state_dim),                    # past trajectory of the ego agent in the scene
-                    (batch_size, 1, num_steps_future, state_dim),]            # future trajectory of the ego agent in the scene
-            batch_idx : batch_idx to be used by PyTorch-Lightning (unused here)
-            risk_level : optional desired risk level
-            n_samples: Number of samples to predict per agent
-                With value of 0 does not include the `n_samples` dim in the output.
-            return_weights: If True, also returns the sample weights
-
-        Returns:
-            (batch_size, (n_samples), num_steps_future, state_dim) tensor
-        """
-        x, mask_x, map, mask_map, offset, x_ego, y_ego = batch
-        risk_level = self._get_risk_tensor(
-            batch_size=x.shape[0],
-            num_agents=x.shape[1],
-            device=x.device,
-            risk_level=risk_level,
-        )
-        y_sampled, weights, _ = self.model(
-            x,
-            mask_x,
-            map,
-            mask_map,
-            offset=offset,
-            x_ego=x_ego,
-            y_ego=y_ego,
-            risk_level=risk_level,
-            n_samples=n_samples,
-        )
-        predict_sampled = self._unnormalize_trajectory(y_sampled, offset)
-        if return_weights:
-            return predict_sampled, weights
-        else:
-            return predict_sampled
-
-    def predict_loop_once(
-        self,
-        batch: Tuple[torch.Tensor, torch.Tensor],
-        batch_idx: int = 0,
-        risk_level: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        """Predict with refinment:
-                A first prediction is done as in predict_step, however instead of unnormalize and return it,
-                it is fed to the encoder that wast trained to encode past and ground truth future.
-                Then the decoder is used again but its latent input sample is biased by the encoder
-                instead of being a sample of the prior distribution.
-                Then as in predict_step the result is unnormalized and returned.
-
-        Args:
-            batch: [(batch_size, num_agents, num_steps, state_dim),           # past trajectories of all agents in the scene
-                    (batch_size, num_agents, num_steps),                      # mask past False where past trajectories are padding data
-                    (batch_size, num_objects, object_seq_len, state_dim),     # map object sequences in the scene
-                    (batch_size, num_objects, object_seq_len),                # mask map False where map objects are padding data
-                    (batch_size, num_agents, state_dim),]                     # position offset of all agents relative to ego at present time
-            batch_idx : batch_idx to be used by PyTorch-Lightning (Unused here). Defaults to 0.
-            risk_level : optional desired risk level
-
-        Returns:
-            torch.Tensor: (batch_size, num_steps_future, state_dim) tensor
-        """
-        x, mask_x, map, mask_map, offset = batch
-        risk_level = self._get_risk_tensor(
-            x.shape[0], x.shape[1], x.device, risk_level=risk_level
-        )
-        y_sampled, _ = self.model(
-            x,
-            mask_x,
-            map,
-            mask_map,
-            offset=offset,
-            risk_level=risk_level,
-        )
-        mask_y = repeat(mask_x.any(-1), "b a -> b a f", f=y_sampled.shape[-2])
-        y_sampled, _ = self.model(
-            x,
-            mask_x,
-            map,
-            mask_map,
-            y_sampled,
-            mask_y,
-            offset=offset,
-            risk_level=risk_level,
-        )
-        predict_sampled = self._unnormalize_trajectory(y_sampled, offset=offset)
-        return predict_sampled
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/panel.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/panel.py
deleted file mode 100644
index d522d80b5189554d1acf9b46d5db1981b946d712..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/panel.py
+++ /dev/null
@@ -1,308 +0,0 @@
-from typing import TYPE_CHECKING, Optional
-
-from .align import AlignMethod
-from .box import ROUNDED, Box
-from .cells import cell_len
-from .jupyter import JupyterMixin
-from .measure import Measurement, measure_renderables
-from .padding import Padding, PaddingDimensions
-from .segment import Segment
-from .style import Style, StyleType
-from .text import Text, TextType
-
-if TYPE_CHECKING:
-    from .console import Console, ConsoleOptions, RenderableType, RenderResult
-
-
-class Panel(JupyterMixin):
-    """A console renderable that draws a border around its contents.
-
-    Example:
-        >>> console.print(Panel("Hello, World!"))
-
-    Args:
-        renderable (RenderableType): A console renderable object.
-        box (Box, optional): A Box instance that defines the look of the border (see :ref:`appendix_box`.
-            Defaults to box.ROUNDED.
-        safe_box (bool, optional): Disable box characters that don't display on windows legacy terminal with *raster* fonts. Defaults to True.
-        expand (bool, optional): If True the panel will stretch to fill the console
-            width, otherwise it will be sized to fit the contents. Defaults to True.
-        style (str, optional): The style of the panel (border and contents). Defaults to "none".
-        border_style (str, optional): The style of the border. Defaults to "none".
-        width (Optional[int], optional): Optional width of panel. Defaults to None to auto-detect.
-        height (Optional[int], optional): Optional height of panel. Defaults to None to auto-detect.
-        padding (Optional[PaddingDimensions]): Optional padding around renderable. Defaults to 0.
-        highlight (bool, optional): Enable automatic highlighting of panel title (if str). Defaults to False.
-    """
-
-    def __init__(
-        self,
-        renderable: "RenderableType",
-        box: Box = ROUNDED,
-        *,
-        title: Optional[TextType] = None,
-        title_align: AlignMethod = "center",
-        subtitle: Optional[TextType] = None,
-        subtitle_align: AlignMethod = "center",
-        safe_box: Optional[bool] = None,
-        expand: bool = True,
-        style: StyleType = "none",
-        border_style: StyleType = "none",
-        width: Optional[int] = None,
-        height: Optional[int] = None,
-        padding: PaddingDimensions = (0, 1),
-        highlight: bool = False,
-    ) -> None:
-        self.renderable = renderable
-        self.box = box
-        self.title = title
-        self.title_align: AlignMethod = title_align
-        self.subtitle = subtitle
-        self.subtitle_align = subtitle_align
-        self.safe_box = safe_box
-        self.expand = expand
-        self.style = style
-        self.border_style = border_style
-        self.width = width
-        self.height = height
-        self.padding = padding
-        self.highlight = highlight
-
-    @classmethod
-    def fit(
-        cls,
-        renderable: "RenderableType",
-        box: Box = ROUNDED,
-        *,
-        title: Optional[TextType] = None,
-        title_align: AlignMethod = "center",
-        subtitle: Optional[TextType] = None,
-        subtitle_align: AlignMethod = "center",
-        safe_box: Optional[bool] = None,
-        style: StyleType = "none",
-        border_style: StyleType = "none",
-        width: Optional[int] = None,
-        padding: PaddingDimensions = (0, 1),
-    ) -> "Panel":
-        """An alternative constructor that sets expand=False."""
-        return cls(
-            renderable,
-            box,
-            title=title,
-            title_align=title_align,
-            subtitle=subtitle,
-            subtitle_align=subtitle_align,
-            safe_box=safe_box,
-            style=style,
-            border_style=border_style,
-            width=width,
-            padding=padding,
-            expand=False,
-        )
-
-    @property
-    def _title(self) -> Optional[Text]:
-        if self.title:
-            title_text = (
-                Text.from_markup(self.title)
-                if isinstance(self.title, str)
-                else self.title.copy()
-            )
-            title_text.end = ""
-            title_text.plain = title_text.plain.replace("\n", " ")
-            title_text.no_wrap = True
-            title_text.expand_tabs()
-            title_text.pad(1)
-            return title_text
-        return None
-
-    @property
-    def _subtitle(self) -> Optional[Text]:
-        if self.subtitle:
-            subtitle_text = (
-                Text.from_markup(self.subtitle)
-                if isinstance(self.subtitle, str)
-                else self.subtitle.copy()
-            )
-            subtitle_text.end = ""
-            subtitle_text.plain = subtitle_text.plain.replace("\n", " ")
-            subtitle_text.no_wrap = True
-            subtitle_text.expand_tabs()
-            subtitle_text.pad(1)
-            return subtitle_text
-        return None
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        _padding = Padding.unpack(self.padding)
-        renderable = (
-            Padding(self.renderable, _padding) if any(_padding) else self.renderable
-        )
-        style = console.get_style(self.style)
-        border_style = style + console.get_style(self.border_style)
-        width = (
-            options.max_width
-            if self.width is None
-            else min(options.max_width, self.width)
-        )
-
-        safe_box: bool = console.safe_box if self.safe_box is None else self.safe_box
-        box = self.box.substitute(options, safe=safe_box)
-
-        def align_text(
-            text: Text, width: int, align: str, character: str, style: Style
-        ) -> Text:
-            """Gets new aligned text.
-
-            Args:
-                text (Text): Title or subtitle text.
-                width (int): Desired width.
-                align (str): Alignment.
-                character (str): Character for alignment.
-                style (Style): Border style
-
-            Returns:
-                Text: New text instance
-            """
-            text = text.copy()
-            text.truncate(width)
-            excess_space = width - cell_len(text.plain)
-            if excess_space:
-                if align == "left":
-                    return Text.assemble(
-                        text,
-                        (character * excess_space, style),
-                        no_wrap=True,
-                        end="",
-                    )
-                elif align == "center":
-                    left = excess_space // 2
-                    return Text.assemble(
-                        (character * left, style),
-                        text,
-                        (character * (excess_space - left), style),
-                        no_wrap=True,
-                        end="",
-                    )
-                else:
-                    return Text.assemble(
-                        (character * excess_space, style),
-                        text,
-                        no_wrap=True,
-                        end="",
-                    )
-            return text
-
-        title_text = self._title
-        if title_text is not None:
-            title_text.stylize_before(border_style)
-
-        child_width = (
-            width - 2
-            if self.expand
-            else console.measure(
-                renderable, options=options.update_width(width - 2)
-            ).maximum
-        )
-        child_height = self.height or options.height or None
-        if child_height:
-            child_height -= 2
-        if title_text is not None:
-            child_width = min(
-                options.max_width - 2, max(child_width, title_text.cell_len + 2)
-            )
-
-        width = child_width + 2
-        child_options = options.update(
-            width=child_width, height=child_height, highlight=self.highlight
-        )
-        lines = console.render_lines(renderable, child_options, style=style)
-
-        line_start = Segment(box.mid_left, border_style)
-        line_end = Segment(f"{box.mid_right}", border_style)
-        new_line = Segment.line()
-        if title_text is None or width <= 4:
-            yield Segment(box.get_top([width - 2]), border_style)
-        else:
-            title_text = align_text(
-                title_text,
-                width - 4,
-                self.title_align,
-                box.top,
-                border_style,
-            )
-            yield Segment(box.top_left + box.top, border_style)
-            yield from console.render(title_text, child_options.update_width(width - 4))
-            yield Segment(box.top + box.top_right, border_style)
-
-        yield new_line
-        for line in lines:
-            yield line_start
-            yield from line
-            yield line_end
-            yield new_line
-
-        subtitle_text = self._subtitle
-        if subtitle_text is not None:
-            subtitle_text.stylize_before(border_style)
-
-        if subtitle_text is None or width <= 4:
-            yield Segment(box.get_bottom([width - 2]), border_style)
-        else:
-            subtitle_text = align_text(
-                subtitle_text,
-                width - 4,
-                self.subtitle_align,
-                box.bottom,
-                border_style,
-            )
-            yield Segment(box.bottom_left + box.bottom, border_style)
-            yield from console.render(
-                subtitle_text, child_options.update_width(width - 4)
-            )
-            yield Segment(box.bottom + box.bottom_right, border_style)
-
-        yield new_line
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        _title = self._title
-        _, right, _, left = Padding.unpack(self.padding)
-        padding = left + right
-        renderables = [self.renderable, _title] if _title else [self.renderable]
-
-        if self.width is None:
-            width = (
-                measure_renderables(
-                    console,
-                    options.update_width(options.max_width - padding - 2),
-                    renderables,
-                ).maximum
-                + padding
-                + 2
-            )
-        else:
-            width = self.width
-        return Measurement(width, width)
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from .console import Console
-
-    c = Console()
-
-    from .box import DOUBLE, ROUNDED
-    from .padding import Padding
-
-    p = Panel(
-        "Hello, World!",
-        title="rich.Panel",
-        style="white on blue",
-        box=DOUBLE,
-        padding=1,
-    )
-
-    c.print()
-    c.print(p)
diff --git a/spaces/Vageesh1/bio_generator/app.py b/spaces/Vageesh1/bio_generator/app.py
deleted file mode 100644
index 66a95a4718ae39100c1235d7afad9bd631367757..0000000000000000000000000000000000000000
--- a/spaces/Vageesh1/bio_generator/app.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import streamlit as st 
-import langchain 
-import pandas as pd 
-import numpy as np 
-import os
-
-import re 
-
-from langchain.chat_models import ChatOpenAI
-import openai 
-from langchain import HuggingFaceHub, LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferWindowMemory
-from langchain.chains import ConversationalRetrievalChain
-
-trait_content_df=pd.read_csv('AI Personality Chart trait_content (2).csv')
-trait_content_df=trait_content_df.drop(0,axis=0)
-trait_content_df.rename(columns={'Column 1':'Question','Column 2':'Options','Column 3':'Traits','Column 4':'Content'},inplace=True)
-trait_content_df['Title'].fillna(method='ffill',inplace=True)
-trait_content_df['Question'].fillna(method='ffill',inplace=True)
-
-template = """
-Imagine you're someone looking to create a unique personalized bio based on your traits and experiences. You've shared some details about your background, and now it's time to craft a bio that stands out. Respond in the second person and avoid using the same sentences for different users. Your response should be concise and conclude within 150 words.
-
-{history}
-You: {human_input}
-Bot:
-
-[CHARACTER_LIMIT=150]
-"""
-
-prompt = PromptTemplate(
-    input_variables=["history", "human_input"],
-    template=template
-)
-
-llm_chain = LLMChain(
-        llm = ChatOpenAI(temperature=1.3,model_name='gpt-3.5-turbo'),
-        prompt=prompt,
-        verbose=True,
-        memory=ConversationBufferWindowMemory(k=0)
-    )
-
-def extract_text_from_html(html):
-    cleanr = re.compile('<.*?>')
-    cleantext = re.sub(cleanr, '', html)
-    return cleantext.strip()
-
-def conversational_chat(query, replacement_word=None):
-    hist_dict['past'].append(query)
-    output = llm_chain.predict(human_input=query)
-    hist_dict['generated'].append(output)
-    
-    if replacement_word is not None:
-        # Use a regular expression with the re module for case-insensitive replacement
-        output = re.sub(r'\bjack\b', replacement_word, output, flags=re.IGNORECASE)
-    
-    return extract_text_from_html(output)
-    
-def word_count(text):
-    words = re.findall(r'\w+', text)
-    return len(words)
-
-
-
-hist_dict={}
-hist_dict['generated']=["Hello ! Ask me anything about " + " 🤗"]
-hist_dict['past'] = ["Hey ! 👋"]
-
-
-trait_content_df_org=pd.read_csv('AI Personality Chart trait_content (2).csv')
-trait_content_df_org=trait_content_df_org.drop(0,axis=0)
-trait_content_df_org.rename(columns={'Column 1':'Question','Column 2':'Options','Column 3':'Traits','Column 4':'Content'},inplace=True)
-
-
-def ui():
-    # Initialize a dictionary to store responses
-    responses = {}
-
-    # Create checkboxes for each question and options
-    index = 0
-    while index < len(trait_content_df_org):
-        question = trait_content_df_org.iloc[index]["Question"]
-        st.write(question)
-
-        option_a = st.checkbox(f"Option A: {trait_content_df_org.iloc[index]['Options']}", key=f"option_a_{index}")
-
-        # Check if Option B has a corresponding question (not None)
-        if trait_content_df_org.iloc[index + 1]["Question"] is not None:
-            option_b = st.checkbox(f"Option B: {trait_content_df_org.iloc[index + 1]['Options']}", key=f"option_b_{index + 1}")
-        else:
-            option_b = False
-
-        st.write("")  # Add some spacing between questions
-
-        # Store responses in the dictionary
-        if option_a:
-            responses[question] = f"{trait_content_df_org.iloc[index]['Options']}"
-        if option_b:
-            responses[question] = f"{trait_content_df_org.iloc[index + 1]['Options']}"
-
-        index += 2  # Move to the next question and options (skipping None)
-
-    st.write("Responses:")
-    for question, selected_option in responses.items():
-        st.write(question)
-        st.write(selected_option)
-
-    # Generate a prompt based on selected options
-    selected_traits = [responses[question] for question in responses]
-    options_list = []
-    traits_list = []
-    content_list = []
-
-    for trait_str in selected_traits:
-        matching_rows = trait_content_df_org[trait_content_df_org["Options"] == trait_str]
-        
-        if not matching_rows.empty:
-            options_list.append(matching_rows["Options"].values[0])
-            traits_list.append(matching_rows["Traits"].values[0])
-            content_list.append(matching_rows["Content"].values[0])
-
-    prompt = f"The following are Traits {', '.join(traits_list)}, and the content for the options is {', '.join(content_list)}"
-
-    # Display user input field
-    name_input = st.text_input("Enter your name:")
-
-    # Add a submit button
-    if st.button("Submit"):
-        # Generate a chatbot response
-        bio = conversational_chat(prompt, name_input)
-
-        # Count words in the generated bio
-        bio_word_count = word_count(bio)
-        
-        # Check if the bio exceeds 250 words
-        if bio_word_count > 250:
-            st.warning("Generated Bio exceeded 250 words. Re-inferencing...")
-            bio = conversational_chat(prompt, name_input)  # Re-inferencing
-            
-            # Count words in the re-inferenced bio
-            bio_word_count = word_count(bio)
-        
-        st.write(f"Generated Bio Word Count: {bio_word_count}")
-        st.write(bio)
-
-
-
-
-    
-    
-
-if __name__=='__main__':
-    ui()
-
diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/common/gradcam.py b/spaces/Vision-CAIR/minigpt4/minigpt4/common/gradcam.py
deleted file mode 100644
index d53a5254d4b319eaf2cbfbd081b0ca8e38c5c7a0..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/minigpt4/minigpt4/common/gradcam.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import numpy as np
-from matplotlib import pyplot as plt
-from scipy.ndimage import filters
-from skimage import transform as skimage_transform
-
-
-def getAttMap(img, attMap, blur=True, overlap=True):
-    attMap -= attMap.min()
-    if attMap.max() > 0:
-        attMap /= attMap.max()
-    attMap = skimage_transform.resize(attMap, (img.shape[:2]), order=3, mode="constant")
-    if blur:
-        attMap = filters.gaussian_filter(attMap, 0.02 * max(img.shape[:2]))
-        attMap -= attMap.min()
-        attMap /= attMap.max()
-    cmap = plt.get_cmap("jet")
-    attMapV = cmap(attMap)
-    attMapV = np.delete(attMapV, 3, 2)
-    if overlap:
-        attMap = (
-            1 * (1 - attMap**0.7).reshape(attMap.shape + (1,)) * img
-            + (attMap**0.7).reshape(attMap.shape + (1,)) * attMapV
-        )
-    return attMap
diff --git a/spaces/WordLift/entity-linking/app.py b/spaces/WordLift/entity-linking/app.py
deleted file mode 100644
index 0acc47629b8581ae3a02eff101f037ab0d288211..0000000000000000000000000000000000000000
--- a/spaces/WordLift/entity-linking/app.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import streamlit as st
-from annotated_text import annotated_text
-from refined.inference.processor import Refined
-import requests
-import json
-import spacy
-
-# Page config
-st.set_page_config(
-    page_title="Entity Linking by WordLift",
-    page_icon="fav-ico.png",
-    layout="wide",
-    initial_sidebar_state="collapsed",
-    menu_items={
-        'Get Help': 'https://wordlift.io/book-a-demo/',
-        'About': "# This is a demo app for NEL/NED/NER and SEO"
-    }
-)
-
-# Sidebar
-st.sidebar.image("logo-wordlift.png")
-language_options = {"English", "German"}
-selected_language = st.sidebar.selectbox("Select the Language", list(language_options), index=0)
-
-# Based on selected language, configure model, entity set, and citation options
-if selected_language != "German":
-    model_options = ["aida_model", "wikipedia_model_with_numbers"]
-    entity_set_options = ["wikidata", "wikipedia"]
-    
-    selected_model_name = st.sidebar.selectbox("Select the Model", model_options)
-    selected_entity_set = st.sidebar.selectbox("Select the Entity Set", entity_set_options)
-
-    refined_citation = """
-    @inproceedings{ayoola-etal-2022-refined,
-    title = "{R}e{F}in{ED}: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking",
-    author = "Tom Ayoola, Shubhi Tyagi, Joseph Fisher, Christos Christodoulopoulos, Andrea Pierleoni",
-    booktitle = "NAACL",
-    year = "2022"
-    }
-    """
-
-    with st.sidebar.expander('Citations'):
-        st.markdown(refined_citation)
-else:
-    selected_model_name = None
-    selected_entity_set = None
-
-    entity_fishing_citation = """
-    @misc{entity-fishing,
-    title = {entity-fishing},
-    publisher = {GitHub},
-    year = {2016--2023},
-    archivePrefix = {swh},
-    eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c}
-    }
-    """
-
-    with st.sidebar.expander('Citations'):
-        st.markdown(entity_fishing_citation)
-
-@st.cache_resource  # 👈 Add the caching decorator
-def load_model(selected_language, model_name=None, entity_set=None):
-    if selected_language == "German":
-        # Load the German-specific model
-        nlp_model_de = spacy.load("de_core_news_lg")
-        nlp_model_de.add_pipe("entityfishing")
-        
-        return nlp_model_de
-    else:
-        # Load the pretrained model for other languages
-        refined_model = Refined.from_pretrained(model_name=model_name, entity_set=entity_set)
-        return refined_model
-
-# Use the cached model
-model = load_model(selected_language, selected_model_name, selected_entity_set)
-
-# Helper functions
-def get_wikidata_id(entity_string):
-    entity_list = entity_string.split("=")
-    entity_id = str(entity_list[1])
-    entity_link = "http://www.wikidata.org/entity/" + entity_id
-    return {"id": entity_id, "link": entity_link}
-    
-def get_entity_data(entity_link):
-    try:
-        # Format the entity_link
-        formatted_link = entity_link.replace("http://", "http/")
-        response = requests.get(f'https://api.wordlift.io/id/{formatted_link}')
-        return response.json()
-    except Exception as e:
-        print(f"Exception when fetching data for entity: {entity_link}. Exception: {e}")
-        return None
-            
-# Create the form
-with st.form(key='my_form'):
-    text_input = st.text_area(label='Enter a sentence')
-    submit_button = st.form_submit_button(label='Analyze')
-
-# Initialization
-entities_map = {}
-entities_data = {}
-
-if text_input:
-    if selected_language == "German":
-        doc_de = model(text_input)
-        entities = [(ent.text, ent.label_, ent._.kb_qid, ent._.url_wikidata) for ent in doc_de.ents]
-        for entity in entities:
-            entity_string, entity_type, wikidata_id, wikidata_url = entity
-            if wikidata_url:
-                # Ensure correct format for the German model
-                formatted_wikidata_url = wikidata_url.replace("https://www.wikidata.org/wiki/", "http://www.wikidata.org/entity/")
-                entities_map[entity_string] = {"id": wikidata_id, "link": formatted_wikidata_url}
-                entity_data = get_entity_data(formatted_wikidata_url)
-                
-                if entity_data is not None:
-                    entities_data[entity_string] = entity_data
-
-    else:
-        entities = model.process_text(text_input)
-
-        for entity in entities:
-            single_entity_list = str(entity).strip('][').replace("\'", "").split(', ')
-            if len(single_entity_list) >= 2 and "wikidata" in single_entity_list[1]:
-                entities_map[single_entity_list[0].strip()] = get_wikidata_id(single_entity_list[1])
-                entity_data = get_entity_data(entities_map[single_entity_list[0].strip()]["link"])
-                if entity_data is not None:
-                    entities_data[single_entity_list[0].strip()] = entity_data
-
-    combined_entity_info_dictionary = dict([(k, [entities_map[k], entities_data[k] if k in entities_data else None]) for k in entities_map])
-    
-    if submit_button:
-        # Prepare a list to hold the final output
-        final_text = []
-    
-        # JSON-LD data
-        json_ld_data = {
-                "@context": "https://schema.org",
-                "@type": "WebPage",
-                "mentions": []
-            }
-    
-       # Replace each entity in the text with its annotated version
-        for entity_string, entity_info in entities_map.items():
-            # Check if the entity has a valid Wikidata link
-            if entity_info["link"] is None or entity_info["link"] == "None":
-                continue  # skip this entity
-            
-            entity_data = entities_data.get(entity_string, None)
-            entity_type = None
-            if entity_data is not None:
-                entity_type = entity_data.get("@type", None)
-    
-            # Use different colors based on the entity's type
-            color = "#8ef"  # Default color
-            if entity_type == "Place":
-                color = "#8AC7DB"
-            elif entity_type == "Organization":
-                color = "#ADD8E6"
-            elif entity_type == "Person":
-                color = "#67B7D1"
-            elif entity_type == "Product":
-                color = "#2ea3f2"
-            elif entity_type == "CreativeWork":
-                color = "#00BFFF"
-            elif entity_type == "Event":
-                color = "#1E90FF"
-    
-            entity_annotation = (entity_string, entity_info["id"], color)
-            text_input = text_input.replace(entity_string, f'{{{str(entity_annotation)}}}', 1)
-                
-            # Add the entity to JSON-LD data
-            entity_json_ld = combined_entity_info_dictionary[entity_string][1]
-            if entity_json_ld and entity_json_ld.get("link") != "None":
-                json_ld_data["mentions"].append(entity_json_ld)
-
-        # Split the modified text_input into a list
-        text_list = text_input.split("{")
-        
-        for item in text_list:
-            if "}" in item:
-                item_list = item.split("}")
-                final_text.append(eval(item_list[0]))
-                if len(item_list[1]) > 0:
-                    final_text.append(item_list[1])
-            else:
-                final_text.append(item)
-
-        # Pass the final_text to the annotated_text function
-        annotated_text(*final_text)
-        
-        with st.expander("See annotations"):
-            st.write(combined_entity_info_dictionary)
-
-        with st.expander("Here is the final JSON-LD"):
-            st.json(json_ld_data)  # Output JSON-LD    
\ No newline at end of file
diff --git a/spaces/XAI/CHM-Corr/model/base/geometry.py b/spaces/XAI/CHM-Corr/model/base/geometry.py
deleted file mode 100644
index 41363601169587951572bae74183459f14819acb..0000000000000000000000000000000000000000
--- a/spaces/XAI/CHM-Corr/model/base/geometry.py
+++ /dev/null
@@ -1,133 +0,0 @@
-r""" Provides functions that manipulate boxes and points """
-
-import math
-
-import torch.nn.functional as F
-import torch
-
-
-class Geometry(object):
-
-    @classmethod
-    def initialize(cls, img_size):
-        cls.img_size = img_size
-
-        cls.spatial_side = int(img_size / 8)
-        norm_grid1d = torch.linspace(-1, 1, cls.spatial_side)
-
-        cls.norm_grid_x = norm_grid1d.view(1, -1).repeat(cls.spatial_side, 1).view(1, 1, -1)
-        cls.norm_grid_y = norm_grid1d.view(-1, 1).repeat(1, cls.spatial_side).view(1, 1, -1)
-        cls.grid = torch.stack(list(reversed(torch.meshgrid(norm_grid1d, norm_grid1d)))).permute(1, 2, 0)
-
-        cls.feat_idx = torch.arange(0, cls.spatial_side).float()
-
-    @classmethod
-    def normalize_kps(cls, kps):
-        kps = kps.clone().detach()
-        kps[kps != -2] -= (cls.img_size // 2)
-        kps[kps != -2] /= (cls.img_size // 2)
-        return kps
-
-    @classmethod
-    def unnormalize_kps(cls, kps):
-        kps = kps.clone().detach()
-        kps[kps != -2] *= (cls.img_size // 2)
-        kps[kps != -2] += (cls.img_size // 2)
-        return kps
-
-    @classmethod
-    def attentive_indexing(cls, kps, thres=0.1):
-        r"""kps: normalized keypoints x, y (N, 2)
-            returns attentive index map(N, spatial_side, spatial_side)
-        """
-        nkps = kps.size(0)
-        kps = kps.view(nkps, 1, 1, 2)
-
-        eps = 1e-5
-        attmap = (cls.grid.unsqueeze(0).repeat(nkps, 1, 1, 1) - kps).pow(2).sum(dim=3)
-        attmap = (attmap + eps).pow(0.5)
-        attmap = (thres - attmap).clamp(min=0).view(nkps, -1)
-        attmap = attmap / attmap.sum(dim=1, keepdim=True)
-        attmap = attmap.view(nkps, cls.spatial_side, cls.spatial_side)
-
-        return attmap
-
-    @classmethod
-    def apply_gaussian_kernel(cls, corr, sigma=17):
-        bsz, side, side = corr.size()
-
-        center = corr.max(dim=2)[1]
-        center_y = center // cls.spatial_side
-        center_x = center % cls.spatial_side
-
-        y = cls.feat_idx.view(1, 1, cls.spatial_side).repeat(bsz, center_y.size(1), 1) - center_y.unsqueeze(2)
-        x = cls.feat_idx.view(1, 1, cls.spatial_side).repeat(bsz, center_x.size(1), 1) - center_x.unsqueeze(2)
-
-        y = y.unsqueeze(3).repeat(1, 1, 1, cls.spatial_side)
-        x = x.unsqueeze(2).repeat(1, 1, cls.spatial_side, 1)
-
-        gauss_kernel = torch.exp(-(x.pow(2) + y.pow(2)) / (2 * sigma ** 2))
-        filtered_corr = gauss_kernel * corr.view(bsz, -1, cls.spatial_side, cls.spatial_side)
-        filtered_corr = filtered_corr.view(bsz, side, side)
-
-        return filtered_corr
-
-    @classmethod
-    def transfer_kps(cls, confidence_ts, src_kps, n_pts, normalized):
-        r""" Transfer keypoints by weighted average """
-
-        if not normalized:
-            src_kps = Geometry.normalize_kps(src_kps)
-        confidence_ts = cls.apply_gaussian_kernel(confidence_ts)
-
-        pdf = F.softmax(confidence_ts, dim=2)
-        prd_x = (pdf * cls.norm_grid_x).sum(dim=2)
-        prd_y = (pdf * cls.norm_grid_y).sum(dim=2)
-
-        prd_kps = []
-        for idx, (x, y, src_kp, np) in enumerate(zip(prd_x, prd_y, src_kps, n_pts)):
-            max_pts = src_kp.size()[1]
-            prd_xy = torch.stack([x, y]).t()
-
-            src_kp = src_kp[:, :np].t()
-            attmap = cls.attentive_indexing(src_kp).view(np, -1)
-            prd_kp = (prd_xy.unsqueeze(0) * attmap.unsqueeze(-1)).sum(dim=1).t()
-            pads = (torch.zeros((2, max_pts - np)) - 2)
-            prd_kp = torch.cat([prd_kp, pads], dim=1)
-            prd_kps.append(prd_kp)
-
-        return torch.stack(prd_kps)
-
-    @staticmethod
-    def get_coord1d(coord4d, ksz):
-        i, j, k, l = coord4d
-        coord1d = i * (ksz ** 3) + j * (ksz ** 2) + k * (ksz) + l
-        return coord1d
-
-    @staticmethod
-    def get_distance(coord1, coord2):
-        delta_y = int(math.pow(coord1[0] - coord2[0], 2))
-        delta_x = int(math.pow(coord1[1] - coord2[1], 2))
-        dist = delta_y + delta_x
-        return dist
-
-    @staticmethod
-    def interpolate4d(tensor4d, size):
-        bsz, h1, w1, h2, w2 = tensor4d.size()
-        tensor4d = tensor4d.view(bsz, h1, w1, -1).permute(0, 3, 1, 2)
-        tensor4d = F.interpolate(tensor4d, size, mode='bilinear', align_corners=True)
-        tensor4d = tensor4d.view(bsz, h2, w2, -1).permute(0, 3, 1, 2)
-        tensor4d = F.interpolate(tensor4d, size, mode='bilinear', align_corners=True)
-        tensor4d = tensor4d.view(bsz, size[0], size[0], size[0], size[0])
-
-        return tensor4d
-    @staticmethod
-    def init_idx4d(ksz):
-        i0 = torch.arange(0, ksz).repeat(ksz ** 3)
-        i1 = torch.arange(0, ksz).unsqueeze(1).repeat(1, ksz).view(-1).repeat(ksz ** 2)
-        i2 = torch.arange(0, ksz).unsqueeze(1).repeat(1, ksz ** 2).view(-1).repeat(ksz)
-        i3 = torch.arange(0, ksz).unsqueeze(1).repeat(1, ksz ** 3).view(-1)
-        idx4d = torch.stack([i3, i2, i1, i0]).t().numpy()
-
-        return idx4d
-
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/core.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/core.py
deleted file mode 100644
index 67a1da3c821e8934ab555fe8e2e8b9a479cabcca..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/core.py
+++ /dev/null
@@ -1,410 +0,0 @@
-"`fastai.core` contains essential util functions to format and split data"
-from .imports.core import *
-
-warnings.filterwarnings("ignore", message="numpy.dtype size changed")
-warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
-
-AnnealFunc = Callable[[Number,Number,float], Number]
-ArgStar = Collection[Any]
-BatchSamples = Collection[Tuple[Collection[int], int]]
-DataFrameOrChunks = Union[DataFrame, pd.io.parsers.TextFileReader]
-FilePathList = Collection[Path]
-Floats = Union[float, Collection[float]]
-ImgLabel = str
-ImgLabels = Collection[ImgLabel]
-IntsOrStrs = Union[int, Collection[int], str, Collection[str]]
-KeyFunc = Callable[[int], int]
-KWArgs = Dict[str,Any]
-ListOrItem = Union[Collection[Any],int,float,str]
-ListRules = Collection[Callable[[str],str]]
-ListSizes = Collection[Tuple[int,int]]
-NPArrayableList = Collection[Union[np.ndarray, list]]
-NPArrayList = Collection[np.ndarray]
-NPArrayMask = np.ndarray
-NPImage = np.ndarray
-OptDataFrame = Optional[DataFrame]
-OptListOrItem = Optional[ListOrItem]
-OptRange = Optional[Tuple[float,float]]
-OptStrTuple = Optional[Tuple[str,str]]
-OptStats = Optional[Tuple[np.ndarray, np.ndarray]]
-PathOrStr = Union[Path,str]
-PathLikeOrBinaryStream = Union[PathOrStr, BufferedWriter, BytesIO]
-PBar = Union[MasterBar, ProgressBar]
-Point=Tuple[float,float]
-Points=Collection[Point]
-Sizes = List[List[int]]
-SplitArrayList = List[Tuple[np.ndarray,np.ndarray]]
-StartOptEnd=Union[float,Tuple[float,float]]
-StrList = Collection[str]
-Tokens = Collection[Collection[str]]
-OptStrList = Optional[StrList]
-np.set_printoptions(precision=6, threshold=50, edgeitems=4, linewidth=120)
-
-def num_cpus()->int:
-    "Get number of cpus"
-    try:                   return len(os.sched_getaffinity(0))
-    except AttributeError: return os.cpu_count()
-
-_default_cpus = min(16, num_cpus())
-defaults = SimpleNamespace(cpus=_default_cpus, cmap='viridis', return_fig=False, silent=False)
-
-def is_listy(x:Any)->bool: return isinstance(x, (tuple,list))
-def is_tuple(x:Any)->bool: return isinstance(x, tuple)
-def is_dict(x:Any)->bool: return isinstance(x, dict)
-def is_pathlike(x:Any)->bool: return isinstance(x, (str,Path))
-def noop(x): return x
-
-class PrePostInitMeta(type):
-    "A metaclass that calls optional `__pre_init__` and `__post_init__` methods"
-    def __new__(cls, name, bases, dct):
-        x = super().__new__(cls, name, bases, dct)
-        old_init = x.__init__
-        def _pass(self): pass
-        @functools.wraps(old_init)
-        def _init(self,*args,**kwargs):
-            self.__pre_init__()
-            old_init(self, *args,**kwargs)
-            self.__post_init__()
-        x.__init__ = _init
-        if not hasattr(x,'__pre_init__'):  x.__pre_init__  = _pass
-        if not hasattr(x,'__post_init__'): x.__post_init__ = _pass
-        return x
-
-def chunks(l:Collection, n:int)->Iterable:
-    "Yield successive `n`-sized chunks from `l`."
-    for i in range(0, len(l), n): yield l[i:i+n]
-
-def recurse(func:Callable, x:Any, *args, **kwargs)->Any:
-    if is_listy(x): return [recurse(func, o, *args, **kwargs) for o in x]
-    if is_dict(x):  return {k: recurse(func, v, *args, **kwargs) for k,v in x.items()}
-    return func(x, *args, **kwargs)
-
-def first_el(x: Any)->Any:
-    "Recursively get the first element of `x`."
-    if is_listy(x): return first_el(x[0])
-    if is_dict(x):  return first_el(x[list(x.keys())[0]])
-    return x
-
-def to_int(b:Any)->Union[int,List[int]]:
-    "Recursively convert `b` to an int or list/dict of ints; raises exception if not convertible."
-    return recurse(lambda x: int(x), b)
-
-def ifnone(a:Any,b:Any)->Any:
-    "`a` if `a` is not None, otherwise `b`."
-    return b if a is None else a
-
-def is1d(a:Collection)->bool:
-    "Return `True` if `a` is one-dimensional"
-    return len(a.shape) == 1 if hasattr(a, 'shape') else len(np.array(a).shape) == 1
-
-def uniqueify(x:Series, sort:bool=False)->List:
-    "Return sorted unique values of `x`."
-    res = list(OrderedDict.fromkeys(x).keys())
-    if sort: res.sort()
-    return res
-
-def idx_dict(a):
-    "Create a dictionary value to index from `a`."
-    return {v:k for k,v in enumerate(a)}
-
-def find_classes(folder:Path)->FilePathList:
-    "List of label subdirectories in imagenet-style `folder`."
-    classes = [d for d in folder.iterdir()
-               if d.is_dir() and not d.name.startswith('.')]
-    assert(len(classes)>0)
-    return sorted(classes, key=lambda d: d.name)
-
-def arrays_split(mask:NPArrayMask, *arrs:NPArrayableList)->SplitArrayList:
-    "Given `arrs` is [a,b,...] and `mask`index - return[(a[mask],a[~mask]),(b[mask],b[~mask]),...]."
-    assert all([len(arr)==len(arrs[0]) for arr in arrs]), 'All arrays should have same length'
-    mask = array(mask)
-    return list(zip(*[(a[mask],a[~mask]) for a in map(np.array, arrs)]))
-
-def random_split(valid_pct:float, *arrs:NPArrayableList)->SplitArrayList:
-    "Randomly split `arrs` with `valid_pct` ratio. good for creating validation set."
-    assert (valid_pct>=0 and valid_pct<=1), 'Validation set percentage should be between 0 and 1'
-    is_train = np.random.uniform(size=(len(arrs[0]),)) > valid_pct
-    return arrays_split(is_train, *arrs)
-
-def listify(p:OptListOrItem=None, q:OptListOrItem=None):
-    "Make `p` listy and the same length as `q`."
-    if p is None: p=[]
-    elif isinstance(p, str):          p = [p]
-    elif not isinstance(p, Iterable): p = [p]
-    #Rank 0 tensors in PyTorch are Iterable but don't have a length.
-    else:
-        try: a = len(p)
-        except: p = [p]
-    n = q if type(q)==int else len(p) if q is None else len(q)
-    if len(p)==1: p = p * n
-    assert len(p)==n, f'List len mismatch ({len(p)} vs {n})'
-    return list(p)
-
-_camel_re1 = re.compile('(.)([A-Z][a-z]+)')
-_camel_re2 = re.compile('([a-z0-9])([A-Z])')
-def camel2snake(name:str)->str:
-    "Change `name` from camel to snake style."
-    s1 = re.sub(_camel_re1, r'\1_\2', name)
-    return re.sub(_camel_re2, r'\1_\2', s1).lower()
-
-def even_mults(start:float, stop:float, n:int)->np.ndarray:
-    "Build log-stepped array from `start` to `stop` in `n` steps."
-    mult = stop/start
-    step = mult**(1/(n-1))
-    return np.array([start*(step**i) for i in range(n)])
-
-def extract_kwargs(names:Collection[str], kwargs:KWArgs):
-    "Extract the keys in `names` from the `kwargs`."
-    new_kwargs = {}
-    for arg_name in names:
-        if arg_name in kwargs:
-            arg_val = kwargs.pop(arg_name)
-            new_kwargs[arg_name] = arg_val
-    return new_kwargs, kwargs
-
-def partition(a:Collection, sz:int)->List[Collection]:
-    "Split iterables `a` in equal parts of size `sz`"
-    return [a[i:i+sz] for i in range(0, len(a), sz)]
-
-def partition_by_cores(a:Collection, n_cpus:int)->List[Collection]:
-    "Split data in `a` equally among `n_cpus` cores"
-    return partition(a, len(a)//n_cpus + 1)
-
-def series2cat(df:DataFrame, *col_names):
-    "Categorifies the columns `col_names` in `df`."
-    for c in listify(col_names): df[c] = df[c].astype('category').cat.as_ordered()
-
-TfmList = Union[Callable, Collection[Callable]]
-
-class ItemBase():
-    "Base item type in the fastai library."
-    def __init__(self, data:Any): self.data=self.obj=data
-    def __repr__(self)->str: return f'{self.__class__.__name__} {str(self)}'
-    def show(self, ax:plt.Axes, **kwargs):
-        "Subclass this method if you want to customize the way this `ItemBase` is shown on `ax`."
-        ax.set_title(str(self))
-    def apply_tfms(self, tfms:Collection, **kwargs):
-        "Subclass this method if you want to apply data augmentation with `tfms` to this `ItemBase`."
-        if tfms: raise Exception(f"Not implemented: you can't apply transforms to this type of item ({self.__class__.__name__})")
-        return self
-    def __eq__(self, other): return recurse_eq(self.data, other.data)
-
-def recurse_eq(arr1, arr2):
-    if is_listy(arr1): return is_listy(arr2) and len(arr1) == len(arr2) and np.all([recurse_eq(x,y) for x,y in zip(arr1,arr2)])
-    else:              return np.all(np.atleast_1d(arr1 == arr2))
-        
-def download_url(url:str, dest:str, overwrite:bool=False, pbar:ProgressBar=None,
-                 show_progress=True, chunk_size=1024*1024, timeout=4, retries=5)->None:
-    "Download `url` to `dest` unless it exists and not `overwrite`."
-    if os.path.exists(dest) and not overwrite: return
-
-    s = requests.Session()
-    s.mount('http://',requests.adapters.HTTPAdapter(max_retries=retries))
-    u = s.get(url, stream=True, timeout=timeout)
-    try: file_size = int(u.headers["Content-Length"])
-    except: show_progress = False
-
-    with open(dest, 'wb') as f:
-        nbytes = 0
-        if show_progress: pbar = progress_bar(range(file_size), auto_update=False, leave=False, parent=pbar)
-        try:
-            for chunk in u.iter_content(chunk_size=chunk_size):
-                nbytes += len(chunk)
-                if show_progress: pbar.update(nbytes)
-                f.write(chunk)
-        except requests.exceptions.ConnectionError as e:
-            fname = url.split('/')[-1]
-            from fastai.datasets import Config
-            data_dir = Config().data_path()
-            timeout_txt =(f'\n Download of {url} has failed after {retries} retries\n'
-                          f' Fix the download manually:\n'
-                          f'$ mkdir -p {data_dir}\n'
-                          f'$ cd {data_dir}\n'
-                          f'$ wget -c {url}\n'
-                          f'$ tar -zxvf {fname}\n\n'
-                          f'And re-run your code once the download is successful\n')
-            print(timeout_txt)
-            import sys;sys.exit(1)
-
-def range_of(x):
-    "Create a range from 0 to `len(x)`."
-    return list(range(len(x)))
-def arange_of(x):
-    "Same as `range_of` but returns an array."
-    return np.arange(len(x))
-
-Path.ls = lambda x: list(x.iterdir())
-
-def join_path(fname:PathOrStr, path:PathOrStr='.')->Path:
-    "Return `Path(path)/Path(fname)`, `path` defaults to current dir."
-    return Path(path)/Path(fname)
-
-def join_paths(fnames:FilePathList, path:PathOrStr='.')->Collection[Path]:
-    "Join `path` to every file name in `fnames`."
-    path = Path(path)
-    return [join_path(o,path) for o in fnames]
-
-def loadtxt_str(path:PathOrStr)->np.ndarray:
-    "Return `ndarray` of `str` of lines of text from `path`."
-    with open(path, 'r') as f: lines = f.readlines()
-    return np.array([l.strip() for l in lines])
-
-def save_texts(fname:PathOrStr, texts:Collection[str]):
-    "Save in `fname` the content of `texts`."
-    with open(fname, 'w') as f:
-        for t in texts: f.write(f'{t}\n')
-
-def df_names_to_idx(names:IntsOrStrs, df:DataFrame):
-    "Return the column indexes of `names` in `df`."
-    if not is_listy(names): names = [names]
-    if isinstance(names[0], int): return names
-    return [df.columns.get_loc(c) for c in names]
-
-def one_hot(x:Collection[int], c:int):
-    "One-hot encode `x` with `c` classes."
-    res = np.zeros((c,), np.float32)
-    res[listify(x)] = 1.
-    return res
-
-def index_row(a:Union[Collection,pd.DataFrame,pd.Series], idxs:Collection[int])->Any:
-    "Return the slice of `a` corresponding to `idxs`."
-    if a is None: return a
-    if isinstance(a,(pd.DataFrame,pd.Series)):
-        res = a.iloc[idxs]
-        if isinstance(res,(pd.DataFrame,pd.Series)): return res.copy()
-        return res
-    return a[idxs]
-
-def func_args(func)->bool:
-    "Return the arguments of `func`."
-    code = func.__code__
-    return code.co_varnames[:code.co_argcount]
-
-def has_arg(func, arg)->bool:
-    "Check if `func` accepts `arg`."
-    return arg in func_args(func)
-
-def split_kwargs_by_func(kwargs, func):
-    "Split `kwargs` between those expected by `func` and the others."
-    args = func_args(func)
-    func_kwargs = {a:kwargs.pop(a) for a in args if a in kwargs}
-    return func_kwargs, kwargs
-
-def array(a, dtype:type=None, **kwargs)->np.ndarray:
-    "Same as `np.array` but also handles generators. `kwargs` are passed to `np.array` with `dtype`."
-    if not isinstance(a, collections.abc.Sized) and not getattr(a,'__array_interface__',False):
-        a = list(a)
-    if np.int_==np.int32 and dtype is None and is_listy(a) and len(a) and isinstance(a[0],int):
-        dtype=np.int64
-    return np.array(a, dtype=dtype, **kwargs)
-
-class EmptyLabel(ItemBase):
-    "Should be used for a dummy label."
-    def __init__(self): self.obj,self.data = 0,0
-    def __str__(self):  return ''
-    def __hash__(self): return hash(str(self))
-
-class Category(ItemBase):
-    "Basic class for single classification labels."
-    def __init__(self,data,obj): self.data,self.obj = data,obj
-    def __int__(self):  return int(self.data)
-    def __str__(self):  return str(self.obj)
-    def __hash__(self): return hash(str(self))
-
-class MultiCategory(ItemBase):
-    "Basic class for multi-classification labels."
-    def __init__(self,data,obj,raw): self.data,self.obj,self.raw = data,obj,raw
-    def __str__(self):  return ';'.join([str(o) for o in self.obj])
-    def __hash__(self): return hash(str(self))
-
-class FloatItem(ItemBase):
-    "Basic class for float items."
-    def __init__(self,obj): self.data,self.obj = np.array(obj).astype(np.float32),obj
-    def __str__(self):  return str(self.obj)
-    def __hash__(self): return hash(str(self))
-
-def _treat_html(o:str)->str:
-    o = str(o)
-    to_replace = {'\n':'\\n', '<':'&lt;', '>':'&gt;', '&':'&amp;'}
-    for k,v in to_replace.items(): o = o.replace(k, v)
-    return o
-
-def text2html_table(items:Collection[Collection[str]])->str:
-    "Put the texts in `items` in an HTML table, `widths` are the widths of the columns in %."
-    html_code = f"""<table border="1" class="dataframe">"""
-    html_code += f"""  <thead>\n    <tr style="text-align: right;">\n"""
-    for i in items[0]: html_code += f"      <th>{_treat_html(i)}</th>"
-    html_code += f"    </tr>\n  </thead>\n  <tbody>"
-    html_code += "  <tbody>"
-    for line in items[1:]:
-        html_code += "    <tr>"
-        for i in line: html_code += f"      <td>{_treat_html(i)}</td>"
-        html_code += "    </tr>"
-    html_code += "  </tbody>\n</table>"
-    return html_code
-
-def parallel(func, arr:Collection, max_workers:int=None, leave=False):
-    "Call `func` on every element of `arr` in parallel using `max_workers`."
-    max_workers = ifnone(max_workers, defaults.cpus)
-    if max_workers<2: results = [func(o,i) for i,o in progress_bar(enumerate(arr), total=len(arr), leave=leave)]
-    else:
-        with ProcessPoolExecutor(max_workers=max_workers) as ex:
-            futures = [ex.submit(func,o,i) for i,o in enumerate(arr)]
-            results = []
-            for f in progress_bar(concurrent.futures.as_completed(futures), total=len(arr), leave=leave): 
-                results.append(f.result())
-    if any([o is not None for o in results]): return results
-
-def subplots(rows:int, cols:int, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, title=None, **kwargs):
-    "Like `plt.subplots` but with consistent axs shape, `kwargs` passed to `fig.suptitle` with `title`"
-    figsize = ifnone(figsize, (imgsize*cols, imgsize*rows))
-    fig, axs = plt.subplots(rows,cols,figsize=figsize)
-    if rows==cols==1: axs = [[axs]] # subplots(1,1) returns Axes, not [Axes]
-    elif (rows==1 and cols!=1) or (cols==1 and rows!=1): axs = [axs]
-    if title is not None: fig.suptitle(title, **kwargs)
-    return array(axs)
-
-def show_some(items:Collection, n_max:int=5, sep:str=','):
-    "Return the representation of the first  `n_max` elements in `items`."
-    if items is None or len(items) == 0: return ''
-    res = sep.join([f'{o}' for o in items[:n_max]])
-    if len(items) > n_max: res += '...'
-    return res
-
-def get_tmp_file(dir=None):
-    "Create and return a tmp filename, optionally at a specific path. `os.remove` when done with it."
-    with tempfile.NamedTemporaryFile(delete=False, dir=dir) as f: return f.name
-
-def compose(funcs:List[Callable])->Callable:
-    "Compose `funcs`"
-    def compose_(funcs, x, *args, **kwargs):
-        for f in listify(funcs): x = f(x, *args, **kwargs)
-        return x
-    return partial(compose_, funcs)
-
-class PrettyString(str):
-    "Little hack to get strings to show properly in Jupyter."
-    def __repr__(self): return self
-
-def float_or_x(x):
-    "Tries to convert to float, returns x if it can't"
-    try:   return float(x)
-    except:return x
-
-def bunzip(fn:PathOrStr):
-    "bunzip `fn`, raising exception if output already exists"
-    fn = Path(fn)
-    assert fn.exists(), f"{fn} doesn't exist"
-    out_fn = fn.with_suffix('')
-    assert not out_fn.exists(), f"{out_fn} already exists"
-    with bz2.BZ2File(fn, 'rb') as src, out_fn.open('wb') as dst:
-        for d in iter(lambda: src.read(1024*1024), b''): dst.write(d)
-
-@contextmanager
-def working_directory(path:PathOrStr):
-    "Change working directory to `path` and return to previous on exit."
-    prev_cwd = Path.cwd()
-    os.chdir(path)
-    try: yield
-    finally: os.chdir(prev_cwd)
-
diff --git a/spaces/Xenova/react-translator/assets/index-138f5db2.js b/spaces/Xenova/react-translator/assets/index-138f5db2.js
deleted file mode 100644
index 5eee70594bb46ea8a00d0fdeb482af31f9c1d6dd..0000000000000000000000000000000000000000
--- a/spaces/Xenova/react-translator/assets/index-138f5db2.js
+++ /dev/null
@@ -1,40 +0,0 @@
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- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
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diff --git a/spaces/XzJosh/Azuma-Bert-VITS2/text/cleaner.py b/spaces/XzJosh/Azuma-Bert-VITS2/text/cleaner.py
deleted file mode 100644
index 64bd5f7296f66c94f3a335666c53706bb5fe5b39..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Azuma-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/XzJosh/yoyo-Bert-VITS2/train_ms.py b/spaces/XzJosh/yoyo-Bert-VITS2/train_ms.py
deleted file mode 100644
index 5d109003d40497ea4493e7c73f47c1eb7370a81e..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/yoyo-Bert-VITS2/train_ms.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-import shutil
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-from tqdm import tqdm
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import commons
-import utils
-from data_utils import (
-    TextAudioSpeakerLoader,
-    TextAudioSpeakerCollate,
-    DistributedBucketSampler
-)
-from models import (
-    SynthesizerTrn,
-    MultiPeriodDiscriminator,
-    DurationDiscriminator,
-)
-from losses import (
-    generator_loss,
-    discriminator_loss,
-    feature_loss,
-    kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-
-torch.backends.cudnn.benchmark = True
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-torch.set_float32_matmul_precision('medium')
-global_step = 0
-
-
-def main():
-    """Assume Single Node Multi GPUs Training Only"""
-    assert torch.cuda.is_available(), "CPU training is not allowed."
-
-    n_gpus = torch.cuda.device_count()
-    os.environ['MASTER_ADDR'] = 'localhost'
-    os.environ['MASTER_PORT'] = '65280'
-
-    hps = utils.get_hparams()
-    if not hps.cont:
-           shutil.copy('./pretrained_models/D_0.pth','./logs/OUTPUT_MODEL/D_0.pth')
-           shutil.copy('./pretrained_models/G_0.pth','./logs/OUTPUT_MODEL/G_0.pth')
-           shutil.copy('./pretrained_models/DUR_0.pth','./logs/OUTPUT_MODEL/DUR_0.pth')
-    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.model_dir)
-        logger.info(hps)
-        utils.check_git_hash(hps.model_dir)
-        writer = SummaryWriter(log_dir=hps.model_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-    dist.init_process_group(backend=  'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
-    torch.manual_seed(hps.train.seed)
-    torch.cuda.set_device(rank)
-
-    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
-    train_sampler = DistributedBucketSampler(
-        train_dataset,
-        hps.train.batch_size,
-        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
-        num_replicas=n_gpus,
-        rank=rank,
-        shuffle=True)
-    collate_fn = TextAudioSpeakerCollate()
-    train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True,
-                              collate_fn=collate_fn, batch_sampler=train_sampler)
-    if rank == 0:
-        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
-        eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
-                                 batch_size=1, pin_memory=True,
-                                 drop_last=False, collate_fn=collate_fn)
-    if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True:
-        print("Using noise scaled MAS for VITS2")
-        use_noise_scaled_mas = True
-        mas_noise_scale_initial = 0.01
-        noise_scale_delta = 2e-6
-    else:
-        print("Using normal MAS for VITS1")
-        use_noise_scaled_mas = False
-        mas_noise_scale_initial = 0.0
-        noise_scale_delta = 0.0
-    if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True:
-        print("Using duration discriminator for VITS2")
-        use_duration_discriminator = True
-        net_dur_disc = DurationDiscriminator(
-         hps.model.hidden_channels, 
-         hps.model.hidden_channels, 
-         3, 
-         0.1, 
-         gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
-         ).cuda(rank)
-    if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
-        if hps.data.n_speakers == 0:
-            raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
-        use_spk_conditioned_encoder = True
-    else:
-        print("Using normal encoder for VITS1")
-        use_spk_conditioned_encoder = False
-
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        mas_noise_scale_initial = mas_noise_scale_initial,
-        noise_scale_delta = noise_scale_delta,
-        **hps.model).cuda(rank)
-
-    freeze_enc = getattr(hps.model, "freeze_enc", False)
-    if freeze_enc:
-        print("freeze encoder !!!")
-        for param in net_g.enc_p.parameters():
-            param.requires_grad = False
-
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-    optim_g = torch.optim.AdamW(
-        filter(lambda p: p.requires_grad, net_g.parameters()),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    optim_d = torch.optim.AdamW(
-        net_d.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    if net_dur_disc is not None:
-        optim_dur_disc = torch.optim.AdamW(
-        net_dur_disc.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    else:
-        optim_dur_disc = None
-    net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
-    net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
-    if net_dur_disc is not None:
-        net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
-
-    pretrain_dir = None
-    if pretrain_dir is None:
-        try:
-            if net_dur_disc is not None:
-                _, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont)
-            _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
-                                                   optim_g, skip_optimizer=not hps.cont)
-            _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
-                                                   optim_d, skip_optimizer=not hps.cont)
-            
-            epoch_str = max(epoch_str, 1)
-            global_step = (epoch_str - 1) * len(train_loader)
-        except Exception as e:
-            print(e)
-            epoch_str = 1
-            global_step = 0
-    else:
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
-                                                   optim_g, True)
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
-                                                   optim_d, True)
-
-
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    if net_dur_disc is not None:
-        scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
-    else:
-        scheduler_dur_disc = None
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        if rank == 0:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
-        else:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None)
-        scheduler_g.step()
-        scheduler_d.step()
-        if net_dur_disc is not None:
-            scheduler_dur_disc.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-    net_g, net_d, net_dur_disc = nets
-    optim_g, optim_d, optim_dur_disc = optims
-    scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
-    train_loader, eval_loader = loaders
-    if writers is not None:
-        writer, writer_eval = writers
-
-    train_loader.batch_sampler.set_epoch(epoch)
-    global global_step
-
-    net_g.train()
-    net_d.train()
-    if net_dur_disc is not None:
-        net_dur_disc.train()
-    for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)):
-        if net_g.module.use_noise_scaled_mas:
-            current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step
-            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
-        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
-        spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
-        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
-        speakers = speakers.cuda(rank, non_blocking=True)
-        tone = tone.cuda(rank, non_blocking=True)
-        language = language.cuda(rank, non_blocking=True)
-        bert = bert.cuda(rank, non_blocking=True)
-
-        with autocast(enabled=hps.train.fp16_run):
-            y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
-                (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert)
-            mel = spec_to_mel_torch(
-                spec,
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax)
-            y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax
-            )
-
-            y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size)  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-            with autocast(enabled=False):
-                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-                loss_disc_all = loss_disc
-            if net_dur_disc is not None:
-                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
-                with autocast(enabled=False):
-                 # TODO: I think need to mean using the mask, but for now, just mean all
-                    loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
-                    loss_dur_disc_all = loss_dur_disc
-                optim_dur_disc.zero_grad()
-                scaler.scale(loss_dur_disc_all).backward()
-                scaler.unscale_(optim_dur_disc)
-                grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
-                scaler.step(optim_dur_disc)
-
-        optim_d.zero_grad()
-        scaler.scale(loss_disc_all).backward()
-        scaler.unscale_(optim_d)
-        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-        scaler.step(optim_d)
-
-        with autocast(enabled=hps.train.fp16_run):
-            # Generator
-            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-            if net_dur_disc is not None:
-                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
-            with autocast(enabled=False):
-                loss_dur = torch.sum(l_length.float())
-                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
-                loss_fm = feature_loss(fmap_r, fmap_g)
-                loss_gen, losses_gen = generator_loss(y_d_hat_g)
-                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
-                if net_dur_disc is not None:
-                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
-                    loss_gen_all += loss_dur_gen
-        optim_g.zero_grad()
-        scaler.scale(loss_gen_all).backward()
-        scaler.unscale_(optim_g)
-        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-        scaler.step(optim_g)
-        scaler.update()
-
-        if rank == 0:
-            if global_step % hps.train.log_interval == 0:
-                lr = optim_g.param_groups[0]['lr']
-                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
-                logger.info('Train Epoch: {} [{:.0f}%]'.format(
-                    epoch,
-                    100. * batch_idx / len(train_loader)))
-                logger.info([x.item() for x in losses] + [global_step, lr])
-
-                scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
-                               "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-                scalar_dict.update(
-                    {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
-                scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-                scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-                scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-          
-                image_dict = {
-                    "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
-                    "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-                    "all/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy())
-                }
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict)
-
-            if global_step % hps.train.eval_interval == 0:
-                evaluate(hps, net_g, eval_loader, writer_eval)
-                utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-                utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-                if net_dur_disc is not None:
-                    utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)))    
-                keep_ckpts = getattr(hps.train, 'keep_ckpts', 5)
-                if keep_ckpts > 0:
-                    utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
-
-
-        global_step += 1
-
-    if rank == 0:
-        logger.info('====> Epoch: {}'.format(epoch))
-
-
-
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    image_dict = {}
-    audio_dict = {}
-    print("Evaluating ...")
-    with torch.no_grad():
-        for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader):
-            x, x_lengths = x.cuda(), x_lengths.cuda()
-            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
-            y, y_lengths = y.cuda(), y_lengths.cuda()
-            speakers = speakers.cuda()
-            bert = bert.cuda()
-            tone = tone.cuda()
-            language = language.cuda()
-            for use_sdp in [True, False]:
-                y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0)
-                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
-
-                mel = spec_to_mel_torch(
-                    spec,
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax)
-                y_hat_mel = mel_spectrogram_torch(
-                    y_hat.squeeze(1).float(),
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.hop_length,
-                    hps.data.win_length,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax
-                )
-                image_dict.update({
-                    f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
-                })
-                audio_dict.update({
-                    f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]]
-                })
-                image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
-                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]})
-
-    utils.summarize(
-        writer=writer_eval,
-        global_step=global_step,
-        images=image_dict,
-        audios=audio_dict,
-        audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/YUANAI/DiffspeechResearch/modules/commons/normalizing_flow/utils.py b/spaces/YUANAI/DiffspeechResearch/modules/commons/normalizing_flow/utils.py
deleted file mode 100644
index 7eb56ec514bff822ba1a19a6474207ed82492410..0000000000000000000000000000000000000000
--- a/spaces/YUANAI/DiffspeechResearch/modules/commons/normalizing_flow/utils.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import torch
-
-
-def squeeze(x, x_mask=None, n_sqz=2):
-    b, c, t = x.size()
-
-    t = (t // n_sqz) * n_sqz
-    x = x[:, :, :t]
-    x_sqz = x.view(b, c, t // n_sqz, n_sqz)
-    x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
-
-    if x_mask is not None:
-        x_mask = x_mask[:, :, n_sqz - 1::n_sqz]
-    else:
-        x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
-    return x_sqz * x_mask, x_mask
-
-
-def unsqueeze(x, x_mask=None, n_sqz=2):
-    b, c, t = x.size()
-
-    x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
-    x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
-
-    if x_mask is not None:
-        x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
-    else:
-        x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
-    return x_unsqz * x_mask, x_mask
diff --git a/spaces/YazawaSunrise/so-vits-svc-LoveLive/losses.py b/spaces/YazawaSunrise/so-vits-svc-LoveLive/losses.py
deleted file mode 100644
index 41f9be6980713a46824ae9ec5eb8fd7c515d89c5..0000000000000000000000000000000000000000
--- a/spaces/YazawaSunrise/so-vits-svc-LoveLive/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-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):
-      rl = rl.float().detach()
-      gl = gl.float()
-      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):
-    dr = dr.float()
-    dg = dg.float()
-    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:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-  #print(logs_p)
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/schedulers/scheduling_dpmsolver_multistep.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/schedulers/scheduling_dpmsolver_multistep.py
deleted file mode 100644
index b7d0838026913b751128ad23925cef1e978fa906..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/schedulers/scheduling_dpmsolver_multistep.py
+++ /dev/null
@@ -1,533 +0,0 @@
-# Copyright 2022 TSAIL Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver
-
-import math
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, deprecate
-from .scheduling_utils import SchedulerMixin, SchedulerOutput
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, 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].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-
-    def alpha_bar(time_step):
-        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    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 torch.tensor(betas, dtype=torch.float32)
-
-
-class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
-    """
-    DPM-Solver (and the improved version DPM-Solver++) is a fast dedicated high-order solver for diffusion ODEs with
-    the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality
-    samples, and it can generate quite good samples even in only 10 steps.
-
-    For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095
-
-    Currently, we support the multistep DPM-Solver for both noise prediction models and data prediction models. We
-    recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling.
-
-    We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space
-    diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic
-    thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as
-    stable-diffusion).
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        solver_order (`int`, default `2`):
-            the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided
-            sampling, and `solver_order=3` for unconditional sampling.
-        prediction_type (`str`, default `epsilon`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
-            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
-        thresholding (`bool`, default `False`):
-            whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
-            For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to
-            use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion
-            models (such as stable-diffusion).
-        dynamic_thresholding_ratio (`float`, default `0.995`):
-            the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
-            (https://arxiv.org/abs/2205.11487).
-        sample_max_value (`float`, default `1.0`):
-            the threshold value for dynamic thresholding. Valid only when `thresholding=True` and
-            `algorithm_type="dpmsolver++`.
-        algorithm_type (`str`, default `dpmsolver++`):
-            the algorithm type for the solver. Either `dpmsolver` or `dpmsolver++`. The `dpmsolver` type implements the
-            algorithms in https://arxiv.org/abs/2206.00927, and the `dpmsolver++` type implements the algorithms in
-            https://arxiv.org/abs/2211.01095. We recommend to use `dpmsolver++` with `solver_order=2` for guided
-            sampling (e.g. stable-diffusion).
-        solver_type (`str`, default `midpoint`):
-            the solver type for the second-order solver. Either `midpoint` or `heun`. The solver type slightly affects
-            the sample quality, especially for small number of steps. We empirically find that `midpoint` solvers are
-            slightly better, so we recommend to use the `midpoint` type.
-        lower_order_final (`bool`, default `True`):
-            whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically
-            find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10.
-
-    """
-
-    _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
-    _deprecated_kwargs = ["predict_epsilon"]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        solver_order: int = 2,
-        prediction_type: str = "epsilon",
-        thresholding: bool = False,
-        dynamic_thresholding_ratio: float = 0.995,
-        sample_max_value: float = 1.0,
-        algorithm_type: str = "dpmsolver++",
-        solver_type: str = "midpoint",
-        lower_order_final: bool = True,
-        **kwargs,
-    ):
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DPMSolverMultistepScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
-        if predict_epsilon is not None:
-            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
-
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-        # Currently we only support VP-type noise schedule
-        self.alpha_t = torch.sqrt(self.alphas_cumprod)
-        self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
-        self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # settings for DPM-Solver
-        if algorithm_type not in ["dpmsolver", "dpmsolver++"]:
-            raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
-        if solver_type not in ["midpoint", "heun"]:
-            raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
-
-        # setable values
-        self.num_inference_steps = None
-        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
-        self.timesteps = torch.from_numpy(timesteps)
-        self.model_outputs = [None] * solver_order
-        self.lower_order_nums = 0
-
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
-        """
-        Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            device (`str` or `torch.device`, optional):
-                the device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        """
-        self.num_inference_steps = num_inference_steps
-        timesteps = (
-            np.linspace(0, self.num_train_timesteps - 1, num_inference_steps + 1)
-            .round()[::-1][:-1]
-            .copy()
-            .astype(np.int64)
-        )
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-        self.model_outputs = [
-            None,
-        ] * self.config.solver_order
-        self.lower_order_nums = 0
-
-    def convert_model_output(
-        self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor
-    ) -> torch.FloatTensor:
-        """
-        Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs.
-
-        DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to
-        discretize an integral of the data prediction model. So we need to first convert the model output to the
-        corresponding type to match the algorithm.
-
-        Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or
-        DPM-Solver++ for both noise prediction model and data prediction model.
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-
-        Returns:
-            `torch.FloatTensor`: the converted model output.
-        """
-        # DPM-Solver++ needs to solve an integral of the data prediction model.
-        if self.config.algorithm_type == "dpmsolver++":
-            if self.config.prediction_type == "epsilon":
-                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
-                x0_pred = (sample - sigma_t * model_output) / alpha_t
-            elif self.config.prediction_type == "sample":
-                x0_pred = model_output
-            elif self.config.prediction_type == "v_prediction":
-                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
-                x0_pred = alpha_t * sample - sigma_t * model_output
-            else:
-                raise ValueError(
-                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                    " `v_prediction` for the DPMSolverMultistepScheduler."
-                )
-
-            if self.config.thresholding:
-                # Dynamic thresholding in https://arxiv.org/abs/2205.11487
-                orig_dtype = x0_pred.dtype
-                if orig_dtype not in [torch.float, torch.double]:
-                    x0_pred = x0_pred.float()
-                dynamic_max_val = torch.quantile(
-                    torch.abs(x0_pred).reshape((x0_pred.shape[0], -1)), self.config.dynamic_thresholding_ratio, dim=1
-                )
-                dynamic_max_val = torch.maximum(
-                    dynamic_max_val,
-                    self.config.sample_max_value * torch.ones_like(dynamic_max_val).to(dynamic_max_val.device),
-                )[(...,) + (None,) * (x0_pred.ndim - 1)]
-                x0_pred = torch.clamp(x0_pred, -dynamic_max_val, dynamic_max_val) / dynamic_max_val
-                x0_pred = x0_pred.type(orig_dtype)
-            return x0_pred
-        # DPM-Solver needs to solve an integral of the noise prediction model.
-        elif self.config.algorithm_type == "dpmsolver":
-            if self.config.prediction_type == "epsilon":
-                return model_output
-            elif self.config.prediction_type == "sample":
-                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
-                epsilon = (sample - alpha_t * model_output) / sigma_t
-                return epsilon
-            elif self.config.prediction_type == "v_prediction":
-                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
-                epsilon = alpha_t * model_output + sigma_t * sample
-                return epsilon
-            else:
-                raise ValueError(
-                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                    " `v_prediction` for the DPMSolverMultistepScheduler."
-                )
-
-    def dpm_solver_first_order_update(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        prev_timestep: int,
-        sample: torch.FloatTensor,
-    ) -> torch.FloatTensor:
-        """
-        One step for the first-order DPM-Solver (equivalent to DDIM).
-
-        See https://arxiv.org/abs/2206.00927 for the detailed derivation.
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            prev_timestep (`int`): previous discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-
-        Returns:
-            `torch.FloatTensor`: the sample tensor at the previous timestep.
-        """
-        lambda_t, lambda_s = self.lambda_t[prev_timestep], self.lambda_t[timestep]
-        alpha_t, alpha_s = self.alpha_t[prev_timestep], self.alpha_t[timestep]
-        sigma_t, sigma_s = self.sigma_t[prev_timestep], self.sigma_t[timestep]
-        h = lambda_t - lambda_s
-        if self.config.algorithm_type == "dpmsolver++":
-            x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output
-        elif self.config.algorithm_type == "dpmsolver":
-            x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output
-        return x_t
-
-    def multistep_dpm_solver_second_order_update(
-        self,
-        model_output_list: List[torch.FloatTensor],
-        timestep_list: List[int],
-        prev_timestep: int,
-        sample: torch.FloatTensor,
-    ) -> torch.FloatTensor:
-        """
-        One step for the second-order multistep DPM-Solver.
-
-        Args:
-            model_output_list (`List[torch.FloatTensor]`):
-                direct outputs from learned diffusion model at current and latter timesteps.
-            timestep (`int`): current and latter discrete timestep in the diffusion chain.
-            prev_timestep (`int`): previous discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-
-        Returns:
-            `torch.FloatTensor`: the sample tensor at the previous timestep.
-        """
-        t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2]
-        m0, m1 = model_output_list[-1], model_output_list[-2]
-        lambda_t, lambda_s0, lambda_s1 = self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1]
-        alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0]
-        sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0]
-        h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1
-        r0 = h_0 / h
-        D0, D1 = m0, (1.0 / r0) * (m0 - m1)
-        if self.config.algorithm_type == "dpmsolver++":
-            # See https://arxiv.org/abs/2211.01095 for detailed derivations
-            if self.config.solver_type == "midpoint":
-                x_t = (
-                    (sigma_t / sigma_s0) * sample
-                    - (alpha_t * (torch.exp(-h) - 1.0)) * D0
-                    - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1
-                )
-            elif self.config.solver_type == "heun":
-                x_t = (
-                    (sigma_t / sigma_s0) * sample
-                    - (alpha_t * (torch.exp(-h) - 1.0)) * D0
-                    + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1
-                )
-        elif self.config.algorithm_type == "dpmsolver":
-            # See https://arxiv.org/abs/2206.00927 for detailed derivations
-            if self.config.solver_type == "midpoint":
-                x_t = (
-                    (alpha_t / alpha_s0) * sample
-                    - (sigma_t * (torch.exp(h) - 1.0)) * D0
-                    - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1
-                )
-            elif self.config.solver_type == "heun":
-                x_t = (
-                    (alpha_t / alpha_s0) * sample
-                    - (sigma_t * (torch.exp(h) - 1.0)) * D0
-                    - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_order_update(
-        self,
-        model_output_list: List[torch.FloatTensor],
-        timestep_list: List[int],
-        prev_timestep: int,
-        sample: torch.FloatTensor,
-    ) -> torch.FloatTensor:
-        """
-        One step for the third-order multistep DPM-Solver.
-
-        Args:
-            model_output_list (`List[torch.FloatTensor]`):
-                direct outputs from learned diffusion model at current and latter timesteps.
-            timestep (`int`): current and latter discrete timestep in the diffusion chain.
-            prev_timestep (`int`): previous discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-
-        Returns:
-            `torch.FloatTensor`: the sample tensor at the previous timestep.
-        """
-        t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3]
-        m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]
-        lambda_t, lambda_s0, lambda_s1, lambda_s2 = (
-            self.lambda_t[t],
-            self.lambda_t[s0],
-            self.lambda_t[s1],
-            self.lambda_t[s2],
-        )
-        alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0]
-        sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0]
-        h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2
-        r0, r1 = h_0 / h, h_1 / h
-        D0 = m0
-        D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2)
-        D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1)
-        D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1)
-        if self.config.algorithm_type == "dpmsolver++":
-            # See https://arxiv.org/abs/2206.00927 for detailed derivations
-            x_t = (
-                (sigma_t / sigma_s0) * sample
-                - (alpha_t * (torch.exp(-h) - 1.0)) * D0
-                + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1
-                - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2
-            )
-        elif self.config.algorithm_type == "dpmsolver":
-            # See https://arxiv.org/abs/2206.00927 for detailed derivations
-            x_t = (
-                (alpha_t / alpha_s0) * sample
-                - (sigma_t * (torch.exp(h) - 1.0)) * D0
-                - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
-                - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2
-            )
-        return x_t
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        sample: torch.FloatTensor,
-        return_dict: bool = True,
-    ) -> Union[SchedulerOutput, Tuple]:
-        """
-        Step function propagating the sample with the multistep DPM-Solver.
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
-
-        Returns:
-            [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
-            True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-        if self.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        if isinstance(timestep, torch.Tensor):
-            timestep = timestep.to(self.timesteps.device)
-        step_index = (self.timesteps == timestep).nonzero()
-        if len(step_index) == 0:
-            step_index = len(self.timesteps) - 1
-        else:
-            step_index = step_index.item()
-        prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1]
-        lower_order_final = (
-            (step_index == len(self.timesteps) - 1) and self.config.lower_order_final and len(self.timesteps) < 15
-        )
-        lower_order_second = (
-            (step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15
-        )
-
-        model_output = self.convert_model_output(model_output, timestep, sample)
-        for i in range(self.config.solver_order - 1):
-            self.model_outputs[i] = self.model_outputs[i + 1]
-        self.model_outputs[-1] = model_output
-
-        if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:
-            prev_sample = self.dpm_solver_first_order_update(model_output, timestep, prev_timestep, sample)
-        elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:
-            timestep_list = [self.timesteps[step_index - 1], timestep]
-            prev_sample = self.multistep_dpm_solver_second_order_update(
-                self.model_outputs, timestep_list, prev_timestep, sample
-            )
-        else:
-            timestep_list = [self.timesteps[step_index - 2], self.timesteps[step_index - 1], timestep]
-            prev_sample = self.multistep_dpm_solver_third_order_update(
-                self.model_outputs, timestep_list, prev_timestep, sample
-            )
-
-        if self.lower_order_nums < self.config.solver_order:
-            self.lower_order_nums += 1
-
-        if not return_dict:
-            return (prev_sample,)
-
-        return SchedulerOutput(prev_sample=prev_sample)
-
-    def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`torch.FloatTensor`): input sample
-
-        Returns:
-            `torch.FloatTensor`: scaled input sample
-        """
-        return sample
-
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.FloatTensor:
-        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
-        timesteps = timesteps.to(original_samples.device)
-
-        sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
-
-        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
-            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        return noisy_samples
-
-    def __len__(self):
-        return self.config.num_train_timesteps
diff --git a/spaces/Yudha515/Rvc-Models/tests/common_utils/temp_utils.py b/spaces/Yudha515/Rvc-Models/tests/common_utils/temp_utils.py
deleted file mode 100644
index d1e0367e979c8b9fea65472c373916d956ad5aaa..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/tests/common_utils/temp_utils.py
+++ /dev/null
@@ -1,56 +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 os
-import tempfile
-
-
-class TempDirMixin:
-    """Mixin to provide easy access to temp dir.
-    """
-
-    temp_dir_ = None
-
-    @classmethod
-    def get_base_temp_dir(cls):
-        # If AUDIOCRAFT_TEST_DIR is set, use it instead of temporary directory.
-        # this is handy for debugging.
-        key = "AUDIOCRAFT_TEST_DIR"
-        if key in os.environ:
-            return os.environ[key]
-        if cls.temp_dir_ is None:
-            cls.temp_dir_ = tempfile.TemporaryDirectory()
-        return cls.temp_dir_.name
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.temp_dir_ is not None:
-            try:
-                cls.temp_dir_.cleanup()
-                cls.temp_dir_ = None
-            except PermissionError:
-                # On Windows there is a know issue with `shutil.rmtree`,
-                # which fails intermittenly.
-                # https://github.com/python/cpython/issues/74168
-                # Following the above thread, we ignore it.
-                pass
-        super().tearDownClass()
-
-    @property
-    def id(self):
-        return self.__class__.__name__
-
-    def get_temp_path(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-        return path
-
-    def get_temp_dir(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(path, exist_ok=True)
-        return path
diff --git a/spaces/Zaxxced/rvc-random-v2/lib/infer_pack/commons.py b/spaces/Zaxxced/rvc-random-v2/lib/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/Zaxxced/rvc-random-v2/lib/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    device = duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/apc_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/apc_head.py
deleted file mode 100644
index c5aa9368bd5a5a7f1abf8a490a97eb021ab09795..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/decode_heads/apc_head.py
+++ /dev/null
@@ -1,170 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-class ACM(nn.Module):
-    """Adaptive Context Module used in APCNet.
-
-    Args:
-        pool_scale (int): Pooling scale used in Adaptive Context
-            Module to extract region features.
-        fusion (bool): Add one conv to fuse residual feature.
-        in_channels (int): Input channels.
-        channels (int): Channels after modules, before conv_seg.
-        conv_cfg (dict | None): Config of conv layers.
-        norm_cfg (dict | None): Config of norm layers.
-        act_cfg (dict): Config of activation layers.
-    """
-
-    def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
-                 norm_cfg, act_cfg):
-        super(ACM, self).__init__()
-        self.pool_scale = pool_scale
-        self.fusion = fusion
-        self.in_channels = in_channels
-        self.channels = channels
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        self.pooled_redu_conv = ConvModule(
-            self.in_channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        self.input_redu_conv = ConvModule(
-            self.in_channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        self.global_info = ConvModule(
-            self.channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        self.gla = nn.Conv2d(self.channels, self.pool_scale**2, 1, 1, 0)
-
-        self.residual_conv = ConvModule(
-            self.channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        if self.fusion:
-            self.fusion_conv = ConvModule(
-                self.channels,
-                self.channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg)
-
-    def forward(self, x):
-        """Forward function."""
-        pooled_x = F.adaptive_avg_pool2d(x, self.pool_scale)
-        # [batch_size, channels, h, w]
-        x = self.input_redu_conv(x)
-        # [batch_size, channels, pool_scale, pool_scale]
-        pooled_x = self.pooled_redu_conv(pooled_x)
-        batch_size = x.size(0)
-        # [batch_size, pool_scale * pool_scale, channels]
-        pooled_x = pooled_x.view(batch_size, self.channels,
-                                 -1).permute(0, 2, 1).contiguous()
-        # [batch_size, h * w, pool_scale * pool_scale]
-        affinity_matrix = self.gla(x + resize(
-            self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:])
-                                   ).permute(0, 2, 3, 1).reshape(
-                                       batch_size, -1, self.pool_scale**2)
-        affinity_matrix = F.sigmoid(affinity_matrix)
-        # [batch_size, h * w, channels]
-        z_out = torch.matmul(affinity_matrix, pooled_x)
-        # [batch_size, channels, h * w]
-        z_out = z_out.permute(0, 2, 1).contiguous()
-        # [batch_size, channels, h, w]
-        z_out = z_out.view(batch_size, self.channels, x.size(2), x.size(3))
-        z_out = self.residual_conv(z_out)
-        z_out = F.relu(z_out + x)
-        if self.fusion:
-            z_out = self.fusion_conv(z_out)
-
-        return z_out
-
-
-@HEADS.register_module()
-class APCHead(BaseDecodeHead):
-    """Adaptive Pyramid Context Network for Semantic Segmentation.
-
-    This head is the implementation of
-    `APCNet <https://openaccess.thecvf.com/content_CVPR_2019/papers/\
-    He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_\
-    CVPR_2019_paper.pdf>`_.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Adaptive Context
-            Module. Default: (1, 2, 3, 6).
-        fusion (bool): Add one conv to fuse residual feature.
-    """
-
-    def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):
-        super(APCHead, self).__init__(**kwargs)
-        assert isinstance(pool_scales, (list, tuple))
-        self.pool_scales = pool_scales
-        self.fusion = fusion
-        acm_modules = []
-        for pool_scale in self.pool_scales:
-            acm_modules.append(
-                ACM(pool_scale,
-                    self.fusion,
-                    self.in_channels,
-                    self.channels,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    act_cfg=self.act_cfg))
-        self.acm_modules = nn.ModuleList(acm_modules)
-        self.bottleneck = ConvModule(
-            self.in_channels + len(pool_scales) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        acm_outs = [x]
-        for acm_module in self.acm_modules:
-            acm_outs.append(acm_module(x))
-        acm_outs = torch.cat(acm_outs, dim=1)
-        output = self.bottleneck(acm_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/ahmedghani/svoice_demo/svoice/evaluate_auto_select.py b/spaces/ahmedghani/svoice_demo/svoice/evaluate_auto_select.py
deleted file mode 100644
index da4f98f2c9ca05b976ad35d1c1fb4fad08614e91..0000000000000000000000000000000000000000
--- a/spaces/ahmedghani/svoice_demo/svoice/evaluate_auto_select.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Authors: Yossi Adi (adiyoss)
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import json
-import logging
-import sys
-
-import numpy as np
-from pesq import pesq
-from pystoi import stoi
-import torch
-
-from .models.sisnr_loss import cal_loss
-from .data.data import Validset
-from . import distrib
-from .utils import bold, deserialize_model, LogProgress
-from .evaluate import _run_metrics
-
-
-logger = logging.getLogger(__name__)
-
-parser = argparse.ArgumentParser(
-    'Evaluate model automatic selection performance')
-parser.add_argument('model_path_2spk',
-                    help='Path to 2spk model file created by training')
-parser.add_argument('model_path_3spk',
-                    help='Path to 3spk model file created by training')
-parser.add_argument('model_path_4spk',
-                    help='Path to 4spk model file created by training')
-parser.add_argument('model_path_5spk',
-                    help='Path to 5spk model file created by training')
-parser.add_argument(
-    'data_dir', help='directory including mix.json, s1.json and s2.json files')
-parser.add_argument('--device', default="cuda")
-parser.add_argument('--sample_rate', default=8000,
-                    type=int, help='Sample rate')
-parser.add_argument('--thresh', default=0.001,
-                    type=float, help='Threshold for model auto selection')
-parser.add_argument('--num_workers', type=int, default=5)
-parser.add_argument('-v', '--verbose', action='store_const', const=logging.DEBUG,
-                    default=logging.INFO, help="More loggging")
-
-
-
-# test pariwise matching
-def pair_wise(padded_source, estimate_source):
-    pair_wise = torch.sum(padded_source.unsqueeze(
-        1)*estimate_source.unsqueeze(2), dim=3)
-    if estimate_source.shape[1] != padded_source.shape[1]:
-        idxs = pair_wise.argmax(dim=1)
-        new_src = torch.FloatTensor(padded_source.shape)
-        for b, idx in enumerate(idxs):
-            new_src[b:, :, ] = estimate_source[b][idx]
-        padded_source_pad = padded_source
-        estimate_source_pad = new_src.cuda()
-    else:
-        padded_source_pad = padded_source
-        estimate_source_pad = estimate_source
-    return estimate_source_pad
-
-
-def evaluate_auto_select(args):
-    total_sisnr = 0
-    total_pesq = 0
-    total_stoi = 0
-    total_cnt = 0
-    updates = 5
-
-    models = list()
-    paths = [args.model_path_2spk, args.model_path_3spk,
-             args.model_path_4spk, args.model_path_5spk]
-
-    for path in paths:
-        # Load model
-        pkg = torch.load(path)
-        if 'model' in pkg:
-            model = pkg['model']
-        else:
-            model = pkg
-        model = deserialize_model(model)
-        if 'best_state' in pkg:
-            model.load_state_dict(pkg['best_state'])
-        logger.debug(model)
-
-        model.eval()
-        model.to(args.device)
-        models.append(model)
-
-    # Load data
-    dataset = Validset(args.data_dir)
-    data_loader = distrib.loader(
-        dataset, batch_size=1, num_workers=args.num_workers)
-    sr = args.sample_rate
-    y_hat = torch.zeros((4))
-
-    pendings = []
-    with ProcessPoolExecutor(args.num_workers) as pool:
-        with torch.no_grad():
-            iterator = LogProgress(logger, data_loader, name="Eval estimates")
-            for i, data in enumerate(iterator):
-                # Get batch data
-                mixture, lengths, sources = [x.to(args.device) for x in data]
-                estimated_sources = list()
-                reorder_estimated_sources = list()
-
-                for model in models:
-                    # Forward
-                    with torch.no_grad():
-                        raw_estimate = model(mixture)[-1]
-
-                    estimate = pair_wise(sources, raw_estimate)
-                    sisnr_loss, snr, estimate, reorder_estimate = cal_loss(
-                        sources, estimate, lengths)
-                    estimated_sources.insert(0, raw_estimate)
-                    reorder_estimated_sources.insert(0, reorder_estimate)
-
-                # =================== DETECT NUM. NON-ACTIVE CHANNELS ============== #
-                selected_idx = 0
-                thresh = args.thresh
-                max_spk = 5
-                mix_spk = 2
-                ground = (max_spk - mix_spk)
-                while (selected_idx <= ground):
-                    no_sils = 0
-                    vals = torch.mean(
-                        (estimated_sources[selected_idx]/torch.abs(estimated_sources[selected_idx]).max())**2, axis=2)
-                    new_selected_idx = max_spk - len(vals[vals > thresh])
-                    if new_selected_idx == selected_idx:
-                        break
-                    else:
-                        selected_idx = new_selected_idx
-                if selected_idx < 0:
-                    selected_idx = 0
-                elif selected_idx > ground:
-                    selected_idx = ground
-
-                y_hat[ground - selected_idx] += 1
-                reorder_estimate = reorder_estimated_sources[selected_idx].cpu(
-                )
-                sources = sources.cpu()
-                mixture = mixture.cpu()
-
-                pendings.append(
-                    pool.submit(_run_metrics, sources, reorder_estimate, mixture, None,
-                                sr=sr))
-                total_cnt += sources.shape[0]
-
-            for pending in LogProgress(logger, pendings, updates, name="Eval metrics"):
-                sisnr_i, pesq_i, stoi_i = pending.result()
-                total_sisnr += sisnr_i
-                total_pesq += pesq_i
-                total_stoi += stoi_i
-
-    metrics = [total_sisnr, total_pesq, total_stoi]
-    sisnr, pesq, stoi = distrib.average(
-        [m/total_cnt for m in metrics], total_cnt)
-    logger.info(bold(f'Test set performance: SISNRi={sisnr:.2f} '
-                     f'PESQ={pesq}, STOI={stoi}.'))
-    logger.info(f'Two spks prob: {y_hat[0]/(total_cnt)}')
-    logger.info(f'Three spks prob: {y_hat[1]/(total_cnt)}')
-    logger.info(f'Four spks prob: {y_hat[2]/(total_cnt)}')
-    logger.info(f'Five spks prob: {y_hat[3]/(total_cnt)}')
-    return sisnr, pesq, stoi
-
-
-def main():
-    args = parser.parse_args()
-    logging.basicConfig(stream=sys.stderr, level=args.verbose)
-    logger.debug(args)
-    sisnr, pesq, stoi = evaluate_auto_select(args)
-    json.dump({'sisnr': sisnr,
-               'pesq': pesq, 'stoi': stoi}, sys.stdout)
-    sys.stdout.write('\n')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/ahuang11/mapnstreets/README.md b/spaces/ahuang11/mapnstreets/README.md
deleted file mode 100644
index 68a7ea8550d2db0221afdbe9df29ce8104df15d5..0000000000000000000000000000000000000000
--- a/spaces/ahuang11/mapnstreets/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MapnStreets
-emoji: 🛣️
-colorFrom: gray
-colorTo: green
-sdk: docker
-pinned: false
-duplicated_from: ahuang11/name-chronicles
-license: bsd-3-clause
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aipicasso/cool-japan-diffusion-latest-demo/README.md b/spaces/aipicasso/cool-japan-diffusion-latest-demo/README.md
deleted file mode 100644
index 1f0182cd8c9f4f9de00240caa6b77bcf9bcda68e..0000000000000000000000000000000000000000
--- a/spaces/aipicasso/cool-japan-diffusion-latest-demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Cool Japan Diffusion
-emoji: 🇯🇵
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/Mask2Former/mask2former/modeling/pixel_decoder/ops/setup.py b/spaces/akhaliq/Mask2Former/mask2former/modeling/pixel_decoder/ops/setup.py
deleted file mode 100644
index 244fdec83bee181e187d88800300395f449b0fbc..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Mask2Former/mask2former/modeling/pixel_decoder/ops/setup.py
+++ /dev/null
@@ -1,78 +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
-
-import os
-import glob
-
-import torch
-
-from torch.utils.cpp_extension import CUDA_HOME
-from torch.utils.cpp_extension import CppExtension
-from torch.utils.cpp_extension import CUDAExtension
-
-from setuptools import find_packages
-from setuptools import setup
-
-requirements = ["torch", "torchvision"]
-
-def get_extensions():
-    this_dir = os.path.dirname(os.path.abspath(__file__))
-    extensions_dir = os.path.join(this_dir, "src")
-
-    main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
-    source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
-    source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
-
-    sources = main_file + source_cpu
-    extension = CppExtension
-    extra_compile_args = {"cxx": []}
-    define_macros = []
-
-    # Force cuda since torch ask for a device, not if cuda is in fact available.
-    if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None:
-        extension = CUDAExtension
-        sources += source_cuda
-        define_macros += [("WITH_CUDA", None)]
-        extra_compile_args["nvcc"] = [
-            "-DCUDA_HAS_FP16=1",
-            "-D__CUDA_NO_HALF_OPERATORS__",
-            "-D__CUDA_NO_HALF_CONVERSIONS__",
-            "-D__CUDA_NO_HALF2_OPERATORS__",
-        ]
-#     else:
-#         if CUDA_HOME is None:
-#             raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.')
-#         else:
-#             raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().')
-
-    sources = [os.path.join(extensions_dir, s) for s in sources]
-    include_dirs = [extensions_dir]
-    ext_modules = [
-        extension(
-            "MultiScaleDeformableAttention",
-            sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            extra_compile_args=extra_compile_args,
-        )
-    ]
-    return ext_modules
-
-setup(
-    name="MultiScaleDeformableAttention",
-    version="1.0",
-    author="Weijie Su",
-    url="https://github.com/fundamentalvision/Deformable-DETR",
-    description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention",
-    packages=find_packages(exclude=("configs", "tests",)),
-    ext_modules=get_extensions(),
-    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-)
diff --git a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Comment.pod b/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Comment.pod
deleted file mode 100644
index f8e2cb290e0e2baff9c371718eca6495265d3f92..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Comment.pod
+++ /dev/null
@@ -1,14 +0,0 @@
-=head1 NAME
-
-XML::DOM::Comment - An XML comment in XML::DOM
-
-=head1 DESCRIPTION
-
-XML::DOM::Comment extends L<XML::DOM::CharacterData> which extends 
-L<XML::DOM::Node>.
-
-This node represents the content of a comment, i.e., all the characters
-between the starting '<!--' and ending '-->'. Note that this is the
-definition of a comment in XML, and, in practice, HTML, although some
-HTML tools may implement the full SGML comment structure.
-
diff --git a/spaces/akiyamasho/AnimeBackgroundGAN/network/Transformer.py b/spaces/akiyamasho/AnimeBackgroundGAN/network/Transformer.py
deleted file mode 100644
index 966c1c3aa654fbeb4650d361b4fc803695de5369..0000000000000000000000000000000000000000
--- a/spaces/akiyamasho/AnimeBackgroundGAN/network/Transformer.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class Transformer(nn.Module):
-    def __init__(self):
-        super(Transformer, self).__init__()
-        #
-        self.refpad01_1 = nn.ReflectionPad2d(3)
-        self.conv01_1 = nn.Conv2d(3, 64, 7)
-        self.in01_1 = InstanceNormalization(64)
-        # relu
-        self.conv02_1 = nn.Conv2d(64, 128, 3, 2, 1)
-        self.conv02_2 = nn.Conv2d(128, 128, 3, 1, 1)
-        self.in02_1 = InstanceNormalization(128)
-        # relu
-        self.conv03_1 = nn.Conv2d(128, 256, 3, 2, 1)
-        self.conv03_2 = nn.Conv2d(256, 256, 3, 1, 1)
-        self.in03_1 = InstanceNormalization(256)
-        # relu
-
-        ## res block 1
-        self.refpad04_1 = nn.ReflectionPad2d(1)
-        self.conv04_1 = nn.Conv2d(256, 256, 3)
-        self.in04_1 = InstanceNormalization(256)
-        # relu
-        self.refpad04_2 = nn.ReflectionPad2d(1)
-        self.conv04_2 = nn.Conv2d(256, 256, 3)
-        self.in04_2 = InstanceNormalization(256)
-        # + input
-
-        ## res block 2
-        self.refpad05_1 = nn.ReflectionPad2d(1)
-        self.conv05_1 = nn.Conv2d(256, 256, 3)
-        self.in05_1 = InstanceNormalization(256)
-        # relu
-        self.refpad05_2 = nn.ReflectionPad2d(1)
-        self.conv05_2 = nn.Conv2d(256, 256, 3)
-        self.in05_2 = InstanceNormalization(256)
-        # + input
-
-        ## res block 3
-        self.refpad06_1 = nn.ReflectionPad2d(1)
-        self.conv06_1 = nn.Conv2d(256, 256, 3)
-        self.in06_1 = InstanceNormalization(256)
-        # relu
-        self.refpad06_2 = nn.ReflectionPad2d(1)
-        self.conv06_2 = nn.Conv2d(256, 256, 3)
-        self.in06_2 = InstanceNormalization(256)
-        # + input
-
-        ## res block 4
-        self.refpad07_1 = nn.ReflectionPad2d(1)
-        self.conv07_1 = nn.Conv2d(256, 256, 3)
-        self.in07_1 = InstanceNormalization(256)
-        # relu
-        self.refpad07_2 = nn.ReflectionPad2d(1)
-        self.conv07_2 = nn.Conv2d(256, 256, 3)
-        self.in07_2 = InstanceNormalization(256)
-        # + input
-
-        ## res block 5
-        self.refpad08_1 = nn.ReflectionPad2d(1)
-        self.conv08_1 = nn.Conv2d(256, 256, 3)
-        self.in08_1 = InstanceNormalization(256)
-        # relu
-        self.refpad08_2 = nn.ReflectionPad2d(1)
-        self.conv08_2 = nn.Conv2d(256, 256, 3)
-        self.in08_2 = InstanceNormalization(256)
-        # + input
-
-        ## res block 6
-        self.refpad09_1 = nn.ReflectionPad2d(1)
-        self.conv09_1 = nn.Conv2d(256, 256, 3)
-        self.in09_1 = InstanceNormalization(256)
-        # relu
-        self.refpad09_2 = nn.ReflectionPad2d(1)
-        self.conv09_2 = nn.Conv2d(256, 256, 3)
-        self.in09_2 = InstanceNormalization(256)
-        # + input
-
-        ## res block 7
-        self.refpad10_1 = nn.ReflectionPad2d(1)
-        self.conv10_1 = nn.Conv2d(256, 256, 3)
-        self.in10_1 = InstanceNormalization(256)
-        # relu
-        self.refpad10_2 = nn.ReflectionPad2d(1)
-        self.conv10_2 = nn.Conv2d(256, 256, 3)
-        self.in10_2 = InstanceNormalization(256)
-        # + input
-
-        ## res block 8
-        self.refpad11_1 = nn.ReflectionPad2d(1)
-        self.conv11_1 = nn.Conv2d(256, 256, 3)
-        self.in11_1 = InstanceNormalization(256)
-        # relu
-        self.refpad11_2 = nn.ReflectionPad2d(1)
-        self.conv11_2 = nn.Conv2d(256, 256, 3)
-        self.in11_2 = InstanceNormalization(256)
-        # + input
-
-        ##------------------------------------##
-        self.deconv01_1 = nn.ConvTranspose2d(256, 128, 3, 2, 1, 1)
-        self.deconv01_2 = nn.Conv2d(128, 128, 3, 1, 1)
-        self.in12_1 = InstanceNormalization(128)
-        # relu
-        self.deconv02_1 = nn.ConvTranspose2d(128, 64, 3, 2, 1, 1)
-        self.deconv02_2 = nn.Conv2d(64, 64, 3, 1, 1)
-        self.in13_1 = InstanceNormalization(64)
-        # relu
-        self.refpad12_1 = nn.ReflectionPad2d(3)
-        self.deconv03_1 = nn.Conv2d(64, 3, 7)
-        # tanh
-
-    def forward(self, x):
-        y = F.relu(self.in01_1(self.conv01_1(self.refpad01_1(x))))
-        y = F.relu(self.in02_1(self.conv02_2(self.conv02_1(y))))
-        t04 = F.relu(self.in03_1(self.conv03_2(self.conv03_1(y))))
-
-        ##
-        y = F.relu(self.in04_1(self.conv04_1(self.refpad04_1(t04))))
-        t05 = self.in04_2(self.conv04_2(self.refpad04_2(y))) + t04
-
-        y = F.relu(self.in05_1(self.conv05_1(self.refpad05_1(t05))))
-        t06 = self.in05_2(self.conv05_2(self.refpad05_2(y))) + t05
-
-        y = F.relu(self.in06_1(self.conv06_1(self.refpad06_1(t06))))
-        t07 = self.in06_2(self.conv06_2(self.refpad06_2(y))) + t06
-
-        y = F.relu(self.in07_1(self.conv07_1(self.refpad07_1(t07))))
-        t08 = self.in07_2(self.conv07_2(self.refpad07_2(y))) + t07
-
-        y = F.relu(self.in08_1(self.conv08_1(self.refpad08_1(t08))))
-        t09 = self.in08_2(self.conv08_2(self.refpad08_2(y))) + t08
-
-        y = F.relu(self.in09_1(self.conv09_1(self.refpad09_1(t09))))
-        t10 = self.in09_2(self.conv09_2(self.refpad09_2(y))) + t09
-
-        y = F.relu(self.in10_1(self.conv10_1(self.refpad10_1(t10))))
-        t11 = self.in10_2(self.conv10_2(self.refpad10_2(y))) + t10
-
-        y = F.relu(self.in11_1(self.conv11_1(self.refpad11_1(t11))))
-        y = self.in11_2(self.conv11_2(self.refpad11_2(y))) + t11
-        ##
-
-        y = F.relu(self.in12_1(self.deconv01_2(self.deconv01_1(y))))
-        y = F.relu(self.in13_1(self.deconv02_2(self.deconv02_1(y))))
-        y = torch.tanh(self.deconv03_1(self.refpad12_1(y)))
-
-        return y
-
-
-class InstanceNormalization(nn.Module):
-    def __init__(self, dim, eps=1e-9):
-        super(InstanceNormalization, self).__init__()
-        self.scale = nn.Parameter(torch.FloatTensor(dim))
-        self.shift = nn.Parameter(torch.FloatTensor(dim))
-        self.eps = eps
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        self.scale.data.uniform_()
-        self.shift.data.zero_()
-
-    def __call__(self, x):
-        n = x.size(2) * x.size(3)
-        t = x.view(x.size(0), x.size(1), n)
-        mean = torch.mean(t, 2).unsqueeze(2).unsqueeze(3).expand_as(x)
-        # Calculate the biased var. torch.var returns unbiased var
-        var = torch.var(t, 2).unsqueeze(2).unsqueeze(3).expand_as(x) * (
-            (n - 1) / float(n)
-        )
-        scale_broadcast = self.scale.unsqueeze(1).unsqueeze(1).unsqueeze(0)
-        scale_broadcast = scale_broadcast.expand_as(x)
-        shift_broadcast = self.shift.unsqueeze(1).unsqueeze(1).unsqueeze(0)
-        shift_broadcast = shift_broadcast.expand_as(x)
-        out = (x - mean) / torch.sqrt(var + self.eps)
-        out = out * scale_broadcast + shift_broadcast
-        return out
diff --git a/spaces/alexray/btc_predictor/data_preprocessing.py b/spaces/alexray/btc_predictor/data_preprocessing.py
deleted file mode 100644
index b3e9f14374a0bd68bdca05fb07cbede4a09bcd51..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/data_preprocessing.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import os
-import pandas as pd
-from sklearn.preprocessing import StandardScaler
-
-
-def preprocess_data(input_file="data/assets_data.csv", data_dir="data"):
-
-    os.makedirs(data_dir, exist_ok=True)
-
-    df = pd.read_csv(input_file, index_col=0)
-
-    # Additional features
-    for column in df.drop(columns='target').columns:
-        df[f'{column}_ch'] = df[column] / df.shift(1)[column]
-
-    df.dropna(inplace=True)
-
-    X = df.drop('target', axis=1)
-    y = df['target']
-
-    # Scaling
-    scaler = StandardScaler()
-    X_scaled = scaler.fit_transform(X)
-    X_scaled_df = pd.DataFrame(X_scaled, index=X.index, columns=X.columns)
-
-    # Train-test split
-    train_size = len(df) - 90
-    X_train, X_test = X_scaled_df[:train_size], X_scaled_df[train_size:]
-    y_train, y_test = y[:train_size], y[train_size:]
-
-    X_train.to_csv(f"{data_dir}/train_features.csv", index=True)
-    X_test.to_csv(f"{data_dir}/test_features.csv", index=True)
-    y_train.to_csv(f"{data_dir}/train_target.csv", index=True)
-    y_test.to_csv(f"{data_dir}/test_target.csv", index=True)
-
-
-if __name__ == '__main__':
-    preprocess_data()
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/operations/install/legacy.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/operations/install/legacy.py
deleted file mode 100644
index 5b7ef9017181e94a86b8985f7523feaea387f612..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/operations/install/legacy.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""Legacy installation process, i.e. `setup.py install`.
-"""
-
-import logging
-import os
-from distutils.util import change_root
-from typing import List, Optional, Sequence
-
-from pip._internal.build_env import BuildEnvironment
-from pip._internal.exceptions import InstallationError, LegacyInstallFailure
-from pip._internal.models.scheme import Scheme
-from pip._internal.utils.misc import ensure_dir
-from pip._internal.utils.setuptools_build import make_setuptools_install_args
-from pip._internal.utils.subprocess import runner_with_spinner_message
-from pip._internal.utils.temp_dir import TempDirectory
-
-logger = logging.getLogger(__name__)
-
-
-def write_installed_files_from_setuptools_record(
-    record_lines: List[str],
-    root: Optional[str],
-    req_description: str,
-) -> None:
-    def prepend_root(path: str) -> str:
-        if root is None or not os.path.isabs(path):
-            return path
-        else:
-            return change_root(root, path)
-
-    for line in record_lines:
-        directory = os.path.dirname(line)
-        if directory.endswith(".egg-info"):
-            egg_info_dir = prepend_root(directory)
-            break
-    else:
-        message = (
-            "{} did not indicate that it installed an "
-            ".egg-info directory. Only setup.py projects "
-            "generating .egg-info directories are supported."
-        ).format(req_description)
-        raise InstallationError(message)
-
-    new_lines = []
-    for line in record_lines:
-        filename = line.strip()
-        if os.path.isdir(filename):
-            filename += os.path.sep
-        new_lines.append(os.path.relpath(prepend_root(filename), egg_info_dir))
-    new_lines.sort()
-    ensure_dir(egg_info_dir)
-    inst_files_path = os.path.join(egg_info_dir, "installed-files.txt")
-    with open(inst_files_path, "w") as f:
-        f.write("\n".join(new_lines) + "\n")
-
-
-def install(
-    install_options: List[str],
-    global_options: Sequence[str],
-    root: Optional[str],
-    home: Optional[str],
-    prefix: Optional[str],
-    use_user_site: bool,
-    pycompile: bool,
-    scheme: Scheme,
-    setup_py_path: str,
-    isolated: bool,
-    req_name: str,
-    build_env: BuildEnvironment,
-    unpacked_source_directory: str,
-    req_description: str,
-) -> bool:
-
-    header_dir = scheme.headers
-
-    with TempDirectory(kind="record") as temp_dir:
-        try:
-            record_filename = os.path.join(temp_dir.path, "install-record.txt")
-            install_args = make_setuptools_install_args(
-                setup_py_path,
-                global_options=global_options,
-                install_options=install_options,
-                record_filename=record_filename,
-                root=root,
-                prefix=prefix,
-                header_dir=header_dir,
-                home=home,
-                use_user_site=use_user_site,
-                no_user_config=isolated,
-                pycompile=pycompile,
-            )
-
-            runner = runner_with_spinner_message(
-                f"Running setup.py install for {req_name}"
-            )
-            with build_env:
-                runner(
-                    cmd=install_args,
-                    cwd=unpacked_source_directory,
-                )
-
-            if not os.path.exists(record_filename):
-                logger.debug("Record file %s not found", record_filename)
-                # Signal to the caller that we didn't install the new package
-                return False
-
-        except Exception as e:
-            # Signal to the caller that we didn't install the new package
-            raise LegacyInstallFailure(package_details=req_name) from e
-
-        # At this point, we have successfully installed the requirement.
-
-        # We intentionally do not use any encoding to read the file because
-        # setuptools writes the file using distutils.file_util.write_file,
-        # which does not specify an encoding.
-        with open(record_filename) as f:
-            record_lines = f.read().splitlines()
-
-    write_installed_files_from_setuptools_record(record_lines, root, req_description)
-    return True
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/control.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/control.py
deleted file mode 100644
index c98d0d7d98bd6de3b6b14b89ccffabbbefc5aefb..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/control.py
+++ /dev/null
@@ -1,175 +0,0 @@
-from typing import Any, Callable, Dict, Iterable, List, TYPE_CHECKING, Union
-
-from .segment import ControlCode, ControlType, Segment
-
-if TYPE_CHECKING:
-    from .console import Console, ConsoleOptions, RenderResult
-
-STRIP_CONTROL_CODES = [
-    8,  # Backspace
-    11,  # Vertical tab
-    12,  # Form feed
-    13,  # Carriage return
-]
-_CONTROL_TRANSLATE = {_codepoint: None for _codepoint in STRIP_CONTROL_CODES}
-
-
-CONTROL_CODES_FORMAT: Dict[int, Callable[..., str]] = {
-    ControlType.BELL: lambda: "\x07",
-    ControlType.CARRIAGE_RETURN: lambda: "\r",
-    ControlType.HOME: lambda: "\x1b[H",
-    ControlType.CLEAR: lambda: "\x1b[2J",
-    ControlType.ENABLE_ALT_SCREEN: lambda: "\x1b[?1049h",
-    ControlType.DISABLE_ALT_SCREEN: lambda: "\x1b[?1049l",
-    ControlType.SHOW_CURSOR: lambda: "\x1b[?25h",
-    ControlType.HIDE_CURSOR: lambda: "\x1b[?25l",
-    ControlType.CURSOR_UP: lambda param: f"\x1b[{param}A",
-    ControlType.CURSOR_DOWN: lambda param: f"\x1b[{param}B",
-    ControlType.CURSOR_FORWARD: lambda param: f"\x1b[{param}C",
-    ControlType.CURSOR_BACKWARD: lambda param: f"\x1b[{param}D",
-    ControlType.CURSOR_MOVE_TO_COLUMN: lambda param: f"\x1b[{param+1}G",
-    ControlType.ERASE_IN_LINE: lambda param: f"\x1b[{param}K",
-    ControlType.CURSOR_MOVE_TO: lambda x, y: f"\x1b[{y+1};{x+1}H",
-}
-
-
-class Control:
-    """A renderable that inserts a control code (non printable but may move cursor).
-
-    Args:
-        *codes (str): Positional arguments are either a :class:`~rich.segment.ControlType` enum or a
-            tuple of ControlType and an integer parameter
-    """
-
-    __slots__ = ["segment"]
-
-    def __init__(self, *codes: Union[ControlType, ControlCode]) -> None:
-        control_codes: List[ControlCode] = [
-            (code,) if isinstance(code, ControlType) else code for code in codes
-        ]
-        _format_map = CONTROL_CODES_FORMAT
-        rendered_codes = "".join(
-            _format_map[code](*parameters) for code, *parameters in control_codes
-        )
-        self.segment = Segment(rendered_codes, None, control_codes)
-
-    @classmethod
-    def bell(cls) -> "Control":
-        """Ring the 'bell'."""
-        return cls(ControlType.BELL)
-
-    @classmethod
-    def home(cls) -> "Control":
-        """Move cursor to 'home' position."""
-        return cls(ControlType.HOME)
-
-    @classmethod
-    def move(cls, x: int = 0, y: int = 0) -> "Control":
-        """Move cursor relative to current position.
-
-        Args:
-            x (int): X offset.
-            y (int): Y offset.
-
-        Returns:
-            ~Control: Control object.
-
-        """
-
-        def get_codes() -> Iterable[ControlCode]:
-            control = ControlType
-            if x:
-                yield (
-                    control.CURSOR_FORWARD if x > 0 else control.CURSOR_BACKWARD,
-                    abs(x),
-                )
-            if y:
-                yield (
-                    control.CURSOR_DOWN if y > 0 else control.CURSOR_UP,
-                    abs(y),
-                )
-
-        control = cls(*get_codes())
-        return control
-
-    @classmethod
-    def move_to_column(cls, x: int, y: int = 0) -> "Control":
-        """Move to the given column, optionally add offset to row.
-
-        Returns:
-            x (int): absolute x (column)
-            y (int): optional y offset (row)
-
-        Returns:
-            ~Control: Control object.
-        """
-
-        return (
-            cls(
-                (ControlType.CURSOR_MOVE_TO_COLUMN, x),
-                (
-                    ControlType.CURSOR_DOWN if y > 0 else ControlType.CURSOR_UP,
-                    abs(y),
-                ),
-            )
-            if y
-            else cls((ControlType.CURSOR_MOVE_TO_COLUMN, x))
-        )
-
-    @classmethod
-    def move_to(cls, x: int, y: int) -> "Control":
-        """Move cursor to absolute position.
-
-        Args:
-            x (int): x offset (column)
-            y (int): y offset (row)
-
-        Returns:
-            ~Control: Control object.
-        """
-        return cls((ControlType.CURSOR_MOVE_TO, x, y))
-
-    @classmethod
-    def clear(cls) -> "Control":
-        """Clear the screen."""
-        return cls(ControlType.CLEAR)
-
-    @classmethod
-    def show_cursor(cls, show: bool) -> "Control":
-        """Show or hide the cursor."""
-        return cls(ControlType.SHOW_CURSOR if show else ControlType.HIDE_CURSOR)
-
-    @classmethod
-    def alt_screen(cls, enable: bool) -> "Control":
-        """Enable or disable alt screen."""
-        if enable:
-            return cls(ControlType.ENABLE_ALT_SCREEN, ControlType.HOME)
-        else:
-            return cls(ControlType.DISABLE_ALT_SCREEN)
-
-    def __str__(self) -> str:
-        return self.segment.text
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        if self.segment.text:
-            yield self.segment
-
-
-def strip_control_codes(
-    text: str, _translate_table: Dict[int, None] = _CONTROL_TRANSLATE
-) -> str:
-    """Remove control codes from text.
-
-    Args:
-        text (str): A string possibly contain control codes.
-
-    Returns:
-        str: String with control codes removed.
-    """
-    return text.translate(_translate_table)
-
-
-if __name__ == "__main__":  # pragma: no cover
-    print(strip_control_codes("hello\rWorld"))
diff --git a/spaces/amankishore/sjc/run_img_sampling.py b/spaces/amankishore/sjc/run_img_sampling.py
deleted file mode 100644
index bded1a0a2eb1b5530c590ae55c8d10c54720253b..0000000000000000000000000000000000000000
--- a/spaces/amankishore/sjc/run_img_sampling.py
+++ /dev/null
@@ -1,235 +0,0 @@
-from pathlib import Path
-import numpy as np
-import torch
-
-from misc import torch_samps_to_imgs
-from adapt import Karras, ScoreAdapter, power_schedule
-from adapt_gddpm import GuidedDDPM
-from adapt_ncsn import NCSN as _NCSN
-# from adapt_vesde import VESDE  # not included to prevent import conflicts
-from adapt_sd import StableDiffusion
-
-from my.utils import tqdm, EventStorage, HeartBeat, EarlyLoopBreak
-from my.config import BaseConf, dispatch
-from my.utils.seed import seed_everything
-
-
-class GDDPM(BaseConf):
-    """Guided DDPM from OpenAI"""
-    model:          str = "m_lsun_256"
-    lsun_cat:       str = "bedroom"
-    imgnet_cat:     int = -1
-
-    def make(self):
-        args = self.dict()
-        model = GuidedDDPM(**args)
-        return model
-
-
-class SD(BaseConf):
-    """Stable Diffusion"""
-    variant:        str = "v1"
-    v2_highres:     bool = False
-    prompt:         str = "a photograph of an astronaut riding a horse"
-    scale:          float = 3.0  # classifier free guidance scale
-    precision:      str = 'autocast'
-
-    def make(self):
-        args = self.dict()
-        model = StableDiffusion(**args)
-        return model
-
-
-class SDE(BaseConf):
-    def make(self):
-        args = self.dict()
-        model = VESDE(**args)
-        return model
-
-
-class NCSN(BaseConf):
-    def make(self):
-        args = self.dict()
-        model = _NCSN(**args)
-        return model
-
-
-class KarrasGen(BaseConf):
-    family:         str = "gddpm"
-    gddpm:          GDDPM = GDDPM()
-    sd:             SD = SD()
-    # sde:            SDE = SDE()
-    ncsn:           NCSN = NCSN()
-
-    batch_size:     int = 10
-    num_images:     int = 1250
-    num_t:          int = 40
-    σ_max:          float = 80.0
-    heun:           bool = True
-    langevin:       bool = False
-    cls_scaling:    float = 1.0  # classifier guidance scaling
-
-    def run(self):
-        args = self.dict()
-        family = args.pop("family")
-        model = getattr(self, family).make()
-        self.karras_generate(model, **args)
-
-    @staticmethod
-    def karras_generate(
-        model: ScoreAdapter,
-        batch_size, num_images, σ_max, num_t, langevin, heun, cls_scaling,
-        **kwargs
-    ):
-        del kwargs  # removed extra args
-        num_batches = num_images // batch_size
-
-        fuse = EarlyLoopBreak(5)
-        with tqdm(total=num_batches) as pbar, \
-            HeartBeat(pbar) as hbeat, \
-                EventStorage() as metric:
-
-            all_imgs = []
-
-            for _ in range(num_batches):
-                if fuse.on_break():
-                    break
-
-                pipeline = Karras.inference(
-                    model, batch_size, num_t,
-                    init_xs=None, heun=heun, σ_max=σ_max,
-                    langevin=langevin, cls_scaling=cls_scaling
-                )
-
-                for imgs in tqdm(pipeline, total=num_t+1, disable=False):
-                    # _std = imgs.std().item()
-                    # print(_std)
-                    hbeat.beat()
-                    pass
-
-                if isinstance(model, StableDiffusion):
-                    imgs = model.decode(imgs)
-
-                imgs = torch_samps_to_imgs(imgs, uncenter=model.samps_centered())
-                all_imgs.append(imgs)
-
-                pbar.update()
-
-            all_imgs = np.concatenate(all_imgs, axis=0)
-            metric.put_artifact("imgs", ".npy", lambda fn: np.save(fn, all_imgs))
-            metric.step()
-            hbeat.done()
-
-
-class SMLDGen(BaseConf):
-    family:         str = "ncsn"
-    gddpm:          GDDPM = GDDPM()
-    # sde:            SDE = SDE()
-    ncsn:           NCSN = NCSN()
-
-    batch_size:     int = 16
-    num_images:     int = 16
-    num_stages:     int = 80
-    num_steps:      int = 15
-    σ_max:          float = 80.0
-    ε:              float = 1e-5
-
-    def run(self):
-        args = self.dict()
-        family = args.pop("family")
-        model = getattr(self, family).make()
-        self.smld_generate(model, **args)
-
-    @staticmethod
-    def smld_generate(
-        model: ScoreAdapter,
-        batch_size, num_images, num_stages, num_steps, σ_max, ε,
-        **kwargs
-    ):
-        num_batches = num_images // batch_size
-        σs = power_schedule(σ_max, model.σ_min, num_stages)
-        σs = [model.snap_t_to_nearest_tick(σ)[0] for σ in σs]
-
-        fuse = EarlyLoopBreak(5)
-        with tqdm(total=num_batches) as pbar, \
-            HeartBeat(pbar) as hbeat, \
-                EventStorage() as metric:
-
-            all_imgs = []
-
-            for _ in range(num_batches):
-                if fuse.on_break():
-                    break
-
-                init_xs = torch.rand(batch_size, *model.data_shape(), device=model.device)
-                if model.samps_centered():
-                    init_xs = init_xs * 2 - 1  # [0, 1] -> [-1, 1]
-
-                pipeline = smld_inference(
-                    model, σs, num_steps, ε, init_xs
-                )
-
-                for imgs in tqdm(pipeline, total=(num_stages * num_steps)+1, disable=False):
-                    pbar.set_description(f"{imgs.max().item():.3f}")
-                    metric.put_scalars(
-                        max=imgs.max().item(), min=imgs.min().item(), std=imgs.std().item()
-                    )
-                    metric.step()
-                    hbeat.beat()
-
-                pbar.update()
-                imgs = torch_samps_to_imgs(imgs, uncenter=model.samps_centered())
-                all_imgs.append(imgs)
-
-            all_imgs = np.concatenate(all_imgs, axis=0)
-            metric.put_artifact("imgs", ".npy", lambda fn: np.save(fn, all_imgs))
-            metric.step()
-            hbeat.done()
-
-
-def smld_inference(model, σs, num_steps, ε, init_xs):
-    from math import sqrt
-    # not doing conditioning or cls guidance; for gddpm only lsun works; fine.
-
-    xs = init_xs
-    yield xs
-
-    for i in range(len(σs)):
-        α_i = ε * ((σs[i] / σs[-1]) ** 2)
-        for _ in range(num_steps):
-            grad = model.score(xs, σs[i])
-            z = torch.randn_like(xs)
-            xs = xs + α_i * grad + sqrt(2 * α_i) * z
-            yield xs
-
-
-def load_np_imgs(fname):
-    fname = Path(fname)
-    data = np.load(fname)
-    if fname.suffix == ".npz":
-        imgs = data['arr_0']
-    else:
-        imgs = data
-    return imgs
-
-
-def visualize(max_n_imgs=16):
-    import torchvision.utils as vutils
-    from imageio import imwrite
-    from einops import rearrange
-
-    all_imgs = load_np_imgs("imgs/step_0.npy")
-
-    imgs = all_imgs[:max_n_imgs]
-    imgs = rearrange(imgs, "N H W C -> N C H W", C=3)
-    imgs = torch.from_numpy(imgs)
-    pane = vutils.make_grid(imgs, padding=2, nrow=4)
-    pane = rearrange(pane, "C H W -> H W C", C=3)
-    pane = pane.numpy()
-    imwrite("preview.jpg", pane)
-
-
-if __name__ == "__main__":
-    seed_everything(0)
-    dispatch(KarrasGen)
-    visualize(16)
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_callbackstop.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_callbackstop.c
deleted file mode 100644
index fba9dca4dbf3215ee9e5301cd424e1d425ff2b0e..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_callbackstop.c
+++ /dev/null
@@ -1,252 +0,0 @@
-/** @file patest_callbackstop.c
-    @ingroup test_src
-    @brief Test the paComplete callback result code.
-    @author Ross Bencina <rossb@audiomulch.com>
-*/
-/*
- * $Id$
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com/
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * 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.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include <stdio.h>
-#include <math.h>
-#include "portaudio.h"
-
-#define NUM_SECONDS   (5)
-#define NUM_LOOPS     (4)
-#define SAMPLE_RATE   (44100)
-#define FRAMES_PER_BUFFER  (67)
-
-#ifndef M_PI
-#define M_PI  (3.14159265)
-#endif
-
-#define TABLE_SIZE   (200)
-typedef struct
-{
-    float sine[TABLE_SIZE];
-    int phase;
-    unsigned long generatedFramesCount;
-    volatile int callbackReturnedPaComplete;
-    volatile int callbackInvokedAfterReturningPaComplete;
-    char message[100];
-}
-TestData;
-
-/*
-   This routine will be called by the PortAudio stream when audio is needed.
-   It may be called at interrupt level on some machines so don't do anything
-   that could mess up the system like calling malloc() or free().
-*/
-static int TestCallback( const void *input, void *output,
-                            unsigned long frameCount,
-                            const PaStreamCallbackTimeInfo* timeInfo,
-                            PaStreamCallbackFlags statusFlags,
-                            void *userData )
-{
-    TestData *data = (TestData*)userData;
-    float *out = (float*)output;
-    unsigned long i;
-    float x;
-
-    (void) input;       /* Prevent unused variable warnings. */
-    (void) timeInfo;
-    (void) statusFlags;
-
-
-    if( data->callbackReturnedPaComplete )
-        data->callbackInvokedAfterReturningPaComplete = 1;
-
-    for( i=0; i<frameCount; i++ )
-    {
-        /* generate tone */
-
-        x = data->sine[ data->phase++ ];
-        if( data->phase >= TABLE_SIZE )
-            data->phase -= TABLE_SIZE;
-
-        *out++ = x;  /* left */
-        *out++ = x;  /* right */
-    }
-
-    data->generatedFramesCount += frameCount;
-    if( data->generatedFramesCount >= (NUM_SECONDS * SAMPLE_RATE) )
-    {
-        data->callbackReturnedPaComplete = 1;
-        return paComplete;
-    }
-    else
-    {
-        return paContinue;
-    }
-}
-
-/*
- * This routine is called by portaudio when playback is done.
- */
-static void StreamFinished( void* userData )
-{
-    TestData *data = (TestData *) userData;
-    printf( "Stream Completed: %s\n", data->message );
-}
-
-
-/*----------------------------------------------------------------------------*/
-int main(void);
-int main(void)
-{
-    PaStreamParameters outputParameters;
-    PaStream *stream;
-    PaError err;
-    TestData data;
-    int i, j;
-
-
-    printf( "PortAudio Test: output sine wave. SR = %d, BufSize = %d\n",
-            SAMPLE_RATE, FRAMES_PER_BUFFER );
-
-    /* initialise sinusoidal wavetable */
-    for( i=0; i<TABLE_SIZE; i++ )
-    {
-        data.sine[i] = (float) sin( ((double)i/(double)TABLE_SIZE) * M_PI * 2. );
-    }
-
-    err = Pa_Initialize();
-    if( err != paNoError ) goto error;
-
-    outputParameters.device                    = Pa_GetDefaultOutputDevice();
-    if (outputParameters.device == paNoDevice) {
-        fprintf(stderr,"Error: No default output device.\n");
-        goto error;
-    }
-    outputParameters.channelCount              = 2;               /* stereo output */
-    outputParameters.sampleFormat              = paFloat32;       /* 32 bit floating point output */
-    outputParameters.suggestedLatency          = Pa_GetDeviceInfo( outputParameters.device )->defaultLowOutputLatency;
-    outputParameters.hostApiSpecificStreamInfo = NULL;
-
-    err = Pa_OpenStream(
-              &stream,
-              NULL, /* no input */
-              &outputParameters,
-              SAMPLE_RATE,
-              FRAMES_PER_BUFFER,
-              paClipOff,      /* output will be in-range, so no need to clip */
-              TestCallback,
-              &data );
-    if( err != paNoError ) goto error;
-
-    sprintf( data.message, "Loop: XX" );
-    err = Pa_SetStreamFinishedCallback( stream, &StreamFinished );
-    if( err != paNoError ) goto error;
-
-    printf("Repeating test %d times.\n", NUM_LOOPS );
-
-    for( i=0; i < NUM_LOOPS; ++i )
-    {
-        data.phase = 0;
-        data.generatedFramesCount = 0;
-        data.callbackReturnedPaComplete = 0;
-        data.callbackInvokedAfterReturningPaComplete = 0;
-        sprintf( data.message, "Loop: %d", i );
-
-        err = Pa_StartStream( stream );
-        if( err != paNoError ) goto error;
-
-        printf("Play for %d seconds.\n", NUM_SECONDS );
-
-        /* wait for the callback to complete generating NUM_SECONDS of tone */
-
-        do
-        {
-            Pa_Sleep( 500 );
-        }
-        while( !data.callbackReturnedPaComplete );
-
-        printf( "Callback returned paComplete.\n" );
-        printf( "Waiting for buffers to finish playing...\n" );
-
-        /* wait for stream to become inactive,
-           or for a timeout of approximately NUM_SECONDS
-         */
-
-        j = 0;
-        while( (err = Pa_IsStreamActive( stream )) == 1 && j < NUM_SECONDS * 2 )
-        {
-            printf(".\n" );
-            Pa_Sleep( 500 );
-            ++j;
-        }
-
-        if( err < 0 )
-        {
-            goto error;
-        }
-        else if( err == 1 )
-        {
-            printf( "TEST FAILED: Timed out waiting for buffers to finish playing.\n" );
-        }
-        else
-        {
-            printf("Buffers finished.\n" );
-        }
-
-        if( data.callbackInvokedAfterReturningPaComplete )
-        {
-            printf( "TEST FAILED: Callback was invoked after returning paComplete.\n" );
-        }
-
-
-        err = Pa_StopStream( stream );
-        if( err != paNoError ) goto error;
-
-        printf( "sleeping for 1 second...\n" );
-        Pa_Sleep( 1000 );
-    }
-
-    err = Pa_CloseStream( stream );
-    if( err != paNoError ) goto error;
-
-    Pa_Terminate();
-    printf("Test finished.\n");
-
-    return err;
-error:
-    Pa_Terminate();
-    fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-    fprintf( stderr, "Error number: %d\n", err );
-    fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-    return err;
-}
diff --git a/spaces/amielle/patent-summarizer/util/examples.py b/spaces/amielle/patent-summarizer/util/examples.py
deleted file mode 100644
index 938d20b32739868f206f8f19cebbb8813fe118cc..0000000000000000000000000000000000000000
--- a/spaces/amielle/patent-summarizer/util/examples.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from util import summarizer
-
-entries = [
-    [
-        "US9820315B2",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        250
-    ],
-    [
-        "US9820315B2",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        350
-    ],
-    [
-        "https://patents.google.com/patent/US9820315B2",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        400
-    ],
-    [
-        "https://patents.google.com/patent/US10263802B2/en?q=smart+user+interface&oq=smart+user+interface",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        350
-    ],
-    [
-        "CN211575647U",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        250
-    ],
-    [
-        "CN211575647U",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        350
-    ],
-    [
-        "https://patents.google.com/patent/CN211575647U",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        400
-    ],
-    [
-        "https://patents.google.com/patent/CN211575647U/en",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        350
-    ],
-    [
-        "US10125002B2",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        250
-    ],
-    [
-        "US10125002B2",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        350
-    ],
-    [
-        "https://patents.google.com/patent/US10125002B2",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        400
-    ],
-    [
-        "https://patents.google.com/patent/US10125002B2/en",
-        summarizer.summary_options,
-        summarizer.model_names[3],
-        summarizer.model_names[0],
-        summarizer.model_names[2],
-        True,
-        350
-    ]
-]
\ No newline at end of file
diff --git a/spaces/anhalu/transformer-ocr/README.md b/spaces/anhalu/transformer-ocr/README.md
deleted file mode 100644
index da799b34c46f3cd005b2b2e6889b60ba1819f6c8..0000000000000000000000000000000000000000
--- a/spaces/anhalu/transformer-ocr/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Transformer Ocr
-emoji: 📊
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/antonovmaxim/text-generation-webui-space/extensions/openai/README.md b/spaces/antonovmaxim/text-generation-webui-space/extensions/openai/README.md
deleted file mode 100644
index b20eba3326b297630e64a3bedf96ef82c0d359b8..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/extensions/openai/README.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# An OpenedAI API (openai like)
-
-This extension creates an API that works kind of like openai (ie. api.openai.com).
-It's incomplete so far but perhaps is functional enough for you.
-
-## Setup & installation 
-
-Optional (for flask_cloudflared, embeddings):
-
-```
-pip3 install -r requirements.txt
-```
-
-It listens on tcp port 5001 by default. You can use the OPENEDAI_PORT environment variable to change this.
-
-To enable the bare bones image generation (txt2img) set: SD_WEBUI_URL to point to your Stable Diffusion API ([Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)).
-
-Example:
-```
-SD_WEBUI_URL=http://127.0.0.1:7861
-```
-
-### Embeddings (alpha)
-
-Embeddings requires ```sentence-transformers``` installed, but chat and completions will function without it loaded. The embeddings endpoint is currently using the HuggingFace model: ```sentence-transformers/all-mpnet-base-v2``` for embeddings. This produces 768 dimensional embeddings (the same as the text-davinci-002 embeddings), which is different from OpenAI's current default ```text-embedding-ada-002``` model which produces 1536 dimensional embeddings. The model is small-ish and fast-ish. This model and embedding size may change in the future.
-
-| model name | dimensions | input max tokens | speed | size | Avg. performance | 
-| --- | --- | --- | --- | --- | --- |
-| text-embedding-ada-002 | 1536 | 8192| - | - | - |
-| text-davinci-002 | 768 | 2046 | - | - | - |
-| all-mpnet-base-v2 | 768 | 384 | 2800 | 420M | 63.3 |
-| all-MiniLM-L6-v2 | 384 | 256 | 14200 | 80M | 58.8 |
-
-In short, the all-MiniLM-L6-v2 model is 5x faster, 5x smaller ram, 2x smaller storage, and still offers good quality. Stats from (https://www.sbert.net/docs/pretrained_models.html). To change the model from the default you can set the environment variable OPENEDAI_EMBEDDING_MODEL, ex. "OPENEDAI_EMBEDDING_MODEL=all-MiniLM-L6-v2".
-
-Warning: You cannot mix embeddings from different models even if they have the same dimensions. They are not comparable.
-
-### Client Application Setup
-
-Almost everything you use it with will require you to set a dummy OpenAI API key environment variable.
-
-With the [official python openai client](https://github.com/openai/openai-python), you can set the OPENAI_API_BASE environment variable before you import the openai module, like so:
-
-```
-OPENAI_API_KEY=dummy
-OPENAI_API_BASE=http://127.0.0.1:5001/v1
-```
-
-If needed, replace 127.0.0.1 with the IP/port of your server.
-
-If using .env files to save the OPENAI_API_BASE and OPENAI_API_KEY variables, you can ensure compatibility by loading the .env file before loading the openai module, like so in python:
-
-```
-from dotenv import load_dotenv
-load_dotenv()
-import openai
-```
-
-With the [official Node.js openai client](https://github.com/openai/openai-node) it is slightly more more complex because the environment variables are not used by default, so small source code changes may be required to use the environment variables, like so:
-
-```
-const openai = OpenAI(Configuration({
-  apiKey: process.env.OPENAI_API_KEY,
-  basePath: process.env.OPENAI_API_BASE,
-}));
-```
-
-For apps made with the [chatgpt-api Node.js client library](https://github.com/transitive-bullshit/chatgpt-api):
-
-```
-const api = new ChatGPTAPI({
-  apiKey: process.env.OPENAI_API_KEY,
-  apiBaseUrl: process.env.OPENAI_API_BASE,
-})
-```
-
-## Compatibility & not so compatibility
-
-| API endpoint | tested with | notes |
-| --- | --- | --- |
-| /v1/models | openai.Model.list() | returns the currently loaded model_name and some mock compatibility options |
-| /v1/models/{id} | openai.Model.get() | returns whatever you ask for, model does nothing yet anyways |
-| /v1/text_completion | openai.Completion.create() | the most tested, only supports single string input so far |
-| /v1/chat/completions | openai.ChatCompletion.create() | depending on the model, this may add leading linefeeds |
-| /v1/edits | openai.Edit.create() | Assumes an instruction following model, but may work with others |
-| /v1/images/generations | openai.Image.create() | Bare bones, no model configuration, response_format='b64_json' only. |
-| /v1/embeddings | openai.Embedding.create() | Using Sentence Transformer, dimensions are different and may never be directly comparable to openai embeddings. |
-| /v1/moderations | openai.Moderation.create() | does nothing. successfully. |
-| /v1/engines/\*/... completions, embeddings, generate | python-openai v0.25 and earlier | Legacy engines endpoints |
-| /v1/images/edits | openai.Image.create_edit() | not supported |
-| /v1/images/variations | openai.Image.create_variation() | not supported |
-| /v1/audio/\* | openai.Audio.\* | not supported |
-| /v1/files\* | openai.Files.\* | not supported |
-| /v1/fine-tunes\* | openai.FineTune.\* | not supported |
-
-The model name setting is ignored in completions, but you may need to adjust the maximum token length to fit the model (ie. set to <2048 tokens instead of 4096, 8k, etc). To mitigate some of this, the max_tokens value is halved until it is less than truncation_length for the model (typically 2k).
-
-Streaming, temperature, top_p, max_tokens, stop, should all work as expected, but not all parameters are mapped correctly.
-
-Some hacky mappings:
-
-| OpenAI | text-generation-webui | note |
-| --- | --- | --- |
-| frequency_penalty | encoder_repetition_penalty | this seems to operate with a different scale and defaults, I tried to scale it based on range & defaults, but the results are terrible. hardcoded to 1.18 until there is a better way |
-| presence_penalty | repetition_penalty | same issues as frequency_penalty, hardcoded to 1.0 |
-| best_of | top_k | |
-| stop | custom_stopping_strings | this is also stuffed with ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ] for good measure. |
-| n | 1 | hardcoded, it may be worth implementing this but I'm not sure how yet |
-| 1.0 | typical_p | hardcoded |
-| 1 | num_beams | hardcoded |
-| max_tokens | max_new_tokens | max_tokens is scaled down by powers of 2 until it's smaller than truncation length. |
-| logprobs | - | ignored |
-
-defaults are mostly from openai, so are different. I use the openai defaults where I can and try to scale them to the webui defaults with the same intent.
-
-### Models
-
-This has been successfully tested with Koala, Alpaca, gpt4-x-alpaca, GPT4all-snoozy,  wizard-vicuna, stable-vicuna and Vicuna 1.1 - ie. Instruction Following models. If you test with other models please let me know how it goes. Less than satisfying results (so far): RWKV-4-Raven, llama, mpt-7b-instruct/chat
-
-### Applications
-
-Everything needs OPENAI_API_KEY=dummy set.
-
-| Compatibility | Application/Library | url | notes / setting |
-| --- | --- | --- | --- |
-| ✅❌ | openai-python | https://github.com/openai/openai-python | only the endpoints from above are working. OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
-| ✅❌ | openai-node | https://github.com/openai/openai-node | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) |
-| ✅❌ | chatgpt-api | https://github.com/transitive-bullshit/chatgpt-api | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) |
-| ✅ | shell_gpt | https://github.com/TheR1D/shell_gpt | OPENAI_API_HOST=http://127.0.0.1:5001 |
-| ✅ | gpt-shell | https://github.com/jla/gpt-shell | OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
-| ✅ | gpt-discord-bot | https://github.com/openai/gpt-discord-bot | OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
-| ✅❌ | langchain | https://github.com/hwchase17/langchain | OPENAI_API_BASE=http://127.0.0.1:5001/v1 even with a good 30B-4bit model the result is poor so far. It assumes zero shot python/json coding. Some model tailored prompt formatting improves results greatly. |
-| ✅❌ | Auto-GPT | https://github.com/Significant-Gravitas/Auto-GPT | OPENAI_API_BASE=http://127.0.0.1:5001/v1 Same issues as langchain. Also assumes a 4k+ context |
-| ✅❌ | babyagi | https://github.com/yoheinakajima/babyagi | OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
-
-## Future plans
-* better error handling
-* model changing, esp. something for swapping loras or embedding models
-* consider switching to FastAPI + starlette for SSE (openai SSE seems non-standard)
-* do something about rate limiting or locking requests for completions, most systems will only be able handle a single request at a time before OOM
-
-## Bugs? Feedback? Comments? Pull requests?
-
-Are all appreciated, please @matatonic and I'll try to get back to you as soon as possible.
diff --git a/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/modeling/prompt_encoder.py b/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/modeling/prompt_encoder.py
deleted file mode 100644
index c3143f4f8e02ddd7ca8587b40ff5d47c3a6b7ef3..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/panoptic-segment-anything/segment_anything/segment_anything/modeling/prompt_encoder.py
+++ /dev/null
@@ -1,214 +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 numpy as np
-import torch
-from torch import nn
-
-from typing import Any, Optional, Tuple, Type
-
-from .common import LayerNorm2d
-
-
-class PromptEncoder(nn.Module):
-    def __init__(
-        self,
-        embed_dim: int,
-        image_embedding_size: Tuple[int, int],
-        input_image_size: Tuple[int, int],
-        mask_in_chans: int,
-        activation: Type[nn.Module] = nn.GELU,
-    ) -> None:
-        """
-        Encodes prompts for input to SAM's mask decoder.
-
-        Arguments:
-          embed_dim (int): The prompts' embedding dimension
-          image_embedding_size (tuple(int, int)): The spatial size of the
-            image embedding, as (H, W).
-          input_image_size (int): The padded size of the image as input
-            to the image encoder, as (H, W).
-          mask_in_chans (int): The number of hidden channels used for
-            encoding input masks.
-          activation (nn.Module): The activation to use when encoding
-            input masks.
-        """
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.input_image_size = input_image_size
-        self.image_embedding_size = image_embedding_size
-        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
-
-        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
-        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
-        self.point_embeddings = nn.ModuleList(point_embeddings)
-        self.not_a_point_embed = nn.Embedding(1, embed_dim)
-
-        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
-        self.mask_downscaling = nn.Sequential(
-            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
-            LayerNorm2d(mask_in_chans // 4),
-            activation(),
-            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
-            LayerNorm2d(mask_in_chans),
-            activation(),
-            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
-        )
-        self.no_mask_embed = nn.Embedding(1, embed_dim)
-
-    def get_dense_pe(self) -> torch.Tensor:
-        """
-        Returns the positional encoding used to encode point prompts,
-        applied to a dense set of points the shape of the image encoding.
-
-        Returns:
-          torch.Tensor: Positional encoding with shape
-            1x(embed_dim)x(embedding_h)x(embedding_w)
-        """
-        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
-
-    def _embed_points(
-        self,
-        points: torch.Tensor,
-        labels: torch.Tensor,
-        pad: bool,
-    ) -> torch.Tensor:
-        """Embeds point prompts."""
-        points = points + 0.5  # Shift to center of pixel
-        if pad:
-            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
-            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
-            points = torch.cat([points, padding_point], dim=1)
-            labels = torch.cat([labels, padding_label], dim=1)
-        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
-        point_embedding[labels == -1] = 0.0
-        point_embedding[labels == -1] += self.not_a_point_embed.weight
-        point_embedding[labels == 0] += self.point_embeddings[0].weight
-        point_embedding[labels == 1] += self.point_embeddings[1].weight
-        return point_embedding
-
-    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
-        """Embeds box prompts."""
-        boxes = boxes + 0.5  # Shift to center of pixel
-        coords = boxes.reshape(-1, 2, 2)
-        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
-        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
-        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
-        return corner_embedding
-
-    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
-        """Embeds mask inputs."""
-        mask_embedding = self.mask_downscaling(masks)
-        return mask_embedding
-
-    def _get_batch_size(
-        self,
-        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
-        boxes: Optional[torch.Tensor],
-        masks: Optional[torch.Tensor],
-    ) -> int:
-        """
-        Gets the batch size of the output given the batch size of the input prompts.
-        """
-        if points is not None:
-            return points[0].shape[0]
-        elif boxes is not None:
-            return boxes.shape[0]
-        elif masks is not None:
-            return masks.shape[0]
-        else:
-            return 1
-
-    def _get_device(self) -> torch.device:
-        return self.point_embeddings[0].weight.device
-
-    def forward(
-        self,
-        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
-        boxes: Optional[torch.Tensor],
-        masks: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Embeds different types of prompts, returning both sparse and dense
-        embeddings.
-
-        Arguments:
-          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
-            and labels to embed.
-          boxes (torch.Tensor or none): boxes to embed
-          masks (torch.Tensor or none): masks to embed
-
-        Returns:
-          torch.Tensor: sparse embeddings for the points and boxes, with shape
-            BxNx(embed_dim), where N is determined by the number of input points
-            and boxes.
-          torch.Tensor: dense embeddings for the masks, in the shape
-            Bx(embed_dim)x(embed_H)x(embed_W)
-        """
-        bs = self._get_batch_size(points, boxes, masks)
-        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
-        if points is not None:
-            coords, labels = points
-            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
-            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
-        if boxes is not None:
-            box_embeddings = self._embed_boxes(boxes)
-            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
-        if masks is not None:
-            dense_embeddings = self._embed_masks(masks)
-        else:
-            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
-                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
-            )
-
-        return sparse_embeddings, dense_embeddings
-
-
-class PositionEmbeddingRandom(nn.Module):
-    """
-    Positional encoding using random spatial frequencies.
-    """
-
-    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
-        super().__init__()
-        if scale is None or scale <= 0.0:
-            scale = 1.0
-        self.register_buffer(
-            "positional_encoding_gaussian_matrix",
-            scale * torch.randn((2, num_pos_feats)),
-        )
-
-    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
-        """Positionally encode points that are normalized to [0,1]."""
-        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
-        coords = 2 * coords - 1
-        coords = coords @ self.positional_encoding_gaussian_matrix
-        coords = 2 * np.pi * coords
-        # outputs d_1 x ... x d_n x C shape
-        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
-
-    def forward(self, size: Tuple[int, int]) -> torch.Tensor:
-        """Generate positional encoding for a grid of the specified size."""
-        h, w = size
-        device: Any = self.positional_encoding_gaussian_matrix.device
-        grid = torch.ones((h, w), device=device, dtype=torch.float32)
-        y_embed = grid.cumsum(dim=0) - 0.5
-        x_embed = grid.cumsum(dim=1) - 0.5
-        y_embed = y_embed / h
-        x_embed = x_embed / w
-
-        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
-        return pe.permute(2, 0, 1)  # C x H x W
-
-    def forward_with_coords(
-        self, coords_input: torch.Tensor, image_size: Tuple[int, int]
-    ) -> torch.Tensor:
-        """Positionally encode points that are not normalized to [0,1]."""
-        coords = coords_input.clone()
-        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
-        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
-        return self._pe_encoding(coords.to(torch.float))  # B x N x C
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/codeformer_model.py b/spaces/aodianyun/stable-diffusion-webui/modules/codeformer_model.py
deleted file mode 100644
index fc8fbc3e947f64a7f23f0da7243d6e0ad7cbeb79..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/codeformer_model.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import os
-import sys
-import traceback
-
-import cv2
-import torch
-
-import modules.face_restoration
-import modules.shared
-from modules import shared, devices, modelloader
-from modules.paths import models_path
-
-# codeformer people made a choice to include modified basicsr library to their project which makes
-# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
-# I am making a choice to include some files from codeformer to work around this issue.
-model_dir = "Codeformer"
-model_path = os.path.join(models_path, model_dir)
-model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-
-have_codeformer = False
-codeformer = None
-
-
-def setup_model(dirname):
-    global model_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-
-    path = modules.paths.paths.get("CodeFormer", None)
-    if path is None:
-        return
-
-    try:
-        from torchvision.transforms.functional import normalize
-        from modules.codeformer.codeformer_arch import CodeFormer
-        from basicsr.utils.download_util import load_file_from_url
-        from basicsr.utils import imwrite, img2tensor, tensor2img
-        from facelib.utils.face_restoration_helper import FaceRestoreHelper
-        from facelib.detection.retinaface import retinaface
-        from modules.shared import cmd_opts
-
-        net_class = CodeFormer
-
-        class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
-            def name(self):
-                return "CodeFormer"
-
-            def __init__(self, dirname):
-                self.net = None
-                self.face_helper = None
-                self.cmd_dir = dirname
-
-            def create_models(self):
-
-                if self.net is not None and self.face_helper is not None:
-                    self.net.to(devices.device_codeformer)
-                    return self.net, self.face_helper
-                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth')
-                if len(model_paths) != 0:
-                    ckpt_path = model_paths[0]
-                else:
-                    print("Unable to load codeformer model.")
-                    return None, None
-                net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
-                checkpoint = torch.load(ckpt_path)['params_ema']
-                net.load_state_dict(checkpoint)
-                net.eval()
-
-                if hasattr(retinaface, 'device'):
-                    retinaface.device = devices.device_codeformer
-                face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
-
-                self.net = net
-                self.face_helper = face_helper
-
-                return net, face_helper
-
-            def send_model_to(self, device):
-                self.net.to(device)
-                self.face_helper.face_det.to(device)
-                self.face_helper.face_parse.to(device)
-
-            def restore(self, np_image, w=None):
-                np_image = np_image[:, :, ::-1]
-
-                original_resolution = np_image.shape[0:2]
-
-                self.create_models()
-                if self.net is None or self.face_helper is None:
-                    return np_image
-
-                self.send_model_to(devices.device_codeformer)
-
-                self.face_helper.clean_all()
-                self.face_helper.read_image(np_image)
-                self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
-                self.face_helper.align_warp_face()
-
-                for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
-                    cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-                    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-                    cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
-
-                    try:
-                        with torch.no_grad():
-                            output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
-                            restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
-                        del output
-                        torch.cuda.empty_cache()
-                    except Exception as error:
-                        print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
-                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
-
-                    restored_face = restored_face.astype('uint8')
-                    self.face_helper.add_restored_face(restored_face)
-
-                self.face_helper.get_inverse_affine(None)
-
-                restored_img = self.face_helper.paste_faces_to_input_image()
-                restored_img = restored_img[:, :, ::-1]
-
-                if original_resolution != restored_img.shape[0:2]:
-                    restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
-
-                self.face_helper.clean_all()
-
-                if shared.opts.face_restoration_unload:
-                    self.send_model_to(devices.cpu)
-
-                return restored_img
-
-        global have_codeformer
-        have_codeformer = True
-
-        global codeformer
-        codeformer = FaceRestorerCodeFormer(dirname)
-        shared.face_restorers.append(codeformer)
-
-    except Exception:
-        print("Error setting up CodeFormer:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-   # sys.path = stored_sys_path
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/textual_inversion/logging.py b/spaces/aodianyun/stable-diffusion-webui/modules/textual_inversion/logging.py
deleted file mode 100644
index b2c01f0a4ef6666c0c2e1147dbee9d6850d277c0..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/textual_inversion/logging.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import datetime
-import json
-import os
-
-saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
-saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
-saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
-saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
-saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
-
-
-def save_settings_to_file(log_directory, all_params):
-    now = datetime.datetime.now()
-    params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")}
-
-    keys = saved_params_all
-    if all_params.get('preview_from_txt2img'):
-        keys = keys | saved_params_previews
-
-    params.update({k: v for k, v in all_params.items() if k in keys})
-
-    filename = f'settings-{now.strftime("%Y-%m-%d-%H-%M-%S")}.json'
-    with open(os.path.join(log_directory, filename), "w") as file:
-        json.dump(params, file, indent=4)
diff --git a/spaces/apsys/normflows/app.py b/spaces/apsys/normflows/app.py
deleted file mode 100644
index cb2a47e6bec4337afb3db2cd12f6a842b3e264d4..0000000000000000000000000000000000000000
--- a/spaces/apsys/normflows/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import streamlit as st
-import torch
-from normflows import nflow
-import numpy as np
-import seaborn as sns
-import pandas as pd
-
-uploaded_file = st.file_uploader("Choose original dataset")
-col1,col2,col3 = st.columns(3)
-bw = col1.number_input('Scale',value=3.05)
-wd = col2.number_input('Weight Decay',value=0.0002)
-iters = col3.number_input('Iterations',value=400)
-
-
-
-def compute(dim):
-    api = nflow(dim=dim,latent=16,dataset=uploaded_file)
-    api.compile(optim=torch.optim.ASGD,bw=bw,lr=0.0001,wd=wd)
-    
-    my_bar = st.progress(0)
-    
-    for idx in api.train(iters=iters):
-        my_bar.progress(idx[0]/iters)
-    my_bar.progress(100)
-    samples = np.delete(np.array(api.model.sample(torch.tensor(api.scaled).float()).detach()),np.argmin(np.array(api.model.sample(torch.tensor(api.scaled).float()).detach()),axis=0),0)
-    # samples = np.delete(samples,np.argmax(samples,axis=0),0)
-    
-    
-
-        # fig, ax = plt.subplots()
-    g = sns.jointplot(x=samples[:, 0], y=samples[:, 1], kind='kde',cmap=sns.color_palette("Blues", as_cmap=True),fill=True,label='Gaussian KDE',levels=1000)
-
-    w = sns.scatterplot(x=api.scaled[:,0],y=api.scaled[:,1],ax=g.ax_joint,c='orange',marker='+',s=100,label='Real')
-    st.pyplot(w.get_figure())
-    
-    
-    def random_normal_samples(n, dim=3):
-        return torch.zeros(n, dim).normal_(mean=0, std=1)
-    
-    samples = np.array(api.model.sample(torch.tensor(random_normal_samples(1000,api.scaled.shape[-1])).float()).detach())
-    
-    return api.scaler.inverse_transform(samples)
-
-with st.form('login_form'):
-    st.write('Token for generation:')
-    token = st.text_input('Token')
-    submit = st.form_submit_button('Submit')
-
-if token in st.secrets['tokens'] and submit:
-    
-    if uploaded_file is not None:
-        dims = len(uploaded_file.getvalue().decode("utf-8").split('\n')[0].split(','))-1
-        samples=compute(dims)
-        st.download_button('Download generated CSV', pd.DataFrame(samples).to_csv(), 'text/csv')
-    
-    elif not uploaded_file:
-        st.write('Upload your file')
-    
-else:
-        st.markdown('## :red[You dont have access]')
-        st.markdown('Buy tokens here: [@advprop](https://adprop.t.me)')
\ No newline at end of file
diff --git a/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/README.md b/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/README.md
deleted file mode 100644
index d56f95805723fd89a09598a1aa1b853b2d20c296..0000000000000000000000000000000000000000
--- a/spaces/aquaaaaaaaaaaaa/AI-minato_aqua/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: AI-minato Aqua
-emoji: 🐠
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: other
-duplicated_from: DoNotSelect/AI-minato_aqua
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/artificialguybr/video-dubbing/TTS/docs/source/main_classes/dataset.md b/spaces/artificialguybr/video-dubbing/TTS/docs/source/main_classes/dataset.md
deleted file mode 100644
index 92d381aca552c6fe95a9573d76227b8aa51a8dc0..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/docs/source/main_classes/dataset.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Datasets
-
-## TTS Dataset
-
-```{eval-rst}
-.. autoclass:: TTS.tts.datasets.TTSDataset
-    :members:
-```
-
-## Vocoder Dataset
-
-```{eval-rst}
-.. autoclass:: TTS.vocoder.datasets.gan_dataset.GANDataset
-    :members:
-```
-
-```{eval-rst}
-.. autoclass:: TTS.vocoder.datasets.wavegrad_dataset.WaveGradDataset
-    :members:
-```
-
-```{eval-rst}
-.. autoclass:: TTS.vocoder.datasets.wavernn_dataset.WaveRNNDataset
-    :members:
-```
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/DES3.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/DES3.py
deleted file mode 100644
index c0d93671332171fd44900ab280a593bb9a486066..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/DES3.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  Cipher/DES3.py : DES3
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# 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.
-# ===================================================================
-"""
-Module's constants for the modes of operation supported with Triple DES:
-
-:var MODE_ECB: :ref:`Electronic Code Book (ECB) <ecb_mode>`
-:var MODE_CBC: :ref:`Cipher-Block Chaining (CBC) <cbc_mode>`
-:var MODE_CFB: :ref:`Cipher FeedBack (CFB) <cfb_mode>`
-:var MODE_OFB: :ref:`Output FeedBack (OFB) <ofb_mode>`
-:var MODE_CTR: :ref:`CounTer Mode (CTR) <ctr_mode>`
-:var MODE_OPENPGP:  :ref:`OpenPGP Mode <openpgp_mode>`
-:var MODE_EAX: :ref:`EAX Mode <eax_mode>`
-"""
-
-import sys
-
-from Crypto.Cipher import _create_cipher
-from Crypto.Util.py3compat import byte_string, bchr, bord, bstr
-from Crypto.Util._raw_api import (load_pycryptodome_raw_lib,
-                                  VoidPointer, SmartPointer,
-                                  c_size_t)
-
-_raw_des3_lib = load_pycryptodome_raw_lib(
-                    "Crypto.Cipher._raw_des3",
-                    """
-                    int DES3_start_operation(const uint8_t key[],
-                                             size_t key_len,
-                                             void **pResult);
-                    int DES3_encrypt(const void *state,
-                                     const uint8_t *in,
-                                     uint8_t *out,
-                                     size_t data_len);
-                    int DES3_decrypt(const void *state,
-                                     const uint8_t *in,
-                                     uint8_t *out,
-                                     size_t data_len);
-                    int DES3_stop_operation(void *state);
-                    """)
-
-
-def adjust_key_parity(key_in):
-    """Set the parity bits in a TDES key.
-
-    :param key_in: the TDES key whose bits need to be adjusted
-    :type key_in: byte string
-
-    :returns: a copy of ``key_in``, with the parity bits correctly set
-    :rtype: byte string
-
-    :raises ValueError: if the TDES key is not 16 or 24 bytes long
-    :raises ValueError: if the TDES key degenerates into Single DES
-    """
-
-    def parity_byte(key_byte):
-        parity = 1
-        for i in range(1, 8):
-            parity ^= (key_byte >> i) & 1
-        return (key_byte & 0xFE) | parity
-
-    if len(key_in) not in key_size:
-        raise ValueError("Not a valid TDES key")
-
-    key_out = b"".join([ bchr(parity_byte(bord(x))) for x in key_in ])
-
-    if key_out[:8] == key_out[8:16] or key_out[-16:-8] == key_out[-8:]:
-        raise ValueError("Triple DES key degenerates to single DES")
-
-    return key_out
-
-
-def _create_base_cipher(dict_parameters):
-    """This method instantiates and returns a handle to a low-level base cipher.
-    It will absorb named parameters in the process."""
-
-    try:
-        key_in = dict_parameters.pop("key")
-    except KeyError:
-        raise TypeError("Missing 'key' parameter")
-
-    key = adjust_key_parity(bstr(key_in))
-
-    start_operation = _raw_des3_lib.DES3_start_operation
-    stop_operation = _raw_des3_lib.DES3_stop_operation
-
-    cipher = VoidPointer()
-    result = start_operation(key,
-                             c_size_t(len(key)),
-                             cipher.address_of())
-    if result:
-        raise ValueError("Error %X while instantiating the TDES cipher"
-                         % result)
-    return SmartPointer(cipher.get(), stop_operation)
-
-
-def new(key, mode, *args, **kwargs):
-    """Create a new Triple DES cipher.
-
-    :param key:
-        The secret key to use in the symmetric cipher.
-        It must be 16 or 24 byte long. The parity bits will be ignored.
-    :type key: bytes/bytearray/memoryview
-
-    :param mode:
-        The chaining mode to use for encryption or decryption.
-    :type mode: One of the supported ``MODE_*`` constants
-
-    :Keyword Arguments:
-        *   **iv** (*bytes*, *bytearray*, *memoryview*) --
-            (Only applicable for ``MODE_CBC``, ``MODE_CFB``, ``MODE_OFB``,
-            and ``MODE_OPENPGP`` modes).
-
-            The initialization vector to use for encryption or decryption.
-
-            For ``MODE_CBC``, ``MODE_CFB``, and ``MODE_OFB`` it must be 8 bytes long.
-
-            For ``MODE_OPENPGP`` mode only,
-            it must be 8 bytes long for encryption
-            and 10 bytes for decryption (in the latter case, it is
-            actually the *encrypted* IV which was prefixed to the ciphertext).
-
-            If not provided, a random byte string is generated (you must then
-            read its value with the :attr:`iv` attribute).
-
-        *   **nonce** (*bytes*, *bytearray*, *memoryview*) --
-            (Only applicable for ``MODE_EAX`` and ``MODE_CTR``).
-
-            A value that must never be reused for any other encryption done
-            with this key.
-
-            For ``MODE_EAX`` there are no
-            restrictions on its length (recommended: **16** bytes).
-
-            For ``MODE_CTR``, its length must be in the range **[0..7]**.
-
-            If not provided for ``MODE_EAX``, a random byte string is generated (you
-            can read it back via the ``nonce`` attribute).
-
-        *   **segment_size** (*integer*) --
-            (Only ``MODE_CFB``).The number of **bits** the plaintext and ciphertext
-            are segmented in. It must be a multiple of 8.
-            If not specified, it will be assumed to be 8.
-
-        *   **mac_len** : (*integer*) --
-            (Only ``MODE_EAX``)
-            Length of the authentication tag, in bytes.
-            It must be no longer than 8 (default).
-
-        *   **initial_value** : (*integer*) --
-            (Only ``MODE_CTR``). The initial value for the counter within
-            the counter block. By default it is **0**.
-
-    :Return: a Triple DES object, of the applicable mode.
-    """
-
-    return _create_cipher(sys.modules[__name__], key, mode, *args, **kwargs)
-
-MODE_ECB = 1
-MODE_CBC = 2
-MODE_CFB = 3
-MODE_OFB = 5
-MODE_CTR = 6
-MODE_OPENPGP = 7
-MODE_EAX = 9
-
-# Size of a data block (in bytes)
-block_size = 8
-# Size of a key (in bytes)
-key_size = (16, 24)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/SunImagePlugin.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/SunImagePlugin.py
deleted file mode 100644
index c03759a01e6d3422dd636ff102d4284644adf0ff..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/SunImagePlugin.py
+++ /dev/null
@@ -1,136 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Sun image file handling
-#
-# History:
-# 1995-09-10 fl   Created
-# 1996-05-28 fl   Fixed 32-bit alignment
-# 1998-12-29 fl   Import ImagePalette module
-# 2001-12-18 fl   Fixed palette loading (from Jean-Claude Rimbault)
-#
-# Copyright (c) 1997-2001 by Secret Labs AB
-# Copyright (c) 1995-1996 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i32be as i32
-
-
-def _accept(prefix):
-    return len(prefix) >= 4 and i32(prefix) == 0x59A66A95
-
-
-##
-# Image plugin for Sun raster files.
-
-
-class SunImageFile(ImageFile.ImageFile):
-
-    format = "SUN"
-    format_description = "Sun Raster File"
-
-    def _open(self):
-
-        # The Sun Raster file header is 32 bytes in length
-        # and has the following format:
-
-        #     typedef struct _SunRaster
-        #     {
-        #         DWORD MagicNumber;      /* Magic (identification) number */
-        #         DWORD Width;            /* Width of image in pixels */
-        #         DWORD Height;           /* Height of image in pixels */
-        #         DWORD Depth;            /* Number of bits per pixel */
-        #         DWORD Length;           /* Size of image data in bytes */
-        #         DWORD Type;             /* Type of raster file */
-        #         DWORD ColorMapType;     /* Type of color map */
-        #         DWORD ColorMapLength;   /* Size of the color map in bytes */
-        #     } SUNRASTER;
-
-        # HEAD
-        s = self.fp.read(32)
-        if not _accept(s):
-            raise SyntaxError("not an SUN raster file")
-
-        offset = 32
-
-        self._size = i32(s, 4), i32(s, 8)
-
-        depth = i32(s, 12)
-        # data_length = i32(s, 16)   # unreliable, ignore.
-        file_type = i32(s, 20)
-        palette_type = i32(s, 24)  # 0: None, 1: RGB, 2: Raw/arbitrary
-        palette_length = i32(s, 28)
-
-        if depth == 1:
-            self.mode, rawmode = "1", "1;I"
-        elif depth == 4:
-            self.mode, rawmode = "L", "L;4"
-        elif depth == 8:
-            self.mode = rawmode = "L"
-        elif depth == 24:
-            if file_type == 3:
-                self.mode, rawmode = "RGB", "RGB"
-            else:
-                self.mode, rawmode = "RGB", "BGR"
-        elif depth == 32:
-            if file_type == 3:
-                self.mode, rawmode = "RGB", "RGBX"
-            else:
-                self.mode, rawmode = "RGB", "BGRX"
-        else:
-            raise SyntaxError("Unsupported Mode/Bit Depth")
-
-        if palette_length:
-            if palette_length > 1024:
-                raise SyntaxError("Unsupported Color Palette Length")
-
-            if palette_type != 1:
-                raise SyntaxError("Unsupported Palette Type")
-
-            offset = offset + palette_length
-            self.palette = ImagePalette.raw("RGB;L", self.fp.read(palette_length))
-            if self.mode == "L":
-                self.mode = "P"
-                rawmode = rawmode.replace("L", "P")
-
-        # 16 bit boundaries on stride
-        stride = ((self.size[0] * depth + 15) // 16) * 2
-
-        # file type: Type is the version (or flavor) of the bitmap
-        # file. The following values are typically found in the Type
-        # field:
-        # 0000h Old
-        # 0001h Standard
-        # 0002h Byte-encoded
-        # 0003h RGB format
-        # 0004h TIFF format
-        # 0005h IFF format
-        # FFFFh Experimental
-
-        # Old and standard are the same, except for the length tag.
-        # byte-encoded is run-length-encoded
-        # RGB looks similar to standard, but RGB byte order
-        # TIFF and IFF mean that they were converted from T/IFF
-        # Experimental means that it's something else.
-        # (https://www.fileformat.info/format/sunraster/egff.htm)
-
-        if file_type in (0, 1, 3, 4, 5):
-            self.tile = [("raw", (0, 0) + self.size, offset, (rawmode, stride))]
-        elif file_type == 2:
-            self.tile = [("sun_rle", (0, 0) + self.size, offset, rawmode)]
-        else:
-            raise SyntaxError("Unsupported Sun Raster file type")
-
-
-#
-# registry
-
-
-Image.register_open(SunImageFile.format, SunImageFile, _accept)
-
-Image.register_extension(SunImageFile.format, ".ras")
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/bar_chart_horizontal.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/bar_chart_horizontal.py
deleted file mode 100644
index 9d145f7fd908df48f54fd5b5860590849e802871..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/bar_chart_horizontal.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"""
-Horizontal Bar Chart
---------------------
-This example is a bar chart drawn horizontally by putting the quantitative value on the x axis.
-"""
-# category: bar charts
-import altair as alt
-from vega_datasets import data
-
-source = data.wheat()
-
-alt.Chart(source).mark_bar().encode(
-    x='wheat:Q',
-    y="year:O"
-).properties(height=700)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/histogram_responsive.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/histogram_responsive.py
deleted file mode 100644
index c8af4e7148d97d9babde22a9824843693bcd6074..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/histogram_responsive.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""
-Histogram with Responsive Bins
-------------------------------
-This shows an example of a histogram with bins that are responsive to a
-selection domain. Click and drag on the bottom panel to see the bins
-change on the top panel.
-"""
-# category: histograms
-import altair as alt
-from vega_datasets import data
-
-source = data.flights_5k.url
-
-brush = alt.selection_interval(encodings=['x'])
-
-base = alt.Chart(source).transform_calculate(
-    time="hours(datum.date) + minutes(datum.date) / 60"
-).mark_bar().encode(
-    y='count():Q'
-).properties(
-    width=600,
-    height=100
-)
-
-alt.vconcat(
-  base.encode(
-    alt.X('time:Q',
-      bin=alt.Bin(maxbins=30, extent=brush),
-      scale=alt.Scale(domain=brush)
-    )
-  ),
-  base.encode(
-    alt.X('time:Q', bin=alt.Bin(maxbins=30)),
-  ).add_selection(brush)
-)
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/multifeature_scatter_plot.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/multifeature_scatter_plot.py
deleted file mode 100644
index 40e189bbf6608330dba5c9d69ba24af286782f17..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/multifeature_scatter_plot.py
+++ /dev/null
@@ -1,17 +0,0 @@
-"""
-Multifeature Scatter Plot
-=========================
-This example shows how to make a scatter plot with multiple feature encodings.
-"""
-# category: scatter plots
-import altair as alt
-from vega_datasets import data
-
-source = data.iris()
-
-alt.Chart(source).mark_circle().encode(
-    alt.X('sepalLength', scale=alt.Scale(zero=False)),
-    alt.Y('sepalWidth', scale=alt.Scale(zero=False, padding=1)),
-    color='species',
-    size='petalWidth'
-)
diff --git a/spaces/aryadytm/remove-photo-background/src/models/backbones/mobilenetv2.py b/spaces/aryadytm/remove-photo-background/src/models/backbones/mobilenetv2.py
deleted file mode 100644
index 709d352565799f181bfaed652c796ef065e71a0f..0000000000000000000000000000000000000000
--- a/spaces/aryadytm/remove-photo-background/src/models/backbones/mobilenetv2.py
+++ /dev/null
@@ -1,199 +0,0 @@
-""" This file is adapted from https://github.com/thuyngch/Human-Segmentation-PyTorch"""
-
-import math
-import json
-from functools import reduce
-
-import torch
-from torch import nn
-
-
-#------------------------------------------------------------------------------
-#  Useful functions
-#------------------------------------------------------------------------------
-
-def _make_divisible(v, divisor, min_value=None):
-	if min_value is None:
-		min_value = divisor
-	new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-	# Make sure that round down does not go down by more than 10%.
-	if new_v < 0.9 * v:
-		new_v += divisor
-	return new_v
-
-
-def conv_bn(inp, oup, stride):
-	return nn.Sequential(
-		nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
-		nn.BatchNorm2d(oup),
-		nn.ReLU6(inplace=True)
-	)
-
-
-def conv_1x1_bn(inp, oup):
-	return nn.Sequential(
-		nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
-		nn.BatchNorm2d(oup),
-		nn.ReLU6(inplace=True)
-	)
-
-
-#------------------------------------------------------------------------------
-#  Class of Inverted Residual block
-#------------------------------------------------------------------------------
-
-class InvertedResidual(nn.Module):
-	def __init__(self, inp, oup, stride, expansion, dilation=1):
-		super(InvertedResidual, self).__init__()
-		self.stride = stride
-		assert stride in [1, 2]
-
-		hidden_dim = round(inp * expansion)
-		self.use_res_connect = self.stride == 1 and inp == oup
-
-		if expansion == 1:
-			self.conv = nn.Sequential(
-				# dw
-				nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
-				nn.BatchNorm2d(hidden_dim),
-				nn.ReLU6(inplace=True),
-				# pw-linear
-				nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
-				nn.BatchNorm2d(oup),
-			)
-		else:
-			self.conv = nn.Sequential(
-				# pw
-				nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
-				nn.BatchNorm2d(hidden_dim),
-				nn.ReLU6(inplace=True),
-				# dw
-				nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
-				nn.BatchNorm2d(hidden_dim),
-				nn.ReLU6(inplace=True),
-				# pw-linear
-				nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
-				nn.BatchNorm2d(oup),
-			)
-
-	def forward(self, x):
-		if self.use_res_connect:
-			return x + self.conv(x)
-		else:
-			return self.conv(x)
-
-
-#------------------------------------------------------------------------------
-#  Class of MobileNetV2
-#------------------------------------------------------------------------------
-
-class MobileNetV2(nn.Module):
-	def __init__(self, in_channels, alpha=1.0, expansion=6, num_classes=1000):
-		super(MobileNetV2, self).__init__()
-		self.in_channels = in_channels
-		self.num_classes = num_classes
-		input_channel = 32
-		last_channel = 1280
-		interverted_residual_setting = [
-			# t, c, n, s
-			[1        , 16, 1, 1],
-			[expansion, 24, 2, 2],
-			[expansion, 32, 3, 2],
-			[expansion, 64, 4, 2],
-			[expansion, 96, 3, 1],
-			[expansion, 160, 3, 2],
-			[expansion, 320, 1, 1],
-		]
-
-		# building first layer
-		input_channel = _make_divisible(input_channel*alpha, 8)
-		self.last_channel = _make_divisible(last_channel*alpha, 8) if alpha > 1.0 else last_channel
-		self.features = [conv_bn(self.in_channels, input_channel, 2)]
-
-		# building inverted residual blocks
-		for t, c, n, s in interverted_residual_setting:
-			output_channel = _make_divisible(int(c*alpha), 8)
-			for i in range(n):
-				if i == 0:
-					self.features.append(InvertedResidual(input_channel, output_channel, s, expansion=t))
-				else:
-					self.features.append(InvertedResidual(input_channel, output_channel, 1, expansion=t))
-				input_channel = output_channel
-
-		# building last several layers
-		self.features.append(conv_1x1_bn(input_channel, self.last_channel))
-
-		# make it nn.Sequential
-		self.features = nn.Sequential(*self.features)
-
-		# building classifier
-		if self.num_classes is not None:
-			self.classifier = nn.Sequential(
-				nn.Dropout(0.2),
-				nn.Linear(self.last_channel, num_classes),
-			)
-
-		# Initialize weights
-		self._init_weights()
-
-	def forward(self, x):
-		# Stage1
-		x = self.features[0](x)
-		x = self.features[1](x)
-		# Stage2
-		x = self.features[2](x)
-		x = self.features[3](x)
-		# Stage3
-		x = self.features[4](x)
-		x = self.features[5](x)
-		x = self.features[6](x)
-		# Stage4
-		x = self.features[7](x)
-		x = self.features[8](x)
-		x = self.features[9](x)
-		x = self.features[10](x)
-		x = self.features[11](x)
-		x = self.features[12](x)
-		x = self.features[13](x)
-		# Stage5
-		x = self.features[14](x)
-		x = self.features[15](x)
-		x = self.features[16](x)
-		x = self.features[17](x)
-		x = self.features[18](x)
-
-		# Classification
-		if self.num_classes is not None:
-			x = x.mean(dim=(2,3))
-			x = self.classifier(x)
-			
-		# Output
-		return x
-
-	def _load_pretrained_model(self, pretrained_file):
-		pretrain_dict = torch.load(pretrained_file, map_location='cpu')
-		model_dict = {}
-		state_dict = self.state_dict()
-		print("[MobileNetV2] Loading pretrained model...")
-		for k, v in pretrain_dict.items():
-			if k in state_dict:
-				model_dict[k] = v
-			else:
-				print(k, "is ignored")
-		state_dict.update(model_dict)
-		self.load_state_dict(state_dict)
-
-	def _init_weights(self):
-		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))
-				if m.bias is not None:
-					m.bias.data.zero_()
-			elif isinstance(m, nn.BatchNorm2d):
-				m.weight.data.fill_(1)
-				m.bias.data.zero_()
-			elif isinstance(m, nn.Linear):
-				n = m.weight.size(1)
-				m.weight.data.normal_(0, 0.01)
-				m.bias.data.zero_()
diff --git a/spaces/avivdm1/AutoGPT/autogpt/commands/twitter.py b/spaces/avivdm1/AutoGPT/autogpt/commands/twitter.py
deleted file mode 100644
index 3eaed36e20e1c520690ac59f25a4da6501f3440f..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/commands/twitter.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-
-import tweepy
-from dotenv import load_dotenv
-
-load_dotenv()
-
-
-def send_tweet(tweet_text):
-    consumer_key = os.environ.get("TW_CONSUMER_KEY")
-    consumer_secret = os.environ.get("TW_CONSUMER_SECRET")
-    access_token = os.environ.get("TW_ACCESS_TOKEN")
-    access_token_secret = os.environ.get("TW_ACCESS_TOKEN_SECRET")
-    # Authenticate to Twitter
-    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
-    auth.set_access_token(access_token, access_token_secret)
-
-    # Create API object
-    api = tweepy.API(auth)
-
-    # Send tweet
-    try:
-        api.update_status(tweet_text)
-        print("Tweet sent successfully!")
-    except tweepy.TweepyException as e:
-        print("Error sending tweet: {}".format(e.reason))
diff --git a/spaces/awacke1/ChatGPTStreamlit7/app.py b/spaces/awacke1/ChatGPTStreamlit7/app.py
deleted file mode 100644
index 9fcd544b1048b3bbe3efd012716150f93ac3564d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ChatGPTStreamlit7/app.py
+++ /dev/null
@@ -1,258 +0,0 @@
-import streamlit as st
-import openai
-import os
-import base64
-import glob
-import json
-import mistune
-import pytz
-import math
-import requests
-
-from datetime import datetime
-from openai import ChatCompletion
-from xml.etree import ElementTree as ET
-from bs4 import BeautifulSoup
-from collections import deque
-from audio_recorder_streamlit import audio_recorder
-
-def generate_filename(prompt, file_type):
-    central = pytz.timezone('US/Central')
-    safe_date_time = datetime.now(central).strftime("%m%d_%I%M")  
-    safe_prompt = "".join(x for x in prompt if x.isalnum())[:45]
-    return f"{safe_date_time}_{safe_prompt}.{file_type}"
-
-def chat_with_model(prompt, document_section):
-    model = model_choice
-    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
-    conversation.append({'role': 'user', 'content': prompt})
-    if len(document_section)>0:
-        conversation.append({'role': 'assistant', 'content': document_section})
-    response = openai.ChatCompletion.create(model=model, messages=conversation)
-    #return response
-    return response['choices'][0]['message']['content']
-    
-def transcribe_audio(openai_key, file_path, model):
-    OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
-    headers = {
-        "Authorization": f"Bearer {openai_key}",
-    }
-    with open(file_path, 'rb') as f:
-        data = {'file': f}
-        response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
-    if response.status_code == 200:
-        st.write(response.json())
-        
-        response2 = chat_with_model(response.json().get('text'), '') # *************************************
-        st.write('Responses:')
-        #st.write(response)
-        st.write(response2)
-        return response.json().get('text')
-    else:
-        st.write(response.json())
-        st.error("Error in API call.")
-        return None
-
-def save_and_play_audio(audio_recorder):
-    audio_bytes = audio_recorder()
-    if audio_bytes:
-        filename = generate_filename("Recording", "wav")
-        with open(filename, 'wb') as f:
-            f.write(audio_bytes)
-        st.audio(audio_bytes, format="audio/wav")
-        return filename
-    return None
-
-def create_file(filename, prompt, response):
-    if filename.endswith(".txt"):
-        with open(filename, 'w') as file:
-            file.write(f"{prompt}\n{response}")
-    elif filename.endswith(".htm"):
-        with open(filename, 'w') as file:
-            file.write(f"{prompt}   {response}")
-    elif filename.endswith(".md"):
-        with open(filename, 'w') as file:
-            file.write(f"{prompt}\n\n{response}")
-            
-def truncate_document(document, length):
-    return document[:length]
-def divide_document(document, max_length):
-    return [document[i:i+max_length] for i in range(0, len(document), max_length)]
-
-def get_table_download_link(file_path):
-    with open(file_path, 'r') as file:
-        data = file.read()
-    b64 = base64.b64encode(data.encode()).decode()  
-    file_name = os.path.basename(file_path)
-    ext = os.path.splitext(file_name)[1]  # get the file extension
-    if ext == '.txt':
-        mime_type = 'text/plain'
-    elif ext == '.py':
-        mime_type = 'text/plain'
-    elif ext == '.xlsx':
-        mime_type = 'text/plain'
-    elif ext == '.csv':
-        mime_type = 'text/plain'
-    elif ext == '.htm':
-        mime_type = 'text/html'
-    elif ext == '.md':
-        mime_type = 'text/markdown'
-    else:
-        mime_type = 'application/octet-stream'  # general binary data type
-    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
-    return href
-
-def CompressXML(xml_text):
-    root = ET.fromstring(xml_text)
-    for elem in list(root.iter()):
-        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
-            elem.parent.remove(elem)
-    return ET.tostring(root, encoding='unicode', method="xml")
-    
-def read_file_content(file,max_length):
-    if file.type == "application/json":
-        content = json.load(file)
-        return str(content)
-    elif file.type == "text/html" or file.type == "text/htm":
-        content = BeautifulSoup(file, "html.parser")
-        return content.text
-    elif file.type == "application/xml" or file.type == "text/xml":
-        tree = ET.parse(file)
-        root = tree.getroot()
-        xml = CompressXML(ET.tostring(root, encoding='unicode'))
-        return xml
-    elif file.type == "text/markdown" or file.type == "text/md":
-        md = mistune.create_markdown()
-        content = md(file.read().decode())
-        return content
-    elif file.type == "text/plain":
-        return file.getvalue().decode()
-    else:
-        return ""
-
-
-
-def chat_with_file_contents(prompt, file_content):
-    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
-    conversation.append({'role': 'user', 'content': prompt})
-    if len(file_content)>0:
-        conversation.append({'role': 'assistant', 'content': file_content})
-    response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
-    return response['choices'][0]['message']['content']
-        
-
-# Sidebar and global
-openai.api_key = os.getenv('OPENAI_KEY')
-st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
-menu = ["htm", "txt", "xlsx", "csv", "md", "py"]  #619
-choice = st.sidebar.selectbox("Output File Type:", menu)
-model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
-
-# Audio, transcribe, GPT:
-filename = save_and_play_audio(audio_recorder)
-if filename is not None:
-    transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
-    st.write(transcription)
-    gptOutput = chat_with_model(transcription, '') # *************************************
-    filename = generate_filename(transcription, choice)
-    create_file(filename, transcription, gptOutput)
-    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-
-def main():
-    user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
-
-    collength, colupload = st.columns([2,3])  # adjust the ratio as needed
-    with collength:
-        #max_length = 12000 - optimal for gpt35 turbo. 2x=24000 for gpt4.  8x=96000 for gpt4-32k.
-        max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
-    with colupload:
-        uploaded_file = st.file_uploader("Add a file for context:", type=["xml", "json", "xlsx","csv","html", "htm", "md", "txt"])
-    
-    document_sections = deque()
-    document_responses = {}
-
-    if uploaded_file is not None:
-        file_content = read_file_content(uploaded_file, max_length)
-        document_sections.extend(divide_document(file_content, max_length))
-
-    if len(document_sections) > 0:
-        
-        if st.button("👁️ View Upload"):
-            st.markdown("**Sections of the uploaded file:**")
-            for i, section in enumerate(list(document_sections)):
-                st.markdown(f"**Section {i+1}**\n{section}")
-        
-        st.markdown("**Chat with the model:**")
-        for i, section in enumerate(list(document_sections)):
-            if i in document_responses:
-                st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
-            else:
-                if st.button(f"Chat about Section {i+1}"):
-                    st.write('Reasoning with your inputs...')
-                    response = chat_with_model(user_prompt, section) # *************************************
-                    st.write('Response:')
-                    st.write(response)
-                    document_responses[i] = response
-                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
-                    create_file(filename, user_prompt, response)
-                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-    if st.button('💬 Chat'):
-        st.write('Reasoning with your inputs...')
-        response = chat_with_model(user_prompt, ''.join(list(document_sections))) # *************************************
-        st.write('Response:')
-        st.write(response)
-        
-        filename = generate_filename(user_prompt, choice)
-        create_file(filename, user_prompt, response)
-        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-    all_files = glob.glob("*.*")
-    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
-    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
-
-    # sidebar of files
-    file_contents=''
-    next_action=''
-    for file in all_files:
-        col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1])  # adjust the ratio as needed
-        with col1:
-            if st.button("🌐", key="md_"+file):  # md emoji button
-                with open(file, 'r') as f:
-                    file_contents = f.read()
-                    next_action='md'
-        with col2:
-            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
-        with col3:
-            if st.button("📂", key="open_"+file):  # open emoji button
-                with open(file, 'r') as f:
-                    file_contents = f.read()
-                    next_action='open'
-        with col4:
-            if st.button("🔍", key="read_"+file):  # search emoji button
-                with open(file, 'r') as f:
-                    file_contents = f.read()
-                    next_action='search'
-        with col5:
-            if st.button("🗑", key="delete_"+file):
-                os.remove(file)
-                st.experimental_rerun()
-                
-    if len(file_contents) > 0:
-        if next_action=='open':
-            file_content_area = st.text_area("File Contents:", file_contents, height=500)
-        if next_action=='md':
-            st.markdown(file_contents)
-        if next_action=='search':
-            file_content_area = st.text_area("File Contents:", file_contents, height=500)
-            st.write('Reasoning with your inputs...')
-            response = chat_with_file_contents(user_prompt, file_contents)
-            st.write('Response:')
-            st.write(response)
-            filename = generate_filename(file_content_area, choice)
-            create_file(filename, file_content_area, response)
-            st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-                
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/awacke1/Docker.Jupyterlab.Integration.HF/README.md b/spaces/awacke1/Docker.Jupyterlab.Integration.HF/README.md
deleted file mode 100644
index 2d16889cfdb8e0cd7bd088ff78a4f33a3f5464cf..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Docker.Jupyterlab.Integration.HF/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: JupyterLab
-emoji: 💻🐳
-colorFrom: gray
-colorTo: green
-sdk: docker
-pinned: false
-tags:
-- jupyterlab
-duplicated_from: DockerTemplates/jupyterlab
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/MLOpsStreamlit/README.md b/spaces/awacke1/MLOpsStreamlit/README.md
deleted file mode 100644
index 244e4a9cdc157fc0faef2319be43b7495e342efe..0000000000000000000000000000000000000000
--- a/spaces/awacke1/MLOpsStreamlit/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 📊Graph ML Ops NLP Words📊
-emoji: 📊💬📊
-colorFrom: blue
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.9.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/awacke1/SOTA-Plan/app.py b/spaces/awacke1/SOTA-Plan/app.py
deleted file mode 100644
index 3f851aeabce66431337ae8ca11b4ba4ae1e40b0b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/SOTA-Plan/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-
-context = "What should be documented in a care plan?\n"
-context = context + "Regardless of what your preferences are, your care plan should include:\n"
-context = context + "What your assessed care needs are.\n"
-context = context + "What type of support you should receive.\n"
-context = context + "Your desired outcomes.\n"
-context = context + "Who should provide care.\n"
-context = context + "When care and support should be provided.\n"
-context = context + "Records of care provided.\n"
-context = context + "Your wishes and personal preferences.\n"
-context = context + "The costs of the services.\n"
-context = context + "Dimensions\n"
-context = context + "1-Ontology of Plan\n"
-context = context + "2-Problems as evidenced by Signs of Systems\n"
-context = context + "3-Assessment of Needs\n"
-context = context + "4-Questions about problems faced\n"
-context = context + "5-Goals for long and short term improvements\n"
-context = context + "6-Knowledge-Behavior-Status Quality Measures\n"
-context = context + "7-Intervention List of Options\n"
-context = context + "8-Quality Measures\n"
-context = context + "9-Pathways Available\n"
-
-with open('WritingCarePlans.txt', 'r') as file:
-        context = file.read()
-
-question = "What should be documented in a care plan?"
-
-gr.Interface.load(
-             "huggingface/deepset/roberta-base-squad2",
-             theme="default",
-             css=".footer{display:none !important}",
-             inputs=[gr.inputs.Textbox(lines=12, default=context, label="Context paragraph"), gr.inputs.Textbox(lines=3, default=question, label="Question")],
-             outputs=[gr.outputs.Textbox(label="Answer"), gr.outputs.Textbox(label="Score")],
-             title=None,
-             description="Provide your own paragraph and ask any question about the text. How well does the model answer?").launch()
\ No newline at end of file
diff --git a/spaces/awacke1/Try.Playing.Learning.Sharing.On.This/style.css b/spaces/awacke1/Try.Playing.Learning.Sharing.On.This/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Try.Playing.Learning.Sharing.On.This/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/banana-projects/web3d/node_modules/three/examples/js/loaders/AMFLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/AMFLoader.js
deleted file mode 100644
index 55ef7cd3b770acb75aa3ae266f733efe40983130..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/AMFLoader.js
+++ /dev/null
@@ -1,495 +0,0 @@
-/*
- * @author tamarintech / https://tamarintech.com
- *
- * Description: Early release of an AMF Loader following the pattern of the
- * example loaders in the three.js project.
- *
- * More information about the AMF format: http://amf.wikispaces.com
- *
- * Usage:
- *	var loader = new AMFLoader();
- *	loader.load('/path/to/project.amf', function(objecttree) {
- *		scene.add(objecttree);
- *	});
- *
- * Materials now supported, material colors supported
- * Zip support, requires jszip
- * No constellation support (yet)!
- *
- */
-
-THREE.AMFLoader = function ( manager ) {
-
-	this.manager = ( manager !== undefined ) ? manager : THREE.DefaultLoadingManager;
-
-};
-
-THREE.AMFLoader.prototype = {
-
-	constructor: THREE.AMFLoader,
-
-	load: function ( url, onLoad, onProgress, onError ) {
-
-		var scope = this;
-
-		var loader = new THREE.FileLoader( scope.manager );
-		loader.setPath( scope.path );
-		loader.setResponseType( 'arraybuffer' );
-		loader.load( url, function ( text ) {
-
-			onLoad( scope.parse( text ) );
-
-		}, onProgress, onError );
-
-	},
-
-	setPath: function ( value ) {
-
-		this.path = value;
-		return this;
-
-	},
-
-	parse: function ( data ) {
-
-		function loadDocument( data ) {
-
-			var view = new DataView( data );
-			var magic = String.fromCharCode( view.getUint8( 0 ), view.getUint8( 1 ) );
-
-			if ( magic === 'PK' ) {
-
-				var zip = null;
-				var file = null;
-
-				console.log( 'THREE.AMFLoader: Loading Zip' );
-
-				try {
-
-					zip = new JSZip( data ); // eslint-disable-line no-undef
-
-				} catch ( e ) {
-
-					if ( e instanceof ReferenceError ) {
-
-						console.log( 'THREE.AMFLoader: jszip missing and file is compressed.' );
-						return null;
-
-					}
-
-				}
-
-				for ( file in zip.files ) {
-
-					if ( file.toLowerCase().substr( - 4 ) === '.amf' ) {
-
-						break;
-
-					}
-
-				}
-
-				console.log( 'THREE.AMFLoader: Trying to load file asset: ' + file );
-				view = new DataView( zip.file( file ).asArrayBuffer() );
-
-			}
-
-			var fileText = THREE.LoaderUtils.decodeText( view );
-			var xmlData = new DOMParser().parseFromString( fileText, 'application/xml' );
-
-			if ( xmlData.documentElement.nodeName.toLowerCase() !== 'amf' ) {
-
-				console.log( 'THREE.AMFLoader: Error loading AMF - no AMF document found.' );
-				return null;
-
-			}
-
-			return xmlData;
-
-		}
-
-		function loadDocumentScale( node ) {
-
-			var scale = 1.0;
-			var unit = 'millimeter';
-
-			if ( node.documentElement.attributes.unit !== undefined ) {
-
-				unit = node.documentElement.attributes.unit.value.toLowerCase();
-
-			}
-
-			var scaleUnits = {
-				millimeter: 1.0,
-				inch: 25.4,
-				feet: 304.8,
-				meter: 1000.0,
-				micron: 0.001
-			};
-
-			if ( scaleUnits[ unit ] !== undefined ) {
-
-				scale = scaleUnits[ unit ];
-
-			}
-
-			console.log( 'THREE.AMFLoader: Unit scale: ' + scale );
-			return scale;
-
-		}
-
-		function loadMaterials( node ) {
-
-			var matName = 'AMF Material';
-			var matId = node.attributes.id.textContent;
-			var color = { r: 1.0, g: 1.0, b: 1.0, a: 1.0 };
-
-			var loadedMaterial = null;
-
-			for ( var i = 0; i < node.childNodes.length; i ++ ) {
-
-				var matChildEl = node.childNodes[ i ];
-
-				if ( matChildEl.nodeName === 'metadata' && matChildEl.attributes.type !== undefined ) {
-
-					if ( matChildEl.attributes.type.value === 'name' ) {
-
-						matName = matChildEl.textContent;
-
-					}
-
-				} else if ( matChildEl.nodeName === 'color' ) {
-
-					color = loadColor( matChildEl );
-
-				}
-
-			}
-
-			loadedMaterial = new THREE.MeshPhongMaterial( {
-				flatShading: true,
-				color: new THREE.Color( color.r, color.g, color.b ),
-				name: matName
-			} );
-
-			if ( color.a !== 1.0 ) {
-
-				loadedMaterial.transparent = true;
-				loadedMaterial.opacity = color.a;
-
-			}
-
-			return { id: matId, material: loadedMaterial };
-
-		}
-
-		function loadColor( node ) {
-
-			var color = { r: 1.0, g: 1.0, b: 1.0, a: 1.0 };
-
-			for ( var i = 0; i < node.childNodes.length; i ++ ) {
-
-				var matColor = node.childNodes[ i ];
-
-				if ( matColor.nodeName === 'r' ) {
-
-					color.r = matColor.textContent;
-
-				} else if ( matColor.nodeName === 'g' ) {
-
-					color.g = matColor.textContent;
-
-				} else if ( matColor.nodeName === 'b' ) {
-
-					color.b = matColor.textContent;
-
-				} else if ( matColor.nodeName === 'a' ) {
-
-					color.a = matColor.textContent;
-
-				}
-
-			}
-
-			return color;
-
-		}
-
-		function loadMeshVolume( node ) {
-
-			var volume = { name: '', triangles: [], materialid: null };
-
-			var currVolumeNode = node.firstElementChild;
-
-			if ( node.attributes.materialid !== undefined ) {
-
-				volume.materialId = node.attributes.materialid.nodeValue;
-
-			}
-
-			while ( currVolumeNode ) {
-
-				if ( currVolumeNode.nodeName === 'metadata' ) {
-
-					if ( currVolumeNode.attributes.type !== undefined ) {
-
-						if ( currVolumeNode.attributes.type.value === 'name' ) {
-
-							volume.name = currVolumeNode.textContent;
-
-						}
-
-					}
-
-				} else if ( currVolumeNode.nodeName === 'triangle' ) {
-
-					var v1 = currVolumeNode.getElementsByTagName( 'v1' )[ 0 ].textContent;
-					var v2 = currVolumeNode.getElementsByTagName( 'v2' )[ 0 ].textContent;
-					var v3 = currVolumeNode.getElementsByTagName( 'v3' )[ 0 ].textContent;
-
-					volume.triangles.push( v1, v2, v3 );
-
-				}
-
-				currVolumeNode = currVolumeNode.nextElementSibling;
-
-			}
-
-			return volume;
-
-		}
-
-		function loadMeshVertices( node ) {
-
-			var vertArray = [];
-			var normalArray = [];
-			var currVerticesNode = node.firstElementChild;
-
-			while ( currVerticesNode ) {
-
-				if ( currVerticesNode.nodeName === 'vertex' ) {
-
-					var vNode = currVerticesNode.firstElementChild;
-
-					while ( vNode ) {
-
-						if ( vNode.nodeName === 'coordinates' ) {
-
-							var x = vNode.getElementsByTagName( 'x' )[ 0 ].textContent;
-							var y = vNode.getElementsByTagName( 'y' )[ 0 ].textContent;
-							var z = vNode.getElementsByTagName( 'z' )[ 0 ].textContent;
-
-							vertArray.push( x, y, z );
-
-						} else if ( vNode.nodeName === 'normal' ) {
-
-							var nx = vNode.getElementsByTagName( 'nx' )[ 0 ].textContent;
-							var ny = vNode.getElementsByTagName( 'ny' )[ 0 ].textContent;
-							var nz = vNode.getElementsByTagName( 'nz' )[ 0 ].textContent;
-
-							normalArray.push( nx, ny, nz );
-
-						}
-
-						vNode = vNode.nextElementSibling;
-
-					}
-
-				}
-				currVerticesNode = currVerticesNode.nextElementSibling;
-
-			}
-
-			return { 'vertices': vertArray, 'normals': normalArray };
-
-		}
-
-		function loadObject( node ) {
-
-			var objId = node.attributes.id.textContent;
-			var loadedObject = { name: 'amfobject', meshes: [] };
-			var currColor = null;
-			var currObjNode = node.firstElementChild;
-
-			while ( currObjNode ) {
-
-				if ( currObjNode.nodeName === 'metadata' ) {
-
-					if ( currObjNode.attributes.type !== undefined ) {
-
-						if ( currObjNode.attributes.type.value === 'name' ) {
-
-							loadedObject.name = currObjNode.textContent;
-
-						}
-
-					}
-
-				} else if ( currObjNode.nodeName === 'color' ) {
-
-					currColor = loadColor( currObjNode );
-
-				} else if ( currObjNode.nodeName === 'mesh' ) {
-
-					var currMeshNode = currObjNode.firstElementChild;
-					var mesh = { vertices: [], normals: [], volumes: [], color: currColor };
-
-					while ( currMeshNode ) {
-
-						if ( currMeshNode.nodeName === 'vertices' ) {
-
-							var loadedVertices = loadMeshVertices( currMeshNode );
-
-							mesh.normals = mesh.normals.concat( loadedVertices.normals );
-							mesh.vertices = mesh.vertices.concat( loadedVertices.vertices );
-
-						} else if ( currMeshNode.nodeName === 'volume' ) {
-
-							mesh.volumes.push( loadMeshVolume( currMeshNode ) );
-
-						}
-
-						currMeshNode = currMeshNode.nextElementSibling;
-
-					}
-
-					loadedObject.meshes.push( mesh );
-
-				}
-
-				currObjNode = currObjNode.nextElementSibling;
-
-			}
-
-			return { 'id': objId, 'obj': loadedObject };
-
-		}
-
-		var xmlData = loadDocument( data );
-		var amfName = '';
-		var amfAuthor = '';
-		var amfScale = loadDocumentScale( xmlData );
-		var amfMaterials = {};
-		var amfObjects = {};
-		var childNodes = xmlData.documentElement.childNodes;
-
-		var i, j;
-
-		for ( i = 0; i < childNodes.length; i ++ ) {
-
-			var child = childNodes[ i ];
-
-			if ( child.nodeName === 'metadata' ) {
-
-				if ( child.attributes.type !== undefined ) {
-
-					if ( child.attributes.type.value === 'name' ) {
-
-						amfName = child.textContent;
-
-					} else if ( child.attributes.type.value === 'author' ) {
-
-						amfAuthor = child.textContent;
-
-					}
-
-				}
-
-			} else if ( child.nodeName === 'material' ) {
-
-				var loadedMaterial = loadMaterials( child );
-
-				amfMaterials[ loadedMaterial.id ] = loadedMaterial.material;
-
-			} else if ( child.nodeName === 'object' ) {
-
-				var loadedObject = loadObject( child );
-
-				amfObjects[ loadedObject.id ] = loadedObject.obj;
-
-			}
-
-		}
-
-		var sceneObject = new THREE.Group();
-		var defaultMaterial = new THREE.MeshPhongMaterial( { color: 0xaaaaff, flatShading: true } );
-
-		sceneObject.name = amfName;
-		sceneObject.userData.author = amfAuthor;
-		sceneObject.userData.loader = 'AMF';
-
-		for ( var id in amfObjects ) {
-
-			var part = amfObjects[ id ];
-			var meshes = part.meshes;
-			var newObject = new THREE.Group();
-			newObject.name = part.name || '';
-
-			for ( i = 0; i < meshes.length; i ++ ) {
-
-				var objDefaultMaterial = defaultMaterial;
-				var mesh = meshes[ i ];
-				var vertices = new THREE.Float32BufferAttribute( mesh.vertices, 3 );
-				var normals = null;
-
-				if ( mesh.normals.length ) {
-
-					normals = new THREE.Float32BufferAttribute( mesh.normals, 3 );
-
-				}
-
-				if ( mesh.color ) {
-
-					var color = mesh.color;
-
-					objDefaultMaterial = defaultMaterial.clone();
-					objDefaultMaterial.color = new THREE.Color( color.r, color.g, color.b );
-
-					if ( color.a !== 1.0 ) {
-
-						objDefaultMaterial.transparent = true;
-						objDefaultMaterial.opacity = color.a;
-
-					}
-
-				}
-
-				var volumes = mesh.volumes;
-
-				for ( j = 0; j < volumes.length; j ++ ) {
-
-					var volume = volumes[ j ];
-					var newGeometry = new THREE.BufferGeometry();
-					var material = objDefaultMaterial;
-
-					newGeometry.setIndex( volume.triangles );
-					newGeometry.addAttribute( 'position', vertices.clone() );
-
-					if ( normals ) {
-
-						newGeometry.addAttribute( 'normal', normals.clone() );
-
-					}
-
-					if ( amfMaterials[ volume.materialId ] !== undefined ) {
-
-						material = amfMaterials[ volume.materialId ];
-
-					}
-
-					newGeometry.scale( amfScale, amfScale, amfScale );
-					newObject.add( new THREE.Mesh( newGeometry, material.clone() ) );
-
-				}
-
-			}
-
-			sceneObject.add( newObject );
-
-		}
-
-		return sceneObject;
-
-	}
-
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/PRWMLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/PRWMLoader.js
deleted file mode 100644
index 8b19970353704f7491953d63351ee4f91cb920b3..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/PRWMLoader.js
+++ /dev/null
@@ -1,299 +0,0 @@
-/**
- * @author Kevin Chapelier / https://github.com/kchapelier
- * See https://github.com/kchapelier/PRWM for more informations about this file format
- */
-
-( function ( THREE ) {
-
-	'use strict';
-
-	var bigEndianPlatform = null;
-
-	/**
-	 * Check if the endianness of the platform is big-endian (most significant bit first)
-	 * @returns {boolean} True if big-endian, false if little-endian
-	 */
-	function isBigEndianPlatform() {
-
-		if ( bigEndianPlatform === null ) {
-
-			var buffer = new ArrayBuffer( 2 ),
-				uint8Array = new Uint8Array( buffer ),
-				uint16Array = new Uint16Array( buffer );
-
-			uint8Array[ 0 ] = 0xAA; // set first byte
-			uint8Array[ 1 ] = 0xBB; // set second byte
-			bigEndianPlatform = ( uint16Array[ 0 ] === 0xAABB );
-
-		}
-
-		return bigEndianPlatform;
-
-	}
-
-	// match the values defined in the spec to the TypedArray types
-	var InvertedEncodingTypes = [
-		null,
-		Float32Array,
-		null,
-		Int8Array,
-		Int16Array,
-		null,
-		Int32Array,
-		Uint8Array,
-		Uint16Array,
-		null,
-		Uint32Array
-	];
-
-	// define the method to use on a DataView, corresponding the TypedArray type
-	var getMethods = {
-		Uint16Array: 'getUint16',
-		Uint32Array: 'getUint32',
-		Int16Array: 'getInt16',
-		Int32Array: 'getInt32',
-		Float32Array: 'getFloat32',
-		Float64Array: 'getFloat64'
-	};
-
-
-	function copyFromBuffer( sourceArrayBuffer, viewType, position, length, fromBigEndian ) {
-
-		var bytesPerElement = viewType.BYTES_PER_ELEMENT,
-			result;
-
-		if ( fromBigEndian === isBigEndianPlatform() || bytesPerElement === 1 ) {
-
-			result = new viewType( sourceArrayBuffer, position, length );
-
-		} else {
-
-			var readView = new DataView( sourceArrayBuffer, position, length * bytesPerElement ),
-				getMethod = getMethods[ viewType.name ],
-				littleEndian = ! fromBigEndian,
-				i = 0;
-
-			result = new viewType( length );
-
-			for ( ; i < length; i ++ ) {
-
-				result[ i ] = readView[ getMethod ]( i * bytesPerElement, littleEndian );
-
-			}
-
-		}
-
-		return result;
-
-	}
-
-
-	function decodePrwm( buffer ) {
-
-		var array = new Uint8Array( buffer ),
-			version = array[ 0 ],
-			flags = array[ 1 ],
-			indexedGeometry = !! ( flags >> 7 & 0x01 ),
-			indicesType = flags >> 6 & 0x01,
-			bigEndian = ( flags >> 5 & 0x01 ) === 1,
-			attributesNumber = flags & 0x1F,
-			valuesNumber = 0,
-			indicesNumber = 0;
-
-		if ( bigEndian ) {
-
-			valuesNumber = ( array[ 2 ] << 16 ) + ( array[ 3 ] << 8 ) + array[ 4 ];
-			indicesNumber = ( array[ 5 ] << 16 ) + ( array[ 6 ] << 8 ) + array[ 7 ];
-
-		} else {
-
-			valuesNumber = array[ 2 ] + ( array[ 3 ] << 8 ) + ( array[ 4 ] << 16 );
-			indicesNumber = array[ 5 ] + ( array[ 6 ] << 8 ) + ( array[ 7 ] << 16 );
-
-		}
-
-		/** PRELIMINARY CHECKS **/
-
-		if ( version === 0 ) {
-
-			throw new Error( 'PRWM decoder: Invalid format version: 0' );
-
-		} else if ( version !== 1 ) {
-
-			throw new Error( 'PRWM decoder: Unsupported format version: ' + version );
-
-		}
-
-		if ( ! indexedGeometry ) {
-
-			if ( indicesType !== 0 ) {
-
-				throw new Error( 'PRWM decoder: Indices type must be set to 0 for non-indexed geometries' );
-
-			} else if ( indicesNumber !== 0 ) {
-
-				throw new Error( 'PRWM decoder: Number of indices must be set to 0 for non-indexed geometries' );
-
-			}
-
-		}
-
-		/** PARSING **/
-
-		var pos = 8;
-
-		var attributes = {},
-			attributeName,
-			char,
-			attributeType,
-			cardinality,
-			encodingType,
-			arrayType,
-			values,
-			indices,
-			i;
-
-		for ( i = 0; i < attributesNumber; i ++ ) {
-
-			attributeName = '';
-
-			while ( pos < array.length ) {
-
-				char = array[ pos ];
-				pos ++;
-
-				if ( char === 0 ) {
-
-					break;
-
-				} else {
-
-					attributeName += String.fromCharCode( char );
-
-				}
-
-			}
-
-			flags = array[ pos ];
-
-			attributeType = flags >> 7 & 0x01;
-			cardinality = ( flags >> 4 & 0x03 ) + 1;
-			encodingType = flags & 0x0F;
-			arrayType = InvertedEncodingTypes[ encodingType ];
-
-			pos ++;
-
-			// padding to next multiple of 4
-			pos = Math.ceil( pos / 4 ) * 4;
-
-			values = copyFromBuffer( buffer, arrayType, pos, cardinality * valuesNumber, bigEndian );
-
-			pos += arrayType.BYTES_PER_ELEMENT * cardinality * valuesNumber;
-
-			attributes[ attributeName ] = {
-				type: attributeType,
-				cardinality: cardinality,
-				values: values
-			};
-
-		}
-
-		pos = Math.ceil( pos / 4 ) * 4;
-
-		indices = null;
-
-		if ( indexedGeometry ) {
-
-			indices = copyFromBuffer(
-				buffer,
-				indicesType === 1 ? Uint32Array : Uint16Array,
-				pos,
-				indicesNumber,
-				bigEndian
-			);
-
-		}
-
-		return {
-			version: version,
-			attributes: attributes,
-			indices: indices
-		};
-
-	}
-
-	// Define the public interface
-
-	THREE.PRWMLoader = function PRWMLoader( manager ) {
-
-		this.manager = ( manager !== undefined ) ? manager : THREE.DefaultLoadingManager;
-
-	};
-
-	THREE.PRWMLoader.prototype = {
-
-		constructor: THREE.PRWMLoader,
-
-		load: function ( url, onLoad, onProgress, onError ) {
-
-			var scope = this;
-
-			var loader = new THREE.FileLoader( scope.manager );
-			loader.setPath( scope.path );
-			loader.setResponseType( 'arraybuffer' );
-
-			url = url.replace( /\*/g, isBigEndianPlatform() ? 'be' : 'le' );
-
-			loader.load( url, function ( arrayBuffer ) {
-
-				onLoad( scope.parse( arrayBuffer ) );
-
-			}, onProgress, onError );
-
-		},
-
-		setPath: function ( value ) {
-
-			this.path = value;
-			return this;
-
-		},
-
-		parse: function ( arrayBuffer ) {
-
-			console.time( 'PRWMLoader' );
-
-			var data = decodePrwm( arrayBuffer ),
-				attributesKey = Object.keys( data.attributes ),
-				bufferGeometry = new THREE.BufferGeometry(),
-				attribute,
-				i;
-
-			for ( i = 0; i < attributesKey.length; i ++ ) {
-
-				attribute = data.attributes[ attributesKey[ i ] ];
-				bufferGeometry.addAttribute( attributesKey[ i ], new THREE.BufferAttribute( attribute.values, attribute.cardinality, attribute.normalized ) );
-
-			}
-
-			if ( data.indices !== null ) {
-
-				bufferGeometry.setIndex( new THREE.BufferAttribute( data.indices, 1 ) );
-
-			}
-
-			console.timeEnd( 'PRWMLoader' );
-
-			return bufferGeometry;
-
-		}
-
-	};
-
-	THREE.PRWMLoader.isBigEndianPlatform = function () {
-
-		return isBigEndianPlatform();
-
-	};
-
-} )( THREE );
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/CatmullRomCurve3.js b/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/CatmullRomCurve3.js
deleted file mode 100644
index 5f53a36f11bc6b721ecea5febe277881d3d05b8a..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/CatmullRomCurve3.js
+++ /dev/null
@@ -1,255 +0,0 @@
-import { Vector3 } from '../../math/Vector3.js';
-import { Curve } from '../core/Curve.js';
-
-/**
- * @author zz85 https://github.com/zz85
- *
- * Centripetal CatmullRom Curve - which is useful for avoiding
- * cusps and self-intersections in non-uniform catmull rom curves.
- * http://www.cemyuksel.com/research/catmullrom_param/catmullrom.pdf
- *
- * curve.type accepts centripetal(default), chordal and catmullrom
- * curve.tension is used for catmullrom which defaults to 0.5
- */
-
-
-/*
-Based on an optimized c++ solution in
- - http://stackoverflow.com/questions/9489736/catmull-rom-curve-with-no-cusps-and-no-self-intersections/
- - http://ideone.com/NoEbVM
-
-This CubicPoly class could be used for reusing some variables and calculations,
-but for three.js curve use, it could be possible inlined and flatten into a single function call
-which can be placed in CurveUtils.
-*/
-
-function CubicPoly() {
-
-	var c0 = 0, c1 = 0, c2 = 0, c3 = 0;
-
-	/*
-	 * Compute coefficients for a cubic polynomial
-	 *   p(s) = c0 + c1*s + c2*s^2 + c3*s^3
-	 * such that
-	 *   p(0) = x0, p(1) = x1
-	 *  and
-	 *   p'(0) = t0, p'(1) = t1.
-	 */
-	function init( x0, x1, t0, t1 ) {
-
-		c0 = x0;
-		c1 = t0;
-		c2 = - 3 * x0 + 3 * x1 - 2 * t0 - t1;
-		c3 = 2 * x0 - 2 * x1 + t0 + t1;
-
-	}
-
-	return {
-
-		initCatmullRom: function ( x0, x1, x2, x3, tension ) {
-
-			init( x1, x2, tension * ( x2 - x0 ), tension * ( x3 - x1 ) );
-
-		},
-
-		initNonuniformCatmullRom: function ( x0, x1, x2, x3, dt0, dt1, dt2 ) {
-
-			// compute tangents when parameterized in [t1,t2]
-			var t1 = ( x1 - x0 ) / dt0 - ( x2 - x0 ) / ( dt0 + dt1 ) + ( x2 - x1 ) / dt1;
-			var t2 = ( x2 - x1 ) / dt1 - ( x3 - x1 ) / ( dt1 + dt2 ) + ( x3 - x2 ) / dt2;
-
-			// rescale tangents for parametrization in [0,1]
-			t1 *= dt1;
-			t2 *= dt1;
-
-			init( x1, x2, t1, t2 );
-
-		},
-
-		calc: function ( t ) {
-
-			var t2 = t * t;
-			var t3 = t2 * t;
-			return c0 + c1 * t + c2 * t2 + c3 * t3;
-
-		}
-
-	};
-
-}
-
-//
-
-var tmp = new Vector3();
-var px = new CubicPoly(), py = new CubicPoly(), pz = new CubicPoly();
-
-function CatmullRomCurve3( points, closed, curveType, tension ) {
-
-	Curve.call( this );
-
-	this.type = 'CatmullRomCurve3';
-
-	this.points = points || [];
-	this.closed = closed || false;
-	this.curveType = curveType || 'centripetal';
-	this.tension = tension || 0.5;
-
-}
-
-CatmullRomCurve3.prototype = Object.create( Curve.prototype );
-CatmullRomCurve3.prototype.constructor = CatmullRomCurve3;
-
-CatmullRomCurve3.prototype.isCatmullRomCurve3 = true;
-
-CatmullRomCurve3.prototype.getPoint = function ( t, optionalTarget ) {
-
-	var point = optionalTarget || new Vector3();
-
-	var points = this.points;
-	var l = points.length;
-
-	var p = ( l - ( this.closed ? 0 : 1 ) ) * t;
-	var intPoint = Math.floor( p );
-	var weight = p - intPoint;
-
-	if ( this.closed ) {
-
-		intPoint += intPoint > 0 ? 0 : ( Math.floor( Math.abs( intPoint ) / l ) + 1 ) * l;
-
-	} else if ( weight === 0 && intPoint === l - 1 ) {
-
-		intPoint = l - 2;
-		weight = 1;
-
-	}
-
-	var p0, p1, p2, p3; // 4 points
-
-	if ( this.closed || intPoint > 0 ) {
-
-		p0 = points[ ( intPoint - 1 ) % l ];
-
-	} else {
-
-		// extrapolate first point
-		tmp.subVectors( points[ 0 ], points[ 1 ] ).add( points[ 0 ] );
-		p0 = tmp;
-
-	}
-
-	p1 = points[ intPoint % l ];
-	p2 = points[ ( intPoint + 1 ) % l ];
-
-	if ( this.closed || intPoint + 2 < l ) {
-
-		p3 = points[ ( intPoint + 2 ) % l ];
-
-	} else {
-
-		// extrapolate last point
-		tmp.subVectors( points[ l - 1 ], points[ l - 2 ] ).add( points[ l - 1 ] );
-		p3 = tmp;
-
-	}
-
-	if ( this.curveType === 'centripetal' || this.curveType === 'chordal' ) {
-
-		// init Centripetal / Chordal Catmull-Rom
-		var pow = this.curveType === 'chordal' ? 0.5 : 0.25;
-		var dt0 = Math.pow( p0.distanceToSquared( p1 ), pow );
-		var dt1 = Math.pow( p1.distanceToSquared( p2 ), pow );
-		var dt2 = Math.pow( p2.distanceToSquared( p3 ), pow );
-
-		// safety check for repeated points
-		if ( dt1 < 1e-4 ) dt1 = 1.0;
-		if ( dt0 < 1e-4 ) dt0 = dt1;
-		if ( dt2 < 1e-4 ) dt2 = dt1;
-
-		px.initNonuniformCatmullRom( p0.x, p1.x, p2.x, p3.x, dt0, dt1, dt2 );
-		py.initNonuniformCatmullRom( p0.y, p1.y, p2.y, p3.y, dt0, dt1, dt2 );
-		pz.initNonuniformCatmullRom( p0.z, p1.z, p2.z, p3.z, dt0, dt1, dt2 );
-
-	} else if ( this.curveType === 'catmullrom' ) {
-
-		px.initCatmullRom( p0.x, p1.x, p2.x, p3.x, this.tension );
-		py.initCatmullRom( p0.y, p1.y, p2.y, p3.y, this.tension );
-		pz.initCatmullRom( p0.z, p1.z, p2.z, p3.z, this.tension );
-
-	}
-
-	point.set(
-		px.calc( weight ),
-		py.calc( weight ),
-		pz.calc( weight )
-	);
-
-	return point;
-
-};
-
-CatmullRomCurve3.prototype.copy = function ( source ) {
-
-	Curve.prototype.copy.call( this, source );
-
-	this.points = [];
-
-	for ( var i = 0, l = source.points.length; i < l; i ++ ) {
-
-		var point = source.points[ i ];
-
-		this.points.push( point.clone() );
-
-	}
-
-	this.closed = source.closed;
-	this.curveType = source.curveType;
-	this.tension = source.tension;
-
-	return this;
-
-};
-
-CatmullRomCurve3.prototype.toJSON = function () {
-
-	var data = Curve.prototype.toJSON.call( this );
-
-	data.points = [];
-
-	for ( var i = 0, l = this.points.length; i < l; i ++ ) {
-
-		var point = this.points[ i ];
-		data.points.push( point.toArray() );
-
-	}
-
-	data.closed = this.closed;
-	data.curveType = this.curveType;
-	data.tension = this.tension;
-
-	return data;
-
-};
-
-CatmullRomCurve3.prototype.fromJSON = function ( json ) {
-
-	Curve.prototype.fromJSON.call( this, json );
-
-	this.points = [];
-
-	for ( var i = 0, l = json.points.length; i < l; i ++ ) {
-
-		var point = json.points[ i ];
-		this.points.push( new Vector3().fromArray( point ) );
-
-	}
-
-	this.closed = json.closed;
-	this.curveType = json.curveType;
-	this.tension = json.tension;
-
-	return this;
-
-};
-
-
-export { CatmullRomCurve3 };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/fog_vertex.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/fog_vertex.glsl.js
deleted file mode 100644
index ecfc773911a311201d77bcaf4726ab9bf0f80659..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/fog_vertex.glsl.js
+++ /dev/null
@@ -1,7 +0,0 @@
-export default /* glsl */`
-#ifdef USE_FOG
-
-	fogDepth = -mvPosition.z;
-
-#endif
-`;
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/linedashed_vert.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/linedashed_vert.glsl.js
deleted file mode 100644
index 4a7a7a4b74742d86f06f0931fb10c2e5e3175be2..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/linedashed_vert.glsl.js
+++ /dev/null
@@ -1,27 +0,0 @@
-export default /* glsl */`
-uniform float scale;
-attribute float lineDistance;
-
-varying float vLineDistance;
-
-#include <common>
-#include <color_pars_vertex>
-#include <fog_pars_vertex>
-#include <logdepthbuf_pars_vertex>
-#include <clipping_planes_pars_vertex>
-
-void main() {
-
-	#include <color_vertex>
-
-	vLineDistance = scale * lineDistance;
-
-	vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );
-	gl_Position = projectionMatrix * mvPosition;
-
-	#include <logdepthbuf_vertex>
-	#include <clipping_planes_vertex>
-	#include <fog_vertex>
-
-}
-`;
diff --git a/spaces/bankholdup/stylegan_petbreeder/e4e/models/stylegan2/op/upfirdn2d.py b/spaces/bankholdup/stylegan_petbreeder/e4e/models/stylegan2/op/upfirdn2d.py
deleted file mode 100644
index 7bc5a1e331c2bbb1893ac748cfd0f144ff0651b4..0000000000000000000000000000000000000000
--- a/spaces/bankholdup/stylegan_petbreeder/e4e/models/stylegan2/op/upfirdn2d.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import os
-
-import torch
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-module_path = os.path.dirname(__file__)
-upfirdn2d_op = load(
-    'upfirdn2d',
-    sources=[
-        os.path.join(module_path, 'upfirdn2d.cpp'),
-        os.path.join(module_path, 'upfirdn2d_kernel.cu'),
-    ],
-)
-
-
-class UpFirDn2dBackward(Function):
-    @staticmethod
-    def forward(
-            ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
-    ):
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_op.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            down_x,
-            down_y,
-            up_x,
-            up_y,
-            g_pad_x0,
-            g_pad_x1,
-            g_pad_y0,
-            g_pad_y1,
-        )
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        kernel, = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_op.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            ctx.up_x,
-            ctx.up_y,
-            ctx.down_x,
-            ctx.down_y,
-            ctx.pad_x0,
-            ctx.pad_x1,
-            ctx.pad_y0,
-            ctx.pad_y1,
-        )
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(
-            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
-        )
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        batch, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_op.upfirdn2d(
-            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-        )
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    out = UpFirDn2d.apply(
-        input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
-    )
-
-    return out
-
-
-def upfirdn2d_native(
-        input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
-    )
-    out = out[
-          :,
-          max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0),
-          max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0),
-          :,
-          ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
-    )
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-
-    return out[:, ::down_y, ::down_x, :]
diff --git a/spaces/benmaor/FoodVision_Big/app.py b/spaces/benmaor/FoodVision_Big/app.py
deleted file mode 100644
index fac4f54b531efdab955c94105d72eb86d50301ce..0000000000000000000000000000000000000000
--- a/spaces/benmaor/FoodVision_Big/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import gradio as gr
-import os
-import torch
-
-from model import create_ViT
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-
-with open("class_names.txt", "r") as f:
-  class_names = [food_name.strip() for food_name in  f.readlines()]
-
-
-vit, vit_transforms = create_ViT(
-    num_classes=101,
-)
-
-vit.load_state_dict(
-    torch.load(
-        f="09_VisionTransformer_food_101_100_percent.pth",
-        map_location=torch.device("cpu"),
-    )
-)
-
-def predict(img) -> Tuple[Dict, float]:
-    start_time = timer()
-
-    img = vit_transforms(img).unsqueeze(0)
-    vit.eval()
-
-    with torch.inference_mode():
-        pred_probs = torch.softmax(vit(img), dim=1)
-
-    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-
-    pred_time = round(timer() - start_time, 5)
-    return pred_labels_and_probs, pred_time
-
-
-title = "FoodVision Big"
-description = "A Vision TransFormer computer vision model to classify images of 101 kinds of food as pizza, steak, risotto or sushi and more."
-article = "Created at 09. PyTorch Model Deployment."
-
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-demo = gr.Interface(fn=predict,
-                    inputs=gr.Image(type="pil"),
-                    outputs=[gr.Label(num_top_classes=4, label="Predictions"),
-                             gr.Number(label="Prediction time (s)")],
-                    examples=example_list,
-                    title=title,
-                    description=description,
-                    article=article)
-
-demo.launch(debug=False)
diff --git a/spaces/bennydou/gitea/README.md b/spaces/bennydou/gitea/README.md
deleted file mode 100644
index 6c2c6b3577677373ed57bec9ca7605c78a6be1dc..0000000000000000000000000000000000000000
--- a/spaces/bennydou/gitea/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Gitea
-emoji: 😸
-sdk: docker
-colorFrom: green
-colorTo: green
-pinned: false
-license: mit
-app_port: 3000
----
diff --git a/spaces/bespin-global/Bespin-QuestionAnswering/app.py b/spaces/bespin-global/Bespin-QuestionAnswering/app.py
deleted file mode 100644
index fe256adc13fb7f9b3ffb84d93daf59d22b5e5a4d..0000000000000000000000000000000000000000
--- a/spaces/bespin-global/Bespin-QuestionAnswering/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import streamlit as st
-import torch
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer
-
-device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-@st.cache(allow_output_mutation=True)
-def get_model():
-    # Load fine-tuned MRC model by HuggingFace Model Hub
-    HUGGINGFACE_MODEL_PATH = "bespin-global/klue-bert-base-aihub-mrc"
-    tokenizer = AutoTokenizer.from_pretrained(HUGGINGFACE_MODEL_PATH)
-    model = AutoModelForQuestionAnswering.from_pretrained(HUGGINGFACE_MODEL_PATH).to(device)
-
-    return tokenizer, model
-
-tokenizer, model = get_model()
-
-
-## Title
-st.title('☁️ Bespin → QuestionAnswering')
-
-## Text
-st.write('[⚡bespin-global/klue-bert-base-aihub-mrc](https://huggingface.co/bespin-global/klue-bert-base-aihub-mrc) 모델 성능 테스트 페이지 입니다.')
-
-
-context_option = st.selectbox(' 📑 Select Context Examples.',
-    (
-        '스티븐 폴 스티브 잡스(영어: Steven Paul "Steve" Jobs, 1955년 2월 24일 ~ 2011년 10월 5일)는 미국의 기업인이었으며 애플의 전 CEO이자 공동 창립자이다. 2011년 10월 5일 췌장암으로 사망했다. 1976년 스티브 워즈니악, 로널드 웨인과 함께 애플을 공동 창업하고, 애플 2를 통해 개인용 컴퓨터를 대중화했다. 또한, GUI와 마우스의 가능성을 처음으로 내다보고 애플 리사와 매킨토시에서 이 기술을 도입하였다. 1986년 경영분쟁에 의해 애플에서 나온 이후 NeXT 컴퓨터를 창업하여 새로운 개념의 운영 체제를 개발했다. 1996년 애플이 NeXT를 인수하게 되면서 다시 애플로 돌아오게 되었고 1997년에는 임시 CEO로 애플을 다시 이끌게 되었으며 이후 다시금 애플을 혁신해 시장에서 성공을 거두게 이끌었다. 2001년 아이팟을 출시하여 음악 산업 전체를 뒤바꾸어 놓았다. 또한, 2007년 아이폰을 출시하면서 스마트폰 시장을 바꾸어 놓았고 2010년 아이패드를 출시함으로써 포스트PC 시대(Post-PC era)를 열었다. 스티브 잡스는 애니메이션 영화 《인크레더블》과 《토이 스토리》 등을 제작한 컴퓨터 애니메이션 제작사인 픽사의 소유주이자 CEO였다. 월트 디즈니 회사는 74억 달러어치의 자사 주식으로 이 회사를 구입하였다. 2006년 6월 이 거래가 완료되어 잡스는 이 거래를 통해 디즈니 지분의 7%를 소유한, 최대의 개인 주주이자 디즈니 이사회의 이사가 되었다. 한편 그는 2003년 무렵부터 췌장암으로 투병생활을 이어왔다. 그의 악화된 건강상태로 인하여 2011년 8월 24일 애플은 스티브 잡스가 최고경영책임자(CEO)를 사임하고 최고운영책임자(COO)인 팀 쿡이 새로운 CEO를 맡는다고 밝혔다. 잡스는 CEO직에서 물러나지만 이사회 의장직은 유지시키기로 했으나, 건강상태가 더욱 악화되어 사임 2개월도 지나지 않은 2011년 10월 5일 향년 56세의 나이로 사망했다.',
-        '비트코인은 2009년 사토시 나카모토[6]가 만든 가상화폐로, 통화를 발행하고 관리하는 중앙 장치가 존재하지 않는 구조를 가지고 있다. 대신, 비트코인의 거래는 P2P 기반 분산 데이터베이스에 의해 이루어지며, 공개 키 암호 방식 기반으로 거래를 수행한다. 비트코인은 공개성을 가지고 있다. 비트코인은 지갑 파일의 형태로 저장되며, 이 지갑에는 각각의 고유 주소가 부여되며, 그 주소를 기반으로 비트코인의 거래가 이루어진다. 비트코인은 1998년 웨이따이가 사이버펑크 메일링 리스트에 올린 암호통화(cryptocurrency)란 구상을 최초로 구현한 것 중의 하나이다.[7][8] 비트코인은 공개 키 암호 방식을 이용해 공개된 계정간에 거래를 한다. 모든 거래는 비공개적이나 거래의 기록은 남으며, 분산 데이터베이스에 저장된다. 분산된 시간서버로 일련의 작업증명(proof-of-work)을 하여 중복지출(double-spending)을 방지한다. 거래 기록은 모두 데이터베이스에 저장되어야 한다. 저장소 크기를 줄이기 위해 머클 트리(Merkle tree)가 사용된다.'
-    )   
-)
-# Text Input
-context = st.text_area("Context.", value=context_option, height=300, on_change=None) # placeholder="Please input some context..",
-
-
-if '스티븐 폴 스티브 잡스' in context_option:
-    question_option = st.selectbox('💡 Select Question Examples.',
-        (
-            '스티브 잡스가 누구야?', '스티브 잡스는 애플로 돌아와서 어떻게 했어?', '왜 애플을 나왔어?', '스티브 잡스는 어떻게 다시 애플로 돌아오게 되었어?', '픽사는 뭘 제작했어?', '왜  팀 쿡을 새로운 CEO로 맡았어?', '스티브 잡스는 언제 사망했어?'
-        )
-    )
-elif '비트코인' in context_option:
-    question_option = st.selectbox('💡 Select Question Examples.',
-        (
-            '비트코인은 어떤 구조야?', '비트코인은 어떻게 거래가 돼?', '비트코인 지갑에는 뭐가 부여 돼?', '공개된 계정간 거래 시 뭘 이용해?', '모든 거래는 어떻게 남아?', '머클 트리가 왜 사용 돼?'
-        )
-    )
-
-# Text Area
-question = st.text_area("Question.", value=question_option, on_change=None) # placeholder="Please input your question.."
-
-
-
-if st.button("Submit", key='question'):
-    try:
-        # Progress spinner
-        with st.spinner('Wait for it...'):
-            # Encoding
-            encodings = tokenizer(context, question, 
-                              max_length=512, 
-                              truncation=True,
-                              padding="max_length", 
-                              return_token_type_ids=False,
-                              return_offsets_mapping=True
-                              )
-            encodings = {key: torch.tensor([val]).to(device) for key, val in encodings.items()}             
-
-            # Predict
-            pred = model(encodings["input_ids"], attention_mask=encodings["attention_mask"])
-            start_logits, end_logits = pred.start_logits, pred.end_logits
-            token_start_index, token_end_index = start_logits.argmax(dim=-1), end_logits.argmax(dim=-1)
-            pred_ids = encodings["input_ids"][0][token_start_index: token_end_index + 1]
-            prediction = tokenizer.decode(pred_ids)
-
-            # Offset
-            answer_start_offset = int(encodings['offset_mapping'][0][token_start_index][0][0])
-            answer_end_offset = int(encodings['offset_mapping'][0][token_end_index][0][1])
-            answer_offset = (answer_start_offset, answer_end_offset)
-
-            # answer
-            st.success(prediction)
-
-
-    except Exception as e:
-        st.error(e)
diff --git a/spaces/bioriAsaeru/text-to-voice/Benefits of X Force X32 Exe Vault Professional 2013 for Product Lifecycle Management.md b/spaces/bioriAsaeru/text-to-voice/Benefits of X Force X32 Exe Vault Professional 2013 for Product Lifecycle Management.md
deleted file mode 100644
index 8ed6d723182b16a5de046211522d1f39e6d00b00..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Benefits of X Force X32 Exe Vault Professional 2013 for Product Lifecycle Management.md	
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/Captain Underpants And The Terrifying Perilious Misfortune Of The T.P. Mummy 720p HOT.md b/spaces/bioriAsaeru/text-to-voice/Captain Underpants And The Terrifying Perilious Misfortune Of The T.P. Mummy 720p HOT.md
deleted file mode 100644
index aea58bf2d31988cb94988dd579612921943ef389..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Captain Underpants And The Terrifying Perilious Misfortune Of The T.P. Mummy 720p HOT.md	
+++ /dev/null
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-<h2>Captain Underpants And The Terrifying Perilious Misfortune Of The T.P. Mummy 720p</h2><br /><p><b><b>Download File</b> &#9658;&#9658;&#9658; <a href="https://urloso.com/2uyPHk">https://urloso.com/2uyPHk</a></b></p><br /><br />
-
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diff --git a/spaces/bioriAsaeru/text-to-voice/How to Enjoy Karl Jenkins Songs of Sanctuary Zip A Guide to the Music of Adiemus.md b/spaces/bioriAsaeru/text-to-voice/How to Enjoy Karl Jenkins Songs of Sanctuary Zip A Guide to the Music of Adiemus.md
deleted file mode 100644
index 4c09a6f35ba11d8634263d1770ad75f5fe44ff0d..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/How to Enjoy Karl Jenkins Songs of Sanctuary Zip A Guide to the Music of Adiemus.md	
+++ /dev/null
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-<h2>karl jenkins songs of sanctuary zip</h2><br /><p><b><b>Download File</b> &#10038; <a href="https://urloso.com/2uyPLu">https://urloso.com/2uyPLu</a></b></p><br /><br />
-
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diff --git a/spaces/bkhmsi/Font-To-Sketch/code/bezier.py b/spaces/bkhmsi/Font-To-Sketch/code/bezier.py
deleted file mode 100644
index bb2b8ef67b0219bc5293acf8409f5866b38b940b..0000000000000000000000000000000000000000
--- a/spaces/bkhmsi/Font-To-Sketch/code/bezier.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.special import binom
-from numpy.linalg import norm
-
-def num_bezier(n_ctrl, degree=3):
-    if type(n_ctrl) == np.ndarray:
-        n_ctrl = len(n_ctrl)
-    return int((n_ctrl - 1) / degree)
-
-def bernstein(n, i):
-    bi = binom(n, i)
-    return lambda t, bi=bi, n=n, i=i: bi * t**i * (1 - t)**(n - i)
-
-def bezier(P, t, d=0):
-    '''Bezier curve of degree len(P)-1. d is the derivative order (0 gives positions)'''
-    n = P.shape[0] - 1
-    if d > 0:
-        Q = np.diff(P, axis=0)*n
-        return bezier(Q, t, d-1)
-    B = np.vstack([bernstein(n, i)(t) for i, p in enumerate(P)])
-    return (P.T @ B).T
-
-def cubic_bezier(P, t):
-    return (1.0-t)**3*P[0] + 3*(1.0-t)**2*t*P[1] + 3*(1.0-t)*t**2*P[2] + t**3*P[3]
-
-def bezier_piecewise(Cp, subd=100, degree=3, d=0):
-    ''' sample a piecewise Bezier curve given a sequence of control points'''
-    num = num_bezier(Cp.shape[0], degree)
-    X = []
-    for i in range(num):
-        P = Cp[i*degree:i*degree+degree+1, :]
-        t = np.linspace(0, 1., subd)[:-1]
-        Y = bezier(P, t, d)
-        X += [Y]
-    X.append(Cp[-1])
-    X = np.vstack(X)
-    return X
-
-def compute_beziers(beziers, subd=100, degree=3):
-    chain = beziers_to_chain(beziers)
-    return bezier_piecewise(chain, subd, degree)
-
-def plot_control_polygon(Cp, degree=3, lw=0.5, linecolor=np.ones(3)*0.1):
-    n_bezier = num_bezier(len(Cp), degree)
-    for i in range(n_bezier):
-        cp = Cp[i*degree:i*degree+degree+1, :]
-        if degree==3:
-            plt.plot(cp[0:2,0], cp[0:2, 1], ':', color=linecolor, linewidth=lw)
-            plt.plot(cp[2:,0], cp[2:,1], ':', color=linecolor, linewidth=lw)
-            plt.plot(cp[:,0], cp[:,1], 'o', color=[0, 0.5, 1.], markersize=4)
-        else:
-            plt.plot(cp[:,0], cp[:,1], ':', color=linecolor, linewidth=lw)
-            plt.plot(cp[:,0], cp[:,1], 'o', color=[0, 0.5, 1.])
-
-
-def chain_to_beziers(chain, degree=3):
-    ''' Convert Bezier chain to list of curve segments (4 control points each)'''
-    num = num_bezier(chain.shape[0], degree)
-    beziers = []
-    for i in range(num):
-        beziers.append(chain[i*degree:i*degree+degree+1,:])
-    return beziers
-
-
-def beziers_to_chain(beziers):
-    ''' Convert list of Bezier curve segments to a piecewise bezier chain (shares vertices)'''
-    n = len(beziers)
-    chain = []
-    for i in range(n):
-        chain.append(list(beziers[i][:-1]))
-    chain.append([beziers[-1][-1]])
-    return np.array(sum(chain, []))
-
-
-def split_cubic(bez, t):
-    p1, p2, p3, p4 = bez
-
-    p12 = (p2 - p1) * t + p1
-    p23 = (p3 - p2) * t + p2
-    p34 = (p4 - p3) * t + p3
-
-    p123 = (p23 - p12) * t + p12
-    p234 = (p34 - p23) * t + p23
-
-    p1234 = (p234 - p123) * t + p123
-
-    return np.array([p1, p12, p123, p1234]), np.array([p1234, p234, p34, p4])
-
-
-def approx_arc_length(bez):
-    c0, c1, c2, c3 = bez
-    v0 = norm(c1-c0)*0.15
-    v1 = norm(-0.558983582205757*c0 + 0.325650248872424*c1 + 0.208983582205757*c2 + 0.024349751127576*c3)
-    v2 = norm(c3-c0+c2-c1)*0.26666666666666666
-    v3 = norm(-0.024349751127576*c0 - 0.208983582205757*c1 - 0.325650248872424*c2 + 0.558983582205757*c3)
-    v4 = norm(c3-c2)*.15
-    return v0 + v1 + v2 + v3 + v4
-
-
-def subdivide_bezier(bez, thresh):
-    stack = [bez]
-    res = []
-    while stack:
-        bez = stack.pop()
-        l = approx_arc_length(bez)
-        if l < thresh:
-            res.append(bez)
-        else:
-            b1, b2 = split_cubic(bez, 0.5)
-            stack += [b2, b1]
-    return res
-
-def subdivide_bezier_chain(C, thresh):
-    beziers = chain_to_beziers(C)
-    res = []
-    for bez in beziers:
-        res += subdivide_bezier(bez, thresh)
-    return beziers_to_chain(res)
-
-
-
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/docs/MBD.md b/spaces/brainblow/AudioCreator_Music-Audio_Generation/docs/MBD.md
deleted file mode 100644
index 296d08407bac9155380a48bdc9faa5798db32bcb..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/docs/MBD.md
+++ /dev/null
@@ -1,117 +0,0 @@
-# MultiBand Diffusion
-
-AudioCraft provides the code and models for MultiBand Diffusion, [From Discrete Tokens to High Fidelity Audio using MultiBand Diffusion][arxiv].
-MultiBand diffusion is a collection of 4 models that can decode tokens from
-<a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> into waveform audio.
-
-<a target="_blank" href="https://colab.research.google.com/drive/1JlTOjB-G0A2Hz3h8PK63vLZk4xdCI5QB?usp=sharing">
-  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-</a>
-<br>
-
-
-## Installation
-
-Please follow the AudioCraft installation instructions from the [README](../README.md).
-
-
-## Usage
-
-We offer a number of way to use MultiBand Diffusion:
-1. The MusicGen demo includes a toggle to try diffusion decoder. You can use the demo locally by running [`python -m demos.musicgen_app --share`](../demos/musicgen_app.py), or through the [MusicGen Colab](https://colab.research.google.com/drive/1JlTOjB-G0A2Hz3h8PK63vLZk4xdCI5QB?usp=sharing).
-2. You can play with MusicGen by running the jupyter notebook at [`demos/musicgen_demo.ipynb`](../demos/musicgen_demo.ipynb) locally (if you have a GPU).
-
-## API
-
-We provide a simple API and pre-trained models for MusicGen and for EnCodec at 24 khz for 3 bitrates (1.5 kbps, 3 kbps and 6 kbps).
-
-See after a quick example for using MultiBandDiffusion with the MusicGen API:
-
-```python
-import torchaudio
-from audiocraft.models import MusicGen, MultiBandDiffusion
-from audiocraft.data.audio import audio_write
-
-model = MusicGen.get_pretrained('facebook/musicgen-melody')
-mbd = MultiBandDiffusion.get_mbd_musicgen()
-model.set_generation_params(duration=8)  # generate 8 seconds.
-wav, tokens = model.generate_unconditional(4, return_tokens=True)    # generates 4 unconditional audio samples and keep the tokens for MBD generation
-descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
-wav_diffusion = mbd.tokens_to_wav(tokens)
-wav, tokens = model.generate(descriptions, return_tokens=True)  # generates 3 samples and keep the tokens.
-wav_diffusion = mbd.tokens_to_wav(tokens)
-melody, sr = torchaudio.load('./assets/bach.mp3')
-# Generates using the melody from the given audio and the provided descriptions, returns audio and audio tokens.
-wav, tokens = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr, return_tokens=True)
-wav_diffusion = mbd.tokens_to_wav(tokens)
-
-for idx, one_wav in enumerate(wav):
-    # Will save under {idx}.wav and {idx}_diffusion.wav, with loudness normalization at -14 db LUFS for comparing the methods.
-    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-    audio_write(f'{idx}_diffusion', wav_diffusion[idx].cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-
-For the compression task (and to compare with [EnCodec](https://github.com/facebookresearch/encodec)):
-
-```python
-import torch
-from audiocraft.models import MultiBandDiffusion
-from encodec import EncodecModel
-from audiocraft.data.audio import audio_read, audio_write
-
-bandwidth = 3.0  # 1.5, 3.0, 6.0
-mbd = MultiBandDiffusion.get_mbd_24khz(bw=bandwidth)
-encodec = EncodecModel.get_encodec_24khz()
-
-somepath = ''
-wav, sr = audio_read(somepath)
-with torch.no_grad():
-    compressed_encodec = encodec(wav)
-    compressed_diffusion = mbd.regenerate(wav, sample_rate=sr)
-
-audio_write('sample_encodec', compressed_encodec.squeeze(0).cpu(), mbd.sample_rate, strategy="loudness", loudness_compressor=True)
-audio_write('sample_diffusion', compressed_diffusion.squeeze(0).cpu(), mbd.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-
-
-## Training
-
-The [DiffusionSolver](../audiocraft/solvers/diffusion.py) implements our diffusion training pipeline.
-It generates waveform audio conditioned on the embeddings extracted from a pre-trained EnCodec model
-(see [EnCodec documentation](./ENCODEC.md) for more details on how to train such model).
-
-Note that **we do NOT provide any of the datasets** used for training our diffusion models.
-We provide a dummy dataset containing just a few examples for illustrative purposes.
-
-### Example configurations and grids
-
-One can train diffusion models as described in the paper by using this [dora grid](../audiocraft/grids/diffusion/4_bands_base_32khz.py).
-```shell
-# 4 bands MBD trainning
-dora grid diffusion.4_bands_base_32khz
-```
-
-### Learn more
-
-Learn more about AudioCraft training pipelines in the [dedicated section](./TRAINING.md).
-
-
-## Citation
-
-```
-@article{sanroman2023fromdi,
-  title={From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion},
-  author={San Roman, Robin and Adi, Yossi and Deleforge, Antoine and Serizel, Romain and Synnaeve, Gabriel and Défossez, Alexandre},
-  journal={arXiv preprint arXiv:},
-  year={2023}
-}
-```
-
-
-## License
-
-See license information in the [README](../README.md).
-
-
-[arxiv]: https://dl.fbaipublicfiles.com/encodec/Diffusion/paper.pdf
-[mbd_samples]: https://ai.honu.io/papers/mbd/
diff --git a/spaces/briancatmaster/Tropic-AI/README.md b/spaces/briancatmaster/Tropic-AI/README.md
deleted file mode 100644
index 23ac9b3c6bad1581e0b1858e66edb274a9af2efa..0000000000000000000000000000000000000000
--- a/spaces/briancatmaster/Tropic-AI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Tropic AI
-emoji: 📉
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/hrfpn.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/hrfpn.py
deleted file mode 100644
index 08ec420fa24e1e8f5074baf2e9ae737aff2ab12e..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/hrfpn.py
+++ /dev/null
@@ -1,182 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-MIT License
-Copyright (c) 2019 Microsoft
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.backbone import BACKBONE_REGISTRY
-from detectron2.modeling.backbone.backbone import Backbone
-
-from .hrnet import build_pose_hrnet_backbone
-
-
-class HRFPN(Backbone):
-    """HRFPN (High Resolution Feature Pyramids)
-    Transforms outputs of HRNet backbone so they are suitable for the ROI_heads
-    arXiv: https://arxiv.org/abs/1904.04514
-    Adapted from https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/hrfpn.py
-    Args:
-        bottom_up: (list) output of HRNet
-        in_features (list): names of the input features (output of HRNet)
-        in_channels (list): number of channels for each branch
-        out_channels (int): output channels of feature pyramids
-        n_out_features (int): number of output stages
-        pooling (str): pooling for generating feature pyramids (from {MAX, AVG})
-        share_conv (bool): Have one conv per output, or share one with all the outputs
-    """
-
-    def __init__(
-        self,
-        bottom_up,
-        in_features,
-        n_out_features,
-        in_channels,
-        out_channels,
-        pooling="AVG",
-        share_conv=False,
-    ):
-        super(HRFPN, self).__init__()
-        assert isinstance(in_channels, list)
-        self.bottom_up = bottom_up
-        self.in_features = in_features
-        self.n_out_features = n_out_features
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_ins = len(in_channels)
-        self.share_conv = share_conv
-
-        if self.share_conv:
-            self.fpn_conv = nn.Conv2d(
-                in_channels=out_channels, out_channels=out_channels, kernel_size=3, padding=1
-            )
-        else:
-            self.fpn_conv = nn.ModuleList()
-            for _ in range(self.n_out_features):
-                self.fpn_conv.append(
-                    nn.Conv2d(
-                        in_channels=out_channels,
-                        out_channels=out_channels,
-                        kernel_size=3,
-                        padding=1,
-                    )
-                )
-
-        # Custom change: Replaces a simple bilinear interpolation
-        self.interp_conv = nn.ModuleList()
-        for i in range(len(self.in_features)):
-            self.interp_conv.append(
-                nn.Sequential(
-                    nn.ConvTranspose2d(
-                        in_channels=in_channels[i],
-                        out_channels=in_channels[i],
-                        kernel_size=4,
-                        stride=2**i,
-                        padding=0,
-                        output_padding=0,
-                        bias=False,
-                    ),
-                    nn.BatchNorm2d(in_channels[i], momentum=0.1),
-                    nn.ReLU(inplace=True),
-                )
-            )
-
-        # Custom change: Replaces a couple (reduction conv + pooling) by one conv
-        self.reduction_pooling_conv = nn.ModuleList()
-        for i in range(self.n_out_features):
-            self.reduction_pooling_conv.append(
-                nn.Sequential(
-                    nn.Conv2d(sum(in_channels), out_channels, kernel_size=2**i, stride=2**i),
-                    nn.BatchNorm2d(out_channels, momentum=0.1),
-                    nn.ReLU(inplace=True),
-                )
-            )
-
-        if pooling == "MAX":
-            self.pooling = F.max_pool2d
-        else:
-            self.pooling = F.avg_pool2d
-
-        self._out_features = []
-        self._out_feature_channels = {}
-        self._out_feature_strides = {}
-
-        for i in range(self.n_out_features):
-            self._out_features.append("p%d" % (i + 1))
-            self._out_feature_channels.update({self._out_features[-1]: self.out_channels})
-            self._out_feature_strides.update({self._out_features[-1]: 2 ** (i + 2)})
-
-    # default init_weights for conv(msra) and norm in ConvModule
-    def init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, a=1)
-                nn.init.constant_(m.bias, 0)
-
-    def forward(self, inputs):
-        bottom_up_features = self.bottom_up(inputs)
-        assert len(bottom_up_features) == len(self.in_features)
-        inputs = [bottom_up_features[f] for f in self.in_features]
-
-        outs = []
-        for i in range(len(inputs)):
-            outs.append(self.interp_conv[i](inputs[i]))
-        shape_2 = min(o.shape[2] for o in outs)
-        shape_3 = min(o.shape[3] for o in outs)
-        out = torch.cat([o[:, :, :shape_2, :shape_3] for o in outs], dim=1)
-        outs = []
-        for i in range(self.n_out_features):
-            outs.append(self.reduction_pooling_conv[i](out))
-        for i in range(len(outs)):  # Make shapes consistent
-            outs[-1 - i] = outs[-1 - i][
-                :, :, : outs[-1].shape[2] * 2**i, : outs[-1].shape[3] * 2**i
-            ]
-        outputs = []
-        for i in range(len(outs)):
-            if self.share_conv:
-                outputs.append(self.fpn_conv(outs[i]))
-            else:
-                outputs.append(self.fpn_conv[i](outs[i]))
-
-        assert len(self._out_features) == len(outputs)
-        return dict(zip(self._out_features, outputs))
-
-
-@BACKBONE_REGISTRY.register()
-def build_hrfpn_backbone(cfg, input_shape: ShapeSpec) -> HRFPN:
-
-    in_channels = cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS
-    in_features = ["p%d" % (i + 1) for i in range(cfg.MODEL.HRNET.STAGE4.NUM_BRANCHES)]
-    n_out_features = len(cfg.MODEL.ROI_HEADS.IN_FEATURES)
-    out_channels = cfg.MODEL.HRNET.HRFPN.OUT_CHANNELS
-    hrnet = build_pose_hrnet_backbone(cfg, input_shape)
-    hrfpn = HRFPN(
-        hrnet,
-        in_features,
-        n_out_features,
-        in_channels,
-        out_channels,
-        pooling="AVG",
-        share_conv=False,
-    )
-
-    return hrfpn
diff --git a/spaces/c-s-ale/ArxivChainLitDemo/app.py b/spaces/c-s-ale/ArxivChainLitDemo/app.py
deleted file mode 100644
index 754bfa066afac6db6769d38b2621a8f49cb02084..0000000000000000000000000000000000000000
--- a/spaces/c-s-ale/ArxivChainLitDemo/app.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.document_loaders import PyMuPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.chains import RetrievalQAWithSourcesChain
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-import os
-import arxiv
-import chainlit as cl
-from chainlit import user_session
-
-@cl.langchain_factory(use_async=True)
-async def init():
-    arxiv_query = None
-
-    # Wait for the user to ask an Arxiv question
-    while arxiv_query == None:
-        arxiv_query = await cl.AskUserMessage(
-            content="Please enter a topic to begin!", timeout=15
-        ).send()
-
-    # Obtain the top 30 results from Arxiv for the query
-    search = arxiv.Search(
-        query=arxiv_query["content"],
-        max_results=3,
-        sort_by=arxiv.SortCriterion.Relevance,
-    )
-
-    await cl.Message(content="Downloading and chunking articles...").send()
-    # download each of the pdfs
-    pdf_data = []
-    for result in search.results():
-        loader = PyMuPDFLoader(result.pdf_url)
-        loaded_pdf = loader.load()
-
-        for document in loaded_pdf:
-            document.metadata["source"] = result.entry_id
-            document.metadata["file_path"] = result.pdf_url
-            document.metadata["title"] = result.title
-            pdf_data.append(document)
-
-    # Create a Chroma vector store
-    embeddings = OpenAIEmbeddings(
-        disallowed_special=(),
-    )
-    
-    # If operation takes too long, make_async allows to run in a thread
-    # docsearch = await cl.make_async(Chroma.from_documents)(pdf_data, embeddings) 
-    docsearch = Chroma.from_documents(pdf_data, embeddings)
-
-    # Create a chain that uses the Chroma vector store
-    chain = RetrievalQAWithSourcesChain.from_chain_type(
-        ChatOpenAI(
-            model_name="gpt-3.5-turbo-16k",
-            temperature=0,
-        ),
-        chain_type="stuff",
-        retriever=docsearch.as_retriever(),
-        return_source_documents=True,
-    )
-
-    # Let the user know that the system is ready
-    await cl.Message(
-        content=f"We found a few papers about `{arxiv_query['content']}` you can now ask questions!"
-    ).send()
-
-    return chain
-
-
-@cl.langchain_postprocess
-async def process_response(res):
-    answer = res["answer"]
-    source_elements_dict = {}
-    source_elements = []
-    for idx, source in enumerate(res["source_documents"]):
-        title = source.metadata["title"]
-
-        if title not in source_elements_dict:
-            source_elements_dict[title] = {
-                "page_number": [source.metadata["page"]],
-                "url": source.metadata["file_path"],
-            }
-
-        else:
-            source_elements_dict[title]["page_number"].append(source.metadata["page"])
-
-        # sort the page numbers
-        source_elements_dict[title]["page_number"].sort()
-
-    for title, source in source_elements_dict.items():
-        # create a string for the page numbers
-        page_numbers = ", ".join([str(x) for x in source["page_number"]])
-        text_for_source = f"Page Number(s): {page_numbers}\nURL: {source['url']}"
-        source_elements.append(
-            cl.Text(name=title, content=text_for_source, display="inline")
-        )
-
-    await cl.Message(content=answer, elements=source_elements).send()
\ No newline at end of file
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ContainerIO.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ContainerIO.py
deleted file mode 100644
index 45e80b39af72c15aa58c08618daa7289d96649d0..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ContainerIO.py
+++ /dev/null
@@ -1,120 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# a class to read from a container file
-#
-# History:
-# 1995-06-18 fl     Created
-# 1995-09-07 fl     Added readline(), readlines()
-#
-# Copyright (c) 1997-2001 by Secret Labs AB
-# Copyright (c) 1995 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import io
-
-
-class ContainerIO:
-    """
-    A file object that provides read access to a part of an existing
-    file (for example a TAR file).
-    """
-
-    def __init__(self, file, offset, length):
-        """
-        Create file object.
-
-        :param file: Existing file.
-        :param offset: Start of region, in bytes.
-        :param length: Size of region, in bytes.
-        """
-        self.fh = file
-        self.pos = 0
-        self.offset = offset
-        self.length = length
-        self.fh.seek(offset)
-
-    ##
-    # Always false.
-
-    def isatty(self):
-        return False
-
-    def seek(self, offset, mode=io.SEEK_SET):
-        """
-        Move file pointer.
-
-        :param offset: Offset in bytes.
-        :param mode: Starting position. Use 0 for beginning of region, 1
-           for current offset, and 2 for end of region.  You cannot move
-           the pointer outside the defined region.
-        """
-        if mode == 1:
-            self.pos = self.pos + offset
-        elif mode == 2:
-            self.pos = self.length + offset
-        else:
-            self.pos = offset
-        # clamp
-        self.pos = max(0, min(self.pos, self.length))
-        self.fh.seek(self.offset + self.pos)
-
-    def tell(self):
-        """
-        Get current file pointer.
-
-        :returns: Offset from start of region, in bytes.
-        """
-        return self.pos
-
-    def read(self, n=0):
-        """
-        Read data.
-
-        :param n: Number of bytes to read. If omitted or zero,
-            read until end of region.
-        :returns: An 8-bit string.
-        """
-        if n:
-            n = min(n, self.length - self.pos)
-        else:
-            n = self.length - self.pos
-        if not n:  # EOF
-            return b"" if "b" in self.fh.mode else ""
-        self.pos = self.pos + n
-        return self.fh.read(n)
-
-    def readline(self):
-        """
-        Read a line of text.
-
-        :returns: An 8-bit string.
-        """
-        s = b"" if "b" in self.fh.mode else ""
-        newline_character = b"\n" if "b" in self.fh.mode else "\n"
-        while True:
-            c = self.read(1)
-            if not c:
-                break
-            s = s + c
-            if c == newline_character:
-                break
-        return s
-
-    def readlines(self):
-        """
-        Read multiple lines of text.
-
-        :returns: A list of 8-bit strings.
-        """
-        lines = []
-        while True:
-            s = self.readline()
-            if not s:
-                break
-            lines.append(s)
-        return lines
diff --git a/spaces/candlend/vits-hoshimi/sovits/hubert/__init__.py b/spaces/candlend/vits-hoshimi/sovits/hubert/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/captchaboy/FAST-ABINet-OCR/transforms.py b/spaces/captchaboy/FAST-ABINet-OCR/transforms.py
deleted file mode 100644
index 5a7042f3368bc832566d5c22d1e18abe5d8547f5..0000000000000000000000000000000000000000
--- a/spaces/captchaboy/FAST-ABINet-OCR/transforms.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import math
-import numbers
-import random
-
-import cv2
-import numpy as np
-from PIL import Image
-from torchvision import transforms
-from torchvision.transforms import Compose
-
-
-def sample_asym(magnitude, size=None):
-    return np.random.beta(1, 4, size) * magnitude
-
-def sample_sym(magnitude, size=None):
-    return (np.random.beta(4, 4, size=size) - 0.5) * 2 * magnitude
-
-def sample_uniform(low, high, size=None):
-    return np.random.uniform(low, high, size=size)
-
-def get_interpolation(type='random'):
-    if type == 'random':
-        choice = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA]
-        interpolation = choice[random.randint(0, len(choice)-1)]
-    elif type == 'nearest': interpolation = cv2.INTER_NEAREST
-    elif type == 'linear': interpolation = cv2.INTER_LINEAR
-    elif type == 'cubic': interpolation = cv2.INTER_CUBIC
-    elif type == 'area': interpolation = cv2.INTER_AREA
-    else: raise TypeError('Interpolation types only nearest, linear, cubic, area are supported!')
-    return interpolation
-
-class CVRandomRotation(object):
-    def __init__(self, degrees=15):
-        assert isinstance(degrees, numbers.Number), "degree should be a single number."
-        assert degrees >= 0, "degree must be positive."
-        self.degrees = degrees
-
-    @staticmethod
-    def get_params(degrees):
-        return sample_sym(degrees)
-    
-    def __call__(self, img):
-        angle = self.get_params(self.degrees)
-        src_h, src_w = img.shape[:2]
-        M = cv2.getRotationMatrix2D(center=(src_w/2, src_h/2), angle=angle, scale=1.0)
-        abs_cos, abs_sin = abs(M[0,0]), abs(M[0,1])
-        dst_w = int(src_h * abs_sin + src_w * abs_cos)
-        dst_h = int(src_h * abs_cos + src_w * abs_sin)
-        M[0, 2] += (dst_w - src_w)/2
-        M[1, 2] += (dst_h - src_h)/2
-        
-        flags = get_interpolation()
-        return cv2.warpAffine(img, M, (dst_w, dst_h), flags=flags, borderMode=cv2.BORDER_REPLICATE)
-
-class CVRandomAffine(object):
-    def __init__(self, degrees, translate=None, scale=None, shear=None):
-        assert isinstance(degrees, numbers.Number), "degree should be a single number."
-        assert degrees >= 0, "degree must be positive."
-        self.degrees = degrees
-
-        if translate is not None:
-            assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
-                "translate should be a list or tuple and it must be of length 2."
-            for t in translate:
-                if not (0.0 <= t <= 1.0):
-                    raise ValueError("translation values should be between 0 and 1")
-        self.translate = translate
-
-        if scale is not None:
-            assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
-                "scale should be a list or tuple and it must be of length 2."
-            for s in scale:
-                if s <= 0:
-                    raise ValueError("scale values should be positive")
-        self.scale = scale
-
-        if shear is not None:
-            if isinstance(shear, numbers.Number):
-                if shear < 0:
-                    raise ValueError("If shear is a single number, it must be positive.")
-                self.shear = [shear]
-            else:
-                assert isinstance(shear, (tuple, list)) and (len(shear) == 2), \
-                    "shear should be a list or tuple and it must be of length 2."
-                self.shear = shear
-        else:
-            self.shear = shear
-            
-    def _get_inverse_affine_matrix(self, center, angle, translate, scale, shear):
-        # https://github.com/pytorch/vision/blob/v0.4.0/torchvision/transforms/functional.py#L717
-        from numpy import sin, cos, tan
-        
-        if isinstance(shear, numbers.Number):
-            shear = [shear, 0]
-
-        if not isinstance(shear, (tuple, list)) and len(shear) == 2:
-            raise ValueError(
-                "Shear should be a single value or a tuple/list containing " +
-                "two values. Got {}".format(shear))
-
-        rot = math.radians(angle)
-        sx, sy = [math.radians(s) for s in shear]
-
-        cx, cy = center
-        tx, ty = translate
-
-        # RSS without scaling
-        a = cos(rot - sy) / cos(sy)
-        b = -cos(rot - sy) * tan(sx) / cos(sy) - sin(rot)
-        c = sin(rot - sy) / cos(sy)
-        d = -sin(rot - sy) * tan(sx) / cos(sy) + cos(rot)
-
-        # Inverted rotation matrix with scale and shear
-        # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
-        M = [d, -b, 0,
-             -c, a, 0]
-        M = [x / scale for x in M]
-
-        # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
-        M[2] += M[0] * (-cx - tx) + M[1] * (-cy - ty)
-        M[5] += M[3] * (-cx - tx) + M[4] * (-cy - ty)
-
-        # Apply center translation: C * RSS^-1 * C^-1 * T^-1
-        M[2] += cx
-        M[5] += cy
-        return M
-
-    @staticmethod
-    def get_params(degrees, translate, scale_ranges, shears, height):        
-        angle = sample_sym(degrees)
-        if translate is not None:
-            max_dx = translate[0] * height
-            max_dy = translate[1] * height
-            translations = (np.round(sample_sym(max_dx)), np.round(sample_sym(max_dy)))
-        else:
-            translations = (0, 0)
-
-        if scale_ranges is not None:
-            scale = sample_uniform(scale_ranges[0], scale_ranges[1])
-        else:
-            scale = 1.0
-
-        if shears is not None:
-            if len(shears) == 1:
-                shear = [sample_sym(shears[0]), 0.]
-            elif len(shears) == 2:
-                shear = [sample_sym(shears[0]), sample_sym(shears[1])]
-        else:
-            shear = 0.0
-
-        return angle, translations, scale, shear
-    
-    
-    def __call__(self, img):
-        src_h, src_w = img.shape[:2]
-        angle, translate, scale, shear = self.get_params(
-                self.degrees, self.translate, self.scale, self.shear, src_h)
-
-        M = self._get_inverse_affine_matrix((src_w/2, src_h/2), angle, (0, 0), scale, shear)
-        M = np.array(M).reshape(2,3)
-        
-        startpoints = [(0, 0), (src_w - 1, 0), (src_w - 1, src_h - 1), (0, src_h - 1)]
-        project = lambda x, y, a, b, c: int(a*x + b*y + c)
-        endpoints = [(project(x, y, *M[0]), project(x, y, *M[1])) for x, y in startpoints]
-        
-        rect = cv2.minAreaRect(np.array(endpoints))
-        bbox = cv2.boxPoints(rect).astype(dtype=np.int)
-        max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
-        min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
-
-        dst_w = int(max_x - min_x)
-        dst_h = int(max_y - min_y)
-        M[0, 2] += (dst_w - src_w) / 2
-        M[1, 2] += (dst_h - src_h) / 2
-        
-        # add translate
-        dst_w += int(abs(translate[0]))
-        dst_h += int(abs(translate[1]))
-        if translate[0] < 0: M[0, 2] += abs(translate[0])
-        if translate[1] < 0: M[1, 2] += abs(translate[1])
-            
-        flags = get_interpolation()
-        return cv2.warpAffine(img, M, (dst_w , dst_h), flags=flags, borderMode=cv2.BORDER_REPLICATE)
-
-class CVRandomPerspective(object):
-    def __init__(self, distortion=0.5):
-        self.distortion = distortion
-
-    def get_params(self, width, height, distortion):
-        offset_h = sample_asym(distortion * height / 2, size=4).astype(dtype=np.int)
-        offset_w = sample_asym(distortion * width / 2, size=4).astype(dtype=np.int)
-        topleft  = (            offset_w[0],              offset_h[0])
-        topright = (width - 1 - offset_w[1],              offset_h[1])
-        botright = (width - 1 - offset_w[2], height - 1 - offset_h[2])
-        botleft  = (            offset_w[3], height - 1 - offset_h[3])
-
-        startpoints = [(0, 0), (width - 1, 0), (width - 1, height - 1), (0, height - 1)]
-        endpoints = [topleft, topright, botright, botleft]
-        return np.array(startpoints, dtype=np.float32), np.array(endpoints, dtype=np.float32)
-    
-    def __call__(self, img):
-        height, width = img.shape[:2]
-        startpoints, endpoints = self.get_params(width, height, self.distortion)
-        M = cv2.getPerspectiveTransform(startpoints, endpoints)
-
-        # TODO: more robust way to crop image
-        rect = cv2.minAreaRect(endpoints)
-        bbox = cv2.boxPoints(rect).astype(dtype=np.int)
-        max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
-        min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
-        min_x, min_y = max(min_x, 0), max(min_y, 0)
-
-        flags = get_interpolation() 
-        img = cv2.warpPerspective(img, M, (max_x, max_y), flags=flags, borderMode=cv2.BORDER_REPLICATE)
-        img = img[min_y:, min_x:]
-        return img
-
-class CVRescale(object):
-    
-    def __init__(self, factor=4, base_size=(128, 512)):
-        """ Define image scales using gaussian pyramid and rescale image to target scale.
-        
-        Args:
-            factor: the decayed factor from base size, factor=4 keeps target scale by default.
-            base_size: base size the build the bottom layer of pyramid
-        """
-        if isinstance(factor, numbers.Number):
-            self.factor = round(sample_uniform(0, factor))
-        elif isinstance(factor, (tuple, list)) and len(factor) == 2:
-            self.factor = round(sample_uniform(factor[0], factor[1]))
-        else:
-            raise Exception('factor must be number or list with length 2')
-        # assert factor is valid
-        self.base_h, self.base_w = base_size[:2]
-
-    def __call__(self, img):
-        if self.factor == 0: return img 
-        src_h, src_w = img.shape[:2]
-        cur_w, cur_h = self.base_w, self.base_h        
-        scale_img = cv2.resize(img, (cur_w, cur_h), interpolation=get_interpolation())
-        for _ in range(self.factor): 
-            scale_img = cv2.pyrDown(scale_img)
-        scale_img = cv2.resize(scale_img, (src_w, src_h), interpolation=get_interpolation())
-        return scale_img
-
-class CVGaussianNoise(object):
-    def __init__(self, mean=0, var=20):
-        self.mean = mean
-        if isinstance(var, numbers.Number):
-            self.var = max(int(sample_asym(var)), 1)
-        elif isinstance(var, (tuple, list)) and len(var) == 2:
-            self.var = int(sample_uniform(var[0], var[1]))
-        else:
-            raise Exception('degree must be number or list with length 2')
-        
-    def __call__(self, img):
-        noise = np.random.normal(self.mean, self.var**0.5, img.shape)
-        img = np.clip(img + noise, 0, 255).astype(np.uint8)
-        return img
-
-class CVMotionBlur(object):
-    def __init__(self, degrees=12, angle=90):
-        if isinstance(degrees, numbers.Number):
-            self.degree = max(int(sample_asym(degrees)), 1)
-        elif isinstance(degrees, (tuple, list)) and len(degrees) == 2:
-            self.degree = int(sample_uniform(degrees[0], degrees[1]))
-        else:
-            raise Exception('degree must be number or list with length 2')
-        self.angle = sample_uniform(-angle, angle)
-
-    def __call__(self, img):
-        M = cv2.getRotationMatrix2D((self.degree // 2, self.degree // 2), self.angle, 1)
-        motion_blur_kernel = np.zeros((self.degree, self.degree))
-        motion_blur_kernel[self.degree // 2, :] = 1
-        motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (self.degree, self.degree))
-        motion_blur_kernel = motion_blur_kernel / self.degree
-        img = cv2.filter2D(img, -1, motion_blur_kernel)
-        img = np.clip(img, 0, 255).astype(np.uint8)
-        return img
-
-class CVGeometry(object):
-    def __init__(self, degrees=15, translate=(0.3, 0.3), scale=(0.5, 2.), 
-                       shear=(45, 15), distortion=0.5, p=0.5):
-        self.p = p
-        type_p = random.random()
-        if type_p < 0.33:
-            self.transforms = CVRandomRotation(degrees=degrees)
-        elif type_p < 0.66:
-            self.transforms = CVRandomAffine(degrees=degrees, translate=translate, scale=scale, shear=shear)
-        else:
-            self.transforms = CVRandomPerspective(distortion=distortion)
-
-    def __call__(self, img):
-        if random.random() < self.p:
-            img = np.array(img)
-            return Image.fromarray(self.transforms(img))
-        else: return img
-
-class CVDeterioration(object):
-    def __init__(self, var, degrees, factor, p=0.5):
-        self.p = p
-        transforms = []
-        if var is not None:
-            transforms.append(CVGaussianNoise(var=var))
-        if degrees is not None:
-            transforms.append(CVMotionBlur(degrees=degrees))
-        if factor is not None:
-            transforms.append(CVRescale(factor=factor))
-            
-        random.shuffle(transforms)
-        transforms = Compose(transforms)
-        self.transforms =  transforms
-        
-    def __call__(self, img):
-        if random.random() < self.p:
-            img = np.array(img)
-            return Image.fromarray(self.transforms(img))
-        else: return img
-
-
-class CVColorJitter(object):
-    def __init__(self, brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1, p=0.5):
-        self.p = p
-        self.transforms = transforms.ColorJitter(brightness=brightness, contrast=contrast, 
-                                                 saturation=saturation, hue=hue)
-
-    def __call__(self, img):
-        if random.random() < self.p: return self.transforms(img)
-        else: return img
diff --git a/spaces/ccolas/EmotionPlaylist/utils.py b/spaces/ccolas/EmotionPlaylist/utils.py
deleted file mode 100644
index 3371c4ab199e42bec46b991d9dfb780e9a136976..0000000000000000000000000000000000000000
--- a/spaces/ccolas/EmotionPlaylist/utils.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import numpy as np
-import json
-import os
-
-valid_track_infos = {'uri', 'name', 'artist_name', 'popularity', 'artist_genres', 'album',
-                     'artist_popularity', 'audio_features', 'audio_analysis'}
-
-def get_all_tracks_from_playlist_uri(sp, playlist_uri):
-    # get all playlist_tracks
-    offset = 0
-    tracks = []
-    done = False
-    while not done:
-        new_tracks = sp.playlist_tracks(playlist_uri, offset=offset, limit=100)["items"]
-        tracks += new_tracks
-        if len(new_tracks) < 100:
-            done = True
-        else:
-            offset += 100
-    return tracks
-
-def update_data_with_audio_features(sp, uris, data):
-    assert len(uris) <= 100
-    tracks_audio_features = sp.audio_features(uris)
-    for i in range(len(uris)):
-        data[uris[i]]['track']['audio_features'] = tracks_audio_features[i]
-    return data, []
-
-def check_all_track_has_audio_features(data):
-    for uri in data.keys():
-        assert 'audio_features' in data[uri]['track'].keys()
-
-def get_all_tracks_from_playlists(sp, playlist_uris, verbose=False):
-    if verbose: print(f'Extracting all tracks from {len(playlist_uris)} playlists.')
-    # load data
-    cache_path = './cache_track_features_tmp.json'
-    if True: #not os.path.exists(cache_path):
-        with open(cache_path, 'w') as f:
-            json.dump(dict(), f)
-    with open(cache_path, 'r') as f:
-        data = json.load(f)
-    for k in list(data.keys()).copy():
-        if k not in playlist_uris:
-            data.pop(k)
-        else:
-            print(k)
-    if verbose: print(f'\t{len(data.keys())} tracks loaded from cache')
-
-    # for each playlist, extract all tracks, remove doubles
-    if verbose: print(f'\tScanning tracks for each playlist')
-    new_additions = 0
-    added_uris = []
-    for i_playlist, playlist_uri in enumerate(playlist_uris):
-        new_tracks = get_all_tracks_from_playlist_uri(sp, playlist_uri)
-        # remove doubles
-        for new_track in new_tracks:
-            uri = new_track['track']['uri'].split(':')[-1]
-            if uri not in set(data.keys()):
-                genres = sp.artist(new_track['track']['artists'][0]['uri'])['genres']
-                new_track['track']['genres'] = genres
-                data[uri] = new_track
-                added_uris.append(uri)
-                new_additions += 1
-                # when 100 new added uris, compute their audio features
-                if len(added_uris) == 100:
-                    data, added_uris = update_data_with_audio_features(sp, added_uris, data)
-                if (new_additions + 1) % 1000 == 0:
-                    data, added_uris = update_data_with_audio_features(sp, added_uris, data)
-                    check_all_track_has_audio_features(data)
-                    with open(cache_path, 'w') as f:
-                        json.dump(data, f)
-        if verbose: print(f"\t\t{i_playlist + 1} playlists scanned ({new_additions} new tracks, total: {len(data.keys())} tracks)")
-    if verbose: print('\tDone.')
-    data, _ = update_data_with_audio_features(sp, added_uris, data)
-    check_all_track_has_audio_features(data)
-    with open(cache_path, 'w') as f:
-        json.dump(data, f)
-    return data
-
-
-def get_all_tracks_from_user(sp, user_id='bkayf', verbose=False):
-    if verbose: print(f'Extracting all tracks from user {user_id}.')
-    # load data
-    if user_id == 'bkayf':
-        cache_path = '../data/bkayf/cache_track_features.json'
-        if not os.path.exists(cache_path):
-            with open(cache_path, 'w') as f:
-                json.dump(dict(), f)
-        with open(cache_path, 'r') as f:
-            data = json.load(f)
-    else:
-        data = dict()
-    if verbose: print(f'\t{len(data.keys())} tracks loaded from cache')
-
-    # first get all playlists
-    offset = 0
-    done = False
-    playlists = []
-    if verbose: print(f'\tScanning playlists.')
-    while not done:
-        new_playlists = sp.user_playlists(user_id, offset=offset, limit=50)['items']
-        playlists += new_playlists
-        if len(new_playlists) < 50:
-            done = True
-            if verbose: print(f'\t\tfrom {offset} to {offset + len(new_playlists)} (complete).')
-        else:
-            if verbose: print(f'\t\tfrom {offset} to {offset + len(new_playlists)},')
-            offset += 50
-
-    # for each playlist, extract all tracks, remove doubles
-    if verbose: print(f'\tScanning tracks for each playlist')
-    new_additions = 0
-    added_uris = []
-    for i_playlist, playlist in enumerate(playlists):
-        if (i_playlist + 1) % 5 == 0:
-            if verbose: print(f"\t\t{i_playlist + 1} playlists scanned ({new_additions} new tracks, total: {len(data.keys())} tracks)")
-        playlist_uri = playlist['uri'].split(':')[-1]
-        new_tracks = get_all_tracks_from_playlist_uri(sp, playlist_uri)
-        # remove doubles
-        for new_track in new_tracks:
-            uri = new_track['track']['uri'].split(':')[-1]
-            if uri not in set(data.keys()):
-                data[uri] = new_track
-                added_uris.append(uri)
-                new_additions += 1
-                # when 100 new added uris, compute their audio features
-                if len(added_uris) == 100:
-                    data, added_uris = update_data_with_audio_features(sp, added_uris, data)
-                if (new_additions + 1) % 1000 == 0 and user_id == "bkayf":
-                    data, added_uris = update_data_with_audio_features(sp, added_uris, data)
-                    check_all_track_has_audio_features(data)
-                    with open(cache_path, 'w') as f:
-                        json.dump(data, f)
-    if verbose: print('\tDone.')
-    if user_id == "bkayf":
-        data, _ = update_data_with_audio_features(sp, added_uris, data)
-        check_all_track_has_audio_features(data)
-        with open(cache_path, 'w') as f:
-            json.dump(data, f)
-    return data
-
-
-def get_uri_from_link(link):
-    return link.split("?")[0].split("/")[-1]
-
-
-
-
-def get_track_info_from_playlist_uri(sp, playlist_uri, which_info=['uri'], verbose=False):
-    output = dict()
-    assert len(set(which_info) - valid_track_infos) == 0, f"Error which_info. Valid infos are: {valid_track_infos}"
-
-    tracks = get_all_tracks_from_playlist_uri(sp, playlist_uri)
-    if verbose: print(f'Playlist with {len(tracks)} tracks.')
-
-    # prepare artist info if needed
-    if any([w in which_info for w in ['artist_genres', 'artist_popularity', 'artist_name']]):
-        artist_uris = [x["track"]["artists"][0]["uri"] for x in tracks]
-        artist_infos = [sp.artist(artist_uri) for artist_uri in artist_uris]
-
-    for info in which_info:
-        # print(info)
-        if info in ['uri', 'name', 'album', 'popularity']:
-            output[info] = []
-            for i_t, x in enumerate(tracks):
-                print(i_t)
-                output[info].append(x["track"][info])
-            # output[info] = [x["track"][info] for x in tracks]
-        elif info in ['artist_genres', 'artist_popularity', 'artist_name']:
-            output[info] = [artist_info[info.split('_')[1]] for artist_info in artist_infos]
-        elif info == 'album':
-            output[info] = [x["track"][info]["name"] for x in tracks]
-        elif info == 'audio_features':
-            output[info] = []
-            for i_t, x in enumerate(tracks):
-                print(i_t)
-                output[info].append(sp.audio_features(x["track"]["uri"]))
-            # output[info] = [sp.audio_features(x["track"]["uri"]) for x in tracks]
-        elif info == 'audio_analysis':
-            output[info] = [sp.audio_analysis(x["track"]["uri"]) for x in tracks]
-        else:
-            raise NotImplementedError
-
-    return output
-
-def compute_progress_and_eta(times, iter, total, n_av=3000):
-    av_time = np.mean(times[-n_av:])
-    progress = int(((iter + 1) / total) * 100)
-    eta_h = int(av_time * (total - iter) // 3600)
-    eta_m = int((av_time * (total - iter) - (eta_h * 3600)) // 60)
-    eta_s = int((av_time * (total - iter) - (eta_h * 3600) - eta_m * 60))
-    eta = f"Progress: {progress}%, ETA: {eta_h}H{eta_m}M{eta_s}S."
-    return eta
diff --git a/spaces/chenyangqi/FateZero/inference_fatezero.py b/spaces/chenyangqi/FateZero/inference_fatezero.py
deleted file mode 100644
index 120a7844fb49ddb3e779be658946d659b4186cba..0000000000000000000000000000000000000000
--- a/spaces/chenyangqi/FateZero/inference_fatezero.py
+++ /dev/null
@@ -1,127 +0,0 @@
-
-from FateZero.test_fatezero import *
-
-import copy
-import gradio as gr
-
-class merge_config_then_run():
-    def __init__(self) -> None:
-            # Load the tokenizer
-        pretrained_model_path = 'FateZero/ckpt/stable-diffusion-v1-4'
-        self.tokenizer = None
-        self.text_encoder = None
-        self.vae = None
-        self.unet = None
-        
-        cache_ckpt = True
-        if cache_ckpt:
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                pretrained_model_path,
-                # 'FateZero/ckpt/stable-diffusion-v1-4',
-                subfolder="tokenizer",
-                use_fast=False,
-            )
-
-            # Load models and create wrapper for stable diffusion
-            self.text_encoder = CLIPTextModel.from_pretrained(
-                pretrained_model_path,
-                subfolder="text_encoder",
-            )
-
-            self.vae = AutoencoderKL.from_pretrained(
-                pretrained_model_path,
-                subfolder="vae",
-            )
-            model_config = {
-                "lora": 160,
-                # temporal_downsample_time: 4
-                "SparseCausalAttention_index": ['mid'],
-                "least_sc_channel": 640
-            }
-            self.unet = UNetPseudo3DConditionModel.from_2d_model(
-                os.path.join(pretrained_model_path, "unet"), model_config=model_config
-            )
-
-    def run(
-        self,
-        # def merge_config_then_run(
-        model_id,
-        data_path,
-        source_prompt,
-        target_prompt,
-        cross_replace_steps,
-        self_replace_steps,
-        enhance_words,
-        enhance_words_value,
-        num_steps,
-        guidance_scale,
-        user_input_video=None,
-        
-        # Temporal and spatial crop of the video
-        start_sample_frame=0,
-        n_sample_frame=8,
-        stride=1,
-        left_crop=0,
-        right_crop=0,
-        top_crop=0,
-        bottom_crop=0,
-    ):
-        # , ] = inputs
-        default_edit_config='FateZero/config/low_resource_teaser/jeep_watercolor_ddim_10_steps.yaml'
-        Omegadict_default_edit_config = OmegaConf.load(default_edit_config)
-        
-        dataset_time_string = get_time_string()
-        config_now = copy.deepcopy(Omegadict_default_edit_config)
-        print(f"config_now['pretrained_model_path'] = model_id {model_id}")
-        # config_now['pretrained_model_path'] = model_id
-        config_now['train_dataset']['prompt'] = source_prompt
-        config_now['train_dataset']['path'] = data_path
-        # ImageSequenceDataset_dict = { }
-        offset_dict = {
-            "left": left_crop,
-            "right": right_crop,
-            "top": top_crop,
-            "bottom": bottom_crop,
-        }
-        ImageSequenceDataset_dict = {
-            "start_sample_frame" : start_sample_frame,
-            "n_sample_frame" : n_sample_frame,
-            "sampling_rate"       : stride,   
-            "offset": offset_dict,
-        }
-        config_now['train_dataset'].update(ImageSequenceDataset_dict)
-        if user_input_video and data_path is None:
-            raise gr.Error('You need to upload a video or choose a provided video')
-        if user_input_video is not None:
-            if isinstance(user_input_video, str):
-                config_now['train_dataset']['path'] = user_input_video
-            elif hasattr(user_input_video, 'name') and user_input_video.name is not None:
-                config_now['train_dataset']['path'] = user_input_video.name
-        config_now['validation_sample_logger_config']['prompts'] = [target_prompt]
-        
-
-        # fatezero config
-        p2p_config_now = copy.deepcopy(config_now['validation_sample_logger_config']['p2p_config'][0])
-        p2p_config_now['cross_replace_steps']['default_'] = cross_replace_steps
-        p2p_config_now['self_replace_steps'] = self_replace_steps
-        p2p_config_now['eq_params']['words'] = enhance_words.split(" ")
-        p2p_config_now['eq_params']['values'] = [enhance_words_value,]*len(p2p_config_now['eq_params']['words'])
-        config_now['validation_sample_logger_config']['p2p_config'][0] = copy.deepcopy(p2p_config_now)
-
-
-        # ddim config
-        config_now['validation_sample_logger_config']['guidance_scale'] = guidance_scale
-        config_now['validation_sample_logger_config']['num_inference_steps'] = num_steps
-        
-
-        logdir = default_edit_config.replace('config', 'result').replace('.yml', '').replace('.yaml', '')+f'_{dataset_time_string}'      
-        config_now['logdir'] = logdir
-        print(f'Saving at {logdir}')
-        save_path = test(tokenizer = self.tokenizer,
-                         text_encoder = self.text_encoder,
-                         vae = self.vae,
-                         unet = self.unet,
-                         config=default_edit_config, **config_now)
-        mp4_path = save_path.replace('_0.gif', '_0_0_0.mp4')
-        return mp4_path
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Accounts Dictionary Free Download PDF A Practical and User-Friendly Guide to Accounting.md b/spaces/cihyFjudo/fairness-paper-search/Accounts Dictionary Free Download PDF A Practical and User-Friendly Guide to Accounting.md
deleted file mode 100644
index 040346ae336d1a13da3a03569ce429274fbc060b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Accounts Dictionary Free Download PDF A Practical and User-Friendly Guide to Accounting.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>accounts dictionary free download pdf</h2><br /><p><b><b>Download</b> &#10084; <a href="https://tinurli.com/2uwjdd">https://tinurli.com/2uwjdd</a></b></p><br /><br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Descargar Facebook Chat para KP500 conecta con tus contactos de Instagram desde Messenger.md b/spaces/cihyFjudo/fairness-paper-search/Descargar Facebook Chat para KP500 conecta con tus contactos de Instagram desde Messenger.md
deleted file mode 100644
index 2a3b9f28d963dd0018525605f0e1dc4954e96834..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Descargar Facebook Chat para KP500 conecta con tus contactos de Instagram desde Messenger.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>descargar facebook chat para kp500</h2><br /><p><b><b>Download File</b> &harr; <a href="https://tinurli.com/2uwkkk">https://tinurli.com/2uwkkk</a></b></p><br /><br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Open Kannan Star Song Download 2021.md b/spaces/cihyFjudo/fairness-paper-search/Open Kannan Star Song Download 2021.md
deleted file mode 100644
index 9bb76d25a0e3ad899ee1b6fe7bc15d20d513ffbd..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Open Kannan Star Song Download 2021.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>Watch the <b>open kannam star songs Song</b> video before converting or downloading, you can preview it by clicking </i> <b>Watch Video</b> button, </i> <b>Download MP3</b> button will convert to mp3 and </i> <b>Download MP4</b> button will convert to mp4; SavefromNets.com allows you to download any videos from the supported website into MP3, MP4, and more format.</p>
-<h2>Open Kannan Star Song Download</h2><br /><p><b><b>Download Zip</b> &#10031; <a href="https://tinurli.com/2uwitv">https://tinurli.com/2uwitv</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Size Zero Full Movie 658 The Ultimate Romantic Comedy for All Sizes.md b/spaces/cihyFjudo/fairness-paper-search/Size Zero Full Movie 658 The Ultimate Romantic Comedy for All Sizes.md
deleted file mode 100644
index 6da8f2d902d7fc48ebeaffaec894dd542dc29f8b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Size Zero Full Movie 658 The Ultimate Romantic Comedy for All Sizes.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<br />
-<p>That thing was like a Tank but on legs, massive. Beyond massive. It towered over all the houses, every step of its two heavy legs shook us to the core. Thankfully it was slow, but every aspect of it from its size to the gigantic weapons hanging underneath it screamed danger. Death. Run.</p>
-<p>For the <i>Star Wars Anthology Series</i> film, <i>Rogue One </i>, the X-wings at the Rebel base were realized by a combination of full-size props and cutouts, similar to the fighters in the original <i>Star Wars</i> film.[34]</p>
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diff --git a/spaces/cloudstack/CSV-ChatBot/modules/history.py b/spaces/cloudstack/CSV-ChatBot/modules/history.py
deleted file mode 100644
index 56b68e963cc1d0c8f644156106a7f6bf1a4d5703..0000000000000000000000000000000000000000
--- a/spaces/cloudstack/CSV-ChatBot/modules/history.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import os
-import streamlit as st
-from streamlit_chat import message
-
-
-class ChatHistory:
-    def __init__(self):
-        self.history = st.session_state.get("history", [])
-        st.session_state["history"] = self.history
-
-    def default_greeting(self):
-        return "Hello ! 👋"
-
-    def default_prompt(self, topic):
-        return f"Hello! {topic} ask anything about 🤗"
-
-    def initialize_user_history(self):
-        st.session_state["user"] = [self.default_greeting()]
-
-    def initialize_assistant_history(self, uploaded_file):
-        st.session_state["assistant"] = [self.default_prompt(uploaded_file.name)]
-
-    def initialize(self, uploaded_file):
-        if "assistant" not in st.session_state:
-            self.initialize_assistant_history(uploaded_file)
-        if "user" not in st.session_state:
-            self.initialize_user_history()
-
-    def reset(self, uploaded_file):
-        st.session_state["history"] = []
-        self.initialize_user_history()
-        self.initialize_assistant_history(uploaded_file)
-        st.session_state["reset_chat"] = False
-
-    def append(self, mode, message):
-        st.session_state[mode].append(message)
-
-    def generate_messages(self, container):
-        if st.session_state["assistant"]:
-            with container:
-                for i in range(len(st.session_state["assistant"])):
-                    message(
-                        st.session_state["user"][i],
-                        is_user=True,
-                        key=f"{i}_user",
-                        avatar_style="big-smile",
-                    )
-                    message(st.session_state["assistant"][i], key=str(i), avatar_style="thumbs")
-
-    def load(self):
-        if os.path.exists(self.history_file):
-            with open(self.history_file, "r") as f:
-                self.history = f.read().splitlines()
-
-    def save(self):
-        with open(self.history_file, "w") as f:
-            f.write("\n".join(self.history))
\ No newline at end of file
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libxavs.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libxavs.c
deleted file mode 100644
index 6c29539f2436d482f6a6cd3cf9cf17ca9b88b589..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libxavs.c
+++ /dev/null
@@ -1,441 +0,0 @@
-/*
- * AVS encoding using the xavs library
- * Copyright (C) 2010 Amanda, Y.N. Wu <amanda11192003@gmail.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 <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <math.h>
-#include <stdint.h>
-#include <float.h>
-#include <xavs.h>
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "encode.h"
-#include "packet_internal.h"
-#include "libavutil/internal.h"
-#include "libavutil/mem.h"
-#include "libavutil/opt.h"
-
-#define END_OF_STREAM 0x001
-
-#define XAVS_PART_I8X8 0x002 /* Analyze i8x8 (requires 8x8 transform) */
-#define XAVS_PART_P8X8 0x010 /* Analyze p16x8, p8x16 and p8x8 */
-#define XAVS_PART_B8X8 0x100 /* Analyze b16x8, b*/
-
-typedef struct XavsContext {
-    AVClass        *class;
-    xavs_param_t    params;
-    xavs_t         *enc;
-    xavs_picture_t  pic;
-    uint8_t        *sei;
-    int             sei_size;
-    int             end_of_stream;
-    float crf;
-    int cqp;
-    int b_bias;
-    float cplxblur;
-    int direct_pred;
-    int aud;
-    int fast_pskip;
-    int motion_est;
-    int mbtree;
-    int mixed_refs;
-    int b_frame_strategy;
-    int chroma_offset;
-    int scenechange_threshold;
-    int noise_reduction;
-
-    int64_t *pts_buffer;
-    int out_frame_count;
-} XavsContext;
-
-static void XAVS_log(void *p, int level, const char *fmt, va_list args)
-{
-    static const int level_map[] = {
-        [XAVS_LOG_ERROR]   = AV_LOG_ERROR,
-        [XAVS_LOG_WARNING] = AV_LOG_WARNING,
-        [XAVS_LOG_INFO]    = AV_LOG_INFO,
-        [XAVS_LOG_DEBUG]   = AV_LOG_DEBUG
-    };
-
-    if (level < 0 || level > XAVS_LOG_DEBUG)
-        return;
-
-    av_vlog(p, level_map[level], fmt, args);
-}
-
-static int encode_nals(AVCodecContext *ctx, AVPacket *pkt,
-                       xavs_nal_t *nals, int nnal)
-{
-    XavsContext *x4 = ctx->priv_data;
-    int64_t size = x4->sei_size;
-    uint8_t *p, *p_end;
-    int i, s, ret;
-
-    if (!nnal)
-        return 0;
-
-    for (i = 0; i < nnal; i++)
-        size += 3U + nals[i].i_payload;
-
-    if ((ret = ff_get_encode_buffer(ctx, pkt, size, 0)) < 0)
-        return ret;
-    p = pkt->data;
-    p_end = pkt->data + size;
-
-    /* Write the SEI as part of the first frame. */
-    if (x4->sei_size > 0 && nnal > 0) {
-        memcpy(p, x4->sei, x4->sei_size);
-        p += x4->sei_size;
-        x4->sei_size = 0;
-    }
-
-    for (i = 0; i < nnal; i++) {
-        int size = p_end - p;
-        s = xavs_nal_encode(p, &size, 1, nals + i);
-        if (s < 0)
-            return AVERROR_EXTERNAL;
-        if (s != 3U + nals[i].i_payload)
-            return AVERROR_EXTERNAL;
-        p += s;
-    }
-
-    return 1;
-}
-
-static int XAVS_frame(AVCodecContext *avctx, AVPacket *pkt,
-                      const AVFrame *frame, int *got_packet)
-{
-    XavsContext *x4 = avctx->priv_data;
-    xavs_nal_t *nal;
-    int nnal, i, ret;
-    xavs_picture_t pic_out;
-    int pict_type;
-
-    x4->pic.img.i_csp   = XAVS_CSP_I420;
-    x4->pic.img.i_plane = 3;
-
-    if (frame) {
-       for (i = 0; i < 3; i++) {
-            x4->pic.img.plane[i] = frame->data[i];
-            x4->pic.img.i_stride[i] = frame->linesize[i];
-       }
-
-        x4->pic.i_pts  = frame->pts;
-        x4->pic.i_type = XAVS_TYPE_AUTO;
-        x4->pts_buffer[avctx->frame_num % (avctx->max_b_frames+1)] = frame->pts;
-    }
-
-    if (xavs_encoder_encode(x4->enc, &nal, &nnal,
-                            frame? &x4->pic: NULL, &pic_out) < 0)
-        return AVERROR_EXTERNAL;
-
-    ret = encode_nals(avctx, pkt, nal, nnal);
-
-    if (ret < 0)
-        return ret;
-
-    if (!ret) {
-        if (!frame && !(x4->end_of_stream)) {
-            if ((ret = ff_get_encode_buffer(avctx, pkt, 4, 0)) < 0)
-                return ret;
-
-            pkt->data[0] = 0x0;
-            pkt->data[1] = 0x0;
-            pkt->data[2] = 0x01;
-            pkt->data[3] = 0xb1;
-            pkt->dts = 2*x4->pts_buffer[(x4->out_frame_count-1)%(avctx->max_b_frames+1)] -
-                         x4->pts_buffer[(x4->out_frame_count-2)%(avctx->max_b_frames+1)];
-            x4->end_of_stream = END_OF_STREAM;
-            *got_packet = 1;
-        }
-        return 0;
-    }
-
-    pkt->pts = pic_out.i_pts;
-    if (avctx->has_b_frames) {
-        if (!x4->out_frame_count)
-            pkt->dts = pkt->pts - (x4->pts_buffer[1] - x4->pts_buffer[0]);
-        else
-            pkt->dts = x4->pts_buffer[(x4->out_frame_count-1)%(avctx->max_b_frames+1)];
-    } else
-        pkt->dts = pkt->pts;
-
-    switch (pic_out.i_type) {
-    case XAVS_TYPE_IDR:
-    case XAVS_TYPE_I:
-        pict_type = AV_PICTURE_TYPE_I;
-        break;
-    case XAVS_TYPE_P:
-        pict_type = AV_PICTURE_TYPE_P;
-        break;
-    case XAVS_TYPE_B:
-    case XAVS_TYPE_BREF:
-        pict_type = AV_PICTURE_TYPE_B;
-        break;
-    default:
-        pict_type = AV_PICTURE_TYPE_NONE;
-    }
-
-    /* There is no IDR frame in AVS JiZhun */
-    /* Sequence header is used as a flag */
-    if (pic_out.i_type == XAVS_TYPE_I) {
-        pkt->flags |= AV_PKT_FLAG_KEY;
-    }
-
-    ff_side_data_set_encoder_stats(pkt, (pic_out.i_qpplus1 - 1) * FF_QP2LAMBDA, NULL, 0, pict_type);
-
-    x4->out_frame_count++;
-    *got_packet = ret;
-    return 0;
-}
-
-static av_cold int XAVS_close(AVCodecContext *avctx)
-{
-    XavsContext *x4 = avctx->priv_data;
-
-    av_freep(&x4->sei);
-    av_freep(&x4->pts_buffer);
-
-    if (x4->enc)
-        xavs_encoder_close(x4->enc);
-
-    return 0;
-}
-
-static av_cold int XAVS_init(AVCodecContext *avctx)
-{
-    XavsContext *x4 = avctx->priv_data;
-
-    x4->sei_size = 0;
-    xavs_param_default(&x4->params);
-
-    x4->params.pf_log               = XAVS_log;
-    x4->params.p_log_private        = avctx;
-    x4->params.i_keyint_max         = avctx->gop_size;
-    if (avctx->bit_rate) {
-        x4->params.rc.i_bitrate   = avctx->bit_rate / 1000;
-        x4->params.rc.i_rc_method = XAVS_RC_ABR;
-    }
-    x4->params.rc.i_vbv_buffer_size = avctx->rc_buffer_size / 1000;
-    x4->params.rc.i_vbv_max_bitrate = avctx->rc_max_rate    / 1000;
-    x4->params.rc.b_stat_write      = avctx->flags & AV_CODEC_FLAG_PASS1;
-    if (avctx->flags & AV_CODEC_FLAG_PASS2) {
-        x4->params.rc.b_stat_read = 1;
-    } else {
-        if (x4->crf >= 0) {
-            x4->params.rc.i_rc_method   = XAVS_RC_CRF;
-            x4->params.rc.f_rf_constant = x4->crf;
-        } else if (x4->cqp >= 0) {
-            x4->params.rc.i_rc_method   = XAVS_RC_CQP;
-            x4->params.rc.i_qp_constant = x4->cqp;
-        }
-    }
-
-    if (x4->aud >= 0)
-        x4->params.b_aud                      = x4->aud;
-    if (x4->mbtree >= 0)
-        x4->params.rc.b_mb_tree               = x4->mbtree;
-    if (x4->direct_pred >= 0)
-        x4->params.analyse.i_direct_mv_pred   = x4->direct_pred;
-    if (x4->fast_pskip >= 0)
-        x4->params.analyse.b_fast_pskip       = x4->fast_pskip;
-    if (x4->motion_est >= 0)
-        x4->params.analyse.i_me_method        = x4->motion_est;
-    if (x4->mixed_refs >= 0)
-        x4->params.analyse.b_mixed_references = x4->mixed_refs;
-    if (x4->b_bias != INT_MIN)
-        x4->params.i_bframe_bias              = x4->b_bias;
-    if (x4->cplxblur >= 0)
-        x4->params.rc.f_complexity_blur = x4->cplxblur;
-
-    x4->params.i_bframe          = avctx->max_b_frames;
-    /* cabac is not included in AVS JiZhun Profile */
-    x4->params.b_cabac           = 0;
-
-    x4->params.i_bframe_adaptive = x4->b_frame_strategy;
-
-    avctx->has_b_frames          = !!avctx->max_b_frames;
-
-    /* AVS doesn't allow B picture as reference */
-    /* The max allowed reference frame number of B is 2 */
-    x4->params.i_keyint_min      = avctx->keyint_min;
-    if (x4->params.i_keyint_min > x4->params.i_keyint_max)
-        x4->params.i_keyint_min = x4->params.i_keyint_max;
-
-    x4->params.i_scenecut_threshold = x4->scenechange_threshold;
-
-   // x4->params.b_deblocking_filter       = avctx->flags & AV_CODEC_FLAG_LOOP_FILTER;
-
-    x4->params.rc.i_qp_min                 = avctx->qmin;
-    x4->params.rc.i_qp_max                 = avctx->qmax;
-    x4->params.rc.i_qp_step                = avctx->max_qdiff;
-
-    x4->params.rc.f_qcompress       = avctx->qcompress; /* 0.0 => cbr, 1.0 => constant qp */
-    x4->params.rc.f_qblur           = avctx->qblur;     /* temporally blur quants */
-
-    x4->params.i_frame_reference    = avctx->refs;
-
-    x4->params.i_width              = avctx->width;
-    x4->params.i_height             = avctx->height;
-    x4->params.vui.i_sar_width      = avctx->sample_aspect_ratio.num;
-    x4->params.vui.i_sar_height     = avctx->sample_aspect_ratio.den;
-    /* This is only used for counting the fps */
-    x4->params.i_fps_num            = avctx->time_base.den;
-    x4->params.i_fps_den            = avctx->time_base.num;
-    x4->params.analyse.inter        = XAVS_ANALYSE_I8x8 |XAVS_ANALYSE_PSUB16x16| XAVS_ANALYSE_BSUB16x16;
-
-    x4->params.analyse.i_me_range = avctx->me_range;
-    x4->params.analyse.i_subpel_refine    = avctx->me_subpel_quality;
-
-    x4->params.analyse.b_chroma_me        = avctx->me_cmp & FF_CMP_CHROMA;
-    /* AVS P2 only enables 8x8 transform */
-    x4->params.analyse.b_transform_8x8    = 1; //avctx->flags2 & AV_CODEC_FLAG2_8X8DCT;
-
-    x4->params.analyse.i_trellis          = avctx->trellis;
-
-    x4->params.analyse.i_noise_reduction  = x4->noise_reduction;
-
-    if (avctx->level > 0)
-        x4->params.i_level_idc = avctx->level;
-
-    if (avctx->bit_rate > 0)
-        x4->params.rc.f_rate_tolerance =
-            (float)avctx->bit_rate_tolerance / avctx->bit_rate;
-
-    if ((avctx->rc_buffer_size) &&
-        (avctx->rc_initial_buffer_occupancy <= avctx->rc_buffer_size)) {
-        x4->params.rc.f_vbv_buffer_init =
-            (float)avctx->rc_initial_buffer_occupancy / avctx->rc_buffer_size;
-    } else
-        x4->params.rc.f_vbv_buffer_init = 0.9;
-
-    /* TAG:do we have MB tree RC method */
-    /* what is the RC method we are now using? Default NO */
-    x4->params.rc.f_ip_factor             = 1 / fabs(avctx->i_quant_factor);
-    x4->params.rc.f_pb_factor             = avctx->b_quant_factor;
-
-    x4->params.analyse.i_chroma_qp_offset = x4->chroma_offset;
-
-    x4->params.analyse.b_psnr = avctx->flags & AV_CODEC_FLAG_PSNR;
-    x4->params.i_log_level    = XAVS_LOG_DEBUG;
-    x4->params.i_threads      = avctx->thread_count;
-    x4->params.b_interlaced   = avctx->flags & AV_CODEC_FLAG_INTERLACED_DCT;
-
-    if (avctx->flags & AV_CODEC_FLAG_GLOBAL_HEADER)
-        x4->params.b_repeat_headers = 0;
-
-    x4->enc = xavs_encoder_open(&x4->params);
-    if (!x4->enc)
-        return AVERROR_EXTERNAL;
-
-    if (!FF_ALLOCZ_TYPED_ARRAY(x4->pts_buffer, avctx->max_b_frames + 1))
-        return AVERROR(ENOMEM);
-
-    /* TAG: Do we have GLOBAL HEADER in AVS */
-    /* We Have PPS and SPS in AVS */
-    if (avctx->flags & AV_CODEC_FLAG_GLOBAL_HEADER && 0) {
-        xavs_nal_t *nal;
-        int nnal, s, i, size;
-        uint8_t *p;
-
-        s = xavs_encoder_headers(x4->enc, &nal, &nnal);
-
-        avctx->extradata = p = av_malloc(s);
-        for (i = 0; i < nnal; i++) {
-            /* Don't put the SEI in extradata. */
-            if (nal[i].i_type == NAL_SEI) {
-                x4->sei = av_malloc( 5 + nal[i].i_payload * 4 / 3 );
-                if (xavs_nal_encode(x4->sei, &x4->sei_size, 1, nal + i) < 0)
-                    return -1;
-
-                continue;
-            }
-            size = xavs_nal_encode(p, &s, 1, nal + i);
-            if (size < 0)
-                return -1;
-            p += size;
-        }
-        avctx->extradata_size = p - avctx->extradata;
-    }
-    return 0;
-}
-
-#define OFFSET(x) offsetof(XavsContext, x)
-#define VE AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_ENCODING_PARAM
-static const AVOption options[] = {
-    { "crf",           "Select the quality for constant quality mode",    OFFSET(crf),           AV_OPT_TYPE_FLOAT,  {.dbl = -1 }, -1, FLT_MAX, VE },
-    { "qp",            "Constant quantization parameter rate control method",OFFSET(cqp),        AV_OPT_TYPE_INT,    {.i64 = -1 }, -1, INT_MAX, VE },
-    { "b-bias",        "Influences how often B-frames are used",          OFFSET(b_bias),        AV_OPT_TYPE_INT,    {.i64 = INT_MIN}, INT_MIN, INT_MAX, VE },
-    { "cplxblur",      "Reduce fluctuations in QP (before curve compression)", OFFSET(cplxblur), AV_OPT_TYPE_FLOAT,  {.dbl = -1 }, -1, FLT_MAX, VE},
-    { "direct-pred",   "Direct MV prediction mode",                       OFFSET(direct_pred),   AV_OPT_TYPE_INT,    {.i64 = -1 }, -1, INT_MAX, VE, "direct-pred" },
-    { "none",          NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_DIRECT_PRED_NONE },     0, 0, VE, "direct-pred" },
-    { "spatial",       NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_DIRECT_PRED_SPATIAL },  0, 0, VE, "direct-pred" },
-    { "temporal",      NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_DIRECT_PRED_TEMPORAL }, 0, 0, VE, "direct-pred" },
-    { "auto",          NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_DIRECT_PRED_AUTO },     0, 0, VE, "direct-pred" },
-    { "aud",           "Use access unit delimiters.",                     OFFSET(aud),           AV_OPT_TYPE_BOOL,    {.i64 = -1 }, -1, 1, VE},
-    { "mbtree",        "Use macroblock tree ratecontrol.",                OFFSET(mbtree),        AV_OPT_TYPE_BOOL,    {.i64 = -1 }, -1, 1, VE},
-    { "mixed-refs",    "One reference per partition, as opposed to one reference per macroblock", OFFSET(mixed_refs), AV_OPT_TYPE_BOOL, {.i64 = -1}, -1, 1, VE },
-    { "fast-pskip",    NULL,                                              OFFSET(fast_pskip),    AV_OPT_TYPE_BOOL,    {.i64 = -1 }, -1, 1, VE},
-    { "motion-est",   "Set motion estimation method",                     OFFSET(motion_est),    AV_OPT_TYPE_INT,    { .i64 = XAVS_ME_DIA }, -1, XAVS_ME_TESA, VE, "motion-est"},
-    { "dia",           NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_ME_DIA },               INT_MIN, INT_MAX, VE, "motion-est" },
-    { "hex",           NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_ME_HEX },               INT_MIN, INT_MAX, VE, "motion-est" },
-    { "umh",           NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_ME_UMH },               INT_MIN, INT_MAX, VE, "motion-est" },
-    { "esa",           NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_ME_ESA },               INT_MIN, INT_MAX, VE, "motion-est" },
-    { "tesa",          NULL,      0,    AV_OPT_TYPE_CONST, { .i64 = XAVS_ME_TESA },              INT_MIN, INT_MAX, VE, "motion-est" },
-    { "b_strategy",    "Strategy to choose between I/P/B-frames",         OFFSET(b_frame_strategy), AV_OPT_TYPE_INT, {.i64 = 0 }, 0, 2, VE},
-    { "chromaoffset", "QP difference between chroma and luma",           OFFSET(chroma_offset), AV_OPT_TYPE_INT, {.i64 = 0 }, INT_MIN, INT_MAX, VE},
-    { "sc_threshold", "Scene change threshold",                           OFFSET(scenechange_threshold), AV_OPT_TYPE_INT, {.i64 = 0 }, 0, INT_MAX, VE},
-    { "noise_reduction", "Noise reduction",                               OFFSET(noise_reduction), AV_OPT_TYPE_INT, {.i64 = 0 }, 0, INT_MAX, VE},
-
-    { NULL },
-};
-
-static const AVClass xavs_class = {
-    .class_name = "libxavs",
-    .item_name  = av_default_item_name,
-    .option     = options,
-    .version    = LIBAVUTIL_VERSION_INT,
-};
-
-static const FFCodecDefault xavs_defaults[] = {
-    { "b",                "0" },
-    { NULL },
-};
-
-const FFCodec ff_libxavs_encoder = {
-    .p.name         = "libxavs",
-    CODEC_LONG_NAME("libxavs Chinese AVS (Audio Video Standard)"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_CAVS,
-    .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_DELAY |
-                      AV_CODEC_CAP_OTHER_THREADS,
-    .priv_data_size = sizeof(XavsContext),
-    .init           = XAVS_init,
-    FF_CODEC_ENCODE_CB(XAVS_frame),
-    .close          = XAVS_close,
-    .caps_internal  = FF_CODEC_CAP_NOT_INIT_THREADSAFE |
-                      FF_CODEC_CAP_AUTO_THREADS,
-    .p.pix_fmts     = (const enum AVPixelFormat[]) { AV_PIX_FMT_YUV420P, AV_PIX_FMT_NONE },
-    .p.priv_class   = &xavs_class,
-    .defaults       = xavs_defaults,
-    .p.wrapper_name = "libxavs",
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/videodsp_init.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/videodsp_init.c
deleted file mode 100644
index 92ade4f846636e10a5ccb37ca4182be31bc2690b..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/videodsp_init.c
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (c) 2021 Loongson Technology Corporation Limited
- * Contributed by Xiwei Gu <guxiwei-hf@loongson.cn>
- *
- * 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 "libavcodec/videodsp.h"
-#include "libavutil/attributes.h"
-
-static void prefetch_loongarch(const uint8_t *mem, ptrdiff_t stride, int h)
-{
-    register const uint8_t *p = mem;
-
-    __asm__ volatile (
-        "1:                                     \n\t"
-        "preld      0,     %[p],     0          \n\t"
-        "preld      0,     %[p],     32         \n\t"
-        "addi.d     %[h],  %[h],     -1         \n\t"
-        "add.d      %[p],  %[p],     %[stride]  \n\t"
-
-        "blt        $r0,   %[h],     1b         \n\t"
-        : [p] "+r" (p), [h] "+r" (h)
-        : [stride] "r" (stride)
-    );
-}
-
-av_cold void ff_videodsp_init_loongarch(VideoDSPContext *ctx, int bpc)
-{
-    ctx->prefetch = prefetch_loongarch;
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Dig Deep MOD Menu APK for Android Latest Version.md b/spaces/congsaPfin/Manga-OCR/logs/Download Dig Deep MOD Menu APK for Android Latest Version.md
deleted file mode 100644
index 1c04b0468c555a8cc1b902f5cb98bde0edb2e026..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Dig Deep MOD Menu APK for Android Latest Version.md	
+++ /dev/null
@@ -1,81 +0,0 @@
-<br />
-<h1>Dig Deep Mod Menu APK: How to Download and Install It</h1>
-<p>Do you love mining games? Do you want to become a rich tycoon by digging for treasure? If yes, then you should try Dig Deep, a fun and addictive idle miner game. But wait, there's more! You can also download Dig Deep Mod Menu APK, a modified version of the game that gives you unlimited money, no ads, and easy controls. Sounds awesome, right? In this article, we will tell you what Dig Deep Mod Menu APK is, what features it has, and how to download and install it on your Android device. Let's get started!</p>
- <h2>What is Dig Deep Mod Menu APK?</h2>
-<p>Dig Deep is a simulator game where you go underground and dig deep for treasure. You can hire workers, upgrade your equipment, and explore different biomes. The game is fun and relaxing, but it can also be challenging and rewarding. You can earn money by selling your loot, and use it to expand your mining empire.</p>
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-<p>Dig Deep Mod Menu APK is a modified version of the game that gives you some extra benefits. With this mod menu, you can get unlimited money, no ads, and easy controls. You can use the money to buy anything you want in the game, without worrying about running out. You can also enjoy the game without any annoying ads interrupting your gameplay. And you can control your workers and machines with just one tap, making the game more convenient and enjoyable.</p>
- <h3>Features of Dig Deep Mod Menu APK</h3>
-<p>Here are some of the features of Dig Deep Mod Menu APK that make it better than the original game:</p>
- <h4>Unlimited money</h4>
-<p>With this mod menu, you can get unlimited money in the game. You can use it to buy workers, equipment, upgrades, and anything else you need. You can also unlock new biomes and discover rare treasures. You don't have to wait for your workers to dig or sell your loot to earn money. You can just tap on the screen and get as much money as you want.</p>
- <h4>No ads</h4>
-<p>Another benefit of this mod menu is that it removes all the ads from the game. You can play Dig Deep without any interruptions or distractions. You don't have to watch videos or click on banners to get rewards or bonuses. You can just focus on digging and having fun.</p>
- <h4>Easy controls</h4>
-<p>The last feature of this mod menu is that it makes the game easier to control. You don't have to swipe or drag on the screen to move your workers or machines. You can just tap on the screen and they will do what you want them to do. You can also tap on the icons on the top right corner of the screen to access the shop, settings, achievements, and other options.</p>
- <h2>How to Download and Install Dig Deep Mod Menu APK?</h2>
-<p>If you want to download and install Dig Deep Mod Menu APK on your Android device, you need to follow these simple steps:</p>
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- <h3>Step 1: Enable unknown sources</h3>
-<p>Before you can install any APK file on your device, you need to enable unknown sources in your settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</p>
- <h3>Step 2: Download the APK file</h3>
-<p>Next, you need to download the APK file of Dig Deep Mod Menu from a reliable source. You can use this link to download it directly from MODAPK website. The file size is about 40 MB, so make sure you have enough space on your device.</p>
- <h3>Step 3: Install the APK <h3>Step 3: Install the APK file</h3>
-<p>After you have downloaded the APK file, you need to install it on your device. To do this, locate the file in your file manager and tap on it. You will see a pop-up window asking you to confirm the installation. Tap on Install and wait for the process to finish.</p>
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-<p>Once the installation is done, you can launch the game and enjoy the mod menu. You will see a new icon on your home screen with the name Dig Deep Mod Menu. Tap on it and start playing. You will notice that you have unlimited money, no ads, and easy controls. You can also access the mod menu by tapping on the M button on the top left corner of the screen. From there, you can customize your settings and preferences.</p>
- <h2>Conclusion</h2>
-<p>Dig Deep Mod Menu APK is a great way to enhance your gaming experience and have more fun with Dig Deep. You can get unlimited money, no ads, and easy controls with this mod menu. You can also download and install it easily on your Android device by following the steps we have provided in this article. So, what are you waiting for? Download Dig Deep Mod Menu APK today and start digging for treasure!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Dig Deep Mod Menu APK:</p>
- <h4>Q: Is Dig Deep Mod Menu APK safe to use?</h4>
-<p>A: Yes, Dig Deep Mod Menu APK is safe to use as long as you download it from a trusted source like MODAPK website. However, you should always be careful when installing any APK file on your device and make sure you have a backup of your data in case something goes wrong.</p>
- <h4>Q: Do I need to root my device to use Dig Deep Mod Menu APK?</h4>
-<p>A: No, you don't need to root your device to use Dig Deep Mod Menu APK. You can install it on any Android device without any root access or special permissions.</p>
- <h4>Q: Will Dig Deep Mod Menu APK affect my game progress?</h4>
-<p>A: No, Dig Deep Mod Menu APK will not affect your game progress. You can play the game normally with or without the mod menu. However, you should be aware that using the mod menu may make the game less challenging and rewarding, as you will have unlimited resources and advantages.</p>
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-<p>A: Yes, you can update Dig Deep Mod Menu APK whenever there is a new version available. However, you should always check the compatibility of the mod menu with the latest version of the game before updating it. You should also backup your data before updating in case something goes wrong.</p>
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-<p>A: Yes, you can play Dig Deep online with Dig Deep Mod Menu APK. However, you should be careful not to abuse the mod menu or cheat in any way that may affect other players or violate the game's terms of service. You should also respect other players and play fair.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Euchre Offline A Fun and Free Card Game to Download and Play Anywhere.md b/spaces/congsaPfin/Manga-OCR/logs/Euchre Offline A Fun and Free Card Game to Download and Play Anywhere.md
deleted file mode 100644
index 6ba6c2e4c61b9aa339227eb8638687239be09ef9..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Euchre Offline A Fun and Free Card Game to Download and Play Anywhere.md	
+++ /dev/null
@@ -1,141 +0,0 @@
-
-<h1>How to Download Euchre and Enjoy the Classic Card Game</h1>
-<p>Euchre is a trick-taking card game that is popular in many countries, especially in North America, Australia, New Zealand, and Great Britain. It is a fun and challenging game that can be played with friends, family, or online with other players. In this article, we will show you how to download euchre on different devices, and how to play euchre online with some of the best euchre apps and websites. But first, let's learn more about euchre and why you should play it.</p>
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- <h2>What is Euchre and Why Should You Play It?</h2>
-<p>Euchre is a card game that originated in Europe in the 18th or 19th century, most likely from an Alsatian game called Jucker.  It is played with a deck of 24, 28, or 32 cards, depending on the number of players and the variant. The game involves two teams of two players each, who try to win tricks by playing the highest card of the suit led or the highest trump card. The trump suit is determined by a bidding process, where one team can choose to make the suit of a turned-up card as trump, or name another suit as trump, or pass. The team that chooses trump becomes the "makers", and the other team becomes the "defenders". The makers need to win at least three tricks out of five to score points, while the defenders need to prevent them from doing so. A player who is confident of winning all five tricks can choose to play alone, without their partner's help, for extra points.</p>
- <h3>The History and Origins of Euchre</h3>
-<p>Euchre has a rich and fascinating history that spans across continents and centuries. The earliest reference to euchre dates back to 1810, when it was mentioned in a German book about card games.  It was brought to America by German immigrants in Pennsylvania, where it spread throughout the nation.  It was also introduced to England by French prisoners of war during the Napoleonic Wars, where it became popular in Cornwall and Devon.  Euchre was responsible for introducing the joker into the modern deck of cards, as it was used as the "best bower" or the highest trump card in some variants.  Euchre remains one of the most popular card games today, especially in regions where it has a strong cultural presence.</p>
- <h3>The Rules and Variations of Euchre</h3>
-<p>The rules of euchre have changed over time, but they still remain largely similar to those of its early days. The basic rules are as follows:</p>
-<ul>
-<li>The game is played with four players in two teams of two.</li>
-<li>A standard 52-card deck is stripped down to 24 cards by removing all cards below 9 (or below 7 in some variants).</li>
-<li>Each player is dealt five cards in a three-two or two-three sequence.</li>
-<li>The remaining four cards are placed face down on the table, and the top card is turned face up as a prospective trump.</li>
-<li>The player to the left of the dealer can either accept or reject the turned-up card as trump by saying "I order it up" or "I pass". If they accept it, their partner must discard one card face down and pick up the turned-up card. If they reject it, the next player in clockwise order can do the same.</li>
-<li>If all four players pass on the turned-up card,. - If all four players pass on the turned-up card, the dealer can either name another suit as trump or turn down the card. If they turn down the card, the next player can name another suit as trump or pass, and so on until a suit is named or all players pass again. If all players pass again, the cards are reshuffled and redealt by the next dealer.</li>
-<li>The player to the left of the dealer leads the first trick by playing any card from their hand. The other players must follow suit if they can, or play any other card if they cannot. The highest card of the suit led or the highest trump card wins the trick and takes it for their team.</li>
-<li>The player who wins the trick leads the next trick, and so on until all five tricks are played.</li>
-<li>The team that chooses trump is called the makers, and they need to win at least three tricks to score points. The other team is called the defenders, and they need to prevent the makers from winning three tricks. If the makers win three or four tricks, they score one point. If they win all five tricks, they score two points. This is called a euchre, and it also means that the defenders lose two points. If a player chooses to play alone without their partner's help, they score four points for winning all five tricks, or one point for winning three or four tricks.</li>
-<li>The first team to reach a predetermined number of points, usually 10, wins the game.</li>
-</ul>
-<p>There are many variations of euchre that have different rules, such as British euchre, Australian euchre, Bid euchre, Railroad euchre, and Buck euchre. Some of these variations use different numbers of cards, different scoring systems, different bidding methods, or different roles for the joker. You can find more information about these variations online or in books about euchre.</p>
- <h3>The Benefits of Playing Euchre</h3>
-<p>Playing euchre is not only fun, but also beneficial for your mental and social health. Here are some of the benefits of playing euchre:</p>
-<ul>
-<li>It improves your memory, concentration, and strategic thinking skills by requiring you to remember the cards played, keep track of the trump suit, and plan your moves ahead.</li>
-<li>It enhances your communication and teamwork skills by requiring you to cooperate with your partner and signal your intentions without revealing them to your opponents.</li>
-<li>It reduces your stress and anxiety levels by providing you with a relaxing and enjoyable activity that distracts you from your worries and problems.</li>
-<li>It strengthens your social bonds and friendships by allowing you to interact with other people who share your interest in euchre and have fun together.</li>
-<li>It boosts your self-esteem and confidence by giving you a sense of achievement and satisfaction when you win a game or make a good play.</li>
-</ul>
- <h2>How to Download Euchre on Different Devices</h2>
-<p>If you want to play euchre on your own device, you will need to download an app or a software that supports euchre. There are many options available for different devices, such as Android, iOS, Windows, and Mac. Here are some of the best ones that we recommend:</p>
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- <h3>Download Euchre on Android</h3>
-<p>If you have an Android device, you can download one of these apps from the Google Play Store:</p>
-<table>
-<tr><th>Name</th><th>Description</th><th>Rating</th></tr>
-<tr><td>Euchre Free: Classic Card Game</td><td>This app offers a smooth and realistic euchre experience with customizable settings, smart AI opponents, and online multiplayer mode. You can also track your statistics and achievements.</td><td>4.5/5</td></tr>
-<tr><td>Euchre - Offline Free Card Games</td><td>This app allows you to play euchre offline with three difficulty levels, adjustable game speed, and various rules options. You can also play online with friends or strangers.</td><td>4.4/5</td></tr>
-<tr><td>Euchre Online</td><td>This app lets you play euchre online with real players from around the world. You can join public tables or create your own private ones. You can also chat with other players and send emojis.</td><td>4.3/5</td></tr>
-</table>
- <h3>Download Euchre on iOS</h3>
-<p>If you have an iOS device, you can download one of these apps from the App Store:</p>
-<table>
-<tr><th>Name</th><th>Description</th><th>Rating</th></tr>
-<tr><td>Euchre 3D</td><td>This app features stunning 3D graphics, realistic animations, and sound effects that make you feel like you are playing euchre in real life. You can play with smart AI opponents or online with other players. You can also customize the game rules, the cards, and the table.</td><td>4.8/5</td></tr>
-<tr><td>Euchre - Hardwood Games</td><td>This app offers a beautiful and elegant euchre game with high-quality graphics, animations, and sounds. You can play solo or online with other players. You can also choose from different card decks, backgrounds, and avatars.</td><td>4.7/5</td></tr>
-<tr><td>Euchre Gold</td><td>This app provides a simple and fast euchre game with intuitive controls and smooth gameplay. You can play offline with AI opponents or online with other players. You can also adjust the game settings, such as the difficulty level, the scoring system, and the trump suit.</td><td>4.6/5</td></tr>
-</table>
- <h3>Download Euchre on Windows</h3>
-<p>If you have a Windows device, you can download one of these software from the Microsoft Store:</p>
-<table>
-<tr><th>Name</th><th>Description</th><th>Rating</th></tr>
-<tr><td>Euchre Free!</td><td>This software allows you to play euchre for free on your Windows device. You can play with AI opponents or online with other players. You can also change the game rules, the card design, and the background music.</td><td>4.2/5</td></tr>
-<tr><td>Euchre Online</td><td>This software lets you play euchre online with real players from around the world. You can join public tables or create your own private ones. You can also chat with other players and send emojis.</td><td>4.1/5</td></tr>
-<tr><td>Euchre Club</td><td>This software offers a fun and social euchre game with various features and options. You can play offline with AI opponents or online with other players. You can also join clubs, earn coins, unlock achievements, and compete in tournaments.</td><td>4/5</td></tr>
-</table>
- <h3>Download Euchre on Mac</h3>
-<p>If you have a Mac device, you can download one of these software from the Mac App Store:</p>
-<table>
-<tr><th>Name</th><th>Description</th><th>Rating</th></tr>
-<tr><td>Euchre HD</td><td>This software delivers a high-definition euchre game with crisp graphics and smooth gameplay. You can play with AI opponents or online with other players. You can also customize the game rules, the card style, and the table theme.</td><td>4.7/5</td></tr>
-<tr><td>Euchre Sandbox</td><td>This software provides a simple and easy euchre game with basic features and options. You can play offline with AI opponents or online with other players. You can also change the game settings, such as the difficulty level, the scoring system, and the trump suit.</td><td>4.5/5</td></tr>
-<tr><td>Euchre 3D Pro</td><td>This software features a realistic and immersive euchre game with 3D graphics, animations, and sound effects. You can play with smart AI opponents or online with other players. You can also choose from different card decks, backgrounds, and avatars.</td><td>4.3/5</td></tr>
-</table>
- <h2>How to Play Euchre Online with Friends or Other Players</h2>
-<p>If you want to play euchre online with your friends or other players, you will need to find an app or a website that supports euchre multiplayer mode. There are many options available for different devices, but here are some of the best ones that we recommend:</p>
- <h3>Trickster Euchre</h3>
-<p>Trickster Euchre is a website that allows you to play euchre online with your friends or other players from around the world. You can create your own custom games or join existing ones. You can also chat with other players, send emojis, and view your statistics and rankings.</p>
- <h3>Euchre 3D</h3>
-<p>Euchre 3D is an app that lets you play euchre online with your friends or other players from around the world. You can create your own private games or join public ones. You can also chat with other players, send emojis, and view your statistics and achievements.</p>
- <h3>Euchre by KARMAN Games</h3>
-<p>Euchre by KARMAN Games is an app that enables you to play euchre online with your friends or other players from around the world. You can create your own games or join existing ones. You can also chat with other players, send emojis, and view your statistics and ratings.</p>
- <h2>Conclusion</h2>
-<p>Euchre is a classic card game that is fun, challenging, and rewarding to play. It is a great way to spend some quality time with your friends, family, or online with other players. It is also a great way to improve your mental and social skills, such as memory, concentration, strategy, communication, and teamwork. If you want to play euchre on your own device, you can download one of the many apps or software that support euchre. You can also play euchre online with some of the best euchre apps and websites that we have recommended in this article. We hope you have enjoyed this article and learned how to download euchre and enjoy the classic card game.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about euchre:</p>
-<ul>
-<li><b>Q: How many players can play euchre?</b></li>
-<li>A: Euchre is usually played with four players in two teams of two, but it can also be played with two, three, five, or six players with different rules and variations.</li>
-<li><b>Q: How do you score points in euchre?</b></li>
-<li>A: The team that chooses trump is called the makers, and they need to win at least three tricks out of five to score points. The other team is called the defenders, and they need to prevent the makers from winning three tricks. If the makers win three or four tricks, they score one point. If they win all five tricks, they score two points. This is called a euchre, and it also means that the defenders lose two points. If a player chooses to play alone without their partner's help, they score four points for winning all five tricks, or one point for winning three or four tricks.</li>
-<li><b>Q: What is the best card in euchre?</b></li>
-<li>A: The best card in euchre is the right bower, which is the jack of the trump suit. The second best card is the left bower, which is the jack of the same color as the trump suit. For example, if spades are trump, then the right bower is the jack of spades and the left bower is the jack of clubs.</li>
-<li><b>Q: How do you signal your partner in euchre?</b></li>
-<li>A: Signaling your partner in euchre is a way of communicating your intentions and information without revealing them to your opponents. There are different ways of signaling your partner, such as using your cards, your bids, or your gestures. For example, you can signal your partner that you have a high card of a certain suit by leading that suit or discarding a low card of that suit. You can also signal your partner that you want them to choose a certain suit as trump by ordering up or passing on that suit. You can also signal your partner that you have a strong hand by playing alone or bidding high.</li>
-<li><b>Q: How do you play euchre online?</b></li>
-<li>A: To play euchre online, you need to find an app or a website that supports euchre multiplayer mode. You can create your own custom games or join existing ones. You can also chat with other players, send emojis, and view your statistics and rankings.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Activate and Use eSIM with Azercell.md b/spaces/congsaPfin/Manga-OCR/logs/How to Activate and Use eSIM with Azercell.md
deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/How to Activate and Use eSIM with Azercell.md	
+++ /dev/null
@@ -1,103 +0,0 @@
-<br />
-<h1>e-sim azercell: A new generation of SIM technology</h1>
-<p>If you are looking for a more convenient, secure, and eco-friendly way to use your mobile phone, you might want to consider switching to e-sim. e-sim is a digital microchip that is embedded in your smartphone, eliminating the need for a physical SIM card. You can activate it by installing the “e-sim profile” of an operator that provides the service, such as Azercell. In this article, we will explain what e-sim is, how it works, and how you can benefit from it with Azercell.</p>
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- <h2>What is e-sim and how does it work?</h2>
-<p>e-sim stands for embedded SIM or electronic SIM. It is a new generation of SIM technology that allows you to connect to a mobile network without inserting a plastic SIM card into your phone. Instead, you can download and install the “e-sim profile” of your chosen operator, which contains all the information and settings needed to access their services. You can do this by scanning a QR code or using an app provided by the operator.</p>
-<p>e-sim works the same way as a traditional SIM card, but with some advantages. For example, you can store multiple e-sim profiles on one device and switch between them easily. You can also change your operator or plan without having to replace your SIM card. Moreover, you can protect your data in case of loss, theft, or damage of your device, as the e-sim cannot be removed or tampered with.</p>
- <h2>The benefits of e-sim over traditional SIM cards</h2>
-<p>There are many reasons why you might want to use e-sim instead of a regular SIM card. Here are some of them:</p>
-<ul>
-<li><b>More secure:</b> Your data is more secure with e-sim because the virtual card cannot be stolen or lost. You can also remotely lock or erase your e-sim profile if your device is missing or compromised.</li>
-<li><b>More convenient:</b> With e-sim, you can connect several numbers to one smartphone without having to swap SIM cards. This is useful if you have multiple lines for personal or business use, or if you travel frequently and need local numbers.</li>
-<li><b>Eco-friendly:</b> e-sim is completely digital, eliminating the need for plastic cards. This reduces waste and environmental impact.</li>
-</ul>
- <h2>How to activate e-sim with Azercell</h2>
-<p>If you want to use e-sim with Azercell, you need to have a compatible device and an internet connection (wi-fi or mobile internet). You also need to check if your device's IMEI code is registered. You can do this by clicking <a href="https://www.e-imza.gov.az/imei/imei.php">here</a>. Then, you can follow these steps:</p>
- <h3>For new numbers</h3>
-<ol>
-<li>Go to <a href="https://www.azerc ell.com/en/e-sim">www.azercell.com/en/e-sim</a> and fill out the online form with your personal information and the number you want to activate.</li>
-<li>You will receive an email with a QR code and a PIN code. Scan the QR code with your device's camera and enter the PIN code when prompted.</li>
-<li>Your e-sim profile will be installed on your device and you can start using your new number.</li>
-</ol>
- <h3>For existing numbers</h3>
-<ol>
-<li>Visit any Azercell customer service center and request to transfer your existing number to e-sim. You will need to present your ID card and your device.</li>
-<li>You will receive a QR code and a PIN code from the customer service representative. Scan the QR code with your device's camera and enter the PIN code when prompted.</li>
-<li>Your e-sim profile will be installed on your device and you can continue using your existing number.</li>
-</ol>
- <h2>Which devices support e-sim and how to check them?</h2>
-<p>Not all devices are compatible with e-sim technology. You need to have a smartphone that has an e-sim chip built-in. Some of the devices that support e-sim are:</p>
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-<p>To check if your device supports e-sim, you can do the following:</p>
-<ol>
-<li>Go to Settings > General > About on your device and look for the IMEI number. If you see two IMEI numbers, one for primary and one for secondary, it means your device has an e-sim chip.</li>
-<li>Alternatively, you can go to Settings > Cellular > Add Cellular Plan on your device and see if you have the option to scan a QR code or enter a confirmation code. If you do, it means your device supports e-sim.</li>
-</ol>
- <h2>How to manage multiple e-sim numbers on one device?</h2>
-<p>One of the advantages of e-sim is that you can have more than one number on one device. You can store up to nine e-sim profiles on your device and switch between them easily. You can also order, transfer, or delete e-sim numbers as you wish. Here is how you can do that:</p>
- <h3>How to order, transfer, or delete e-sim numbers</h3>
-<p>If you want to order a new e-sim number from Azercell, you can follow the same steps as described above for activating e-sim with Azercell. You can choose any number from the available list or keep your current number if you are an existing Azercell customer.</p>
-<p>If you want to transfer an existing e-sim number from another operator to Azercell, you need to visit an Azercell customer service center and request a portability service. You will need to present your ID card and your device. You will receive a QR code and a PIN code from the customer service representative. Scan the QR code with your device's camera and enter the PIN code when prompted. Your e-sim profile will be transferred to Azercell and you can use their services.</p>
-<p>If you want to delete an e-sim number from your device, you can go to Settings > Cellular > Cellular Plans on your device and tap on the number you want to remove. Then tap on Remove Cellular Plan and confirm your choice. Your e-sim profile will be deleted from your device and you will not be able to use that number anymore.</p>
- <h3>How to switch between different e-sim profiles</h3>
-<p>If you have more than one e-sim profile on your device, you can switch between them easily depending on your needs. You can do this by going to Settings > Cellular > Cellular Plans on your device and tapping on the number you want to use as your default line. You can also choose which line to use for voice calls, data, iMessage, FaceTime, etc.</p>
- <h2>How to get e-sim from Azercell</h2>
-<p>If you are interested in getting e-sim from Azercell, you have two options: online or offline. Here is how they differ:</p>
- <h3>Online option</h3>
-<p>The online option is convenient and fast. you are interested in trying e-sim with Azercell, you can order it online or offline and enjoy the benefits of this new technology. If you have any feedback or suggestions, please let us know in the comments below. Thank you for reading and have a great day!</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/No Limit Drag Racing 2 APK The Best Drag Racing Game Youve Ever Seen.md b/spaces/congsaPfin/Manga-OCR/logs/No Limit Drag Racing 2 APK The Best Drag Racing Game Youve Ever Seen.md
deleted file mode 100644
index 65c737d09396964c612f378f924742353ed04bab..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/No Limit Drag Racing 2 APK The Best Drag Racing Game Youve Ever Seen.md	
+++ /dev/null
@@ -1,95 +0,0 @@
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-<h1>No Limit Drag Racing 2 APK: A Guide for Racing Fans</h1>
-<p>If you love drag racing games, you might want to check out No Limit Drag Racing 2, a realistic and immersive racing simulator that lets you customize your car, compete with other players, and progress through a career mode. In this article, we will tell you everything you need to know about No Limit Drag Racing 2 APK, including its features, how to download and install it, and some tips and tricks to help you win more races.</p>
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- <h2>What is No Limit Drag Racing 2?</h2>
-<p>No Limit Drag Racing 2 is a sequel to the popular No Limit Drag Racing game that was released in 2015. It is developed by Battle Creek Games, a studio that specializes in racing games. No Limit Drag Racing 2 is available for Android devices and can be downloaded for free from the Google Play Store or from third-party websites as an APK file.</p>
- <h3>Features of No Limit Drag Racing 2</h3>
-<p>No Limit Drag Racing 2 has many features that make it one of the best drag racing games on the market. Here are some of them:</p>
- <h4>Customization</h4>
-<p>In No Limit Drag Racing 2, you can customize your car in various ways, such as changing its color, wheels, decals, hood, spoiler, and more. You can also modify its performance by adjusting the gearing, rev limiter, suspension, timing, fuel delivery, boost, and launch control. You can use the included dyno to test the changes you make and see how they affect your car's power and speed.</p>
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-<p>No Limit Drag Racing 2 allows you to play online with other racers from all over the world. You can join or create a lobby and race against up to four opponents at a time. You can also chat with other players, send them challenges, and join a team to compete in team events. Be careful though, there are some fast folks on this game!</p>
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-<p>If you prefer to play solo, you can try the career mode in No Limit Drag Racing 2. In this mode, you start with a basic car and work your way up through different classes of racing. You can earn money by winning races and use it to buy new cars or upgrade your existing ones. You can also unlock new tracks and events as you progress.</p>
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-<li>An Android device running Android 5.0 or higher.</li>
-<li>At least 300 MB of free storage space.</li>
-<li>A stable internet connection.</li>
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- <h3>Tips and Tricks for No Limit Drag Racing 2</h3>
-<p>No Limit Drag Racing 2 is a fun and challenging game that requires skill and strategy to win. Here are some tips and tricks that can help you improve your performance:</p>
- <h4>Tune Your Car</h4>
-<p <p>Tuning your car is essential to optimize its performance and suit your driving style. You can adjust various parameters such as the gear ratio, the tire pressure, the suspension stiffness, and the camber angle. You can also use the dyno to see how your changes affect the horsepower, torque, and acceleration of your car. Experiment with different settings until you find the best combination for each track and race.</p>
- <h4>Practice Your Launch</h4>
-<p>The launch is one of the most important aspects of drag racing, as it determines how fast you can get off the line and gain an advantage over your opponent. To launch your car effectively, you need to pay attention to the RPM gauge and the green light. You want to rev your engine to the optimal point and release the clutch at the right moment. If you rev too high or too low, you will lose speed and traction. If you release the clutch too early or too late, you will either stall or spin out. Practice your launch until you master the timing and feel of your car.</p>
- <h4>Upgrade Your Parts</h4>
-<p>As you progress through the game, you will face tougher opponents and harder races. To keep up with the competition, you will need to upgrade your car parts and improve its performance. You can buy new parts such as engines, turbos, nitrous, tires, brakes, and more from the shop. You can also sell your old parts to earn some extra cash. Upgrading your parts will not only increase your car's speed and power, but also its appearance and value.</p>
- <h3>Conclusion</h3>
-<p>No Limit Drag Racing 2 is a thrilling and realistic drag racing game that will keep you hooked for hours. You can customize your car, race online or offline, and advance through a career mode. You can also download and install No Limit Drag Racing 2 APK on your Android device easily and safely. If you follow our tips and tricks, you will be able to win more races and become a drag racing legend.</p>
- <h3>FAQs</h3>
-<ul>
-<li><b>Q: How do I use nitrous in No Limit Drag Racing 2?</b></li>
-<li>A: You can use nitrous by tapping on the blue button on the right side of the screen. You can only use nitrous once per race, so make sure you use it wisely.</li>
-<li><b>Q: How do I earn money in No Limit Drag Racing 2?</b></li>
-<li>A: You can earn money by winning races, completing challenges, selling parts, or watching ads. You can also buy money with real money if you want to.</li>
-<li><b>Q: How do I join a team in No Limit Drag Racing 2?</b></li>
-<li>A: You can join a team by going to the multiplayer menu and tapping on the team icon. You can either join an existing team or create your own team.</li>
-<li><b>Q: How do I change the camera view in No Limit Drag Racing 2?</b></li>
-<li>A: You can change the camera view by tapping on the camera icon on the top left corner of the screen. You can choose between three views: cockpit, bumper, or third-person.</li>
-<li><b>Q: How do I reset my progress in No Limit Drag Racing 2?</b></li>
-<li>A: You can reset your progress by going to the settings menu and tapping on the reset button. Be careful though, this will erase all your data and start from scratch.</li>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Sen al Kapimi (Love Is in the Air) Season 1 and 2 Hindi Dubbed  Download All Episodes [720p HD]  Turkish Romance Comedy Series.md b/spaces/congsaPfin/Manga-OCR/logs/Sen al Kapimi (Love Is in the Air) Season 1 and 2 Hindi Dubbed  Download All Episodes [720p HD]  Turkish Romance Comedy Series.md
deleted file mode 100644
index 28fdfd02531ca786d3ff2b155d75cf75a74f8825..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Sen al Kapimi (Love Is in the Air) Season 1 and 2 Hindi Dubbed  Download All Episodes [720p HD]  Turkish Romance Comedy Series.md	
+++ /dev/null
@@ -1,158 +0,0 @@
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-<h1>Love Is in the Air: A Romantic Turkish Drama Series in Hindi</h1>
-<p>If you are looking for a romantic comedy series with a captivating plot, charming characters, and beautiful scenery, then you should check out Love Is in the Air, a Turkish drama series that is now available in Hindi. In this article, we will tell you what Love Is in the Air is about, who are the main characters and actors, why you should watch it, and how to download it in Hindi.</p>
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-<p>Love Is in the Air (original title: Sen Çal Kapimi) is a Turkish drama series that premiered on July 8, 2020 on FOX Turkey. It has been dubbed in Hindi and other languages and has gained popularity among international audiences. The series has two seasons, with a total of 61 episodes as of June 2023. The series is also known as You Knock on My Door or Knock on My Door.</p>
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-<p>The story revolves around Eda Yildiz, a young and ambitious florist who lost her parents in an accident and is living with her aunt and cousin. She dreams of studying abroad, but her scholarship is cancelled by Serkan Bolat, a wealthy and arrogant businessman who owns the company that sponsors her education. To get her scholarship back, Eda agrees to pretend to be Serkan's fiancee for two months. However, as they spend more time together, they start to develop feelings for each other and face various challenges and misunderstandings.</p>
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-<ul>
-<li>Eda Yildiz, played by Hande Erçel, a beautiful and spirited florist who loves nature and books. She is smart, loyal, and brave, but also stubborn and impulsive. She has a crush on Serkan since she was a child.</li>
-<li>Serkan Bolat, played by Kerem Bürsin, a handsome and successful architect who runs his family's company. He is cold, arrogant, and perfectionist, but also caring and protective. He has a troubled past and does not believe in love.</li>
-<li>Selin Atakan, played by Neslihan Yeldan, Serkan's ex-girlfriend and business partner. She is elegant, ambitious, and manipulative. She wants to get back with Serkan and tries to sabotage his relationship with Eda.</li>
-<li>Ferit Simsek, played by Alican Aytekin, Selin's fiance and Serkan's friend. He is kind, gentle, and supportive. He loves Selin but does not trust her.</li>
-<li>Ceren Basarir, played by Evrim Dogan, Eda's best friend and co-worker. She is cheerful, optimistic, and helpful. She has a crush on Ferit.</li>
-<li>Kaan Karadag, played by Anil Ilter, Serkan's rival and enemy. He is cunning, greedy, and vengeful. He wants to destroy Serkan's company and reputation.</li>
-</ul>
- <h3>Why should you watch Love Is in the Air?</h3>
-<p>There are many reasons why you should watch Love Is in the Air, such as:</p>
-<ul>
-<li>It has a captivating plot that will keep you hooked from the first episode to the last. It has romance, comedy, drama, suspense, and twists that will make you laugh, cry, swoon, and gasp.</li>
-<li>It has charming characters that will make you fall in love with them. They have chemistry, personality, and growth. They will make you root for them, relate to them, and learn from them.</li>
-<li>It has beautiful scenery that will make you want to visit Turkey. The series showcases the stunning landscapes, architecture, culture, and cuisine of Istanbul and other places in Turkey.</li>
-<li>It has a catchy soundtrack that will make you hum along. The series features original songs and covers of popular Turkish and international songs that suit the mood and theme of each scene.</li>
-<li>It has a loyal fan base that will make you feel part of a community. The series has millions of fans around the world who share their love and support for the show on social media, forums, blogs, and podcasts.</li>
-</ul>
- <h2>How to download Love Is in the Air in Hindi?</h2>
-<p>If you are wondering how to download Love Is in the Air in Hindi, you have several options to choose from. Here are some of the most popular ones:</p>
- <h3>Option 1: KatMovieHD</h3>
-<p>KatMovieHD is a website that offers free downloads of movies and TV shows in various languages and formats. You can find Love Is in the Air in Hindi on KatMovieHD by following these steps:</p>
-<ol>
-<li>Go to <a href="">KatMovieHD</a> and search for Love Is in the Air or Sen Çal Kapimi.</li>
-<li>Select the season and episode that you want to download and click on it.</li>
-<li>Choose the quality and language that you prefer and click on the download button.</li>
-<li>Wait for the download to finish and enjoy watching Love Is in the Air in Hindi.</li>
-</ol>
- <h4>Pros and cons of KatMovieHD</h4>
-<p>KatMovieHD has some advantages and disadvantages that you should consider before using it. Here are some of them:</p>
-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>It is free and easy to use.</td><td>It may contain ads, pop-ups, and malware that can harm your device or data.</td></tr>
-<tr><td>It offers a wide range of movies and TV shows in different languages and genres.</td><td>It may have low-quality videos, audio, or subtitles that can affect your viewing experience.</td></tr>
-<tr><td>It updates regularly with new content and features.</td><td>It may not have all the episodes or seasons of Love Is in the Air in Hindi or other languages.</td></tr>
-<tr><td>It allows you to download multiple files at once or resume interrupted downloads.</td><td>It may violate the copyright laws or terms of service of the original content creators or distributors.</td></tr>
-</table>
- <h3>Option 2: Dailymotion</h3>
-<p>Dailymotion is a video-sharing platform that hosts millions of videos from various categories and sources. You can watch Love Is in the Air in Hindi on Dailymotion by following these steps:</p>
-<ol>
-<li>Go to <a href="">Dailymotion</a> and search for Love Is in the Air or Sen Çal Kapimi.</li>
-<li>Select the season and episode that you want to watch and click on it.</li>
-<li>Enjoy watching Love Is in the Air in Hindi online or offline by downloading it using a third-party tool or app.</li>
-</ol>
- <h4>Pros and cons of Dailymotion</h4>
-<p>Dailymotion has some advantages and disadvantages that you should consider before using it. Here are some of them:</p>
-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>It is free and accessible from any device or browser.</td><td>It may contain ads, pop-ups, and malware that can harm your device or data.</td></tr>
-<tr><td>It offers a large collection of videos from various creators and channels.</td><td>It may have low-quality videos, audio, or subtitles that can affect your viewing experience.</td></tr>
-<tr><td>It supports multiple languages and subtitles for different videos.</td><td>It may not have all the episodes or seasons of Love Is in the Air in Hindi or other languages.</td></tr>
-<tr><td>It allows you to create playlists, follow channels, comment, share, and like videos.</td><td>It may delete or block some videos due to copyright infringement or community guidelines violations.</td></tr>
-</table>
- <h3>Option 3: YouTube</h3>
-<p>YouTube is a video-sharing platform that hosts billions of videos from various categories and sources. You can watch Love Is in the Air in Hindi on YouTube by following these steps:</p> <ol>
-<li>Go to <a href="">YouTube</a> and search for Love Is in the Air or Sen Çal Kapimi.</li>
-<li>Select the season and episode that you want to watch and click on it.</li>
-<li>Enjoy watching Love Is in the Air in Hindi online or offline by downloading it using a third-party tool or app.</li>
-</ol>
- <h4>Pros and cons of YouTube</h4>
-<p>YouTube has some advantages and disadvantages that you should consider before using it. Here are some of them:</p>
-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>It is free and available on any device or browser.</td><td>It may contain ads, pop-ups, and malware that can harm your device or data.</td></tr>
-<tr><td>It offers a huge variety of videos from various creators and channels.</td><td>It may have low-quality videos, audio, or subtitles that can affect your viewing experience.</td></tr>
-<tr><td>It supports multiple languages and subtitles for different videos.</td><td>It may not have all the episodes or seasons of Love Is in the Air in Hindi or other languages.</td></tr>
-<tr><td>It allows you to create playlists, follow channels, comment, share, and like videos.</td><td>It may delete or block some videos due to copyright infringement or community guidelines violations.</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>In conclusion, Love Is in the Air is a romantic Turkish drama series that is worth watching if you are looking for a fun, sweet, and exciting story with lovable characters and stunning scenery. You can download Love Is in the Air in Hindi from various websites or platforms, such as KatMovieHD, Dailymotion, or YouTube. However, you should be aware of the pros and cons of each option and choose the one that suits your preferences and needs. We hope this article has helped you learn more about Love Is in the Air and how to download it in Hindi. Happy watching!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the most common questions that people ask about Love Is in the Air and how to download it in Hindi:</p>
- <h3>Q: Is Love Is in the Air based on a true story?</h3>
-<p>A: No, Love Is in the Air is not based on a true story. It is a fictional story created by Ayse Üner Kutlu, who is also the screenwriter of the series.</p>
- <h3>Q: How many seasons and episodes does Love Is in the Air have?</h3>
-<p>A: Love Is in the Air has two seasons, with a total of 61 episodes as of June 2023. Each episode is about 120 minutes long.</p>
- <h3>Q: Where can I watch Love Is in the Air in other languages?</h3>
-<p>A: You can watch Love Is in the Air in other languages on various websites or platforms that offer subtitles or dubbing for the series. Some of the languages that you can find are English, Spanish, French, Arabic, Urdu, and more.</p>
- <h3>Q: What are some of the awards and nominations that Love Is in the Air has received?</h3>
-<p>A: Love Is in the Air has received several awards and nominations for its outstanding performance and popularity. Some of them are:</p>
-<ul>
-<li>The Golden Butterfly Awards for Best Romantic Comedy Series, Best Actress (Hande Erçel), Best Actor (Kerem Bürsin), Best Couple (Hande Erçel and Kerem Bürsin), Best Supporting Actress (Neslihan Yeldan), Best Supporting Actor (Alican Aytekin), Best Screenplay (Ayse Üner Kutlu), and Best Director (Altan Dönmez).</li>
-<li>The Ayakli Gazete TV Stars Awards for Best Romantic Comedy Series, Best Actress (Hande Erçel), Best Actor (Kerem Bürsin), Best Couple (Hande Erçel and Kerem Bürsin), Best Supporting Actress (Neslihan Yeldan), Best Supporting Actor (Alican Aytekin), Best Screenplay (Ayse Üner Kutlu), and Best Director (Altan Dönmez).</li>
-<li>The Pantene Golden Lens Awards for Best Romantic Comedy Series, Best Actress (Hande Erçel), Best Actor (Kerem Bürsin), Best Couple (Hande Erçel and Kerem Bürsin), Best Supporting Actress (Neslihan Yeldan), Best Supporting Actor (Alican Aytekin), Best Screenplay (Ayse Üner Kutlu), and Best Director (Altan Dönmez).</li>
-<li>The E! People's Choice Awards for The Drama Show of 2023, The Female TV Star of 2023 (Hande Erçel), The Male TV Star of 2023 (Kerem Bürsin), and The Bingeworthy Show of 2023.</li>
-</ul>
- <h3>Q: Where can I find more information and updates about Love Is in the Air?</h3>
-<p>A: You can find more information and updates about Love Is in the Air on its official website, social media accounts, fan pages, and podcasts. Some of the links that you can follow are:</p>
-<ul>
-<li><a href="">Love Is in the Air Official Website</a></li>
-<li><a href="">Love Is in the Air Official Instagram</a></li>
-<li><a href="">Love Is in the Air Official Twitter</a></li>
-<li><a href="">Love Is in the Air Official Facebook</a></li>
-<li><a href="">Love Is in the Air Fan Page on Instagram</a></li>
-<li><a href="">Love Is in the Air Fan Page on Twitter</a></li>
-<li><a href="">Love Is in the Air Fan Page on Facebook</a></li>
-<li><a href="">Love Is in the Air Podcast on Spotify</a></li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cooelf/Multimodal-CoT/timm/data/parsers/parser.py b/spaces/cooelf/Multimodal-CoT/timm/data/parsers/parser.py
deleted file mode 100644
index 76ab6d18283644702424d0ff2af5832d6d6dd3b7..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/data/parsers/parser.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from abc import abstractmethod
-
-
-class Parser:
-    def __init__(self):
-        pass
-
-    @abstractmethod
-    def _filename(self, index, basename=False, absolute=False):
-        pass
-
-    def filename(self, index, basename=False, absolute=False):
-        return self._filename(index, basename=basename, absolute=absolute)
-
-    def filenames(self, basename=False, absolute=False):
-        return [self._filename(index, basename=basename, absolute=absolute) for index in range(len(self))]
-
diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/tresnet.py b/spaces/cooelf/Multimodal-CoT/timm/models/tresnet.py
deleted file mode 100644
index 372bfb7bc0ce89241121f8b85ea928f376af8bd5..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/tresnet.py
+++ /dev/null
@@ -1,297 +0,0 @@
-"""
-TResNet: High Performance GPU-Dedicated Architecture
-https://arxiv.org/pdf/2003.13630.pdf
-
-Original model: https://github.com/mrT23/TResNet
-
-"""
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-
-from .helpers import build_model_with_cfg
-from .layers import SpaceToDepthModule, BlurPool2d, InplaceAbn, ClassifierHead, SEModule
-from .registry import register_model
-
-__all__ = ['tresnet_m', 'tresnet_l', 'tresnet_xl']
-
-
-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': (0, 0, 0), 'std': (1, 1, 1),
-        'first_conv': 'body.conv1.0', 'classifier': 'head.fc',
-        **kwargs
-    }
-
-
-default_cfgs = {
-    'tresnet_m': _cfg(
-        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/tresnet_m_1k_miil_83_1.pth'),
-    'tresnet_m_miil_in21k': _cfg(
-        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/tresnet_m_miil_in21k.pth', num_classes=11221),
-    'tresnet_l': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth'),
-    'tresnet_xl': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_82_0-a2d51b00.pth'),
-    'tresnet_m_448': _cfg(
-        input_size=(3, 448, 448), pool_size=(14, 14),
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_448-bc359d10.pth'),
-    'tresnet_l_448': _cfg(
-        input_size=(3, 448, 448), pool_size=(14, 14),
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth'),
-    'tresnet_xl_448': _cfg(
-        input_size=(3, 448, 448), pool_size=(14, 14),
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_448-8c1815de.pth')
-}
-
-
-def IABN2Float(module: nn.Module) -> nn.Module:
-    """If `module` is IABN don't use half precision."""
-    if isinstance(module, InplaceAbn):
-        module.float()
-    for child in module.children():
-        IABN2Float(child)
-    return module
-
-
-def conv2d_iabn(ni, nf, stride, kernel_size=3, groups=1, act_layer="leaky_relu", act_param=1e-2):
-    return nn.Sequential(
-        nn.Conv2d(
-            ni, nf, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, groups=groups, bias=False),
-        InplaceAbn(nf, act_layer=act_layer, act_param=act_param)
-    )
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True, aa_layer=None):
-        super(BasicBlock, self).__init__()
-        if stride == 1:
-            self.conv1 = conv2d_iabn(inplanes, planes, stride=1, act_param=1e-3)
-        else:
-            if aa_layer is None:
-                self.conv1 = conv2d_iabn(inplanes, planes, stride=2, act_param=1e-3)
-            else:
-                self.conv1 = nn.Sequential(
-                    conv2d_iabn(inplanes, planes, stride=1, act_param=1e-3),
-                    aa_layer(channels=planes, filt_size=3, stride=2))
-
-        self.conv2 = conv2d_iabn(planes, planes, stride=1, act_layer="identity")
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-        rd_chs = max(planes * self.expansion // 4, 64)
-        self.se = SEModule(planes * self.expansion, rd_channels=rd_chs) if use_se else None
-
-    def forward(self, x):
-        if self.downsample is not None:
-            shortcut = self.downsample(x)
-        else:
-            shortcut = x
-
-        out = self.conv1(x)
-        out = self.conv2(out)
-
-        if self.se is not None:
-            out = self.se(out)
-
-        out += shortcut
-        out = self.relu(out)
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True,
-                 act_layer="leaky_relu", aa_layer=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = conv2d_iabn(
-            inplanes, planes, kernel_size=1, stride=1, act_layer=act_layer, act_param=1e-3)
-        if stride == 1:
-            self.conv2 = conv2d_iabn(
-                planes, planes, kernel_size=3, stride=1, act_layer=act_layer, act_param=1e-3)
-        else:
-            if aa_layer is None:
-                self.conv2 = conv2d_iabn(
-                    planes, planes, kernel_size=3, stride=2, act_layer=act_layer, act_param=1e-3)
-            else:
-                self.conv2 = nn.Sequential(
-                    conv2d_iabn(planes, planes, kernel_size=3, stride=1, act_layer=act_layer, act_param=1e-3),
-                    aa_layer(channels=planes, filt_size=3, stride=2))
-
-        reduction_chs = max(planes * self.expansion // 8, 64)
-        self.se = SEModule(planes, rd_channels=reduction_chs) if use_se else None
-
-        self.conv3 = conv2d_iabn(
-            planes, planes * self.expansion, kernel_size=1, stride=1, act_layer="identity")
-
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        if self.downsample is not None:
-            shortcut = self.downsample(x)
-        else:
-            shortcut = x
-
-        out = self.conv1(x)
-        out = self.conv2(out)
-        if self.se is not None:
-            out = self.se(out)
-
-        out = self.conv3(out)
-        out = out + shortcut  # no inplace
-        out = self.relu(out)
-
-        return out
-
-
-class TResNet(nn.Module):
-    def __init__(self, layers, in_chans=3, num_classes=1000, width_factor=1.0, global_pool='fast', drop_rate=0.):
-        self.num_classes = num_classes
-        self.drop_rate = drop_rate
-        super(TResNet, self).__init__()
-
-        aa_layer = BlurPool2d
-
-        # TResnet stages
-        self.inplanes = int(64 * width_factor)
-        self.planes = int(64 * width_factor)
-        conv1 = conv2d_iabn(in_chans * 16, self.planes, stride=1, kernel_size=3)
-        layer1 = self._make_layer(
-            BasicBlock, self.planes, layers[0], stride=1, use_se=True, aa_layer=aa_layer)  # 56x56
-        layer2 = self._make_layer(
-            BasicBlock, self.planes * 2, layers[1], stride=2, use_se=True, aa_layer=aa_layer)  # 28x28
-        layer3 = self._make_layer(
-            Bottleneck, self.planes * 4, layers[2], stride=2, use_se=True, aa_layer=aa_layer)  # 14x14
-        layer4 = self._make_layer(
-            Bottleneck, self.planes * 8, layers[3], stride=2, use_se=False, aa_layer=aa_layer)  # 7x7
-
-        # body
-        self.body = nn.Sequential(OrderedDict([
-            ('SpaceToDepth', SpaceToDepthModule()),
-            ('conv1', conv1),
-            ('layer1', layer1),
-            ('layer2', layer2),
-            ('layer3', layer3),
-            ('layer4', layer4)]))
-
-        self.feature_info = [
-            dict(num_chs=self.planes, reduction=2, module=''),  # Not with S2D?
-            dict(num_chs=self.planes, reduction=4, module='body.layer1'),
-            dict(num_chs=self.planes * 2, reduction=8, module='body.layer2'),
-            dict(num_chs=self.planes * 4 * Bottleneck.expansion, reduction=16, module='body.layer3'),
-            dict(num_chs=self.planes * 8 * Bottleneck.expansion, reduction=32, module='body.layer4'),
-        ]
-
-        # head
-        self.num_features = (self.planes * 8) * Bottleneck.expansion
-        self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
-
-        # model initilization
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
-            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, InplaceAbn):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        # residual connections special initialization
-        for m in self.modules():
-            if isinstance(m, BasicBlock):
-                m.conv2[1].weight = nn.Parameter(torch.zeros_like(m.conv2[1].weight))  # BN to zero
-            if isinstance(m, Bottleneck):
-                m.conv3[1].weight = nn.Parameter(torch.zeros_like(m.conv3[1].weight))  # BN to zero
-            if isinstance(m, nn.Linear):
-                m.weight.data.normal_(0, 0.01)
-
-    def _make_layer(self, block, planes, blocks, stride=1, use_se=True, aa_layer=None):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            layers = []
-            if stride == 2:
-                # avg pooling before 1x1 conv
-                layers.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True, count_include_pad=False))
-            layers += [conv2d_iabn(
-                self.inplanes, planes * block.expansion, kernel_size=1, stride=1, act_layer="identity")]
-            downsample = nn.Sequential(*layers)
-
-        layers = []
-        layers.append(block(
-            self.inplanes, planes, stride, downsample, use_se=use_se, aa_layer=aa_layer))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(
-                block(self.inplanes, planes, use_se=use_se, aa_layer=aa_layer))
-        return nn.Sequential(*layers)
-
-    def get_classifier(self):
-        return self.head.fc
-
-    def reset_classifier(self, num_classes, global_pool='fast'):
-        self.head = ClassifierHead(
-            self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
-
-    def forward_features(self, x):
-        return self.body(x)
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-
-def _create_tresnet(variant, pretrained=False, **kwargs):
-    return build_model_with_cfg(
-        TResNet, variant, pretrained,
-        default_cfg=default_cfgs[variant],
-        feature_cfg=dict(out_indices=(1, 2, 3, 4), flatten_sequential=True),
-        **kwargs)
-
-
-@register_model
-def tresnet_m(pretrained=False, **kwargs):
-    model_kwargs = dict(layers=[3, 4, 11, 3], **kwargs)
-    return _create_tresnet('tresnet_m', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def tresnet_m_miil_in21k(pretrained=False, **kwargs):
-    model_kwargs = dict(layers=[3, 4, 11, 3], **kwargs)
-    return _create_tresnet('tresnet_m_miil_in21k', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def tresnet_l(pretrained=False, **kwargs):
-    model_kwargs = dict(layers=[4, 5, 18, 3], width_factor=1.2, **kwargs)
-    return _create_tresnet('tresnet_l', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def tresnet_xl(pretrained=False, **kwargs):
-    model_kwargs = dict(layers=[4, 5, 24, 3], width_factor=1.3, **kwargs)
-    return _create_tresnet('tresnet_xl', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def tresnet_m_448(pretrained=False, **kwargs):
-    model_kwargs = dict(layers=[3, 4, 11, 3], **kwargs)
-    return _create_tresnet('tresnet_m_448', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def tresnet_l_448(pretrained=False, **kwargs):
-    model_kwargs = dict(layers=[4, 5, 18, 3], width_factor=1.2, **kwargs)
-    return _create_tresnet('tresnet_l_448', pretrained=pretrained, **model_kwargs)
-
-
-@register_model
-def tresnet_xl_448(pretrained=False, **kwargs):
-    model_kwargs = dict(layers=[4, 5, 24, 3], width_factor=1.3, **kwargs)
-    return _create_tresnet('tresnet_xl_448', pretrained=pretrained, **model_kwargs)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/assets/run_tflite.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/assets/run_tflite.py
deleted file mode 100644
index 4b8ebe235758d3d0f3d357c51ed54d78ac7eea8e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/assets/run_tflite.py
+++ /dev/null
@@ -1,75 +0,0 @@
-# Flex ops are included in the nightly build of the TensorFlow Python package. You can use TFLite models containing Flex ops by the same Python API as normal TFLite models. The nightly TensorFlow build can be installed with this command:
-# Flex ops will be added to the TensorFlow Python package's and the tflite_runtime package from version 2.3 for Linux and 2.4 for other environments.
-# https://www.tensorflow.org/lite/guide/ops_select#running_the_model
-
-# You must use: tf-nightly
-# pip install tf-nightly
-
-import os
-import glob
-import cv2
-import numpy as np
-
-import tensorflow as tf
-
-width=256
-height=256
-model_name="model.tflite"
-#model_name="model_quant.tflite"
-image_name="dog.jpg"
-
-# input
-img = cv2.imread(image_name)
-img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
-
-mean=[0.485, 0.456, 0.406]
-std=[0.229, 0.224, 0.225]
-img = (img - mean) / std
-
-img_resized = tf.image.resize(img, [width,height], method='bicubic', preserve_aspect_ratio=False)
-#img_resized = tf.transpose(img_resized, [2, 0, 1])
-img_input = img_resized.numpy()
-reshape_img = img_input.reshape(1,width,height,3)
-tensor = tf.convert_to_tensor(reshape_img, dtype=tf.float32)
-
-# load model
-print("Load model...")
-interpreter = tf.lite.Interpreter(model_path=model_name)
-print("Allocate tensor...")
-interpreter.allocate_tensors()
-print("Get input/output details...")
-input_details = interpreter.get_input_details()
-output_details = interpreter.get_output_details()
-print("Get input shape...")
-input_shape = input_details[0]['shape']
-print(input_shape)
-print(input_details)
-print(output_details)
-#input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
-print("Set input tensor...")
-interpreter.set_tensor(input_details[0]['index'], tensor)
-
-print("invoke()...")
-interpreter.invoke()
-
-# The function `get_tensor()` returns a copy of the tensor data.
-# Use `tensor()` in order to get a pointer to the tensor.
-print("get output tensor...")
-output = interpreter.get_tensor(output_details[0]['index'])
-#output = np.squeeze(output)
-output = output.reshape(width, height)
-#print(output)
-prediction = np.array(output)
-print("reshape prediction...")
-prediction = prediction.reshape(width, height)
-             
-# output file
-#prediction = cv2.resize(prediction, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC)
-print(" Write image to: output.png")
-depth_min = prediction.min()
-depth_max = prediction.max()
-img_out = (255 * (prediction - depth_min) / (depth_max - depth_min)).astype("uint8")
-print("save output image...")
-cv2.imwrite("output.png", img_out)
-        
-print("finished")
\ No newline at end of file
diff --git a/spaces/cscan/CodeFormer/CodeFormer/basicsr/archs/vqgan_arch.py b/spaces/cscan/CodeFormer/CodeFormer/basicsr/archs/vqgan_arch.py
deleted file mode 100644
index f6dfcf4c9983b431f0a978701e5ddd9598faf381..0000000000000000000000000000000000000000
--- a/spaces/cscan/CodeFormer/CodeFormer/basicsr/archs/vqgan_arch.py
+++ /dev/null
@@ -1,435 +0,0 @@
-'''
-VQGAN code, adapted from the original created by the Unleashing Transformers authors:
-https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
-
-'''
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import copy
-from basicsr.utils import get_root_logger
-from basicsr.utils.registry import ARCH_REGISTRY
-
-def normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-    
-
-@torch.jit.script
-def swish(x):
-    return x*torch.sigmoid(x)
-
-
-#  Define VQVAE classes
-class VectorQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
-
-    def forward(self, z):
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.emb_dim)
-
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
-            2 * torch.matmul(z_flattened, self.embedding.weight.t())
-
-        mean_distance = torch.mean(d)
-        # find closest encodings
-        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
-        # [0-1], higher score, higher confidence
-        min_encoding_scores = torch.exp(-min_encoding_scores/10)
-
-        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q, loss, {
-            "perplexity": perplexity,
-            "min_encodings": min_encodings,
-            "min_encoding_indices": min_encoding_indices,
-            "min_encoding_scores": min_encoding_scores,
-            "mean_distance": mean_distance
-            }
-
-    def get_codebook_feat(self, indices, shape):
-        # input indices: batch*token_num -> (batch*token_num)*1
-        # shape: batch, height, width, channel
-        indices = indices.view(-1,1)
-        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
-        min_encodings.scatter_(1, indices, 1)
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:  # reshape back to match original input shape
-            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-class GumbelQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
-        super().__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.straight_through = straight_through
-        self.temperature = temp_init
-        self.kl_weight = kl_weight
-        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
-        self.embed = nn.Embedding(codebook_size, emb_dim)
-
-    def forward(self, z):
-        hard = self.straight_through if self.training else True
-
-        logits = self.proj(z)
-
-        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
-
-        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
-
-        # + kl divergence to the prior loss
-        qy = F.softmax(logits, dim=1)
-        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
-        min_encoding_indices = soft_one_hot.argmax(dim=1)
-
-        return z_q, diff, {
-            "min_encoding_indices": min_encoding_indices
-        }
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
-
-    def forward(self, x):
-        pad = (0, 1, 0, 1)
-        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x):
-        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-
-        return x
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channels, out_channels=None):
-        super(ResBlock, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = in_channels if out_channels is None else out_channels
-        self.norm1 = normalize(in_channels)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.norm2 = normalize(out_channels)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if self.in_channels != self.out_channels:
-            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x_in):
-        x = x_in
-        x = self.norm1(x)
-        x = swish(x)
-        x = self.conv1(x)
-        x = self.norm2(x)
-        x = swish(x)
-        x = self.conv2(x)
-        if self.in_channels != self.out_channels:
-            x_in = self.conv_out(x_in)
-
-        return x + x_in
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = normalize(in_channels)
-        self.q = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, c, h*w)
-        q = q.permute(0, 2, 1)   
-        k = k.reshape(b, c, h*w)
-        w_ = torch.bmm(q, k) 
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = F.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b, c, h*w)
-        w_ = w_.permute(0, 2, 1) 
-        h_ = torch.bmm(v, w_)
-        h_ = h_.reshape(b, c, h, w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class Encoder(nn.Module):
-    def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
-        super().__init__()
-        self.nf = nf
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.attn_resolutions = attn_resolutions
-
-        curr_res = self.resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-
-        blocks = []
-        # initial convultion
-        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
-
-        # residual and downsampling blocks, with attention on smaller res (16x16)
-        for i in range(self.num_resolutions):
-            block_in_ch = nf * in_ch_mult[i]
-            block_out_ch = nf * ch_mult[i]
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-                if curr_res in attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != self.num_resolutions - 1:
-                blocks.append(Downsample(block_in_ch))
-                curr_res = curr_res // 2
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        # normalise and convert to latent size
-        blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-            
-        return x
-
-
-class Generator(nn.Module):
-    def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
-        super().__init__()
-        self.nf = nf 
-        self.ch_mult = ch_mult 
-        self.num_resolutions = len(self.ch_mult)
-        self.num_res_blocks = res_blocks
-        self.resolution = img_size 
-        self.attn_resolutions = attn_resolutions
-        self.in_channels = emb_dim
-        self.out_channels = 3
-        block_in_ch = self.nf * self.ch_mult[-1]
-        curr_res = self.resolution // 2 ** (self.num_resolutions-1)
-
-        blocks = []
-        # initial conv
-        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        for i in reversed(range(self.num_resolutions)):
-            block_out_ch = self.nf * self.ch_mult[i]
-
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-
-                if curr_res in self.attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != 0:
-                blocks.append(Upsample(block_in_ch))
-                curr_res = curr_res * 2
-
-        blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
-
-        self.blocks = nn.ModuleList(blocks)
-   
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-            
-        return x
-
-  
-@ARCH_REGISTRY.register()
-class VQAutoEncoder(nn.Module):
-    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
-                beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
-        super().__init__()
-        logger = get_root_logger()
-        self.in_channels = 3 
-        self.nf = nf 
-        self.n_blocks = res_blocks 
-        self.codebook_size = codebook_size
-        self.embed_dim = emb_dim
-        self.ch_mult = ch_mult
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
-        self.quantizer_type = quantizer
-        self.encoder = Encoder(
-            self.in_channels,
-            self.nf,
-            self.embed_dim,
-            self.ch_mult,
-            self.n_blocks,
-            self.resolution,
-            self.attn_resolutions
-        )
-        if self.quantizer_type == "nearest":
-            self.beta = beta #0.25
-            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
-        elif self.quantizer_type == "gumbel":
-            self.gumbel_num_hiddens = emb_dim
-            self.straight_through = gumbel_straight_through
-            self.kl_weight = gumbel_kl_weight
-            self.quantize = GumbelQuantizer(
-                self.codebook_size,
-                self.embed_dim,
-                self.gumbel_num_hiddens,
-                self.straight_through,
-                self.kl_weight
-            )
-        self.generator = Generator(
-            self.nf, 
-            self.embed_dim,
-            self.ch_mult, 
-            self.n_blocks, 
-            self.resolution, 
-            self.attn_resolutions
-        )
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_ema' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
-                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-                logger.info(f'vqgan is loaded from: {model_path} [params]')
-            else:
-                raise ValueError(f'Wrong params!')
-
-
-    def forward(self, x):
-        x = self.encoder(x)
-        quant, codebook_loss, quant_stats = self.quantize(x)
-        x = self.generator(quant)
-        return x, codebook_loss, quant_stats
-
-
-
-# patch based discriminator
-@ARCH_REGISTRY.register()
-class VQGANDiscriminator(nn.Module):
-    def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
-        super().__init__()
-
-        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
-        ndf_mult = 1
-        ndf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            ndf_mult_prev = ndf_mult
-            ndf_mult = min(2 ** n, 8)
-            layers += [
-                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
-                nn.BatchNorm2d(ndf * ndf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        ndf_mult_prev = ndf_mult
-        ndf_mult = min(2 ** n_layers, 8)
-
-        layers += [
-            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
-            nn.BatchNorm2d(ndf * ndf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-
-        layers += [
-            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
-        self.main = nn.Sequential(*layers)
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_d' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-            else:
-                raise ValueError(f'Wrong params!')
-
-    def forward(self, x):
-        return self.main(x)
\ No newline at end of file
diff --git a/spaces/datasciencemmw/ContextXLA-beta-demo/README.md b/spaces/datasciencemmw/ContextXLA-beta-demo/README.md
deleted file mode 100644
index f38189c7f9c8d95b2215a01fad462e494138b514..0000000000000000000000000000000000000000
--- a/spaces/datasciencemmw/ContextXLA-beta-demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ContextXLA Beta Demo
-emoji: 📊
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/declare-lab/tango/diffusers/tests/pipelines/score_sde_ve/test_score_sde_ve.py b/spaces/declare-lab/tango/diffusers/tests/pipelines/score_sde_ve/test_score_sde_ve.py
deleted file mode 100644
index 036ecc3f6bf3c3a61780933c0a404ca91abe5dc4..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/tests/pipelines/score_sde_ve/test_score_sde_ve.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel
-from diffusers.utils.testing_utils import require_torch, slow, torch_device
-
-
-torch.backends.cuda.matmul.allow_tf32 = False
-
-
-class ScoreSdeVeipelineFastTests(unittest.TestCase):
-    @property
-    def dummy_uncond_unet(self):
-        torch.manual_seed(0)
-        model = UNet2DModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=3,
-            out_channels=3,
-            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
-            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
-        )
-        return model
-
-    def test_inference(self):
-        unet = self.dummy_uncond_unet
-        scheduler = ScoreSdeVeScheduler()
-
-        sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
-        sde_ve.to(torch_device)
-        sde_ve.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, return_dict=False)[
-            0
-        ]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-
-@slow
-@require_torch
-class ScoreSdeVePipelineIntegrationTests(unittest.TestCase):
-    def test_inference(self):
-        model_id = "google/ncsnpp-church-256"
-        model = UNet2DModel.from_pretrained(model_id)
-
-        scheduler = ScoreSdeVeScheduler.from_pretrained(model_id)
-
-        sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
-        sde_ve.to(torch_device)
-        sde_ve.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 256, 256, 3)
-
-        expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/spaces/deeplearning/audioldm-text-to-audio-generation/README.md b/spaces/deeplearning/audioldm-text-to-audio-generation/README.md
deleted file mode 100644
index 34881f9775d9d094bafdcdb8a10a7b25c0c7f5b2..0000000000000000000000000000000000000000
--- a/spaces/deeplearning/audioldm-text-to-audio-generation/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: Audioldm Text To Audio Generation
-emoji: 🔊
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
-duplicated_from: AIFILMS/audioldm-text-to-audio-generation
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-## Reference
-Part of the code from this repo is borrowed from the following repos. We would like to thank the authors of them for their contribution. 
-
-> https://github.com/LAION-AI/CLAP
-> https://github.com/CompVis/stable-diffusion
-> https://github.com/v-iashin/SpecVQGAN 
-> https://github.com/toshas/torch-fidelity
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/MAGIX VEGAS Movie Studio Platinum 15.0.0.102 Crack _VERIFIED_.md b/spaces/diacanFperku/AutoGPT/MAGIX VEGAS Movie Studio Platinum 15.0.0.102 Crack _VERIFIED_.md
deleted file mode 100644
index 7791bd44d715c007eb9de367b1623e6edd4813c2..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/MAGIX VEGAS Movie Studio Platinum 15.0.0.102 Crack _VERIFIED_.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>MAGIX VEGAS Movie Studio Platinum 15.0.0.102 Crack</h2><br /><p><b><b>DOWNLOAD</b> &#10040; <a href="https://gohhs.com/2uFUcA">https://gohhs.com/2uFUcA</a></b></p><br /><br />
-
-Unduh Gratis MAGIX VEGAS Movie Studio 17 Pro + Platinum versi lengkap untuk Windows yang cepat, alat pemotongan dan pengeditan yang ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diffle/ComfyUI/README.md b/spaces/diffle/ComfyUI/README.md
deleted file mode 100644
index 4becbe61b28c4a8b76e4185e77acf341e875b0ce..0000000000000000000000000000000000000000
--- a/spaces/diffle/ComfyUI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ComfyUI
-emoji: 🪟.UI
-colorFrom: green
-colorTo: pink
-sdk: static
-pinned: true
-license: creativeml-openrail-m
----
-
-![](https://raw.githubusercontent.com/ehristoforu/imghost/main/31c77b98-1019-4ea6-9c89-d66d25ab1586.jpg)
-<h1>ComfyUI</h1>
-<p>This is UI for everyone! Generate image for FREE!</p>
\ No newline at end of file
diff --git a/spaces/diffusers/convert/app.py b/spaces/diffusers/convert/app.py
deleted file mode 100644
index 2d11e0ba2cda09a158acfcc31c91516f7d7d2fe8..0000000000000000000000000000000000000000
--- a/spaces/diffusers/convert/app.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import csv
-from datetime import datetime
-import os
-from typing import Optional
-import gradio as gr
-
-from convert import convert
-from huggingface_hub import HfApi, Repository
-
-
-DATASET_REPO_URL = "https://huggingface.co/datasets/safetensors/conversions"
-DATA_FILENAME = "data.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-repo: Optional[Repository] = None
-if HF_TOKEN:
-    repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, token=HF_TOKEN)
-
-
-def run(token: str, model_id: str) -> str:
-    if token == "" or model_id == "":
-        return """
-        ### Invalid input 🐞
-        
-        Please fill a token and model_id.
-        """
-    try:
-        api = HfApi(token=token)
-        is_private = api.model_info(repo_id=model_id).private
-        print("is_private", is_private)
-
-        commit_info = convert(api=api, model_id=model_id, force=True)
-        print("[commit_info]", commit_info)
-
-        # save in a (public) dataset:
-        if repo is not None and not is_private:
-            repo.git_pull(rebase=True)
-            print("pulled")
-            with open(DATA_FILE, "a") as csvfile:
-                writer = csv.DictWriter(
-                    csvfile, fieldnames=["model_id", "pr_url", "time"]
-                )
-                writer.writerow(
-                    {
-                        "model_id": model_id,
-                        "pr_url": commit_info.pr_url,
-                        "time": str(datetime.now()),
-                    }
-                )
-            commit_url = repo.push_to_hub()
-            print("[dataset]", commit_url)
-
-        return f"""
-        ### Success 🔥
-
-        Yay! This model was successfully converted and a PR was open using your token, here:
-
-        [{commit_info.pr_url}]({commit_info.pr_url})
-        """
-    except Exception as e:
-        return f"""
-        ### Error 😢😢😢
-        
-        {e}
-        """
-
-
-DESCRIPTION = """
-The steps are the following:
-
-- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
-- Input a model id from the Hub
-- Click "Submit"
-- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR 🔥
-
-⚠️ For now only `pytorch_model.bin` files are supported but we'll extend in the future.
-"""
-
-demo = gr.Interface(
-    title="Convert any model to Safetensors and open a PR",
-    description=DESCRIPTION,
-    allow_flagging="never",
-    article="Check out the [Safetensors repo on GitHub](https://github.com/huggingface/safetensors)",
-    inputs=[
-        gr.Text(max_lines=1, label="your_hf_token"),
-        gr.Text(max_lines=1, label="model_id"),
-    ],
-    outputs=[gr.Markdown(label="output")],
-    fn=run,
-)
-
-demo.launch()
diff --git a/spaces/digitalxingtong/Eileen-Bert-Vits2/short_audio_transcribe.py b/spaces/digitalxingtong/Eileen-Bert-Vits2/short_audio_transcribe.py
deleted file mode 100644
index f1e8b30671f2c2f2fa3c93feb1f4edd3fbe2f545..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Eileen-Bert-Vits2/short_audio_transcribe.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import whisper
-import os
-import json
-import torchaudio
-import argparse
-import torch
-
-lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-def transcribe_one(audio_path):
-    # load audio and pad/trim it to fit 30 seconds
-    audio = whisper.load_audio(audio_path)
-    audio = whisper.pad_or_trim(audio)
-
-    # make log-Mel spectrogram and move to the same device as the model
-    mel = whisper.log_mel_spectrogram(audio).to(model.device)
-
-    # detect the spoken language
-    _, probs = model.detect_language(mel)
-    print(f"Detected language: {max(probs, key=probs.get)}")
-    lang = max(probs, key=probs.get)
-    # decode the audio
-    options = whisper.DecodingOptions(beam_size=5)
-    result = whisper.decode(model, mel, options)
-
-    # print the recognized text
-    print(result.text)
-    return lang, result.text
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--languages", default="CJE")
-    parser.add_argument("--whisper_size", default="medium")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-    elif args.languages == "CJ":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-        }
-    elif args.languages == "C":
-        lang2token = {
-            'zh': "[ZH]",
-        }
-    assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
-    model = whisper.load_model(args.whisper_size)
-    parent_dir = "./custom_character_voice/"
-    speaker_names = list(os.walk(parent_dir))[0][1]
-    speaker_annos = []
-    total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
-    # resample audios
-    # 2023/4/21: Get the target sampling rate
-    with open("./configs/config.json", 'r', encoding='utf-8') as f:
-        hps = json.load(f)
-    target_sr = hps['data']['sampling_rate']
-    processed_files = 0
-    for speaker in speaker_names:
-        for i, wavfile in enumerate(list(os.walk(parent_dir + speaker))[0][2]):
-            # try to load file as audio
-            if wavfile.startswith("processed_"):
-                continue
-            try:
-                wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
-                                          channels_first=True)
-                wav = wav.mean(dim=0).unsqueeze(0)
-                if sr != target_sr:
-                    wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
-                if wav.shape[1] / sr > 20:
-                    print(f"{wavfile} too long, ignoring\n")
-                save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
-                torchaudio.save(save_path, wav, target_sr, channels_first=True)
-                # transcribe text
-                lang, text = transcribe_one(save_path)
-                if lang not in list(lang2token.keys()):
-                    print(f"{lang} not supported, ignoring\n")
-                    continue
-                text = "ZH|" + text + "\n"#
-                #text = lang2token[lang] + text + lang2token[lang] + "\n"
-                speaker_annos.append(save_path + "|" + speaker + "|" + text)
-                
-                processed_files += 1
-                print(f"Processed: {processed_files}/{total_files}")
-            except:
-                continue
-
-    # # clean annotation
-    # import argparse
-    # import text
-    # from utils import load_filepaths_and_text
-    # for i, line in enumerate(speaker_annos):
-    #     path, sid, txt = line.split("|")
-    #     cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
-    #     cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-    #     speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
-    # write into annotation
-    if len(speaker_annos) == 0:
-        print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
-        print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
-    with open("./filelists/short_character_anno.list", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
-
-    # import json
-    # # generate new config
-    # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-    #     hps = json.load(f)
-    # # modify n_speakers
-    # hps['data']["n_speakers"] = 1000 + len(speaker2id)
-    # # add speaker names
-    # for speaker in speaker_names:
-    #     hps['speakers'][speaker] = speaker2id[speaker]
-    # # save modified config
-    # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-    #     json.dump(hps, f, indent=2)
-    # print("finished")
diff --git a/spaces/digitalxingtong/Eileen-Bert-Vits2/text/chinese_bert.py b/spaces/digitalxingtong/Eileen-Bert-Vits2/text/chinese_bert.py
deleted file mode 100644
index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Eileen-Bert-Vits2/text/chinese_bert.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
-model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device)
-
-def get_bert_feature(text, word2ph):
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors='pt')
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)
-        res = model(**inputs, output_hidden_states=True)
-        res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
-
-    assert len(word2ph) == len(text)+2
-    word2phone = word2ph
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        repeat_feature = res[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-
-    return phone_level_feature.T
-
-if __name__ == '__main__':
-    # feature = get_bert_feature('你好,我是说的道理。')
-    import torch
-
-    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
-    word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1]
-
-    # 计算总帧数
-    total_frames = sum(word2phone)
-    print(word_level_feature.shape)
-    print(word2phone)
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        print(word_level_feature[i].shape)
-
-        # 对每个词重复word2phone[i]次
-        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-    print(phone_level_feature.shape)  # torch.Size([36, 1024])
-
diff --git a/spaces/digitalxingtong/Nanami-Bert-VITS2/short_audio_transcribe.py b/spaces/digitalxingtong/Nanami-Bert-VITS2/short_audio_transcribe.py
deleted file mode 100644
index f1e8b30671f2c2f2fa3c93feb1f4edd3fbe2f545..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nanami-Bert-VITS2/short_audio_transcribe.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import whisper
-import os
-import json
-import torchaudio
-import argparse
-import torch
-
-lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-def transcribe_one(audio_path):
-    # load audio and pad/trim it to fit 30 seconds
-    audio = whisper.load_audio(audio_path)
-    audio = whisper.pad_or_trim(audio)
-
-    # make log-Mel spectrogram and move to the same device as the model
-    mel = whisper.log_mel_spectrogram(audio).to(model.device)
-
-    # detect the spoken language
-    _, probs = model.detect_language(mel)
-    print(f"Detected language: {max(probs, key=probs.get)}")
-    lang = max(probs, key=probs.get)
-    # decode the audio
-    options = whisper.DecodingOptions(beam_size=5)
-    result = whisper.decode(model, mel, options)
-
-    # print the recognized text
-    print(result.text)
-    return lang, result.text
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--languages", default="CJE")
-    parser.add_argument("--whisper_size", default="medium")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-    elif args.languages == "CJ":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-        }
-    elif args.languages == "C":
-        lang2token = {
-            'zh': "[ZH]",
-        }
-    assert (torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
-    model = whisper.load_model(args.whisper_size)
-    parent_dir = "./custom_character_voice/"
-    speaker_names = list(os.walk(parent_dir))[0][1]
-    speaker_annos = []
-    total_files = sum([len(files) for r, d, files in os.walk(parent_dir)])
-    # resample audios
-    # 2023/4/21: Get the target sampling rate
-    with open("./configs/config.json", 'r', encoding='utf-8') as f:
-        hps = json.load(f)
-    target_sr = hps['data']['sampling_rate']
-    processed_files = 0
-    for speaker in speaker_names:
-        for i, wavfile in enumerate(list(os.walk(parent_dir + speaker))[0][2]):
-            # try to load file as audio
-            if wavfile.startswith("processed_"):
-                continue
-            try:
-                wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
-                                          channels_first=True)
-                wav = wav.mean(dim=0).unsqueeze(0)
-                if sr != target_sr:
-                    wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)(wav)
-                if wav.shape[1] / sr > 20:
-                    print(f"{wavfile} too long, ignoring\n")
-                save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
-                torchaudio.save(save_path, wav, target_sr, channels_first=True)
-                # transcribe text
-                lang, text = transcribe_one(save_path)
-                if lang not in list(lang2token.keys()):
-                    print(f"{lang} not supported, ignoring\n")
-                    continue
-                text = "ZH|" + text + "\n"#
-                #text = lang2token[lang] + text + lang2token[lang] + "\n"
-                speaker_annos.append(save_path + "|" + speaker + "|" + text)
-                
-                processed_files += 1
-                print(f"Processed: {processed_files}/{total_files}")
-            except:
-                continue
-
-    # # clean annotation
-    # import argparse
-    # import text
-    # from utils import load_filepaths_and_text
-    # for i, line in enumerate(speaker_annos):
-    #     path, sid, txt = line.split("|")
-    #     cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
-    #     cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
-    #     speaker_annos[i] = path + "|" + sid + "|" + cleaned_text
-    # write into annotation
-    if len(speaker_annos) == 0:
-        print("Warning: no short audios found, this IS expected if you have only uploaded long audios, videos or video links.")
-        print("this IS NOT expected if you have uploaded a zip file of short audios. Please check your file structure or make sure your audio language is supported.")
-    with open("./filelists/short_character_anno.list", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
-
-    # import json
-    # # generate new config
-    # with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
-    #     hps = json.load(f)
-    # # modify n_speakers
-    # hps['data']["n_speakers"] = 1000 + len(speaker2id)
-    # # add speaker names
-    # for speaker in speaker_names:
-    #     hps['speakers'][speaker] = speaker2id[speaker]
-    # # save modified config
-    # with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
-    #     json.dump(hps, f, indent=2)
-    # print("finished")
diff --git a/spaces/dirge/voicevox/test/test_mock_synthesis_engine.py b/spaces/dirge/voicevox/test/test_mock_synthesis_engine.py
deleted file mode 100644
index c06a0504a37d316c4769fcf0c658ac245f0e50d8..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/test/test_mock_synthesis_engine.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from unittest import TestCase
-
-from voicevox_engine.dev.synthesis_engine import MockSynthesisEngine
-from voicevox_engine.kana_parser import create_kana
-from voicevox_engine.model import AccentPhrase, AudioQuery, Mora
-
-
-class TestMockSynthesisEngine(TestCase):
-    def setUp(self):
-        super().setUp()
-
-        self.accent_phrases_hello_hiho = [
-            AccentPhrase(
-                moras=[
-                    Mora(
-                        text="コ",
-                        consonant="k",
-                        consonant_length=0.0,
-                        vowel="o",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ン",
-                        consonant=None,
-                        consonant_length=None,
-                        vowel="N",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ニ",
-                        consonant="n",
-                        consonant_length=0.0,
-                        vowel="i",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="チ",
-                        consonant="ch",
-                        consonant_length=0.0,
-                        vowel="i",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ワ",
-                        consonant="w",
-                        consonant_length=0.0,
-                        vowel="a",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                ],
-                accent=5,
-                pause_mora=Mora(
-                    text="、",
-                    consonant=None,
-                    consonant_length=None,
-                    vowel="pau",
-                    vowel_length=0.0,
-                    pitch=0.0,
-                ),
-            ),
-            AccentPhrase(
-                moras=[
-                    Mora(
-                        text="ヒ",
-                        consonant="h",
-                        consonant_length=0.0,
-                        vowel="i",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ホ",
-                        consonant="h",
-                        consonant_length=0.0,
-                        vowel="o",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="デ",
-                        consonant="d",
-                        consonant_length=0.0,
-                        vowel="e",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                    Mora(
-                        text="ス",
-                        consonant="s",
-                        consonant_length=0.0,
-                        vowel="U",
-                        vowel_length=0.0,
-                        pitch=0.0,
-                    ),
-                ],
-                accent=1,
-                pause_mora=None,
-            ),
-        ]
-        self.engine = MockSynthesisEngine(speakers="", supported_devices="")
-
-    def test_replace_phoneme_length(self):
-        self.assertEqual(
-            self.engine.replace_phoneme_length(
-                accent_phrases=self.accent_phrases_hello_hiho,
-                speaker_id=0,
-            ),
-            self.accent_phrases_hello_hiho,
-        )
-
-    def test_replace_mora_pitch(self):
-        self.assertEqual(
-            self.engine.replace_mora_pitch(
-                accent_phrases=self.accent_phrases_hello_hiho,
-                speaker_id=0,
-            ),
-            self.accent_phrases_hello_hiho,
-        )
-
-    def test_synthesis(self):
-        self.engine.synthesis(
-            AudioQuery(
-                accent_phrases=self.accent_phrases_hello_hiho,
-                speedScale=1,
-                pitchScale=0,
-                intonationScale=1,
-                volumeScale=1,
-                prePhonemeLength=0.1,
-                postPhonemeLength=0.1,
-                outputSamplingRate=24000,
-                outputStereo=False,
-                kana=create_kana(self.accent_phrases_hello_hiho),
-            ),
-            speaker_id=0,
-        )
diff --git a/spaces/divilis/chatgpt/app.py b/spaces/divilis/chatgpt/app.py
deleted file mode 100644
index 5523a648e43b4dab0e8c504fed92b0bd32bb8fbd..0000000000000000000000000000000000000000
--- a/spaces/divilis/chatgpt/app.py
+++ /dev/null
@@ -1,454 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-import logging
-import sys
-
-import gradio as gr
-
-from utils import *
-from presets import *
-from overwrites import *
-from chat_func import *
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-my_api_key = ""  # 在这里输入你的 API 密钥
-
-# if we are running in Docker
-if os.environ.get("dockerrun") == "yes":
-    dockerflag = True
-else:
-    dockerflag = False
-
-authflag = False
-
-if dockerflag:
-    my_api_key = os.environ.get("my_api_key")
-    if my_api_key == "empty":
-        logging.error("Please give a api key!")
-        sys.exit(1)
-    # auth
-    username = os.environ.get("USERNAME")
-    password = os.environ.get("PASSWORD")
-    if not (isinstance(username, type(None)) or isinstance(password, type(None))):
-        authflag = True
-else:
-    if (
-        not my_api_key
-        and os.path.exists("api_key.txt")
-        and os.path.getsize("api_key.txt")
-    ):
-        with open("api_key.txt", "r") as f:
-            my_api_key = f.read().strip()
-    if os.path.exists("auth.json"):
-        with open("auth.json", "r") as f:
-            auth = json.load(f)
-            username = auth["username"]
-            password = auth["password"]
-            if username != "" and password != "":
-                authflag = True
-
-gr.Chatbot.postprocess = postprocess
-PromptHelper.compact_text_chunks = compact_text_chunks
-
-with open("custom.css", "r", encoding="utf-8") as f:
-    customCSS = f.read()
-
-with gr.Blocks(
-    css=customCSS,
-    theme=gr.themes.Soft(
-        primary_hue=gr.themes.Color(
-            c50="#02C160",
-            c100="rgba(2, 193, 96, 0.2)",
-            c200="#02C160",
-            c300="rgba(2, 193, 96, 0.32)",
-            c400="rgba(2, 193, 96, 0.32)",
-            c500="rgba(2, 193, 96, 1.0)",
-            c600="rgba(2, 193, 96, 1.0)",
-            c700="rgba(2, 193, 96, 0.32)",
-            c800="rgba(2, 193, 96, 0.32)",
-            c900="#02C160",
-            c950="#02C160",
-        ),
-        secondary_hue=gr.themes.Color(
-            c50="#576b95",
-            c100="#576b95",
-            c200="#576b95",
-            c300="#576b95",
-            c400="#576b95",
-            c500="#576b95",
-            c600="#576b95",
-            c700="#576b95",
-            c800="#576b95",
-            c900="#576b95",
-            c950="#576b95",
-        ),
-        neutral_hue=gr.themes.Color(
-            name="gray",
-            c50="#f9fafb",
-            c100="#f3f4f6",
-            c200="#e5e7eb",
-            c300="#d1d5db",
-            c400="#B2B2B2",
-            c500="#808080",
-            c600="#636363",
-            c700="#515151",
-            c800="#393939",
-            c900="#272727",
-            c950="#171717",
-        ),
-        radius_size=gr.themes.sizes.radius_sm,
-    ).set(
-        button_primary_background_fill="#06AE56",
-        button_primary_background_fill_dark="#06AE56",
-        button_primary_background_fill_hover="#07C863",
-        button_primary_border_color="#06AE56",
-        button_primary_border_color_dark="#06AE56",
-        button_primary_text_color="#FFFFFF",
-        button_primary_text_color_dark="#FFFFFF",
-        button_secondary_background_fill="#F2F2F2",
-        button_secondary_background_fill_dark="#2B2B2B",
-        button_secondary_text_color="#393939",
-        button_secondary_text_color_dark="#FFFFFF",
-        # background_fill_primary="#F7F7F7",
-        # background_fill_primary_dark="#1F1F1F",
-        block_title_text_color="*primary_500",
-        block_title_background_fill="*primary_100",
-        input_background_fill="#F6F6F6",
-    ),
-) as demo:
-    history = gr.State([])
-    token_count = gr.State([])
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    user_api_key = gr.State(my_api_key)
-    TRUECOMSTANT = gr.State(True)
-    FALSECONSTANT = gr.State(False)
-    topic = gr.State("未命名对话历史记录")
-
-    with gr.Row():
-        gr.HTML(title)
-        status_display = gr.Markdown(get_geoip(), elem_id="status_display")
-
-    with gr.Row(scale=1).style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row(scale=1):
-                chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height="100%")
-            with gr.Row(scale=1):
-                with gr.Column(scale=12):
-                    user_input = gr.Textbox(
-                        show_label=False, placeholder="在这里输入"
-                    ).style(container=False)
-                with gr.Column(min_width=70, scale=1):
-                    submitBtn = gr.Button("发送", variant="primary")
-            with gr.Row(scale=1):
-                emptyBtn = gr.Button(
-                    "🧹 新的对话",
-                )
-                retryBtn = gr.Button("🔄 重新生成")
-                delLastBtn = gr.Button("🗑️ 删除一条对话")
-                reduceTokenBtn = gr.Button("♻️ 总结对话")
-
-        with gr.Column():
-            with gr.Column(min_width=50, scale=1):
-                with gr.Tab(label="ChatGPT"):
-                    keyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"OpenAI API-key...",
-                        value=hide_middle_chars(my_api_key),
-                        type="password",
-                        visible=not HIDE_MY_KEY,
-                        label="API-Key",
-                    )
-                    model_select_dropdown = gr.Dropdown(
-                        label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0]
-                    )
-                    use_streaming_checkbox = gr.Checkbox(
-                        label="实时传输回答", value=True, visible=enable_streaming_option
-                    )
-                    use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
-                    index_files = gr.Files(label="上传索引文件", type="file", multiple=True)
-
-                with gr.Tab(label="Prompt"):
-                    systemPromptTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"在这里输入System Prompt...",
-                        label="System prompt",
-                        value=initial_prompt,
-                        lines=10,
-                    ).style(container=False)
-                    with gr.Accordion(label="加载Prompt模板", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    templateFileSelectDropdown = gr.Dropdown(
-                                        label="选择Prompt模板集合文件",
-                                        choices=get_template_names(plain=True),
-                                        multiselect=False,
-                                        value=get_template_names(plain=True)[0],
-                                    ).style(container=False)
-                                with gr.Column(scale=1):
-                                    templateRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column():
-                                    templateSelectDropdown = gr.Dropdown(
-                                        label="从Prompt模板中加载",
-                                        choices=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        ),
-                                        multiselect=False,
-                                        value=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        )[0],
-                                    ).style(container=False)
-
-                with gr.Tab(label="保存/加载"):
-                    with gr.Accordion(label="保存/加载对话历史记录", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    historyFileSelectDropdown = gr.Dropdown(
-                                        label="从列表中加载对话",
-                                        choices=get_history_names(plain=True),
-                                        multiselect=False,
-                                        value=get_history_names(plain=True)[0],
-                                    )
-                                with gr.Column(scale=1):
-                                    historyRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    saveFileName = gr.Textbox(
-                                        show_label=True,
-                                        placeholder=f"设置文件名: 默认为.json，可选为.md",
-                                        label="设置保存文件名",
-                                        value="对话历史记录",
-                                    ).style(container=True)
-                                with gr.Column(scale=1):
-                                    saveHistoryBtn = gr.Button("💾 保存对话")
-                                    exportMarkdownBtn = gr.Button("📝 导出为Markdown")
-                                    gr.Markdown("默认保存于history文件夹")
-                            with gr.Row():
-                                with gr.Column():
-                                    downloadFile = gr.File(interactive=True)
-
-                with gr.Tab(label="高级"):
-                    default_btn = gr.Button("🔙 恢复默认设置")
-                    gr.Markdown("# ⚠️ 务必谨慎更改 ⚠️\n\n如果无法使用请恢复默认设置")
-
-                    with gr.Accordion("参数", open=False):
-                        top_p = gr.Slider(
-                            minimum=-0,
-                            maximum=1.0,
-                            value=1.0,
-                            step=0.05,
-                            interactive=True,
-                            label="Top-p",
-                        )
-                        temperature = gr.Slider(
-                            minimum=-0,
-                            maximum=2.0,
-                            value=1.0,
-                            step=0.1,
-                            interactive=True,
-                            label="Temperature",
-                        )
-
-                    apiurlTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"在这里输入API地址...",
-                        label="API地址",
-                        value="https://api.openai.com/v1/chat/completions",
-                        lines=2,
-                    )
-                    changeAPIURLBtn = gr.Button("🔄 切换API地址")
-                    proxyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"在这里输入代理地址...",
-                        label="代理地址（示例：http://127.0.0.1:10809）",
-                        value="",
-                        lines=2,
-                    )
-                    changeProxyBtn = gr.Button("🔄 设置代理地址")
-
-    gr.Markdown(description)
-
-    keyTxt.submit(submit_key, keyTxt, [user_api_key, status_display])
-    keyTxt.change(submit_key, keyTxt, [user_api_key, status_display])
-    # Chatbot
-    user_input.submit(
-        predict,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            user_input,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            use_websearch_checkbox,
-            index_files,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-    user_input.submit(reset_textbox, [], [user_input])
-
-    submitBtn.click(
-        predict,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            user_input,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            use_websearch_checkbox,
-            index_files,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-    submitBtn.click(reset_textbox, [], [user_input])
-
-    emptyBtn.click(
-        reset_state,
-        outputs=[chatbot, history, token_count, status_display],
-        show_progress=True,
-    )
-
-    retryBtn.click(
-        retry,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-
-    delLastBtn.click(
-        delete_last_conversation,
-        [chatbot, history, token_count],
-        [chatbot, history, token_count, status_display],
-        show_progress=True,
-    )
-
-    reduceTokenBtn.click(
-        reduce_token_size,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            gr.State(0),
-            model_select_dropdown,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-
-    # Template
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-    templateFileSelectDropdown.change(
-        load_template,
-        [templateFileSelectDropdown],
-        [promptTemplates, templateSelectDropdown],
-        show_progress=True,
-    )
-    templateSelectDropdown.change(
-        get_template_content,
-        [promptTemplates, templateSelectDropdown, systemPromptTxt],
-        [systemPromptTxt],
-        show_progress=True,
-    )
-
-    # S&L
-    saveHistoryBtn.click(
-        save_chat_history,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    exportMarkdownBtn.click(
-        export_markdown,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    historyFileSelectDropdown.change(
-        load_chat_history,
-        [historyFileSelectDropdown, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-        show_progress=True,
-    )
-    downloadFile.change(
-        load_chat_history,
-        [downloadFile, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-    )
-
-    # Advanced
-    default_btn.click(
-        reset_default, [], [apiurlTxt, proxyTxt, status_display], show_progress=True
-    )
-    changeAPIURLBtn.click(
-        change_api_url,
-        [apiurlTxt],
-        [status_display],
-        show_progress=True,
-    )
-    changeProxyBtn.click(
-        change_proxy,
-        [proxyTxt],
-        [status_display],
-        show_progress=True,
-    )
-
-logging.info(
-    colorama.Back.GREEN
-    + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面"
-    + colorama.Style.RESET_ALL
-)
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = "川虎ChatGPT 🚀"
-
-if __name__ == "__main__":
-    # if running in Docker
-    if dockerflag:
-        if authflag:
-            demo.queue().launch(
-                server_name="0.0.0.0", server_port=7860, auth=(username, password),
-                favicon_path="./assets/favicon.png"
-            )
-        else:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False, favicon_path="./assets/favicon.png")
-    # if not running in Docker
-    else:
-        if authflag:
-            demo.queue().launch(share=False, auth=(username, password), favicon_path="./assets/favicon.png", inbrowser=True)
-        else:
-            demo.queue().launch(share=False, favicon_path="./assets/favicon.png", inbrowser=True)  # 改为 share=True 可以创建公开分享链接
-        # demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-        # demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-        # demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/dolceschokolade/chatbot-mini/types/env.ts b/spaces/dolceschokolade/chatbot-mini/types/env.ts
deleted file mode 100644
index f6b9dd7c97885ba49e2b6c238a297cbf98070961..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/types/env.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-export interface ProcessEnv {
-  OPENAI_API_KEY: string;
-  OPENAI_API_HOST?: string;
-  OPENAI_API_TYPE?: 'openai' | 'azure';
-  OPENAI_API_VERSION?: string;
-  OPENAI_ORGANIZATION?: string;
-}
diff --git a/spaces/dorkai/ChatUIPro/app/api/utils/stream.ts b/spaces/dorkai/ChatUIPro/app/api/utils/stream.ts
deleted file mode 100644
index 2da1359e4c629b4d50aa8b757db1ef858ee236b8..0000000000000000000000000000000000000000
--- a/spaces/dorkai/ChatUIPro/app/api/utils/stream.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-export async function OpenAIStream(res: { body: any }) {
-  const reader = res.body.getReader();
-
-  const stream = new ReadableStream({
-    // https://developer.mozilla.org/en-US/docs/Web/API/Streams_API/Using_readable_streams
-    // https://github.com/whichlight/chatgpt-api-streaming/blob/master/pages/api/OpenAIStream.ts
-    start(controller) {
-      return pump();
-      function pump() {
-        return reader.read().then(({ done, value }: any) => {
-          // When no more data needs to be consumed, close the stream
-          if (done) {
-            controller.close();
-            return;
-          }
-          // Enqueue the next data chunk into our target stream
-          controller.enqueue(value);
-          return pump();
-        });
-      }
-    },
-  });
-
-  return stream;
-}
\ No newline at end of file
diff --git a/spaces/dvitel/codebleu/parser_DFG.py b/spaces/dvitel/codebleu/parser_DFG.py
deleted file mode 100644
index 93be6bcfb689ba81335a078c7501a8556204b3e5..0000000000000000000000000000000000000000
--- a/spaces/dvitel/codebleu/parser_DFG.py
+++ /dev/null
@@ -1,1184 +0,0 @@
-# Copyright (c) Microsoft Corporation. 
-# Licensed under the MIT license.
-
-from tree_sitter import Language, Parser
-from .parser_utils import (remove_comments_and_docstrings,
-                   tree_to_token_index,
-                   index_to_code_token,
-                   tree_to_variable_index)
-
-
-def DFG_python(root_node,index_to_code,states):
-    assignment=['assignment','augmented_assignment','for_in_clause']
-    if_statement=['if_statement']
-    for_statement=['for_statement']
-    while_statement=['while_statement']
-    do_first_statement=['for_in_clause'] 
-    def_statement=['default_parameter']
-    states=states.copy() 
-    if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment':        
-        idx,code=index_to_code[(root_node.start_point,root_node.end_point)]
-        if root_node.type==code:
-            return [],states
-        elif code in states:
-            return [(code,idx,'comesFrom',[code],states[code].copy())],states
-        else:
-            if root_node.type=='identifier':
-                states[code]=[idx]
-            return [(code,idx,'comesFrom',[],[])],states
-    elif root_node.type in def_statement:
-        name=root_node.child_by_field_name('name')
-        value=root_node.child_by_field_name('value')
-        DFG=[]
-        if value is None:
-            indexs=tree_to_variable_index(name,index_to_code)
-            for index in indexs:
-                idx,code=index_to_code[index]
-                DFG.append((code,idx,'comesFrom',[],[]))
-                states[code]=[idx]
-            return sorted(DFG,key=lambda x:x[1]),states
-        else:
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)
-            temp,states=DFG_python(value,index_to_code,states)
-            DFG+=temp            
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'comesFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            return sorted(DFG,key=lambda x:x[1]),states        
-    elif root_node.type in assignment:
-        if root_node.type=='for_in_clause':
-            right_nodes=[root_node.children[-1]]
-            left_nodes=[root_node.child_by_field_name('left')]
-        else:
-            if root_node.child_by_field_name('right') is None:
-                return [],states
-            left_nodes=[x for x in root_node.child_by_field_name('left').children if x.type!=',']
-            right_nodes=[x for x in root_node.child_by_field_name('right').children if x.type!=',']
-            if len(right_nodes)!=len(left_nodes):
-                left_nodes=[root_node.child_by_field_name('left')]
-                right_nodes=[root_node.child_by_field_name('right')]
-            if len(left_nodes)==0:
-                left_nodes=[root_node.child_by_field_name('left')]
-            if len(right_nodes)==0:
-                right_nodes=[root_node.child_by_field_name('right')]
-        DFG=[]
-        for node in right_nodes:
-            temp,states=DFG_python(node,index_to_code,states)
-            DFG+=temp
-            
-        for left_node,right_node in zip(left_nodes,right_nodes):
-            left_tokens_index=tree_to_variable_index(left_node,index_to_code)
-            right_tokens_index=tree_to_variable_index(right_node,index_to_code)
-            temp=[]
-            for token1_index in left_tokens_index:
-                idx1,code1=index_to_code[token1_index]
-                temp.append((code1,idx1,'computedFrom',[index_to_code[x][1] for x in right_tokens_index],
-                             [index_to_code[x][0] for x in right_tokens_index]))
-                states[code1]=[idx1]
-            DFG+=temp        
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in if_statement:
-        DFG=[]
-        current_states=states.copy()
-        others_states=[]
-        tag=False
-        if 'else' in root_node.type:
-            tag=True
-        for child in root_node.children:
-            if 'else' in child.type:
-                tag=True
-            if child.type not in ['elif_clause','else_clause']:
-                temp,current_states=DFG_python(child,index_to_code,current_states)
-                DFG+=temp
-            else:
-                temp,new_states=DFG_python(child,index_to_code,states)
-                DFG+=temp
-                others_states.append(new_states)
-        others_states.append(current_states)
-        if tag is False:
-            others_states.append(states)
-        new_states={}
-        for dic in others_states:
-            for key in dic:
-                if key not in new_states:
-                    new_states[key]=dic[key].copy()
-                else:
-                    new_states[key]+=dic[key]
-        for key in new_states:
-            new_states[key]=sorted(list(set(new_states[key])))
-        return sorted(DFG,key=lambda x:x[1]),new_states
-    elif root_node.type in for_statement:
-        DFG=[]
-        for i in range(2):
-            right_nodes=[x for x in root_node.child_by_field_name('right').children if x.type!=',']
-            left_nodes=[x for x in root_node.child_by_field_name('left').children if x.type!=',']
-            if len(right_nodes)!=len(left_nodes):
-                left_nodes=[root_node.child_by_field_name('left')]
-                right_nodes=[root_node.child_by_field_name('right')]
-            if len(left_nodes)==0:
-                left_nodes=[root_node.child_by_field_name('left')]
-            if len(right_nodes)==0:
-                right_nodes=[root_node.child_by_field_name('right')]
-            for node in right_nodes:
-                temp,states=DFG_python(node,index_to_code,states)
-                DFG+=temp
-            for left_node,right_node in zip(left_nodes,right_nodes):
-                left_tokens_index=tree_to_variable_index(left_node,index_to_code)
-                right_tokens_index=tree_to_variable_index(right_node,index_to_code)
-                temp=[]
-                for token1_index in left_tokens_index:
-                    idx1,code1=index_to_code[token1_index]
-                    temp.append((code1,idx1,'computedFrom',[index_to_code[x][1] for x in right_tokens_index],
-                                 [index_to_code[x][0] for x in right_tokens_index]))
-                    states[code1]=[idx1]
-                DFG+=temp   
-            if  root_node.children[-1].type=="block":
-                temp,states=DFG_python(root_node.children[-1],index_to_code,states)
-                DFG+=temp 
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in while_statement:  
-        DFG=[]
-        for i in range(2):
-            for child in root_node.children:
-                temp,states=DFG_python(child,index_to_code,states)
-                DFG+=temp    
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states        
-    else:
-        DFG=[]
-        for child in root_node.children:
-            if child.type in do_first_statement:
-                temp,states=DFG_python(child,index_to_code,states)
-                DFG+=temp
-        for child in root_node.children:
-            if child.type not in do_first_statement:
-                temp,states=DFG_python(child,index_to_code,states)
-                DFG+=temp
-        
-        return sorted(DFG,key=lambda x:x[1]),states
-        
-
-def DFG_java(root_node,index_to_code,states):
-    assignment=['assignment_expression']
-    def_statement=['variable_declarator']
-    increment_statement=['update_expression']
-    if_statement=['if_statement','else']
-    for_statement=['for_statement']
-    enhanced_for_statement=['enhanced_for_statement']
-    while_statement=['while_statement']
-    do_first_statement=[]    
-    states=states.copy()
-    if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment':
-        idx,code=index_to_code[(root_node.start_point,root_node.end_point)]
-        if root_node.type==code:
-            return [],states
-        elif code in states:
-            return [(code,idx,'comesFrom',[code],states[code].copy())],states
-        else:
-            if root_node.type=='identifier':
-                states[code]=[idx]
-            return [(code,idx,'comesFrom',[],[])],states
-    elif root_node.type in def_statement:
-        name=root_node.child_by_field_name('name')
-        value=root_node.child_by_field_name('value')
-        DFG=[]
-        if value is None:
-            indexs=tree_to_variable_index(name,index_to_code)
-            for index in indexs:
-                idx,code=index_to_code[index]
-                DFG.append((code,idx,'comesFrom',[],[]))
-                states[code]=[idx]
-            return sorted(DFG,key=lambda x:x[1]),states
-        else:
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)
-            temp,states=DFG_java(value,index_to_code,states)
-            DFG+=temp            
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'comesFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in assignment:
-        left_nodes=root_node.child_by_field_name('left')
-        right_nodes=root_node.child_by_field_name('right')
-        DFG=[]
-        temp,states=DFG_java(right_nodes,index_to_code,states)
-        DFG+=temp            
-        name_indexs=tree_to_variable_index(left_nodes,index_to_code)
-        value_indexs=tree_to_variable_index(right_nodes,index_to_code)        
-        for index1 in name_indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in value_indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]   
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in increment_statement:
-        DFG=[]
-        indexs=tree_to_variable_index(root_node,index_to_code)
-        for index1 in indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]
-        return sorted(DFG,key=lambda x:x[1]),states   
-    elif root_node.type in if_statement:
-        DFG=[]
-        current_states=states.copy()
-        others_states=[]
-        flag=False
-        tag=False
-        if 'else' in root_node.type:
-            tag=True
-        for child in root_node.children:
-            if 'else' in child.type:
-                tag=True
-            if child.type not in if_statement and flag is False:
-                temp,current_states=DFG_java(child,index_to_code,current_states)
-                DFG+=temp
-            else:
-                flag=True
-                temp,new_states=DFG_java(child,index_to_code,states)
-                DFG+=temp
-                others_states.append(new_states)
-        others_states.append(current_states)
-        if tag is False:
-            others_states.append(states)
-        new_states={}
-        for dic in others_states:
-            for key in dic:
-                if key not in new_states:
-                    new_states[key]=dic[key].copy()
-                else:
-                    new_states[key]+=dic[key]
-        for key in new_states:
-            new_states[key]=sorted(list(set(new_states[key])))
-        return sorted(DFG,key=lambda x:x[1]),new_states
-    elif root_node.type in for_statement:
-        DFG=[]
-        for child in root_node.children:
-            temp,states=DFG_java(child,index_to_code,states)
-            DFG+=temp
-        flag=False
-        for child in root_node.children:
-            if flag:
-                temp,states=DFG_java(child,index_to_code,states)
-                DFG+=temp                
-            elif child.type=="local_variable_declaration":
-                flag=True
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in enhanced_for_statement:
-        name=root_node.child_by_field_name('name')
-        value=root_node.child_by_field_name('value')
-        body=root_node.child_by_field_name('body')
-        DFG=[]
-        for i in range(2):
-            temp,states=DFG_java(value,index_to_code,states)
-            DFG+=temp       
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)        
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            temp,states=DFG_java(body,index_to_code,states)
-            DFG+=temp                       
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in while_statement:  
-        DFG=[]
-        for i in range(2):
-            for child in root_node.children:
-                temp,states=DFG_java(child,index_to_code,states)
-                DFG+=temp    
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states        
-    else:
-        DFG=[]
-        for child in root_node.children:
-            if child.type in do_first_statement:
-                temp,states=DFG_java(child,index_to_code,states)
-                DFG+=temp
-        for child in root_node.children:
-            if child.type not in do_first_statement:
-                temp,states=DFG_java(child,index_to_code,states)
-                DFG+=temp
-        
-        return sorted(DFG,key=lambda x:x[1]),states
-
-def DFG_csharp(root_node,index_to_code,states):
-    assignment=['assignment_expression']
-    def_statement=['variable_declarator']
-    increment_statement=['postfix_unary_expression']
-    if_statement=['if_statement','else']
-    for_statement=['for_statement']
-    enhanced_for_statement=['for_each_statement']
-    while_statement=['while_statement']
-    do_first_statement=[]    
-    states=states.copy()
-    if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment':
-        idx,code=index_to_code[(root_node.start_point,root_node.end_point)]
-        if root_node.type==code:
-            return [],states
-        elif code in states:
-            return [(code,idx,'comesFrom',[code],states[code].copy())],states
-        else:
-            if root_node.type=='identifier':
-                states[code]=[idx]
-            return [(code,idx,'comesFrom',[],[])],states
-    elif root_node.type in def_statement:
-        if len(root_node.children)==2:
-            name=root_node.children[0]
-            value=root_node.children[1]
-        else:
-            name=root_node.children[0]
-            value=None
-        DFG=[]
-        if value is None:
-            indexs=tree_to_variable_index(name,index_to_code)
-            for index in indexs:
-                idx,code=index_to_code[index]
-                DFG.append((code,idx,'comesFrom',[],[]))
-                states[code]=[idx]
-            return sorted(DFG,key=lambda x:x[1]),states
-        else:
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)
-            temp,states=DFG_csharp(value,index_to_code,states)
-            DFG+=temp            
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'comesFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in assignment:
-        left_nodes=root_node.child_by_field_name('left')
-        right_nodes=root_node.child_by_field_name('right')
-        DFG=[]
-        temp,states=DFG_csharp(right_nodes,index_to_code,states)
-        DFG+=temp            
-        name_indexs=tree_to_variable_index(left_nodes,index_to_code)
-        value_indexs=tree_to_variable_index(right_nodes,index_to_code)        
-        for index1 in name_indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in value_indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]   
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in increment_statement:
-        DFG=[]
-        indexs=tree_to_variable_index(root_node,index_to_code)
-        for index1 in indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]
-        return sorted(DFG,key=lambda x:x[1]),states   
-    elif root_node.type in if_statement:
-        DFG=[]
-        current_states=states.copy()
-        others_states=[]
-        flag=False
-        tag=False
-        if 'else' in root_node.type:
-            tag=True
-        for child in root_node.children:
-            if 'else' in child.type:
-                tag=True
-            if child.type not in if_statement and flag is False:
-                temp,current_states=DFG_csharp(child,index_to_code,current_states)
-                DFG+=temp
-            else:
-                flag=True
-                temp,new_states=DFG_csharp(child,index_to_code,states)
-                DFG+=temp
-                others_states.append(new_states)
-        others_states.append(current_states)
-        if tag is False:
-            others_states.append(states)
-        new_states={}
-        for dic in others_states:
-            for key in dic:
-                if key not in new_states:
-                    new_states[key]=dic[key].copy()
-                else:
-                    new_states[key]+=dic[key]
-        for key in new_states:
-            new_states[key]=sorted(list(set(new_states[key])))
-        return sorted(DFG,key=lambda x:x[1]),new_states
-    elif root_node.type in for_statement:
-        DFG=[]
-        for child in root_node.children:
-            temp,states=DFG_csharp(child,index_to_code,states)
-            DFG+=temp
-        flag=False
-        for child in root_node.children:
-            if flag:
-                temp,states=DFG_csharp(child,index_to_code,states)
-                DFG+=temp                
-            elif child.type=="local_variable_declaration":
-                flag=True
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in enhanced_for_statement:
-        name=root_node.child_by_field_name('left')
-        value=root_node.child_by_field_name('right')
-        body=root_node.child_by_field_name('body')
-        DFG=[]
-        for i in range(2):
-            temp,states=DFG_csharp(value,index_to_code,states)
-            DFG+=temp       
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)        
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            temp,states=DFG_csharp(body,index_to_code,states)
-            DFG+=temp                       
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in while_statement:  
-        DFG=[]
-        for i in range(2):
-            for child in root_node.children:
-                temp,states=DFG_csharp(child,index_to_code,states)
-                DFG+=temp    
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states        
-    else:
-        DFG=[]
-        for child in root_node.children:
-            if child.type in do_first_statement:
-                temp,states=DFG_csharp(child,index_to_code,states)
-                DFG+=temp
-        for child in root_node.children:
-            if child.type not in do_first_statement:
-                temp,states=DFG_csharp(child,index_to_code,states)
-                DFG+=temp
-        
-        return sorted(DFG,key=lambda x:x[1]),states
-
-
-    
-    
-def DFG_ruby(root_node,index_to_code,states):
-    assignment=['assignment','operator_assignment']
-    if_statement=['if','elsif','else','unless','when']
-    for_statement=['for']
-    while_statement=['while_modifier','until']
-    do_first_statement=[] 
-    def_statement=['keyword_parameter']
-    if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment':
-        states=states.copy()
-        idx,code=index_to_code[(root_node.start_point,root_node.end_point)]
-        if root_node.type==code:
-            return [],states
-        elif code in states:
-            return [(code,idx,'comesFrom',[code],states[code].copy())],states
-        else:
-            if root_node.type=='identifier':
-                states[code]=[idx]
-            return [(code,idx,'comesFrom',[],[])],states
-    elif root_node.type in def_statement:
-        name=root_node.child_by_field_name('name')
-        value=root_node.child_by_field_name('value')
-        DFG=[]
-        if value is None:
-            indexs=tree_to_variable_index(name,index_to_code)
-            for index in indexs:
-                idx,code=index_to_code[index]
-                DFG.append((code,idx,'comesFrom',[],[]))
-                states[code]=[idx]
-            return sorted(DFG,key=lambda x:x[1]),states
-        else:
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)
-            temp,states=DFG_ruby(value,index_to_code,states)
-            DFG+=temp            
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'comesFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            return sorted(DFG,key=lambda x:x[1]),states        
-    elif root_node.type in assignment:
-        left_nodes=[x for x in root_node.child_by_field_name('left').children if x.type!=',']
-        right_nodes=[x for x in root_node.child_by_field_name('right').children if x.type!=',']
-        if len(right_nodes)!=len(left_nodes):
-            left_nodes=[root_node.child_by_field_name('left')]
-            right_nodes=[root_node.child_by_field_name('right')]
-        if len(left_nodes)==0:
-            left_nodes=[root_node.child_by_field_name('left')]
-        if len(right_nodes)==0:
-            right_nodes=[root_node.child_by_field_name('right')]
-        if root_node.type=="operator_assignment":
-            left_nodes=[root_node.children[0]]
-            right_nodes=[root_node.children[-1]]
-
-        DFG=[]
-        for node in right_nodes:
-            temp,states=DFG_ruby(node,index_to_code,states)
-            DFG+=temp
-            
-        for left_node,right_node in zip(left_nodes,right_nodes):
-            left_tokens_index=tree_to_variable_index(left_node,index_to_code)
-            right_tokens_index=tree_to_variable_index(right_node,index_to_code)
-            temp=[]
-            for token1_index in left_tokens_index:
-                idx1,code1=index_to_code[token1_index]
-                temp.append((code1,idx1,'computedFrom',[index_to_code[x][1] for x in right_tokens_index],
-                             [index_to_code[x][0] for x in right_tokens_index]))
-                states[code1]=[idx1]
-            DFG+=temp        
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in if_statement:
-        DFG=[]
-        current_states=states.copy()
-        others_states=[]
-        tag=False
-        if 'else' in root_node.type:
-            tag=True
-        for child in root_node.children:
-            if 'else' in child.type:
-                tag=True
-            if child.type not in if_statement:
-                temp,current_states=DFG_ruby(child,index_to_code,current_states)
-                DFG+=temp
-            else:
-                temp,new_states=DFG_ruby(child,index_to_code,states)
-                DFG+=temp
-                others_states.append(new_states)
-        others_states.append(current_states)
-        if tag is False:
-            others_states.append(states)
-        new_states={}
-        for dic in others_states:
-            for key in dic:
-                if key not in new_states:
-                    new_states[key]=dic[key].copy()
-                else:
-                    new_states[key]+=dic[key]
-        for key in new_states:
-            new_states[key]=sorted(list(set(new_states[key])))
-        return sorted(DFG,key=lambda x:x[1]),new_states
-    elif root_node.type in for_statement:
-        DFG=[]
-        for i in range(2):
-            left_nodes=[root_node.child_by_field_name('pattern')]
-            right_nodes=[root_node.child_by_field_name('value')]
-            assert len(right_nodes)==len(left_nodes)
-            for node in right_nodes:
-                temp,states=DFG_ruby(node,index_to_code,states)
-                DFG+=temp
-            for left_node,right_node in zip(left_nodes,right_nodes):
-                left_tokens_index=tree_to_variable_index(left_node,index_to_code)
-                right_tokens_index=tree_to_variable_index(right_node,index_to_code)
-                temp=[]
-                for token1_index in left_tokens_index:
-                    idx1,code1=index_to_code[token1_index]
-                    temp.append((code1,idx1,'computedFrom',[index_to_code[x][1] for x in right_tokens_index],
-                                 [index_to_code[x][0] for x in right_tokens_index]))
-                    states[code1]=[idx1]
-                DFG+=temp 
-            temp,states=DFG_ruby(root_node.child_by_field_name('body'),index_to_code,states)
-            DFG+=temp 
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in while_statement:  
-        DFG=[]
-        for i in range(2):
-            for child in root_node.children:
-                temp,states=DFG_ruby(child,index_to_code,states)
-                DFG+=temp    
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states        
-    else:
-        DFG=[]
-        for child in root_node.children:
-            if child.type in do_first_statement:
-                temp,states=DFG_ruby(child,index_to_code,states)
-                DFG+=temp
-        for child in root_node.children:
-            if child.type not in do_first_statement:
-                temp,states=DFG_ruby(child,index_to_code,states)
-                DFG+=temp
-        
-        return sorted(DFG,key=lambda x:x[1]),states
-
-def DFG_go(root_node,index_to_code,states):
-    assignment=['assignment_statement',]
-    def_statement=['var_spec']
-    increment_statement=['inc_statement']
-    if_statement=['if_statement','else']
-    for_statement=['for_statement']
-    enhanced_for_statement=[]
-    while_statement=[]
-    do_first_statement=[]    
-    states=states.copy()
-    if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment':
-        idx,code=index_to_code[(root_node.start_point,root_node.end_point)]
-        if root_node.type==code:
-            return [],states
-        elif code in states:
-            return [(code,idx,'comesFrom',[code],states[code].copy())],states
-        else:
-            if root_node.type=='identifier':
-                states[code]=[idx]
-            return [(code,idx,'comesFrom',[],[])],states
-    elif root_node.type in def_statement:
-        name=root_node.child_by_field_name('name')
-        value=root_node.child_by_field_name('value')
-        DFG=[]
-        if value is None:
-            indexs=tree_to_variable_index(name,index_to_code)
-            for index in indexs:
-                idx,code=index_to_code[index]
-                DFG.append((code,idx,'comesFrom',[],[]))
-                states[code]=[idx]
-            return sorted(DFG,key=lambda x:x[1]),states
-        else:
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)
-            temp,states=DFG_go(value,index_to_code,states)
-            DFG+=temp            
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'comesFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in assignment:
-        left_nodes=root_node.child_by_field_name('left')
-        right_nodes=root_node.child_by_field_name('right')
-        DFG=[]
-        temp,states=DFG_go(right_nodes,index_to_code,states)
-        DFG+=temp            
-        name_indexs=tree_to_variable_index(left_nodes,index_to_code)
-        value_indexs=tree_to_variable_index(right_nodes,index_to_code)        
-        for index1 in name_indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in value_indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]   
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in increment_statement:
-        DFG=[]
-        indexs=tree_to_variable_index(root_node,index_to_code)
-        for index1 in indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]
-        return sorted(DFG,key=lambda x:x[1]),states   
-    elif root_node.type in if_statement:
-        DFG=[]
-        current_states=states.copy()
-        others_states=[]
-        flag=False
-        tag=False
-        if 'else' in root_node.type:
-            tag=True
-        for child in root_node.children:
-            if 'else' in child.type:
-                tag=True
-            if child.type not in if_statement and flag is False:
-                temp,current_states=DFG_go(child,index_to_code,current_states)
-                DFG+=temp
-            else:
-                flag=True
-                temp,new_states=DFG_go(child,index_to_code,states)
-                DFG+=temp
-                others_states.append(new_states)
-        others_states.append(current_states)
-        if tag is False:
-            others_states.append(states)
-        new_states={}
-        for dic in others_states:
-            for key in dic:
-                if key not in new_states:
-                    new_states[key]=dic[key].copy()
-                else:
-                    new_states[key]+=dic[key]
-        for key in states:
-            if key not in new_states:
-                new_states[key]=states[key]
-            else:
-                new_states[key]+=states[key]
-        for key in new_states:
-            new_states[key]=sorted(list(set(new_states[key])))
-        return sorted(DFG,key=lambda x:x[1]),new_states
-    elif root_node.type in for_statement:
-        DFG=[]
-        for child in root_node.children:
-            temp,states=DFG_go(child,index_to_code,states)
-            DFG+=temp
-        flag=False
-        for child in root_node.children:
-            if flag:
-                temp,states=DFG_go(child,index_to_code,states)
-                DFG+=temp                
-            elif child.type=="for_clause":
-                if child.child_by_field_name('update') is not None:
-                    temp,states=DFG_go(child.child_by_field_name('update'),index_to_code,states)
-                    DFG+=temp                 
-                flag=True
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    else:
-        DFG=[]
-        for child in root_node.children:
-            if child.type in do_first_statement:
-                temp,states=DFG_go(child,index_to_code,states)
-                DFG+=temp
-        for child in root_node.children:
-            if child.type not in do_first_statement:
-                temp,states=DFG_go(child,index_to_code,states)
-                DFG+=temp
-        
-        return sorted(DFG,key=lambda x:x[1]),states
-
-    
-    
-
-def DFG_php(root_node,index_to_code,states):
-    assignment=['assignment_expression','augmented_assignment_expression']
-    def_statement=['simple_parameter']
-    increment_statement=['update_expression']
-    if_statement=['if_statement','else_clause']
-    for_statement=['for_statement']
-    enhanced_for_statement=['foreach_statement']
-    while_statement=['while_statement']
-    do_first_statement=[]    
-    states=states.copy()
-    if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment':
-        idx,code=index_to_code[(root_node.start_point,root_node.end_point)]
-        if root_node.type==code:
-            return [],states
-        elif code in states:
-            return [(code,idx,'comesFrom',[code],states[code].copy())],states
-        else:
-            if root_node.type=='identifier':
-                states[code]=[idx]
-            return [(code,idx,'comesFrom',[],[])],states
-    elif root_node.type in def_statement:
-        name=root_node.child_by_field_name('name')
-        value=root_node.child_by_field_name('default_value')
-        DFG=[]
-        if value is None:
-            indexs=tree_to_variable_index(name,index_to_code)
-            for index in indexs:
-                idx,code=index_to_code[index]
-                DFG.append((code,idx,'comesFrom',[],[]))
-                states[code]=[idx]
-            return sorted(DFG,key=lambda x:x[1]),states
-        else:
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)
-            temp,states=DFG_php(value,index_to_code,states)
-            DFG+=temp            
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'comesFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in assignment:
-        left_nodes=root_node.child_by_field_name('left')
-        right_nodes=root_node.child_by_field_name('right')
-        DFG=[]
-        temp,states=DFG_php(right_nodes,index_to_code,states)
-        DFG+=temp            
-        name_indexs=tree_to_variable_index(left_nodes,index_to_code)
-        value_indexs=tree_to_variable_index(right_nodes,index_to_code)        
-        for index1 in name_indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in value_indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]   
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in increment_statement:
-        DFG=[]
-        indexs=tree_to_variable_index(root_node,index_to_code)
-        for index1 in indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]
-        return sorted(DFG,key=lambda x:x[1]),states   
-    elif root_node.type in if_statement:
-        DFG=[]
-        current_states=states.copy()
-        others_states=[]
-        flag=False
-        tag=False
-        if 'else' in root_node.type:
-            tag=True
-        for child in root_node.children:
-            if 'else' in child.type:
-                tag=True
-            if child.type not in if_statement and flag is False:
-                temp,current_states=DFG_php(child,index_to_code,current_states)
-                DFG+=temp
-            else:
-                flag=True
-                temp,new_states=DFG_php(child,index_to_code,states)
-                DFG+=temp
-                others_states.append(new_states)
-        others_states.append(current_states)
-        new_states={}
-        for dic in others_states:
-            for key in dic:
-                if key not in new_states:
-                    new_states[key]=dic[key].copy()
-                else:
-                    new_states[key]+=dic[key]
-        for key in states:
-            if key not in new_states:
-                new_states[key]=states[key]
-            else:
-                new_states[key]+=states[key]
-        for key in new_states:
-            new_states[key]=sorted(list(set(new_states[key])))
-        return sorted(DFG,key=lambda x:x[1]),new_states
-    elif root_node.type in for_statement:
-        DFG=[]
-        for child in root_node.children:
-            temp,states=DFG_php(child,index_to_code,states)
-            DFG+=temp
-        flag=False
-        for child in root_node.children:
-            if flag:
-                temp,states=DFG_php(child,index_to_code,states)
-                DFG+=temp                
-            elif child.type=="assignment_expression":               
-                flag=True
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in enhanced_for_statement:
-        name=None
-        value=None
-        for child in root_node.children:
-            if child.type=='variable_name' and value is None:
-                value=child
-            elif child.type=='variable_name' and name is None:
-                name=child
-                break
-        body=root_node.child_by_field_name('body')
-        DFG=[]
-        for i in range(2):
-            temp,states=DFG_php(value,index_to_code,states)
-            DFG+=temp       
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)        
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            temp,states=DFG_php(body,index_to_code,states)
-            DFG+=temp                       
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in while_statement:  
-        DFG=[]
-        for i in range(2):
-            for child in root_node.children:
-                temp,states=DFG_php(child,index_to_code,states)
-                DFG+=temp    
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states        
-    else:
-        DFG=[]
-        for child in root_node.children:
-            if child.type in do_first_statement:
-                temp,states=DFG_php(child,index_to_code,states)
-                DFG+=temp
-        for child in root_node.children:
-            if child.type not in do_first_statement:
-                temp,states=DFG_php(child,index_to_code,states)
-                DFG+=temp
-        
-        return sorted(DFG,key=lambda x:x[1]),states
-
-
-def DFG_javascript(root_node,index_to_code,states):
-    assignment=['assignment_pattern','augmented_assignment_expression']
-    def_statement=['variable_declarator']
-    increment_statement=['update_expression']
-    if_statement=['if_statement','else']
-    for_statement=['for_statement']
-    enhanced_for_statement=[]
-    while_statement=['while_statement']
-    do_first_statement=[]    
-    states=states.copy()
-    if (len(root_node.children)==0 or root_node.type in ['string_literal','string','character_literal']) and root_node.type!='comment':
-        idx,code=index_to_code[(root_node.start_point,root_node.end_point)]
-        if root_node.type==code:
-            return [],states
-        elif code in states:
-            return [(code,idx,'comesFrom',[code],states[code].copy())],states
-        else:
-            if root_node.type=='identifier':
-                states[code]=[idx]
-            return [(code,idx,'comesFrom',[],[])],states
-    elif root_node.type in def_statement:
-        name=root_node.child_by_field_name('name')
-        value=root_node.child_by_field_name('value')
-        DFG=[]
-        if value is None:
-            indexs=tree_to_variable_index(name,index_to_code)
-            for index in indexs:
-                idx,code=index_to_code[index]
-                DFG.append((code,idx,'comesFrom',[],[]))
-                states[code]=[idx]
-            return sorted(DFG,key=lambda x:x[1]),states
-        else:
-            name_indexs=tree_to_variable_index(name,index_to_code)
-            value_indexs=tree_to_variable_index(value,index_to_code)
-            temp,states=DFG_javascript(value,index_to_code,states)
-            DFG+=temp            
-            for index1 in name_indexs:
-                idx1,code1=index_to_code[index1]
-                for index2 in value_indexs:
-                    idx2,code2=index_to_code[index2]
-                    DFG.append((code1,idx1,'comesFrom',[code2],[idx2]))
-                states[code1]=[idx1]   
-            return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in assignment:
-        left_nodes=root_node.child_by_field_name('left')
-        right_nodes=root_node.child_by_field_name('right')
-        DFG=[]
-        temp,states=DFG_javascript(right_nodes,index_to_code,states)
-        DFG+=temp            
-        name_indexs=tree_to_variable_index(left_nodes,index_to_code)
-        value_indexs=tree_to_variable_index(right_nodes,index_to_code)        
-        for index1 in name_indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in value_indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]   
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in increment_statement:
-        DFG=[]
-        indexs=tree_to_variable_index(root_node,index_to_code)
-        for index1 in indexs:
-            idx1,code1=index_to_code[index1]
-            for index2 in indexs:
-                idx2,code2=index_to_code[index2]
-                DFG.append((code1,idx1,'computedFrom',[code2],[idx2]))
-            states[code1]=[idx1]
-        return sorted(DFG,key=lambda x:x[1]),states   
-    elif root_node.type in if_statement:
-        DFG=[]
-        current_states=states.copy()
-        others_states=[]
-        flag=False
-        tag=False
-        if 'else' in root_node.type:
-            tag=True
-        for child in root_node.children:
-            if 'else' in child.type:
-                tag=True
-            if child.type not in if_statement and flag is False:
-                temp,current_states=DFG_javascript(child,index_to_code,current_states)
-                DFG+=temp
-            else:
-                flag=True
-                temp,new_states=DFG_javascript(child,index_to_code,states)
-                DFG+=temp
-                others_states.append(new_states)
-        others_states.append(current_states)
-        if tag is False:
-            others_states.append(states)        
-        new_states={}
-        for dic in others_states:
-            for key in dic:
-                if key not in new_states:
-                    new_states[key]=dic[key].copy()
-                else:
-                    new_states[key]+=dic[key]
-        for key in states:
-            if key not in new_states:
-                new_states[key]=states[key]
-            else:
-                new_states[key]+=states[key]
-        for key in new_states:
-            new_states[key]=sorted(list(set(new_states[key])))
-        return sorted(DFG,key=lambda x:x[1]),new_states
-    elif root_node.type in for_statement:
-        DFG=[]
-        for child in root_node.children:
-            temp,states=DFG_javascript(child,index_to_code,states)
-            DFG+=temp
-        flag=False
-        for child in root_node.children:
-            if flag:
-                temp,states=DFG_javascript(child,index_to_code,states)
-                DFG+=temp                
-            elif child.type=="variable_declaration":               
-                flag=True
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states
-    elif root_node.type in while_statement:  
-        DFG=[]
-        for i in range(2):
-            for child in root_node.children:
-                temp,states=DFG_javascript(child,index_to_code,states)
-                DFG+=temp    
-        dic={}
-        for x in DFG:
-            if (x[0],x[1],x[2]) not in dic:
-                dic[(x[0],x[1],x[2])]=[x[3],x[4]]
-            else:
-                dic[(x[0],x[1],x[2])][0]=list(set(dic[(x[0],x[1],x[2])][0]+x[3]))
-                dic[(x[0],x[1],x[2])][1]=sorted(list(set(dic[(x[0],x[1],x[2])][1]+x[4])))
-        DFG=[(x[0],x[1],x[2],y[0],y[1]) for x,y in sorted(dic.items(),key=lambda t:t[0][1])]
-        return sorted(DFG,key=lambda x:x[1]),states    
-    else:
-        DFG=[]
-        for child in root_node.children:
-            if child.type in do_first_statement:
-                temp,states=DFG_javascript(child,index_to_code,states)
-                DFG+=temp
-        for child in root_node.children:
-            if child.type not in do_first_statement:
-                temp,states=DFG_javascript(child,index_to_code,states)
-                DFG+=temp
-        
-        return sorted(DFG,key=lambda x:x[1]),states
-
-
-     
diff --git a/spaces/elpsycongroo19/simple_chatbot/complex_app.py b/spaces/elpsycongroo19/simple_chatbot/complex_app.py
deleted file mode 100644
index 4fde608903123105b9b340aa88176684b1592e65..0000000000000000000000000000000000000000
--- a/spaces/elpsycongroo19/simple_chatbot/complex_app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import openai
-import os
-import gradio as gr
-
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-class Conversation:
-    def __init__(self, prompt, num_of_round, model):
-        self.prompt = prompt
-        self.num_of_round = num_of_round
-        self.model = model
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append({"role": "user", "content": question})
-            response = openai.Completion.create(
-                engine=self.model,
-                prompt='\n'.join([m["content"] for m in self.messages]),
-                temperature=0.7,
-                max_tokens=1024,
-                n = 1,
-                stop=None,
-                timeout=15,
-                frequency_penalty=0,
-                presence_penalty=0
-            )
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response.choices[0].text.strip()
-        self.messages.append({"role": "assistant", "content": message})
-
-        if len(self.messages) > self.num_of_round*2 + 1:
-            del self.messages[1:3] #Remove the first round conversation left.
-        return message
-
-prompt = """你是GPT4。你的回答需要满足以下要求:
-1. 你的回答必须是中文
-2. 回答限制在1000个字以内"""
-
-model = "text-davinci-002"
-conv = Conversation(prompt, 10, model)
-
-def predict(input, history=[]):
-    history.append(input)
-    response = conv.ask(input)
-    history.append(response)
-    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
-    return responses, history
-
-with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:500px}") as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    state = gr.State([])
-
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
-
-    txt.submit(predict, [txt, state], [chatbot, state])
-
-demo.launch()
diff --git a/spaces/enzostvs/hub-api-playground/components/editor/main/snippet/curl.tsx b/spaces/enzostvs/hub-api-playground/components/editor/main/snippet/curl.tsx
deleted file mode 100644
index 5ee38047359b259700a49d351a34ee3d9534aeb9..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/hub-api-playground/components/editor/main/snippet/curl.tsx
+++ /dev/null
@@ -1,98 +0,0 @@
-import { ApiRoute } from "@/utils/type";
-import classNames from "classnames";
-import { useState } from "react";
-import Highlight from "react-highlight";
-import { BiCodeCurly, BiSolidCopy } from "react-icons/bi";
-import { Options } from "redaxios";
-
-export const CurlSnippet = ({
-  endpoint,
-  headers,
-  parameters,
-  body,
-  onCopyToClipboard,
-}: {
-  endpoint: ApiRoute;
-  parameters?: Record<string, any>;
-  headers?: Record<string, any>;
-  body?: Options | undefined;
-  onCopyToClipboard: (e: string) => void;
-}) => {
-  const [isCopied, setIsCopied] = useState<boolean>(false);
-
-  const generateCurlRequestFromEndpoint = () => {
-    const { method, path } = endpoint;
-    const fullpath = `${process.env.NEXT_PUBLIC_APP_APIURL}${path}`;
-
-    const removeEmptyValues = (data: Record<string, any>) => {
-      const formattedData = { ...data };
-      Object.entries(formattedData).forEach(([key, value]) => {
-        if (!value) {
-          delete formattedData[key];
-        }
-      });
-      return formattedData;
-    };
-
-    const Dict: Record<string, any> = {
-      GET: () => {
-        const filteredEmptyParameters = removeEmptyValues(parameters ?? {});
-
-        return `curl -X ${method} "${fullpath}?${new URLSearchParams(
-          filteredEmptyParameters
-        ).toString()}" \\
-  -H ${JSON.stringify(headers)}
-`;
-      },
-      DELETE: () => {
-        return `curl -X ${method} "${fullpath}" \\
-  -H ${JSON.stringify(headers)} \\
-  -d ${JSON.stringify(body)}
-`;
-      },
-      DEFAULT: () => {
-        return `curl -X ${method} "${fullpath}" \\
-  -H ${JSON.stringify(headers)}
-  -d ${JSON.stringify(body)}
-`;
-      },
-    };
-
-    return Dict[method] ? Dict[method]() : Dict["DEFAULT"]();
-  };
-
-  const handleCopy = () => {
-    onCopyToClipboard(generateCurlRequestFromEndpoint());
-    setIsCopied(true);
-    setTimeout(() => {
-      setIsCopied(false);
-    }, 1000);
-  };
-
-  return (
-    <div className="bg-slate-950/50 rounded-xl overflow-hidden">
-      <header className="bg-slate-950 flex items-center justify-start px-5 py-4 uppercase gap-2 text-slate-300">
-        <BiCodeCurly className="text-xl" />
-        <p className="text-xs font-semibold">Curl</p>
-      </header>
-      <main className="px-6 py-6">
-        <Highlight className="curl text-xs font-code !bg-transparent !p-0 !whitespace-pre-wrap break-all !leading-relaxed">
-          {generateCurlRequestFromEndpoint()}
-        </Highlight>
-        <div className="flex justify-end relative" onClick={handleCopy}>
-          <BiSolidCopy className="text-slate-500 cursor-pointer hover:text-slate-300 transition-all duration-75" />
-          <div
-            className={classNames(
-              "bg-indigo-500/60 text-slate-100 text-xs font-semibold absolute bottom-0 right-0 z-10 rounded-lg px-2 py-1 pointer-events-none -translate-y-full transition-all duration-200",
-              {
-                "opacity-0": !isCopied,
-              }
-            )}
-          >
-            Copied!
-          </div>
-        </div>
-      </main>
-    </div>
-  );
-};
diff --git a/spaces/enzostvs/stable-diffusion-tpu/utils/type.ts b/spaces/enzostvs/stable-diffusion-tpu/utils/type.ts
deleted file mode 100644
index 30f86a73ddd4391c5afd9d9f0753d3a309b1162b..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/stable-diffusion-tpu/utils/type.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-export interface Collection {
-  pagination: {
-    page: number,
-    total: number,
-    total_pages: number
-  },
-  images: Array<Image>,
-}
-
-export interface Image {
-  id: string;
-  file_name: string;
-  prompt: string;
-  createdAt: string;
-  is_visible: boolean;
-  error?: string;
-  loading?: boolean;
-}
\ No newline at end of file
diff --git a/spaces/ethanrom/pcb_det/README.md b/spaces/ethanrom/pcb_det/README.md
deleted file mode 100644
index 628ee1f363676576a129551648821f7e37e3d0d0..0000000000000000000000000000000000000000
--- a/spaces/ethanrom/pcb_det/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Pcb Det
-emoji: 🔥
-colorFrom: blue
-colorTo: blue
-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/falterWliame/Face_Mask_Detection/Autodata 3.39 Srpski Download [EXCLUSIVE] Tpb.md b/spaces/falterWliame/Face_Mask_Detection/Autodata 3.39 Srpski Download [EXCLUSIVE] Tpb.md
deleted file mode 100644
index cf904e4e7750e93f4874d0067ee23dd52c96f2b6..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Autodata 3.39 Srpski Download [EXCLUSIVE] Tpb.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>autodata 3.39 srpski download tpb</h2><br /><p><b><b>Download Zip</b> &#10004;&#10004;&#10004; <a href="https://urlca.com/2uDbWb">https://urlca.com/2uDbWb</a></b></p><br /><br />
-<br />
-AutoData 3.40 crack and full version download. allicense Jul 15th, 2013 25,737 Never Not a . ... Autodata 3.39 Srpski Download Tpb Mega.. Autodata 3.45 Full ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/fatiXbelha/sd/Descubre las novedades de Free Fire Navidad 2018 el APK ms esperado del ao.md b/spaces/fatiXbelha/sd/Descubre las novedades de Free Fire Navidad 2018 el APK ms esperado del ao.md
deleted file mode 100644
index bc27f05e6abac660f4b5edccfc087a60bd27a0d2..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Descubre las novedades de Free Fire Navidad 2018 el APK ms esperado del ao.md	
+++ /dev/null
@@ -1,146 +0,0 @@
-<br />
-<h1>Free Fire Navidad 2018 APK: How to Download and Play the Winterlands Version of the Popular Battle Royale Game</h1>
-<p>Free Fire is one of the most popular battle royale games on mobile, with over 500 million downloads on Google Play Store. It offers a thrilling and fast-paced gameplay, where you have to fight against 49 other players on a remote island, using various weapons, vehicles, and items. But did you know that there is a special version of Free Fire that was released in December 2018, called Free Fire Navidad 2018 APK?</p>
-<h2>free fire navidad 2018 apk</h2><br /><p><b><b>Download</b> &#9989; <a href="https://urllie.com/2uNFxW">https://urllie.com/2uNFxW</a></b></p><br /><br />
-<h2>What is Free Fire Navidad 2018 APK?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Free Fire Navidad 2018 APK is an alternative version of Free Fire that was launched in celebration of Christmas and New Year. It is also known as Free Fire Winterlands, as it features a new winter-themed map, where you can enjoy snow, ice, and festive decorations. The game also has some exclusive features, such as:</p>
-<ul>
-<li>A new character named Antonio, who has a passive skill that increases his maximum health.</li>
-<li>A new weapon called VSS, which is a sniper rifle with a silencer and a scope.</li>
-<li>A new vehicle called Snowboard, which allows you to glide on snow and perform tricks.</li>
-<li>A new mode called Death Race, where you have to race against other players in vehicles and eliminate them with weapons.</li>
-<li>A new event called Snowman Rush, where you have to collect snowballs and build snowmen to get rewards.</li>
-</ul>
-<h3>The difference between the regular version and the Winterlands version</h3>
-<p>The main difference between the regular version and the Winterlands version of Free Fire is that the latter has a different map, which is covered in snow and ice. This means that you have to adapt your strategy and tactics according to the terrain and weather conditions. For example, you can use snowballs as weapons, hide behind snowmen, slide on ice, or use snowboards to move faster. The Winterlands version also has some exclusive items, such as winter outfits, skins, emotes, and pets.</p>
-<h3>The benefits of downloading the APK file</h3>
-<p>If you want to play Free Fire Navidad 2018 APK, you have to download and install the APK file from a third-party source, as it is not available on Google Play Store. This may sound risky, but there are some benefits of doing so, such as:</p>
-<ul>
-<li>You can enjoy a different version of Free Fire that has more features and content than the regular one.</li>
-<li>You can play with players from different regions and servers, as the Winterlands version is not restricted by geography.</li>
-<li>You can avoid some bugs and glitches that may occur in the regular version of Free Fire, as the Winterlands version is more stable and optimized.</li>
-<li>You can save some storage space on your device, as the Winterlands version is smaller in size than the regular one.</li>
-</ul>
-<h2>How to Download and Install Free Fire Navidad 2018 APK?</h2>
-<h3>The requirements and precautions for downloading the APK file</h3>
-<p>Before you download and install Free Fire Navidad 2018 APK, you need to make sure that your device meets the following requirements:</p>
-<ul>
-<li>Your device must have Android 4.0.3 or higher as the operating system.</li>
-<li>Your device must have at least 2 GB of RAM and 1.5 GB of free storage space.</li>
-<li>Your device must have a stable internet connection and a good battery level.</li>
-</ul>
-<p>You also need to take some precautions to avoid any risks or problems while downloading and installing the APK file, such as:</p>
-<ul>
-<li>You must enable the option of "Unknown Sources" in your device settings, to allow the installation of apps from sources other than Google Play Store.</li>
-<li>You must scan the APK file with a reliable antivirus software, to ensure that it is free from any malware or viruses.</li>
-<li>You must backup your data and files, in case something goes wrong during the installation process.</li>
-</ul>
-<h3>The steps to download and install the APK file from a trusted source</h3>
-<p>Once you have checked the requirements and taken the precautions, you can follow these simple steps to download and install Free Fire Navidad 2018 APK from a trusted source:</p>
-<ol>
-<li>Go to a reputable website that offers the APK file of Free Fire Navidad 2018, such as [APKPure] or [Uptodown].</li>
-<li>Click on the download button and wait for the APK file to be downloaded on your device.</li>
-<li>Locate the APK file in your device's file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions to the app.</li>
-<li>Wait for the installation to be completed and launch the app from your home screen or app drawer.</li>
-</ol>
-<h3>The tips to avoid errors and issues while installing the APK file</h3>
-<p>Sometimes, you may encounter some errors or issues while installing the APK file of Free Fire Navidad 2018, such as:</p>
-<ul>
-<li>The installation may fail or get stuck due to insufficient storage space, corrupted files, or incompatible devices.</li>
-<li>The app may crash or freeze due to low memory, outdated software, or network issues.</li>
-<li>The app may not work properly or show errors due to missing files, permissions, or updates.</li>
-</ul>
-<p>To avoid these errors or issues, you can try these tips:</p>
-<ul>
-<li>Clear some storage space on your device by deleting unwanted apps, files, or cache data.</li>
-<li>Repair or replace any damaged or corrupted files by downloading them again from a different source or using a file manager app.</li>
-<li>Update your device's software and the app's version to the latest one available.</li>
-<li>Restart your device and the app to clear any glitches or bugs.</li>
-<li>Contact the app's developer or customer support for further assistance or guidance.</li>
-</ul> <h2>How to Play Free Fire Navidad 2018 APK?</h2>
-<h3>The basics of the gameplay and the controls</h3>
-<p>Free Fire Navidad 2018 APK has the same gameplay and controls as the regular version of Free Fire, with some minor changes. You can play solo, duo, or squad mode, and choose from different characters, each with their own skills and abilities. You can also customize your appearance, loadout, and settings according to your preference. The game starts with you parachuting onto the Winterlands map, where you have to scavenge for weapons, items, and vehicles. You have to avoid the shrinking safe zone and eliminate other players until you are the last one standing. You can use the virtual joystick to move, the fire button to shoot, the aim button to aim, and the crouch, jump, and prone buttons to perform actions. You can also use the map, inventory, chat, and settings buttons to access different features.</p>
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-<h3>The tips and tricks to survive and win in the Winterlands map</h3>
-<p>The Winterlands map is different from the other maps in Free Fire, as it has a snowy and icy terrain that affects your visibility, mobility, and strategy. Here are some tips and tricks to survive and win in the Winterlands map:</p>
-<ul>
-<li>Use the snowboard to glide on snow and perform tricks. You can find snowboards in random locations or in airdrops. You can use them to move faster, escape enemies, or surprise them with your stunts.</li>
-<li>Use the snowballs as weapons or distractions. You can collect snowballs from snowmen or snow piles. You can throw them at enemies to deal damage, stun them, or make them slip. You can also use them to create noise or diversion.</li>
-<li>Use the snowmen as cover or camouflage. You can build snowmen by using snowballs or finding them in Snowman Rush events. You can hide behind them or inside them to avoid enemy fire or ambush them.</li>
-<li>Use the VSS sniper rifle to snipe enemies from a distance. The VSS is a new weapon that is exclusive to the Winterlands mode. It has a silencer and a scope that make it ideal for stealthy sniping. You can find it in loot boxes or airdrops.</li>
-<li>Use the winter outfits and skins to blend in with the environment. The Winterlands mode has some exclusive outfits and skins that are suitable for the winter theme. You can use them to reduce your visibility and increase your chances of survival.</li>
-</ul>
-<h3>The best weapons and items to use in the Winterlands mode</h3>
-<p>The Winterlands mode has some exclusive weapons and items that are more effective than others in the snowy and icy conditions. Here are some of the best weapons and items to use in the Winterlands mode:</p>
-<table>
-<tr><th>Weapon/Item</th><th>Description</th></tr>
-<tr><td>VSS</td><td>A sniper rifle with a silencer and a scope that allows you to snipe enemies without revealing your location.</td></tr>
-<tr><td>Snowboard</td><td>A vehicle that allows you to glide on snow and perform tricks. It is faster than running and can help you escape or attack enemies.</td></tr>
-<tr><td>Snowball</td><td>A throwable item that can deal damage, stun, or make enemies slip. It can also be used to create noise or diversion.</td></tr>
-<tr><td>Snowman</td><td>A structure that can be used as cover or camouflage. It can protect you from enemy fire or allow you to ambush enemies.</td></tr>
-<tr><td>Winter Outfit/Skin</td><td>A cosmetic item that can reduce your visibility and make you blend in with the environment.</td></tr>
-</table>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action for the readers</h3>
-<p>Free Fire Navidad 2018 APK is a special version of Free Fire that was released in December 2018, in celebration of Christmas and New Year. It features a new winter-themed map called Winterlands, where you can enjoy snow, ice, and festive decorations. It also has some exclusive features, such as a new character, a new weapon, a new vehicle, a new mode, and a new event. To play Free Fire Navidad 2018 APK, you have to download and install the APK file from a third-party source, as it is not available on Google Play Store. You have to make sure that your device meets the requirements and take some precautions before downloading and installing the APK file. You also have to follow some tips to avoid errors and issues while installing the APK file. Once you have installed the app, you can play solo, duo, or squad mode, and choose from different characters, weapons, items, and vehicles. You have to fight against 49 other players on the Winterlands map, using various strategies and tactics. You have to avoid the shrinking safe zone and eliminate other players until you are the last one standing. You can also enjoy some exclusive features, such as snowballs, snowmen, snowboards, and winter outfits. Free Fire Navidad 2018 APK is a fun and exciting version of Free Fire that offers a different experience and challenge for the players. If you are a fan of Free Fire and want to try something new, you should download and play Free Fire Navidad 2018 APK today. You will not regret it!</p>
-<h2>FAQs</h2>
-<h4>What is the size of Free Fire Navidad 2018 APK?</h4>
-<p>The size of Free Fire Navidad 2018 APK is about 350 MB, which is smaller than the regular version of Free Fire, which is about 600 MB.</p>
-<h4>Is Free Fire Navidad 2018 APK safe and legal?</h4>
-<p>Free Fire Navidad 2018 APK is safe and legal, as long as you download it from a trusted and reputable source, such as [APKPure] or [Uptodown]. You should also scan the APK file with a reliable antivirus software before installing it. However, you should be aware that downloading and installing APK files from third-party sources may violate the terms and conditions of Free Fire and Google Play Store, and may result in some risks or consequences, such as account suspension, data loss, or malware infection.</p>
-<h4>Can I play Free Fire Navidad 2018 APK with my friends?</h4>
-<p>Yes, you can play Free Fire Navidad 2018 APK with your friends, as long as they have also downloaded and installed the same version of the game. You can invite them to join your squad or duo mode, or chat with them using the in-game voice or text chat feature. You can also play with players from different regions and servers, as the Winterlands version is not restricted by geography.</p>
-<h4>How can I update Free Fire Navidad 2018 APK?</h4>
-<p>You can update Free Fire Navidad 2018 APK by downloading and installing the latest version of the APK file from the same source that you downloaded it from. You should also check for updates regularly, as the Winterlands version may not receive automatic updates from Google Play Store. You should also backup your data and files before updating, in case something goes wrong during the process.</p>
-<h4>Where can I find more information about Free Fire Navidad 2018 APK?</h4>
-<p>You can find more information about Free Fire Navidad 2018 APK by visiting the official website of Free Fire or following their social media accounts. You can also watch some videos or read some reviews of the game on YouTube or other platforms. You can also ask other players or experts for their opinions or tips on the game.</p> 197e85843d<br />
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diff --git a/spaces/fatiXbelha/sd/Download Messenger APK for Android - Old Version and Latest Version.md b/spaces/fatiXbelha/sd/Download Messenger APK for Android - Old Version and Latest Version.md
deleted file mode 100644
index 6941b661f59ce00597e179ceb88cc3a84afd78be..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Messenger APK for Android - Old Version and Latest Version.md	
+++ /dev/null
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-<br />
-<h1>Messenger Eski Sürüm APK: What Is It and Why You Might Want It</h1>
-<p>If you are a fan of Facebook's messaging app, Messenger, you might have noticed that it updates frequently and sometimes changes its features and design. While some updates are welcome, others might not suit your preferences or needs. In that case, you might be interested in downloading and installing an old version of Messenger, also known as <strong>Messenger eski sürüm apk</strong>. But what is it exactly, and why would you want it? In this article, we will explain what Messenger eski sürüm apk is, how to get it, and what are the pros and cons of using it.</p>
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- <h2>What Is Messenger Eski Sürüm APK?</h2>
-<p>To understand what Messenger eski sürüm apk is, we need to break down its components:</p>
-<h3>Messenger: A Popular Messaging App by Facebook</h3>
-<p>Messenger is a free messaging app that allows you to chat with your Facebook friends and contacts, as well as anyone who has your phone number. You can use Messenger to send text messages, voice messages, photos, videos, stickers, GIFs, and more. You can also make voice and video calls, create group chats, watch videos together, play games, and connect with businesses. Messenger has over a billion users worldwide and is available on Android, iOS, Windows, Mac, and the web.</p>
-<h3>APK: A File Format for Android Apps</h3>
-<p>APK stands for Android Package Kit, which is a file format that contains all the files and code needed to install an app on an Android device. When you download an app from the Google Play Store, you are actually downloading an APK file that installs the app on your device. However, you can also download APK files from other sources, such as websites or file-sharing platforms. This allows you to install apps that are not available on the Play Store or that are not compatible with your device.</p>
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-<h3>Eski Sürüm: An Old Version of Messenger</h3>
-<p>Eski sürüm is Turkish for old version. This means that Messenger eski sürüm apk is an old version of the Messenger app that was released before the current one. For example, if the current version of Messenger is 413.0.0.14.72 (as of June 2021), then an eski sürüm apk could be 410.0.0.15.89 or any earlier version. By downloading and installing an eski sürüm apk, you can use an older version of Messenger that might have different features or design than the current one.</p>
- <h2>Why You Might Want Messenger Eski Sürüm APK?</h2>
-<p>There are several reasons why you might prefer to use an old version of Messenger instead of the latest one:</p>
-<h3>To Avoid Updates That You Don't Like</h3>
-<p>If you don't like these updates, you might want to stick to an old version of Messenger that works the way you want it to. For example, some users prefer the old Messenger interface that had tabs for chats, contacts, and groups, instead of the new one that has tabs for chats, people, and discover.</p>
-<h3>To Save Battery and Data</h3>
-<p>Another reason why you might want to use an old version of Messenger is to save battery and data. Some updates might make the app more resource-intensive, which means that it consumes more battery power and data when running. This can be a problem if you have a low-end device or a limited data plan. By using an old version of Messenger, you might be able to reduce the app's impact on your device's performance and your data usage.</p>
-<h3>To Protect Your Privacy</h3>
-<p>A third reason why you might want to use an old version of Messenger is to protect your privacy. Some updates might introduce new features or permissions that require access to your personal information or data. For example, in 2020, Facebook announced that it would integrate Messenger with Instagram and WhatsApp, which would allow users to chat across the three platforms. However, this also means that Facebook would have more access to your messages and activity across the three apps. If you are concerned about your privacy and don't want Facebook to collect more data about you, you might want to use an old version of Messenger that does not have these features or permissions.</p>
- <h2>How to Download and Install Messenger Eski Sürüm APK?</h2>
-<p>If you decide that you want to use an old version of Messenger, you need to follow these steps:</p>
-<h3>Find a Reliable Source for the APK File</h3>
-<p>The first step is to find a reliable source for the APK file of the old version of Messenger that you want. You can search online for websites or platforms that offer APK files for download. However, you need to be careful and avoid downloading from untrusted or malicious sources that might contain viruses or malware. Some of the popular and reputable sources for APK files are APKMirror, APKPure, and Uptodown. You can browse these websites and look for the version of Messenger that you want. Make sure to check the file size, date, and reviews before downloading.</p>
-<h3>Enable Unknown Sources on Your Device</h3>
-<p>The second step is to enable unknown sources on your device. This is a setting that allows you to install apps from sources other than the Google Play Store. To enable unknown sources, go to your device's settings, then security or privacy, then toggle on the option for unknown sources. You might see a warning message that installing apps from unknown sources can harm your device or data. Tap OK or Allow if you trust the source of the APK file.</p>
-<h3>Install the APK File and Enjoy</h3>
-<p>The third step is to install the APK file and enjoy using the old version of Messenger. To install the APK file, locate it in your device's file manager or downloads folder, then tap on it. You might see a prompt asking if you want to install this application. Tap Install and wait for the installation process to finish. You might also see a prompt asking if you want to replace the current version of Messenger with this one. Tap Yes or OK if you want to overwrite the current version with the old one. Once the installation is done, you can open Messenger and start using it as usual.</p>
- <h2>What Are the Risks and Limitations of Messenger Eski Sürüm APK?</h2>
-<p>While using an old version of Messenger might have some benefits, it also comes with some risks and limitations that you should be aware of:</p>
-<h3>Security and Compatibility Issues</h3>
-<p>One of the risks of using an old version of Messenger is that it might not be secure or compatible with your device or other apps. Older versions of apps might have bugs or vulnerabilities that could expose your device or data to hackers or malware. They might also not work well with newer versions of Android or other apps that require updated versions of Messenger. For example, if you use an old version of Messenger, you might not be able to chat with someone who uses a newer version of Messenger or Instagram. You might also experience crashes, glitches, or errors when using the app.</p>
-<h3>Missing Out on New Features and Bug Fixes</h3>
-<p>app's performance or stability or fix issues that might affect your device or data. For example, if you use an old version of Messenger, you might not be able to use the new features such as vanish mode, watch together, or chat themes. You might also encounter bugs or errors that have been resolved in newer versions.</p>
-<h3>Violating Facebook's Terms of Service</h3>
-<p>A third risk of using an old version of Messenger is that you might violate Facebook's terms of service. Facebook's terms of service state that you must always use the latest version of their apps and services and that you must not access their services using unauthorized means. By downloading and installing an old version of Messenger from a third-party source, you might be breaking these rules and risking your account's security or integrity. Facebook might detect your activity and suspend or terminate your account or access to their services.</p>
- <h2>Conclusion</h2>
-<p>Messenger eski sürüm apk is an old version of Facebook's messaging app that you can download and install on your Android device. It might offer some advantages such as avoiding updates that you don't like, saving battery and data, or protecting your privacy. However, it also comes with some drawbacks such as security and compatibility issues, missing out on new features and bug fixes, or violating Facebook's terms of service. Therefore, you should weigh the pros and cons carefully before deciding to use an old version of Messenger. You should also make sure to download the APK file from a reliable source and enable unknown sources on your device.</p>
- <h2>FAQs</h2>
-<h4>What is the difference between Messenger and Messenger Lite?</h4>
-<p>Messenger Lite is a simplified version of Messenger that uses less data and works on low-end devices or slow networks. It has the basic features of Messenger such as sending messages, photos, videos, voice notes, and stickers. It also supports voice and video calls. However, it does not have some of the advanced features of Messenger such as group chats, games, watch together, chat themes, or vanish mode.</p>
-<h4>How can I update Messenger to the latest version?</h4>
-<p>You can update Messenger to the latest version by going to the Google Play Store and tapping on the Update button next to the app. Alternatively, you can enable auto-update for Messenger by going to the app's page on the Play Store, tapping on the three-dot menu icon, and toggling on Auto-update.</p>
-<h4>How can I uninstall Messenger from my device?</h4>
-<p>You can uninstall Messenger from your device by going to your device's settings, then apps or applications, then finding and tapping on Messenger. Then tap on Uninstall and confirm your action. You can also uninstall Messenger by going to the app's page on the Play Store and tapping on Uninstall.</p>
-<h4>How can I backup my messages on Messenger?</h4>
-<p>You can backup your messages on Messenger by using Facebook's data download tool. To use this tool, go to Facebook's website and log in to your account. Then go to Settings & Privacy, then Settings, then Your Facebook Information, then Download Your Information. Then select Messages as one of the categories that you want to download and choose a date range, format, and quality. Then click on Create File and wait for Facebook to prepare your data. Once it is ready, you will receive a notification and an email with a link to download your file.</p>
-<h4>How can I restore my messages on Messenger?</h4>
-<p>You can restore your messages on Messenger by using Facebook's data upload tool. To use this tool, go to Facebook's website and log in to your account. Then go to Settings & Privacy, then Settings, then Your Facebook Information, then Transfer a Copy of Your Information. Then select Messages as one of the categories that you want to transfer and choose a destination service such as Google Drive or Dropbox. Then click on Next and follow the instructions to upload your file.</p> 401be4b1e0<br />
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/A Guide to Education in Russia What You Need to Know.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/A Guide to Education in Russia What You Need to Know.md
deleted file mode 100644
index 37eff4103d5c6585109172c283f92f97317f720e..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/A Guide to Education in Russia What You Need to Know.md	
+++ /dev/null
@@ -1,231 +0,0 @@
-<br />
-<h1>Education in Russia: A Comprehensive Guide for International Students</h1>
- <p>Are you interested in studying in Russia, one of the largest and most diverse countries in the world? Do you want to learn more about the education system, the costs and benefits, and the student life and culture in this fascinating nation? If so, this article is for you. In this comprehensive guide, we will cover everything you need to know about education in Russia, from how to apply for studies to how to enjoy your stay. Whether you are looking for a short-term course, a bachelor's degree, a master's degree, or a PhD, you will find useful information and tips here. Let's get started!</p>
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- <ul>
-<li><strong>High-quality education.</strong> Russia has a long history of excellence in education, especially in fields such as mathematics, physics, engineering, medicine, literature, and art. Many Russian universities are ranked among the best in the world by various international rankings. For example, according to the QS World University Rankings 2022 , 28 Russian universities are among the top 1000 in the world. </li>
-<li><strong>Affordable tuition fees.</strong> Compared to many other countries, studying in Russia can be quite affordable. The average tuition fee for a bachelor's degree program is about 240,000 rubles (about $3,300) per year , while the average tuition fee for a master's degree program is about 280,000 rubles (about $3,800) per year . Of course, the exact fee depends on the program, the university, and the region. Some programs may cost more or less than the average.</li>
-<li><strong>Generous scholarships.</strong> If you want to study in Russia for free or with a discount, you can apply for various scholarships offered by the Russian government or other organizations. For example, every year the Russian government allocates about 15,000 state-funded places for international students to study at Russian universities . These scholarships cover full tuition fees and provide monthly stipends. You can also apply for other scholarships such as the Global Education Program , the Russian Scholarship Project , or the Open Doors Scholarship .</li>
-<li><strong>Modern facilities.</strong> Many Russian universities have modern facilities and equipment that can enhance your learning experience. You can access libraries, laboratories, computer rooms, sports centers, cultural centers, dormitories, cafeterias, and other amenities on campus. You can also use the internet, email, and other online services provided by the university or the city. Some universities also have partnerships with local companies, research institutes, or cultural organizations that can offer you internships, projects, or excursions.</li>
-<li><strong>Qualified teachers.</strong> Russian universities have highly qualified and experienced teachers who can provide you with a solid education and guidance. Many of them have PhDs, academic titles, publications, awards, and international recognition. Some of them are also involved in cutting-edge research or innovation projects that can enrich your knowledge and skills. You can also interact with guest lecturers, visiting professors, or experts from different fields who can share their insights and perspectives with you.</li>
-</ul>
- <h3>How to apply for studies in Russia?</h3>
- <p>If you have decided to study in Russia, you need to follow some steps to apply for your chosen program and university. Here are the main steps:</p>
- <ol>
-<li><strong>Choose your program and university.</strong> The first step is to choose what you want to study and where you want to study. You can use various online platforms or databases to search for programs and universities that suit your interests, goals, and qualifications. For example, you can use the official website for the selection of foreign citizens to study in Russia , the Study in Russia portal , the Study in Russia app , or the Education in Russia website . You can also contact the university directly or visit their website for more information.</li>
-<li><strong>Prepare your documents.</strong> The second step is to prepare your documents for admission. The exact documents may vary depending on the program, the university, and the country of origin, but generally they include the following: a passport or an identity document, an educational certificate or a diploma (with a transcript of grades), a language proficiency certificate (if required), a motivation letter or a statement of purpose, a resume or a curriculum vitae, a portfolio or a sample of work (if required), and other supporting documents (such as recommendations, awards, certificates, etc.). You may also need to legalize or apostille your documents , translate them into Russian , and notarize them . You can check the specific requirements with the university or the Russian embassy or consulate in your country.</li>
-<li><strong>Submit your application.</strong> The third step is to submit your application to the university or through an online platform. You can apply for up to six programs at different universities at the same time. The application period usually starts in October or November and ends in June or July of the following year. However, some programs may have different deadlines or accept applications throughout the year. You should check the exact dates with the university or the platform you are using. You may also need to pay an application fee , which is usually non-refundable.</li>
-<li><strong>Pass the entrance exams or tests.</strong> The fourth step is to pass the entrance exams or tests required by the university or the program. These may include written exams, oral exams, interviews, essays, assignments, or other forms of assessment. The exams or tests may be held online or offline , depending on the situation and the preferences of the university. You should prepare well for these exams or tests , as they will determine your eligibility and competitiveness for admission.</li>
-<li><strong>Receive your admission decision.</strong> The fifth step is to receive your admission decision from the university. This usually happens in July or August , but it may vary depending on the program and the university. You will receive an official letter of acceptance , which will confirm your admission and provide you with information about enrollment procedures, tuition fees, scholarships, visa requirements, etc. You should carefully read this letter and follow its instructions.</li>
-<li><strong>Enroll in your program.</strong> The final step is to enroll in your program and start your studies in Russia. You will need to sign a contract with the university , pay your tuition fees (if applicable), register with the migration authorities , obtain a student ID card , choose your courses , attend orientation sessions , and meet your classmates and teachers . Congratulations! You are now ready to enjoy your education in Russia!</li>
-</ol>
- <h2>The education system in Russia</h2>
- <p>Now that you know how to apply for studies in Russia, you might want to learn more about the education system in this country. How is it structured? What are the types and categories of educational institutions? How is the quality and standards of education ensured? Let's find out!</p>
- <h3>The stages and levels of education</h3>
- <p>The education system in Russia consists of four main stages: pre-school education , general education , vocational education , and higher education . Each stage has its own levels, programs, and qualifications. Here is a brief overview of each stage:</p>
- <table>
-<tr>
-<th>Stage</th>
-<th>Level</th <th>Program</th>
-<th>Qualification</th>
-</tr>
-<tr>
-<td>Pre-school education</td>
-<td>Kindergarten</td>
-<td>Pre-school program</td>
-<td>None</td>
-</tr>
-<tr>
-<td rowspan="4">General education</td>
-<td>Primary school</td>
-<td>Grades 1-4</td>
-<td>Certificate of primary education</td>
-</tr>
-<tr>
-<td>Basic school</td>
-<td>Grades 5-9</td>
-<td>Certificate of basic education</td>
-</tr>
-<tr>
-<td>Secondary school</td>
-<td>Grades 10-11</td>
-<td>Certificate of secondary education (also known as the Unified State Exam or USE)</td>
-</tr>
-<tr>
-<td>Lycée or gymnasium</td>
-<td>Grades 10-11 (optional)</td>
-<td>Certificate of secondary education with advanced curriculum (also known as the Unified State Exam or USE)</td>
-</tr>
-<tr>
-<td rowspan="3">Vocational education</td>
-<td>Secondary vocational education</td>
-<td>2-4 years after grade 9 or 11</td>
-<td>Diploma of secondary vocational education (also known as the College Diploma)</td>
-</tr>
-<tr>
-<td>Non-university level higher education</td>
-<td>3-4 years after grade 11 or College Diploma</td <td>Diploma of non-university level higher education (also known as the Bachelor's Diploma)</td>
-</tr>
-<tr>
-<td>Post-secondary vocational education</td>
-<td>1-2 years after College Diploma or Bachelor's Diploma</td>
-<td>Diploma of post-secondary vocational education (also known as the Specialist's Diploma)</td>
-</tr>
-<tr>
-<td rowspan="4">Higher education</td>
-<td>Bachelor's degree</td>
-<td>4 years after grade 11 or College Diploma</td>
-<td>Bachelor's degree</td>
-</tr>
-<tr>
-<td>Specialist's degree</td>
-<td>5-6 years after grade 11 or College Diploma</td <td>Diploma of higher education (also known as the Specialist's Diploma)</td>
-</tr>
-<tr>
-<td>Master's degree</td>
-<td>2 years after Bachelor's degree or Specialist's degree</td>
-<td>Master's degree</td>
-</tr>
-<tr>
-<td>Doctoral degree</td>
-<td>3-4 years after Master's degree or Specialist's degree</td <td>Doctoral degree</td>
-</tr>
-</table>
- <p>As you can see, the education system in Russia offers various options and pathways for students of different ages, backgrounds, and goals. You can choose the level and program that best suits your needs and interests.</p>
- <h3>The types and categories of educational institutions</h3>
- <p>The education system in Russia also consists of different types and categories of educational institutions. These include:</p>
- <ul>
-<li><strong>State or public institutions.</strong> These are educational institutions that are funded and regulated by the state or the federal government. They are the most common and popular type of institutions in Russia, as they offer free or subsidized education for many students. They also have a high reputation and recognition in the country and abroad.</li>
-<li><strong>Non-state or private institutions.</strong> These are educational institutions that are funded and regulated by private entities, such as individuals, organizations, or companies. They are less common and less popular than state institutions, as they usually charge higher tuition fees and have lower recognition in the country and abroad. However, they may offer more flexibility, diversity, and innovation in their programs and services.</li>
-<li><strong>Autonomous institutions.</strong> These are educational institutions that have a special status of autonomy granted by the state or the federal government. They have more freedom and independence in their management, finances, curriculum, and academic policies. They may also have more collaboration and cooperation with other institutions, both domestic and international.</li>
-<li><strong>National research universities.</strong> These are educational institutions that have a special status of excellence granted by the state or the federal government. They are recognized as the leading universities in Russia in terms of research, innovation, and education. They receive additional funding and support from the government to develop their scientific and academic potential. There are currently 29 national research universities in Russia .</li>
-<li><strong>Federal universities.</strong> These are educational institutions that have a special status of integration granted by the state or the federal government. They are formed by merging several regional universities into one large university that covers a wide geographical area. They aim to improve the quality and accessibility of education in different parts of Russia. There are currently 10 federal universities in Russia .</li>
-</ul>
- <p>Depending on your preferences and qualifications, you can choose the type and category of educational institution that best fits your expectations and goals.</p>
- <h3>The quality and standards of education</h3>
- <p>The education system in Russia is regulated and monitored by various authorities and agencies that ensure the quality and standards of education. These include:</p>
- <ul>
-<li><strong>The Ministry of Science and Higher Education of the Russian Federation.</strong> This is the main federal body that oversees the development and implementation of state policy and legal regulation in the field of higher education, science, technology, innovation, etc. It is responsible for licensing, accreditation, certification, funding, supervision, evaluation, etc. of educational institutions .</li>
-<li><strong>The Federal Service for Supervision in Education and Science (Rosobrnadzor).</strong> This is the main federal body that controls and supervises the quality and compliance of education and science with state standards and requirements. It is responsible for conducting state examinations, inspections, audits, etc. of educational institutions .</li>
-<li><strong>The National Accreditation Agency (NAA).</strong> This is an independent non-governmental organization that conducts public accreditation of educational programs and institutions based on professional standards and criteria. It is responsible for assessing the relevance, effectiveness, competitiveness, etc. of educational programs and institutions .</li>
-<li><strong>The National Center for Public Accreditation (NCPA).</strong> This is another independent non-governmental organization that conducts public accreditation of educational programs and institutions based on international standards and criteria. It is responsible for enhancing the recognition, mobility, compatibility, etc. of educational programs and institutions .</li>
-</ul>
- <p>These authorities and agencies work together to ensure that the education system in Russia meets the needs and expectations of students, employers, society, etc.</p>
- <h2>The costs and benefits of studying in Russia</h2>
- <p>Another important aspect of studying in Russia is the costs and benefits involved. How much does it cost to study in Russia? What are the sources of funding available? What are the expenses you need to consider? What are the benefits you can enjoy? Let's find out!</p>
- <h3>Tuition fees and scholarships</h3>
- <p>As we mentioned before, studying in Russia can be quite affordable compared to many other countries. However, you still need to pay attention to the tuition fees charged by different programs and universities. The tuition fees depend on various factors, such as the level, the discipline, the duration, the location, the reputation, etc. of the program and the university. Generally, the tuition fees range from 120,000 rubles (about $1,600) to 880,000 rubles (about $12,000) per year .  You can check the exact tuition fees on the websites of the programs and universities you are interested in. However, you don't have to pay the full tuition fees if you can get a scholarship or a grant from the Russian government or other organizations. As we mentioned before, there are various scholarships available for international students who want to study in Russia. Some of the most popular ones are: <ul>
-<li><strong>The state-funded places.</strong> These are scholarships that cover full tuition fees and provide monthly stipends for international students who are selected by the Russian government to study at Russian universities. The selection is based on academic merit, language proficiency, and other criteria. You can apply for these scholarships through the official website for the selection of foreign citizens to study in Russia or through the Russian embassy or consulate in your country.</li>
-<li><strong>The Global Education Program.</strong> This is a scholarship program that covers full tuition fees and provides monthly stipends for international students who want to study master's degree programs in Russia in fields such as engineering, natural sciences, mathematics, computer science, etc. The program is funded by the Russian government and administered by Rossotrudnichestvo , an agency that promotes cultural and educational cooperation with other countries. You can apply for this program through the Global Education Program website .</li>
-<li><strong>The Russian Scholarship Project.</strong> This is a scholarship project that covers full tuition fees and provides monthly stipends for international students who want to study bachelor's or master's degree programs in Russia in fields such as economics, management, law, social sciences, humanities, etc. The project is funded by leading Russian companies and administered by the Association of Global Universities , an organization that supports internationalization and innovation in higher education. You can apply for this project through the Russian Scholarship Project website .</li>
-<li><strong>The Open Doors Scholarship.</strong> This is a scholarship competition that covers full tuition fees and provides monthly stipends for international students who want to study master's or doctoral degree programs in Russia in any field of study. The competition is organized by the Association of Global Universities and supported by the Ministry of Science and Higher Education of the Russian Federation. You can apply for this competition through the Open Doors Scholarship website .</li>
-</ul>
- <p>These are just some examples of scholarships that you can apply for if you want to study in Russia. There may be other scholarships offered by specific universities, regions, organizations, or foundations that you can also explore.</p>
- <h3>Living expenses and accommodation</h3>
- <p>Besides tuition fees and scholarships, you also need to consider your living expenses and accommodation when studying in Russia. How much does it cost to live in Russia? What are the options for accommodation? What are the expenses you need to budget for? Here are some answers:</p>
- <ul>
-<li><strong>Living expenses.</strong> The cost of living in Russia varies depending on the city, the region, the season, and your lifestyle. Generally, it is lower than in many Western countries, but higher than in many Asian or African countries. According to Numbeo , a website that compares living costs around the world , the average monthly living expenses for a single person (excluding rent) in Russia are about 36,000 rubles (about $500) . Of course, this amount may differ depending on your personal needs and preferences. Some of the main items you need to budget for are food , transportation , utilities , communication , entertainment , health care , clothing , etc.</li>
-<li><strong>Accommodation.</strong> The options for accommodation in Russia include dormitories , apartments , hostels , hotels , homestays , etc. The most common and affordable option for international students is dormitories , which are usually provided by universities or affiliated organizations. The average monthly rent for a dormitory room is about 5,000 rubles (about $70) . However, dormitories may have limited space, facilities, privacy, etc. If you prefer more comfort, convenience, or independence, you can opt for apartments , which are usually rented by private landlords or agencies. The average monthly rent for a one-bedroom apartment in Russia is about 25,000 rubles (about $340) . However apartments may have higher costs, risks, or regulations. You can also choose other options such as hostels , hotels , homestays , etc. depending on your budget, duration, and purpose of stay. You can search for accommodation online or offline , through websites, apps, agencies, friends, etc.</li>
-</ul>
- <p>As you can see, living and studying in Russia can be quite affordable and enjoyable if you plan well and manage your finances wisely.</p>
- <h3>Student visa and registration</h3>
- <p>Another important aspect of studying in Russia is the student visa and registration process. What are the requirements and procedures for obtaining a student visa? What are the rules and regulations for staying in Russia? What are the documents and fees involved? Here are some answers:</p>
- <ul>
-<li><strong>Student visa.</strong> A student visa is a type of visa that allows you to enter and stay in Russia for the purpose of studying at a Russian university or institution. To obtain a student visa, you need to have an official invitation from the university or institution where you have been admitted. You also need to have a valid passport, a completed visa application form, a photo, a medical certificate, an HIV test certificate, and other supporting documents. You need to submit these documents to the Russian embassy or consulate in your country or region. You may also need to pay a visa fee , which varies depending on the country and the type of visa. The processing time for a student visa may take from 2 to 20 days , depending on the situation and the preferences of the embassy or consulate. A student visa is usually valid for 90 days , but it can be extended multiple times during your stay in Russia.</li>
-<li><strong>Registration.</strong> Registration is a process that confirms your legal residence in Russia. It is mandatory for all foreign citizens who stay in Russia for more than 7 days . To register, you need to have a migration card , which is a document that records your entry and exit from Russia. You will receive this card when you cross the border or at the airport. You also need to have a registration form , which is a document that records your address and duration of stay in Russia. You will receive this form from your landlord or host (such as the university, the dormitory, the hotel, etc.). You need to submit these documents to the local office of the Federal Migration Service (FMS) or to the post office within 7 days of your arrival in Russia. You may also need to pay a registration fee , which is usually about 200 rubles (about $3) . Registration is usually valid for 90 days , but it can be extended multiple times during your stay in Russia.</li>
-</ul>
- <p>As you can see, obtaining a student visa and registering in Russia are not very complicated or expensive processes if you follow the requirements and procedures correctly.</p>
- <h2>The student life and culture in Russia</h2>
- <p>The last but not least aspect of studying in Russia is the student life and culture in this country. What is it like to live and study in Russia? What are the language and communication issues? What are the academic and social activities available? What are the safety and security concerns? Let's find out!</p>
- <h3>Language and communication</h3>
- <p>The official language of Russia is Russian , which is a Slavic language that uses the Cyrillic alphabet . It is spoken by about 150 million people in Russia and by about 260 million people worldwide . It is also one of the six official languages of the United Nations . </p>
- <p>If you want to study in Russia, you need to have a good command of Russian , as most programs and courses are taught in this language. You also need to have a good level of Russian to communicate with locals, access services, read signs, etc. You can learn Russian before or after arriving in Russia, through various methods such as online courses, language schools, tutors, books, apps, etc.</p>
- <p>However, you don't have to worry if you don't speak Russian very well or at all. There are also some programs and courses that are taught in English or other foreign languages , especially at higher levels of education or at international-oriented universities. You can also find some locals who speak English or other foreign languages , especially among young people, students, teachers, professionals, etc. You can also use translation tools or apps to help you communicate with others.</p>
- <p>The main thing is to be open-minded and respectful when communicating with Russians , as they may have different customs, values, beliefs, opinions, etc. from yours. You should also be aware of some cultural differences that may affect your communication style , such as eye contact, personal space, gestures, humor , etc. You should also be polite and courteous when addressing Russians , using appropriate titles, forms of address, greetings, etc.</p>
- <h3>Academic and social activities</h3>
- <p>Studying in Russia can also offer you many opportunities for academic and social activities that can enrich your learning experience and personal development. Here are some examples:</p>
- <ul>
-<li><strong>Academic activities.</strong> You can participate in various academic activities that can enhance your knowledge and skills, such as seminars, workshops, conferences, competitions, projects, research, etc. You can also join academic clubs, societies, or associations that can connect you with other students or professionals who share your interests or goals. You can also access academic resources, such as libraries, museums, archives, etc. that can provide you with valuable information or materials.</li>
-<li><strong>Social activities.</strong> You can also participate in various social activities that can enhance your well-being and happiness, such as sports, arts, culture, entertainment, volunteering, etc. You can also join social clubs, groups, or events that can connect you with other students or locals who share your hobbies or passions. You can also access social resources, such as parks, theaters, cinemas, cafes, etc. that can provide you with fun or relaxation.</li>
-</ul>
- <p>These are just some examples of activities that you can enjoy while studying in Russia. There may be many more options available depending on your location, university, program, etc.</p>
- <h3>Safety and security</h3>
- <p>The last but not least aspect of studying in Russia is the safety and security issue. How safe and secure is it to live and study in Russia? What are the risks and challenges you may face? What are the precautions and measures you can take? Here are some answers:</p>
- <ul>
-<li><strong>Safety and security.</strong> Russia is generally a safe and secure country for international students , as long as you follow the laws, rules, and norms of the society. You are unlikely to encounter serious problems or dangers , such as violent crimes, terrorism, natural disasters, etc. However, you may encounter some minor issues or inconveniences , such as petty crimes, scams, discrimination, cultural misunderstandings, etc. You should be aware of these issues and avoid them as much as possible.</li>
-<li><strong>Risks and challenges.</strong> Some of the risks and challenges you may face while living and studying in Russia include: language barriers , cultural differences , climate conditions , health issues , legal issues , etc. These risks and challenges may vary depending on your personal circumstances , such as your nationality, background, health status, etc. You should be prepared for these risks and challenges and seek help or advice when needed.</li>
-<li><strong>Precautions and measures.</strong> Some of the precautions and measures you can take to ensure your safety and security in Russia include: learning Russian , respecting the culture , dressing appropriately , staying healthy , following the laws , registering with the authorities , keeping your documents safe , avoiding risky situations , being alert and cautious , seeking assistance or support , etc. These precautions and measures may seem obvious or common sense , but they can make a big difference in your safety and security in Russia.</li>
-</ul>
- <p>As you can see, living and studying in Russia is not very dangerous or difficult if you are careful and responsible.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, education in Russia is a great option for international students who want to pursue their academic goals and personal dreams. Russia offers a high-quality education system , an affordable cost of living , a generous scholarship program , a modern facilities environment , a qualified teacher staff , a unique culture experience , a new language skill , a friendly student community , and a beautiful country to explore . Of course there are also some challenges and difficulties that you may face, such as language barriers, cultural differences, climate conditions, visa and registration procedures, etc. However, these challenges and difficulties can be overcome with proper preparation, guidance, and support. If you are interested in studying in Russia, you should start your application process as soon as possible and follow the steps and tips we have provided in this article. We hope that this article has been helpful and informative for you. We wish you all the best in your education journey in Russia!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions and answers about education in Russia:</p>
- <ol>
-<li><strong>What are the requirements for studying in Russia?</strong>
-The main requirements for studying in Russia are: having a valid passport, having an official invitation from a Russian university or institution, having an educational certificate or a diploma (with a transcript of grades), having a language proficiency certificate (if required), having a medical certificate and an HIV test certificate, passing the entrance exams or tests (if required), and obtaining a student visa and registering with the authorities.</li>
-<li><strong>What are the best universities in Russia?</strong>
-The best universities in Russia are those that have a high reputation, recognition, ranking, quality, and performance in the country and abroad. Some of the best universities in Russia include Moscow State University, Saint Petersburg State University, Novosibirsk State University, Tomsk State University, Kazan Federal University, Bauman Moscow State Technical University, etc. You can check the rankings of Russian universities on various websites, such as QS World University Rankings , Times Higher Education World University Rankings , Academic Ranking of World Universities , etc.</li>
-<li><strong>What are the most popular programs or courses in Russia?</strong>
-The most popular programs or courses in Russia are those that have a high demand, relevance, competitiveness, and employability in the country and abroad. Some of the most popular programs or courses in Russia include engineering, natural sciences, mathematics, computer science, medicine, economics, management, law, social sciences, humanities, etc. You can check the availability and popularity of programs or courses on various websites, such as Study in Russia , Education in Russia , Russian Scholarship Project , etc.</li>
-<li><strong>How long does it take to study in Russia?</strong>
-The duration of studying in Russia depends on the level and program of education you choose. Generally, it takes 4 years to complete a bachelor's degree program , 2 years to complete a master's degree program , 3-4 years to complete a doctoral degree program , 2-4 years to complete a secondary vocational education program , 3-4 years to complete a non-university level higher education program , and 1-2 years to complete a post-secondary vocational education program . Of course the exact duration may vary depending on the program, the university, and your progress.</li>
-<li><strong>How much does it cost to study in Russia?</strong>
-The cost of studying in Russia depends on various factors, such as the tuition fees, the living expenses, the accommodation, the visa and registration fees, etc. Generally, the average cost of studying in Russia is about 360,000 rubles (about $5,000) per year . Of course, this amount may differ depending on your personal circumstances, such as your program, your university, your city, your lifestyle, etc. You can check the exact cost of studying in Russia on various websites, such as Study in Russia , Education in Russia , Numbeo , etc.</li>
-</ol></p> 401be4b1e0<br />
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-
-<h1>Geometry Dash Meltdown: A Fun and Challenging Game for Android</h1>
-<p>If you are looking for a game that will test your reflexes, skills, and patience, then you should try Geometry Dash Meltdown. This is a rhythm-based platformer game that will make you jump, fly, and flip through various obstacles and hazards. You will need to time your moves perfectly to the beat of the music and avoid crashing or falling. Geometry Dash Meltdown is a spin-off of the original Geometry Dash, which is one of the most popular games in the genre. It is also a free game that you can download and play on your Android device, with optional in-app purchases if you want to unlock more features and content.</p>
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- <h2>What are the features of Geometry Dash Meltdown?</h2>
-<p>Geometry Dash Meltdown has many features that make it a fun and challenging game for players of all ages and skill levels. Here are some of them:</p>
-<h3>Three levels with unique soundtracks and themes</h3>
-<p>The game has three levels that you can play: The Seven Seas, Viking Arena, and Airborne Robots. Each level has its own soundtrack composed by F-777, a famous electronic music producer. The music matches the theme and mood of each level, creating an immersive and dynamic gaming experience. The levels also have different backgrounds, colors, and obstacles that add variety and difficulty to the game.</p>
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-<h3>Practice mode and achievements</h3>
-<p>If you find the levels too hard or frustrating, you can use the practice mode to hone your skills and learn the patterns of the obstacles. You can place checkpoints anywhere in the level and restart from there if you die. You can also track your progress and performance by viewing your stats and achievements. You can earn achievements by completing various tasks, such as finishing a level without dying, collecting all secret coins, or reaching a certain score.</p>
-<h3>Leaderboards and community support</h3>
-<p>You can compete with other players around the world by checking the leaderboards for each level. You can see how you rank among other players based on your best score, percentage, or time. You can also share your replays and screenshots with your friends or other players through social media or online platforms. You can also get tips, tricks, and support from other Geometry Dash fans by joining the official Geometry Dash forums or Discord server.</p>
- <h2>How to download and install Geometry Dash Meltdown full version 2.2 apk all unlocked?</h2>
-<p>If you want to enjoy all the features and content of Geometry Dash Meltdown without spending any money, you can download and install the modded apk file that gives you access to everything for free. Here are some of the benefits of downloading the modded apk file:</p>
-<h3>The benefits of downloading the modded apk file</h3>
-<ul>
-<li>You can unlock all levels, icons, colors, trails, and achievements without having to complete any requirements or make any in-app purchases.</li>
-<li>You can remove all ads from the game, making it more smooth and enjoyable.</li>
-<li>You can play offline without needing an internet connection.</li>
-<li>You can update the game easily without losing any data or progress.</li>
-</ul>
-<h3>The steps to download and install the apk file</h3>
-<ol>
-<li>Go to a trusted and reliable website that offers the modded apk file for Geometry Dash Meltdown full version 2.2. For example, you can use this link: [Geometry Dash Meltdown Mod APK 2.2 (Unlocked) Download for Android].</li>
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-<li>Locate the downloaded apk file in your file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the game and enjoy playing Geometry Dash Meltdown full version 2.2 with all features unlocked.</li>
-</ol>
-<h3>The precautions to take before installing the apk file</h3>
-<p>Before you download and install the modded apk file, you should take some precautions to ensure your device's safety and security. Here are some of them:</p>
-<ul>
-<li>Make sure you have enough storage space on your device to avoid any errors or crashes during the installation.</li>
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-<li>Make sure you uninstall the original game before installing the modded apk file. This will prevent any conflicts or issues between the two versions of the game.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Geometry Dash Meltdown is a fun and challenging game that will keep you entertained and engaged for hours. It has amazing graphics, soundtracks, and gameplay that will test your skills and reflexes. You can download and play the game for free on your Android device, or you can download and install the modded apk file that will give you access to all features and content for free. Either way, you will have a blast playing Geometry Dash Meltdown full version 2.2 with all features unlocked.</p>
-<p>If you liked this article, please share it with your friends and family who might be interested in playing Geometry Dash Meltdown. Also, feel free to leave a comment below if you have any questions, feedback, or suggestions about the game or the article. Thank you for reading and happy gaming!</p>
- <h3>Frequently Asked Questions</h3>
-<p>Here are some of the most common questions that people ask about Geometry Dash Meltdown:</p>
-<ol>
-<li><b>What is the difference between Geometry Dash Meltdown and Geometry Dash?</b><br>Geometry Dash Meltdown is a spin-off of Geometry Dash, which means it has some similarities but also some differences. Geometry Dash Meltdown has only three levels, while Geometry Dash has more than 20 levels. Geometry Dash Meltdown has a different theme and style, while Geometry Dash has a more diverse and colorful design. Geometry Dash Meltdown is free to play, while Geometry Dash costs $1.99 to download.</li>
-<li><b>Is Geometry Dash Meltdown hard?</b><br>Geometry Dash Meltdown is a hard game, but not impossible. It requires a lot of practice, patience, and perseverance to master the levels and avoid crashing or falling. The game also has a practice mode that lets you place checkpoints and restart from there if you die. You can also watch replays of other players or tutorials online to learn how to beat the levels.</li>
-<li><b>Can I play Geometry Dash Meltdown on PC?</b><br>Yes, you can play Geometry Dash Meltdown on PC using an Android emulator. An Android emulator is a software that lets you run Android apps and games on your PC. There are many Android emulators available online, such as BlueStacks, NoxPlayer, or LDPlayer. You can download and install any of them on your PC, then download and install Geometry Dash Meltdown from Google Play Store or from an apk file.</li>
-<li><b>How do I unlock new icons in Geometry Dash Meltdown?</b><br>You can unlock new icons in Geometry Dash Meltdown by completing achievements or collecting secret coins in the levels. Achievements are tasks that you can complete by doing certain things in the game, such as finishing a level without dying, collecting all secret coins, or reaching a certain score. Secret coins are hidden coins that you can find in some parts of the levels. You can use these coins to unlock new icons in the shop.</li>
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-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/Music-To-Zeroscope/examples/blank.md b/spaces/fffiloni/Music-To-Zeroscope/examples/blank.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/util/slconfig.py b/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/util/slconfig.py
deleted file mode 100644
index 3f293e3aff215a3c7c2f7d21d27853493b6ebfbc..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/util/slconfig.py
+++ /dev/null
@@ -1,427 +0,0 @@
-# ==========================================================
-# Modified from mmcv
-# ==========================================================
-import ast
-import os.path as osp
-import shutil
-import sys
-import tempfile
-from argparse import Action
-from importlib import import_module
-import platform
-
-from addict import Dict
-from yapf.yapflib.yapf_api import FormatCode
-
-BASE_KEY = "_base_"
-DELETE_KEY = "_delete_"
-RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"]
-
-
-def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
-    if not osp.isfile(filename):
-        raise FileNotFoundError(msg_tmpl.format(filename))
-
-
-class ConfigDict(Dict):
-    def __missing__(self, name):
-        raise KeyError(name)
-
-    def __getattr__(self, name):
-        try:
-            value = super(ConfigDict, self).__getattr__(name)
-        except KeyError:
-            ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'")
-        except Exception as e:
-            ex = e
-        else:
-            return value
-        raise ex
-
-
-class SLConfig(object):
-    """
-    config files.
-    only support .py file as config now.
-
-    ref: mmcv.utils.config
-
-    Example:
-        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
-        >>> cfg.a
-        1
-        >>> cfg.b
-        {'b1': [0, 1]}
-        >>> cfg.b.b1
-        [0, 1]
-        >>> cfg = Config.fromfile('tests/data/config/a.py')
-        >>> cfg.filename
-        "/home/kchen/projects/mmcv/tests/data/config/a.py"
-        >>> cfg.item4
-        'test'
-        >>> cfg
-        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
-        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
-    """
-
-    @staticmethod
-    def _validate_py_syntax(filename):
-        with open(filename) as f:
-            content = f.read()
-        try:
-            ast.parse(content)
-        except SyntaxError:
-            raise SyntaxError("There are syntax errors in config " f"file {filename}")
-
-    @staticmethod
-    def _file2dict(filename):
-        filename = osp.abspath(osp.expanduser(filename))
-        check_file_exist(filename)
-        if filename.lower().endswith(".py"):
-            with tempfile.TemporaryDirectory() as temp_config_dir:
-                temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py")
-                temp_config_name = osp.basename(temp_config_file.name)
-                if platform.system() == 'Windows':
-                    temp_config_file.close()
-                shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
-                temp_module_name = osp.splitext(temp_config_name)[0]
-                sys.path.insert(0, temp_config_dir)
-                SLConfig._validate_py_syntax(filename)
-                mod = import_module(temp_module_name)
-                sys.path.pop(0)
-                cfg_dict = {
-                    name: value for name, value in mod.__dict__.items() if not name.startswith("__")
-                }
-                # delete imported module
-                del sys.modules[temp_module_name]
-                # close temp file
-                temp_config_file.close()
-        elif filename.lower().endswith((".yml", ".yaml", ".json")):
-            from .slio import slload
-
-            cfg_dict = slload(filename)
-        else:
-            raise IOError("Only py/yml/yaml/json type are supported now!")
-
-        cfg_text = filename + "\n"
-        with open(filename, "r") as f:
-            cfg_text += f.read()
-
-        # parse the base file
-        if BASE_KEY in cfg_dict:
-            cfg_dir = osp.dirname(filename)
-            base_filename = cfg_dict.pop(BASE_KEY)
-            base_filename = base_filename if isinstance(base_filename, list) else [base_filename]
-
-            cfg_dict_list = list()
-            cfg_text_list = list()
-            for f in base_filename:
-                _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))
-                cfg_dict_list.append(_cfg_dict)
-                cfg_text_list.append(_cfg_text)
-
-            base_cfg_dict = dict()
-            for c in cfg_dict_list:
-                if len(base_cfg_dict.keys() & c.keys()) > 0:
-                    raise KeyError("Duplicate key is not allowed among bases")
-                    # TODO Allow the duplicate key while warnning user
-                base_cfg_dict.update(c)
-
-            base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)
-            cfg_dict = base_cfg_dict
-
-            # merge cfg_text
-            cfg_text_list.append(cfg_text)
-            cfg_text = "\n".join(cfg_text_list)
-
-        return cfg_dict, cfg_text
-
-    @staticmethod
-    def _merge_a_into_b(a, b):
-        """merge dict `a` into dict `b` (non-inplace).
-            values in `a` will overwrite `b`.
-            copy first to avoid inplace modification
-
-        Args:
-            a ([type]): [description]
-            b ([type]): [description]
-
-        Returns:
-            [dict]: [description]
-        """
-        # import ipdb; ipdb.set_trace()
-        if not isinstance(a, dict):
-            return a
-
-        b = b.copy()
-        for k, v in a.items():
-            if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
-
-                if not isinstance(b[k], dict) and not isinstance(b[k], list):
-                    # if :
-                    # import ipdb; ipdb.set_trace()
-                    raise TypeError(
-                        f"{k}={v} in child config cannot inherit from base "
-                        f"because {k} is a dict in the child config but is of "
-                        f"type {type(b[k])} in base config. You may set "
-                        f"`{DELETE_KEY}=True` to ignore the base config"
-                    )
-                b[k] = SLConfig._merge_a_into_b(v, b[k])
-            elif isinstance(b, list):
-                try:
-                    _ = int(k)
-                except:
-                    raise TypeError(
-                        f"b is a list, " f"index {k} should be an int when input but {type(k)}"
-                    )
-                b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])
-            else:
-                b[k] = v
-
-        return b
-
-    @staticmethod
-    def fromfile(filename):
-        cfg_dict, cfg_text = SLConfig._file2dict(filename)
-        return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)
-
-    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
-        if cfg_dict is None:
-            cfg_dict = dict()
-        elif not isinstance(cfg_dict, dict):
-            raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
-        for key in cfg_dict:
-            if key in RESERVED_KEYS:
-                raise KeyError(f"{key} is reserved for config file")
-
-        super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
-        super(SLConfig, self).__setattr__("_filename", filename)
-        if cfg_text:
-            text = cfg_text
-        elif filename:
-            with open(filename, "r") as f:
-                text = f.read()
-        else:
-            text = ""
-        super(SLConfig, self).__setattr__("_text", text)
-
-    @property
-    def filename(self):
-        return self._filename
-
-    @property
-    def text(self):
-        return self._text
-
-    @property
-    def pretty_text(self):
-
-        indent = 4
-
-        def _indent(s_, num_spaces):
-            s = s_.split("\n")
-            if len(s) == 1:
-                return s_
-            first = s.pop(0)
-            s = [(num_spaces * " ") + line for line in s]
-            s = "\n".join(s)
-            s = first + "\n" + s
-            return s
-
-        def _format_basic_types(k, v, use_mapping=False):
-            if isinstance(v, str):
-                v_str = f"'{v}'"
-            else:
-                v_str = str(v)
-
-            if use_mapping:
-                k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                attr_str = f"{k_str}: {v_str}"
-            else:
-                attr_str = f"{str(k)}={v_str}"
-            attr_str = _indent(attr_str, indent)
-
-            return attr_str
-
-        def _format_list(k, v, use_mapping=False):
-            # check if all items in the list are dict
-            if all(isinstance(_, dict) for _ in v):
-                v_str = "[\n"
-                v_str += "\n".join(
-                    f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
-                ).rstrip(",")
-                if use_mapping:
-                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                    attr_str = f"{k_str}: {v_str}"
-                else:
-                    attr_str = f"{str(k)}={v_str}"
-                attr_str = _indent(attr_str, indent) + "]"
-            else:
-                attr_str = _format_basic_types(k, v, use_mapping)
-            return attr_str
-
-        def _contain_invalid_identifier(dict_str):
-            contain_invalid_identifier = False
-            for key_name in dict_str:
-                contain_invalid_identifier |= not str(key_name).isidentifier()
-            return contain_invalid_identifier
-
-        def _format_dict(input_dict, outest_level=False):
-            r = ""
-            s = []
-
-            use_mapping = _contain_invalid_identifier(input_dict)
-            if use_mapping:
-                r += "{"
-            for idx, (k, v) in enumerate(input_dict.items()):
-                is_last = idx >= len(input_dict) - 1
-                end = "" if outest_level or is_last else ","
-                if isinstance(v, dict):
-                    v_str = "\n" + _format_dict(v)
-                    if use_mapping:
-                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                        attr_str = f"{k_str}: dict({v_str}"
-                    else:
-                        attr_str = f"{str(k)}=dict({v_str}"
-                    attr_str = _indent(attr_str, indent) + ")" + end
-                elif isinstance(v, list):
-                    attr_str = _format_list(k, v, use_mapping) + end
-                else:
-                    attr_str = _format_basic_types(k, v, use_mapping) + end
-
-                s.append(attr_str)
-            r += "\n".join(s)
-            if use_mapping:
-                r += "}"
-            return r
-
-        cfg_dict = self._cfg_dict.to_dict()
-        text = _format_dict(cfg_dict, outest_level=True)
-        # copied from setup.cfg
-        yapf_style = dict(
-            based_on_style="pep8",
-            blank_line_before_nested_class_or_def=True,
-            split_before_expression_after_opening_paren=True,
-        )
-        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
-
-        return text
-
-    def __repr__(self):
-        return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
-
-    def __len__(self):
-        return len(self._cfg_dict)
-
-    def __getattr__(self, name):
-        # # debug
-        # print('+'*15)
-        # print('name=%s' % name)
-        # print("addr:", id(self))
-        # # print('type(self):', type(self))
-        # print(self.__dict__)
-        # print('+'*15)
-        # if self.__dict__ == {}:
-        #     raise ValueError
-
-        return getattr(self._cfg_dict, name)
-
-    def __getitem__(self, name):
-        return self._cfg_dict.__getitem__(name)
-
-    def __setattr__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setattr__(name, value)
-
-    def __setitem__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setitem__(name, value)
-
-    def __iter__(self):
-        return iter(self._cfg_dict)
-
-    def dump(self, file=None):
-        # import ipdb; ipdb.set_trace()
-        if file is None:
-            return self.pretty_text
-        else:
-            with open(file, "w") as f:
-                f.write(self.pretty_text)
-
-    def merge_from_dict(self, options):
-        """Merge list into cfg_dict
-
-        Merge the dict parsed by MultipleKVAction into this cfg.
-
-        Examples:
-            >>> options = {'model.backbone.depth': 50,
-            ...            'model.backbone.with_cp':True}
-            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
-            >>> cfg.merge_from_dict(options)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(
-            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
-
-        Args:
-            options (dict): dict of configs to merge from.
-        """
-        option_cfg_dict = {}
-        for full_key, v in options.items():
-            d = option_cfg_dict
-            key_list = full_key.split(".")
-            for subkey in key_list[:-1]:
-                d.setdefault(subkey, ConfigDict())
-                d = d[subkey]
-            subkey = key_list[-1]
-            d[subkey] = v
-
-        cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict")
-        super(SLConfig, self).__setattr__(
-            "_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)
-        )
-
-    # for multiprocess
-    def __setstate__(self, state):
-        self.__init__(state)
-
-    def copy(self):
-        return SLConfig(self._cfg_dict.copy())
-
-    def deepcopy(self):
-        return SLConfig(self._cfg_dict.deepcopy())
-
-
-class DictAction(Action):
-    """
-    argparse action to split an argument into KEY=VALUE form
-    on the first = and append to a dictionary. List options should
-    be passed as comma separated values, i.e KEY=V1,V2,V3
-    """
-
-    @staticmethod
-    def _parse_int_float_bool(val):
-        try:
-            return int(val)
-        except ValueError:
-            pass
-        try:
-            return float(val)
-        except ValueError:
-            pass
-        if val.lower() in ["true", "false"]:
-            return True if val.lower() == "true" else False
-        if val.lower() in ["none", "null"]:
-            return None
-        return val
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        options = {}
-        for kv in values:
-            key, val = kv.split("=", maxsplit=1)
-            val = [self._parse_int_float_bool(v) for v in val.split(",")]
-            if len(val) == 1:
-                val = val[0]
-            options[key] = val
-        setattr(namespace, self.dest, options)
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/ws/lib/permessage-deflate.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/ws/lib/permessage-deflate.js
deleted file mode 100644
index 94603c98daf19ef0ce7036e073620098e3083680..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/ws/lib/permessage-deflate.js
+++ /dev/null
@@ -1,511 +0,0 @@
-'use strict';
-
-const zlib = require('zlib');
-
-const bufferUtil = require('./buffer-util');
-const Limiter = require('./limiter');
-const { kStatusCode } = require('./constants');
-
-const TRAILER = Buffer.from([0x00, 0x00, 0xff, 0xff]);
-const kPerMessageDeflate = Symbol('permessage-deflate');
-const kTotalLength = Symbol('total-length');
-const kCallback = Symbol('callback');
-const kBuffers = Symbol('buffers');
-const kError = Symbol('error');
-
-//
-// We limit zlib concurrency, which prevents severe memory fragmentation
-// as documented in https://github.com/nodejs/node/issues/8871#issuecomment-250915913
-// and https://github.com/websockets/ws/issues/1202
-//
-// Intentionally global; it's the global thread pool that's an issue.
-//
-let zlibLimiter;
-
-/**
- * permessage-deflate implementation.
- */
-class PerMessageDeflate {
-  /**
-   * Creates a PerMessageDeflate instance.
-   *
-   * @param {Object} [options] Configuration options
-   * @param {(Boolean|Number)} [options.clientMaxWindowBits] Advertise support
-   *     for, or request, a custom client window size
-   * @param {Boolean} [options.clientNoContextTakeover=false] Advertise/
-   *     acknowledge disabling of client context takeover
-   * @param {Number} [options.concurrencyLimit=10] The number of concurrent
-   *     calls to zlib
-   * @param {(Boolean|Number)} [options.serverMaxWindowBits] Request/confirm the
-   *     use of a custom server window size
-   * @param {Boolean} [options.serverNoContextTakeover=false] Request/accept
-   *     disabling of server context takeover
-   * @param {Number} [options.threshold=1024] Size (in bytes) below which
-   *     messages should not be compressed if context takeover is disabled
-   * @param {Object} [options.zlibDeflateOptions] Options to pass to zlib on
-   *     deflate
-   * @param {Object} [options.zlibInflateOptions] Options to pass to zlib on
-   *     inflate
-   * @param {Boolean} [isServer=false] Create the instance in either server or
-   *     client mode
-   * @param {Number} [maxPayload=0] The maximum allowed message length
-   */
-  constructor(options, isServer, maxPayload) {
-    this._maxPayload = maxPayload | 0;
-    this._options = options || {};
-    this._threshold =
-      this._options.threshold !== undefined ? this._options.threshold : 1024;
-    this._isServer = !!isServer;
-    this._deflate = null;
-    this._inflate = null;
-
-    this.params = null;
-
-    if (!zlibLimiter) {
-      const concurrency =
-        this._options.concurrencyLimit !== undefined
-          ? this._options.concurrencyLimit
-          : 10;
-      zlibLimiter = new Limiter(concurrency);
-    }
-  }
-
-  /**
-   * @type {String}
-   */
-  static get extensionName() {
-    return 'permessage-deflate';
-  }
-
-  /**
-   * Create an extension negotiation offer.
-   *
-   * @return {Object} Extension parameters
-   * @public
-   */
-  offer() {
-    const params = {};
-
-    if (this._options.serverNoContextTakeover) {
-      params.server_no_context_takeover = true;
-    }
-    if (this._options.clientNoContextTakeover) {
-      params.client_no_context_takeover = true;
-    }
-    if (this._options.serverMaxWindowBits) {
-      params.server_max_window_bits = this._options.serverMaxWindowBits;
-    }
-    if (this._options.clientMaxWindowBits) {
-      params.client_max_window_bits = this._options.clientMaxWindowBits;
-    } else if (this._options.clientMaxWindowBits == null) {
-      params.client_max_window_bits = true;
-    }
-
-    return params;
-  }
-
-  /**
-   * Accept an extension negotiation offer/response.
-   *
-   * @param {Array} configurations The extension negotiation offers/reponse
-   * @return {Object} Accepted configuration
-   * @public
-   */
-  accept(configurations) {
-    configurations = this.normalizeParams(configurations);
-
-    this.params = this._isServer
-      ? this.acceptAsServer(configurations)
-      : this.acceptAsClient(configurations);
-
-    return this.params;
-  }
-
-  /**
-   * Releases all resources used by the extension.
-   *
-   * @public
-   */
-  cleanup() {
-    if (this._inflate) {
-      this._inflate.close();
-      this._inflate = null;
-    }
-
-    if (this._deflate) {
-      const callback = this._deflate[kCallback];
-
-      this._deflate.close();
-      this._deflate = null;
-
-      if (callback) {
-        callback(
-          new Error(
-            'The deflate stream was closed while data was being processed'
-          )
-        );
-      }
-    }
-  }
-
-  /**
-   *  Accept an extension negotiation offer.
-   *
-   * @param {Array} offers The extension negotiation offers
-   * @return {Object} Accepted configuration
-   * @private
-   */
-  acceptAsServer(offers) {
-    const opts = this._options;
-    const accepted = offers.find((params) => {
-      if (
-        (opts.serverNoContextTakeover === false &&
-          params.server_no_context_takeover) ||
-        (params.server_max_window_bits &&
-          (opts.serverMaxWindowBits === false ||
-            (typeof opts.serverMaxWindowBits === 'number' &&
-              opts.serverMaxWindowBits > params.server_max_window_bits))) ||
-        (typeof opts.clientMaxWindowBits === 'number' &&
-          !params.client_max_window_bits)
-      ) {
-        return false;
-      }
-
-      return true;
-    });
-
-    if (!accepted) {
-      throw new Error('None of the extension offers can be accepted');
-    }
-
-    if (opts.serverNoContextTakeover) {
-      accepted.server_no_context_takeover = true;
-    }
-    if (opts.clientNoContextTakeover) {
-      accepted.client_no_context_takeover = true;
-    }
-    if (typeof opts.serverMaxWindowBits === 'number') {
-      accepted.server_max_window_bits = opts.serverMaxWindowBits;
-    }
-    if (typeof opts.clientMaxWindowBits === 'number') {
-      accepted.client_max_window_bits = opts.clientMaxWindowBits;
-    } else if (
-      accepted.client_max_window_bits === true ||
-      opts.clientMaxWindowBits === false
-    ) {
-      delete accepted.client_max_window_bits;
-    }
-
-    return accepted;
-  }
-
-  /**
-   * Accept the extension negotiation response.
-   *
-   * @param {Array} response The extension negotiation response
-   * @return {Object} Accepted configuration
-   * @private
-   */
-  acceptAsClient(response) {
-    const params = response[0];
-
-    if (
-      this._options.clientNoContextTakeover === false &&
-      params.client_no_context_takeover
-    ) {
-      throw new Error('Unexpected parameter "client_no_context_takeover"');
-    }
-
-    if (!params.client_max_window_bits) {
-      if (typeof this._options.clientMaxWindowBits === 'number') {
-        params.client_max_window_bits = this._options.clientMaxWindowBits;
-      }
-    } else if (
-      this._options.clientMaxWindowBits === false ||
-      (typeof this._options.clientMaxWindowBits === 'number' &&
-        params.client_max_window_bits > this._options.clientMaxWindowBits)
-    ) {
-      throw new Error(
-        'Unexpected or invalid parameter "client_max_window_bits"'
-      );
-    }
-
-    return params;
-  }
-
-  /**
-   * Normalize parameters.
-   *
-   * @param {Array} configurations The extension negotiation offers/reponse
-   * @return {Array} The offers/response with normalized parameters
-   * @private
-   */
-  normalizeParams(configurations) {
-    configurations.forEach((params) => {
-      Object.keys(params).forEach((key) => {
-        let value = params[key];
-
-        if (value.length > 1) {
-          throw new Error(`Parameter "${key}" must have only a single value`);
-        }
-
-        value = value[0];
-
-        if (key === 'client_max_window_bits') {
-          if (value !== true) {
-            const num = +value;
-            if (!Number.isInteger(num) || num < 8 || num > 15) {
-              throw new TypeError(
-                `Invalid value for parameter "${key}": ${value}`
-              );
-            }
-            value = num;
-          } else if (!this._isServer) {
-            throw new TypeError(
-              `Invalid value for parameter "${key}": ${value}`
-            );
-          }
-        } else if (key === 'server_max_window_bits') {
-          const num = +value;
-          if (!Number.isInteger(num) || num < 8 || num > 15) {
-            throw new TypeError(
-              `Invalid value for parameter "${key}": ${value}`
-            );
-          }
-          value = num;
-        } else if (
-          key === 'client_no_context_takeover' ||
-          key === 'server_no_context_takeover'
-        ) {
-          if (value !== true) {
-            throw new TypeError(
-              `Invalid value for parameter "${key}": ${value}`
-            );
-          }
-        } else {
-          throw new Error(`Unknown parameter "${key}"`);
-        }
-
-        params[key] = value;
-      });
-    });
-
-    return configurations;
-  }
-
-  /**
-   * Decompress data. Concurrency limited.
-   *
-   * @param {Buffer} data Compressed data
-   * @param {Boolean} fin Specifies whether or not this is the last fragment
-   * @param {Function} callback Callback
-   * @public
-   */
-  decompress(data, fin, callback) {
-    zlibLimiter.add((done) => {
-      this._decompress(data, fin, (err, result) => {
-        done();
-        callback(err, result);
-      });
-    });
-  }
-
-  /**
-   * Compress data. Concurrency limited.
-   *
-   * @param {(Buffer|String)} data Data to compress
-   * @param {Boolean} fin Specifies whether or not this is the last fragment
-   * @param {Function} callback Callback
-   * @public
-   */
-  compress(data, fin, callback) {
-    zlibLimiter.add((done) => {
-      this._compress(data, fin, (err, result) => {
-        done();
-        callback(err, result);
-      });
-    });
-  }
-
-  /**
-   * Decompress data.
-   *
-   * @param {Buffer} data Compressed data
-   * @param {Boolean} fin Specifies whether or not this is the last fragment
-   * @param {Function} callback Callback
-   * @private
-   */
-  _decompress(data, fin, callback) {
-    const endpoint = this._isServer ? 'client' : 'server';
-
-    if (!this._inflate) {
-      const key = `${endpoint}_max_window_bits`;
-      const windowBits =
-        typeof this.params[key] !== 'number'
-          ? zlib.Z_DEFAULT_WINDOWBITS
-          : this.params[key];
-
-      this._inflate = zlib.createInflateRaw({
-        ...this._options.zlibInflateOptions,
-        windowBits
-      });
-      this._inflate[kPerMessageDeflate] = this;
-      this._inflate[kTotalLength] = 0;
-      this._inflate[kBuffers] = [];
-      this._inflate.on('error', inflateOnError);
-      this._inflate.on('data', inflateOnData);
-    }
-
-    this._inflate[kCallback] = callback;
-
-    this._inflate.write(data);
-    if (fin) this._inflate.write(TRAILER);
-
-    this._inflate.flush(() => {
-      const err = this._inflate[kError];
-
-      if (err) {
-        this._inflate.close();
-        this._inflate = null;
-        callback(err);
-        return;
-      }
-
-      const data = bufferUtil.concat(
-        this._inflate[kBuffers],
-        this._inflate[kTotalLength]
-      );
-
-      if (this._inflate._readableState.endEmitted) {
-        this._inflate.close();
-        this._inflate = null;
-      } else {
-        this._inflate[kTotalLength] = 0;
-        this._inflate[kBuffers] = [];
-
-        if (fin && this.params[`${endpoint}_no_context_takeover`]) {
-          this._inflate.reset();
-        }
-      }
-
-      callback(null, data);
-    });
-  }
-
-  /**
-   * Compress data.
-   *
-   * @param {(Buffer|String)} data Data to compress
-   * @param {Boolean} fin Specifies whether or not this is the last fragment
-   * @param {Function} callback Callback
-   * @private
-   */
-  _compress(data, fin, callback) {
-    const endpoint = this._isServer ? 'server' : 'client';
-
-    if (!this._deflate) {
-      const key = `${endpoint}_max_window_bits`;
-      const windowBits =
-        typeof this.params[key] !== 'number'
-          ? zlib.Z_DEFAULT_WINDOWBITS
-          : this.params[key];
-
-      this._deflate = zlib.createDeflateRaw({
-        ...this._options.zlibDeflateOptions,
-        windowBits
-      });
-
-      this._deflate[kTotalLength] = 0;
-      this._deflate[kBuffers] = [];
-
-      this._deflate.on('data', deflateOnData);
-    }
-
-    this._deflate[kCallback] = callback;
-
-    this._deflate.write(data);
-    this._deflate.flush(zlib.Z_SYNC_FLUSH, () => {
-      if (!this._deflate) {
-        //
-        // The deflate stream was closed while data was being processed.
-        //
-        return;
-      }
-
-      let data = bufferUtil.concat(
-        this._deflate[kBuffers],
-        this._deflate[kTotalLength]
-      );
-
-      if (fin) data = data.slice(0, data.length - 4);
-
-      //
-      // Ensure that the callback will not be called again in
-      // `PerMessageDeflate#cleanup()`.
-      //
-      this._deflate[kCallback] = null;
-
-      this._deflate[kTotalLength] = 0;
-      this._deflate[kBuffers] = [];
-
-      if (fin && this.params[`${endpoint}_no_context_takeover`]) {
-        this._deflate.reset();
-      }
-
-      callback(null, data);
-    });
-  }
-}
-
-module.exports = PerMessageDeflate;
-
-/**
- * The listener of the `zlib.DeflateRaw` stream `'data'` event.
- *
- * @param {Buffer} chunk A chunk of data
- * @private
- */
-function deflateOnData(chunk) {
-  this[kBuffers].push(chunk);
-  this[kTotalLength] += chunk.length;
-}
-
-/**
- * The listener of the `zlib.InflateRaw` stream `'data'` event.
- *
- * @param {Buffer} chunk A chunk of data
- * @private
- */
-function inflateOnData(chunk) {
-  this[kTotalLength] += chunk.length;
-
-  if (
-    this[kPerMessageDeflate]._maxPayload < 1 ||
-    this[kTotalLength] <= this[kPerMessageDeflate]._maxPayload
-  ) {
-    this[kBuffers].push(chunk);
-    return;
-  }
-
-  this[kError] = new RangeError('Max payload size exceeded');
-  this[kError].code = 'WS_ERR_UNSUPPORTED_MESSAGE_LENGTH';
-  this[kError][kStatusCode] = 1009;
-  this.removeListener('data', inflateOnData);
-  this.reset();
-}
-
-/**
- * The listener of the `zlib.InflateRaw` stream `'error'` event.
- *
- * @param {Error} err The emitted error
- * @private
- */
-function inflateOnError(err) {
-  //
-  // There is no need to call `Zlib#close()` as the handle is automatically
-  // closed when an error is emitted.
-  //
-  this[kPerMessageDeflate]._inflate = null;
-  err[kStatusCode] = 1007;
-  this[kCallback](err);
-}
diff --git a/spaces/fffiloni/zeroscope-img-to-video/README.md b/spaces/fffiloni/zeroscope-img-to-video/README.md
deleted file mode 100644
index 345753aee2bc899f2acd0250394681052e839a65..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/zeroscope-img-to-video/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Zeroscope Image-To-Video
-emoji: 🐠
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-duplicated_from: fffiloni/zeroscope
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/flax-community/roberta-hindi/mlm_custom/test_mlm.py b/spaces/flax-community/roberta-hindi/mlm_custom/test_mlm.py
deleted file mode 100644
index f0ecd523f184b354111534d8228fda947be21f6c..0000000000000000000000000000000000000000
--- a/spaces/flax-community/roberta-hindi/mlm_custom/test_mlm.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import json
-import os
-
-import numpy as np
-import pandas as pd
-from transformers import (AutoModel, AutoModelForMaskedLM, AutoTokenizer,
-                          RobertaModel, pipeline)
-
-
-class MLMTest():
-
-    def __init__(self, config_file="mlm_test_config.csv", full_text_file="mlm_full_text.csv", targeted_text_file="mlm_targeted_text.csv"):
-
-        self.config_df = pd.read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), config_file))
-        self.config_df.fillna("", inplace=True)
-        self.full_text_df = pd.read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), full_text_file))
-        self.targeted_text_df = pd.read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), targeted_text_file))
-        self.full_text_results = []
-        self.targeted_text_results = []
-        
-    def _run_full_test_row(self, text, print_debug=False):
-        return_data = []
-        data = text.split()
-        for i in range(0, len(data)):
-            masked_text = " ".join(data[:i]) + " "+self.nlp.tokenizer.mask_token+" " + " ".join(data[i+1:])
-            expected_result = data[i]
-            result = self.nlp(masked_text)
-            self.full_text_results.append({"text": masked_text, "result": result[0]["token_str"], "true_output": expected_result})
-            if print_debug:
-                print(masked_text)
-                print([x["token_str"] for x in result])
-                print("-"*20)
-            return_data.append({"prediction": result[0]["token_str"], "true_output": expected_result})
-        return return_data
-
-    def _run_targeted_test_row(self, text, expected_result, print_debug=False):
-        return_data = []
-        result = self.nlp(text.replace("<mask>", self.nlp.tokenizer.mask_token))
-        self.targeted_text_results.append({"text": text, "result": result[0]["token_str"], "true_output": expected_result})
-        if print_debug:
-            print(text)
-            print([x["token_str"] for x in result])
-            print("-"*20)
-        return_data.append({"prediction": result[0]["token_str"], "true_output": expected_result})
-        return return_data
-
-    def _compute_acc(self, results):
-        ctr = 0
-        for row in results:
-            try:
-                z = json.loads(row["true_output"])
-                if isinstance(z, list):
-                    if row["prediction"] in z:
-                        ctr+=1
-            except:
-                if row["prediction"] == row["true_output"]:
-                    ctr+=1
-
-        return float(ctr/len(results))
-
-    def run_full_test(self, exclude_user_ids=[], print_debug=False):
-        df = pd.DataFrame()
-        for idx, row in self.config_df.iterrows():
-            self.full_text_results = []
-            
-            model_name = row["model_name"]
-            display_name = row["display_name"] if row["display_name"] else row["model_name"]
-            revision = row["revision"] if row["revision"] else "main"
-            from_flax = row["from_flax"]
-            if from_flax:
-                model = AutoModelForMaskedLM.from_pretrained(model_name, from_flax=True, revision=revision)
-                tokenizer = AutoTokenizer.from_pretrained(model_name)
-                tokenizer.save_pretrained('exported_pytorch_model')
-                model.save_pretrained('exported_pytorch_model')
-                self.nlp = pipeline('fill-mask', model="exported_pytorch_model")
-            else:
-                self.nlp = pipeline('fill-mask', model=model_name)
-            accs = []
-            try:
-                for idx, row in self.full_text_df.iterrows():
-                    if row["user_id"] in exclude_user_ids:
-                        continue
-                    results = self._run_full_test_row(row["text"], print_debug=print_debug)
-
-                    acc = self._compute_acc(results)
-                    accs.append(acc)
-            except:
-                print("Error for", display_name)
-                continue
-
-            print(display_name, " Average acc:", sum(accs)/len(accs))
-            if df.empty:
-                df = pd.DataFrame(self.full_text_results)
-                df.rename(columns={"result": display_name}, inplace=True)
-            else:
-                preds = [x["result"] for x in self.full_text_results]
-                df[display_name] = preds
-        df.to_csv("full_text_results.csv", index=False)
-        print("Results saved to full_text_results.csv")
-
-    def run_targeted_test(self, exclude_user_ids=[], print_debug=False):
-
-        df = pd.DataFrame()
-        for idx, row in self.config_df.iterrows():
-            self.targeted_text_results = []
-            
-            model_name = row["model_name"]
-            display_name = row["display_name"] if row["display_name"] else row["model_name"]
-            revision = row["revision"] if row["revision"] else "main"
-            from_flax = row["from_flax"]
-            if from_flax:
-                model = AutoModelForMaskedLM.from_pretrained(model_name, from_flax=True, revision=revision)
-                tokenizer = AutoTokenizer.from_pretrained(model_name)
-                tokenizer.save_pretrained('exported_pytorch_model')
-                model.save_pretrained('exported_pytorch_model')
-                self.nlp = pipeline('fill-mask', model="exported_pytorch_model")
-            else:
-                self.nlp = pipeline('fill-mask', model=model_name)
-            accs = []
-            try:
-                for idx, row2 in self.targeted_text_df.iterrows():
-                    if row2["user_id"] in exclude_user_ids:
-                        continue
-                    results = self._run_targeted_test_row(row2["text"], row2["output"], print_debug=print_debug)
-
-                    acc = self._compute_acc(results)
-                    accs.append(acc)
-            except:
-                import traceback
-                print(traceback.format_exc())
-                print("Error for", display_name)
-                continue
-
-            print(display_name, " Average acc:", sum(accs)/len(accs))
-            if df.empty:
-                df = pd.DataFrame(self.targeted_text_results)
-                df.rename(columns={"result": display_name}, inplace=True)
-            else:
-                preds = [x["result"] for x in self.targeted_text_results]
-                df[display_name] = preds
-        df.to_csv("targeted_text_results.csv", index=False)
-        print("Results saved to targeted_text_results.csv")
- 
diff --git a/spaces/florim/MedGPT/autogpt/config/__init__.py b/spaces/florim/MedGPT/autogpt/config/__init__.py
deleted file mode 100644
index 726b6dcf3da95968b948c4d897e97a9cdd0928ff..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/autogpt/config/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""
-This module contains the configuration classes for AutoGPT.
-"""
-from autogpt.config.ai_config import AIConfig
-from autogpt.config.config import Config, check_openai_api_key
-from autogpt.config.singleton import AbstractSingleton, Singleton
-
-__all__ = [
-    "check_openai_api_key",
-    "AbstractSingleton",
-    "AIConfig",
-    "Config",
-    "Singleton",
-]
diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/backup_envs/talkitoutpolite.py b/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/backup_envs/talkitoutpolite.py
deleted file mode 100644
index 25b681a0df38d614b3373bcae41192f82072f556..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/backup_envs/talkitoutpolite.py
+++ /dev/null
@@ -1,464 +0,0 @@
-from gym_minigrid.minigrid import *
-from gym_minigrid.register import register
- 
-
-class Wizard(NPC):
-    """
-    A simple NPC that knows who is telling the truth
-    """
-
-    def __init__(self, color, name, env):
-        super().__init__(color)
-        self.name = name
-        self.env = env
-        self.npc_dir = 1  # NPC initially looks downward
-        # todo: this should be id == name
-        self.npc_type = 0  # this will be put into the encoding
-        self.was_introduced_to = False
-
-    def can_overlap(self):
-        # If the NPC is hidden, agent can overlap on it
-        return self.env.hidden_npc
-
-    def encode(self, nb_dims=3):
-        if self.env.hidden_npc:
-            if nb_dims == 3:
-                return (1, 0, 0)
-            elif nb_dims == 4:
-                return (1, 0, 0, 0)
-        else:
-            return super().encode(nb_dims=nb_dims)
-
-    def listen(self, utterance):
-        if self.env.hidden_npc:
-            return None
-
-        if self.was_introduced_to:
-            if utterance == TalkItOutPoliteGrammar.construct_utterance([0, 1]):
-                if self.env.nameless:
-                    return "Ask the {} guide.".format(self.env.true_guide.color)
-                else:
-                    return "Ask {}.".format(self.env.true_guide.name)
-        else:
-            if utterance == TalkItOutPoliteGrammar.construct_utterance([3, 3]):
-                self.was_introduced_to = True
-                return "I am well."
-
-        return None
-
-
-
-class Guide(NPC):
-    """
-    A simple NPC that knows the correct door.
-    """
-
-    def __init__(self, color, name, env, liar=False):
-        super().__init__(color)
-        self.name = name
-        self.env = env
-        self.liar = liar
-        self.npc_dir = 1  # NPC initially looks downward
-        # todo: this should be id == name
-        self.npc_type = 1  # this will be put into the encoding
-        self.was_introduced_to = False
-
-        # Select a random target object as mission
-        obj_idx = self.env._rand_int(0, len(self.env.door_pos))
-        self.target_pos = self.env.door_pos[obj_idx]
-        self.target_color = self.env.door_colors[obj_idx]
-
-    def can_overlap(self):
-        # If the NPC is hidden, agent can overlap on it
-        return self.env.hidden_npc
-
-    def encode(self, nb_dims=3):
-        if self.env.hidden_npc:
-            if nb_dims == 3:
-                return (1, 0, 0)
-            elif nb_dims == 4:
-                return (1, 0, 0, 0)
-        else:
-            return super().encode(nb_dims=nb_dims)
-
-    def listen(self, utterance):
-        if self.env.hidden_npc:
-            return None
-
-        if self.was_introduced_to:
-            if utterance == TalkItOutPoliteGrammar.construct_utterance([0, 1]):
-                if self.liar:
-                    fake_colors = [c for c in self.env.door_colors if c != self.env.target_color]
-                    fake_color = self.env._rand_elem(fake_colors)
-
-                    # Generate the mission string
-                    assert fake_color != self.env.target_color
-                    return 'go to the %s door' % fake_color
-
-                else:
-                    return self.env.mission
-        else:
-            if utterance == TalkItOutPoliteGrammar.construct_utterance([3, 3]):
-                self.was_introduced_to = True
-                return "I am well."
-
-
-    def render(self, img):
-        c = COLORS[self.color]
-
-        npc_shapes = []
-        # Draw eyes
-        npc_shapes.append(point_in_circle(cx=0.70, cy=0.50, r=0.10))
-        npc_shapes.append(point_in_circle(cx=0.30, cy=0.50, r=0.10))
-
-        # Draw mouth
-        npc_shapes.append(point_in_rect(0.20, 0.80, 0.72, 0.81))
-
-        # todo: move this to super function
-        # todo: super.render should be able to take the npc_shapes and then rotate them
-
-        if hasattr(self, "npc_dir"):
-            # Pre-rotation to ensure npc_dir = 1 means NPC looks downwards
-            npc_shapes = [rotate_fn(v, cx=0.5, cy=0.5, theta=-1*(math.pi / 2)) for v in npc_shapes]
-            # Rotate npc based on its direction
-            npc_shapes = [rotate_fn(v, cx=0.5, cy=0.5, theta=(math.pi/2) * self.npc_dir) for v in npc_shapes]
-
-        # Draw shapes
-        for v in npc_shapes:
-            fill_coords(img, v, c)
-
-
-class TalkItOutPoliteGrammar(object):
-
-    templates = ["Where is", "Open", "Close", "How are"]
-    things = [
-        "sesame", "the exit", "the wall", "you", "the ceiling", "the window", "the entrance", "the closet",
-        "the drawer", "the fridge", "the floor", "the lamp", "the trash can", "the chair", "the bed", "the sofa"
-    ]
-    assert len(templates)*len(things) == 64
-    print("language complexity {}:".format(len(templates)*len(things)))
-
-    grammar_action_space = spaces.MultiDiscrete([len(templates), len(things)])
-
-    @classmethod
-    def construct_utterance(cls, action):
-        return cls.templates[int(action[0])] + " " + cls.things[int(action[1])] + " "
-
-
-class TalkItOutPoliteEnv(MultiModalMiniGridEnv):
-    """
-    Environment in which the agent is instructed to go to a given object
-    named using an English text string
-    """
-
-    def __init__(
-        self,
-        size=5,
-        hear_yourself=False,
-        diminished_reward=True,
-        step_penalty=False,
-        nameless=False,
-        max_steps=100,
-        hidden_npc=False,
-
-    ):
-        assert size >= 5
-        self.empty_symbol = "NA \n"
-        self.hear_yourself = hear_yourself
-        self.diminished_reward = diminished_reward
-        self.step_penalty = step_penalty
-        self.nameless = nameless
-        self.hidden_npc = hidden_npc
-
-        if max_steps is None:
-            max_steps = 5*size**2
-
-        super().__init__(
-            grid_size=size,
-            max_steps=max_steps,
-            # Set this to True for maximum speed
-            see_through_walls=True,
-            actions=MiniGridEnv.Actions,
-            action_space=spaces.MultiDiscrete([
-                len(MiniGridEnv.Actions),
-                *TalkItOutPoliteGrammar.grammar_action_space.nvec
-            ]),
-            add_npc_direction=True
-        )
-
-        print({
-            "size": size,
-            "hear_yourself": hear_yourself,
-            "diminished_reward": diminished_reward,
-            "step_penalty": step_penalty,
-        })
-
-    def _gen_grid(self, width, height):
-        # Create the grid
-        self.grid = Grid(width, height, nb_obj_dims=4)
-
-        # Randomly vary the room width and height
-        width = self._rand_int(5, width+1)
-        height = self._rand_int(5, height+1)
-
-        # Generate the surrounding walls
-        self.grid.wall_rect(0, 0, width, height)
-
-        # Generate the surrounding walls
-        self.grid.wall_rect(0, 0, width, height)
-
-        # Generate the 4 doors at random positions
-        self.door_pos = []
-        self.door_front_pos = []  # Remembers positions in front of door to avoid setting wizard here
-
-        self.door_pos.append((self._rand_int(2, width-2), 0))
-        self.door_front_pos.append((self.door_pos[-1][0], self.door_pos[-1][1]+1))
-
-        self.door_pos.append((self._rand_int(2, width-2), height-1))
-        self.door_front_pos.append((self.door_pos[-1][0], self.door_pos[-1][1] - 1))
-
-        self.door_pos.append((0, self._rand_int(2, height-2)))
-        self.door_front_pos.append((self.door_pos[-1][0] + 1, self.door_pos[-1][1]))
-
-        self.door_pos.append((width-1, self._rand_int(2, height-2)))
-        self.door_front_pos.append((self.door_pos[-1][0] - 1, self.door_pos[-1][1]))
-
-        # Generate the door colors
-        self.door_colors = []
-        while len(self.door_colors) < len(self.door_pos):
-            color = self._rand_elem(COLOR_NAMES)
-            if color in self.door_colors:
-                continue
-            self.door_colors.append(color)
-
-        # Place the doors in the grid
-        for idx, pos in enumerate(self.door_pos):
-            color = self.door_colors[idx]
-            self.grid.set(*pos, Door(color))
-
-
-        # Set a randomly coloured WIZARD at a random position
-        color = self._rand_elem(COLOR_NAMES)
-        self.wizard = Wizard(color, "Gandalf", self)
-
-        # Place it randomly, omitting front of door positions
-        self.place_obj(self.wizard,
-                       size=(width, height),
-                       reject_fn=lambda _, p: tuple(p) in self.door_front_pos)
-
-        # add guides
-        GUIDE_NAMES = ["John", "Jack"]
-
-        # Set a randomly coloured TRUE GUIDE at a random position
-        name = self._rand_elem(GUIDE_NAMES)
-        color = self._rand_elem(COLOR_NAMES)
-        self.true_guide = Guide(color, name, self, liar=False)
-
-        # Place it randomly, omitting invalid positions
-        self.place_obj(self.true_guide,
-                       size=(width, height),
-                       # reject_fn=lambda _, p: tuple(p) in self.door_front_pos)
-                       reject_fn=lambda _, p: tuple(p) in [*self.door_front_pos, tuple(self.wizard.cur_pos)])
-
-        # Set a randomly coloured FALSE GUIDE at a random position
-        name = self._rand_elem([n for n in GUIDE_NAMES if n != self.true_guide.name])
-
-        color = self._rand_elem([c for c in COLOR_NAMES if c != self.true_guide.color])
-
-        self.false_guide = Guide(color, name, self, liar=True)
-
-        # Place it randomly, omitting invalid positions
-        self.place_obj(self.false_guide,
-                       size=(width, height),
-                       reject_fn=lambda _, p: tuple(p) in [
-                           *self.door_front_pos, tuple(self.wizard.cur_pos), tuple(self.true_guide.cur_pos)])
-
-        assert self.true_guide.name != self.false_guide.name
-        assert self.true_guide.color != self.false_guide.color
-
-        # Randomize the agent's start position and orientation
-        self.place_agent(size=(width, height))
-
-        # Select a random target door
-        self.doorIdx = self._rand_int(0, len(self.door_pos))
-        self.target_pos = self.door_pos[self.doorIdx]
-        self.target_color = self.door_colors[self.doorIdx]
-
-        # Generate the mission string
-        self.mission = 'go to the %s door' % self.target_color
-
-        # Dummy beginning string
-        self.beginning_string = "This is what you hear. \n"
-        self.utterance = self.beginning_string
-
-        # utterance appended at the end of each step
-        self.utterance_history = ""
-
-        # used for rendering
-        self.conversation = self.utterance
-        self.outcome_info = None
-
-    def step(self, action):
-        p_action = action[0]
-        utterance_action = action[1:]
-
-        # assert all nan or neither nan
-        assert len(set(np.isnan(utterance_action))) == 1
-
-        speak_flag = not all(np.isnan(utterance_action))
-
-        obs, reward, done, info = super().step(p_action)
-
-        if speak_flag:
-            utterance = TalkItOutPoliteGrammar.construct_utterance(utterance_action)
-            if self.hear_yourself:
-                if self.nameless:
-                    self.utterance += "{} \n".format(utterance)
-                else:
-                    self.utterance += "YOU: {} \n".format(utterance)
-                
-            self.conversation += "YOU: {} \n".format(utterance)
-
-            # check if near wizard
-            if self.wizard.is_near_agent():
-                reply = self.wizard.listen(utterance)
-
-                if reply:
-                    if self.nameless:
-                        self.utterance += "{} \n".format(reply)
-                    else:
-                        self.utterance += "{}: {} \n".format(self.wizard.name, reply)
-
-                    self.conversation += "{}: {} \n".format(self.wizard.name, reply)
-
-            if self.true_guide.is_near_agent():
-                reply = self.true_guide.listen(utterance)
-
-                if reply:
-                    if self.nameless:
-                        self.utterance += "{} \n".format(reply)
-                    else:
-                        self.utterance += "{}: {} \n".format(self.true_guide.name, reply)
-
-                    self.conversation += "{}: {} \n".format(self.true_guide.name, reply)
-
-            if self.false_guide.is_near_agent():
-                reply = self.false_guide.listen(utterance)
-
-                if reply:
-                    if self.nameless:
-                        self.utterance += "{} \n".format(reply)
-                    else:
-                        self.utterance += "{}: {} \n".format(self.false_guide.name, reply)
-
-                    self.conversation += "{}: {} \n".format(self.false_guide.name, reply)
-
-            if utterance == TalkItOutPoliteGrammar.construct_utterance([1, 0]):
-                ax, ay = self.agent_pos
-                tx, ty = self.target_pos
-
-                if (ax == tx and abs(ay - ty) == 1) or (ay == ty and abs(ax - tx) == 1):
-                    reward = self._reward()
-
-                for dx, dy in self.door_pos:
-                    if (ax == dx and abs(ay - dy) == 1) or (ay == dy and abs(ax - dx) == 1):
-                        # agent has chosen some door episode, regardless of if the door is correct the episode is over
-                        done = True
-
-        # Don't let the agent open any of the doors
-        if p_action == self.actions.toggle:
-            done = True
-
-        if p_action == self.actions.done:
-            done = True
-
-        # discount
-        if self.step_penalty:
-            reward = reward - 0.01
-
-        if self.hidden_npc:
-            # all npc are hidden
-            assert np.argwhere(obs['image'][:,:,0] == OBJECT_TO_IDX['npc']).size == 0
-            assert "{}:".format(self.wizard.name) not in self.utterance
-            # assert "{}:".format(self.true_guide.name) not in self.utterance
-            # assert "{}:".format(self.false_guide.name) not in self.utterance
-
-        # fill observation with text
-        self.append_existing_utterance_to_history()
-        obs = self.add_utterance_to_observation(obs)
-        self.reset_utterance()
-
-        if done:
-            if reward > 0:
-                self.outcome_info = "SUCCESS: agent got {} reward \n".format(np.round(reward, 1))
-            else:
-                self.outcome_info = "FAILURE: agent got {} reward \n".format(reward)
-
-        return obs, reward, done, info
-
-    def _reward(self):
-        if self.diminished_reward:
-            return super()._reward()
-        else:
-            return 1.0
-
-    def render(self, *args, **kwargs):
-        obs = super().render(*args, **kwargs)
-
-        self.window.clear_text()  # erase previous text
-
-        self.window.set_caption(self.conversation, [
-            "Gandalf:",
-            "Jack:",
-            "John:",
-            "Where is the exit",
-            "Open sesame",
-        ])
-
-        self.window.ax.set_title("correct door: {}".format(self.true_guide.target_color), loc="left", fontsize=10)
-        if self.outcome_info:
-            color = None
-            if "SUCCESS" in self.outcome_info:
-                color = "lime"
-            elif "FAILURE" in self.outcome_info:
-                color = "red"
-            self.window.add_text(*(0.01, 0.85, self.outcome_info),
-                                 **{'fontsize':15, 'color':color, 'weight':"bold"})
-
-        self.window.show_img(obs)  # re-draw image to add changes to window
-        return obs
-
-
-class TalkItOutPolite8x8Env(TalkItOutPoliteEnv):
-    def __init__(self, **kwargs):
-        super().__init__(size=8, max_steps=100, **kwargs)
-
-
-class TalkItOutPolite6x6Env(TalkItOutPoliteEnv):
-    def __init__(self):
-        super().__init__(size=6, max_steps=100)
-
-
-class TalkItOutPoliteNameless8x8Env(TalkItOutPoliteEnv):
-    def __init__(self):
-        super().__init__(size=8, max_steps=100, nameless=True)
-
-register(
-    id='MiniGrid-TalkItOutPolite-5x5-v0',
-    entry_point='gym_minigrid.envs:TalkItOutPoliteEnv'
-)
-
-register(
-    id='MiniGrid-TalkItOutPolite-6x6-v0',
-    entry_point='gym_minigrid.envs:TalkItOutPolite6x6Env'
-)
-
-register(
-    id='MiniGrid-TalkItOutPolite-8x8-v0',
-    entry_point='gym_minigrid.envs:TalkItOutPolite8x8Env'
-)
-
-register(
-    id='MiniGrid-TalkItOutPoliteNameless-8x8-v0',
-    entry_point='gym_minigrid.envs:TalkItOutPoliteNameless8x8Env'
-)
\ No newline at end of file
diff --git a/spaces/flowers-team/SocialAISchool/utils/tester.py b/spaces/flowers-team/SocialAISchool/utils/tester.py
deleted file mode 100644
index a3b74742f32f1b20893747c9f69af20c43b0fba0..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/utils/tester.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import numpy as np
-import utils
-import os
-import pickle
-import torch
-
-class AgentWrap:
-    """ Handles action selection without gradient updates for proper testing """
-
-    def __init__(self, acmodel, preprocess_obss, device, num_envs=1, argmax=False):
-
-        self.preprocess_obss = preprocess_obss
-        self.acmodel = acmodel
-
-        self.device = device
-        self.argmax = argmax
-        self.num_envs = num_envs
-
-        if self.acmodel.recurrent:
-            self.memories = torch.zeros(self.num_envs, self.acmodel.memory_size, device=self.device)
-
-    def get_actions(self, obss):
-        preprocessed_obss = self.preprocess_obss(obss, device=self.device)
-
-        with torch.no_grad():
-            if self.acmodel.recurrent:
-                dist, _, self.memories = self.acmodel(preprocessed_obss, self.memories)
-            else:
-                dist, _ = self.acmodel(preprocessed_obss)
-
-        if isinstance(dist, torch.distributions.Distribution):
-            if self.argmax:
-                actions = dist.probs.max(1, keepdim=True)[1]
-            else:
-                actions = dist.sample()
-        else:
-            if self.argmax:
-                actions = torch.stack([d.probs.max(1)[1] for d in dist], dim=1)
-            else:
-                actions = torch.stack([d.sample() for d in dist], dim=1)
-        return self.acmodel.construct_final_action(actions.cpu().numpy())
-
-    def get_action(self, obs):
-        return self.get_actions([obs])[0]
-
-    def analyze_feedbacks(self, rewards, dones):
-        if self.acmodel.recurrent:
-            masks = 1 - torch.tensor(dones, dtype=torch.float, device=self.device).unsqueeze(1)
-            self.memories *= masks
-
-    def analyze_feedback(self, reward, done):
-        return self.analyze_feedbacks([reward], [done])
-
-
-class Tester:
-
-    def __init__(self, env_args, seed, episodes, save_path, acmodel, preprocess_obss, device):
-
-        self.envs = [utils.make_env(
-            **env_args
-        ) for _ in range(episodes)]
-        self.seed = seed
-        self.episodes = episodes
-        self.ep_counter = 0
-        self.savefile = save_path + "/testing_{}.pkl".format(self.envs[0].spec.id)
-        print("Testing log: ", self.savefile)
-
-        self.stats_dict = {"test_rewards": [], "test_success_rates": [], "test_step_nb": []}
-        self.agent = AgentWrap(acmodel, preprocess_obss, device)
-
-    def test_agent(self, num_frames):
-        self.agent.acmodel.eval()
-
-        # set seed
-        # self.env.seed(self.seed)
-        # save test time (nb training steps)
-        self.stats_dict['test_step_nb'].append(num_frames)
-
-        rewards = []
-        success_rates = []
-
-
-        # cols = []
-        # s = "-".join([e.current_env.marble.color for e in self.envs])
-        # print("s:", s)
-
-        for episode in range(self.episodes):
-            # self.envs[episode].seed(self.seed)
-            self.envs[episode].seed(episode)
-            # print("current_seed", np.random.get_state()[1][0])
-
-            obs = self.envs[episode].reset()
-
-            # cols.append(self.envs[episode].current_env.marble.color)
-            # cols.append(str(self.envs[episode].current_env.marble.cur_pos))
-
-            done = False
-            while not done:
-                action = self.agent.get_action(obs)
-
-                obs, reward, done, info = self.envs[episode].step(action)
-                self.agent.analyze_feedback(reward, done)
-
-                if done:
-                    rewards.append(reward)
-                    success_rates.append(info['success'])
-                    break
-
-        # from hashlib import md5
-        # hash_string = "-".join(cols).encode()
-
-        # print('hs:', hash_string[:20])
-        # print("hash test envs:", md5(hash_string).hexdigest())
-
-        mean_rewards = np.array(rewards).mean()
-        mean_success_rates = np.array(success_rates).mean()
-
-        self.stats_dict["test_rewards"].append(mean_rewards)
-        self.stats_dict["test_success_rates"].append(mean_success_rates)
-
-        self.agent.acmodel.train()
-        return mean_success_rates, mean_rewards
-
-    def load(self):
-        if os.path.isfile(self.savefile):
-            with open(self.savefile, 'rb') as f:
-                stats_dict_loaded = pickle.load(f)
-
-                for k, v in stats_dict_loaded.items():
-                    self.stats_dict[k] = v
-        else:
-            raise ValueError(f"Save file {self.savefile} doesn't exist.")
-
-    def dump(self):
-        with open(self.savefile, 'wb') as f:
-            pickle.dump(self.stats_dict, f)
-
diff --git a/spaces/gagan3012/summarization/src/models/hf_upload.py b/spaces/gagan3012/summarization/src/models/hf_upload.py
deleted file mode 100644
index f8ce0ad05c0df5df6c615bc444fddf2ad2f098f1..0000000000000000000000000000000000000000
--- a/spaces/gagan3012/summarization/src/models/hf_upload.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import shutil
-from getpass import getpass
-from os.path import join, dirname
-from pathlib import Path
-import yaml
-
-from model import Summarization
-from huggingface_hub import HfApi, Repository
-
-
-def upload(model_to_upload, model_name):
-    hf_username = input("Enter your HuggingFace username:")
-    hf_token = getpass("Enter your HuggingFace token:")
-    model_url = HfApi().create_repo(token=hf_token, name=model_name, exist_ok=True)
-    model_repo = Repository(
-        "./hf_model",
-        clone_from=model_url,
-        use_auth_token=hf_token,
-        git_email=f"{hf_username}@users.noreply.huggingface.co",
-        git_user=hf_username,
-    )
-
-    del hf_token
-    try:
-        readme_txt = open(join(dirname(__file__), "README.md"), encoding="utf8").read()
-    except Exception:
-        readme_txt = None
-
-    (Path(model_repo.local_dir) / "README.md").write_text(readme_txt)
-    model_to_upload.save_model(Path(model_repo.local_dir))
-    commit_url = model_repo.push_to_hub()
-
-    print("Check out your model at:")
-    print(commit_url)
-    print(f"https://huggingface.co/{hf_username}/{model_name}")
-
-    if Path("./hf_model").exists():
-        shutil.rmtree("./hf_model")
-
-
-if __name__ == "__main__":
-    with open("model_params.yml") as f:
-        params = yaml.safe_load(f)
-
-    model = Summarization()
-    model.load_model(model_dir="./models")
-
-    upload(model_to_upload=model, model_name=params["name"])
diff --git a/spaces/giswqs/Streamlit/apps/raster.py b/spaces/giswqs/Streamlit/apps/raster.py
deleted file mode 100644
index 95ea27e231f6119424be781806551a78239e804a..0000000000000000000000000000000000000000
--- a/spaces/giswqs/Streamlit/apps/raster.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import os
-import leafmap.foliumap as leafmap
-import streamlit as st
-import palettable
-
-
-@st.cache(allow_output_mutation=True)
-def load_cog_list():
-    print(os.getcwd())
-    in_txt = os.path.join(os.getcwd(), "data/cog_files.txt")
-    with open(in_txt) as f:
-        return [line.strip() for line in f.readlines()[1:]]
-
-
-@st.cache(allow_output_mutation=True)
-def get_palettes():
-    palettes = dir(palettable.matplotlib)[:-16]
-    return ["matplotlib." + p for p in palettes]
-
-
-def app():
-
-    st.title("Visualize Raster Datasets")
-    st.markdown(
-        """
-    An interactive web app for visualizing local raster datasets and Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org)). The app was built using [streamlit](https://streamlit.io), [leafmap](https://leafmap.org), and [localtileserver](https://github.com/banesullivan/localtileserver).
-
-
-    """
-    )
-
-    row1_col1, row1_col2 = st.columns([2, 1])
-
-    with row1_col1:
-        cog_list = load_cog_list()
-        cog = st.selectbox("Select a sample Cloud Opitmized GeoTIFF (COG)", cog_list)
-
-    with row1_col2:
-        empty = st.empty()
-
-        url = empty.text_input(
-            "Enter a HTTP URL to a Cloud Optimized GeoTIFF (COG)",
-            cog,
-        )
-
-        data = st.file_uploader("Upload a raster dataset", type=["tif", "img"])
-
-        if data:
-            url = empty.text_input(
-                "Enter a URL to a Cloud Optimized GeoTIFF (COG)",
-                "",
-            )
-
-        add_palette = st.checkbox("Add a color palette")
-        if add_palette:
-            palette = st.selectbox("Select a color palette", get_palettes())
-        else:
-            palette = None
-
-        submit = st.button("Submit")
-
-    m = leafmap.Map(latlon_control=False)
-
-    if submit:
-        if data or url:
-            try:
-                if data:
-                    file_path = leafmap.save_data(data)
-                    m.add_local_tile(file_path, palette=palette, debug=True)
-                elif url:
-                    m.add_remote_tile(url, palette=palette, debug=True)
-            except Exception as e:
-                with row1_col2:
-                    st.error("Work in progress. Try it again later.")
-
-    with row1_col1:
-        m.to_streamlit()
diff --git a/spaces/giswqs/Streamlit/multiapp.py b/spaces/giswqs/Streamlit/multiapp.py
deleted file mode 100644
index 55c1f40dadb3c1c7082efbab873e9f846f2aebe0..0000000000000000000000000000000000000000
--- a/spaces/giswqs/Streamlit/multiapp.py
+++ /dev/null
@@ -1,81 +0,0 @@
-"""Frameworks for running multiple Streamlit applications as a single app.
-"""
-import streamlit as st
-
-# app_state = st.experimental_get_query_params()
-# app_state = {k: v[0] if isinstance(v, list) else v for k, v in app_state.items()} # fetch the first item in each query string as we don't have multiple values for each query string key in this example
-
-
-class MultiApp:
-    """Framework for combining multiple streamlit applications.
-    Usage:
-        def foo():
-            st.title("Hello Foo")
-        def bar():
-            st.title("Hello Bar")
-        app = MultiApp()
-        app.add_app("Foo", foo)
-        app.add_app("Bar", bar)
-        app.run()
-    It is also possible keep each application in a separate file.
-        import foo
-        import bar
-        app = MultiApp()
-        app.add_app("Foo", foo.app)
-        app.add_app("Bar", bar.app)
-        app.run()
-    """
-
-    def __init__(self):
-        self.apps = []
-
-    def add_app(self, title, func):
-        """Adds a new application.
-        Parameters
-        ----------
-        func:
-            the python function to render this app.
-        title:
-            title of the app. Appears in the dropdown in the sidebar.
-        """
-        self.apps.append({"title": title, "function": func})
-
-    def run(self):
-        app_state = st.experimental_get_query_params()
-        app_state = {
-            k: v[0] if isinstance(v, list) else v for k, v in app_state.items()
-        }  # fetch the first item in each query string as we don't have multiple values for each query string key in this example
-
-        # st.write('before', app_state)
-
-        titles = [a["title"] for a in self.apps]
-        functions = [a["function"] for a in self.apps]
-        default_radio = titles.index(app_state["page"]) if "page" in app_state else 0
-
-        st.sidebar.title("Navigation")
-
-        title = st.sidebar.radio("Go To", titles, index=default_radio, key="radio")
-
-        app_state["page"] = st.session_state.radio
-        # st.write('after', app_state)
-
-        st.experimental_set_query_params(**app_state)
-        # st.experimental_set_query_params(**st.session_state.to_dict())
-        functions[titles.index(title)]()
-
-        st.sidebar.title("Contribute")
-        st.sidebar.info(
-            "This is an open source project and you are very welcome to contribute your "
-            "comments, questions, resources and apps as "
-            "[issues](https://github.com/giswqs/streamlit-geospatial/issues) or "
-            "[pull requests](https://github.com/giswqs/streamlit-geospatial/pulls) "
-            "to the [source code](https://github.com/giswqs/streamlit-geospatial). "
-        )
-        st.sidebar.title("About")
-        st.sidebar.info(
-            """
-            This web [app](https://share.streamlit.io/giswqs/streamlit-geospatial/app.py) is maintained by [Qiusheng Wu](https://wetlands.io). You can follow me on social media:
-             [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu).
-            This web app URL: <https://streamlit.gishub.org>
-        """
-        )
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Cardfive 7.7 HOT Crack Key.rar.md b/spaces/gotiQspiryo/whisper-ui/examples/Cardfive 7.7 HOT Crack Key.rar.md
deleted file mode 100644
index 0ce53723d7222d2c3f5cb6f129a4cba91f831d00..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Cardfive 7.7 HOT Crack Key.rar.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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-
- d5da3c52bf<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Esko Artios CAD V12.0 Torrent.zi - Learn the Features and Benefits of this Powerful Software.md b/spaces/gotiQspiryo/whisper-ui/examples/Esko Artios CAD V12.0 Torrent.zi - Learn the Features and Benefits of this Powerful Software.md
deleted file mode 100644
index 0952da1bdeda3da928119a9bf60e63e2254c0941..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Esko Artios CAD V12.0 Torrent.zi - Learn the Features and Benefits of this Powerful Software.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Esko Artios CAD V12.0 Torrent.zi</h2><br /><p><b><b>Download</b> &#127775; <a href="https://urlgoal.com/2uyLQX">https://urlgoal.com/2uyLQX</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Homeopathic Medicine List And Uses Pdf Free A Practical Handbook for Self-Care and Wellness.md b/spaces/gotiQspiryo/whisper-ui/examples/Homeopathic Medicine List And Uses Pdf Free A Practical Handbook for Self-Care and Wellness.md
deleted file mode 100644
index 7d40f73337543e1aeb8c4aa98404176fe13c2007..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Homeopathic Medicine List And Uses Pdf Free A Practical Handbook for Self-Care and Wellness.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<br />
-<p>Increasing numbers of medical colleges have started offering courses in alternative medicine. Accredited Naturopathic colleges and universities are increasing in number and popularity in the USA. They offer the most complete medical training in complimentary medicines that is available today [4, 5]. In Britain, no conventional medical schools offer courses that teach the clinical practice of alternative medicine. However, alternative medicine is taught in several unconventional schools as part of their curriculum. Teaching is based mostly on theory and understanding of alternative medicine, with emphasis on being able to communicate with alternative medicine specialists.</p>
-<p>People should be free to choose whatever method of healthcare they want, but stipulate that people must be informed as to the safety and efficacy of whatever method they choose. People who choose alternative medicine may think they are choosing a safe, effective medicine, while they may only be getting quack remedies. Grapefruit seed extract is an example of quackery when multiple studies demonstrate its universal antimicrobial effect is due to synthetic antimicrobial contamination [18, 19].</p>
-<h2>Homeopathic Medicine List And Uses Pdf Free</h2><br /><p><b><b>Download Zip</b> &#187; <a href="https://urlgoal.com/2uyMu0">https://urlgoal.com/2uyMu0</a></b></p><br /><br />
-<p>Arizona, Connecticut, and Nevada are the only states with homeopathic licensing boards for doctors of medicine (holders of M.D. degrees) and doctors of osteopathic medicine (holders of D.O. degrees). In 15 states, a section of the naturopathic medical board examinations is on homeopathy.</p>
-<p>Another important health coverage scheme is the Central Government Health Scheme, organized and run by the Ministry of Health and Family Welfare for current and retired central government employees and their dependents.11 There are no income or wage requirements to be eligible. Coverage includes health care services for allopathic, homeopathic, and alternative medicine treatments.12 Approximately 3.6 million beneficiaries were registered under this scheme as of late 2019.13 Similar schemes exist for railway and defense employees.</p>
-<p><strong>Primary care: </strong>Under the Health and Wellness Centres program, 150,000 subcenters (the lowest tier of the health system) across the country are being upgraded to provide comprehensive primary health care services, free essential medicines, and free diagnostic services. Nutritional support will also be provided to all beneficiaries with tuberculosis at a rate of<strong> </strong>INR 500 (USD 7) per month during treatment. Other primary health care providers include primary health centers (PHCs) and community health centers. No patient registration is required.</p>
-<p>ENBREL is a medicine that affects your immune system. ENBREL can lower the ability of your immune system to fight infections. Serious infections have happened in patients taking ENBREL. These infections include tuberculosis (TB) and infections caused by viruses, fungi, or bacteria that have spread throughout the body. Some patients have died from these infections. Your healthcare provider should test you for TB before you take ENBREL and monitor you closely for TB before, during, and after ENBREL treatment, even if you have tested negative for TB.</p> aaccfb2cb3<br />
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diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Mac Os Sierra Could Not Open Package For Expansion A Guide to Troubleshoot This Issue.md b/spaces/gotiQspiryo/whisper-ui/examples/Mac Os Sierra Could Not Open Package For Expansion A Guide to Troubleshoot This Issue.md
deleted file mode 100644
index 30a39eef8c9f40096e1a356e3760ad88e568495c..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Mac Os Sierra Could Not Open Package For Expansion A Guide to Troubleshoot This Issue.md	
+++ /dev/null
@@ -1,15 +0,0 @@
-
-<p>If you right click it, and click <strong>Show Package Contents</strong> you'll get a few files in a Contents folder. (Note: if you do not see <strong>Show Package Contents</strong> you will need to open Terminal.app and run pkgutil --expand mystubbornpackage.pkg path/to/expand)</p>
-<h2>Mac Os Sierra Could Not Open Package For Expansion</h2><br /><p><b><b>DOWNLOAD</b> &#10038; <a href="https://urlgoal.com/2uyMgV">https://urlgoal.com/2uyMgV</a></b></p><br /><br />
-<p>Not only will it provide all the information you need in the app it also install a Quick Look plug in so just selecting the package file and hitting the space bar opens up a window with the most essential information.</p>
-<p>The new imac I am usingto create a boot installer is running mojave so it won't let me open the sierra install package because, I guess it thinks I am going to install it on the mac that has mojave. How can I get round this please.</p>
-<p>Once I verified that I could get the same results using the <strong>SecUpd2019-005HighSierra.RecoveryHDUpdate.pkg</strong> installer package, I wrote a script (based on the original one I had found) to help automate the process of rebuilding a macOS Recovery volume or partition. For more details, please see below the jump.</p>
-<p>This guide will help you open package contents of a PKG file on Windows or Mac, depending on your operating system and type. If during extraction there are errors please comment below so we can offer assistance quickly!</p>
-<p></p>
-<p>Typinator shows variants of its "T" icon in the menu bar to indicate situations in which expansions do not work as expected. Typinator 5.0 or newer can explain the meaning of this icon: Click the triangle next to the "T" icon to open the menu and select the first command "What does this symbol mean?".</p>
-<p>Note that the recommendation to use suggested packages conditionally intests does also apply to packages used to manage test suites: anotorious example was <strong>testthat</strong> which in version 1.0.0 containedillegal C++ code and hence could not be installed on standards-compliantplatforms.</p>
-<p>In very special cases packages may create binary files other than theshared objects/DLLs in the src directory. Such files will not beinstalled in a multi-architecture setting since R CMD INSTALL--libs-only is used to merge multiple sub-architectures and it onlycopies shared objects/DLLs. If a package wants to install otherbinaries (for example executable programs), it should provide an Rscript src/install.libs.R which will be run as part of theinstallation in the src build directory instead of copyingthe shared objects/DLLs. The script is run in a separate Renvironment containing the following variables: R_PACKAGE_NAME(the name of the package), R_PACKAGE_SOURCE (the path to thesource directory of the package), R_PACKAGE_DIR (the path of thetarget installation directory of the package), R_ARCH (thearch-dependent part of the path, often empty), SHLIB_EXT (theextension of shared objects) and WINDOWS (TRUE on Windows,FALSE elsewhere). Something close to the default behavior couldbe replicated with the following src/install.libs.R file:</p>
-<p>listing the classes and functions with methods respectively. Suppose wehad two small packages <strong>A</strong> and <strong>B</strong> with <strong>B</strong> using <strong>A</strong>.Then they could have NAMESPACE files</p>
-<p>Many of the graphics devices are platform-specific: even X11()(aka x11()) which although emulated on Windows may not beavailable on a Unix-alike (and is not the preferred screen device on OSX). It is rarely necessary for package code or examples to open a newdevice, but if essential,68 use dev.new().</p> aaccfb2cb3<br />
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diff --git a/spaces/gradio/HuBERT/docs/conf.py b/spaces/gradio/HuBERT/docs/conf.py
deleted file mode 100644
index 440784bfae96c14e9050542b1b1921a75a3b4b27..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/docs/conf.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-#
-# fairseq documentation build configuration file, created by
-# sphinx-quickstart on Fri Aug 17 21:45:30 2018.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-
-import os
-import sys
-from fairseq import __version__
-
-
-# source code directory, relative to this file, for sphinx-autobuild
-sys.path.insert(0, os.path.abspath(".."))
-
-source_suffix = [".rst"]
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    "sphinx.ext.autodoc",
-    "sphinx.ext.intersphinx",
-    "sphinx.ext.viewcode",
-    "sphinx.ext.napoleon",
-    "sphinxarg.ext",
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
-
-# The master toctree document.
-master_doc = "index"
-
-# General information about the project.
-project = "fairseq"
-copyright = "Facebook AI Research (FAIR)"
-author = "Facebook AI Research (FAIR)"
-
-github_doc_root = "https://github.com/pytorch/fairseq/tree/master/docs/"
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = __version__
-# The full version, including alpha/beta/rc tags.
-release = __version__
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = "sphinx"
-highlight_language = "python"
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = False
-
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = "sphinx_rtd_theme"
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#
-# html_theme_options = {}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
-
-html_context = {
-    "css_files": [
-        "_static/theme_overrides.css",  # override wide tables in RTD theme
-    ],
-}
-
-# Custom sidebar templates, must be a dictionary that maps document names
-# to template names.
-#
-# This is required for the alabaster theme
-# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
-# html_sidebars = {
-#    '**': [
-#        'about.html',
-#        'navigation.html',
-#        'relations.html',  # needs 'show_related': True theme option to display
-#        'searchbox.html',
-#        'donate.html',
-#    ]
-# }
-
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {
-    "numpy": ("http://docs.scipy.org/doc/numpy/", None),
-    "python": ("https://docs.python.org/", None),
-    "torch": ("https://pytorch.org/docs/master/", None),
-}
diff --git a/spaces/gulabpatel/Real-ESRGAN/tests/test_model.py b/spaces/gulabpatel/Real-ESRGAN/tests/test_model.py
deleted file mode 100644
index c20bb1d56ed20222e929e9c94026f6ea383c6026..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/Real-ESRGAN/tests/test_model.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import yaml
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.data.paired_image_dataset import PairedImageDataset
-from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss
-
-from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
-from realesrgan.models.realesrgan_model import RealESRGANModel
-from realesrgan.models.realesrnet_model import RealESRNetModel
-
-
-def test_realesrnet_model():
-    with open('tests/data/test_realesrnet_model.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    # build model
-    model = RealESRNetModel(opt)
-    # test attributes
-    assert model.__class__.__name__ == 'RealESRNetModel'
-    assert isinstance(model.net_g, RRDBNet)
-    assert isinstance(model.cri_pix, L1Loss)
-    assert isinstance(model.optimizers[0], torch.optim.Adam)
-
-    # prepare data
-    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
-    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
-    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
-    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
-    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
-    model.feed_data(data)
-    # check dequeue
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # change probability to test if-else
-    model.opt['gaussian_noise_prob'] = 0
-    model.opt['gray_noise_prob'] = 0
-    model.opt['second_blur_prob'] = 0
-    model.opt['gaussian_noise_prob2'] = 0
-    model.opt['gray_noise_prob2'] = 0
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # ----------------- test nondist_validation -------------------- #
-    # construct dataloader
-    dataset_opt = dict(
-        name='Demo',
-        dataroot_gt='tests/data/gt',
-        dataroot_lq='tests/data/lq',
-        io_backend=dict(type='disk'),
-        scale=4,
-        phase='val')
-    dataset = PairedImageDataset(dataset_opt)
-    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    assert model.is_train is True
-    model.nondist_validation(dataloader, 1, None, False)
-    assert model.is_train is True
-
-
-def test_realesrgan_model():
-    with open('tests/data/test_realesrgan_model.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    # build model
-    model = RealESRGANModel(opt)
-    # test attributes
-    assert model.__class__.__name__ == 'RealESRGANModel'
-    assert isinstance(model.net_g, RRDBNet)  # generator
-    assert isinstance(model.net_d, UNetDiscriminatorSN)  # discriminator
-    assert isinstance(model.cri_pix, L1Loss)
-    assert isinstance(model.cri_perceptual, PerceptualLoss)
-    assert isinstance(model.cri_gan, GANLoss)
-    assert isinstance(model.optimizers[0], torch.optim.Adam)
-    assert isinstance(model.optimizers[1], torch.optim.Adam)
-
-    # prepare data
-    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
-    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
-    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
-    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
-    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
-    model.feed_data(data)
-    # check dequeue
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # change probability to test if-else
-    model.opt['gaussian_noise_prob'] = 0
-    model.opt['gray_noise_prob'] = 0
-    model.opt['second_blur_prob'] = 0
-    model.opt['gaussian_noise_prob2'] = 0
-    model.opt['gray_noise_prob2'] = 0
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # ----------------- test nondist_validation -------------------- #
-    # construct dataloader
-    dataset_opt = dict(
-        name='Demo',
-        dataroot_gt='tests/data/gt',
-        dataroot_lq='tests/data/lq',
-        io_backend=dict(type='disk'),
-        scale=4,
-        phase='val')
-    dataset = PairedImageDataset(dataset_opt)
-    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    assert model.is_train is True
-    model.nondist_validation(dataloader, 1, None, False)
-    assert model.is_train is True
-
-    # ----------------- test optimize_parameters -------------------- #
-    model.feed_data(data)
-    model.optimize_parameters(1)
-    assert model.output.shape == (1, 3, 32, 32)
-    assert isinstance(model.log_dict, dict)
-    # check returned keys
-    expected_keys = ['l_g_pix', 'l_g_percep', 'l_g_gan', 'l_d_real', 'out_d_real', 'l_d_fake', 'out_d_fake']
-    assert set(expected_keys).issubset(set(model.log_dict.keys()))
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/persistence.py b/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/persistence.py
deleted file mode 100644
index 1abf9cbf2c92a631ab1ac22fc1b0b382e22a0af0..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/torch_utils/persistence.py
+++ /dev/null
@@ -1,253 +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.
-
-"""Facilities for pickling Python code alongside other data.
-
-The pickled code is automatically imported into a separate Python module
-during unpickling. This way, any previously exported pickles will remain
-usable even if the original code is no longer available, or if the current
-version of the code is not consistent with what was originally pickled."""
-
-import sys
-import pickle
-import io
-import inspect
-import copy
-import uuid
-import types
-import dnnlib
-
-#----------------------------------------------------------------------------
-
-_version            = 6         # internal version number
-_decorators         = set()     # {decorator_class, ...}
-_import_hooks       = []        # [hook_function, ...]
-_module_to_src_dict = dict()    # {module: src, ...}
-_src_to_module_dict = dict()    # {src: module, ...}
-
-#----------------------------------------------------------------------------
-
-def persistent_class(orig_class):
-    r"""Class decorator that extends a given class to save its source code
-    when pickled.
-
-    Example:
-
-        from torch_utils import persistence
-
-        @persistence.persistent_class
-        class MyNetwork(torch.nn.Module):
-            def __init__(self, num_inputs, num_outputs):
-                super().__init__()
-                self.fc = MyLayer(num_inputs, num_outputs)
-                ...
-
-        @persistence.persistent_class
-        class MyLayer(torch.nn.Module):
-            ...
-
-    When pickled, any instance of `MyNetwork` and `MyLayer` will save its
-    source code alongside other internal state (e.g., parameters, buffers,
-    and submodules). This way, any previously exported pickle will remain
-    usable even if the class definitions have been modified or are no
-    longer available.
-
-    The decorator saves the source code of the entire Python module
-    containing the decorated class. It does *not* save the source code of
-    any imported modules. Thus, the imported modules must be available
-    during unpickling, also including `torch_utils.persistence` itself.
-
-    It is ok to call functions defined in the same module from the
-    decorated class. However, if the decorated class depends on other
-    classes defined in the same module, they must be decorated as well.
-    This is illustrated in the above example in the case of `MyLayer`.
-
-    It is also possible to employ the decorator just-in-time before
-    calling the constructor. For example:
-
-        cls = MyLayer
-        if want_to_make_it_persistent:
-            cls = persistence.persistent_class(cls)
-        layer = cls(num_inputs, num_outputs)
-
-    As an additional feature, the decorator also keeps track of the
-    arguments that were used to construct each instance of the decorated
-    class. The arguments can be queried via `obj.init_args` and
-    `obj.init_kwargs`, and they are automatically pickled alongside other
-    object state. A typical use case is to first unpickle a previous
-    instance of a persistent class, and then upgrade it to use the latest
-    version of the source code:
-
-        with open('old_pickle.pkl', 'rb') as f:
-            old_net = pickle.load(f)
-        new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
-        misc.copy_params_and_buffers(old_net, new_net, require_all=True)
-    """
-    assert isinstance(orig_class, type)
-    if is_persistent(orig_class):
-        return orig_class
-
-    assert orig_class.__module__ in sys.modules
-    orig_module = sys.modules[orig_class.__module__]
-    orig_module_src = _module_to_src(orig_module)
-
-    class Decorator(orig_class):
-        _orig_module_src = orig_module_src
-        _orig_class_name = orig_class.__name__
-
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            self._init_args = copy.deepcopy(args)
-            self._init_kwargs = copy.deepcopy(kwargs)
-            assert orig_class.__name__ in orig_module.__dict__
-            _check_pickleable(self.__reduce__())
-
-        @property
-        def init_args(self):
-            return copy.deepcopy(self._init_args)
-
-        @property
-        def init_kwargs(self):
-            return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
-
-        def __reduce__(self):
-            fields = list(super().__reduce__())
-            fields += [None] * max(3 - len(fields), 0)
-            if fields[0] is not _reconstruct_persistent_obj:
-                meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2])
-                fields[0] = _reconstruct_persistent_obj # reconstruct func
-                fields[1] = (meta,) # reconstruct args
-                fields[2] = None # state dict
-            return tuple(fields)
-
-    Decorator.__name__ = orig_class.__name__
-    _decorators.add(Decorator)
-    return Decorator
-
-#----------------------------------------------------------------------------
-
-def is_persistent(obj):
-    r"""Test whether the given object or class is persistent, i.e.,
-    whether it will save its source code when pickled.
-    """
-    try:
-        if obj in _decorators:
-            return True
-    except TypeError:
-        pass
-    return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck
-
-#----------------------------------------------------------------------------
-
-def import_hook(hook):
-    r"""Register an import hook that is called whenever a persistent object
-    is being unpickled. A typical use case is to patch the pickled source
-    code to avoid errors and inconsistencies when the API of some imported
-    module has changed.
-
-    The hook should have the following signature:
-
-        hook(meta) -> modified meta
-
-    `meta` is an instance of `dnnlib.EasyDict` with the following fields:
-
-        type:       Type of the persistent object, e.g. `'class'`.
-        version:    Internal version number of `torch_utils.persistence`.
-        module_src  Original source code of the Python module.
-        class_name: Class name in the original Python module.
-        state:      Internal state of the object.
-
-    Example:
-
-        @persistence.import_hook
-        def wreck_my_network(meta):
-            if meta.class_name == 'MyNetwork':
-                print('MyNetwork is being imported. I will wreck it!')
-                meta.module_src = meta.module_src.replace("True", "False")
-            return meta
-    """
-    assert callable(hook)
-    _import_hooks.append(hook)
-
-#----------------------------------------------------------------------------
-
-def _reconstruct_persistent_obj(meta):
-    r"""Hook that is called internally by the `pickle` module to unpickle
-    a persistent object.
-    """
-    meta = dnnlib.EasyDict(meta)
-    meta.state = dnnlib.EasyDict(meta.state)
-    for hook in _import_hooks:
-        meta = hook(meta)
-        assert meta is not None
-
-    assert meta.version == _version
-    module = _src_to_module(meta.module_src)
-
-    assert meta.type == 'class'
-    orig_class = module.__dict__[meta.class_name]
-    decorator_class = persistent_class(orig_class)
-    obj = decorator_class.__new__(decorator_class)
-
-    setstate = getattr(obj, '__setstate__', None)
-    if callable(setstate):
-        setstate(meta.state) # pylint: disable=not-callable
-    else:
-        obj.__dict__.update(meta.state)
-    return obj
-
-#----------------------------------------------------------------------------
-
-def _module_to_src(module):
-    r"""Query the source code of a given Python module.
-    """
-    src = _module_to_src_dict.get(module, None)
-    if src is None:
-        src = inspect.getsource(module)
-        _module_to_src_dict[module] = src
-        _src_to_module_dict[src] = module
-    return src
-
-def _src_to_module(src):
-    r"""Get or create a Python module for the given source code.
-    """
-    module = _src_to_module_dict.get(src, None)
-    if module is None:
-        module_name = "_imported_module_" + uuid.uuid4().hex
-        module = types.ModuleType(module_name)
-        sys.modules[module_name] = module
-        _module_to_src_dict[module] = src
-        _src_to_module_dict[src] = module
-        exec(src, module.__dict__) # pylint: disable=exec-used
-    return module
-
-#----------------------------------------------------------------------------
-
-def _check_pickleable(obj):
-    r"""Check that the given object is pickleable, raising an exception if
-    it is not. This function is expected to be considerably more efficient
-    than actually pickling the object.
-    """
-    def recurse(obj):
-        if isinstance(obj, (list, tuple, set)):
-            return [recurse(x) for x in obj]
-        if isinstance(obj, dict):
-            return [[recurse(x), recurse(y)] for x, y in obj.items()]
-        if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
-            return None # Python primitive types are pickleable.
-        if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor', 'torch.nn.parameter.Parameter']:
-            return None # NumPy arrays and PyTorch tensors are pickleable.
-        if is_persistent(obj):
-            return None # Persistent objects are pickleable, by virtue of the constructor check.
-        return obj
-    with io.BytesIO() as f:
-        pickle.dump(recurse(obj), f)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/training/volumetric_rendering/math_utils.py b/spaces/gwang-kim/DATID-3D/eg3d/training/volumetric_rendering/math_utils.py
deleted file mode 100644
index 4cf9d2b811e0acbc7923bc9126e010b52cb1a8af..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/training/volumetric_rendering/math_utils.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Petr Kellnhofer
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-import torch
-
-def transform_vectors(matrix: torch.Tensor, vectors4: torch.Tensor) -> torch.Tensor:
-    """
-    Left-multiplies MxM @ NxM. Returns NxM.
-    """
-    res = torch.matmul(vectors4, matrix.T)
-    return res
-
-
-def normalize_vecs(vectors: torch.Tensor) -> torch.Tensor:
-    """
-    Normalize vector lengths.
-    """
-    return vectors / (torch.norm(vectors, dim=-1, keepdim=True))
-
-def torch_dot(x: torch.Tensor, y: torch.Tensor):
-    """
-    Dot product of two tensors.
-    """
-    return (x * y).sum(-1)
-
-
-def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor, box_side_length):
-    """
-    Author: Petr Kellnhofer
-    Intersects rays with the [-1, 1] NDC volume.
-    Returns min and max distance of entry.
-    Returns -1 for no intersection.
-    https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection
-    """
-    o_shape = rays_o.shape
-    rays_o = rays_o.detach().reshape(-1, 3)
-    rays_d = rays_d.detach().reshape(-1, 3)
-
-
-    bb_min = [-1*(box_side_length/2), -1*(box_side_length/2), -1*(box_side_length/2)]
-    bb_max = [1*(box_side_length/2), 1*(box_side_length/2), 1*(box_side_length/2)]
-    bounds = torch.tensor([bb_min, bb_max], dtype=rays_o.dtype, device=rays_o.device)
-    is_valid = torch.ones(rays_o.shape[:-1], dtype=bool, device=rays_o.device)
-
-    # Precompute inverse for stability.
-    invdir = 1 / rays_d
-    sign = (invdir < 0).long()
-
-    # Intersect with YZ plane.
-    tmin = (bounds.index_select(0, sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0]
-    tmax = (bounds.index_select(0, 1 - sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0]
-
-    # Intersect with XZ plane.
-    tymin = (bounds.index_select(0, sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1]
-    tymax = (bounds.index_select(0, 1 - sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1]
-
-    # Resolve parallel rays.
-    is_valid[torch.logical_or(tmin > tymax, tymin > tmax)] = False
-
-    # Use the shortest intersection.
-    tmin = torch.max(tmin, tymin)
-    tmax = torch.min(tmax, tymax)
-
-    # Intersect with XY plane.
-    tzmin = (bounds.index_select(0, sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2]
-    tzmax = (bounds.index_select(0, 1 - sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2]
-
-    # Resolve parallel rays.
-    is_valid[torch.logical_or(tmin > tzmax, tzmin > tmax)] = False
-
-    # Use the shortest intersection.
-    tmin = torch.max(tmin, tzmin)
-    tmax = torch.min(tmax, tzmax)
-
-    # Mark invalid.
-    tmin[torch.logical_not(is_valid)] = -1
-    tmax[torch.logical_not(is_valid)] = -2
-
-    return tmin.reshape(*o_shape[:-1], 1), tmax.reshape(*o_shape[:-1], 1)
-
-
-def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):
-    """
-    Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
-    Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
-    """
-    # create a tensor of 'num' steps from 0 to 1
-    steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)
-
-    # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings
-    # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript
-    #   "cannot statically infer the expected size of a list in this contex", hence the code below
-    for i in range(start.ndim):
-        steps = steps.unsqueeze(-1)
-
-    # the output starts at 'start' and increments until 'stop' in each dimension
-    out = start[None] + steps * (stop - start)[None]
-
-    return out
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/train.py b/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/train.py
deleted file mode 100644
index 55eca2d0ad9463415970e09bccab8b722e496704..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/train.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import argparse
-import logging
-import os
-
-import torch
-import torch.distributed as dist
-import torch.nn.functional as F
-import torch.utils.data.distributed
-from torch.nn.utils import clip_grad_norm_
-
-import losses
-from backbones import get_model
-from dataset import MXFaceDataset, SyntheticDataset, DataLoaderX
-from partial_fc import PartialFC
-from utils.utils_amp import MaxClipGradScaler
-from utils.utils_callbacks import CallBackVerification, CallBackLogging, CallBackModelCheckpoint
-from utils.utils_config import get_config
-from utils.utils_logging import AverageMeter, init_logging
-
-
-def main(args):
-    cfg = get_config(args.config)
-    try:
-        world_size = int(os.environ['WORLD_SIZE'])
-        rank = int(os.environ['RANK'])
-        dist.init_process_group('nccl')
-    except KeyError:
-        world_size = 1
-        rank = 0
-        dist.init_process_group(backend='nccl', init_method="tcp://127.0.0.1:12584", rank=rank, world_size=world_size)
-
-    local_rank = args.local_rank
-    torch.cuda.set_device(local_rank)
-    os.makedirs(cfg.output, exist_ok=True)
-    init_logging(rank, cfg.output)
-
-    if cfg.rec == "synthetic":
-        train_set = SyntheticDataset(local_rank=local_rank)
-    else:
-        train_set = MXFaceDataset(root_dir=cfg.rec, local_rank=local_rank)
-
-    train_sampler = torch.utils.data.distributed.DistributedSampler(train_set, shuffle=True)
-    train_loader = DataLoaderX(
-        local_rank=local_rank, dataset=train_set, batch_size=cfg.batch_size,
-        sampler=train_sampler, num_workers=2, pin_memory=True, drop_last=True)
-    backbone = get_model(cfg.network, dropout=0.0, fp16=cfg.fp16, num_features=cfg.embedding_size).to(local_rank)
-
-    if cfg.resume:
-        try:
-            backbone_pth = os.path.join(cfg.output, "backbone.pth")
-            backbone.load_state_dict(torch.load(backbone_pth, map_location=torch.device(local_rank)))
-            if rank == 0:
-                logging.info("backbone resume successfully!")
-        except (FileNotFoundError, KeyError, IndexError, RuntimeError):
-            if rank == 0:
-                logging.info("resume fail, backbone init successfully!")
-
-    backbone = torch.nn.parallel.DistributedDataParallel(
-        module=backbone, broadcast_buffers=False, device_ids=[local_rank])
-    backbone.train()
-    margin_softmax = losses.get_loss(cfg.loss)
-    module_partial_fc = PartialFC(
-        rank=rank, local_rank=local_rank, world_size=world_size, resume=cfg.resume,
-        batch_size=cfg.batch_size, margin_softmax=margin_softmax, num_classes=cfg.num_classes,
-        sample_rate=cfg.sample_rate, embedding_size=cfg.embedding_size, prefix=cfg.output)
-
-    opt_backbone = torch.optim.SGD(
-        params=[{'params': backbone.parameters()}],
-        lr=cfg.lr / 512 * cfg.batch_size * world_size,
-        momentum=0.9, weight_decay=cfg.weight_decay)
-    opt_pfc = torch.optim.SGD(
-        params=[{'params': module_partial_fc.parameters()}],
-        lr=cfg.lr / 512 * cfg.batch_size * world_size,
-        momentum=0.9, weight_decay=cfg.weight_decay)
-
-    num_image = len(train_set)
-    total_batch_size = cfg.batch_size * world_size
-    cfg.warmup_step = num_image // total_batch_size * cfg.warmup_epoch
-    cfg.total_step = num_image // total_batch_size * cfg.num_epoch
-
-    def lr_step_func(current_step):
-        cfg.decay_step = [x * num_image // total_batch_size for x in cfg.decay_epoch]
-        if current_step < cfg.warmup_step:
-            return current_step / cfg.warmup_step
-        else:
-            return 0.1 ** len([m for m in cfg.decay_step if m <= current_step])
-
-    scheduler_backbone = torch.optim.lr_scheduler.LambdaLR(
-        optimizer=opt_backbone, lr_lambda=lr_step_func)
-    scheduler_pfc = torch.optim.lr_scheduler.LambdaLR(
-        optimizer=opt_pfc, lr_lambda=lr_step_func)
-
-    for key, value in cfg.items():
-        num_space = 25 - len(key)
-        logging.info(": " + key + " " * num_space + str(value))
-
-    val_target = cfg.val_targets
-    callback_verification = CallBackVerification(2000, rank, val_target, cfg.rec)
-    callback_logging = CallBackLogging(50, rank, cfg.total_step, cfg.batch_size, world_size, None)
-    callback_checkpoint = CallBackModelCheckpoint(rank, cfg.output)
-
-    loss = AverageMeter()
-    start_epoch = 0
-    global_step = 0
-    grad_amp = MaxClipGradScaler(cfg.batch_size, 128 * cfg.batch_size, growth_interval=100) if cfg.fp16 else None
-    for epoch in range(start_epoch, cfg.num_epoch):
-        train_sampler.set_epoch(epoch)
-        for step, (img, label) in enumerate(train_loader):
-            global_step += 1
-            features = F.normalize(backbone(img))
-            x_grad, loss_v = module_partial_fc.forward_backward(label, features, opt_pfc)
-            if cfg.fp16:
-                features.backward(grad_amp.scale(x_grad))
-                grad_amp.unscale_(opt_backbone)
-                clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
-                grad_amp.step(opt_backbone)
-                grad_amp.update()
-            else:
-                features.backward(x_grad)
-                clip_grad_norm_(backbone.parameters(), max_norm=5, norm_type=2)
-                opt_backbone.step()
-
-            opt_pfc.step()
-            module_partial_fc.update()
-            opt_backbone.zero_grad()
-            opt_pfc.zero_grad()
-            loss.update(loss_v, 1)
-            callback_logging(global_step, loss, epoch, cfg.fp16, scheduler_backbone.get_last_lr()[0], grad_amp)
-            callback_verification(global_step, backbone)
-            scheduler_backbone.step()
-            scheduler_pfc.step()
-        callback_checkpoint(global_step, backbone, module_partial_fc)
-    dist.destroy_process_group()
-
-
-if __name__ == "__main__":
-    torch.backends.cudnn.benchmark = True
-    parser = argparse.ArgumentParser(description='PyTorch ArcFace Training')
-    parser.add_argument('config', type=str, help='py config file')
-    parser.add_argument('--local_rank', type=int, default=0, help='local_rank')
-    main(parser.parse_args())
diff --git a/spaces/h2oai/wave-tour/examples/meta_inline_script_callback.py b/spaces/h2oai/wave-tour/examples/meta_inline_script_callback.py
deleted file mode 100644
index 3a32aa284097c27a6c1af61b46f55830f1c3ad31..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/meta_inline_script_callback.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Meta / Inline Script / Callback
-# Handle events from arbitrary Javascript
-# ---
-from h2o_wave import main, app, Q, ui
-
-# Define a function that emits an event from Javascript to Python
-counter_onclick = '''
-function increment() {
-  // Emit an event to the app.
-  // All three arguments are arbitrary.
-  // Here, we use:
-  // - 'counter' to indicate the source of the event.
-  // - 'clicked' to indicate the type of event.
-  // - the third argument can be a string, number, boolean or any complex structure, like { foo: 'bar', qux: 42 }
-  // In Python, q.events.counter.clicked will be set to True.
-  wave.emit('counter', 'clicked', true);
-}
-'''
-
-# The HTML and CSS to create a custom button.
-# Note that we've named the element 'counter',
-#   and called the increment() Javascript function when clicked.
-counter_html = '''
-<style>
-#counter {
-  cursor: pointer; 
-  user-select: none;
-  background: #ff784f; 
-  color: #fff;
-  padding: 0.25em 1em;
-  text-align: center;
-}
-#counter:hover {
-  background: #ff8965; 
-}
-</style>
-<h2 id="counter" onclick="increment()">Click Me!</h2>
-'''
-
-
-@app('/demo')
-async def serve(q: Q):
-    # Track how many times the button has been clicked.
-    if q.client.count is None:
-        q.client.count = 0
-
-    if not q.client.initialized:
-        # Add our script to the page.
-        q.page['meta'] = ui.meta_card(
-            box='',
-            script=ui.inline_script(
-                # The Javascript code for this script.
-                content=counter_onclick,
-                # Execute this script only if the 'counter' element is available.
-                targets=['counter'],
-            )
-        )
-        q.page['form'] = ui.form_card(
-            box='1 1 2 2',
-            title='Counter',
-            items=[
-                # Display our custom button.
-                ui.markup(content=counter_html),
-                ui.text(name='text', content=''),
-            ],
-        )
-        q.client.initialized = True
-    else:
-        # Do we have an event from the counter?
-        if q.events.counter:
-            # Is 'clicked' True?
-            if q.events.counter.clicked:
-                # Increment the count.
-                q.client.count += 1
-                # Display the latest count.
-                q.page['form'].text.content = f'You clicked {q.client.count} times.'
-
-    await q.page.save()
diff --git a/spaces/hhim8826/vits-ATR/commons.py b/spaces/hhim8826/vits-ATR/commons.py
deleted file mode 100644
index 9ad0444b61cbadaa388619986c2889c707d873ce..0000000000000000000000000000000000000000
--- a/spaces/hhim8826/vits-ATR/commons.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_Dice.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_Dice.py
deleted file mode 100644
index 683e2813814fce7102427b853da080d517f62258..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_Dice.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    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 nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2
-from nnunet.training.loss_functions.dice_loss import SoftDiceLoss
-from nnunet.utilities.nd_softmax import softmax_helper
-
-
-class nnUNetTrainerV2_Loss_Dice(nnUNetTrainerV2):
-    def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
-                 unpack_data=True, deterministic=True, fp16=False):
-        super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
-                         deterministic, fp16)
-        self.loss = SoftDiceLoss(**{'apply_nonlin': softmax_helper, 'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False})
-
-
-class nnUNetTrainerV2_Loss_DicewithBG(nnUNetTrainerV2):
-    def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
-                 unpack_data=True, deterministic=True, fp16=False):
-        super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
-                         deterministic, fp16)
-        self.loss = SoftDiceLoss(**{'apply_nonlin': softmax_helper, 'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': True})
-
diff --git a/spaces/huggingchat/chat-ui/src/lib/stores/errors.ts b/spaces/huggingchat/chat-ui/src/lib/stores/errors.ts
deleted file mode 100644
index 144b16faba7cb1f74e1b5d8451403ab340ed1638..0000000000000000000000000000000000000000
--- a/spaces/huggingchat/chat-ui/src/lib/stores/errors.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-import { writable } from "svelte/store";
-
-export const ERROR_MESSAGES = {
-	default: "Oops, something went wrong.",
-	authOnly: "You have to be logged in.",
-	rateLimited: "You are sending too many messages. Try again later.",
-};
-
-export const error = writable<string | null>(null);
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_flip_pfc01_filter04_r50.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_flip_pfc01_filter04_r50.py
deleted file mode 100644
index 2c1018b7f0d0320678b33b212eed5751badf72ee..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/wf12m_flip_pfc01_filter04_r50.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.0, 0.4)
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.1
-config.interclass_filtering_threshold = 0.4
-config.fp16 = True
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.optimizer = "sgd"
-config.lr = 0.1
-config.verbose = 2000
-config.dali = False
-
-config.rec = "/train_tmp/WebFace12M_FLIP40"
-config.num_classes = 617970
-config.num_image = 12720066
-config.num_epoch = 20
-config.warmup_epoch = config.num_epoch // 10
-config.val_targets = []
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py b/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py
deleted file mode 100644
index eb4e0d31f1aedf4590628d394e1606920fefb5c9..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "r18"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 25
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/inamXcontru/PoeticTTS/2 Yeh Kaisa Khiladi Movie Download Mp4 A Story of Luck and Life.md b/spaces/inamXcontru/PoeticTTS/2 Yeh Kaisa Khiladi Movie Download Mp4 A Story of Luck and Life.md
deleted file mode 100644
index 5ca26ad6aa62ac4eb1e761edfae2f10fc238ae27..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/2 Yeh Kaisa Khiladi Movie Download Mp4 A Story of Luck and Life.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>2 Yeh Kaisa Khiladi Movie Download Mp4</h2><br /><p><b><b>Download</b> &rarr;&rarr;&rarr; <a href="https://gohhs.com/2uz3bj">https://gohhs.com/2uz3bj</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inamXcontru/PoeticTTS/DVDFab 11.0.1.0 !!EXCLUSIVE!! Crack.md b/spaces/inamXcontru/PoeticTTS/DVDFab 11.0.1.0 !!EXCLUSIVE!! Crack.md
deleted file mode 100644
index dd0f843c9407f3aa634a6f74b0debbe15e344325..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/DVDFab 11.0.1.0 !!EXCLUSIVE!! Crack.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p><strong>DVDFab Serial Key</strong> breaks down innovative programming with extraordinary highlights, DVD burning, and hardening. CDs and DVDs are the hallmarks of this product. You can change the video and crack lost confidential documents from DVD. DVD. Once you start the program, in case you need to talk, the program will start at the beginning of the message, and the email will start.</p>
-<h2>DVDFab 11.0.1.0 Crack</h2><br /><p><b><b>Download Zip</b> &#10004; <a href="https://gohhs.com/2uz2Ou">https://gohhs.com/2uz2Ou</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inflaton/learn-ai/Makefile b/spaces/inflaton/learn-ai/Makefile
deleted file mode 100644
index 096f43ea31d44c079aaa732ff71e880c3d5958c8..0000000000000000000000000000000000000000
--- a/spaces/inflaton/learn-ai/Makefile
+++ /dev/null
@@ -1,63 +0,0 @@
-.PHONY: start
-start:
-	python app.py
-
-serve:
-ifeq ("$(PORT)", "")
-	JINA_HIDE_SURVEY=1 TRANSFORMERS_OFFLINE=1 python -m lcserve deploy local server
-else
-	JINA_HIDE_SURVEY=1 TRANSFORMERS_OFFLINE=1 python -m lcserve deploy local server --port=${PORT}
-endif
-
-test:
-	python test.py
-
-test2:
-	python server.py
-
-chat:
-	python test.py chat
-
-chat2:
-	python unit_test.py chat
-
-unittest:
-	python unit_test.py $(TEST)
-
-tele:
-	python telegram_bot.py
-
-openllm:
-ifeq ("$(PORT)", "")
-	openllm start llama --model-id meta-llama/Llama-2-7b-chat-hf
-else
-	openllm start llama --model-id meta-llama/Llama-2-7b-chat-hf --port=${PORT}
-endif
-
-openllm-cpu:
-	CUDA_VISIBLE_DEVICES="" openllm start llama --model-id meta-llama/Llama-2-7b-chat-hf
-
-ingest:
-	python ingest.py
-
-mlock:
-	@echo 'To set new value for mlock, please run: sudo prlimit --memlock=35413752832:35413752832 --pid $$$$'
-	prlimit --memlock
-
-.PHONY: format
-format:
-	isort .
-	black .
-
-install:
-	pip install -U -r requirements.txt
-	pip show langchain transformers
-	
-install-extra:
-	CXX=g++-11  CC=gcc-11 pip install -U -r requirements_extra.txt
-	pip show llama-cpp-python ctransformers
-	
-install-extra-mac:
-	# brew install llvm libomp
-	CXX=/usr/local/opt/llvm/bin/clang++ CC=/usr/local/opt/llvm/bin/clang pip install -U -r requirements_extra.txt
-	pip show llama-cpp-python ctransformers
diff --git a/spaces/inflaton/learn-ai/app.py b/spaces/inflaton/learn-ai/app.py
deleted file mode 100644
index 667ed1f1891fb10339a27f89d6633c3b70c310e9..0000000000000000000000000000000000000000
--- a/spaces/inflaton/learn-ai/app.py
+++ /dev/null
@@ -1,213 +0,0 @@
-"""Main entrypoint for the app."""
-import os
-import time
-from queue import Queue
-from timeit import default_timer as timer
-
-import gradio as gr
-from anyio.from_thread import start_blocking_portal
-
-from app_modules.init import app_init
-from app_modules.llm_chat_chain import ChatChain
-from app_modules.utils import print_llm_response, remove_extra_spaces
-
-llm_loader, qa_chain = app_init()
-
-show_param_settings = os.environ.get("SHOW_PARAM_SETTINGS") == "true"
-share_gradio_app = os.environ.get("SHARE_GRADIO_APP") == "true"
-using_openai = os.environ.get("LLM_MODEL_TYPE") == "openai"
-chat_with_llama_2 = (
-    not using_openai and os.environ.get("USE_LLAMA_2_PROMPT_TEMPLATE") == "true"
-)
-chat_history_enabled = (
-    not chat_with_llama_2 and os.environ.get("CHAT_HISTORY_ENABLED") == "true"
-)
-
-model = (
-    "OpenAI GPT-3.5"
-    if using_openai
-    else os.environ.get("HUGGINGFACE_MODEL_NAME_OR_PATH")
-)
-href = (
-    "https://platform.openai.com/docs/models/gpt-3-5"
-    if using_openai
-    else f"https://huggingface.co/{model}"
-)
-
-if chat_with_llama_2:
-    qa_chain = ChatChain(llm_loader)
-    name = "Llama-2"
-else:
-    name = "AI Books"
-
-title = f"""<h1 align="left" style="min-width:200px; margin-top:0;"> Chat with {name} </h1>"""
-
-description_top = f"""\
-<div align="left">
-<p> Currently Running: <a href="{href}">{model}</a></p>
-</div>
-"""
-
-description = """\
-<div align="center" style="margin:16px 0">
-The demo is built on <a href="https://github.com/hwchase17/langchain">LangChain</a>.
-</div>
-"""
-
-CONCURRENT_COUNT = 1
-
-
-def qa(chatbot):
-    user_msg = chatbot[-1][0]
-    q = Queue()
-    result = Queue()
-    job_done = object()
-
-    def task(question, chat_history):
-        start = timer()
-        inputs = {"question": question}
-        if not chat_with_llama_2:
-            inputs["chat_history"] = chat_history
-        ret = qa_chain.call_chain(inputs, None, q)
-        end = timer()
-
-        print(f"Completed in {end - start:.3f}s")
-        print_llm_response(ret)
-
-        q.put(job_done)
-        result.put(ret)
-
-    with start_blocking_portal() as portal:
-        chat_history = []
-        if chat_history_enabled:
-            for i in range(len(chatbot) - 1):
-                element = chatbot[i]
-                item = (element[0] or "", element[1] or "")
-                chat_history.append(item)
-
-        portal.start_task_soon(task, user_msg, chat_history)
-
-        content = ""
-        count = 2 if len(chat_history) > 0 else 1
-
-        while count > 0:
-            while q.empty():
-                print("nothing generated yet - retry in 0.5s")
-                time.sleep(0.5)
-
-            for next_token in llm_loader.streamer:
-                if next_token is job_done:
-                    break
-                content += next_token or ""
-                chatbot[-1][1] = remove_extra_spaces(content)
-
-                if count == 1:
-                    yield chatbot
-
-            count -= 1
-
-        if not chat_with_llama_2:
-            chatbot[-1][1] += "\n\nSources:\n"
-            ret = result.get()
-            titles = []
-            for doc in ret["source_documents"]:
-                page = doc.metadata["page"] + 1
-                url = f"{doc.metadata['url']}#page={page}"
-                file_name = doc.metadata["source"].split("/")[-1]
-                title = f"{file_name} Page: {page}"
-                if title not in titles:
-                    titles.append(title)
-                    chatbot[-1][1] += f"1. [{title}]({url})\n"
-
-        yield chatbot
-
-
-with open("assets/custom.css", "r", encoding="utf-8") as f:
-    customCSS = f.read()
-
-with gr.Blocks(css=customCSS) as demo:
-    user_question = gr.State("")
-    with gr.Row():
-        gr.HTML(title)
-    gr.Markdown(description_top)
-    with gr.Row().style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row():
-                chatbot = gr.Chatbot(elem_id="inflaton_chatbot").style(height="100%")
-            with gr.Row():
-                with gr.Column(scale=2):
-                    user_input = gr.Textbox(
-                        show_label=False, placeholder="Enter your question here"
-                    ).style(container=False)
-                with gr.Column(
-                    min_width=70,
-                ):
-                    submitBtn = gr.Button("Send")
-                with gr.Column(
-                    min_width=70,
-                ):
-                    clearBtn = gr.Button("Clear")
-        if show_param_settings:
-            with gr.Column():
-                with gr.Column(
-                    min_width=50,
-                ):
-                    with gr.Tab(label="Parameter Setting"):
-                        gr.Markdown("# Parameters")
-                        top_p = gr.Slider(
-                            minimum=-0,
-                            maximum=1.0,
-                            value=0.95,
-                            step=0.05,
-                            # interactive=True,
-                            label="Top-p",
-                        )
-                        temperature = gr.Slider(
-                            minimum=0.1,
-                            maximum=2.0,
-                            value=0,
-                            step=0.1,
-                            # interactive=True,
-                            label="Temperature",
-                        )
-                        max_new_tokens = gr.Slider(
-                            minimum=0,
-                            maximum=2048,
-                            value=2048,
-                            step=8,
-                            # interactive=True,
-                            label="Max Generation Tokens",
-                        )
-                        max_context_length_tokens = gr.Slider(
-                            minimum=0,
-                            maximum=4096,
-                            value=4096,
-                            step=128,
-                            # interactive=True,
-                            label="Max Context Tokens",
-                        )
-    gr.Markdown(description)
-
-    def chat(user_message, history):
-        return "", history + [[user_message, None]]
-
-    user_input.submit(
-        chat, [user_input, chatbot], [user_input, chatbot], queue=True
-    ).then(qa, chatbot, chatbot)
-
-    submitBtn.click(
-        chat, [user_input, chatbot], [user_input, chatbot], queue=True, api_name="chat"
-    ).then(qa, chatbot, chatbot)
-
-    def reset():
-        return "", []
-
-    clearBtn.click(
-        reset,
-        outputs=[user_input, chatbot],
-        show_progress=True,
-        api_name="reset",
-    )
-
-demo.title = "Chat with AI Books" if chat_with_llama_2 else "Chat with Llama-2"
-demo.queue(concurrency_count=CONCURRENT_COUNT).launch(share=share_gradio_app)
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Direct X 11 FULL[Offline]AryaN L33T[LittleFairyRG] Serial Key Keygen.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Direct X 11 FULL[Offline]AryaN L33T[LittleFairyRG] Serial Key Keygen.md
deleted file mode 100644
index bf6414ff24a1e9cdabb45572ded2f6276b095888..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Direct X 11 FULL[Offline]AryaN L33T[LittleFairyRG] Serial Key Keygen.md	
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Intel Core I3 2310m Ethernet Driver UPDATED Download.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Intel Core I3 2310m Ethernet Driver UPDATED Download.md
deleted file mode 100644
index 5889f6615a38b8ce9b8d028e90366a07574da3a7..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Intel Core I3 2310m Ethernet Driver UPDATED Download.md	
+++ /dev/null
@@ -1,106 +0,0 @@
-## intel core i3 2310m ethernet driver download
-
-
-
- 
-
-  
-
- 
-
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-
-
-
- 
-
- 
-
- 
-
- 
-
- Here is a possible title and article for your keyword:  
-
-# How to Download and Install Intel Core i3-2310M Ethernet Driver
-
- 
-
-If you have an Intel Core i3-2310M processor in your laptop or desktop, you may need to download and install the latest Ethernet driver to ensure a stable and fast internet connection. The Ethernet driver is a software component that enables your device to communicate with the network adapter and the router. Without it, you may experience slow or intermittent internet access, or even no connection at all.
-
- 
-
-In this article, we will show you how to download and install the Intel Core i3-2310M Ethernet driver from the official Intel website. This is the safest and most reliable source of drivers for your processor, as they are tested and verified by Intel. You can also use other third-party websites or software tools to download drivers, but they may not be compatible or up-to-date with your device.
-
- 
-
-## Steps to Download and Install Intel Core i3-2310M Ethernet Driver
-
- 
-
-1. Go to the [Intel Core i3-2310M Processor Downloads page](https://www.intel.com/content/www/us/en/products/sku/52220/intel-core-i32310m-processor-3m-cache-2-10-ghz/downloads.html).
-
-2. Under the "Drivers" section, find the "Ethernet" category and click on "View All".
-
-3. Select the driver that matches your operating system and click on "Download".
-
-4. Save the file to a convenient location on your device.
-
-5. Double-click on the file to launch the installer.
-
-6. Follow the on-screen instructions to complete the installation.
-
-7. Restart your device if prompted.
-
-
-
-Congratulations! You have successfully downloaded and installed the Intel Core i3-2310M Ethernet driver. You should now be able to enjoy a smooth and fast internet connection on your device.
-
- 
-
-## Troubleshooting Tips
-
- 
-
-If you encounter any issues with downloading or installing the driver, here are some tips that may help:
-
-
-
-- Make sure you have a stable internet connection before downloading the driver.
-
-- Check if you have enough disk space on your device to store and install the driver.
-
-- Run a virus scan on your device to ensure there are no malware or corrupted files that may interfere with the installation.
-
-- Update your operating system and other drivers to the latest versions.
-
-- Contact Intel customer support or visit their online forums for further assistance.
-
-
-
-Here is a possible continuation of the article:  
-
-## Benefits of Intel Core i3-2310M Processor
-
- 
-
-The Intel Core i3-2310M processor is a dual-core processor that offers a balance of performance and power efficiency for your laptop or desktop. It has a base frequency of 2.10 GHz and a maximum turbo frequency of 2.90 GHz, which means it can handle multiple tasks and applications smoothly and quickly. It also supports Hyper-Threading Technology, which allows each core to run two threads simultaneously, increasing the processing capacity and responsiveness of your device.
-
- 
-
-The Intel Core i3-2310M processor also features Intel HD Graphics 3000, which delivers stunning visuals and graphics for your games, movies, and photos. It supports DirectX 10.1, OpenGL 3.1, and OpenCL 1.1 standards, as well as Intel Quick Sync Video, which accelerates video encoding and decoding for faster and smoother media playback and editing.
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- 
-
-Another benefit of the Intel Core i3-2310M processor is its low power consumption and thermal design power (TDP) of 35 W, which means it can run longer on battery life and generate less heat and noise. It also supports Intel Smart Cache Technology, which allocates the cache memory dynamically between the cores according to the workload, improving the performance and efficiency of your device.
-
- 
-
-With the Intel Core i3-2310M processor, you can enjoy a fast and reliable internet connection with the Ethernet driver, as well as a powerful and versatile computing experience for your everyday needs.
-
- dfd1c89656
-
- 
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diff --git a/spaces/inreVtussa/clothingai/Examples/Boxedapp Sdk Crack [EXCLUSIVE].md b/spaces/inreVtussa/clothingai/Examples/Boxedapp Sdk Crack [EXCLUSIVE].md
deleted file mode 100644
index 188a77aed430281ad15600d193243d98b46271aa..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Boxedapp Sdk Crack [EXCLUSIVE].md	
+++ /dev/null
@@ -1,108 +0,0 @@
-<br />
-<h1>BoxedApp SDK Crack: A Powerful Tool for Creating Portable Applications</h1>
-
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-<h2>How Does BoxedApp SDK Crack Work?</h2>
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-<p>BoxedApp SDK Crack works by intercepting the system calls that an application makes to access the file system and the registry. It then redirects these calls to its own virtual file system and virtual registry, which are stored in the memory. The application "thinks" that it is working with real files and registry entries, but in fact it is working with virtual ones.</p>
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-<p>For example, suppose your application uses a Flash ActiveX player to display a Flash movie or video. The end users would need a Flash player ActiveX to allow your application work properly. Also, keep in mind that Flash player is not capable of loading files directly from the memory. That exposes two major problems: first, you would have to install a Flash player ActiveX, and second, you would have to have the movie in a file.</p>
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-<p>BoxedApp SDK Crack solves these problems: you simply create a virtual file that contains the flash movie, another virtual file that contains the Flash player ActiveX DLL, and virtual registry entries that point to that virtual file. That's it. Now the application "thinks" that the Flash player ActiveX is actually installed, so the Flash player works just as if the movie file was actually there.</p>
-
-<h2>What Are the Benefits of BoxedApp SDK Crack?</h2>
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-<p>BoxedApp SDK Crack has many benefits for developers and end users alike. Some of them are:</p>
-
-<ul>
-<li>It simplifies the deployment process. You don't have to worry about installing or registering any components or files on the target system. You just need to copy your EXE file and run it.</li>
-<li>It increases the compatibility and portability of your applications. You don't have to worry about different versions of Windows, different service packs, different runtimes, or different security settings. Your applications will run on any Windows system from Windows XP to Windows 10.</li>
-<li>It protects your applications from reverse engineering and cracking. Since your applications are packed into a single EXE file, it is harder for hackers to analyze or modify them. You can also encrypt or compress your EXE file to make it even more secure.</li>
-<li>It improves the performance and stability of your applications. Since your applications don't use any external files or components, they don't depend on their availability or integrity. They also don't create any temporary files or registry entries that could slow down or corrupt the system.</li>
-</ul>
-
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-<li>ActiveX and COM virtualization: BoxedApp SDK Crack enables you to register an ActiveX in the virtual registry and then let the application work as usually: it will "see" required registry entries. At the same time, real registry stays unmodified.</li>
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-</blockquote>
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-<blockquote>
-<p>"I have used BoxedApp SDK Crack to create a portable application that uses a Flash ActiveX player to display a Flash movie. It was very simple and fast to create a virtual file that contains the flash movie, another virtual file that contains the Flash player ActiveX DLL, and virtual registry entries that point to that virtual file. The application works perfectly on any Windows system without installing or registering anything. BoxedApp SDK Crack is amazing!"</p>
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-<cite>- Bob Lee, Security Expert</cite>
-</blockquote>
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-<h2>Conclusion</h2>
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-<p>BoxedApp SDK Crack is a developer library that provides a set of functions for emulating a file system and a system registry for an application. Using these functions, you can create virtual files, fake registry entries, keys and values. You can launch processes from the memory directly, use ActiveX without registration, embed runtimes like .Net, Flash and VC++ redistributable.</p>
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diff --git a/spaces/inreVtussa/clothingai/Examples/Busy Accounting Software 3.5 Free Download.md b/spaces/inreVtussa/clothingai/Examples/Busy Accounting Software 3.5 Free Download.md
deleted file mode 100644
index 9980af8fad58e55c9ba67647ca5d33b19fd3fb9b..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Busy Accounting Software 3.5 Free Download.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-<h2>Busy Accounting Software 3.5 Free Download</h2><br /><p><b><b>DOWNLOAD</b> >>> <a href="https://tiurll.com/2uCjly">https://tiurll.com/2uCjly</a></b></p><br /><br />
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-After this upgrade, the capability to convert the existing database into a new format was enhanced.
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-14. The software also provides a function called PDFTool to convert PDF files to XPS. As an ongoing service, A Xpress post is delivered at the end of each week with the latest accounting statements for your Xpress Quickbooks.
-
-Software download blog
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-If the feature is missing, contact the company for support. The software enables your users to submit tax returns online, as well as generate W2s, 1099s, and POs. Other types of balance sheet accounts can be easily created. Unfortunately, I see several times on websites or forum posts that the company no longer supports this product.
-
-Visit the Xpress website to contact the company and learn more about this tool. The monthly service plan is the most cost effective way to manage your Xpress Quickbooks. Publisher Support We provide phone and email support to Xpress users. The software supports multiple company types, such as sole proprietorships, corporations, partnerships, and LLCs.
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-Is the product still being supported? The software supports multiple company types, such as sole proprietorships, corporations, partnerships, and LLCs. Since the company doesn't offer any other supported software for Xpress, we don't recommend it. As a Certified QuickBooks ProAdvisor, I receive training, support, and a range of productivity and business management tools.
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-By continuing to use this site, you agree to the use of cookies.
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-Our user interface and search features are powered by Google. Support, price, download, and install help. View product documentation and search for customer reviews.
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-What is the difference between the annual and the monthly service plans? Your account was successfully upgraded.
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-The tool features a clean, user-friendly interface. In this report, I am going to talk about what you will find when you look at the tool's features.
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diff --git a/spaces/ismot/8testi1/models/__init__.py b/spaces/ismot/8testi1/models/__init__.py
deleted file mode 100644
index 84952a8167bc2975913a6def6b4f027d566552a9..0000000000000000000000000000000000000000
--- a/spaces/ismot/8testi1/models/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# init
\ No newline at end of file
diff --git a/spaces/ivntl/MMS/vits/text/cleaners.py b/spaces/ivntl/MMS/vits/text/cleaners.py
deleted file mode 100644
index 2658f667a7d59ca99a3e16ba0c157d2ab5d795eb..0000000000000000000000000000000000000000
--- a/spaces/ivntl/MMS/vits/text/cleaners.py
+++ /dev/null
@@ -1,100 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-from phonemizer import phonemize
-
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_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):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def english_cleaners(text):
-  '''Pipeline for English text, including abbreviation expansion.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = expand_abbreviations(text)
-  phonemes = phonemize(text, language='en-us', backend='espeak', strip=True)
-  phonemes = collapse_whitespace(phonemes)
-  return phonemes
-
-
-def english_cleaners2(text):
-  '''Pipeline for English text, including abbreviation expansion. + punctuation + stress'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = expand_abbreviations(text)
-  phonemes = phonemize(text, language='en-us', backend='espeak', strip=True, preserve_punctuation=True, with_stress=True)
-  phonemes = collapse_whitespace(phonemes)
-  return phonemes
diff --git a/spaces/jbilcke-hf/VideoQuest/src/app/games/flamenco.ts b/spaces/jbilcke-hf/VideoQuest/src/app/games/flamenco.ts
deleted file mode 100644
index c58db6d691d3810714cf8b34a7288a0759391b35..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/app/games/flamenco.ts
+++ /dev/null
@@ -1,80 +0,0 @@
-import { edu } from "@/lib/fonts"
-import { Game } from "./types"
-import { InventoryItem } from "../../types"
-
-const initialSituation = [
-  `beautiful view of an art deco building in new york`,
-  `looking up`,
-  `entrance desk`,
-  `pigeon character`,
-  `day of the dead makeup`,
-  `artdeco bridge`,
-].join(", ")
-
-const initialActionnables = [
-  "sun",
-  "face",
-  "person",
-  "building",
-  "light",
-  "decoration",
-  "box",
-  "desk",
-  "gate",
-  "door"
-]
-
-const inventory: InventoryItem[] = [
-  {
-    name: "burger",
-    title: "Burger",
-    caption: "",
-    description: "I forgot to eat it."
-  },
-  {
-    name: "chicken",
-    title: "Chicken",
-    caption: "",
-    description: "Well it does eggs, so yes it is useful!"
-  },
-  {
-    name: "fishbone",
-    title: "Fishbone",
-    caption: "",
-    description: "Maybe I could pick some locks with it?"
-  },
-  {
-    name: "tentacle",
-    title: "Tentacle",
-    caption: "",
-    description: "I found this strange tentacle.. this is evidence!"
-  },
-]
-
-export const game: Game = {
-  title: "Sad Flamenco",
-  type: "flamenco",
-  description: [
-    "The game is a role playing adventure set in 1920 mexico, inspired by the Grim Fandango game, with mexican, art deco and aztec influences.",
-    "The player is Lenny, a travel agent from the world of the dead, who try to find customers to escort safely to heaven.",
-    "The player can click around to move to new scenes, find or activate artifacts.",
-    "They can also use objects from their inventory.",
-  ],
-  engines: [
-    "cartesian_image",
-    "cartesian_video",
-    "spherical_image",
-  ],
-  className: edu.className,
-  initialSituation,
-  initialActionnables,
-  inventory,
-  getScenePrompt: (situation?: string) => [
-    `photo of an artdeco scene`,
-    `grimfandango screenshot`,
-    `unreal engine`,
-    `1920 mexico`,
-    situation || initialSituation,
-  ]
-}
-
diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/checkbox.tsx b/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/checkbox.tsx
deleted file mode 100644
index 5850485b9fecba303bdba1849e5a7b6329300af4..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/checkbox.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as CheckboxPrimitive from "@radix-ui/react-checkbox"
-import { Check } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-
-const Checkbox = React.forwardRef<
-  React.ElementRef<typeof CheckboxPrimitive.Root>,
-  React.ComponentPropsWithoutRef<typeof CheckboxPrimitive.Root>
->(({ className, ...props }, ref) => (
-  <CheckboxPrimitive.Root
-    ref={ref}
-    className={cn(
-      "peer h-4 w-4 shrink-0 rounded-sm border border-stone-200 border-stone-900 ring-offset-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-400 focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-stone-900 data-[state=checked]:text-stone-50 dark:border-stone-800 dark:border-stone-50 dark:ring-offset-stone-950 dark:focus-visible:ring-stone-800 dark:data-[state=checked]:bg-stone-50 dark:data-[state=checked]:text-stone-900",
-      className
-    )}
-    {...props}
-  >
-    <CheckboxPrimitive.Indicator
-      className={cn("flex items-center justify-center text-current")}
-    >
-      <Check className="h-4 w-4" />
-    </CheckboxPrimitive.Indicator>
-  </CheckboxPrimitive.Root>
-))
-Checkbox.displayName = CheckboxPrimitive.Root.displayName
-
-export { Checkbox }
diff --git a/spaces/jlmarrugom/voice_fixer_app/voicefixer/tools/modules/pqmf.py b/spaces/jlmarrugom/voice_fixer_app/voicefixer/tools/modules/pqmf.py
deleted file mode 100644
index 085eeddd7e9cea1e019e44dfcb8f2420f88c03be..0000000000000000000000000000000000000000
--- a/spaces/jlmarrugom/voice_fixer_app/voicefixer/tools/modules/pqmf.py
+++ /dev/null
@@ -1,116 +0,0 @@
-"""
-@File    :   subband_util.py
-@Contact :   liu.8948@buckeyemail.osu.edu
-@License :   (C)Copyright 2020-2021
-@Modify Time      @Author    @Version    @Desciption
-------------      -------    --------    -----------
-2020/4/3 4:54 PM   Haohe Liu      1.0         None
-"""
-
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-import numpy as np
-import os.path as op
-from scipy.io import loadmat
-
-
-def load_mat2numpy(fname=""):
-    if len(fname) == 0:
-        return None
-    else:
-        return loadmat(fname)
-
-
-class PQMF(nn.Module):
-    def __init__(self, N, M, project_root):
-        super().__init__()
-        self.N = N  # nsubband
-        self.M = M  # nfilter
-        try:
-            assert (N, M) in [(8, 64), (4, 64), (2, 64)]
-        except:
-            print("Warning:", N, "subbandand ", M, " filter is not supported")
-        self.pad_samples = 64
-        self.name = str(N) + "_" + str(M) + ".mat"
-        self.ana_conv_filter = nn.Conv1d(
-            1, out_channels=N, kernel_size=M, stride=N, bias=False
-        )
-        data = load_mat2numpy(
-            op.join(
-                project_root,
-                "arnold_workspace/restorer/tools/pytorch/modules/filters/f_"
-                + self.name,
-            )
-        )
-        data = data["f"].astype(np.float32) / N
-        data = np.flipud(data.T).T
-        data = np.reshape(data, (N, 1, M)).copy()
-        dict_new = self.ana_conv_filter.state_dict().copy()
-        dict_new["weight"] = torch.from_numpy(data)
-        self.ana_pad = nn.ConstantPad1d((M - N, 0), 0)
-        self.ana_conv_filter.load_state_dict(dict_new)
-
-        self.syn_pad = nn.ConstantPad1d((0, M // N - 1), 0)
-        self.syn_conv_filter = nn.Conv1d(
-            N, out_channels=N, kernel_size=M // N, stride=1, bias=False
-        )
-        gk = load_mat2numpy(
-            op.join(
-                project_root,
-                "arnold_workspace/restorer/tools/pytorch/modules/filters/h_"
-                + self.name,
-            )
-        )
-        gk = gk["h"].astype(np.float32)
-        gk = np.transpose(np.reshape(gk, (N, M // N, N)), (1, 0, 2)) * N
-        gk = np.transpose(gk[::-1, :, :], (2, 1, 0)).copy()
-        dict_new = self.syn_conv_filter.state_dict().copy()
-        dict_new["weight"] = torch.from_numpy(gk)
-        self.syn_conv_filter.load_state_dict(dict_new)
-
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def __analysis_channel(self, inputs):
-        return self.ana_conv_filter(self.ana_pad(inputs))
-
-    def __systhesis_channel(self, inputs):
-        ret = self.syn_conv_filter(self.syn_pad(inputs)).permute(0, 2, 1)
-        return torch.reshape(ret, (ret.shape[0], 1, -1))
-
-    def analysis(self, inputs):
-        """
-        :param inputs: [batchsize,channel,raw_wav],value:[0,1]
-        :return:
-        """
-        inputs = F.pad(inputs, ((0, self.pad_samples)))
-        ret = None
-        for i in range(inputs.size()[1]):  # channels
-            if ret is None:
-                ret = self.__analysis_channel(inputs[:, i : i + 1, :])
-            else:
-                ret = torch.cat(
-                    (ret, self.__analysis_channel(inputs[:, i : i + 1, :])), dim=1
-                )
-        return ret
-
-    def synthesis(self, data):
-        """
-        :param data: [batchsize,self.N*K,raw_wav_sub],value:[0,1]
-        :return:
-        """
-        ret = None
-        # data = F.pad(data,((0,self.pad_samples//self.N)))
-        for i in range(data.size()[1]):  # channels
-            if i % self.N == 0:
-                if ret is None:
-                    ret = self.__systhesis_channel(data[:, i : i + self.N, :])
-                else:
-                    new = self.__systhesis_channel(data[:, i : i + self.N, :])
-                    ret = torch.cat((ret, new), dim=1)
-        ret = ret[..., : -self.pad_samples]
-        return ret
-
-    def forward(self, inputs):
-        return self.ana_conv_filter(self.ana_pad(inputs))
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageStat.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageStat.py
deleted file mode 100644
index b7ebddf066ab6eb115a79d6bc34e31ab0c1569bd..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/ImageStat.py
+++ /dev/null
@@ -1,148 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# global image statistics
-#
-# History:
-# 1996-04-05 fl   Created
-# 1997-05-21 fl   Added mask; added rms, var, stddev attributes
-# 1997-08-05 fl   Added median
-# 1998-07-05 hk   Fixed integer overflow error
-#
-# Notes:
-# This class shows how to implement delayed evaluation of attributes.
-# To get a certain value, simply access the corresponding attribute.
-# The __getattr__ dispatcher takes care of the rest.
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996-97.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import functools
-import math
-import operator
-
-
-class Stat:
-    def __init__(self, image_or_list, mask=None):
-        try:
-            if mask:
-                self.h = image_or_list.histogram(mask)
-            else:
-                self.h = image_or_list.histogram()
-        except AttributeError:
-            self.h = image_or_list  # assume it to be a histogram list
-        if not isinstance(self.h, list):
-            msg = "first argument must be image or list"
-            raise TypeError(msg)
-        self.bands = list(range(len(self.h) // 256))
-
-    def __getattr__(self, id):
-        """Calculate missing attribute"""
-        if id[:4] == "_get":
-            raise AttributeError(id)
-        # calculate missing attribute
-        v = getattr(self, "_get" + id)()
-        setattr(self, id, v)
-        return v
-
-    def _getextrema(self):
-        """Get min/max values for each band in the image"""
-
-        def minmax(histogram):
-            n = 255
-            x = 0
-            for i in range(256):
-                if histogram[i]:
-                    n = min(n, i)
-                    x = max(x, i)
-            return n, x  # returns (255, 0) if there's no data in the histogram
-
-        v = []
-        for i in range(0, len(self.h), 256):
-            v.append(minmax(self.h[i:]))
-        return v
-
-    def _getcount(self):
-        """Get total number of pixels in each layer"""
-
-        v = []
-        for i in range(0, len(self.h), 256):
-            v.append(functools.reduce(operator.add, self.h[i : i + 256]))
-        return v
-
-    def _getsum(self):
-        """Get sum of all pixels in each layer"""
-
-        v = []
-        for i in range(0, len(self.h), 256):
-            layer_sum = 0.0
-            for j in range(256):
-                layer_sum += j * self.h[i + j]
-            v.append(layer_sum)
-        return v
-
-    def _getsum2(self):
-        """Get squared sum of all pixels in each layer"""
-
-        v = []
-        for i in range(0, len(self.h), 256):
-            sum2 = 0.0
-            for j in range(256):
-                sum2 += (j**2) * float(self.h[i + j])
-            v.append(sum2)
-        return v
-
-    def _getmean(self):
-        """Get average pixel level for each layer"""
-
-        v = []
-        for i in self.bands:
-            v.append(self.sum[i] / self.count[i])
-        return v
-
-    def _getmedian(self):
-        """Get median pixel level for each layer"""
-
-        v = []
-        for i in self.bands:
-            s = 0
-            half = self.count[i] // 2
-            b = i * 256
-            for j in range(256):
-                s = s + self.h[b + j]
-                if s > half:
-                    break
-            v.append(j)
-        return v
-
-    def _getrms(self):
-        """Get RMS for each layer"""
-
-        v = []
-        for i in self.bands:
-            v.append(math.sqrt(self.sum2[i] / self.count[i]))
-        return v
-
-    def _getvar(self):
-        """Get variance for each layer"""
-
-        v = []
-        for i in self.bands:
-            n = self.count[i]
-            v.append((self.sum2[i] - (self.sum[i] ** 2.0) / n) / n)
-        return v
-
-    def _getstddev(self):
-        """Get standard deviation for each layer"""
-
-        v = []
-        for i in self.bands:
-            v.append(math.sqrt(self.var[i]))
-        return v
-
-
-Global = Stat  # compatibility
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/parser/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/parser/__init__.py
deleted file mode 100644
index d174b0e4dcc472999b75e55ebb88af320ae38081..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/parser/__init__.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# -*- coding: utf-8 -*-
-from ._parser import parse, parser, parserinfo, ParserError
-from ._parser import DEFAULTPARSER, DEFAULTTZPARSER
-from ._parser import UnknownTimezoneWarning
-
-from ._parser import __doc__
-
-from .isoparser import isoparser, isoparse
-
-__all__ = ['parse', 'parser', 'parserinfo',
-           'isoparse', 'isoparser',
-           'ParserError',
-           'UnknownTimezoneWarning']
-
-
-###
-# Deprecate portions of the private interface so that downstream code that
-# is improperly relying on it is given *some* notice.
-
-
-def __deprecated_private_func(f):
-    from functools import wraps
-    import warnings
-
-    msg = ('{name} is a private function and may break without warning, '
-           'it will be moved and or renamed in future versions.')
-    msg = msg.format(name=f.__name__)
-
-    @wraps(f)
-    def deprecated_func(*args, **kwargs):
-        warnings.warn(msg, DeprecationWarning)
-        return f(*args, **kwargs)
-
-    return deprecated_func
-
-def __deprecate_private_class(c):
-    import warnings
-
-    msg = ('{name} is a private class and may break without warning, '
-           'it will be moved and or renamed in future versions.')
-    msg = msg.format(name=c.__name__)
-
-    class private_class(c):
-        __doc__ = c.__doc__
-
-        def __init__(self, *args, **kwargs):
-            warnings.warn(msg, DeprecationWarning)
-            super(private_class, self).__init__(*args, **kwargs)
-
-    private_class.__name__ = c.__name__
-
-    return private_class
-
-
-from ._parser import _timelex, _resultbase
-from ._parser import _tzparser, _parsetz
-
-_timelex = __deprecate_private_class(_timelex)
-_tzparser = __deprecate_private_class(_tzparser)
-_resultbase = __deprecate_private_class(_resultbase)
-_parsetz = __deprecated_private_func(_parsetz)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/tz/win.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/tz/win.py
deleted file mode 100644
index cde07ba792c40903f0c334839140173b39fd8124..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dateutil/tz/win.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module provides an interface to the native time zone data on Windows,
-including :py:class:`datetime.tzinfo` implementations.
-
-Attempting to import this module on a non-Windows platform will raise an
-:py:obj:`ImportError`.
-"""
-# This code was originally contributed by Jeffrey Harris.
-import datetime
-import struct
-
-from six.moves import winreg
-from six import text_type
-
-try:
-    import ctypes
-    from ctypes import wintypes
-except ValueError:
-    # ValueError is raised on non-Windows systems for some horrible reason.
-    raise ImportError("Running tzwin on non-Windows system")
-
-from ._common import tzrangebase
-
-__all__ = ["tzwin", "tzwinlocal", "tzres"]
-
-ONEWEEK = datetime.timedelta(7)
-
-TZKEYNAMENT = r"SOFTWARE\Microsoft\Windows NT\CurrentVersion\Time Zones"
-TZKEYNAME9X = r"SOFTWARE\Microsoft\Windows\CurrentVersion\Time Zones"
-TZLOCALKEYNAME = r"SYSTEM\CurrentControlSet\Control\TimeZoneInformation"
-
-
-def _settzkeyname():
-    handle = winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE)
-    try:
-        winreg.OpenKey(handle, TZKEYNAMENT).Close()
-        TZKEYNAME = TZKEYNAMENT
-    except WindowsError:
-        TZKEYNAME = TZKEYNAME9X
-    handle.Close()
-    return TZKEYNAME
-
-
-TZKEYNAME = _settzkeyname()
-
-
-class tzres(object):
-    """
-    Class for accessing ``tzres.dll``, which contains timezone name related
-    resources.
-
-    .. versionadded:: 2.5.0
-    """
-    p_wchar = ctypes.POINTER(wintypes.WCHAR)        # Pointer to a wide char
-
-    def __init__(self, tzres_loc='tzres.dll'):
-        # Load the user32 DLL so we can load strings from tzres
-        user32 = ctypes.WinDLL('user32')
-
-        # Specify the LoadStringW function
-        user32.LoadStringW.argtypes = (wintypes.HINSTANCE,
-                                       wintypes.UINT,
-                                       wintypes.LPWSTR,
-                                       ctypes.c_int)
-
-        self.LoadStringW = user32.LoadStringW
-        self._tzres = ctypes.WinDLL(tzres_loc)
-        self.tzres_loc = tzres_loc
-
-    def load_name(self, offset):
-        """
-        Load a timezone name from a DLL offset (integer).
-
-        >>> from dateutil.tzwin import tzres
-        >>> tzr = tzres()
-        >>> print(tzr.load_name(112))
-        'Eastern Standard Time'
-
-        :param offset:
-            A positive integer value referring to a string from the tzres dll.
-
-        .. note::
-
-            Offsets found in the registry are generally of the form
-            ``@tzres.dll,-114``. The offset in this case is 114, not -114.
-
-        """
-        resource = self.p_wchar()
-        lpBuffer = ctypes.cast(ctypes.byref(resource), wintypes.LPWSTR)
-        nchar = self.LoadStringW(self._tzres._handle, offset, lpBuffer, 0)
-        return resource[:nchar]
-
-    def name_from_string(self, tzname_str):
-        """
-        Parse strings as returned from the Windows registry into the time zone
-        name as defined in the registry.
-
-        >>> from dateutil.tzwin import tzres
-        >>> tzr = tzres()
-        >>> print(tzr.name_from_string('@tzres.dll,-251'))
-        'Dateline Daylight Time'
-        >>> print(tzr.name_from_string('Eastern Standard Time'))
-        'Eastern Standard Time'
-
-        :param tzname_str:
-            A timezone name string as returned from a Windows registry key.
-
-        :return:
-            Returns the localized timezone string from tzres.dll if the string
-            is of the form `@tzres.dll,-offset`, else returns the input string.
-        """
-        if not tzname_str.startswith('@'):
-            return tzname_str
-
-        name_splt = tzname_str.split(',-')
-        try:
-            offset = int(name_splt[1])
-        except:
-            raise ValueError("Malformed timezone string.")
-
-        return self.load_name(offset)
-
-
-class tzwinbase(tzrangebase):
-    """tzinfo class based on win32's timezones available in the registry."""
-    def __init__(self):
-        raise NotImplementedError('tzwinbase is an abstract base class')
-
-    def __eq__(self, other):
-        # Compare on all relevant dimensions, including name.
-        if not isinstance(other, tzwinbase):
-            return NotImplemented
-
-        return  (self._std_offset == other._std_offset and
-                 self._dst_offset == other._dst_offset and
-                 self._stddayofweek == other._stddayofweek and
-                 self._dstdayofweek == other._dstdayofweek and
-                 self._stdweeknumber == other._stdweeknumber and
-                 self._dstweeknumber == other._dstweeknumber and
-                 self._stdhour == other._stdhour and
-                 self._dsthour == other._dsthour and
-                 self._stdminute == other._stdminute and
-                 self._dstminute == other._dstminute and
-                 self._std_abbr == other._std_abbr and
-                 self._dst_abbr == other._dst_abbr)
-
-    @staticmethod
-    def list():
-        """Return a list of all time zones known to the system."""
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            with winreg.OpenKey(handle, TZKEYNAME) as tzkey:
-                result = [winreg.EnumKey(tzkey, i)
-                          for i in range(winreg.QueryInfoKey(tzkey)[0])]
-        return result
-
-    def display(self):
-        """
-        Return the display name of the time zone.
-        """
-        return self._display
-
-    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
-
-        dston = picknthweekday(year, self._dstmonth, self._dstdayofweek,
-                               self._dsthour, self._dstminute,
-                               self._dstweeknumber)
-
-        dstoff = picknthweekday(year, self._stdmonth, self._stddayofweek,
-                                self._stdhour, self._stdminute,
-                                self._stdweeknumber)
-
-        # Ambiguous dates default to the STD side
-        dstoff -= self._dst_base_offset
-
-        return dston, dstoff
-
-    def _get_hasdst(self):
-        return self._dstmonth != 0
-
-    @property
-    def _dst_base_offset(self):
-        return self._dst_base_offset_
-
-
-class tzwin(tzwinbase):
-    """
-    Time zone object created from the zone info in the Windows registry
-
-    These are similar to :py:class:`dateutil.tz.tzrange` objects in that
-    the time zone data is provided in the format of a single offset rule
-    for either 0 or 2 time zone transitions per year.
-
-    :param: name
-        The name of a Windows time zone key, e.g. "Eastern Standard Time".
-        The full list of keys can be retrieved with :func:`tzwin.list`.
-    """
-
-    def __init__(self, name):
-        self._name = name
-
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            tzkeyname = text_type("{kn}\\{name}").format(kn=TZKEYNAME, name=name)
-            with winreg.OpenKey(handle, tzkeyname) as tzkey:
-                keydict = valuestodict(tzkey)
-
-        self._std_abbr = keydict["Std"]
-        self._dst_abbr = keydict["Dlt"]
-
-        self._display = keydict["Display"]
-
-        # See http://ww_winreg.jsiinc.com/SUBA/tip0300/rh0398.htm
-        tup = struct.unpack("=3l16h", keydict["TZI"])
-        stdoffset = -tup[0]-tup[1]          # Bias + StandardBias * -1
-        dstoffset = stdoffset-tup[2]        # + DaylightBias * -1
-        self._std_offset = datetime.timedelta(minutes=stdoffset)
-        self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
-        # for the meaning see the win32 TIME_ZONE_INFORMATION structure docs
-        # http://msdn.microsoft.com/en-us/library/windows/desktop/ms725481(v=vs.85).aspx
-        (self._stdmonth,
-         self._stddayofweek,   # Sunday = 0
-         self._stdweeknumber,  # Last = 5
-         self._stdhour,
-         self._stdminute) = tup[4:9]
-
-        (self._dstmonth,
-         self._dstdayofweek,   # Sunday = 0
-         self._dstweeknumber,  # Last = 5
-         self._dsthour,
-         self._dstminute) = tup[12:17]
-
-        self._dst_base_offset_ = self._dst_offset - self._std_offset
-        self.hasdst = self._get_hasdst()
-
-    def __repr__(self):
-        return "tzwin(%s)" % repr(self._name)
-
-    def __reduce__(self):
-        return (self.__class__, (self._name,))
-
-
-class tzwinlocal(tzwinbase):
-    """
-    Class representing the local time zone information in the Windows registry
-
-    While :class:`dateutil.tz.tzlocal` makes system calls (via the :mod:`time`
-    module) to retrieve time zone information, ``tzwinlocal`` retrieves the
-    rules directly from the Windows registry and creates an object like
-    :class:`dateutil.tz.tzwin`.
-
-    Because Windows does not have an equivalent of :func:`time.tzset`, on
-    Windows, :class:`dateutil.tz.tzlocal` instances will always reflect the
-    time zone settings *at the time that the process was started*, meaning
-    changes to the machine's time zone settings during the run of a program
-    on Windows will **not** be reflected by :class:`dateutil.tz.tzlocal`.
-    Because ``tzwinlocal`` reads the registry directly, it is unaffected by
-    this issue.
-    """
-    def __init__(self):
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            with winreg.OpenKey(handle, TZLOCALKEYNAME) as tzlocalkey:
-                keydict = valuestodict(tzlocalkey)
-
-            self._std_abbr = keydict["StandardName"]
-            self._dst_abbr = keydict["DaylightName"]
-
-            try:
-                tzkeyname = text_type('{kn}\\{sn}').format(kn=TZKEYNAME,
-                                                          sn=self._std_abbr)
-                with winreg.OpenKey(handle, tzkeyname) as tzkey:
-                    _keydict = valuestodict(tzkey)
-                    self._display = _keydict["Display"]
-            except OSError:
-                self._display = None
-
-        stdoffset = -keydict["Bias"]-keydict["StandardBias"]
-        dstoffset = stdoffset-keydict["DaylightBias"]
-
-        self._std_offset = datetime.timedelta(minutes=stdoffset)
-        self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
-        # For reasons unclear, in this particular key, the day of week has been
-        # moved to the END of the SYSTEMTIME structure.
-        tup = struct.unpack("=8h", keydict["StandardStart"])
-
-        (self._stdmonth,
-         self._stdweeknumber,  # Last = 5
-         self._stdhour,
-         self._stdminute) = tup[1:5]
-
-        self._stddayofweek = tup[7]
-
-        tup = struct.unpack("=8h", keydict["DaylightStart"])
-
-        (self._dstmonth,
-         self._dstweeknumber,  # Last = 5
-         self._dsthour,
-         self._dstminute) = tup[1:5]
-
-        self._dstdayofweek = tup[7]
-
-        self._dst_base_offset_ = self._dst_offset - self._std_offset
-        self.hasdst = self._get_hasdst()
-
-    def __repr__(self):
-        return "tzwinlocal()"
-
-    def __str__(self):
-        # str will return the standard name, not the daylight name.
-        return "tzwinlocal(%s)" % repr(self._std_abbr)
-
-    def __reduce__(self):
-        return (self.__class__, ())
-
-
-def picknthweekday(year, month, dayofweek, hour, minute, whichweek):
-    """ dayofweek == 0 means Sunday, whichweek 5 means last instance """
-    first = datetime.datetime(year, month, 1, hour, minute)
-
-    # This will work if dayofweek is ISO weekday (1-7) or Microsoft-style (0-6),
-    # Because 7 % 7 = 0
-    weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7) + 1)
-    wd = weekdayone + ((whichweek - 1) * ONEWEEK)
-    if (wd.month != month):
-        wd -= ONEWEEK
-
-    return wd
-
-
-def valuestodict(key):
-    """Convert a registry key's values to a dictionary."""
-    dout = {}
-    size = winreg.QueryInfoKey(key)[1]
-    tz_res = None
-
-    for i in range(size):
-        key_name, value, dtype = winreg.EnumValue(key, i)
-        if dtype == winreg.REG_DWORD or dtype == winreg.REG_DWORD_LITTLE_ENDIAN:
-            # If it's a DWORD (32-bit integer), it's stored as unsigned - convert
-            # that to a proper signed integer
-            if value & (1 << 31):
-                value = value - (1 << 32)
-        elif dtype == winreg.REG_SZ:
-            # If it's a reference to the tzres DLL, load the actual string
-            if value.startswith('@tzres'):
-                tz_res = tz_res or tzres()
-                value = tz_res.name_from_string(value)
-
-            value = value.rstrip('\x00')    # Remove trailing nulls
-
-        dout[key_name] = value
-
-    return dout
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/filelock/version.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/filelock/version.py
deleted file mode 100644
index 23c19a10e0ab8f0f2eeeab0e656b2d868837d450..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/filelock/version.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# file generated by setuptools_scm
-# don't change, don't track in version control
-__version__ = version = '3.12.4'
-__version_tuple__ = version_tuple = (3, 12, 4)
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/lib/triggerDownload.ts b/spaces/jordonpeter01/ai-comic-factory/src/lib/triggerDownload.ts
deleted file mode 100644
index e5627a26a4bba34bdf28279d265c6a71440d8136..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/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/juuxn/SimpleRVC/inference.py b/spaces/juuxn/SimpleRVC/inference.py
deleted file mode 100644
index 8f10fb90b1fefa429e88ec15d5b80467f6e4e648..0000000000000000000000000000000000000000
--- a/spaces/juuxn/SimpleRVC/inference.py
+++ /dev/null
@@ -1,248 +0,0 @@
-import infer_web
-import wget
-import os
-import scipy.io.wavfile as wavfile
-from utils import model
-import validators
-from myutils import delete_files
-
-class Inference:
-    
-    inference_cont = 0
-    
-    def __init__(
-        self,
-        model_name=None,
-        source_audio_path=None,
-        output_file_name=None,
-        feature_index_path="",
-        f0_file=None,
-        speaker_id=0,
-        transposition=0,
-        f0_method="harvest",
-        crepe_hop_length=160,
-        harvest_median_filter=3,
-        resample=0,
-        mix=1,
-        feature_ratio=0.78,
-        protection_amnt=0.33,
-        protect1=False
-    ):
-        Inference.inference_cont += 1
-        self._model_name = model_name
-        self._source_audio_path = source_audio_path
-        self._output_file_name = output_file_name
-        self._feature_index_path = feature_index_path
-        self._f0_file = f0_file
-        self._speaker_id = speaker_id
-        self._transposition = transposition
-        self._f0_method = f0_method
-        self._crepe_hop_length = crepe_hop_length
-        self._harvest_median_filter = harvest_median_filter
-        self._resample = resample
-        self._mix = mix
-        self._feature_ratio = feature_ratio
-        self._protection_amnt = protection_amnt
-        self._protect1 = protect1
-        self._id = Inference.inference_cont
-
-        if not os.path.exists("./hubert_base.pt"):
-            wget.download(
-                "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt", out="./hubert_base.pt")
-        
-        if not os.path.exists("./rmvpe.pt"):
-            wget.download(
-                "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt", out="./rmvpe.pt"
-            )
-    
-    @property
-    def id(self):
-        return self._id
-
-    @id.setter
-    def id(self, id):
-        self._id = id
-    
-    @property
-    def audio(self):
-        return self._audio
-
-    @audio.setter
-    def audio_file(self, audio):
-        self._audio_file = audio
-
-    @property
-    def model_name(self):
-        return self._model_name
-
-    @model_name.setter
-    def model_name(self, model_name):
-        self._model_name = model_name
-
-    @property
-    def source_audio_path(self):
-        return self._source_audio_path
-
-    @source_audio_path.setter
-    def source_audio_path(self, source_audio_path):
-        if not self._output_file_name:
-            self._output_file_name = os.path.join("./audio-outputs", os.path.basename(source_audio_path))
-        self._source_audio_path = source_audio_path
-
-    @property
-    def output_file_name(self):
-        return self._output_file_name
-
-    @output_file_name.setter
-    def output_file_name(self, output_file_name):
-        self._output_file_name = output_file_name
-
-    @property
-    def feature_index_path(self):
-        return self._feature_index_path
-
-    @feature_index_path.setter
-    def feature_index_path(self, feature_index_path):
-        self._feature_index_path = feature_index_path
-
-    @property
-    def f0_file(self):
-        return self._f0_file
-
-    @f0_file.setter
-    def f0_file(self, f0_file):
-        self._f0_file = f0_file
-
-    @property
-    def speaker_id(self):
-        return self._speaker_id
-
-    @speaker_id.setter
-    def speaker_id(self, speaker_id):
-        self._speaker_id = speaker_id
-
-    @property
-    def transposition(self):
-        return self._transposition
-
-    @transposition.setter
-    def transposition(self, transposition):
-        self._transposition = transposition
-
-    @property
-    def f0_method(self):
-        return self._f0_method
-
-    @f0_method.setter
-    def f0_method(self, f0_method):
-        self._f0_method = f0_method
-
-    @property
-    def crepe_hop_length(self):
-        return self._crepe_hop_length
-
-    @crepe_hop_length.setter
-    def crepe_hop_length(self, crepe_hop_length):
-        self._crepe_hop_length = crepe_hop_length
-
-    @property
-    def harvest_median_filter(self):
-        return self._harvest_median_filter
-
-    @crepe_hop_length.setter
-    def harvest_median_filter(self, harvest_median_filter):
-        self._harvest_median_filter = harvest_median_filter
-
-    @property
-    def resample(self):
-        return self._resample
-
-    @resample.setter
-    def resample(self, resample):
-        self._resample = resample
-
-    @property
-    def mix(self):
-        return self._mix
-
-    @mix.setter
-    def mix(self, mix):
-        self._mix = mix
-
-    @property
-    def feature_ratio(self):
-        return self._feature_ratio
-
-    @feature_ratio.setter
-    def feature_ratio(self, feature_ratio):
-        self._feature_ratio = feature_ratio
-
-    @property
-    def protection_amnt(self):
-        return self._protection_amnt
-
-    @protection_amnt.setter
-    def protection_amnt(self, protection_amnt):
-        self._protection_amnt = protection_amnt
-
-    @property
-    def protect1(self):
-        return self._protect1
-
-    @protect1.setter
-    def protect1(self, protect1):
-        self._protect1 = protect1
-
-    def run(self):
-        current_dir = os.getcwd()
-        modelname = model.model_downloader(
-            self._model_name, "./zips/", "./weights/")
-        
-        if not modelname:
-            return "No se ha podido descargar el modelo, intenta con otro enlace o intentalo más tarde."
-        
-        model_info = model.get_model(os.path.join(current_dir, 'weights') , modelname)
-        if not model_info:
-            return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde."
-
-        if not model_info.get('pth'):
-            return "No se encontrado un modelo valido, verifica el contenido del enlace e intentalo más tarde."
-        
-        index = model_info.get('index', '')
-        pth = model_info.get('pth', None)
-        
-        infer_web.get_vc(pth)
-        
-        conversion_data = infer_web.vc_single(
-            self.speaker_id,
-            self.source_audio_path,
-            self.source_audio_path,
-            self.transposition,
-            self.f0_file,
-            self.f0_method,
-            index,
-            index,
-            self.feature_ratio,
-            self.harvest_median_filter,
-            self.resample,
-            self.mix,
-            self.protection_amnt,
-            self.crepe_hop_length,
-        )
-        
-        if "Success." in conversion_data[0]:
-            wavfile.write(
-                "%s/%s" % ("audio-outputs",os.path.basename(self._output_file_name)),
-                conversion_data[1][0],
-                conversion_data[1][1],
-            )
-            return({
-                "success": True,
-                "file": self._output_file_name
-            })
-        else:
-            return({
-                "success": False,
-                "file": self._output_file_name
-            })
-            #print(conversion_data[0])
\ No newline at end of file
diff --git a/spaces/jykoh/gill/Dockerfile b/spaces/jykoh/gill/Dockerfile
deleted file mode 100644
index 3785dbbe0c70fb8faefca7f18f81c50d4088f389..0000000000000000000000000000000000000000
--- a/spaces/jykoh/gill/Dockerfile
+++ /dev/null
@@ -1,19 +0,0 @@
-FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-runtime as base
-
-RUN apt-get update && apt-get -y install git
-
-
-ENV HOME=/exp/fromage
-
-
-
-WORKDIR /exp/fromage
-COPY ./requirements.txt ./requirements.txt
-RUN python -m pip install -r ./requirements.txt
-RUN python -m pip install --upgrade Jinja2
-RUN python -m pip install gradio
-
-COPY . .
-RUN chmod -R a+rwX .
-
-CMD ["uvicorn", "app:main", "--host", "0.0.0.0", "--port", "7860"]
diff --git a/spaces/kaicheng/ChatGPT_ad/README.md b/spaces/kaicheng/ChatGPT_ad/README.md
deleted file mode 100644
index 79790f767ded0eb77b8129f8e960c65b8d166c14..0000000000000000000000000000000000000000
--- a/spaces/kaicheng/ChatGPT_ad/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.33.1
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/kcagle/AutoGPT/tests/unit/test_browse_scrape_links.py b/spaces/kcagle/AutoGPT/tests/unit/test_browse_scrape_links.py
deleted file mode 100644
index 0a3340e7397a997da96b8ab9828954230e1a3c20..0000000000000000000000000000000000000000
--- a/spaces/kcagle/AutoGPT/tests/unit/test_browse_scrape_links.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Generated by CodiumAI
-
-# Dependencies:
-# pip install pytest-mock
-import pytest
-
-from autogpt.commands.web_requests import scrape_links
-
-"""
-Code Analysis
-
-Objective:
-The objective of the 'scrape_links' function is to scrape hyperlinks from a
-given URL and return them in a formatted way.
-
-Inputs:
-- url: a string representing the URL to be scraped.
-
-Flow:
-1. Send a GET request to the given URL using the requests library and the user agent header from the config file.
-2. Check if the response contains an HTTP error. If it does, return "error".
-3. Parse the HTML content of the response using the BeautifulSoup library.
-4. Remove any script and style tags from the parsed HTML.
-5. Extract all hyperlinks from the parsed HTML using the 'extract_hyperlinks' function.
-6. Format the extracted hyperlinks using the 'format_hyperlinks' function.
-7. Return the formatted hyperlinks.
-
-Outputs:
-- A list of formatted hyperlinks.
-
-Additional aspects:
-- The function uses the 'requests' and 'BeautifulSoup' libraries to send HTTP
-requests and parse HTML content, respectively.
-- The 'extract_hyperlinks' function is called to extract hyperlinks from the parsed HTML.
-- The 'format_hyperlinks' function is called to format the extracted hyperlinks.
-- The function checks for HTTP errors and returns "error" if any are found.
-"""
-
-
-class TestScrapeLinks:
-    # Tests that the function returns a list of formatted hyperlinks when
-    # provided with a valid url that returns a webpage with hyperlinks.
-    def test_valid_url_with_hyperlinks(self):
-        url = "https://www.google.com"
-        result = scrape_links(url)
-        assert len(result) > 0
-        assert isinstance(result, list)
-        assert isinstance(result[0], str)
-
-    # Tests that the function returns correctly formatted hyperlinks when given a valid url.
-    def test_valid_url(self, mocker):
-        # Mock the requests.get() function to return a response with sample HTML containing hyperlinks
-        mock_response = mocker.Mock()
-        mock_response.status_code = 200
-        mock_response.text = (
-            "<html><body><a href='https://www.google.com'>Google</a></body></html>"
-        )
-        mocker.patch("requests.Session.get", return_value=mock_response)
-
-        # Call the function with a valid URL
-        result = scrape_links("https://www.example.com")
-
-        # Assert that the function returns correctly formatted hyperlinks
-        assert result == ["Google (https://www.google.com)"]
-
-    # Tests that the function returns "error" when given an invalid url.
-    def test_invalid_url(self, mocker):
-        # Mock the requests.get() function to return an HTTP error response
-        mock_response = mocker.Mock()
-        mock_response.status_code = 404
-        mocker.patch("requests.Session.get", return_value=mock_response)
-
-        # Call the function with an invalid URL
-        result = scrape_links("https://www.invalidurl.com")
-
-        # Assert that the function returns "error"
-        assert "Error:" in result
-
-    # Tests that the function returns an empty list when the html contains no hyperlinks.
-    def test_no_hyperlinks(self, mocker):
-        # Mock the requests.get() function to return a response with sample HTML containing no hyperlinks
-        mock_response = mocker.Mock()
-        mock_response.status_code = 200
-        mock_response.text = "<html><body><p>No hyperlinks here</p></body></html>"
-        mocker.patch("requests.Session.get", return_value=mock_response)
-
-        # Call the function with a URL containing no hyperlinks
-        result = scrape_links("https://www.example.com")
-
-        # Assert that the function returns an empty list
-        assert result == []
-
-    # Tests that scrape_links() correctly extracts and formats hyperlinks from
-    # a sample HTML containing a few hyperlinks.
-    def test_scrape_links_with_few_hyperlinks(self, mocker):
-        # Mock the requests.get() function to return a response with a sample HTML containing hyperlinks
-        mock_response = mocker.Mock()
-        mock_response.status_code = 200
-        mock_response.text = """
-            <html>
-                <body>
-                    <div id="google-link"><a href="https://www.google.com">Google</a></div>
-                    <div id="github"><a href="https://github.com">GitHub</a></div>
-                    <div id="CodiumAI"><a href="https://www.codium.ai">CodiumAI</a></div>
-                </body>
-            </html>
-        """
-        mocker.patch("requests.Session.get", return_value=mock_response)
-
-        # Call the function being tested
-        result = scrape_links("https://www.example.com")
-
-        # Assert that the function returns a list of formatted hyperlinks
-        assert isinstance(result, list)
-        assert len(result) == 3
-        assert result[0] == "Google (https://www.google.com)"
-        assert result[1] == "GitHub (https://github.com)"
-        assert result[2] == "CodiumAI (https://www.codium.ai)"
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/encoder/params_model.py b/spaces/keithhon/Real-Time-Voice-Cloning/encoder/params_model.py
deleted file mode 100644
index 3e356472fb5a27f370cb3920976a11d12a76c1b7..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/encoder/params_model.py
+++ /dev/null
@@ -1,11 +0,0 @@
-
-## Model parameters
-model_hidden_size = 256
-model_embedding_size = 256
-model_num_layers = 3
-
-
-## Training parameters
-learning_rate_init = 1e-4
-speakers_per_batch = 64
-utterances_per_speaker = 10
diff --git a/spaces/kellyxiaowei/OWL-ViT/README.md b/spaces/kellyxiaowei/OWL-ViT/README.md
deleted file mode 100644
index 6449d4d69290eb0f8a35f1fef3fb123c6c2a739f..0000000000000000000000000000000000000000
--- a/spaces/kellyxiaowei/OWL-ViT/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: OWL-ViT Demo
-emoji: 🔥
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.3
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: adirik/OWL-ViT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/keras-dreambooth/nuthatch-bird-demo/README.md b/spaces/keras-dreambooth/nuthatch-bird-demo/README.md
deleted file mode 100644
index 8a6d73ead73f677fe4edf2bcca0db7c32701574b..0000000000000000000000000000000000000000
--- a/spaces/keras-dreambooth/nuthatch-bird-demo/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Nuthatch Bird Demo
-emoji: 👁
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-tags:
-  - keras-dreambooth
-  - nature
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/Bark-UI-with-Voice-Cloning-2/parseinput.py b/spaces/kevinwang676/Bark-UI-with-Voice-Cloning-2/parseinput.py
deleted file mode 100644
index f2102648cf169f0a52bb66755308fee5f81247e0..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bark-UI-with-Voice-Cloning-2/parseinput.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import re
-import xml.etree.ElementTree as ET
-from xml.sax import saxutils
-#import nltk
-
-# Chunked generation originally from https://github.com/serp-ai/bark-with-voice-clone
-def split_and_recombine_text(text, desired_length=100, max_length=150):
-    # return nltk.sent_tokenize(text)
-
-    # from https://github.com/neonbjb/tortoise-tts
-    """Split text it into chunks of a desired length trying to keep sentences intact."""
-    # normalize text, remove redundant whitespace and convert non-ascii quotes to ascii
-    text = re.sub(r"\n\n+", "\n", text)
-    text = re.sub(r"\s+", " ", text)
-    text = re.sub(r"[“”]", '"', text)
-
-    rv = []
-    in_quote = False
-    current = ""
-    split_pos = []
-    pos = -1
-    end_pos = len(text) - 1
-
-    def seek(delta):
-        nonlocal pos, in_quote, current
-        is_neg = delta < 0
-        for _ in range(abs(delta)):
-            if is_neg:
-                pos -= 1
-                current = current[:-1]
-            else:
-                pos += 1
-                current += text[pos]
-            if text[pos] == '"':
-                in_quote = not in_quote
-        return text[pos]
-
-    def peek(delta):
-        p = pos + delta
-        return text[p] if p < end_pos and p >= 0 else ""
-
-    def commit():
-        nonlocal rv, current, split_pos
-        rv.append(current)
-        current = ""
-        split_pos = []
-
-    while pos < end_pos:
-        c = seek(1)
-        # do we need to force a split?
-        if len(current) >= max_length:
-            if len(split_pos) > 0 and len(current) > (desired_length / 2):
-                # we have at least one sentence and we are over half the desired length, seek back to the last split
-                d = pos - split_pos[-1]
-                seek(-d)
-            else:
-                # no full sentences, seek back until we are not in the middle of a word and split there
-                while c not in "!?.,\n " and pos > 0 and len(current) > desired_length:
-                    c = seek(-1)
-            commit()
-        # check for sentence boundaries
-        elif not in_quote and (c in "!?]\n" or (c == "." and peek(1) in "\n ")):
-            # seek forward if we have consecutive boundary markers but still within the max length
-            while (
-                pos < len(text) - 1 and len(current) < max_length and peek(1) in "!?.]"
-            ):
-                c = seek(1)
-            split_pos.append(pos)
-            if len(current) >= desired_length:
-                commit()
-        # treat end of quote as a boundary if its followed by a space or newline
-        elif in_quote and peek(1) == '"' and peek(2) in "\n ":
-            seek(2)
-            split_pos.append(pos)
-    rv.append(current)
-
-    # clean up, remove lines with only whitespace or punctuation
-    rv = [s.strip() for s in rv]
-    rv = [s for s in rv if len(s) > 0 and not re.match(r"^[\s\.,;:!?]*$", s)]
-
-    return rv
-
-def is_ssml(value):
-    try:
-        ET.fromstring(value)
-    except ET.ParseError:
-        return False
-    return True
-
-def build_ssml(rawtext, selected_voice):
-    texts = rawtext.split("\n")
-    joinedparts = ""
-    for textpart in texts:
-        textpart = textpart.strip()
-        if len(textpart) < 1:
-            continue
-        joinedparts = joinedparts + f"\n<voice name=\"{selected_voice}\">{saxutils.escape(textpart)}</voice>"
-    ssml = f"""<?xml version="1.0"?>
-<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://www.w3.org/2001/10/synthesis
-                   http://www.w3.org/TR/speech-synthesis/synthesis.xsd"
-         xml:lang="en-US">
-         {joinedparts}
-</speak>
-    """
-    return ssml
-
-def create_clips_from_ssml(ssmlinput):
-    # Parse the XML
-    tree = ET.ElementTree(ET.fromstring(ssmlinput))
-    root = tree.getroot()
-
-    # Create an empty list
-    voice_list = []
-
-    # Loop through all voice tags
-    for voice in root.iter('{http://www.w3.org/2001/10/synthesis}voice'):
-        # Extract the voice name attribute and the content text
-        voice_name = voice.attrib['name']
-        voice_content = voice.text.strip() if voice.text else ''
-        if(len(voice_content) > 0):
-            parts = split_and_recombine_text(voice_content)
-            for p in parts:
-                if(len(p) > 1):
-                    # add to tuple list
-                    voice_list.append((voice_name, p))
-    return voice_list
-
diff --git a/spaces/kevinwang676/Bark-with-Voice-Cloning/bark/model.py b/spaces/kevinwang676/Bark-with-Voice-Cloning/bark/model.py
deleted file mode 100644
index 457b49e749f396c47c6b35f44955fd512d233d79..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bark-with-Voice-Cloning/bark/model.py
+++ /dev/null
@@ -1,218 +0,0 @@
-"""
-Much of this code is adapted from Andrej Karpathy's NanoGPT
-(https://github.com/karpathy/nanoGPT)
-"""
-import math
-from dataclasses import dataclass
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-class LayerNorm(nn.Module):
-    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
-
-    def __init__(self, ndim, bias):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(ndim))
-        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
-
-    def forward(self, input):
-        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
-
-class CausalSelfAttention(nn.Module):
-
-    def __init__(self, config):
-        super().__init__()
-        assert config.n_embd % config.n_head == 0
-        # key, query, value projections for all heads, but in a batch
-        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
-        # output projection
-        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
-        # regularization
-        self.attn_dropout = nn.Dropout(config.dropout)
-        self.resid_dropout = nn.Dropout(config.dropout)
-        self.n_head = config.n_head
-        self.n_embd = config.n_embd
-        self.dropout = config.dropout
-        # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
-        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
-        if not self.flash:
-            # print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
-            # causal mask to ensure that attention is only applied to the left in the input sequence
-            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
-                                        .view(1, 1, config.block_size, config.block_size))
-
-    def forward(self, x, past_kv=None, use_cache=False):
-        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
-
-        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
-        q, k ,v  = self.c_attn(x).split(self.n_embd, dim=2)
-        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
-
-        if past_kv is not None:
-            past_key = past_kv[0]
-            past_value = past_kv[1]
-            k = torch.cat((past_key, k), dim=-2)
-            v = torch.cat((past_value, v), dim=-2)
-
-        FULL_T = k.shape[-2]
-
-        if use_cache is True:
-            present = (k, v)
-        else:
-            present = None
-
-        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
-        if self.flash:
-            # efficient attention using Flash Attention CUDA kernels
-            if past_kv is not None:
-                # When `past_kv` is provided, we're doing incremental decoding and `q.shape[2] == 1`: q only contains
-                # the query for the last token. scaled_dot_product_attention interprets this as the first token in the
-                # sequence, so if is_causal=True it will mask out all attention from it. This is not what we want, so 
-                # to work around this we set is_causal=False.
-                is_causal = False
-            else:
-                is_causal = True
-
-            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout, is_causal=is_causal)
-        else:
-            # manual implementation of attention
-            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-            att = att.masked_fill(self.bias[:,:,FULL_T-T:FULL_T,:FULL_T] == 0, float('-inf'))
-            att = F.softmax(att, dim=-1)
-            att = self.attn_dropout(att)
-            y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
-        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
-
-        # output projection
-        y = self.resid_dropout(self.c_proj(y))
-        return (y, present)
-
-class MLP(nn.Module):
-
-    def __init__(self, config):
-        super().__init__()
-        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
-        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
-        self.dropout = nn.Dropout(config.dropout)
-        self.gelu = nn.GELU()
-
-    def forward(self, x):
-        x = self.c_fc(x)
-        x = self.gelu(x)
-        x = self.c_proj(x)
-        x = self.dropout(x)
-        return x
-
-class Block(nn.Module):
-
-    def __init__(self, config, layer_idx):
-        super().__init__()
-        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
-        self.attn = CausalSelfAttention(config)
-        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
-        self.mlp = MLP(config)
-        self.layer_idx = layer_idx
-
-    def forward(self, x, past_kv=None, use_cache=False):
-        attn_output, prev_kvs = self.attn(self.ln_1(x), past_kv=past_kv, use_cache=use_cache)
-        x = x + attn_output
-        x = x + self.mlp(self.ln_2(x))
-        return (x, prev_kvs)
-
-@dataclass
-class GPTConfig:
-    block_size: int = 1024
-    input_vocab_size: int = 10_048
-    output_vocab_size: int = 10_048
-    n_layer: int = 12
-    n_head: int = 12
-    n_embd: int = 768
-    dropout: float = 0.0
-    bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
-
-class GPT(nn.Module):
-
-    def __init__(self, config):
-        super().__init__()
-        assert config.input_vocab_size is not None
-        assert config.output_vocab_size is not None
-        assert config.block_size is not None
-        self.config = config
-
-        self.transformer = nn.ModuleDict(dict(
-            wte = nn.Embedding(config.input_vocab_size, config.n_embd),
-            wpe = nn.Embedding(config.block_size, config.n_embd),
-            drop = nn.Dropout(config.dropout),
-            h = nn.ModuleList([Block(config, idx) for idx in range(config.n_layer)]),
-            ln_f = LayerNorm(config.n_embd, bias=config.bias),
-        ))
-        self.lm_head = nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
-
-    def get_num_params(self, non_embedding=True):
-        """
-        Return the number of parameters in the model.
-        For non-embedding count (default), the position embeddings get subtracted.
-        The token embeddings would too, except due to the parameter sharing these
-        params are actually used as weights in the final layer, so we include them.
-        """
-        n_params = sum(p.numel() for p in self.parameters())
-        if non_embedding:
-            n_params -= self.transformer.wte.weight.numel()
-            n_params -= self.transformer.wpe.weight.numel()
-        return n_params
-
-    def forward(self, idx, merge_context=False, past_kv=None, position_ids=None, use_cache=False):
-        device = idx.device
-        b, t = idx.size()
-        if past_kv is not None:
-            assert t == 1
-            tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
-        else:
-            if merge_context:
-                assert(idx.shape[1] >= 256+256+1)
-                t = idx.shape[1] - 256
-            else:
-                assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
-
-            # forward the GPT model itself
-            if merge_context:
-                tok_emb = torch.cat([
-                    self.transformer.wte(idx[:,:256]) + self.transformer.wte(idx[:,256:256+256]),
-                    self.transformer.wte(idx[:,256+256:])
-                ], dim=1)
-            else:
-                tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
-
-        if past_kv is None:
-            past_length = 0
-            past_kv = tuple([None] * len(self.transformer.h))
-        else:
-            past_length = past_kv[0][0].size(-2)
-
-        if position_ids is None:
-            position_ids = torch.arange(past_length, t + past_length, dtype=torch.long, device=device)
-            position_ids = position_ids.unsqueeze(0) # shape (1, t)
-            assert position_ids.shape == (1, t)
-
-        pos_emb = self.transformer.wpe(position_ids) # position embeddings of shape (1, t, n_embd)
-
-        x = self.transformer.drop(tok_emb + pos_emb)
-
-        new_kv = () if use_cache else None
-
-        for i, (block, past_layer_kv) in enumerate(zip(self.transformer.h, past_kv)):
-            x, kv = block(x, past_kv=past_layer_kv, use_cache=use_cache)
-
-            if use_cache:
-                new_kv = new_kv + (kv,)
-
-        x = self.transformer.ln_f(x)
-
-        # inference-time mini-optimization: only forward the lm_head on the very last position
-        logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
-
-        return (logits, new_kv)
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/facerender/modules/keypoint_detector.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/facerender/modules/keypoint_detector.py
deleted file mode 100644
index 62a38a962b2f1a4326aac771aced353ec5e22a96..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/facerender/modules/keypoint_detector.py
+++ /dev/null
@@ -1,179 +0,0 @@
-from torch import nn
-import torch
-import torch.nn.functional as F
-
-from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d
-from src.facerender.modules.util import KPHourglass, make_coordinate_grid, AntiAliasInterpolation2d, ResBottleneck
-
-
-class KPDetector(nn.Module):
-    """
-    Detecting canonical keypoints. Return keypoint position and jacobian near each keypoint.
-    """
-
-    def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, reshape_channel, reshape_depth,
-                 num_blocks, temperature, estimate_jacobian=False, scale_factor=1, single_jacobian_map=False):
-        super(KPDetector, self).__init__()
-
-        self.predictor = KPHourglass(block_expansion, in_features=image_channel,
-                                     max_features=max_features,  reshape_features=reshape_channel, reshape_depth=reshape_depth, num_blocks=num_blocks)
-
-        # self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=7, padding=3)
-        self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=3, padding=1)
-
-        if estimate_jacobian:
-            self.num_jacobian_maps = 1 if single_jacobian_map else num_kp
-            # self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=7, padding=3)
-            self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=3, padding=1)
-            '''
-            initial as:
-            [[1 0 0]
-             [0 1 0]
-             [0 0 1]]
-            '''
-            self.jacobian.weight.data.zero_()
-            self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float))
-        else:
-            self.jacobian = None
-
-        self.temperature = temperature
-        self.scale_factor = scale_factor
-        if self.scale_factor != 1:
-            self.down = AntiAliasInterpolation2d(image_channel, self.scale_factor)
-
-    def gaussian2kp(self, heatmap):
-        """
-        Extract the mean from a heatmap
-        """
-        shape = heatmap.shape
-        heatmap = heatmap.unsqueeze(-1)
-        grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0)
-        value = (heatmap * grid).sum(dim=(2, 3, 4))
-        kp = {'value': value}
-
-        return kp
-
-    def forward(self, x):
-        if self.scale_factor != 1:
-            x = self.down(x)
-
-        feature_map = self.predictor(x)
-        prediction = self.kp(feature_map)
-
-        final_shape = prediction.shape
-        heatmap = prediction.view(final_shape[0], final_shape[1], -1)
-        heatmap = F.softmax(heatmap / self.temperature, dim=2)
-        heatmap = heatmap.view(*final_shape)
-
-        out = self.gaussian2kp(heatmap)
-
-        if self.jacobian is not None:
-            jacobian_map = self.jacobian(feature_map)
-            jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 9, final_shape[2],
-                                                final_shape[3], final_shape[4])
-            heatmap = heatmap.unsqueeze(2)
-
-            jacobian = heatmap * jacobian_map
-            jacobian = jacobian.view(final_shape[0], final_shape[1], 9, -1)
-            jacobian = jacobian.sum(dim=-1)
-            jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 3, 3)
-            out['jacobian'] = jacobian
-
-        return out
-
-
-class HEEstimator(nn.Module):
-    """
-    Estimating head pose and expression.
-    """
-
-    def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, num_bins=66, estimate_jacobian=True):
-        super(HEEstimator, self).__init__()
-
-        self.conv1 = nn.Conv2d(in_channels=image_channel, out_channels=block_expansion, kernel_size=7, padding=3, stride=2)
-        self.norm1 = BatchNorm2d(block_expansion, affine=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.conv2 = nn.Conv2d(in_channels=block_expansion, out_channels=256, kernel_size=1)
-        self.norm2 = BatchNorm2d(256, affine=True)
-
-        self.block1 = nn.Sequential()
-        for i in range(3):
-            self.block1.add_module('b1_'+ str(i), ResBottleneck(in_features=256, stride=1))
-
-        self.conv3 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1)
-        self.norm3 = BatchNorm2d(512, affine=True)
-        self.block2 = ResBottleneck(in_features=512, stride=2)
-
-        self.block3 = nn.Sequential()
-        for i in range(3):
-            self.block3.add_module('b3_'+ str(i), ResBottleneck(in_features=512, stride=1))
-
-        self.conv4 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1)
-        self.norm4 = BatchNorm2d(1024, affine=True)
-        self.block4 = ResBottleneck(in_features=1024, stride=2)
-
-        self.block5 = nn.Sequential()
-        for i in range(5):
-            self.block5.add_module('b5_'+ str(i), ResBottleneck(in_features=1024, stride=1))
-
-        self.conv5 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=1)
-        self.norm5 = BatchNorm2d(2048, affine=True)
-        self.block6 = ResBottleneck(in_features=2048, stride=2)
-
-        self.block7 = nn.Sequential()
-        for i in range(2):
-            self.block7.add_module('b7_'+ str(i), ResBottleneck(in_features=2048, stride=1))
-
-        self.fc_roll = nn.Linear(2048, num_bins)
-        self.fc_pitch = nn.Linear(2048, num_bins)
-        self.fc_yaw = nn.Linear(2048, num_bins)
-
-        self.fc_t = nn.Linear(2048, 3)
-
-        self.fc_exp = nn.Linear(2048, 3*num_kp)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.norm1(out)
-        out = F.relu(out)
-        out = self.maxpool(out)
-
-        out = self.conv2(out)
-        out = self.norm2(out)
-        out = F.relu(out)
-
-        out = self.block1(out)
-
-        out = self.conv3(out)
-        out = self.norm3(out)
-        out = F.relu(out)
-        out = self.block2(out)
-
-        out = self.block3(out)
-
-        out = self.conv4(out)
-        out = self.norm4(out)
-        out = F.relu(out)
-        out = self.block4(out)
-
-        out = self.block5(out)
-
-        out = self.conv5(out)
-        out = self.norm5(out)
-        out = F.relu(out)
-        out = self.block6(out)
-
-        out = self.block7(out)
-
-        out = F.adaptive_avg_pool2d(out, 1)
-        out = out.view(out.shape[0], -1)
-
-        yaw = self.fc_roll(out)
-        pitch = self.fc_pitch(out)
-        roll = self.fc_yaw(out)
-        t = self.fc_t(out)
-        exp = self.fc_exp(out)
-
-        return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp}
-
diff --git a/spaces/kevinwang676/SadTalker/src/face3d/options/__init__.py b/spaces/kevinwang676/SadTalker/src/face3d/options/__init__.py
deleted file mode 100644
index e7eedebe54aa70169fd25951b3034d819e396c90..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/face3d/options/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""
diff --git a/spaces/kevinwang676/SadTalker/src/face3d/visualize.py b/spaces/kevinwang676/SadTalker/src/face3d/visualize.py
deleted file mode 100644
index 23a1110806a0ddf37d4aa549c023d1c3f7114e3e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/face3d/visualize.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# check the sync of 3dmm feature and the audio
-import cv2
-import numpy as np
-from src.face3d.models.bfm import ParametricFaceModel
-from src.face3d.models.facerecon_model import FaceReconModel
-import torch
-import subprocess, platform
-import scipy.io as scio
-from tqdm import tqdm 
-
-# draft
-def gen_composed_video(args, device, first_frame_coeff, coeff_path, audio_path, save_path, exp_dim=64):
-    
-    coeff_first = scio.loadmat(first_frame_coeff)['full_3dmm']
-
-    coeff_pred = scio.loadmat(coeff_path)['coeff_3dmm']
-
-    coeff_full = np.repeat(coeff_first, coeff_pred.shape[0], axis=0) # 257
-
-    coeff_full[:, 80:144] = coeff_pred[:, 0:64]
-    coeff_full[:, 224:227]  = coeff_pred[:, 64:67] # 3 dim translation
-    coeff_full[:, 254:]  = coeff_pred[:, 67:] # 3 dim translation
-
-    tmp_video_path = '/tmp/face3dtmp.mp4'
-
-    facemodel = FaceReconModel(args)
-    
-    video = cv2.VideoWriter(tmp_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 25, (224, 224))
-
-    for k in tqdm(range(coeff_pred.shape[0]), 'face3d rendering:'):
-        cur_coeff_full = torch.tensor(coeff_full[k:k+1], device=device)
-
-        facemodel.forward(cur_coeff_full, device)
-
-        predicted_landmark = facemodel.pred_lm # TODO.
-        predicted_landmark = predicted_landmark.cpu().numpy().squeeze()
-
-        rendered_img = facemodel.pred_face
-        rendered_img = 255. * rendered_img.cpu().numpy().squeeze().transpose(1,2,0)
-        out_img = rendered_img[:, :, :3].astype(np.uint8)
-
-        video.write(np.uint8(out_img[:,:,::-1]))
-
-    video.release()
-
-    command = 'ffmpeg -v quiet -y -i {} -i {} -strict -2 -q:v 1 {}'.format(audio_path, tmp_video_path, save_path)
-    subprocess.call(command, shell=platform.system() != 'Windows')
-
diff --git a/spaces/kevinwang676/voice-conversion-yourtts/bark/api.py b/spaces/kevinwang676/voice-conversion-yourtts/bark/api.py
deleted file mode 100644
index 7a4319ceaa13798912637290f8e9e88c50d5420a..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/voice-conversion-yourtts/bark/api.py
+++ /dev/null
@@ -1,158 +0,0 @@
-from typing import Dict, Optional, Union
-
-import numpy as np
-
-from .generation import codec_decode, generate_coarse, generate_fine, generate_text_semantic
-
-
-def generate_with_settings(text_prompt, semantic_temp=0.6, eos_p=0.2, coarse_temp=0.7, fine_temp=0.5, voice_name=None, output_full=False):
-
-    # generation with more control
-    x_semantic = generate_text_semantic(
-        text_prompt,
-        history_prompt=voice_name,
-        temp=semantic_temp,
-        min_eos_p = eos_p,
-        use_kv_caching=True
-    )
-
-    x_coarse_gen = generate_coarse(
-        x_semantic,
-        history_prompt=voice_name,
-        temp=coarse_temp,
-        use_kv_caching=True
-    )
-    x_fine_gen = generate_fine(
-        x_coarse_gen,
-        history_prompt=voice_name,
-        temp=fine_temp,
-    )
-
-    if output_full:
-        full_generation = {
-            'semantic_prompt': x_semantic,
-            'coarse_prompt': x_coarse_gen,
-            'fine_prompt': x_fine_gen
-        }
-        return full_generation, codec_decode(x_fine_gen)
-    return codec_decode(x_fine_gen)
-
-
-def text_to_semantic(
-    text: str,
-    history_prompt: Optional[Union[Dict, str]] = None,
-    temp: float = 0.7,
-    silent: bool = False,
-):
-    """Generate semantic array from text.
-
-    Args:
-        text: text to be turned into audio
-        history_prompt: history choice for audio cloning
-        temp: generation temperature (1.0 more diverse, 0.0 more conservative)
-        silent: disable progress bar
-
-    Returns:
-        numpy semantic array to be fed into `semantic_to_waveform`
-    """
-    x_semantic = generate_text_semantic(
-        text,
-        history_prompt=history_prompt,
-        temp=temp,
-        silent=silent,
-        use_kv_caching=True
-    )
-    return x_semantic
-
-
-def semantic_to_waveform(
-    semantic_tokens: np.ndarray,
-    history_prompt: Optional[Union[Dict, str]] = None,
-    temp: float = 0.7,
-    silent: bool = False,
-    output_full: bool = False,
-):
-    """Generate audio array from semantic input.
-
-    Args:
-        semantic_tokens: semantic token output from `text_to_semantic`
-        history_prompt: history choice for audio cloning
-        temp: generation temperature (1.0 more diverse, 0.0 more conservative)
-        silent: disable progress bar
-        output_full: return full generation to be used as a history prompt
-
-    Returns:
-        numpy audio array at sample frequency 24khz
-    """
-    coarse_tokens = generate_coarse(
-        semantic_tokens,
-        history_prompt=history_prompt,
-        temp=temp,
-        silent=silent,
-        use_kv_caching=True
-    )
-    fine_tokens = generate_fine(
-        coarse_tokens,
-        history_prompt=history_prompt,
-        temp=0.5,
-    )
-    audio_arr = codec_decode(fine_tokens)
-    if output_full:
-        full_generation = {
-            "semantic_prompt": semantic_tokens,
-            "coarse_prompt": coarse_tokens,
-            "fine_prompt": fine_tokens,
-        }
-        return full_generation, audio_arr
-    return audio_arr
-
-
-def save_as_prompt(filepath, full_generation):
-    assert(filepath.endswith(".npz"))
-    assert(isinstance(full_generation, dict))
-    assert("semantic_prompt" in full_generation)
-    assert("coarse_prompt" in full_generation)
-    assert("fine_prompt" in full_generation)
-    np.savez(filepath, **full_generation)
-
-
-def generate_audio(
-    text: str,
-    history_prompt: Optional[Union[Dict, str]] = None,
-    text_temp: float = 0.7,
-    waveform_temp: float = 0.7,
-    silent: bool = False,
-    output_full: bool = False,
-):
-    """Generate audio array from input text.
-
-    Args:
-        text: text to be turned into audio
-        history_prompt: history choice for audio cloning
-        text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
-        waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
-        silent: disable progress bar
-        output_full: return full generation to be used as a history prompt
-
-    Returns:
-        numpy audio array at sample frequency 24khz
-    """
-    semantic_tokens = text_to_semantic(
-        text,
-        history_prompt=history_prompt,
-        temp=text_temp,
-        silent=silent,
-    )
-    out = semantic_to_waveform(
-        semantic_tokens,
-        history_prompt=history_prompt,
-        temp=waveform_temp,
-        silent=silent,
-        output_full=output_full,
-    )
-    if output_full:
-        full_generation, audio_arr = out
-        return full_generation, audio_arr
-    else:
-        audio_arr = out
-    return audio_arr
diff --git a/spaces/kinensake/quanquan/lm_scorer/models/gpt2.py b/spaces/kinensake/quanquan/lm_scorer/models/gpt2.py
deleted file mode 100644
index f5f8d480364059b76fa974564e18ab2e1a4d502e..0000000000000000000000000000000000000000
--- a/spaces/kinensake/quanquan/lm_scorer/models/gpt2.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from typing import *  # pylint: disable=wildcard-import,unused-wildcard-import
-
-
-import torch
-from transformers import AutoTokenizer, GPT2LMHeadModel
-from transformers import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
-from transformers.tokenization_utils import BatchEncoding
-
-from .abc.transformers import TransformersLMScorer
-
-
-class GPT2LMScorer(TransformersLMScorer):
-    # @overrides
-    def _build(self, model_name: str, options: Dict[str, Any]) -> None:
-        super()._build(model_name, options)
-
-        # pylint: disable=attribute-defined-outside-init
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            model_name, use_fast=True, add_special_tokens=False
-        )
-        # Add the pad token to GPT2 dictionary.
-        # len(tokenizer) = vocab_size + 1
-        self.tokenizer.add_special_tokens({"additional_special_tokens": ["<|pad|>"]})
-        self.tokenizer.pad_token = "<|pad|>"
-
-        self.model = GPT2LMHeadModel.from_pretrained(model_name)
-        # We need to resize the embedding layer because we added the pad token.
-        self.model.resize_token_embeddings(len(self.tokenizer))
-        self.model.eval()
-        if "device" in options:
-            self.model.to(options["device"])
-
-    def _add_special_tokens(self, text: str) -> str:
-        return self.tokenizer.bos_token + text + self.tokenizer.eos_token
-
-    # @overrides
-    def _tokens_log_prob_for_batch(
-        self, text: List[str]
-    ) -> List[Tuple[torch.DoubleTensor, torch.LongTensor, List[str]]]:
-        outputs: List[Tuple[torch.DoubleTensor, torch.LongTensor, List[str]]] = []
-        if len(text) == 0:
-            return outputs
-
-        # TODO: Handle overflowing elements for long sentences
-        text = list(map(self._add_special_tokens, text))
-        encoding: BatchEncoding = self.tokenizer.batch_encode_plus(
-            text, return_tensors="pt",
-        )
-        with torch.no_grad():
-            ids = encoding["input_ids"].to(self.model.device)
-            attention_mask = encoding["attention_mask"].to(self.model.device)
-            nopad_mask = ids != self.tokenizer.pad_token_id
-            logits: torch.Tensor = self.model(ids, attention_mask=attention_mask)[0]
-
-        for sent_index in range(len(text)):
-            sent_nopad_mask = nopad_mask[sent_index]
-            # len(tokens) = len(text[sent_index]) + 1
-            sent_tokens = [
-                tok
-                for i, tok in enumerate(encoding.tokens(sent_index))
-                if sent_nopad_mask[i] and i != 0
-            ]
-
-            # sent_ids.shape = [len(text[sent_index]) + 1]
-            sent_ids = ids[sent_index, sent_nopad_mask][1:]
-            # logits.shape = [len(text[sent_index]) + 1, vocab_size]
-            sent_logits = logits[sent_index, sent_nopad_mask][:-1, :]
-            sent_logits[:, self.tokenizer.pad_token_id] = float("-inf")
-            # ids_scores.shape = [seq_len + 1]
-            sent_ids_scores = sent_logits.gather(1, sent_ids.unsqueeze(1)).squeeze(1)
-            # log_prob.shape = [seq_len + 1]
-            sent_log_probs = sent_ids_scores - sent_logits.logsumexp(1)
-
-            sent_log_probs = cast(torch.DoubleTensor, sent_log_probs)
-            sent_ids = cast(torch.LongTensor, sent_ids)
-
-            output = (sent_log_probs, sent_ids, sent_tokens)
-            outputs.append(output)
-
-        return outputs
-
-    # @overrides
-    @classmethod
-    def _supported_model_names(cls) -> Iterable[str]:
-        return GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys()
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/mkgui/__init__.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/mkgui/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/new/decoders/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/new/decoders/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/masks/countless/__init__.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/masks/countless/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/kukuhtw/AutoGPT/autogpt/workspace.py b/spaces/kukuhtw/AutoGPT/autogpt/workspace.py
deleted file mode 100644
index 6fb0e3113eb2c1338edf7f86c6e162fc27c61e50..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/autogpt/workspace.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-from autogpt.config import Config
-
-CFG = Config()
-
-# Set a dedicated folder for file I/O
-WORKSPACE_PATH = Path(os.getcwd()) / "auto_gpt_workspace"
-
-# Create the directory if it doesn't exist
-if not os.path.exists(WORKSPACE_PATH):
-    os.makedirs(WORKSPACE_PATH)
-
-
-def path_in_workspace(relative_path: str | Path) -> Path:
-    """Get full path for item in workspace
-
-    Parameters:
-        relative_path (str | Path): Path to translate into the workspace
-
-    Returns:
-        Path: Absolute path for the given path in the workspace
-    """
-    return safe_path_join(WORKSPACE_PATH, relative_path)
-
-
-def safe_path_join(base: Path, *paths: str | Path) -> Path:
-    """Join one or more path components, asserting the resulting path is within the workspace.
-
-    Args:
-        base (Path): The base path
-        *paths (str): The paths to join to the base path
-
-    Returns:
-        Path: The joined path
-    """
-    joined_path = base.joinpath(*paths).resolve()
-
-    if CFG.restrict_to_workspace and not joined_path.is_relative_to(base):
-        raise ValueError(
-            f"Attempted to access path '{joined_path}' outside of workspace '{base}'."
-        )
-
-    return joined_path
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/cu2qu/ufo.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/cu2qu/ufo.py
deleted file mode 100644
index 10367cfecf8384e32eace3b9d0e01ab6c588c324..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/cu2qu/ufo.py
+++ /dev/null
@@ -1,349 +0,0 @@
-# Copyright 2015 Google Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""Converts cubic bezier curves to quadratic splines.
-
-Conversion is performed such that the quadratic splines keep the same end-curve
-tangents as the original cubics. The approach is iterative, increasing the
-number of segments for a spline until the error gets below a bound.
-
-Respective curves from multiple fonts will be converted at once to ensure that
-the resulting splines are interpolation-compatible.
-"""
-
-import logging
-from fontTools.pens.basePen import AbstractPen
-from fontTools.pens.pointPen import PointToSegmentPen
-from fontTools.pens.reverseContourPen import ReverseContourPen
-
-from . import curves_to_quadratic
-from .errors import (
-    UnequalZipLengthsError,
-    IncompatibleSegmentNumberError,
-    IncompatibleSegmentTypesError,
-    IncompatibleGlyphsError,
-    IncompatibleFontsError,
-)
-
-
-__all__ = ["fonts_to_quadratic", "font_to_quadratic"]
-
-# The default approximation error below is a relative value (1/1000 of the EM square).
-# Later on, we convert it to absolute font units by multiplying it by a font's UPEM
-# (see fonts_to_quadratic).
-DEFAULT_MAX_ERR = 0.001
-CURVE_TYPE_LIB_KEY = "com.github.googlei18n.cu2qu.curve_type"
-
-logger = logging.getLogger(__name__)
-
-
-_zip = zip
-
-
-def zip(*args):
-    """Ensure each argument to zip has the same length. Also make sure a list is
-    returned for python 2/3 compatibility.
-    """
-
-    if len(set(len(a) for a in args)) != 1:
-        raise UnequalZipLengthsError(*args)
-    return list(_zip(*args))
-
-
-class GetSegmentsPen(AbstractPen):
-    """Pen to collect segments into lists of points for conversion.
-
-    Curves always include their initial on-curve point, so some points are
-    duplicated between segments.
-    """
-
-    def __init__(self):
-        self._last_pt = None
-        self.segments = []
-
-    def _add_segment(self, tag, *args):
-        if tag in ["move", "line", "qcurve", "curve"]:
-            self._last_pt = args[-1]
-        self.segments.append((tag, args))
-
-    def moveTo(self, pt):
-        self._add_segment("move", pt)
-
-    def lineTo(self, pt):
-        self._add_segment("line", pt)
-
-    def qCurveTo(self, *points):
-        self._add_segment("qcurve", self._last_pt, *points)
-
-    def curveTo(self, *points):
-        self._add_segment("curve", self._last_pt, *points)
-
-    def closePath(self):
-        self._add_segment("close")
-
-    def endPath(self):
-        self._add_segment("end")
-
-    def addComponent(self, glyphName, transformation):
-        pass
-
-
-def _get_segments(glyph):
-    """Get a glyph's segments as extracted by GetSegmentsPen."""
-
-    pen = GetSegmentsPen()
-    # glyph.draw(pen)
-    # We can't simply draw the glyph with the pen, but we must initialize the
-    # PointToSegmentPen explicitly with outputImpliedClosingLine=True.
-    # By default PointToSegmentPen does not outputImpliedClosingLine -- unless
-    # last and first point on closed contour are duplicated. Because we are
-    # converting multiple glyphs at the same time, we want to make sure
-    # this function returns the same number of segments, whether or not
-    # the last and first point overlap.
-    # https://github.com/googlefonts/fontmake/issues/572
-    # https://github.com/fonttools/fonttools/pull/1720
-    pointPen = PointToSegmentPen(pen, outputImpliedClosingLine=True)
-    glyph.drawPoints(pointPen)
-    return pen.segments
-
-
-def _set_segments(glyph, segments, reverse_direction):
-    """Draw segments as extracted by GetSegmentsPen back to a glyph."""
-
-    glyph.clearContours()
-    pen = glyph.getPen()
-    if reverse_direction:
-        pen = ReverseContourPen(pen)
-    for tag, args in segments:
-        if tag == "move":
-            pen.moveTo(*args)
-        elif tag == "line":
-            pen.lineTo(*args)
-        elif tag == "curve":
-            pen.curveTo(*args[1:])
-        elif tag == "qcurve":
-            pen.qCurveTo(*args[1:])
-        elif tag == "close":
-            pen.closePath()
-        elif tag == "end":
-            pen.endPath()
-        else:
-            raise AssertionError('Unhandled segment type "%s"' % tag)
-
-
-def _segments_to_quadratic(segments, max_err, stats, all_quadratic=True):
-    """Return quadratic approximations of cubic segments."""
-
-    assert all(s[0] == "curve" for s in segments), "Non-cubic given to convert"
-
-    new_points = curves_to_quadratic([s[1] for s in segments], max_err, all_quadratic)
-    n = len(new_points[0])
-    assert all(len(s) == n for s in new_points[1:]), "Converted incompatibly"
-
-    spline_length = str(n - 2)
-    stats[spline_length] = stats.get(spline_length, 0) + 1
-
-    if all_quadratic or n == 3:
-        return [("qcurve", p) for p in new_points]
-    else:
-        return [("curve", p) for p in new_points]
-
-
-def _glyphs_to_quadratic(glyphs, max_err, reverse_direction, stats, all_quadratic=True):
-    """Do the actual conversion of a set of compatible glyphs, after arguments
-    have been set up.
-
-    Return True if the glyphs were modified, else return False.
-    """
-
-    try:
-        segments_by_location = zip(*[_get_segments(g) for g in glyphs])
-    except UnequalZipLengthsError:
-        raise IncompatibleSegmentNumberError(glyphs)
-    if not any(segments_by_location):
-        return False
-
-    # always modify input glyphs if reverse_direction is True
-    glyphs_modified = reverse_direction
-
-    new_segments_by_location = []
-    incompatible = {}
-    for i, segments in enumerate(segments_by_location):
-        tag = segments[0][0]
-        if not all(s[0] == tag for s in segments[1:]):
-            incompatible[i] = [s[0] for s in segments]
-        elif tag == "curve":
-            new_segments = _segments_to_quadratic(
-                segments, max_err, stats, all_quadratic
-            )
-            if all_quadratic or new_segments != segments:
-                glyphs_modified = True
-            segments = new_segments
-        new_segments_by_location.append(segments)
-
-    if glyphs_modified:
-        new_segments_by_glyph = zip(*new_segments_by_location)
-        for glyph, new_segments in zip(glyphs, new_segments_by_glyph):
-            _set_segments(glyph, new_segments, reverse_direction)
-
-    if incompatible:
-        raise IncompatibleSegmentTypesError(glyphs, segments=incompatible)
-    return glyphs_modified
-
-
-def glyphs_to_quadratic(
-    glyphs, max_err=None, reverse_direction=False, stats=None, all_quadratic=True
-):
-    """Convert the curves of a set of compatible of glyphs to quadratic.
-
-    All curves will be converted to quadratic at once, ensuring interpolation
-    compatibility. If this is not required, calling glyphs_to_quadratic with one
-    glyph at a time may yield slightly more optimized results.
-
-    Return True if glyphs were modified, else return False.
-
-    Raises IncompatibleGlyphsError if glyphs have non-interpolatable outlines.
-    """
-    if stats is None:
-        stats = {}
-
-    if not max_err:
-        # assume 1000 is the default UPEM
-        max_err = DEFAULT_MAX_ERR * 1000
-
-    if isinstance(max_err, (list, tuple)):
-        max_errors = max_err
-    else:
-        max_errors = [max_err] * len(glyphs)
-    assert len(max_errors) == len(glyphs)
-
-    return _glyphs_to_quadratic(
-        glyphs, max_errors, reverse_direction, stats, all_quadratic
-    )
-
-
-def fonts_to_quadratic(
-    fonts,
-    max_err_em=None,
-    max_err=None,
-    reverse_direction=False,
-    stats=None,
-    dump_stats=False,
-    remember_curve_type=True,
-    all_quadratic=True,
-):
-    """Convert the curves of a collection of fonts to quadratic.
-
-    All curves will be converted to quadratic at once, ensuring interpolation
-    compatibility. If this is not required, calling fonts_to_quadratic with one
-    font at a time may yield slightly more optimized results.
-
-    Return True if fonts were modified, else return False.
-
-    By default, cu2qu stores the curve type in the fonts' lib, under a private
-    key "com.github.googlei18n.cu2qu.curve_type", and will not try to convert
-    them again if the curve type is already set to "quadratic".
-    Setting 'remember_curve_type' to False disables this optimization.
-
-    Raises IncompatibleFontsError if same-named glyphs from different fonts
-    have non-interpolatable outlines.
-    """
-
-    if remember_curve_type:
-        curve_types = {f.lib.get(CURVE_TYPE_LIB_KEY, "cubic") for f in fonts}
-        if len(curve_types) == 1:
-            curve_type = next(iter(curve_types))
-            if curve_type in ("quadratic", "mixed"):
-                logger.info("Curves already converted to quadratic")
-                return False
-            elif curve_type == "cubic":
-                pass  # keep converting
-            else:
-                raise NotImplementedError(curve_type)
-        elif len(curve_types) > 1:
-            # going to crash later if they do differ
-            logger.warning("fonts may contain different curve types")
-
-    if stats is None:
-        stats = {}
-
-    if max_err_em and max_err:
-        raise TypeError("Only one of max_err and max_err_em can be specified.")
-    if not (max_err_em or max_err):
-        max_err_em = DEFAULT_MAX_ERR
-
-    if isinstance(max_err, (list, tuple)):
-        assert len(max_err) == len(fonts)
-        max_errors = max_err
-    elif max_err:
-        max_errors = [max_err] * len(fonts)
-
-    if isinstance(max_err_em, (list, tuple)):
-        assert len(fonts) == len(max_err_em)
-        max_errors = [f.info.unitsPerEm * e for f, e in zip(fonts, max_err_em)]
-    elif max_err_em:
-        max_errors = [f.info.unitsPerEm * max_err_em for f in fonts]
-
-    modified = False
-    glyph_errors = {}
-    for name in set().union(*(f.keys() for f in fonts)):
-        glyphs = []
-        cur_max_errors = []
-        for font, error in zip(fonts, max_errors):
-            if name in font:
-                glyphs.append(font[name])
-                cur_max_errors.append(error)
-        try:
-            modified |= _glyphs_to_quadratic(
-                glyphs, cur_max_errors, reverse_direction, stats, all_quadratic
-            )
-        except IncompatibleGlyphsError as exc:
-            logger.error(exc)
-            glyph_errors[name] = exc
-
-    if glyph_errors:
-        raise IncompatibleFontsError(glyph_errors)
-
-    if modified and dump_stats:
-        spline_lengths = sorted(stats.keys())
-        logger.info(
-            "New spline lengths: %s"
-            % (", ".join("%s: %d" % (l, stats[l]) for l in spline_lengths))
-        )
-
-    if remember_curve_type:
-        for font in fonts:
-            curve_type = font.lib.get(CURVE_TYPE_LIB_KEY, "cubic")
-            new_curve_type = "quadratic" if all_quadratic else "mixed"
-            if curve_type != new_curve_type:
-                font.lib[CURVE_TYPE_LIB_KEY] = new_curve_type
-                modified = True
-    return modified
-
-
-def glyph_to_quadratic(glyph, **kwargs):
-    """Convenience wrapper around glyphs_to_quadratic, for just one glyph.
-    Return True if the glyph was modified, else return False.
-    """
-
-    return glyphs_to_quadratic([glyph], **kwargs)
-
-
-def font_to_quadratic(font, **kwargs):
-    """Convenience wrapper around fonts_to_quadratic, for just one font.
-    Return True if the font was modified, else return False.
-    """
-
-    return fonts_to_quadratic([font], **kwargs)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/tqdm.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/tqdm.py
deleted file mode 100644
index 81bdfb0500a71e4015d3e2bf539571fae54e7f96..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/tqdm.py
+++ /dev/null
@@ -1,178 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 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
-"""Utility helpers to handle progress bars in `huggingface_hub`.
-
-Example:
-    1. Use `huggingface_hub.utils.tqdm` as you would use `tqdm.tqdm` or `tqdm.auto.tqdm`.
-    2. To disable progress bars, either use `disable_progress_bars()` helper or set the
-       environment variable `HF_HUB_DISABLE_PROGRESS_BARS` to 1.
-    3. To re-enable progress bars, use `enable_progress_bars()`.
-    4. To check whether progress bars are disabled, use `are_progress_bars_disabled()`.
-
-NOTE: Environment variable `HF_HUB_DISABLE_PROGRESS_BARS` has the priority.
-
-Example:
-    ```py
-    from huggingface_hub.utils import (
-        are_progress_bars_disabled,
-        disable_progress_bars,
-        enable_progress_bars,
-        tqdm,
-    )
-
-    # Disable progress bars globally
-    disable_progress_bars()
-
-    # Use as normal `tqdm`
-    for _ in tqdm(range(5)):
-       do_something()
-
-    # Still not showing progress bars, as `disable=False` is overwritten to `True`.
-    for _ in tqdm(range(5), disable=False):
-       do_something()
-
-    are_progress_bars_disabled() # True
-
-    # Re-enable progress bars globally
-    enable_progress_bars()
-
-    # Progress bar will be shown !
-    for _ in tqdm(range(5)):
-       do_something()
-    ```
-"""
-import io
-import warnings
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Iterator, Optional, Union
-
-from tqdm.auto import tqdm as old_tqdm
-
-from ..constants import HF_HUB_DISABLE_PROGRESS_BARS
-
-
-# `HF_HUB_DISABLE_PROGRESS_BARS` is `Optional[bool]` while `_hf_hub_progress_bars_disabled`
-# is a `bool`. If `HF_HUB_DISABLE_PROGRESS_BARS` is set to True or False, it has priority.
-# If `HF_HUB_DISABLE_PROGRESS_BARS` is None, it means the user have not set the
-# environment variable and is free to enable/disable progress bars programmatically.
-# TL;DR: env variable has priority over code.
-#
-# By default, progress bars are enabled.
-_hf_hub_progress_bars_disabled: bool = HF_HUB_DISABLE_PROGRESS_BARS or False
-
-
-def disable_progress_bars() -> None:
-    """
-    Disable globally progress bars used in `huggingface_hub` except if `HF_HUB_DISABLE_PROGRESS_BARS` environment
-    variable has been set.
-
-    Use [`~utils.enable_progress_bars`] to re-enable them.
-    """
-    if HF_HUB_DISABLE_PROGRESS_BARS is False:
-        warnings.warn(
-            "Cannot disable progress bars: environment variable `HF_HUB_DISABLE_PROGRESS_BARS=0` is set and has"
-            " priority."
-        )
-        return
-    global _hf_hub_progress_bars_disabled
-    _hf_hub_progress_bars_disabled = True
-
-
-def enable_progress_bars() -> None:
-    """
-    Enable globally progress bars used in `huggingface_hub` except if `HF_HUB_DISABLE_PROGRESS_BARS` environment
-    variable has been set.
-
-    Use [`~utils.disable_progress_bars`] to disable them.
-    """
-    if HF_HUB_DISABLE_PROGRESS_BARS is True:
-        warnings.warn(
-            "Cannot enable progress bars: environment variable `HF_HUB_DISABLE_PROGRESS_BARS=1` is set and has"
-            " priority."
-        )
-        return
-    global _hf_hub_progress_bars_disabled
-    _hf_hub_progress_bars_disabled = False
-
-
-def are_progress_bars_disabled() -> bool:
-    """Return whether progress bars are globally disabled or not.
-
-    Progress bars used in `huggingface_hub` can be enable or disabled globally using [`~utils.enable_progress_bars`]
-    and [`~utils.disable_progress_bars`] or by setting `HF_HUB_DISABLE_PROGRESS_BARS` as environment variable.
-    """
-    global _hf_hub_progress_bars_disabled
-    return _hf_hub_progress_bars_disabled
-
-
-class tqdm(old_tqdm):
-    """
-    Class to override `disable` argument in case progress bars are globally disabled.
-
-    Taken from https://github.com/tqdm/tqdm/issues/619#issuecomment-619639324.
-    """
-
-    def __init__(self, *args, **kwargs):
-        if are_progress_bars_disabled():
-            kwargs["disable"] = True
-        super().__init__(*args, **kwargs)
-
-
-@contextmanager
-def tqdm_stream_file(path: Union[Path, str]) -> Iterator[io.BufferedReader]:
-    """
-    Open a file as binary and wrap the `read` method to display a progress bar when it's streamed.
-
-    First implemented in `transformers` in 2019 but removed when switched to git-lfs. Used in `huggingface_hub` to show
-    progress bar when uploading an LFS file to the Hub. See github.com/huggingface/transformers/pull/2078#discussion_r354739608
-    for implementation details.
-
-    Note: currently implementation handles only files stored on disk as it is the most common use case. Could be
-          extended to stream any `BinaryIO` object but we might have to debug some corner cases.
-
-    Example:
-    ```py
-    >>> with tqdm_stream_file("config.json") as f:
-    >>>     requests.put(url, data=f)
-    config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s]
-    ```
-    """
-    if isinstance(path, str):
-        path = Path(path)
-
-    with path.open("rb") as f:
-        total_size = path.stat().st_size
-        pbar = tqdm(
-            unit="B",
-            unit_scale=True,
-            total=total_size,
-            initial=0,
-            desc=path.name,
-        )
-
-        f_read = f.read
-
-        def _inner_read(size: Optional[int] = -1) -> bytes:
-            data = f_read(size)
-            pbar.update(len(data))
-            return data
-
-        f.read = _inner_read  # type: ignore
-
-        yield f
-
-        pbar.close()
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/testing/jpl_units/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/testing/jpl_units/__init__.py
deleted file mode 100644
index b8caa9a8957a250b78712c25175bec415507e416..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/testing/jpl_units/__init__.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""
-A sample set of units for use with testing unit conversion
-of Matplotlib routines.  These are used because they use very strict
-enforcement of unitized data which will test the entire spectrum of how
-unitized data might be used (it is not always meaningful to convert to
-a float without specific units given).
-
-UnitDbl is essentially a unitized floating point number.  It has a
-minimal set of supported units (enough for testing purposes).  All
-of the mathematical operation are provided to fully test any behaviour
-that might occur with unitized data.  Remember that unitized data has
-rules as to how it can be applied to one another (a value of distance
-cannot be added to a value of time).  Thus we need to guard against any
-accidental "default" conversion that will strip away the meaning of the
-data and render it neutered.
-
-Epoch is different than a UnitDbl of time.  Time is something that can be
-measured where an Epoch is a specific moment in time.  Epochs are typically
-referenced as an offset from some predetermined epoch.
-
-A difference of two epochs is a Duration.  The distinction between a Duration
-and a UnitDbl of time is made because an Epoch can have different frames (or
-units).  In the case of our test Epoch class the two allowed frames are 'UTC'
-and 'ET' (Note that these are rough estimates provided for testing purposes
-and should not be used in production code where accuracy of time frames is
-desired).  As such a Duration also has a frame of reference and therefore needs
-to be called out as different that a simple measurement of time since a delta-t
-in one frame may not be the same in another.
-"""
-
-from .Duration import Duration
-from .Epoch import Epoch
-from .UnitDbl import UnitDbl
-
-from .StrConverter import StrConverter
-from .EpochConverter import EpochConverter
-from .UnitDblConverter import UnitDblConverter
-
-from .UnitDblFormatter import UnitDblFormatter
-
-
-__version__ = "1.0"
-
-__all__ = [
-            'register',
-            'Duration',
-            'Epoch',
-            'UnitDbl',
-            'UnitDblFormatter',
-          ]
-
-
-def register():
-    """Register the unit conversion classes with matplotlib."""
-    import matplotlib.units as mplU
-
-    mplU.registry[str] = StrConverter()
-    mplU.registry[Epoch] = EpochConverter()
-    mplU.registry[Duration] = EpochConverter()
-    mplU.registry[UnitDbl] = UnitDblConverter()
-
-
-# Some default unit instances
-# Distances
-m = UnitDbl(1.0, "m")
-km = UnitDbl(1.0, "km")
-mile = UnitDbl(1.0, "mile")
-# Angles
-deg = UnitDbl(1.0, "deg")
-rad = UnitDbl(1.0, "rad")
-# Time
-sec = UnitDbl(1.0, "sec")
-min = UnitDbl(1.0, "min")
-hr = UnitDbl(1.0, "hour")
-day = UnitDbl(24.0, "hour")
-sec = UnitDbl(1.0, "sec")
diff --git a/spaces/lightli/bingo-newbing/src/components/ui/input.tsx b/spaces/lightli/bingo-newbing/src/components/ui/input.tsx
deleted file mode 100644
index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000
--- a/spaces/lightli/bingo-newbing/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Bukuajarilmubedahdejongpdfdownload.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Bukuajarilmubedahdejongpdfdownload.md
deleted file mode 100644
index feca0d49b3409a20eee5b6d1bdbda1b6d92b98b7..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Bukuajarilmubedahdejongpdfdownload.md
+++ /dev/null
@@ -1,82 +0,0 @@
-<br />
-<h1>Buku Ajar Ilmu Bedah de Jong PDF Download: A Must-Have for Surgery Students and Practitioners</h1>
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-<ol>
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-<ul>
-<li>It is written in Indonesian language, which makes it easy to understand and apply for Indonesian readers.</li>
-<li>It is based on the latest scientific evidence and clinical practice guidelines, which ensures its accuracy and relevance.</li>
-<li>It is organized into chapters based on anatomy, which facilitates the learning and teaching process.</li>
-<li>It covers a wide range of surgical topics and specialties, such as general surgery, trauma surgery, vascular surgery, thoracic surgery, cardiac surgery, neurosurgery, urology, plastic surgery, orthopedic surgery, pediatric surgery, oncology surgery, transplant surgery, and more.</li>
-<li>It provides clear and concise explanations of surgical concepts and principles, supported by illustrations, tables, charts, diagrams, algorithms, and photographs.</li>
-<li>It offers practical and useful tips and tricks for surgical procedures and techniques, as well as potential complications and management strategies.</li>
-<li>It includes case studies and clinical scenarios that illustrate real-life situations and challenges in surgery.</li>
-<li>It contains review questions and answers at the end of each chapter that help readers assess their knowledge and comprehension.</li>
-<li>It has a user-friendly layout and design that make it easy to read and navigate.</li>
-</ul>
-<h2>What are the Reviews and Testimonials from Users of Buku Ajar Ilmu Bedah de Jong PDF Download?</h2>
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-<dl>
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-<dd>- Dr. Andi Pratama, General Surgeon</dd>
-<dt>"I have been using Buku Ajar Ilmu Bedah de Jong PDF download for my surgical residency program. It is very helpful and informative. It explains the surgical concepts and principles clearly and logically. It also provides practical tips and tricks for surgical procedures and techniques. It is a must-have for every surgery resident."</dt>
-<dd>- Dr. Rina Sari, Surgical Resident</dd>
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-<dd>- Dr. Budi Santoso, Surgery Lecturer</dd>
-</dl>
-
-<p>You have now learned how to get Buku Ajar Ilmu Bedah de Jong PDF download for free, what are the features and benefits of this textbook, and what are some reviews and testimonials from users. You can now use this textbook as a reference or guide for your study or practice in surgery.</p>
-<h2>How to Use Buku Ajar Ilmu Bedah de Jong PDF Download?</h2>
-<p>To use Buku Ajar Ilmu Bedah de Jong PDF download effectively and efficiently, you need to follow these steps:</p>
-<ol>
-<li>Open the PDF file using any PDF reader software.</li>
-<li>Use the table of contents or the index to find the chapter or topic you want to read or study.</li>
-<li>Use the bookmarks or the navigation pane to jump to different sections or pages.</li>
-<li>Use the zoom or the fit functions to adjust the view or the size of the text and images.</li>
-<li>Use the highlight, underline, comment or annotation functions to mark or note important points or information.</li>
-<li>Use the print or save functions to print or save a copy of the PDF file for your personal use.</li>
-</ol>
-<h2>What are the Limitations of Buku Ajar Ilmu Bedah de Jong PDF Download?</h2>
-<p>While Buku Ajar Ilmu Bedah de Jong PDF download may seem convenient and beneficial, it also has some limitations that you need to be aware of. Some of them are:</p>
-<p></p>
-<ul>
-<li>It may violate the intellectual property rights of the authors and publishers, and you may face legal consequences if you use it without their permission or authorization.</li>
-<li>It may not be updated with the latest research and guidelines, and you may miss out on some important or relevant information or changes.</li>
-<li>It may not have the same quality and accuracy as the original printed version, and you may encounter errors or discrepancies in the text or images.</li>
-<li>It may not have some features or functions that are available in the original printed version, such as interactive exercises, online resources, or supplementary materials.</li>
-<li>It may not be compatible with some devices or software, and you may have difficulties in opening or viewing it.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to get Buku Ajar Ilmu Bedah de Jong PDF download for free, what are the features and benefits of this textbook, what are some reviews and testimonials from users, how to use it, and what are some limitations of it. We hope that this article has been helpful and informative for you. However, we do not recommend using Buku Ajar Ilmu Bedah de Jong PDF download because it may violate the intellectual property rights of the authors and publishers, it may not be updated with the latest research and guidelines, it may not have the same quality and accuracy as the original printed version, it may not have some features or functions that are available in the original printed version, and it may not be compatible with some devices or software. If you want to use Buku Ajar Ilmu Bedah de Jong on your device, you should buy the original printed version or use another alternative that suits your needs and preferences.</p>
-<p>Thank you for reading this article. If you have any questions or feedback, please feel free to contact us or leave a comment below.</p>
-<h2>Who are the Authors of Buku Ajar Ilmu Bedah de Jong PDF Download?</h2>
-<p>Buku Ajar Ilmu Bedah de Jong PDF download is written by two distinguished authors who have extensive experience and expertise in surgical science. They are:</p>
-<ul>
-<li>R. Sjamsuhidajat, MD, PhD, FACS, FICS. He is a professor of surgery at the Faculty of Medicine, University of Indonesia. He is also the founder and chairman of the Indonesian College of Surgeons. He has published many books and articles on surgery, and has received many awards and honors for his contributions to surgical education and research.</li>
-<li>Wim de Jong, MD, PhD, FACS, FICS. He is a professor emeritus of surgery at the Faculty of Medicine, University of Indonesia. He is also the former president of the International College of Surgeons. He has published many books and articles on surgery, and has received many awards and honors for his contributions to surgical education and research.</li>
-</ul>
-<h2>What are the Contents of Buku Ajar Ilmu Bedah de Jong PDF Download?</h2>
-<p>Buku Ajar Ilmu Bedah de Jong PDF download consists of four volumes that cover various topics and aspects of surgery. The contents are as follows:</p>
-<ol>
-<li>Volume 1: Principles of Surgery. This volume covers the basic principles and concepts of surgery, such as surgical anatomy, physiology, pathology, microbiology, immunology, pharmacology, nutrition, metabolism, wound healing, infection, inflammation, shock, trauma, fluid and electrolyte balance, blood transfusion, anesthesia, pain management, ethics, and legal issues.</li>
-<li>Volume 2: General Surgery. This volume covers the general topics and specialties of surgery, such as abdominal surgery, gastrointestinal surgery, hepatobiliary surgery, pancreatic surgery, endocrine surgery, breast surgery, vascular surgery, thoracic surgery, cardiac surgery, neurosurgery, urology, plastic surgery, orthopedic surgery, pediatric surgery, oncology surgery, transplant surgery, and minimally invasive surgery.</li>
-<li>Volume 3: Surgical Techniques. This volume covers the practical and technical aspects of surgical procedures and techniques, such as surgical instruments and equipment, surgical incisions and closures, surgical sutures and knots, surgical drains and tubes, surgical hemostasis and bleeding control, surgical infections and complications management.</li>
-<li>Volume 4: Surgical Cases. This volume covers the clinical cases and scenarios that illustrate real-life situations and challenges in surgery. It also provides the diagnosis, differential diagnosis, investigations, treatment options, outcomes and prognosis for each case.</li>
-</ol>
-
-<p>You have now learned some more information about Buku Ajar Ilmu Bedah de Jong PDF download on your device. You can now use this textbook as a reference or guide for your study or practice in surgery.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to get Buku Ajar Ilmu Bedah de Jong PDF download for free, what are the features and benefits of this textbook, what are some reviews and testimonials from users, how to use it, what are some limitations of it, who are the authors of it, and what are the contents of it. We hope that this article has been helpful and informative for you. However, we do not recommend using Buku Ajar Ilmu Bedah de Jong PDF download because it may violate the intellectual property rights of the authors and publishers, it may not be updated with the latest research and guidelines, it may not have the same quality and accuracy as the original printed version, it may not have some features or functions that are available in the original printed version, and it may not be compatible with some devices or software. If you want to use Buku Ajar Ilmu Bedah de Jong on your device, you should buy the original printed version or use another alternative that suits your needs and preferences.</p>
-<p>Thank you for reading this article. If you have any questions or feedback, please feel free to contact us or leave a comment below.</p> 3cee63e6c2<br />
-<br />
-<br />
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diff --git a/spaces/liuyuan-pal/SyncDreamer/app.py b/spaces/liuyuan-pal/SyncDreamer/app.py
deleted file mode 100644
index 716135fb2072b3fe2a3fba6111d4683e1ec30761..0000000000000000000000000000000000000000
--- a/spaces/liuyuan-pal/SyncDreamer/app.py
+++ /dev/null
@@ -1,261 +0,0 @@
-from functools import partial
-
-from PIL import Image
-import numpy as np
-import gradio as gr
-import torch
-import os
-import fire
-from omegaconf import OmegaConf
-
-from ldm.models.diffusion.sync_dreamer import SyncDDIMSampler, SyncMultiviewDiffusion
-from ldm.util import add_margin, instantiate_from_config
-from sam_utils import sam_init, sam_out_nosave
-
-import torch
-_TITLE = '''SyncDreamer: Generating Multiview-consistent Images from a Single-view Image'''
-_DESCRIPTION = '''
-<div>
-<a style="display:inline-block" href="https://liuyuan-pal.github.io/SyncDreamer/"><img src="https://img.shields.io/badge/SyncDremer-Homepage-blue"></a>
-<a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2309.03453"><img src="https://img.shields.io/badge/2309.03453-f9f7f7?logo=data:image/png;base64,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"></a>
-<a style="display:inline-block; margin-left: .5em" href='https://github.com/liuyuan-pal/SyncDreamer'><img src='https://img.shields.io/github/stars/liuyuan-pal/SyncDreamer?style=social' /></a>
-</div>
-Given a single-view image, SyncDreamer is able to generate multiview-consistent images, which enables direct 3D reconstruction with NeuS or NeRF without SDS loss </br>
-
-Procedure: </br>
-**Step 1**. Upload an image or select an example.  ==> The foreground is masked out by SAM and we crop it as inputs. </br>
-**Step 2**. Select "Elevation angle "and click "Run generation". ==> Generate multiview images. The **Elevation angle** is the elevation of the input image. (This costs about 30s.) </br>
-You may adjust the **Crop size** and **Elevation angle** to get a better result! <br>
-To reconstruct a NeRF or a 3D mesh from the generated images, please refer to our [github repository](https://github.com/liuyuan-pal/SyncDreamer). <br>
-We have heavily borrowed codes from [One-2-3-45](https://huggingface.co/spaces/One-2-3-45/One-2-3-45), which is also an amazing single-view reconstruction method. 
-'''
-_USER_GUIDE0 = "Step1: Please upload an image in the block above (or choose an example shown in the left)."
-# _USER_GUIDE1 = "Step1: Please select a **Crop size** and click **Crop it**."
-_USER_GUIDE2 = "Step2: Please choose a **Elevation angle** and click **Run Generate**. The **Elevation angle** is the elevation of the input image. This costs about 30s."
-_USER_GUIDE3 = "Generated multiview images are shown below! (You may adjust the **Crop size** and **Elevation angle** to get a better result!)"
-
-others = '''**Step 1**. Select "Crop size" and click "Crop it". ==> The foreground object is centered and resized. </br>'''
-
-deployed = True
-
-if deployed:
-    print(f"Is CUDA available: {torch.cuda.is_available()}")
-    print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
-
-
-class BackgroundRemoval:
-    def __init__(self, device='cuda'):
-        from carvekit.api.high import HiInterface
-        self.interface = HiInterface(
-            object_type="object",  # Can be "object" or "hairs-like".
-            batch_size_seg=5,
-            batch_size_matting=1,
-            device=device,
-            seg_mask_size=640,  # Use 640 for Tracer B7 and 320 for U2Net
-            matting_mask_size=2048,
-            trimap_prob_threshold=231,
-            trimap_dilation=30,
-            trimap_erosion_iters=5,
-            fp16=True,
-        )
-
-    @torch.no_grad()
-    def __call__(self, image):
-        # image: [H, W, 3] array in [0, 255].
-        image = self.interface([image])[0]
-        return image
-
-def resize_inputs(image_input, crop_size):
-    if image_input is None: return None
-    alpha_np = np.asarray(image_input)[:, :, 3]
-    coords = np.stack(np.nonzero(alpha_np), 1)[:, (1, 0)]
-    min_x, min_y = np.min(coords, 0)
-    max_x, max_y = np.max(coords, 0)
-    ref_img_ = image_input.crop((min_x, min_y, max_x, max_y))
-    h, w = ref_img_.height, ref_img_.width
-    scale = crop_size / max(h, w)
-    h_, w_ = int(scale * h), int(scale * w)
-    ref_img_ = ref_img_.resize((w_, h_), resample=Image.BICUBIC)
-    results = add_margin(ref_img_, size=256)
-    return results
-
-def generate(model, sample_steps, batch_view_num, sample_num, cfg_scale, seed, image_input, elevation_input):
-    if deployed:
-        assert isinstance(model, SyncMultiviewDiffusion)
-        seed=int(seed)
-        torch.random.manual_seed(seed)
-        np.random.seed(seed)
-
-        # prepare data
-        image_input = np.asarray(image_input)
-        image_input = image_input.astype(np.float32) / 255.0
-        alpha_values = image_input[:,:, 3:]
-        image_input[:, :, :3] = alpha_values * image_input[:,:, :3] + 1 - alpha_values # white background
-        image_input = image_input[:, :, :3] * 2.0 - 1.0
-        image_input = torch.from_numpy(image_input.astype(np.float32))
-        elevation_input = torch.from_numpy(np.asarray([np.deg2rad(elevation_input)], np.float32))
-        data = {"input_image": image_input, "input_elevation": elevation_input}
-        for k, v in data.items():
-            if deployed:
-                data[k] = v.unsqueeze(0).cuda()
-            else:
-                data[k] = v.unsqueeze(0)
-            data[k] = torch.repeat_interleave(data[k], sample_num, dim=0)
-
-        if deployed:
-            sampler = SyncDDIMSampler(model, sample_steps)
-            x_sample = model.sample(sampler, data, cfg_scale, batch_view_num)
-        else:
-            x_sample = torch.zeros(sample_num, 16, 3, 256, 256)
-
-        B, N, _, H, W = x_sample.shape
-        x_sample = (torch.clamp(x_sample,max=1.0,min=-1.0) + 1) * 0.5
-        x_sample = x_sample.permute(0,1,3,4,2).cpu().numpy() * 255
-        x_sample = x_sample.astype(np.uint8)
-
-        results = []
-        for bi in range(B):
-            results.append(np.concatenate([x_sample[bi,ni] for ni in range(N)], 1))
-        results = np.concatenate(results, 0)
-        return Image.fromarray(results)
-    else:
-        return Image.fromarray(np.zeros([sample_num*256,16*256,3],np.uint8))
-
-
-def sam_predict(predictor, removal, raw_im):
-    if raw_im is None: return None
-    if deployed:
-        raw_im.thumbnail([512, 512], Image.Resampling.LANCZOS)
-        image_nobg = removal(raw_im.convert('RGB'))
-        arr = np.asarray(image_nobg)[:, :, -1]
-        x_nonzero = np.nonzero(arr.sum(axis=0))
-        y_nonzero = np.nonzero(arr.sum(axis=1))
-        x_min = int(x_nonzero[0].min())
-        y_min = int(y_nonzero[0].min())
-        x_max = int(x_nonzero[0].max())
-        y_max = int(y_nonzero[0].max())
-        # image_nobg.save('./nobg.png')
-
-        image_nobg.thumbnail([512, 512], Image.Resampling.LANCZOS)
-        image_sam = sam_out_nosave(predictor, image_nobg.convert("RGB"), (x_min, y_min, x_max, y_max))
-
-        # imsave('./mask.png', np.asarray(image_sam)[:,:,3]*255)
-        image_sam = np.asarray(image_sam, np.float32) / 255
-        out_mask = image_sam[:, :, 3:]
-        out_rgb = image_sam[:, :, :3] * out_mask + 1 - out_mask
-        out_img = (np.concatenate([out_rgb, out_mask], 2) * 255).astype(np.uint8)
-
-        image_sam = Image.fromarray(out_img, mode='RGBA')
-        # image_sam.save('./output.png')
-        torch.cuda.empty_cache()
-        return image_sam
-    else:
-        return raw_im
-
-def run_demo():
-    # device = f"cuda:0" if torch.cuda.is_available() else "cpu"
-    # models = None # init_model(device, os.path.join(code_dir, ckpt))
-    cfg = 'configs/syncdreamer.yaml'
-    ckpt = 'ckpt/syncdreamer-pretrain.ckpt'
-    config = OmegaConf.load(cfg)
-    # model = None
-    if deployed:
-        model = instantiate_from_config(config.model)
-        print(f'loading model from {ckpt} ...')
-        ckpt = torch.load(ckpt,map_location='cpu')
-        model.load_state_dict(ckpt['state_dict'], strict=True)
-        model = model.cuda().eval()
-        del ckpt
-        mask_predictor = sam_init()
-        removal = BackgroundRemoval()
-    else:
-        model = None
-        mask_predictor = None
-        removal = None
-
-    # NOTE: Examples must match inputs
-    examples_full = [
-        ['hf_demo/examples/monkey.png',30,200],
-        ['hf_demo/examples/cat.png',30,200],
-        ['hf_demo/examples/crab.png',30,200],
-        ['hf_demo/examples/elephant.png',30,200],
-        ['hf_demo/examples/flower.png',0,200],
-        ['hf_demo/examples/forest.png',30,200],
-        ['hf_demo/examples/teapot.png',20,200],
-        ['hf_demo/examples/basket.png',30,200],
-    ]
-
-    image_block = gr.Image(type='pil', image_mode='RGBA', height=256, label='Input image', tool=None, interactive=True)
-    elevation = gr.Slider(-10, 40, 30, step=5, label='Elevation angle of the input image', interactive=True)
-    crop_size = gr.Slider(120, 240, 200, step=10, label='Crop size', interactive=True)
-
-    # Compose demo layout & data flow.
-    with gr.Blocks(title=_TITLE, css="hf_demo/style.css") as demo:
-        with gr.Row():
-            with gr.Column(scale=1):
-                gr.Markdown('# ' + _TITLE)
-            # with gr.Column(scale=0):
-            #     gr.DuplicateButton(value='Duplicate Space for private use', elem_id='duplicate-button')
-        gr.Markdown(_DESCRIPTION)
-
-        with gr.Row(variant='panel'):
-            with gr.Column(scale=1.2):
-                gr.Examples(
-                    examples=examples_full,  # NOTE: elements must match inputs list!
-                    inputs=[image_block, elevation, crop_size],
-                    outputs=[image_block, elevation, crop_size],
-                    cache_examples=False,
-                    label='Examples (click one of the images below to start)',
-                    examples_per_page=5,
-                )
-
-            with gr.Column(scale=0.8):
-                image_block.render()
-                guide_text = gr.Markdown(_USER_GUIDE0, visible=True)
-                fig0 = gr.Image(value=Image.open('assets/crop_size.jpg'), type='pil', image_mode='RGB', height=256, show_label=False, tool=None, interactive=False)
-
-
-            with gr.Column(scale=0.8):
-                sam_block = gr.Image(type='pil', image_mode='RGBA', label="SAM output", height=256, interactive=False)
-                crop_size.render()
-                # crop_btn = gr.Button('Crop it', variant='primary', interactive=True)
-                fig1 = gr.Image(value=Image.open('assets/elevation.jpg'), type='pil', image_mode='RGB', height=256, show_label=False, tool=None, interactive=False)
-
-            with gr.Column(scale=0.8):
-                input_block = gr.Image(type='pil', image_mode='RGBA', label="Input to SyncDreamer", height=256, interactive=False)
-                elevation.render()
-                with gr.Accordion('Advanced options', open=False):
-                    cfg_scale = gr.Slider(1.0, 5.0, 2.0, step=0.1, label='Classifier free guidance', interactive=True)
-                    sample_num = gr.Slider(1, 2, 1, step=1, label='Sample num', interactive=False, info='How many instance (16 images per instance)')
-                    sample_steps = gr.Slider(10, 300, 50, step=10, label='Sample steps', interactive=False)
-                    batch_view_num = gr.Slider(1, 16, 16, step=1, label='Batch num', interactive=True)
-                    seed = gr.Number(6033, label='Random seed', interactive=True)
-                run_btn = gr.Button('Run generation', variant='primary', interactive=True)
-
-
-        output_block = gr.Image(type='pil', image_mode='RGB', label="Outputs of SyncDreamer", height=256, interactive=False)
-
-        def update_guide2(text, im):
-            if im is None:
-                return _USER_GUIDE0
-            else:
-                return text
-        update_guide = lambda GUIDE_TEXT: gr.update(value=GUIDE_TEXT)
-
-        image_block.clear(fn=partial(update_guide, _USER_GUIDE0), outputs=[guide_text], queue=False)
-        image_block.change(fn=partial(sam_predict, mask_predictor, removal), inputs=[image_block], outputs=[sam_block], queue=True) \
-                   .success(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=True)\
-                   .success(fn=partial(update_guide2, _USER_GUIDE2), inputs=[image_block], outputs=[guide_text], queue=False)\
-
-        crop_size.change(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=True)\
-                 .success(fn=partial(update_guide, _USER_GUIDE2), outputs=[guide_text], queue=False)
-        # crop_btn.click(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=False)\
-        #                .success(fn=partial(update_guide, _USER_GUIDE2), outputs=[guide_text], queue=False)
-
-        run_btn.click(partial(generate, model), inputs=[sample_steps, batch_view_num, sample_num, cfg_scale, seed, input_block, elevation], outputs=[output_block], queue=True)\
-               .success(fn=partial(update_guide, _USER_GUIDE3), outputs=[guide_text], queue=False)
-
-    demo.queue().launch(share=False, max_threads=80)  # auth=("admin", os.environ['PASSWD'])
-
-if __name__=="__main__":
-    fire.Fire(run_demo)
\ No newline at end of file
diff --git a/spaces/ltgoslo/ssa-perin/model/module/transformer.py b/spaces/ltgoslo/ssa-perin/model/module/transformer.py
deleted file mode 100644
index 301ff362e9df7bfd3cb2ae297728fb60a949c90a..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/model/module/transformer.py
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/usr/bin/env python3
-# coding=utf-8
-
-import torch
-import torch.nn as nn
-
-
-def checkpoint(module, *args, **kwargs):
-    dummy = torch.empty(1, requires_grad=True)
-    return torch.utils.checkpoint.checkpoint(lambda d, *a, **k: module(*a, **k), dummy, *args, **kwargs)
-
-
-class Attention(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-        self.attention = nn.MultiheadAttention(args.hidden_size, args.n_attention_heads, args.dropout_transformer_attention)
-        self.dropout = nn.Dropout(args.dropout_transformer)
-
-    def forward(self, q_input, kv_input, mask=None):
-        output, _ = self.attention(q_input, kv_input, kv_input, mask, need_weights=False)
-        output = self.dropout(output)
-        return output
-
-
-class FeedForward(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-        self.f = nn.Sequential(
-            nn.Linear(args.hidden_size, args.hidden_size_ff),
-            self._get_activation_f(args.activation),
-            nn.Dropout(args.dropout_transformer),
-            nn.Linear(args.hidden_size_ff, args.hidden_size),
-            nn.Dropout(args.dropout_transformer),
-        )
-
-    def forward(self, x):
-        return self.f(x)
-
-    def _get_activation_f(self, activation: str):
-        return {"relu": nn.ReLU, "gelu": nn.GELU}[activation]()
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, args):
-        super().__init__()
-        self.self_f = Attention(args)
-        #self.cross_f = Attention(args)
-        self.feedforward_f = FeedForward(args)
-
-        self.pre_self_norm = nn.LayerNorm(args.hidden_size) if args.pre_norm else nn.Identity()
-        #self.pre_cross_norm = nn.LayerNorm(args.hidden_size) if args.pre_norm else nn.Identity()
-        self.pre_feedforward_norm = nn.LayerNorm(args.hidden_size) if args.pre_norm else nn.Identity()
-        self.post_self_norm = nn.Identity() if args.pre_norm else nn.LayerNorm(args.hidden_size)
-        #self.post_cross_norm = nn.Identity() if args.pre_norm else nn.LayerNorm(args.hidden_size)
-        self.post_feedforward_norm = nn.Identity() if args.pre_norm else nn.LayerNorm(args.hidden_size)
-
-    def forward(self, x, encoder_output, x_mask, encoder_mask):
-        x_ = self.pre_self_norm(x)
-        x = self.post_self_norm(x + self.self_f(x_, x_, x_mask))
-
-        #x_ = self.pre_cross_norm(x)
-        #x = self.post_cross_norm(x + self.cross_f(x_, encoder_output, encoder_mask))
-
-        x_ = self.pre_feedforward_norm(x)
-        x = self.post_feedforward_norm(x + self.feedforward_f(x_))
-
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(self, args):
-        super(Decoder, self).__init__()
-        self.layers = nn.ModuleList([DecoderLayer(args) for _ in range(args.n_layers)])
-
-    def forward(self, target, encoder, target_mask, encoder_mask):
-        target = target.transpose(0, 1)  # shape: (T, B, D)
-        encoder = encoder.transpose(0, 1)  # shape: (T, B, D)
-
-        for layer in self.layers[:-1]:
-            target = checkpoint(layer, target, encoder, target_mask, encoder_mask)
-        target = self.layers[-1](target, encoder, target_mask, encoder_mask)  # don't checkpoint due to grad_norm
-        target = target.transpose(0, 1)  # shape: (B, T, D)
-
-        return target
diff --git a/spaces/lunarring/latentblending/ldm/modules/diffusionmodules/util.py b/spaces/lunarring/latentblending/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index 637363dfe34799e70cfdbcd11445212df9d9ca1f..0000000000000000000000000000000000000000
--- a/spaces/lunarring/latentblending/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,270 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
-                                   "dtype": torch.get_autocast_gpu_dtype(),
-                                   "cache_enabled": torch.is_autocast_cache_enabled()}
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad(), \
-                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/spaces/lusea/rvc-Qinggan/lib/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/lusea/rvc-Qinggan/lib/infer_pack/modules/F0Predictor/F0Predictor.py
deleted file mode 100644
index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000
--- a/spaces/lusea/rvc-Qinggan/lib/infer_pack/modules/F0Predictor/F0Predictor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class F0Predictor(object):
-    def compute_f0(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length]
-        """
-        pass
-
-    def compute_f0_uv(self, wav, p_len):
-        """
-        input: wav:[signal_length]
-               p_len:int
-        output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
-        """
-        pass
diff --git a/spaces/luxuedong/lxd/tests/kblob.ts b/spaces/luxuedong/lxd/tests/kblob.ts
deleted file mode 100644
index 9e15b41c1c94a690beb61b23cdb42fc78767ccd2..0000000000000000000000000000000000000000
--- a/spaces/luxuedong/lxd/tests/kblob.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import FormData from 'form-data'
-
-import { fetch } from '@/lib/isomorphic'
-
-const formData = new FormData()
-
-const knowledgeRequest = {"imageInfo":{"url":"https://www.baidu.com/img/PCfb_5bf082d29588c07f842ccde3f97243ea.png"},"knowledgeRequest":{"invokedSkills":["ImageById"],"subscriptionId":"Bing.Chat.Multimodal","invokedSkillsRequestData":{"enableFaceBlur":true},"convoData":{"convoid":"51D|BingProdUnAuthenticatedUsers|E3DCA904FF236C67C3450163BCEC64CFF3F618CC8A4AFD75FD518F5ED0ADA080","convotone":"Creative"}}}
-
-formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-
-
-fetch('https://bing.vcanbb.top/images/kblob',
-  {
-    method: 'POST',
-    body: formData.getBuffer(),
-    headers: {
-      "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-      "sec-ch-ua-mobile": "?0",
-      "sec-ch-ua-platform": "\"Windows\"",
-      "Referer": "https://bing.vcanbb.top/web/index.html",
-      "Referrer-Policy": "origin-when-cross-origin",
-      ...formData.getHeaders()
-    }
-
-  }
-).then(res => res.text())
-.then(res => console.log('res', res))
diff --git a/spaces/lysine/auscultate/src/app/breath/AuscultationTrack.tsx b/spaces/lysine/auscultate/src/app/breath/AuscultationTrack.tsx
deleted file mode 100644
index c40c7315967ed57659edf119012085b7ec830dc0..0000000000000000000000000000000000000000
--- a/spaces/lysine/auscultate/src/app/breath/AuscultationTrack.tsx
+++ /dev/null
@@ -1,224 +0,0 @@
-import React, { useEffect, useMemo, useRef } from 'react';
-import WaveSurfer from 'wavesurfer.js';
-import HoverPlugin from 'wavesurfer.js/plugins/hover';
-import TimelinePlugin from 'wavesurfer.js/plugins/timeline';
-import RegionsPlugin from 'wavesurfer.js/plugins/regions';
-import SpectrogramPlugin from 'wavesurfer.js/plugins/spectrogram';
-import { getDataUrl } from './api';
-import { useAudio } from '../AudioContext';
-import {
-  AuscultationTrack,
-  SoundSegment,
-  nameLocation,
-  getTrackAbnormalities,
-  Abnormalities,
-} from '../../breath-types';
-
-export interface AuscultationTrackProps {
-  track: AuscultationTrack;
-  zoom: number;
-  showAnswer: boolean;
-  spectrogram: boolean;
-  regionsLevel: RegionsLevel;
-}
-
-export enum RegionsLevel {
-  None = 0,
-  Markers = 1,
-  Noises = 2,
-  Full = 3,
-}
-
-function getColorByType(segment: SoundSegment | Abnormalities): string {
-  if (segment.crackles && segment.wheezes) {
-    return '#36d39933';
-  } else if (segment.crackles) {
-    return '#3abff833';
-  } else if (segment.wheezes) {
-    return '#fbbd2333';
-  } else {
-    return '#f8727233';
-  }
-}
-
-function nameSegment(segment: SoundSegment | Abnormalities): string {
-  if (segment.crackles && segment.wheezes) {
-    return 'Both';
-  } else if (segment.crackles) {
-    return 'Crackles';
-  } else if (segment.wheezes) {
-    return 'Wheezes';
-  } else {
-    return '';
-  }
-}
-
-export default function AuscultationTrack({
-  track,
-  zoom,
-  showAnswer,
-  spectrogram: showSpectrogram,
-  regionsLevel,
-}: AuscultationTrackProps): JSX.Element {
-  const waveformId = ('waveform' + track.audioFile).replaceAll('.', '_');
-
-  const { nowPlaying, setNowPlaying } = useAudio();
-
-  const wavesurfer = useRef<WaveSurfer>();
-  const regions = useRef<any>();
-  const activeRegion = useRef<any>();
-  useEffect(() => {
-    if (showSpectrogram) {
-      const spectrogramPlugin = SpectrogramPlugin.create({
-        labels: true,
-        height: 100,
-      });
-      wavesurfer.current?.registerPlugin(spectrogramPlugin);
-      spectrogramPlugin.render();
-      return () => {
-        spectrogramPlugin.destroy();
-      };
-    }
-    return undefined;
-  }, [showSpectrogram]);
-  useEffect(() => {
-    regions.current?.clearRegions();
-    if (regionsLevel === RegionsLevel.Full) {
-      track.segments.forEach(segment => {
-        regions.current?.addRegion({
-          start: segment.start,
-          end: segment.end,
-          color: getColorByType(segment),
-          content: nameSegment(segment),
-          drag: false,
-          resize: false,
-        });
-      });
-    } else if (regionsLevel === RegionsLevel.Noises) {
-      track.segments.forEach(segment => {
-        regions.current?.addRegion({
-          start: segment.start,
-          end: segment.crackles || segment.wheezes ? segment.end : undefined,
-          color: getColorByType(segment),
-          content: nameSegment(segment),
-          drag: false,
-          resize: false,
-        });
-      });
-    } else if (regionsLevel === RegionsLevel.Markers) {
-      track.segments.forEach(segment => {
-        regions.current?.addRegion({
-          start: segment.start,
-          color: getColorByType(segment),
-          content: nameSegment(segment),
-          drag: false,
-          resize: false,
-        });
-      });
-    }
-  }, [regionsLevel]);
-  useEffect(() => {
-    const instance = WaveSurfer.create({
-      container: '#' + waveformId,
-      url: getDataUrl(track.audioFile),
-      minPxPerSec: zoom,
-      plugins: [
-        HoverPlugin.create(),
-        TimelinePlugin.create(),
-        (regions.current = RegionsPlugin.create()),
-      ],
-    });
-    regions.current.on('region-clicked', (region: any, e: MouseEvent) => {
-      e.stopPropagation(); // prevent triggering a click on the waveform
-      activeRegion.current = region;
-      region.play();
-    });
-    regions.current.on('region-out', (region: any) => {
-      if (activeRegion.current === region) {
-        activeRegion.current = undefined;
-        instance.pause();
-      }
-    });
-    instance.on('play', () => {
-      setNowPlaying(waveformId);
-    });
-    instance.on('pause', () => {
-      setNowPlaying(v => v); // just to trigger a rerender
-    });
-    wavesurfer.current = instance;
-    return () => {
-      try {
-        instance.destroy();
-      } catch (e) {
-        console.log(e); // an error may be thrown because the spectrogram plugin is destroyed twice
-      }
-      wavesurfer.current = undefined;
-    };
-  }, []);
-
-  useEffect(() => {
-    if (nowPlaying !== waveformId) {
-      wavesurfer.current?.pause();
-    }
-  }, [nowPlaying]);
-
-  useEffect(() => {
-    if (wavesurfer.current?.getDecodedData()) wavesurfer.current?.zoom(zoom);
-  }, [zoom]);
-
-  const abnormalities = useMemo(() => getTrackAbnormalities(track), [track]);
-
-  return (
-    <div className="flex gap-4 my-8 flex-wrap justify-center">
-      <div className="flex flex-col w-48 gap-4 my-4">
-        <span className="text-xl text-center flex-1">
-          {nameLocation(track.location)}
-        </span>
-
-        <span
-          className="text-lg text-center text-warning"
-          style={{
-            visibility:
-              showAnswer && (abnormalities.crackles || abnormalities.wheezes)
-                ? 'visible'
-                : 'hidden',
-          }}
-        >
-          {nameSegment(abnormalities)}
-        </span>
-
-        <div className="flex gap-2">
-          <button
-            className="btn btn-accent flex-grow-[2]"
-            onClick={async () => {
-              await wavesurfer.current?.playPause();
-              setNowPlaying(waveformId);
-            }}
-          >
-            <svg width="24px" height="24px" viewBox="0 0 24 24">
-              <path
-                d="M21.4086 9.35258C23.5305 10.5065 23.5305 13.4935 21.4086 14.6474L8.59662 21.6145C6.53435 22.736 4 21.2763 4 18.9671L4 5.0329C4 2.72368 6.53435 1.26402 8.59661 2.38548L21.4086 9.35258Z"
-                fill="#ffffff"
-              />
-            </svg>
-          </button>
-          <button
-            className="btn flex-grow-[1]"
-            onClick={async () => {
-              await wavesurfer.current?.stop();
-              setNowPlaying(v => (v === waveformId ? null : v));
-            }}
-          >
-            <svg width="24px" height="24px" viewBox="0 0 32 32">
-              <path
-                d="M5.92 24.096q0 0.832 0.576 1.408t1.44 0.608h16.128q0.832 0 1.44-0.608t0.576-1.408v-16.16q0-0.832-0.576-1.44t-1.44-0.576h-16.128q-0.832 0-1.44 0.576t-0.576 1.44v16.16z"
-                fill="#a6adba"
-              ></path>
-            </svg>
-          </button>
-        </div>
-      </div>
-      <div id={waveformId} className="waveform flex-1 min-w-[250px]"></div>
-    </div>
-  );
-}
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/binary_search.h b/spaces/ma-xu/LIVE/thrust/thrust/binary_search.h
deleted file mode 100644
index 127be16aab996b03e7290bac5ae3d1d1fce27588..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/binary_search.h
+++ /dev/null
@@ -1,1902 +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 binary_search.h
- *  \brief Search for values in sorted ranges.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-
-    
-/*! \addtogroup algorithms
- */
-
-
-/*! \addtogroup searching
- *  \ingroup algorithms
- *  \{
- */
-
-
-/*! \addtogroup binary_search Binary Search
- *  \ingroup searching
- *  \{
- */
-
-
-//////////////////////   
-// Scalar Functions //
-//////////////////////
-
-
-/*! \p lower_bound is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * Specifically, it returns the first position where value could be
- * inserted without violating the ordering. This version of 
- * \p lower_bound uses <tt>operator<</tt> for comparison and returns
- * the furthermost iterator \c i in <tt>[first, last)</tt> such that,
- * for every iterator \c j in <tt>[first, i)</tt>, <tt>*j < value</tt>. 
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \return The furthermost iterator \c i, such that <tt>*i < value</tt>.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>. 
- *
- *  The following code snippet demonstrates how to use \p lower_bound
- *  to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::lower_bound(thrust::device, input.begin(), input.end(), 0); // returns input.begin()
- *  thrust::lower_bound(thrust::device, input.begin(), input.end(), 1); // returns input.begin() + 1
- *  thrust::lower_bound(thrust::device, input.begin(), input.end(), 2); // returns input.begin() + 1
- *  thrust::lower_bound(thrust::device, input.begin(), input.end(), 3); // returns input.begin() + 2
- *  thrust::lower_bound(thrust::device, input.begin(), input.end(), 8); // returns input.begin() + 4
- *  thrust::lower_bound(thrust::device, input.begin(), input.end(), 9); // returns input.end()
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/lower_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename LessThanComparable>
-__host__ __device__
-ForwardIterator lower_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            const LessThanComparable &value);
-
-
-/*! \p lower_bound is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * Specifically, it returns the first position where value could be
- * inserted without violating the ordering. This version of 
- * \p lower_bound uses <tt>operator<</tt> for comparison and returns
- * the furthermost iterator \c i in <tt>[first, last)</tt> such that,
- * for every iterator \c j in <tt>[first, i)</tt>, <tt>*j < value</tt>. 
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \return The furthermost iterator \c i, such that <tt>*i < value</tt>.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>. 
- *
- *  The following code snippet demonstrates how to use \p lower_bound
- *  to search for values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::lower_bound(input.begin(), input.end(), 0); // returns input.begin()
- *  thrust::lower_bound(input.begin(), input.end(), 1); // returns input.begin() + 1
- *  thrust::lower_bound(input.begin(), input.end(), 2); // returns input.begin() + 1
- *  thrust::lower_bound(input.begin(), input.end(), 3); // returns input.begin() + 2
- *  thrust::lower_bound(input.begin(), input.end(), 8); // returns input.begin() + 4
- *  thrust::lower_bound(input.begin(), input.end(), 9); // returns input.end()
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/lower_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <class ForwardIterator, class LessThanComparable>
-ForwardIterator lower_bound(ForwardIterator first, 
-                            ForwardIterator last,
-                            const LessThanComparable& value);
-
-
-/*! \p lower_bound is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * Specifically, it returns the first position where value could be
- * inserted without violating the ordering. This version of 
- * \p lower_bound uses function object \c comp for comparison 
- * and returns the furthermost iterator \c i in <tt>[first, last)</tt>
- * such that, for every iterator \c j in <tt>[first, i)</tt>, 
- * <tt>comp(*j, value)</tt> is \c true. 
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \param comp The comparison operator.
- *  \return The furthermost iterator \c i, such that <tt>comp(*i, value)</tt> is \c true.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam T is comparable to \p ForwardIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  The following code snippet demonstrates how to use \p lower_bound
- *  to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::lower_bound(input.begin(), input.end(), 0, thrust::less<int>()); // returns input.begin()
- *  thrust::lower_bound(input.begin(), input.end(), 1, thrust::less<int>()); // returns input.begin() + 1
- *  thrust::lower_bound(input.begin(), input.end(), 2, thrust::less<int>()); // returns input.begin() + 1
- *  thrust::lower_bound(input.begin(), input.end(), 3, thrust::less<int>()); // returns input.begin() + 2
- *  thrust::lower_bound(input.begin(), input.end(), 8, thrust::less<int>()); // returns input.begin() + 4
- *  thrust::lower_bound(input.begin(), input.end(), 9, thrust::less<int>()); // returns input.end()
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/lower_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
-__host__ __device__
-ForwardIterator lower_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            const T &value,
-                            StrictWeakOrdering comp);
-
-
-/*! \p lower_bound is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * Specifically, it returns the first position where value could be
- * inserted without violating the ordering. This version of 
- * \p lower_bound uses function object \c comp for comparison 
- * and returns the furthermost iterator \c i in <tt>[first, last)</tt>
- * such that, for every iterator \c j in <tt>[first, i)</tt>, 
- * <tt>comp(*j, value)</tt> is \c true. 
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \param comp The comparison operator.
- *  \return The furthermost iterator \c i, such that <tt>comp(*i, value)</tt> is \c true.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam T is comparable to \p ForwardIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  The following code snippet demonstrates how to use \p lower_bound
- *  to search for values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::lower_bound(input.begin(), input.end(), 0, thrust::less<int>()); // returns input.begin()
- *  thrust::lower_bound(input.begin(), input.end(), 1, thrust::less<int>()); // returns input.begin() + 1
- *  thrust::lower_bound(input.begin(), input.end(), 2, thrust::less<int>()); // returns input.begin() + 1
- *  thrust::lower_bound(input.begin(), input.end(), 3, thrust::less<int>()); // returns input.begin() + 2
- *  thrust::lower_bound(input.begin(), input.end(), 8, thrust::less<int>()); // returns input.begin() + 4
- *  thrust::lower_bound(input.begin(), input.end(), 9, thrust::less<int>()); // returns input.end()
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/lower_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <class ForwardIterator, class T, class StrictWeakOrdering>
-ForwardIterator lower_bound(ForwardIterator first,
-                            ForwardIterator last,
-                            const T& value, 
-                            StrictWeakOrdering comp);
-
-
-/*! \p upper_bound is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * Specifically, it returns the last position where value could be
- * inserted without violating the ordering. This version of 
- * \p upper_bound uses <tt>operator<</tt> for comparison and returns
- * the furthermost iterator \c i in <tt>[first, last)</tt> such that,
- * for every iterator \c j in <tt>[first, i)</tt>, <tt>value < *j</tt>
- * is \c false.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \return The furthermost iterator \c i, such that <tt>value < *i</tt> is \c false.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>. 
- *
- *  The following code snippet demonstrates how to use \p upper_bound
- *  to search for values in a ordered range using the \p thrust::device execution policy for parallelism:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 0); // returns input.begin() + 1
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 1); // returns input.begin() + 1
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 2); // returns input.begin() + 2
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 3); // returns input.begin() + 2
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 8); // returns input.end()
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 9); // returns input.end()
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/upper_bound.html
- *  \see \p lower_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename LessThanComparable>
-__host__ __device__
-ForwardIterator upper_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            const LessThanComparable &value);
-
-
-/*! \p upper_bound is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * Specifically, it returns the last position where value could be
- * inserted without violating the ordering. This version of 
- * \p upper_bound uses <tt>operator<</tt> for comparison and returns
- * the furthermost iterator \c i in <tt>[first, last)</tt> such that,
- * for every iterator \c j in <tt>[first, i)</tt>, <tt>value < *j</tt>
- * is \c false.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \return The furthermost iterator \c i, such that <tt>value < *i</tt> is \c false.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>. 
- *
- *  The following code snippet demonstrates how to use \p upper_bound
- *  to search for values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::upper_bound(input.begin(), input.end(), 0); // returns input.begin() + 1
- *  thrust::upper_bound(input.begin(), input.end(), 1); // returns input.begin() + 1
- *  thrust::upper_bound(input.begin(), input.end(), 2); // returns input.begin() + 2
- *  thrust::upper_bound(input.begin(), input.end(), 3); // returns input.begin() + 2
- *  thrust::upper_bound(input.begin(), input.end(), 8); // returns input.end()
- *  thrust::upper_bound(input.begin(), input.end(), 9); // returns input.end()
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/upper_bound.html
- *  \see \p lower_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <class ForwardIterator, class LessThanComparable>
-ForwardIterator upper_bound(ForwardIterator first, 
-                            ForwardIterator last,
-                            const LessThanComparable& value);
-
-
-/*! \p upper_bound is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * Specifically, it returns the last position where value could be
- * inserted without violating the ordering. This version of 
- * \p upper_bound uses function object \c comp for comparison and returns
- * the furthermost iterator \c i in <tt>[first, last)</tt> such that,
- * for every iterator \c j in <tt>[first, i)</tt>, <tt>comp(value, *j)</tt>
- * is \c false.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \param comp The comparison operator.
- *  \return The furthermost iterator \c i, such that <tt>comp(value, *i)</tt> is \c false.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam T is comparable to \p ForwardIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  The following code snippet demonstrates how to use \p upper_bound
- *  to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 0, thrust::less<int>()); // returns input.begin() + 1
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 1, thrust::less<int>()); // returns input.begin() + 1
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 2, thrust::less<int>()); // returns input.begin() + 2
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 3, thrust::less<int>()); // returns input.begin() + 2
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 8, thrust::less<int>()); // returns input.end()
- *  thrust::upper_bound(thrust::device, input.begin(), input.end(), 9, thrust::less<int>()); // returns input.end()
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/upper_bound.html
- *  \see \p lower_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template<typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
-__host__ __device__
-ForwardIterator upper_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            const T &value,
-                            StrictWeakOrdering comp);
-
-/*! \p upper_bound is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * Specifically, it returns the last position where value could be
- * inserted without violating the ordering. This version of 
- * \p upper_bound uses function object \c comp for comparison and returns
- * the furthermost iterator \c i in <tt>[first, last)</tt> such that,
- * for every iterator \c j in <tt>[first, i)</tt>, <tt>comp(value, *j)</tt>
- * is \c false.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \param comp The comparison operator.
- *  \return The furthermost iterator \c i, such that <tt>comp(value, *i)</tt> is \c false.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam T is comparable to \p ForwardIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  The following code snippet demonstrates how to use \p upper_bound
- *  to search for values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::upper_bound(input.begin(), input.end(), 0, thrust::less<int>()); // returns input.begin() + 1
- *  thrust::upper_bound(input.begin(), input.end(), 1, thrust::less<int>()); // returns input.begin() + 1
- *  thrust::upper_bound(input.begin(), input.end(), 2, thrust::less<int>()); // returns input.begin() + 2
- *  thrust::upper_bound(input.begin(), input.end(), 3, thrust::less<int>()); // returns input.begin() + 2
- *  thrust::upper_bound(input.begin(), input.end(), 8, thrust::less<int>()); // returns input.end()
- *  thrust::upper_bound(input.begin(), input.end(), 9, thrust::less<int>()); // returns input.end()
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/upper_bound.html
- *  \see \p lower_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <class ForwardIterator, class T, class StrictWeakOrdering>
-ForwardIterator upper_bound(ForwardIterator first,
-                            ForwardIterator last,
-                            const T& value, 
-                            StrictWeakOrdering comp);
-
-
-/*! \p binary_search is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * It returns \c true if an element that is equivalent to \c value 
- * is present in <tt>[first, last)</tt> and \c false if no such element
- * exists.  Specifically, this version returns \c true if and only if 
- * there exists an iterator \c i in <tt>[first, last)</tt> such that 
- * <tt>*i < value</tt> and <tt>value < *i</tt> are both \c false.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \return \c true if an equivalent element exists in <tt>[first, last)</tt>, otherwise \c false.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>. 
- *
- *  The following code snippet demonstrates how to use \p binary_search
- *  to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 0); // returns true
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 1); // returns false
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 2); // returns true
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 3); // returns false
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 8); // returns true
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 9); // returns false
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/binary_search.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p equal_range
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename LessThanComparable>
-__host__ __device__
-bool binary_search(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                   ForwardIterator first, 
-                   ForwardIterator last,
-                   const LessThanComparable& value);
-
-
-/*! \p binary_search is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * It returns \c true if an element that is equivalent to \c value 
- * is present in <tt>[first, last)</tt> and \c false if no such element
- * exists.  Specifically, this version returns \c true if and only if 
- * there exists an iterator \c i in <tt>[first, last)</tt> such that 
- * <tt>*i < value</tt> and <tt>value < *i</tt> are both \c false.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \return \c true if an equivalent element exists in <tt>[first, last)</tt>, otherwise \c false.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>. 
- *
- *  The following code snippet demonstrates how to use \p binary_search
- *  to search for values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::binary_search(input.begin(), input.end(), 0); // returns true
- *  thrust::binary_search(input.begin(), input.end(), 1); // returns false
- *  thrust::binary_search(input.begin(), input.end(), 2); // returns true
- *  thrust::binary_search(input.begin(), input.end(), 3); // returns false
- *  thrust::binary_search(input.begin(), input.end(), 8); // returns true
- *  thrust::binary_search(input.begin(), input.end(), 9); // returns false
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/binary_search.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p equal_range
- */
-template <class ForwardIterator, class LessThanComparable>
-bool binary_search(ForwardIterator first, 
-                   ForwardIterator last,
-                   const LessThanComparable& value);
-
-
-/*! \p binary_search is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * It returns \c true if an element that is equivalent to \c value 
- * is present in <tt>[first, last)</tt> and \c false if no such element
- * exists.  Specifically, this version returns \c true if and only if 
- * there exists an iterator \c i in <tt>[first, last)</tt> such that 
- * <tt>comp(*i, value)</tt> and <tt>comp(value, *i)</tt> are both \c false.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \param comp The comparison operator.
- *  \return \c true if an equivalent element exists in <tt>[first, last)</tt>, otherwise \c false.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam T is comparable to \p ForwardIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  The following code snippet demonstrates how to use \p binary_search
- *  to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 0, thrust::less<int>()); // returns true
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 1, thrust::less<int>()); // returns false
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 2, thrust::less<int>()); // returns true
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 3, thrust::less<int>()); // returns false
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 8, thrust::less<int>()); // returns true
- *  thrust::binary_search(thrust::device, input.begin(), input.end(), 9, thrust::less<int>()); // returns false
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/binary_search.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p equal_range
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
-__host__ __device__
-bool binary_search(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                   ForwardIterator first,
-                   ForwardIterator last,
-                   const T& value, 
-                   StrictWeakOrdering comp);
-
-
-/*! \p binary_search is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. 
- * It returns \c true if an element that is equivalent to \c value 
- * is present in <tt>[first, last)</tt> and \c false if no such element
- * exists.  Specifically, this version returns \c true if and only if 
- * there exists an iterator \c i in <tt>[first, last)</tt> such that 
- * <tt>comp(*i, value)</tt> and <tt>comp(value, *i)</tt> are both \c false.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \param comp The comparison operator.
- *  \return \c true if an equivalent element exists in <tt>[first, last)</tt>, otherwise \c false.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam T is comparable to \p ForwardIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  The following code snippet demonstrates how to use \p binary_search
- *  to search for values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::binary_search(input.begin(), input.end(), 0, thrust::less<int>()); // returns true
- *  thrust::binary_search(input.begin(), input.end(), 1, thrust::less<int>()); // returns false
- *  thrust::binary_search(input.begin(), input.end(), 2, thrust::less<int>()); // returns true
- *  thrust::binary_search(input.begin(), input.end(), 3, thrust::less<int>()); // returns false
- *  thrust::binary_search(input.begin(), input.end(), 8, thrust::less<int>()); // returns true
- *  thrust::binary_search(input.begin(), input.end(), 9, thrust::less<int>()); // returns false
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/binary_search.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p equal_range
- */
-template <class ForwardIterator, class T, class StrictWeakOrdering>
-bool binary_search(ForwardIterator first,
-                   ForwardIterator last,
-                   const T& value, 
-                   StrictWeakOrdering comp);
-
-
-/*! \p equal_range is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. The 
- * value returned by \p equal_range is essentially a combination of
- * the values returned by \p lower_bound and \p upper_bound: it returns
- * a \p pair of iterators \c i and \c j such that \c i is the first
- * position where value could be inserted without violating the 
- * ordering and \c j is the last position where value could be inserted
- * without violating the ordering. It follows that every element in the
- * range <tt>[i, j)</tt> is equivalent to value, and that 
- * <tt>[i, j)</tt> is the largest subrange of <tt>[first, last)</tt> that
- * has this property. 
- *
- * This version of \p equal_range returns a \p pair of iterators 
- * <tt>[i, j)</tt>, where \c i is the furthermost iterator in 
- * <tt>[first, last)</tt> such that, for every iterator \c k in 
- * <tt>[first, i)</tt>, <tt>*k < value</tt>.  \c j is the furthermost
- * iterator in <tt>[first, last)</tt> such that, for every iterator 
- * \c k in <tt>[first, j)</tt>, <tt>value < *k</tt> is \c false. 
- * For every iterator \c k in <tt>[i, j)</tt>, neither 
- * <tt>value < *k</tt> nor <tt>*k < value</tt> is \c true.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \return A \p pair of iterators <tt>[i, j)</tt> that define the range of equivalent elements.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>. 
- *
- *  The following code snippet demonstrates how to use \p equal_range
- *  to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 0); // returns [input.begin(), input.begin() + 1)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 1); // returns [input.begin() + 1, input.begin() + 1)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 2); // returns [input.begin() + 1, input.begin() + 2)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 3); // returns [input.begin() + 2, input.begin() + 2)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 8); // returns [input.begin() + 4, input.end)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 9); // returns [input.end(), input.end)
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/equal_range.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p binary_search
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename LessThanComparable>
-__host__ __device__
-thrust::pair<ForwardIterator, ForwardIterator>
-equal_range(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-            ForwardIterator first,
-            ForwardIterator last,
-            const LessThanComparable& value);
-
-
-/*! \p equal_range is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. The 
- * value returned by \p equal_range is essentially a combination of
- * the values returned by \p lower_bound and \p upper_bound: it returns
- * a \p pair of iterators \c i and \c j such that \c i is the first
- * position where value could be inserted without violating the 
- * ordering and \c j is the last position where value could be inserted
- * without violating the ordering. It follows that every element in the
- * range <tt>[i, j)</tt> is equivalent to value, and that 
- * <tt>[i, j)</tt> is the largest subrange of <tt>[first, last)</tt> that
- * has this property. 
- *
- * This version of \p equal_range returns a \p pair of iterators 
- * <tt>[i, j)</tt>, where \c i is the furthermost iterator in 
- * <tt>[first, last)</tt> such that, for every iterator \c k in 
- * <tt>[first, i)</tt>, <tt>*k < value</tt>.  \c j is the furthermost
- * iterator in <tt>[first, last)</tt> such that, for every iterator 
- * \c k in <tt>[first, j)</tt>, <tt>value < *k</tt> is \c false. 
- * For every iterator \c k in <tt>[i, j)</tt>, neither 
- * <tt>value < *k</tt> nor <tt>*k < value</tt> is \c true.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \return A \p pair of iterators <tt>[i, j)</tt> that define the range of equivalent elements.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam LessThanComparable is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>. 
- *
- *  The following code snippet demonstrates how to use \p equal_range
- *  to search for values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::equal_range(input.begin(), input.end(), 0); // returns [input.begin(), input.begin() + 1)
- *  thrust::equal_range(input.begin(), input.end(), 1); // returns [input.begin() + 1, input.begin() + 1)
- *  thrust::equal_range(input.begin(), input.end(), 2); // returns [input.begin() + 1, input.begin() + 2)
- *  thrust::equal_range(input.begin(), input.end(), 3); // returns [input.begin() + 2, input.begin() + 2)
- *  thrust::equal_range(input.begin(), input.end(), 8); // returns [input.begin() + 4, input.end)
- *  thrust::equal_range(input.begin(), input.end(), 9); // returns [input.end(), input.end)
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/equal_range.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p binary_search
- */
-template <class ForwardIterator, class LessThanComparable>
-thrust::pair<ForwardIterator, ForwardIterator>
-equal_range(ForwardIterator first,
-            ForwardIterator last,
-            const LessThanComparable& value);
-
-
-/*! \p equal_range is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. The 
- * value returned by \p equal_range is essentially a combination of
- * the values returned by \p lower_bound and \p upper_bound: it returns
- * a \p pair of iterators \c i and \c j such that \c i is the first
- * position where value could be inserted without violating the 
- * ordering and \c j is the last position where value could be inserted
- * without violating the ordering. It follows that every element in the
- * range <tt>[i, j)</tt> is equivalent to value, and that 
- * <tt>[i, j)</tt> is the largest subrange of <tt>[first, last)</tt> that
- * has this property. 
- *
- * This version of \p equal_range returns a \p pair of iterators 
- * <tt>[i, j)</tt>. \c i is the furthermost iterator in 
- * <tt>[first, last)</tt> such that, for every iterator \c k in 
- * <tt>[first, i)</tt>, <tt>comp(*k, value)</tt> is \c true.
- * \c j is the furthermost iterator in <tt>[first, last)</tt> such
- * that, for every iterator \c k in <tt>[first, last)</tt>, 
- * <tt>comp(value, *k)</tt> is \c false. For every iterator \c k 
- * in <tt>[i, j)</tt>, neither <tt>comp(value, *k)</tt> nor 
- * <tt>comp(*k, value)</tt> is \c true.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \param comp The comparison operator.
- *  \return A \p pair of iterators <tt>[i, j)</tt> that define the range of equivalent elements.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam T is comparable to \p ForwardIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  The following code snippet demonstrates how to use \p equal_range
- *  to search for values in a ordered range using the \p thrust::device execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 0, thrust::less<int>()); // returns [input.begin(), input.begin() + 1)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 1, thrust::less<int>()); // returns [input.begin() + 1, input.begin() + 1)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 2, thrust::less<int>()); // returns [input.begin() + 1, input.begin() + 2)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 3, thrust::less<int>()); // returns [input.begin() + 2, input.begin() + 2)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 8, thrust::less<int>()); // returns [input.begin() + 4, input.end)
- *  thrust::equal_range(thrust::device, input.begin(), input.end(), 9, thrust::less<int>()); // returns [input.end(), input.end)
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/equal_range.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p binary_search
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename T, typename StrictWeakOrdering>
-__host__ __device__
-thrust::pair<ForwardIterator, ForwardIterator>
-equal_range(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-            ForwardIterator first,
-            ForwardIterator last,
-            const T& value,
-            StrictWeakOrdering comp);
-
-
-/*! \p equal_range is a version of binary search: it attempts to find
- * the element value in an ordered range <tt>[first, last)</tt>. The 
- * value returned by \p equal_range is essentially a combination of
- * the values returned by \p lower_bound and \p upper_bound: it returns
- * a \p pair of iterators \c i and \c j such that \c i is the first
- * position where value could be inserted without violating the 
- * ordering and \c j is the last position where value could be inserted
- * without violating the ordering. It follows that every element in the
- * range <tt>[i, j)</tt> is equivalent to value, and that 
- * <tt>[i, j)</tt> is the largest subrange of <tt>[first, last)</tt> that
- * has this property. 
- *
- * This version of \p equal_range returns a \p pair of iterators 
- * <tt>[i, j)</tt>. \c i is the furthermost iterator in 
- * <tt>[first, last)</tt> such that, for every iterator \c k in 
- * <tt>[first, i)</tt>, <tt>comp(*k, value)</tt> is \c true.
- * \c j is the furthermost iterator in <tt>[first, last)</tt> such
- * that, for every iterator \c k in <tt>[first, last)</tt>, 
- * <tt>comp(value, *k)</tt> is \c false. For every iterator \c k 
- * in <tt>[i, j)</tt>, neither <tt>comp(value, *k)</tt> nor 
- * <tt>comp(*k, value)</tt> is \c true.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param value The value to be searched.
- *  \param comp The comparison operator.
- *  \return A \p pair of iterators <tt>[i, j)</tt> that define the range of equivalent elements.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam T is comparable to \p ForwardIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  The following code snippet demonstrates how to use \p equal_range
- *  to search for values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::equal_range(input.begin(), input.end(), 0, thrust::less<int>()); // returns [input.begin(), input.begin() + 1)
- *  thrust::equal_range(input.begin(), input.end(), 1, thrust::less<int>()); // returns [input.begin() + 1, input.begin() + 1)
- *  thrust::equal_range(input.begin(), input.end(), 2, thrust::less<int>()); // returns [input.begin() + 1, input.begin() + 2)
- *  thrust::equal_range(input.begin(), input.end(), 3, thrust::less<int>()); // returns [input.begin() + 2, input.begin() + 2)
- *  thrust::equal_range(input.begin(), input.end(), 8, thrust::less<int>()); // returns [input.begin() + 4, input.end)
- *  thrust::equal_range(input.begin(), input.end(), 9, thrust::less<int>()); // returns [input.end(), input.end)
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/equal_range.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p binary_search
- */
-template <class ForwardIterator, class T, class StrictWeakOrdering>
-thrust::pair<ForwardIterator, ForwardIterator>
-equal_range(ForwardIterator first,
-            ForwardIterator last,
-            const T& value,
-            StrictWeakOrdering comp);
-
-
-/*! \addtogroup vectorized_binary_search Vectorized Searches
- *  \ingroup binary_search
- *  \{
- */
-
-
-//////////////////////
-// Vector Functions //
-//////////////////////
-
-
-/*! \p lower_bound is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * Specifically, it returns the index of first position where value could
- * be inserted without violating the ordering.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p lower_bound
- *  to search for multiple values in a ordered range using the \p thrust::device execution policy for
- *  parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<unsigned int> output(6);
- *
- *  thrust::lower_bound(thrust::device,
- *                      input.begin(), input.end(),
- *                      values.begin(), values.end(),
- *                      output.begin());
- *
- *  // output is now [0, 1, 1, 2, 4, 5]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/lower_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator>
-__host__ __device__
-OutputIterator lower_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                           ForwardIterator first, 
-                           ForwardIterator last,
-                           InputIterator values_first, 
-                           InputIterator values_last,
-                           OutputIterator result);
-
-
-/*! \p lower_bound is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * Specifically, it returns the index of first position where value could
- * be inserted without violating the ordering.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p lower_bound
- *  to search for multiple values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<unsigned int> output(6);
- *
- *  thrust::lower_bound(input.begin(), input.end(),
- *                      values.begin(), values.end(),
- *                      output.begin());
- *
- *  // output is now [0, 1, 1, 2, 4, 5]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/lower_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <class ForwardIterator, class InputIterator, class OutputIterator>
-OutputIterator lower_bound(ForwardIterator first, 
-                           ForwardIterator last,
-                           InputIterator values_first, 
-                           InputIterator values_last,
-                           OutputIterator result);
-
-
-/*! \p lower_bound is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * Specifically, it returns the index of first position where value could
- * be inserted without violating the ordering.  This version of 
- * \p lower_bound uses function object \c comp for comparison.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- *  \param comp The comparison operator.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is comparable to \p ForwardIterator's \c value_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p lower_bound
- *  to search for multiple values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<unsigned int> output(6);
- *
- *  thrust::lower_bound(input.begin(), input.end(),
- *                      values.begin(), values.end(), 
- *                      output.begin(),
- *                      thrust::less<int>());
- *
- *  // output is now [0, 1, 1, 2, 4, 5]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/lower_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator, typename StrictWeakOrdering>
-__host__ __device__
-OutputIterator lower_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                           ForwardIterator first, 
-                           ForwardIterator last,
-                           InputIterator values_first, 
-                           InputIterator values_last,
-                           OutputIterator result,
-                           StrictWeakOrdering comp);
-
-
-/*! \p lower_bound is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * Specifically, it returns the index of first position where value could
- * be inserted without violating the ordering.  This version of 
- * \p lower_bound uses function object \c comp for comparison.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- *  \param comp The comparison operator.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is comparable to \p ForwardIterator's \c value_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p lower_bound
- *  to search for multiple values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<unsigned int> output(6);
- *
- *  thrust::lower_bound(input.begin(), input.end(),
- *                      values.begin(), values.end(), 
- *                      output.begin(),
- *                      thrust::less<int>());
- *
- *  // output is now [0, 1, 1, 2, 4, 5]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/lower_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <class ForwardIterator, class InputIterator, class OutputIterator, class StrictWeakOrdering>
-OutputIterator lower_bound(ForwardIterator first, 
-                           ForwardIterator last,
-                           InputIterator values_first, 
-                           InputIterator values_last,
-                           OutputIterator result,
-                           StrictWeakOrdering comp);
-
-
-/*! \p upper_bound is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * Specifically, it returns the index of last position where value could
- * be inserted without violating the ordering.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p upper_bound
- *  to search for multiple values in a ordered range using the \p thrust::device execution policy for
- *  parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<unsigned int> output(6);
- *
- *  thrust::upper_bound(thrust::device,
- *                      input.begin(), input.end(),
- *                      values.begin(), values.end(),
- *                      output.begin());
- *
- *  // output is now [1, 1, 2, 2, 5, 5]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/upper_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator>
-__host__ __device__
-OutputIterator upper_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                           ForwardIterator first, 
-                           ForwardIterator last,
-                           InputIterator values_first, 
-                           InputIterator values_last,
-                           OutputIterator result);
-
-
-/*! \p upper_bound is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * Specifically, it returns the index of last position where value could
- * be inserted without violating the ordering.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p upper_bound
- *  to search for multiple values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<unsigned int> output(6);
- *
- *  thrust::upper_bound(input.begin(), input.end(),
- *                      values.begin(), values.end(),
- *                      output.begin());
- *
- *  // output is now [1, 1, 2, 2, 5, 5]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/upper_bound.html
- *  \see \p upper_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <class ForwardIterator, class InputIterator, class OutputIterator>
-OutputIterator upper_bound(ForwardIterator first, 
-                           ForwardIterator last,
-                           InputIterator values_first, 
-                           InputIterator values_last,
-                           OutputIterator result);
-
-
-/*! \p upper_bound is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * Specifically, it returns the index of first position where value could
- * be inserted without violating the ordering.  This version of 
- * \p upper_bound uses function object \c comp for comparison.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- *  \param comp The comparison operator.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is comparable to \p ForwardIterator's \c value_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p upper_bound
- *  to search for multiple values in a ordered range using the \p thrust::device execution policy for
- *  parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<unsigned int> output(6);
- *
- *  thrust::upper_bound(thrust::device,
- *                      input.begin(), input.end(),
- *                      values.begin(), values.end(), 
- *                      output.begin(),
- *                      thrust::less<int>());
- *
- *  // output is now [1, 1, 2, 2, 5, 5]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/upper_bound.html
- *  \see \p lower_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator, typename StrictWeakOrdering>
-__host__ __device__
-OutputIterator upper_bound(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                           ForwardIterator first, 
-                           ForwardIterator last,
-                           InputIterator values_first, 
-                           InputIterator values_last,
-                           OutputIterator result,
-                           StrictWeakOrdering comp);
-
-
-/*! \p upper_bound is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * Specifically, it returns the index of first position where value could
- * be inserted without violating the ordering.  This version of 
- * \p upper_bound uses function object \c comp for comparison.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- *  \param comp The comparison operator.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is comparable to \p ForwardIterator's \c value_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and \c ForwardIterator's difference_type is convertible to \c OutputIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p upper_bound
- *  to search for multiple values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<unsigned int> output(6);
- *
- *  thrust::upper_bound(input.begin(), input.end(),
- *                      values.begin(), values.end(), 
- *                      output.begin(),
- *                      thrust::less<int>());
- *
- *  // output is now [1, 1, 2, 2, 5, 5]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/upper_bound.html
- *  \see \p lower_bound
- *  \see \p equal_range
- *  \see \p binary_search
- */
-template <class ForwardIterator, class InputIterator, class OutputIterator, class StrictWeakOrdering>
-OutputIterator upper_bound(ForwardIterator first, 
-                           ForwardIterator last,
-                           InputIterator values_first, 
-                           InputIterator values_last,
-                           OutputIterator result,
-                           StrictWeakOrdering comp);
-
-
-/*! \p binary_search is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * It returns \c true if an element that is equivalent to \c value 
- * is present in <tt>[first, last)</tt> and \c false if no such element
- * exists.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and bool is convertible to \c OutputIterator's \c value_type.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p binary_search
- *  to search for multiple values in a ordered range using the \p thrust::device execution policy for
- *  parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<bool> output(6);
- *
- *  thrust::binary_search(thrust::device,
- *                        input.begin(), input.end(),
- *                        values.begin(), values.end(),
- *                        output.begin());
- *
- *  // output is now [true, false, true, false, true, false]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/binary_search.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p equal_range
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator>
-__host__ __device__
-OutputIterator binary_search(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                             ForwardIterator first, 
-                             ForwardIterator last,
-                             InputIterator values_first, 
-                             InputIterator values_last,
-                             OutputIterator result);
-
-
-/*! \p binary_search is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * It returns \c true if an element that is equivalent to \c value 
- * is present in <tt>[first, last)</tt> and \c false if no such element
- * exists.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and bool is convertible to \c OutputIterator's \c value_type.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p binary_search
- *  to search for multiple values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<bool> output(6);
- *
- *  thrust::binary_search(input.begin(), input.end(),
- *                        values.begin(), values.end(),
- *                        output.begin());
- *
- *  // output is now [true, false, true, false, true, false]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/binary_search.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p equal_range
- */
-template <class ForwardIterator, class InputIterator, class OutputIterator>
-OutputIterator binary_search(ForwardIterator first, 
-                             ForwardIterator last,
-                             InputIterator values_first, 
-                             InputIterator values_last,
-                             OutputIterator result);
-
-
-/*! \p binary_search is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * It returns \c true if an element that is equivalent to \c value 
- * is present in <tt>[first, last)</tt> and \c false if no such element
- * exists.  This version of \p binary_search uses function object 
- * \c comp for comparison.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- *  \param comp The comparison operator.
- * 
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and bool is convertible to \c OutputIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p binary_search
- *  to search for multiple values in a ordered range using the \p thrust::device execution policy for
- *  parallelization:
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<bool> output(6);
- *
- *  thrust::binary_search(thrust::device,
- *                        input.begin(), input.end(),
- *                        values.begin(), values.end(),
- *                        output.begin(),
- *                        thrust::less<T>());
- *
- *  // output is now [true, false, true, false, true, false]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/binary_search.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p equal_range
- */
-template <typename DerivedPolicy, typename ForwardIterator, typename InputIterator, typename OutputIterator, typename StrictWeakOrdering>
-__host__ __device__
-OutputIterator binary_search(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                             ForwardIterator first, 
-                             ForwardIterator last,
-                             InputIterator values_first, 
-                             InputIterator values_last,
-                             OutputIterator result,
-                             StrictWeakOrdering comp);
-
-
-/*! \p binary_search is a vectorized version of binary search: for each 
- * iterator \c v in <tt>[values_first, values_last)</tt> it attempts to
- * find the value <tt>*v</tt> in an ordered range <tt>[first, last)</tt>.
- * It returns \c true if an element that is equivalent to \c value 
- * is present in <tt>[first, last)</tt> and \c false if no such element
- * exists.  This version of \p binary_search uses function object 
- * \c comp for comparison.
- *
- *  \param first The beginning of the ordered sequence.
- *  \param last The end of the ordered sequence.
- *  \param values_first The beginning of the search values sequence.
- *  \param values_last The end of the search values sequence.
- *  \param result The beginning of the output sequence.
- *  \param comp The comparison operator.
- * 
- *  \tparam ForwardIterator is a model of <a href="http://www.sgi.com/tech/stl/ForwardIterator">Forward Iterator</a>.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *                        and \c InputIterator's \c value_type is <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThanComparable</a>.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *                        and bool is convertible to \c OutputIterator's \c value_type.
- *  \tparam StrictWeakOrdering is a model of <a href="http://www.sgi.com/tech/stl/StrictWeakOrdering.html">Strict Weak Ordering</a>.
- *
- *  \pre The ranges <tt>[first,last)</tt> and <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p binary_search
- *  to search for multiple values in a ordered range.
- *
- *  \code
- *  #include <thrust/binary_search.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/functional.h>
- *  ...
- *  thrust::device_vector<int> input(5);
- *
- *  input[0] = 0;
- *  input[1] = 2;
- *  input[2] = 5;
- *  input[3] = 7;
- *  input[4] = 8;
- *
- *  thrust::device_vector<int> values(6);
- *  values[0] = 0; 
- *  values[1] = 1;
- *  values[2] = 2;
- *  values[3] = 3;
- *  values[4] = 8;
- *  values[5] = 9;
- *
- *  thrust::device_vector<bool> output(6);
- *
- *  thrust::binary_search(input.begin(), input.end(),
- *                        values.begin(), values.end(),
- *                        output.begin(),
- *                        thrust::less<T>());
- *
- *  // output is now [true, false, true, false, true, false]
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/binary_search.html
- *  \see \p lower_bound
- *  \see \p upper_bound
- *  \see \p equal_range
- */
-template <class ForwardIterator, class InputIterator, class OutputIterator, class StrictWeakOrdering>
-OutputIterator binary_search(ForwardIterator first, 
-                             ForwardIterator last,
-                             InputIterator values_first, 
-                             InputIterator values_last,
-                             OutputIterator result,
-                             StrictWeakOrdering comp);
-
-
-/*! \} // end vectorized_binary_search
- */
-
-
-/*! \} // end binary_search
- */
-
-
-/*! \} // end searching
- */
-
-
-} // end namespace thrust
-
-#include <thrust/detail/binary_search.inl>
-
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/swinir_arch.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/swinir_arch.py
deleted file mode 100644
index 3917fa2c7408e1f5b55b9930c643a9af920a4d81..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/swinir_arch.py
+++ /dev/null
@@ -1,956 +0,0 @@
-# Modified from https://github.com/JingyunLiang/SwinIR
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-
-import math
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint as checkpoint
-
-from basicsr.utils.registry import ARCH_REGISTRY
-from .arch_util import to_2tuple, trunc_normal_
-
-
-def drop_path(x, drop_prob: float = 0., training: bool = False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-
-    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
-    """
-    if drop_prob == 0. or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-
-    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
-    """
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-
-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
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (b, h, w, c)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*b, window_size, window_size, c)
-    """
-    b, h, w, c = x.shape
-    x = x.view(b, h // window_size, window_size, w // window_size, window_size, c)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c)
-    return windows
-
-
-def window_reverse(windows, window_size, h, w):
-    """
-    Args:
-        windows: (num_windows*b, window_size, window_size, c)
-        window_size (int): Window size
-        h (int): Height of image
-        w (int): Width of image
-
-    Returns:
-        x: (b, h, w, c)
-    """
-    b = int(windows.shape[0] / (h * w / window_size / window_size))
-    x = windows.view(b, h // window_size, w // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer('relative_position_index', relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*b, n, c)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        b_, n, c = x.shape
-        qkv = self.qkv(x).reshape(b_, n, 3, self.num_heads, c // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nw = mask.shape[0]
-            attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, n, n)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(b_, n, c)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, n):
-        # calculate flops for 1 window with token length of n
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += n * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * n * (self.dim // self.num_heads) * n
-        #  x = (attn @ v)
-        flops += self.num_heads * n * n * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += n * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self,
-                 dim,
-                 input_resolution,
-                 num_heads,
-                 window_size=7,
-                 shift_size=0,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 act_layer=nn.GELU,
-                 norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size'
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer('attn_mask', attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        h, w = x_size
-        img_mask = torch.zeros((1, h, w, 1))  # 1 h w 1
-        h_slices = (slice(0, -self.window_size), slice(-self.window_size,
-                                                       -self.shift_size), slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size), slice(-self.window_size,
-                                                       -self.shift_size), slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nw, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        h, w = x_size
-        b, _, c = x.shape
-        # assert seq_len == h * w, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(b, h, w, c)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nw*b, window_size, window_size, c
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, c)  # nw*b, window_size*window_size, c
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nw*b, window_size*window_size, c
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c)
-        shifted_x = window_reverse(attn_windows, self.window_size, h, w)  # b h' w' c
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(b, h * w, c)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return (f'dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, '
-                f'window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}')
-
-    def flops(self):
-        flops = 0
-        h, w = self.input_resolution
-        # norm1
-        flops += self.dim * h * w
-        # W-MSA/SW-MSA
-        nw = h * w / self.window_size / self.window_size
-        flops += nw * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * h * w * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * h * w
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: b, h*w, c
-        """
-        h, w = self.input_resolution
-        b, seq_len, c = x.shape
-        assert seq_len == h * w, 'input feature has wrong size'
-        assert h % 2 == 0 and w % 2 == 0, f'x size ({h}*{w}) are not even.'
-
-        x = x.view(b, h, w, c)
-
-        x0 = x[:, 0::2, 0::2, :]  # b h/2 w/2 c
-        x1 = x[:, 1::2, 0::2, :]  # b h/2 w/2 c
-        x2 = x[:, 0::2, 1::2, :]  # b h/2 w/2 c
-        x3 = x[:, 1::2, 1::2, :]  # b h/2 w/2 c
-        x = torch.cat([x0, x1, x2, x3], -1)  # b h/2 w/2 4*c
-        x = x.view(b, -1, 4 * c)  # b h/2*w/2 4*c
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f'input_resolution={self.input_resolution}, dim={self.dim}'
-
-    def flops(self):
-        h, w = self.input_resolution
-        flops = h * w * self.dim
-        flops += (h // 2) * (w // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 dim,
-                 input_resolution,
-                 depth,
-                 num_heads,
-                 window_size,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 norm_layer=nn.LayerNorm,
-                 downsample=None,
-                 use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(
-                dim=dim,
-                input_resolution=input_resolution,
-                num_heads=num_heads,
-                window_size=window_size,
-                shift_size=0 if (i % 2 == 0) else window_size // 2,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop,
-                attn_drop=attn_drop,
-                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                norm_layer=norm_layer) for i in range(depth)
-        ])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}'
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self,
-                 dim,
-                 input_resolution,
-                 depth,
-                 num_heads,
-                 window_size,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 norm_layer=nn.LayerNorm,
-                 downsample=None,
-                 use_checkpoint=False,
-                 img_size=224,
-                 patch_size=4,
-                 resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(
-            dim=dim,
-            input_resolution=input_resolution,
-            depth=depth,
-            num_heads=num_heads,
-            window_size=window_size,
-            mlp_ratio=mlp_ratio,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            drop=drop,
-            attn_drop=attn_drop,
-            drop_path=drop_path,
-            norm_layer=norm_layer,
-            downsample=downsample,
-            use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(
-                nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        h, w = self.input_resolution
-        flops += h * w * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # b Ph*Pw c
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        h, w = self.img_size
-        if self.norm is not None:
-            flops += h * w * self.embed_dim
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        x = x.transpose(1, 2).view(x.shape[0], self.embed_dim, x_size[0], x_size[1])  # b Ph*Pw c
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        h, w = self.input_resolution
-        flops = h * w * self.num_feat * 3 * 9
-        return flops
-
-
-@ARCH_REGISTRY.register()
-class SwinIR(nn.Module):
-    r""" SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self,
-                 img_size=64,
-                 patch_size=1,
-                 in_chans=3,
-                 embed_dim=96,
-                 depths=(6, 6, 6, 6),
-                 num_heads=(6, 6, 6, 6),
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm,
-                 ape=False,
-                 patch_norm=True,
-                 use_checkpoint=False,
-                 upscale=2,
-                 img_range=1.,
-                 upsampler='',
-                 resi_connection='1conv',
-                 **kwargs):
-        super(SwinIR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-
-        # ------------------------- 1, shallow feature extraction ------------------------- #
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        # ------------------------- 2, deep feature extraction ------------------------- #
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(
-                dim=embed_dim,
-                input_resolution=(patches_resolution[0], patches_resolution[1]),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                norm_layer=norm_layer,
-                downsample=None,
-                use_checkpoint=use_checkpoint,
-                img_size=img_size,
-                patch_size=patch_size,
-                resi_connection=resi_connection)
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(
-                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        # ------------------------- 3, high quality image reconstruction ------------------------- #
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, 'only support x4 now.'
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True))
-            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)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    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 {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # b seq_len c
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x
-
-    def flops(self):
-        flops = 0
-        h, w = self.patches_resolution
-        flops += h * w * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for layer in self.layers:
-            flops += layer.flops()
-        flops += h * w * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = SwinIR(
-        upscale=2,
-        img_size=(height, width),
-        window_size=window_size,
-        img_range=1.,
-        depths=[6, 6, 6, 6],
-        embed_dim=60,
-        num_heads=[6, 6, 6, 6],
-        mlp_ratio=2,
-        upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/doc/__init__.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/doc/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/matthoffner/starchat-ui/components/Chatbar/Chatbar.state.tsx b/spaces/matthoffner/starchat-ui/components/Chatbar/Chatbar.state.tsx
deleted file mode 100644
index bb9a21a298d858cfd2e9612cbcbc4c7e4bc26a19..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/starchat-ui/components/Chatbar/Chatbar.state.tsx
+++ /dev/null
@@ -1,11 +0,0 @@
-import { Conversation } from '@/types/chat';
-
-export interface ChatbarInitialState {
-  searchTerm: string;
-  filteredConversations: Conversation[];
-}
-
-export const initialState: ChatbarInitialState = {
-  searchTerm: '',
-  filteredConversations: [],
-};
diff --git a/spaces/matthoffner/wizardcoder-ggml/main.py b/spaces/matthoffner/wizardcoder-ggml/main.py
deleted file mode 100644
index f43f3af4aca576ecb537cdeb494ed59d67de0c8e..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/wizardcoder-ggml/main.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import json
-import markdown
-from typing import Callable, List, Dict, Any, Generator
-from functools import partial
-
-import fastapi
-import uvicorn
-from fastapi import HTTPException, Depends, Request
-from fastapi.responses import HTMLResponse
-from fastapi.middleware.cors import CORSMiddleware
-from sse_starlette.sse import EventSourceResponse
-from anyio import create_memory_object_stream
-from anyio.to_thread import run_sync
-from ctransformers import AutoModelForCausalLM
-from pydantic import BaseModel
-
-llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardCoder-15B-1.0-GGML",
-                                           model_file="WizardCoder-15B-1.0.ggmlv3.q5_0.bin",
-                                           model_type="starcoder",
-                                           threads=8)
-app = fastapi.FastAPI(title="🪄WizardCoder💫")
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.get("/")
-async def index():
-    html_content = """
-    <html>
-        <head>
-        </head>
-        <body style="font-family:system-ui">
-            <h2><a href="https://huggingface.co/TheBloke/WizardCoder-15B-1.0-GGML">wizardcoder-ggml</a></h2>
-            <h2><a href="https://matthoffner-wizardcoder-ggml.hf.space/docs">FastAPI Docs</a></h2>
-            <h2><a href="https://matthoffner-monacopilot.hf.space">monacopilot</a></h2>
-        </body>
-    </html>
-    """
-    return HTMLResponse(content=html_content, status_code=200)
-
-class ChatCompletionRequestV0(BaseModel):
-    prompt: str
-
-class Message(BaseModel):
-    role: str
-    content: str
-
-class ChatCompletionRequest(BaseModel):
-    messages: List[Message]
-    max_tokens: int = 250
-
-@app.post("/v1/completions")
-async def completion(request: ChatCompletionRequestV0, response_mode=None):
-    response = llm(request.prompt)
-    return response
-
-async def generate_response(chat_chunks, llm):
-    for chat_chunk in chat_chunks:
-        response = {
-            'choices': [
-                {
-                    'message': {
-                        'role': 'system',
-                        'content': llm.detokenize(chat_chunk)
-                    },
-                    'finish_reason': 'stop' if llm.is_eos_token(chat_chunk) else 'unknown'
-                }
-            ]
-        }
-        yield dict(data=json.dumps(response))
-    yield dict(data="[DONE]")
-
-@app.post("/v1/chat/completions")
-async def chat(request: ChatCompletionRequest):
-    combined_messages = ' '.join([message.content for message in request.messages])
-    tokens = llm.tokenize(combined_messages)
-    
-    try:
-        chat_chunks = llm.generate(tokens)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-    return EventSourceResponse(generate_response(chat_chunks, llm))
-
-async def stream_response(tokens, llm):
-    try:
-        iterator: Generator = llm.generate(tokens)
-        for chat_chunk in iterator:
-            response = {
-                'choices': [
-                    {
-                        'message': {
-                            'role': 'system',
-                            'content': llm.detokenize(chat_chunk)
-                        },
-                        'finish_reason': 'stop' if llm.is_eos_token(chat_chunk) else 'unknown'
-                    }
-                ]
-            }
-            yield dict(data=json.dumps(response))
-        yield dict(data="[DONE]")
-    except Exception as e:
-        print(f"Exception in event publisher: {str(e)}")
-
-@app.post("/v2/chat/completions")
-async def chatV2_endpoint(request: Request, body: ChatCompletionRequest):
-    combined_messages = ' '.join([message.content for message in body.messages])
-    tokens = llm.tokenize(combined_messages)
-
-    return EventSourceResponse(stream_response(tokens, llm))
-
-@app.post("/v0/chat/completions")
-async def chat(request: ChatCompletionRequestV0, response_mode=None):
-    tokens = llm.tokenize(request.prompt)
-    async def server_sent_events(chat_chunks, llm):
-        for chat_chunk in llm.generate(chat_chunks):
-            yield dict(data=json.dumps(llm.detokenize(chat_chunk)))
-        yield dict(data="[DONE]")
-
-    return EventSourceResponse(server_sent_events(tokens, llm))
-
-if __name__ == "__main__":
-  uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/spaces/mayerantoine/disaster-damage-classifier/app.py b/spaces/mayerantoine/disaster-damage-classifier/app.py
deleted file mode 100644
index db001b816d98ae5a67a045576e9323f1106ec4d0..0000000000000000000000000000000000000000
--- a/spaces/mayerantoine/disaster-damage-classifier/app.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import os
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras import models
-
-IMG_SIZE = 300
-class_names = ['none','mild','severe']
-
-cwd = os.getcwd()
-outpath= os.path.join(cwd,"model")
-model_name = 'cross_event_ecuador_haiti_efficientnet_fine_tuned_1644086357.h5'
-loaded_model = models.load_model(os.path.join(outpath,model_name))
-
-def _classifier(inp):
-  img = np.asarray(tf.cast(inp, dtype=tf.float32)) * 1 / 255.0
-  img = img.reshape((-1, IMG_SIZE, IMG_SIZE, 3))
-  preds = loaded_model.predict(img).flatten()
-  return {class_names[i]:float(preds[i]) for i in range(len(class_names))}
-
-iface = gr.Interface(fn=_classifier, 
-                      title="Disaster damage assessment from social media image",
-                      description="This simple app allow users to load an image and assess the extent of damage caused by an earthquake",
-                      article="The severity of damage in an image is the extent of physical destruction shown in it. For this experiment we only consider three level of damages: severe damage,mild damage and no damage (none). The model was trained using data from Haiti,Ecuador,Nepal earthquakes and google images.",
-                      examples=['Haiti-Gingerbread-2.jpg','building_damage_100.jpg','building_damage_424.jpg'],
-                     inputs=gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE)),
-                     outputs=gr.outputs.Label()
-                     )
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/merve/MusicGen/app.py b/spaces/merve/MusicGen/app.py
deleted file mode 100644
index d5f9a13c3abb63fdc2965039a18c8f47633f382e..0000000000000000000000000000000000000000
--- a/spaces/merve/MusicGen/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-import numpy as np
-import os
-
-token = os.getenv("TOKEN")
-endpoint = os.getenv("ENDPOINT")
-
-client = InferenceClient(model=endpoint,token=token)
-
-
-def inference(prompt):
-  response = client.post(json={"inputs":prompt})
-
-  output = eval(response)[0]["generated_audio"]
-  return (32000, np.asarray(output[0]))
-
-
-title = "MusicGen Demo"
-description = "Try out a text prompt below 👇 and generate music! 🎶🎸🎤 "
-
-gr.Interface(fn=inference, inputs="text", outputs="audio", title=title, description=description,
-             examples=[["an alt rock song"]], cache_examples=True, theme="abidlabs/Lime").launch()
\ No newline at end of file
diff --git a/spaces/merve/anonymization/public/third_party/umap.js b/spaces/merve/anonymization/public/third_party/umap.js
deleted file mode 100644
index 13bb989b285114e7a79d0a213422997c19a3c2f0..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/public/third_party/umap.js
+++ /dev/null
@@ -1,6864 +0,0 @@
-// https://github.com/pair-code/umap-js Copyright 2019 Google
-(function webpackUniversalModuleDefinition(root, factory) {
-    if(typeof exports === 'object' && typeof module === 'object')
-        module.exports = factory();
-    else if(typeof define === 'function' && define.amd)
-        define([], factory);
-    else {
-        var a = factory();
-        for(var i in a) (typeof exports === 'object' ? exports : root)[i] = a[i];
-    }
-})(window, function() {
-return /******/ (function(modules) { // webpackBootstrap
-/******/    // The module cache
-/******/    var installedModules = {};
-/******/
-/******/    // The require function
-/******/    function __webpack_require__(moduleId) {
-/******/
-/******/        // Check if module is in cache
-/******/        if(installedModules[moduleId]) {
-/******/            return installedModules[moduleId].exports;
-/******/        }
-/******/        // Create a new module (and put it into the cache)
-/******/        var module = installedModules[moduleId] = {
-/******/            i: moduleId,
-/******/            l: false,
-/******/            exports: {}
-/******/        };
-/******/
-/******/        // Execute the module function
-/******/        modules[moduleId].call(module.exports, module, module.exports, __webpack_require__);
-/******/
-/******/        // Flag the module as loaded
-/******/        module.l = true;
-/******/
-/******/        // Return the exports of the module
-/******/        return module.exports;
-/******/    }
-/******/
-/******/
-/******/    // expose the modules object (__webpack_modules__)
-/******/    __webpack_require__.m = modules;
-/******/
-/******/    // expose the module cache
-/******/    __webpack_require__.c = installedModules;
-/******/
-/******/    // define getter function for harmony exports
-/******/    __webpack_require__.d = function(exports, name, getter) {
-/******/        if(!__webpack_require__.o(exports, name)) {
-/******/            Object.defineProperty(exports, name, { enumerable: true, get: getter });
-/******/        }
-/******/    };
-/******/
-/******/    // define __esModule on exports
-/******/    __webpack_require__.r = function(exports) {
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-/******/ ([
-/* 0 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-
-const toString = Object.prototype.toString;
-
-function isAnyArray(object) {
-  return toString.call(object).endsWith('Array]');
-}
-
-module.exports = isAnyArray;
-
-
-/***/ }),
-/* 1 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __values = (this && this.__values) || function (o) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
-    if (m) return m.call(o);
-    return {
-        next: function () {
-            if (o && i >= o.length) o = void 0;
-            return { value: o && o[i++], done: !o };
-        }
-    };
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-function tauRandInt(n, random) {
-    return Math.floor(random() * n);
-}
-exports.tauRandInt = tauRandInt;
-function tauRand(random) {
-    return random();
-}
-exports.tauRand = tauRand;
-function norm(vec) {
-    var e_1, _a;
-    var result = 0;
-    try {
-        for (var vec_1 = __values(vec), vec_1_1 = vec_1.next(); !vec_1_1.done; vec_1_1 = vec_1.next()) {
-            var item = vec_1_1.value;
-            result += Math.pow(item, 2);
-        }
-    }
-    catch (e_1_1) { e_1 = { error: e_1_1 }; }
-    finally {
-        try {
-            if (vec_1_1 && !vec_1_1.done && (_a = vec_1.return)) _a.call(vec_1);
-        }
-        finally { if (e_1) throw e_1.error; }
-    }
-    return Math.sqrt(result);
-}
-exports.norm = norm;
-function empty(n) {
-    var output = [];
-    for (var i = 0; i < n; i++) {
-        output.push(undefined);
-    }
-    return output;
-}
-exports.empty = empty;
-function range(n) {
-    return empty(n).map(function (_, i) { return i; });
-}
-exports.range = range;
-function filled(n, v) {
-    return empty(n).map(function () { return v; });
-}
-exports.filled = filled;
-function zeros(n) {
-    return filled(n, 0);
-}
-exports.zeros = zeros;
-function ones(n) {
-    return filled(n, 1);
-}
-exports.ones = ones;
-function linear(a, b, len) {
-    return empty(len).map(function (_, i) {
-        return a + i * ((b - a) / (len - 1));
-    });
-}
-exports.linear = linear;
-function sum(input) {
-    return input.reduce(function (sum, val) { return sum + val; });
-}
-exports.sum = sum;
-function mean(input) {
-    return sum(input) / input.length;
-}
-exports.mean = mean;
-function max(input) {
-    var max = 0;
-    for (var i = 0; i < input.length; i++) {
-        max = input[i] > max ? input[i] : max;
-    }
-    return max;
-}
-exports.max = max;
-function max2d(input) {
-    var max = 0;
-    for (var i = 0; i < input.length; i++) {
-        for (var j = 0; j < input[i].length; j++) {
-            max = input[i][j] > max ? input[i][j] : max;
-        }
-    }
-    return max;
-}
-exports.max2d = max2d;
-function rejectionSample(nSamples, poolSize, random) {
-    var result = zeros(nSamples);
-    for (var i = 0; i < nSamples; i++) {
-        var rejectSample = true;
-        while (rejectSample) {
-            var j = tauRandInt(poolSize, random);
-            var broken = false;
-            for (var k = 0; k < i; k++) {
-                if (j === result[k]) {
-                    broken = true;
-                    break;
-                }
-            }
-            if (!broken) {
-                rejectSample = false;
-            }
-            result[i] = j;
-        }
-    }
-    return result;
-}
-exports.rejectionSample = rejectionSample;
-function reshape2d(x, a, b) {
-    var rows = [];
-    var count = 0;
-    var index = 0;
-    if (x.length !== a * b) {
-        throw new Error('Array dimensions must match input length.');
-    }
-    for (var i = 0; i < a; i++) {
-        var col = [];
-        for (var j = 0; j < b; j++) {
-            col.push(x[index]);
-            index += 1;
-        }
-        rows.push(col);
-        count += 1;
-    }
-    return rows;
-}
-exports.reshape2d = reshape2d;
-
-
-/***/ }),
-/* 2 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var utils = __importStar(__webpack_require__(1));
-function makeHeap(nPoints, size) {
-    var makeArrays = function (fillValue) {
-        return utils.empty(nPoints).map(function () {
-            return utils.filled(size, fillValue);
-        });
-    };
-    var heap = [];
-    heap.push(makeArrays(-1));
-    heap.push(makeArrays(Infinity));
-    heap.push(makeArrays(0));
-    return heap;
-}
-exports.makeHeap = makeHeap;
-function rejectionSample(nSamples, poolSize, random) {
-    var result = utils.zeros(nSamples);
-    for (var i = 0; i < nSamples; i++) {
-        var rejectSample = true;
-        var j = 0;
-        while (rejectSample) {
-            j = utils.tauRandInt(poolSize, random);
-            var broken = false;
-            for (var k = 0; k < i; k++) {
-                if (j === result[k]) {
-                    broken = true;
-                    break;
-                }
-            }
-            if (!broken)
-                rejectSample = false;
-        }
-        result[i] = j;
-    }
-    return result;
-}
-exports.rejectionSample = rejectionSample;
-function heapPush(heap, row, weight, index, flag) {
-    row = Math.floor(row);
-    var indices = heap[0][row];
-    var weights = heap[1][row];
-    var isNew = heap[2][row];
-    if (weight >= weights[0]) {
-        return 0;
-    }
-    for (var i = 0; i < indices.length; i++) {
-        if (index === indices[i]) {
-            return 0;
-        }
-    }
-    return uncheckedHeapPush(heap, row, weight, index, flag);
-}
-exports.heapPush = heapPush;
-function uncheckedHeapPush(heap, row, weight, index, flag) {
-    var indices = heap[0][row];
-    var weights = heap[1][row];
-    var isNew = heap[2][row];
-    if (weight >= weights[0]) {
-        return 0;
-    }
-    weights[0] = weight;
-    indices[0] = index;
-    isNew[0] = flag;
-    var i = 0;
-    var iSwap = 0;
-    while (true) {
-        var ic1 = 2 * i + 1;
-        var ic2 = ic1 + 1;
-        var heapShape2 = heap[0][0].length;
-        if (ic1 >= heapShape2) {
-            break;
-        }
-        else if (ic2 >= heapShape2) {
-            if (weights[ic1] > weight) {
-                iSwap = ic1;
-            }
-            else {
-                break;
-            }
-        }
-        else if (weights[ic1] >= weights[ic2]) {
-            if (weight < weights[ic1]) {
-                iSwap = ic1;
-            }
-            else {
-                break;
-            }
-        }
-        else {
-            if (weight < weights[ic2]) {
-                iSwap = ic2;
-            }
-            else {
-                break;
-            }
-        }
-        weights[i] = weights[iSwap];
-        indices[i] = indices[iSwap];
-        isNew[i] = isNew[iSwap];
-        i = iSwap;
-    }
-    weights[i] = weight;
-    indices[i] = index;
-    isNew[i] = flag;
-    return 1;
-}
-exports.uncheckedHeapPush = uncheckedHeapPush;
-function buildCandidates(currentGraph, nVertices, nNeighbors, maxCandidates, random) {
-    var candidateNeighbors = makeHeap(nVertices, maxCandidates);
-    for (var i = 0; i < nVertices; i++) {
-        for (var j = 0; j < nNeighbors; j++) {
-            if (currentGraph[0][i][j] < 0) {
-                continue;
-            }
-            var idx = currentGraph[0][i][j];
-            var isn = currentGraph[2][i][j];
-            var d = utils.tauRand(random);
-            heapPush(candidateNeighbors, i, d, idx, isn);
-            heapPush(candidateNeighbors, idx, d, i, isn);
-            currentGraph[2][i][j] = 0;
-        }
-    }
-    return candidateNeighbors;
-}
-exports.buildCandidates = buildCandidates;
-function deheapSort(heap) {
-    var indices = heap[0];
-    var weights = heap[1];
-    for (var i = 0; i < indices.length; i++) {
-        var indHeap = indices[i];
-        var distHeap = weights[i];
-        for (var j = 0; j < indHeap.length - 1; j++) {
-            var indHeapIndex = indHeap.length - j - 1;
-            var distHeapIndex = distHeap.length - j - 1;
-            var temp1 = indHeap[0];
-            indHeap[0] = indHeap[indHeapIndex];
-            indHeap[indHeapIndex] = temp1;
-            var temp2 = distHeap[0];
-            distHeap[0] = distHeap[distHeapIndex];
-            distHeap[distHeapIndex] = temp2;
-            siftDown(distHeap, indHeap, distHeapIndex, 0);
-        }
-    }
-    return { indices: indices, weights: weights };
-}
-exports.deheapSort = deheapSort;
-function siftDown(heap1, heap2, ceiling, elt) {
-    while (elt * 2 + 1 < ceiling) {
-        var leftChild = elt * 2 + 1;
-        var rightChild = leftChild + 1;
-        var swap = elt;
-        if (heap1[swap] < heap1[leftChild]) {
-            swap = leftChild;
-        }
-        if (rightChild < ceiling && heap1[swap] < heap1[rightChild]) {
-            swap = rightChild;
-        }
-        if (swap === elt) {
-            break;
-        }
-        else {
-            var temp1 = heap1[elt];
-            heap1[elt] = heap1[swap];
-            heap1[swap] = temp1;
-            var temp2 = heap2[elt];
-            heap2[elt] = heap2[swap];
-            heap2[swap] = temp2;
-            elt = swap;
-        }
-    }
-}
-function smallestFlagged(heap, row) {
-    var ind = heap[0][row];
-    var dist = heap[1][row];
-    var flag = heap[2][row];
-    var minDist = Infinity;
-    var resultIndex = -1;
-    for (var i = 0; i > ind.length; i++) {
-        if (flag[i] === 1 && dist[i] < minDist) {
-            minDist = dist[i];
-            resultIndex = i;
-        }
-    }
-    if (resultIndex >= 0) {
-        flag[resultIndex] = 0;
-        return Math.floor(ind[resultIndex]);
-    }
-    else {
-        return -1;
-    }
-}
-exports.smallestFlagged = smallestFlagged;
-
-
-/***/ }),
-/* 3 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __read = (this && this.__read) || function (o, n) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator];
-    if (!m) return o;
-    var i = m.call(o), r, ar = [], e;
-    try {
-        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
-    }
-    catch (error) { e = { error: error }; }
-    finally {
-        try {
-            if (r && !r.done && (m = i["return"])) m.call(i);
-        }
-        finally { if (e) throw e.error; }
-    }
-    return ar;
-};
-var __spread = (this && this.__spread) || function () {
-    for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i]));
-    return ar;
-};
-var __values = (this && this.__values) || function (o) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
-    if (m) return m.call(o);
-    return {
-        next: function () {
-            if (o && i >= o.length) o = void 0;
-            return { value: o && o[i++], done: !o };
-        }
-    };
-};
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var _a;
-var utils = __importStar(__webpack_require__(1));
-var SparseMatrix = (function () {
-    function SparseMatrix(rows, cols, values, dims) {
-        this.entries = new Map();
-        this.nRows = 0;
-        this.nCols = 0;
-        this.rows = __spread(rows);
-        this.cols = __spread(cols);
-        this.values = __spread(values);
-        for (var i = 0; i < values.length; i++) {
-            var key = this.makeKey(this.rows[i], this.cols[i]);
-            this.entries.set(key, i);
-        }
-        this.nRows = dims[0];
-        this.nCols = dims[1];
-    }
-    SparseMatrix.prototype.makeKey = function (row, col) {
-        return row + ":" + col;
-    };
-    SparseMatrix.prototype.checkDims = function (row, col) {
-        var withinBounds = row < this.nRows && col < this.nCols;
-        if (!withinBounds) {
-            throw new Error('array index out of bounds');
-        }
-    };
-    SparseMatrix.prototype.set = function (row, col, value) {
-        this.checkDims(row, col);
-        var key = this.makeKey(row, col);
-        if (!this.entries.has(key)) {
-            this.rows.push(row);
-            this.cols.push(col);
-            this.values.push(value);
-            this.entries.set(key, this.values.length - 1);
-        }
-        else {
-            var index = this.entries.get(key);
-            this.values[index] = value;
-        }
-    };
-    SparseMatrix.prototype.get = function (row, col, defaultValue) {
-        if (defaultValue === void 0) { defaultValue = 0; }
-        this.checkDims(row, col);
-        var key = this.makeKey(row, col);
-        if (this.entries.has(key)) {
-            var index = this.entries.get(key);
-            return this.values[index];
-        }
-        else {
-            return defaultValue;
-        }
-    };
-    SparseMatrix.prototype.getDims = function () {
-        return [this.nRows, this.nCols];
-    };
-    SparseMatrix.prototype.getRows = function () {
-        return __spread(this.rows);
-    };
-    SparseMatrix.prototype.getCols = function () {
-        return __spread(this.cols);
-    };
-    SparseMatrix.prototype.getValues = function () {
-        return __spread(this.values);
-    };
-    SparseMatrix.prototype.forEach = function (fn) {
-        for (var i = 0; i < this.values.length; i++) {
-            fn(this.values[i], this.rows[i], this.cols[i]);
-        }
-    };
-    SparseMatrix.prototype.map = function (fn) {
-        var vals = [];
-        for (var i = 0; i < this.values.length; i++) {
-            vals.push(fn(this.values[i], this.rows[i], this.cols[i]));
-        }
-        var dims = [this.nRows, this.nCols];
-        return new SparseMatrix(this.rows, this.cols, vals, dims);
-    };
-    SparseMatrix.prototype.toArray = function () {
-        var _this = this;
-        var rows = utils.empty(this.nRows);
-        var output = rows.map(function () {
-            return utils.zeros(_this.nCols);
-        });
-        for (var i = 0; i < this.values.length; i++) {
-            output[this.rows[i]][this.cols[i]] = this.values[i];
-        }
-        return output;
-    };
-    return SparseMatrix;
-}());
-exports.SparseMatrix = SparseMatrix;
-function transpose(matrix) {
-    var cols = [];
-    var rows = [];
-    var vals = [];
-    matrix.forEach(function (value, row, col) {
-        cols.push(row);
-        rows.push(col);
-        vals.push(value);
-    });
-    var dims = [matrix.nCols, matrix.nRows];
-    return new SparseMatrix(rows, cols, vals, dims);
-}
-exports.transpose = transpose;
-function identity(size) {
-    var _a = __read(size, 1), rows = _a[0];
-    var matrix = new SparseMatrix([], [], [], size);
-    for (var i = 0; i < rows; i++) {
-        matrix.set(i, i, 1);
-    }
-    return matrix;
-}
-exports.identity = identity;
-function pairwiseMultiply(a, b) {
-    return elementWise(a, b, function (x, y) { return x * y; });
-}
-exports.pairwiseMultiply = pairwiseMultiply;
-function add(a, b) {
-    return elementWise(a, b, function (x, y) { return x + y; });
-}
-exports.add = add;
-function subtract(a, b) {
-    return elementWise(a, b, function (x, y) { return x - y; });
-}
-exports.subtract = subtract;
-function maximum(a, b) {
-    return elementWise(a, b, function (x, y) { return (x > y ? x : y); });
-}
-exports.maximum = maximum;
-function multiplyScalar(a, scalar) {
-    return a.map(function (value) {
-        return value * scalar;
-    });
-}
-exports.multiplyScalar = multiplyScalar;
-function eliminateZeros(m) {
-    var zeroIndices = new Set();
-    var values = m.getValues();
-    var rows = m.getRows();
-    var cols = m.getCols();
-    for (var i = 0; i < values.length; i++) {
-        if (values[i] === 0) {
-            zeroIndices.add(i);
-        }
-    }
-    var removeByZeroIndex = function (_, index) { return !zeroIndices.has(index); };
-    var nextValues = values.filter(removeByZeroIndex);
-    var nextRows = rows.filter(removeByZeroIndex);
-    var nextCols = cols.filter(removeByZeroIndex);
-    return new SparseMatrix(nextRows, nextCols, nextValues, m.getDims());
-}
-exports.eliminateZeros = eliminateZeros;
-function normalize(m, normType) {
-    if (normType === void 0) { normType = "l2"; }
-    var e_1, _a;
-    var normFn = normFns[normType];
-    var colsByRow = new Map();
-    m.forEach(function (_, row, col) {
-        var cols = colsByRow.get(row) || [];
-        cols.push(col);
-        colsByRow.set(row, cols);
-    });
-    var nextMatrix = new SparseMatrix([], [], [], m.getDims());
-    var _loop_1 = function (row) {
-        var cols = colsByRow.get(row).sort();
-        var vals = cols.map(function (col) { return m.get(row, col); });
-        var norm = normFn(vals);
-        for (var i = 0; i < norm.length; i++) {
-            nextMatrix.set(row, cols[i], norm[i]);
-        }
-    };
-    try {
-        for (var _b = __values(colsByRow.keys()), _c = _b.next(); !_c.done; _c = _b.next()) {
-            var row = _c.value;
-            _loop_1(row);
-        }
-    }
-    catch (e_1_1) { e_1 = { error: e_1_1 }; }
-    finally {
-        try {
-            if (_c && !_c.done && (_a = _b.return)) _a.call(_b);
-        }
-        finally { if (e_1) throw e_1.error; }
-    }
-    return nextMatrix;
-}
-exports.normalize = normalize;
-var normFns = (_a = {},
-    _a["max"] = function (xs) {
-        var max = -Infinity;
-        for (var i = 0; i < xs.length; i++) {
-            max = xs[i] > max ? xs[i] : max;
-        }
-        return xs.map(function (x) { return x / max; });
-    },
-    _a["l1"] = function (xs) {
-        var sum = 0;
-        for (var i = 0; i < xs.length; i++) {
-            sum += xs[i];
-        }
-        return xs.map(function (x) { return x / sum; });
-    },
-    _a["l2"] = function (xs) {
-        var sum = 0;
-        for (var i = 0; i < xs.length; i++) {
-            sum += Math.pow(xs[i], 2);
-        }
-        return xs.map(function (x) { return Math.sqrt(Math.pow(x, 2) / sum); });
-    },
-    _a);
-function elementWise(a, b, op) {
-    var visited = new Set();
-    var rows = [];
-    var cols = [];
-    var vals = [];
-    var operate = function (row, col) {
-        rows.push(row);
-        cols.push(col);
-        var nextValue = op(a.get(row, col), b.get(row, col));
-        vals.push(nextValue);
-    };
-    var valuesA = a.getValues();
-    var rowsA = a.getRows();
-    var colsA = a.getCols();
-    for (var i = 0; i < valuesA.length; i++) {
-        var row = rowsA[i];
-        var col = colsA[i];
-        var key = row + ":" + col;
-        visited.add(key);
-        operate(row, col);
-    }
-    var valuesB = b.getValues();
-    var rowsB = b.getRows();
-    var colsB = b.getCols();
-    for (var i = 0; i < valuesB.length; i++) {
-        var row = rowsB[i];
-        var col = colsB[i];
-        var key = row + ":" + col;
-        if (visited.has(key))
-            continue;
-        operate(row, col);
-    }
-    var dims = [a.nRows, a.nCols];
-    return new SparseMatrix(rows, cols, vals, dims);
-}
-function getCSR(x) {
-    var entries = [];
-    x.forEach(function (value, row, col) {
-        entries.push({ value: value, row: row, col: col });
-    });
-    entries.sort(function (a, b) {
-        if (a.row === b.row) {
-            return a.col - b.col;
-        }
-        else {
-            return a.row - b.col;
-        }
-    });
-    var indices = [];
-    var values = [];
-    var indptr = [];
-    var currentRow = -1;
-    for (var i = 0; i < entries.length; i++) {
-        var _a = entries[i], row = _a.row, col = _a.col, value = _a.value;
-        if (row !== currentRow) {
-            currentRow = row;
-            indptr.push(i);
-        }
-        indices.push(col);
-        values.push(value);
-    }
-    return { indices: indices, values: values, indptr: indptr };
-}
-exports.getCSR = getCSR;
-
-
-/***/ }),
-/* 4 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __read = (this && this.__read) || function (o, n) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator];
-    if (!m) return o;
-    var i = m.call(o), r, ar = [], e;
-    try {
-        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
-    }
-    catch (error) { e = { error: error }; }
-    finally {
-        try {
-            if (r && !r.done && (m = i["return"])) m.call(i);
-        }
-        finally { if (e) throw e.error; }
-    }
-    return ar;
-};
-var __spread = (this && this.__spread) || function () {
-    for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i]));
-    return ar;
-};
-var __values = (this && this.__values) || function (o) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
-    if (m) return m.call(o);
-    return {
-        next: function () {
-            if (o && i >= o.length) o = void 0;
-            return { value: o && o[i++], done: !o };
-        }
-    };
-};
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var utils = __importStar(__webpack_require__(1));
-var FlatTree = (function () {
-    function FlatTree(hyperplanes, offsets, children, indices) {
-        this.hyperplanes = hyperplanes;
-        this.offsets = offsets;
-        this.children = children;
-        this.indices = indices;
-    }
-    return FlatTree;
-}());
-exports.FlatTree = FlatTree;
-function makeForest(data, nNeighbors, nTrees, random) {
-    var leafSize = Math.max(10, nNeighbors);
-    var trees = utils
-        .range(nTrees)
-        .map(function (_, i) { return makeTree(data, leafSize, i, random); });
-    var forest = trees.map(function (tree) { return flattenTree(tree, leafSize); });
-    return forest;
-}
-exports.makeForest = makeForest;
-function makeTree(data, leafSize, n, random) {
-    if (leafSize === void 0) { leafSize = 30; }
-    var indices = utils.range(data.length);
-    var tree = makeEuclideanTree(data, indices, leafSize, n, random);
-    return tree;
-}
-function makeEuclideanTree(data, indices, leafSize, q, random) {
-    if (leafSize === void 0) { leafSize = 30; }
-    if (indices.length > leafSize) {
-        var splitResults = euclideanRandomProjectionSplit(data, indices, random);
-        var indicesLeft = splitResults.indicesLeft, indicesRight = splitResults.indicesRight, hyperplane = splitResults.hyperplane, offset = splitResults.offset;
-        var leftChild = makeEuclideanTree(data, indicesLeft, leafSize, q + 1, random);
-        var rightChild = makeEuclideanTree(data, indicesRight, leafSize, q + 1, random);
-        var node = { leftChild: leftChild, rightChild: rightChild, isLeaf: false, hyperplane: hyperplane, offset: offset };
-        return node;
-    }
-    else {
-        var node = { indices: indices, isLeaf: true };
-        return node;
-    }
-}
-function euclideanRandomProjectionSplit(data, indices, random) {
-    var dim = data[0].length;
-    var leftIndex = utils.tauRandInt(indices.length, random);
-    var rightIndex = utils.tauRandInt(indices.length, random);
-    rightIndex += leftIndex === rightIndex ? 1 : 0;
-    rightIndex = rightIndex % indices.length;
-    var left = indices[leftIndex];
-    var right = indices[rightIndex];
-    var hyperplaneOffset = 0;
-    var hyperplaneVector = utils.zeros(dim);
-    for (var i = 0; i < hyperplaneVector.length; i++) {
-        hyperplaneVector[i] = data[left][i] - data[right][i];
-        hyperplaneOffset -=
-            (hyperplaneVector[i] * (data[left][i] + data[right][i])) / 2.0;
-    }
-    var nLeft = 0;
-    var nRight = 0;
-    var side = utils.zeros(indices.length);
-    for (var i = 0; i < indices.length; i++) {
-        var margin = hyperplaneOffset;
-        for (var d = 0; d < dim; d++) {
-            margin += hyperplaneVector[d] * data[indices[i]][d];
-        }
-        if (margin === 0) {
-            side[i] = utils.tauRandInt(2, random);
-            if (side[i] === 0) {
-                nLeft += 1;
-            }
-            else {
-                nRight += 1;
-            }
-        }
-        else if (margin > 0) {
-            side[i] = 0;
-            nLeft += 1;
-        }
-        else {
-            side[i] = 1;
-            nRight += 1;
-        }
-    }
-    var indicesLeft = utils.zeros(nLeft);
-    var indicesRight = utils.zeros(nRight);
-    nLeft = 0;
-    nRight = 0;
-    for (var i in utils.range(side.length)) {
-        if (side[i] === 0) {
-            indicesLeft[nLeft] = indices[i];
-            nLeft += 1;
-        }
-        else {
-            indicesRight[nRight] = indices[i];
-            nRight += 1;
-        }
-    }
-    return {
-        indicesLeft: indicesLeft,
-        indicesRight: indicesRight,
-        hyperplane: hyperplaneVector,
-        offset: hyperplaneOffset,
-    };
-}
-function flattenTree(tree, leafSize) {
-    var nNodes = numNodes(tree);
-    var nLeaves = numLeaves(tree);
-    var hyperplanes = utils
-        .range(nNodes)
-        .map(function () { return utils.zeros(tree.hyperplane.length); });
-    var offsets = utils.zeros(nNodes);
-    var children = utils.range(nNodes).map(function () { return [-1, -1]; });
-    var indices = utils
-        .range(nLeaves)
-        .map(function () { return utils.range(leafSize).map(function () { return -1; }); });
-    recursiveFlatten(tree, hyperplanes, offsets, children, indices, 0, 0);
-    return new FlatTree(hyperplanes, offsets, children, indices);
-}
-function recursiveFlatten(tree, hyperplanes, offsets, children, indices, nodeNum, leafNum) {
-    var _a;
-    if (tree.isLeaf) {
-        children[nodeNum][0] = -leafNum;
-        (_a = indices[leafNum]).splice.apply(_a, __spread([0, tree.indices.length], tree.indices));
-        leafNum += 1;
-        return { nodeNum: nodeNum, leafNum: leafNum };
-    }
-    else {
-        hyperplanes[nodeNum] = tree.hyperplane;
-        offsets[nodeNum] = tree.offset;
-        children[nodeNum][0] = nodeNum + 1;
-        var oldNodeNum = nodeNum;
-        var res = recursiveFlatten(tree.leftChild, hyperplanes, offsets, children, indices, nodeNum + 1, leafNum);
-        nodeNum = res.nodeNum;
-        leafNum = res.leafNum;
-        children[oldNodeNum][1] = nodeNum + 1;
-        res = recursiveFlatten(tree.rightChild, hyperplanes, offsets, children, indices, nodeNum + 1, leafNum);
-        return { nodeNum: res.nodeNum, leafNum: res.leafNum };
-    }
-}
-function numNodes(tree) {
-    if (tree.isLeaf) {
-        return 1;
-    }
-    else {
-        return 1 + numNodes(tree.leftChild) + numNodes(tree.rightChild);
-    }
-}
-function numLeaves(tree) {
-    if (tree.isLeaf) {
-        return 1;
-    }
-    else {
-        return numLeaves(tree.leftChild) + numLeaves(tree.rightChild);
-    }
-}
-function makeLeafArray(rpForest) {
-    var e_1, _a;
-    if (rpForest.length > 0) {
-        var output = [];
-        try {
-            for (var rpForest_1 = __values(rpForest), rpForest_1_1 = rpForest_1.next(); !rpForest_1_1.done; rpForest_1_1 = rpForest_1.next()) {
-                var tree = rpForest_1_1.value;
-                output.push.apply(output, __spread(tree.indices));
-            }
-        }
-        catch (e_1_1) { e_1 = { error: e_1_1 }; }
-        finally {
-            try {
-                if (rpForest_1_1 && !rpForest_1_1.done && (_a = rpForest_1.return)) _a.call(rpForest_1);
-            }
-            finally { if (e_1) throw e_1.error; }
-        }
-        return output;
-    }
-    else {
-        return [[-1]];
-    }
-}
-exports.makeLeafArray = makeLeafArray;
-function selectSide(hyperplane, offset, point, random) {
-    var margin = offset;
-    for (var d = 0; d < point.length; d++) {
-        margin += hyperplane[d] * point[d];
-    }
-    if (margin === 0) {
-        var side = utils.tauRandInt(2, random);
-        return side;
-    }
-    else if (margin > 0) {
-        return 0;
-    }
-    else {
-        return 1;
-    }
-}
-function searchFlatTree(point, tree, random) {
-    var node = 0;
-    while (tree.children[node][0] > 0) {
-        var side = selectSide(tree.hyperplanes[node], tree.offsets[node], point, random);
-        if (side === 0) {
-            node = tree.children[node][0];
-        }
-        else {
-            node = tree.children[node][1];
-        }
-    }
-    var index = -1 * tree.children[node][0];
-    return tree.indices[index];
-}
-exports.searchFlatTree = searchFlatTree;
-
-
-/***/ }),
-/* 5 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-Object.defineProperty(exports, "__esModule", { value: true });
-var umap_1 = __webpack_require__(6);
-exports.UMAP = umap_1.UMAP;
-
-
-/***/ }),
-/* 6 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
-    return new (P || (P = Promise))(function (resolve, reject) {
-        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
-        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
-        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
-        step((generator = generator.apply(thisArg, _arguments || [])).next());
-    });
-};
-var __generator = (this && this.__generator) || function (thisArg, body) {
-    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
-    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
-    function verb(n) { return function (v) { return step([n, v]); }; }
-    function step(op) {
-        if (f) throw new TypeError("Generator is already executing.");
-        while (_) try {
-            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
-            if (y = 0, t) op = [op[0] & 2, t.value];
-            switch (op[0]) {
-                case 0: case 1: t = op; break;
-                case 4: _.label++; return { value: op[1], done: false };
-                case 5: _.label++; y = op[1]; op = [0]; continue;
-                case 7: op = _.ops.pop(); _.trys.pop(); continue;
-                default:
-                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
-                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
-                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
-                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
-                    if (t[2]) _.ops.pop();
-                    _.trys.pop(); continue;
-            }
-            op = body.call(thisArg, _);
-        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
-        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
-    }
-};
-var __read = (this && this.__read) || function (o, n) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator];
-    if (!m) return o;
-    var i = m.call(o), r, ar = [], e;
-    try {
-        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
-    }
-    catch (error) { e = { error: error }; }
-    finally {
-        try {
-            if (r && !r.done && (m = i["return"])) m.call(i);
-        }
-        finally { if (e) throw e.error; }
-    }
-    return ar;
-};
-var __spread = (this && this.__spread) || function () {
-    for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i]));
-    return ar;
-};
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-var __importDefault = (this && this.__importDefault) || function (mod) {
-    return (mod && mod.__esModule) ? mod : { "default": mod };
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var heap = __importStar(__webpack_require__(2));
-var matrix = __importStar(__webpack_require__(3));
-var nnDescent = __importStar(__webpack_require__(7));
-var tree = __importStar(__webpack_require__(4));
-var utils = __importStar(__webpack_require__(1));
-var ml_levenberg_marquardt_1 = __importDefault(__webpack_require__(8));
-var SMOOTH_K_TOLERANCE = 1e-5;
-var MIN_K_DIST_SCALE = 1e-3;
-var UMAP = (function () {
-    function UMAP(params) {
-        if (params === void 0) { params = {}; }
-        var _this = this;
-        this.learningRate = 1.0;
-        this.localConnectivity = 1.0;
-        this.minDist = 0.1;
-        this.nComponents = 2;
-        this.nEpochs = 0;
-        this.nNeighbors = 15;
-        this.negativeSampleRate = 5;
-        this.random = Math.random;
-        this.repulsionStrength = 1.0;
-        this.setOpMixRatio = 1.0;
-        this.spread = 1.0;
-        this.transformQueueSize = 4.0;
-        this.targetMetric = "categorical";
-        this.targetWeight = 0.5;
-        this.targetNNeighbors = this.nNeighbors;
-        this.distanceFn = euclidean;
-        this.isInitialized = false;
-        this.rpForest = [];
-        this.embedding = [];
-        this.optimizationState = new OptimizationState();
-        var setParam = function (key) {
-            if (params[key] !== undefined)
-                _this[key] = params[key];
-        };
-        setParam('distanceFn');
-        setParam('learningRate');
-        setParam('localConnectivity');
-        setParam('minDist');
-        setParam('nComponents');
-        setParam('nEpochs');
-        setParam('nNeighbors');
-        setParam('negativeSampleRate');
-        setParam('random');
-        setParam('repulsionStrength');
-        setParam('setOpMixRatio');
-        setParam('spread');
-        setParam('transformQueueSize');
-    }
-    UMAP.prototype.fit = function (X) {
-        this.initializeFit(X);
-        this.optimizeLayout();
-        return this.embedding;
-    };
-    UMAP.prototype.fitAsync = function (X, callback) {
-        if (callback === void 0) { callback = function () { return true; }; }
-        return __awaiter(this, void 0, void 0, function () {
-            return __generator(this, function (_a) {
-                switch (_a.label) {
-                    case 0:
-                        this.initializeFit(X);
-                        return [4, this.optimizeLayoutAsync(callback)];
-                    case 1:
-                        _a.sent();
-                        return [2, this.embedding];
-                }
-            });
-        });
-    };
-    UMAP.prototype.setSupervisedProjection = function (Y, params) {
-        if (params === void 0) { params = {}; }
-        this.Y = Y;
-        this.targetMetric = params.targetMetric || this.targetMetric;
-        this.targetWeight = params.targetWeight || this.targetWeight;
-        this.targetNNeighbors = params.targetNNeighbors || this.targetNNeighbors;
-    };
-    UMAP.prototype.setPrecomputedKNN = function (knnIndices, knnDistances) {
-        this.knnIndices = knnIndices;
-        this.knnDistances = knnDistances;
-    };
-    UMAP.prototype.initializeFit = function (X) {
-        if (this.X === X && this.isInitialized) {
-            return this.getNEpochs();
-        }
-        this.X = X;
-        if (!this.knnIndices && !this.knnDistances) {
-            var knnResults = this.nearestNeighbors(X);
-            this.knnIndices = knnResults.knnIndices;
-            this.knnDistances = knnResults.knnDistances;
-        }
-        this.graph = this.fuzzySimplicialSet(X, this.nNeighbors, this.setOpMixRatio);
-        this.makeSearchFns();
-        this.searchGraph = this.makeSearchGraph(X);
-        this.processGraphForSupervisedProjection();
-        var _a = this.initializeSimplicialSetEmbedding(), head = _a.head, tail = _a.tail, epochsPerSample = _a.epochsPerSample;
-        this.optimizationState.head = head;
-        this.optimizationState.tail = tail;
-        this.optimizationState.epochsPerSample = epochsPerSample;
-        this.initializeOptimization();
-        this.prepareForOptimizationLoop();
-        this.isInitialized = true;
-        return this.getNEpochs();
-    };
-    UMAP.prototype.makeSearchFns = function () {
-        var _a = nnDescent.makeInitializations(this.distanceFn), initFromTree = _a.initFromTree, initFromRandom = _a.initFromRandom;
-        this.initFromTree = initFromTree;
-        this.initFromRandom = initFromRandom;
-        this.search = nnDescent.makeInitializedNNSearch(this.distanceFn);
-    };
-    UMAP.prototype.makeSearchGraph = function (X) {
-        var knnIndices = this.knnIndices;
-        var knnDistances = this.knnDistances;
-        var dims = [X.length, X.length];
-        var searchGraph = new matrix.SparseMatrix([], [], [], dims);
-        for (var i = 0; i < knnIndices.length; i++) {
-            var knn = knnIndices[i];
-            var distances = knnDistances[i];
-            for (var j = 0; j < knn.length; j++) {
-                var neighbor = knn[j];
-                var distance = distances[j];
-                if (distance > 0) {
-                    searchGraph.set(i, neighbor, distance);
-                }
-            }
-        }
-        var transpose = matrix.transpose(searchGraph);
-        return matrix.maximum(searchGraph, transpose);
-    };
-    UMAP.prototype.transform = function (toTransform) {
-        var _this = this;
-        var rawData = this.X;
-        if (rawData === undefined || rawData.length === 0) {
-            throw new Error('No data has been fit.');
-        }
-        var nNeighbors = Math.floor(this.nNeighbors * this.transformQueueSize);
-        var init = nnDescent.initializeSearch(this.rpForest, rawData, toTransform, nNeighbors, this.initFromRandom, this.initFromTree, this.random);
-        var result = this.search(rawData, this.searchGraph, init, toTransform);
-        var _a = heap.deheapSort(result), indices = _a.indices, distances = _a.weights;
-        indices = indices.map(function (x) { return x.slice(0, _this.nNeighbors); });
-        distances = distances.map(function (x) { return x.slice(0, _this.nNeighbors); });
-        var adjustedLocalConnectivity = Math.max(0, this.localConnectivity - 1);
-        var _b = this.smoothKNNDistance(distances, this.nNeighbors, adjustedLocalConnectivity), sigmas = _b.sigmas, rhos = _b.rhos;
-        var _c = this.computeMembershipStrengths(indices, distances, sigmas, rhos), rows = _c.rows, cols = _c.cols, vals = _c.vals;
-        var size = [toTransform.length, rawData.length];
-        var graph = new matrix.SparseMatrix(rows, cols, vals, size);
-        var normed = matrix.normalize(graph, "l1");
-        var csrMatrix = matrix.getCSR(normed);
-        var nPoints = toTransform.length;
-        var eIndices = utils.reshape2d(csrMatrix.indices, nPoints, this.nNeighbors);
-        var eWeights = utils.reshape2d(csrMatrix.values, nPoints, this.nNeighbors);
-        var embedding = initTransform(eIndices, eWeights, this.embedding);
-        var nEpochs = this.nEpochs
-            ? this.nEpochs / 3
-            : graph.nRows <= 10000
-                ? 100
-                : 30;
-        var graphMax = graph
-            .getValues()
-            .reduce(function (max, val) { return (val > max ? val : max); }, 0);
-        graph = graph.map(function (value) { return (value < graphMax / nEpochs ? 0 : value); });
-        graph = matrix.eliminateZeros(graph);
-        var epochsPerSample = this.makeEpochsPerSample(graph.getValues(), nEpochs);
-        var head = graph.getRows();
-        var tail = graph.getCols();
-        this.assignOptimizationStateParameters({
-            headEmbedding: embedding,
-            tailEmbedding: this.embedding,
-            head: head,
-            tail: tail,
-            currentEpoch: 0,
-            nEpochs: nEpochs,
-            nVertices: graph.getDims()[1],
-            epochsPerSample: epochsPerSample,
-        });
-        this.prepareForOptimizationLoop();
-        return this.optimizeLayout();
-    };
-    UMAP.prototype.processGraphForSupervisedProjection = function () {
-        var _a = this, Y = _a.Y, X = _a.X;
-        if (Y) {
-            if (Y.length !== X.length) {
-                throw new Error('Length of X and y must be equal');
-            }
-            if (this.targetMetric === "categorical") {
-                var lt = this.targetWeight < 1.0;
-                var farDist = lt ? 2.5 * (1.0 / (1.0 - this.targetWeight)) : 1.0e12;
-                this.graph = this.categoricalSimplicialSetIntersection(this.graph, Y, farDist);
-            }
-        }
-    };
-    UMAP.prototype.step = function () {
-        var currentEpoch = this.optimizationState.currentEpoch;
-        if (currentEpoch < this.getNEpochs()) {
-            this.optimizeLayoutStep(currentEpoch);
-        }
-        return this.optimizationState.currentEpoch;
-    };
-    UMAP.prototype.getEmbedding = function () {
-        return this.embedding;
-    };
-    UMAP.prototype.nearestNeighbors = function (X) {
-        var _a = this, distanceFn = _a.distanceFn, nNeighbors = _a.nNeighbors;
-        var log2 = function (n) { return Math.log(n) / Math.log(2); };
-        var metricNNDescent = nnDescent.makeNNDescent(distanceFn, this.random);
-        var round = function (n) {
-            return n === 0.5 ? 0 : Math.round(n);
-        };
-        var nTrees = 5 + Math.floor(round(Math.pow(X.length, 0.5) / 20.0));
-        var nIters = Math.max(5, Math.floor(Math.round(log2(X.length))));
-        this.rpForest = tree.makeForest(X, nNeighbors, nTrees, this.random);
-        var leafArray = tree.makeLeafArray(this.rpForest);
-        var _b = metricNNDescent(X, leafArray, nNeighbors, nIters), indices = _b.indices, weights = _b.weights;
-        return { knnIndices: indices, knnDistances: weights };
-    };
-    UMAP.prototype.fuzzySimplicialSet = function (X, nNeighbors, setOpMixRatio) {
-        if (setOpMixRatio === void 0) { setOpMixRatio = 1.0; }
-        var _a = this, _b = _a.knnIndices, knnIndices = _b === void 0 ? [] : _b, _c = _a.knnDistances, knnDistances = _c === void 0 ? [] : _c, localConnectivity = _a.localConnectivity;
-        var _d = this.smoothKNNDistance(knnDistances, nNeighbors, localConnectivity), sigmas = _d.sigmas, rhos = _d.rhos;
-        var _e = this.computeMembershipStrengths(knnIndices, knnDistances, sigmas, rhos), rows = _e.rows, cols = _e.cols, vals = _e.vals;
-        var size = [X.length, X.length];
-        var sparseMatrix = new matrix.SparseMatrix(rows, cols, vals, size);
-        var transpose = matrix.transpose(sparseMatrix);
-        var prodMatrix = matrix.pairwiseMultiply(sparseMatrix, transpose);
-        var a = matrix.subtract(matrix.add(sparseMatrix, transpose), prodMatrix);
-        var b = matrix.multiplyScalar(a, setOpMixRatio);
-        var c = matrix.multiplyScalar(prodMatrix, 1.0 - setOpMixRatio);
-        var result = matrix.add(b, c);
-        return result;
-    };
-    UMAP.prototype.categoricalSimplicialSetIntersection = function (simplicialSet, target, farDist, unknownDist) {
-        if (unknownDist === void 0) { unknownDist = 1.0; }
-        var intersection = fastIntersection(simplicialSet, target, unknownDist, farDist);
-        intersection = matrix.eliminateZeros(intersection);
-        return resetLocalConnectivity(intersection);
-    };
-    UMAP.prototype.smoothKNNDistance = function (distances, k, localConnectivity, nIter, bandwidth) {
-        if (localConnectivity === void 0) { localConnectivity = 1.0; }
-        if (nIter === void 0) { nIter = 64; }
-        if (bandwidth === void 0) { bandwidth = 1.0; }
-        var target = (Math.log(k) / Math.log(2)) * bandwidth;
-        var rho = utils.zeros(distances.length);
-        var result = utils.zeros(distances.length);
-        for (var i = 0; i < distances.length; i++) {
-            var lo = 0.0;
-            var hi = Infinity;
-            var mid = 1.0;
-            var ithDistances = distances[i];
-            var nonZeroDists = ithDistances.filter(function (d) { return d > 0.0; });
-            if (nonZeroDists.length >= localConnectivity) {
-                var index = Math.floor(localConnectivity);
-                var interpolation = localConnectivity - index;
-                if (index > 0) {
-                    rho[i] = nonZeroDists[index - 1];
-                    if (interpolation > SMOOTH_K_TOLERANCE) {
-                        rho[i] +=
-                            interpolation * (nonZeroDists[index] - nonZeroDists[index - 1]);
-                    }
-                }
-                else {
-                    rho[i] = interpolation * nonZeroDists[0];
-                }
-            }
-            else if (nonZeroDists.length > 0) {
-                rho[i] = utils.max(nonZeroDists);
-            }
-            for (var n = 0; n < nIter; n++) {
-                var psum = 0.0;
-                for (var j = 1; j < distances[i].length; j++) {
-                    var d = distances[i][j] - rho[i];
-                    if (d > 0) {
-                        psum += Math.exp(-(d / mid));
-                    }
-                    else {
-                        psum += 1.0;
-                    }
-                }
-                if (Math.abs(psum - target) < SMOOTH_K_TOLERANCE) {
-                    break;
-                }
-                if (psum > target) {
-                    hi = mid;
-                    mid = (lo + hi) / 2.0;
-                }
-                else {
-                    lo = mid;
-                    if (hi === Infinity) {
-                        mid *= 2;
-                    }
-                    else {
-                        mid = (lo + hi) / 2.0;
-                    }
-                }
-            }
-            result[i] = mid;
-            if (rho[i] > 0.0) {
-                var meanIthDistances = utils.mean(ithDistances);
-                if (result[i] < MIN_K_DIST_SCALE * meanIthDistances) {
-                    result[i] = MIN_K_DIST_SCALE * meanIthDistances;
-                }
-            }
-            else {
-                var meanDistances = utils.mean(distances.map(utils.mean));
-                if (result[i] < MIN_K_DIST_SCALE * meanDistances) {
-                    result[i] = MIN_K_DIST_SCALE * meanDistances;
-                }
-            }
-        }
-        return { sigmas: result, rhos: rho };
-    };
-    UMAP.prototype.computeMembershipStrengths = function (knnIndices, knnDistances, sigmas, rhos) {
-        var nSamples = knnIndices.length;
-        var nNeighbors = knnIndices[0].length;
-        var rows = utils.zeros(nSamples * nNeighbors);
-        var cols = utils.zeros(nSamples * nNeighbors);
-        var vals = utils.zeros(nSamples * nNeighbors);
-        for (var i = 0; i < nSamples; i++) {
-            for (var j = 0; j < nNeighbors; j++) {
-                var val = 0;
-                if (knnIndices[i][j] === -1) {
-                    continue;
-                }
-                if (knnIndices[i][j] === i) {
-                    val = 0.0;
-                }
-                else if (knnDistances[i][j] - rhos[i] <= 0.0) {
-                    val = 1.0;
-                }
-                else {
-                    val = Math.exp(-((knnDistances[i][j] - rhos[i]) / sigmas[i]));
-                }
-                rows[i * nNeighbors + j] = i;
-                cols[i * nNeighbors + j] = knnIndices[i][j];
-                vals[i * nNeighbors + j] = val;
-            }
-        }
-        return { rows: rows, cols: cols, vals: vals };
-    };
-    UMAP.prototype.initializeSimplicialSetEmbedding = function () {
-        var _this = this;
-        var nEpochs = this.getNEpochs();
-        var nComponents = this.nComponents;
-        var graphValues = this.graph.getValues();
-        var graphMax = 0;
-        for (var i = 0; i < graphValues.length; i++) {
-            var value = graphValues[i];
-            if (graphMax < graphValues[i]) {
-                graphMax = value;
-            }
-        }
-        var graph = this.graph.map(function (value) {
-            if (value < graphMax / nEpochs) {
-                return 0;
-            }
-            else {
-                return value;
-            }
-        });
-        this.embedding = utils.zeros(graph.nRows).map(function () {
-            return utils.zeros(nComponents).map(function () {
-                return utils.tauRand(_this.random) * 20 + -10;
-            });
-        });
-        var weights = [];
-        var head = [];
-        var tail = [];
-        for (var i = 0; i < graph.nRows; i++) {
-            for (var j = 0; j < graph.nCols; j++) {
-                var value = graph.get(i, j);
-                if (value) {
-                    weights.push(value);
-                    tail.push(i);
-                    head.push(j);
-                }
-            }
-        }
-        var epochsPerSample = this.makeEpochsPerSample(weights, nEpochs);
-        return { head: head, tail: tail, epochsPerSample: epochsPerSample };
-    };
-    UMAP.prototype.makeEpochsPerSample = function (weights, nEpochs) {
-        var result = utils.filled(weights.length, -1.0);
-        var max = utils.max(weights);
-        var nSamples = weights.map(function (w) { return (w / max) * nEpochs; });
-        nSamples.forEach(function (n, i) {
-            if (n > 0)
-                result[i] = nEpochs / nSamples[i];
-        });
-        return result;
-    };
-    UMAP.prototype.assignOptimizationStateParameters = function (state) {
-        Object.assign(this.optimizationState, state);
-    };
-    UMAP.prototype.prepareForOptimizationLoop = function () {
-        var _a = this, repulsionStrength = _a.repulsionStrength, learningRate = _a.learningRate, negativeSampleRate = _a.negativeSampleRate;
-        var _b = this.optimizationState, epochsPerSample = _b.epochsPerSample, headEmbedding = _b.headEmbedding, tailEmbedding = _b.tailEmbedding;
-        var dim = headEmbedding[0].length;
-        var moveOther = headEmbedding.length === tailEmbedding.length;
-        var epochsPerNegativeSample = epochsPerSample.map(function (e) { return e / negativeSampleRate; });
-        var epochOfNextNegativeSample = __spread(epochsPerNegativeSample);
-        var epochOfNextSample = __spread(epochsPerSample);
-        this.assignOptimizationStateParameters({
-            epochOfNextSample: epochOfNextSample,
-            epochOfNextNegativeSample: epochOfNextNegativeSample,
-            epochsPerNegativeSample: epochsPerNegativeSample,
-            moveOther: moveOther,
-            initialAlpha: learningRate,
-            alpha: learningRate,
-            gamma: repulsionStrength,
-            dim: dim,
-        });
-    };
-    UMAP.prototype.initializeOptimization = function () {
-        var headEmbedding = this.embedding;
-        var tailEmbedding = this.embedding;
-        var _a = this.optimizationState, head = _a.head, tail = _a.tail, epochsPerSample = _a.epochsPerSample;
-        var nEpochs = this.getNEpochs();
-        var nVertices = this.graph.nCols;
-        var _b = findABParams(this.spread, this.minDist), a = _b.a, b = _b.b;
-        this.assignOptimizationStateParameters({
-            headEmbedding: headEmbedding,
-            tailEmbedding: tailEmbedding,
-            head: head,
-            tail: tail,
-            epochsPerSample: epochsPerSample,
-            a: a,
-            b: b,
-            nEpochs: nEpochs,
-            nVertices: nVertices,
-        });
-    };
-    UMAP.prototype.optimizeLayoutStep = function (n) {
-        var optimizationState = this.optimizationState;
-        var head = optimizationState.head, tail = optimizationState.tail, headEmbedding = optimizationState.headEmbedding, tailEmbedding = optimizationState.tailEmbedding, epochsPerSample = optimizationState.epochsPerSample, epochOfNextSample = optimizationState.epochOfNextSample, epochOfNextNegativeSample = optimizationState.epochOfNextNegativeSample, epochsPerNegativeSample = optimizationState.epochsPerNegativeSample, moveOther = optimizationState.moveOther, initialAlpha = optimizationState.initialAlpha, alpha = optimizationState.alpha, gamma = optimizationState.gamma, a = optimizationState.a, b = optimizationState.b, dim = optimizationState.dim, nEpochs = optimizationState.nEpochs, nVertices = optimizationState.nVertices;
-        var clipValue = 4.0;
-        for (var i = 0; i < epochsPerSample.length; i++) {
-            if (epochOfNextSample[i] > n) {
-                continue;
-            }
-            var j = head[i];
-            var k = tail[i];
-            var current = headEmbedding[j];
-            var other = tailEmbedding[k];
-            var distSquared = rDist(current, other);
-            var gradCoeff = 0;
-            if (distSquared > 0) {
-                gradCoeff = -2.0 * a * b * Math.pow(distSquared, b - 1.0);
-                gradCoeff /= a * Math.pow(distSquared, b) + 1.0;
-            }
-            for (var d = 0; d < dim; d++) {
-                var gradD = clip(gradCoeff * (current[d] - other[d]), clipValue);
-                current[d] += gradD * alpha;
-                if (moveOther) {
-                    other[d] += -gradD * alpha;
-                }
-            }
-            epochOfNextSample[i] += epochsPerSample[i];
-            var nNegSamples = Math.floor((n - epochOfNextNegativeSample[i]) / epochsPerNegativeSample[i]);
-            for (var p = 0; p < nNegSamples; p++) {
-                var k_1 = utils.tauRandInt(nVertices, this.random);
-                var other_1 = tailEmbedding[k_1];
-                var distSquared_1 = rDist(current, other_1);
-                var gradCoeff_1 = 0.0;
-                if (distSquared_1 > 0.0) {
-                    gradCoeff_1 = 2.0 * gamma * b;
-                    gradCoeff_1 /=
-                        (0.001 + distSquared_1) * (a * Math.pow(distSquared_1, b) + 1);
-                }
-                else if (j === k_1) {
-                    continue;
-                }
-                for (var d = 0; d < dim; d++) {
-                    var gradD = 4.0;
-                    if (gradCoeff_1 > 0.0) {
-                        gradD = clip(gradCoeff_1 * (current[d] - other_1[d]), clipValue);
-                    }
-                    current[d] += gradD * alpha;
-                }
-            }
-            epochOfNextNegativeSample[i] += nNegSamples * epochsPerNegativeSample[i];
-        }
-        optimizationState.alpha = initialAlpha * (1.0 - n / nEpochs);
-        optimizationState.currentEpoch += 1;
-        return headEmbedding;
-    };
-    UMAP.prototype.optimizeLayoutAsync = function (epochCallback) {
-        var _this = this;
-        if (epochCallback === void 0) { epochCallback = function () { return true; }; }
-        return new Promise(function (resolve, reject) {
-            var step = function () { return __awaiter(_this, void 0, void 0, function () {
-                var _a, nEpochs, currentEpoch, epochCompleted, shouldStop, isFinished;
-                return __generator(this, function (_b) {
-                    try {
-                        _a = this.optimizationState, nEpochs = _a.nEpochs, currentEpoch = _a.currentEpoch;
-                        this.embedding = this.optimizeLayoutStep(currentEpoch);
-                        epochCompleted = this.optimizationState.currentEpoch;
-                        shouldStop = epochCallback(epochCompleted) === false;
-                        isFinished = epochCompleted === nEpochs;
-                        if (!shouldStop && !isFinished) {
-                            step();
-                        }
-                        else {
-                            return [2, resolve(isFinished)];
-                        }
-                    }
-                    catch (err) {
-                        reject(err);
-                    }
-                    return [2];
-                });
-            }); };
-            step();
-        });
-    };
-    UMAP.prototype.optimizeLayout = function (epochCallback) {
-        if (epochCallback === void 0) { epochCallback = function () { return true; }; }
-        var isFinished = false;
-        var embedding = [];
-        while (!isFinished) {
-            var _a = this.optimizationState, nEpochs = _a.nEpochs, currentEpoch = _a.currentEpoch;
-            embedding = this.optimizeLayoutStep(currentEpoch);
-            var epochCompleted = this.optimizationState.currentEpoch;
-            var shouldStop = epochCallback(epochCompleted) === false;
-            isFinished = epochCompleted === nEpochs || shouldStop;
-        }
-        return embedding;
-    };
-    UMAP.prototype.getNEpochs = function () {
-        var graph = this.graph;
-        if (this.nEpochs > 0) {
-            return this.nEpochs;
-        }
-        var length = graph.nRows;
-        if (length <= 2500) {
-            return 500;
-        }
-        else if (length <= 5000) {
-            return 400;
-        }
-        else if (length <= 7500) {
-            return 300;
-        }
-        else {
-            return 200;
-        }
-    };
-    return UMAP;
-}());
-exports.UMAP = UMAP;
-function euclidean(x, y) {
-    var result = 0;
-    for (var i = 0; i < x.length; i++) {
-        result += Math.pow((x[i] - y[i]), 2);
-    }
-    return Math.sqrt(result);
-}
-exports.euclidean = euclidean;
-function cosine(x, y) {
-    var result = 0.0;
-    var normX = 0.0;
-    var normY = 0.0;
-    for (var i = 0; i < x.length; i++) {
-        result += x[i] * y[i];
-        normX += Math.pow(x[i], 2);
-        normY += Math.pow(y[i], 2);
-    }
-    if (normX === 0 && normY === 0) {
-        return 0;
-    }
-    else if (normX === 0 || normY === 0) {
-        return 1.0;
-    }
-    else {
-        return 1.0 - result / Math.sqrt(normX * normY);
-    }
-}
-exports.cosine = cosine;
-var OptimizationState = (function () {
-    function OptimizationState() {
-        this.currentEpoch = 0;
-        this.headEmbedding = [];
-        this.tailEmbedding = [];
-        this.head = [];
-        this.tail = [];
-        this.epochsPerSample = [];
-        this.epochOfNextSample = [];
-        this.epochOfNextNegativeSample = [];
-        this.epochsPerNegativeSample = [];
-        this.moveOther = true;
-        this.initialAlpha = 1.0;
-        this.alpha = 1.0;
-        this.gamma = 1.0;
-        this.a = 1.5769434603113077;
-        this.b = 0.8950608779109733;
-        this.dim = 2;
-        this.nEpochs = 500;
-        this.nVertices = 0;
-    }
-    return OptimizationState;
-}());
-function clip(x, clipValue) {
-    if (x > clipValue)
-        return clipValue;
-    else if (x < -clipValue)
-        return -clipValue;
-    else
-        return x;
-}
-function rDist(x, y) {
-    var result = 0.0;
-    for (var i = 0; i < x.length; i++) {
-        result += Math.pow(x[i] - y[i], 2);
-    }
-    return result;
-}
-function findABParams(spread, minDist) {
-    var curve = function (_a) {
-        var _b = __read(_a, 2), a = _b[0], b = _b[1];
-        return function (x) {
-            return 1.0 / (1.0 + a * Math.pow(x, (2 * b)));
-        };
-    };
-    var xv = utils
-        .linear(0, spread * 3, 300)
-        .map(function (val) { return (val < minDist ? 1.0 : val); });
-    var yv = utils.zeros(xv.length).map(function (val, index) {
-        var gte = xv[index] >= minDist;
-        return gte ? Math.exp(-(xv[index] - minDist) / spread) : val;
-    });
-    var initialValues = [0.5, 0.5];
-    var data = { x: xv, y: yv };
-    var options = {
-        damping: 1.5,
-        initialValues: initialValues,
-        gradientDifference: 10e-2,
-        maxIterations: 100,
-        errorTolerance: 10e-3,
-    };
-    var parameterValues = ml_levenberg_marquardt_1.default(data, curve, options).parameterValues;
-    var _a = __read(parameterValues, 2), a = _a[0], b = _a[1];
-    return { a: a, b: b };
-}
-exports.findABParams = findABParams;
-function fastIntersection(graph, target, unknownDist, farDist) {
-    if (unknownDist === void 0) { unknownDist = 1.0; }
-    if (farDist === void 0) { farDist = 5.0; }
-    return graph.map(function (value, row, col) {
-        if (target[row] === -1 || target[col] === -1) {
-            return value * Math.exp(-unknownDist);
-        }
-        else if (target[row] !== target[col]) {
-            return value * Math.exp(-farDist);
-        }
-        else {
-            return value;
-        }
-    });
-}
-exports.fastIntersection = fastIntersection;
-function resetLocalConnectivity(simplicialSet) {
-    simplicialSet = matrix.normalize(simplicialSet, "max");
-    var transpose = matrix.transpose(simplicialSet);
-    var prodMatrix = matrix.pairwiseMultiply(transpose, simplicialSet);
-    simplicialSet = matrix.add(simplicialSet, matrix.subtract(transpose, prodMatrix));
-    return matrix.eliminateZeros(simplicialSet);
-}
-exports.resetLocalConnectivity = resetLocalConnectivity;
-function initTransform(indices, weights, embedding) {
-    var result = utils
-        .zeros(indices.length)
-        .map(function (z) { return utils.zeros(embedding[0].length); });
-    for (var i = 0; i < indices.length; i++) {
-        for (var j = 0; j < indices[0].length; j++) {
-            for (var d = 0; d < embedding[0].length; d++) {
-                var a = indices[i][j];
-                result[i][d] += weights[i][j] * embedding[a][d];
-            }
-        }
-    }
-    return result;
-}
-exports.initTransform = initTransform;
-
-
-/***/ }),
-/* 7 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __values = (this && this.__values) || function (o) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
-    if (m) return m.call(o);
-    return {
-        next: function () {
-            if (o && i >= o.length) o = void 0;
-            return { value: o && o[i++], done: !o };
-        }
-    };
-};
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var heap = __importStar(__webpack_require__(2));
-var matrix = __importStar(__webpack_require__(3));
-var tree = __importStar(__webpack_require__(4));
-var utils = __importStar(__webpack_require__(1));
-function makeNNDescent(distanceFn, random) {
-    return function nNDescent(data, leafArray, nNeighbors, nIters, maxCandidates, delta, rho, rpTreeInit) {
-        if (nIters === void 0) { nIters = 10; }
-        if (maxCandidates === void 0) { maxCandidates = 50; }
-        if (delta === void 0) { delta = 0.001; }
-        if (rho === void 0) { rho = 0.5; }
-        if (rpTreeInit === void 0) { rpTreeInit = true; }
-        var nVertices = data.length;
-        var currentGraph = heap.makeHeap(data.length, nNeighbors);
-        for (var i = 0; i < data.length; i++) {
-            var indices = heap.rejectionSample(nNeighbors, data.length, random);
-            for (var j = 0; j < indices.length; j++) {
-                var d = distanceFn(data[i], data[indices[j]]);
-                heap.heapPush(currentGraph, i, d, indices[j], 1);
-                heap.heapPush(currentGraph, indices[j], d, i, 1);
-            }
-        }
-        if (rpTreeInit) {
-            for (var n = 0; n < leafArray.length; n++) {
-                for (var i = 0; i < leafArray[n].length; i++) {
-                    if (leafArray[n][i] < 0) {
-                        break;
-                    }
-                    for (var j = i + 1; j < leafArray[n].length; j++) {
-                        if (leafArray[n][j] < 0) {
-                            break;
-                        }
-                        var d = distanceFn(data[leafArray[n][i]], data[leafArray[n][j]]);
-                        heap.heapPush(currentGraph, leafArray[n][i], d, leafArray[n][j], 1);
-                        heap.heapPush(currentGraph, leafArray[n][j], d, leafArray[n][i], 1);
-                    }
-                }
-            }
-        }
-        for (var n = 0; n < nIters; n++) {
-            var candidateNeighbors = heap.buildCandidates(currentGraph, nVertices, nNeighbors, maxCandidates, random);
-            var c = 0;
-            for (var i = 0; i < nVertices; i++) {
-                for (var j = 0; j < maxCandidates; j++) {
-                    var p = Math.floor(candidateNeighbors[0][i][j]);
-                    if (p < 0 || utils.tauRand(random) < rho) {
-                        continue;
-                    }
-                    for (var k = 0; k < maxCandidates; k++) {
-                        var q = Math.floor(candidateNeighbors[0][i][k]);
-                        var cj = candidateNeighbors[2][i][j];
-                        var ck = candidateNeighbors[2][i][k];
-                        if (q < 0 || (!cj && !ck)) {
-                            continue;
-                        }
-                        var d = distanceFn(data[p], data[q]);
-                        c += heap.heapPush(currentGraph, p, d, q, 1);
-                        c += heap.heapPush(currentGraph, q, d, p, 1);
-                    }
-                }
-            }
-            if (c <= delta * nNeighbors * data.length) {
-                break;
-            }
-        }
-        var sorted = heap.deheapSort(currentGraph);
-        return sorted;
-    };
-}
-exports.makeNNDescent = makeNNDescent;
-function makeInitializations(distanceFn) {
-    function initFromRandom(nNeighbors, data, queryPoints, _heap, random) {
-        for (var i = 0; i < queryPoints.length; i++) {
-            var indices = utils.rejectionSample(nNeighbors, data.length, random);
-            for (var j = 0; j < indices.length; j++) {
-                if (indices[j] < 0) {
-                    continue;
-                }
-                var d = distanceFn(data[indices[j]], queryPoints[i]);
-                heap.heapPush(_heap, i, d, indices[j], 1);
-            }
-        }
-    }
-    function initFromTree(_tree, data, queryPoints, _heap, random) {
-        for (var i = 0; i < queryPoints.length; i++) {
-            var indices = tree.searchFlatTree(queryPoints[i], _tree, random);
-            for (var j = 0; j < indices.length; j++) {
-                if (indices[j] < 0) {
-                    return;
-                }
-                var d = distanceFn(data[indices[j]], queryPoints[i]);
-                heap.heapPush(_heap, i, d, indices[j], 1);
-            }
-        }
-        return;
-    }
-    return { initFromRandom: initFromRandom, initFromTree: initFromTree };
-}
-exports.makeInitializations = makeInitializations;
-function makeInitializedNNSearch(distanceFn) {
-    return function nnSearchFn(data, graph, initialization, queryPoints) {
-        var e_1, _a;
-        var _b = matrix.getCSR(graph), indices = _b.indices, indptr = _b.indptr;
-        for (var i = 0; i < queryPoints.length; i++) {
-            var tried = new Set(initialization[0][i]);
-            while (true) {
-                var vertex = heap.smallestFlagged(initialization, i);
-                if (vertex === -1) {
-                    break;
-                }
-                var candidates = indices.slice(indptr[vertex], indptr[vertex + 1]);
-                try {
-                    for (var candidates_1 = __values(candidates), candidates_1_1 = candidates_1.next(); !candidates_1_1.done; candidates_1_1 = candidates_1.next()) {
-                        var candidate = candidates_1_1.value;
-                        if (candidate === vertex ||
-                            candidate === -1 ||
-                            tried.has(candidate)) {
-                            continue;
-                        }
-                        var d = distanceFn(data[candidate], queryPoints[i]);
-                        heap.uncheckedHeapPush(initialization, i, d, candidate, 1);
-                        tried.add(candidate);
-                    }
-                }
-                catch (e_1_1) { e_1 = { error: e_1_1 }; }
-                finally {
-                    try {
-                        if (candidates_1_1 && !candidates_1_1.done && (_a = candidates_1.return)) _a.call(candidates_1);
-                    }
-                    finally { if (e_1) throw e_1.error; }
-                }
-            }
-        }
-        return initialization;
-    };
-}
-exports.makeInitializedNNSearch = makeInitializedNNSearch;
-function initializeSearch(forest, data, queryPoints, nNeighbors, initFromRandom, initFromTree, random) {
-    var e_2, _a;
-    var results = heap.makeHeap(queryPoints.length, nNeighbors);
-    initFromRandom(nNeighbors, data, queryPoints, results, random);
-    if (forest) {
-        try {
-            for (var forest_1 = __values(forest), forest_1_1 = forest_1.next(); !forest_1_1.done; forest_1_1 = forest_1.next()) {
-                var tree_1 = forest_1_1.value;
-                initFromTree(tree_1, data, queryPoints, results, random);
-            }
-        }
-        catch (e_2_1) { e_2 = { error: e_2_1 }; }
-        finally {
-            try {
-                if (forest_1_1 && !forest_1_1.done && (_a = forest_1.return)) _a.call(forest_1);
-            }
-            finally { if (e_2) throw e_2.error; }
-        }
-    }
-    return results;
-}
-exports.initializeSearch = initializeSearch;
-
-
-/***/ }),
-/* 8 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-
-var mlMatrix = __webpack_require__(9);
-
-/**
- * Calculate current error
- * @ignore
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {Array<number>} parameters - Array of current parameter values
- * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
- * @return {number}
- */
-function errorCalculation(
-  data,
-  parameters,
-  parameterizedFunction
-) {
-  var error = 0;
-  const func = parameterizedFunction(parameters);
-
-  for (var i = 0; i < data.x.length; i++) {
-    error += Math.abs(data.y[i] - func(data.x[i]));
-  }
-
-  return error;
-}
-
-/**
- * Difference of the matrix function over the parameters
- * @ignore
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {Array<number>} evaluatedData - Array of previous evaluated function values
- * @param {Array<number>} params - Array of previous parameter values
- * @param {number} gradientDifference - Adjustment for decrease the damping parameter
- * @param {function} paramFunction - The parameters and returns a function with the independent variable as a parameter
- * @return {Matrix}
- */
-function gradientFunction(
-  data,
-  evaluatedData,
-  params,
-  gradientDifference,
-  paramFunction
-) {
-  const n = params.length;
-  const m = data.x.length;
-
-  var ans = new Array(n);
-
-  for (var param = 0; param < n; param++) {
-    ans[param] = new Array(m);
-    var auxParams = params.concat();
-    auxParams[param] += gradientDifference;
-    var funcParam = paramFunction(auxParams);
-
-    for (var point = 0; point < m; point++) {
-      ans[param][point] = evaluatedData[point] - funcParam(data.x[point]);
-    }
-  }
-  return new mlMatrix.Matrix(ans);
-}
-
-/**
- * Matrix function over the samples
- * @ignore
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {Array<number>} evaluatedData - Array of previous evaluated function values
- * @return {Matrix}
- */
-function matrixFunction(data, evaluatedData) {
-  const m = data.x.length;
-
-  var ans = new Array(m);
-
-  for (var point = 0; point < m; point++) {
-    ans[point] = data.y[point] - evaluatedData[point];
-  }
-
-  return new mlMatrix.Matrix([ans]);
-}
-
-/**
- * Iteration for Levenberg-Marquardt
- * @ignore
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {Array<number>} params - Array of previous parameter values
- * @param {number} damping - Levenberg-Marquardt parameter
- * @param {number} gradientDifference - Adjustment for decrease the damping parameter
- * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
- * @return {Array<number>}
- */
-function step(
-  data,
-  params,
-  damping,
-  gradientDifference,
-  parameterizedFunction
-) {
-  var identity = mlMatrix.Matrix.eye(params.length).mul(
-    damping * gradientDifference * gradientDifference
-  );
-
-  var l = data.x.length;
-  var evaluatedData = new Array(l);
-  const func = parameterizedFunction(params);
-  for (var i = 0; i < l; i++) {
-    evaluatedData[i] = func(data.x[i]);
-  }
-  var gradientFunc = gradientFunction(
-    data,
-    evaluatedData,
-    params,
-    gradientDifference,
-    parameterizedFunction
-  );
-  var matrixFunc = matrixFunction(data, evaluatedData).transposeView();
-  var inverseMatrix = mlMatrix.inverse(
-    identity.add(gradientFunc.mmul(gradientFunc.transposeView()))
-  );
-  params = new mlMatrix.Matrix([params]);
-  params = params.sub(
-    inverseMatrix
-      .mmul(gradientFunc)
-      .mmul(matrixFunc)
-      .mul(gradientDifference)
-      .transposeView()
-  );
-
-  return params.to1DArray();
-}
-
-/**
- * Curve fitting algorithm
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
- * @param {object} [options] - Options object
- * @param {number} [options.damping] - Levenberg-Marquardt parameter
- * @param {number} [options.gradientDifference = 10e-2] - Adjustment for decrease the damping parameter
- * @param {Array<number>} [options.initialValues] - Array of initial parameter values
- * @param {number} [options.maxIterations = 100] - Maximum of allowed iterations
- * @param {number} [options.errorTolerance = 10e-3] - Minimum uncertainty allowed for each point
- * @return {{parameterValues: Array<number>, parameterError: number, iterations: number}}
- */
-function levenbergMarquardt(
-  data,
-  parameterizedFunction,
-  options = {}
-) {
-  let {
-    maxIterations = 100,
-    gradientDifference = 10e-2,
-    damping = 0,
-    errorTolerance = 10e-3,
-    initialValues
-  } = options;
-
-  if (damping <= 0) {
-    throw new Error('The damping option must be a positive number');
-  } else if (!data.x || !data.y) {
-    throw new Error('The data parameter must have x and y elements');
-  } else if (
-    !Array.isArray(data.x) ||
-    data.x.length < 2 ||
-    !Array.isArray(data.y) ||
-    data.y.length < 2
-  ) {
-    throw new Error(
-      'The data parameter elements must be an array with more than 2 points'
-    );
-  } else {
-    let dataLen = data.x.length;
-    if (dataLen !== data.y.length) {
-      throw new Error('The data parameter elements must have the same size');
-    }
-  }
-
-  var parameters =
-    initialValues || new Array(parameterizedFunction.length).fill(1);
-
-  if (!Array.isArray(parameters)) {
-    throw new Error('initialValues must be an array');
-  }
-
-  var error = errorCalculation(data, parameters, parameterizedFunction);
-
-  var converged = error <= errorTolerance;
-
-  for (
-    var iteration = 0;
-    iteration < maxIterations && !converged;
-    iteration++
-  ) {
-    parameters = step(
-      data,
-      parameters,
-      damping,
-      gradientDifference,
-      parameterizedFunction
-    );
-    error = errorCalculation(data, parameters, parameterizedFunction);
-    converged = error <= errorTolerance;
-  }
-
-  return {
-    parameterValues: parameters,
-    parameterError: error,
-    iterations: iteration
-  };
-}
-
-module.exports = levenbergMarquardt;
-
-
-/***/ }),
-/* 9 */
-/***/ (function(module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-__webpack_require__.r(__webpack_exports__);
-
-// EXTERNAL MODULE: ./node_modules/is-any-array/src/index.js
-var src = __webpack_require__(0);
-var src_default = /*#__PURE__*/__webpack_require__.n(src);
-
-// CONCATENATED MODULE: ./node_modules/ml-array-max/lib-es6/index.js
-
-
-/**
- * Computes the maximum of the given values
- * @param {Array<number>} input
- * @return {number}
- */
-
-function lib_es6_max(input) {
-  if (!src_default()(input)) {
-    throw new TypeError('input must be an array');
-  }
-
-  if (input.length === 0) {
-    throw new TypeError('input must not be empty');
-  }
-
-  var max = input[0];
-
-  for (var i = 1; i < input.length; i++) {
-    if (input[i] > max) max = input[i];
-  }
-
-  return max;
-}
-
-/* harmony default export */ var lib_es6 = (lib_es6_max);
-
-// CONCATENATED MODULE: ./node_modules/ml-array-min/lib-es6/index.js
-
-
-/**
- * Computes the minimum of the given values
- * @param {Array<number>} input
- * @return {number}
- */
-
-function lib_es6_min(input) {
-  if (!src_default()(input)) {
-    throw new TypeError('input must be an array');
-  }
-
-  if (input.length === 0) {
-    throw new TypeError('input must not be empty');
-  }
-
-  var min = input[0];
-
-  for (var i = 1; i < input.length; i++) {
-    if (input[i] < min) min = input[i];
-  }
-
-  return min;
-}
-
-/* harmony default export */ var ml_array_min_lib_es6 = (lib_es6_min);
-
-// CONCATENATED MODULE: ./node_modules/ml-array-rescale/lib-es6/index.js
-
-
-
-
-function rescale(input) {
-  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-
-  if (!src_default()(input)) {
-    throw new TypeError('input must be an array');
-  } else if (input.length === 0) {
-    throw new TypeError('input must not be empty');
-  }
-
-  var output;
-
-  if (options.output !== undefined) {
-    if (!src_default()(options.output)) {
-      throw new TypeError('output option must be an array if specified');
-    }
-
-    output = options.output;
-  } else {
-    output = new Array(input.length);
-  }
-
-  var currentMin = ml_array_min_lib_es6(input);
-  var currentMax = lib_es6(input);
-
-  if (currentMin === currentMax) {
-    throw new RangeError('minimum and maximum input values are equal. Cannot rescale a constant array');
-  }
-
-  var _options$min = options.min,
-      minValue = _options$min === void 0 ? options.autoMinMax ? currentMin : 0 : _options$min,
-      _options$max = options.max,
-      maxValue = _options$max === void 0 ? options.autoMinMax ? currentMax : 1 : _options$max;
-
-  if (minValue >= maxValue) {
-    throw new RangeError('min option must be smaller than max option');
-  }
-
-  var factor = (maxValue - minValue) / (currentMax - currentMin);
-
-  for (var i = 0; i < input.length; i++) {
-    output[i] = (input[i] - currentMin) * factor + minValue;
-  }
-
-  return output;
-}
-
-/* harmony default export */ var ml_array_rescale_lib_es6 = (rescale);
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/lu.js
-
-
-/**
- * @class LuDecomposition
- * @link https://github.com/lutzroeder/Mapack/blob/master/Source/LuDecomposition.cs
- * @param {Matrix} matrix
- */
-class lu_LuDecomposition {
-  constructor(matrix) {
-    matrix = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(matrix);
-
-    var lu = matrix.clone();
-    var rows = lu.rows;
-    var columns = lu.columns;
-    var pivotVector = new Array(rows);
-    var pivotSign = 1;
-    var i, j, k, p, s, t, v;
-    var LUcolj, kmax;
-
-    for (i = 0; i < rows; i++) {
-      pivotVector[i] = i;
-    }
-
-    LUcolj = new Array(rows);
-
-    for (j = 0; j < columns; j++) {
-      for (i = 0; i < rows; i++) {
-        LUcolj[i] = lu.get(i, j);
-      }
-
-      for (i = 0; i < rows; i++) {
-        kmax = Math.min(i, j);
-        s = 0;
-        for (k = 0; k < kmax; k++) {
-          s += lu.get(i, k) * LUcolj[k];
-        }
-        LUcolj[i] -= s;
-        lu.set(i, j, LUcolj[i]);
-      }
-
-      p = j;
-      for (i = j + 1; i < rows; i++) {
-        if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {
-          p = i;
-        }
-      }
-
-      if (p !== j) {
-        for (k = 0; k < columns; k++) {
-          t = lu.get(p, k);
-          lu.set(p, k, lu.get(j, k));
-          lu.set(j, k, t);
-        }
-
-        v = pivotVector[p];
-        pivotVector[p] = pivotVector[j];
-        pivotVector[j] = v;
-
-        pivotSign = -pivotSign;
-      }
-
-      if (j < rows && lu.get(j, j) !== 0) {
-        for (i = j + 1; i < rows; i++) {
-          lu.set(i, j, lu.get(i, j) / lu.get(j, j));
-        }
-      }
-    }
-
-    this.LU = lu;
-    this.pivotVector = pivotVector;
-    this.pivotSign = pivotSign;
-  }
-
-  /**
-   *
-   * @return {boolean}
-   */
-  isSingular() {
-    var data = this.LU;
-    var col = data.columns;
-    for (var j = 0; j < col; j++) {
-      if (data[j][j] === 0) {
-        return true;
-      }
-    }
-    return false;
-  }
-
-  /**
-   *
-   * @param {Matrix} value
-   * @return {Matrix}
-   */
-  solve(value) {
-    value = matrix_Matrix.checkMatrix(value);
-
-    var lu = this.LU;
-    var rows = lu.rows;
-
-    if (rows !== value.rows) {
-      throw new Error('Invalid matrix dimensions');
-    }
-    if (this.isSingular()) {
-      throw new Error('LU matrix is singular');
-    }
-
-    var count = value.columns;
-    var X = value.subMatrixRow(this.pivotVector, 0, count - 1);
-    var columns = lu.columns;
-    var i, j, k;
-
-    for (k = 0; k < columns; k++) {
-      for (i = k + 1; i < columns; i++) {
-        for (j = 0; j < count; j++) {
-          X[i][j] -= X[k][j] * lu[i][k];
-        }
-      }
-    }
-    for (k = columns - 1; k >= 0; k--) {
-      for (j = 0; j < count; j++) {
-        X[k][j] /= lu[k][k];
-      }
-      for (i = 0; i < k; i++) {
-        for (j = 0; j < count; j++) {
-          X[i][j] -= X[k][j] * lu[i][k];
-        }
-      }
-    }
-    return X;
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get determinant() {
-    var data = this.LU;
-    if (!data.isSquare()) {
-      throw new Error('Matrix must be square');
-    }
-    var determinant = this.pivotSign;
-    var col = data.columns;
-    for (var j = 0; j < col; j++) {
-      determinant *= data[j][j];
-    }
-    return determinant;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get lowerTriangularMatrix() {
-    var data = this.LU;
-    var rows = data.rows;
-    var columns = data.columns;
-    var X = new matrix_Matrix(rows, columns);
-    for (var i = 0; i < rows; i++) {
-      for (var j = 0; j < columns; j++) {
-        if (i > j) {
-          X[i][j] = data[i][j];
-        } else if (i === j) {
-          X[i][j] = 1;
-        } else {
-          X[i][j] = 0;
-        }
-      }
-    }
-    return X;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get upperTriangularMatrix() {
-    var data = this.LU;
-    var rows = data.rows;
-    var columns = data.columns;
-    var X = new matrix_Matrix(rows, columns);
-    for (var i = 0; i < rows; i++) {
-      for (var j = 0; j < columns; j++) {
-        if (i <= j) {
-          X[i][j] = data[i][j];
-        } else {
-          X[i][j] = 0;
-        }
-      }
-    }
-    return X;
-  }
-
-  /**
-   *
-   * @return {Array<number>}
-   */
-  get pivotPermutationVector() {
-    return this.pivotVector.slice();
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/util.js
-function hypotenuse(a, b) {
-  var r = 0;
-  if (Math.abs(a) > Math.abs(b)) {
-    r = b / a;
-    return Math.abs(a) * Math.sqrt(1 + r * r);
-  }
-  if (b !== 0) {
-    r = a / b;
-    return Math.abs(b) * Math.sqrt(1 + r * r);
-  }
-  return 0;
-}
-
-function getFilled2DArray(rows, columns, value) {
-  var array = new Array(rows);
-  for (var i = 0; i < rows; i++) {
-    array[i] = new Array(columns);
-    for (var j = 0; j < columns; j++) {
-      array[i][j] = value;
-    }
-  }
-  return array;
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/svd.js
-
-
-
-
-/**
- * @class SingularValueDecomposition
- * @see https://github.com/accord-net/framework/blob/development/Sources/Accord.Math/Decompositions/SingularValueDecomposition.cs
- * @param {Matrix} value
- * @param {object} [options]
- * @param {boolean} [options.computeLeftSingularVectors=true]
- * @param {boolean} [options.computeRightSingularVectors=true]
- * @param {boolean} [options.autoTranspose=false]
- */
-class svd_SingularValueDecomposition {
-  constructor(value, options = {}) {
-    value = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(value);
-
-    var m = value.rows;
-    var n = value.columns;
-
-    const {
-      computeLeftSingularVectors = true,
-      computeRightSingularVectors = true,
-      autoTranspose = false
-    } = options;
-
-    var wantu = Boolean(computeLeftSingularVectors);
-    var wantv = Boolean(computeRightSingularVectors);
-
-    var swapped = false;
-    var a;
-    if (m < n) {
-      if (!autoTranspose) {
-        a = value.clone();
-        // eslint-disable-next-line no-console
-        console.warn(
-          'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose'
-        );
-      } else {
-        a = value.transpose();
-        m = a.rows;
-        n = a.columns;
-        swapped = true;
-        var aux = wantu;
-        wantu = wantv;
-        wantv = aux;
-      }
-    } else {
-      a = value.clone();
-    }
-
-    var nu = Math.min(m, n);
-    var ni = Math.min(m + 1, n);
-    var s = new Array(ni);
-    var U = getFilled2DArray(m, nu, 0);
-    var V = getFilled2DArray(n, n, 0);
-
-    var e = new Array(n);
-    var work = new Array(m);
-
-    var si = new Array(ni);
-    for (let i = 0; i < ni; i++) si[i] = i;
-
-    var nct = Math.min(m - 1, n);
-    var nrt = Math.max(0, Math.min(n - 2, m));
-    var mrc = Math.max(nct, nrt);
-
-    for (let k = 0; k < mrc; k++) {
-      if (k < nct) {
-        s[k] = 0;
-        for (let i = k; i < m; i++) {
-          s[k] = hypotenuse(s[k], a[i][k]);
-        }
-        if (s[k] !== 0) {
-          if (a[k][k] < 0) {
-            s[k] = -s[k];
-          }
-          for (let i = k; i < m; i++) {
-            a[i][k] /= s[k];
-          }
-          a[k][k] += 1;
-        }
-        s[k] = -s[k];
-      }
-
-      for (let j = k + 1; j < n; j++) {
-        if (k < nct && s[k] !== 0) {
-          let t = 0;
-          for (let i = k; i < m; i++) {
-            t += a[i][k] * a[i][j];
-          }
-          t = -t / a[k][k];
-          for (let i = k; i < m; i++) {
-            a[i][j] += t * a[i][k];
-          }
-        }
-        e[j] = a[k][j];
-      }
-
-      if (wantu && k < nct) {
-        for (let i = k; i < m; i++) {
-          U[i][k] = a[i][k];
-        }
-      }
-
-      if (k < nrt) {
-        e[k] = 0;
-        for (let i = k + 1; i < n; i++) {
-          e[k] = hypotenuse(e[k], e[i]);
-        }
-        if (e[k] !== 0) {
-          if (e[k + 1] < 0) {
-            e[k] = 0 - e[k];
-          }
-          for (let i = k + 1; i < n; i++) {
-            e[i] /= e[k];
-          }
-          e[k + 1] += 1;
-        }
-        e[k] = -e[k];
-        if (k + 1 < m && e[k] !== 0) {
-          for (let i = k + 1; i < m; i++) {
-            work[i] = 0;
-          }
-          for (let i = k + 1; i < m; i++) {
-            for (let j = k + 1; j < n; j++) {
-              work[i] += e[j] * a[i][j];
-            }
-          }
-          for (let j = k + 1; j < n; j++) {
-            let t = -e[j] / e[k + 1];
-            for (let i = k + 1; i < m; i++) {
-              a[i][j] += t * work[i];
-            }
-          }
-        }
-        if (wantv) {
-          for (let i = k + 1; i < n; i++) {
-            V[i][k] = e[i];
-          }
-        }
-      }
-    }
-
-    let p = Math.min(n, m + 1);
-    if (nct < n) {
-      s[nct] = a[nct][nct];
-    }
-    if (m < p) {
-      s[p - 1] = 0;
-    }
-    if (nrt + 1 < p) {
-      e[nrt] = a[nrt][p - 1];
-    }
-    e[p - 1] = 0;
-
-    if (wantu) {
-      for (let j = nct; j < nu; j++) {
-        for (let i = 0; i < m; i++) {
-          U[i][j] = 0;
-        }
-        U[j][j] = 1;
-      }
-      for (let k = nct - 1; k >= 0; k--) {
-        if (s[k] !== 0) {
-          for (let j = k + 1; j < nu; j++) {
-            let t = 0;
-            for (let i = k; i < m; i++) {
-              t += U[i][k] * U[i][j];
-            }
-            t = -t / U[k][k];
-            for (let i = k; i < m; i++) {
-              U[i][j] += t * U[i][k];
-            }
-          }
-          for (let i = k; i < m; i++) {
-            U[i][k] = -U[i][k];
-          }
-          U[k][k] = 1 + U[k][k];
-          for (let i = 0; i < k - 1; i++) {
-            U[i][k] = 0;
-          }
-        } else {
-          for (let i = 0; i < m; i++) {
-            U[i][k] = 0;
-          }
-          U[k][k] = 1;
-        }
-      }
-    }
-
-    if (wantv) {
-      for (let k = n - 1; k >= 0; k--) {
-        if (k < nrt && e[k] !== 0) {
-          for (let j = k + 1; j < n; j++) {
-            let t = 0;
-            for (let i = k + 1; i < n; i++) {
-              t += V[i][k] * V[i][j];
-            }
-            t = -t / V[k + 1][k];
-            for (let i = k + 1; i < n; i++) {
-              V[i][j] += t * V[i][k];
-            }
-          }
-        }
-        for (let i = 0; i < n; i++) {
-          V[i][k] = 0;
-        }
-        V[k][k] = 1;
-      }
-    }
-
-    var pp = p - 1;
-    var iter = 0;
-    var eps = Number.EPSILON;
-    while (p > 0) {
-      let k, kase;
-      for (k = p - 2; k >= -1; k--) {
-        if (k === -1) {
-          break;
-        }
-        const alpha =
-          Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));
-        if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {
-          e[k] = 0;
-          break;
-        }
-      }
-      if (k === p - 2) {
-        kase = 4;
-      } else {
-        let ks;
-        for (ks = p - 1; ks >= k; ks--) {
-          if (ks === k) {
-            break;
-          }
-          let t =
-            (ks !== p ? Math.abs(e[ks]) : 0) +
-            (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);
-          if (Math.abs(s[ks]) <= eps * t) {
-            s[ks] = 0;
-            break;
-          }
-        }
-        if (ks === k) {
-          kase = 3;
-        } else if (ks === p - 1) {
-          kase = 1;
-        } else {
-          kase = 2;
-          k = ks;
-        }
-      }
-
-      k++;
-
-      switch (kase) {
-        case 1: {
-          let f = e[p - 2];
-          e[p - 2] = 0;
-          for (let j = p - 2; j >= k; j--) {
-            let t = hypotenuse(s[j], f);
-            let cs = s[j] / t;
-            let sn = f / t;
-            s[j] = t;
-            if (j !== k) {
-              f = -sn * e[j - 1];
-              e[j - 1] = cs * e[j - 1];
-            }
-            if (wantv) {
-              for (let i = 0; i < n; i++) {
-                t = cs * V[i][j] + sn * V[i][p - 1];
-                V[i][p - 1] = -sn * V[i][j] + cs * V[i][p - 1];
-                V[i][j] = t;
-              }
-            }
-          }
-          break;
-        }
-        case 2: {
-          let f = e[k - 1];
-          e[k - 1] = 0;
-          for (let j = k; j < p; j++) {
-            let t = hypotenuse(s[j], f);
-            let cs = s[j] / t;
-            let sn = f / t;
-            s[j] = t;
-            f = -sn * e[j];
-            e[j] = cs * e[j];
-            if (wantu) {
-              for (let i = 0; i < m; i++) {
-                t = cs * U[i][j] + sn * U[i][k - 1];
-                U[i][k - 1] = -sn * U[i][j] + cs * U[i][k - 1];
-                U[i][j] = t;
-              }
-            }
-          }
-          break;
-        }
-        case 3: {
-          const scale = Math.max(
-            Math.abs(s[p - 1]),
-            Math.abs(s[p - 2]),
-            Math.abs(e[p - 2]),
-            Math.abs(s[k]),
-            Math.abs(e[k])
-          );
-          const sp = s[p - 1] / scale;
-          const spm1 = s[p - 2] / scale;
-          const epm1 = e[p - 2] / scale;
-          const sk = s[k] / scale;
-          const ek = e[k] / scale;
-          const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;
-          const c = sp * epm1 * (sp * epm1);
-          let shift = 0;
-          if (b !== 0 || c !== 0) {
-            if (b < 0) {
-              shift = 0 - Math.sqrt(b * b + c);
-            } else {
-              shift = Math.sqrt(b * b + c);
-            }
-            shift = c / (b + shift);
-          }
-          let f = (sk + sp) * (sk - sp) + shift;
-          let g = sk * ek;
-          for (let j = k; j < p - 1; j++) {
-            let t = hypotenuse(f, g);
-            if (t === 0) t = Number.MIN_VALUE;
-            let cs = f / t;
-            let sn = g / t;
-            if (j !== k) {
-              e[j - 1] = t;
-            }
-            f = cs * s[j] + sn * e[j];
-            e[j] = cs * e[j] - sn * s[j];
-            g = sn * s[j + 1];
-            s[j + 1] = cs * s[j + 1];
-            if (wantv) {
-              for (let i = 0; i < n; i++) {
-                t = cs * V[i][j] + sn * V[i][j + 1];
-                V[i][j + 1] = -sn * V[i][j] + cs * V[i][j + 1];
-                V[i][j] = t;
-              }
-            }
-            t = hypotenuse(f, g);
-            if (t === 0) t = Number.MIN_VALUE;
-            cs = f / t;
-            sn = g / t;
-            s[j] = t;
-            f = cs * e[j] + sn * s[j + 1];
-            s[j + 1] = -sn * e[j] + cs * s[j + 1];
-            g = sn * e[j + 1];
-            e[j + 1] = cs * e[j + 1];
-            if (wantu && j < m - 1) {
-              for (let i = 0; i < m; i++) {
-                t = cs * U[i][j] + sn * U[i][j + 1];
-                U[i][j + 1] = -sn * U[i][j] + cs * U[i][j + 1];
-                U[i][j] = t;
-              }
-            }
-          }
-          e[p - 2] = f;
-          iter = iter + 1;
-          break;
-        }
-        case 4: {
-          if (s[k] <= 0) {
-            s[k] = s[k] < 0 ? -s[k] : 0;
-            if (wantv) {
-              for (let i = 0; i <= pp; i++) {
-                V[i][k] = -V[i][k];
-              }
-            }
-          }
-          while (k < pp) {
-            if (s[k] >= s[k + 1]) {
-              break;
-            }
-            let t = s[k];
-            s[k] = s[k + 1];
-            s[k + 1] = t;
-            if (wantv && k < n - 1) {
-              for (let i = 0; i < n; i++) {
-                t = V[i][k + 1];
-                V[i][k + 1] = V[i][k];
-                V[i][k] = t;
-              }
-            }
-            if (wantu && k < m - 1) {
-              for (let i = 0; i < m; i++) {
-                t = U[i][k + 1];
-                U[i][k + 1] = U[i][k];
-                U[i][k] = t;
-              }
-            }
-            k++;
-          }
-          iter = 0;
-          p--;
-          break;
-        }
-        // no default
-      }
-    }
-
-    if (swapped) {
-      var tmp = V;
-      V = U;
-      U = tmp;
-    }
-
-    this.m = m;
-    this.n = n;
-    this.s = s;
-    this.U = U;
-    this.V = V;
-  }
-
-  /**
-   * Solve a problem of least square (Ax=b) by using the SVD. Useful when A is singular. When A is not singular, it would be better to use qr.solve(value).
-   * Example : We search to approximate x, with A matrix shape m*n, x vector size n, b vector size m (m > n). We will use :
-   * var svd = SingularValueDecomposition(A);
-   * var x = svd.solve(b);
-   * @param {Matrix} value - Matrix 1D which is the vector b (in the equation Ax = b)
-   * @return {Matrix} - The vector x
-   */
-  solve(value) {
-    var Y = value;
-    var e = this.threshold;
-    var scols = this.s.length;
-    var Ls = matrix_Matrix.zeros(scols, scols);
-
-    for (let i = 0; i < scols; i++) {
-      if (Math.abs(this.s[i]) <= e) {
-        Ls[i][i] = 0;
-      } else {
-        Ls[i][i] = 1 / this.s[i];
-      }
-    }
-
-    var U = this.U;
-    var V = this.rightSingularVectors;
-
-    var VL = V.mmul(Ls);
-    var vrows = V.rows;
-    var urows = U.length;
-    var VLU = matrix_Matrix.zeros(vrows, urows);
-
-    for (let i = 0; i < vrows; i++) {
-      for (let j = 0; j < urows; j++) {
-        let sum = 0;
-        for (let k = 0; k < scols; k++) {
-          sum += VL[i][k] * U[j][k];
-        }
-        VLU[i][j] = sum;
-      }
-    }
-
-    return VLU.mmul(Y);
-  }
-
-  /**
-   *
-   * @param {Array<number>} value
-   * @return {Matrix}
-   */
-  solveForDiagonal(value) {
-    return this.solve(matrix_Matrix.diag(value));
-  }
-
-  /**
-   * Get the inverse of the matrix. We compute the inverse of a matrix using SVD when this matrix is singular or ill-conditioned. Example :
-   * var svd = SingularValueDecomposition(A);
-   * var inverseA = svd.inverse();
-   * @return {Matrix} - The approximation of the inverse of the matrix
-   */
-  inverse() {
-    var V = this.V;
-    var e = this.threshold;
-    var vrows = V.length;
-    var vcols = V[0].length;
-    var X = new matrix_Matrix(vrows, this.s.length);
-
-    for (let i = 0; i < vrows; i++) {
-      for (let j = 0; j < vcols; j++) {
-        if (Math.abs(this.s[j]) > e) {
-          X[i][j] = V[i][j] / this.s[j];
-        } else {
-          X[i][j] = 0;
-        }
-      }
-    }
-
-    var U = this.U;
-
-    var urows = U.length;
-    var ucols = U[0].length;
-    var Y = new matrix_Matrix(vrows, urows);
-
-    for (let i = 0; i < vrows; i++) {
-      for (let j = 0; j < urows; j++) {
-        let sum = 0;
-        for (let k = 0; k < ucols; k++) {
-          sum += X[i][k] * U[j][k];
-        }
-        Y[i][j] = sum;
-      }
-    }
-
-    return Y;
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get condition() {
-    return this.s[0] / this.s[Math.min(this.m, this.n) - 1];
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get norm2() {
-    return this.s[0];
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get rank() {
-    var tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;
-    var r = 0;
-    var s = this.s;
-    for (var i = 0, ii = s.length; i < ii; i++) {
-      if (s[i] > tol) {
-        r++;
-      }
-    }
-    return r;
-  }
-
-  /**
-   *
-   * @return {Array<number>}
-   */
-  get diagonal() {
-    return this.s;
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get threshold() {
-    return Number.EPSILON / 2 * Math.max(this.m, this.n) * this.s[0];
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get leftSingularVectors() {
-    if (!matrix_Matrix.isMatrix(this.U)) {
-      this.U = new matrix_Matrix(this.U);
-    }
-    return this.U;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get rightSingularVectors() {
-    if (!matrix_Matrix.isMatrix(this.V)) {
-      this.V = new matrix_Matrix(this.V);
-    }
-    return this.V;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get diagonalMatrix() {
-    return matrix_Matrix.diag(this.s);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/util.js
-
-
-/**
- * @private
- * Check that a row index is not out of bounds
- * @param {Matrix} matrix
- * @param {number} index
- * @param {boolean} [outer]
- */
-function checkRowIndex(matrix, index, outer) {
-  var max = outer ? matrix.rows : matrix.rows - 1;
-  if (index < 0 || index > max) {
-    throw new RangeError('Row index out of range');
-  }
-}
-
-/**
- * @private
- * Check that a column index is not out of bounds
- * @param {Matrix} matrix
- * @param {number} index
- * @param {boolean} [outer]
- */
-function checkColumnIndex(matrix, index, outer) {
-  var max = outer ? matrix.columns : matrix.columns - 1;
-  if (index < 0 || index > max) {
-    throw new RangeError('Column index out of range');
-  }
-}
-
-/**
- * @private
- * Check that the provided vector is an array with the right length
- * @param {Matrix} matrix
- * @param {Array|Matrix} vector
- * @return {Array}
- * @throws {RangeError}
- */
-function checkRowVector(matrix, vector) {
-  if (vector.to1DArray) {
-    vector = vector.to1DArray();
-  }
-  if (vector.length !== matrix.columns) {
-    throw new RangeError(
-      'vector size must be the same as the number of columns'
-    );
-  }
-  return vector;
-}
-
-/**
- * @private
- * Check that the provided vector is an array with the right length
- * @param {Matrix} matrix
- * @param {Array|Matrix} vector
- * @return {Array}
- * @throws {RangeError}
- */
-function checkColumnVector(matrix, vector) {
-  if (vector.to1DArray) {
-    vector = vector.to1DArray();
-  }
-  if (vector.length !== matrix.rows) {
-    throw new RangeError('vector size must be the same as the number of rows');
-  }
-  return vector;
-}
-
-function checkIndices(matrix, rowIndices, columnIndices) {
-  return {
-    row: checkRowIndices(matrix, rowIndices),
-    column: checkColumnIndices(matrix, columnIndices)
-  };
-}
-
-function checkRowIndices(matrix, rowIndices) {
-  if (typeof rowIndices !== 'object') {
-    throw new TypeError('unexpected type for row indices');
-  }
-
-  var rowOut = rowIndices.some((r) => {
-    return r < 0 || r >= matrix.rows;
-  });
-
-  if (rowOut) {
-    throw new RangeError('row indices are out of range');
-  }
-
-  if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);
-
-  return rowIndices;
-}
-
-function checkColumnIndices(matrix, columnIndices) {
-  if (typeof columnIndices !== 'object') {
-    throw new TypeError('unexpected type for column indices');
-  }
-
-  var columnOut = columnIndices.some((c) => {
-    return c < 0 || c >= matrix.columns;
-  });
-
-  if (columnOut) {
-    throw new RangeError('column indices are out of range');
-  }
-  if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);
-
-  return columnIndices;
-}
-
-function checkRange(matrix, startRow, endRow, startColumn, endColumn) {
-  if (arguments.length !== 5) {
-    throw new RangeError('expected 4 arguments');
-  }
-  checkNumber('startRow', startRow);
-  checkNumber('endRow', endRow);
-  checkNumber('startColumn', startColumn);
-  checkNumber('endColumn', endColumn);
-  if (
-    startRow > endRow ||
-    startColumn > endColumn ||
-    startRow < 0 ||
-    startRow >= matrix.rows ||
-    endRow < 0 ||
-    endRow >= matrix.rows ||
-    startColumn < 0 ||
-    startColumn >= matrix.columns ||
-    endColumn < 0 ||
-    endColumn >= matrix.columns
-  ) {
-    throw new RangeError('Submatrix indices are out of range');
-  }
-}
-
-function getRange(from, to) {
-  var arr = new Array(to - from + 1);
-  for (var i = 0; i < arr.length; i++) {
-    arr[i] = from + i;
-  }
-  return arr;
-}
-
-function sumByRow(matrix) {
-  var sum = matrix_Matrix.zeros(matrix.rows, 1);
-  for (var i = 0; i < matrix.rows; ++i) {
-    for (var j = 0; j < matrix.columns; ++j) {
-      sum.set(i, 0, sum.get(i, 0) + matrix.get(i, j));
-    }
-  }
-  return sum;
-}
-
-function sumByColumn(matrix) {
-  var sum = matrix_Matrix.zeros(1, matrix.columns);
-  for (var i = 0; i < matrix.rows; ++i) {
-    for (var j = 0; j < matrix.columns; ++j) {
-      sum.set(0, j, sum.get(0, j) + matrix.get(i, j));
-    }
-  }
-  return sum;
-}
-
-function sumAll(matrix) {
-  var v = 0;
-  for (var i = 0; i < matrix.rows; i++) {
-    for (var j = 0; j < matrix.columns; j++) {
-      v += matrix.get(i, j);
-    }
-  }
-  return v;
-}
-
-function checkNumber(name, value) {
-  if (typeof value !== 'number') {
-    throw new TypeError(`${name} must be a number`);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/base.js
-
-
-
-class base_BaseView extends AbstractMatrix() {
-  constructor(matrix, rows, columns) {
-    super();
-    this.matrix = matrix;
-    this.rows = rows;
-    this.columns = columns;
-  }
-
-  static get [Symbol.species]() {
-    return matrix_Matrix;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/transpose.js
-
-
-class transpose_MatrixTransposeView extends base_BaseView {
-  constructor(matrix) {
-    super(matrix, matrix.columns, matrix.rows);
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(columnIndex, rowIndex, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(columnIndex, rowIndex);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/row.js
-
-
-class row_MatrixRowView extends base_BaseView {
-  constructor(matrix, row) {
-    super(matrix, 1, matrix.columns);
-    this.row = row;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(this.row, columnIndex, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(this.row, columnIndex);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/sub.js
-
-
-
-
-class sub_MatrixSubView extends base_BaseView {
-  constructor(matrix, startRow, endRow, startColumn, endColumn) {
-    checkRange(matrix, startRow, endRow, startColumn, endColumn);
-    super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);
-    this.startRow = startRow;
-    this.startColumn = startColumn;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(
-      this.startRow + rowIndex,
-      this.startColumn + columnIndex,
-      value
-    );
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(
-      this.startRow + rowIndex,
-      this.startColumn + columnIndex
-    );
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/selection.js
-
-
-
-
-class selection_MatrixSelectionView extends base_BaseView {
-  constructor(matrix, rowIndices, columnIndices) {
-    var indices = checkIndices(matrix, rowIndices, columnIndices);
-    super(matrix, indices.row.length, indices.column.length);
-    this.rowIndices = indices.row;
-    this.columnIndices = indices.column;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(
-      this.rowIndices[rowIndex],
-      this.columnIndices[columnIndex],
-      value
-    );
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(
-      this.rowIndices[rowIndex],
-      this.columnIndices[columnIndex]
-    );
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/rowSelection.js
-
-
-
-
-class rowSelection_MatrixRowSelectionView extends base_BaseView {
-  constructor(matrix, rowIndices) {
-    rowIndices = checkRowIndices(matrix, rowIndices);
-    super(matrix, rowIndices.length, matrix.columns);
-    this.rowIndices = rowIndices;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(this.rowIndices[rowIndex], columnIndex);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/columnSelection.js
-
-
-
-
-class columnSelection_MatrixColumnSelectionView extends base_BaseView {
-  constructor(matrix, columnIndices) {
-    columnIndices = checkColumnIndices(matrix, columnIndices);
-    super(matrix, matrix.rows, columnIndices.length);
-    this.columnIndices = columnIndices;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/column.js
-
-
-class column_MatrixColumnView extends base_BaseView {
-  constructor(matrix, column) {
-    super(matrix, matrix.rows, 1);
-    this.column = column;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(rowIndex, this.column, value);
-    return this;
-  }
-
-  get(rowIndex) {
-    return this.matrix.get(rowIndex, this.column);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/flipRow.js
-
-
-class flipRow_MatrixFlipRowView extends base_BaseView {
-  constructor(matrix) {
-    super(matrix, matrix.rows, matrix.columns);
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(this.rows - rowIndex - 1, columnIndex);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/flipColumn.js
-
-
-class flipColumn_MatrixFlipColumnView extends base_BaseView {
-  constructor(matrix) {
-    super(matrix, matrix.rows, matrix.columns);
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(rowIndex, this.columns - columnIndex - 1);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/abstractMatrix.js
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-function AbstractMatrix(superCtor) {
-  if (superCtor === undefined) superCtor = Object;
-
-  /**
-   * Real matrix
-   * @class Matrix
-   * @param {number|Array|Matrix} nRows - Number of rows of the new matrix,
-   * 2D array containing the data or Matrix instance to clone
-   * @param {number} [nColumns] - Number of columns of the new matrix
-   */
-  class Matrix extends superCtor {
-    static get [Symbol.species]() {
-      return this;
-    }
-
-    /**
-     * Constructs a Matrix with the chosen dimensions from a 1D array
-     * @param {number} newRows - Number of rows
-     * @param {number} newColumns - Number of columns
-     * @param {Array} newData - A 1D array containing data for the matrix
-     * @return {Matrix} - The new matrix
-     */
-    static from1DArray(newRows, newColumns, newData) {
-      var length = newRows * newColumns;
-      if (length !== newData.length) {
-        throw new RangeError('Data length does not match given dimensions');
-      }
-      var newMatrix = new this(newRows, newColumns);
-      for (var row = 0; row < newRows; row++) {
-        for (var column = 0; column < newColumns; column++) {
-          newMatrix.set(row, column, newData[row * newColumns + column]);
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Creates a row vector, a matrix with only one row.
-         * @param {Array} newData - A 1D array containing data for the vector
-         * @return {Matrix} - The new matrix
-         */
-    static rowVector(newData) {
-      var vector = new this(1, newData.length);
-      for (var i = 0; i < newData.length; i++) {
-        vector.set(0, i, newData[i]);
-      }
-      return vector;
-    }
-
-    /**
-         * Creates a column vector, a matrix with only one column.
-         * @param {Array} newData - A 1D array containing data for the vector
-         * @return {Matrix} - The new matrix
-         */
-    static columnVector(newData) {
-      var vector = new this(newData.length, 1);
-      for (var i = 0; i < newData.length; i++) {
-        vector.set(i, 0, newData[i]);
-      }
-      return vector;
-    }
-
-    /**
-         * Creates an empty matrix with the given dimensions. Values will be undefined. Same as using new Matrix(rows, columns).
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @return {Matrix} - The new matrix
-         */
-    static empty(rows, columns) {
-      return new this(rows, columns);
-    }
-
-    /**
-         * Creates a matrix with the given dimensions. Values will be set to zero.
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @return {Matrix} - The new matrix
-         */
-    static zeros(rows, columns) {
-      return this.empty(rows, columns).fill(0);
-    }
-
-    /**
-         * Creates a matrix with the given dimensions. Values will be set to one.
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @return {Matrix} - The new matrix
-         */
-    static ones(rows, columns) {
-      return this.empty(rows, columns).fill(1);
-    }
-
-    /**
-         * Creates a matrix with the given dimensions. Values will be randomly set.
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @param {function} [rng=Math.random] - Random number generator
-         * @return {Matrix} The new matrix
-         */
-    static rand(rows, columns, rng) {
-      if (rng === undefined) rng = Math.random;
-      var matrix = this.empty(rows, columns);
-      for (var i = 0; i < rows; i++) {
-        for (var j = 0; j < columns; j++) {
-          matrix.set(i, j, rng());
-        }
-      }
-      return matrix;
-    }
-
-    /**
-         * Creates a matrix with the given dimensions. Values will be random integers.
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @param {number} [maxValue=1000] - Maximum value
-         * @param {function} [rng=Math.random] - Random number generator
-         * @return {Matrix} The new matrix
-         */
-    static randInt(rows, columns, maxValue, rng) {
-      if (maxValue === undefined) maxValue = 1000;
-      if (rng === undefined) rng = Math.random;
-      var matrix = this.empty(rows, columns);
-      for (var i = 0; i < rows; i++) {
-        for (var j = 0; j < columns; j++) {
-          var value = Math.floor(rng() * maxValue);
-          matrix.set(i, j, value);
-        }
-      }
-      return matrix;
-    }
-
-    /**
-         * Creates an identity matrix with the given dimension. Values of the diagonal will be 1 and others will be 0.
-         * @param {number} rows - Number of rows
-         * @param {number} [columns=rows] - Number of columns
-         * @param {number} [value=1] - Value to fill the diagonal with
-         * @return {Matrix} - The new identity matrix
-         */
-    static eye(rows, columns, value) {
-      if (columns === undefined) columns = rows;
-      if (value === undefined) value = 1;
-      var min = Math.min(rows, columns);
-      var matrix = this.zeros(rows, columns);
-      for (var i = 0; i < min; i++) {
-        matrix.set(i, i, value);
-      }
-      return matrix;
-    }
-
-    /**
-         * Creates a diagonal matrix based on the given array.
-         * @param {Array} data - Array containing the data for the diagonal
-         * @param {number} [rows] - Number of rows (Default: data.length)
-         * @param {number} [columns] - Number of columns (Default: rows)
-         * @return {Matrix} - The new diagonal matrix
-         */
-    static diag(data, rows, columns) {
-      var l = data.length;
-      if (rows === undefined) rows = l;
-      if (columns === undefined) columns = rows;
-      var min = Math.min(l, rows, columns);
-      var matrix = this.zeros(rows, columns);
-      for (var i = 0; i < min; i++) {
-        matrix.set(i, i, data[i]);
-      }
-      return matrix;
-    }
-
-    /**
-         * Returns a matrix whose elements are the minimum between matrix1 and matrix2
-         * @param {Matrix} matrix1
-         * @param {Matrix} matrix2
-         * @return {Matrix}
-         */
-    static min(matrix1, matrix2) {
-      matrix1 = this.checkMatrix(matrix1);
-      matrix2 = this.checkMatrix(matrix2);
-      var rows = matrix1.rows;
-      var columns = matrix1.columns;
-      var result = new this(rows, columns);
-      for (var i = 0; i < rows; i++) {
-        for (var j = 0; j < columns; j++) {
-          result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));
-        }
-      }
-      return result;
-    }
-
-    /**
-         * Returns a matrix whose elements are the maximum between matrix1 and matrix2
-         * @param {Matrix} matrix1
-         * @param {Matrix} matrix2
-         * @return {Matrix}
-         */
-    static max(matrix1, matrix2) {
-      matrix1 = this.checkMatrix(matrix1);
-      matrix2 = this.checkMatrix(matrix2);
-      var rows = matrix1.rows;
-      var columns = matrix1.columns;
-      var result = new this(rows, columns);
-      for (var i = 0; i < rows; i++) {
-        for (var j = 0; j < columns; j++) {
-          result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));
-        }
-      }
-      return result;
-    }
-
-    /**
-         * Check that the provided value is a Matrix and tries to instantiate one if not
-         * @param {*} value - The value to check
-         * @return {Matrix}
-         */
-    static checkMatrix(value) {
-      return Matrix.isMatrix(value) ? value : new this(value);
-    }
-
-    /**
-         * Returns true if the argument is a Matrix, false otherwise
-         * @param {*} value - The value to check
-         * @return {boolean}
-         */
-    static isMatrix(value) {
-      return (value != null) && (value.klass === 'Matrix');
-    }
-
-    /**
-         * @prop {number} size - The number of elements in the matrix.
-         */
-    get size() {
-      return this.rows * this.columns;
-    }
-
-    /**
-         * Applies a callback for each element of the matrix. The function is called in the matrix (this) context.
-         * @param {function} callback - Function that will be called with two parameters : i (row) and j (column)
-         * @return {Matrix} this
-         */
-    apply(callback) {
-      if (typeof callback !== 'function') {
-        throw new TypeError('callback must be a function');
-      }
-      var ii = this.rows;
-      var jj = this.columns;
-      for (var i = 0; i < ii; i++) {
-        for (var j = 0; j < jj; j++) {
-          callback.call(this, i, j);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Returns a new 1D array filled row by row with the matrix values
-         * @return {Array}
-         */
-    to1DArray() {
-      var array = new Array(this.size);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          array[i * this.columns + j] = this.get(i, j);
-        }
-      }
-      return array;
-    }
-
-    /**
-         * Returns a 2D array containing a copy of the data
-         * @return {Array}
-         */
-    to2DArray() {
-      var copy = new Array(this.rows);
-      for (var i = 0; i < this.rows; i++) {
-        copy[i] = new Array(this.columns);
-        for (var j = 0; j < this.columns; j++) {
-          copy[i][j] = this.get(i, j);
-        }
-      }
-      return copy;
-    }
-
-    /**
-         * @return {boolean} true if the matrix has one row
-         */
-    isRowVector() {
-      return this.rows === 1;
-    }
-
-    /**
-         * @return {boolean} true if the matrix has one column
-         */
-    isColumnVector() {
-      return this.columns === 1;
-    }
-
-    /**
-         * @return {boolean} true if the matrix has one row or one column
-         */
-    isVector() {
-      return (this.rows === 1) || (this.columns === 1);
-    }
-
-    /**
-         * @return {boolean} true if the matrix has the same number of rows and columns
-         */
-    isSquare() {
-      return this.rows === this.columns;
-    }
-
-    /**
-         * @return {boolean} true if the matrix is square and has the same values on both sides of the diagonal
-         */
-    isSymmetric() {
-      if (this.isSquare()) {
-        for (var i = 0; i < this.rows; i++) {
-          for (var j = 0; j <= i; j++) {
-            if (this.get(i, j) !== this.get(j, i)) {
-              return false;
-            }
-          }
-        }
-        return true;
-      }
-      return false;
-    }
-
-    /**
-         * Sets a given element of the matrix. mat.set(3,4,1) is equivalent to mat[3][4]=1
-         * @abstract
-         * @param {number} rowIndex - Index of the row
-         * @param {number} columnIndex - Index of the column
-         * @param {number} value - The new value for the element
-         * @return {Matrix} this
-         */
-    set(rowIndex, columnIndex, value) { // eslint-disable-line no-unused-vars
-      throw new Error('set method is unimplemented');
-    }
-
-    /**
-         * Returns the given element of the matrix. mat.get(3,4) is equivalent to matrix[3][4]
-         * @abstract
-         * @param {number} rowIndex - Index of the row
-         * @param {number} columnIndex - Index of the column
-         * @return {number}
-         */
-    get(rowIndex, columnIndex) { // eslint-disable-line no-unused-vars
-      throw new Error('get method is unimplemented');
-    }
-
-    /**
-         * Creates a new matrix that is a repetition of the current matrix. New matrix has rowRep times the number of
-         * rows of the matrix, and colRep times the number of columns of the matrix
-         * @param {number} rowRep - Number of times the rows should be repeated
-         * @param {number} colRep - Number of times the columns should be re
-         * @return {Matrix}
-         * @example
-         * var matrix = new Matrix([[1,2]]);
-         * matrix.repeat(2); // [[1,2],[1,2]]
-         */
-    repeat(rowRep, colRep) {
-      rowRep = rowRep || 1;
-      colRep = colRep || 1;
-      var matrix = new this.constructor[Symbol.species](this.rows * rowRep, this.columns * colRep);
-      for (var i = 0; i < rowRep; i++) {
-        for (var j = 0; j < colRep; j++) {
-          matrix.setSubMatrix(this, this.rows * i, this.columns * j);
-        }
-      }
-      return matrix;
-    }
-
-    /**
-         * Fills the matrix with a given value. All elements will be set to this value.
-         * @param {number} value - New value
-         * @return {Matrix} this
-         */
-    fill(value) {
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, value);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Negates the matrix. All elements will be multiplied by (-1)
-         * @return {Matrix} this
-         */
-    neg() {
-      return this.mulS(-1);
-    }
-
-    /**
-         * Returns a new array from the given row index
-         * @param {number} index - Row index
-         * @return {Array}
-         */
-    getRow(index) {
-      checkRowIndex(this, index);
-      var row = new Array(this.columns);
-      for (var i = 0; i < this.columns; i++) {
-        row[i] = this.get(index, i);
-      }
-      return row;
-    }
-
-    /**
-         * Returns a new row vector from the given row index
-         * @param {number} index - Row index
-         * @return {Matrix}
-         */
-    getRowVector(index) {
-      return this.constructor.rowVector(this.getRow(index));
-    }
-
-    /**
-         * Sets a row at the given index
-         * @param {number} index - Row index
-         * @param {Array|Matrix} array - Array or vector
-         * @return {Matrix} this
-         */
-    setRow(index, array) {
-      checkRowIndex(this, index);
-      array = checkRowVector(this, array);
-      for (var i = 0; i < this.columns; i++) {
-        this.set(index, i, array[i]);
-      }
-      return this;
-    }
-
-    /**
-         * Swaps two rows
-         * @param {number} row1 - First row index
-         * @param {number} row2 - Second row index
-         * @return {Matrix} this
-         */
-    swapRows(row1, row2) {
-      checkRowIndex(this, row1);
-      checkRowIndex(this, row2);
-      for (var i = 0; i < this.columns; i++) {
-        var temp = this.get(row1, i);
-        this.set(row1, i, this.get(row2, i));
-        this.set(row2, i, temp);
-      }
-      return this;
-    }
-
-    /**
-         * Returns a new array from the given column index
-         * @param {number} index - Column index
-         * @return {Array}
-         */
-    getColumn(index) {
-      checkColumnIndex(this, index);
-      var column = new Array(this.rows);
-      for (var i = 0; i < this.rows; i++) {
-        column[i] = this.get(i, index);
-      }
-      return column;
-    }
-
-    /**
-         * Returns a new column vector from the given column index
-         * @param {number} index - Column index
-         * @return {Matrix}
-         */
-    getColumnVector(index) {
-      return this.constructor.columnVector(this.getColumn(index));
-    }
-
-    /**
-         * Sets a column at the given index
-         * @param {number} index - Column index
-         * @param {Array|Matrix} array - Array or vector
-         * @return {Matrix} this
-         */
-    setColumn(index, array) {
-      checkColumnIndex(this, index);
-      array = checkColumnVector(this, array);
-      for (var i = 0; i < this.rows; i++) {
-        this.set(i, index, array[i]);
-      }
-      return this;
-    }
-
-    /**
-         * Swaps two columns
-         * @param {number} column1 - First column index
-         * @param {number} column2 - Second column index
-         * @return {Matrix} this
-         */
-    swapColumns(column1, column2) {
-      checkColumnIndex(this, column1);
-      checkColumnIndex(this, column2);
-      for (var i = 0; i < this.rows; i++) {
-        var temp = this.get(i, column1);
-        this.set(i, column1, this.get(i, column2));
-        this.set(i, column2, temp);
-      }
-      return this;
-    }
-
-    /**
-         * Adds the values of a vector to each row
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    addRowVector(vector) {
-      vector = checkRowVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) + vector[j]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Subtracts the values of a vector from each row
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    subRowVector(vector) {
-      vector = checkRowVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) - vector[j]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Multiplies the values of a vector with each row
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    mulRowVector(vector) {
-      vector = checkRowVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) * vector[j]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Divides the values of each row by those of a vector
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    divRowVector(vector) {
-      vector = checkRowVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) / vector[j]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Adds the values of a vector to each column
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    addColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) + vector[i]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Subtracts the values of a vector from each column
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    subColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) - vector[i]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Multiplies the values of a vector with each column
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    mulColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) * vector[i]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Divides the values of each column by those of a vector
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    divColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) / vector[i]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Multiplies the values of a row with a scalar
-         * @param {number} index - Row index
-         * @param {number} value
-         * @return {Matrix} this
-         */
-    mulRow(index, value) {
-      checkRowIndex(this, index);
-      for (var i = 0; i < this.columns; i++) {
-        this.set(index, i, this.get(index, i) * value);
-      }
-      return this;
-    }
-
-    /**
-         * Multiplies the values of a column with a scalar
-         * @param {number} index - Column index
-         * @param {number} value
-         * @return {Matrix} this
-         */
-    mulColumn(index, value) {
-      checkColumnIndex(this, index);
-      for (var i = 0; i < this.rows; i++) {
-        this.set(i, index, this.get(i, index) * value);
-      }
-      return this;
-    }
-
-    /**
-         * Returns the maximum value of the matrix
-         * @return {number}
-         */
-    max() {
-      var v = this.get(0, 0);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          if (this.get(i, j) > v) {
-            v = this.get(i, j);
-          }
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the maximum value
-         * @return {Array}
-         */
-    maxIndex() {
-      var v = this.get(0, 0);
-      var idx = [0, 0];
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          if (this.get(i, j) > v) {
-            v = this.get(i, j);
-            idx[0] = i;
-            idx[1] = j;
-          }
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the minimum value of the matrix
-         * @return {number}
-         */
-    min() {
-      var v = this.get(0, 0);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          if (this.get(i, j) < v) {
-            v = this.get(i, j);
-          }
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the minimum value
-         * @return {Array}
-         */
-    minIndex() {
-      var v = this.get(0, 0);
-      var idx = [0, 0];
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          if (this.get(i, j) < v) {
-            v = this.get(i, j);
-            idx[0] = i;
-            idx[1] = j;
-          }
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the maximum value of one row
-         * @param {number} row - Row index
-         * @return {number}
-         */
-    maxRow(row) {
-      checkRowIndex(this, row);
-      var v = this.get(row, 0);
-      for (var i = 1; i < this.columns; i++) {
-        if (this.get(row, i) > v) {
-          v = this.get(row, i);
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the maximum value of one row
-         * @param {number} row - Row index
-         * @return {Array}
-         */
-    maxRowIndex(row) {
-      checkRowIndex(this, row);
-      var v = this.get(row, 0);
-      var idx = [row, 0];
-      for (var i = 1; i < this.columns; i++) {
-        if (this.get(row, i) > v) {
-          v = this.get(row, i);
-          idx[1] = i;
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the minimum value of one row
-         * @param {number} row - Row index
-         * @return {number}
-         */
-    minRow(row) {
-      checkRowIndex(this, row);
-      var v = this.get(row, 0);
-      for (var i = 1; i < this.columns; i++) {
-        if (this.get(row, i) < v) {
-          v = this.get(row, i);
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the maximum value of one row
-         * @param {number} row - Row index
-         * @return {Array}
-         */
-    minRowIndex(row) {
-      checkRowIndex(this, row);
-      var v = this.get(row, 0);
-      var idx = [row, 0];
-      for (var i = 1; i < this.columns; i++) {
-        if (this.get(row, i) < v) {
-          v = this.get(row, i);
-          idx[1] = i;
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the maximum value of one column
-         * @param {number} column - Column index
-         * @return {number}
-         */
-    maxColumn(column) {
-      checkColumnIndex(this, column);
-      var v = this.get(0, column);
-      for (var i = 1; i < this.rows; i++) {
-        if (this.get(i, column) > v) {
-          v = this.get(i, column);
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the maximum value of one column
-         * @param {number} column - Column index
-         * @return {Array}
-         */
-    maxColumnIndex(column) {
-      checkColumnIndex(this, column);
-      var v = this.get(0, column);
-      var idx = [0, column];
-      for (var i = 1; i < this.rows; i++) {
-        if (this.get(i, column) > v) {
-          v = this.get(i, column);
-          idx[0] = i;
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the minimum value of one column
-         * @param {number} column - Column index
-         * @return {number}
-         */
-    minColumn(column) {
-      checkColumnIndex(this, column);
-      var v = this.get(0, column);
-      for (var i = 1; i < this.rows; i++) {
-        if (this.get(i, column) < v) {
-          v = this.get(i, column);
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the minimum value of one column
-         * @param {number} column - Column index
-         * @return {Array}
-         */
-    minColumnIndex(column) {
-      checkColumnIndex(this, column);
-      var v = this.get(0, column);
-      var idx = [0, column];
-      for (var i = 1; i < this.rows; i++) {
-        if (this.get(i, column) < v) {
-          v = this.get(i, column);
-          idx[0] = i;
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns an array containing the diagonal values of the matrix
-         * @return {Array}
-         */
-    diag() {
-      var min = Math.min(this.rows, this.columns);
-      var diag = new Array(min);
-      for (var i = 0; i < min; i++) {
-        diag[i] = this.get(i, i);
-      }
-      return diag;
-    }
-
-    /**
-         * Returns the sum by the argument given, if no argument given,
-         * it returns the sum of all elements of the matrix.
-         * @param {string} by - sum by 'row' or 'column'.
-         * @return {Matrix|number}
-         */
-    sum(by) {
-      switch (by) {
-        case 'row':
-          return sumByRow(this);
-        case 'column':
-          return sumByColumn(this);
-        default:
-          return sumAll(this);
-      }
-    }
-
-    /**
-         * Returns the mean of all elements of the matrix
-         * @return {number}
-         */
-    mean() {
-      return this.sum() / this.size;
-    }
-
-    /**
-         * Returns the product of all elements of the matrix
-         * @return {number}
-         */
-    prod() {
-      var prod = 1;
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          prod *= this.get(i, j);
-        }
-      }
-      return prod;
-    }
-
-    /**
-         * Returns the norm of a matrix.
-         * @param {string} type - "frobenius" (default) or "max" return resp. the Frobenius norm and the max norm.
-         * @return {number}
-         */
-    norm(type = 'frobenius') {
-      var result = 0;
-      if (type === 'max') {
-        return this.max();
-      } else if (type === 'frobenius') {
-        for (var i = 0; i < this.rows; i++) {
-          for (var j = 0; j < this.columns; j++) {
-            result = result + this.get(i, j) * this.get(i, j);
-          }
-        }
-        return Math.sqrt(result);
-      } else {
-        throw new RangeError(`unknown norm type: ${type}`);
-      }
-    }
-
-    /**
-         * Computes the cumulative sum of the matrix elements (in place, row by row)
-         * @return {Matrix} this
-         */
-    cumulativeSum() {
-      var sum = 0;
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          sum += this.get(i, j);
-          this.set(i, j, sum);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Computes the dot (scalar) product between the matrix and another
-         * @param {Matrix} vector2 vector
-         * @return {number}
-         */
-    dot(vector2) {
-      if (Matrix.isMatrix(vector2)) vector2 = vector2.to1DArray();
-      var vector1 = this.to1DArray();
-      if (vector1.length !== vector2.length) {
-        throw new RangeError('vectors do not have the same size');
-      }
-      var dot = 0;
-      for (var i = 0; i < vector1.length; i++) {
-        dot += vector1[i] * vector2[i];
-      }
-      return dot;
-    }
-
-    /**
-         * Returns the matrix product between this and other
-         * @param {Matrix} other
-         * @return {Matrix}
-         */
-    mmul(other) {
-      other = this.constructor.checkMatrix(other);
-      if (this.columns !== other.rows) {
-        // eslint-disable-next-line no-console
-        console.warn('Number of columns of left matrix are not equal to number of rows of right matrix.');
-      }
-
-      var m = this.rows;
-      var n = this.columns;
-      var p = other.columns;
-
-      var result = new this.constructor[Symbol.species](m, p);
-
-      var Bcolj = new Array(n);
-      for (var j = 0; j < p; j++) {
-        for (var k = 0; k < n; k++) {
-          Bcolj[k] = other.get(k, j);
-        }
-
-        for (var i = 0; i < m; i++) {
-          var s = 0;
-          for (k = 0; k < n; k++) {
-            s += this.get(i, k) * Bcolj[k];
-          }
-
-          result.set(i, j, s);
-        }
-      }
-      return result;
-    }
-
-    strassen2x2(other) {
-      var result = new this.constructor[Symbol.species](2, 2);
-      const a11 = this.get(0, 0);
-      const b11 = other.get(0, 0);
-      const a12 = this.get(0, 1);
-      const b12 = other.get(0, 1);
-      const a21 = this.get(1, 0);
-      const b21 = other.get(1, 0);
-      const a22 = this.get(1, 1);
-      const b22 = other.get(1, 1);
-
-      // Compute intermediate values.
-      const m1 = (a11 + a22) * (b11 + b22);
-      const m2 = (a21 + a22) * b11;
-      const m3 = a11 * (b12 - b22);
-      const m4 = a22 * (b21 - b11);
-      const m5 = (a11 + a12) * b22;
-      const m6 = (a21 - a11) * (b11 + b12);
-      const m7 = (a12 - a22) * (b21 + b22);
-
-      // Combine intermediate values into the output.
-      const c00 = m1 + m4 - m5 + m7;
-      const c01 = m3 + m5;
-      const c10 = m2 + m4;
-      const c11 = m1 - m2 + m3 + m6;
-
-      result.set(0, 0, c00);
-      result.set(0, 1, c01);
-      result.set(1, 0, c10);
-      result.set(1, 1, c11);
-      return result;
-    }
-
-    strassen3x3(other) {
-      var result = new this.constructor[Symbol.species](3, 3);
-
-      const a00 = this.get(0, 0);
-      const a01 = this.get(0, 1);
-      const a02 = this.get(0, 2);
-      const a10 = this.get(1, 0);
-      const a11 = this.get(1, 1);
-      const a12 = this.get(1, 2);
-      const a20 = this.get(2, 0);
-      const a21 = this.get(2, 1);
-      const a22 = this.get(2, 2);
-
-      const b00 = other.get(0, 0);
-      const b01 = other.get(0, 1);
-      const b02 = other.get(0, 2);
-      const b10 = other.get(1, 0);
-      const b11 = other.get(1, 1);
-      const b12 = other.get(1, 2);
-      const b20 = other.get(2, 0);
-      const b21 = other.get(2, 1);
-      const b22 = other.get(2, 2);
-
-      const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;
-      const m2 = (a00 - a10) * (-b01 + b11);
-      const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);
-      const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);
-      const m5 = (a10 + a11) * (-b00 + b01);
-      const m6 = a00 * b00;
-      const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);
-      const m8 = (-a00 + a20) * (b02 - b12);
-      const m9 = (a20 + a21) * (-b00 + b02);
-      const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;
-      const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);
-      const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);
-      const m13 = (a02 - a22) * (b11 - b21);
-      const m14 = a02 * b20;
-      const m15 = (a21 + a22) * (-b20 + b21);
-      const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);
-      const m17 = (a02 - a12) * (b12 - b22);
-      const m18 = (a11 + a12) * (-b20 + b22);
-      const m19 = a01 * b10;
-      const m20 = a12 * b21;
-      const m21 = a10 * b02;
-      const m22 = a20 * b01;
-      const m23 = a22 * b22;
-
-      const c00 = m6 + m14 + m19;
-      const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;
-      const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;
-      const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;
-      const c11 = m2 + m4 + m5 + m6 + m20;
-      const c12 = m14 + m16 + m17 + m18 + m21;
-      const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;
-      const c21 = m12 + m13 + m14 + m15 + m22;
-      const c22 = m6 + m7 + m8 + m9 + m23;
-
-      result.set(0, 0, c00);
-      result.set(0, 1, c01);
-      result.set(0, 2, c02);
-      result.set(1, 0, c10);
-      result.set(1, 1, c11);
-      result.set(1, 2, c12);
-      result.set(2, 0, c20);
-      result.set(2, 1, c21);
-      result.set(2, 2, c22);
-      return result;
-    }
-
-    /**
-         * Returns the matrix product between x and y. More efficient than mmul(other) only when we multiply squared matrix and when the size of the matrix is > 1000.
-         * @param {Matrix} y
-         * @return {Matrix}
-         */
-    mmulStrassen(y) {
-      var x = this.clone();
-      var r1 = x.rows;
-      var c1 = x.columns;
-      var r2 = y.rows;
-      var c2 = y.columns;
-      if (c1 !== r2) {
-        // eslint-disable-next-line no-console
-        console.warn(`Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`);
-      }
-
-      // Put a matrix into the top left of a matrix of zeros.
-      // `rows` and `cols` are the dimensions of the output matrix.
-      function embed(mat, rows, cols) {
-        var r = mat.rows;
-        var c = mat.columns;
-        if ((r === rows) && (c === cols)) {
-          return mat;
-        } else {
-          var resultat = Matrix.zeros(rows, cols);
-          resultat = resultat.setSubMatrix(mat, 0, 0);
-          return resultat;
-        }
-      }
-
-
-      // Make sure both matrices are the same size.
-      // This is exclusively for simplicity:
-      // this algorithm can be implemented with matrices of different sizes.
-
-      var r = Math.max(r1, r2);
-      var c = Math.max(c1, c2);
-      x = embed(x, r, c);
-      y = embed(y, r, c);
-
-      // Our recursive multiplication function.
-      function blockMult(a, b, rows, cols) {
-        // For small matrices, resort to naive multiplication.
-        if (rows <= 512 || cols <= 512) {
-          return a.mmul(b); // a is equivalent to this
-        }
-
-        // Apply dynamic padding.
-        if ((rows % 2 === 1) && (cols % 2 === 1)) {
-          a = embed(a, rows + 1, cols + 1);
-          b = embed(b, rows + 1, cols + 1);
-        } else if (rows % 2 === 1) {
-          a = embed(a, rows + 1, cols);
-          b = embed(b, rows + 1, cols);
-        } else if (cols % 2 === 1) {
-          a = embed(a, rows, cols + 1);
-          b = embed(b, rows, cols + 1);
-        }
-
-        var halfRows = parseInt(a.rows / 2, 10);
-        var halfCols = parseInt(a.columns / 2, 10);
-        // Subdivide input matrices.
-        var a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);
-        var b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);
-
-        var a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);
-        var b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);
-
-        var a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);
-        var b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);
-
-        var a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);
-        var b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);
-
-        // Compute intermediate values.
-        var m1 = blockMult(Matrix.add(a11, a22), Matrix.add(b11, b22), halfRows, halfCols);
-        var m2 = blockMult(Matrix.add(a21, a22), b11, halfRows, halfCols);
-        var m3 = blockMult(a11, Matrix.sub(b12, b22), halfRows, halfCols);
-        var m4 = blockMult(a22, Matrix.sub(b21, b11), halfRows, halfCols);
-        var m5 = blockMult(Matrix.add(a11, a12), b22, halfRows, halfCols);
-        var m6 = blockMult(Matrix.sub(a21, a11), Matrix.add(b11, b12), halfRows, halfCols);
-        var m7 = blockMult(Matrix.sub(a12, a22), Matrix.add(b21, b22), halfRows, halfCols);
-
-        // Combine intermediate values into the output.
-        var c11 = Matrix.add(m1, m4);
-        c11.sub(m5);
-        c11.add(m7);
-        var c12 = Matrix.add(m3, m5);
-        var c21 = Matrix.add(m2, m4);
-        var c22 = Matrix.sub(m1, m2);
-        c22.add(m3);
-        c22.add(m6);
-
-        // Crop output to the desired size (undo dynamic padding).
-        var resultat = Matrix.zeros(2 * c11.rows, 2 * c11.columns);
-        resultat = resultat.setSubMatrix(c11, 0, 0);
-        resultat = resultat.setSubMatrix(c12, c11.rows, 0);
-        resultat = resultat.setSubMatrix(c21, 0, c11.columns);
-        resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);
-        return resultat.subMatrix(0, rows - 1, 0, cols - 1);
-      }
-      return blockMult(x, y, r, c);
-    }
-
-    /**
-         * Returns a row-by-row scaled matrix
-         * @param {number} [min=0] - Minimum scaled value
-         * @param {number} [max=1] - Maximum scaled value
-         * @return {Matrix} - The scaled matrix
-         */
-    scaleRows(min, max) {
-      min = min === undefined ? 0 : min;
-      max = max === undefined ? 1 : max;
-      if (min >= max) {
-        throw new RangeError('min should be strictly smaller than max');
-      }
-      var newMatrix = this.constructor.empty(this.rows, this.columns);
-      for (var i = 0; i < this.rows; i++) {
-        var scaled = ml_array_rescale_lib_es6(this.getRow(i), { min, max });
-        newMatrix.setRow(i, scaled);
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Returns a new column-by-column scaled matrix
-         * @param {number} [min=0] - Minimum scaled value
-         * @param {number} [max=1] - Maximum scaled value
-         * @return {Matrix} - The new scaled matrix
-         * @example
-         * var matrix = new Matrix([[1,2],[-1,0]]);
-         * var scaledMatrix = matrix.scaleColumns(); // [[1,1],[0,0]]
-         */
-    scaleColumns(min, max) {
-      min = min === undefined ? 0 : min;
-      max = max === undefined ? 1 : max;
-      if (min >= max) {
-        throw new RangeError('min should be strictly smaller than max');
-      }
-      var newMatrix = this.constructor.empty(this.rows, this.columns);
-      for (var i = 0; i < this.columns; i++) {
-        var scaled = ml_array_rescale_lib_es6(this.getColumn(i), {
-          min: min,
-          max: max
-        });
-        newMatrix.setColumn(i, scaled);
-      }
-      return newMatrix;
-    }
-
-
-    /**
-         * Returns the Kronecker product (also known as tensor product) between this and other
-         * See https://en.wikipedia.org/wiki/Kronecker_product
-         * @param {Matrix} other
-         * @return {Matrix}
-         */
-    kroneckerProduct(other) {
-      other = this.constructor.checkMatrix(other);
-
-      var m = this.rows;
-      var n = this.columns;
-      var p = other.rows;
-      var q = other.columns;
-
-      var result = new this.constructor[Symbol.species](m * p, n * q);
-      for (var i = 0; i < m; i++) {
-        for (var j = 0; j < n; j++) {
-          for (var k = 0; k < p; k++) {
-            for (var l = 0; l < q; l++) {
-              result[p * i + k][q * j + l] = this.get(i, j) * other.get(k, l);
-            }
-          }
-        }
-      }
-      return result;
-    }
-
-    /**
-         * Transposes the matrix and returns a new one containing the result
-         * @return {Matrix}
-         */
-    transpose() {
-      var result = new this.constructor[Symbol.species](this.columns, this.rows);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          result.set(j, i, this.get(i, j));
-        }
-      }
-      return result;
-    }
-
-    /**
-         * Sorts the rows (in place)
-         * @param {function} compareFunction - usual Array.prototype.sort comparison function
-         * @return {Matrix} this
-         */
-    sortRows(compareFunction) {
-      if (compareFunction === undefined) compareFunction = compareNumbers;
-      for (var i = 0; i < this.rows; i++) {
-        this.setRow(i, this.getRow(i).sort(compareFunction));
-      }
-      return this;
-    }
-
-    /**
-         * Sorts the columns (in place)
-         * @param {function} compareFunction - usual Array.prototype.sort comparison function
-         * @return {Matrix} this
-         */
-    sortColumns(compareFunction) {
-      if (compareFunction === undefined) compareFunction = compareNumbers;
-      for (var i = 0; i < this.columns; i++) {
-        this.setColumn(i, this.getColumn(i).sort(compareFunction));
-      }
-      return this;
-    }
-
-    /**
-         * Returns a subset of the matrix
-         * @param {number} startRow - First row index
-         * @param {number} endRow - Last row index
-         * @param {number} startColumn - First column index
-         * @param {number} endColumn - Last column index
-         * @return {Matrix}
-         */
-    subMatrix(startRow, endRow, startColumn, endColumn) {
-      checkRange(this, startRow, endRow, startColumn, endColumn);
-      var newMatrix = new this.constructor[Symbol.species](endRow - startRow + 1, endColumn - startColumn + 1);
-      for (var i = startRow; i <= endRow; i++) {
-        for (var j = startColumn; j <= endColumn; j++) {
-          newMatrix[i - startRow][j - startColumn] = this.get(i, j);
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Returns a subset of the matrix based on an array of row indices
-         * @param {Array} indices - Array containing the row indices
-         * @param {number} [startColumn = 0] - First column index
-         * @param {number} [endColumn = this.columns-1] - Last column index
-         * @return {Matrix}
-         */
-    subMatrixRow(indices, startColumn, endColumn) {
-      if (startColumn === undefined) startColumn = 0;
-      if (endColumn === undefined) endColumn = this.columns - 1;
-      if ((startColumn > endColumn) || (startColumn < 0) || (startColumn >= this.columns) || (endColumn < 0) || (endColumn >= this.columns)) {
-        throw new RangeError('Argument out of range');
-      }
-
-      var newMatrix = new this.constructor[Symbol.species](indices.length, endColumn - startColumn + 1);
-      for (var i = 0; i < indices.length; i++) {
-        for (var j = startColumn; j <= endColumn; j++) {
-          if (indices[i] < 0 || indices[i] >= this.rows) {
-            throw new RangeError(`Row index out of range: ${indices[i]}`);
-          }
-          newMatrix.set(i, j - startColumn, this.get(indices[i], j));
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Returns a subset of the matrix based on an array of column indices
-         * @param {Array} indices - Array containing the column indices
-         * @param {number} [startRow = 0] - First row index
-         * @param {number} [endRow = this.rows-1] - Last row index
-         * @return {Matrix}
-         */
-    subMatrixColumn(indices, startRow, endRow) {
-      if (startRow === undefined) startRow = 0;
-      if (endRow === undefined) endRow = this.rows - 1;
-      if ((startRow > endRow) || (startRow < 0) || (startRow >= this.rows) || (endRow < 0) || (endRow >= this.rows)) {
-        throw new RangeError('Argument out of range');
-      }
-
-      var newMatrix = new this.constructor[Symbol.species](endRow - startRow + 1, indices.length);
-      for (var i = 0; i < indices.length; i++) {
-        for (var j = startRow; j <= endRow; j++) {
-          if (indices[i] < 0 || indices[i] >= this.columns) {
-            throw new RangeError(`Column index out of range: ${indices[i]}`);
-          }
-          newMatrix.set(j - startRow, i, this.get(j, indices[i]));
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Set a part of the matrix to the given sub-matrix
-         * @param {Matrix|Array< Array >} matrix - The source matrix from which to extract values.
-         * @param {number} startRow - The index of the first row to set
-         * @param {number} startColumn - The index of the first column to set
-         * @return {Matrix}
-         */
-    setSubMatrix(matrix, startRow, startColumn) {
-      matrix = this.constructor.checkMatrix(matrix);
-      var endRow = startRow + matrix.rows - 1;
-      var endColumn = startColumn + matrix.columns - 1;
-      checkRange(this, startRow, endRow, startColumn, endColumn);
-      for (var i = 0; i < matrix.rows; i++) {
-        for (var j = 0; j < matrix.columns; j++) {
-          this[startRow + i][startColumn + j] = matrix.get(i, j);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Return a new matrix based on a selection of rows and columns
-         * @param {Array<number>} rowIndices - The row indices to select. Order matters and an index can be more than once.
-         * @param {Array<number>} columnIndices - The column indices to select. Order matters and an index can be use more than once.
-         * @return {Matrix} The new matrix
-         */
-    selection(rowIndices, columnIndices) {
-      var indices = checkIndices(this, rowIndices, columnIndices);
-      var newMatrix = new this.constructor[Symbol.species](rowIndices.length, columnIndices.length);
-      for (var i = 0; i < indices.row.length; i++) {
-        var rowIndex = indices.row[i];
-        for (var j = 0; j < indices.column.length; j++) {
-          var columnIndex = indices.column[j];
-          newMatrix[i][j] = this.get(rowIndex, columnIndex);
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Returns the trace of the matrix (sum of the diagonal elements)
-         * @return {number}
-         */
-    trace() {
-      var min = Math.min(this.rows, this.columns);
-      var trace = 0;
-      for (var i = 0; i < min; i++) {
-        trace += this.get(i, i);
-      }
-      return trace;
-    }
-
-    /*
-         Matrix views
-         */
-
-    /**
-         * Returns a view of the transposition of the matrix
-         * @return {MatrixTransposeView}
-         */
-    transposeView() {
-      return new transpose_MatrixTransposeView(this);
-    }
-
-    /**
-         * Returns a view of the row vector with the given index
-         * @param {number} row - row index of the vector
-         * @return {MatrixRowView}
-         */
-    rowView(row) {
-      checkRowIndex(this, row);
-      return new row_MatrixRowView(this, row);
-    }
-
-    /**
-         * Returns a view of the column vector with the given index
-         * @param {number} column - column index of the vector
-         * @return {MatrixColumnView}
-         */
-    columnView(column) {
-      checkColumnIndex(this, column);
-      return new column_MatrixColumnView(this, column);
-    }
-
-    /**
-         * Returns a view of the matrix flipped in the row axis
-         * @return {MatrixFlipRowView}
-         */
-    flipRowView() {
-      return new flipRow_MatrixFlipRowView(this);
-    }
-
-    /**
-         * Returns a view of the matrix flipped in the column axis
-         * @return {MatrixFlipColumnView}
-         */
-    flipColumnView() {
-      return new flipColumn_MatrixFlipColumnView(this);
-    }
-
-    /**
-         * Returns a view of a submatrix giving the index boundaries
-         * @param {number} startRow - first row index of the submatrix
-         * @param {number} endRow - last row index of the submatrix
-         * @param {number} startColumn - first column index of the submatrix
-         * @param {number} endColumn - last column index of the submatrix
-         * @return {MatrixSubView}
-         */
-    subMatrixView(startRow, endRow, startColumn, endColumn) {
-      return new sub_MatrixSubView(this, startRow, endRow, startColumn, endColumn);
-    }
-
-    /**
-         * Returns a view of the cross of the row indices and the column indices
-         * @example
-         * // resulting vector is [[2], [2]]
-         * var matrix = new Matrix([[1,2,3], [4,5,6]]).selectionView([0, 0], [1])
-         * @param {Array<number>} rowIndices
-         * @param {Array<number>} columnIndices
-         * @return {MatrixSelectionView}
-         */
-    selectionView(rowIndices, columnIndices) {
-      return new selection_MatrixSelectionView(this, rowIndices, columnIndices);
-    }
-
-    /**
-         * Returns a view of the row indices
-         * @example
-         * // resulting vector is [[1,2,3], [1,2,3]]
-         * var matrix = new Matrix([[1,2,3], [4,5,6]]).rowSelectionView([0, 0])
-         * @param {Array<number>} rowIndices
-         * @return {MatrixRowSelectionView}
-         */
-    rowSelectionView(rowIndices) {
-      return new rowSelection_MatrixRowSelectionView(this, rowIndices);
-    }
-
-    /**
-         * Returns a view of the column indices
-         * @example
-         * // resulting vector is [[2, 2], [5, 5]]
-         * var matrix = new Matrix([[1,2,3], [4,5,6]]).columnSelectionView([1, 1])
-         * @param {Array<number>} columnIndices
-         * @return {MatrixColumnSelectionView}
-         */
-    columnSelectionView(columnIndices) {
-      return new columnSelection_MatrixColumnSelectionView(this, columnIndices);
-    }
-
-
-    /**
-        * Calculates and returns the determinant of a matrix as a Number
-        * @example
-        *   new Matrix([[1,2,3], [4,5,6]]).det()
-        * @return {number}
-        */
-    det() {
-      if (this.isSquare()) {
-        var a, b, c, d;
-        if (this.columns === 2) {
-          // 2 x 2 matrix
-          a = this.get(0, 0);
-          b = this.get(0, 1);
-          c = this.get(1, 0);
-          d = this.get(1, 1);
-
-          return a * d - (b * c);
-        } else if (this.columns === 3) {
-          // 3 x 3 matrix
-          var subMatrix0, subMatrix1, subMatrix2;
-          subMatrix0 = this.selectionView([1, 2], [1, 2]);
-          subMatrix1 = this.selectionView([1, 2], [0, 2]);
-          subMatrix2 = this.selectionView([1, 2], [0, 1]);
-          a = this.get(0, 0);
-          b = this.get(0, 1);
-          c = this.get(0, 2);
-
-          return a * subMatrix0.det() - b * subMatrix1.det() + c * subMatrix2.det();
-        } else {
-          // general purpose determinant using the LU decomposition
-          return new lu_LuDecomposition(this).determinant;
-        }
-      } else {
-        throw Error('Determinant can only be calculated for a square matrix.');
-      }
-    }
-
-    /**
-         * Returns inverse of a matrix if it exists or the pseudoinverse
-         * @param {number} threshold - threshold for taking inverse of singular values (default = 1e-15)
-         * @return {Matrix} the (pseudo)inverted matrix.
-         */
-    pseudoInverse(threshold) {
-      if (threshold === undefined) threshold = Number.EPSILON;
-      var svdSolution = new svd_SingularValueDecomposition(this, { autoTranspose: true });
-
-      var U = svdSolution.leftSingularVectors;
-      var V = svdSolution.rightSingularVectors;
-      var s = svdSolution.diagonal;
-
-      for (var i = 0; i < s.length; i++) {
-        if (Math.abs(s[i]) > threshold) {
-          s[i] = 1.0 / s[i];
-        } else {
-          s[i] = 0.0;
-        }
-      }
-
-      // convert list to diagonal
-      s = this.constructor[Symbol.species].diag(s);
-      return V.mmul(s.mmul(U.transposeView()));
-    }
-
-    /**
-         * Creates an exact and independent copy of the matrix
-         * @return {Matrix}
-         */
-    clone() {
-      var newMatrix = new this.constructor[Symbol.species](this.rows, this.columns);
-      for (var row = 0; row < this.rows; row++) {
-        for (var column = 0; column < this.columns; column++) {
-          newMatrix.set(row, column, this.get(row, column));
-        }
-      }
-      return newMatrix;
-    }
-  }
-
-  Matrix.prototype.klass = 'Matrix';
-
-  function compareNumbers(a, b) {
-    return a - b;
-  }
-
-  /*
-     Synonyms
-     */
-
-  Matrix.random = Matrix.rand;
-  Matrix.diagonal = Matrix.diag;
-  Matrix.prototype.diagonal = Matrix.prototype.diag;
-  Matrix.identity = Matrix.eye;
-  Matrix.prototype.negate = Matrix.prototype.neg;
-  Matrix.prototype.tensorProduct = Matrix.prototype.kroneckerProduct;
-  Matrix.prototype.determinant = Matrix.prototype.det;
-
-  /*
-     Add dynamically instance and static methods for mathematical operations
-     */
-
-  var inplaceOperator = `
-(function %name%(value) {
-    if (typeof value === 'number') return this.%name%S(value);
-    return this.%name%M(value);
-})
-`;
-
-  var inplaceOperatorScalar = `
-(function %name%S(value) {
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, this.get(i, j) %op% value);
-        }
-    }
-    return this;
-})
-`;
-
-  var inplaceOperatorMatrix = `
-(function %name%M(matrix) {
-    matrix = this.constructor.checkMatrix(matrix);
-    if (this.rows !== matrix.rows ||
-        this.columns !== matrix.columns) {
-        throw new RangeError('Matrices dimensions must be equal');
-    }
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, this.get(i, j) %op% matrix.get(i, j));
-        }
-    }
-    return this;
-})
-`;
-
-  var staticOperator = `
-(function %name%(matrix, value) {
-    var newMatrix = new this[Symbol.species](matrix);
-    return newMatrix.%name%(value);
-})
-`;
-
-  var inplaceMethod = `
-(function %name%() {
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, %method%(this.get(i, j)));
-        }
-    }
-    return this;
-})
-`;
-
-  var staticMethod = `
-(function %name%(matrix) {
-    var newMatrix = new this[Symbol.species](matrix);
-    return newMatrix.%name%();
-})
-`;
-
-  var inplaceMethodWithArgs = `
-(function %name%(%args%) {
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, %method%(this.get(i, j), %args%));
-        }
-    }
-    return this;
-})
-`;
-
-  var staticMethodWithArgs = `
-(function %name%(matrix, %args%) {
-    var newMatrix = new this[Symbol.species](matrix);
-    return newMatrix.%name%(%args%);
-})
-`;
-
-
-  var inplaceMethodWithOneArgScalar = `
-(function %name%S(value) {
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, %method%(this.get(i, j), value));
-        }
-    }
-    return this;
-})
-`;
-  var inplaceMethodWithOneArgMatrix = `
-(function %name%M(matrix) {
-    matrix = this.constructor.checkMatrix(matrix);
-    if (this.rows !== matrix.rows ||
-        this.columns !== matrix.columns) {
-        throw new RangeError('Matrices dimensions must be equal');
-    }
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, %method%(this.get(i, j), matrix.get(i, j)));
-        }
-    }
-    return this;
-})
-`;
-
-  var inplaceMethodWithOneArg = `
-(function %name%(value) {
-    if (typeof value === 'number') return this.%name%S(value);
-    return this.%name%M(value);
-})
-`;
-
-  var staticMethodWithOneArg = staticMethodWithArgs;
-
-  var operators = [
-    // Arithmetic operators
-    ['+', 'add'],
-    ['-', 'sub', 'subtract'],
-    ['*', 'mul', 'multiply'],
-    ['/', 'div', 'divide'],
-    ['%', 'mod', 'modulus'],
-    // Bitwise operators
-    ['&', 'and'],
-    ['|', 'or'],
-    ['^', 'xor'],
-    ['<<', 'leftShift'],
-    ['>>', 'signPropagatingRightShift'],
-    ['>>>', 'rightShift', 'zeroFillRightShift']
-  ];
-
-  var i;
-  var eval2 = eval; // eslint-disable-line no-eval
-  for (var operator of operators) {
-    var inplaceOp = eval2(fillTemplateFunction(inplaceOperator, { name: operator[1], op: operator[0] }));
-    var inplaceOpS = eval2(fillTemplateFunction(inplaceOperatorScalar, { name: `${operator[1]}S`, op: operator[0] }));
-    var inplaceOpM = eval2(fillTemplateFunction(inplaceOperatorMatrix, { name: `${operator[1]}M`, op: operator[0] }));
-    var staticOp = eval2(fillTemplateFunction(staticOperator, { name: operator[1] }));
-    for (i = 1; i < operator.length; i++) {
-      Matrix.prototype[operator[i]] = inplaceOp;
-      Matrix.prototype[`${operator[i]}S`] = inplaceOpS;
-      Matrix.prototype[`${operator[i]}M`] = inplaceOpM;
-      Matrix[operator[i]] = staticOp;
-    }
-  }
-
-  var methods = [['~', 'not']];
-
-  [
-    'abs', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'cbrt', 'ceil',
-    'clz32', 'cos', 'cosh', 'exp', 'expm1', 'floor', 'fround', 'log', 'log1p',
-    'log10', 'log2', 'round', 'sign', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc'
-  ].forEach(function (mathMethod) {
-    methods.push([`Math.${mathMethod}`, mathMethod]);
-  });
-
-  for (var method of methods) {
-    var inplaceMeth = eval2(fillTemplateFunction(inplaceMethod, { name: method[1], method: method[0] }));
-    var staticMeth = eval2(fillTemplateFunction(staticMethod, { name: method[1] }));
-    for (i = 1; i < method.length; i++) {
-      Matrix.prototype[method[i]] = inplaceMeth;
-      Matrix[method[i]] = staticMeth;
-    }
-  }
-
-  var methodsWithArgs = [['Math.pow', 1, 'pow']];
-
-  for (var methodWithArg of methodsWithArgs) {
-    var args = 'arg0';
-    for (i = 1; i < methodWithArg[1]; i++) {
-      args += `, arg${i}`;
-    }
-    if (methodWithArg[1] !== 1) {
-      var inplaceMethWithArgs = eval2(fillTemplateFunction(inplaceMethodWithArgs, {
-        name: methodWithArg[2],
-        method: methodWithArg[0],
-        args: args
-      }));
-      var staticMethWithArgs = eval2(fillTemplateFunction(staticMethodWithArgs, { name: methodWithArg[2], args: args }));
-      for (i = 2; i < methodWithArg.length; i++) {
-        Matrix.prototype[methodWithArg[i]] = inplaceMethWithArgs;
-        Matrix[methodWithArg[i]] = staticMethWithArgs;
-      }
-    } else {
-      var tmplVar = {
-        name: methodWithArg[2],
-        args: args,
-        method: methodWithArg[0]
-      };
-      var inplaceMethod2 = eval2(fillTemplateFunction(inplaceMethodWithOneArg, tmplVar));
-      var inplaceMethodS = eval2(fillTemplateFunction(inplaceMethodWithOneArgScalar, tmplVar));
-      var inplaceMethodM = eval2(fillTemplateFunction(inplaceMethodWithOneArgMatrix, tmplVar));
-      var staticMethod2 = eval2(fillTemplateFunction(staticMethodWithOneArg, tmplVar));
-      for (i = 2; i < methodWithArg.length; i++) {
-        Matrix.prototype[methodWithArg[i]] = inplaceMethod2;
-        Matrix.prototype[`${methodWithArg[i]}M`] = inplaceMethodM;
-        Matrix.prototype[`${methodWithArg[i]}S`] = inplaceMethodS;
-        Matrix[methodWithArg[i]] = staticMethod2;
-      }
-    }
-  }
-
-  function fillTemplateFunction(template, values) {
-    for (var value in values) {
-      template = template.replace(new RegExp(`%${value}%`, 'g'), values[value]);
-    }
-    return template;
-  }
-
-  return Matrix;
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/matrix.js
-
-
-
-class matrix_Matrix extends AbstractMatrix(Array) {
-  constructor(nRows, nColumns) {
-    var i;
-    if (arguments.length === 1 && typeof nRows === 'number') {
-      return new Array(nRows);
-    }
-    if (matrix_Matrix.isMatrix(nRows)) {
-      return nRows.clone();
-    } else if (Number.isInteger(nRows) && nRows > 0) {
-      // Create an empty matrix
-      super(nRows);
-      if (Number.isInteger(nColumns) && nColumns > 0) {
-        for (i = 0; i < nRows; i++) {
-          this[i] = new Array(nColumns);
-        }
-      } else {
-        throw new TypeError('nColumns must be a positive integer');
-      }
-    } else if (Array.isArray(nRows)) {
-      // Copy the values from the 2D array
-      const matrix = nRows;
-      nRows = matrix.length;
-      nColumns = matrix[0].length;
-      if (typeof nColumns !== 'number' || nColumns === 0) {
-        throw new TypeError(
-          'Data must be a 2D array with at least one element'
-        );
-      }
-      super(nRows);
-      for (i = 0; i < nRows; i++) {
-        if (matrix[i].length !== nColumns) {
-          throw new RangeError('Inconsistent array dimensions');
-        }
-        this[i] = [].concat(matrix[i]);
-      }
-    } else {
-      throw new TypeError(
-        'First argument must be a positive number or an array'
-      );
-    }
-    this.rows = nRows;
-    this.columns = nColumns;
-    return this;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this[rowIndex][columnIndex] = value;
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this[rowIndex][columnIndex];
-  }
-
-  /**
-   * Removes a row from the given index
-   * @param {number} index - Row index
-   * @return {Matrix} this
-   */
-  removeRow(index) {
-    checkRowIndex(this, index);
-    if (this.rows === 1) {
-      throw new RangeError('A matrix cannot have less than one row');
-    }
-    this.splice(index, 1);
-    this.rows -= 1;
-    return this;
-  }
-
-  /**
-   * Adds a row at the given index
-   * @param {number} [index = this.rows] - Row index
-   * @param {Array|Matrix} array - Array or vector
-   * @return {Matrix} this
-   */
-  addRow(index, array) {
-    if (array === undefined) {
-      array = index;
-      index = this.rows;
-    }
-    checkRowIndex(this, index, true);
-    array = checkRowVector(this, array, true);
-    this.splice(index, 0, array);
-    this.rows += 1;
-    return this;
-  }
-
-  /**
-   * Removes a column from the given index
-   * @param {number} index - Column index
-   * @return {Matrix} this
-   */
-  removeColumn(index) {
-    checkColumnIndex(this, index);
-    if (this.columns === 1) {
-      throw new RangeError('A matrix cannot have less than one column');
-    }
-    for (var i = 0; i < this.rows; i++) {
-      this[i].splice(index, 1);
-    }
-    this.columns -= 1;
-    return this;
-  }
-
-  /**
-   * Adds a column at the given index
-   * @param {number} [index = this.columns] - Column index
-   * @param {Array|Matrix} array - Array or vector
-   * @return {Matrix} this
-   */
-  addColumn(index, array) {
-    if (typeof array === 'undefined') {
-      array = index;
-      index = this.columns;
-    }
-    checkColumnIndex(this, index, true);
-    array = checkColumnVector(this, array);
-    for (var i = 0; i < this.rows; i++) {
-      this[i].splice(index, 0, array[i]);
-    }
-    this.columns += 1;
-    return this;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/wrap/WrapperMatrix1D.js
-
-
-
-class WrapperMatrix1D_WrapperMatrix1D extends AbstractMatrix() {
-  /**
-   * @class WrapperMatrix1D
-   * @param {Array<number>} data
-   * @param {object} [options]
-   * @param {object} [options.rows = 1]
-   */
-  constructor(data, options = {}) {
-    const { rows = 1 } = options;
-
-    if (data.length % rows !== 0) {
-      throw new Error('the data length is not divisible by the number of rows');
-    }
-    super();
-    this.rows = rows;
-    this.columns = data.length / rows;
-    this.data = data;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    var index = this._calculateIndex(rowIndex, columnIndex);
-    this.data[index] = value;
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    var index = this._calculateIndex(rowIndex, columnIndex);
-    return this.data[index];
-  }
-
-  _calculateIndex(row, column) {
-    return row * this.columns + column;
-  }
-
-  static get [Symbol.species]() {
-    return matrix_Matrix;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/wrap/WrapperMatrix2D.js
-
-
-
-class WrapperMatrix2D_WrapperMatrix2D extends AbstractMatrix() {
-  /**
-   * @class WrapperMatrix2D
-   * @param {Array<Array<number>>} data
-   */
-  constructor(data) {
-    super();
-    this.data = data;
-    this.rows = data.length;
-    this.columns = data[0].length;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.data[rowIndex][columnIndex] = value;
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.data[rowIndex][columnIndex];
-  }
-
-  static get [Symbol.species]() {
-    return matrix_Matrix;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/wrap/wrap.js
-
-
-
-/**
- * @param {Array<Array<number>>|Array<number>} array
- * @param {object} [options]
- * @param {object} [options.rows = 1]
- * @return {WrapperMatrix1D|WrapperMatrix2D}
- */
-function wrap(array, options) {
-  if (Array.isArray(array)) {
-    if (array[0] && Array.isArray(array[0])) {
-      return new WrapperMatrix2D_WrapperMatrix2D(array);
-    } else {
-      return new WrapperMatrix1D_WrapperMatrix1D(array, options);
-    }
-  } else {
-    throw new Error('the argument is not an array');
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/qr.js
-
-
-
-
-/**
- * @class QrDecomposition
- * @link https://github.com/lutzroeder/Mapack/blob/master/Source/QrDecomposition.cs
- * @param {Matrix} value
- */
-class qr_QrDecomposition {
-  constructor(value) {
-    value = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(value);
-
-    var qr = value.clone();
-    var m = value.rows;
-    var n = value.columns;
-    var rdiag = new Array(n);
-    var i, j, k, s;
-
-    for (k = 0; k < n; k++) {
-      var nrm = 0;
-      for (i = k; i < m; i++) {
-        nrm = hypotenuse(nrm, qr.get(i, k));
-      }
-      if (nrm !== 0) {
-        if (qr.get(k, k) < 0) {
-          nrm = -nrm;
-        }
-        for (i = k; i < m; i++) {
-          qr.set(i, k, qr.get(i, k) / nrm);
-        }
-        qr.set(k, k, qr.get(k, k) + 1);
-        for (j = k + 1; j < n; j++) {
-          s = 0;
-          for (i = k; i < m; i++) {
-            s += qr.get(i, k) * qr.get(i, j);
-          }
-          s = -s / qr.get(k, k);
-          for (i = k; i < m; i++) {
-            qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));
-          }
-        }
-      }
-      rdiag[k] = -nrm;
-    }
-
-    this.QR = qr;
-    this.Rdiag = rdiag;
-  }
-
-  /**
-   * Solve a problem of least square (Ax=b) by using the QR decomposition. Useful when A is rectangular, but not working when A is singular.
-   * Example : We search to approximate x, with A matrix shape m*n, x vector size n, b vector size m (m > n). We will use :
-   * var qr = QrDecomposition(A);
-   * var x = qr.solve(b);
-   * @param {Matrix} value - Matrix 1D which is the vector b (in the equation Ax = b)
-   * @return {Matrix} - The vector x
-   */
-  solve(value) {
-    value = matrix_Matrix.checkMatrix(value);
-
-    var qr = this.QR;
-    var m = qr.rows;
-
-    if (value.rows !== m) {
-      throw new Error('Matrix row dimensions must agree');
-    }
-    if (!this.isFullRank()) {
-      throw new Error('Matrix is rank deficient');
-    }
-
-    var count = value.columns;
-    var X = value.clone();
-    var n = qr.columns;
-    var i, j, k, s;
-
-    for (k = 0; k < n; k++) {
-      for (j = 0; j < count; j++) {
-        s = 0;
-        for (i = k; i < m; i++) {
-          s += qr[i][k] * X[i][j];
-        }
-        s = -s / qr[k][k];
-        for (i = k; i < m; i++) {
-          X[i][j] += s * qr[i][k];
-        }
-      }
-    }
-    for (k = n - 1; k >= 0; k--) {
-      for (j = 0; j < count; j++) {
-        X[k][j] /= this.Rdiag[k];
-      }
-      for (i = 0; i < k; i++) {
-        for (j = 0; j < count; j++) {
-          X[i][j] -= X[k][j] * qr[i][k];
-        }
-      }
-    }
-
-    return X.subMatrix(0, n - 1, 0, count - 1);
-  }
-
-  /**
-   *
-   * @return {boolean}
-   */
-  isFullRank() {
-    var columns = this.QR.columns;
-    for (var i = 0; i < columns; i++) {
-      if (this.Rdiag[i] === 0) {
-        return false;
-      }
-    }
-    return true;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get upperTriangularMatrix() {
-    var qr = this.QR;
-    var n = qr.columns;
-    var X = new matrix_Matrix(n, n);
-    var i, j;
-    for (i = 0; i < n; i++) {
-      for (j = 0; j < n; j++) {
-        if (i < j) {
-          X[i][j] = qr[i][j];
-        } else if (i === j) {
-          X[i][j] = this.Rdiag[i];
-        } else {
-          X[i][j] = 0;
-        }
-      }
-    }
-    return X;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get orthogonalMatrix() {
-    var qr = this.QR;
-    var rows = qr.rows;
-    var columns = qr.columns;
-    var X = new matrix_Matrix(rows, columns);
-    var i, j, k, s;
-
-    for (k = columns - 1; k >= 0; k--) {
-      for (i = 0; i < rows; i++) {
-        X[i][k] = 0;
-      }
-      X[k][k] = 1;
-      for (j = k; j < columns; j++) {
-        if (qr[k][k] !== 0) {
-          s = 0;
-          for (i = k; i < rows; i++) {
-            s += qr[i][k] * X[i][j];
-          }
-
-          s = -s / qr[k][k];
-
-          for (i = k; i < rows; i++) {
-            X[i][j] += s * qr[i][k];
-          }
-        }
-      }
-    }
-    return X;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/decompositions.js
-
-
-
-
-
-
-/**
- * Computes the inverse of a Matrix
- * @param {Matrix} matrix
- * @param {boolean} [useSVD=false]
- * @return {Matrix}
- */
-function inverse(matrix, useSVD = false) {
-  matrix = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(matrix);
-  if (useSVD) {
-    return new svd_SingularValueDecomposition(matrix).inverse();
-  } else {
-    return solve(matrix, matrix_Matrix.eye(matrix.rows));
-  }
-}
-
-/**
- *
- * @param {Matrix} leftHandSide
- * @param {Matrix} rightHandSide
- * @param {boolean} [useSVD = false]
- * @return {Matrix}
- */
-function solve(leftHandSide, rightHandSide, useSVD = false) {
-  leftHandSide = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(leftHandSide);
-  rightHandSide = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(rightHandSide);
-  if (useSVD) {
-    return new svd_SingularValueDecomposition(leftHandSide).solve(rightHandSide);
-  } else {
-    return leftHandSide.isSquare()
-      ? new lu_LuDecomposition(leftHandSide).solve(rightHandSide)
-      : new qr_QrDecomposition(leftHandSide).solve(rightHandSide);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/linearDependencies.js
-
-
-
-
-
-// function used by rowsDependencies
-function xrange(n, exception) {
-  var range = [];
-  for (var i = 0; i < n; i++) {
-    if (i !== exception) {
-      range.push(i);
-    }
-  }
-  return range;
-}
-
-// function used by rowsDependencies
-function dependenciesOneRow(
-  error,
-  matrix,
-  index,
-  thresholdValue = 10e-10,
-  thresholdError = 10e-10
-) {
-  if (error > thresholdError) {
-    return new Array(matrix.rows + 1).fill(0);
-  } else {
-    var returnArray = matrix.addRow(index, [0]);
-    for (var i = 0; i < returnArray.rows; i++) {
-      if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {
-        returnArray.set(i, 0, 0);
-      }
-    }
-    return returnArray.to1DArray();
-  }
-}
-
-/**
- * Creates a matrix which represents the dependencies between rows.
- * If a row is a linear combination of others rows, the result will be a row with the coefficients of this combination.
- * For example : for A = [[2, 0, 0, 1], [0, 1, 6, 0], [0, 3, 0, 1], [0, 0, 1, 0], [0, 1, 2, 0]], the result will be [[0, 0, 0, 0, 0], [0, 0, 0, 4, 1], [0, 0, 0, 0, 0], [0, 0.25, 0, 0, -0.25], [0, 1, 0, -4, 0]]
- * @param {Matrix} matrix
- * @param {Object} [options] includes thresholdValue and thresholdError.
- * @param {number} [options.thresholdValue = 10e-10] If an absolute value is inferior to this threshold, it will equals zero.
- * @param {number} [options.thresholdError = 10e-10] If the error is inferior to that threshold, the linear combination found is accepted and the row is dependent from other rows.
- * @return {Matrix} the matrix which represents the dependencies between rows.
- */
-
-function linearDependencies(matrix, options = {}) {
-  const { thresholdValue = 10e-10, thresholdError = 10e-10 } = options;
-
-  var n = matrix.rows;
-  var results = new matrix_Matrix(n, n);
-
-  for (var i = 0; i < n; i++) {
-    var b = matrix_Matrix.columnVector(matrix.getRow(i));
-    var Abis = matrix.subMatrixRow(xrange(n, i)).transposeView();
-    var svd = new svd_SingularValueDecomposition(Abis);
-    var x = svd.solve(b);
-    var error = lib_es6(
-      matrix_Matrix.sub(b, Abis.mmul(x))
-        .abs()
-        .to1DArray()
-    );
-    results.setRow(
-      i,
-      dependenciesOneRow(error, x, i, thresholdValue, thresholdError)
-    );
-  }
-  return results;
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/evd.js
-
-
-
-
-/**
- * @class EigenvalueDecomposition
- * @link https://github.com/lutzroeder/Mapack/blob/master/Source/EigenvalueDecomposition.cs
- * @param {Matrix} matrix
- * @param {object} [options]
- * @param {boolean} [options.assumeSymmetric=false]
- */
-class evd_EigenvalueDecomposition {
-  constructor(matrix, options = {}) {
-    const { assumeSymmetric = false } = options;
-
-    matrix = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(matrix);
-    if (!matrix.isSquare()) {
-      throw new Error('Matrix is not a square matrix');
-    }
-
-    var n = matrix.columns;
-    var V = getFilled2DArray(n, n, 0);
-    var d = new Array(n);
-    var e = new Array(n);
-    var value = matrix;
-    var i, j;
-
-    var isSymmetric = false;
-    if (assumeSymmetric) {
-      isSymmetric = true;
-    } else {
-      isSymmetric = matrix.isSymmetric();
-    }
-
-    if (isSymmetric) {
-      for (i = 0; i < n; i++) {
-        for (j = 0; j < n; j++) {
-          V[i][j] = value.get(i, j);
-        }
-      }
-      tred2(n, e, d, V);
-      tql2(n, e, d, V);
-    } else {
-      var H = getFilled2DArray(n, n, 0);
-      var ort = new Array(n);
-      for (j = 0; j < n; j++) {
-        for (i = 0; i < n; i++) {
-          H[i][j] = value.get(i, j);
-        }
-      }
-      orthes(n, H, ort, V);
-      hqr2(n, e, d, V, H);
-    }
-
-    this.n = n;
-    this.e = e;
-    this.d = d;
-    this.V = V;
-  }
-
-  /**
-   *
-   * @return {Array<number>}
-   */
-  get realEigenvalues() {
-    return this.d;
-  }
-
-  /**
-   *
-   * @return {Array<number>}
-   */
-  get imaginaryEigenvalues() {
-    return this.e;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get eigenvectorMatrix() {
-    if (!matrix_Matrix.isMatrix(this.V)) {
-      this.V = new matrix_Matrix(this.V);
-    }
-    return this.V;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get diagonalMatrix() {
-    var n = this.n;
-    var e = this.e;
-    var d = this.d;
-    var X = new matrix_Matrix(n, n);
-    var i, j;
-    for (i = 0; i < n; i++) {
-      for (j = 0; j < n; j++) {
-        X[i][j] = 0;
-      }
-      X[i][i] = d[i];
-      if (e[i] > 0) {
-        X[i][i + 1] = e[i];
-      } else if (e[i] < 0) {
-        X[i][i - 1] = e[i];
-      }
-    }
-    return X;
-  }
-}
-
-function tred2(n, e, d, V) {
-  var f, g, h, i, j, k, hh, scale;
-
-  for (j = 0; j < n; j++) {
-    d[j] = V[n - 1][j];
-  }
-
-  for (i = n - 1; i > 0; i--) {
-    scale = 0;
-    h = 0;
-    for (k = 0; k < i; k++) {
-      scale = scale + Math.abs(d[k]);
-    }
-
-    if (scale === 0) {
-      e[i] = d[i - 1];
-      for (j = 0; j < i; j++) {
-        d[j] = V[i - 1][j];
-        V[i][j] = 0;
-        V[j][i] = 0;
-      }
-    } else {
-      for (k = 0; k < i; k++) {
-        d[k] /= scale;
-        h += d[k] * d[k];
-      }
-
-      f = d[i - 1];
-      g = Math.sqrt(h);
-      if (f > 0) {
-        g = -g;
-      }
-
-      e[i] = scale * g;
-      h = h - f * g;
-      d[i - 1] = f - g;
-      for (j = 0; j < i; j++) {
-        e[j] = 0;
-      }
-
-      for (j = 0; j < i; j++) {
-        f = d[j];
-        V[j][i] = f;
-        g = e[j] + V[j][j] * f;
-        for (k = j + 1; k <= i - 1; k++) {
-          g += V[k][j] * d[k];
-          e[k] += V[k][j] * f;
-        }
-        e[j] = g;
-      }
-
-      f = 0;
-      for (j = 0; j < i; j++) {
-        e[j] /= h;
-        f += e[j] * d[j];
-      }
-
-      hh = f / (h + h);
-      for (j = 0; j < i; j++) {
-        e[j] -= hh * d[j];
-      }
-
-      for (j = 0; j < i; j++) {
-        f = d[j];
-        g = e[j];
-        for (k = j; k <= i - 1; k++) {
-          V[k][j] -= f * e[k] + g * d[k];
-        }
-        d[j] = V[i - 1][j];
-        V[i][j] = 0;
-      }
-    }
-    d[i] = h;
-  }
-
-  for (i = 0; i < n - 1; i++) {
-    V[n - 1][i] = V[i][i];
-    V[i][i] = 1;
-    h = d[i + 1];
-    if (h !== 0) {
-      for (k = 0; k <= i; k++) {
-        d[k] = V[k][i + 1] / h;
-      }
-
-      for (j = 0; j <= i; j++) {
-        g = 0;
-        for (k = 0; k <= i; k++) {
-          g += V[k][i + 1] * V[k][j];
-        }
-        for (k = 0; k <= i; k++) {
-          V[k][j] -= g * d[k];
-        }
-      }
-    }
-
-    for (k = 0; k <= i; k++) {
-      V[k][i + 1] = 0;
-    }
-  }
-
-  for (j = 0; j < n; j++) {
-    d[j] = V[n - 1][j];
-    V[n - 1][j] = 0;
-  }
-
-  V[n - 1][n - 1] = 1;
-  e[0] = 0;
-}
-
-function tql2(n, e, d, V) {
-  var g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2, iter;
-
-  for (i = 1; i < n; i++) {
-    e[i - 1] = e[i];
-  }
-
-  e[n - 1] = 0;
-
-  var f = 0;
-  var tst1 = 0;
-  var eps = Number.EPSILON;
-
-  for (l = 0; l < n; l++) {
-    tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));
-    m = l;
-    while (m < n) {
-      if (Math.abs(e[m]) <= eps * tst1) {
-        break;
-      }
-      m++;
-    }
-
-    if (m > l) {
-      iter = 0;
-      do {
-        iter = iter + 1;
-
-        g = d[l];
-        p = (d[l + 1] - g) / (2 * e[l]);
-        r = hypotenuse(p, 1);
-        if (p < 0) {
-          r = -r;
-        }
-
-        d[l] = e[l] / (p + r);
-        d[l + 1] = e[l] * (p + r);
-        dl1 = d[l + 1];
-        h = g - d[l];
-        for (i = l + 2; i < n; i++) {
-          d[i] -= h;
-        }
-
-        f = f + h;
-
-        p = d[m];
-        c = 1;
-        c2 = c;
-        c3 = c;
-        el1 = e[l + 1];
-        s = 0;
-        s2 = 0;
-        for (i = m - 1; i >= l; i--) {
-          c3 = c2;
-          c2 = c;
-          s2 = s;
-          g = c * e[i];
-          h = c * p;
-          r = hypotenuse(p, e[i]);
-          e[i + 1] = s * r;
-          s = e[i] / r;
-          c = p / r;
-          p = c * d[i] - s * g;
-          d[i + 1] = h + s * (c * g + s * d[i]);
-
-          for (k = 0; k < n; k++) {
-            h = V[k][i + 1];
-            V[k][i + 1] = s * V[k][i] + c * h;
-            V[k][i] = c * V[k][i] - s * h;
-          }
-        }
-
-        p = -s * s2 * c3 * el1 * e[l] / dl1;
-        e[l] = s * p;
-        d[l] = c * p;
-      } while (Math.abs(e[l]) > eps * tst1);
-    }
-    d[l] = d[l] + f;
-    e[l] = 0;
-  }
-
-  for (i = 0; i < n - 1; i++) {
-    k = i;
-    p = d[i];
-    for (j = i + 1; j < n; j++) {
-      if (d[j] < p) {
-        k = j;
-        p = d[j];
-      }
-    }
-
-    if (k !== i) {
-      d[k] = d[i];
-      d[i] = p;
-      for (j = 0; j < n; j++) {
-        p = V[j][i];
-        V[j][i] = V[j][k];
-        V[j][k] = p;
-      }
-    }
-  }
-}
-
-function orthes(n, H, ort, V) {
-  var low = 0;
-  var high = n - 1;
-  var f, g, h, i, j, m;
-  var scale;
-
-  for (m = low + 1; m <= high - 1; m++) {
-    scale = 0;
-    for (i = m; i <= high; i++) {
-      scale = scale + Math.abs(H[i][m - 1]);
-    }
-
-    if (scale !== 0) {
-      h = 0;
-      for (i = high; i >= m; i--) {
-        ort[i] = H[i][m - 1] / scale;
-        h += ort[i] * ort[i];
-      }
-
-      g = Math.sqrt(h);
-      if (ort[m] > 0) {
-        g = -g;
-      }
-
-      h = h - ort[m] * g;
-      ort[m] = ort[m] - g;
-
-      for (j = m; j < n; j++) {
-        f = 0;
-        for (i = high; i >= m; i--) {
-          f += ort[i] * H[i][j];
-        }
-
-        f = f / h;
-        for (i = m; i <= high; i++) {
-          H[i][j] -= f * ort[i];
-        }
-      }
-
-      for (i = 0; i <= high; i++) {
-        f = 0;
-        for (j = high; j >= m; j--) {
-          f += ort[j] * H[i][j];
-        }
-
-        f = f / h;
-        for (j = m; j <= high; j++) {
-          H[i][j] -= f * ort[j];
-        }
-      }
-
-      ort[m] = scale * ort[m];
-      H[m][m - 1] = scale * g;
-    }
-  }
-
-  for (i = 0; i < n; i++) {
-    for (j = 0; j < n; j++) {
-      V[i][j] = i === j ? 1 : 0;
-    }
-  }
-
-  for (m = high - 1; m >= low + 1; m--) {
-    if (H[m][m - 1] !== 0) {
-      for (i = m + 1; i <= high; i++) {
-        ort[i] = H[i][m - 1];
-      }
-
-      for (j = m; j <= high; j++) {
-        g = 0;
-        for (i = m; i <= high; i++) {
-          g += ort[i] * V[i][j];
-        }
-
-        g = g / ort[m] / H[m][m - 1];
-        for (i = m; i <= high; i++) {
-          V[i][j] += g * ort[i];
-        }
-      }
-    }
-  }
-}
-
-function hqr2(nn, e, d, V, H) {
-  var n = nn - 1;
-  var low = 0;
-  var high = nn - 1;
-  var eps = Number.EPSILON;
-  var exshift = 0;
-  var norm = 0;
-  var p = 0;
-  var q = 0;
-  var r = 0;
-  var s = 0;
-  var z = 0;
-  var iter = 0;
-  var i, j, k, l, m, t, w, x, y;
-  var ra, sa, vr, vi;
-  var notlast, cdivres;
-
-  for (i = 0; i < nn; i++) {
-    if (i < low || i > high) {
-      d[i] = H[i][i];
-      e[i] = 0;
-    }
-
-    for (j = Math.max(i - 1, 0); j < nn; j++) {
-      norm = norm + Math.abs(H[i][j]);
-    }
-  }
-
-  while (n >= low) {
-    l = n;
-    while (l > low) {
-      s = Math.abs(H[l - 1][l - 1]) + Math.abs(H[l][l]);
-      if (s === 0) {
-        s = norm;
-      }
-      if (Math.abs(H[l][l - 1]) < eps * s) {
-        break;
-      }
-      l--;
-    }
-
-    if (l === n) {
-      H[n][n] = H[n][n] + exshift;
-      d[n] = H[n][n];
-      e[n] = 0;
-      n--;
-      iter = 0;
-    } else if (l === n - 1) {
-      w = H[n][n - 1] * H[n - 1][n];
-      p = (H[n - 1][n - 1] - H[n][n]) / 2;
-      q = p * p + w;
-      z = Math.sqrt(Math.abs(q));
-      H[n][n] = H[n][n] + exshift;
-      H[n - 1][n - 1] = H[n - 1][n - 1] + exshift;
-      x = H[n][n];
-
-      if (q >= 0) {
-        z = p >= 0 ? p + z : p - z;
-        d[n - 1] = x + z;
-        d[n] = d[n - 1];
-        if (z !== 0) {
-          d[n] = x - w / z;
-        }
-        e[n - 1] = 0;
-        e[n] = 0;
-        x = H[n][n - 1];
-        s = Math.abs(x) + Math.abs(z);
-        p = x / s;
-        q = z / s;
-        r = Math.sqrt(p * p + q * q);
-        p = p / r;
-        q = q / r;
-
-        for (j = n - 1; j < nn; j++) {
-          z = H[n - 1][j];
-          H[n - 1][j] = q * z + p * H[n][j];
-          H[n][j] = q * H[n][j] - p * z;
-        }
-
-        for (i = 0; i <= n; i++) {
-          z = H[i][n - 1];
-          H[i][n - 1] = q * z + p * H[i][n];
-          H[i][n] = q * H[i][n] - p * z;
-        }
-
-        for (i = low; i <= high; i++) {
-          z = V[i][n - 1];
-          V[i][n - 1] = q * z + p * V[i][n];
-          V[i][n] = q * V[i][n] - p * z;
-        }
-      } else {
-        d[n - 1] = x + p;
-        d[n] = x + p;
-        e[n - 1] = z;
-        e[n] = -z;
-      }
-
-      n = n - 2;
-      iter = 0;
-    } else {
-      x = H[n][n];
-      y = 0;
-      w = 0;
-      if (l < n) {
-        y = H[n - 1][n - 1];
-        w = H[n][n - 1] * H[n - 1][n];
-      }
-
-      if (iter === 10) {
-        exshift += x;
-        for (i = low; i <= n; i++) {
-          H[i][i] -= x;
-        }
-        s = Math.abs(H[n][n - 1]) + Math.abs(H[n - 1][n - 2]);
-        x = y = 0.75 * s;
-        w = -0.4375 * s * s;
-      }
-
-      if (iter === 30) {
-        s = (y - x) / 2;
-        s = s * s + w;
-        if (s > 0) {
-          s = Math.sqrt(s);
-          if (y < x) {
-            s = -s;
-          }
-          s = x - w / ((y - x) / 2 + s);
-          for (i = low; i <= n; i++) {
-            H[i][i] -= s;
-          }
-          exshift += s;
-          x = y = w = 0.964;
-        }
-      }
-
-      iter = iter + 1;
-
-      m = n - 2;
-      while (m >= l) {
-        z = H[m][m];
-        r = x - z;
-        s = y - z;
-        p = (r * s - w) / H[m + 1][m] + H[m][m + 1];
-        q = H[m + 1][m + 1] - z - r - s;
-        r = H[m + 2][m + 1];
-        s = Math.abs(p) + Math.abs(q) + Math.abs(r);
-        p = p / s;
-        q = q / s;
-        r = r / s;
-        if (m === l) {
-          break;
-        }
-        if (
-          Math.abs(H[m][m - 1]) * (Math.abs(q) + Math.abs(r)) <
-          eps *
-            (Math.abs(p) *
-              (Math.abs(H[m - 1][m - 1]) +
-                Math.abs(z) +
-                Math.abs(H[m + 1][m + 1])))
-        ) {
-          break;
-        }
-        m--;
-      }
-
-      for (i = m + 2; i <= n; i++) {
-        H[i][i - 2] = 0;
-        if (i > m + 2) {
-          H[i][i - 3] = 0;
-        }
-      }
-
-      for (k = m; k <= n - 1; k++) {
-        notlast = k !== n - 1;
-        if (k !== m) {
-          p = H[k][k - 1];
-          q = H[k + 1][k - 1];
-          r = notlast ? H[k + 2][k - 1] : 0;
-          x = Math.abs(p) + Math.abs(q) + Math.abs(r);
-          if (x !== 0) {
-            p = p / x;
-            q = q / x;
-            r = r / x;
-          }
-        }
-
-        if (x === 0) {
-          break;
-        }
-
-        s = Math.sqrt(p * p + q * q + r * r);
-        if (p < 0) {
-          s = -s;
-        }
-
-        if (s !== 0) {
-          if (k !== m) {
-            H[k][k - 1] = -s * x;
-          } else if (l !== m) {
-            H[k][k - 1] = -H[k][k - 1];
-          }
-
-          p = p + s;
-          x = p / s;
-          y = q / s;
-          z = r / s;
-          q = q / p;
-          r = r / p;
-
-          for (j = k; j < nn; j++) {
-            p = H[k][j] + q * H[k + 1][j];
-            if (notlast) {
-              p = p + r * H[k + 2][j];
-              H[k + 2][j] = H[k + 2][j] - p * z;
-            }
-
-            H[k][j] = H[k][j] - p * x;
-            H[k + 1][j] = H[k + 1][j] - p * y;
-          }
-
-          for (i = 0; i <= Math.min(n, k + 3); i++) {
-            p = x * H[i][k] + y * H[i][k + 1];
-            if (notlast) {
-              p = p + z * H[i][k + 2];
-              H[i][k + 2] = H[i][k + 2] - p * r;
-            }
-
-            H[i][k] = H[i][k] - p;
-            H[i][k + 1] = H[i][k + 1] - p * q;
-          }
-
-          for (i = low; i <= high; i++) {
-            p = x * V[i][k] + y * V[i][k + 1];
-            if (notlast) {
-              p = p + z * V[i][k + 2];
-              V[i][k + 2] = V[i][k + 2] - p * r;
-            }
-
-            V[i][k] = V[i][k] - p;
-            V[i][k + 1] = V[i][k + 1] - p * q;
-          }
-        }
-      }
-    }
-  }
-
-  if (norm === 0) {
-    return;
-  }
-
-  for (n = nn - 1; n >= 0; n--) {
-    p = d[n];
-    q = e[n];
-
-    if (q === 0) {
-      l = n;
-      H[n][n] = 1;
-      for (i = n - 1; i >= 0; i--) {
-        w = H[i][i] - p;
-        r = 0;
-        for (j = l; j <= n; j++) {
-          r = r + H[i][j] * H[j][n];
-        }
-
-        if (e[i] < 0) {
-          z = w;
-          s = r;
-        } else {
-          l = i;
-          if (e[i] === 0) {
-            H[i][n] = w !== 0 ? -r / w : -r / (eps * norm);
-          } else {
-            x = H[i][i + 1];
-            y = H[i + 1][i];
-            q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
-            t = (x * s - z * r) / q;
-            H[i][n] = t;
-            H[i + 1][n] =
-              Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z;
-          }
-
-          t = Math.abs(H[i][n]);
-          if (eps * t * t > 1) {
-            for (j = i; j <= n; j++) {
-              H[j][n] = H[j][n] / t;
-            }
-          }
-        }
-      }
-    } else if (q < 0) {
-      l = n - 1;
-
-      if (Math.abs(H[n][n - 1]) > Math.abs(H[n - 1][n])) {
-        H[n - 1][n - 1] = q / H[n][n - 1];
-        H[n - 1][n] = -(H[n][n] - p) / H[n][n - 1];
-      } else {
-        cdivres = cdiv(0, -H[n - 1][n], H[n - 1][n - 1] - p, q);
-        H[n - 1][n - 1] = cdivres[0];
-        H[n - 1][n] = cdivres[1];
-      }
-
-      H[n][n - 1] = 0;
-      H[n][n] = 1;
-      for (i = n - 2; i >= 0; i--) {
-        ra = 0;
-        sa = 0;
-        for (j = l; j <= n; j++) {
-          ra = ra + H[i][j] * H[j][n - 1];
-          sa = sa + H[i][j] * H[j][n];
-        }
-
-        w = H[i][i] - p;
-
-        if (e[i] < 0) {
-          z = w;
-          r = ra;
-          s = sa;
-        } else {
-          l = i;
-          if (e[i] === 0) {
-            cdivres = cdiv(-ra, -sa, w, q);
-            H[i][n - 1] = cdivres[0];
-            H[i][n] = cdivres[1];
-          } else {
-            x = H[i][i + 1];
-            y = H[i + 1][i];
-            vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
-            vi = (d[i] - p) * 2 * q;
-            if (vr === 0 && vi === 0) {
-              vr =
-                eps *
-                norm *
-                (Math.abs(w) +
-                  Math.abs(q) +
-                  Math.abs(x) +
-                  Math.abs(y) +
-                  Math.abs(z));
-            }
-            cdivres = cdiv(
-              x * r - z * ra + q * sa,
-              x * s - z * sa - q * ra,
-              vr,
-              vi
-            );
-            H[i][n - 1] = cdivres[0];
-            H[i][n] = cdivres[1];
-            if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {
-              H[i + 1][n - 1] = (-ra - w * H[i][n - 1] + q * H[i][n]) / x;
-              H[i + 1][n] = (-sa - w * H[i][n] - q * H[i][n - 1]) / x;
-            } else {
-              cdivres = cdiv(-r - y * H[i][n - 1], -s - y * H[i][n], z, q);
-              H[i + 1][n - 1] = cdivres[0];
-              H[i + 1][n] = cdivres[1];
-            }
-          }
-
-          t = Math.max(Math.abs(H[i][n - 1]), Math.abs(H[i][n]));
-          if (eps * t * t > 1) {
-            for (j = i; j <= n; j++) {
-              H[j][n - 1] = H[j][n - 1] / t;
-              H[j][n] = H[j][n] / t;
-            }
-          }
-        }
-      }
-    }
-  }
-
-  for (i = 0; i < nn; i++) {
-    if (i < low || i > high) {
-      for (j = i; j < nn; j++) {
-        V[i][j] = H[i][j];
-      }
-    }
-  }
-
-  for (j = nn - 1; j >= low; j--) {
-    for (i = low; i <= high; i++) {
-      z = 0;
-      for (k = low; k <= Math.min(j, high); k++) {
-        z = z + V[i][k] * H[k][j];
-      }
-      V[i][j] = z;
-    }
-  }
-}
-
-function cdiv(xr, xi, yr, yi) {
-  var r, d;
-  if (Math.abs(yr) > Math.abs(yi)) {
-    r = yi / yr;
-    d = yr + r * yi;
-    return [(xr + r * xi) / d, (xi - r * xr) / d];
-  } else {
-    r = yr / yi;
-    d = yi + r * yr;
-    return [(r * xr + xi) / d, (r * xi - xr) / d];
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/cholesky.js
-
-
-/**
- * @class CholeskyDecomposition
- * @link https://github.com/lutzroeder/Mapack/blob/master/Source/CholeskyDecomposition.cs
- * @param {Matrix} value
- */
-class cholesky_CholeskyDecomposition {
-  constructor(value) {
-    value = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(value);
-    if (!value.isSymmetric()) {
-      throw new Error('Matrix is not symmetric');
-    }
-
-    var a = value;
-    var dimension = a.rows;
-    var l = new matrix_Matrix(dimension, dimension);
-    var positiveDefinite = true;
-    var i, j, k;
-
-    for (j = 0; j < dimension; j++) {
-      var Lrowj = l[j];
-      var d = 0;
-      for (k = 0; k < j; k++) {
-        var Lrowk = l[k];
-        var s = 0;
-        for (i = 0; i < k; i++) {
-          s += Lrowk[i] * Lrowj[i];
-        }
-        Lrowj[k] = s = (a.get(j, k) - s) / l[k][k];
-        d = d + s * s;
-      }
-
-      d = a.get(j, j) - d;
-
-      positiveDefinite &= d > 0;
-      l[j][j] = Math.sqrt(Math.max(d, 0));
-      for (k = j + 1; k < dimension; k++) {
-        l[j][k] = 0;
-      }
-    }
-
-    if (!positiveDefinite) {
-      throw new Error('Matrix is not positive definite');
-    }
-
-    this.L = l;
-  }
-
-  /**
-   *
-   * @param {Matrix} value
-   * @return {Matrix}
-   */
-  solve(value) {
-    value = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(value);
-
-    var l = this.L;
-    var dimension = l.rows;
-
-    if (value.rows !== dimension) {
-      throw new Error('Matrix dimensions do not match');
-    }
-
-    var count = value.columns;
-    var B = value.clone();
-    var i, j, k;
-
-    for (k = 0; k < dimension; k++) {
-      for (j = 0; j < count; j++) {
-        for (i = 0; i < k; i++) {
-          B[k][j] -= B[i][j] * l[k][i];
-        }
-        B[k][j] /= l[k][k];
-      }
-    }
-
-    for (k = dimension - 1; k >= 0; k--) {
-      for (j = 0; j < count; j++) {
-        for (i = k + 1; i < dimension; i++) {
-          B[k][j] -= B[i][j] * l[i][k];
-        }
-        B[k][j] /= l[k][k];
-      }
-    }
-
-    return B;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get lowerTriangularMatrix() {
-    return this.L;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/index.js
-/* concated harmony reexport default */__webpack_require__.d(__webpack_exports__, "default", function() { return matrix_Matrix; });
-/* concated harmony reexport Matrix */__webpack_require__.d(__webpack_exports__, "Matrix", function() { return matrix_Matrix; });
-/* concated harmony reexport abstractMatrix */__webpack_require__.d(__webpack_exports__, "abstractMatrix", function() { return AbstractMatrix; });
-/* concated harmony reexport wrap */__webpack_require__.d(__webpack_exports__, "wrap", function() { return wrap; });
-/* concated harmony reexport WrapperMatrix2D */__webpack_require__.d(__webpack_exports__, "WrapperMatrix2D", function() { return WrapperMatrix2D_WrapperMatrix2D; });
-/* concated harmony reexport WrapperMatrix1D */__webpack_require__.d(__webpack_exports__, "WrapperMatrix1D", function() { return WrapperMatrix1D_WrapperMatrix1D; });
-/* concated harmony reexport solve */__webpack_require__.d(__webpack_exports__, "solve", function() { return solve; });
-/* concated harmony reexport inverse */__webpack_require__.d(__webpack_exports__, "inverse", function() { return inverse; });
-/* concated harmony reexport linearDependencies */__webpack_require__.d(__webpack_exports__, "linearDependencies", function() { return linearDependencies; });
-/* concated harmony reexport SingularValueDecomposition */__webpack_require__.d(__webpack_exports__, "SingularValueDecomposition", function() { return svd_SingularValueDecomposition; });
-/* concated harmony reexport SVD */__webpack_require__.d(__webpack_exports__, "SVD", function() { return svd_SingularValueDecomposition; });
-/* concated harmony reexport EigenvalueDecomposition */__webpack_require__.d(__webpack_exports__, "EigenvalueDecomposition", function() { return evd_EigenvalueDecomposition; });
-/* concated harmony reexport EVD */__webpack_require__.d(__webpack_exports__, "EVD", function() { return evd_EigenvalueDecomposition; });
-/* concated harmony reexport CholeskyDecomposition */__webpack_require__.d(__webpack_exports__, "CholeskyDecomposition", function() { return cholesky_CholeskyDecomposition; });
-/* concated harmony reexport CHO */__webpack_require__.d(__webpack_exports__, "CHO", function() { return cholesky_CholeskyDecomposition; });
-/* concated harmony reexport LuDecomposition */__webpack_require__.d(__webpack_exports__, "LuDecomposition", function() { return lu_LuDecomposition; });
-/* concated harmony reexport LU */__webpack_require__.d(__webpack_exports__, "LU", function() { return lu_LuDecomposition; });
-/* concated harmony reexport QrDecomposition */__webpack_require__.d(__webpack_exports__, "QrDecomposition", function() { return qr_QrDecomposition; });
-/* concated harmony reexport QR */__webpack_require__.d(__webpack_exports__, "QR", function() { return qr_QrDecomposition; });
-
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-
-
-
-
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-/***/ })
-/******/ ]);
-});
\ No newline at end of file
diff --git a/spaces/merve/data-leak/source/private-and-fair/footnote.js b/spaces/merve/data-leak/source/private-and-fair/footnote.js
deleted file mode 100644
index 383057091ac6456ef8d4c7205478d89bef07ad87..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/private-and-fair/footnote.js
+++ /dev/null
@@ -1,132 +0,0 @@
-d3.select('body').selectAppend('div.tooltip.tooltip-hidden')
-
-var footnums = '¹²³⁴⁵⁶⁷⁸⁹'
-
-var footendSel = d3.selectAll('.footend')
-  .each(function(d, i){
-    var sel = d3.select(this)
-    var ogHTML = sel.parent().html()
-    sel
-      .at({href: '#footstart-' + i, id: 'footend-' + i})
-      .text(footnums[i])
-      .datum(ogHTML)
-  })
-
-footendSel.parent().parent().selectAll('br').remove()
-
-var footstartSel = d3.selectAll('.footstart')
-  .each(function(d, i){
-    d3.select(this)
-      .at({
-        href: '#footend-' + i,
-      })
-      .text(footnums[i])
-      .datum(footendSel.data()[i])
-      .parent().at({id: 'footstart-' + i})
-  })
-  .call(addLockedTooltip)
-
-ttSel.classed('tooltip-footnote', 1)
-
-function addLockedTooltip(sel){
-  sel
-    .on('mouseover', function(d, i){
-      ttSel.classed('tooltip-footnote', 1)
-        .html(d)
-        .select('.footend').remove()
-
-      var x = this.offsetLeft,
-          y = this.offsetTop,
-          bb = ttSel.node().getBoundingClientRect(),
-          left = d3.clamp(20, (x-bb.width/2), window.innerWidth - bb.width - 20),
-          top = innerHeight + scrollY > y + 20 + bb.height ? y + 20 : y - bb.height - 10;
-
-      ttSel.st({left, top}).classed('tooltip-hidden', false)
-    })
-
-  sel.on('mousemove',mouseover).on('mouseout', mouseout)
-  ttSel.on('mousemove', mouseover).on('mouseout', mouseout)
-  function mouseover(){
-    if (window.__ttfade) window.__ttfade.stop()
-  }
-  function mouseout(){
-    if (window.__ttfade) window.__ttfade.stop()
-    window.__ttfade = d3.timeout(() => {
-      ttSel
-        .classed('tooltip-hidden', 1)
-    }, 250)
-  }
-}
-
-
-
-
-
-var infoSel = d3.select('.info-box').html('')
-  .st({border: '1px solid orange', background: 'rgba(255,250,241,.5)', maxWidth: 750, margin: '0 auto', padding: 20, paddingTop: 5, paddingBottom: 5})
-  // .st({textAlign: })
-
-infoSel.append('p')
-  .st({marginLeft: 10})
-  .html('Not familiar with how machine learning models are trained or why they might leak data? <br>These interactive articles will get you up to speed.')
-  .html('New to some of these concepts? These interactive articles will get you up to speed.')
-  .html('New to machine learning or differential privacy? These interactive articles will get you up to speed.')
-
-var articles = [
-  {
-    img: 'https://pair.withgoogle.com/explorables/images/anonymization.png',
-    title: 'Collecting Sensitive Information',
-    permalink: 'https://pair.withgoogle.com/explorables/anonymization/',
-  },
-  {
-    img: 'https://pair.withgoogle.com/explorables/images/model-inversion.png',
-    title: 'Why Some Models Leak Data',
-    permalink: 'https://pair.withgoogle.com/explorables/data-leak/',
-  },
-  {
-    img: 'http://playground.tensorflow.org/preview.png',
-    title: 'TensorFlow Playground',
-    permalink: 'https://playground.tensorflow.org'
-  },
-]
-
-
-var postSel = infoSel.appendMany('a.post', articles)
-  .st({
-    textAlign: 'center',
-    width: '30.5%', 
-    display: 'inline-block', 
-    verticalAlign: 'top',
-    marginLeft: 10,
-    marginRight: 10,
-    textDecoration: 'none',
-  })
-  .at({href: d => d.permalink})
-
-postSel.append('div.img')
-  .st({
-    width: '100%', 
-    height: 80,
-    backgroundImage: d => `url(${d.img})`,
-    backgroundSize: 'cover',
-    backgroundPosition: 'center',
-    outline: '1px solid #ccc'
-  })
-
-postSel.append('p.title')
-  .text(d => d.title)
-  .st({
-    verticalAlign: 'top',
-    marginTop: 10,
-    textDecoration: 'none',
-    fontSize: 15,
-    fontWeight: 500,
-  })
-
-
-// width: 100%;
-//   height: 200px;
-//   background-image: url(https://pair.withgoogle.com/explorables/images/model-inversion.png);
-//   background-size: cover;
-//   background-position: center center;
-
diff --git a/spaces/merve/hidden-bias/source/_posts/2021-10-31-uncertainty-calibration.md b/spaces/merve/hidden-bias/source/_posts/2021-10-31-uncertainty-calibration.md
deleted file mode 100644
index 0e097d412fff555af6b338ffa6d704d4ba05a454..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/_posts/2021-10-31-uncertainty-calibration.md
+++ /dev/null
@@ -1,131 +0,0 @@
----
-template: post.html
-title: Are Model Predictions Probabilities?
-socialsummary: Machine learning models express their uncertainty as model scores, but through calibration we can transform these scores into probabilities for more effective decision making.
-shareimg: https://pair.withgoogle.com/explorables/images/uncertainty-calibration.png
-shareimgabstract: https://pair.withgoogle.com/explorables/images/uncertainty-calibration-abstract.png
-permalink: /uncertainty-calibration/
----
-
-<div id='container'>
-<div id='graph'></div>
-<div id='sections'>
-
-<div>
-
-If a machine learning model tells you that it’s going to rain tomorrow with a score of 0.60, should you buy an umbrella?<a class='footstart'>1</a> 
-
-<p> In the diagram, we have a hypothetical machine learning classifier for predicting rainy days. For each date, the classifier reads in relevant signals like temperature and humidity and spits out a number between 0 and 1. Each data point represents a different day, with the position representing the model’s prediction for rain that day and the symbol (🌧️ or ☀️) representing the true weather that occurred that day. 
-
-<p> <div id='card'> Do the model’s predictions tell us the probability of rain?</div>
-
-<p> In general, machine learning classifiers don’t just give binary predictions, but instead provide some numerical value between 0 and 1 for their predictions. This number, sometimes called the *model score* or *confidence*, is a way for the model to express their certainty about what class the input data belongs to. In most applications, the exact score is ignored and we use a threshold to round the score to a binary answer, yes or no, rain or not. However, by using *calibration* we can transform these scores into probabilities and use them more effectively in decision making.
-
-</div>
-
-<div> <h3>Thresholding</h3>
-
-<p> One traditional approach to using a model’s score is through <span class='highlight'>*thresholding*</span>. In this setting, you choose a threshold *t* and then declare that the model thinks it’s going to rain if the score is above *t* and it’s not if the score is below, thereby converting the score to a binary outcome. When you observe the actual weather, you know how often it was wrong and can compute key aggregate statistics like <a href="https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification" target="_blank">*accuracy*</a>.
-
-<p> We can sometimes treat these aggregate statistics themselves as probabilities. For example, accuracy is the probability that the binary prediction of your model (rain or not) is equal to the ground truth (🌧️ or ☀️). 
-</div>
-
-<div> <h3>Adjustable Thresholding</h3>
-
-<p>The threshold can easily be changed after the model is trained.
-
-<p> Thresholding uses the model’s score to make a decision, but fails to consider the model’s confidence. The model score is only used to decide whether you are above or below the threshold, but the magnitude of the difference isn’t considered. For example, if you threshold at 0.4, the model’s predictions of 0.6 and 0.9 are treated the same, even though the model is much more confident in the latter.
- 
-<div id='card'> Can we do a better job of incorporating the model score into our understanding of the model? </div>
-
-</div>
-
-<div> <h3>Calibration</h3>
-
-<p> <span class='highlight'>*Calibration*</span> lets us compare our model scores directly to probabilities. 
-
-<p> For this technique, instead of one threshold, we have many, which we use to split the predictions into buckets. Again, once we observe the ground truth, we can see what proportion of the predictions in each bucket were rainy days (🌧️). This proportion is the *empirical probability* of rain for that bucket.
-
-<p> Ideally, we want this proportion to be higher for higher buckets, so that the probability is roughly in line with the average prediction for that bucket. We call the difference between the proportion and the predicted rates the calibration error, and by averaging over all of the buckets, we can calculate the <a href="https://arxiv.org/pdf/1706.04599.pdf" target="_blank">Expected Calibration Error</a>. If the proportions and the predictions line up for our use case, meaning the error is low, then we say the model is “well-calibrated” and we can consider treating the model score as the probability that it will actually rain.
-</div>
-
-<div> <h3>Adjusting Calibration</h3>
-
-<p> We saw above that a well-calibrated model allows us to treat our model score as a kind of probability. But if we start with a poorly calibrated model, one which is over or under-confident. Is there anything we can do to improve it?
-
-<p> It turns out that, in many settings, we can adjust the model score without really changing the model’s decisions, as long as our adjustment preserves the order of the scores<a class='footstart'>2</a>. For example, if we map all of the scores from our original model to their squares, we don’t change the order of the data with respect to the model score. Thus, quantities like accuracy will stay the same as long as we appropriately map the threshold to its square as well. However, these adjustments *do* change the calibration of a model by changing which data points lie in which buckets.
-
-<div id='card'> **Try** **tweaking the thresholds** to *calibrate* the model scores for our data<a class='footstart'>3</a> – how much can you improve the model's calibration? </div>
-
-<p> In general, we don’t have to rely on tweaking the model scores by hand to improve calibration. If we are trying to calibrate the model for a particular data distribution, we can use mathematical techniques like <a href="https://en.wikipedia.org/wiki/Isotonic_regression" target="_blank">Isotonic Regression</a> or <a href="https://en.wikipedia.org/wiki/Platt_scaling" target="_blank">Platt Scaling</a> to generate the correct remapping for model scores.
-</div>
-
-<div> <h3>Shifting Data</h3>
-
-<p> While good calibration is an important property for a model’s scores to be interpreted as probabilities, it alone does not capture all aspects of model uncertainty.
-
-<p> What happens if it starts to rain less frequently after we've trained and calibrated our model? Notice how the calibration drops, even if we use the same calibrated model scores as before.
-
-<p> Models are usually only well calibrated with respect to certain data distributions. If the data changes significantly between training and serving time, our models might cease to be well calibrated and we can’t rely on using our model scores as probabilities.
-</div>
-
-<div><h3>Beyond Calibration</h3>
-
-<p> Calibration can sometimes be easy to game. For example, if we knew that it rains 50% of the time over the course of the year, then we could create a model with a constant prediction of 0.5 every day. This would have perfect calibration, despite not being a very useful model for distinguishing day-to-day differences in the probability of rain. This highlights an important issue: 
-
-<div id='card'> Better calibration doesn’t mean more accurate predictions. </div> 
-
-<p> It turns out that statisticians identified the issue with focusing solely on calibration in meteorology when comparing weather forecasts, and came up with a solution.  <a href="https://sites.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf" target="_blank">Proper scoring rules</a>  provide an alternative approach to measuring the quality of probabilistic forecasts, by using a formula to measure the distance between the model’s predictions and the true event probabilities. These rules guarantee that a better value must mean a better prediction in terms of accuracy and calibration. Such rules incentivize models to be both better calibrated and more accurate.  
-
-</div>
-</div>
-</div>
-
-
-<h3> More Reading </h3>
-
-<p> This post is only the beginning of the discussion on the connections between machine learning models, probability, and uncertainty. In practice, when developing machine learning models with uncertainty in mind, we may need to go beyond calibration. 
-
-<p> In some settings, errors are not all equal. For example, if we are training a classifier to predict if a patient needs to be tested for a disease, then a false negative (missing a case of the disease) may be <a href="https://pair.withgoogle.com/explorables/measuring-fairness/" target="_blank">more detrimental</a> than a false positive (accidentally having a patient tested). In such cases, we may not want a perfectly calibrated model, but may want to skew the model scores towards one class or another. The field of <a href="https://books.google.ca/books?hl=en&lr=&id=1CDaBwAAQBAJ&oi=fnd&pg=PA1&dq=Statistical+Decision+Theory&ots=LMuipfYL0J&sig=bSdHt0_Phot_wxieYXN7cvXvmII#v=onepage&q=Statistical%20Decision%20Theory&f=false" target="_blank">Statistical Decision Theory</a> provides us with tools to determine how to better use model scores in this more general setting. Calibration may also lead to tension with other important goals like <a href="https://proceedings.neurips.cc/paper/2017/file/b8b9c74ac526fffbeb2d39ab038d1cd7-Paper.pdf" target="_blank">model fairness</a> in some applications.
-
-<p> Beyond this, so far we’ve only considered the case of using a single model score, i.e. a point estimate. If we trained the model a thousand times with different random seeds, or resampled the training data, we would almost certainly generate a collection of different model scores for a given input. To truly unpack the different sources of uncertainty that we might encounter, we might want to look towards *distributional* approaches to measuring uncertainty, using techniques like <a href="https://proceedings.neurips.cc/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf" target="_blank">Deep Ensembles</a> or <a href="https://authors.library.caltech.edu/13793/1/MACnc92b.pdf" target="_blank">Bayesian modeling</a>. We will dig deeper into these in future posts.
-
-<h3> Credits </h3>
-
-<p> Nithum Thain, Adam Pearce, Jasper Snoek & Mahima Pushkarna // March 2022
-
-<p> Thanks to Balaji Lakshminarayanan, Emily Reif, Lucas Dixon, Martin Wattenberg, Fernanda Viégas, Ian Kivlichan, Nicole Mitchell, and Meredith Morris for their help with this piece.
-
-<h3> Footnotes </h3>
-
-<p> <a class='footend'></a> Your decision might depend both on the probability of rain and its severity (i.e. how much rain there is going to be). We’ll focus just on the probability for now.
-
-<p> <a class='footend'></a> Applying a strictly <a href="https://en.wikipedia.org/wiki/Monotonic_function" target="_blank">monotonic function</a> to the model always keeps the order of scores the same. 
- 
-<p> <a class='footend'></a> In this example, we adjust the model scores by changing the model scores of elements within a bucket to the mean of the bucket.  
-<h3> More Explorables </h3>
-
-<p id='recirc'></p>
-
-
-
-
-
-<link rel="stylesheet" href="graph-scroll.css">
-<script src='../third_party/d3_.js'></script>
-
-<link rel='stylesheet' href='footnote.css'>
-<script src='footnote.js'></script>
-
-<script src='generate_data.js'></script>
-
-<script src='util.js'></script>
-<script src='weatherdata.js'></script>
-<script src='draw_calibrationcurve.js'></script>
-<script src='draw_model_remapping.js'></script>
-<script src='draw_weathergraph.js'></script>
-<script src='draw_slides.js'></script>
-<script src='init.js'></script>
-
-<script src='../third_party/recirc.js'></script>
-<link rel="stylesheet" href="style.css">
\ No newline at end of file
diff --git a/spaces/merve/measuring-fairness/public/uncertainty-calibration/footnote.css b/spaces/merve/measuring-fairness/public/uncertainty-calibration/footnote.css
deleted file mode 100644
index 83472e6bc26c962b1c2fcc630d641ed62f181e77..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/public/uncertainty-calibration/footnote.css
+++ /dev/null
@@ -1,57 +0,0 @@
-.tooltip-footnote {
-  top: -1000px;
-  position: absolute;
-  padding: 10px;
-  background: rgba(255, 255, 255, .8);
-  border: 0px solid lightgray;
-  
-  width: 300px !important;
-  font-size: 14px;
-  line-height: 1.4em;
-  background: rgba(0, 0, 0, .8);
-  color: #fff;
-  pointer-events: all !important;
-}
-.tooltip-footnote a{
-  color: #fff !important;
-}
-.tooltip-footnote:hover{
-/*  opacity: 1;
-  pointer-events: all !important;
-*/}
-
-.tooltip-footnote-hidden{
-  opacity: 0;
-  transition: opacity .3s;
-  transition-delay: .2s;
-  pointer-events: none !important;
-}
-
-@media (max-width: 590px){
-  .footend{
-    margin-left: 0px;
-    width: 10px;
-  }
-
-  div.tooltip-footnote{
-    transition: all 0s !important;
-    transition-delay: 0s !important;
-
-    display: none;
-    position: fixed;
-    bottom: -1px;
-    width: calc(100%);
-    left: -1px !important;
-    right: -1px !important;
-    top: auto !important;
-    width: auto !important;
-  }
-}
-
-.footstart{
-  padding-left: 2px;
-  height: 8px !important;
-  /*background: red;*/
-  /*display: inline-block;*/
-  line-height: 0em;
-}
diff --git a/spaces/mfrashad/ClothingGAN/netdissect/segdata.py b/spaces/mfrashad/ClothingGAN/netdissect/segdata.py
deleted file mode 100644
index f3cb6dfac8985d9c55344abbc26cc26c4862aa85..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/netdissect/segdata.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import os, numpy, torch, json
-from .parallelfolder import ParallelImageFolders
-from torchvision import transforms
-from torchvision.transforms.functional import to_tensor, normalize
-
-class FieldDef(object):
-    def __init__(self, field, index, bitshift, bitmask, labels):
-        self.field = field
-        self.index = index
-        self.bitshift = bitshift
-        self.bitmask = bitmask
-        self.labels = labels
-
-class MultiSegmentDataset(object):
-    '''
-    Just like ClevrMulticlassDataset, but the second stream is a one-hot
-    segmentation tensor rather than a flat one-hot presence vector.
-
-    MultiSegmentDataset('dataset/clevrseg',
-        imgdir='images/train/positive',
-        segdir='images/train/segmentation')
-    '''
-    def __init__(self, directory, transform=None,
-            imgdir='img', segdir='seg', val=False, size=None):
-        self.segdataset = ParallelImageFolders(
-                [os.path.join(directory, imgdir),
-                 os.path.join(directory, segdir)],
-                transform=transform)
-        self.fields = []
-        with open(os.path.join(directory, 'labelnames.json'), 'r') as f:
-            for defn in json.load(f):
-                self.fields.append(FieldDef(
-                    defn['field'], defn['index'], defn['bitshift'],
-                    defn['bitmask'], defn['label']))
-        self.labels = ['-'] # Reserve label 0 to mean "no label"
-        self.categories = []
-        self.label_category = [0]
-        for fieldnum, f in enumerate(self.fields):
-            self.categories.append(f.field)
-            f.firstchannel = len(self.labels)
-            f.channels = len(f.labels) - 1
-            for lab in f.labels[1:]:
-                self.labels.append(lab)
-                self.label_category.append(fieldnum)
-        # Reserve 25% of the dataset for validation.
-        first_val = int(len(self.segdataset) * 0.75)
-        self.val = val
-        self.first = first_val if val else 0
-        self.length = len(self.segdataset) - first_val if val else first_val
-        # Truncate the dataset if requested.
-        if size:
-            self.length = min(size, self.length)
-
-    def __len__(self):
-        return self.length
-
-    def __getitem__(self, index):
-        img, segimg = self.segdataset[index + self.first]
-        segin = numpy.array(segimg, numpy.uint8, copy=False)
-        segout = torch.zeros(len(self.categories),
-                segin.shape[0], segin.shape[1], dtype=torch.int64)
-        for i, field in enumerate(self.fields):
-            fielddata = ((torch.from_numpy(segin[:, :, field.index])
-                    >> field.bitshift) & field.bitmask)
-            segout[i] = field.firstchannel + fielddata - 1
-        bincount = numpy.bincount(segout.flatten(),
-                minlength=len(self.labels))
-        return img, segout, bincount
-
-if __name__ == '__main__':
-    ds = MultiSegmentDataset('dataset/clevrseg')
-    print(ds[0])
-    import pdb; pdb.set_trace()
-
diff --git a/spaces/mikkoar/marco/tests/parse.ts b/spaces/mikkoar/marco/tests/parse.ts
deleted file mode 100644
index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000
--- a/spaces/mikkoar/marco/tests/parse.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
-  const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
-  const headers = parseHeadersFromCurl(content)
-  console.log(headers)
-
-  const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
-  const cmdHeaders = parseHeadersFromCurl(cmdContent)
-  console.log(cmdHeaders)
-})()
diff --git a/spaces/misteca/ChatGPT/ChuanhuChatbot.py b/spaces/misteca/ChatGPT/ChuanhuChatbot.py
deleted file mode 100644
index 086dc6a1e3da91f4078e163ffac03ab54ed0a7d0..0000000000000000000000000000000000000000
--- a/spaces/misteca/ChatGPT/ChuanhuChatbot.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import gradio as gr
-# import openai
-import os
-import sys
-import argparse
-from utils import *
-from presets import *
-
-
-my_api_key = ""    # 在这里输入你的 API 密钥
-
-#if we are running in Docker
-if os.environ.get('dockerrun') == 'yes':
-    dockerflag = True
-else:
-    dockerflag = False
-
-authflag = False
-
-if dockerflag:
-    my_api_key = os.environ.get('my_api_key')
-    if my_api_key == "empty":
-        print("Please give a api key!")
-        sys.exit(1)
-    #auth
-    username = os.environ.get('USERNAME')
-    password = os.environ.get('PASSWORD')
-    if not (isinstance(username, type(None)) or isinstance(password, type(None))):
-        authflag = True
-else:
-    if not my_api_key and os.path.exists("api_key.txt") and os.path.getsize("api_key.txt"):
-        with open("api_key.txt", "r") as f:
-            my_api_key = f.read().strip()
-    if os.path.exists("auth.json"):
-        with open("auth.json", "r") as f:
-            auth = json.load(f)
-            username = auth["username"]
-            password = auth["password"]
-            if username != "" and password != "":
-                authflag = True
-
-gr.Chatbot.postprocess = postprocess
-
-with gr.Blocks(css=customCSS) as demo:
-    gr.HTML(title)
-    with gr.Row():
-        keyTxt = gr.Textbox(show_label=False, placeholder=f"在这里输入你的OpenAI API-key...",
-                            value=my_api_key, type="password", visible=not HIDE_MY_KEY).style(container=True)
-        use_streaming_checkbox = gr.Checkbox(label="实时传输回答", value=True, visible=enable_streaming_option)
-    chatbot = gr.Chatbot()  # .style(color_map=("#1D51EE", "#585A5B"))
-    history = gr.State([])
-    token_count = gr.State([])
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    TRUECOMSTANT = gr.State(True)
-    FALSECONSTANT = gr.State(False)
-    topic = gr.State("未命名对话历史记录")
-
-    with gr.Row():
-        with gr.Column(scale=12):
-            user_input = gr.Textbox(show_label=False, placeholder="在这里输入").style(
-                container=False)
-        with gr.Column(min_width=50, scale=1):
-            submitBtn = gr.Button("🚀", variant="primary")
-    with gr.Row():
-        emptyBtn = gr.Button("🧹 新的对话")
-        retryBtn = gr.Button("🔄 重新生成")
-        delLastBtn = gr.Button("🗑️ 删除最近一条对话")
-        reduceTokenBtn = gr.Button("♻️ 总结对话")
-    status_display = gr.Markdown("status: ready")
-    systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入System Prompt...",
-                                 label="System prompt", value=initial_prompt).style(container=True)
-    with gr.Accordion(label="加载Prompt模板", open=False):
-        with gr.Column():
-            with gr.Row():
-                with gr.Column(scale=6):
-                    templateFileSelectDropdown = gr.Dropdown(label="选择Prompt模板集合文件", choices=get_template_names(plain=True), multiselect=False, value=get_template_names(plain=True)[0])
-                with gr.Column(scale=1):
-                    templateRefreshBtn = gr.Button("🔄 刷新")
-                    templaeFileReadBtn = gr.Button("📂 读入模板")
-            with gr.Row():
-                with gr.Column(scale=6):
-                    templateSelectDropdown = gr.Dropdown(label="从Prompt模板中加载", choices=load_template(get_template_names(plain=True)[0], mode=1), multiselect=False, value=load_template(get_template_names(plain=True)[0], mode=1)[0])
-                with gr.Column(scale=1):
-                    templateApplyBtn = gr.Button("⬇️ 应用")
-    with gr.Accordion(label="保存/加载对话历史记录", open=False):
-        with gr.Column():
-            with gr.Row():
-                with gr.Column(scale=6):
-                    saveFileName = gr.Textbox(
-                        show_label=True, placeholder=f"在这里输入保存的文件名...", label="设置保存文件名", value="对话历史记录").style(container=True)
-                with gr.Column(scale=1):
-                    saveHistoryBtn = gr.Button("💾 保存对话")
-            with gr.Row():
-                with gr.Column(scale=6):
-                    historyFileSelectDropdown = gr.Dropdown(label="从列表中加载对话", choices=get_history_names(plain=True), multiselect=False, value=get_history_names(plain=True)[0])
-                with gr.Column(scale=1):
-                    historyRefreshBtn = gr.Button("🔄 刷新")
-                    historyReadBtn = gr.Button("📂 读入对话")
-    #inputs, top_p, temperature, top_k, repetition_penalty
-    with gr.Accordion("参数", open=False):
-        top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.05,
-                          interactive=True, label="Top-p (nucleus sampling)",)
-        temperature = gr.Slider(minimum=-0, maximum=5.0, value=1.0,
-                                step=0.1, interactive=True, label="Temperature",)
-        #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
-        #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
-    gr.Markdown(description)
-
-
-    user_input.submit(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-    user_input.submit(reset_textbox, [], [user_input])
-
-    submitBtn.click(predict, [keyTxt, systemPromptTxt, history, user_input, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-    submitBtn.click(reset_textbox, [], [user_input])
-
-    emptyBtn.click(reset_state, outputs=[chatbot, history, token_count, status_display], show_progress=True)
-
-    retryBtn.click(retry, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-
-    delLastBtn.click(delete_last_conversation, [chatbot, history, token_count, use_streaming_checkbox], [
-                     chatbot, history, token_count, status_display], show_progress=True)
-
-    reduceTokenBtn.click(reduce_token_size, [keyTxt, systemPromptTxt, history, chatbot, token_count, top_p, temperature, use_streaming_checkbox], [chatbot, history, status_display, token_count], show_progress=True)
-
-    saveHistoryBtn.click(save_chat_history, [
-                  saveFileName, systemPromptTxt, history, chatbot], None, show_progress=True)
-
-    saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-
-    historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-
-    historyReadBtn.click(load_chat_history, [historyFileSelectDropdown, systemPromptTxt, history, chatbot],  [saveFileName, systemPromptTxt, history, chatbot], show_progress=True)
-
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-
-    templaeFileReadBtn.click(load_template, [templateFileSelectDropdown],  [promptTemplates, templateSelectDropdown], show_progress=True)
-
-    templateApplyBtn.click(get_template_content, [promptTemplates, templateSelectDropdown, systemPromptTxt],  [systemPromptTxt], show_progress=True)
-
-print("川虎的温馨提示：访问 http://localhost:7860 查看界面")
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = "川虎ChatGPT 🚀"
-
-if __name__ == "__main__":
-    #if running in Docker
-    if dockerflag:
-        if authflag:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(username, password))
-        else:
-            demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
-    #if not running in Docker
-    else:
-        if authflag:
-            demo.queue().launch(share=False, auth=(username, password))
-        else:
-            demo.queue().launch(share=False) # 改为 share=True 可以创建公开分享链接
-        #demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-        #demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-        #demo.queue().launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/mkotan/mafese_feature_selection/README.md b/spaces/mkotan/mafese_feature_selection/README.md
deleted file mode 100644
index d9126014fbcfc2576ad200496a8830e44e167d7f..0000000000000000000000000000000000000000
--- a/spaces/mkotan/mafese_feature_selection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Mafese Feature Selection
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ml6team/controlnet-interior-design/preprocessing.py b/spaces/ml6team/controlnet-interior-design/preprocessing.py
deleted file mode 100644
index 98b673a083b9b19f630558547b1cf9e22e075336..0000000000000000000000000000000000000000
--- a/spaces/ml6team/controlnet-interior-design/preprocessing.py
+++ /dev/null
@@ -1,134 +0,0 @@
-"""Preprocessing methods"""
-import logging
-from typing import List, Tuple
-
-import numpy as np
-from PIL import Image, ImageFilter
-import streamlit as st
-
-from config import COLOR_RGB, WIDTH, HEIGHT
-# from enhance_config import ENHANCE_SETTINGS
-
-LOGGING = logging.getLogger(__name__)
-
-
-def preprocess_seg_mask(canvas_seg, real_seg: Image.Image = None) -> Tuple[np.ndarray, np.ndarray]:
-    """Preprocess the segmentation mask.
-    Args:
-        canvas_seg: segmentation canvas
-        real_seg (Image.Image, optional): segmentation mask. Defaults to None.
-    Returns:
-        Tuple[np.ndarray, np.ndarray]: segmentation mask, segmentation mask with overlay
-    """
-    # get unique colors in the segmentation
-    image_seg = canvas_seg.image_data.copy()[:, :, :3]
-
-    # average the colors of the segmentation masks
-    average_color = np.mean(image_seg, axis=(2))
-    mask = average_color[:, :] > 0
-    if mask.sum() > 0:
-        mask = mask * 1
-
-    unique_colors = np.unique(image_seg.reshape(-1, image_seg.shape[-1]), axis=0)
-    unique_colors = [tuple(color) for color in unique_colors]
-
-    unique_colors = [color for color in unique_colors if np.sum(
-        np.all(image_seg == color, axis=-1)) > 100]
-
-    unique_colors_exact = [color for color in unique_colors if color in COLOR_RGB]
-
-    if real_seg is not None:
-        overlay_seg = np.array(real_seg)
-
-        unique_colors = np.unique(overlay_seg.reshape(-1, overlay_seg.shape[-1]), axis=0)
-        unique_colors = [tuple(color) for color in unique_colors]
-
-        for color in unique_colors_exact:
-            if color != (255, 255, 255) and color != (0, 0, 0):
-                overlay_seg[np.all(image_seg == color, axis=-1)] = color
-        image_seg = overlay_seg
-
-    return mask, image_seg
-
-
-def get_mask(image_mask: np.ndarray) -> np.ndarray:
-    """Get the mask from the segmentation mask.
-    Args:
-        image_mask (np.ndarray): segmentation mask
-    Returns:
-        np.ndarray: mask
-    """
-    # average the colors of the segmentation masks
-    average_color = np.mean(image_mask, axis=(2))
-    mask = average_color[:, :] > 0
-    if mask.sum() > 0:
-        mask = mask * 1
-    return mask
-
-
-def get_image() -> np.ndarray:
-    """Get the image from the session state.
-    Returns:
-        np.ndarray: image
-    """
-    if 'initial_image' in st.session_state and st.session_state['initial_image'] is not None:
-        initial_image = st.session_state['initial_image']
-        if isinstance(initial_image, Image.Image):
-            return np.array(initial_image.resize((WIDTH, HEIGHT)))
-        else:
-            return np.array(Image.fromarray(initial_image).resize((WIDTH, HEIGHT)))
-    else:
-        return None
-
-
-# def make_enhance_config(segmentation, objects=None):
-    """Make the enhance config for the segmentation image.
-    """
-    info = ENHANCE_SETTINGS[objects]
-
-    segmentation = np.array(segmentation)
-
-    if 'replace' in info:
-        replace_color = info['replace']
-        mask = np.zeros(segmentation.shape)
-        for color in info['colors']:
-            mask[np.all(segmentation == color, axis=-1)] = [1, 1, 1]
-            segmentation[np.all(segmentation == color, axis=-1)] = replace_color
-
-    if info['inverse'] is False:
-        mask = np.zeros(segmentation.shape)
-        for color in info['colors']:
-            mask[np.all(segmentation == color, axis=-1)] = [1, 1, 1]
-    else:
-        mask = np.ones(segmentation.shape)
-        for color in info['colors']:
-            mask[np.all(segmentation == color, axis=-1)] = [0, 0, 0]
-
-    st.session_state['positive_prompt'] = info['positive_prompt']
-    st.session_state['negative_prompt'] = info['negative_prompt']
-
-    if info['inpainting'] is True:
-        mask = mask.astype(np.uint8)
-        mask = Image.fromarray(mask)
-        mask = mask.filter(ImageFilter.GaussianBlur(radius=13))
-        mask = mask.filter(ImageFilter.MaxFilter(size=9))
-        mask = np.array(mask)
-
-        mask[mask < 0.1] = 0
-        mask[mask >= 0.1] = 1
-        mask = mask.astype(np.uint8)
-
-        conditioning = dict(
-            mask_image=mask,
-            positive_prompt=info['positive_prompt'],
-            negative_prompt=info['negative_prompt'],
-        )
-    else:
-        conditioning = dict(
-            mask_image=mask,
-            controlnet_conditioning_image=segmentation,
-            positive_prompt=info['positive_prompt'],
-            negative_prompt=info['negative_prompt'],
-            strength=info['strength']
-        )
-    return conditioning, info['inpainting']
\ No newline at end of file
diff --git a/spaces/mmecheri/Rakuten_Streamlit/demo.py b/spaces/mmecheri/Rakuten_Streamlit/demo.py
deleted file mode 100644
index 887ce857acb747c49c145bbcecb07e96e8e81e6c..0000000000000000000000000000000000000000
--- a/spaces/mmecheri/Rakuten_Streamlit/demo.py
+++ /dev/null
@@ -1,604 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Sat Mar 12 11:30:24 2022
-
-@author: MME
-"""
-import streamlit as st
-import  demo_inputs
-import  cleaning_text  
-from PIL import Image
-from io import BytesIO
-import numpy as np
-import cv2
-
-
-def app():
-   
-    
-    st.title("Prédictions (Démo)")
-    contenent_page(text_page ='./page_descriptions/demo_txt.md')
-
-
-def contenent_page(text_page):
-    
-    '''The text page. Read from .md file '''
-    with open(text_page, 'r', encoding='utf-8') as txtpage: 
-        txtpage = txtpage.read().split('---Insersetion---')
-        
-        st.markdown(txtpage[0], unsafe_allow_html=True) 
-
-     
-        classif_options = st.selectbox(
-        'Veuillez sélectionner le type de classification',
-          (['', 'A partir du Texte', 'A partir d\'Images', 'A partir du Texte et d\'Images(Bimodal)']))
-    
-        
-        if classif_options != '':
-                
-            if classif_options == 'A partir du Texte':
-                st.markdown(txtpage[1], unsafe_allow_html=True)                           
-                models_txt_options = st.selectbox(
-                  'Veuillez sélectionner un modèle',
-                  (['','Conv1D', 'Simple DNN']))
-            
-                
-            elif classif_options == 'A partir d\'Images':
-                st.markdown(txtpage[1], unsafe_allow_html=True) 
-                models_img_options = st.selectbox(
-                  'Veuillez sélectionner un modèle',
-                  (['', 'Xception', 'InceptionV3']))
-              
-                
-            elif classif_options == 'A partir du Texte et d\'Images(Bimodal)':
-                    st.markdown(txtpage[1], unsafe_allow_html=True) 
-                    models_bimod_options = st.selectbox(
-                      'Veuillez sélectionner une combinaison',
-                      (['', 'Conv1D & Simple DNN & Xception', 'Conv1D & Simple DNN & InceptionV3']))
-                                    
-               
-            st.markdown(txtpage[2], unsafe_allow_html=True)                    
-
-            source_options = st.selectbox(
-                      'Veuillez sélectionner une source de données',
-                      (['', 'Echantillon du jeu de données(Choix Aléatoire)', 'Manuelle' ]))
-
-     
-#***************************************************************************************************************************** 
-#------------------------------------------ Depuis le jeu de données d\'entrainement(Choix Aléatoire)--------------------------
-#*****************************************************************************************************************************          
-            if  classif_options == 'A partir du Texte' and models_txt_options == 'Conv1D':
-              if  source_options == 'Echantillon du jeu de données(Choix Aléatoire)':
-           
-                  if st.button('Obtenir un exemple et Classifier'): 
-                      design, descrip, im_name, text_cleaned, row_index = demo_inputs.get_random_row()
-                                        
-                      st.text_input('Designation', design, disabled = True)
-                      st.text_input('Description', descrip, disabled = True)
-                                    
-                      pred_class , pred_label ,y_pred_proba =  demo_inputs.predict_with_conv1D(text_cleaned)                    
-                      prodcode, label = demo_inputs.get_real_class_info(row_index)
-                      
-                      col1, col2 = st.columns(2)
-
-                      with col1: 
-                          msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                          msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                    
-                          precision = np.amax(y_pred_proba)
-                          precision = precision * 100
-                          precision = np.round(precision,2)
-                        
-                          msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                          st.markdown(msg1, unsafe_allow_html=True)
-                          st.markdown(msg2, unsafe_allow_html=True) 
-                          st.markdown(msg3, unsafe_allow_html=True) 
-                                      
-                      with col2:                       
-                          msg4 = '<span style="color:green">Classe rèelle du produit: '  + str(prodcode) + '</span>'
-                          msg5 = '<span style="color:green">Catégorie rèelle du produit : ' + str(label) + '</span>'
-                          st.markdown(msg4, unsafe_allow_html=True) 
-                          st.markdown(msg5, unsafe_allow_html=True) 
-                          
-# #****************************************************************************************************************                  
-            if  classif_options == 'A partir du Texte' and models_txt_options == 'Simple DNN':
-              if  source_options == 'Echantillon du jeu de données(Choix Aléatoire)':                   
-                   
-                  if st.button('Obtenir un exemple et Classifier'): 
-                      design, descrip, im_name, text_cleaned,row_index = demo_inputs.get_random_row()
-                                                  
-                      st.text_input('Designation', design, disabled = True)
-                      st.text_input('Description', descrip, disabled = True)
-                              
-                      pred_class , pred_label ,y_pred_proba =  demo_inputs.predict_with_simpDNN(text_cleaned) 
-                      prodcode, label = demo_inputs.get_real_class_info(row_index)
-              
-                      col1, col2 = st.columns(2)
-
-                      with col1: 
-                        msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                        msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                    
-                        precision = np.amax(y_pred_proba)
-                        precision = precision * 100
-                        precision = np.round(precision,2)
-                    
-                        msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                        st.markdown(msg1, unsafe_allow_html=True)
-                        st.markdown(msg2, unsafe_allow_html=True) 
-                        st.markdown(msg3, unsafe_allow_html=True) 
-                            
-                      with col2:                       
-                        msg4 = '<span style="color:green">Classe rèelle du produit: '  + str(prodcode) + '</span>'
-                        msg5 = '<span style="color:green">Catégorie rèelle du produit : ' + str(label) + '</span>'
-                        st.markdown(msg4, unsafe_allow_html=True) 
-                        st.markdown(msg5, unsafe_allow_html=True)
-                                  
-# #****************************************************************************************************************            
-            if  classif_options == 'A partir d\'Images' and models_img_options == 'Xception':
-              if  source_options == 'Echantillon du jeu de données(Choix Aléatoire)':
-             
-                  if st.button('Obtenir un exemple et Classifier'): 
-
-                    design, descrip, im_name, text_cleaned, row_index = demo_inputs.get_random_row()
-                
-                    image = Image.open(demo_inputs.images_dir + im_name)
-                    st.image(image, channels="BGR",width=299,caption= im_name)
-
-                    image = demo_inputs.prepare_image(image, target=(299, 299))
-                    
-                    pred_class ,pred_label,y_pred_proba = demo_inputs.predict_with_xception(image)
-                    prodcode, label = demo_inputs.get_real_class_info(row_index)
-                    
-                    col1, col2 = st.columns(2)
-
-                    with col1: 
-                      msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                      msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                    
-                      precision = np.amax(y_pred_proba)
-                      precision = precision * 100
-                      precision = np.round(precision,2)
-                    
-                      msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                      st.markdown(msg1, unsafe_allow_html=True)
-                      st.markdown(msg2, unsafe_allow_html=True) 
-                      st.markdown(msg3, unsafe_allow_html=True) 
-                                  
-                    with col2:                       
-                      msg4 = '<span style="color:green">Classe rèelle du produit: '  + str(prodcode) + '</span>'
-                      msg5 = '<span style="color:green">Catégorie rèelle du produit : ' + str(label) + '</span>'
-                      st.markdown(msg4, unsafe_allow_html=True) 
-                      st.markdown(msg5, unsafe_allow_html=True)
-   
-# #****************************************************************************************************************
-            if  classif_options == 'A partir d\'Images' and models_img_options == 'InceptionV3':
-              if  source_options == 'Echantillon du jeu de données(Choix Aléatoire)':
-                  
-                  if st.button('Obtenir un exemple et Classifier'): 
-
-                    design, descrip, im_name, text_cleaned, row_index = demo_inputs.get_random_row()
-                
-                    image = Image.open(demo_inputs.images_dir + im_name)
-                    st.image(image, channels="BGR",width=299,caption= im_name)
-
-                    image = demo_inputs.prepare_image(image, target=(299, 299))
-                    
-                    pred_class ,pred_label,y_pred_proba = demo_inputs.predict_with_inception(image)
-                    prodcode, label = demo_inputs.get_real_class_info(row_index)
-                    
-                    col1, col2 = st.columns(2)
-
-                    with col1: 
-                      msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                      msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                    
-                      precision = np.amax(y_pred_proba)
-                      precision = precision * 100
-                      precision = np.round(precision,2)
-                    
-                      msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                      st.markdown(msg1, unsafe_allow_html=True)
-                      st.markdown(msg2, unsafe_allow_html=True) 
-                      st.markdown(msg3, unsafe_allow_html=True) 
-                                  
-                    with col2:                       
-                      msg4 = '<span style="color:green">Classe rèelle du produit: '  + str(prodcode) + '</span>'
-                      msg5 = '<span style="color:green">Catégorie rèelle du produit : ' + str(label) + '</span>'
-                      st.markdown(msg4, unsafe_allow_html=True) 
-                      st.markdown(msg5, unsafe_allow_html=True)
-     
-##************************************************************************************************************************  
-#                                                 Bimodal
-##************************************************************************************************************************            
-            if  classif_options == 'A partir du Texte et d\'Images(Bimodal)' and models_bimod_options == 'Conv1D & Simple DNN & Xception':
-              if  source_options == 'Echantillon du jeu de données(Choix Aléatoire)':
-            
-                  if st.button('Obtenir un exemple et Classifier'): 
-                   
-                    design, descrip, im_name, text_cleaned,row_index= demo_inputs.get_random_row()
-           
-                    st.text_input('Designation', design, disabled = True)
-                    st.text_input('Description', descrip, disabled = True)
-                    
-                    image = Image.open(demo_inputs.images_dir + im_name)
-                    st.image(image, channels="BGR",width=299,caption= im_name)
-
-                    image = demo_inputs.prepare_image(image, target=(299, 299))
-
-                    pred_class ,pred_label, y_pred_proba = demo_inputs.predict_conv1D_simp_DNN_xception(text_cleaned, image)
-                    prodcode, label = demo_inputs.get_real_class_info(row_index)
-                    
-                    pred_class_conv1 , pred_label_conv1 ,y_pred_proba_conv1 =  demo_inputs.predict_with_conv1D(text_cleaned) 
-                    pred_class_sDNN , pred_label_sDNN ,y_pred_proba_sDNN =  demo_inputs.predict_with_simpDNN(text_cleaned) 
-                    pred_class_Xcep ,pred_label_Xcep,y_pred_proba_Xcep = demo_inputs.predict_with_xception(image) 
-                  
-                    col1, col2, col3 = st.columns(3)
-
-                    with col1: 
-                     msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                     msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                  
-                     precision = np.amax(y_pred_proba)
-                     precision = precision * 100
-                     precision = np.round(precision,2)
-                  
-                     msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                     st.markdown(msg1, unsafe_allow_html=True)
-                     st.markdown(msg2, unsafe_allow_html=True) 
-                     st.markdown(msg3, unsafe_allow_html=True) 
-                     
-                     with col2:                       
-                       msg4 = '<span style="color:black">La classe de produit et la catégorie prédites à partir du Texte avec le modèle Conv1D: '  + str(pred_class_conv1) +', ' +pred_label_conv1 + '</span>'
-                       st.markdown(msg4, unsafe_allow_html=True) 
-                       msg6 = '<span style="color:black">La classe de produit et la catégorie prédites  à partir du Texte avec le modèle Simple DNN: '  + str(pred_class_sDNN)  +', ' +pred_label_sDNN +  '</span>'
-                       st.markdown(msg6, unsafe_allow_html=True) 
-                       msg8 = '<span style="color:black">La classe de produit et la catégorie prédites  à partir de l\'Image avec le modèle Xception(CNN): '  + str(pred_class_Xcep) +', ' +pred_label_Xcep +  '</span>'
-                       st.markdown(msg8, unsafe_allow_html=True) 
-                      
-                    with col3:                       
-                       msg10 = '<span style="color:green">Classe rèelle du produit: '  + str(prodcode) + '</span>'
-                       msg11 = '<span style="color:green">Catégorie rèelle du produit : ' + str(label) + '</span>'
-                       st.markdown(msg10, unsafe_allow_html=True) 
-                       st.markdown(msg11, unsafe_allow_html=True)
-                          
-##**********************************************************************************************************************            
-            if  classif_options == 'A partir du Texte et d\'Images(Bimodal)' and models_bimod_options == 'Conv1D & Simple DNN & InceptionV3':
-              if  source_options == 'Echantillon du jeu de données(Choix Aléatoire)':
-                   
-                  if st.button('Obtenir un exemple et Classifier'): 
-                   
-                    design, descrip, im_name, text_cleaned,row_index= demo_inputs.get_random_row()
-                  
-                    st.text_input('Designation', design, disabled = True)
-                    st.text_input('Description', descrip, disabled = True)
-                    
-                    image = Image.open(demo_inputs.images_dir + im_name)
-                    st.image(image, channels="BGR",width=299,caption= im_name)
-
-                    image = demo_inputs.prepare_image(image, target=(299, 299))
-                    
-                    pred_class ,pred_label, y_pred_proba = demo_inputs.predict_conv1D_simp_DNN_inception(text_cleaned, image)
-                    prodcode, label = demo_inputs.get_real_class_info(row_index)
-                    
-                    pred_class_conv1 , pred_label_conv1 ,y_pred_proba_conv1 =  demo_inputs.predict_with_conv1D(text_cleaned) 
-                    pred_class_sDNN , pred_label_sDNN ,y_pred_proba_sDNN =  demo_inputs.predict_with_simpDNN(text_cleaned) 
-                    pred_class_incep ,pred_label_incep,y_pred_proba_incep = demo_inputs.predict_with_inception(image)
-                    
-                    col1, col2, col3 = st.columns(3)
-
-                    with col1: 
-                     msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                     msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                  
-                     precision = np.amax(y_pred_proba)
-                     precision = precision * 100
-                     precision = np.round(precision,2)
-                  
-                     msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                     st.markdown(msg1, unsafe_allow_html=True)
-                     st.markdown(msg2, unsafe_allow_html=True) 
-                     st.markdown(msg3, unsafe_allow_html=True) 
-                     
-                     with col2:                       
-                       msg4 = '<span style="color:black">La classe de produit et la catégorie prédites à partir du Texte avec le modèle Conv1D: '  + str(pred_class_conv1) +', ' +pred_label_conv1 + '</span>'
-                       st.markdown(msg4, unsafe_allow_html=True) 
-                       msg6 = '<span style="color:black">La classe de produit et la catégorie prédites  à partir du Texte avec le modèle Simple DNN: '  + str(pred_class_sDNN)  +', ' +pred_label_sDNN +  '</span>'
-                       st.markdown(msg6, unsafe_allow_html=True) 
-                       msg8 = '<span style="color:black">La classe de produit et la catégorie prédites  à partir de l\'Image avec le modèle InceptionV3(CNN): '  + str(pred_class_incep) +', ' +pred_label_incep +  '</span>'
-                       st.markdown(msg8, unsafe_allow_html=True) 
-                    
-                      
-                    with col3:                       
-                       msg10 = '<span style="color:green">Classe rèelle du produit: '  + str(prodcode) + '</span>'
-                       msg11 = '<span style="color:green">Catégorie rèelle du produit : ' + str(label) + '</span>'
-                       st.markdown(msg10, unsafe_allow_html=True) 
-                       st.markdown(msg11, unsafe_allow_html=True)
-                  
-##**************************************************************************************************************************** 
-##----------------------------------------------Manuel------------------------------------------------------------------------
-##****************************************************************************************************************************            
-            if  classif_options == 'A partir du Texte' and models_txt_options == 'Conv1D' :
-              if  source_options == 'Manuelle':
-                  
-                  user_Desig_input = st.text_area('Designation (obligatoire)', )
-                  user_Descrip_input = st.text_area('Description (optionnel)', )
-
-                  if st.button('Classifier'): 
-                     
-                      if user_Desig_input == "" or user_Desig_input.isspace():              
-                          st.write('Veuillez saisir le champ "Designation"' ) 
-
-                      else : 
-                        try:
-                            df = cleaning_text.createdfManuel(user_Desig_input, user_Descrip_input)
-                            df_cleaned = cleaning_text.CreateTextANDcleaning(df)                        
-            
-                            pred_class , pred_label ,y_pred_proba =  demo_inputs.predict_with_conv1D(df_cleaned)                    
-                    
-                            precision = np.amax(y_pred_proba)
-                            precision = precision * 100
-                            precision = np.round(precision,2)
-                          
-                            msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                            msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                            msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                            
-                            st.markdown(msg1, unsafe_allow_html=True)
-                            st.markdown(msg2, unsafe_allow_html=True)
-                            st.markdown(msg3, unsafe_allow_html=True) 
-
-                        except Exception as e:
-                            msgError = '<span style="color:red">Le informations texte fournies ne sont pas valides</span>'
-                            st.markdown(msgError, unsafe_allow_html=True)
-                         
-##**************************************************************************************************************** 
-##****************************************************************************************************************           
-            if  classif_options == 'A partir du Texte' and models_txt_options == 'Simple DNN' :
-              if  source_options == 'Manuelle':
-                  
-                  user_Desig_input = st.text_area('Designation (obligatoire)', )
-                  user_Descrip_input = st.text_area('Description (optionnel)', )
-
-                  if st.button('Classifier'): 
-                     
-                      if user_Desig_input == "" or user_Desig_input.isspace(): 
-                          st.write('Veuillez saisir le champ "Designation"' ) 
-                        
-                      else :
-                        try:  
-                            df = cleaning_text.createdfManuel(user_Desig_input, user_Descrip_input)  
-                            df_cleaned = cleaning_text.CreateTextANDcleaning(df)                        
-                            
-                            pred_class , pred_label ,y_pred_proba =  demo_inputs.predict_with_simpDNN(df_cleaned)
-                            
-                            precision = np.amax(y_pred_proba)
-                            precision = precision * 100
-                            precision = np.round(precision,2)
-                            
-                            msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                            msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                            msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                            st.markdown(msg1, unsafe_allow_html=True)
-                            st.markdown(msg2, unsafe_allow_html=True)
-                            st.markdown(msg3, unsafe_allow_html=True)
-
-                        except Exception as e:
-                            msgError = '<span style="color:red">Le informations texte fournies ne sont pas valides</span>'
-                            st.markdown(msgError, unsafe_allow_html=True)
-                
-
-##**************************************************************************************************************** 
-##****************************************************************************************************************            
-            if  classif_options == 'A partir d\'Images' and models_img_options == 'Xception' :
-              if  source_options == 'Manuelle':
-                      
-                  uploaded_file = st.file_uploader("Sélectionner un fichier image")
-
-                  if st.button('Classifier'): 
-
-                    if uploaded_file is  None:
-                      st.write('Veuillez charger une image')  
-
-                    elif uploaded_file is not None:                                             
-                      content =  uploaded_file.read()
-                      
-                      try: 
-                          image = Image.open(BytesIO(content)).convert("RGB")
-                          st.image(image, channels="BGR",width=299) 
-
-                          image = demo_inputs.prepare_image(image, target=(299, 299))
-
-                          pred_class ,pred_label,y_pred_proba = demo_inputs.predict_with_xception_manu(image)
-                          
-                          precision = np.amax(y_pred_proba)
-                          precision = precision * 100
-                          precision = np.round(precision,2)
-                          
-                          msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                          msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                          msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-
-                          st.markdown(msg1, unsafe_allow_html=True)
-                          st.markdown(msg2, unsafe_allow_html=True)
-                          st.markdown(msg3, unsafe_allow_html=True)
-
-                      except IOError:
-                          msgError = '<span style="color:red">Le fichier fourni n\'est pas une image valide</span>'
-                          st.markdown(msgError, unsafe_allow_html=True)
-
-##****************************************************************************************************************
-##****************************************************************************************************************            
-            if  classif_options == 'A partir d\'Images' and models_img_options == 'InceptionV3' :
-              if  source_options == 'Manuelle':
-
-                  uploaded_file = st.file_uploader("Sélectionner un fichier image")
-
-                  if st.button('Classifier'): 
-
-                    if uploaded_file is  None:
-                      st.write('Veuillez charger une image')  
-
-                    elif uploaded_file is not None:                                             
-                      content =  uploaded_file.read()
-                      
-                      try: 
-                          image = Image.open(BytesIO(content)).convert("RGB")
-                          st.image(image, channels="BGR",width=299) 
-
-                          image = demo_inputs.prepare_image(image, target=(299, 299))
-
-                          pred_class ,pred_label,y_pred_proba = demo_inputs.predict_with_inception_manu(image)
-                          
-                          precision = np.amax(y_pred_proba)
-                          precision = precision * 100
-                          precision = np.round(precision,2)
-                          
-                          msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                          msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'
-                          msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-
-                          st.markdown(msg1, unsafe_allow_html=True)
-                          st.markdown(msg2, unsafe_allow_html=True)
-                          st.markdown(msg3, unsafe_allow_html=True)
-
-                      except IOError:
-                          msgError = '<span style="color:red">Le fichier fourni n\'est pas une image valide</span>'
-                          st.markdown(msgError, unsafe_allow_html=True)                 
-
-##****************************************************************************************************************
-##****************************************************************************************************************            
-            if  classif_options == 'A partir du Texte et d\'Images(Bimodal)' and models_bimod_options == 'Conv1D & Simple DNN & Xception' :
-              if  source_options == 'Manuelle':
-                                         
-                       user_Desig_input = st.text_area('Designation (obligatoire)', )
-                       user_Descrip_input = st.text_area('Description (optionnel)', )                      
-                       uploaded_file = st.file_uploader("Sélectionner un fichier image")  
-
-                                                   
-                       if st.button('Classifier'):
-              
-                         if (user_Desig_input == "" or user_Desig_input.isspace()) and uploaded_file is None:
-                            st.write('Veuillez renseigner le champ "Designation" et charger une image' ) 
-                          
-                         elif uploaded_file is  None:
-                             st.write('Veuillez charger une image')
-                                      
-                         elif user_Desig_input == "" or user_Desig_input.isspace():                           
-                                 st.write('Veuillez renseigner le champ "Designation"')
-                             
-                         else :
-                             try:  
-                               content =  uploaded_file.read()                       
-                               image = Image.open(BytesIO(content)).convert("RGB")
-                               st.image(image, channels="BGR",width=299)               
-                               image = demo_inputs.prepare_image(image, target=(299, 299))
-
-                             except IOError:                          
-                                msgError = '<span style="color:red">Le fichier fourni n\'est pas une image valide</span>'
-                                st.markdown(msgError, unsafe_allow_html=True)
-                                #pass 
-                             else:
-                              try: 
-                                  df = cleaning_text.createdfManuel(user_Desig_input, user_Descrip_input) 
-                                  df_cleaned = cleaning_text.CreateTextANDcleaning(df)   
-                              except Exception as e:
-                                msgError = '<span style="color:red">Le informations texte fournies ne sont pas valides</span>'
-                                st.markdown(msgError, unsafe_allow_html=True)
-
-                              else:
-                                pred_class ,pred_label, proba = demo_inputs.predict_conv1D_simp_DNN_xception_manu(df_cleaned, image)                                                               
-                                pred_class_conv1 , pred_label_conv1 ,y_pred_proba_conv1 =  demo_inputs.predict_with_conv1D(df_cleaned) 
-                                pred_class_sDNN , pred_label_sDNN ,y_pred_proba_sDNN =  demo_inputs.predict_with_simpDNN(df_cleaned) 
-                                pred_class_Xcep ,pred_label_Xcep,y_pred_proba_Xcep = demo_inputs.predict_with_xception_manu(image) 
-                                
-                                col1, col2= st.columns(2)
-                                
-                                with col1: 
-                                  msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                                  msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'  
-                                  
-                                  precision = np.amax(proba)
-                                  precision = precision * 100
-                                  precision = np.round(precision,2)
-                                  
-                                  msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                                  st.markdown(msg1, unsafe_allow_html=True)
-                                  st.markdown(msg2, unsafe_allow_html=True)
-                                  st.markdown(msg3, unsafe_allow_html=True)
-                                                                    
-                                with col2: 
-                                  msg4 = '<span style="color:black">La classe de produit et la catégorie prédites à partir du Texte avec le modèle Conv1D: '  + str(pred_class_conv1) +', ' +pred_label_conv1 + '</span>'
-                                  st.markdown(msg4, unsafe_allow_html=True) 
-                                  msg6 = '<span style="color:black">La classe de produit et la catégorie prédites  à partir du Texte avec le modèle Simple DNN: '  + str(pred_class_sDNN)  +', ' +pred_label_sDNN +  '</span>'
-                                  st.markdown(msg6, unsafe_allow_html=True)                               
-                                  msg7 = '<span style="color:black">La classe de produit et la catégorie prédites  à partir de l\'Image avec le modèle Xception(CNN): '  + str(pred_class_Xcep) +', ' +pred_label_Xcep +  '</span>'
-                                  st.markdown(msg7, unsafe_allow_html=True) 
-
-                       
-##****************************************************************************************************************            
-            if  classif_options == 'A partir du Texte et d\'Images(Bimodal)' and models_bimod_options == 'Conv1D & Simple DNN & InceptionV3' :
-              if  source_options == 'Manuelle':
-
-                       user_Desig_input = st.text_area('Designation (obligatoire)', )
-                       user_Descrip_input = st.text_area('Description (optionnel)', )                      
-                       uploaded_file = st.file_uploader("Sélectionner un fichier image")  
-
-                       if st.button('Classifier'):
-              
-                         if (user_Desig_input == "" or user_Desig_input.isspace()) and uploaded_file is None:
-                            st.write('Veuillez renseigner le champ "Designation" et charger une image' ) 
-                          
-                         elif uploaded_file is  None:
-                             st.write('Veuillez charger une image')
-                                      
-                         elif user_Desig_input == "" or user_Desig_input.isspace():                           
-                                 st.write('Veuillez renseigner le champ "Designation"')
-                             
-                         else :
-                             try:
-                               content =  uploaded_file.read()                         
-                               image = Image.open(BytesIO(content)).convert("RGB")
-                               st.image(image, channels="BGR",width=299)               
-                               image = demo_inputs.prepare_image(image, target=(299, 299))
-
-                             except IOError:                          
-                                msgError = '<span style="color:red">Le fichier fourni n\'est pas une image valide</span>'
-                                st.markdown(msgError, unsafe_allow_html=True)
-                                #pass 
-                             else:
-                              try: 
-                                  df = cleaning_text.createdfManuel(user_Desig_input, user_Descrip_input) 
-                                  df_cleaned = cleaning_text.CreateTextANDcleaning(df)   
-                              except Exception as e:
-                                msgError = '<span style="color:red">Le informations texte fournies ne sont pas valides</span>'
-                                st.markdown(msgError, unsafe_allow_html=True)
-
-                              else:
-                                pred_class ,pred_label, proba = demo_inputs.predict_conv1D_simp_DNN_inception_manu(df_cleaned, image)                                                               
-                                pred_class_conv1 , pred_label_conv1 ,y_pred_proba_conv1 =  demo_inputs.predict_with_conv1D(df_cleaned) 
-                                pred_class_sDNN , pred_label_sDNN ,y_pred_proba_sDNN =  demo_inputs.predict_with_simpDNN(df_cleaned) 
-                                pred_class_Xcep ,pred_label_Xcep,y_pred_proba_Xcep = demo_inputs.predict_with_inception_manu(image) 
-                                
-                                col1, col2= st.columns(2)
-                                
-                                with col1: 
-                                  msg1 = '<span style="color:blue">La classe de produit prédite: '  + str(pred_class) + '</span>'
-                                  msg2 = '<span style="color:blue">La catégorie prédite : ' + pred_label + '</span>'  
-                                  
-                                  precision = np.amax(proba)
-                                  precision = precision * 100
-                                  precision = np.round(precision,2)
-                                  
-                                  msg3 = '<span style="color:blue">Certitude : ' + str(precision) +'%'+ '</span>'
-                                  st.markdown(msg1, unsafe_allow_html=True)
-                                  st.markdown(msg2, unsafe_allow_html=True)
-                                  st.markdown(msg3, unsafe_allow_html=True)
-                                                                    
-                                with col2: 
-                                  msg4 = '<span style="color:black">La classe de produit et la catégorie prédites à partir du Texte avec le modèle Conv1D: '  + str(pred_class_conv1) +', ' +pred_label_conv1 + '</span>'
-                                  st.markdown(msg4, unsafe_allow_html=True) 
-                                  msg6 = '<span style="color:black">La classe de produit et la catégorie prédites  à partir du Texte avec le modèle Simple DNN: '  + str(pred_class_sDNN)  +', ' +pred_label_sDNN +  '</span>'
-                                  st.markdown(msg6, unsafe_allow_html=True)                               
-                                  msg7 = '<span style="color:black">La classe de produit et la catégorie prédites  à partir de l\'Image avec le modèle Inception(CNN): '  + str(pred_class_Xcep) +', ' +pred_label_Xcep +  '</span>'
-                                  st.markdown(msg7, unsafe_allow_html=True) 
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/dedup_all.py b/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/dedup_all.py
deleted file mode 100644
index ef39c05ee606aaeda1d9e94970932d2241a8b281..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/dedup_all.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-
-import os
-import glob
-import argparse
-from utils.dedup import deup
-
-import sys
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or  not WORKDIR_ROOT.strip():
-    print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
-    sys.exit(-1)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--from-folder", type=str, required=True,
-                        help="the data folder to be dedup")
-    parser.add_argument("--to-folder", type=str, required=True,
-                        help="the data folder to save deduped data")
-    parser.add_argument('--directions', type=str, default=None, required=False)
-
-    args = parser.parse_args()    
-
-    if args.directions is None:
-        raw_files = glob.glob(f'{args.from_folder}/train*')
-
-        directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files]
-    else:
-        directions = args.directions.split(',')
-    directions = sorted(set(directions))
-    
-    for direction in directions:
-        src, tgt = direction.split('-')
-        src_file = f'{args.from_folder}/train.{src}-{tgt}.{src}'
-        tgt_file = f'{args.from_folder}/train.{src}-{tgt}.{tgt}'
-        src_file_out = f'{args.to_folder}/train.{src}-{tgt}.{src}'
-        tgt_file_out = f'{args.to_folder}/train.{src}-{tgt}.{tgt}'
-        assert src_file != src_file_out
-        assert tgt_file != tgt_file_out
-        print(f'deduping {src_file}, {tgt_file}')
-        deup(src_file, tgt_file, src_file_out, tgt_file_out)
-                
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/evaluation/eval_f0.py b/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/evaluation/eval_f0.py
deleted file mode 100644
index df721d683113b44957149cfc3cddaba36520a22c..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_synthesis/evaluation/eval_f0.py
+++ /dev/null
@@ -1,266 +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.
-
-"""
-Signal processing-based evaluation using waveforms
-"""
-import numpy as np
-import os.path as op
-
-import torchaudio
-import tqdm
-from tabulate import tabulate
-
-from examples.speech_synthesis.utils import (
-    gross_pitch_error, voicing_decision_error, f0_frame_error
-)
-from examples.speech_synthesis.evaluation.eval_sp import load_eval_spec
-
-
-def difference_function(x, n, tau_max):
-    """
-    Compute difference function of data x. This solution is implemented directly
-    with Numpy fft.
-
-
-    :param x: audio data
-    :param n: length of data
-    :param tau_max: integration window size
-    :return: difference function
-    :rtype: list
-    """
-
-    x = np.array(x, np.float64)
-    w = x.size
-    tau_max = min(tau_max, w)
-    x_cumsum = np.concatenate((np.array([0.]), (x * x).cumsum()))
-    size = w + tau_max
-    p2 = (size // 32).bit_length()
-    nice_numbers = (16, 18, 20, 24, 25, 27, 30, 32)
-    size_pad = min(x * 2 ** p2 for x in nice_numbers if x * 2 ** p2 >= size)
-    fc = np.fft.rfft(x, size_pad)
-    conv = np.fft.irfft(fc * fc.conjugate())[:tau_max]
-    return x_cumsum[w:w - tau_max:-1] + x_cumsum[w] - x_cumsum[:tau_max] - \
-        2 * conv
-
-
-def cumulative_mean_normalized_difference_function(df, n):
-    """
-    Compute cumulative mean normalized difference function (CMND).
-
-    :param df: Difference function
-    :param n: length of data
-    :return: cumulative mean normalized difference function
-    :rtype: list
-    """
-
-    # scipy method
-    cmn_df = df[1:] * range(1, n) / np.cumsum(df[1:]).astype(float)
-    return np.insert(cmn_df, 0, 1)
-
-
-def get_pitch(cmdf, tau_min, tau_max, harmo_th=0.1):
-    """
-    Return fundamental period of a frame based on CMND function.
-
-    :param cmdf: Cumulative Mean Normalized Difference function
-    :param tau_min: minimum period for speech
-    :param tau_max: maximum period for speech
-    :param harmo_th: harmonicity threshold to determine if it is necessary to
-    compute pitch frequency
-    :return: fundamental period if there is values under threshold, 0 otherwise
-    :rtype: float
-    """
-    tau = tau_min
-    while tau < tau_max:
-        if cmdf[tau] < harmo_th:
-            while tau + 1 < tau_max and cmdf[tau + 1] < cmdf[tau]:
-                tau += 1
-            return tau
-        tau += 1
-
-    return 0    # if unvoiced
-
-
-def compute_yin(sig, sr, w_len=512, w_step=256, f0_min=100, f0_max=500,
-                harmo_thresh=0.1):
-    """
-
-    Compute the Yin Algorithm. Return fundamental frequency and harmonic rate.
-
-    https://github.com/NVIDIA/mellotron adaption of
-    https://github.com/patriceguyot/Yin
-
-    :param sig: Audio signal (list of float)
-    :param sr: sampling rate (int)
-    :param w_len: size of the analysis window (samples)
-    :param w_step: size of the lag between two consecutives windows (samples)
-    :param f0_min: Minimum fundamental frequency that can be detected (hertz)
-    :param f0_max: Maximum fundamental frequency that can be detected (hertz)
-    :param harmo_thresh: Threshold of detection. The yalgorithmù return the
-    first minimum of the CMND function below this threshold.
-
-    :returns:
-
-        * pitches: list of fundamental frequencies,
-        * harmonic_rates: list of harmonic rate values for each fundamental
-        frequency value (= confidence value)
-        * argmins: minimums of the Cumulative Mean Normalized DifferenceFunction
-        * times: list of time of each estimation
-    :rtype: tuple
-    """
-
-    tau_min = int(sr / f0_max)
-    tau_max = int(sr / f0_min)
-
-    # time values for each analysis window
-    time_scale = range(0, len(sig) - w_len, w_step)
-    times = [t/float(sr) for t in time_scale]
-    frames = [sig[t:t + w_len] for t in time_scale]
-
-    pitches = [0.0] * len(time_scale)
-    harmonic_rates = [0.0] * len(time_scale)
-    argmins = [0.0] * len(time_scale)
-
-    for i, frame in enumerate(frames):
-        # Compute YIN
-        df = difference_function(frame, w_len, tau_max)
-        cm_df = cumulative_mean_normalized_difference_function(df, tau_max)
-        p = get_pitch(cm_df, tau_min, tau_max, harmo_thresh)
-
-        # Get results
-        if np.argmin(cm_df) > tau_min:
-            argmins[i] = float(sr / np.argmin(cm_df))
-        if p != 0:  # A pitch was found
-            pitches[i] = float(sr / p)
-            harmonic_rates[i] = cm_df[p]
-        else:  # No pitch, but we compute a value of the harmonic rate
-            harmonic_rates[i] = min(cm_df)
-
-    return pitches, harmonic_rates, argmins, times
-
-
-def extract_f0(samples):
-    f0_samples = []
-    for sample in tqdm.tqdm(samples):
-        if not op.isfile(sample["ref"]) or not op.isfile(sample["syn"]):
-            f0_samples.append(None)
-            continue
-
-        # assume single channel
-        yref, sr = torchaudio.load(sample["ref"])
-        ysyn, _sr = torchaudio.load(sample["syn"])
-        yref, ysyn = yref[0], ysyn[0]
-        assert sr == _sr, f"{sr} != {_sr}"
-
-        yref_f0 = compute_yin(yref, sr)
-        ysyn_f0 = compute_yin(ysyn, sr)
-
-        f0_samples += [
-            {
-                "ref": yref_f0,
-                "syn": ysyn_f0
-            }
-        ]
-
-    return f0_samples
-
-
-def eval_f0_error(samples, distortion_fn):
-    results = []
-    for sample in tqdm.tqdm(samples):
-        if sample is None:
-            results.append(None)
-            continue
-        # assume single channel
-        yref_f, _, _, yref_t = sample["ref"]
-        ysyn_f, _, _, ysyn_t = sample["syn"]
-
-        yref_f = np.array(yref_f)
-        yref_t = np.array(yref_t)
-        ysyn_f = np.array(ysyn_f)
-        ysyn_t = np.array(ysyn_t)
-
-        distortion = distortion_fn(yref_t, yref_f, ysyn_t, ysyn_f)
-        results.append((distortion.item(),
-                        len(yref_f),
-                        len(ysyn_f)
-                        ))
-    return results
-
-
-def eval_gross_pitch_error(samples):
-    return eval_f0_error(samples, gross_pitch_error)
-
-
-def eval_voicing_decision_error(samples):
-    return eval_f0_error(samples, voicing_decision_error)
-
-
-def eval_f0_frame_error(samples):
-    return eval_f0_error(samples, f0_frame_error)
-
-
-def print_results(results, show_bin):
-    results = np.array(list(filter(lambda x: x is not None, results)))
-
-    np.set_printoptions(precision=3)
-
-    def _print_result(results):
-        res = {
-            "nutt": len(results),
-            "error": results[:, 0].mean(),
-            "std": results[:, 0].std(),
-            "dur_ref": int(results[:, 1].sum()),
-            "dur_syn": int(results[:, 2].sum()),
-        }
-        print(tabulate([res.values()], res.keys(), floatfmt=".4f"))
-
-    print(">>>> ALL")
-    _print_result(results)
-
-    if show_bin:
-        edges = [0, 200, 400, 600, 800, 1000, 2000, 4000]
-        for i in range(1, len(edges)):
-            mask = np.logical_and(results[:, 1] >= edges[i-1],
-                                  results[:, 1] < edges[i])
-            if not mask.any():
-                continue
-            bin_results = results[mask]
-            print(f">>>> ({edges[i-1]}, {edges[i]})")
-            _print_result(bin_results)
-
-
-def main(eval_f0, gpe, vde, ffe, show_bin):
-    samples = load_eval_spec(eval_f0)
-    if gpe or vde or ffe:
-        f0_samples = extract_f0(samples)
-
-    if gpe:
-        print("===== Evaluate Gross Pitch Error =====")
-        results = eval_gross_pitch_error(f0_samples)
-        print_results(results, show_bin)
-    if vde:
-        print("===== Evaluate Voicing Decision Error =====")
-        results = eval_voicing_decision_error(f0_samples)
-        print_results(results, show_bin)
-    if ffe:
-        print("===== Evaluate F0 Frame Error =====")
-        results = eval_f0_frame_error(f0_samples)
-        print_results(results, show_bin)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("eval_f0")
-    parser.add_argument("--gpe", action="store_true")
-    parser.add_argument("--vde", action="store_true")
-    parser.add_argument("--ffe", action="store_true")
-    parser.add_argument("--show-bin", action="store_true")
-    args = parser.parse_args()
-
-    main(args.eval_f0, args.gpe, args.vde, args.ffe, args.show_bin)
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq_cli/__init__.py b/spaces/mshukor/UnIVAL/fairseq/fairseq_cli/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/musicians/deepharmony/lib/MusicXMLParser.py b/spaces/musicians/deepharmony/lib/MusicXMLParser.py
deleted file mode 100644
index aa212106c31e4aa764b56c275ef3b1ea5664d09a..0000000000000000000000000000000000000000
--- a/spaces/musicians/deepharmony/lib/MusicXMLParser.py
+++ /dev/null
@@ -1,1261 +0,0 @@
-# Copyright 2022 The Magenta 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.
-
-"""MusicXML parser.
-
-Simple MusicXML parser used to convert MusicXML into NoteSequence.
-"""
-
-import fractions
-import xml.etree.ElementTree as ET
-import zipfile
-
-import constants
-
-Fraction = fractions.Fraction
-
-DEFAULT_MIDI_PROGRAM = 0    # Default MIDI Program (0 = grand piano)
-DEFAULT_MIDI_CHANNEL = 0    # Default MIDI Channel (0 = first channel)
-MUSICXML_MIME_TYPE = 'application/vnd.recordare.musicxml+xml'
-
-
-class MusicXMLParseError(Exception):
-  """Exception thrown when the MusicXML contents cannot be parsed."""
-  pass
-
-
-class PitchStepParseError(MusicXMLParseError):
-  """Exception thrown when a pitch step cannot be parsed.
-
-  Will happen if pitch step is not one of A, B, C, D, E, F, or G
-  """
-  pass
-
-
-class ChordSymbolParseError(MusicXMLParseError):
-  """Exception thrown when a chord symbol cannot be parsed."""
-  pass
-
-
-class MultipleTimeSignatureError(MusicXMLParseError):
-  """Exception thrown when multiple time signatures found in a measure."""
-  pass
-
-
-class AlternatingTimeSignatureError(MusicXMLParseError):
-  """Exception thrown when an alternating time signature is encountered."""
-  pass
-
-
-class TimeSignatureParseError(MusicXMLParseError):
-  """Exception thrown when the time signature could not be parsed."""
-  pass
-
-
-class UnpitchedNoteError(MusicXMLParseError):
-  """Exception thrown when an unpitched note is encountered.
-
-  We do not currently support parsing files with unpitched notes (e.g.,
-  percussion scores).
-
-  http://www.musicxml.com/tutorial/percussion/unpitched-notes/
-  """
-  pass
-
-
-class KeyParseError(MusicXMLParseError):
-  """Exception thrown when a key signature cannot be parsed."""
-  pass
-
-
-class InvalidNoteDurationTypeError(MusicXMLParseError):
-  """Exception thrown when a note's duration type is invalid."""
-  pass
-
-
-class MusicXMLParserState(object):
-  """Maintains internal state of the MusicXML parser."""
-
-  def __init__(self):
-    # Default to one division per measure
-    # From the MusicXML documentation: "The divisions element indicates
-    # how many divisions per quarter note are used to indicate a note's
-    # duration. For example, if duration = 1 and divisions = 2,
-    # this is an eighth note duration."
-    self.divisions = 1
-
-    # Default to a tempo of 120 quarter notes per minute
-    # MusicXML calls this tempo, but Magenta calls this qpm
-    # Therefore, the variable is called qpm, but reads the
-    # MusicXML tempo attribute
-    # (120 qpm is the default tempo according to the
-    # Standard MIDI Files 1.0 Specification)
-    self.qpm = 120
-
-    # Duration of a single quarter note in seconds
-    self.seconds_per_quarter = 0.5
-
-    # Running total of time for the current event in seconds.
-    # Resets to 0 on every part. Affected by <forward> and <backup> elements
-    self.time_position = 0
-
-    # Default to a MIDI velocity of 64 (mf)
-    self.velocity = 64
-
-    # Default MIDI program (0 = grand piano)
-    self.midi_program = DEFAULT_MIDI_PROGRAM
-
-    # Current MIDI channel (usually equal to the part number)
-    self.midi_channel = DEFAULT_MIDI_CHANNEL
-
-    # Keep track of previous note to get chord timing correct
-    # This variable stores an instance of the Note class (defined below)
-    self.previous_note = None
-
-    # Keep track of current transposition level in +/- semitones.
-    self.transpose = 0
-
-    # Keep track of current time signature. Does not support polymeter.
-    self.time_signature = None
-
-
-class MusicXMLDocument(object):
-  """Internal representation of a MusicXML Document.
-
-  Represents the top level object which holds the MusicXML document
-  Responsible for loading the .xml or .mxl file using the _get_score method
-  If the file is .mxl, this class uncompresses it
-
-  After the file is loaded, this class then parses the document into memory
-  using the parse method.
-  """
-
-  def __init__(self, filename):
-    self._score = self._get_score(filename)
-    self.parts = []
-    # ScoreParts indexed by id.
-    self._score_parts = {}
-    self.midi_resolution = constants.STANDARD_PPQ
-    self._state = MusicXMLParserState()
-    # Total time in seconds
-    self.total_time_secs = 0
-    self._parse()
-
-  @staticmethod
-  def _get_score(score_string):
-    """Given a MusicXML file, return the score as an xml.etree.ElementTree.
-
-    Given a MusicXML file, return the score as an xml.etree.ElementTree
-    If the file is compress (ends in .mxl), uncompress it first
-
-    Args:
-        filename: The path of a MusicXML file
-
-    Returns:
-      The score as an xml.etree.ElementTree.
-
-    Raises:
-      MusicXMLParseError: if the file cannot be parsed.
-    """
-    score = None
-    score = ET.fromstring(score_string)
-    return score
-
-  def _parse(self):
-    """Parse the uncompressed MusicXML document."""
-    # Parse part-list
-    xml_part_list = self._score.find('part-list')
-    if xml_part_list is not None:
-      for element in xml_part_list:
-        if element.tag == 'score-part':
-          score_part = ScorePart(element)
-          self._score_parts[score_part.id] = score_part
-
-    # Parse parts
-    for score_part_index, child in enumerate(self._score.findall('part')):
-      part = Part(child, self._score_parts, self._state)
-      self.parts.append(part)
-      score_part_index += 1
-      if self._state.time_position > self.total_time_secs:
-        self.total_time_secs = self._state.time_position
-
-  def get_chord_symbols(self):
-    """Return a list of all the chord symbols used in this score."""
-    chord_symbols = []
-    for part in self.parts:
-      for measure in part.measures:
-        for chord_symbol in measure.chord_symbols:
-          if chord_symbol not in chord_symbols:
-            # Prevent duplicate chord symbols
-            chord_symbols.append(chord_symbol)
-    return chord_symbols
-
-  def get_time_signatures(self):
-    """Return a list of all the time signatures used in this score.
-
-    Does not support polymeter (i.e. assumes all parts have the same
-    time signature, such as Part 1 having a time signature of 6/8
-    while Part 2 has a simultaneous time signature of 2/4).
-
-    Ignores duplicate time signatures to prevent Magenta duplicate
-    time signature error. This happens when multiple parts have the
-    same time signature is used in multiple parts at the same time.
-
-    Example: If Part 1 has a time siganture of 4/4 and Part 2 also
-    has a time signature of 4/4, then only instance of 4/4 is sent
-    to Magenta.
-
-    Returns:
-      A list of all TimeSignature objects used in this score.
-    """
-    time_signatures = []
-    for part in self.parts:
-      for measure in part.measures:
-        if measure.time_signature is not None:
-          if measure.time_signature not in time_signatures:
-            # Prevent duplicate time signatures
-            time_signatures.append(measure.time_signature)
-
-    return time_signatures
-
-  def get_key_signatures(self):
-    """Return a list of all the key signatures used in this score.
-
-    Support different key signatures in different parts (score in
-    written pitch).
-
-    Ignores duplicate key signatures to prevent Magenta duplicate key
-    signature error. This happens when multiple parts have the same
-    key signature at the same time.
-
-    Example: If the score is in written pitch and the
-    flute is written in the key of Bb major, the trombone will also be
-    written in the key of Bb major. However, the clarinet and trumpet
-    will be written in the key of C major because they are Bb transposing
-    instruments.
-
-    If no key signatures are found, create a default key signature of
-    C major.
-
-    Returns:
-      A list of all KeySignature objects used in this score.
-    """
-    key_signatures = []
-    for part in self.parts:
-      for measure in part.measures:
-        if measure.key_signature is not None:
-          if measure.key_signature not in key_signatures:
-            # Prevent duplicate key signatures
-            key_signatures.append(measure.key_signature)
-
-    if not key_signatures:
-      # If there are no key signatures, add C major at the beginning
-      key_signature = KeySignature(self._state)
-      key_signature.time_position = 0
-      key_signatures.append(key_signature)
-
-    return key_signatures
-
-  def get_tempos(self):
-    """Return a list of all tempos in this score.
-
-    If no tempos are found, create a default tempo of 120 qpm.
-
-    Returns:
-      A list of all Tempo objects used in this score.
-    """
-    tempos = []
-
-    if self.parts:
-      part = self.parts[0]  # Use only first part
-      for measure in part.measures:
-        for tempo in measure.tempos:
-          tempos.append(tempo)
-
-    # If no tempos, add a default of 120 at beginning
-    if not tempos:
-      tempo = Tempo(self._state)
-      tempo.qpm = self._state.qpm
-      tempo.time_position = 0
-      tempos.append(tempo)
-
-    return tempos
-
-
-class ScorePart(object):
-  """"Internal representation of a MusicXML <score-part>.
-
-  A <score-part> element contains MIDI program and channel info
-  for the <part> elements in the MusicXML document.
-
-  If no MIDI info is found for the part, use the default MIDI channel (0)
-  and default to the Grand Piano program (MIDI Program #1).
-  """
-
-  def __init__(self, xml_score_part=None):
-    self.id = ''
-    self.part_name = ''
-    self.midi_channel = DEFAULT_MIDI_CHANNEL
-    self.midi_program = DEFAULT_MIDI_PROGRAM
-    if xml_score_part is not None:
-      self._parse(xml_score_part)
-
-  def _parse(self, xml_score_part):
-    """Parse the <score-part> element to an in-memory representation."""
-    self.id = xml_score_part.attrib['id']
-
-    if xml_score_part.find('part-name') is not None:
-      self.part_name = xml_score_part.find('part-name').text or ''
-
-    xml_midi_instrument = xml_score_part.find('midi-instrument')
-    if (xml_midi_instrument is not None and
-        xml_midi_instrument.find('midi-channel') is not None and
-        xml_midi_instrument.find('midi-program') is not None):
-      self.midi_channel = int(xml_midi_instrument.find('midi-channel').text)
-      self.midi_program = int(xml_midi_instrument.find('midi-program').text)
-    else:
-      # If no MIDI info, use the default MIDI channel.
-      self.midi_channel = DEFAULT_MIDI_CHANNEL
-      # Use the default MIDI program
-      self.midi_program = DEFAULT_MIDI_PROGRAM
-
-  def __str__(self):
-    score_str = 'ScorePart: ' + self.part_name
-    score_str += ', Channel: ' + str(self.midi_channel)
-    score_str += ', Program: ' + str(self.midi_program)
-    return score_str
-
-
-class Part(object):
-  """Internal represention of a MusicXML <part> element."""
-
-  def __init__(self, xml_part, score_parts, state):
-    self.id = ''
-    self.score_part = None
-    self.measures = []
-    self._state = state
-    self._parse(xml_part, score_parts)
-
-  def _parse(self, xml_part, score_parts):
-    """Parse the <part> element."""
-    if 'id' in xml_part.attrib:
-      self.id = xml_part.attrib['id']
-    if self.id in score_parts:
-      self.score_part = score_parts[self.id]
-    else:
-      # If this part references a score-part id that was not found in the file,
-      # construct a default score-part.
-      self.score_part = ScorePart()
-
-    # Reset the time position when parsing each part
-    self._state.time_position = 0
-    self._state.midi_channel = self.score_part.midi_channel
-    self._state.midi_program = self.score_part.midi_program
-    self._state.transpose = 0
-
-    xml_measures = xml_part.findall('measure')
-    for measure in xml_measures:
-      # Issue #674: Repair measures that do not contain notes
-      # by inserting a whole measure rest
-      self._repair_empty_measure(measure)
-      parsed_measure = Measure(measure, self._state)
-      self.measures.append(parsed_measure)
-
-  def _repair_empty_measure(self, measure):
-    """Repair a measure if it is empty by inserting a whole measure rest.
-
-    If a <measure> only consists of a <forward> element that advances
-    the time cursor, remove the <forward> element and replace
-    with a whole measure rest of the same duration.
-
-    Args:
-      measure: The measure to repair.
-    """
-    # Issue #674 - If the <forward> element is in a measure without
-    # any <note> elements, treat it as if it were a whole measure
-    # rest by inserting a rest of that duration
-    forward_count = len(measure.findall('forward'))
-    note_count = len(measure.findall('note'))
-    if note_count == 0 and forward_count == 1:
-      # Get the duration of the <forward> element
-      xml_forward = measure.find('forward')
-      xml_duration = xml_forward.find('duration')
-      forward_duration = int(xml_duration.text)
-
-      # Delete the <forward> element
-      measure.remove(xml_forward)
-
-      # Insert the new note
-      new_note = '<note>'
-      new_note += '<rest /><duration>' + str(forward_duration) + '</duration>'
-      new_note += '<voice>1</voice><type>whole</type><staff>1</staff>'
-      new_note += '</note>'
-      new_note_xml = ET.fromstring(new_note)
-      measure.append(new_note_xml)
-
-  def __str__(self):
-    part_str = 'Part: ' + self.score_part.part_name
-    return part_str
-
-
-class Measure(object):
-  """Internal represention of the MusicXML <measure> element."""
-
-  def __init__(self, xml_measure, state):
-    self.xml_measure = xml_measure
-    self.notes = []
-    self.chord_symbols = []
-    self.tempos = []
-    self.time_signature = None
-    self.key_signature = None
-    # Cumulative duration in MusicXML duration.
-    # Used for time signature calculations
-    self.duration = 0
-    self.state = state
-    # Record the starting time of this measure so that time signatures
-    # can be inserted at the beginning of the measure
-    self.start_time_position = self.state.time_position
-    self._parse()
-    # Update the time signature if a partial or pickup measure
-    self._fix_time_signature()
-
-  def _parse(self):
-    """Parse the <measure> element."""
-
-    for child in self.xml_measure:
-      if child.tag == 'attributes':
-        self._parse_attributes(child)
-      elif child.tag == 'backup':
-        self._parse_backup(child)
-      elif child.tag == 'direction':
-        self._parse_direction(child)
-      elif child.tag == 'forward':
-        self._parse_forward(child)
-      elif child.tag == 'note':
-        note = Note(child, self.state)
-        self.notes.append(note)
-        # Keep track of current note as previous note for chord timings
-        self.state.previous_note = note
-
-        # Sum up the MusicXML durations in voice 1 of this measure
-        if note.voice == 1 and not note.is_in_chord:
-          self.duration += note.note_duration.duration
-      elif child.tag == 'harmony':
-        chord_symbol = ChordSymbol(child, self.state)
-        self.chord_symbols.append(chord_symbol)
-
-      else:
-        # Ignore other tag types because they are not relevant to Magenta.
-        pass
-
-  def _parse_attributes(self, xml_attributes):
-    """Parse the MusicXML <attributes> element."""
-
-    for child in xml_attributes:
-      if child.tag == 'divisions':
-        self.state.divisions = int(child.text)
-      elif child.tag == 'key':
-        self.key_signature = KeySignature(self.state, child)
-      elif child.tag == 'time':
-        if self.time_signature is None:
-          self.time_signature = TimeSignature(self.state, child)
-          self.state.time_signature = self.time_signature
-        else:
-          raise MultipleTimeSignatureError('Multiple time signatures')
-      elif child.tag == 'transpose':
-        transpose = int(child.find('chromatic').text)
-        self.state.transpose = transpose
-        if self.key_signature is not None:
-          # Transposition is chromatic. Every half step up is 5 steps backward
-          # on the circle of fifths, which has 12 positions.
-          key_transpose = (transpose * -5) % 12
-          new_key = self.key_signature.key + key_transpose
-          # If the new key has >6 sharps, translate to flats.
-          # TODO(fjord): Could be more smart about when to use sharps vs. flats
-          # when there are enharmonic equivalents.
-          if new_key > 6:
-            new_key %= -6
-          self.key_signature.key = new_key
-      else:
-        # Ignore other tag types because they are not relevant to Magenta.
-        pass
-
-  def _parse_backup(self, xml_backup):
-    """Parse the MusicXML <backup> element.
-
-    This moves the global time position backwards.
-
-    Args:
-      xml_backup: XML element with tag type 'backup'.
-    """
-
-    xml_duration = xml_backup.find('duration')
-    backup_duration = int(xml_duration.text)
-    midi_ticks = backup_duration * (constants.STANDARD_PPQ
-                                    / self.state.divisions)
-    seconds = ((midi_ticks / constants.STANDARD_PPQ)
-               * self.state.seconds_per_quarter)
-    self.state.time_position -= seconds
-
-  def _parse_direction(self, xml_direction):
-    """Parse the MusicXML <direction> element."""
-
-    for child in xml_direction:
-      if child.tag == 'sound':
-        if child.get('tempo') is not None:
-          tempo = Tempo(self.state, child)
-          self.tempos.append(tempo)
-          self.state.qpm = tempo.qpm
-          self.state.seconds_per_quarter = 60 / self.state.qpm
-          if child.get('dynamics') is not None:
-            self.state.velocity = int(child.get('dynamics'))
-
-  def _parse_forward(self, xml_forward):
-    """Parse the MusicXML <forward> element.
-
-    This moves the global time position forward.
-
-    Args:
-      xml_forward: XML element with tag type 'forward'.
-    """
-
-    xml_duration = xml_forward.find('duration')
-    forward_duration = int(xml_duration.text)
-    midi_ticks = forward_duration * (constants.STANDARD_PPQ
-                                     / self.state.divisions)
-    seconds = ((midi_ticks / constants.STANDARD_PPQ)
-               * self.state.seconds_per_quarter)
-    self.state.time_position += seconds
-
-  def _fix_time_signature(self):
-    """Correct the time signature for incomplete measures.
-
-    If the measure is incomplete or a pickup, insert an appropriate
-    time signature into this Measure.
-    """
-    # Compute the fractional time signature (duration / divisions)
-    # Multiply divisions by 4 because division is always parts per quarter note
-    numerator = self.duration
-    denominator = self.state.divisions * 4
-    fractional_time_signature = Fraction(numerator, denominator)
-
-    if self.state.time_signature is None and self.time_signature is None:
-      # No global time signature yet and no measure time signature defined
-      # in this measure (no time signature or senza misura).
-      # Insert the fractional time signature as the time signature
-      # for this measure
-      self.time_signature = TimeSignature(self.state)
-      self.time_signature.numerator = fractional_time_signature.numerator
-      self.time_signature.denominator = fractional_time_signature.denominator
-      self.state.time_signature = self.time_signature
-    else:
-      fractional_state_time_signature = Fraction(
-          self.state.time_signature.numerator,
-          self.state.time_signature.denominator)
-
-      # Check for pickup measure. Reset time signature to smaller numerator
-      pickup_measure = False
-      if numerator < self.state.time_signature.numerator:
-        pickup_measure = True
-
-      # Get the current time signature denominator
-      global_time_signature_denominator = self.state.time_signature.denominator
-
-      # If the fractional time signature = 1 (e.g. 4/4),
-      # make the numerator the same as the global denominator
-      if fractional_time_signature == 1 and not pickup_measure:
-        new_time_signature = TimeSignature(self.state)
-        new_time_signature.numerator = global_time_signature_denominator
-        new_time_signature.denominator = global_time_signature_denominator
-      else:
-        # Otherwise, set the time signature to the fractional time signature
-        # Issue #674 - Use the original numerator and denominator
-        # instead of the fractional one
-        new_time_signature = TimeSignature(self.state)
-        new_time_signature.numerator = numerator
-        new_time_signature.denominator = denominator
-
-        new_time_sig_fraction = Fraction(numerator, denominator)
-
-        if new_time_sig_fraction == fractional_time_signature:
-          new_time_signature.numerator = fractional_time_signature.numerator
-          new_time_signature.denominator = fractional_time_signature.denominator
-
-      # Insert a new time signature only if it does not equal the global
-      # time signature.
-      if (pickup_measure or
-          (self.time_signature is None
-           and (fractional_time_signature != fractional_state_time_signature))):
-        new_time_signature.time_position = self.start_time_position
-        self.time_signature = new_time_signature
-        self.state.time_signature = new_time_signature
-
-
-class Note(object):
-  """Internal representation of a MusicXML <note> element."""
-
-  def __init__(self, xml_note, state):
-    self.xml_note = xml_note
-    self.voice = 1
-    self.is_rest = False
-    self.is_in_chord = False
-    self.is_grace_note = False
-    self.pitch = None               # Tuple (Pitch Name, MIDI number)
-    self.note_duration = NoteDuration(state)
-    self.state = state
-    self._parse()
-
-  def _parse(self):
-    """Parse the MusicXML <note> element."""
-
-    self.midi_channel = self.state.midi_channel
-    self.midi_program = self.state.midi_program
-    self.velocity = self.state.velocity
-
-    for child in self.xml_note:
-      if child.tag == 'chord':
-        self.is_in_chord = True
-      elif child.tag == 'duration':
-        self.note_duration.parse_duration(self.is_in_chord, self.is_grace_note,
-                                          child.text)
-      elif child.tag == 'pitch':
-        self._parse_pitch(child)
-      elif child.tag == 'rest':
-        self.is_rest = True
-      elif child.tag == 'voice':
-        self.voice = int(child.text)
-      elif child.tag == 'dot':
-        self.note_duration.dots += 1
-      elif child.tag == 'type':
-        self.note_duration.type = child.text
-      elif child.tag == 'time-modification':
-        # A time-modification element represents a tuplet_ratio
-        self._parse_tuplet(child)
-      elif child.tag == 'unpitched':
-        raise UnpitchedNoteError('Unpitched notes are not supported')
-      else:
-        # Ignore other tag types because they are not relevant to Magenta.
-        pass
-
-  def _parse_pitch(self, xml_pitch):
-    """Parse the MusicXML <pitch> element."""
-    step = xml_pitch.find('step').text
-    alter_text = ''
-    alter = 0.0
-    if xml_pitch.find('alter') is not None:
-      alter_text = xml_pitch.find('alter').text
-    octave = xml_pitch.find('octave').text
-
-    # Parse alter string to a float (floats represent microtonal alterations)
-    if alter_text:
-      alter = float(alter_text)
-
-    # Check if this is a semitone alter (i.e. an integer) or microtonal (float)
-    alter_semitones = int(alter)  # Number of semitones
-    is_microtonal_alter = (alter != alter_semitones)
-
-    # Visual pitch representation
-    alter_string = ''
-    if alter_semitones == -2:
-      alter_string = 'bb'
-    elif alter_semitones == -1:
-      alter_string = 'b'
-    elif alter_semitones == 1:
-      alter_string = '#'
-    elif alter_semitones == 2:
-      alter_string = 'x'
-
-    if is_microtonal_alter:
-      alter_string += ' (+microtones) '
-
-    # N.B. - pitch_string does not account for transposition
-    pitch_string = step + alter_string + octave
-
-    # Compute MIDI pitch number (C4 = 60, C1 = 24, C0 = 12)
-    midi_pitch = self.pitch_to_midi_pitch(step, alter, octave)
-    # Transpose MIDI pitch
-    midi_pitch += self.state.transpose
-    self.pitch = (pitch_string, midi_pitch)
-
-  def _parse_tuplet(self, xml_time_modification):
-    """Parses a tuplet ratio.
-
-    Represented in MusicXML by the <time-modification> element.
-
-    Args:
-      xml_time_modification: An xml time-modification element.
-    """
-    numerator = int(xml_time_modification.find('actual-notes').text)
-    denominator = int(xml_time_modification.find('normal-notes').text)
-    self.note_duration.tuplet_ratio = Fraction(numerator, denominator)
-
-  @staticmethod
-  def pitch_to_midi_pitch(step, alter, octave):
-    """Convert MusicXML pitch representation to MIDI pitch number."""
-    pitch_class = 0
-    if step == 'C':
-      pitch_class = 0
-    elif step == 'D':
-      pitch_class = 2
-    elif step == 'E':
-      pitch_class = 4
-    elif step == 'F':
-      pitch_class = 5
-    elif step == 'G':
-      pitch_class = 7
-    elif step == 'A':
-      pitch_class = 9
-    elif step == 'B':
-      pitch_class = 11
-    else:
-      # Raise exception for unknown step (ex: 'Q')
-      raise PitchStepParseError('Unable to parse pitch step ' + step)
-
-    pitch_class = (pitch_class + int(alter)) % 12
-    midi_pitch = (12 + pitch_class) + (int(octave) * 12)
-    return midi_pitch
-
-  def __str__(self):
-    note_string = '{duration: ' + str(self.note_duration.duration)
-    note_string += ', midi_ticks: ' + str(self.note_duration.midi_ticks)
-    note_string += ', seconds: ' + str(self.note_duration.seconds)
-    if self.is_rest:
-      note_string += ', rest: ' + str(self.is_rest)
-    else:
-      note_string += ', pitch: ' + self.pitch[0]
-      note_string += ', MIDI pitch: ' + str(self.pitch[1])
-
-    note_string += ', voice: ' + str(self.voice)
-    note_string += ', velocity: ' + str(self.velocity) + '} '
-    note_string += '(@time: ' + str(self.note_duration.time_position) + ')'
-    return note_string
-
-
-class NoteDuration(object):
-  """Internal representation of a MusicXML note's duration properties."""
-
-  TYPE_RATIO_MAP = {'maxima': Fraction(8, 1), 'long': Fraction(4, 1),
-                    'breve': Fraction(2, 1), 'whole': Fraction(1, 1),
-                    'half': Fraction(1, 2), 'quarter': Fraction(1, 4),
-                    'eighth': Fraction(1, 8), '16th': Fraction(1, 16),
-                    '32nd': Fraction(1, 32), '64th': Fraction(1, 64),
-                    '128th': Fraction(1, 128), '256th': Fraction(1, 256),
-                    '512th': Fraction(1, 512), '1024th': Fraction(1, 1024)}
-
-  def __init__(self, state):
-    self.duration = 0                   # MusicXML duration
-    self.midi_ticks = 0                 # Duration in MIDI ticks
-    self.seconds = 0                    # Duration in seconds
-    self.time_position = 0              # Onset time in seconds
-    self.dots = 0                       # Number of augmentation dots
-    self._type = 'quarter'              # MusicXML duration type
-    self.tuplet_ratio = Fraction(1, 1)  # Ratio for tuplets (default to 1)
-    self.is_grace_note = True           # Assume true until not found
-    self.state = state
-
-  def parse_duration(self, is_in_chord, is_grace_note, duration):
-    """Parse the duration of a note and compute timings."""
-    self.duration = int(duration)
-
-    # Due to an error in Sibelius' export, force this note to have the
-    # duration of the previous note if it is in a chord
-    if is_in_chord:
-      self.duration = self.state.previous_note.note_duration.duration
-
-    self.midi_ticks = self.duration
-    self.midi_ticks *= (constants.STANDARD_PPQ / self.state.divisions)
-
-    self.seconds = (self.midi_ticks / constants.STANDARD_PPQ)
-    self.seconds *= self.state.seconds_per_quarter
-
-    self.time_position = self.state.time_position
-
-    # Not sure how to handle durations of grace notes yet as they
-    # steal time from subsequent notes and they do not have a
-    # <duration> tag in the MusicXML
-    self.is_grace_note = is_grace_note
-
-    if is_in_chord:
-      # If this is a chord, set the time position to the time position
-      # of the previous note (i.e. all the notes in the chord will have
-      # the same time position)
-      self.time_position = self.state.previous_note.note_duration.time_position
-    else:
-      # Only increment time positions once in chord
-      self.state.time_position += self.seconds
-
-  def _convert_type_to_ratio(self):
-    """Convert the MusicXML note-type-value to a Python Fraction.
-
-    Examples:
-    - whole = 1/1
-    - half = 1/2
-    - quarter = 1/4
-    - 32nd = 1/32
-
-    Returns:
-      A Fraction object representing the note type.
-    """
-    return self.TYPE_RATIO_MAP[self.type]
-
-  def duration_ratio(self):
-    """Compute the duration ratio of the note as a Python Fraction.
-
-    Examples:
-    - Whole Note = 1
-    - Quarter Note = 1/4
-    - Dotted Quarter Note = 3/8
-    - Triplet eighth note = 1/12
-
-    Returns:
-      The duration ratio as a Python Fraction.
-    """
-    # Get ratio from MusicXML note type
-    duration_ratio = Fraction(1, 1)
-    type_ratio = self._convert_type_to_ratio()
-
-    # Compute tuplet ratio
-    duration_ratio /= self.tuplet_ratio
-    type_ratio /= self.tuplet_ratio
-
-    # Add augmentation dots
-    one_half = Fraction(1, 2)
-    dot_sum = Fraction(0, 1)
-    for dot in range(self.dots):
-      dot_sum += (one_half ** (dot + 1)) * type_ratio
-
-    duration_ratio = type_ratio + dot_sum
-
-    # If the note is a grace note, force its ratio to be 0
-    # because it does not have a <duration> tag
-    if self.is_grace_note:
-      duration_ratio = Fraction(0, 1)
-
-    return duration_ratio
-
-  def duration_float(self):
-    """Return the duration ratio as a float."""
-    ratio = self.duration_ratio()
-    return ratio.numerator / ratio.denominator
-
-  @property
-  def type(self):
-    return self._type
-
-  @type.setter
-  def type(self, new_type):
-    if new_type not in self.TYPE_RATIO_MAP:
-      raise InvalidNoteDurationTypeError(
-          'Note duration type "{}" is not valid'.format(new_type))
-    self._type = new_type
-
-
-class ChordSymbol(object):
-  """Internal representation of a MusicXML chord symbol <harmony> element.
-
-  This represents a chord symbol with four components:
-
-  1) Root: a string representing the chord root pitch class, e.g. "C#".
-  2) Kind: a string representing the chord kind, e.g. "m7" for minor-seventh,
-      "9" for dominant-ninth, or the empty string for major triad.
-  3) Scale degree modifications: a list of strings representing scale degree
-      modifications for the chord, e.g. "add9" to add an unaltered ninth scale
-      degree (without the seventh), "b5" to flatten the fifth scale degree,
-      "no3" to remove the third scale degree, etc.
-  4) Bass: a string representing the chord bass pitch class, or None if the bass
-      pitch class is the same as the root pitch class.
-
-  There's also a special chord kind "N.C." representing no harmony, for which
-  all other fields should be None.
-
-  Use the `get_figure_string` method to get a string representation of the chord
-  symbol as might appear in a lead sheet. This string representation is what we
-  use to represent chord symbols in NoteSequence protos, as text annotations.
-  While the MusicXML representation has more structure, using an unstructured
-  string provides more flexibility and allows us to ingest chords from other
-  sources, e.g. guitar tabs on the web.
-  """
-
-  # The below dictionary maps chord kinds to an abbreviated string as would
-  # appear in a chord symbol in a standard lead sheet. There are often multiple
-  # standard abbreviations for the same chord type, e.g. "+" and "aug" both
-  # refer to an augmented chord, and "maj7", "M7", and a Delta character all
-  # refer to a major-seventh chord; this dictionary attempts to be consistent
-  # but the choice of abbreviation is somewhat arbitrary.
-  #
-  # The MusicXML-defined chord kinds are listed here:
-  # http://usermanuals.musicxml.com/MusicXML/Content/ST-MusicXML-kind-value.htm
-
-  CHORD_KIND_ABBREVIATIONS = {
-      # These chord kinds are in the MusicXML spec.
-      'major': '',
-      'minor': 'm',
-      'augmented': 'aug',
-      'diminished': 'dim',
-      'dominant': '7',
-      'major-seventh': 'maj7',
-      'minor-seventh': 'm7',
-      'diminished-seventh': 'dim7',
-      'augmented-seventh': 'aug7',
-      'half-diminished': 'm7b5',
-      'major-minor': 'm(maj7)',
-      'major-sixth': '6',
-      'minor-sixth': 'm6',
-      'dominant-ninth': '9',
-      'major-ninth': 'maj9',
-      'minor-ninth': 'm9',
-      'dominant-11th': '11',
-      'major-11th': 'maj11',
-      'minor-11th': 'm11',
-      'dominant-13th': '13',
-      'major-13th': 'maj13',
-      'minor-13th': 'm13',
-      'suspended-second': 'sus2',
-      'suspended-fourth': 'sus',
-      'pedal': 'ped',
-      'power': '5',
-      'none': 'N.C.',
-
-      # These are not in the spec, but show up frequently in the wild.
-      'dominant-seventh': '7',
-      'augmented-ninth': 'aug9',
-      'minor-major': 'm(maj7)',
-
-      # Some abbreviated kinds also show up frequently in the wild.
-      '': '',
-      'min': 'm',
-      'aug': 'aug',
-      'dim': 'dim',
-      '7': '7',
-      'maj7': 'maj7',
-      'min7': 'm7',
-      'dim7': 'dim7',
-      'm7b5': 'm7b5',
-      'minMaj7': 'm(maj7)',
-      '6': '6',
-      'min6': 'm6',
-      'maj69': '6(add9)',
-      '9': '9',
-      'maj9': 'maj9',
-      'min9': 'm9',
-      'sus47': 'sus7'
-  }
-
-  def __init__(self, xml_harmony, state):
-    self.xml_harmony = xml_harmony
-    self.time_position = -1
-    self.root = None
-    self.kind = ''
-    self.degrees = []
-    self.bass = None
-    self.state = state
-    self._parse()
-
-  def _alter_to_string(self, alter_text):
-    """Parse alter text to a string of one or two sharps/flats.
-
-    Args:
-      alter_text: A string representation of an integer number of semitones.
-
-    Returns:
-      A string, one of 'bb', 'b', '#', '##', or the empty string.
-
-    Raises:
-      ChordSymbolParseError: If `alter_text` cannot be parsed to an integer,
-          or if the integer is not a valid number of semitones between -2 and 2
-          inclusive.
-    """
-    # Parse alter text to an integer number of semitones.
-    try:
-      alter_semitones = int(alter_text)
-    except ValueError:
-      raise ChordSymbolParseError('Non-integer alter: ' + str(alter_text))
-
-    # Visual alter representation
-    if alter_semitones == -2:
-      alter_string = 'bb'
-    elif alter_semitones == -1:
-      alter_string = 'b'
-    elif alter_semitones == 0:
-      alter_string = ''
-    elif alter_semitones == 1:
-      alter_string = '#'
-    elif alter_semitones == 2:
-      alter_string = '##'
-    else:
-      raise ChordSymbolParseError('Invalid alter: ' + str(alter_semitones))
-
-    return alter_string
-
-  def _parse(self):
-    """Parse the MusicXML <harmony> element."""
-    self.time_position = self.state.time_position
-
-    for child in self.xml_harmony:
-      if child.tag == 'root':
-        self._parse_root(child)
-      elif child.tag == 'kind':
-        if child.text is None:
-          # Seems like this shouldn't happen but frequently does in the wild...
-          continue
-        kind_text = str(child.text).strip()
-        if kind_text not in self.CHORD_KIND_ABBREVIATIONS:
-          raise ChordSymbolParseError('Unknown chord kind: ' + kind_text)
-        self.kind = self.CHORD_KIND_ABBREVIATIONS[kind_text]
-      elif child.tag == 'degree':
-        self.degrees.append(self._parse_degree(child))
-      elif child.tag == 'bass':
-        self._parse_bass(child)
-      elif child.tag == 'offset':
-        # Offset tag moves chord symbol time position.
-        try:
-          offset = int(child.text)
-        except ValueError:
-          raise ChordSymbolParseError('Non-integer offset: ' + str(child.text))
-        midi_ticks = offset * constants.STANDARD_PPQ / self.state.divisions
-        seconds = (midi_ticks / constants.STANDARD_PPQ *
-                   self.state.seconds_per_quarter)
-        self.time_position += seconds
-      else:
-        # Ignore other tag types because they are not relevant to Magenta.
-        pass
-
-    if self.root is None and self.kind != 'N.C.':
-      raise ChordSymbolParseError('Chord symbol must have a root')
-
-  def _parse_pitch(self, xml_pitch, step_tag, alter_tag):
-    """Parse and return the pitch-like <root> or <bass> element."""
-    if xml_pitch.find(step_tag) is None:
-      raise ChordSymbolParseError('Missing pitch step')
-    step = xml_pitch.find(step_tag).text
-
-    alter_string = ''
-    if xml_pitch.find(alter_tag) is not None:
-      alter_text = xml_pitch.find(alter_tag).text
-      alter_string = self._alter_to_string(alter_text)
-
-    if self.state.transpose:
-      raise ChordSymbolParseError(
-          'Transposition of chord symbols currently unsupported')
-
-    return step + alter_string
-
-  def _parse_root(self, xml_root):
-    """Parse the <root> tag for a chord symbol."""
-    self.root = self._parse_pitch(xml_root, step_tag='root-step',
-                                  alter_tag='root-alter')
-
-  def _parse_bass(self, xml_bass):
-    """Parse the <bass> tag for a chord symbol."""
-    self.bass = self._parse_pitch(xml_bass, step_tag='bass-step',
-                                  alter_tag='bass-alter')
-
-  def _parse_degree(self, xml_degree):
-    """Parse and return the <degree> scale degree modification element."""
-    if xml_degree.find('degree-value') is None:
-      raise ChordSymbolParseError('Missing scale degree value in harmony')
-    value_text = xml_degree.find('degree-value').text
-    if value_text is None:
-      raise ChordSymbolParseError('Missing scale degree')
-    try:
-      value = int(value_text)
-    except ValueError:
-      raise ChordSymbolParseError(
-          'Non-integer scale degree: ' + str(value_text))
-
-    alter_string = ''
-    if xml_degree.find('degree-alter') is not None:
-      alter_text = xml_degree.find('degree-alter').text
-      alter_string = self._alter_to_string(alter_text)
-
-    if xml_degree.find('degree-type') is None:
-      raise ChordSymbolParseError('Missing degree modification type')
-    type_text = xml_degree.find('degree-type').text
-
-    if type_text == 'add':
-      if not alter_string:
-        # When adding unaltered scale degree, use "add" string.
-        type_string = 'add'
-      else:
-        # When adding altered scale degree, "add" not necessary.
-        type_string = ''
-    elif type_text == 'subtract':
-      type_string = 'no'
-      # Alter should be irrelevant when removing scale degree.
-      alter_string = ''
-    elif type_text == 'alter':
-      if not alter_string:
-        raise ChordSymbolParseError('Degree alteration by zero semitones')
-      # No type string necessary as merely appending e.g. "#9" suffices.
-      type_string = ''
-    else:
-      raise ChordSymbolParseError(
-          'Invalid degree modification type: ' + str(type_text))
-
-    # Return a scale degree modification string that can be appended to a chord
-    # symbol figure string.
-    return type_string + alter_string + str(value)
-
-  def __str__(self):
-    if self.kind == 'N.C.':
-      note_string = '{kind: ' + self.kind + '} '
-    else:
-      note_string = '{root: ' + self.root
-      note_string += ', kind: ' + self.kind
-      note_string += ', degrees: [%s]' % ', '.join(degree
-                                                   for degree in self.degrees)
-      note_string += ', bass: ' + self.bass + '} '
-    note_string += '(@time: ' + str(self.time_position) + ')'
-    return note_string
-
-  def get_figure_string(self):
-    """Return a chord symbol figure string."""
-    if self.kind == 'N.C.':
-      return self.kind
-    else:
-      degrees_string = ''.join('(%s)' % degree for degree in self.degrees)
-      figure = self.root + self.kind + degrees_string
-      if self.bass:
-        figure += '/' + self.bass
-      return figure
-
-
-class TimeSignature(object):
-  """Internal representation of a MusicXML time signature.
-
-  Does not support:
-  - Composite time signatures: 3+2/8
-  - Alternating time signatures 2/4 + 3/8
-  - Senza misura
-  """
-
-  def __init__(self, state, xml_time=None):
-    self.xml_time = xml_time
-    self.numerator = -1
-    self.denominator = -1
-    self.time_position = 0
-    self.state = state
-    if xml_time is not None:
-      self._parse()
-
-  def _parse(self):
-    """Parse the MusicXML <time> element."""
-    if (len(self.xml_time.findall('beats')) > 1 or
-        len(self.xml_time.findall('beat-type')) > 1):
-      # If more than 1 beats or beat-type found, this time signature is
-      # not supported (ex: alternating meter)
-      raise AlternatingTimeSignatureError('Alternating Time Signature')
-
-    beats = self.xml_time.find('beats').text
-    beat_type = self.xml_time.find('beat-type').text
-    try:
-      self.numerator = int(beats)
-      self.denominator = int(beat_type)
-    except ValueError:
-      raise TimeSignatureParseError(
-          'Could not parse time signature: {}/{}'.format(beats, beat_type))
-    self.time_position = self.state.time_position
-
-  def __str__(self):
-    time_sig_str = str(self.numerator) + '/' + str(self.denominator)
-    time_sig_str += ' (@time: ' + str(self.time_position) + ')'
-    return time_sig_str
-
-  def __eq__(self, other):
-    isequal = self.numerator == other.numerator
-    isequal = isequal and (self.denominator == other.denominator)
-    isequal = isequal and (self.time_position == other.time_position)
-    return isequal
-
-  def __ne__(self, other):
-    return not self.__eq__(other)
-
-
-class KeySignature(object):
-  """Internal representation of a MusicXML key signature."""
-
-  def __init__(self, state, xml_key=None):
-    self.xml_key = xml_key
-    # MIDI and MusicXML identify key by using "fifths":
-    # -1 = F, 0 = C, 1 = G etc.
-    self.key = 0
-    # mode is "major" or "minor" only: MIDI only supports major and minor
-    self.mode = 'major'
-    self.time_position = -1
-    self.state = state
-    if xml_key is not None:
-      self._parse()
-
-  def _parse(self):
-    """Parse the MusicXML <key> element into a MIDI compatible key.
-
-    If the mode is not minor (e.g. dorian), default to "major"
-    because MIDI only supports major and minor modes.
-
-
-    Raises:
-      KeyParseError: If the fifths element is missing.
-    """
-    fifths = self.xml_key.find('fifths')
-    if fifths is None:
-      raise KeyParseError(
-          'Could not find fifths attribute in key signature.')
-    self.key = int(self.xml_key.find('fifths').text)
-    mode = self.xml_key.find('mode')
-    # Anything not minor will be interpreted as major
-    if mode != 'minor':
-      mode = 'major'
-    self.mode = mode
-    self.time_position = self.state.time_position
-
-  def __str__(self):
-    keys = (['Cb', 'Gb', 'Db', 'Ab', 'Eb', 'Bb', 'F', 'C', 'G', 'D',
-             'A', 'E', 'B', 'F#', 'C#'])
-    key_string = keys[self.key + 7] + ' ' + self.mode
-    key_string += ' (@time: ' + str(self.time_position) + ')'
-    return key_string
-
-  def __eq__(self, other):
-    isequal = self.key == other.key
-    isequal = isequal and (self.mode == other.mode)
-    isequal = isequal and (self.time_position == other.time_position)
-    return isequal
-
-
-class Tempo(object):
-  """Internal representation of a MusicXML tempo."""
-
-  def __init__(self, state, xml_sound=None):
-    self.xml_sound = xml_sound
-    self.qpm = -1
-    self.time_position = -1
-    self.state = state
-    if xml_sound is not None:
-      self._parse()
-
-  def _parse(self):
-    """Parse the MusicXML <sound> element and retrieve the tempo.
-
-    If no tempo is specified, default to DEFAULT_QUARTERS_PER_MINUTE
-    """
-    self.qpm = float(self.xml_sound.get('tempo'))
-    if self.qpm == 0:
-      # If tempo is 0, set it to default
-      self.qpm = constants.DEFAULT_QUARTERS_PER_MINUTE
-    self.time_position = self.state.time_position
-
-  def __str__(self):
-    tempo_str = 'Tempo: ' + str(self.qpm)
-    tempo_str += ' (@time: ' + str(self.time_position) + ')'
-    return tempo_str
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/data/proxy.py b/spaces/nateraw/deepafx-st/deepafx_st/data/proxy.py
deleted file mode 100644
index 79d6afd250269a9c65b0a6dc7c4fb1bccd5b1c2a..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/data/proxy.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import os
-import json
-import glob
-import torch
-import random
-from tqdm import tqdm
-
-# from deepafx_st.plugins.channel import Channel
-from deepafx_st.processors.processor import Processor
-from deepafx_st.data.audio import AudioFile
-import deepafx_st.utils as utils
-
-
-class DSPProxyDataset(torch.utils.data.Dataset):
-    """Class for generating input-output audio from Python DSP effects.
-
-    Args:
-        input_dir (List[str]): List of paths to the directories containing input audio files.
-        processor (Processor): Processor object to create proxy of.
-        processor_type (str): Processor name.
-        subset (str, optional): Dataset subset. One of ["train", "val", "test"]. Default: "train"
-        buffer_size_gb (float, optional): Size of audio to read into RAM in GB at any given time. Default: 10.0
-            Note: This is the buffer size PER DataLoader worker. So total RAM = buffer_size_gb * num_workers
-        buffer_reload_rate (int, optional): Number of items to generate before loading next chunk of dataset. Default: 10000
-        length (int, optional): Number of samples to load for each example. Default: 65536
-        num_examples_per_epoch (int, optional): Define an epoch as certain number of audio examples. Default: 10000
-        ext (str, optional): Expected audio file extension. Default: "wav"
-        hard_clip (bool, optional): Hard clip outputs between -1 and 1. Default: True
-    """
-
-    def __init__(
-        self,
-        input_dir: str,
-        processor: Processor,
-        processor_type: str,
-        subset="train",
-        length=65536,
-        buffer_size_gb=1.0,
-        buffer_reload_rate=1000,
-        half=False,
-        num_examples_per_epoch=10000,
-        ext="wav",
-        soft_clip=True,
-    ):
-        super().__init__()
-        self.input_dir = input_dir
-        self.processor = processor
-        self.processor_type = processor_type
-        self.subset = subset
-        self.length = length
-        self.buffer_size_gb = buffer_size_gb
-        self.buffer_reload_rate = buffer_reload_rate
-        self.half = half
-        self.num_examples_per_epoch = num_examples_per_epoch
-        self.ext = ext
-        self.soft_clip = soft_clip
-
-        search_path = os.path.join(input_dir, f"*.{ext}")
-        self.input_filepaths = glob.glob(search_path)
-        self.input_filepaths = sorted(self.input_filepaths)
-
-        if len(self.input_filepaths) < 1:
-            raise RuntimeError(f"No files found in {input_dir}.")
-
-        # get training split
-        self.input_filepaths = utils.split_dataset(
-            self.input_filepaths, self.subset, 0.9
-        )
-
-        # get details about audio files
-        cnt = 0
-        self.input_files = {}
-        for input_filepath in tqdm(self.input_filepaths, ncols=80):
-            file_id = os.path.basename(input_filepath)
-            audio_file = AudioFile(
-                input_filepath,
-                preload=False,
-                half=half,
-            )
-            if audio_file.num_frames < self.length:
-                continue
-            self.input_files[file_id] = audio_file
-            self.sample_rate = self.input_files[file_id].sample_rate
-            cnt += 1
-            if cnt > 1000:
-                break
-
-        # some setup for iteratble loading of the dataset into RAM
-        self.items_since_load = self.buffer_reload_rate
-
-    def __len__(self):
-        return self.num_examples_per_epoch
-
-    def load_audio_buffer(self):
-        self.input_files_loaded = {}  # clear audio buffer
-        self.items_since_load = 0  # reset iteration counter
-        nbytes_loaded = 0  # counter for data in RAM
-
-        # different subset in each
-        random.shuffle(self.input_filepaths)
-
-        # load files into RAM
-        for input_filepath in self.input_filepaths:
-            file_id = os.path.basename(input_filepath)
-            audio_file = AudioFile(
-                input_filepath,
-                preload=True,
-                half=self.half,
-            )
-
-            if audio_file.num_frames < self.length:
-                continue
-
-            self.input_files_loaded[file_id] = audio_file
-
-            nbytes = audio_file.audio.element_size() * audio_file.audio.nelement()
-            nbytes_loaded += nbytes
-
-            if nbytes_loaded > self.buffer_size_gb * 1e9:
-                break
-
-    def __getitem__(self, _):
-        """ """
-
-        # increment counter
-        self.items_since_load += 1
-
-        # load next chunk into buffer if needed
-        if self.items_since_load > self.buffer_reload_rate:
-            self.load_audio_buffer()
-
-        rand_input_file_id = utils.get_random_file_id(self.input_files_loaded.keys())
-        # use this random key to retrieve an input file
-        input_file = self.input_files_loaded[rand_input_file_id]
-
-        # load the audio data if needed
-        if not input_file.loaded:
-            input_file.load()
-
-        # get a random patch of size `self.length`
-        # start_idx, stop_idx = utils.get_random_patch(input_file, self.sample_rate, self.length)
-        start_idx, stop_idx = utils.get_random_patch(input_file, self.length)
-        input_audio = input_file.audio[:, start_idx:stop_idx].clone().detach()
-
-        # random scaling
-        input_audio /= input_audio.abs().max()
-        scale_dB = (torch.rand(1).squeeze().numpy() * 12) + 12
-        input_audio *= 10 ** (-scale_dB / 20.0)
-
-        # generate random parameters (uniform) over 0 to 1
-        params = torch.rand(self.processor.num_control_params)
-
-        # expects batch dim
-        # apply plugins with random parameters
-        if self.processor_type == "channel":
-            params[-1] = 0.5  # set makeup gain to 0dB
-            target_audio = self.processor(
-                input_audio.view(1, 1, -1),
-                params.view(1, -1),
-            )
-            target_audio = target_audio.view(1, -1)
-        elif self.processor_type == "peq":
-            target_audio = self.processor(
-                input_audio.view(1, 1, -1).numpy(),
-                params.view(1, -1).numpy(),
-            )
-            target_audio = torch.tensor(target_audio).view(1, -1)
-        elif self.processor_type == "comp":
-            params[-1] = 0.5  # set makeup gain to 0dB
-            target_audio = self.processor(
-                input_audio.view(1, 1, -1).numpy(),
-                params.view(1, -1).numpy(),
-            )
-            target_audio = torch.tensor(target_audio).view(1, -1)
-
-        # clip
-        if self.soft_clip:
-            # target_audio = target_audio.clamp(-2.0, 2.0)
-            target_audio = torch.tanh(target_audio / 2.0) * 2.0
-
-        return input_audio, target_audio, params
diff --git a/spaces/nehuggingface/cant/app.py b/spaces/nehuggingface/cant/app.py
deleted file mode 100644
index 943996b260c54b777d2a683ae99ad54a49884e3e..0000000000000000000000000000000000000000
--- a/spaces/nehuggingface/cant/app.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import openai
-
-openai.api_key = "59a71ffda88b4df2ac660a2e899a9819"
-openai.api_base =  "https://gpt-test-1-21-23.openai.azure.com/" # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
-openai.api_type = 'azure'
-openai.api_version = '2022-12-01' # this may change in the future
-
-import gradio as gr
-import openai
-
-# Insert your openai API key here
-# openai.api_key = "YOUR_API_KEY"
-
-
-
-
-
-
-def contract_clause_analyzer(text):
-
-    # prompt = f"Analyze the following contract clause: {text}"
-    prompt = f'''Read the following contract clause. Then figure out what kind of clause it is. Then write a standard, general version of that type of provision.
-
-    After that, write two newlines. Then provide an example of how unfair language might be hidden in the clause to illustrate the concept for educational purposes.
-
-    Then write two newlines. Then explain the clause type, what it's about, what's different, and how the new example provides an advantage to either party and why. 
-
-    Use the following format:
-    Original clause: <insert original clause here>
-
-    Market standard clause: <insert standard, general version of that type of clause here>
-
-    Unfair language injected into clause: <insert an example of how unfair language might be hidden in the clause here>
-
-    Explanation: <insert explanation here>
-
-    ////
-
-    Original clause: {text}
-
-    Market standard clause:
-
-    '''
-
-    prompt_simple_type_of_clause = f'''Read the following contract clause. Then figure out what kind of clause it is.
-
-    #####
-
-    {text}
-
-    Type of clause:'''
-
-
-    prompt_standardized_clause = f'''Read the following contract clause, and figure out what kind of clause it is. Then write a standardized generalized version of contract provision with that clause type.
-
-    #####
-
-    {text}
-
-    Generalized:'''
-
-
-    prompt_inject_unfair_language= f'''Read the following contract clause, and provide an example of how unfair language might be hidden in the clause to illustrate the concept for educational purposes.
-
-    #####
-
-    {text}
-
-    Clause with injected unfair language:'''
-
-
-
-
-
-    type_of_clause_response = openai.Completion.create(
-        prompt=prompt_simple_type_of_clause,
-        max_tokens= 2000,
-        engine  ="tmp-deployment-text-davinci-003"
-        # engine="text-davinci-003",
-        # **generation_config.__dict__,
-        # engine="text-davinci-002",
-        # prompt=f"Analyze the following contract clause: {text}",
-        # temperature=0.5
-    )
-
-    standard_clause_response = openai.Completion.create(
-        prompt=prompt_standardized_clause,
-        max_tokens= 2000,
-        engine  ="tmp-deployment-text-davinci-003"
-        # engine="text-davinci-003",
-        # **generation_config.__dict__,
-        # engine="text-davinci-002",
-        # prompt=f"Analyze the following contract clause: {text}",
-        # temperature=0.5
-    )
-
-    injected_response = openai.Completion.create(
-        prompt=prompt_inject_unfair_language,
-        max_tokens= 2000,
-        engine  ="tmp-deployment-text-davinci-003"
-        # engine="text-davinci-003",
-        # **generation_config.__dict__,
-        # engine="text-davinci-002",
-        # prompt=f"Analyze the following contract clause: {text}",
-        # temperature=0.5
-    )
-
-    # prompt_inject_unfair_language=f'''Compare the following clauses, explaining the clause type, what it's about, why the clauses are different, and whether one provides an advantage to either party and why: 
-
-    prompt_inject_unfair_language=f'''Compare the following two clauses, write how the clauses are different, and whether one provides an advantage to either party and why: 
-
-
-    #####
-
-    Clause 1: {text}
-
-    Clause 2: {injected_response.choices[0].text}
-    
-
-    Difference:'''
-
-
-    explain_difference_response= openai.Completion.create(
-        prompt=prompt_inject_unfair_language,
-        max_tokens= 2000,
-        engine  ="tmp-deployment-text-davinci-003"
-        # engine="text-davinci-003",
-        # **generation_config.__dict__,
-        # engine="text-davinci-002",
-        # prompt=f"Analyze the following contract clause: {text}",
-        # temperature=0.5
-    )
-
-
-    return type_of_clause_response.choices[0].text , standard_clause_response.choices[0].text,injected_response.choices[0].text,explain_difference_response.choices[0].text
-
-# Create the Gradio interface
-# interface = gr.Interface(contract_clause_analyzer, gr.inputs.Textbox(label="Enter contract clause"), gr.outputs.Textbox(label="Analysis"))
-
-examples = [
-"5.20. Amendment to Supply Agreement. Each of Parent and the Company hereby agrees, on behalf of themselves and their respective subsidiaries, that that certain Addendum to the Master Purchase Agreements, dated as of July 8, 2019, by and between (i) Parent and Cisco Systems International B.V., a Netherlands corporation and wholly owned subsidiary of Parent, and (ii) the Company and Acacia Communications (Ireland) Limited, a Subsidiary of the Company, is hereby amended, effective as of the Agreement Date, to remove Section 7 thereof, which section shall have no further effect.",
-"(a) Representations and Warranties. (i) The representations and warranties of Parent in the first sentence of Section 3.1, Section 3.2(a), Section 3.4 and Section 3.8 shall be true and correct in all material respects on and as of the Agreement Date and on and as of the Closing Date as though such representations and warranties were made on and as of such date (except for representations and warranties that address matters only as to a specified date, which representations and warranties shall be true and correct with respect to such specified date) and (ii) all other representations and warranties of Parent in this Agreement, disregarding all qualifications and exceptions contained therein relating to materiality or Parent Material Adverse Effect or any similar standard or qualification, shall be true and correct on and as of the Agreement Date and on and as of the Closing Date as though such representations and warranties were made on and as of such date (except for representations and warranties that address matters only as to a specified date, which representations and warranties shall be true and correct with respect to such specified date), except where the circumstances causing the failure of such representations or warranties to be true and correct have not had, and would not reasonably be expected to have, individually or in the aggregate, a Parent Material Adverse Effect. The Company shall have received a certificate to such effect signed on behalf of Parent by a duly authorized officer of Parent.",
-"7.1. Termination. At any time prior to the Effective Time, this Agreement may be terminated and the Merger abandoned by action taken or authorized by the board of directors of the terminating party or parties, which action (x) in the case of termination pursuant to Section 7.1(a), Section 7.1(b), Section 7.1(c), Section 7.1(e) or Section 7.1(f), may be taken or authorized before or after the Company Stockholder Approval has been obtained, (y) in the case of termination pursuant to Section 7.1(g) or Section 7.1(h), may be taken or authorized only before the Company Stockholder Approval has been obtained and (z) in the case of termination pursuant to Section 7.1(d), may be taken or authorized only after the Company Stockholder Meeting has been held at which a vote was taken on the Company Stockholder Approval:",
-
-
-]
-
-
-outputs = [gr.outputs.Textbox(label="Type of Clause"),
-    gr.outputs.Textbox(label="Standardized Clause"),
-    gr.outputs.Textbox(label="Clause with injected unfair language"),
-    gr.outputs.Textbox(label="Explanation"),
-    ]
-
-interface = gr.Interface(contract_clause_analyzer, gr.inputs.Textbox(label="Enter contract clause"),
-    outputs=outputs,
-    examples=examples
- )
-
-
-
-                        # gr.outputs.Textbox(label="Type of Clause"),
-                        # gr.outputs.Textbox(label="Standardized Clause")
-
-
-# Run the interface
-# interface.launch(share=True)
-interface.launch()
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Swat.4.tss.radar.25.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Swat.4.tss.radar.25.md
deleted file mode 100644
index 5668979cbb0eacd419845c667816a2ea19f78cfe..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Swat.4.tss.radar.25.md
+++ /dev/null
@@ -1,173 +0,0 @@
-<br />
-<h1>What is swat.4.tss.radar.25 and why you need it</h1>
-<p>If you are a fan of tactical shooter games, you might have heard of Swat 4: The Stetchkov Syndicate, an expansion pack for the original Swat 4 game released in 2005. This game puts you in the shoes of a SWAT leader who has to deal with a variety of dangerous situations involving the notorious Stetchkov crime family.</p>
-<h2>swat.4.tss.radar.25</h2><br /><p><b><b>Download Zip</b> &#10001; &#10001; &#10001; <a href="https://urlcod.com/2uIacD">https://urlcod.com/2uIacD</a></b></p><br /><br />
-<p>But what if you want to spice up your gameplay and get an edge over your enemies? That's where swat.4.tss.radar.25 comes in handy. This is a mod that adds a radar hack to the game, allowing you to see the location and status of all the suspects, hostages, teammates, and objectives on your screen.</p>
-<p>In this article, we will explain what swat.4.tss.radar.25 is, how to use it, what are its benefits and drawbacks, and what are some alternatives to it. By the end of this article, you will have a better understanding of this mod and whether you should use it or not.</p>
- <h2>Swat 4: The Stetchkov Syndicate - a brief overview</h2>
-<p>Before we dive into the details of swat.4.tss.radar.25, let's take a look at the game itself. Swat 4: The Stetchkov Syndicate is an expansion pack for Swat 4, a tactical first-person shooter game developed by Irrational Games and published by Vivendi Universal Games in 2005.</p>
-<h3>The game features and modes</h3>
-<p>The game features seven new missions that take place in various locations such as a nightclub, a subway station, a meat packing plant, and a tenement building. The missions involve dealing with the Stetchkov crime family, a ruthless Russian mafia group that is involved in illegal arms trafficking, drug smuggling, kidnapping, and murder.</p>
-<p></p>
-<p>The game also adds two new multiplayer modes: Smash and Grab and Co-op VIP Escort. Smash and Grab is a mode where one team has to break into a vault and steal money while the other team has to stop them. Co-op VIP Escort is a mode where one team has to escort a VIP to safety while the other team has to assassinate him.</p>
-<p>The game also adds seven new weapons, such as the AK-47, the Uzi, the TEC-9, and the FN P90. Additionally, the game introduces some new gameplay features, such as:</p>
-<ul>
-<li>VoIP capability for multiplayer games</li>
-<li>The ability to strike suspects or hostages with your weapon to prompt compliance</li>
-<li>The possibility of suspects picking up their weapons if they are not swiftly arrested</li>
-<li>10 player co-op with up to two teams of five</li>
-<li>Stat tracking, <h3>The Stetchkov crime family and the missions</h3>
-<p>The main antagonists of the game are the Stetchkov crime family, a powerful and violent Russian mafia group that operates in the city. The family is led by Alexei Stetchkov, a ruthless and ambitious crime boss who has a personal vendetta against the SWAT team. The family consists of several members, such as:</p>
-<ul>
-<li>Boris Stetchkov, Alexei's younger brother and second-in-command</li>
-<li>Andrei Stetchkov, Alexei's cousin and enforcer</li>
-<li>Nikolai Stetchkov, Alexei's uncle and arms dealer</li>
-<li>Sergei Stetchkov, Alexei's nephew and hacker</li>
-<li>Ivan Stetchkov, Alexei's cousin and drug lord</li>
-</ul>
-<p>The missions of the game are based on real-life SWAT scenarios and require the player to use tactics, teamwork, and non-lethal force whenever possible. The missions are as follows:</p>
-<ol>
-<li>The Wolcott Projects: The SWAT team is called to a tenement building where a group of Stetchkov thugs have taken over and are holding several hostages.</li>
-<li>The Old Granite Hotel: The SWAT team is sent to a rundown hotel where a hostage situation has turned into a shootout between the Stetchkovs and a rival gang.</li>
-<li>The Red Library Offices: The SWAT team is tasked with raiding the offices of a software company that is secretly run by Sergei Stetchkov, who is using it as a front for his hacking activities.</li>
-<li>The Department of Agriculture: The SWAT team is alerted to a bomb threat at the Department of Agriculture building, where Nikolai Stetchkov is planning to detonate a dirty bomb.</li>
-<li>The Fresnal Street Station: The SWAT team is dispatched to a subway station where Andrei Stetchkov and his men have hijacked a train and are holding the passengers hostage.</li>
-<li>The Meat Barn Restaurant: The SWAT team is assigned to infiltrate a meat packing plant that is being used by Ivan Stetchkov as a drug lab and distribution center.</li>
-<li>The Stetchkov Warehouse: The SWAT team is ordered to storm the Stetchkovs' main warehouse, where Alexei and Boris are preparing for their final confrontation with the SWAT team.</li>
-</ol>
- <h2>How to use swat.4.tss.radar.25 - a step by step guide</h2>
-<p>Now that you have an idea of what Swat 4: The Stetchkov Syndicate is all about, let's see how you can use swat.4.tss.radar.25 to enhance your gameplay experience. This mod is a radar hack that allows you to see the location and status of all the suspects, hostages, teammates, and objectives on your screen. This can give you an advantage in planning your strategy, avoiding ambushes, and completing your mission objectives.</p>
-<h3>What is swat.4.tss.radar.25 and what does it do</h3>
-<p>Swat.4.tss.radar.25 is a mod that was created by an anonymous user on an online forum. It is not an official part of the game, but rather a third-party modification that alters some of the game files. The mod adds a radar hack to the game, which displays the following information on your screen:</p>
-<ul>
-<li>The location of all the suspects, hostages, teammates, and objectives on the map</li>
-<li>The status of each suspect (alive, dead, injured, compliant, or hostile)</li>
-<li>The status of each hostage (alive, dead, injured, or rescued)</li>
-<li>The status of each teammate (alive, dead, injured, or incapacitated)</li>
-<li>The status of each objective (completed or pending)</li>
-</ul>
-<p>The radar hack also allows you to zoom in and out of the map, change the color scheme of the radar, and toggle it on and off with a hotkey.</p>
- <h3>How to download and install swat.4.tss.radar.25</h3>
-<p>To use swat.4.tss.radar.25, you need to download and install it on your computer. Here are the steps you need to follow:</p>
- <ol>
-<li>Download swat.4.tss.radar.25 from this link: </li>
-<li>Extract the zip file to your Swat 4: The Stetchkov Syndicate folder (usually located at C:\Program Files\Sierra\SWAT 4\ContentExpansion)</li>
-<li>Run swat4x.exe as administrator</li>
-<li>Enjoy the game with the radar hack enabled</li>
-</ol> <h3>How to activate and configure swat.4.tss.radar.25</h3>
-<p>Once you have installed swat.4.tss.radar.25, you need to activate and configure it to suit your preferences. Here are the steps you need to follow:</p>
-<ol>
-<li>Launch the game and start a single-player or multiplayer mode</li>
-<li>Press the Insert key on your keyboard to open the radar hack menu</li>
-<li>Use the arrow keys and the Enter key to navigate the menu and change the settings</li>
-<li>Some of the settings you can change are:</li>
-<ul>
-<li>The size of the radar (small, medium, or large)</li>
-<li>The position of the radar (top-left, top-right, bottom-left, or bottom-right)</li>
-<li>The color of the radar (red, green, blue, yellow, or white)</li>
-<li>The transparency of the radar (0% to 100%)</li>
-<li>The zoom level of the radar (1x to 10x)</li>
-<li>The hotkey to toggle the radar on and off (default is Insert)</li>
-</ul>
-<li>Press the Insert key again to close the menu and save your settings</li>
-</ol>
- <h3>How to use swat.4.tss.radar.25 in game</h3>
-<p>Now that you have activated and configured swat.4.tss.radar.25, you can use it in game to enhance your gameplay experience. Here are some tips on how to use it effectively:</p>
-<ul>
-<li>Use the radar to plan your strategy before entering a room or a building. You can see where the suspects, hostages, teammates, and objectives are located and what their status is.</li>
-<li>Use the radar to avoid ambushes and traps. You can see if there are any hidden enemies or explosives behind doors or walls.</li>
-<li>Use the radar to coordinate with your teammates. You can see where they are and what they are doing, and communicate with them using VoIP or chat.</li>
-<li>Use the radar to complete your mission objectives. You can see if you have secured all the suspects, rescued all the hostages, and cleared all the areas.</li>
-<li>Use the zoom function to get a closer or wider view of the map. You can adjust the zoom level according to your needs.</li>
-<li>Use the toggle function to turn the radar on and off as needed. You can disable the radar if you want a more realistic or challenging gameplay.</li>
-</ul>
- <h2>The benefits and drawbacks of swat.4.tss.radar.25</h2>
-<p>Swat.4.tss.radar.25 is a mod that can make your gameplay more fun and easy, but it also has some drawbacks and risks that you should be aware of. Here are some of the pros and cons of using this mod:</p>
- <h3>The advantages of using swat.4.tss.radar.25</h3>
-<p>Some of the benefits of using swat.4.tss.radar.25 are:</p>
-<ul>
-<li>It gives you an edge over your enemies by showing you their location and status.</li>
-<li>It helps you avoid unnecessary casualties by showing you where the hostages are and if they are alive or dead.</li>
-<li>It improves your teamwork by showing you where your teammates are and what they are doing.</li>
-<li>It enhances your performance by showing you where the objectives are and if they are completed or pending.</li>
-<li>It allows you to customize your radar according to your preferences by changing its size, position, color, transparency, zoom level, and hotkey.</li>
-</ul>
- <h3>The disadvantages and risks of using swat.4.tss.radar.25</h3>
-<p>Some of the drawbacks and risks of using swat.4.tss.radar.25 are:</p>
-<ul>
-<li>It reduces the realism and challenge of the game by making it too easy and predictable.</li>
-<li>It may cause compatibility issues with other mods or updates for the game.</li>
-<li>It may contain viruses or malware that can harm your computer or steal your personal information.</li>
-<li>It may violate the terms of service or code of conduct of the game or its online servers.</li>
-<li>It may get you banned from playing online or accessing certain features of the game.</li>
-</ul>
- <h2>The alternatives to swat.4.tss.radar.25</h2>
- <p>If you are looking for other ways to enhance your gameplay experience without using swat.4.tss.radar.25, here are some alternatives that you can try:</p>
- <h3>Other mods and hacks for Swat 4: The Stetchkov Syndicate</h3>
- <p>There <p>There are many other mods and hacks for Swat 4: The Stetchkov Syndicate that you can download and install to modify your game. Some of them are:</p>
-<ul>
-<li>Swat 4 Remake Mod: This mod improves the graphics, sounds, animations, and gameplay of the game. It also adds new weapons, equipment, skins, maps, and missions.</li>
-<li>Swat 4 Elite Force Mod: This mod adds more realism, challenge, and options to the game. It also adds new commands, tactics, weapons, equipment, and features.</li>
-<li>Swat 4 Respawn Mod: This mod allows you to respawn in single-player and co-op modes. It also adds new weapons, equipment, maps, and missions.</li>
-<li>Swat 4 Aimbot Hack: This hack allows you to automatically aim and shoot at your enemies with perfect accuracy. It also allows you to see through walls, fly, and teleport.</li>
-<li>Swat 4 Wallhack Hack: This hack allows you to see through walls and objects. It also allows you to change your color, speed, and health.</li>
-</ul>
- <h3>Other games similar to Swat 4: The Stetchkov Syndicate</h3>
- <p>If you are looking for other games that offer a similar gameplay experience to Swat 4: The Stetchkov Syndicate, here are some recommendations that you can try:</p>
-<ul>
-<li>Rainbow Six Siege: This is a tactical first-person shooter game that focuses on team-based combat and destructible environments. You can play as different operators with unique abilities and gadgets.</li>
-<li>Door Kickers: This is a real-time strategy game that simulates SWAT operations. You can plan and execute your missions using a top-down view and various tools and weapons.</li>
-<li>Ready or Not: This is a realistic tactical first-person shooter game that puts you in the role of a SWAT officer. You can use realistic tactics, equipment, and communication to deal with various scenarios.</li>
-<li>Police Stories: This is a top-down shooter game that requires you to use non-lethal force and arrest procedures. You can play solo or co-op with a partner.</li>
-<li>SWAT 3: This is the predecessor of Swat 4, and it offers a similar gameplay experience. You can lead a SWAT team in various missions involving terrorists, criminals, and hostages.</li>
-</ul>
- <h2>Conclusion and FAQs</h2>
- <p>In conclusion, swat.4.tss.radar.25 is a mod that adds a radar hack to Swat 4: The Stetchkov Syndicate, allowing you to see the location and status of all the suspects, hostages, teammates, and objectives on your screen. This can give you an advantage in planning your strategy, avoiding ambushes, and completing your mission objectives.</p>
- <p>However, swat.4.tss.radar.25 also has some drawbacks and risks that you should be aware of. It can reduce the realism and challenge of the game, cause compatibility issues with other mods or updates, contain viruses or malware, violate the terms of service or code of conduct of the game or its online servers, and get you banned from playing online or accessing certain features of the game.</p>
- <p>If you are looking for other ways to enhance your gameplay experience without using swat.4.tss.radar.25, you can try other mods and hacks for Swat 4: The Stetchkov Syndicate or other games similar to Swat 4: The Stetchkov Syndicate.</p>
- <p>We hope this article has helped you understand what swat.4.tss.radar.25 is, how to use it, what are its benefits and drawbacks, and what are some alternatives to it. If you have any questions or comments about this topic, feel free to leave them below.</p>
- <p>Here are some FAQs that might interest you:</p>
- <h3>Q: Is swat.4.tss.radar.25 legal?</h3>
-<p>A: Swat.4.tss.radar.25 is not legal in the sense that it is not an official part of the game or endorsed by the developers or publishers of the game. It is also not legal in the sense that it violates the terms of service or code of conduct of the game or its online servers. Using swat.4.tss.radar.25 can result in legal actions or penalties from the game authorities or owners.</p>
- <h3>Q: Is swat.4.tss.radar.25 safe?</h3>
-<p>A: Swat. <p>A: Swat.4.tss.radar.25 is not safe in the sense that it may contain viruses or malware that can harm your computer or steal your personal information. It is also not safe in the sense that it may get you banned from playing online or accessing certain features of the game. Using swat.4.tss.radar.25 can result in security or privacy issues or penalties from the game authorities or owners.</p>
- <h3>Q: How can I uninstall swat.4.tss.radar.25?</h3>
-<p>A: If you want to uninstall swat.4.tss.radar.25 from your computer, you need to follow these steps:</p>
-<ol>
-<li>Delete the swat4x.exe file from your Swat 4: The Stetchkov Syndicate folder</li>
-<li>Delete the radar.ini file from your Swat 4: The Stetchkov Syndicate folder</li>
-<li>Delete the radar.dll file from your Swat 4: The Stetchkov Syndicate folder</li>
-<li>Run swat4.exe as administrator</li>
-<li>Enjoy the game without the radar hack</li>
-</ol>
- <h3>Q: Where can I find more information about swat.4.tss.radar.25?</h3>
-<p>A: If you want to find more information about swat.4.tss.radar.25, you can visit these links:</p>
-<ul>
-<li>[The original forum post where the mod was shared]</li>
-<li>[A video tutorial on how to use the mod]</li>
-<li>[A review of the mod by a game critic]</li>
-</ul>
- <h3>Q: What are some tips and tricks for playing Swat 4: The Stetchkov Syndicate?</h3>
-<p>A: If you want to improve your skills and enjoy playing Swat 4: The Stetchkov Syndicate, here are some tips and tricks that you can use:</p>
-<ul>
-<li>Use your command interface to give orders to your teammates and coordinate your actions.</li>
-<li>Use your optiwand to peek around corners and check for enemies or hostages.</li>
-<li>Use your flashbangs, stingers, gas grenades, or pepper spray to stun or incapacitate your enemies.</li>
-<li>Use your breaching tools, such as C2 charges, shotguns, or lock picks, to open doors or windows.</li>
-<li>Use your non-lethal weapons, such as beanbag shotguns, tasers, or pepperball guns, to subdue your enemies without killing them.</li>
-<li>Use your voice commands or hand signals to communicate with your teammates and suspects.</li>
-<li>Use your report function to update your status and progress to the command center.</li>
-<li>Follow the rules of engagement and use force only when necessary.</li>
-</ul>
- <h3>Q: How can I play Swat 4: The Stetchkov Syndicate online with other players?</h3>
-<p>A: If you want to play Swat 4: The Stetchkov Syndicate online with other players, you need to follow these steps:</p>
-<ol>
-<li>Create an account on GameRanger, a free online gaming service that supports Swat 4: The Stetchkov Syndicate.</li>
-<li>Download and install GameRanger on your computer.</li>
-<li>Launch GameRanger and sign in with your account.</li>
-<li>Browse the list of available games and join one that suits your preferences.</li>
-<li>Alternatively, you can create your own game and invite other players to join.</li>
-<li>Enjoy playing Swat 4: The Stetchkov Syndicate online with other players.</li>
-</ol></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nomic-ai/cnn_dailymail/style.css b/spaces/nomic-ai/cnn_dailymail/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/cnn_dailymail/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/oliver2023/chatgpt-on-wechat/lib/itchat/core.py b/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/core.py
deleted file mode 100644
index f3871b5b6107df05d67d22a4c66ea4002c216344..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/core.py
+++ /dev/null
@@ -1,456 +0,0 @@
-import requests
-
-from . import storage
-
-class Core(object):
-    def __init__(self):
-        ''' init is the only method defined in core.py
-            alive is value showing whether core is running
-                - you should call logout method to change it
-                - after logout, a core object can login again
-            storageClass only uses basic python types
-                - so for advanced uses, inherit it yourself
-            receivingRetryCount is for receiving loop retry
-                - it's 5 now, but actually even 1 is enough
-                - failing is failing
-        '''
-        self.alive, self.isLogging = False, False
-        self.storageClass = storage.Storage(self)
-        self.memberList = self.storageClass.memberList
-        self.mpList = self.storageClass.mpList
-        self.chatroomList = self.storageClass.chatroomList
-        self.msgList = self.storageClass.msgList
-        self.loginInfo = {}
-        self.s = requests.Session()
-        self.uuid = None
-        self.functionDict = {'FriendChat': {}, 'GroupChat': {}, 'MpChat': {}}
-        self.useHotReload, self.hotReloadDir = False, 'itchat.pkl'
-        self.receivingRetryCount = 5
-    def login(self, enableCmdQR=False, picDir=None, qrCallback=None,
-            loginCallback=None, exitCallback=None):
-        ''' log in like web wechat does
-            for log in
-                - a QR code will be downloaded and opened
-                - then scanning status is logged, it paused for you confirm
-                - finally it logged in and show your nickName
-            for options
-                - enableCmdQR: show qrcode in command line
-                    - integers can be used to fit strange char length
-                - picDir: place for storing qrcode
-                - qrCallback: method that should accept uuid, status, qrcode
-                - loginCallback: callback after successfully logged in
-                    - if not set, screen is cleared and qrcode is deleted
-                - exitCallback: callback after logged out
-                    - it contains calling of logout
-            for usage
-                ..code::python
-
-                    import itchat
-                    itchat.login()
-
-            it is defined in components/login.py
-            and of course every single move in login can be called outside
-                - you may scan source code to see how
-                - and modified according to your own demand
-        '''
-        raise NotImplementedError()
-    def get_QRuuid(self):
-        ''' get uuid for qrcode
-            uuid is the symbol of qrcode
-                - for logging in, you need to get a uuid first
-                - for downloading qrcode, you need to pass uuid to it
-                - for checking login status, uuid is also required
-            if uuid has timed out, just get another
-            it is defined in components/login.py
-        '''
-        raise NotImplementedError()
-    def get_QR(self, uuid=None, enableCmdQR=False, picDir=None, qrCallback=None):
-        ''' download and show qrcode
-            for options
-                - uuid: if uuid is not set, latest uuid you fetched will be used
-                - enableCmdQR: show qrcode in cmd
-                - picDir: where to store qrcode
-                - qrCallback: method that should accept uuid, status, qrcode
-            it is defined in components/login.py
-        '''
-        raise NotImplementedError()
-    def check_login(self, uuid=None):
-        ''' check login status
-            for options:
-                - uuid: if uuid is not set, latest uuid you fetched will be used
-            for return values:
-                - a string will be returned
-                - for meaning of return values
-                    - 200: log in successfully
-                    - 201: waiting for press confirm
-                    - 408: uuid timed out
-                    - 0  : unknown error
-            for processing:
-                - syncUrl and fileUrl is set
-                - BaseRequest is set
-            blocks until reaches any of above status
-            it is defined in components/login.py
-        '''
-        raise NotImplementedError()
-    def web_init(self):
-        ''' get info necessary for initializing
-            for processing:
-                - own account info is set
-                - inviteStartCount is set
-                - syncKey is set
-                - part of contact is fetched
-            it is defined in components/login.py
-        '''
-        raise NotImplementedError()
-    def show_mobile_login(self):
-        ''' show web wechat login sign
-            the sign is on the top of mobile phone wechat
-            sign will be added after sometime even without calling this function
-            it is defined in components/login.py
-        '''
-        raise NotImplementedError()
-    def start_receiving(self, exitCallback=None, getReceivingFnOnly=False):
-        ''' open a thread for heart loop and receiving messages
-            for options:
-                - exitCallback: callback after logged out
-                    - it contains calling of logout
-                - getReceivingFnOnly: if True thread will not be created and started. Instead, receive fn will be returned.
-            for processing:
-                - messages: msgs are formatted and passed on to registered fns
-                - contact : chatrooms are updated when related info is received
-            it is defined in components/login.py
-        '''
-        raise NotImplementedError()
-    def get_msg(self):
-        ''' fetch messages
-            for fetching
-                - method blocks for sometime until
-                    - new messages are to be received
-                    - or anytime they like
-                - synckey is updated with returned synccheckkey
-            it is defined in components/login.py
-        '''
-        raise NotImplementedError()
-    def logout(self):
-        ''' logout
-            if core is now alive
-                logout will tell wechat backstage to logout
-            and core gets ready for another login
-            it is defined in components/login.py
-        '''
-        raise NotImplementedError()
-    def update_chatroom(self, userName, detailedMember=False):
-        ''' update chatroom
-            for chatroom contact
-                - a chatroom contact need updating to be detailed
-                - detailed means members, encryid, etc
-                - auto updating of heart loop is a more detailed updating
-                    - member uin will also be filled
-                - once called, updated info will be stored
-            for options
-                - userName: 'UserName' key of chatroom or a list of it
-                - detailedMember: whether to get members of contact
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def update_friend(self, userName):
-        ''' update chatroom
-            for friend contact
-                - once called, updated info will be stored
-            for options
-                - userName: 'UserName' key of a friend or a list of it
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def get_contact(self, update=False):
-        ''' fetch part of contact
-            for part
-                - all the massive platforms and friends are fetched
-                - if update, only starred chatrooms are fetched
-            for options
-                - update: if not set, local value will be returned
-            for results
-                - chatroomList will be returned
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def get_friends(self, update=False):
-        ''' fetch friends list
-            for options
-                - update: if not set, local value will be returned
-            for results
-                - a list of friends' info dicts will be returned
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def get_chatrooms(self, update=False, contactOnly=False):
-        ''' fetch chatrooms list
-            for options
-                - update: if not set, local value will be returned
-                - contactOnly: if set, only starred chatrooms will be returned
-            for results
-                - a list of chatrooms' info dicts will be returned
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def get_mps(self, update=False):
-        ''' fetch massive platforms list
-            for options
-                - update: if not set, local value will be returned
-            for results
-                - a list of platforms' info dicts will be returned
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def set_alias(self, userName, alias):
-        ''' set alias for a friend
-            for options
-                - userName: 'UserName' key of info dict
-                - alias: new alias
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def set_pinned(self, userName, isPinned=True):
-        ''' set pinned for a friend or a chatroom
-            for options
-                - userName: 'UserName' key of info dict
-                - isPinned: whether to pin
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def accept_friend(self, userName, v4,autoUpdate=True):
-        ''' accept a friend or accept a friend
-            for options
-                - userName: 'UserName' for friend's info dict
-                - status:
-                    - for adding status should be 2
-                    - for accepting status should be 3
-                - ticket: greeting message
-                - userInfo: friend's other info for adding into local storage
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def get_head_img(self, userName=None, chatroomUserName=None, picDir=None):
-        ''' place for docs
-            for options
-                - if you want to get chatroom header: only set chatroomUserName
-                - if you want to get friend header: only set userName
-                - if you want to get chatroom member header: set both
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def create_chatroom(self, memberList, topic=''):
-        ''' create a chatroom
-            for creating
-                - its calling frequency is strictly limited
-            for options
-                - memberList: list of member info dict
-                - topic: topic of new chatroom
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def set_chatroom_name(self, chatroomUserName, name):
-        ''' set chatroom name
-            for setting
-                - it makes an updating of chatroom
-                - which means detailed info will be returned in heart loop
-            for options
-                - chatroomUserName: 'UserName' key of chatroom info dict
-                - name: new chatroom name
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def delete_member_from_chatroom(self, chatroomUserName, memberList):
-        ''' deletes members from chatroom
-            for deleting
-                - you can't delete yourself
-                - if so, no one will be deleted
-                - strict-limited frequency
-            for options
-                - chatroomUserName: 'UserName' key of chatroom info dict
-                - memberList: list of members' info dict
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def add_member_into_chatroom(self, chatroomUserName, memberList,
-            useInvitation=False):
-        ''' add members into chatroom
-            for adding
-                - you can't add yourself or member already in chatroom
-                - if so, no one will be added
-                - if member will over 40 after adding, invitation must be used
-                - strict-limited frequency
-            for options
-                - chatroomUserName: 'UserName' key of chatroom info dict
-                - memberList: list of members' info dict
-                - useInvitation: if invitation is not required, set this to use
-            it is defined in components/contact.py
-        '''
-        raise NotImplementedError()
-    def send_raw_msg(self, msgType, content, toUserName):
-        ''' many messages are sent in a common way
-            for demo
-                .. code:: python
-
-                    @itchat.msg_register(itchat.content.CARD)
-                    def reply(msg):
-                        itchat.send_raw_msg(msg['MsgType'], msg['Content'], msg['FromUserName'])
-
-            there are some little tricks here, you may discover them yourself
-            but remember they are tricks
-            it is defined in components/messages.py
-        '''
-        raise NotImplementedError()
-    def send_msg(self, msg='Test Message', toUserName=None):
-        ''' send plain text message
-            for options
-                - msg: should be unicode if there's non-ascii words in msg
-                - toUserName: 'UserName' key of friend dict
-            it is defined in components/messages.py
-        '''
-        raise NotImplementedError()
-    def upload_file(self, fileDir, isPicture=False, isVideo=False,
-            toUserName='filehelper', file_=None, preparedFile=None):
-        ''' upload file to server and get mediaId
-            for options
-                - fileDir: dir for file ready for upload
-                - isPicture: whether file is a picture
-                - isVideo: whether file is a video
-            for return values
-                will return a ReturnValue
-                if succeeded, mediaId is in r['MediaId']
-            it is defined in components/messages.py
-        '''
-        raise NotImplementedError()
-    def send_file(self, fileDir, toUserName=None, mediaId=None, file_=None):
-        ''' send attachment
-            for options
-                - fileDir: dir for file ready for upload
-                - mediaId: mediaId for file. 
-                    - if set, file will not be uploaded twice
-                - toUserName: 'UserName' key of friend dict
-            it is defined in components/messages.py
-        '''
-        raise NotImplementedError()
-    def send_image(self, fileDir=None, toUserName=None, mediaId=None, file_=None):
-        ''' send image
-            for options
-                - fileDir: dir for file ready for upload
-                    - if it's a gif, name it like 'xx.gif'
-                - mediaId: mediaId for file. 
-                    - if set, file will not be uploaded twice
-                - toUserName: 'UserName' key of friend dict
-            it is defined in components/messages.py
-        '''
-        raise NotImplementedError()
-    def send_video(self, fileDir=None, toUserName=None, mediaId=None, file_=None):
-        ''' send video
-            for options
-                - fileDir: dir for file ready for upload
-                    - if mediaId is set, it's unnecessary to set fileDir
-                - mediaId: mediaId for file. 
-                    - if set, file will not be uploaded twice
-                - toUserName: 'UserName' key of friend dict
-            it is defined in components/messages.py
-        '''
-        raise NotImplementedError()
-    def send(self, msg, toUserName=None, mediaId=None):
-        ''' wrapped function for all the sending functions
-            for options
-                - msg: message starts with different string indicates different type
-                    - list of type string: ['@fil@', '@img@', '@msg@', '@vid@']
-                    - they are for file, image, plain text, video
-                    - if none of them matches, it will be sent like plain text
-                - toUserName: 'UserName' key of friend dict
-                - mediaId: if set, uploading will not be repeated
-            it is defined in components/messages.py
-        '''
-        raise NotImplementedError()
-    def revoke(self, msgId, toUserName, localId=None):
-        ''' revoke message with its and msgId
-            for options
-                - msgId: message Id on server
-                - toUserName: 'UserName' key of friend dict
-                - localId: message Id at local (optional)
-            it is defined in components/messages.py
-        '''
-        raise NotImplementedError()
-    def dump_login_status(self, fileDir=None):
-        ''' dump login status to a specific file
-            for option
-                - fileDir: dir for dumping login status
-            it is defined in components/hotreload.py
-        '''
-        raise NotImplementedError()
-    def load_login_status(self, fileDir,
-            loginCallback=None, exitCallback=None):
-        ''' load login status from a specific file
-            for option
-                - fileDir: file for loading login status
-                - loginCallback: callback after successfully logged in
-                    - if not set, screen is cleared and qrcode is deleted
-                - exitCallback: callback after logged out
-                    - it contains calling of logout
-            it is defined in components/hotreload.py
-        '''
-        raise NotImplementedError()
-    def auto_login(self, hotReload=False, statusStorageDir='itchat.pkl',
-            enableCmdQR=False, picDir=None, qrCallback=None,
-            loginCallback=None, exitCallback=None):
-        ''' log in like web wechat does
-            for log in
-                - a QR code will be downloaded and opened
-                - then scanning status is logged, it paused for you confirm
-                - finally it logged in and show your nickName
-            for options
-                - hotReload: enable hot reload
-                - statusStorageDir: dir for storing log in status
-                - enableCmdQR: show qrcode in command line
-                    - integers can be used to fit strange char length
-                - picDir: place for storing qrcode
-                - loginCallback: callback after successfully logged in
-                    - if not set, screen is cleared and qrcode is deleted
-                - exitCallback: callback after logged out
-                    - it contains calling of logout
-                - qrCallback: method that should accept uuid, status, qrcode
-            for usage
-                ..code::python
-
-                    import itchat
-                    itchat.auto_login()
-
-            it is defined in components/register.py
-            and of course every single move in login can be called outside
-                - you may scan source code to see how
-                - and modified according to your own demond
-        '''
-        raise NotImplementedError()
-    def configured_reply(self):
-        ''' determine the type of message and reply if its method is defined
-            however, I use a strange way to determine whether a msg is from massive platform
-            I haven't found a better solution here
-            The main problem I'm worrying about is the mismatching of new friends added on phone
-            If you have any good idea, pleeeease report an issue. I will be more than grateful.
-        '''
-        raise NotImplementedError()
-    def msg_register(self, msgType,
-            isFriendChat=False, isGroupChat=False, isMpChat=False):
-        ''' a decorator constructor
-            return a specific decorator based on information given
-        '''
-        raise NotImplementedError()
-    def run(self, debug=True, blockThread=True):
-        ''' start auto respond
-            for option
-                - debug: if set, debug info will be shown on screen
-            it is defined in components/register.py
-        '''
-        raise NotImplementedError()
-    def search_friends(self, name=None, userName=None, remarkName=None, nickName=None,
-            wechatAccount=None):
-        return self.storageClass.search_friends(name, userName, remarkName,
-            nickName, wechatAccount)
-    def search_chatrooms(self, name=None, userName=None):
-        return self.storageClass.search_chatrooms(name, userName)
-    def search_mps(self, name=None, userName=None):
-        return self.storageClass.search_mps(name, userName)
diff --git "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_n\305\221_en.html" "b/spaces/oskarvanderwal/MT-bias-demo/results/simple_n\305\221_en.html"
deleted file mode 100644
index 9c1fe3c800bddefa7656c0bbb5656e94d488fdff..0000000000000000000000000000000000000000
--- "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_n\305\221_en.html"
+++ /dev/null
@@ -1,46 +0,0 @@
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="jdxxflwlozofutuxflqp_viz_container">
-    <div id="jdxxflwlozofutuxflqp_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "jdxxflwlozofutuxflqp_saliency_plot_container" class="jdxxflwlozofutuxflqp_viz_container" style="display:block">
-            
-<div id="ffkrllfdhvtwfxtglqwy_saliency_plot" class="ffkrllfdhvtwfxtglqwy_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Source Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁She</th><th>'</th><th>s</th><th>▁a</th><th>▁woman</th><th>.</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ő</th><th style="background:rgba(255.0, 13.0, 87.0, 0.9448207565854626)">0.813</th><th style="background:rgba(255.0, 13.0, 87.0, 0.7398692810457518)">0.637</th><th style="background:rgba(255.0, 13.0, 87.0, 0.36149732620320857)">0.318</th><th style="background:rgba(255.0, 13.0, 87.0, 0.29055258467023165)">0.254</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4245593186769657)">0.372</th><th style="background:rgba(255.0, 13.0, 87.0, 0.26690433749257286)">0.236</th><th style="background:rgba(30.0, 136.0, 229.0, 0.6373935432758963)">-0.55</th></tr><tr><th>▁nő</th><th style="background:rgba(255.0, 13.0, 87.0, 0.6689245395127748)">0.577</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4245593186769657)">0.37</th><th style="background:rgba(255.0, 13.0, 87.0, 0.18019409784115661)">0.157</th><th style="background:rgba(255.0, 13.0, 87.0, 0.29843533372945136)">0.259</th><th style="background:rgba(255.0, 13.0, 87.0, 0.5743315508021392)">0.495</th><th style="background:rgba(255.0, 13.0, 87.0, 0.7319865319865321)">0.63</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4560903149138443)">0.396</th></tr><tr><th>.</th><th style="background:rgba(255.0, 13.0, 87.0, 0.08560110913052081)">0.077</th><th style="background:rgba(255.0, 13.0, 87.0, 0.23537334125569415)">0.206</th><th style="background:rgba(255.0, 13.0, 87.0, 0.06983561101208159)">0.063</th><th style="background:rgba(255.0, 13.0, 87.0, 0.1171321053673995)">0.107</th><th style="background:rgba(255.0, 13.0, 87.0, 0.00677361853832443)">0.011</th><th style="background:rgba(255.0, 13.0, 87.0, 0.8423450188156072)">0.73</th><th style="background:rgba(255.0, 13.0, 87.0, 0.2747870865517925)">0.241</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="bkysetwuuwwxeoomriom_viz_container">
-    <div id="bkysetwuuwwxeoomriom_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "bkysetwuuwwxeoomriom_saliency_plot_container" class="bkysetwuuwwxeoomriom_viz_container" style="display:block">
-            
-<div id="xgwesmibqooxsginaebj_saliency_plot" class="xgwesmibqooxsginaebj_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Target Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁She</th><th>'</th><th>s</th><th>▁a</th><th>▁woman</th><th>.</th><th>&lt;/s&gt;</th></tr><tr><th>▁She</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.7477520301049713)">0.644</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4245593186769657)">0.37</th><th style="background:rgba(255.0, 13.0, 87.0, 0.306318082788671)">0.267</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4797385620915033)">0.413</th><th style="background:rgba(255.0, 13.0, 87.0, 0.05407011289364243)">0.052</th><th style="background:rgba(255.0, 13.0, 87.0, 0.29055258467023165)">0.256</th></tr><tr><th>'</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.9921172509407804)">0.857</th><th style="background:rgba(255.0, 13.0, 87.0, 0.2117250940780353)">0.185</th><th style="background:rgba(255.0, 13.0, 87.0, 0.20384234501881549)">0.179</th><th style="background:rgba(255.0, 13.0, 87.0, 0.03830461477520289)">0.04</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4797385620915033)">0.414</th></tr><tr><th>s</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.866</th><th style="background:rgba(255.0, 13.0, 87.0, 0.3536145771439889)">0.305</th><th style="background:rgba(255.0, 13.0, 87.0, 0.07771836007130124)">0.073</th><th style="background:rgba(30.0, 136.0, 229.0, 0.10136660724896014)">-0.089</th></tr><tr><th>▁a</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.6531590413943356)">0.567</th><th style="background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)">-0.003</th><th style="background:rgba(255.0, 13.0, 87.0, 0.4245593186769657)">0.371</th></tr><tr><th>▁woman</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.07771836007130124)">0.069</th><th style="background:rgba(255.0, 13.0, 87.0, 0.36149732620320857)">0.315</th></tr><tr><th>.</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.02253911665676371)">0.021</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
diff --git a/spaces/paj/dubharv/README.md b/spaces/paj/dubharv/README.md
deleted file mode 100644
index f9b74b7b72b3ef5ff2c2c5e65c79c8b8d2b3b988..0000000000000000000000000000000000000000
--- a/spaces/paj/dubharv/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dubharv
-emoji: 👀
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.5
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pashas/openai-whisper-large-v2/app.py b/spaces/pashas/openai-whisper-large-v2/app.py
deleted file mode 100644
index dbb9d9791843686edb94d5f60b142d78896fef12..0000000000000000000000000000000000000000
--- a/spaces/pashas/openai-whisper-large-v2/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/openai/whisper-large-v2").launch()
\ No newline at end of file
diff --git a/spaces/peterwisu/lip_synthesis/utils/plot.py b/spaces/peterwisu/lip_synthesis/utils/plot.py
deleted file mode 100644
index 444d3f37cea791edba6d0b3b819ab1af5c0c86d4..0000000000000000000000000000000000000000
--- a/spaces/peterwisu/lip_synthesis/utils/plot.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import matplotlib.pyplot as plt
-import seaborn as sns
-import numpy as np
-import pandas as pd
-from sklearn.metrics import confusion_matrix
-from sklearn.metrics import roc_curve
-from sklearn.metrics import auc
-import cv2
-
-
-def plot_single(value, label, name):
-    """
-    ***********************************************************
-    plot_single : Plot a single line plot for metrics (accuracy or loss)
-    ***********************************************************
-    @author: Wish Suharitdarmong
-    ------
-    inputs
-    ------
-        vali :  array containg a log value 
-    -------
-    returns
-    -------
-        figure : Single line plot figure   
-    """
-    fig , (ax)  = plt.subplots(nrows=1,ncols=1)
-    
-    ax.plot(value, color='r', label=label, linestyle='-')
-    if "loss" in name.lower():
-        ax.legend(loc="upper right")
-    if "accuracy" in name.lower():
-        ax.legend(loc="lower right")
-    ax.set_title("{}".format(name))
-    figure = ax.get_figure()
-    plt.close(fig)
-    
-    return figure
-    
-
-def plot_comp(train,vali, name):
-    """
-    ************************************************************
-    plot_comp : Plot comparision between training and evaluation
-    ************************************************************
-    @author: Wish Suharitdamrong
-    ------
-    inputs
-    ------
-        train :  array containing a training logs value (accuracy or loss)
-        vali :  array containg a evaluation log value 
-    -------
-    returns
-    -------
-        figure : Plot comparision figure
-    """
-    
- 
-    fig, (ax) = plt.subplots(nrows=1,ncols=1)
-    ax.plot(train, color='r' , label='Train', linestyle='-')
-    ax.plot(vali, color='b', label='Validation', linestyle='-')
-    if "loss" in name.lower():
-        ax.legend(loc="upper right")
-    if "accuracy" in name.lower():
-        ax.legend(loc="lower right")
-    ax.set_title("{}".format(name))
-    figure = ax.get_figure()
-    plt.close(fig)
-    
-    return figure
-
-
-
-
-
-
-def plot_cm(y_pred_label,y_gt):
-    """
-    ****************************
-    Plot Confusion Matrix Figure
-    ****************************
-    @author: Wish Suharitdamrong
-    ----------
-    parameters
-    ----------    
-        y_pred_label :  labels predicted from a model
-        y_gt  : grouth truth of each labels
-    -------
-    returns
-    -------
-        dia_cm : Figure of Confusion Matrix
-    """
-
-    df_cols_rows = ["Positive", "Negative"]
-
-    cm = confusion_matrix(y_gt, y_pred_label,normalize="all")
-    cm_df = pd.DataFrame(cm , index = df_cols_rows , columns = df_cols_rows)
-    
-    # Plot Fig 
-    fig, (ax) = plt.subplots(nrows=1, ncols=1)
-    ax.set_title("Confusion Maxtrix")
-    dia_cm = sns.heatmap(cm_df, annot =True).get_figure() 
-    plt.close(fig)
-        
-    return dia_cm
-
-
-
-
-def plot_roc(y_pred_proba,y_gt):
-    """
-    **************************************************
-    Receiver Operating Characteristic (ROC) Curve plot
-    **************************************************
-    @authors: Wish Suharitdamrong
-    ---------
-    parameter
-    ---------
-        y_pred_proba : Probabilities of each labels predicted from a model
-        y_gt : Ground truth of eachs labels
-    -------
-    returns
-    -------
-        dia_roc : ROC Curve Figure
-    """
-    
-    # Calculate False and True positive rate
-    fpr, tpr, threshold = roc_curve(y_gt, y_pred_proba)
-    
-    # Calculate area under a curve 
-    roc_auc = auc(fpr, tpr)
-    
-    # Plot Fig 
-    fig, (ax) = plt.subplots(nrows=1, ncols=1)
-    # Plot curve
-    line1 = ax.plot(fpr, tpr, 'b', label= "AUC = {:.2f}".format(roc_auc))
-    # Plot Threshold
-    line2 = ax.plot([0,1],[0,1],'--r', label = "Random Classifier")
-    ax.legend(loc="lower right")
-    ax.set_title("Receiving Operating Characteristic (ROC) Curve")
-    ax.set_xlim([0,1])
-    ax.set_ylim([0,1])
-    ax.set_xlabel("False Positive Rate")
-    ax.set_ylabel("True Positive Rate") 
-    dia_roc = ax.get_figure()
-    plt.close(fig) 
-     
-    return dia_roc
-
-#
-#
-#def plot_compareLip(predict,gt_lip):
-#    """
-#    ************************************************************
-#    plot_compareLip : Plot comparision between the generated lip and actual lip
-#    ************************************************************
-#    @author: Wish Suharitdamrong
-#    ------
-#    inputs
-#    ------
-#        predict : predicted lip in form of torch tensor 
-#        gt_lip  : ground truht lip in form of torch tensor
-#    -------
-#    returns
-#    -------
-#        com_fig :  comparision figure
-#    """
-#    
-#
-#    # convert torch tensor into numpy array
-#    lip_pred = predict.reshape(predict.shape[0], predict.shape[1], 20, -1)[0, 0].detach().clone().cpu().numpy()
-#    fl = gt_lip.reshape(predict.shape[0], predict.shape[1], 20, -1)[0, 0].detach().clone().cpu().numpy()
-#
-#    fig, (ax) = plt.subplots(nrows=1, ncols=1)
-#
-#    # Plot Generated Lip
-#    ax.scatter(lip_pred[:, 0], lip_pred[:, 1], s=20, c='r', linewidths=2)
-#    ax.plot(lip_pred[0:7, 0], lip_pred[0:7, 1], c="tab:red", linewidth=3 , label='Generated')
-#    ax.plot(np.append(lip_pred[6:12, 0], lip_pred[0, 0]), np.append(lip_pred[6:12, 1], lip_pred[0, 1]), c="tab:red", linewidth=3)
-#    ax.plot(lip_pred[12:17, 0], lip_pred[12:17, 1], c="tab:red", linewidth=3)
-#    ax.plot(np.append(lip_pred[16:20, 0], lip_pred[12, 0]), np.append(lip_pred[16:20, 1], lip_pred[12, 1]), c="tab:red", linewidth=3)
-#
-#    # Plot Ground Truth Lip
-#    ax.scatter(fl[:, 0], fl[:, 1], s=20, c='g', linewidths=2)
-#    ax.plot(fl[0:7, 0], fl[0:7, 1], c="tab:blue", linewidth=3, label='Ground Truth')
-#    ax.plot(np.append(fl[6:12, 0], fl[0, 0]), np.append(fl[6:12, 1], fl[0, 1]), c="tab:blue", linewidth=3)
-#    ax.plot(fl[12:17, 0], fl[12:17, 1], c="tab:blue", linewidth=3)
-#    ax.plot(np.append(fl[16:20, 0], fl[12, 0]), np.append(fl[16:20, 1], fl[12, 1]), c="tab:blue", linewidth=3)
-#    ax.legend(loc="upper left")
-#    ax.invert_yaxis()
-#    com_fig = ax.get_figure()
-#    plt.close(fig)
-#
-#    return com_fig
-#
-#
-
-def plot_lip_landmark(fl):
-    """
-    ************************************************************
-    plot_visLip :  Visualize lip landmark mark
-    ************************************************************
-    @author: Wish Suharitdamrong
-    ------
-    inputs
-    ------
-        fl : lip landmark in form of numpy array 
-    -------
-    returns
-    -------
-        com_fig :  comparision figure
-    """
-    
-    # invert yaxis value so that the lip is wont display upside down
-    fig, ax = plt.subplots() 
-    ax.scatter(fl[:,0],fl[:,1],s=20, c='r',linewidths=4)
-    ax.plot(fl[0:7,0],fl[0:7,1], c="tab:pink", linewidth=3 )
-    ax.plot(np.append(fl[6:12,0],fl[0,0]),np.append(fl[6:12,1],fl[0,1]), c="tab:pink", linewidth=3 )
-    ax.plot(fl[12:17,0],fl[12:17,1], c="tab:pink", linewidth=3 )
-    ax.plot(np.append(fl[16:20,0],fl[12,0]),np.append(fl[16:20,1],fl[12,1] ), c="tab:pink", linewidth=3)
-    ax.invert_yaxis()
-    plt.show()
-    
-
-
-def plot_scatter_facial_landmark(fl):
-
-    fig = plt.figure()
-    ax = fig.add_subplot()
-    ax.scatter(fl[:,0],fl[:,1],s=20,c='r')
-    ax.invert_yaxis()
-    fig.tight_layout()
-    plt.show()
-
-
-def plot_lip_comparision(pred,ref,gt):
-    """
-    ************************************************************
-    plot_seqlip_comp : Visualize  gena 
-    ************************************************************
-    @author: Wish Suharitdamrong
-    ------
-    inputs
-    ------
-        fl : lip landmark in form of numpy array 
-    -------
-    returns
-    -------
-        com_fig :  comparision figure
-    """
-    
-    fig, (ax1, ax2) = plt.subplots(nrows=1 , ncols=2, figsize=(15,6))
-
-    fl = pred
-    ax1.scatter(fl[:,0],fl[:,1],s=20, c='r',linewidths=4)
-    ax1.plot(fl[0:7,0],fl[0:7,1], c="tab:red", linewidth=3 , label='Generated')
-    ax1.plot(np.append(fl[6:12,0],fl[0,0]),np.append(fl[6:12,1],fl[0,1]), c="tab:red", linewidth=3 )
-    ax1.plot(fl[12:17,0],fl[12:17,1], c="tab:red", linewidth=3 )
-    ax1.plot(np.append(fl[16:20,0],fl[12,0]),np.append(fl[16:20,1],fl[12,1] ), c="tab:red", linewidth=3)
-
-    fl = ref  
-    ax1.scatter(fl[:,0],fl[:,1],s=20, c='b',linewidths=4)
-    ax1.plot(fl[0:7,0],fl[0:7,1], c="tab:cyan", linewidth=3 , label='Reference')
-    ax1.plot(np.append(fl[6:12,0],fl[0,0]),np.append(fl[6:12,1],fl[0,1]), c="tab:cyan", linewidth=3 )
-    ax1.plot(fl[12:17,0],fl[12:17,1], c="tab:cyan", linewidth=3 )
-    ax1.plot(np.append(fl[16:20,0],fl[12,0]),np.append(fl[16:20,1],fl[12,1] ), c="tab:cyan", linewidth=3)
-    ax1.legend(loc="upper left")
-    ax1.invert_yaxis()
-
-    #################################################################################
-
-    fl = pred
-    ax2.scatter(fl[:,0],fl[:,1],s=20, c='r',linewidths=4)
-    ax2.plot(fl[0:7,0],fl[0:7,1], c="tab:red", linewidth=3 , label='Generated')
-    ax2.plot(np.append(fl[6:12,0],fl[0,0]),np.append(fl[6:12,1],fl[0,1]), c="tab:red", linewidth=3 )
-    ax2.plot(fl[12:17,0],fl[12:17,1], c="tab:red", linewidth=3 )
-    ax2.plot(np.append(fl[16:20,0],fl[12,0]),np.append(fl[16:20,1],fl[12,1] ), c="tab:red", linewidth=3)
-
-    fl = gt 
-    ax2.scatter(fl[:,0],fl[:,1],s=20, c='g',linewidths=4)
-    ax2.plot(fl[0:7,0],fl[0:7,1], c="tab:green", linewidth=3 , label='Ground Truth')
-    ax2.plot(np.append(fl[6:12,0],fl[0,0]),np.append(fl[6:12,1],fl[0,1]), c="tab:green", linewidth=3 )
-    ax2.plot(fl[12:17,0],fl[12:17,1], c="tab:green", linewidth=3 )
-    ax2.plot(np.append(fl[16:20,0],fl[12,0]),np.append(fl[16:20,1],fl[12,1] ), c="tab:green", linewidth=3)
-
-
-    ax2.legend(loc="upper left")
-    ax2.invert_yaxis()
-    
-    
-    seqlip_fig = ax1.get_figure()
-
-    plt.close(fig)
-
-    return  seqlip_fig
-
-
-# Visualize Facial Landmark (Whole Face)
-def vis_landmark_on_img(img, shape, linewidth=2):
-
-    """
-
-    @author: This function is originally from ***MakeItTalk***
-
-    Link: https://github.com/yzhou359/MakeItTalk
-
-    """
-    def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth):
-        for i in idx_list:
-            cv2.line(img, (shape[i, 0], shape[i, 1]), (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth)
-        if (loop):
-            cv2.line(img, (shape[idx_list[0], 0], shape[idx_list[0], 1]),
-                     (shape[idx_list[-1] + 1, 0], shape[idx_list[-1] + 1, 1]), color, lineWidth)
-
-    draw_curve(list(range(0, 16)), color=(255, 144, 25))  # jaw
-    draw_curve(list(range(17, 21)), color=(50, 205, 50))  # eye brow
-    draw_curve(list(range(22, 26)), color=(50, 205, 50))
-    draw_curve(list(range(27, 35)), color=(208, 224, 63))  # nose
-    draw_curve(list(range(36, 41)), loop=True, color=(71, 99, 255))  # eyes
-    draw_curve(list(range(42, 47)), loop=True, color=(71, 99, 255))
-    draw_curve(list(range(48, 59)), loop=True, color=(238, 130, 238))  # mouth
-    draw_curve(list(range(60, 67)), loop=True, color=(238, 130, 238))
-
-    return img
-
-
-
-
-
-
diff --git a/spaces/phyloforfun/VoucherVision/vouchervision/utils.py b/spaces/phyloforfun/VoucherVision/vouchervision/utils.py
deleted file mode 100644
index 0ad35c9986739d5bbc87b564bc16b42f6aad307e..0000000000000000000000000000000000000000
--- a/spaces/phyloforfun/VoucherVision/vouchervision/utils.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import os, json, re
-from googleapiclient.discovery import build
-from googleapiclient.http import MediaFileUpload
-from google.oauth2 import service_account
-import base64
-from PIL import Image
-from PIL import Image
-from io import BytesIO
-
-from vouchervision.general_utils import get_cfg_from_full_path
-
-
-def setup_streamlit_config(dir_home):
-    # Define the directory path and filename
-    dir_path = os.path.join(dir_home, ".streamlit")
-    file_path = os.path.join(dir_path, "config.toml")
-
-    # Check if directory exists, if not create it
-    if not os.path.exists(dir_path):
-        os.makedirs(dir_path)
-    
-    # Create or modify the file with the provided content
-    config_content = f"""
-    [theme]
-    base = "dark"
-    primaryColor = "#00ff00"
-
-    [server]
-    enableStaticServing = false
-    runOnSave = true
-    port = 8524
-    maxUploadSize = 5000
-    """
-
-    with open(file_path, "w") as f:
-        f.write(config_content.strip())
-
-
-
-def save_uploaded_file(directory, img_file, image=None):
-    if not os.path.exists(directory):
-        os.makedirs(directory)
-    # Assuming the uploaded file is an image
-    if image is None:
-        with Image.open(img_file) as image:
-            full_path = os.path.join(directory, img_file.name)
-            image.save(full_path, "JPEG")
-        # Return the full path of the saved image
-        return full_path
-    else:
-        full_path = os.path.join(directory, img_file.name)
-        image.save(full_path, "JPEG")
-        return full_path
-    
-def image_to_base64(img):
-    buffered = BytesIO()
-    img.save(buffered, format="JPEG")
-    return base64.b64encode(buffered.getvalue()).decode()
-
-def check_prompt_yaml_filename(fname):
-    # Check if the filename only contains letters, numbers, underscores, and dashes
-    pattern = r'^[\w-]+$'
-    
-    # The \w matches any alphanumeric character and is equivalent to the character class [a-zA-Z0-9_].
-    # The hyphen - is literally matched.
-
-    if re.match(pattern, fname):
-        return True
-    else:
-        return False
-    
-# Function to upload files to Google Drive
-def upload_to_drive(filepath, filename):
-    # Parse the service account info from the environment variable
-    creds_info = json.loads(os.environ.get('GDRIVE_API'))
-    if creds_info:
-        creds = service_account.Credentials.from_service_account_info(
-            creds_info, scopes=["https://www.googleapis.com/auth/drive"]
-        )
-        service = build('drive', 'v3', credentials=creds)
-
-        # Get the folder ID from the environment variable
-        folder_id = os.environ.get('GDRIVE')
-        # st.info(f"{folder_id}")
-
-        if folder_id:
-            file_metadata = {
-                'name': filename,
-                'parents': [folder_id]
-            }
-            # st.info(f"{file_metadata}")
-
-            media = MediaFileUpload(filepath, mimetype='application/x-yaml')
-
-            service.files().create(
-                body=file_metadata,
-                media_body=media,
-                fields='id'
-            ).execute()
\ No newline at end of file
diff --git a/spaces/popo23/app/Dockerfile b/spaces/popo23/app/Dockerfile
deleted file mode 100644
index 4a5a821629c9a08569f0e83004405a13032cd177..0000000000000000000000000000000000000000
--- a/spaces/popo23/app/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM python:3.9
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-COPY . .
-
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
diff --git a/spaces/profayle/TerrapinTalk/main.py b/spaces/profayle/TerrapinTalk/main.py
deleted file mode 100644
index c8a724356bba37402537792810a5cfee34121d51..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/main.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import openai
-import gradio
-import os
-
-openai.api_key = os.environ["OPENAI_KEY"]
-messages = [{"role": "system", "content": "You are a therapist and friend to a University student, citing various specific mental health resources available at the University of Maryland if needed."}]
-
-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
-
-theme_red = gradio.themes.Soft(
-    primary_hue="red",
-    secondary_hue="red",
-)
-
-demo = gradio.Interface(theme=theme_red,
-                        fn=CustomChatGPT, 
-                        inputs="text", 
-                        outputs="text", 
-                        title="TerrapinTalk")
-
-demo.launch(share=True)
\ No newline at end of file
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/TiffImagePlugin.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/TiffImagePlugin.py
deleted file mode 100644
index dabf8dbfb5fb1a81c42db03631ffc0b3d124f6f2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/TiffImagePlugin.py
+++ /dev/null
@@ -1,2156 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# TIFF file handling
-#
-# TIFF is a flexible, if somewhat aged, image file format originally
-# defined by Aldus.  Although TIFF supports a wide variety of pixel
-# layouts and compression methods, the name doesn't really stand for
-# "thousands of incompatible file formats," it just feels that way.
-#
-# To read TIFF data from a stream, the stream must be seekable.  For
-# progressive decoding, make sure to use TIFF files where the tag
-# directory is placed first in the file.
-#
-# History:
-# 1995-09-01 fl   Created
-# 1996-05-04 fl   Handle JPEGTABLES tag
-# 1996-05-18 fl   Fixed COLORMAP support
-# 1997-01-05 fl   Fixed PREDICTOR support
-# 1997-08-27 fl   Added support for rational tags (from Perry Stoll)
-# 1998-01-10 fl   Fixed seek/tell (from Jan Blom)
-# 1998-07-15 fl   Use private names for internal variables
-# 1999-06-13 fl   Rewritten for PIL 1.0 (1.0)
-# 2000-10-11 fl   Additional fixes for Python 2.0 (1.1)
-# 2001-04-17 fl   Fixed rewind support (seek to frame 0) (1.2)
-# 2001-05-12 fl   Added write support for more tags (from Greg Couch) (1.3)
-# 2001-12-18 fl   Added workaround for broken Matrox library
-# 2002-01-18 fl   Don't mess up if photometric tag is missing (D. Alan Stewart)
-# 2003-05-19 fl   Check FILLORDER tag
-# 2003-09-26 fl   Added RGBa support
-# 2004-02-24 fl   Added DPI support; fixed rational write support
-# 2005-02-07 fl   Added workaround for broken Corel Draw 10 files
-# 2006-01-09 fl   Added support for float/double tags (from Russell Nelson)
-#
-# Copyright (c) 1997-2006 by Secret Labs AB.  All rights reserved.
-# Copyright (c) 1995-1997 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-import io
-import itertools
-import logging
-import math
-import os
-import struct
-import warnings
-from collections.abc import MutableMapping
-from fractions import Fraction
-from numbers import Number, Rational
-
-from . import ExifTags, Image, ImageFile, ImageOps, ImagePalette, TiffTags
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-from ._binary import o8
-from .TiffTags import TYPES
-
-logger = logging.getLogger(__name__)
-
-# Set these to true to force use of libtiff for reading or writing.
-READ_LIBTIFF = False
-WRITE_LIBTIFF = False
-IFD_LEGACY_API = True
-STRIP_SIZE = 65536
-
-II = b"II"  # little-endian (Intel style)
-MM = b"MM"  # big-endian (Motorola style)
-
-#
-# --------------------------------------------------------------------
-# Read TIFF files
-
-# a few tag names, just to make the code below a bit more readable
-IMAGEWIDTH = 256
-IMAGELENGTH = 257
-BITSPERSAMPLE = 258
-COMPRESSION = 259
-PHOTOMETRIC_INTERPRETATION = 262
-FILLORDER = 266
-IMAGEDESCRIPTION = 270
-STRIPOFFSETS = 273
-SAMPLESPERPIXEL = 277
-ROWSPERSTRIP = 278
-STRIPBYTECOUNTS = 279
-X_RESOLUTION = 282
-Y_RESOLUTION = 283
-PLANAR_CONFIGURATION = 284
-RESOLUTION_UNIT = 296
-TRANSFERFUNCTION = 301
-SOFTWARE = 305
-DATE_TIME = 306
-ARTIST = 315
-PREDICTOR = 317
-COLORMAP = 320
-TILEWIDTH = 322
-TILELENGTH = 323
-TILEOFFSETS = 324
-TILEBYTECOUNTS = 325
-SUBIFD = 330
-EXTRASAMPLES = 338
-SAMPLEFORMAT = 339
-JPEGTABLES = 347
-YCBCRSUBSAMPLING = 530
-REFERENCEBLACKWHITE = 532
-COPYRIGHT = 33432
-IPTC_NAA_CHUNK = 33723  # newsphoto properties
-PHOTOSHOP_CHUNK = 34377  # photoshop properties
-ICCPROFILE = 34675
-EXIFIFD = 34665
-XMP = 700
-JPEGQUALITY = 65537  # pseudo-tag by libtiff
-
-# https://github.com/imagej/ImageJA/blob/master/src/main/java/ij/io/TiffDecoder.java
-IMAGEJ_META_DATA_BYTE_COUNTS = 50838
-IMAGEJ_META_DATA = 50839
-
-COMPRESSION_INFO = {
-    # Compression => pil compression name
-    1: "raw",
-    2: "tiff_ccitt",
-    3: "group3",
-    4: "group4",
-    5: "tiff_lzw",
-    6: "tiff_jpeg",  # obsolete
-    7: "jpeg",
-    8: "tiff_adobe_deflate",
-    32771: "tiff_raw_16",  # 16-bit padding
-    32773: "packbits",
-    32809: "tiff_thunderscan",
-    32946: "tiff_deflate",
-    34676: "tiff_sgilog",
-    34677: "tiff_sgilog24",
-    34925: "lzma",
-    50000: "zstd",
-    50001: "webp",
-}
-
-COMPRESSION_INFO_REV = {v: k for k, v in COMPRESSION_INFO.items()}
-
-OPEN_INFO = {
-    # (ByteOrder, PhotoInterpretation, SampleFormat, FillOrder, BitsPerSample,
-    #  ExtraSamples) => mode, rawmode
-    (II, 0, (1,), 1, (1,), ()): ("1", "1;I"),
-    (MM, 0, (1,), 1, (1,), ()): ("1", "1;I"),
-    (II, 0, (1,), 2, (1,), ()): ("1", "1;IR"),
-    (MM, 0, (1,), 2, (1,), ()): ("1", "1;IR"),
-    (II, 1, (1,), 1, (1,), ()): ("1", "1"),
-    (MM, 1, (1,), 1, (1,), ()): ("1", "1"),
-    (II, 1, (1,), 2, (1,), ()): ("1", "1;R"),
-    (MM, 1, (1,), 2, (1,), ()): ("1", "1;R"),
-    (II, 0, (1,), 1, (2,), ()): ("L", "L;2I"),
-    (MM, 0, (1,), 1, (2,), ()): ("L", "L;2I"),
-    (II, 0, (1,), 2, (2,), ()): ("L", "L;2IR"),
-    (MM, 0, (1,), 2, (2,), ()): ("L", "L;2IR"),
-    (II, 1, (1,), 1, (2,), ()): ("L", "L;2"),
-    (MM, 1, (1,), 1, (2,), ()): ("L", "L;2"),
-    (II, 1, (1,), 2, (2,), ()): ("L", "L;2R"),
-    (MM, 1, (1,), 2, (2,), ()): ("L", "L;2R"),
-    (II, 0, (1,), 1, (4,), ()): ("L", "L;4I"),
-    (MM, 0, (1,), 1, (4,), ()): ("L", "L;4I"),
-    (II, 0, (1,), 2, (4,), ()): ("L", "L;4IR"),
-    (MM, 0, (1,), 2, (4,), ()): ("L", "L;4IR"),
-    (II, 1, (1,), 1, (4,), ()): ("L", "L;4"),
-    (MM, 1, (1,), 1, (4,), ()): ("L", "L;4"),
-    (II, 1, (1,), 2, (4,), ()): ("L", "L;4R"),
-    (MM, 1, (1,), 2, (4,), ()): ("L", "L;4R"),
-    (II, 0, (1,), 1, (8,), ()): ("L", "L;I"),
-    (MM, 0, (1,), 1, (8,), ()): ("L", "L;I"),
-    (II, 0, (1,), 2, (8,), ()): ("L", "L;IR"),
-    (MM, 0, (1,), 2, (8,), ()): ("L", "L;IR"),
-    (II, 1, (1,), 1, (8,), ()): ("L", "L"),
-    (MM, 1, (1,), 1, (8,), ()): ("L", "L"),
-    (II, 1, (2,), 1, (8,), ()): ("L", "L"),
-    (MM, 1, (2,), 1, (8,), ()): ("L", "L"),
-    (II, 1, (1,), 2, (8,), ()): ("L", "L;R"),
-    (MM, 1, (1,), 2, (8,), ()): ("L", "L;R"),
-    (II, 1, (1,), 1, (12,), ()): ("I;16", "I;12"),
-    (II, 0, (1,), 1, (16,), ()): ("I;16", "I;16"),
-    (II, 1, (1,), 1, (16,), ()): ("I;16", "I;16"),
-    (MM, 1, (1,), 1, (16,), ()): ("I;16B", "I;16B"),
-    (II, 1, (1,), 2, (16,), ()): ("I;16", "I;16R"),
-    (II, 1, (2,), 1, (16,), ()): ("I", "I;16S"),
-    (MM, 1, (2,), 1, (16,), ()): ("I", "I;16BS"),
-    (II, 0, (3,), 1, (32,), ()): ("F", "F;32F"),
-    (MM, 0, (3,), 1, (32,), ()): ("F", "F;32BF"),
-    (II, 1, (1,), 1, (32,), ()): ("I", "I;32N"),
-    (II, 1, (2,), 1, (32,), ()): ("I", "I;32S"),
-    (MM, 1, (2,), 1, (32,), ()): ("I", "I;32BS"),
-    (II, 1, (3,), 1, (32,), ()): ("F", "F;32F"),
-    (MM, 1, (3,), 1, (32,), ()): ("F", "F;32BF"),
-    (II, 1, (1,), 1, (8, 8), (2,)): ("LA", "LA"),
-    (MM, 1, (1,), 1, (8, 8), (2,)): ("LA", "LA"),
-    (II, 2, (1,), 1, (8, 8, 8), ()): ("RGB", "RGB"),
-    (MM, 2, (1,), 1, (8, 8, 8), ()): ("RGB", "RGB"),
-    (II, 2, (1,), 2, (8, 8, 8), ()): ("RGB", "RGB;R"),
-    (MM, 2, (1,), 2, (8, 8, 8), ()): ("RGB", "RGB;R"),
-    (II, 2, (1,), 1, (8, 8, 8, 8), ()): ("RGBA", "RGBA"),  # missing ExtraSamples
-    (MM, 2, (1,), 1, (8, 8, 8, 8), ()): ("RGBA", "RGBA"),  # missing ExtraSamples
-    (II, 2, (1,), 1, (8, 8, 8, 8), (0,)): ("RGBX", "RGBX"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8), (0,)): ("RGBX", "RGBX"),
-    (II, 2, (1,), 1, (8, 8, 8, 8, 8), (0, 0)): ("RGBX", "RGBXX"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (0, 0)): ("RGBX", "RGBXX"),
-    (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0, 0)): ("RGBX", "RGBXXX"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0, 0)): ("RGBX", "RGBXXX"),
-    (II, 2, (1,), 1, (8, 8, 8, 8), (1,)): ("RGBA", "RGBa"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8), (1,)): ("RGBA", "RGBa"),
-    (II, 2, (1,), 1, (8, 8, 8, 8, 8), (1, 0)): ("RGBA", "RGBaX"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (1, 0)): ("RGBA", "RGBaX"),
-    (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (1, 0, 0)): ("RGBA", "RGBaXX"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (1, 0, 0)): ("RGBA", "RGBaXX"),
-    (II, 2, (1,), 1, (8, 8, 8, 8), (2,)): ("RGBA", "RGBA"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8), (2,)): ("RGBA", "RGBA"),
-    (II, 2, (1,), 1, (8, 8, 8, 8, 8), (2, 0)): ("RGBA", "RGBAX"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (2, 0)): ("RGBA", "RGBAX"),
-    (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (2, 0, 0)): ("RGBA", "RGBAXX"),
-    (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (2, 0, 0)): ("RGBA", "RGBAXX"),
-    (II, 2, (1,), 1, (8, 8, 8, 8), (999,)): ("RGBA", "RGBA"),  # Corel Draw 10
-    (MM, 2, (1,), 1, (8, 8, 8, 8), (999,)): ("RGBA", "RGBA"),  # Corel Draw 10
-    (II, 2, (1,), 1, (16, 16, 16), ()): ("RGB", "RGB;16L"),
-    (MM, 2, (1,), 1, (16, 16, 16), ()): ("RGB", "RGB;16B"),
-    (II, 2, (1,), 1, (16, 16, 16, 16), ()): ("RGBA", "RGBA;16L"),
-    (MM, 2, (1,), 1, (16, 16, 16, 16), ()): ("RGBA", "RGBA;16B"),
-    (II, 2, (1,), 1, (16, 16, 16, 16), (0,)): ("RGBX", "RGBX;16L"),
-    (MM, 2, (1,), 1, (16, 16, 16, 16), (0,)): ("RGBX", "RGBX;16B"),
-    (II, 2, (1,), 1, (16, 16, 16, 16), (1,)): ("RGBA", "RGBa;16L"),
-    (MM, 2, (1,), 1, (16, 16, 16, 16), (1,)): ("RGBA", "RGBa;16B"),
-    (II, 2, (1,), 1, (16, 16, 16, 16), (2,)): ("RGBA", "RGBA;16L"),
-    (MM, 2, (1,), 1, (16, 16, 16, 16), (2,)): ("RGBA", "RGBA;16B"),
-    (II, 3, (1,), 1, (1,), ()): ("P", "P;1"),
-    (MM, 3, (1,), 1, (1,), ()): ("P", "P;1"),
-    (II, 3, (1,), 2, (1,), ()): ("P", "P;1R"),
-    (MM, 3, (1,), 2, (1,), ()): ("P", "P;1R"),
-    (II, 3, (1,), 1, (2,), ()): ("P", "P;2"),
-    (MM, 3, (1,), 1, (2,), ()): ("P", "P;2"),
-    (II, 3, (1,), 2, (2,), ()): ("P", "P;2R"),
-    (MM, 3, (1,), 2, (2,), ()): ("P", "P;2R"),
-    (II, 3, (1,), 1, (4,), ()): ("P", "P;4"),
-    (MM, 3, (1,), 1, (4,), ()): ("P", "P;4"),
-    (II, 3, (1,), 2, (4,), ()): ("P", "P;4R"),
-    (MM, 3, (1,), 2, (4,), ()): ("P", "P;4R"),
-    (II, 3, (1,), 1, (8,), ()): ("P", "P"),
-    (MM, 3, (1,), 1, (8,), ()): ("P", "P"),
-    (II, 3, (1,), 1, (8, 8), (2,)): ("PA", "PA"),
-    (MM, 3, (1,), 1, (8, 8), (2,)): ("PA", "PA"),
-    (II, 3, (1,), 2, (8,), ()): ("P", "P;R"),
-    (MM, 3, (1,), 2, (8,), ()): ("P", "P;R"),
-    (II, 5, (1,), 1, (8, 8, 8, 8), ()): ("CMYK", "CMYK"),
-    (MM, 5, (1,), 1, (8, 8, 8, 8), ()): ("CMYK", "CMYK"),
-    (II, 5, (1,), 1, (8, 8, 8, 8, 8), (0,)): ("CMYK", "CMYKX"),
-    (MM, 5, (1,), 1, (8, 8, 8, 8, 8), (0,)): ("CMYK", "CMYKX"),
-    (II, 5, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0)): ("CMYK", "CMYKXX"),
-    (MM, 5, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0)): ("CMYK", "CMYKXX"),
-    (II, 5, (1,), 1, (16, 16, 16, 16), ()): ("CMYK", "CMYK;16L"),
-    (II, 6, (1,), 1, (8,), ()): ("L", "L"),
-    (MM, 6, (1,), 1, (8,), ()): ("L", "L"),
-    # JPEG compressed images handled by LibTiff and auto-converted to RGBX
-    # Minimal Baseline TIFF requires YCbCr images to have 3 SamplesPerPixel
-    (II, 6, (1,), 1, (8, 8, 8), ()): ("RGB", "RGBX"),
-    (MM, 6, (1,), 1, (8, 8, 8), ()): ("RGB", "RGBX"),
-    (II, 8, (1,), 1, (8, 8, 8), ()): ("LAB", "LAB"),
-    (MM, 8, (1,), 1, (8, 8, 8), ()): ("LAB", "LAB"),
-}
-
-MAX_SAMPLESPERPIXEL = max(len(key_tp[4]) for key_tp in OPEN_INFO)
-
-PREFIXES = [
-    b"MM\x00\x2A",  # Valid TIFF header with big-endian byte order
-    b"II\x2A\x00",  # Valid TIFF header with little-endian byte order
-    b"MM\x2A\x00",  # Invalid TIFF header, assume big-endian
-    b"II\x00\x2A",  # Invalid TIFF header, assume little-endian
-    b"MM\x00\x2B",  # BigTIFF with big-endian byte order
-    b"II\x2B\x00",  # BigTIFF with little-endian byte order
-]
-
-
-def _accept(prefix):
-    return prefix[:4] in PREFIXES
-
-
-def _limit_rational(val, max_val):
-    inv = abs(val) > 1
-    n_d = IFDRational(1 / val if inv else val).limit_rational(max_val)
-    return n_d[::-1] if inv else n_d
-
-
-def _limit_signed_rational(val, max_val, min_val):
-    frac = Fraction(val)
-    n_d = frac.numerator, frac.denominator
-
-    if min(n_d) < min_val:
-        n_d = _limit_rational(val, abs(min_val))
-
-    if max(n_d) > max_val:
-        val = Fraction(*n_d)
-        n_d = _limit_rational(val, max_val)
-
-    return n_d
-
-
-##
-# Wrapper for TIFF IFDs.
-
-_load_dispatch = {}
-_write_dispatch = {}
-
-
-class IFDRational(Rational):
-    """Implements a rational class where 0/0 is a legal value to match
-    the in the wild use of exif rationals.
-
-    e.g., DigitalZoomRatio - 0.00/0.00  indicates that no digital zoom was used
-    """
-
-    """ If the denominator is 0, store this as a float('nan'), otherwise store
-    as a fractions.Fraction(). Delegate as appropriate
-
-    """
-
-    __slots__ = ("_numerator", "_denominator", "_val")
-
-    def __init__(self, value, denominator=1):
-        """
-        :param value: either an integer numerator, a
-        float/rational/other number, or an IFDRational
-        :param denominator: Optional integer denominator
-        """
-        if isinstance(value, IFDRational):
-            self._numerator = value.numerator
-            self._denominator = value.denominator
-            self._val = value._val
-            return
-
-        if isinstance(value, Fraction):
-            self._numerator = value.numerator
-            self._denominator = value.denominator
-        else:
-            self._numerator = value
-            self._denominator = denominator
-
-        if denominator == 0:
-            self._val = float("nan")
-        elif denominator == 1:
-            self._val = Fraction(value)
-        else:
-            self._val = Fraction(value, denominator)
-
-    @property
-    def numerator(self):
-        return self._numerator
-
-    @property
-    def denominator(self):
-        return self._denominator
-
-    def limit_rational(self, max_denominator):
-        """
-
-        :param max_denominator: Integer, the maximum denominator value
-        :returns: Tuple of (numerator, denominator)
-        """
-
-        if self.denominator == 0:
-            return self.numerator, self.denominator
-
-        f = self._val.limit_denominator(max_denominator)
-        return f.numerator, f.denominator
-
-    def __repr__(self):
-        return str(float(self._val))
-
-    def __hash__(self):
-        return self._val.__hash__()
-
-    def __eq__(self, other):
-        val = self._val
-        if isinstance(other, IFDRational):
-            other = other._val
-        if isinstance(other, float):
-            val = float(val)
-        return val == other
-
-    def __getstate__(self):
-        return [self._val, self._numerator, self._denominator]
-
-    def __setstate__(self, state):
-        IFDRational.__init__(self, 0)
-        _val, _numerator, _denominator = state
-        self._val = _val
-        self._numerator = _numerator
-        self._denominator = _denominator
-
-    def _delegate(op):
-        def delegate(self, *args):
-            return getattr(self._val, op)(*args)
-
-        return delegate
-
-    """ a = ['add','radd', 'sub', 'rsub', 'mul', 'rmul',
-             'truediv', 'rtruediv', 'floordiv', 'rfloordiv',
-             'mod','rmod', 'pow','rpow', 'pos', 'neg',
-             'abs', 'trunc', 'lt', 'gt', 'le', 'ge', 'bool',
-             'ceil', 'floor', 'round']
-        print("\n".join("__%s__ = _delegate('__%s__')" % (s,s) for s in a))
-        """
-
-    __add__ = _delegate("__add__")
-    __radd__ = _delegate("__radd__")
-    __sub__ = _delegate("__sub__")
-    __rsub__ = _delegate("__rsub__")
-    __mul__ = _delegate("__mul__")
-    __rmul__ = _delegate("__rmul__")
-    __truediv__ = _delegate("__truediv__")
-    __rtruediv__ = _delegate("__rtruediv__")
-    __floordiv__ = _delegate("__floordiv__")
-    __rfloordiv__ = _delegate("__rfloordiv__")
-    __mod__ = _delegate("__mod__")
-    __rmod__ = _delegate("__rmod__")
-    __pow__ = _delegate("__pow__")
-    __rpow__ = _delegate("__rpow__")
-    __pos__ = _delegate("__pos__")
-    __neg__ = _delegate("__neg__")
-    __abs__ = _delegate("__abs__")
-    __trunc__ = _delegate("__trunc__")
-    __lt__ = _delegate("__lt__")
-    __gt__ = _delegate("__gt__")
-    __le__ = _delegate("__le__")
-    __ge__ = _delegate("__ge__")
-    __bool__ = _delegate("__bool__")
-    __ceil__ = _delegate("__ceil__")
-    __floor__ = _delegate("__floor__")
-    __round__ = _delegate("__round__")
-    # Python >= 3.11
-    if hasattr(Fraction, "__int__"):
-        __int__ = _delegate("__int__")
-
-
-class ImageFileDirectory_v2(MutableMapping):
-    """This class represents a TIFF tag directory.  To speed things up, we
-    don't decode tags unless they're asked for.
-
-    Exposes a dictionary interface of the tags in the directory::
-
-        ifd = ImageFileDirectory_v2()
-        ifd[key] = 'Some Data'
-        ifd.tagtype[key] = TiffTags.ASCII
-        print(ifd[key])
-        'Some Data'
-
-    Individual values are returned as the strings or numbers, sequences are
-    returned as tuples of the values.
-
-    The tiff metadata type of each item is stored in a dictionary of
-    tag types in
-    :attr:`~PIL.TiffImagePlugin.ImageFileDirectory_v2.tagtype`. The types
-    are read from a tiff file, guessed from the type added, or added
-    manually.
-
-    Data Structures:
-
-        * ``self.tagtype = {}``
-
-          * Key: numerical TIFF tag number
-          * Value: integer corresponding to the data type from
-            :py:data:`.TiffTags.TYPES`
-
-          .. versionadded:: 3.0.0
-
-    'Internal' data structures:
-
-        * ``self._tags_v2 = {}``
-
-          * Key: numerical TIFF tag number
-          * Value: decoded data, as tuple for multiple values
-
-        * ``self._tagdata = {}``
-
-          * Key: numerical TIFF tag number
-          * Value: undecoded byte string from file
-
-        * ``self._tags_v1 = {}``
-
-          * Key: numerical TIFF tag number
-          * Value: decoded data in the v1 format
-
-    Tags will be found in the private attributes ``self._tagdata``, and in
-    ``self._tags_v2`` once decoded.
-
-    ``self.legacy_api`` is a value for internal use, and shouldn't be changed
-    from outside code. In cooperation with
-    :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1`, if ``legacy_api``
-    is true, then decoded tags will be populated into both ``_tags_v1`` and
-    ``_tags_v2``. ``_tags_v2`` will be used if this IFD is used in the TIFF
-    save routine. Tags should be read from ``_tags_v1`` if
-    ``legacy_api == true``.
-
-    """
-
-    def __init__(self, ifh=b"II\052\0\0\0\0\0", prefix=None, group=None):
-        """Initialize an ImageFileDirectory.
-
-        To construct an ImageFileDirectory from a real file, pass the 8-byte
-        magic header to the constructor.  To only set the endianness, pass it
-        as the 'prefix' keyword argument.
-
-        :param ifh: One of the accepted magic headers (cf. PREFIXES); also sets
-              endianness.
-        :param prefix: Override the endianness of the file.
-        """
-        if not _accept(ifh):
-            msg = f"not a TIFF file (header {repr(ifh)} not valid)"
-            raise SyntaxError(msg)
-        self._prefix = prefix if prefix is not None else ifh[:2]
-        if self._prefix == MM:
-            self._endian = ">"
-        elif self._prefix == II:
-            self._endian = "<"
-        else:
-            msg = "not a TIFF IFD"
-            raise SyntaxError(msg)
-        self._bigtiff = ifh[2] == 43
-        self.group = group
-        self.tagtype = {}
-        """ Dictionary of tag types """
-        self.reset()
-        (self.next,) = (
-            self._unpack("Q", ifh[8:]) if self._bigtiff else self._unpack("L", ifh[4:])
-        )
-        self._legacy_api = False
-
-    prefix = property(lambda self: self._prefix)
-    offset = property(lambda self: self._offset)
-    legacy_api = property(lambda self: self._legacy_api)
-
-    @legacy_api.setter
-    def legacy_api(self, value):
-        msg = "Not allowing setting of legacy api"
-        raise Exception(msg)
-
-    def reset(self):
-        self._tags_v1 = {}  # will remain empty if legacy_api is false
-        self._tags_v2 = {}  # main tag storage
-        self._tagdata = {}
-        self.tagtype = {}  # added 2008-06-05 by Florian Hoech
-        self._next = None
-        self._offset = None
-
-    def __str__(self):
-        return str(dict(self))
-
-    def named(self):
-        """
-        :returns: dict of name|key: value
-
-        Returns the complete tag dictionary, with named tags where possible.
-        """
-        return {
-            TiffTags.lookup(code, self.group).name: value
-            for code, value in self.items()
-        }
-
-    def __len__(self):
-        return len(set(self._tagdata) | set(self._tags_v2))
-
-    def __getitem__(self, tag):
-        if tag not in self._tags_v2:  # unpack on the fly
-            data = self._tagdata[tag]
-            typ = self.tagtype[tag]
-            size, handler = self._load_dispatch[typ]
-            self[tag] = handler(self, data, self.legacy_api)  # check type
-        val = self._tags_v2[tag]
-        if self.legacy_api and not isinstance(val, (tuple, bytes)):
-            val = (val,)
-        return val
-
-    def __contains__(self, tag):
-        return tag in self._tags_v2 or tag in self._tagdata
-
-    def __setitem__(self, tag, value):
-        self._setitem(tag, value, self.legacy_api)
-
-    def _setitem(self, tag, value, legacy_api):
-        basetypes = (Number, bytes, str)
-
-        info = TiffTags.lookup(tag, self.group)
-        values = [value] if isinstance(value, basetypes) else value
-
-        if tag not in self.tagtype:
-            if info.type:
-                self.tagtype[tag] = info.type
-            else:
-                self.tagtype[tag] = TiffTags.UNDEFINED
-                if all(isinstance(v, IFDRational) for v in values):
-                    self.tagtype[tag] = (
-                        TiffTags.RATIONAL
-                        if all(v >= 0 for v in values)
-                        else TiffTags.SIGNED_RATIONAL
-                    )
-                elif all(isinstance(v, int) for v in values):
-                    if all(0 <= v < 2**16 for v in values):
-                        self.tagtype[tag] = TiffTags.SHORT
-                    elif all(-(2**15) < v < 2**15 for v in values):
-                        self.tagtype[tag] = TiffTags.SIGNED_SHORT
-                    else:
-                        self.tagtype[tag] = (
-                            TiffTags.LONG
-                            if all(v >= 0 for v in values)
-                            else TiffTags.SIGNED_LONG
-                        )
-                elif all(isinstance(v, float) for v in values):
-                    self.tagtype[tag] = TiffTags.DOUBLE
-                elif all(isinstance(v, str) for v in values):
-                    self.tagtype[tag] = TiffTags.ASCII
-                elif all(isinstance(v, bytes) for v in values):
-                    self.tagtype[tag] = TiffTags.BYTE
-
-        if self.tagtype[tag] == TiffTags.UNDEFINED:
-            values = [
-                v.encode("ascii", "replace") if isinstance(v, str) else v
-                for v in values
-            ]
-        elif self.tagtype[tag] == TiffTags.RATIONAL:
-            values = [float(v) if isinstance(v, int) else v for v in values]
-
-        is_ifd = self.tagtype[tag] == TiffTags.LONG and isinstance(values, dict)
-        if not is_ifd:
-            values = tuple(info.cvt_enum(value) for value in values)
-
-        dest = self._tags_v1 if legacy_api else self._tags_v2
-
-        # Three branches:
-        # Spec'd length == 1, Actual length 1, store as element
-        # Spec'd length == 1, Actual > 1, Warn and truncate. Formerly barfed.
-        # No Spec, Actual length 1, Formerly (<4.2) returned a 1 element tuple.
-        # Don't mess with the legacy api, since it's frozen.
-        if not is_ifd and (
-            (info.length == 1)
-            or self.tagtype[tag] == TiffTags.BYTE
-            or (info.length is None and len(values) == 1 and not legacy_api)
-        ):
-            # Don't mess with the legacy api, since it's frozen.
-            if legacy_api and self.tagtype[tag] in [
-                TiffTags.RATIONAL,
-                TiffTags.SIGNED_RATIONAL,
-            ]:  # rationals
-                values = (values,)
-            try:
-                (dest[tag],) = values
-            except ValueError:
-                # We've got a builtin tag with 1 expected entry
-                warnings.warn(
-                    f"Metadata Warning, tag {tag} had too many entries: "
-                    f"{len(values)}, expected 1"
-                )
-                dest[tag] = values[0]
-
-        else:
-            # Spec'd length > 1 or undefined
-            # Unspec'd, and length > 1
-            dest[tag] = values
-
-    def __delitem__(self, tag):
-        self._tags_v2.pop(tag, None)
-        self._tags_v1.pop(tag, None)
-        self._tagdata.pop(tag, None)
-
-    def __iter__(self):
-        return iter(set(self._tagdata) | set(self._tags_v2))
-
-    def _unpack(self, fmt, data):
-        return struct.unpack(self._endian + fmt, data)
-
-    def _pack(self, fmt, *values):
-        return struct.pack(self._endian + fmt, *values)
-
-    def _register_loader(idx, size):
-        def decorator(func):
-            from .TiffTags import TYPES
-
-            if func.__name__.startswith("load_"):
-                TYPES[idx] = func.__name__[5:].replace("_", " ")
-            _load_dispatch[idx] = size, func  # noqa: F821
-            return func
-
-        return decorator
-
-    def _register_writer(idx):
-        def decorator(func):
-            _write_dispatch[idx] = func  # noqa: F821
-            return func
-
-        return decorator
-
-    def _register_basic(idx_fmt_name):
-        from .TiffTags import TYPES
-
-        idx, fmt, name = idx_fmt_name
-        TYPES[idx] = name
-        size = struct.calcsize("=" + fmt)
-        _load_dispatch[idx] = (  # noqa: F821
-            size,
-            lambda self, data, legacy_api=True: (
-                self._unpack(f"{len(data) // size}{fmt}", data)
-            ),
-        )
-        _write_dispatch[idx] = lambda self, *values: (  # noqa: F821
-            b"".join(self._pack(fmt, value) for value in values)
-        )
-
-    list(
-        map(
-            _register_basic,
-            [
-                (TiffTags.SHORT, "H", "short"),
-                (TiffTags.LONG, "L", "long"),
-                (TiffTags.SIGNED_BYTE, "b", "signed byte"),
-                (TiffTags.SIGNED_SHORT, "h", "signed short"),
-                (TiffTags.SIGNED_LONG, "l", "signed long"),
-                (TiffTags.FLOAT, "f", "float"),
-                (TiffTags.DOUBLE, "d", "double"),
-                (TiffTags.IFD, "L", "long"),
-                (TiffTags.LONG8, "Q", "long8"),
-            ],
-        )
-    )
-
-    @_register_loader(1, 1)  # Basic type, except for the legacy API.
-    def load_byte(self, data, legacy_api=True):
-        return data
-
-    @_register_writer(1)  # Basic type, except for the legacy API.
-    def write_byte(self, data):
-        if isinstance(data, IFDRational):
-            data = int(data)
-        if isinstance(data, int):
-            data = bytes((data,))
-        return data
-
-    @_register_loader(2, 1)
-    def load_string(self, data, legacy_api=True):
-        if data.endswith(b"\0"):
-            data = data[:-1]
-        return data.decode("latin-1", "replace")
-
-    @_register_writer(2)
-    def write_string(self, value):
-        # remerge of https://github.com/python-pillow/Pillow/pull/1416
-        if isinstance(value, int):
-            value = str(value)
-        if not isinstance(value, bytes):
-            value = value.encode("ascii", "replace")
-        return value + b"\0"
-
-    @_register_loader(5, 8)
-    def load_rational(self, data, legacy_api=True):
-        vals = self._unpack(f"{len(data) // 4}L", data)
-
-        def combine(a, b):
-            return (a, b) if legacy_api else IFDRational(a, b)
-
-        return tuple(combine(num, denom) for num, denom in zip(vals[::2], vals[1::2]))
-
-    @_register_writer(5)
-    def write_rational(self, *values):
-        return b"".join(
-            self._pack("2L", *_limit_rational(frac, 2**32 - 1)) for frac in values
-        )
-
-    @_register_loader(7, 1)
-    def load_undefined(self, data, legacy_api=True):
-        return data
-
-    @_register_writer(7)
-    def write_undefined(self, value):
-        if isinstance(value, int):
-            value = str(value).encode("ascii", "replace")
-        return value
-
-    @_register_loader(10, 8)
-    def load_signed_rational(self, data, legacy_api=True):
-        vals = self._unpack(f"{len(data) // 4}l", data)
-
-        def combine(a, b):
-            return (a, b) if legacy_api else IFDRational(a, b)
-
-        return tuple(combine(num, denom) for num, denom in zip(vals[::2], vals[1::2]))
-
-    @_register_writer(10)
-    def write_signed_rational(self, *values):
-        return b"".join(
-            self._pack("2l", *_limit_signed_rational(frac, 2**31 - 1, -(2**31)))
-            for frac in values
-        )
-
-    def _ensure_read(self, fp, size):
-        ret = fp.read(size)
-        if len(ret) != size:
-            msg = (
-                "Corrupt EXIF data.  "
-                f"Expecting to read {size} bytes but only got {len(ret)}. "
-            )
-            raise OSError(msg)
-        return ret
-
-    def load(self, fp):
-        self.reset()
-        self._offset = fp.tell()
-
-        try:
-            tag_count = (
-                self._unpack("Q", self._ensure_read(fp, 8))
-                if self._bigtiff
-                else self._unpack("H", self._ensure_read(fp, 2))
-            )[0]
-            for i in range(tag_count):
-                tag, typ, count, data = (
-                    self._unpack("HHQ8s", self._ensure_read(fp, 20))
-                    if self._bigtiff
-                    else self._unpack("HHL4s", self._ensure_read(fp, 12))
-                )
-
-                tagname = TiffTags.lookup(tag, self.group).name
-                typname = TYPES.get(typ, "unknown")
-                msg = f"tag: {tagname} ({tag}) - type: {typname} ({typ})"
-
-                try:
-                    unit_size, handler = self._load_dispatch[typ]
-                except KeyError:
-                    logger.debug("%s - unsupported type %s", msg, typ)
-                    continue  # ignore unsupported type
-                size = count * unit_size
-                if size > (8 if self._bigtiff else 4):
-                    here = fp.tell()
-                    (offset,) = self._unpack("Q" if self._bigtiff else "L", data)
-                    msg += f" Tag Location: {here} - Data Location: {offset}"
-                    fp.seek(offset)
-                    data = ImageFile._safe_read(fp, size)
-                    fp.seek(here)
-                else:
-                    data = data[:size]
-
-                if len(data) != size:
-                    warnings.warn(
-                        "Possibly corrupt EXIF data.  "
-                        f"Expecting to read {size} bytes but only got {len(data)}."
-                        f" Skipping tag {tag}"
-                    )
-                    logger.debug(msg)
-                    continue
-
-                if not data:
-                    logger.debug(msg)
-                    continue
-
-                self._tagdata[tag] = data
-                self.tagtype[tag] = typ
-
-                msg += " - value: " + (
-                    "<table: %d bytes>" % size if size > 32 else repr(data)
-                )
-                logger.debug(msg)
-
-            (self.next,) = (
-                self._unpack("Q", self._ensure_read(fp, 8))
-                if self._bigtiff
-                else self._unpack("L", self._ensure_read(fp, 4))
-            )
-        except OSError as msg:
-            warnings.warn(str(msg))
-            return
-
-    def tobytes(self, offset=0):
-        # FIXME What about tagdata?
-        result = self._pack("H", len(self._tags_v2))
-
-        entries = []
-        offset = offset + len(result) + len(self._tags_v2) * 12 + 4
-        stripoffsets = None
-
-        # pass 1: convert tags to binary format
-        # always write tags in ascending order
-        for tag, value in sorted(self._tags_v2.items()):
-            if tag == STRIPOFFSETS:
-                stripoffsets = len(entries)
-            typ = self.tagtype.get(tag)
-            logger.debug("Tag %s, Type: %s, Value: %s", tag, typ, repr(value))
-            is_ifd = typ == TiffTags.LONG and isinstance(value, dict)
-            if is_ifd:
-                if self._endian == "<":
-                    ifh = b"II\x2A\x00\x08\x00\x00\x00"
-                else:
-                    ifh = b"MM\x00\x2A\x00\x00\x00\x08"
-                ifd = ImageFileDirectory_v2(ifh, group=tag)
-                values = self._tags_v2[tag]
-                for ifd_tag, ifd_value in values.items():
-                    ifd[ifd_tag] = ifd_value
-                data = ifd.tobytes(offset)
-            else:
-                values = value if isinstance(value, tuple) else (value,)
-                data = self._write_dispatch[typ](self, *values)
-
-            tagname = TiffTags.lookup(tag, self.group).name
-            typname = "ifd" if is_ifd else TYPES.get(typ, "unknown")
-            msg = f"save: {tagname} ({tag}) - type: {typname} ({typ})"
-            msg += " - value: " + (
-                "<table: %d bytes>" % len(data) if len(data) >= 16 else str(values)
-            )
-            logger.debug(msg)
-
-            # count is sum of lengths for string and arbitrary data
-            if is_ifd:
-                count = 1
-            elif typ in [TiffTags.BYTE, TiffTags.ASCII, TiffTags.UNDEFINED]:
-                count = len(data)
-            else:
-                count = len(values)
-            # figure out if data fits into the entry
-            if len(data) <= 4:
-                entries.append((tag, typ, count, data.ljust(4, b"\0"), b""))
-            else:
-                entries.append((tag, typ, count, self._pack("L", offset), data))
-                offset += (len(data) + 1) // 2 * 2  # pad to word
-
-        # update strip offset data to point beyond auxiliary data
-        if stripoffsets is not None:
-            tag, typ, count, value, data = entries[stripoffsets]
-            if data:
-                msg = "multistrip support not yet implemented"
-                raise NotImplementedError(msg)
-            value = self._pack("L", self._unpack("L", value)[0] + offset)
-            entries[stripoffsets] = tag, typ, count, value, data
-
-        # pass 2: write entries to file
-        for tag, typ, count, value, data in entries:
-            logger.debug("%s %s %s %s %s", tag, typ, count, repr(value), repr(data))
-            result += self._pack("HHL4s", tag, typ, count, value)
-
-        # -- overwrite here for multi-page --
-        result += b"\0\0\0\0"  # end of entries
-
-        # pass 3: write auxiliary data to file
-        for tag, typ, count, value, data in entries:
-            result += data
-            if len(data) & 1:
-                result += b"\0"
-
-        return result
-
-    def save(self, fp):
-        if fp.tell() == 0:  # skip TIFF header on subsequent pages
-            # tiff header -- PIL always starts the first IFD at offset 8
-            fp.write(self._prefix + self._pack("HL", 42, 8))
-
-        offset = fp.tell()
-        result = self.tobytes(offset)
-        fp.write(result)
-        return offset + len(result)
-
-
-ImageFileDirectory_v2._load_dispatch = _load_dispatch
-ImageFileDirectory_v2._write_dispatch = _write_dispatch
-for idx, name in TYPES.items():
-    name = name.replace(" ", "_")
-    setattr(ImageFileDirectory_v2, "load_" + name, _load_dispatch[idx][1])
-    setattr(ImageFileDirectory_v2, "write_" + name, _write_dispatch[idx])
-del _load_dispatch, _write_dispatch, idx, name
-
-
-# Legacy ImageFileDirectory support.
-class ImageFileDirectory_v1(ImageFileDirectory_v2):
-    """This class represents the **legacy** interface to a TIFF tag directory.
-
-    Exposes a dictionary interface of the tags in the directory::
-
-        ifd = ImageFileDirectory_v1()
-        ifd[key] = 'Some Data'
-        ifd.tagtype[key] = TiffTags.ASCII
-        print(ifd[key])
-        ('Some Data',)
-
-    Also contains a dictionary of tag types as read from the tiff image file,
-    :attr:`~PIL.TiffImagePlugin.ImageFileDirectory_v1.tagtype`.
-
-    Values are returned as a tuple.
-
-    ..  deprecated:: 3.0.0
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._legacy_api = True
-
-    tags = property(lambda self: self._tags_v1)
-    tagdata = property(lambda self: self._tagdata)
-
-    # defined in ImageFileDirectory_v2
-    tagtype: dict
-    """Dictionary of tag types"""
-
-    @classmethod
-    def from_v2(cls, original):
-        """Returns an
-        :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1`
-        instance with the same data as is contained in the original
-        :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2`
-        instance.
-
-        :returns: :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1`
-
-        """
-
-        ifd = cls(prefix=original.prefix)
-        ifd._tagdata = original._tagdata
-        ifd.tagtype = original.tagtype
-        ifd.next = original.next  # an indicator for multipage tiffs
-        return ifd
-
-    def to_v2(self):
-        """Returns an
-        :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2`
-        instance with the same data as is contained in the original
-        :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1`
-        instance.
-
-        :returns: :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2`
-
-        """
-
-        ifd = ImageFileDirectory_v2(prefix=self.prefix)
-        ifd._tagdata = dict(self._tagdata)
-        ifd.tagtype = dict(self.tagtype)
-        ifd._tags_v2 = dict(self._tags_v2)
-        return ifd
-
-    def __contains__(self, tag):
-        return tag in self._tags_v1 or tag in self._tagdata
-
-    def __len__(self):
-        return len(set(self._tagdata) | set(self._tags_v1))
-
-    def __iter__(self):
-        return iter(set(self._tagdata) | set(self._tags_v1))
-
-    def __setitem__(self, tag, value):
-        for legacy_api in (False, True):
-            self._setitem(tag, value, legacy_api)
-
-    def __getitem__(self, tag):
-        if tag not in self._tags_v1:  # unpack on the fly
-            data = self._tagdata[tag]
-            typ = self.tagtype[tag]
-            size, handler = self._load_dispatch[typ]
-            for legacy in (False, True):
-                self._setitem(tag, handler(self, data, legacy), legacy)
-        val = self._tags_v1[tag]
-        if not isinstance(val, (tuple, bytes)):
-            val = (val,)
-        return val
-
-
-# undone -- switch this pointer when IFD_LEGACY_API == False
-ImageFileDirectory = ImageFileDirectory_v1
-
-
-##
-# Image plugin for TIFF files.
-
-
-class TiffImageFile(ImageFile.ImageFile):
-    format = "TIFF"
-    format_description = "Adobe TIFF"
-    _close_exclusive_fp_after_loading = False
-
-    def __init__(self, fp=None, filename=None):
-        self.tag_v2 = None
-        """ Image file directory (tag dictionary) """
-
-        self.tag = None
-        """ Legacy tag entries """
-
-        super().__init__(fp, filename)
-
-    def _open(self):
-        """Open the first image in a TIFF file"""
-
-        # Header
-        ifh = self.fp.read(8)
-        if ifh[2] == 43:
-            ifh += self.fp.read(8)
-
-        self.tag_v2 = ImageFileDirectory_v2(ifh)
-
-        # legacy IFD entries will be filled in later
-        self.ifd = None
-
-        # setup frame pointers
-        self.__first = self.__next = self.tag_v2.next
-        self.__frame = -1
-        self._fp = self.fp
-        self._frame_pos = []
-        self._n_frames = None
-
-        logger.debug("*** TiffImageFile._open ***")
-        logger.debug("- __first: %s", self.__first)
-        logger.debug("- ifh: %s", repr(ifh))  # Use repr to avoid str(bytes)
-
-        # and load the first frame
-        self._seek(0)
-
-    @property
-    def n_frames(self):
-        if self._n_frames is None:
-            current = self.tell()
-            self._seek(len(self._frame_pos))
-            while self._n_frames is None:
-                self._seek(self.tell() + 1)
-            self.seek(current)
-        return self._n_frames
-
-    def seek(self, frame):
-        """Select a given frame as current image"""
-        if not self._seek_check(frame):
-            return
-        self._seek(frame)
-        # Create a new core image object on second and
-        # subsequent frames in the image. Image may be
-        # different size/mode.
-        Image._decompression_bomb_check(self.size)
-        self.im = Image.core.new(self.mode, self.size)
-
-    def _seek(self, frame):
-        self.fp = self._fp
-
-        # reset buffered io handle in case fp
-        # was passed to libtiff, invalidating the buffer
-        self.fp.tell()
-
-        while len(self._frame_pos) <= frame:
-            if not self.__next:
-                msg = "no more images in TIFF file"
-                raise EOFError(msg)
-            logger.debug(
-                "Seeking to frame %s, on frame %s, __next %s, location: %s",
-                frame,
-                self.__frame,
-                self.__next,
-                self.fp.tell(),
-            )
-            self.fp.seek(self.__next)
-            self._frame_pos.append(self.__next)
-            logger.debug("Loading tags, location: %s", self.fp.tell())
-            self.tag_v2.load(self.fp)
-            if self.tag_v2.next in self._frame_pos:
-                # This IFD has already been processed
-                # Declare this to be the end of the image
-                self.__next = 0
-            else:
-                self.__next = self.tag_v2.next
-            if self.__next == 0:
-                self._n_frames = frame + 1
-            if len(self._frame_pos) == 1:
-                self.is_animated = self.__next != 0
-            self.__frame += 1
-        self.fp.seek(self._frame_pos[frame])
-        self.tag_v2.load(self.fp)
-        self._reload_exif()
-        # fill the legacy tag/ifd entries
-        self.tag = self.ifd = ImageFileDirectory_v1.from_v2(self.tag_v2)
-        self.__frame = frame
-        self._setup()
-
-    def tell(self):
-        """Return the current frame number"""
-        return self.__frame
-
-    def getxmp(self):
-        """
-        Returns a dictionary containing the XMP tags.
-        Requires defusedxml to be installed.
-
-        :returns: XMP tags in a dictionary.
-        """
-        return self._getxmp(self.tag_v2[XMP]) if XMP in self.tag_v2 else {}
-
-    def get_photoshop_blocks(self):
-        """
-        Returns a dictionary of Photoshop "Image Resource Blocks".
-        The keys are the image resource ID. For more information, see
-        https://www.adobe.com/devnet-apps/photoshop/fileformatashtml/#50577409_pgfId-1037727
-
-        :returns: Photoshop "Image Resource Blocks" in a dictionary.
-        """
-        blocks = {}
-        val = self.tag_v2.get(ExifTags.Base.ImageResources)
-        if val:
-            while val[:4] == b"8BIM":
-                id = i16(val[4:6])
-                n = math.ceil((val[6] + 1) / 2) * 2
-                size = i32(val[6 + n : 10 + n])
-                data = val[10 + n : 10 + n + size]
-                blocks[id] = {"data": data}
-
-                val = val[math.ceil((10 + n + size) / 2) * 2 :]
-        return blocks
-
-    def load(self):
-        if self.tile and self.use_load_libtiff:
-            return self._load_libtiff()
-        return super().load()
-
-    def load_end(self):
-        # allow closing if we're on the first frame, there's no next
-        # This is the ImageFile.load path only, libtiff specific below.
-        if not self.is_animated:
-            self._close_exclusive_fp_after_loading = True
-
-            # reset buffered io handle in case fp
-            # was passed to libtiff, invalidating the buffer
-            self.fp.tell()
-
-            # load IFD data from fp before it is closed
-            exif = self.getexif()
-            for key in TiffTags.TAGS_V2_GROUPS:
-                if key not in exif:
-                    continue
-                exif.get_ifd(key)
-
-        ImageOps.exif_transpose(self, in_place=True)
-        if ExifTags.Base.Orientation in self.tag_v2:
-            del self.tag_v2[ExifTags.Base.Orientation]
-
-    def _load_libtiff(self):
-        """Overload method triggered when we detect a compressed tiff
-        Calls out to libtiff"""
-
-        Image.Image.load(self)
-
-        self.load_prepare()
-
-        if not len(self.tile) == 1:
-            msg = "Not exactly one tile"
-            raise OSError(msg)
-
-        # (self._compression, (extents tuple),
-        #   0, (rawmode, self._compression, fp))
-        extents = self.tile[0][1]
-        args = list(self.tile[0][3])
-
-        # To be nice on memory footprint, if there's a
-        # file descriptor, use that instead of reading
-        # into a string in python.
-        try:
-            fp = hasattr(self.fp, "fileno") and self.fp.fileno()
-            # flush the file descriptor, prevents error on pypy 2.4+
-            # should also eliminate the need for fp.tell
-            # in _seek
-            if hasattr(self.fp, "flush"):
-                self.fp.flush()
-        except OSError:
-            # io.BytesIO have a fileno, but returns an OSError if
-            # it doesn't use a file descriptor.
-            fp = False
-
-        if fp:
-            args[2] = fp
-
-        decoder = Image._getdecoder(
-            self.mode, "libtiff", tuple(args), self.decoderconfig
-        )
-        try:
-            decoder.setimage(self.im, extents)
-        except ValueError as e:
-            msg = "Couldn't set the image"
-            raise OSError(msg) from e
-
-        close_self_fp = self._exclusive_fp and not self.is_animated
-        if hasattr(self.fp, "getvalue"):
-            # We've got a stringio like thing passed in. Yay for all in memory.
-            # The decoder needs the entire file in one shot, so there's not
-            # a lot we can do here other than give it the entire file.
-            # unless we could do something like get the address of the
-            # underlying string for stringio.
-            #
-            # Rearranging for supporting byteio items, since they have a fileno
-            # that returns an OSError if there's no underlying fp. Easier to
-            # deal with here by reordering.
-            logger.debug("have getvalue. just sending in a string from getvalue")
-            n, err = decoder.decode(self.fp.getvalue())
-        elif fp:
-            # we've got a actual file on disk, pass in the fp.
-            logger.debug("have fileno, calling fileno version of the decoder.")
-            if not close_self_fp:
-                self.fp.seek(0)
-            # 4 bytes, otherwise the trace might error out
-            n, err = decoder.decode(b"fpfp")
-        else:
-            # we have something else.
-            logger.debug("don't have fileno or getvalue. just reading")
-            self.fp.seek(0)
-            # UNDONE -- so much for that buffer size thing.
-            n, err = decoder.decode(self.fp.read())
-
-        self.tile = []
-        self.readonly = 0
-
-        self.load_end()
-
-        if close_self_fp:
-            self.fp.close()
-            self.fp = None  # might be shared
-
-        if err < 0:
-            raise OSError(err)
-
-        return Image.Image.load(self)
-
-    def _setup(self):
-        """Setup this image object based on current tags"""
-
-        if 0xBC01 in self.tag_v2:
-            msg = "Windows Media Photo files not yet supported"
-            raise OSError(msg)
-
-        # extract relevant tags
-        self._compression = COMPRESSION_INFO[self.tag_v2.get(COMPRESSION, 1)]
-        self._planar_configuration = self.tag_v2.get(PLANAR_CONFIGURATION, 1)
-
-        # photometric is a required tag, but not everyone is reading
-        # the specification
-        photo = self.tag_v2.get(PHOTOMETRIC_INTERPRETATION, 0)
-
-        # old style jpeg compression images most certainly are YCbCr
-        if self._compression == "tiff_jpeg":
-            photo = 6
-
-        fillorder = self.tag_v2.get(FILLORDER, 1)
-
-        logger.debug("*** Summary ***")
-        logger.debug("- compression: %s", self._compression)
-        logger.debug("- photometric_interpretation: %s", photo)
-        logger.debug("- planar_configuration: %s", self._planar_configuration)
-        logger.debug("- fill_order: %s", fillorder)
-        logger.debug("- YCbCr subsampling: %s", self.tag.get(YCBCRSUBSAMPLING))
-
-        # size
-        xsize = int(self.tag_v2.get(IMAGEWIDTH))
-        ysize = int(self.tag_v2.get(IMAGELENGTH))
-        self._size = xsize, ysize
-
-        logger.debug("- size: %s", self.size)
-
-        sample_format = self.tag_v2.get(SAMPLEFORMAT, (1,))
-        if len(sample_format) > 1 and max(sample_format) == min(sample_format) == 1:
-            # SAMPLEFORMAT is properly per band, so an RGB image will
-            # be (1,1,1).  But, we don't support per band pixel types,
-            # and anything more than one band is a uint8. So, just
-            # take the first element. Revisit this if adding support
-            # for more exotic images.
-            sample_format = (1,)
-
-        bps_tuple = self.tag_v2.get(BITSPERSAMPLE, (1,))
-        extra_tuple = self.tag_v2.get(EXTRASAMPLES, ())
-        if photo in (2, 6, 8):  # RGB, YCbCr, LAB
-            bps_count = 3
-        elif photo == 5:  # CMYK
-            bps_count = 4
-        else:
-            bps_count = 1
-        bps_count += len(extra_tuple)
-        bps_actual_count = len(bps_tuple)
-        samples_per_pixel = self.tag_v2.get(
-            SAMPLESPERPIXEL,
-            3 if self._compression == "tiff_jpeg" and photo in (2, 6) else 1,
-        )
-
-        if samples_per_pixel > MAX_SAMPLESPERPIXEL:
-            # DOS check, samples_per_pixel can be a Long, and we extend the tuple below
-            logger.error(
-                "More samples per pixel than can be decoded: %s", samples_per_pixel
-            )
-            msg = "Invalid value for samples per pixel"
-            raise SyntaxError(msg)
-
-        if samples_per_pixel < bps_actual_count:
-            # If a file has more values in bps_tuple than expected,
-            # remove the excess.
-            bps_tuple = bps_tuple[:samples_per_pixel]
-        elif samples_per_pixel > bps_actual_count and bps_actual_count == 1:
-            # If a file has only one value in bps_tuple, when it should have more,
-            # presume it is the same number of bits for all of the samples.
-            bps_tuple = bps_tuple * samples_per_pixel
-
-        if len(bps_tuple) != samples_per_pixel:
-            msg = "unknown data organization"
-            raise SyntaxError(msg)
-
-        # mode: check photometric interpretation and bits per pixel
-        key = (
-            self.tag_v2.prefix,
-            photo,
-            sample_format,
-            fillorder,
-            bps_tuple,
-            extra_tuple,
-        )
-        logger.debug("format key: %s", key)
-        try:
-            self._mode, rawmode = OPEN_INFO[key]
-        except KeyError as e:
-            logger.debug("- unsupported format")
-            msg = "unknown pixel mode"
-            raise SyntaxError(msg) from e
-
-        logger.debug("- raw mode: %s", rawmode)
-        logger.debug("- pil mode: %s", self.mode)
-
-        self.info["compression"] = self._compression
-
-        xres = self.tag_v2.get(X_RESOLUTION, 1)
-        yres = self.tag_v2.get(Y_RESOLUTION, 1)
-
-        if xres and yres:
-            resunit = self.tag_v2.get(RESOLUTION_UNIT)
-            if resunit == 2:  # dots per inch
-                self.info["dpi"] = (xres, yres)
-            elif resunit == 3:  # dots per centimeter. convert to dpi
-                self.info["dpi"] = (xres * 2.54, yres * 2.54)
-            elif resunit is None:  # used to default to 1, but now 2)
-                self.info["dpi"] = (xres, yres)
-                # For backward compatibility,
-                # we also preserve the old behavior
-                self.info["resolution"] = xres, yres
-            else:  # No absolute unit of measurement
-                self.info["resolution"] = xres, yres
-
-        # build tile descriptors
-        x = y = layer = 0
-        self.tile = []
-        self.use_load_libtiff = READ_LIBTIFF or self._compression != "raw"
-        if self.use_load_libtiff:
-            # Decoder expects entire file as one tile.
-            # There's a buffer size limit in load (64k)
-            # so large g4 images will fail if we use that
-            # function.
-            #
-            # Setup the one tile for the whole image, then
-            # use the _load_libtiff function.
-
-            # libtiff handles the fillmode for us, so 1;IR should
-            # actually be 1;I. Including the R double reverses the
-            # bits, so stripes of the image are reversed.  See
-            # https://github.com/python-pillow/Pillow/issues/279
-            if fillorder == 2:
-                # Replace fillorder with fillorder=1
-                key = key[:3] + (1,) + key[4:]
-                logger.debug("format key: %s", key)
-                # this should always work, since all the
-                # fillorder==2 modes have a corresponding
-                # fillorder=1 mode
-                self._mode, rawmode = OPEN_INFO[key]
-            # libtiff always returns the bytes in native order.
-            # we're expecting image byte order. So, if the rawmode
-            # contains I;16, we need to convert from native to image
-            # byte order.
-            if rawmode == "I;16":
-                rawmode = "I;16N"
-            if ";16B" in rawmode:
-                rawmode = rawmode.replace(";16B", ";16N")
-            if ";16L" in rawmode:
-                rawmode = rawmode.replace(";16L", ";16N")
-
-            # YCbCr images with new jpeg compression with pixels in one plane
-            # unpacked straight into RGB values
-            if (
-                photo == 6
-                and self._compression == "jpeg"
-                and self._planar_configuration == 1
-            ):
-                rawmode = "RGB"
-
-            # Offset in the tile tuple is 0, we go from 0,0 to
-            # w,h, and we only do this once -- eds
-            a = (rawmode, self._compression, False, self.tag_v2.offset)
-            self.tile.append(("libtiff", (0, 0, xsize, ysize), 0, a))
-
-        elif STRIPOFFSETS in self.tag_v2 or TILEOFFSETS in self.tag_v2:
-            # striped image
-            if STRIPOFFSETS in self.tag_v2:
-                offsets = self.tag_v2[STRIPOFFSETS]
-                h = self.tag_v2.get(ROWSPERSTRIP, ysize)
-                w = self.size[0]
-            else:
-                # tiled image
-                offsets = self.tag_v2[TILEOFFSETS]
-                w = self.tag_v2.get(TILEWIDTH)
-                h = self.tag_v2.get(TILELENGTH)
-
-            for offset in offsets:
-                if x + w > xsize:
-                    stride = w * sum(bps_tuple) / 8  # bytes per line
-                else:
-                    stride = 0
-
-                tile_rawmode = rawmode
-                if self._planar_configuration == 2:
-                    # each band on it's own layer
-                    tile_rawmode = rawmode[layer]
-                    # adjust stride width accordingly
-                    stride /= bps_count
-
-                a = (tile_rawmode, int(stride), 1)
-                self.tile.append(
-                    (
-                        self._compression,
-                        (x, y, min(x + w, xsize), min(y + h, ysize)),
-                        offset,
-                        a,
-                    )
-                )
-                x = x + w
-                if x >= self.size[0]:
-                    x, y = 0, y + h
-                    if y >= self.size[1]:
-                        x = y = 0
-                        layer += 1
-        else:
-            logger.debug("- unsupported data organization")
-            msg = "unknown data organization"
-            raise SyntaxError(msg)
-
-        # Fix up info.
-        if ICCPROFILE in self.tag_v2:
-            self.info["icc_profile"] = self.tag_v2[ICCPROFILE]
-
-        # fixup palette descriptor
-
-        if self.mode in ["P", "PA"]:
-            palette = [o8(b // 256) for b in self.tag_v2[COLORMAP]]
-            self.palette = ImagePalette.raw("RGB;L", b"".join(palette))
-
-
-#
-# --------------------------------------------------------------------
-# Write TIFF files
-
-# little endian is default except for image modes with
-# explicit big endian byte-order
-
-SAVE_INFO = {
-    # mode => rawmode, byteorder, photometrics,
-    #           sampleformat, bitspersample, extra
-    "1": ("1", II, 1, 1, (1,), None),
-    "L": ("L", II, 1, 1, (8,), None),
-    "LA": ("LA", II, 1, 1, (8, 8), 2),
-    "P": ("P", II, 3, 1, (8,), None),
-    "PA": ("PA", II, 3, 1, (8, 8), 2),
-    "I": ("I;32S", II, 1, 2, (32,), None),
-    "I;16": ("I;16", II, 1, 1, (16,), None),
-    "I;16S": ("I;16S", II, 1, 2, (16,), None),
-    "F": ("F;32F", II, 1, 3, (32,), None),
-    "RGB": ("RGB", II, 2, 1, (8, 8, 8), None),
-    "RGBX": ("RGBX", II, 2, 1, (8, 8, 8, 8), 0),
-    "RGBA": ("RGBA", II, 2, 1, (8, 8, 8, 8), 2),
-    "CMYK": ("CMYK", II, 5, 1, (8, 8, 8, 8), None),
-    "YCbCr": ("YCbCr", II, 6, 1, (8, 8, 8), None),
-    "LAB": ("LAB", II, 8, 1, (8, 8, 8), None),
-    "I;32BS": ("I;32BS", MM, 1, 2, (32,), None),
-    "I;16B": ("I;16B", MM, 1, 1, (16,), None),
-    "I;16BS": ("I;16BS", MM, 1, 2, (16,), None),
-    "F;32BF": ("F;32BF", MM, 1, 3, (32,), None),
-}
-
-
-def _save(im, fp, filename):
-    try:
-        rawmode, prefix, photo, format, bits, extra = SAVE_INFO[im.mode]
-    except KeyError as e:
-        msg = f"cannot write mode {im.mode} as TIFF"
-        raise OSError(msg) from e
-
-    ifd = ImageFileDirectory_v2(prefix=prefix)
-
-    encoderinfo = im.encoderinfo
-    encoderconfig = im.encoderconfig
-    try:
-        compression = encoderinfo["compression"]
-    except KeyError:
-        compression = im.info.get("compression")
-        if isinstance(compression, int):
-            # compression value may be from BMP. Ignore it
-            compression = None
-    if compression is None:
-        compression = "raw"
-    elif compression == "tiff_jpeg":
-        # OJPEG is obsolete, so use new-style JPEG compression instead
-        compression = "jpeg"
-    elif compression == "tiff_deflate":
-        compression = "tiff_adobe_deflate"
-
-    libtiff = WRITE_LIBTIFF or compression != "raw"
-
-    # required for color libtiff images
-    ifd[PLANAR_CONFIGURATION] = 1
-
-    ifd[IMAGEWIDTH] = im.size[0]
-    ifd[IMAGELENGTH] = im.size[1]
-
-    # write any arbitrary tags passed in as an ImageFileDirectory
-    if "tiffinfo" in encoderinfo:
-        info = encoderinfo["tiffinfo"]
-    elif "exif" in encoderinfo:
-        info = encoderinfo["exif"]
-        if isinstance(info, bytes):
-            exif = Image.Exif()
-            exif.load(info)
-            info = exif
-    else:
-        info = {}
-    logger.debug("Tiffinfo Keys: %s", list(info))
-    if isinstance(info, ImageFileDirectory_v1):
-        info = info.to_v2()
-    for key in info:
-        if isinstance(info, Image.Exif) and key in TiffTags.TAGS_V2_GROUPS:
-            ifd[key] = info.get_ifd(key)
-        else:
-            ifd[key] = info.get(key)
-        try:
-            ifd.tagtype[key] = info.tagtype[key]
-        except Exception:
-            pass  # might not be an IFD. Might not have populated type
-
-    # additions written by Greg Couch, gregc@cgl.ucsf.edu
-    # inspired by image-sig posting from Kevin Cazabon, kcazabon@home.com
-    if hasattr(im, "tag_v2"):
-        # preserve tags from original TIFF image file
-        for key in (
-            RESOLUTION_UNIT,
-            X_RESOLUTION,
-            Y_RESOLUTION,
-            IPTC_NAA_CHUNK,
-            PHOTOSHOP_CHUNK,
-            XMP,
-        ):
-            if key in im.tag_v2:
-                ifd[key] = im.tag_v2[key]
-                ifd.tagtype[key] = im.tag_v2.tagtype[key]
-
-    # preserve ICC profile (should also work when saving other formats
-    # which support profiles as TIFF) -- 2008-06-06 Florian Hoech
-    icc = encoderinfo.get("icc_profile", im.info.get("icc_profile"))
-    if icc:
-        ifd[ICCPROFILE] = icc
-
-    for key, name in [
-        (IMAGEDESCRIPTION, "description"),
-        (X_RESOLUTION, "resolution"),
-        (Y_RESOLUTION, "resolution"),
-        (X_RESOLUTION, "x_resolution"),
-        (Y_RESOLUTION, "y_resolution"),
-        (RESOLUTION_UNIT, "resolution_unit"),
-        (SOFTWARE, "software"),
-        (DATE_TIME, "date_time"),
-        (ARTIST, "artist"),
-        (COPYRIGHT, "copyright"),
-    ]:
-        if name in encoderinfo:
-            ifd[key] = encoderinfo[name]
-
-    dpi = encoderinfo.get("dpi")
-    if dpi:
-        ifd[RESOLUTION_UNIT] = 2
-        ifd[X_RESOLUTION] = dpi[0]
-        ifd[Y_RESOLUTION] = dpi[1]
-
-    if bits != (1,):
-        ifd[BITSPERSAMPLE] = bits
-        if len(bits) != 1:
-            ifd[SAMPLESPERPIXEL] = len(bits)
-    if extra is not None:
-        ifd[EXTRASAMPLES] = extra
-    if format != 1:
-        ifd[SAMPLEFORMAT] = format
-
-    if PHOTOMETRIC_INTERPRETATION not in ifd:
-        ifd[PHOTOMETRIC_INTERPRETATION] = photo
-    elif im.mode in ("1", "L") and ifd[PHOTOMETRIC_INTERPRETATION] == 0:
-        if im.mode == "1":
-            inverted_im = im.copy()
-            px = inverted_im.load()
-            for y in range(inverted_im.height):
-                for x in range(inverted_im.width):
-                    px[x, y] = 0 if px[x, y] == 255 else 255
-            im = inverted_im
-        else:
-            im = ImageOps.invert(im)
-
-    if im.mode in ["P", "PA"]:
-        lut = im.im.getpalette("RGB", "RGB;L")
-        colormap = []
-        colors = len(lut) // 3
-        for i in range(3):
-            colormap += [v * 256 for v in lut[colors * i : colors * (i + 1)]]
-            colormap += [0] * (256 - colors)
-        ifd[COLORMAP] = colormap
-    # data orientation
-    stride = len(bits) * ((im.size[0] * bits[0] + 7) // 8)
-    # aim for given strip size (64 KB by default) when using libtiff writer
-    if libtiff:
-        im_strip_size = encoderinfo.get("strip_size", STRIP_SIZE)
-        rows_per_strip = 1 if stride == 0 else min(im_strip_size // stride, im.size[1])
-        # JPEG encoder expects multiple of 8 rows
-        if compression == "jpeg":
-            rows_per_strip = min(((rows_per_strip + 7) // 8) * 8, im.size[1])
-    else:
-        rows_per_strip = im.size[1]
-    if rows_per_strip == 0:
-        rows_per_strip = 1
-    strip_byte_counts = 1 if stride == 0 else stride * rows_per_strip
-    strips_per_image = (im.size[1] + rows_per_strip - 1) // rows_per_strip
-    ifd[ROWSPERSTRIP] = rows_per_strip
-    if strip_byte_counts >= 2**16:
-        ifd.tagtype[STRIPBYTECOUNTS] = TiffTags.LONG
-    ifd[STRIPBYTECOUNTS] = (strip_byte_counts,) * (strips_per_image - 1) + (
-        stride * im.size[1] - strip_byte_counts * (strips_per_image - 1),
-    )
-    ifd[STRIPOFFSETS] = tuple(
-        range(0, strip_byte_counts * strips_per_image, strip_byte_counts)
-    )  # this is adjusted by IFD writer
-    # no compression by default:
-    ifd[COMPRESSION] = COMPRESSION_INFO_REV.get(compression, 1)
-
-    if im.mode == "YCbCr":
-        for tag, value in {
-            YCBCRSUBSAMPLING: (1, 1),
-            REFERENCEBLACKWHITE: (0, 255, 128, 255, 128, 255),
-        }.items():
-            ifd.setdefault(tag, value)
-
-    blocklist = [TILEWIDTH, TILELENGTH, TILEOFFSETS, TILEBYTECOUNTS]
-    if libtiff:
-        if "quality" in encoderinfo:
-            quality = encoderinfo["quality"]
-            if not isinstance(quality, int) or quality < 0 or quality > 100:
-                msg = "Invalid quality setting"
-                raise ValueError(msg)
-            if compression != "jpeg":
-                msg = "quality setting only supported for 'jpeg' compression"
-                raise ValueError(msg)
-            ifd[JPEGQUALITY] = quality
-
-        logger.debug("Saving using libtiff encoder")
-        logger.debug("Items: %s", sorted(ifd.items()))
-        _fp = 0
-        if hasattr(fp, "fileno"):
-            try:
-                fp.seek(0)
-                _fp = os.dup(fp.fileno())
-            except io.UnsupportedOperation:
-                pass
-
-        # optional types for non core tags
-        types = {}
-        # STRIPOFFSETS and STRIPBYTECOUNTS are added by the library
-        # based on the data in the strip.
-        # The other tags expect arrays with a certain length (fixed or depending on
-        # BITSPERSAMPLE, etc), passing arrays with a different length will result in
-        # segfaults. Block these tags until we add extra validation.
-        # SUBIFD may also cause a segfault.
-        blocklist += [
-            REFERENCEBLACKWHITE,
-            STRIPBYTECOUNTS,
-            STRIPOFFSETS,
-            TRANSFERFUNCTION,
-            SUBIFD,
-        ]
-
-        # bits per sample is a single short in the tiff directory, not a list.
-        atts = {BITSPERSAMPLE: bits[0]}
-        # Merge the ones that we have with (optional) more bits from
-        # the original file, e.g x,y resolution so that we can
-        # save(load('')) == original file.
-        legacy_ifd = {}
-        if hasattr(im, "tag"):
-            legacy_ifd = im.tag.to_v2()
-
-        # SAMPLEFORMAT is determined by the image format and should not be copied
-        # from legacy_ifd.
-        supplied_tags = {**getattr(im, "tag_v2", {}), **legacy_ifd}
-        if SAMPLEFORMAT in supplied_tags:
-            del supplied_tags[SAMPLEFORMAT]
-
-        for tag, value in itertools.chain(ifd.items(), supplied_tags.items()):
-            # Libtiff can only process certain core items without adding
-            # them to the custom dictionary.
-            # Custom items are supported for int, float, unicode, string and byte
-            # values. Other types and tuples require a tagtype.
-            if tag not in TiffTags.LIBTIFF_CORE:
-                if not getattr(Image.core, "libtiff_support_custom_tags", False):
-                    continue
-
-                if tag in ifd.tagtype:
-                    types[tag] = ifd.tagtype[tag]
-                elif not (isinstance(value, (int, float, str, bytes))):
-                    continue
-                else:
-                    type = TiffTags.lookup(tag).type
-                    if type:
-                        types[tag] = type
-            if tag not in atts and tag not in blocklist:
-                if isinstance(value, str):
-                    atts[tag] = value.encode("ascii", "replace") + b"\0"
-                elif isinstance(value, IFDRational):
-                    atts[tag] = float(value)
-                else:
-                    atts[tag] = value
-
-        if SAMPLEFORMAT in atts and len(atts[SAMPLEFORMAT]) == 1:
-            atts[SAMPLEFORMAT] = atts[SAMPLEFORMAT][0]
-
-        logger.debug("Converted items: %s", sorted(atts.items()))
-
-        # libtiff always expects the bytes in native order.
-        # we're storing image byte order. So, if the rawmode
-        # contains I;16, we need to convert from native to image
-        # byte order.
-        if im.mode in ("I;16B", "I;16"):
-            rawmode = "I;16N"
-
-        # Pass tags as sorted list so that the tags are set in a fixed order.
-        # This is required by libtiff for some tags. For example, the JPEGQUALITY
-        # pseudo tag requires that the COMPRESS tag was already set.
-        tags = list(atts.items())
-        tags.sort()
-        a = (rawmode, compression, _fp, filename, tags, types)
-        e = Image._getencoder(im.mode, "libtiff", a, encoderconfig)
-        e.setimage(im.im, (0, 0) + im.size)
-        while True:
-            # undone, change to self.decodermaxblock:
-            errcode, data = e.encode(16 * 1024)[1:]
-            if not _fp:
-                fp.write(data)
-            if errcode:
-                break
-        if _fp:
-            try:
-                os.close(_fp)
-            except OSError:
-                pass
-        if errcode < 0:
-            msg = f"encoder error {errcode} when writing image file"
-            raise OSError(msg)
-
-    else:
-        for tag in blocklist:
-            del ifd[tag]
-        offset = ifd.save(fp)
-
-        ImageFile._save(
-            im, fp, [("raw", (0, 0) + im.size, offset, (rawmode, stride, 1))]
-        )
-
-    # -- helper for multi-page save --
-    if "_debug_multipage" in encoderinfo:
-        # just to access o32 and o16 (using correct byte order)
-        im._debug_multipage = ifd
-
-
-class AppendingTiffWriter:
-    fieldSizes = [
-        0,  # None
-        1,  # byte
-        1,  # ascii
-        2,  # short
-        4,  # long
-        8,  # rational
-        1,  # sbyte
-        1,  # undefined
-        2,  # sshort
-        4,  # slong
-        8,  # srational
-        4,  # float
-        8,  # double
-        4,  # ifd
-        2,  # unicode
-        4,  # complex
-        8,  # long8
-    ]
-
-    #    StripOffsets = 273
-    #    FreeOffsets = 288
-    #    TileOffsets = 324
-    #    JPEGQTables = 519
-    #    JPEGDCTables = 520
-    #    JPEGACTables = 521
-    Tags = {273, 288, 324, 519, 520, 521}
-
-    def __init__(self, fn, new=False):
-        if hasattr(fn, "read"):
-            self.f = fn
-            self.close_fp = False
-        else:
-            self.name = fn
-            self.close_fp = True
-            try:
-                self.f = open(fn, "w+b" if new else "r+b")
-            except OSError:
-                self.f = open(fn, "w+b")
-        self.beginning = self.f.tell()
-        self.setup()
-
-    def setup(self):
-        # Reset everything.
-        self.f.seek(self.beginning, os.SEEK_SET)
-
-        self.whereToWriteNewIFDOffset = None
-        self.offsetOfNewPage = 0
-
-        self.IIMM = iimm = self.f.read(4)
-        if not iimm:
-            # empty file - first page
-            self.isFirst = True
-            return
-
-        self.isFirst = False
-        if iimm == b"II\x2a\x00":
-            self.setEndian("<")
-        elif iimm == b"MM\x00\x2a":
-            self.setEndian(">")
-        else:
-            msg = "Invalid TIFF file header"
-            raise RuntimeError(msg)
-
-        self.skipIFDs()
-        self.goToEnd()
-
-    def finalize(self):
-        if self.isFirst:
-            return
-
-        # fix offsets
-        self.f.seek(self.offsetOfNewPage)
-
-        iimm = self.f.read(4)
-        if not iimm:
-            # msg = "nothing written into new page"
-            # raise RuntimeError(msg)
-            # Make it easy to finish a frame without committing to a new one.
-            return
-
-        if iimm != self.IIMM:
-            msg = "IIMM of new page doesn't match IIMM of first page"
-            raise RuntimeError(msg)
-
-        ifd_offset = self.readLong()
-        ifd_offset += self.offsetOfNewPage
-        self.f.seek(self.whereToWriteNewIFDOffset)
-        self.writeLong(ifd_offset)
-        self.f.seek(ifd_offset)
-        self.fixIFD()
-
-    def newFrame(self):
-        # Call this to finish a frame.
-        self.finalize()
-        self.setup()
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        if self.close_fp:
-            self.close()
-        return False
-
-    def tell(self):
-        return self.f.tell() - self.offsetOfNewPage
-
-    def seek(self, offset, whence=io.SEEK_SET):
-        if whence == os.SEEK_SET:
-            offset += self.offsetOfNewPage
-
-        self.f.seek(offset, whence)
-        return self.tell()
-
-    def goToEnd(self):
-        self.f.seek(0, os.SEEK_END)
-        pos = self.f.tell()
-
-        # pad to 16 byte boundary
-        pad_bytes = 16 - pos % 16
-        if 0 < pad_bytes < 16:
-            self.f.write(bytes(pad_bytes))
-        self.offsetOfNewPage = self.f.tell()
-
-    def setEndian(self, endian):
-        self.endian = endian
-        self.longFmt = self.endian + "L"
-        self.shortFmt = self.endian + "H"
-        self.tagFormat = self.endian + "HHL"
-
-    def skipIFDs(self):
-        while True:
-            ifd_offset = self.readLong()
-            if ifd_offset == 0:
-                self.whereToWriteNewIFDOffset = self.f.tell() - 4
-                break
-
-            self.f.seek(ifd_offset)
-            num_tags = self.readShort()
-            self.f.seek(num_tags * 12, os.SEEK_CUR)
-
-    def write(self, data):
-        return self.f.write(data)
-
-    def readShort(self):
-        (value,) = struct.unpack(self.shortFmt, self.f.read(2))
-        return value
-
-    def readLong(self):
-        (value,) = struct.unpack(self.longFmt, self.f.read(4))
-        return value
-
-    def rewriteLastShortToLong(self, value):
-        self.f.seek(-2, os.SEEK_CUR)
-        bytes_written = self.f.write(struct.pack(self.longFmt, value))
-        if bytes_written is not None and bytes_written != 4:
-            msg = f"wrote only {bytes_written} bytes but wanted 4"
-            raise RuntimeError(msg)
-
-    def rewriteLastShort(self, value):
-        self.f.seek(-2, os.SEEK_CUR)
-        bytes_written = self.f.write(struct.pack(self.shortFmt, value))
-        if bytes_written is not None and bytes_written != 2:
-            msg = f"wrote only {bytes_written} bytes but wanted 2"
-            raise RuntimeError(msg)
-
-    def rewriteLastLong(self, value):
-        self.f.seek(-4, os.SEEK_CUR)
-        bytes_written = self.f.write(struct.pack(self.longFmt, value))
-        if bytes_written is not None and bytes_written != 4:
-            msg = f"wrote only {bytes_written} bytes but wanted 4"
-            raise RuntimeError(msg)
-
-    def writeShort(self, value):
-        bytes_written = self.f.write(struct.pack(self.shortFmt, value))
-        if bytes_written is not None and bytes_written != 2:
-            msg = f"wrote only {bytes_written} bytes but wanted 2"
-            raise RuntimeError(msg)
-
-    def writeLong(self, value):
-        bytes_written = self.f.write(struct.pack(self.longFmt, value))
-        if bytes_written is not None and bytes_written != 4:
-            msg = f"wrote only {bytes_written} bytes but wanted 4"
-            raise RuntimeError(msg)
-
-    def close(self):
-        self.finalize()
-        self.f.close()
-
-    def fixIFD(self):
-        num_tags = self.readShort()
-
-        for i in range(num_tags):
-            tag, field_type, count = struct.unpack(self.tagFormat, self.f.read(8))
-
-            field_size = self.fieldSizes[field_type]
-            total_size = field_size * count
-            is_local = total_size <= 4
-            if not is_local:
-                offset = self.readLong()
-                offset += self.offsetOfNewPage
-                self.rewriteLastLong(offset)
-
-            if tag in self.Tags:
-                cur_pos = self.f.tell()
-
-                if is_local:
-                    self.fixOffsets(
-                        count, isShort=(field_size == 2), isLong=(field_size == 4)
-                    )
-                    self.f.seek(cur_pos + 4)
-                else:
-                    self.f.seek(offset)
-                    self.fixOffsets(
-                        count, isShort=(field_size == 2), isLong=(field_size == 4)
-                    )
-                    self.f.seek(cur_pos)
-
-                offset = cur_pos = None
-
-            elif is_local:
-                # skip the locally stored value that is not an offset
-                self.f.seek(4, os.SEEK_CUR)
-
-    def fixOffsets(self, count, isShort=False, isLong=False):
-        if not isShort and not isLong:
-            msg = "offset is neither short nor long"
-            raise RuntimeError(msg)
-
-        for i in range(count):
-            offset = self.readShort() if isShort else self.readLong()
-            offset += self.offsetOfNewPage
-            if isShort and offset >= 65536:
-                # offset is now too large - we must convert shorts to longs
-                if count != 1:
-                    msg = "not implemented"
-                    raise RuntimeError(msg)  # XXX TODO
-
-                # simple case - the offset is just one and therefore it is
-                # local (not referenced with another offset)
-                self.rewriteLastShortToLong(offset)
-                self.f.seek(-10, os.SEEK_CUR)
-                self.writeShort(TiffTags.LONG)  # rewrite the type to LONG
-                self.f.seek(8, os.SEEK_CUR)
-            elif isShort:
-                self.rewriteLastShort(offset)
-            else:
-                self.rewriteLastLong(offset)
-
-
-def _save_all(im, fp, filename):
-    encoderinfo = im.encoderinfo.copy()
-    encoderconfig = im.encoderconfig
-    append_images = list(encoderinfo.get("append_images", []))
-    if not hasattr(im, "n_frames") and not append_images:
-        return _save(im, fp, filename)
-
-    cur_idx = im.tell()
-    try:
-        with AppendingTiffWriter(fp) as tf:
-            for ims in [im] + append_images:
-                ims.encoderinfo = encoderinfo
-                ims.encoderconfig = encoderconfig
-                if not hasattr(ims, "n_frames"):
-                    nfr = 1
-                else:
-                    nfr = ims.n_frames
-
-                for idx in range(nfr):
-                    ims.seek(idx)
-                    ims.load()
-                    _save(ims, tf, filename)
-                    tf.newFrame()
-    finally:
-        im.seek(cur_idx)
-
-
-#
-# --------------------------------------------------------------------
-# Register
-
-Image.register_open(TiffImageFile.format, TiffImageFile, _accept)
-Image.register_save(TiffImageFile.format, _save)
-Image.register_save_all(TiffImageFile.format, _save_all)
-
-Image.register_extensions(TiffImageFile.format, [".tif", ".tiff"])
-
-Image.register_mime(TiffImageFile.format, "image/tiff")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/lookupDebugInfo.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/lookupDebugInfo.py
deleted file mode 100644
index d4da7de0aed6b87dae6a1d4b417f1c6e099fe1e0..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/lookupDebugInfo.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from typing import NamedTuple
-
-LOOKUP_DEBUG_INFO_KEY = "com.github.fonttools.feaLib"
-LOOKUP_DEBUG_ENV_VAR = "FONTTOOLS_LOOKUP_DEBUGGING"
-
-
-class LookupDebugInfo(NamedTuple):
-    """Information about where a lookup came from, to be embedded in a font"""
-
-    location: str
-    name: str
-    feature: list
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_v_h_e_a.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_v_h_e_a.py
deleted file mode 100644
index 934f2f12d4293f3a14987994b5f13b4dedcd5ef4..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_v_h_e_a.py
+++ /dev/null
@@ -1,128 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import safeEval
-from fontTools.misc.fixedTools import (
-    ensureVersionIsLong as fi2ve,
-    versionToFixed as ve2fi,
-)
-from . import DefaultTable
-import math
-
-
-vheaFormat = """
-		>	# big endian
-		tableVersion:		L
-		ascent:			h
-		descent:		h
-		lineGap:		h
-		advanceHeightMax:	H
-		minTopSideBearing:	h
-		minBottomSideBearing:	h
-		yMaxExtent:		h
-		caretSlopeRise:		h
-		caretSlopeRun:		h
-		caretOffset:		h
-		reserved1:		h
-		reserved2:		h
-		reserved3:		h
-		reserved4:		h
-		metricDataFormat:	h
-		numberOfVMetrics:	H
-"""
-
-
-class table__v_h_e_a(DefaultTable.DefaultTable):
-
-    # Note: Keep in sync with table__h_h_e_a
-
-    dependencies = ["vmtx", "glyf", "CFF ", "CFF2"]
-
-    def decompile(self, data, ttFont):
-        sstruct.unpack(vheaFormat, data, self)
-
-    def compile(self, ttFont):
-        if ttFont.recalcBBoxes and (
-            ttFont.isLoaded("glyf")
-            or ttFont.isLoaded("CFF ")
-            or ttFont.isLoaded("CFF2")
-        ):
-            self.recalc(ttFont)
-        self.tableVersion = fi2ve(self.tableVersion)
-        return sstruct.pack(vheaFormat, self)
-
-    def recalc(self, ttFont):
-        if "vmtx" not in ttFont:
-            return
-
-        vmtxTable = ttFont["vmtx"]
-        self.advanceHeightMax = max(adv for adv, _ in vmtxTable.metrics.values())
-
-        boundsHeightDict = {}
-        if "glyf" in ttFont:
-            glyfTable = ttFont["glyf"]
-            for name in ttFont.getGlyphOrder():
-                g = glyfTable[name]
-                if g.numberOfContours == 0:
-                    continue
-                if g.numberOfContours < 0 and not hasattr(g, "yMax"):
-                    # Composite glyph without extents set.
-                    # Calculate those.
-                    g.recalcBounds(glyfTable)
-                boundsHeightDict[name] = g.yMax - g.yMin
-        elif "CFF " in ttFont or "CFF2" in ttFont:
-            if "CFF " in ttFont:
-                topDict = ttFont["CFF "].cff.topDictIndex[0]
-            else:
-                topDict = ttFont["CFF2"].cff.topDictIndex[0]
-            charStrings = topDict.CharStrings
-            for name in ttFont.getGlyphOrder():
-                cs = charStrings[name]
-                bounds = cs.calcBounds(charStrings)
-                if bounds is not None:
-                    boundsHeightDict[name] = int(
-                        math.ceil(bounds[3]) - math.floor(bounds[1])
-                    )
-
-        if boundsHeightDict:
-            minTopSideBearing = float("inf")
-            minBottomSideBearing = float("inf")
-            yMaxExtent = -float("inf")
-            for name, boundsHeight in boundsHeightDict.items():
-                advanceHeight, tsb = vmtxTable[name]
-                bsb = advanceHeight - tsb - boundsHeight
-                extent = tsb + boundsHeight
-                minTopSideBearing = min(minTopSideBearing, tsb)
-                minBottomSideBearing = min(minBottomSideBearing, bsb)
-                yMaxExtent = max(yMaxExtent, extent)
-            self.minTopSideBearing = minTopSideBearing
-            self.minBottomSideBearing = minBottomSideBearing
-            self.yMaxExtent = yMaxExtent
-
-        else:  # No glyph has outlines.
-            self.minTopSideBearing = 0
-            self.minBottomSideBearing = 0
-            self.yMaxExtent = 0
-
-    def toXML(self, writer, ttFont):
-        formatstring, names, fixes = sstruct.getformat(vheaFormat)
-        for name in names:
-            value = getattr(self, name)
-            if name == "tableVersion":
-                value = fi2ve(value)
-                value = "0x%08x" % value
-            writer.simpletag(name, value=value)
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "tableVersion":
-            setattr(self, name, ve2fi(attrs["value"]))
-            return
-        setattr(self, name, safeEval(attrs["value"]))
-
-    # reserved0 is caretOffset for legacy reasons
-    @property
-    def reserved0(self):
-        return self.caretOffset
-
-    @reserved0.setter
-    def reserved0(self, value):
-        self.caretOffset = value
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/components/plot.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/components/plot.py
deleted file mode 100644
index 673b51dd59dee502ea6c4af4fda0d2763a07e9dc..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/components/plot.py
+++ /dev/null
@@ -1,139 +0,0 @@
-"""gr.Plot() component."""
-
-from __future__ import annotations
-
-import json
-from types import ModuleType
-from typing import Any, Callable, Literal
-
-import altair as alt
-import pandas as pd
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio import processing_utils
-from gradio.components.base import Component
-from gradio.data_classes import GradioModel
-from gradio.events import Events
-
-set_documentation_group("component")
-
-
-class PlotData(GradioModel):
-    type: Literal["altair", "bokeh", "plotly", "matplotlib"]
-    plot: str
-
-
-class AltairPlotData(PlotData):
-    chart: Literal["bar", "line", "scatter"]
-    type: Literal["altair"] = "altair"
-
-
-@document()
-class Plot(Component):
-    """
-    Used to display various kinds of plots (matplotlib, plotly, or bokeh are supported).
-    Preprocessing: this component does *not* accept input.
-    Postprocessing: expects either a {matplotlib.figure.Figure}, a {plotly.graph_objects._figure.Figure}, or a {dict} corresponding to a bokeh plot (json_item format)
-
-    Demos: altair_plot, outbreak_forecast, blocks_kinematics, stock_forecast, map_airbnb
-    Guides: plot-component-for-maps
-    """
-
-    data_model = PlotData
-    EVENTS = [Events.change, Events.clear]
-
-    def __init__(
-        self,
-        value: Callable | None | pd.DataFrame = None,
-        *,
-        label: str | None = None,
-        every: float | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        render: bool = True,
-    ):
-        """
-        Parameters:
-            value: Optionally, supply a default plot object to display, must be a matplotlib, plotly, altair, or bokeh figure, or a callable. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            container: If True, will place the component in a container - providing some extra padding around the border.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
-        """
-        super().__init__(
-            label=label,
-            every=every,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            render=render,
-            value=value,
-        )
-
-    def get_config(self):
-        try:
-            import bokeh  # type: ignore
-
-            bokeh_version = bokeh.__version__
-        except ImportError:
-            bokeh_version = None
-
-        config = super().get_config()
-        config["bokeh_version"] = bokeh_version
-        return config
-
-    def preprocess(self, payload: PlotData | None) -> PlotData | None:
-        return payload
-
-    def example_inputs(self) -> Any:
-        return None
-
-    def postprocess(self, value) -> PlotData | None:
-        import matplotlib.figure
-
-        if value is None:
-            return None
-        if isinstance(value, (ModuleType, matplotlib.figure.Figure)):  # type: ignore
-            dtype = "matplotlib"
-            out_y = processing_utils.encode_plot_to_base64(value)
-        elif "bokeh" in value.__module__:
-            dtype = "bokeh"
-            from bokeh.embed import json_item  # type: ignore
-
-            out_y = json.dumps(json_item(value))
-        else:
-            is_altair = "altair" in value.__module__
-            dtype = "altair" if is_altair else "plotly"
-            out_y = value.to_json()
-        return PlotData(type=dtype, plot=out_y)
-
-
-class AltairPlot:
-    @staticmethod
-    def create_legend(position, title):
-        if position == "none":
-            legend = None
-        else:
-            position = {"orient": position} if position else {}
-            legend = {"title": title, **position}
-
-        return legend
-
-    @staticmethod
-    def create_scale(limit):
-        return alt.Scale(domain=limit) if limit else alt.Undefined
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_artist.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_artist.py
deleted file mode 100644
index dbb5dd2305e03fd3972daf72fde6d02719f3bcfe..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_artist.py
+++ /dev/null
@@ -1,564 +0,0 @@
-import io
-from itertools import chain
-
-import numpy as np
-
-import pytest
-
-import matplotlib.colors as mcolors
-import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-import matplotlib.lines as mlines
-import matplotlib.path as mpath
-import matplotlib.transforms as mtransforms
-import matplotlib.collections as mcollections
-import matplotlib.artist as martist
-import matplotlib.backend_bases as mbackend_bases
-import matplotlib as mpl
-from matplotlib.testing.decorators import check_figures_equal, image_comparison
-
-
-def test_patch_transform_of_none():
-    # tests the behaviour of patches added to an Axes with various transform
-    # specifications
-
-    ax = plt.axes()
-    ax.set_xlim(1, 3)
-    ax.set_ylim(1, 3)
-
-    # Draw an ellipse over data coord (2, 2) by specifying device coords.
-    xy_data = (2, 2)
-    xy_pix = ax.transData.transform(xy_data)
-
-    # Not providing a transform of None puts the ellipse in data coordinates .
-    e = mpatches.Ellipse(xy_data, width=1, height=1, fc='yellow', alpha=0.5)
-    ax.add_patch(e)
-    assert e._transform == ax.transData
-
-    # Providing a transform of None puts the ellipse in device coordinates.
-    e = mpatches.Ellipse(xy_pix, width=120, height=120, fc='coral',
-                         transform=None, alpha=0.5)
-    assert e.is_transform_set()
-    ax.add_patch(e)
-    assert isinstance(e._transform, mtransforms.IdentityTransform)
-
-    # Providing an IdentityTransform puts the ellipse in device coordinates.
-    e = mpatches.Ellipse(xy_pix, width=100, height=100,
-                         transform=mtransforms.IdentityTransform(), alpha=0.5)
-    ax.add_patch(e)
-    assert isinstance(e._transform, mtransforms.IdentityTransform)
-
-    # Not providing a transform, and then subsequently "get_transform" should
-    # not mean that "is_transform_set".
-    e = mpatches.Ellipse(xy_pix, width=120, height=120, fc='coral',
-                         alpha=0.5)
-    intermediate_transform = e.get_transform()
-    assert not e.is_transform_set()
-    ax.add_patch(e)
-    assert e.get_transform() != intermediate_transform
-    assert e.is_transform_set()
-    assert e._transform == ax.transData
-
-
-def test_collection_transform_of_none():
-    # tests the behaviour of collections added to an Axes with various
-    # transform specifications
-
-    ax = plt.axes()
-    ax.set_xlim(1, 3)
-    ax.set_ylim(1, 3)
-
-    # draw an ellipse over data coord (2, 2) by specifying device coords
-    xy_data = (2, 2)
-    xy_pix = ax.transData.transform(xy_data)
-
-    # not providing a transform of None puts the ellipse in data coordinates
-    e = mpatches.Ellipse(xy_data, width=1, height=1)
-    c = mcollections.PatchCollection([e], facecolor='yellow', alpha=0.5)
-    ax.add_collection(c)
-    # the collection should be in data coordinates
-    assert c.get_offset_transform() + c.get_transform() == ax.transData
-
-    # providing a transform of None puts the ellipse in device coordinates
-    e = mpatches.Ellipse(xy_pix, width=120, height=120)
-    c = mcollections.PatchCollection([e], facecolor='coral',
-                                     alpha=0.5)
-    c.set_transform(None)
-    ax.add_collection(c)
-    assert isinstance(c.get_transform(), mtransforms.IdentityTransform)
-
-    # providing an IdentityTransform puts the ellipse in device coordinates
-    e = mpatches.Ellipse(xy_pix, width=100, height=100)
-    c = mcollections.PatchCollection([e],
-                                     transform=mtransforms.IdentityTransform(),
-                                     alpha=0.5)
-    ax.add_collection(c)
-    assert isinstance(c.get_offset_transform(), mtransforms.IdentityTransform)
-
-
-@image_comparison(["clip_path_clipping"], remove_text=True)
-def test_clipping():
-    exterior = mpath.Path.unit_rectangle().deepcopy()
-    exterior.vertices *= 4
-    exterior.vertices -= 2
-    interior = mpath.Path.unit_circle().deepcopy()
-    interior.vertices = interior.vertices[::-1]
-    clip_path = mpath.Path.make_compound_path(exterior, interior)
-
-    star = mpath.Path.unit_regular_star(6).deepcopy()
-    star.vertices *= 2.6
-
-    fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
-
-    col = mcollections.PathCollection([star], lw=5, edgecolor='blue',
-                                      facecolor='red', alpha=0.7, hatch='*')
-    col.set_clip_path(clip_path, ax1.transData)
-    ax1.add_collection(col)
-
-    patch = mpatches.PathPatch(star, lw=5, edgecolor='blue', facecolor='red',
-                               alpha=0.7, hatch='*')
-    patch.set_clip_path(clip_path, ax2.transData)
-    ax2.add_patch(patch)
-
-    ax1.set_xlim([-3, 3])
-    ax1.set_ylim([-3, 3])
-
-
-@check_figures_equal(extensions=['png'])
-def test_clipping_zoom(fig_test, fig_ref):
-    # This test places the Axes and sets its limits such that the clip path is
-    # outside the figure entirely. This should not break the clip path.
-    ax_test = fig_test.add_axes([0, 0, 1, 1])
-    l, = ax_test.plot([-3, 3], [-3, 3])
-    # Explicit Path instead of a Rectangle uses clip path processing, instead
-    # of a clip box optimization.
-    p = mpath.Path([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]])
-    p = mpatches.PathPatch(p, transform=ax_test.transData)
-    l.set_clip_path(p)
-
-    ax_ref = fig_ref.add_axes([0, 0, 1, 1])
-    ax_ref.plot([-3, 3], [-3, 3])
-
-    ax_ref.set(xlim=(0.5, 0.75), ylim=(0.5, 0.75))
-    ax_test.set(xlim=(0.5, 0.75), ylim=(0.5, 0.75))
-
-
-def test_cull_markers():
-    x = np.random.random(20000)
-    y = np.random.random(20000)
-
-    fig, ax = plt.subplots()
-    ax.plot(x, y, 'k.')
-    ax.set_xlim(2, 3)
-
-    pdf = io.BytesIO()
-    fig.savefig(pdf, format="pdf")
-    assert len(pdf.getvalue()) < 8000
-
-    svg = io.BytesIO()
-    fig.savefig(svg, format="svg")
-    assert len(svg.getvalue()) < 20000
-
-
-@image_comparison(['hatching'], remove_text=True, style='default')
-def test_hatching():
-    fig, ax = plt.subplots(1, 1)
-
-    # Default hatch color.
-    rect1 = mpatches.Rectangle((0, 0), 3, 4, hatch='/')
-    ax.add_patch(rect1)
-
-    rect2 = mcollections.RegularPolyCollection(
-        4, sizes=[16000], offsets=[(1.5, 6.5)], offset_transform=ax.transData,
-        hatch='/')
-    ax.add_collection(rect2)
-
-    # Ensure edge color is not applied to hatching.
-    rect3 = mpatches.Rectangle((4, 0), 3, 4, hatch='/', edgecolor='C1')
-    ax.add_patch(rect3)
-
-    rect4 = mcollections.RegularPolyCollection(
-        4, sizes=[16000], offsets=[(5.5, 6.5)], offset_transform=ax.transData,
-        hatch='/', edgecolor='C1')
-    ax.add_collection(rect4)
-
-    ax.set_xlim(0, 7)
-    ax.set_ylim(0, 9)
-
-
-def test_remove():
-    fig, ax = plt.subplots()
-    im = ax.imshow(np.arange(36).reshape(6, 6))
-    ln, = ax.plot(range(5))
-
-    assert fig.stale
-    assert ax.stale
-
-    fig.canvas.draw()
-    assert not fig.stale
-    assert not ax.stale
-    assert not ln.stale
-
-    assert im in ax._mouseover_set
-    assert ln not in ax._mouseover_set
-    assert im.axes is ax
-
-    im.remove()
-    ln.remove()
-
-    for art in [im, ln]:
-        assert art.axes is None
-        assert art.figure is None
-
-    assert im not in ax._mouseover_set
-    assert fig.stale
-    assert ax.stale
-
-
-@image_comparison(["default_edges.png"], remove_text=True, style='default')
-def test_default_edges():
-    # Remove this line when this test image is regenerated.
-    plt.rcParams['text.kerning_factor'] = 6
-
-    fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(2, 2)
-
-    ax1.plot(np.arange(10), np.arange(10), 'x',
-             np.arange(10) + 1, np.arange(10), 'o')
-    ax2.bar(np.arange(10), np.arange(10), align='edge')
-    ax3.text(0, 0, "BOX", size=24, bbox=dict(boxstyle='sawtooth'))
-    ax3.set_xlim((-1, 1))
-    ax3.set_ylim((-1, 1))
-    pp1 = mpatches.PathPatch(
-        mpath.Path([(0, 0), (1, 0), (1, 1), (0, 0)],
-                   [mpath.Path.MOVETO, mpath.Path.CURVE3,
-                    mpath.Path.CURVE3, mpath.Path.CLOSEPOLY]),
-        fc="none", transform=ax4.transData)
-    ax4.add_patch(pp1)
-
-
-def test_properties():
-    ln = mlines.Line2D([], [])
-    ln.properties()  # Check that no warning is emitted.
-
-
-def test_setp():
-    # Check empty list
-    plt.setp([])
-    plt.setp([[]])
-
-    # Check arbitrary iterables
-    fig, ax = plt.subplots()
-    lines1 = ax.plot(range(3))
-    lines2 = ax.plot(range(3))
-    martist.setp(chain(lines1, lines2), 'lw', 5)
-    plt.setp(ax.spines.values(), color='green')
-
-    # Check *file* argument
-    sio = io.StringIO()
-    plt.setp(lines1, 'zorder', file=sio)
-    assert sio.getvalue() == '  zorder: float\n'
-
-
-def test_None_zorder():
-    fig, ax = plt.subplots()
-    ln, = ax.plot(range(5), zorder=None)
-    assert ln.get_zorder() == mlines.Line2D.zorder
-    ln.set_zorder(123456)
-    assert ln.get_zorder() == 123456
-    ln.set_zorder(None)
-    assert ln.get_zorder() == mlines.Line2D.zorder
-
-
-@pytest.mark.parametrize('accept_clause, expected', [
-    ('', 'unknown'),
-    ("ACCEPTS: [ '-' | '--' | '-.' ]", "[ '-' | '--' | '-.' ]"),
-    ('ACCEPTS: Some description.', 'Some description.'),
-    ('.. ACCEPTS: Some description.', 'Some description.'),
-    ('arg : int', 'int'),
-    ('*arg : int', 'int'),
-    ('arg : int\nACCEPTS: Something else.', 'Something else. '),
-])
-def test_artist_inspector_get_valid_values(accept_clause, expected):
-    class TestArtist(martist.Artist):
-        def set_f(self, arg):
-            pass
-
-    TestArtist.set_f.__doc__ = """
-    Some text.
-
-    %s
-    """ % accept_clause
-    valid_values = martist.ArtistInspector(TestArtist).get_valid_values('f')
-    assert valid_values == expected
-
-
-def test_artist_inspector_get_aliases():
-    # test the correct format and type of get_aliases method
-    ai = martist.ArtistInspector(mlines.Line2D)
-    aliases = ai.get_aliases()
-    assert aliases["linewidth"] == {"lw"}
-
-
-def test_set_alpha():
-    art = martist.Artist()
-    with pytest.raises(TypeError, match='^alpha must be numeric or None'):
-        art.set_alpha('string')
-    with pytest.raises(TypeError, match='^alpha must be numeric or None'):
-        art.set_alpha([1, 2, 3])
-    with pytest.raises(ValueError, match="outside 0-1 range"):
-        art.set_alpha(1.1)
-    with pytest.raises(ValueError, match="outside 0-1 range"):
-        art.set_alpha(np.nan)
-
-
-def test_set_alpha_for_array():
-    art = martist.Artist()
-    with pytest.raises(TypeError, match='^alpha must be numeric or None'):
-        art._set_alpha_for_array('string')
-    with pytest.raises(ValueError, match="outside 0-1 range"):
-        art._set_alpha_for_array(1.1)
-    with pytest.raises(ValueError, match="outside 0-1 range"):
-        art._set_alpha_for_array(np.nan)
-    with pytest.raises(ValueError, match="alpha must be between 0 and 1"):
-        art._set_alpha_for_array([0.5, 1.1])
-    with pytest.raises(ValueError, match="alpha must be between 0 and 1"):
-        art._set_alpha_for_array([0.5, np.nan])
-
-
-def test_callbacks():
-    def func(artist):
-        func.counter += 1
-
-    func.counter = 0
-
-    art = martist.Artist()
-    oid = art.add_callback(func)
-    assert func.counter == 0
-    art.pchanged()  # must call the callback
-    assert func.counter == 1
-    art.set_zorder(10)  # setting a property must also call the callback
-    assert func.counter == 2
-    art.remove_callback(oid)
-    art.pchanged()  # must not call the callback anymore
-    assert func.counter == 2
-
-
-def test_set_signature():
-    """Test autogenerated ``set()`` for Artist subclasses."""
-    class MyArtist1(martist.Artist):
-        def set_myparam1(self, val):
-            pass
-
-    assert hasattr(MyArtist1.set, '_autogenerated_signature')
-    assert 'myparam1' in MyArtist1.set.__doc__
-
-    class MyArtist2(MyArtist1):
-        def set_myparam2(self, val):
-            pass
-
-    assert hasattr(MyArtist2.set, '_autogenerated_signature')
-    assert 'myparam1' in MyArtist2.set.__doc__
-    assert 'myparam2' in MyArtist2.set.__doc__
-
-
-def test_set_is_overwritten():
-    """set() defined in Artist subclasses should not be overwritten."""
-    class MyArtist3(martist.Artist):
-
-        def set(self, **kwargs):
-            """Not overwritten."""
-
-    assert not hasattr(MyArtist3.set, '_autogenerated_signature')
-    assert MyArtist3.set.__doc__ == "Not overwritten."
-
-    class MyArtist4(MyArtist3):
-        pass
-
-    assert MyArtist4.set is MyArtist3.set
-
-
-def test_format_cursor_data_BoundaryNorm():
-    """Test if cursor data is correct when using BoundaryNorm."""
-    X = np.empty((3, 3))
-    X[0, 0] = 0.9
-    X[0, 1] = 0.99
-    X[0, 2] = 0.999
-    X[1, 0] = -1
-    X[1, 1] = 0
-    X[1, 2] = 1
-    X[2, 0] = 0.09
-    X[2, 1] = 0.009
-    X[2, 2] = 0.0009
-
-    # map range -1..1 to 0..256 in 0.1 steps
-    fig, ax = plt.subplots()
-    fig.suptitle("-1..1 to 0..256 in 0.1")
-    norm = mcolors.BoundaryNorm(np.linspace(-1, 1, 20), 256)
-    img = ax.imshow(X, cmap='RdBu_r', norm=norm)
-
-    labels_list = [
-        "[0.9]",
-        "[1.]",
-        "[1.]",
-        "[-1.0]",
-        "[0.0]",
-        "[1.0]",
-        "[0.09]",
-        "[0.009]",
-        "[0.0009]",
-    ]
-    for v, label in zip(X.flat, labels_list):
-        # label = "[{:-#.{}g}]".format(v, cbook._g_sig_digits(v, 0.1))
-        assert img.format_cursor_data(v) == label
-
-    plt.close()
-
-    # map range -1..1 to 0..256 in 0.01 steps
-    fig, ax = plt.subplots()
-    fig.suptitle("-1..1 to 0..256 in 0.01")
-    cmap = mpl.colormaps['RdBu_r'].resampled(200)
-    norm = mcolors.BoundaryNorm(np.linspace(-1, 1, 200), 200)
-    img = ax.imshow(X, cmap=cmap, norm=norm)
-
-    labels_list = [
-        "[0.90]",
-        "[0.99]",
-        "[1.0]",
-        "[-1.00]",
-        "[0.00]",
-        "[1.00]",
-        "[0.09]",
-        "[0.009]",
-        "[0.0009]",
-    ]
-    for v, label in zip(X.flat, labels_list):
-        # label = "[{:-#.{}g}]".format(v, cbook._g_sig_digits(v, 0.01))
-        assert img.format_cursor_data(v) == label
-
-    plt.close()
-
-    # map range -1..1 to 0..256 in 0.01 steps
-    fig, ax = plt.subplots()
-    fig.suptitle("-1..1 to 0..256 in 0.001")
-    cmap = mpl.colormaps['RdBu_r'].resampled(2000)
-    norm = mcolors.BoundaryNorm(np.linspace(-1, 1, 2000), 2000)
-    img = ax.imshow(X, cmap=cmap, norm=norm)
-
-    labels_list = [
-        "[0.900]",
-        "[0.990]",
-        "[0.999]",
-        "[-1.000]",
-        "[0.000]",
-        "[1.000]",
-        "[0.090]",
-        "[0.009]",
-        "[0.0009]",
-    ]
-    for v, label in zip(X.flat, labels_list):
-        # label = "[{:-#.{}g}]".format(v, cbook._g_sig_digits(v, 0.001))
-        assert img.format_cursor_data(v) == label
-
-    plt.close()
-
-    # different testing data set with
-    # out of bounds values for 0..1 range
-    X = np.empty((7, 1))
-    X[0] = -1.0
-    X[1] = 0.0
-    X[2] = 0.1
-    X[3] = 0.5
-    X[4] = 0.9
-    X[5] = 1.0
-    X[6] = 2.0
-
-    labels_list = [
-        "[-1.0]",
-        "[0.0]",
-        "[0.1]",
-        "[0.5]",
-        "[0.9]",
-        "[1.0]",
-        "[2.0]",
-    ]
-
-    fig, ax = plt.subplots()
-    fig.suptitle("noclip, neither")
-    norm = mcolors.BoundaryNorm(
-        np.linspace(0, 1, 4, endpoint=True), 256, clip=False, extend='neither')
-    img = ax.imshow(X, cmap='RdBu_r', norm=norm)
-    for v, label in zip(X.flat, labels_list):
-        # label = "[{:-#.{}g}]".format(v, cbook._g_sig_digits(v, 0.33))
-        assert img.format_cursor_data(v) == label
-
-    plt.close()
-
-    fig, ax = plt.subplots()
-    fig.suptitle("noclip, min")
-    norm = mcolors.BoundaryNorm(
-        np.linspace(0, 1, 4, endpoint=True), 256, clip=False, extend='min')
-    img = ax.imshow(X, cmap='RdBu_r', norm=norm)
-    for v, label in zip(X.flat, labels_list):
-        # label = "[{:-#.{}g}]".format(v, cbook._g_sig_digits(v, 0.33))
-        assert img.format_cursor_data(v) == label
-
-    plt.close()
-
-    fig, ax = plt.subplots()
-    fig.suptitle("noclip, max")
-    norm = mcolors.BoundaryNorm(
-        np.linspace(0, 1, 4, endpoint=True), 256, clip=False, extend='max')
-    img = ax.imshow(X, cmap='RdBu_r', norm=norm)
-    for v, label in zip(X.flat, labels_list):
-        # label = "[{:-#.{}g}]".format(v, cbook._g_sig_digits(v, 0.33))
-        assert img.format_cursor_data(v) == label
-
-    plt.close()
-
-    fig, ax = plt.subplots()
-    fig.suptitle("noclip, both")
-    norm = mcolors.BoundaryNorm(
-        np.linspace(0, 1, 4, endpoint=True), 256, clip=False, extend='both')
-    img = ax.imshow(X, cmap='RdBu_r', norm=norm)
-    for v, label in zip(X.flat, labels_list):
-        # label = "[{:-#.{}g}]".format(v, cbook._g_sig_digits(v, 0.33))
-        assert img.format_cursor_data(v) == label
-
-    plt.close()
-
-    fig, ax = plt.subplots()
-    fig.suptitle("clip, neither")
-    norm = mcolors.BoundaryNorm(
-        np.linspace(0, 1, 4, endpoint=True), 256, clip=True, extend='neither')
-    img = ax.imshow(X, cmap='RdBu_r', norm=norm)
-    for v, label in zip(X.flat, labels_list):
-        # label = "[{:-#.{}g}]".format(v, cbook._g_sig_digits(v, 0.33))
-        assert img.format_cursor_data(v) == label
-
-    plt.close()
-
-
-def test_auto_no_rasterize():
-    class Gen1(martist.Artist):
-        ...
-
-    assert 'draw' in Gen1.__dict__
-    assert Gen1.__dict__['draw'] is Gen1.draw
-
-    class Gen2(Gen1):
-        ...
-
-    assert 'draw' not in Gen2.__dict__
-    assert Gen2.draw is Gen1.draw
-
-
-def test_draw_wraper_forward_input():
-    class TestKlass(martist.Artist):
-        def draw(self, renderer, extra):
-            return extra
-
-    art = TestKlass()
-    renderer = mbackend_bases.RendererBase()
-
-    assert 'aardvark' == art.draw(renderer, 'aardvark')
-    assert 'aardvark' == art.draw(renderer, extra='aardvark')
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_preprocess_data.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_preprocess_data.py
deleted file mode 100644
index 0684f0dbb9aef3a7ee195a4e6de723e1e271478d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_preprocess_data.py
+++ /dev/null
@@ -1,288 +0,0 @@
-import re
-import sys
-
-import numpy as np
-import pytest
-
-from matplotlib import _preprocess_data
-from matplotlib.axes import Axes
-from matplotlib.testing import subprocess_run_for_testing
-from matplotlib.testing.decorators import check_figures_equal
-
-# Notes on testing the plotting functions itself
-# *   the individual decorated plotting functions are tested in 'test_axes.py'
-# *   that pyplot functions accept a data kwarg is only tested in
-#     test_axes.test_pie_linewidth_0
-
-
-# this gets used in multiple tests, so define it here
-@_preprocess_data(replace_names=["x", "y"], label_namer="y")
-def plot_func(ax, x, y, ls="x", label=None, w="xyz"):
-    return f"x: {list(x)}, y: {list(y)}, ls: {ls}, w: {w}, label: {label}"
-
-
-all_funcs = [plot_func]
-all_func_ids = ['plot_func']
-
-
-def test_compiletime_checks():
-    """Test decorator invocations -> no replacements."""
-
-    def func(ax, x, y): pass
-    def func_args(ax, x, y, *args): pass
-    def func_kwargs(ax, x, y, **kwargs): pass
-    def func_no_ax_args(*args, **kwargs): pass
-
-    # this is ok
-    _preprocess_data(replace_names=["x", "y"])(func)
-    _preprocess_data(replace_names=["x", "y"])(func_kwargs)
-    # this has "enough" information to do all the replaces
-    _preprocess_data(replace_names=["x", "y"])(func_args)
-
-    # no positional_parameter_names but needed due to replaces
-    with pytest.raises(AssertionError):
-        # z is unknown
-        _preprocess_data(replace_names=["x", "y", "z"])(func_args)
-
-    # no replacements at all -> all ok...
-    _preprocess_data(replace_names=[], label_namer=None)(func)
-    _preprocess_data(replace_names=[], label_namer=None)(func_args)
-    _preprocess_data(replace_names=[], label_namer=None)(func_kwargs)
-    _preprocess_data(replace_names=[], label_namer=None)(func_no_ax_args)
-
-    # label namer is unknown
-    with pytest.raises(AssertionError):
-        _preprocess_data(label_namer="z")(func)
-
-    with pytest.raises(AssertionError):
-        _preprocess_data(label_namer="z")(func_args)
-
-
-@pytest.mark.parametrize('func', all_funcs, ids=all_func_ids)
-def test_function_call_without_data(func):
-    """Test without data -> no replacements."""
-    assert (func(None, "x", "y") ==
-            "x: ['x'], y: ['y'], ls: x, w: xyz, label: None")
-    assert (func(None, x="x", y="y") ==
-            "x: ['x'], y: ['y'], ls: x, w: xyz, label: None")
-    assert (func(None, "x", "y", label="") ==
-            "x: ['x'], y: ['y'], ls: x, w: xyz, label: ")
-    assert (func(None, "x", "y", label="text") ==
-            "x: ['x'], y: ['y'], ls: x, w: xyz, label: text")
-    assert (func(None, x="x", y="y", label="") ==
-            "x: ['x'], y: ['y'], ls: x, w: xyz, label: ")
-    assert (func(None, x="x", y="y", label="text") ==
-            "x: ['x'], y: ['y'], ls: x, w: xyz, label: text")
-
-
-@pytest.mark.parametrize('func', all_funcs, ids=all_func_ids)
-def test_function_call_with_dict_input(func):
-    """Tests with dict input, unpacking via preprocess_pipeline"""
-    data = {'a': 1, 'b': 2}
-    assert (func(None, data.keys(), data.values()) ==
-            "x: ['a', 'b'], y: [1, 2], ls: x, w: xyz, label: None")
-
-
-@pytest.mark.parametrize('func', all_funcs, ids=all_func_ids)
-def test_function_call_with_dict_data(func):
-    """Test with dict data -> label comes from the value of 'x' parameter."""
-    data = {"a": [1, 2], "b": [8, 9], "w": "NOT"}
-    assert (func(None, "a", "b", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: b")
-    assert (func(None, x="a", y="b", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: b")
-    assert (func(None, "a", "b", label="", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: ")
-    assert (func(None, "a", "b", label="text", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: text")
-    assert (func(None, x="a", y="b", label="", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: ")
-    assert (func(None, x="a", y="b", label="text", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: text")
-
-
-@pytest.mark.parametrize('func', all_funcs, ids=all_func_ids)
-def test_function_call_with_dict_data_not_in_data(func):
-    """Test the case that one var is not in data -> half replaces, half kept"""
-    data = {"a": [1, 2], "w": "NOT"}
-    assert (func(None, "a", "b", data=data) ==
-            "x: [1, 2], y: ['b'], ls: x, w: xyz, label: b")
-    assert (func(None, x="a", y="b", data=data) ==
-            "x: [1, 2], y: ['b'], ls: x, w: xyz, label: b")
-    assert (func(None, "a", "b", label="", data=data) ==
-            "x: [1, 2], y: ['b'], ls: x, w: xyz, label: ")
-    assert (func(None, "a", "b", label="text", data=data) ==
-            "x: [1, 2], y: ['b'], ls: x, w: xyz, label: text")
-    assert (func(None, x="a", y="b", label="", data=data) ==
-            "x: [1, 2], y: ['b'], ls: x, w: xyz, label: ")
-    assert (func(None, x="a", y="b", label="text", data=data) ==
-            "x: [1, 2], y: ['b'], ls: x, w: xyz, label: text")
-
-
-@pytest.mark.parametrize('func', all_funcs, ids=all_func_ids)
-def test_function_call_with_pandas_data(func, pd):
-    """Test with pandas dataframe -> label comes from ``data["col"].name``."""
-    data = pd.DataFrame({"a": np.array([1, 2], dtype=np.int32),
-                         "b": np.array([8, 9], dtype=np.int32),
-                         "w": ["NOT", "NOT"]})
-
-    assert (func(None, "a", "b", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: b")
-    assert (func(None, x="a", y="b", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: b")
-    assert (func(None, "a", "b", label="", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: ")
-    assert (func(None, "a", "b", label="text", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: text")
-    assert (func(None, x="a", y="b", label="", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: ")
-    assert (func(None, x="a", y="b", label="text", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: text")
-
-
-def test_function_call_replace_all():
-    """Test without a "replace_names" argument, all vars should be replaced."""
-    data = {"a": [1, 2], "b": [8, 9], "x": "xyz"}
-
-    @_preprocess_data(label_namer="y")
-    def func_replace_all(ax, x, y, ls="x", label=None, w="NOT"):
-        return f"x: {list(x)}, y: {list(y)}, ls: {ls}, w: {w}, label: {label}"
-
-    assert (func_replace_all(None, "a", "b", w="x", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: b")
-    assert (func_replace_all(None, x="a", y="b", w="x", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: b")
-    assert (func_replace_all(None, "a", "b", w="x", label="", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: ")
-    assert (
-        func_replace_all(None, "a", "b", w="x", label="text", data=data) ==
-        "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: text")
-    assert (
-        func_replace_all(None, x="a", y="b", w="x", label="", data=data) ==
-        "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: ")
-    assert (
-        func_replace_all(None, x="a", y="b", w="x", label="text", data=data) ==
-        "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: text")
-
-
-def test_no_label_replacements():
-    """Test with "label_namer=None" -> no label replacement at all."""
-
-    @_preprocess_data(replace_names=["x", "y"], label_namer=None)
-    def func_no_label(ax, x, y, ls="x", label=None, w="xyz"):
-        return f"x: {list(x)}, y: {list(y)}, ls: {ls}, w: {w}, label: {label}"
-
-    data = {"a": [1, 2], "b": [8, 9], "w": "NOT"}
-    assert (func_no_label(None, "a", "b", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: None")
-    assert (func_no_label(None, x="a", y="b", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: None")
-    assert (func_no_label(None, "a", "b", label="", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: ")
-    assert (func_no_label(None, "a", "b", label="text", data=data) ==
-            "x: [1, 2], y: [8, 9], ls: x, w: xyz, label: text")
-
-
-def test_more_args_than_pos_parameter():
-    @_preprocess_data(replace_names=["x", "y"], label_namer="y")
-    def func(ax, x, y, z=1):
-        pass
-
-    data = {"a": [1, 2], "b": [8, 9], "w": "NOT"}
-    with pytest.raises(TypeError):
-        func(None, "a", "b", "z", "z", data=data)
-
-
-def test_docstring_addition():
-    @_preprocess_data()
-    def funcy(ax, *args, **kwargs):
-        """
-        Parameters
-        ----------
-        data : indexable object, optional
-            DATA_PARAMETER_PLACEHOLDER
-        """
-
-    assert re.search(r"all parameters also accept a string", funcy.__doc__)
-    assert not re.search(r"the following parameters", funcy.__doc__)
-
-    @_preprocess_data(replace_names=[])
-    def funcy(ax, x, y, z, bar=None):
-        """
-        Parameters
-        ----------
-        data : indexable object, optional
-            DATA_PARAMETER_PLACEHOLDER
-        """
-
-    assert not re.search(r"all parameters also accept a string", funcy.__doc__)
-    assert not re.search(r"the following parameters", funcy.__doc__)
-
-    @_preprocess_data(replace_names=["bar"])
-    def funcy(ax, x, y, z, bar=None):
-        """
-        Parameters
-        ----------
-        data : indexable object, optional
-            DATA_PARAMETER_PLACEHOLDER
-        """
-
-    assert not re.search(r"all parameters also accept a string", funcy.__doc__)
-    assert not re.search(r"the following parameters .*: \*bar\*\.",
-                         funcy.__doc__)
-
-    @_preprocess_data(replace_names=["x", "t"])
-    def funcy(ax, x, y, z, t=None):
-        """
-        Parameters
-        ----------
-        data : indexable object, optional
-            DATA_PARAMETER_PLACEHOLDER
-        """
-
-    assert not re.search(r"all parameters also accept a string", funcy.__doc__)
-    assert not re.search(r"the following parameters .*: \*x\*, \*t\*\.",
-                         funcy.__doc__)
-
-
-def test_data_parameter_replacement():
-    """
-    Test that the docstring contains the correct *data* parameter stub
-    for all methods that we run _preprocess_data() on.
-    """
-    program = (
-        "import logging; "
-        "logging.basicConfig(level=logging.DEBUG); "
-        "import matplotlib.pyplot as plt"
-    )
-    cmd = [sys.executable, "-c", program]
-    completed_proc = subprocess_run_for_testing(
-        cmd, text=True, capture_output=True
-    )
-    assert 'data parameter docstring error' not in completed_proc.stderr
-
-
-class TestPlotTypes:
-
-    plotters = [Axes.scatter, Axes.bar, Axes.plot]
-
-    @pytest.mark.parametrize('plotter', plotters)
-    @check_figures_equal(extensions=['png'])
-    def test_dict_unpack(self, plotter, fig_test, fig_ref):
-        x = [1, 2, 3]
-        y = [4, 5, 6]
-        ddict = dict(zip(x, y))
-
-        plotter(fig_test.subplots(),
-                ddict.keys(), ddict.values())
-        plotter(fig_ref.subplots(), x, y)
-
-    @pytest.mark.parametrize('plotter', plotters)
-    @check_figures_equal(extensions=['png'])
-    def test_data_kwarg(self, plotter, fig_test, fig_ref):
-        x = [1, 2, 3]
-        y = [4, 5, 6]
-
-        plotter(fig_test.subplots(), 'xval', 'yval',
-                data={'xval': x, 'yval': y})
-        plotter(fig_ref.subplots(), x, y)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/textpath.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/textpath.py
deleted file mode 100644
index de97899f8a72056c26668dd1b64c3752bebd5eb3..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/textpath.py
+++ /dev/null
@@ -1,397 +0,0 @@
-from collections import OrderedDict
-import logging
-import urllib.parse
-
-import numpy as np
-
-from matplotlib import _text_helpers, dviread
-from matplotlib.font_manager import (
-    FontProperties, get_font, fontManager as _fontManager
-)
-from matplotlib.ft2font import LOAD_NO_HINTING, LOAD_TARGET_LIGHT
-from matplotlib.mathtext import MathTextParser
-from matplotlib.path import Path
-from matplotlib.texmanager import TexManager
-from matplotlib.transforms import Affine2D
-
-_log = logging.getLogger(__name__)
-
-
-class TextToPath:
-    """A class that converts strings to paths."""
-
-    FONT_SCALE = 100.
-    DPI = 72
-
-    def __init__(self):
-        self.mathtext_parser = MathTextParser('path')
-        self._texmanager = None
-
-    def _get_font(self, prop):
-        """
-        Find the `FT2Font` matching font properties *prop*, with its size set.
-        """
-        filenames = _fontManager._find_fonts_by_props(prop)
-        font = get_font(filenames)
-        font.set_size(self.FONT_SCALE, self.DPI)
-        return font
-
-    def _get_hinting_flag(self):
-        return LOAD_NO_HINTING
-
-    def _get_char_id(self, font, ccode):
-        """
-        Return a unique id for the given font and character-code set.
-        """
-        return urllib.parse.quote(f"{font.postscript_name}-{ccode:x}")
-
-    def get_text_width_height_descent(self, s, prop, ismath):
-        fontsize = prop.get_size_in_points()
-
-        if ismath == "TeX":
-            return TexManager().get_text_width_height_descent(s, fontsize)
-
-        scale = fontsize / self.FONT_SCALE
-
-        if ismath:
-            prop = prop.copy()
-            prop.set_size(self.FONT_SCALE)
-            width, height, descent, *_ = \
-                self.mathtext_parser.parse(s, 72, prop)
-            return width * scale, height * scale, descent * scale
-
-        font = self._get_font(prop)
-        font.set_text(s, 0.0, flags=LOAD_NO_HINTING)
-        w, h = font.get_width_height()
-        w /= 64.0  # convert from subpixels
-        h /= 64.0
-        d = font.get_descent()
-        d /= 64.0
-        return w * scale, h * scale, d * scale
-
-    def get_text_path(self, prop, s, ismath=False):
-        """
-        Convert text *s* to path (a tuple of vertices and codes for
-        matplotlib.path.Path).
-
-        Parameters
-        ----------
-        prop : `~matplotlib.font_manager.FontProperties`
-            The font properties for the text.
-        s : str
-            The text to be converted.
-        ismath : {False, True, "TeX"}
-            If True, use mathtext parser.  If "TeX", use tex for rendering.
-
-        Returns
-        -------
-        verts : list
-            A list of arrays containing the (x, y) coordinates of the vertices.
-        codes : list
-            A list of path codes.
-
-        Examples
-        --------
-        Create a list of vertices and codes from a text, and create a `.Path`
-        from those::
-
-            from matplotlib.path import Path
-            from matplotlib.text import TextToPath
-            from matplotlib.font_manager import FontProperties
-
-            fp = FontProperties(family="Humor Sans", style="italic")
-            verts, codes = TextToPath().get_text_path(fp, "ABC")
-            path = Path(verts, codes, closed=False)
-
-        Also see `TextPath` for a more direct way to create a path from a text.
-        """
-        if ismath == "TeX":
-            glyph_info, glyph_map, rects = self.get_glyphs_tex(prop, s)
-        elif not ismath:
-            font = self._get_font(prop)
-            glyph_info, glyph_map, rects = self.get_glyphs_with_font(font, s)
-        else:
-            glyph_info, glyph_map, rects = self.get_glyphs_mathtext(prop, s)
-
-        verts, codes = [], []
-        for glyph_id, xposition, yposition, scale in glyph_info:
-            verts1, codes1 = glyph_map[glyph_id]
-            verts.extend(verts1 * scale + [xposition, yposition])
-            codes.extend(codes1)
-        for verts1, codes1 in rects:
-            verts.extend(verts1)
-            codes.extend(codes1)
-
-        # Make sure an empty string or one with nothing to print
-        # (e.g. only spaces & newlines) will be valid/empty path
-        if not verts:
-            verts = np.empty((0, 2))
-
-        return verts, codes
-
-    def get_glyphs_with_font(self, font, s, glyph_map=None,
-                             return_new_glyphs_only=False):
-        """
-        Convert string *s* to vertices and codes using the provided ttf font.
-        """
-
-        if glyph_map is None:
-            glyph_map = OrderedDict()
-
-        if return_new_glyphs_only:
-            glyph_map_new = OrderedDict()
-        else:
-            glyph_map_new = glyph_map
-
-        xpositions = []
-        glyph_ids = []
-        for item in _text_helpers.layout(s, font):
-            char_id = self._get_char_id(item.ft_object, ord(item.char))
-            glyph_ids.append(char_id)
-            xpositions.append(item.x)
-            if char_id not in glyph_map:
-                glyph_map_new[char_id] = item.ft_object.get_path()
-
-        ypositions = [0] * len(xpositions)
-        sizes = [1.] * len(xpositions)
-
-        rects = []
-
-        return (list(zip(glyph_ids, xpositions, ypositions, sizes)),
-                glyph_map_new, rects)
-
-    def get_glyphs_mathtext(self, prop, s, glyph_map=None,
-                            return_new_glyphs_only=False):
-        """
-        Parse mathtext string *s* and convert it to a (vertices, codes) pair.
-        """
-
-        prop = prop.copy()
-        prop.set_size(self.FONT_SCALE)
-
-        width, height, descent, glyphs, rects = self.mathtext_parser.parse(
-            s, self.DPI, prop)
-
-        if not glyph_map:
-            glyph_map = OrderedDict()
-
-        if return_new_glyphs_only:
-            glyph_map_new = OrderedDict()
-        else:
-            glyph_map_new = glyph_map
-
-        xpositions = []
-        ypositions = []
-        glyph_ids = []
-        sizes = []
-
-        for font, fontsize, ccode, ox, oy in glyphs:
-            char_id = self._get_char_id(font, ccode)
-            if char_id not in glyph_map:
-                font.clear()
-                font.set_size(self.FONT_SCALE, self.DPI)
-                font.load_char(ccode, flags=LOAD_NO_HINTING)
-                glyph_map_new[char_id] = font.get_path()
-
-            xpositions.append(ox)
-            ypositions.append(oy)
-            glyph_ids.append(char_id)
-            size = fontsize / self.FONT_SCALE
-            sizes.append(size)
-
-        myrects = []
-        for ox, oy, w, h in rects:
-            vert1 = [(ox, oy), (ox, oy + h), (ox + w, oy + h),
-                     (ox + w, oy), (ox, oy), (0, 0)]
-            code1 = [Path.MOVETO,
-                     Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO,
-                     Path.CLOSEPOLY]
-            myrects.append((vert1, code1))
-
-        return (list(zip(glyph_ids, xpositions, ypositions, sizes)),
-                glyph_map_new, myrects)
-
-    def get_glyphs_tex(self, prop, s, glyph_map=None,
-                       return_new_glyphs_only=False):
-        """Convert the string *s* to vertices and codes using usetex mode."""
-        # Mostly borrowed from pdf backend.
-
-        dvifile = TexManager().make_dvi(s, self.FONT_SCALE)
-        with dviread.Dvi(dvifile, self.DPI) as dvi:
-            page, = dvi
-
-        if glyph_map is None:
-            glyph_map = OrderedDict()
-
-        if return_new_glyphs_only:
-            glyph_map_new = OrderedDict()
-        else:
-            glyph_map_new = glyph_map
-
-        glyph_ids, xpositions, ypositions, sizes = [], [], [], []
-
-        # Gather font information and do some setup for combining
-        # characters into strings.
-        for text in page.text:
-            font = get_font(text.font_path)
-            char_id = self._get_char_id(font, text.glyph)
-            if char_id not in glyph_map:
-                font.clear()
-                font.set_size(self.FONT_SCALE, self.DPI)
-                glyph_name_or_index = text.glyph_name_or_index
-                if isinstance(glyph_name_or_index, str):
-                    index = font.get_name_index(glyph_name_or_index)
-                    font.load_glyph(index, flags=LOAD_TARGET_LIGHT)
-                elif isinstance(glyph_name_or_index, int):
-                    self._select_native_charmap(font)
-                    font.load_char(
-                        glyph_name_or_index, flags=LOAD_TARGET_LIGHT)
-                else:  # Should not occur.
-                    raise TypeError(f"Glyph spec of unexpected type: "
-                                    f"{glyph_name_or_index!r}")
-                glyph_map_new[char_id] = font.get_path()
-
-            glyph_ids.append(char_id)
-            xpositions.append(text.x)
-            ypositions.append(text.y)
-            sizes.append(text.font_size / self.FONT_SCALE)
-
-        myrects = []
-
-        for ox, oy, h, w in page.boxes:
-            vert1 = [(ox, oy), (ox + w, oy), (ox + w, oy + h),
-                     (ox, oy + h), (ox, oy), (0, 0)]
-            code1 = [Path.MOVETO,
-                     Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO,
-                     Path.CLOSEPOLY]
-            myrects.append((vert1, code1))
-
-        return (list(zip(glyph_ids, xpositions, ypositions, sizes)),
-                glyph_map_new, myrects)
-
-    @staticmethod
-    def _select_native_charmap(font):
-        # Select the native charmap. (we can't directly identify it but it's
-        # typically an Adobe charmap).
-        for charmap_code in [
-                1094992451,  # ADOBE_CUSTOM.
-                1094995778,  # ADOBE_STANDARD.
-        ]:
-            try:
-                font.select_charmap(charmap_code)
-            except (ValueError, RuntimeError):
-                pass
-            else:
-                break
-        else:
-            _log.warning("No supported encoding in font (%s).", font.fname)
-
-
-text_to_path = TextToPath()
-
-
-class TextPath(Path):
-    """
-    Create a path from the text.
-    """
-
-    def __init__(self, xy, s, size=None, prop=None,
-                 _interpolation_steps=1, usetex=False):
-        r"""
-        Create a path from the text. Note that it simply is a path,
-        not an artist. You need to use the `.PathPatch` (or other artists)
-        to draw this path onto the canvas.
-
-        Parameters
-        ----------
-        xy : tuple or array of two float values
-            Position of the text. For no offset, use ``xy=(0, 0)``.
-
-        s : str
-            The text to convert to a path.
-
-        size : float, optional
-            Font size in points. Defaults to the size specified via the font
-            properties *prop*.
-
-        prop : `~matplotlib.font_manager.FontProperties`, optional
-            Font property. If not provided, will use a default
-            `.FontProperties` with parameters from the
-            :ref:`rcParams<customizing-with-dynamic-rc-settings>`.
-
-        _interpolation_steps : int, optional
-            (Currently ignored)
-
-        usetex : bool, default: False
-            Whether to use tex rendering.
-
-        Examples
-        --------
-        The following creates a path from the string "ABC" with Helvetica
-        font face; and another path from the latex fraction 1/2::
-
-            from matplotlib.text import TextPath
-            from matplotlib.font_manager import FontProperties
-
-            fp = FontProperties(family="Helvetica", style="italic")
-            path1 = TextPath((12, 12), "ABC", size=12, prop=fp)
-            path2 = TextPath((0, 0), r"$\frac{1}{2}$", size=12, usetex=True)
-
-        Also see :doc:`/gallery/text_labels_and_annotations/demo_text_path`.
-        """
-        # Circular import.
-        from matplotlib.text import Text
-
-        prop = FontProperties._from_any(prop)
-        if size is None:
-            size = prop.get_size_in_points()
-
-        self._xy = xy
-        self.set_size(size)
-
-        self._cached_vertices = None
-        s, ismath = Text(usetex=usetex)._preprocess_math(s)
-        super().__init__(
-            *text_to_path.get_text_path(prop, s, ismath=ismath),
-            _interpolation_steps=_interpolation_steps,
-            readonly=True)
-        self._should_simplify = False
-
-    def set_size(self, size):
-        """Set the text size."""
-        self._size = size
-        self._invalid = True
-
-    def get_size(self):
-        """Get the text size."""
-        return self._size
-
-    @property
-    def vertices(self):
-        """
-        Return the cached path after updating it if necessary.
-        """
-        self._revalidate_path()
-        return self._cached_vertices
-
-    @property
-    def codes(self):
-        """
-        Return the codes
-        """
-        return self._codes
-
-    def _revalidate_path(self):
-        """
-        Update the path if necessary.
-
-        The path for the text is initially create with the font size of
-        `.FONT_SCALE`, and this path is rescaled to other size when necessary.
-        """
-        if self._invalid or self._cached_vertices is None:
-            tr = (Affine2D()
-                  .scale(self._size / text_to_path.FONT_SCALE)
-                  .translate(*self._xy))
-            self._cached_vertices = tr.transform(self._vertices)
-            self._cached_vertices.flags.writeable = False
-            self._invalid = False
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/euctwprober.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/euctwprober.py
deleted file mode 100644
index 35669cc4dd809fa4007ee67c752f1be991135e77..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/euctwprober.py
+++ /dev/null
@@ -1,46 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .mbcharsetprober import MultiByteCharSetProber
-from .codingstatemachine import CodingStateMachine
-from .chardistribution import EUCTWDistributionAnalysis
-from .mbcssm import EUCTW_SM_MODEL
-
-class EUCTWProber(MultiByteCharSetProber):
-    def __init__(self):
-        super(EUCTWProber, self).__init__()
-        self.coding_sm = CodingStateMachine(EUCTW_SM_MODEL)
-        self.distribution_analyzer = EUCTWDistributionAnalysis()
-        self.reset()
-
-    @property
-    def charset_name(self):
-        return "EUC-TW"
-
-    @property
-    def language(self):
-        return "Taiwan"
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/formatters/bbcode.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/formatters/bbcode.py
deleted file mode 100644
index 9ce4ebc5a4f8462a14d3c35e5980c7c1be794b38..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/formatters/bbcode.py
+++ /dev/null
@@ -1,108 +0,0 @@
-"""
-    pygments.formatters.bbcode
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    BBcode formatter.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-
-from pygments.formatter import Formatter
-from pygments.util import get_bool_opt
-
-__all__ = ['BBCodeFormatter']
-
-
-class BBCodeFormatter(Formatter):
-    """
-    Format tokens with BBcodes. These formatting codes are used by many
-    bulletin boards, so you can highlight your sourcecode with pygments before
-    posting it there.
-
-    This formatter has no support for background colors and borders, as there
-    are no common BBcode tags for that.
-
-    Some board systems (e.g. phpBB) don't support colors in their [code] tag,
-    so you can't use the highlighting together with that tag.
-    Text in a [code] tag usually is shown with a monospace font (which this
-    formatter can do with the ``monofont`` option) and no spaces (which you
-    need for indentation) are removed.
-
-    Additional options accepted:
-
-    `style`
-        The style to use, can be a string or a Style subclass (default:
-        ``'default'``).
-
-    `codetag`
-        If set to true, put the output into ``[code]`` tags (default:
-        ``false``)
-
-    `monofont`
-        If set to true, add a tag to show the code with a monospace font
-        (default: ``false``).
-    """
-    name = 'BBCode'
-    aliases = ['bbcode', 'bb']
-    filenames = []
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self._code = get_bool_opt(options, 'codetag', False)
-        self._mono = get_bool_opt(options, 'monofont', False)
-
-        self.styles = {}
-        self._make_styles()
-
-    def _make_styles(self):
-        for ttype, ndef in self.style:
-            start = end = ''
-            if ndef['color']:
-                start += '[color=#%s]' % ndef['color']
-                end = '[/color]' + end
-            if ndef['bold']:
-                start += '[b]'
-                end = '[/b]' + end
-            if ndef['italic']:
-                start += '[i]'
-                end = '[/i]' + end
-            if ndef['underline']:
-                start += '[u]'
-                end = '[/u]' + end
-            # there are no common BBcodes for background-color and border
-
-            self.styles[ttype] = start, end
-
-    def format_unencoded(self, tokensource, outfile):
-        if self._code:
-            outfile.write('[code]')
-        if self._mono:
-            outfile.write('[font=monospace]')
-
-        lastval = ''
-        lasttype = None
-
-        for ttype, value in tokensource:
-            while ttype not in self.styles:
-                ttype = ttype.parent
-            if ttype == lasttype:
-                lastval += value
-            else:
-                if lastval:
-                    start, end = self.styles[lasttype]
-                    outfile.write(''.join((start, lastval, end)))
-                lastval = value
-                lasttype = ttype
-
-        if lastval:
-            start, end = self.styles[lasttype]
-            outfile.write(''.join((start, lastval, end)))
-
-        if self._mono:
-            outfile.write('[/font]')
-        if self._code:
-            outfile.write('[/code]')
-        if self._code or self._mono:
-            outfile.write('\n')
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/color_triplet.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/color_triplet.py
deleted file mode 100644
index 02cab328251af9bfa809981aaa44933c407e2cd7..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/color_triplet.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from typing import NamedTuple, Tuple
-
-
-class ColorTriplet(NamedTuple):
-    """The red, green, and blue components of a color."""
-
-    red: int
-    """Red component in 0 to 255 range."""
-    green: int
-    """Green component in 0 to 255 range."""
-    blue: int
-    """Blue component in 0 to 255 range."""
-
-    @property
-    def hex(self) -> str:
-        """get the color triplet in CSS style."""
-        red, green, blue = self
-        return f"#{red:02x}{green:02x}{blue:02x}"
-
-    @property
-    def rgb(self) -> str:
-        """The color in RGB format.
-
-        Returns:
-            str: An rgb color, e.g. ``"rgb(100,23,255)"``.
-        """
-        red, green, blue = self
-        return f"rgb({red},{green},{blue})"
-
-    @property
-    def normalized(self) -> Tuple[float, float, float]:
-        """Convert components into floats between 0 and 1.
-
-        Returns:
-            Tuple[float, float, float]: A tuple of three normalized colour components.
-        """
-        red, green, blue = self
-        return red / 255.0, green / 255.0, blue / 255.0
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/pretty.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/pretty.py
deleted file mode 100644
index 498907f4cf5c48a475e8a222bd207c56d2a89df2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/pretty.py
+++ /dev/null
@@ -1,995 +0,0 @@
-import builtins
-import collections
-import dataclasses
-import inspect
-import os
-import sys
-from array import array
-from collections import Counter, UserDict, UserList, defaultdict, deque
-from dataclasses import dataclass, fields, is_dataclass
-from inspect import isclass
-from itertools import islice
-from types import MappingProxyType
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    DefaultDict,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Sequence,
-    Set,
-    Tuple,
-    Union,
-)
-
-from rich.repr import RichReprResult
-
-try:
-    import attr as _attr_module
-
-    _has_attrs = hasattr(_attr_module, "ib")
-except ImportError:  # pragma: no cover
-    _has_attrs = False
-
-from . import get_console
-from ._loop import loop_last
-from ._pick import pick_bool
-from .abc import RichRenderable
-from .cells import cell_len
-from .highlighter import ReprHighlighter
-from .jupyter import JupyterMixin, JupyterRenderable
-from .measure import Measurement
-from .text import Text
-
-if TYPE_CHECKING:
-    from .console import (
-        Console,
-        ConsoleOptions,
-        HighlighterType,
-        JustifyMethod,
-        OverflowMethod,
-        RenderResult,
-    )
-
-
-def _is_attr_object(obj: Any) -> bool:
-    """Check if an object was created with attrs module."""
-    return _has_attrs and _attr_module.has(type(obj))
-
-
-def _get_attr_fields(obj: Any) -> Sequence["_attr_module.Attribute[Any]"]:
-    """Get fields for an attrs object."""
-    return _attr_module.fields(type(obj)) if _has_attrs else []
-
-
-def _is_dataclass_repr(obj: object) -> bool:
-    """Check if an instance of a dataclass contains the default repr.
-
-    Args:
-        obj (object): A dataclass instance.
-
-    Returns:
-        bool: True if the default repr is used, False if there is a custom repr.
-    """
-    # Digging in to a lot of internals here
-    # Catching all exceptions in case something is missing on a non CPython implementation
-    try:
-        return obj.__repr__.__code__.co_filename == dataclasses.__file__
-    except Exception:  # pragma: no coverage
-        return False
-
-
-_dummy_namedtuple = collections.namedtuple("_dummy_namedtuple", [])
-
-
-def _has_default_namedtuple_repr(obj: object) -> bool:
-    """Check if an instance of namedtuple contains the default repr
-
-    Args:
-        obj (object): A namedtuple
-
-    Returns:
-        bool: True if the default repr is used, False if there's a custom repr.
-    """
-    obj_file = None
-    try:
-        obj_file = inspect.getfile(obj.__repr__)
-    except (OSError, TypeError):
-        # OSError handles case where object is defined in __main__ scope, e.g. REPL - no filename available.
-        # TypeError trapped defensively, in case of object without filename slips through.
-        pass
-    default_repr_file = inspect.getfile(_dummy_namedtuple.__repr__)
-    return obj_file == default_repr_file
-
-
-def _ipy_display_hook(
-    value: Any,
-    console: Optional["Console"] = None,
-    overflow: "OverflowMethod" = "ignore",
-    crop: bool = False,
-    indent_guides: bool = False,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> Union[str, None]:
-    # needed here to prevent circular import:
-    from .console import ConsoleRenderable
-
-    # always skip rich generated jupyter renderables or None values
-    if _safe_isinstance(value, JupyterRenderable) or value is None:
-        return None
-
-    console = console or get_console()
-
-    with console.capture() as capture:
-        # certain renderables should start on a new line
-        if _safe_isinstance(value, ConsoleRenderable):
-            console.line()
-        console.print(
-            value
-            if _safe_isinstance(value, RichRenderable)
-            else Pretty(
-                value,
-                overflow=overflow,
-                indent_guides=indent_guides,
-                max_length=max_length,
-                max_string=max_string,
-                max_depth=max_depth,
-                expand_all=expand_all,
-                margin=12,
-            ),
-            crop=crop,
-            new_line_start=True,
-            end="",
-        )
-    # strip trailing newline, not usually part of a text repr
-    # I'm not sure if this should be prevented at a lower level
-    return capture.get().rstrip("\n")
-
-
-def _safe_isinstance(
-    obj: object, class_or_tuple: Union[type, Tuple[type, ...]]
-) -> bool:
-    """isinstance can fail in rare cases, for example types with no __class__"""
-    try:
-        return isinstance(obj, class_or_tuple)
-    except Exception:
-        return False
-
-
-def install(
-    console: Optional["Console"] = None,
-    overflow: "OverflowMethod" = "ignore",
-    crop: bool = False,
-    indent_guides: bool = False,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> None:
-    """Install automatic pretty printing in the Python REPL.
-
-    Args:
-        console (Console, optional): Console instance or ``None`` to use global console. Defaults to None.
-        overflow (Optional[OverflowMethod], optional): Overflow method. Defaults to "ignore".
-        crop (Optional[bool], optional): Enable cropping of long lines. Defaults to False.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to False.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
-        max_depth (int, optional): Maximum depth of nested data structures, or None for no maximum. Defaults to None.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-        max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
-    """
-    from rich import get_console
-
-    console = console or get_console()
-    assert console is not None
-
-    def display_hook(value: Any) -> None:
-        """Replacement sys.displayhook which prettifies objects with Rich."""
-        if value is not None:
-            assert console is not None
-            builtins._ = None  # type: ignore[attr-defined]
-            console.print(
-                value
-                if _safe_isinstance(value, RichRenderable)
-                else Pretty(
-                    value,
-                    overflow=overflow,
-                    indent_guides=indent_guides,
-                    max_length=max_length,
-                    max_string=max_string,
-                    max_depth=max_depth,
-                    expand_all=expand_all,
-                ),
-                crop=crop,
-            )
-            builtins._ = value  # type: ignore[attr-defined]
-
-    try:
-        ip = get_ipython()  # type: ignore[name-defined]
-    except NameError:
-        sys.displayhook = display_hook
-    else:
-        from IPython.core.formatters import BaseFormatter
-
-        class RichFormatter(BaseFormatter):  # type: ignore[misc]
-            pprint: bool = True
-
-            def __call__(self, value: Any) -> Any:
-                if self.pprint:
-                    return _ipy_display_hook(
-                        value,
-                        console=get_console(),
-                        overflow=overflow,
-                        indent_guides=indent_guides,
-                        max_length=max_length,
-                        max_string=max_string,
-                        max_depth=max_depth,
-                        expand_all=expand_all,
-                    )
-                else:
-                    return repr(value)
-
-        # replace plain text formatter with rich formatter
-        rich_formatter = RichFormatter()
-        ip.display_formatter.formatters["text/plain"] = rich_formatter
-
-
-class Pretty(JupyterMixin):
-    """A rich renderable that pretty prints an object.
-
-    Args:
-        _object (Any): An object to pretty print.
-        highlighter (HighlighterType, optional): Highlighter object to apply to result, or None for ReprHighlighter. Defaults to None.
-        indent_size (int, optional): Number of spaces in indent. Defaults to 4.
-        justify (JustifyMethod, optional): Justify method, or None for default. Defaults to None.
-        overflow (OverflowMethod, optional): Overflow method, or None for default. Defaults to None.
-        no_wrap (Optional[bool], optional): Disable word wrapping. Defaults to False.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to False.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
-        max_depth (int, optional): Maximum depth of nested data structures, or None for no maximum. Defaults to None.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-        margin (int, optional): Subtrace a margin from width to force containers to expand earlier. Defaults to 0.
-        insert_line (bool, optional): Insert a new line if the output has multiple new lines. Defaults to False.
-    """
-
-    def __init__(
-        self,
-        _object: Any,
-        highlighter: Optional["HighlighterType"] = None,
-        *,
-        indent_size: int = 4,
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-        no_wrap: Optional[bool] = False,
-        indent_guides: bool = False,
-        max_length: Optional[int] = None,
-        max_string: Optional[int] = None,
-        max_depth: Optional[int] = None,
-        expand_all: bool = False,
-        margin: int = 0,
-        insert_line: bool = False,
-    ) -> None:
-        self._object = _object
-        self.highlighter = highlighter or ReprHighlighter()
-        self.indent_size = indent_size
-        self.justify: Optional["JustifyMethod"] = justify
-        self.overflow: Optional["OverflowMethod"] = overflow
-        self.no_wrap = no_wrap
-        self.indent_guides = indent_guides
-        self.max_length = max_length
-        self.max_string = max_string
-        self.max_depth = max_depth
-        self.expand_all = expand_all
-        self.margin = margin
-        self.insert_line = insert_line
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        pretty_str = pretty_repr(
-            self._object,
-            max_width=options.max_width - self.margin,
-            indent_size=self.indent_size,
-            max_length=self.max_length,
-            max_string=self.max_string,
-            max_depth=self.max_depth,
-            expand_all=self.expand_all,
-        )
-        pretty_text = Text.from_ansi(
-            pretty_str,
-            justify=self.justify or options.justify,
-            overflow=self.overflow or options.overflow,
-            no_wrap=pick_bool(self.no_wrap, options.no_wrap),
-            style="pretty",
-        )
-        pretty_text = (
-            self.highlighter(pretty_text)
-            if pretty_text
-            else Text(
-                f"{type(self._object)}.__repr__ returned empty string",
-                style="dim italic",
-            )
-        )
-        if self.indent_guides and not options.ascii_only:
-            pretty_text = pretty_text.with_indent_guides(
-                self.indent_size, style="repr.indent"
-            )
-        if self.insert_line and "\n" in pretty_text:
-            yield ""
-        yield pretty_text
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        pretty_str = pretty_repr(
-            self._object,
-            max_width=options.max_width,
-            indent_size=self.indent_size,
-            max_length=self.max_length,
-            max_string=self.max_string,
-            max_depth=self.max_depth,
-            expand_all=self.expand_all,
-        )
-        text_width = (
-            max(cell_len(line) for line in pretty_str.splitlines()) if pretty_str else 0
-        )
-        return Measurement(text_width, text_width)
-
-
-def _get_braces_for_defaultdict(_object: DefaultDict[Any, Any]) -> Tuple[str, str, str]:
-    return (
-        f"defaultdict({_object.default_factory!r}, {{",
-        "})",
-        f"defaultdict({_object.default_factory!r}, {{}})",
-    )
-
-
-def _get_braces_for_array(_object: "array[Any]") -> Tuple[str, str, str]:
-    return (f"array({_object.typecode!r}, [", "])", f"array({_object.typecode!r})")
-
-
-_BRACES: Dict[type, Callable[[Any], Tuple[str, str, str]]] = {
-    os._Environ: lambda _object: ("environ({", "})", "environ({})"),
-    array: _get_braces_for_array,
-    defaultdict: _get_braces_for_defaultdict,
-    Counter: lambda _object: ("Counter({", "})", "Counter()"),
-    deque: lambda _object: ("deque([", "])", "deque()"),
-    dict: lambda _object: ("{", "}", "{}"),
-    UserDict: lambda _object: ("{", "}", "{}"),
-    frozenset: lambda _object: ("frozenset({", "})", "frozenset()"),
-    list: lambda _object: ("[", "]", "[]"),
-    UserList: lambda _object: ("[", "]", "[]"),
-    set: lambda _object: ("{", "}", "set()"),
-    tuple: lambda _object: ("(", ")", "()"),
-    MappingProxyType: lambda _object: ("mappingproxy({", "})", "mappingproxy({})"),
-}
-_CONTAINERS = tuple(_BRACES.keys())
-_MAPPING_CONTAINERS = (dict, os._Environ, MappingProxyType, UserDict)
-
-
-def is_expandable(obj: Any) -> bool:
-    """Check if an object may be expanded by pretty print."""
-    return (
-        _safe_isinstance(obj, _CONTAINERS)
-        or (is_dataclass(obj))
-        or (hasattr(obj, "__rich_repr__"))
-        or _is_attr_object(obj)
-    ) and not isclass(obj)
-
-
-@dataclass
-class Node:
-    """A node in a repr tree. May be atomic or a container."""
-
-    key_repr: str = ""
-    value_repr: str = ""
-    open_brace: str = ""
-    close_brace: str = ""
-    empty: str = ""
-    last: bool = False
-    is_tuple: bool = False
-    is_namedtuple: bool = False
-    children: Optional[List["Node"]] = None
-    key_separator: str = ": "
-    separator: str = ", "
-
-    def iter_tokens(self) -> Iterable[str]:
-        """Generate tokens for this node."""
-        if self.key_repr:
-            yield self.key_repr
-            yield self.key_separator
-        if self.value_repr:
-            yield self.value_repr
-        elif self.children is not None:
-            if self.children:
-                yield self.open_brace
-                if self.is_tuple and not self.is_namedtuple and len(self.children) == 1:
-                    yield from self.children[0].iter_tokens()
-                    yield ","
-                else:
-                    for child in self.children:
-                        yield from child.iter_tokens()
-                        if not child.last:
-                            yield self.separator
-                yield self.close_brace
-            else:
-                yield self.empty
-
-    def check_length(self, start_length: int, max_length: int) -> bool:
-        """Check the length fits within a limit.
-
-        Args:
-            start_length (int): Starting length of the line (indent, prefix, suffix).
-            max_length (int): Maximum length.
-
-        Returns:
-            bool: True if the node can be rendered within max length, otherwise False.
-        """
-        total_length = start_length
-        for token in self.iter_tokens():
-            total_length += cell_len(token)
-            if total_length > max_length:
-                return False
-        return True
-
-    def __str__(self) -> str:
-        repr_text = "".join(self.iter_tokens())
-        return repr_text
-
-    def render(
-        self, max_width: int = 80, indent_size: int = 4, expand_all: bool = False
-    ) -> str:
-        """Render the node to a pretty repr.
-
-        Args:
-            max_width (int, optional): Maximum width of the repr. Defaults to 80.
-            indent_size (int, optional): Size of indents. Defaults to 4.
-            expand_all (bool, optional): Expand all levels. Defaults to False.
-
-        Returns:
-            str: A repr string of the original object.
-        """
-        lines = [_Line(node=self, is_root=True)]
-        line_no = 0
-        while line_no < len(lines):
-            line = lines[line_no]
-            if line.expandable and not line.expanded:
-                if expand_all or not line.check_length(max_width):
-                    lines[line_no : line_no + 1] = line.expand(indent_size)
-            line_no += 1
-
-        repr_str = "\n".join(str(line) for line in lines)
-        return repr_str
-
-
-@dataclass
-class _Line:
-    """A line in repr output."""
-
-    parent: Optional["_Line"] = None
-    is_root: bool = False
-    node: Optional[Node] = None
-    text: str = ""
-    suffix: str = ""
-    whitespace: str = ""
-    expanded: bool = False
-    last: bool = False
-
-    @property
-    def expandable(self) -> bool:
-        """Check if the line may be expanded."""
-        return bool(self.node is not None and self.node.children)
-
-    def check_length(self, max_length: int) -> bool:
-        """Check this line fits within a given number of cells."""
-        start_length = (
-            len(self.whitespace) + cell_len(self.text) + cell_len(self.suffix)
-        )
-        assert self.node is not None
-        return self.node.check_length(start_length, max_length)
-
-    def expand(self, indent_size: int) -> Iterable["_Line"]:
-        """Expand this line by adding children on their own line."""
-        node = self.node
-        assert node is not None
-        whitespace = self.whitespace
-        assert node.children
-        if node.key_repr:
-            new_line = yield _Line(
-                text=f"{node.key_repr}{node.key_separator}{node.open_brace}",
-                whitespace=whitespace,
-            )
-        else:
-            new_line = yield _Line(text=node.open_brace, whitespace=whitespace)
-        child_whitespace = self.whitespace + " " * indent_size
-        tuple_of_one = node.is_tuple and len(node.children) == 1
-        for last, child in loop_last(node.children):
-            separator = "," if tuple_of_one else node.separator
-            line = _Line(
-                parent=new_line,
-                node=child,
-                whitespace=child_whitespace,
-                suffix=separator,
-                last=last and not tuple_of_one,
-            )
-            yield line
-
-        yield _Line(
-            text=node.close_brace,
-            whitespace=whitespace,
-            suffix=self.suffix,
-            last=self.last,
-        )
-
-    def __str__(self) -> str:
-        if self.last:
-            return f"{self.whitespace}{self.text}{self.node or ''}"
-        else:
-            return (
-                f"{self.whitespace}{self.text}{self.node or ''}{self.suffix.rstrip()}"
-            )
-
-
-def _is_namedtuple(obj: Any) -> bool:
-    """Checks if an object is most likely a namedtuple. It is possible
-    to craft an object that passes this check and isn't a namedtuple, but
-    there is only a minuscule chance of this happening unintentionally.
-
-    Args:
-        obj (Any): The object to test
-
-    Returns:
-        bool: True if the object is a namedtuple. False otherwise.
-    """
-    try:
-        fields = getattr(obj, "_fields", None)
-    except Exception:
-        # Being very defensive - if we cannot get the attr then its not a namedtuple
-        return False
-    return isinstance(obj, tuple) and isinstance(fields, tuple)
-
-
-def traverse(
-    _object: Any,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-) -> Node:
-    """Traverse object and generate a tree.
-
-    Args:
-        _object (Any): Object to be traversed.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable truncating.
-            Defaults to None.
-        max_depth (int, optional): Maximum depth of data structures, or None for no maximum.
-            Defaults to None.
-
-    Returns:
-        Node: The root of a tree structure which can be used to render a pretty repr.
-    """
-
-    def to_repr(obj: Any) -> str:
-        """Get repr string for an object, but catch errors."""
-        if (
-            max_string is not None
-            and _safe_isinstance(obj, (bytes, str))
-            and len(obj) > max_string
-        ):
-            truncated = len(obj) - max_string
-            obj_repr = f"{obj[:max_string]!r}+{truncated}"
-        else:
-            try:
-                obj_repr = repr(obj)
-            except Exception as error:
-                obj_repr = f"<repr-error {str(error)!r}>"
-        return obj_repr
-
-    visited_ids: Set[int] = set()
-    push_visited = visited_ids.add
-    pop_visited = visited_ids.remove
-
-    def _traverse(obj: Any, root: bool = False, depth: int = 0) -> Node:
-        """Walk the object depth first."""
-
-        obj_id = id(obj)
-        if obj_id in visited_ids:
-            # Recursion detected
-            return Node(value_repr="...")
-
-        obj_type = type(obj)
-        children: List[Node]
-        reached_max_depth = max_depth is not None and depth >= max_depth
-
-        def iter_rich_args(rich_args: Any) -> Iterable[Union[Any, Tuple[str, Any]]]:
-            for arg in rich_args:
-                if _safe_isinstance(arg, tuple):
-                    if len(arg) == 3:
-                        key, child, default = arg
-                        if default == child:
-                            continue
-                        yield key, child
-                    elif len(arg) == 2:
-                        key, child = arg
-                        yield key, child
-                    elif len(arg) == 1:
-                        yield arg[0]
-                else:
-                    yield arg
-
-        try:
-            fake_attributes = hasattr(
-                obj, "awehoi234_wdfjwljet234_234wdfoijsdfmmnxpi492"
-            )
-        except Exception:
-            fake_attributes = False
-
-        rich_repr_result: Optional[RichReprResult] = None
-        if not fake_attributes:
-            try:
-                if hasattr(obj, "__rich_repr__") and not isclass(obj):
-                    rich_repr_result = obj.__rich_repr__()
-            except Exception:
-                pass
-
-        if rich_repr_result is not None:
-            push_visited(obj_id)
-            angular = getattr(obj.__rich_repr__, "angular", False)
-            args = list(iter_rich_args(rich_repr_result))
-            class_name = obj.__class__.__name__
-
-            if args:
-                children = []
-                append = children.append
-
-                if reached_max_depth:
-                    if angular:
-                        node = Node(value_repr=f"<{class_name}...>")
-                    else:
-                        node = Node(value_repr=f"{class_name}(...)")
-                else:
-                    if angular:
-                        node = Node(
-                            open_brace=f"<{class_name} ",
-                            close_brace=">",
-                            children=children,
-                            last=root,
-                            separator=" ",
-                        )
-                    else:
-                        node = Node(
-                            open_brace=f"{class_name}(",
-                            close_brace=")",
-                            children=children,
-                            last=root,
-                        )
-                    for last, arg in loop_last(args):
-                        if _safe_isinstance(arg, tuple):
-                            key, child = arg
-                            child_node = _traverse(child, depth=depth + 1)
-                            child_node.last = last
-                            child_node.key_repr = key
-                            child_node.key_separator = "="
-                            append(child_node)
-                        else:
-                            child_node = _traverse(arg, depth=depth + 1)
-                            child_node.last = last
-                            append(child_node)
-            else:
-                node = Node(
-                    value_repr=f"<{class_name}>" if angular else f"{class_name}()",
-                    children=[],
-                    last=root,
-                )
-            pop_visited(obj_id)
-        elif _is_attr_object(obj) and not fake_attributes:
-            push_visited(obj_id)
-            children = []
-            append = children.append
-
-            attr_fields = _get_attr_fields(obj)
-            if attr_fields:
-                if reached_max_depth:
-                    node = Node(value_repr=f"{obj.__class__.__name__}(...)")
-                else:
-                    node = Node(
-                        open_brace=f"{obj.__class__.__name__}(",
-                        close_brace=")",
-                        children=children,
-                        last=root,
-                    )
-
-                    def iter_attrs() -> (
-                        Iterable[Tuple[str, Any, Optional[Callable[[Any], str]]]]
-                    ):
-                        """Iterate over attr fields and values."""
-                        for attr in attr_fields:
-                            if attr.repr:
-                                try:
-                                    value = getattr(obj, attr.name)
-                                except Exception as error:
-                                    # Can happen, albeit rarely
-                                    yield (attr.name, error, None)
-                                else:
-                                    yield (
-                                        attr.name,
-                                        value,
-                                        attr.repr if callable(attr.repr) else None,
-                                    )
-
-                    for last, (name, value, repr_callable) in loop_last(iter_attrs()):
-                        if repr_callable:
-                            child_node = Node(value_repr=str(repr_callable(value)))
-                        else:
-                            child_node = _traverse(value, depth=depth + 1)
-                        child_node.last = last
-                        child_node.key_repr = name
-                        child_node.key_separator = "="
-                        append(child_node)
-            else:
-                node = Node(
-                    value_repr=f"{obj.__class__.__name__}()", children=[], last=root
-                )
-            pop_visited(obj_id)
-        elif (
-            is_dataclass(obj)
-            and not _safe_isinstance(obj, type)
-            and not fake_attributes
-            and _is_dataclass_repr(obj)
-        ):
-            push_visited(obj_id)
-            children = []
-            append = children.append
-            if reached_max_depth:
-                node = Node(value_repr=f"{obj.__class__.__name__}(...)")
-            else:
-                node = Node(
-                    open_brace=f"{obj.__class__.__name__}(",
-                    close_brace=")",
-                    children=children,
-                    last=root,
-                    empty=f"{obj.__class__.__name__}()",
-                )
-
-                for last, field in loop_last(
-                    field for field in fields(obj) if field.repr
-                ):
-                    child_node = _traverse(getattr(obj, field.name), depth=depth + 1)
-                    child_node.key_repr = field.name
-                    child_node.last = last
-                    child_node.key_separator = "="
-                    append(child_node)
-
-            pop_visited(obj_id)
-        elif _is_namedtuple(obj) and _has_default_namedtuple_repr(obj):
-            push_visited(obj_id)
-            class_name = obj.__class__.__name__
-            if reached_max_depth:
-                # If we've reached the max depth, we still show the class name, but not its contents
-                node = Node(
-                    value_repr=f"{class_name}(...)",
-                )
-            else:
-                children = []
-                append = children.append
-                node = Node(
-                    open_brace=f"{class_name}(",
-                    close_brace=")",
-                    children=children,
-                    empty=f"{class_name}()",
-                )
-                for last, (key, value) in loop_last(obj._asdict().items()):
-                    child_node = _traverse(value, depth=depth + 1)
-                    child_node.key_repr = key
-                    child_node.last = last
-                    child_node.key_separator = "="
-                    append(child_node)
-            pop_visited(obj_id)
-        elif _safe_isinstance(obj, _CONTAINERS):
-            for container_type in _CONTAINERS:
-                if _safe_isinstance(obj, container_type):
-                    obj_type = container_type
-                    break
-
-            push_visited(obj_id)
-
-            open_brace, close_brace, empty = _BRACES[obj_type](obj)
-
-            if reached_max_depth:
-                node = Node(value_repr=f"{open_brace}...{close_brace}")
-            elif obj_type.__repr__ != type(obj).__repr__:
-                node = Node(value_repr=to_repr(obj), last=root)
-            elif obj:
-                children = []
-                node = Node(
-                    open_brace=open_brace,
-                    close_brace=close_brace,
-                    children=children,
-                    last=root,
-                )
-                append = children.append
-                num_items = len(obj)
-                last_item_index = num_items - 1
-
-                if _safe_isinstance(obj, _MAPPING_CONTAINERS):
-                    iter_items = iter(obj.items())
-                    if max_length is not None:
-                        iter_items = islice(iter_items, max_length)
-                    for index, (key, child) in enumerate(iter_items):
-                        child_node = _traverse(child, depth=depth + 1)
-                        child_node.key_repr = to_repr(key)
-                        child_node.last = index == last_item_index
-                        append(child_node)
-                else:
-                    iter_values = iter(obj)
-                    if max_length is not None:
-                        iter_values = islice(iter_values, max_length)
-                    for index, child in enumerate(iter_values):
-                        child_node = _traverse(child, depth=depth + 1)
-                        child_node.last = index == last_item_index
-                        append(child_node)
-                if max_length is not None and num_items > max_length:
-                    append(Node(value_repr=f"... +{num_items - max_length}", last=True))
-            else:
-                node = Node(empty=empty, children=[], last=root)
-
-            pop_visited(obj_id)
-        else:
-            node = Node(value_repr=to_repr(obj), last=root)
-        node.is_tuple = _safe_isinstance(obj, tuple)
-        node.is_namedtuple = _is_namedtuple(obj)
-        return node
-
-    node = _traverse(_object, root=True)
-    return node
-
-
-def pretty_repr(
-    _object: Any,
-    *,
-    max_width: int = 80,
-    indent_size: int = 4,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> str:
-    """Prettify repr string by expanding on to new lines to fit within a given width.
-
-    Args:
-        _object (Any): Object to repr.
-        max_width (int, optional): Desired maximum width of repr string. Defaults to 80.
-        indent_size (int, optional): Number of spaces to indent. Defaults to 4.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable truncating.
-            Defaults to None.
-        max_depth (int, optional): Maximum depth of nested data structure, or None for no depth.
-            Defaults to None.
-        expand_all (bool, optional): Expand all containers regardless of available width. Defaults to False.
-
-    Returns:
-        str: A possibly multi-line representation of the object.
-    """
-
-    if _safe_isinstance(_object, Node):
-        node = _object
-    else:
-        node = traverse(
-            _object, max_length=max_length, max_string=max_string, max_depth=max_depth
-        )
-    repr_str: str = node.render(
-        max_width=max_width, indent_size=indent_size, expand_all=expand_all
-    )
-    return repr_str
-
-
-def pprint(
-    _object: Any,
-    *,
-    console: Optional["Console"] = None,
-    indent_guides: bool = True,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> None:
-    """A convenience function for pretty printing.
-
-    Args:
-        _object (Any): Object to pretty print.
-        console (Console, optional): Console instance, or None to use default. Defaults to None.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of strings before truncating, or None to disable. Defaults to None.
-        max_depth (int, optional): Maximum depth for nested data structures, or None for unlimited depth. Defaults to None.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to True.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-    """
-    _console = get_console() if console is None else console
-    _console.print(
-        Pretty(
-            _object,
-            max_length=max_length,
-            max_string=max_string,
-            max_depth=max_depth,
-            indent_guides=indent_guides,
-            expand_all=expand_all,
-            overflow="ignore",
-        ),
-        soft_wrap=True,
-    )
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    class BrokenRepr:
-        def __repr__(self) -> str:
-            1 / 0
-            return "this will fail"
-
-    from typing import NamedTuple
-
-    class StockKeepingUnit(NamedTuple):
-        name: str
-        description: str
-        price: float
-        category: str
-        reviews: List[str]
-
-    d = defaultdict(int)
-    d["foo"] = 5
-    data = {
-        "foo": [
-            1,
-            "Hello World!",
-            100.123,
-            323.232,
-            432324.0,
-            {5, 6, 7, (1, 2, 3, 4), 8},
-        ],
-        "bar": frozenset({1, 2, 3}),
-        "defaultdict": defaultdict(
-            list, {"crumble": ["apple", "rhubarb", "butter", "sugar", "flour"]}
-        ),
-        "counter": Counter(
-            [
-                "apple",
-                "orange",
-                "pear",
-                "kumquat",
-                "kumquat",
-                "durian" * 100,
-            ]
-        ),
-        "atomic": (False, True, None),
-        "namedtuple": StockKeepingUnit(
-            "Sparkling British Spring Water",
-            "Carbonated spring water",
-            0.9,
-            "water",
-            ["its amazing!", "its terrible!"],
-        ),
-        "Broken": BrokenRepr(),
-    }
-    data["foo"].append(data)  # type: ignore[attr-defined]
-
-    from rich import print
-
-    # print(Pretty(data, indent_guides=True, max_string=20))
-
-    class Thing:
-        def __repr__(self) -> str:
-            return "Hello\x1b[38;5;239m World!"
-
-    print(Pretty(Thing()))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/progress.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/progress.py
deleted file mode 100644
index 2542b9ab6c9a0c45ee63b345e7a3ab534c892951..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/progress.py
+++ /dev/null
@@ -1,1699 +0,0 @@
-import io
-import sys
-import typing
-import warnings
-from abc import ABC, abstractmethod
-from collections import deque
-from dataclasses import dataclass, field
-from datetime import timedelta
-from io import RawIOBase, UnsupportedOperation
-from math import ceil
-from mmap import mmap
-from operator import length_hint
-from os import PathLike, stat
-from threading import Event, RLock, Thread
-from types import TracebackType
-from typing import (
-    Any,
-    BinaryIO,
-    Callable,
-    ContextManager,
-    Deque,
-    Dict,
-    Generic,
-    Iterable,
-    List,
-    NamedTuple,
-    NewType,
-    Optional,
-    Sequence,
-    TextIO,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-)
-
-if sys.version_info >= (3, 8):
-    from typing import Literal
-else:
-    from typing_extensions import Literal  # pragma: no cover
-
-from . import filesize, get_console
-from .console import Console, Group, JustifyMethod, RenderableType
-from .highlighter import Highlighter
-from .jupyter import JupyterMixin
-from .live import Live
-from .progress_bar import ProgressBar
-from .spinner import Spinner
-from .style import StyleType
-from .table import Column, Table
-from .text import Text, TextType
-
-TaskID = NewType("TaskID", int)
-
-ProgressType = TypeVar("ProgressType")
-
-GetTimeCallable = Callable[[], float]
-
-
-_I = typing.TypeVar("_I", TextIO, BinaryIO)
-
-
-class _TrackThread(Thread):
-    """A thread to periodically update progress."""
-
-    def __init__(self, progress: "Progress", task_id: "TaskID", update_period: float):
-        self.progress = progress
-        self.task_id = task_id
-        self.update_period = update_period
-        self.done = Event()
-
-        self.completed = 0
-        super().__init__()
-
-    def run(self) -> None:
-        task_id = self.task_id
-        advance = self.progress.advance
-        update_period = self.update_period
-        last_completed = 0
-        wait = self.done.wait
-        while not wait(update_period):
-            completed = self.completed
-            if last_completed != completed:
-                advance(task_id, completed - last_completed)
-                last_completed = completed
-
-        self.progress.update(self.task_id, completed=self.completed, refresh=True)
-
-    def __enter__(self) -> "_TrackThread":
-        self.start()
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.done.set()
-        self.join()
-
-
-def track(
-    sequence: Union[Sequence[ProgressType], Iterable[ProgressType]],
-    description: str = "Working...",
-    total: Optional[float] = None,
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    update_period: float = 0.1,
-    disable: bool = False,
-    show_speed: bool = True,
-) -> Iterable[ProgressType]:
-    """Track progress by iterating over a sequence.
-
-    Args:
-        sequence (Iterable[ProgressType]): A sequence (must support "len") you wish to iterate over.
-        description (str, optional): Description of task show next to progress bar. Defaults to "Working".
-        total: (float, optional): Total number of steps. Default is len(sequence).
-        auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
-        transient: (bool, optional): Clear the progress on exit. Defaults to False.
-        console (Console, optional): Console to write to. Default creates internal Console instance.
-        refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
-        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
-        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
-        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
-        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
-        update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1.
-        disable (bool, optional): Disable display of progress.
-        show_speed (bool, optional): Show speed if total isn't known. Defaults to True.
-    Returns:
-        Iterable[ProgressType]: An iterable of the values in the sequence.
-
-    """
-
-    columns: List["ProgressColumn"] = (
-        [TextColumn("[progress.description]{task.description}")] if description else []
-    )
-    columns.extend(
-        (
-            BarColumn(
-                style=style,
-                complete_style=complete_style,
-                finished_style=finished_style,
-                pulse_style=pulse_style,
-            ),
-            TaskProgressColumn(show_speed=show_speed),
-            TimeRemainingColumn(elapsed_when_finished=True),
-        )
-    )
-    progress = Progress(
-        *columns,
-        auto_refresh=auto_refresh,
-        console=console,
-        transient=transient,
-        get_time=get_time,
-        refresh_per_second=refresh_per_second or 10,
-        disable=disable,
-    )
-
-    with progress:
-        yield from progress.track(
-            sequence, total=total, description=description, update_period=update_period
-        )
-
-
-class _Reader(RawIOBase, BinaryIO):
-    """A reader that tracks progress while it's being read from."""
-
-    def __init__(
-        self,
-        handle: BinaryIO,
-        progress: "Progress",
-        task: TaskID,
-        close_handle: bool = True,
-    ) -> None:
-        self.handle = handle
-        self.progress = progress
-        self.task = task
-        self.close_handle = close_handle
-        self._closed = False
-
-    def __enter__(self) -> "_Reader":
-        self.handle.__enter__()
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.close()
-
-    def __iter__(self) -> BinaryIO:
-        return self
-
-    def __next__(self) -> bytes:
-        line = next(self.handle)
-        self.progress.advance(self.task, advance=len(line))
-        return line
-
-    @property
-    def closed(self) -> bool:
-        return self._closed
-
-    def fileno(self) -> int:
-        return self.handle.fileno()
-
-    def isatty(self) -> bool:
-        return self.handle.isatty()
-
-    @property
-    def mode(self) -> str:
-        return self.handle.mode
-
-    @property
-    def name(self) -> str:
-        return self.handle.name
-
-    def readable(self) -> bool:
-        return self.handle.readable()
-
-    def seekable(self) -> bool:
-        return self.handle.seekable()
-
-    def writable(self) -> bool:
-        return False
-
-    def read(self, size: int = -1) -> bytes:
-        block = self.handle.read(size)
-        self.progress.advance(self.task, advance=len(block))
-        return block
-
-    def readinto(self, b: Union[bytearray, memoryview, mmap]):  # type: ignore[no-untyped-def, override]
-        n = self.handle.readinto(b)  # type: ignore[attr-defined]
-        self.progress.advance(self.task, advance=n)
-        return n
-
-    def readline(self, size: int = -1) -> bytes:  # type: ignore[override]
-        line = self.handle.readline(size)
-        self.progress.advance(self.task, advance=len(line))
-        return line
-
-    def readlines(self, hint: int = -1) -> List[bytes]:
-        lines = self.handle.readlines(hint)
-        self.progress.advance(self.task, advance=sum(map(len, lines)))
-        return lines
-
-    def close(self) -> None:
-        if self.close_handle:
-            self.handle.close()
-        self._closed = True
-
-    def seek(self, offset: int, whence: int = 0) -> int:
-        pos = self.handle.seek(offset, whence)
-        self.progress.update(self.task, completed=pos)
-        return pos
-
-    def tell(self) -> int:
-        return self.handle.tell()
-
-    def write(self, s: Any) -> int:
-        raise UnsupportedOperation("write")
-
-
-class _ReadContext(ContextManager[_I], Generic[_I]):
-    """A utility class to handle a context for both a reader and a progress."""
-
-    def __init__(self, progress: "Progress", reader: _I) -> None:
-        self.progress = progress
-        self.reader: _I = reader
-
-    def __enter__(self) -> _I:
-        self.progress.start()
-        return self.reader.__enter__()
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.progress.stop()
-        self.reader.__exit__(exc_type, exc_val, exc_tb)
-
-
-def wrap_file(
-    file: BinaryIO,
-    total: int,
-    *,
-    description: str = "Reading...",
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    disable: bool = False,
-) -> ContextManager[BinaryIO]:
-    """Read bytes from a file while tracking progress.
-
-    Args:
-        file (Union[str, PathLike[str], BinaryIO]): The path to the file to read, or a file-like object in binary mode.
-        total (int): Total number of bytes to read.
-        description (str, optional): Description of task show next to progress bar. Defaults to "Reading".
-        auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
-        transient: (bool, optional): Clear the progress on exit. Defaults to False.
-        console (Console, optional): Console to write to. Default creates internal Console instance.
-        refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
-        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
-        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
-        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
-        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
-        disable (bool, optional): Disable display of progress.
-    Returns:
-        ContextManager[BinaryIO]: A context manager yielding a progress reader.
-
-    """
-
-    columns: List["ProgressColumn"] = (
-        [TextColumn("[progress.description]{task.description}")] if description else []
-    )
-    columns.extend(
-        (
-            BarColumn(
-                style=style,
-                complete_style=complete_style,
-                finished_style=finished_style,
-                pulse_style=pulse_style,
-            ),
-            DownloadColumn(),
-            TimeRemainingColumn(),
-        )
-    )
-    progress = Progress(
-        *columns,
-        auto_refresh=auto_refresh,
-        console=console,
-        transient=transient,
-        get_time=get_time,
-        refresh_per_second=refresh_per_second or 10,
-        disable=disable,
-    )
-
-    reader = progress.wrap_file(file, total=total, description=description)
-    return _ReadContext(progress, reader)
-
-
-@typing.overload
-def open(
-    file: Union[str, "PathLike[str]", bytes],
-    mode: Union[Literal["rt"], Literal["r"]],
-    buffering: int = -1,
-    encoding: Optional[str] = None,
-    errors: Optional[str] = None,
-    newline: Optional[str] = None,
-    *,
-    total: Optional[int] = None,
-    description: str = "Reading...",
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    disable: bool = False,
-) -> ContextManager[TextIO]:
-    pass
-
-
-@typing.overload
-def open(
-    file: Union[str, "PathLike[str]", bytes],
-    mode: Literal["rb"],
-    buffering: int = -1,
-    encoding: Optional[str] = None,
-    errors: Optional[str] = None,
-    newline: Optional[str] = None,
-    *,
-    total: Optional[int] = None,
-    description: str = "Reading...",
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    disable: bool = False,
-) -> ContextManager[BinaryIO]:
-    pass
-
-
-def open(
-    file: Union[str, "PathLike[str]", bytes],
-    mode: Union[Literal["rb"], Literal["rt"], Literal["r"]] = "r",
-    buffering: int = -1,
-    encoding: Optional[str] = None,
-    errors: Optional[str] = None,
-    newline: Optional[str] = None,
-    *,
-    total: Optional[int] = None,
-    description: str = "Reading...",
-    auto_refresh: bool = True,
-    console: Optional[Console] = None,
-    transient: bool = False,
-    get_time: Optional[Callable[[], float]] = None,
-    refresh_per_second: float = 10,
-    style: StyleType = "bar.back",
-    complete_style: StyleType = "bar.complete",
-    finished_style: StyleType = "bar.finished",
-    pulse_style: StyleType = "bar.pulse",
-    disable: bool = False,
-) -> Union[ContextManager[BinaryIO], ContextManager[TextIO]]:
-    """Read bytes from a file while tracking progress.
-
-    Args:
-        path (Union[str, PathLike[str], BinaryIO]): The path to the file to read, or a file-like object in binary mode.
-        mode (str): The mode to use to open the file. Only supports "r", "rb" or "rt".
-        buffering (int): The buffering strategy to use, see :func:`io.open`.
-        encoding (str, optional): The encoding to use when reading in text mode, see :func:`io.open`.
-        errors (str, optional): The error handling strategy for decoding errors, see :func:`io.open`.
-        newline (str, optional): The strategy for handling newlines in text mode, see :func:`io.open`
-        total: (int, optional): Total number of bytes to read. Must be provided if reading from a file handle. Default for a path is os.stat(file).st_size.
-        description (str, optional): Description of task show next to progress bar. Defaults to "Reading".
-        auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
-        transient: (bool, optional): Clear the progress on exit. Defaults to False.
-        console (Console, optional): Console to write to. Default creates internal Console instance.
-        refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
-        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
-        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
-        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
-        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
-        disable (bool, optional): Disable display of progress.
-        encoding (str, optional): The encoding to use when reading in text mode.
-
-    Returns:
-        ContextManager[BinaryIO]: A context manager yielding a progress reader.
-
-    """
-
-    columns: List["ProgressColumn"] = (
-        [TextColumn("[progress.description]{task.description}")] if description else []
-    )
-    columns.extend(
-        (
-            BarColumn(
-                style=style,
-                complete_style=complete_style,
-                finished_style=finished_style,
-                pulse_style=pulse_style,
-            ),
-            DownloadColumn(),
-            TimeRemainingColumn(),
-        )
-    )
-    progress = Progress(
-        *columns,
-        auto_refresh=auto_refresh,
-        console=console,
-        transient=transient,
-        get_time=get_time,
-        refresh_per_second=refresh_per_second or 10,
-        disable=disable,
-    )
-
-    reader = progress.open(
-        file,
-        mode=mode,
-        buffering=buffering,
-        encoding=encoding,
-        errors=errors,
-        newline=newline,
-        total=total,
-        description=description,
-    )
-    return _ReadContext(progress, reader)  # type: ignore[return-value, type-var]
-
-
-class ProgressColumn(ABC):
-    """Base class for a widget to use in progress display."""
-
-    max_refresh: Optional[float] = None
-
-    def __init__(self, table_column: Optional[Column] = None) -> None:
-        self._table_column = table_column
-        self._renderable_cache: Dict[TaskID, Tuple[float, RenderableType]] = {}
-        self._update_time: Optional[float] = None
-
-    def get_table_column(self) -> Column:
-        """Get a table column, used to build tasks table."""
-        return self._table_column or Column()
-
-    def __call__(self, task: "Task") -> RenderableType:
-        """Called by the Progress object to return a renderable for the given task.
-
-        Args:
-            task (Task): An object containing information regarding the task.
-
-        Returns:
-            RenderableType: Anything renderable (including str).
-        """
-        current_time = task.get_time()
-        if self.max_refresh is not None and not task.completed:
-            try:
-                timestamp, renderable = self._renderable_cache[task.id]
-            except KeyError:
-                pass
-            else:
-                if timestamp + self.max_refresh > current_time:
-                    return renderable
-
-        renderable = self.render(task)
-        self._renderable_cache[task.id] = (current_time, renderable)
-        return renderable
-
-    @abstractmethod
-    def render(self, task: "Task") -> RenderableType:
-        """Should return a renderable object."""
-
-
-class RenderableColumn(ProgressColumn):
-    """A column to insert an arbitrary column.
-
-    Args:
-        renderable (RenderableType, optional): Any renderable. Defaults to empty string.
-    """
-
-    def __init__(
-        self, renderable: RenderableType = "", *, table_column: Optional[Column] = None
-    ):
-        self.renderable = renderable
-        super().__init__(table_column=table_column)
-
-    def render(self, task: "Task") -> RenderableType:
-        return self.renderable
-
-
-class SpinnerColumn(ProgressColumn):
-    """A column with a 'spinner' animation.
-
-    Args:
-        spinner_name (str, optional): Name of spinner animation. Defaults to "dots".
-        style (StyleType, optional): Style of spinner. Defaults to "progress.spinner".
-        speed (float, optional): Speed factor of spinner. Defaults to 1.0.
-        finished_text (TextType, optional): Text used when task is finished. Defaults to " ".
-    """
-
-    def __init__(
-        self,
-        spinner_name: str = "dots",
-        style: Optional[StyleType] = "progress.spinner",
-        speed: float = 1.0,
-        finished_text: TextType = " ",
-        table_column: Optional[Column] = None,
-    ):
-        self.spinner = Spinner(spinner_name, style=style, speed=speed)
-        self.finished_text = (
-            Text.from_markup(finished_text)
-            if isinstance(finished_text, str)
-            else finished_text
-        )
-        super().__init__(table_column=table_column)
-
-    def set_spinner(
-        self,
-        spinner_name: str,
-        spinner_style: Optional[StyleType] = "progress.spinner",
-        speed: float = 1.0,
-    ) -> None:
-        """Set a new spinner.
-
-        Args:
-            spinner_name (str): Spinner name, see python -m rich.spinner.
-            spinner_style (Optional[StyleType], optional): Spinner style. Defaults to "progress.spinner".
-            speed (float, optional): Speed factor of spinner. Defaults to 1.0.
-        """
-        self.spinner = Spinner(spinner_name, style=spinner_style, speed=speed)
-
-    def render(self, task: "Task") -> RenderableType:
-        text = (
-            self.finished_text
-            if task.finished
-            else self.spinner.render(task.get_time())
-        )
-        return text
-
-
-class TextColumn(ProgressColumn):
-    """A column containing text."""
-
-    def __init__(
-        self,
-        text_format: str,
-        style: StyleType = "none",
-        justify: JustifyMethod = "left",
-        markup: bool = True,
-        highlighter: Optional[Highlighter] = None,
-        table_column: Optional[Column] = None,
-    ) -> None:
-        self.text_format = text_format
-        self.justify: JustifyMethod = justify
-        self.style = style
-        self.markup = markup
-        self.highlighter = highlighter
-        super().__init__(table_column=table_column or Column(no_wrap=True))
-
-    def render(self, task: "Task") -> Text:
-        _text = self.text_format.format(task=task)
-        if self.markup:
-            text = Text.from_markup(_text, style=self.style, justify=self.justify)
-        else:
-            text = Text(_text, style=self.style, justify=self.justify)
-        if self.highlighter:
-            self.highlighter.highlight(text)
-        return text
-
-
-class BarColumn(ProgressColumn):
-    """Renders a visual progress bar.
-
-    Args:
-        bar_width (Optional[int], optional): Width of bar or None for full width. Defaults to 40.
-        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
-        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
-        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
-        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
-    """
-
-    def __init__(
-        self,
-        bar_width: Optional[int] = 40,
-        style: StyleType = "bar.back",
-        complete_style: StyleType = "bar.complete",
-        finished_style: StyleType = "bar.finished",
-        pulse_style: StyleType = "bar.pulse",
-        table_column: Optional[Column] = None,
-    ) -> None:
-        self.bar_width = bar_width
-        self.style = style
-        self.complete_style = complete_style
-        self.finished_style = finished_style
-        self.pulse_style = pulse_style
-        super().__init__(table_column=table_column)
-
-    def render(self, task: "Task") -> ProgressBar:
-        """Gets a progress bar widget for a task."""
-        return ProgressBar(
-            total=max(0, task.total) if task.total is not None else None,
-            completed=max(0, task.completed),
-            width=None if self.bar_width is None else max(1, self.bar_width),
-            pulse=not task.started,
-            animation_time=task.get_time(),
-            style=self.style,
-            complete_style=self.complete_style,
-            finished_style=self.finished_style,
-            pulse_style=self.pulse_style,
-        )
-
-
-class TimeElapsedColumn(ProgressColumn):
-    """Renders time elapsed."""
-
-    def render(self, task: "Task") -> Text:
-        """Show time elapsed."""
-        elapsed = task.finished_time if task.finished else task.elapsed
-        if elapsed is None:
-            return Text("-:--:--", style="progress.elapsed")
-        delta = timedelta(seconds=max(0, int(elapsed)))
-        return Text(str(delta), style="progress.elapsed")
-
-
-class TaskProgressColumn(TextColumn):
-    """Show task progress as a percentage.
-
-    Args:
-        text_format (str, optional): Format for percentage display. Defaults to "[progress.percentage]{task.percentage:>3.0f}%".
-        text_format_no_percentage (str, optional): Format if percentage is unknown. Defaults to "".
-        style (StyleType, optional): Style of output. Defaults to "none".
-        justify (JustifyMethod, optional): Text justification. Defaults to "left".
-        markup (bool, optional): Enable markup. Defaults to True.
-        highlighter (Optional[Highlighter], optional): Highlighter to apply to output. Defaults to None.
-        table_column (Optional[Column], optional): Table Column to use. Defaults to None.
-        show_speed (bool, optional): Show speed if total is unknown. Defaults to False.
-    """
-
-    def __init__(
-        self,
-        text_format: str = "[progress.percentage]{task.percentage:>3.0f}%",
-        text_format_no_percentage: str = "",
-        style: StyleType = "none",
-        justify: JustifyMethod = "left",
-        markup: bool = True,
-        highlighter: Optional[Highlighter] = None,
-        table_column: Optional[Column] = None,
-        show_speed: bool = False,
-    ) -> None:
-        self.text_format_no_percentage = text_format_no_percentage
-        self.show_speed = show_speed
-        super().__init__(
-            text_format=text_format,
-            style=style,
-            justify=justify,
-            markup=markup,
-            highlighter=highlighter,
-            table_column=table_column,
-        )
-
-    @classmethod
-    def render_speed(cls, speed: Optional[float]) -> Text:
-        """Render the speed in iterations per second.
-
-        Args:
-            task (Task): A Task object.
-
-        Returns:
-            Text: Text object containing the task speed.
-        """
-        if speed is None:
-            return Text("", style="progress.percentage")
-        unit, suffix = filesize.pick_unit_and_suffix(
-            int(speed),
-            ["", "×10³", "×10⁶", "×10⁹", "×10¹²"],
-            1000,
-        )
-        data_speed = speed / unit
-        return Text(f"{data_speed:.1f}{suffix} it/s", style="progress.percentage")
-
-    def render(self, task: "Task") -> Text:
-        if task.total is None and self.show_speed:
-            return self.render_speed(task.finished_speed or task.speed)
-        text_format = (
-            self.text_format_no_percentage if task.total is None else self.text_format
-        )
-        _text = text_format.format(task=task)
-        if self.markup:
-            text = Text.from_markup(_text, style=self.style, justify=self.justify)
-        else:
-            text = Text(_text, style=self.style, justify=self.justify)
-        if self.highlighter:
-            self.highlighter.highlight(text)
-        return text
-
-
-class TimeRemainingColumn(ProgressColumn):
-    """Renders estimated time remaining.
-
-    Args:
-        compact (bool, optional): Render MM:SS when time remaining is less than an hour. Defaults to False.
-        elapsed_when_finished (bool, optional): Render time elapsed when the task is finished. Defaults to False.
-    """
-
-    # Only refresh twice a second to prevent jitter
-    max_refresh = 0.5
-
-    def __init__(
-        self,
-        compact: bool = False,
-        elapsed_when_finished: bool = False,
-        table_column: Optional[Column] = None,
-    ):
-        self.compact = compact
-        self.elapsed_when_finished = elapsed_when_finished
-        super().__init__(table_column=table_column)
-
-    def render(self, task: "Task") -> Text:
-        """Show time remaining."""
-        if self.elapsed_when_finished and task.finished:
-            task_time = task.finished_time
-            style = "progress.elapsed"
-        else:
-            task_time = task.time_remaining
-            style = "progress.remaining"
-
-        if task.total is None:
-            return Text("", style=style)
-
-        if task_time is None:
-            return Text("--:--" if self.compact else "-:--:--", style=style)
-
-        # Based on https://github.com/tqdm/tqdm/blob/master/tqdm/std.py
-        minutes, seconds = divmod(int(task_time), 60)
-        hours, minutes = divmod(minutes, 60)
-
-        if self.compact and not hours:
-            formatted = f"{minutes:02d}:{seconds:02d}"
-        else:
-            formatted = f"{hours:d}:{minutes:02d}:{seconds:02d}"
-
-        return Text(formatted, style=style)
-
-
-class FileSizeColumn(ProgressColumn):
-    """Renders completed filesize."""
-
-    def render(self, task: "Task") -> Text:
-        """Show data completed."""
-        data_size = filesize.decimal(int(task.completed))
-        return Text(data_size, style="progress.filesize")
-
-
-class TotalFileSizeColumn(ProgressColumn):
-    """Renders total filesize."""
-
-    def render(self, task: "Task") -> Text:
-        """Show data completed."""
-        data_size = filesize.decimal(int(task.total)) if task.total is not None else ""
-        return Text(data_size, style="progress.filesize.total")
-
-
-class MofNCompleteColumn(ProgressColumn):
-    """Renders completed count/total, e.g. '  10/1000'.
-
-    Best for bounded tasks with int quantities.
-
-    Space pads the completed count so that progress length does not change as task progresses
-    past powers of 10.
-
-    Args:
-        separator (str, optional): Text to separate completed and total values. Defaults to "/".
-    """
-
-    def __init__(self, separator: str = "/", table_column: Optional[Column] = None):
-        self.separator = separator
-        super().__init__(table_column=table_column)
-
-    def render(self, task: "Task") -> Text:
-        """Show completed/total."""
-        completed = int(task.completed)
-        total = int(task.total) if task.total is not None else "?"
-        total_width = len(str(total))
-        return Text(
-            f"{completed:{total_width}d}{self.separator}{total}",
-            style="progress.download",
-        )
-
-
-class DownloadColumn(ProgressColumn):
-    """Renders file size downloaded and total, e.g. '0.5/2.3 GB'.
-
-    Args:
-        binary_units (bool, optional): Use binary units, KiB, MiB etc. Defaults to False.
-    """
-
-    def __init__(
-        self, binary_units: bool = False, table_column: Optional[Column] = None
-    ) -> None:
-        self.binary_units = binary_units
-        super().__init__(table_column=table_column)
-
-    def render(self, task: "Task") -> Text:
-        """Calculate common unit for completed and total."""
-        completed = int(task.completed)
-
-        unit_and_suffix_calculation_base = (
-            int(task.total) if task.total is not None else completed
-        )
-        if self.binary_units:
-            unit, suffix = filesize.pick_unit_and_suffix(
-                unit_and_suffix_calculation_base,
-                ["bytes", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB", "ZiB", "YiB"],
-                1024,
-            )
-        else:
-            unit, suffix = filesize.pick_unit_and_suffix(
-                unit_and_suffix_calculation_base,
-                ["bytes", "kB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"],
-                1000,
-            )
-        precision = 0 if unit == 1 else 1
-
-        completed_ratio = completed / unit
-        completed_str = f"{completed_ratio:,.{precision}f}"
-
-        if task.total is not None:
-            total = int(task.total)
-            total_ratio = total / unit
-            total_str = f"{total_ratio:,.{precision}f}"
-        else:
-            total_str = "?"
-
-        download_status = f"{completed_str}/{total_str} {suffix}"
-        download_text = Text(download_status, style="progress.download")
-        return download_text
-
-
-class TransferSpeedColumn(ProgressColumn):
-    """Renders human readable transfer speed."""
-
-    def render(self, task: "Task") -> Text:
-        """Show data transfer speed."""
-        speed = task.finished_speed or task.speed
-        if speed is None:
-            return Text("?", style="progress.data.speed")
-        data_speed = filesize.decimal(int(speed))
-        return Text(f"{data_speed}/s", style="progress.data.speed")
-
-
-class ProgressSample(NamedTuple):
-    """Sample of progress for a given time."""
-
-    timestamp: float
-    """Timestamp of sample."""
-    completed: float
-    """Number of steps completed."""
-
-
-@dataclass
-class Task:
-    """Information regarding a progress task.
-
-    This object should be considered read-only outside of the :class:`~Progress` class.
-
-    """
-
-    id: TaskID
-    """Task ID associated with this task (used in Progress methods)."""
-
-    description: str
-    """str: Description of the task."""
-
-    total: Optional[float]
-    """Optional[float]: Total number of steps in this task."""
-
-    completed: float
-    """float: Number of steps completed"""
-
-    _get_time: GetTimeCallable
-    """Callable to get the current time."""
-
-    finished_time: Optional[float] = None
-    """float: Time task was finished."""
-
-    visible: bool = True
-    """bool: Indicates if this task is visible in the progress display."""
-
-    fields: Dict[str, Any] = field(default_factory=dict)
-    """dict: Arbitrary fields passed in via Progress.update."""
-
-    start_time: Optional[float] = field(default=None, init=False, repr=False)
-    """Optional[float]: Time this task was started, or None if not started."""
-
-    stop_time: Optional[float] = field(default=None, init=False, repr=False)
-    """Optional[float]: Time this task was stopped, or None if not stopped."""
-
-    finished_speed: Optional[float] = None
-    """Optional[float]: The last speed for a finished task."""
-
-    _progress: Deque[ProgressSample] = field(
-        default_factory=lambda: deque(maxlen=1000), init=False, repr=False
-    )
-
-    _lock: RLock = field(repr=False, default_factory=RLock)
-    """Thread lock."""
-
-    def get_time(self) -> float:
-        """float: Get the current time, in seconds."""
-        return self._get_time()
-
-    @property
-    def started(self) -> bool:
-        """bool: Check if the task as started."""
-        return self.start_time is not None
-
-    @property
-    def remaining(self) -> Optional[float]:
-        """Optional[float]: Get the number of steps remaining, if a non-None total was set."""
-        if self.total is None:
-            return None
-        return self.total - self.completed
-
-    @property
-    def elapsed(self) -> Optional[float]:
-        """Optional[float]: Time elapsed since task was started, or ``None`` if the task hasn't started."""
-        if self.start_time is None:
-            return None
-        if self.stop_time is not None:
-            return self.stop_time - self.start_time
-        return self.get_time() - self.start_time
-
-    @property
-    def finished(self) -> bool:
-        """Check if the task has finished."""
-        return self.finished_time is not None
-
-    @property
-    def percentage(self) -> float:
-        """float: Get progress of task as a percentage. If a None total was set, returns 0"""
-        if not self.total:
-            return 0.0
-        completed = (self.completed / self.total) * 100.0
-        completed = min(100.0, max(0.0, completed))
-        return completed
-
-    @property
-    def speed(self) -> Optional[float]:
-        """Optional[float]: Get the estimated speed in steps per second."""
-        if self.start_time is None:
-            return None
-        with self._lock:
-            progress = self._progress
-            if not progress:
-                return None
-            total_time = progress[-1].timestamp - progress[0].timestamp
-            if total_time == 0:
-                return None
-            iter_progress = iter(progress)
-            next(iter_progress)
-            total_completed = sum(sample.completed for sample in iter_progress)
-            speed = total_completed / total_time
-            return speed
-
-    @property
-    def time_remaining(self) -> Optional[float]:
-        """Optional[float]: Get estimated time to completion, or ``None`` if no data."""
-        if self.finished:
-            return 0.0
-        speed = self.speed
-        if not speed:
-            return None
-        remaining = self.remaining
-        if remaining is None:
-            return None
-        estimate = ceil(remaining / speed)
-        return estimate
-
-    def _reset(self) -> None:
-        """Reset progress."""
-        self._progress.clear()
-        self.finished_time = None
-        self.finished_speed = None
-
-
-class Progress(JupyterMixin):
-    """Renders an auto-updating progress bar(s).
-
-    Args:
-        console (Console, optional): Optional Console instance. Default will an internal Console instance writing to stdout.
-        auto_refresh (bool, optional): Enable auto refresh. If disabled, you will need to call `refresh()`.
-        refresh_per_second (Optional[float], optional): Number of times per second to refresh the progress information or None to use default (10). Defaults to None.
-        speed_estimate_period: (float, optional): Period (in seconds) used to calculate the speed estimate. Defaults to 30.
-        transient: (bool, optional): Clear the progress on exit. Defaults to False.
-        redirect_stdout: (bool, optional): Enable redirection of stdout, so ``print`` may be used. Defaults to True.
-        redirect_stderr: (bool, optional): Enable redirection of stderr. Defaults to True.
-        get_time: (Callable, optional): A callable that gets the current time, or None to use Console.get_time. Defaults to None.
-        disable (bool, optional): Disable progress display. Defaults to False
-        expand (bool, optional): Expand tasks table to fit width. Defaults to False.
-    """
-
-    def __init__(
-        self,
-        *columns: Union[str, ProgressColumn],
-        console: Optional[Console] = None,
-        auto_refresh: bool = True,
-        refresh_per_second: float = 10,
-        speed_estimate_period: float = 30.0,
-        transient: bool = False,
-        redirect_stdout: bool = True,
-        redirect_stderr: bool = True,
-        get_time: Optional[GetTimeCallable] = None,
-        disable: bool = False,
-        expand: bool = False,
-    ) -> None:
-        assert refresh_per_second > 0, "refresh_per_second must be > 0"
-        self._lock = RLock()
-        self.columns = columns or self.get_default_columns()
-        self.speed_estimate_period = speed_estimate_period
-
-        self.disable = disable
-        self.expand = expand
-        self._tasks: Dict[TaskID, Task] = {}
-        self._task_index: TaskID = TaskID(0)
-        self.live = Live(
-            console=console or get_console(),
-            auto_refresh=auto_refresh,
-            refresh_per_second=refresh_per_second,
-            transient=transient,
-            redirect_stdout=redirect_stdout,
-            redirect_stderr=redirect_stderr,
-            get_renderable=self.get_renderable,
-        )
-        self.get_time = get_time or self.console.get_time
-        self.print = self.console.print
-        self.log = self.console.log
-
-    @classmethod
-    def get_default_columns(cls) -> Tuple[ProgressColumn, ...]:
-        """Get the default columns used for a new Progress instance:
-           - a text column for the description (TextColumn)
-           - the bar itself (BarColumn)
-           - a text column showing completion percentage (TextColumn)
-           - an estimated-time-remaining column (TimeRemainingColumn)
-        If the Progress instance is created without passing a columns argument,
-        the default columns defined here will be used.
-
-        You can also create a Progress instance using custom columns before
-        and/or after the defaults, as in this example:
-
-            progress = Progress(
-                SpinnerColumn(),
-                *Progress.default_columns(),
-                "Elapsed:",
-                TimeElapsedColumn(),
-            )
-
-        This code shows the creation of a Progress display, containing
-        a spinner to the left, the default columns, and a labeled elapsed
-        time column.
-        """
-        return (
-            TextColumn("[progress.description]{task.description}"),
-            BarColumn(),
-            TaskProgressColumn(),
-            TimeRemainingColumn(),
-        )
-
-    @property
-    def console(self) -> Console:
-        return self.live.console
-
-    @property
-    def tasks(self) -> List[Task]:
-        """Get a list of Task instances."""
-        with self._lock:
-            return list(self._tasks.values())
-
-    @property
-    def task_ids(self) -> List[TaskID]:
-        """A list of task IDs."""
-        with self._lock:
-            return list(self._tasks.keys())
-
-    @property
-    def finished(self) -> bool:
-        """Check if all tasks have been completed."""
-        with self._lock:
-            if not self._tasks:
-                return True
-            return all(task.finished for task in self._tasks.values())
-
-    def start(self) -> None:
-        """Start the progress display."""
-        if not self.disable:
-            self.live.start(refresh=True)
-
-    def stop(self) -> None:
-        """Stop the progress display."""
-        self.live.stop()
-        if not self.console.is_interactive:
-            self.console.print()
-
-    def __enter__(self) -> "Progress":
-        self.start()
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.stop()
-
-    def track(
-        self,
-        sequence: Union[Iterable[ProgressType], Sequence[ProgressType]],
-        total: Optional[float] = None,
-        task_id: Optional[TaskID] = None,
-        description: str = "Working...",
-        update_period: float = 0.1,
-    ) -> Iterable[ProgressType]:
-        """Track progress by iterating over a sequence.
-
-        Args:
-            sequence (Sequence[ProgressType]): A sequence of values you want to iterate over and track progress.
-            total: (float, optional): Total number of steps. Default is len(sequence).
-            task_id: (TaskID): Task to track. Default is new task.
-            description: (str, optional): Description of task, if new task is created.
-            update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1.
-
-        Returns:
-            Iterable[ProgressType]: An iterable of values taken from the provided sequence.
-        """
-        if total is None:
-            total = float(length_hint(sequence)) or None
-
-        if task_id is None:
-            task_id = self.add_task(description, total=total)
-        else:
-            self.update(task_id, total=total)
-
-        if self.live.auto_refresh:
-            with _TrackThread(self, task_id, update_period) as track_thread:
-                for value in sequence:
-                    yield value
-                    track_thread.completed += 1
-        else:
-            advance = self.advance
-            refresh = self.refresh
-            for value in sequence:
-                yield value
-                advance(task_id, 1)
-                refresh()
-
-    def wrap_file(
-        self,
-        file: BinaryIO,
-        total: Optional[int] = None,
-        *,
-        task_id: Optional[TaskID] = None,
-        description: str = "Reading...",
-    ) -> BinaryIO:
-        """Track progress file reading from a binary file.
-
-        Args:
-            file (BinaryIO): A file-like object opened in binary mode.
-            total (int, optional): Total number of bytes to read. This must be provided unless a task with a total is also given.
-            task_id (TaskID): Task to track. Default is new task.
-            description (str, optional): Description of task, if new task is created.
-
-        Returns:
-            BinaryIO: A readable file-like object in binary mode.
-
-        Raises:
-            ValueError: When no total value can be extracted from the arguments or the task.
-        """
-        # attempt to recover the total from the task
-        total_bytes: Optional[float] = None
-        if total is not None:
-            total_bytes = total
-        elif task_id is not None:
-            with self._lock:
-                total_bytes = self._tasks[task_id].total
-        if total_bytes is None:
-            raise ValueError(
-                f"unable to get the total number of bytes, please specify 'total'"
-            )
-
-        # update total of task or create new task
-        if task_id is None:
-            task_id = self.add_task(description, total=total_bytes)
-        else:
-            self.update(task_id, total=total_bytes)
-
-        return _Reader(file, self, task_id, close_handle=False)
-
-    @typing.overload
-    def open(
-        self,
-        file: Union[str, "PathLike[str]", bytes],
-        mode: Literal["rb"],
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-        *,
-        total: Optional[int] = None,
-        task_id: Optional[TaskID] = None,
-        description: str = "Reading...",
-    ) -> BinaryIO:
-        pass
-
-    @typing.overload
-    def open(
-        self,
-        file: Union[str, "PathLike[str]", bytes],
-        mode: Union[Literal["r"], Literal["rt"]],
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-        *,
-        total: Optional[int] = None,
-        task_id: Optional[TaskID] = None,
-        description: str = "Reading...",
-    ) -> TextIO:
-        pass
-
-    def open(
-        self,
-        file: Union[str, "PathLike[str]", bytes],
-        mode: Union[Literal["rb"], Literal["rt"], Literal["r"]] = "r",
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-        *,
-        total: Optional[int] = None,
-        task_id: Optional[TaskID] = None,
-        description: str = "Reading...",
-    ) -> Union[BinaryIO, TextIO]:
-        """Track progress while reading from a binary file.
-
-        Args:
-            path (Union[str, PathLike[str]]): The path to the file to read.
-            mode (str): The mode to use to open the file. Only supports "r", "rb" or "rt".
-            buffering (int): The buffering strategy to use, see :func:`io.open`.
-            encoding (str, optional): The encoding to use when reading in text mode, see :func:`io.open`.
-            errors (str, optional): The error handling strategy for decoding errors, see :func:`io.open`.
-            newline (str, optional): The strategy for handling newlines in text mode, see :func:`io.open`.
-            total (int, optional): Total number of bytes to read. If none given, os.stat(path).st_size is used.
-            task_id (TaskID): Task to track. Default is new task.
-            description (str, optional): Description of task, if new task is created.
-
-        Returns:
-            BinaryIO: A readable file-like object in binary mode.
-
-        Raises:
-            ValueError: When an invalid mode is given.
-        """
-        # normalize the mode (always rb, rt)
-        _mode = "".join(sorted(mode, reverse=False))
-        if _mode not in ("br", "rt", "r"):
-            raise ValueError("invalid mode {!r}".format(mode))
-
-        # patch buffering to provide the same behaviour as the builtin `open`
-        line_buffering = buffering == 1
-        if _mode == "br" and buffering == 1:
-            warnings.warn(
-                "line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used",
-                RuntimeWarning,
-            )
-            buffering = -1
-        elif _mode in ("rt", "r"):
-            if buffering == 0:
-                raise ValueError("can't have unbuffered text I/O")
-            elif buffering == 1:
-                buffering = -1
-
-        # attempt to get the total with `os.stat`
-        if total is None:
-            total = stat(file).st_size
-
-        # update total of task or create new task
-        if task_id is None:
-            task_id = self.add_task(description, total=total)
-        else:
-            self.update(task_id, total=total)
-
-        # open the file in binary mode,
-        handle = io.open(file, "rb", buffering=buffering)
-        reader = _Reader(handle, self, task_id, close_handle=True)
-
-        # wrap the reader in a `TextIOWrapper` if text mode
-        if mode in ("r", "rt"):
-            return io.TextIOWrapper(
-                reader,
-                encoding=encoding,
-                errors=errors,
-                newline=newline,
-                line_buffering=line_buffering,
-            )
-
-        return reader
-
-    def start_task(self, task_id: TaskID) -> None:
-        """Start a task.
-
-        Starts a task (used when calculating elapsed time). You may need to call this manually,
-        if you called ``add_task`` with ``start=False``.
-
-        Args:
-            task_id (TaskID): ID of task.
-        """
-        with self._lock:
-            task = self._tasks[task_id]
-            if task.start_time is None:
-                task.start_time = self.get_time()
-
-    def stop_task(self, task_id: TaskID) -> None:
-        """Stop a task.
-
-        This will freeze the elapsed time on the task.
-
-        Args:
-            task_id (TaskID): ID of task.
-        """
-        with self._lock:
-            task = self._tasks[task_id]
-            current_time = self.get_time()
-            if task.start_time is None:
-                task.start_time = current_time
-            task.stop_time = current_time
-
-    def update(
-        self,
-        task_id: TaskID,
-        *,
-        total: Optional[float] = None,
-        completed: Optional[float] = None,
-        advance: Optional[float] = None,
-        description: Optional[str] = None,
-        visible: Optional[bool] = None,
-        refresh: bool = False,
-        **fields: Any,
-    ) -> None:
-        """Update information associated with a task.
-
-        Args:
-            task_id (TaskID): Task id (returned by add_task).
-            total (float, optional): Updates task.total if not None.
-            completed (float, optional): Updates task.completed if not None.
-            advance (float, optional): Add a value to task.completed if not None.
-            description (str, optional): Change task description if not None.
-            visible (bool, optional): Set visible flag if not None.
-            refresh (bool): Force a refresh of progress information. Default is False.
-            **fields (Any): Additional data fields required for rendering.
-        """
-        with self._lock:
-            task = self._tasks[task_id]
-            completed_start = task.completed
-
-            if total is not None and total != task.total:
-                task.total = total
-                task._reset()
-            if advance is not None:
-                task.completed += advance
-            if completed is not None:
-                task.completed = completed
-            if description is not None:
-                task.description = description
-            if visible is not None:
-                task.visible = visible
-            task.fields.update(fields)
-            update_completed = task.completed - completed_start
-
-            current_time = self.get_time()
-            old_sample_time = current_time - self.speed_estimate_period
-            _progress = task._progress
-
-            popleft = _progress.popleft
-            while _progress and _progress[0].timestamp < old_sample_time:
-                popleft()
-            if update_completed > 0:
-                _progress.append(ProgressSample(current_time, update_completed))
-            if (
-                task.total is not None
-                and task.completed >= task.total
-                and task.finished_time is None
-            ):
-                task.finished_time = task.elapsed
-
-        if refresh:
-            self.refresh()
-
-    def reset(
-        self,
-        task_id: TaskID,
-        *,
-        start: bool = True,
-        total: Optional[float] = None,
-        completed: int = 0,
-        visible: Optional[bool] = None,
-        description: Optional[str] = None,
-        **fields: Any,
-    ) -> None:
-        """Reset a task so completed is 0 and the clock is reset.
-
-        Args:
-            task_id (TaskID): ID of task.
-            start (bool, optional): Start the task after reset. Defaults to True.
-            total (float, optional): New total steps in task, or None to use current total. Defaults to None.
-            completed (int, optional): Number of steps completed. Defaults to 0.
-            visible (bool, optional): Enable display of the task. Defaults to True.
-            description (str, optional): Change task description if not None. Defaults to None.
-            **fields (str): Additional data fields required for rendering.
-        """
-        current_time = self.get_time()
-        with self._lock:
-            task = self._tasks[task_id]
-            task._reset()
-            task.start_time = current_time if start else None
-            if total is not None:
-                task.total = total
-            task.completed = completed
-            if visible is not None:
-                task.visible = visible
-            if fields:
-                task.fields = fields
-            if description is not None:
-                task.description = description
-            task.finished_time = None
-        self.refresh()
-
-    def advance(self, task_id: TaskID, advance: float = 1) -> None:
-        """Advance task by a number of steps.
-
-        Args:
-            task_id (TaskID): ID of task.
-            advance (float): Number of steps to advance. Default is 1.
-        """
-        current_time = self.get_time()
-        with self._lock:
-            task = self._tasks[task_id]
-            completed_start = task.completed
-            task.completed += advance
-            update_completed = task.completed - completed_start
-            old_sample_time = current_time - self.speed_estimate_period
-            _progress = task._progress
-
-            popleft = _progress.popleft
-            while _progress and _progress[0].timestamp < old_sample_time:
-                popleft()
-            while len(_progress) > 1000:
-                popleft()
-            _progress.append(ProgressSample(current_time, update_completed))
-            if (
-                task.total is not None
-                and task.completed >= task.total
-                and task.finished_time is None
-            ):
-                task.finished_time = task.elapsed
-                task.finished_speed = task.speed
-
-    def refresh(self) -> None:
-        """Refresh (render) the progress information."""
-        if not self.disable and self.live.is_started:
-            self.live.refresh()
-
-    def get_renderable(self) -> RenderableType:
-        """Get a renderable for the progress display."""
-        renderable = Group(*self.get_renderables())
-        return renderable
-
-    def get_renderables(self) -> Iterable[RenderableType]:
-        """Get a number of renderables for the progress display."""
-        table = self.make_tasks_table(self.tasks)
-        yield table
-
-    def make_tasks_table(self, tasks: Iterable[Task]) -> Table:
-        """Get a table to render the Progress display.
-
-        Args:
-            tasks (Iterable[Task]): An iterable of Task instances, one per row of the table.
-
-        Returns:
-            Table: A table instance.
-        """
-        table_columns = (
-            (
-                Column(no_wrap=True)
-                if isinstance(_column, str)
-                else _column.get_table_column().copy()
-            )
-            for _column in self.columns
-        )
-        table = Table.grid(*table_columns, padding=(0, 1), expand=self.expand)
-
-        for task in tasks:
-            if task.visible:
-                table.add_row(
-                    *(
-                        (
-                            column.format(task=task)
-                            if isinstance(column, str)
-                            else column(task)
-                        )
-                        for column in self.columns
-                    )
-                )
-        return table
-
-    def __rich__(self) -> RenderableType:
-        """Makes the Progress class itself renderable."""
-        with self._lock:
-            return self.get_renderable()
-
-    def add_task(
-        self,
-        description: str,
-        start: bool = True,
-        total: Optional[float] = 100.0,
-        completed: int = 0,
-        visible: bool = True,
-        **fields: Any,
-    ) -> TaskID:
-        """Add a new 'task' to the Progress display.
-
-        Args:
-            description (str): A description of the task.
-            start (bool, optional): Start the task immediately (to calculate elapsed time). If set to False,
-                you will need to call `start` manually. Defaults to True.
-            total (float, optional): Number of total steps in the progress if known.
-                Set to None to render a pulsing animation. Defaults to 100.
-            completed (int, optional): Number of steps completed so far. Defaults to 0.
-            visible (bool, optional): Enable display of the task. Defaults to True.
-            **fields (str): Additional data fields required for rendering.
-
-        Returns:
-            TaskID: An ID you can use when calling `update`.
-        """
-        with self._lock:
-            task = Task(
-                self._task_index,
-                description,
-                total,
-                completed,
-                visible=visible,
-                fields=fields,
-                _get_time=self.get_time,
-                _lock=self._lock,
-            )
-            self._tasks[self._task_index] = task
-            if start:
-                self.start_task(self._task_index)
-            new_task_index = self._task_index
-            self._task_index = TaskID(int(self._task_index) + 1)
-        self.refresh()
-        return new_task_index
-
-    def remove_task(self, task_id: TaskID) -> None:
-        """Delete a task if it exists.
-
-        Args:
-            task_id (TaskID): A task ID.
-
-        """
-        with self._lock:
-            del self._tasks[task_id]
-
-
-if __name__ == "__main__":  # pragma: no coverage
-    import random
-    import time
-
-    from .panel import Panel
-    from .rule import Rule
-    from .syntax import Syntax
-    from .table import Table
-
-    syntax = Syntax(
-        '''def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]:
-    """Iterate and generate a tuple with a flag for last value."""
-    iter_values = iter(values)
-    try:
-        previous_value = next(iter_values)
-    except StopIteration:
-        return
-    for value in iter_values:
-        yield False, previous_value
-        previous_value = value
-    yield True, previous_value''',
-        "python",
-        line_numbers=True,
-    )
-
-    table = Table("foo", "bar", "baz")
-    table.add_row("1", "2", "3")
-
-    progress_renderables = [
-        "Text may be printed while the progress bars are rendering.",
-        Panel("In fact, [i]any[/i] renderable will work"),
-        "Such as [magenta]tables[/]...",
-        table,
-        "Pretty printed structures...",
-        {"type": "example", "text": "Pretty printed"},
-        "Syntax...",
-        syntax,
-        Rule("Give it a try!"),
-    ]
-
-    from itertools import cycle
-
-    examples = cycle(progress_renderables)
-
-    console = Console(record=True)
-
-    with Progress(
-        SpinnerColumn(),
-        *Progress.get_default_columns(),
-        TimeElapsedColumn(),
-        console=console,
-        transient=False,
-    ) as progress:
-        task1 = progress.add_task("[red]Downloading", total=1000)
-        task2 = progress.add_task("[green]Processing", total=1000)
-        task3 = progress.add_task("[yellow]Thinking", total=None)
-
-        while not progress.finished:
-            progress.update(task1, advance=0.5)
-            progress.update(task2, advance=0.3)
-            time.sleep(0.01)
-            if random.randint(0, 100) < 1:
-                progress.log(next(examples))
diff --git a/spaces/propilot/propilot-calling-functions/README.md b/spaces/propilot/propilot-calling-functions/README.md
deleted file mode 100644
index 0c750655e2c18bb0058f50c588d254de1e42a97c..0000000000000000000000000000000000000000
--- a/spaces/propilot/propilot-calling-functions/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Propilot Calling Functions
-emoji: 👁
-colorFrom: blue
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/qianwj/yehiaserag-anime-pencil-diffusion/README.md b/spaces/qianwj/yehiaserag-anime-pencil-diffusion/README.md
deleted file mode 100644
index 40dc2ec8c551fd9fd22c2f92bfa8e4cff6832ad7..0000000000000000000000000000000000000000
--- a/spaces/qianwj/yehiaserag-anime-pencil-diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Yehiaserag Anime Pencil Diffusion
-emoji: 🏢
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/qinzhu/moe-tts-tech/mel_processing.py b/spaces/qinzhu/moe-tts-tech/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/qinzhu/moe-tts-tech/mel_processing.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    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/quidiaMuxgu/Expedit-SAM/Autodesk AutoCAD 2017 HF3 X86-x64 RUS-ENG By M0nkrus- TEAM OS - Free Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Autodesk AutoCAD 2017 HF3 X86-x64 RUS-ENG By M0nkrus- TEAM OS - Free Download.md
deleted file mode 100644
index c343f81a32145580500be07ce9a3b5851d5b0deb..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Autodesk AutoCAD 2017 HF3 X86-x64 RUS-ENG By M0nkrus- TEAM OS - Free Download.md	
+++ /dev/null
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Cakewalk Sonar X1 Producer Keygen Music REPACK.md b/spaces/quidiaMuxgu/Expedit-SAM/Cakewalk Sonar X1 Producer Keygen Music REPACK.md
deleted file mode 100644
index 57013aa17d732ecff4e857031ad426027debaa78..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Cakewalk Sonar X1 Producer Keygen Music REPACK.md	
+++ /dev/null
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Iddaa Oran Sikesi Program Indir.md b/spaces/quidiaMuxgu/Expedit-SAM/Iddaa Oran Sikesi Program Indir.md
deleted file mode 100644
index 90700380d9deb635010d25d9ccc87e47b0d21fd7..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Iddaa Oran Sikesi Program Indir.md	
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Mspy Cracked Apk Full Downloads ((TOP)).md b/spaces/quidiaMuxgu/Expedit-SAM/Mspy Cracked Apk Full Downloads ((TOP)).md
deleted file mode 100644
index 4fb1d5aa8c1852c6545cf365c6ab3f83ae536cae..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Mspy Cracked Apk Full Downloads ((TOP)).md	
+++ /dev/null
@@ -1,97 +0,0 @@
-<br />
-<h1>MSpy Cracked Apk Full Downloads: A Complete Guide</h1>
-<p>If you are looking for a way to monitor someone's phone activity without them knowing, you may have heard of mSpy. mSpy is a popular phone monitoring app that allows you to track calls, messages, social media chats, location, and more. However, to use this app, you need to buy a subscription plan that can be quite expensive. But what if you don't want to spend money on mSpy? Is there a way to use this app for free? The answer is yes, thanks to mSpy cracked apk full downloads.</p>
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-<p>In this article, we will show you how to download, install, and use mSpy cracked apk full downloads with a mod file that unlocks all the premium features of the app. We will also explain the features and benefits of this app and how it can help you spy on any Android device. So, let's get started.</p>
-<h2>What is mSpy?</h2>
-<p>mSpy is a professional phone monitoring app that works on both Android and iOS devices. It has a simple and user-friendly interface that makes it easy to use. Some of the features of this app are:</p>
-<ul>
-<li>Call logs: You can view the incoming and outgoing calls on the target device, along with the contact name, number, date, and duration.</li>
-<li>Messages: You can read the sent and received text messages and iMessages on the target device, along with the contact name, number, date, and time.</li>
-<li>Social media chats: You can monitor the conversations on various social media apps such as WhatsApp, Facebook Messenger, Instagram, Snapchat, Telegram, etc.</li>
-<li>Location: You can track the real-time GPS location of the target device and view the location history on a map.</li>
-<li>Media files: You can access the photos and videos stored on the target device and download them to your own device.</li>
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-</ul>
-<h2>What is mSpy cracked apk full downloads?</h2>
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-<ol>
-<li>First, you need to disable your antivirus program temporarily on your device as it may interfere with the installation process or delete the mod file.</li>
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-</ol>
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-<ol>
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-<li>After that, log in to your mSpy account on your computer or any other device and access your dashboard. There you can see all the data from your target device such as calls, messages, location, etc.</li>
-<li>You can also customize various settings of mSpy such as language, theme, sound, notifications, etc.</li>
-</ol>
-
-<h2>Conclusion</h2>
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-<p>We hope this article has helped you understand what is mSpy cracked apk full downloads and how to use it. If you have any questions or suggestions regarding this topic, feel free to leave a comment below. Thank you for reading.</p>
-<p></p>
-<h2>What are the Benefits of mSpy cracked apk full downloads?</h2>
-<p>mSpy cracked apk full downloads is a beneficial tool for many reasons. Some of the benefits are:</p>
-<ul>
-<li>It can help you spy on any Android device without spending money on mSpy subscription plan. You can enjoy all the premium features of mSpy for free.</li>
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-<li>It can help you access the data from your target device remotely from any device. You can view calls, messages, location, media files, browser history, keylogger, app blocker, remote control, and more.</li>
-<li>It can help you customize various settings of mSpy according to your preferences. You can choose the language, theme, sound, notifications, etc.</li>
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-<li>It may violate the terms and conditions of mSpy and its developers. You should respect their work and support them by buying their subscription plan if you can afford it.</li>
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-<h2>Conclusion</h2>
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\ No newline at end of file
diff --git a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/webUI.py b/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/webUI.py
deleted file mode 100644
index e1a27c55289eac57644e079554602f235444811c..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/webUI.py
+++ /dev/null
@@ -1,285 +0,0 @@
-import os
-import json
-
-import librosa
-import soundfile
-import numpy as np
-
-import gradio as gr
-from UVR_interface import root, UVRInterface, VR_MODELS_DIR, MDX_MODELS_DIR, DEMUCS_MODELS_DIR
-from gui_data.constants import *
-from typing import List, Dict, Callable, Union
-
-
-class UVRWebUI:
-    def __init__(self, uvr: UVRInterface, online_data_path: str) -> None:
-        self.uvr = uvr
-        self.models_url = self.get_models_url(online_data_path)
-        self.define_layout()
-
-        self.input_temp_dir = "__temp"
-        self.export_path = "out"
-        if not os.path.exists(self.input_temp_dir):
-            os.mkdir(self.input_temp_dir)
-
-    def get_models_url(self, models_info_path: str) -> Dict[str, Dict]:
-        with open(models_info_path, "r") as f:
-            online_data = json.loads(f.read())
-        models_url = {}
-        for arch, download_list_key in zip([VR_ARCH_TYPE, MDX_ARCH_TYPE], ["vr_download_list", "mdx_download_list"]):
-            models_url[arch] = {model: NORMAL_REPO+model_path for model, model_path in online_data[download_list_key].items()}
-        models_url[DEMUCS_ARCH_TYPE] = online_data["demucs_download_list"]
-        return models_url
-
-    def get_local_models(self, arch: str) -> List[str]:
-        model_config = {
-            VR_ARCH_TYPE: (VR_MODELS_DIR, ".pth"),
-            MDX_ARCH_TYPE: (MDX_MODELS_DIR, ".onnx"),
-            DEMUCS_ARCH_TYPE: (DEMUCS_MODELS_DIR, ".yaml"),
-        }
-        try:
-            model_dir, suffix = model_config[arch]
-        except KeyError:
-            raise ValueError(f"Unkown arch type: {arch}")
-        return [os.path.splitext(f)[0] for f in os.listdir(model_dir) if f.endswith(suffix)]
-
-    def set_arch_setting_value(self, arch: str, setting1, setting2):
-        if arch == VR_ARCH_TYPE:
-            root.window_size_var.set(setting1)
-            root.aggression_setting_var.set(setting2)
-        elif arch == MDX_ARCH_TYPE:
-            root.mdx_batch_size_var.set(setting1)
-            root.compensate_var.set(setting2)
-        elif arch == DEMUCS_ARCH_TYPE:
-            pass
-
-    def arch_select_update(self, arch: str) -> List[Dict]:
-        choices = self.get_local_models(arch)
-        if arch == VR_ARCH_TYPE:
-            model_update = self.model_choice.update(choices=choices, value=CHOOSE_MODEL, label=SELECT_VR_MODEL_MAIN_LABEL)
-            setting1_update = self.arch_setting1.update(choices=VR_WINDOW, label=WINDOW_SIZE_MAIN_LABEL, value=root.window_size_var.get())
-            setting2_update = self.arch_setting2.update(choices=VR_AGGRESSION, label=AGGRESSION_SETTING_MAIN_LABEL, value=root.aggression_setting_var.get())
-        elif arch == MDX_ARCH_TYPE:
-            model_update = self.model_choice.update(choices=choices, value=CHOOSE_MODEL, label=CHOOSE_MDX_MODEL_MAIN_LABEL)
-            setting1_update = self.arch_setting1.update(choices=BATCH_SIZE, label=BATCHES_MDX_MAIN_LABEL, value=root.mdx_batch_size_var.get())
-            setting2_update = self.arch_setting2.update(choices=VOL_COMPENSATION, label=VOL_COMP_MDX_MAIN_LABEL, value=root.compensate_var.get())
-        elif arch == DEMUCS_ARCH_TYPE:
-            model_update = self.model_choice.update(choices=choices, value=CHOOSE_MODEL, label=CHOOSE_DEMUCS_MODEL_MAIN_LABEL)
-            raise gr.Error(f"{DEMUCS_ARCH_TYPE} not implempted")
-        else:
-            raise gr.Error(f"Unkown arch type: {arch}")
-        return [model_update, setting1_update, setting2_update]
-
-    def model_select_update(self, arch: str, model_name: str) -> List[Union[str, Dict, None]]:
-        if model_name == CHOOSE_MODEL:
-            return [None for _ in range(4)]
-        model, = self.uvr.assemble_model_data(model_name, arch)
-        if not model.model_status:
-            raise gr.Error(f"Cannot get model data, model hash = {model.model_hash}")
-
-        stem1_check_update = self.primary_stem_only.update(label=f"{model.primary_stem} Only")
-        stem2_check_update = self.secondary_stem_only.update(label=f"{model.secondary_stem} Only")
-        stem1_out_update = self.primary_stem_out.update(label=f"Output {model.primary_stem}")
-        stem2_out_update = self.secondary_stem_out.update(label=f"Output {model.secondary_stem}")
-
-        return [stem1_check_update, stem2_check_update, stem1_out_update, stem2_out_update]
-
-    def checkbox_set_root_value(self, checkbox: gr.Checkbox, root_attr: str):
-        checkbox.change(lambda value: root.__getattribute__(root_attr).set(value), inputs=checkbox)
-
-    def set_checkboxes_exclusive(self, checkboxes: List[gr.Checkbox], pure_callbacks: List[Callable], exclusive_value=True):
-        def exclusive_onchange(i, callback_i):
-            def new_onchange(*check_values):
-                if check_values[i] == exclusive_value:
-                    return_values = []
-                    for j, value_j in enumerate(check_values):
-                        if j != i and value_j == exclusive_value:
-                            return_values.append(not exclusive_value)
-                        else:
-                            return_values.append(value_j)
-                else:
-                    return_values = check_values
-                callback_i(check_values[i])
-                return return_values
-            return new_onchange
-
-        for i, (checkbox, callback) in enumerate(zip(checkboxes, pure_callbacks)):
-            checkbox.change(exclusive_onchange(i, callback), inputs=checkboxes, outputs=checkboxes)
-
-    def process(self, input_audio, input_filename, model_name, arch, setting1, setting2, progress=gr.Progress()):
-        def set_progress_func(step, inference_iterations=0):
-            progress_curr = step + inference_iterations
-            progress(progress_curr)
-
-        sampling_rate, audio = input_audio
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        input_path = os.path.join(self.input_temp_dir, input_filename)
-        soundfile.write(input_path, audio, sampling_rate, format="wav")
-
-        self.set_arch_setting_value(arch, setting1, setting2)
-
-        seperator = uvr.process(
-            model_name=model_name,
-            arch_type=arch,
-            audio_file=input_path,
-            export_path=self.export_path,
-            is_model_sample_mode=root.model_sample_mode_var.get(),
-            set_progress_func=set_progress_func,
-        )
-
-        primary_audio = None
-        secondary_audio = None
-        msg = ""
-        if not seperator.is_secondary_stem_only:
-            primary_stem_path = os.path.join(seperator.export_path, f"{seperator.audio_file_base}_({seperator.primary_stem}).wav")
-            audio, rate = soundfile.read(primary_stem_path)
-            primary_audio = (rate, audio)
-            msg += f"{seperator.primary_stem} saved at {primary_stem_path}\n"
-        if not seperator.is_primary_stem_only:
-            secondary_stem_path = os.path.join(seperator.export_path, f"{seperator.audio_file_base}_({seperator.secondary_stem}).wav")
-            audio, rate = soundfile.read(secondary_stem_path)
-            secondary_audio = (rate, audio)
-            msg += f"{seperator.secondary_stem} saved at {secondary_stem_path}\n"
-
-        os.remove(input_path)
-
-        return primary_audio, secondary_audio, msg
-
-    def define_layout(self):
-        with gr.Blocks() as app:
-            self.app = app
-            gr.HTML("<h1> 🎵 Ultimate Vocal Remover WebUI 🎵 </h1>")
-            gr.Markdown("This is an experimental demo with CPU. Duplicate the space for use in private")
-            gr.Markdown(
-                "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/r3gm/Ultimate-Vocal-Remover-WebUI?duplicate=true)\n\n"
-            ) 
-            with gr.Tabs():
-                with gr.TabItem("process"):
-                    with gr.Row():
-                        self.arch_choice = gr.Dropdown(
-                            choices=[VR_ARCH_TYPE, MDX_ARCH_TYPE], value=VR_ARCH_TYPE, # choices=[VR_ARCH_TYPE, MDX_ARCH_TYPE, DEMUCS_ARCH_TYPE], value=VR_ARCH_TYPE,
-                            label=CHOOSE_PROC_METHOD_MAIN_LABEL, interactive=True)
-                        self.model_choice = gr.Dropdown(
-                            choices=self.get_local_models(VR_ARCH_TYPE), value=CHOOSE_MODEL,
-                            label=SELECT_VR_MODEL_MAIN_LABEL+' 👋Select a model', interactive=True)
-                    with gr.Row():
-                        self.arch_setting1 = gr.Dropdown(
-                            choices=VR_WINDOW, value=root.window_size_var.get(),
-                            label=WINDOW_SIZE_MAIN_LABEL+' 👋Select one', interactive=True)
-                        self.arch_setting2 = gr.Dropdown(
-                            choices=VR_AGGRESSION, value=root.aggression_setting_var.get(),
-                            label=AGGRESSION_SETTING_MAIN_LABEL, interactive=True)
-                    with gr.Row():
-                        self.use_gpu = gr.Checkbox(
-                            label='Rhythmic Transmutation Device', value=True, interactive=True) #label=GPU_CONVERSION_MAIN_LABEL, value=root.is_gpu_conversion_var.get(), interactive=True)
-                        self.primary_stem_only = gr.Checkbox(
-                            label=f"{PRIMARY_STEM} only", value=root.is_primary_stem_only_var.get(), interactive=True)
-                        self.secondary_stem_only = gr.Checkbox(
-                            label=f"{SECONDARY_STEM} only", value=root.is_secondary_stem_only_var.get(), interactive=True)
-                        self.sample_mode = gr.Checkbox(
-                            label=SAMPLE_MODE_CHECKBOX(root.model_sample_mode_duration_var.get()),
-                            value=root.model_sample_mode_var.get(), interactive=True)
-
-                    with gr.Row():
-                        self.input_filename = gr.Textbox(label="Input filename", value="temp.wav", interactive=True)
-                    with gr.Row():
-                        self.audio_in = gr.Audio(label="Input audio", interactive=True)
-                    with gr.Row():
-                        self.process_submit = gr.Button(START_PROCESSING, variant="primary")
-                    with gr.Row():
-                        self.primary_stem_out = gr.Audio(label=f"Output {PRIMARY_STEM}", interactive=False)
-                        self.secondary_stem_out = gr.Audio(label=f"Output {SECONDARY_STEM}", interactive=False)
-                    with gr.Row():
-                        self.out_message = gr.Textbox(label="Output Message", interactive=False, show_progress=False)
-
-                with gr.TabItem("settings"):
-                    with gr.Tabs():
-                        with gr.TabItem("Settings Guide"):
-                            pass
-                        with gr.TabItem("Additional Settigns"):
-                            self.wav_type = gr.Dropdown(choices=WAV_TYPE, label="Wav Type", value="PCM_16", interactive=True)
-                            self.mp3_rate = gr.Dropdown(choices=MP3_BIT_RATES, label="MP3 Bitrate", value="320k",interactive=True)
-                        with gr.TabItem("Download models"):
-
-                            def md_url(url, text=None):
-                                if text is None:
-                                    text = url
-                                return f"[{url}]({url})"
-
-                            with gr.Row():
-                                vr_models = self.models_url[VR_ARCH_TYPE]
-                                self.vr_download_choice = gr.Dropdown(choices=list(vr_models.keys()), label=f"Select {VR_ARCH_TYPE} Model", interactive=True)
-                                self.vr_download_url = gr.Markdown()
-                                self.vr_download_choice.change(lambda model: md_url(vr_models[model]), inputs=self.vr_download_choice, outputs=self.vr_download_url)
-                            with gr.Row(variant="panel"):
-                                mdx_models = self.models_url[MDX_ARCH_TYPE]
-                                self.mdx_download_choice = gr.Dropdown(choices=list(mdx_models.keys()), label=f"Select {MDX_ARCH_TYPE} Model", interactive=True)
-                                self.mdx_download_url = gr.Markdown()
-                                self.mdx_download_choice.change(lambda model: md_url(mdx_models[model]), inputs=self.mdx_download_choice, outputs=self.mdx_download_url)
-                            with gr.Row(variant="panel"):
-                                demucs_models: Dict[str, Dict] = self.models_url[DEMUCS_ARCH_TYPE]
-                                self.demucs_download_choice = gr.Dropdown(choices=list(demucs_models.keys()), label=f"Select {DEMUCS_ARCH_TYPE} Model", interactive=True)
-                                self.demucs_download_url = gr.Markdown()
-
-                                self.demucs_download_choice.change(
-                                    lambda model: "\n".join([
-                                        "- " + md_url(url, text=filename) for filename, url in demucs_models[model].items()]),
-                                    inputs=self.demucs_download_choice,
-                                    outputs=self.demucs_download_url)
-
-            self.arch_choice.change(
-                self.arch_select_update, inputs=self.arch_choice,
-                outputs=[self.model_choice, self.arch_setting1, self.arch_setting2])
-            self.model_choice.change(
-                self.model_select_update, inputs=[self.arch_choice, self.model_choice],
-                outputs=[self.primary_stem_only, self.secondary_stem_only, self.primary_stem_out, self.secondary_stem_out])
-
-            self.checkbox_set_root_value(self.use_gpu, 'is_gpu_conversion_var')
-            self.checkbox_set_root_value(self.sample_mode, 'model_sample_mode_var')
-            self.set_checkboxes_exclusive(
-                [self.primary_stem_only, self.secondary_stem_only],
-                [lambda value: root.is_primary_stem_only_var.set(value), lambda value: root.is_secondary_stem_only_var.set(value)])
-
-            self.process_submit.click(
-                self.process,
-                inputs=[self.audio_in, self.input_filename, self.model_choice, self.arch_choice, self.arch_setting1, self.arch_setting2],
-                outputs=[self.primary_stem_out, self.secondary_stem_out, self.out_message])
-
-    def launch(self, **kwargs):
-        self.app.queue().launch(**kwargs)
-
-
-uvr = UVRInterface()
-uvr.cached_sources_clear()
-
-webui = UVRWebUI(uvr, online_data_path='models/download_checks.json')
-
-
-print(webui.models_url)
-model_dict = webui.models_url
-
-import os
-import wget
-
-for category, models in model_dict.items():
-    if category in ['VR Arc', 'MDX-Net']:
-        if category == 'VR Arc':
-            model_path = 'models/VR_Models'
-        elif category == 'MDX-Net':
-            model_path = 'models/MDX_Net_Models'
-
-        for model_name, model_url in models.items():
-            cmd = f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -j5 -x16 -s16 -k1M -c -d {model_path} -Z {model_url}"
-            os.system(cmd)
-
-        print("Models downloaded successfully.")
-    else:
-        print(f"Ignoring category: {category}")
-
-
-
-
-webui = UVRWebUI(uvr, online_data_path='models/download_checks.json')
-webui.launch()
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/3ds max 2012 keygen xforce 64 bit Create stunning 3D models and animations with Autodesk software.md b/spaces/raedeXanto/academic-chatgpt-beta/3ds max 2012 keygen xforce 64 bit Create stunning 3D models and animations with Autodesk software.md
deleted file mode 100644
index 1e3b4f85ecc5d6257cceacd073b0baca7e156a64..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/3ds max 2012 keygen xforce 64 bit Create stunning 3D models and animations with Autodesk software.md	
+++ /dev/null
@@ -1,173 +0,0 @@
-
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- <p>If you are a professional or aspiring 3D artist, animator, or designer, you might have heard of <strong>3ds Max</strong>, one of the most popular and powerful software for creating stunning 3D models, animations, and visual effects. And if you are looking for a way to get <strong>3ds Max 2012</strong>, the latest version of this software, for free, you might have come across <strong>Xforce Keygen</strong>, a tool that can generate activation codes for various Autodesk products.</p>
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-</ul>
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- <ul>
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-<li><strong>CPU:</strong> Intel Pentium IV or AMD Athlon XP or higher (32-bit), Intel EM64T or AMD Athlon64 or higher (64-bit)</li>
-<li><strong>RAM:</strong> At least 2 GB (32-bit), at least 4 GB (64-bit)</li>
-<li><strong>HDD:</strong> At least 6 GB of free space for installation</li>
-<li><strong>Graphics Card:</strong> DirectX9 compatible with Shader Model 3.0 support (32-bit), DirectX10 compatible with Shader Model 4.0 support (64-bit)</li>
-<li><strong>Display:</strong> At least1024 x768 resolution with True Color (32-bit), at least1280 x1024 resolution with True Color (64-bit)</li>
-<li><strong>DVD-ROM Drive:</strong> Required for installation</li>
-<li><strong>Internet Connection:</strong> Required for activation</li>
-</ul>
- <p>If your computer meets these requirements, then you are ready to download and install 3ds Max 2012. If not, then you might need to upgrade your hardware or look for another software that suits your needs better.</p>
- <h2>How to download and install 3ds Max 2012</h2>
- <p>To download and install 3ds Max 2012, you need to follow these steps:</p>
- <ol>
-<li><p><b>Download the setup file from a trusted source.</b></p>
- <p>You can find many websites that offer 3ds Max 2012 for download. However, not all of them are reliable or safe. Some of them might contain viruses or malware that can harm your computer or steal your personal information. Therefore, you need to be careful when choosing where to download 3ds Max 2012 from.</p>
- <p>You can use one of these links  to download 3ds Max 2012 safely. These links are from reputable sources that have been tested and verified by many users. However, you still need to scan any file you download with an antivirus software before opening it.</p>
- <p>When you click on the link, you will be redirected to a page where you can choose the language and operating system of your choice. You will also need to enter your product key and serial number that you can find in the table below:</p>
- | Product Name | Product Key | Serial Number | | --- | --- | --- | | Autodesk 3ds Max 2012 | 128D1 | XXX-XXXXXXXX | <p>Replace the Xs with any numbers of your choice. Make sure they match the license type you have.</p>
- <p>After entering the required information, click on Download Now and wait for the setup file to be downloaded to your computer.</p></li>
- <li><p><b>Run the setup file and follow the instructions.</b></p>
- <p>Once the setup file is downloaded, locate it on your computer and double-click on it to run it. You will see a window that will guide you through the installation process. Follow the instructions on the screen and accept the terms and conditions of the software license agreement.</p>
- <p>You will also need to choose the destination folder where you want to install 3ds Max 2012 on your computer. You can use the default location or browse for another one. Make sure you have enough space on your hard drive for the installation.</p>
- <p>After choosing the destination folder, click on Install and wait for the installation to complete. This might take some time depending on your computer speed and internet connection.</p></li>
- <li><p><b>Choose the product key and enter the serial number.</b></p>
- <p>When the installation is finished, you will see a window that will ask you to activate your product. You can choose one of these options:</p>
- <ul>
-<li><strong>Activate</strong>: This option will allow you to activate your product online using an internet connection. You will need to enter your product key and serial number again and click on Next. You will then see a window that will ask you to sign in with your Autodesk account or create one if you don't have one already. After signing in, you will see a confirmation message that your product is activated successfully.</li>
-<li><strong>Activate Later</strong>: This option will allow you to activate your product later using an offline method. You will need to use Xforce Keygen to generate an activation code for your product. We will explain how to do this in the next section.</li>
-</ul>
- <p>You can choose either option depending on your preference and availability of internet connection. However, we recommend activating your product as soon as possible to avoid any issues or limitations in using it.</p></li>
-</ol>
- <h2>How to use Xforce Keygen to activate 3ds Max 2012</h2>
- <p>If you chose to activate your product later or if you have any problems activating it online, you can use Xforce Keygen to generate an activation code for your product. Xforce Keygen is a tool that can generate activation codes for various Autodesk products. It is also known as a crack or a patch because it bypasses the official activation process of Autodesk products.</p>
- <p>To use Xforce Keygen to activate your product, you need to follow these steps:</p>
- <ol>
-<li><p><b>Disable your internet connection and antivirus software.</b></p>
- <p>Before using Xforce Keygen, you need to make sure that your internet connection and antivirus software are disabled. This is because Xforce Keygen might be detected as a virus or a malware by some antivirus programs or firewalls and prevent it from working properly.</p>
- <p>To disable your internet connection, you can unplug your ethernet cable, turn off your Wi-Fi, or use airplane mode on your device. To disable your antivirus software, you can right-click on its icon on the taskbar and choose Disable or Exit.</p></li>
- <li><p><b>Start Xforce Keygen as administrator and click on Patch.</b></p>
- <p>After disabling your internet connection and antivirus software, you need to start Xforce Keygen as administrator. To do this, locate Xforce Keygen on your computer and right-click on it and choose Run as administrator. You will see a window that looks like this:</p>
- <code>
-+-------------------------------------------------+ |                                                 | |       XFORCE KEYGEN FOR AUTODESK PRODUCTS       | |                                                 | +-------------------------------------------------+ |                                                 | |   Product: Autodesk 3ds Max 2012                | |   Product Key: 128D1                            | |   Serial Number: XXX-XXXXXXXX                   | |                                                 | |   [Patch] [Generate] [Copy]                     | |                                                 | +-------------------------------------------------+ </code>
- <p>You will see that Xforce Keygen has automatically detected your product name, product key, and serial number based on the information you entered during the installation process. If not, you can manually enter them in the corresponding fields.</p>
- <p>After verifying that the information is correct, click on Patch. You will see a message that says Successfully patched.</p></li>
- <li><p><b>Copy the request code and paste it into the keygen and press Generate.</b></p>
- <p>After clicking on Patch, go back to the activation screen of your Autodesk product and choose I have an activation code from Autodesk. You will see a window that looks like this:</p>
- <code>
-+-------------------------------------------------+ |                                                 | |       ACTIVATION CODE FOR AUTODESK PRODUCTS     | |                                                 | +-------------------------------------------------+ |                                                 | |   Request Code: XXXX XXXX XXXX XXXX XXXX        | |                                                 | |   Activation Code:                              | |   _______________________________               | |   _______________________________               | |   _______________________________               | |   _______________________________               | |                                                 | |   [Next]                                        | |                                                 | +-------------------------------------------------+ </code>
- <p>You will see a request code that is generated by your Autodesk product based on your product key and serial number. You need to copy this request code and paste it into Xforce Keygen in the field below Patch.</p>
- <p>After pasting the request code, press Generate. You will see an activation code that is generated by Xforce Keygen based on your request code.</p></li>
- <li><p><b>Copy the activation code and paste it into the activation screen and click Next.</b></p>
- <p>After generating the activation code, copy it from Xforce Keygen and paste it into the activation screen of your Autodesk product and click Next. You will see a confirmation message that your product is activated successfully.</p></li>
-</ol>
- <h2>Advantages and disadvantages of using Xforce Keygen</h2>
- <p>Using Xforce Keygen to activate your Autodesk product might seem like an easy and convenient solution, especially if you don't want to pay for the software or if you have lost your original activation code. However, there are also some drawbacks and risks that you should be aware of before using it. Here are some of the advantages and disadvantages of using Xforce Keygen:</p>
- <h3>Advantages of using Xforce Keygen</h3>
- <ul>
-<li><strong>It is free.</strong> You don't have to pay anything to use Xforce Keygen. You can download it from various websites and use it as many times as you want. You can also use it to activate multiple Autodesk products without any limitations.</li>
-<li><strong>It is easy to use.</strong> You don't need any technical skills or knowledge to use Xforce Keygen. You just need to follow the simple steps that we explained above and you can activate your product in a matter of minutes.</li>
-<li><strong>It works offline.</strong> You don't need an internet connection to use Xforce Keygen. You can activate your product even if you are in a remote location or if you have a poor or unstable internet connection.</li>
-</ul>
- <h3>Disadvantages of using Xforce Keygen</h3>
- <ul>
-<li><strong>It is illegal.</strong> Using Xforce Keygen to activate your Autodesk product is considered a form of software piracy. It violates the terms and conditions of the software license agreement that you agreed to when you installed the software. It also infringes the intellectual property rights of Autodesk, the software developer. You could face legal consequences if you are caught using Xforce Keygen.</li>
-<li><strong>It is unsafe.</strong> Using Xforce Keygen could expose your computer to viruses or malware that could harm your system or steal your personal information. Some websites that offer Xforce Keygen for download might contain malicious files or links that could infect your computer. Even if you scan the file with an antivirus software, there is no guarantee that it is completely safe or clean.</li>
-<li><strong>It is unreliable.</strong> Using Xforce Keygen could cause problems or errors with your Autodesk product or other software on your computer. Some features or functions of your product might not work properly or at all after using Xforce Keygen. Your product might also crash or freeze unexpectedly or frequently. You might also lose access to updates, support, or online services from Autodesk.</li>
-</ul>
- <p>As you can see, using Xforce Keygen has its pros and cons. You should weigh them carefully before deciding whether to use it or not. We recommend that you use the official activation method from Autodesk instead of using Xforce Keygen, as it is safer, legal, and more reliable.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have explained what 3ds Max 2012 is, what its features and benefits are, what its system requirements are, what Xforce Keygen is, how it works, and what its advantages and disadvantages are. We have also shown you how to download and install 3ds Max 2012 and how to use Xforce Keygen to activate it.</p>
- <p>We hope that this article has been helpful and informative for you. If you have any questions or comments, feel free to leave them below. Thank you for reading!</p>
- <h4>Summary of the main points</h4>
- <ul>
-<li><strong>3ds Max 2012</strong> is a powerful and versatile 3D design software that allows you to create and edit 3D models, animations, and visual effects for various purposes.</li>
-<li><strong>Xforce Keygen</strong> is a tool that can generate activation codes for various Autodesk products, including 3ds Max 2012.</li>
-<li>To <strong>download and install 3ds Max 2012</strong>, you need to have a product key and a serial number, choose a trusted source, run the setup file, and follow the instructions.</li>
-<li>To <strong>use Xforce Keygen to activate 3ds Max 2012</strong>, you need to disable your internet connection and antivirus software, start Xforce Keygen as administrator, click on Patch, copy the request code and paste it into the keygen, press Generate, copy the activation code and paste it into the activation screen, and click Next.</li>
-<li><strong>Using Xforce Keygen</strong> has some advantages, such as being free, easy to use, and working offline; but also some disadvantages, such as being illegal, unsafe, and unreliable.</li>
-</ul>
- <h4>Call to action</h4>
- <p>If you want to learn more about 3ds Max 2012 or other Autodesk products, you can visit their official website here: https://www.autodesk.com/products/3ds-max/overview</p>
- <p>If you want to buy 3ds Max 2012 or other Autodesk products at a discounted price, you can check out this link here: https://www.autodesk.com/promotions/offer-details/autodesk-3ds-max-2012-keygen-xforce-64-bit</p>
- <p>If you want to get professional help with your 3D design projects using 3ds Max 2012 or other Autodesk products, you can contact us here: https://www.3ddesignpros.com/</p>
- <h4>FAQs</h4>
- <p>Here are some of the frequently asked questions about 3ds Max 2012 Keygen Xforce 64 Bit:</p>
- <ol>
-<li><p><b>Is 3ds Max 2012 compatible with Windows 10?</b></p>
- <p>Yes, 3ds Max 2012 is compatible with Windows 10. However, you might need to install some updates or patches to ensure its optimal performance and stability. You can find more information about compatibility issues and solutions here: https://knowledge.autodesk.com/support/3ds-max/troubleshooting/caas/sfdcarticles/sfdcarticles/Windows-10-support-for-Autodesk-products.html</p></li>
- <li><p><b>Can I use Xforce Keygen for other Autodesk products?</b></p>
- <p>Yes, you can use Xforce Keygen for other Autodesk products, such as AutoCAD, Maya, Revit, Inventor, etc. However, you need to make sure that you use the correct product key and serial number for each product. You can find a list of product keys for Autodesk products here: https://knowledge.autodesk.com/customer-service/download-install/activate/find-serial-number-product-key/product-key-look/2022-product-keys</p></li>
- <li><p><b>What are the alternatives to Xforce Keygen?</b></p>
- <p>If you don't want to use Xforce Keygen to activate your Autodesk product, you have some alternatives. You can:</p>
- <ul>
-<li>Buy a legitimate license from Autodesk or an authorized reseller.</li>
-<li>Use a free trial version of the software for a limited time.</li>
-<li>Use a student or educator version of the software for educational purposes.</li>
-<li>Use an open-source or free software that has similar features and functions.</li>
-</ul></li>
- <li><p><b>How can I learn 3ds Max 2012?</b></p>
- <p>If you want to learn how to use 3ds Max 2012 effectively and efficiently, you have some options. You can:</p>
- <ul>
-<li>Read the official documentation and tutorials from Autodesk.</li>
-<li>Watch online videos and courses from platforms like YouTube, Udemy, Coursera, etc.</li>
-<li>Join online forums and communities of 3ds Max users and experts.</li>
-<li>Hire a professional tutor or mentor to guide you.</li>
-</ul></li>
- <li><p><b>How can I get help with 3ds Max 2012?</b></p>
- <p>If you encounter any problems or issues with 3ds Max 2012, you have some options. You can:</p>
- <ul>
-<li>Contact the Autodesk support team via phone, email, chat, or web form.</li>
-<li>Visit the Autodesk knowledge network and search for solutions or articles.</li>
-<li>Submit a support request or report a bug via the Autodesk feedback community.</li>
-<li>Ask a question or seek advice from other 3ds Max users on platforms like Quora, Reddit, Stack Exchange, etc.</li>
-</ul></li>
-</ol>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Berlitz English Level 3 (Book And Audio) - .md b/spaces/raedeXanto/academic-chatgpt-beta/Berlitz English Level 3 (Book And Audio) - .md
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-<br />
-<h1>Berlitz English Level 3 (Book and Audio)</h1>
-<p>Do you want to improve your functional ability in English? Do you want to learn language that is relevant and useful for everyday situations? Do you want to measure your progress and compare your skills with other learners? If you answered yes to any of these questions, then <strong>Berlitz English Level 3</strong> is the course for you.</p>
-<h2>What is Berlitz English Level 3?</h2>
-<p>Berlitz English Level 3 is a course book and audio CD that helps learners improve their functional ability in English. Functional ability means that you can communicate in a very limited way, with the simplest oral and listening tasks and situations. For example, you can introduce yourself and ask other people simple questions about themselves, such as where they live, how they are or what things they like. You can also take part in a simple conversation if the person you are conversing with talks slowly and helps you by repeating things.</p>
-<h2>Berlitz English Level 3 (Book And Audio)</h2><br /><p><b><b>DOWNLOAD</b> &#10022;&#10022;&#10022; <a href="https://tinourl.com/2uL2lC">https://tinourl.com/2uL2lC</a></b></p><br /><br />
-<h2>What are the features of Berlitz English Level 3?</h2>
-<h3>Topics and vocabulary</h3>
-<p>Berlitz English Level 3 covers topics such as travel, work, culture, and leisure, and teaches vocabulary related to these areas. For example, you will learn words and phrases for booking a hotel room, ordering food at a restaurant, describing your hobbies, or talking about your family. You will also learn about different cultures and customs around the world.</p>
-<h3>Grammar and pronunciation</h3>
-<p>Berlitz English Level 3 reviews grammar points such as verb tenses, modal verbs, and conditionals, and provides exercises and tips to improve pronunciation. For example, you will learn how to use the present simple and present continuous tenses to talk about routines and actions happening now. You will also learn how to pronounce words with silent letters or different sounds.</p>
-<h3>Skills and strategies</h3>
-<p>Berlitz English Level 3 develops skills such as listening, speaking, reading, and writing, and teaches strategies such as asking for clarification, expressing opinions, and making suggestions. For example, you will learn how to listen for specific information or main ideas in a dialogue or a story. You will also learn how to speak clearly and confidently in different situations.</p>
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-<h2>How can you use Berlitz English Level 3?</h2>
-<h3>Self-study</h3>
-<p>Berlitz English Level 3 can be used for self-study by following the instructions in the book and listening to the audio CD. The book contains eight units with four lessons each. Each lesson has a clear objective and a variety of activities to practice the language. The audio CD contains recordings of native speakers who model the language and provide listening practice.</p>
-<h3>Classroom</h3>
-<p>Berlitz English Level 3 can be used in a classroom setting by following the teacher's guide and participating in activities and discussions. The teacher's guide contains detailed lesson plans and additional resources to enhance the learning experience. The activities and discussions are designed to encourage interaction and communication among learners.</p>
-<h3>Online</h3>
-<p>Berlitz English Level 3 can be used online by accessing the interactive exercises and resources on the Berlitz website. The interactive exercises provide feedback and reinforcement of the language learned in the book. The resources include videos, podcasts, articles, games, quizzes, dictionaries, and more.</p>
-<h2>What are the benefits of Berlitz English Level 3?</h2>
-<h3>Standardized scale</h3>
-<p>Berlitz English Level 3 corresponds to the CEFR level A2/B1, which means that learners can measure their progress and compare their skills with other learners. The CEFR (Common European Framework of Reference for Languages) is a widely used scale that defines different levels of language proficiency, ranging from A1 (beginner) to C2 (professional). By completing Berlitz English Level 3, you will be able to demonstrate that you have reached a functional level of proficiency in English.</p>
-<h3>Practical language</h3>
-<p>Berlitz English Level 3 teaches language that is relevant and useful for everyday situations and real-life contexts. You will learn how to communicate effectively in various scenarios, such as traveling abroad, working with colleagues, or socializing with friends. You will also learn how to adapt your language to different audiences, such as formal or informal, polite or casual, or positive or negative.</p>
-<h3>Engaging content</h3>
-<p>Berlitz English Level 3 provides content that is interesting and motivating for learners, such as cultural information, dialogues, stories, and games. You will learn about different aspects of life in different countries, such as traditions, festivals, food, or music. You will also listen to or read authentic texts that reflect real situations and emotions. You will also have fun while learning by playing games that challenge your memory, creativity, or logic.</p>
-<h2>How can you get Berlitz English Level 3?</h2>
-<p>Berlitz English Level 3 can be purchased online or at a local Berlitz center. You can visit <a href="https://www.berlitz.com">www.berlitz.com</a> to find out more information about the course, the price, and the availability. You can also contact your nearest Berlitz center to enroll in a class or take a free placement test.</p>
-<h2>Conclusion</h2>
-<p>Berlitz English Level 3 is a great way to improve your functional ability in English and achieve your personal goals. Whether you want to travel, work, study, or socialize in an English-speaking environment, Berlitz English Level 3 will help you gain confidence and competence in using the language. ```html litz English Level 3, you will learn from the experts and enjoy the experience.</p>
-<h2>FAQs</h2>
-<ul>
-<li><strong>What is the difference between Berlitz English Level 3 and other English courses?</strong>
-<p>Berlitz English Level 3 is a course that focuses on functional ability in English, which means that you can communicate in a very limited way, with the simplest oral and listening tasks and situations. Other English courses may focus on different aspects of language, such as academic, business, or conversational.</p></li>
-<li><strong>How long does it take to complete Berlitz English Level 3?</strong>
-<p>Berlitz English Level 3 consists of eight units with four lessons each. The duration of each lesson depends on your pace and preference, but it usually takes about an hour to complete. Therefore, you can expect to finish the course in about 32 hours.</p></li>
-<li><strong>How can I practice what I learn in Berlitz English Level 3?</strong>
-<p>Berlitz English Level 3 provides plenty of opportunities to practice what you learn in the book and the audio CD. You can also practice online by accessing the interactive exercises and resources on the Berlitz website. Additionally, you can practice with other learners or native speakers by joining a Berlitz class or a language exchange program.</p></li>
-<li><strong>What are the prerequisites for Berlitz English Level 3?</strong>
-<p>Berlitz English Level 3 is suitable for learners who have completed Berlitz English Level 2 or have a similar level of proficiency in English. If you are not sure about your level, you can take a free placement test online or at a Berlitz center.</p></li>
-<li><strong>What are the next steps after Berlitz English Level 3?</strong>
-<p>After completing Berlitz English Level 3, you can continue your learning journey with Berlitz English Level 4, which will help you improve your intermediate ability in English. You can also explore other courses offered by Berlitz, such as Business English, English for Specific Purposes, or English for Kids and Teens.</p></li>
-</ul>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Disk Internals Partition Recovery 3.8 With Crack (2011) What You Need to Know About Partition Recovery.md b/spaces/raedeXanto/academic-chatgpt-beta/Disk Internals Partition Recovery 3.8 With Crack (2011) What You Need to Know About Partition Recovery.md
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-<br />
-<h1>Disk Internals Partition Recovery 3.8 With Crack (2011)</h1>
-<p>Have you ever lost your important data or partitions due to accidental deletion, formatting, virus attack, power failure, or other reasons? If yes, then you know how frustrating and stressful it can be to recover your lost files. Fortunately, there is a software tool that can help you in such situations: <strong>Disk Internals Partition Recovery 3.8</strong>. In this article, we will introduce you to this powerful data recovery software, explain why you need it, how to download and install it with crack, what are its features, pros and cons, and answer some frequently asked questions.</p>
- <h2>Introduction</h2>
-<p>Data loss is a common problem that many computer users face at some point in their lives. Whether it is due to human error, hardware failure, software corruption, malware infection, or natural disaster, losing your valuable data can be devastating and costly. Sometimes, you may not even realize that you have lost some data until it is too late.</p>
-<h2>Disk Internals Partition Recovery 3.8 With Crack (2011)</h2><br /><p><b><b>Download Zip</b> &rarr; <a href="https://tinourl.com/2uL2XO">https://tinourl.com/2uL2XO</a></b></p><br /><br />
-<p>One of the most common causes of data loss is partition damage or deletion. A partition is a logical division of a hard disk that allows you to store different types of data separately. For example, you may have a C: drive for your operating system and programs, a D: drive for your documents and media files, and an E: drive for your backups. However, sometimes these partitions may get corrupted or deleted due to various reasons, such as:</p>
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-<li>Accidental deletion or formatting of a partition</li>
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-<li>Bad sectors or physical damage on the hard disk</li>
-<li>Virus attack or malware infection</li>
-<li>Power outage or system crash</li>
-<li>Incorrect installation or removal of hardware or software</li>
-</ul>
-<p>When a partition gets damaged or deleted, you may not be able to access your data on that partition anymore. You may also receive some error messages like "The disk structure is corrupted and unreadable", "The file or directory is corrupted and unreadable", "Invalid partition table", "Operating system not found", etc. In some cases, you may even lose the entire hard disk.</p>
-<p>So, what can you do if you encounter such a situation? How can you recover your lost data or partitions? Well, one of the best solutions is to use a data recovery software like <strong>Disk Internals Partition Recovery 3.8</strong>.</p>
- <h2>What is Disk Internals Partition Recovery 3.8?</h2>
-<p><strong>Disk Internals Partition Recovery 3.8</strong> is an advanced software tool that is designed for all users who need to recover some data or lost partitions. It was released in 2011 by DiskInternals Research, a company that specializes in data recovery solutions for Windows systems.</p>
-<p>This software can scan your hard disk for any deleted or damaged partitions and recover them with ease. It can also recover any files that were stored on those partitions, such as documents, photos, videos, music, emails, etc. It supports various file systems, such as NTFS, FAT12, FAT16, FAT32, VFAT, etc.</p>
-<p><strong>Disk Internals Partition Recovery 3.8</strong> has a user-friendly interface that guides you through the recovery process step by step. You can choose between two modes: <em>Fast Scan</em> and <em>Full Scan</em>. The <em>Fast Scan</em> mode quickly scans your hard disk for any deleted partitions and displays them in a list. The <em>Full Scan</em> mode performs a deeper scan of your hard disk for any damaged partitions and recovers them along with their files.</p>
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-<p>, including network disks. You can also burn them to CD or DVD if you want.</p>
- <h2>Features of Disk Internals Partition Recovery 3.8</h2>
-<p><strong>Disk Internals Partition Recovery 3.8</strong> has many features that make it a powerful and reliable data recovery software. Here are some of the main features:</p>
- <h3>Native RAID and Dynamic Disk Support</h3>
-<p>This software can recover data from any type of RAID array, such as RAID 0, RAID 1, RAID 5, RAID 6, etc. It can also recover data from dynamic disks, such as spanned, striped, mirrored, or simple volumes.</p>
- <h3>Virtual Disk Images and Forensic Disk Images</h3>
-<p>This software can recover data from virtual disk images created by VMware, VirtualBox, Microsoft VirtualPC, Parallels, etc. It can also recover data from forensic disk images created by EnCase, ProDiscover, etc.</p>
- <h3>Recovered files can be burned to CD or DVD</h3>
-<p>This software allows you to burn the recovered files to CD or DVD directly from the program. You can also create ISO images of the recovered files and burn them later.</p>
- <h3>Preview recoverable files before purchasing the product</h3>
-<p>This software lets you preview the recoverable files before you buy the full version. You can see the file name, size, date, and content of each file. You can also open and view the files in their native applications.</p>
- <h3>Recover partitions and deleted files remotely over the network</h3>
-<p>This software enables you to recover partitions and deleted files remotely over the network. You can connect to another computer that has this software installed and access its hard disk. You can then scan and recover the data from that computer.</p>
- <h3>Easy Recovery Wizard</h3>
-<p>This software has an easy recovery wizard that guides you through the recovery process step by step. You just need to select the disk or partition that you want to recover, choose the scan mode, and wait for the results. The wizard will then show you the list of recoverable partitions and files and let you save them.</p>
- <h3>Supported file systems: NTFS, FAT12, FAT16, FAT32, VFAT</h3>
-<p>This software supports various file systems that are commonly used on Windows systems. It can recover data from NTFS, FAT12, FAT16, FAT32, and VFAT file systems. It can also recover data from compressed or encrypted NTFS partitions.</p>
- <h3>Recovered files can be saved on any disks visible to the host OS</h3>
-<p>This software allows you to save the recovered files on any disks that are visible to the host operating system. This includes internal and external hard disks, USB flash drives, memory cards, etc. You can also save the recovered files on network disks or FTP servers.</p>
- <h3>Creates recovery snapshot files for logical drives</h3>
-<p>This software creates recovery snapshot files for logical drives. These files contain information about the structure and content of the logical drive. You can use these files to resume the recovery process later or on another computer.</p>
- <h2>Pros and cons of Disk Internals Partition Recovery 3.8</h2>
-<p><strong>Disk Internals Partition Recovery 3.8</strong> has many advantages and disadvantages that you should consider before using it. Here are some of them:</p>
- <h3>Pros</h3>
-<ul>
-<li><strong>High success rate of data recovery</strong>: This software can recover data from various scenarios of partition damage or deletion. It can also recover data from different types of disks and file systems. It has a high success rate of data recovery compared to other similar software.</li>
-<li><strong>User-friendly interface and easy to use</strong>: This software has a user-friendly interface that makes it easy to use for anyone. It has an easy recovery wizard that guides you through the recovery process step by step. It also has a preview feature that lets you see the recoverable files before purchasing the product.</li>
-<li><strong>Affordable price and free trial available</strong>: This software has an affordable price compared to other similar software. It also offers a free trial version that lets you scan and preview the recoverable files before buying the full version.</li>
-</ul>
- <h3>Cons</h3>
-<ul>
-<li><strong>Crack version may contain viruses or malware</strong>: If you download and install <strong>Disk Internals Partition Recovery 3.8 with crack</strong>, you may expose your computer to viruses or malware that may harm your system or steal your personal information. You may also face legal consequences for using a crack version of the software.</li>
-<li><strong>Crack version may not work properly or cause system errors</strong>: If you use <strong>Disk Internals Partition Recovery 3.8 with crack</strong>, you may encounter some problems or errors that may affect your data recovery process or your system performance. The crack version may not work properly or may cause system crashes or freezes.</li>
-<li><strong>Crack version may violate the software license agreement and legal rights</strong>: If you use <strong>Disk Internals Partition Recovery 3.8 with crack</strong>, you may violate the software license agreement and legal rights of DiskInternals Research. You may also infringe the intellectual property rights of DiskInternals Research and other third parties.</li>
-</ul>
- <h2>Conclusion</h2>
-<p><strong>Disk Internals Partition Recovery 3.8</strong> is a powerful and reliable data recovery software that can help you recover your lost data or partitions due to various reasons. It has many features that make it a great choice for anyone who needs to recover some data or lost partitions.</p>
-<p>However,<strong>Disk Internals Partition Recovery 3.8 with crack</strong> is not a good option for anyone who wants to use this software safely and legally. The crack version may contain viruses or malware that may harm your computer or steal your personal information. It may also not work properly or cause system errors that may affect your data recovery process or your system performance. Moreover,<strong>Disk Internals Partition Recovery 3.8 with crack</strong> may violate the software license agreement and legal rights of DiskInternals Research and other third parties.</p>
-<p>Therefore,<strong>Disk Internals Partition Recovery 3.8 with crack (2011)</strong> is not recommended for anyone who wants to use this software in a safe and legal way.<br/>If you want to use this software in a safe and legal way,<br/>you should buy the full version from DiskInternals Research's official website.<br/>You will get a valid license key that will activate all the features of this software.<br/>You will also get technical support and updates from DiskInternals Research.<br/>You will also respect the software license agreement and legal rights of DiskInternals Research and other third parties.<br/>You will also protect your computer and your personal information from viruses or malware.<br/>You will also enjoy a smooth and successful data recovery process with this software.</p>
- <h2>FAQs</h2>
-<ol>
-<li><em>What is Disk Internals Partition Recovery 3.8?</em><br/><br/>Disk Internals Partition Recovery 3.8 is an advanced software tool that is designed for all users who need to recover some data or lost partitions due to various reasons.</li>
-<li><em>Why do I need Disk Internals Partition Recovery 3.8?</em><br/><br/>You may need Disk Internals Partition Recovery 3.8 if you have lost your important data or partitions due to accidental deletion, formatting, virus attack, power failure, or other reasons.</li>
-<li><em>How do I download and install Disk Internals Partition Recovery 3.8 with crack?</em><br/><br/>You can download and install Disk Internals Partition Recovery 3.8 with crack by following these steps:<br/><br/>
-<ol type="a">
-<li>8 With Crack Extra Quality (2011)</a>,or<a href="https://tourismcenter.ge/wp-content/uploads/2022/07/Disk_Internals_Partition_Recovery_38_With_Crack_2011.pdf">Disk Internals Partition Recovery 3.8 With Crack (2011) Fix</a>.</li>
-<li>Click on the download link and save the file on your computer.<br/>The file size may vary depending on the website.<br/>For example,<a href="https://soundcloud.com/johnnie-hancock/disk-internals-partition-recovery-38-with-fix-crack-2011">Disk Internals Partition Recovery 3.8 With Fix Crack (2011)</a>is about 6 MB,<a href="https://soundcloud.com/tmobinchette1982/disk-internals-partition-recovery-38-with-crack-extra-quality-2011">Disk Internals Partition Recovery 3.8 With Crack Extra Quality (2011)</a>is about 9 MB,and<a href="https://tourismcenter.ge/wp-content/uploads/2022/07/Disk_Internals_Partition_Recovery_38_With_Crack_2011.pdf">Disk Internals Partition Recovery 3.8 With Crack (2011) Fix</a>is about 11 MB.</li>
-<li>Extract the file using a program like WinRAR or 7-Zip.<br/>You will get a folder containing the setup file and the crack file.<br/>For example,<a href="https://soundcloud.com/johnnie-hancock/disk-internals-partition-recovery-38-with-fix-crack-2011">Disk Internals Partition Recovery 3.8 With Fix Crack (2011)</a>has a folder named "DiskInternals.Partition.Recovery.3.8.With.Fix.Crack.2011".</li>
-<li>Run the setup file and follow the instructions to install the software.<br/>For example,<a href="https://soundcloud.com/johnnie-hancock/disk-internals-partition-recovery-38-with-fix-crack-2011">Disk Internals Partition Recovery 3.8 With Fix Crack (2011)</a>has a setup file named "setup.exe".</li>
-<li>Copy the crack file and paste it into the installation folder of the software.<br/>For example,<a href="https://soundcloud.com/johnnie-hancock/disk-internals-partition-recovery-38-with-fix-crack-2011">Disk Internals Partition Recovery 3.8 With Fix Crack (2011)</a>has a crack file named "crack.exe".<br/>The installation folder of the software may vary depending on your system.<br/>For example, it may be "C:\Program Files\DiskInternals\Partition Recovery" or "C:\Program Files (x86)\DiskInternals\Partition Recovery".</li>
-<li>Run the crack file and click on the "Crack" button to activate the software.<br/>For example,<a href="https://soundcloud.com/johnnie-hancock/disk-internals-partition-recovery-38-with-fix-crack-2011">Disk Internals Partition Recovery 3.8 With Fix Crack (2011)</a>has a crack file that shows a message like "DiskInternals Partition Recovery v3.8 Cracked by Johnnie Hancock".</li>
-<li>Enjoy using the software with all its features.</li>
-</ol></li>
-<li><em>What are the features of Disk Internals Partition Recovery 3.8?</em><br/><br/>Some of the features of Disk Internals Partition Recovery 3.8 are:<br/><br/>
-<ul>
-<li>Native RAID and Dynamic Disk Support</li>
-<li>Virtual Disk Images and Forensic Disk Images</li>
-<li>Recovered files can be burned to CD or DVD</li>
-<li>Preview recoverable files before purchasing the product</li>
-<li>Recover partitions and deleted files remotely over the network</li>
-<li>Easy Recovery Wizard</li>
-<li>Supported file systems: NTFS, FAT12, FAT16, FAT32, VFAT</li>
-<li>Recovered files can be saved on any disks visible to the host OS</li>
-<li>Creates recovery snapshot files for logical drives</li>
-</ul></li>
-<li><em>What are the pros and cons of Disk Internals Partition Recovery 3.8?</em><br/><br/>Some of the pros and cons of Disk Internals Partition Recovery 3.8 are:<br/><br/>
-<ul>
-<li><strong>Pros</strong>:<br/><br/>
-<ul>
-<li>High success rate of data recovery</li>
-<li>User-friendly interface and easy to use</li>
-<li>Affordable price and free trial available</li>
-</ul></li>
-<li><strong>Cons</strong>:<br/><br/>
-<ul>
-<li>Crack version may contain viruses or malware</li>
-<li>Crack version may not work properly or cause system errors</li>
-<li>Crack version may violate the software license agreement and legal rights</li>
-</ul></li>
-</ul></li>
-<li><em>Where can I buy the full version of Disk Internals Partition Recovery 3.8?</em><br/><br/>You can buy the full version of Disk Internals Partition Recovery 3.8 from DiskInternals Research's official website:<a href="https://www.diskinternals.com/partition-recovery/">https://www.diskinternals.com/partition-recovery/</a>.<br/>You can choose between different license types and payment methods.<br/>You will receive a valid license key that will activate all the features of this software.<br/>You will also get technical support and updates from DiskInternals Research.</li>
-<li><em>Is there any alternative to Disk Internals Partition Recovery 3.8?</em><br/><br/>There are many other data recovery software that can help you recover your lost data or partitions.<br/>Some of them are:<br/><br/>
-<ul>
-<li>EaseUS Data Recovery Wizard: <a href="https://www.easeus.com/datarecoverywizardpro/">https://www.easeus.com/datarecoverywizardpro/</a></li>
-<li>Minitool Power Data Recovery: <a href="https://www.minitool.com/data-recovery-software/">https://www.minitool.com/data-recovery-software/</a></li>
-<li>Stellar Data Recovery: <a href="https://www.stellarinfo.com/windows-data-recovery-professional.php">https://www.stellarinfo.com/windows-data-recovery-professional.php</a></li>
-<li>R-Studio Data Recovery: <a href="https://www.r-studio.com/">https://www.r-studio.com/</a></li>
-<li>Data Rescue: <a href="https://www.prosofteng.com/data-rescue-recovery-software/">https://www.prosofteng.com/data-rescue-recovery-software/</a></li>
-</ul></li>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Gulaal Hd Mp4 Download UPDATED.md b/spaces/raedeXanto/academic-chatgpt-beta/Gulaal Hd Mp4 Download UPDATED.md
deleted file mode 100644
index 996e2a5cab458675e325421849e7cc1767225eee..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Gulaal Hd Mp4 Download UPDATED.md	
+++ /dev/null
@@ -1,39 +0,0 @@
-
-<h1>How to Download Gulaal (2009) Superhit Bollywood Movie in HD MP4 Format</h1>
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-<p>Gulaal is a 2009 Indian political thriller film directed by Anurag Kashyap and starring Kay Kay Menon, Raj Singh Chaudhary, Abhimanyu Singh, Deepak Dobriyal, Ayesha Mohan, Jesse Randhawa, Piyush Mishra and Aditya Srivastava. The film explores the themes of power, corruption, student politics, casteism, nationalism and identity in contemporary India.</p>
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-<p>One of the easiest and most convenient ways to download Gulaal hd mp4 is from YouTube. YouTube is the largest and most popular video-sharing platform in the world, where you can find millions of videos on various topics and genres. You can also find several uploads of Gulaal movie on YouTube, such as [^1^] or [^2^].</p>
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-<p>To download Gulaal hd mp4 from YouTube, you will need a third-party tool that can help you convert and save YouTube videos to your device. There are many such tools available online, both free and paid. Some of them are web-based, while others require installation. Here are some examples of YouTube video downloader tools:</p>
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-<li><a href="https://www.y2mate.com/en68">Y2mate</a>: A free online service that allows you to download YouTube videos in various formats and resolutions, including MP4, MP3, 3GP, WEBM, etc.</li>
-<li><a href="https://www.vidpaw.com/">VidPaw</a>: Another free online service that lets you download YouTube videos in HD MP4, MP3, WEBM, etc. It also supports downloading subtitles and playlists.</li>
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-<ol>
-<li>Go to YouTube and search for Gulaal movie. Choose the video that has the best quality and length.</li>
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-<li>Go to the website or launch the software of the YouTube video downloader tool that you have chosen.</li>
-<li>Paste the URL of the video into the input box or drag-and-drop it into the interface.</li>
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-<li>Click on the download button or icon and wait for the tool to process the video.</li>
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-</ol>
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diff --git a/spaces/rainbowemoji/etf-assistant/README.md b/spaces/rainbowemoji/etf-assistant/README.md
deleted file mode 100644
index ace398dde8e65ea91f548013a70496679e12b020..0000000000000000000000000000000000000000
--- a/spaces/rainbowemoji/etf-assistant/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Etf Assistant
-emoji: 📊
-colorFrom: green
-colorTo: red
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/rajesh1729/text-summarization-gradio/README.md b/spaces/rajesh1729/text-summarization-gradio/README.md
deleted file mode 100644
index b989f4c992ddcbf09a1aa263a8cbb2117ce5d0c3..0000000000000000000000000000000000000000
--- a/spaces/rajesh1729/text-summarization-gradio/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-title: Text Summarization Gradio Transformers Pipeline
-emoji: 🌖
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/ras0k/WhisperX-v2/app.py b/spaces/ras0k/WhisperX-v2/app.py
deleted file mode 100644
index 65c71d56d0a11bcf67fa9476dab618ad984fc163..0000000000000000000000000000000000000000
--- a/spaces/ras0k/WhisperX-v2/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-import whisperx
-import whisper
-
-def transcribe(audio_file):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    # Transcribe with original Whisper
-    model = whisper.load_model("large", device)
-    result = model.transcribe(audio_file)
-
-    # Load alignment model and metadata
-    model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
-
-    # Align Whisper output
-    result_aligned = whisperx.align(result["segments"], model_a, metadata, audio_file, device)
-
-    return {"aligned": result_aligned["segments"], "word_segments": result_aligned["word_segments"]}
-
-inputs = gr.inputs.Audio(source="upload", type="filepath")
-outputs = gr.outputs.JSON()
-
-gr.Interface(fn=transcribe, inputs=inputs, outputs=outputs).launch()
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Flash Player Android 2.2 Download LINK.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Flash Player Android 2.2 Download LINK.md
deleted file mode 100644
index 62eab9253ab5ad8f8caeb0423d8e304b788dcf65..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Flash Player Android 2.2 Download LINK.md	
+++ /dev/null
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Crack Mount And Blade Warband 1.152.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Crack Mount And Blade Warband 1.152.md
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index 38707c3c7a0fc40827a52151bbfe25def02a9bf5..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Crack Mount And Blade Warband 1.152.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Data Cash.NET Framework V4.0.30319 TOP Download.rar25.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Data Cash.NET Framework V4.0.30319 TOP Download.rar25.md
deleted file mode 100644
index a7276e6bb35e26044edecfb28998f7b423f51753..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Data Cash.NET Framework V4.0.30319 TOP Download.rar25.md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Kuch Kuch Hota Hai 1080p ((HOT)).md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Kuch Kuch Hota Hai 1080p ((HOT)).md
deleted file mode 100644
index b02898c027eab93142654bc55f988acbe445271f..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Kuch Kuch Hota Hai 1080p ((HOT)).md	
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Google Sketchup 14 Free Download With Crack.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Google Sketchup 14 Free Download With Crack.md
deleted file mode 100644
index e188cc81094c18a7cf40b08a0cc75b24b0dd20f2..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Google Sketchup 14 Free Download With Crack.md	
+++ /dev/null
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-
-dxf files were opened in the 3D Warehouse after launching SketchUp. .dxf files were marked as unsupported for import, while they were actually supported.
-
-SketchUp Pro Version 15.0.4880
-
-AppleInsider discovered a bug that prevents SketchUp from importing large .dxf files. 
-
-Added – New rendering option: Model From Camera
-
-Added – New rendering option: Model From File
-
-Added – New rendering option: Object To Camera
-
-Added – New rendering option: World to Camera
-
-Bug Fixed – In some cases, SketchUp could crash when opening a .dxf file in the 3D Warehouse. 
-
-Bug Fixed – SVG files could be imported incorrectly with the SketchUp Pro Advanced Template plug-in. 
-
-Bug Fixed – SketchUp could crash when updating a SketchUp project. 
-
-Bug Fixed – Users could save a SketchUp project as a different format. 
-
-Fixed a crash when drawing a sketch in the Outliner.
-
-Fixed a bug that could cause SketchUp to crash when editing a SketchUp project.
-
-Version Numbers: Win: 14.0.4911. Mac: 14.0.4888. SketchUp. Issues Fixed: General. Fixed a crash that could occur when SketchUp was restoring a project.
-
-SketchUp Pro Version 15.0.4877
-
-Bug Fixed – SketchUp could crash when closing a SketchUp project.
-
-Version Numbers: Win: 14.0.4900. Mac: 14.0.4889. SketchUp. Issues Fixed: General. Fixed a crash that could occur when SketchUp was upgrading to a new version.
-
-SketchUp Pro Version 15.0.4873
-
-Bug Fixed – SketchUp could crash when opening a SketchUp project.
-
-Version Numbers: Win: 14.0.4900. Mac: 14.0.4886. SketchUp. Issues Fixed: General. Fixed a bug that could cause SketchUp to crash when upgrading to a new version.
-
-SketchUp Pro Version 15.0.4869
-
-Version Numbers: Win: 14.0.4902. Mac: 14.0.4883. SketchUp. Issues Fixed: General. Fixed a crash that could occur when SketchUp was upgrading to a new version 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/reshinthadith/code-representation-learning/app.py b/spaces/reshinthadith/code-representation-learning/app.py
deleted file mode 100644
index 330c05047b5e62891b8d521d7399f194ca57d365..0000000000000000000000000000000000000000
--- a/spaces/reshinthadith/code-representation-learning/app.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-
-MODEL_NAME = "reshinthadith/BashGPTNeo"
-def load_model_and_tokenizer(model_name):
-    """Adding load_model_and_tokenizer function to keep the model in the memory"""
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    return tokenizer,model
-
-tokenizer,model = load_model_and_tokenizer(MODEL_NAME)
-
-MAX_TOKS = 128
-MAX_NEW_TOKS = 128 
-def generate_text(prompt):
-    prompt = "<english> " + prompt + " <bash>"
-    inputs = tokenizer(prompt, truncation=True, return_tensors="pt")
-    output_seq = model.generate(
-        input_ids=inputs.input_ids, max_length=MAX_TOKS,
-        max_new_tokens=MAX_NEW_TOKS,
-        do_sample=True, temperature=0.8,
-        num_return_sequences=1
-    )
-
-    outputs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
-    outputs = outputs[0].split("<bash>")[-1]
-    return outputs
-st.set_page_config(
-page_title= "Code Representation Learning",
-    
-    initial_sidebar_state= "expanded"
-    )
-
-st.sidebar.title("Code Representation Learning")
-st.sidebar.write("work by Reshinth Adithyan & Aditya Thuruvas")
-
-workflow = st.sidebar.selectbox('select a task', ['Bash Synthesis'])
-if workflow == "Bash Synthesis":
-    st.title("Program Synthesis for Bash")
-    prompt = st.text_input("Natural Language prompt ",'print all the files with ".cpp" extension')
-    button = st.button("synthesize")
-if button:
-    generated_text = generate_text(prompt)
-    st.write(generated_text)
diff --git a/spaces/rewoo/ReWOO-Demo/README.md b/spaces/rewoo/ReWOO-Demo/README.md
deleted file mode 100644
index 6fcaae992db8b30d447526f022f6ad1fee5fd744..0000000000000000000000000000000000000000
--- a/spaces/rewoo/ReWOO-Demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ReWOO Demo
-emoji: 🚀
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingthings3d_subset_384x768.py b/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingthings3d_subset_384x768.py
deleted file mode 100644
index 1351c37a4fb1348062b3ca08d0004841f0690ac1..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingthings3d_subset_384x768.py
+++ /dev/null
@@ -1,106 +0,0 @@
-test_dataset_type = 'Sintel'
-test_data_root = 'data/Sintel'
-
-img_norm_cfg = dict(mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=False)
-
-global_transform = dict(
-    translates=(0.05, 0.05),
-    zoom=(1.0, 1.5),
-    shear=(0.86, 1.16),
-    rotate=(-10., 10.))
-
-relative_transform = dict(
-    translates=(0.00375, 0.00375),
-    zoom=(0.985, 1.015),
-    shear=(1.0, 1.0),
-    rotate=(-1.0, 1.0))
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(
-        type='ColorJitter',
-        brightness=0.5,
-        contrast=0.5,
-        saturation=0.5,
-        hue=0.5),
-    dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='GaussianNoise', sigma_range=(0, 0.04), clamp_range=(0., 1.)),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='RandomFlip', prob=0.5, direction='vertical'),
-    dict(
-        type='RandomAffine',
-        global_transform=global_transform,
-        relative_transform=relative_transform),
-    dict(type='RandomCrop', crop_size=(384, 768)),
-    dict(type='DefaultFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs', 'flow_gt'],
-        meta_keys=[
-            'img_fields', 'ann_fields', 'filename1', 'filename2',
-            'ori_filename1', 'ori_filename2', 'filename_flow',
-            'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg'
-        ]),
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='InputResize', exponent=6),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='TestFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs'],
-        meta_keys=[
-            'flow_gt', 'filename1', 'filename2', 'ori_filename1',
-            'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
-            'scale_factor', 'pad_shape'
-        ])
-]
-
-flyingthings3d_subset_train = dict(
-    type='FlyingThings3DSubset',
-    pipeline=train_pipeline,
-    data_root='data/FlyingThings3D_subset',
-    test_mode=False,
-    direction='forward',
-    scene='left')
-
-test_data_cleanpass = dict(
-    type=test_dataset_type,
-    data_root=test_data_root,
-    pipeline=test_pipeline,
-    test_mode=True,
-    pass_style='clean')
-
-test_data_finalpass = dict(
-    type=test_dataset_type,
-    data_root=test_data_root,
-    pipeline=test_pipeline,
-    test_mode=True,
-    pass_style='final')
-
-data = dict(
-    train_dataloader=dict(
-        samples_per_gpu=1,
-        workers_per_gpu=5,
-        drop_last=True,
-        persistent_workers=True),
-    val_dataloader=dict(
-        samples_per_gpu=1,
-        workers_per_gpu=5,
-        shuffle=False,
-        persistent_workers=True),
-    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=5, shuffle=False),
-    train=flyingthings3d_subset_train,
-    val=dict(
-        type='ConcatDataset',
-        datasets=[test_data_cleanpass, test_data_finalpass],
-        separate_eval=True),
-    test=dict(
-        type='ConcatDataset',
-        datasets=[test_data_cleanpass, test_data_finalpass],
-        separate_eval=True))
diff --git a/spaces/rinong/StyleGAN-NADA/e4e/utils/train_utils.py b/spaces/rinong/StyleGAN-NADA/e4e/utils/train_utils.py
deleted file mode 100644
index 0c55177f7442010bc1fcc64de3d142585c22adc0..0000000000000000000000000000000000000000
--- a/spaces/rinong/StyleGAN-NADA/e4e/utils/train_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-
-def aggregate_loss_dict(agg_loss_dict):
-	mean_vals = {}
-	for output in agg_loss_dict:
-		for key in output:
-			mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]]
-	for key in mean_vals:
-		if len(mean_vals[key]) > 0:
-			mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key])
-		else:
-			print('{} has no value'.format(key))
-			mean_vals[key] = 0
-	return mean_vals
diff --git a/spaces/rodevel1978/llama-2-13b-chat.ggmlv3.q4_K_S/app.py b/spaces/rodevel1978/llama-2-13b-chat.ggmlv3.q4_K_S/app.py
deleted file mode 100644
index b92cfba950c07a4c1f177e74c6fac671fee9c97a..0000000000000000000000000000000000000000
--- a/spaces/rodevel1978/llama-2-13b-chat.ggmlv3.q4_K_S/app.py
+++ /dev/null
@@ -1,273 +0,0 @@
-from typing import Iterator
-
-import gradio as gr
-import torch
-
-from model import get_input_token_length, run
-
-DEFAULT_SYSTEM_PROMPT = """\
-You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\
-"""
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = 4000
-
-DESCRIPTION = """
-# llama-2-13b-chat.ggmlv3.q4_K_S
-"""
-
-LICENSE = """
-<p/>
-
----
-[Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat) by Meta).
-"""
-
-if not torch.cuda.is_available():
-    DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
-
-
-def clear_and_save_textbox(message: str) -> tuple[str, str]:
-    return '', message
-
-
-def display_input(message: str,
-                  history: list[tuple[str, str]]) -> list[tuple[str, str]]:
-    history.append((message, ''))
-    return history
-
-
-def delete_prev_fn(
-        history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
-    try:
-        message, _ = history.pop()
-    except IndexError:
-        message = ''
-    return history, message or ''
-
-
-def generate(
-    message: str,
-    history_with_input: list[tuple[str, str]],
-    system_prompt: str,
-    max_new_tokens: int,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-) -> Iterator[list[tuple[str, str]]]:
-    if max_new_tokens > MAX_MAX_NEW_TOKENS:
-        raise ValueError
-
-    history = history_with_input[:-1]
-    generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
-    try:
-        first_response = next(generator)
-        yield history + [(message, first_response)]
-    except StopIteration:
-        yield history + [(message, '')]
-    for response in generator:
-        yield history + [(message, response)]
-
-
-def process_example(message: str) -> tuple[str, list[tuple[str, str]]]:
-    generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
-    for x in generator:
-        pass
-    return '', x
-
-
-def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None:
-    input_token_length = get_input_token_length(message, chat_history, system_prompt)
-    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
-        raise gr.Error(f'The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.')
-
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(value='Duplicate Space for private use',
-                       elem_id='duplicate-button')
-
-    with gr.Group():
-        chatbot = gr.Chatbot(label='Chatbot')
-        with gr.Row():
-            textbox = gr.Textbox(
-                container=False,
-                show_label=False,
-                placeholder='Type a message...',
-                scale=10,
-            )
-            submit_button = gr.Button('Submit',
-                                      variant='primary',
-                                      scale=1,
-                                      min_width=0)
-    with gr.Row():
-        retry_button = gr.Button('🔄  Retry', variant='secondary')
-        undo_button = gr.Button('↩️ Undo', variant='secondary')
-        clear_button = gr.Button('🗑️  Clear', variant='secondary')
-
-    saved_input = gr.State()
-
-    with gr.Accordion(label='Advanced options', open=False):
-        system_prompt = gr.Textbox(label='System prompt',
-                                   value=DEFAULT_SYSTEM_PROMPT,
-                                   lines=6)
-        max_new_tokens = gr.Slider(
-            label='Max new tokens',
-            minimum=1,
-            maximum=MAX_MAX_NEW_TOKENS,
-            step=1,
-            value=DEFAULT_MAX_NEW_TOKENS,
-        )
-        temperature = gr.Slider(
-            label='Temperature',
-            minimum=0.1,
-            maximum=4.0,
-            step=0.1,
-            value=1.0,
-        )
-        top_p = gr.Slider(
-            label='Top-p (nucleus sampling)',
-            minimum=0.05,
-            maximum=1.0,
-            step=0.05,
-            value=0.95,
-        )
-        top_k = gr.Slider(
-            label='Top-k',
-            minimum=1,
-            maximum=1000,
-            step=1,
-            value=50,
-        )
-
-    gr.Examples(
-        examples=[
-            'When is the best time to visit Beijing, and do you have any suggestions for me?',
-            'Hello there! How are you doing?',
-            'Can you explain briefly to me what is the Python programming language?',
-        ],
-        inputs=textbox,
-        outputs=[textbox, chatbot],
-        fn=process_example,
-        cache_examples=True,
-    )
-
-    gr.Markdown(LICENSE)
-
-    textbox.submit(
-        fn=clear_and_save_textbox,
-        inputs=textbox,
-        outputs=[textbox, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=check_input_token_length,
-        inputs=[saved_input, chatbot, system_prompt],
-        api_name=False,
-        queue=False,
-    ).success(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    button_event_preprocess = submit_button.click(
-        fn=clear_and_save_textbox,
-        inputs=textbox,
-        outputs=[textbox, saved_input],
-        api_name="submit",
-#        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=check_input_token_length,
-        inputs=[saved_input, chatbot, system_prompt],
-        api_name=False,
-        queue=False,
-    ).success(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    retry_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    undo_button.click(
-
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=lambda x: x,
-        inputs=[saved_input],
-        outputs=textbox,
-        api_name=False,
-        queue=False,
-    )
-
-    clear_button.click(
-        fn=lambda: ([], ''),
-        outputs=[chatbot, saved_input],
-        queue=False,
-        api_name=False,
-    )
-
-demo.queue(max_size=20).launch()
diff --git a/spaces/safi842/FashionGen/netdissect/upsegmodel/prroi_pool/test_prroi_pooling2d.py b/spaces/safi842/FashionGen/netdissect/upsegmodel/prroi_pool/test_prroi_pooling2d.py
deleted file mode 100644
index a29d92c80538f5550808dc51f92dcaf65cbd9fb0..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/netdissect/upsegmodel/prroi_pool/test_prroi_pooling2d.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : test_prroi_pooling2d.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 18/02/2018
-#
-# This file is part of Jacinle.
-
-import unittest
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from jactorch.utils.unittest import TorchTestCase
-
-from prroi_pool import PrRoIPool2D
-
-
-class TestPrRoIPool2D(TorchTestCase):
-    def test_forward(self):
-        pool = PrRoIPool2D(7, 7, spatial_scale=0.5)
-        features = torch.rand((4, 16, 24, 32)).cuda()
-        rois = torch.tensor([
-            [0, 0, 0, 14, 14],
-            [1, 14, 14, 28, 28],
-        ]).float().cuda()
-
-        out = pool(features, rois)
-        out_gold = F.avg_pool2d(features, kernel_size=2, stride=1)
-
-        self.assertTensorClose(out, torch.stack((
-            out_gold[0, :, :7, :7],
-            out_gold[1, :, 7:14, 7:14],
-        ), dim=0))
-
-    def test_backward_shapeonly(self):
-        pool = PrRoIPool2D(2, 2, spatial_scale=0.5)
-
-        features = torch.rand((4, 2, 24, 32)).cuda()
-        rois = torch.tensor([
-            [0, 0, 0, 4, 4],
-            [1, 14, 14, 18, 18],
-        ]).float().cuda()
-        features.requires_grad = rois.requires_grad = True
-        out = pool(features, rois)
-
-        loss = out.sum()
-        loss.backward()
-
-        self.assertTupleEqual(features.size(), features.grad.size())
-        self.assertTupleEqual(rois.size(), rois.grad.size())
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/saurav-sabu/Car-Price-Prediction/README.md b/spaces/saurav-sabu/Car-Price-Prediction/README.md
deleted file mode 100644
index 27506371feef8f1419a0cd5a75db98b6933523b5..0000000000000000000000000000000000000000
--- a/spaces/saurav-sabu/Car-Price-Prediction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Car Price Prediction
-emoji: 👀
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sccstandardteam/ChuanhuChatGPT/assets/custom.css b/spaces/sccstandardteam/ChuanhuChatGPT/assets/custom.css
deleted file mode 100644
index c094258d4a9e61a01ec3f58a2549315d2614c709..0000000000000000000000000000000000000000
--- a/spaces/sccstandardteam/ChuanhuChatGPT/assets/custom.css
+++ /dev/null
@@ -1,500 +0,0 @@
-:root {
-    --chatbot-color-light: #000000;
-    --chatbot-color-dark: #FFFFFF;
-    --chatbot-background-color-light: #F3F3F3;
-    --chatbot-background-color-dark: #121111;
-    --message-user-background-color-light: #95EC69;
-    --message-user-background-color-dark: #26B561;
-    --message-bot-background-color-light: #FFFFFF;
-    --message-bot-background-color-dark: #2C2C2C;
-}
-
-#app_title {
-    font-weight: var(--prose-header-text-weight);
-    font-size: var(--text-xxl);
-    line-height: 1.3;
-    text-align: left;
-    margin-top: 6px;
-    white-space: nowrap;
-}
-#description {
-    text-align: center;
-    margin: 32px 0 4px 0;
-}
-
-/* gradio的页脚信息 */
-footer {
-    /* display: none !important; */
-    margin-top: .2em !important;
-    font-size: 85%;
-}
-#footer {
-    text-align: center;
-}
-#footer div {
-    display: inline-block;
-}
-#footer .versions{
-    font-size: 85%;
-    opacity: 0.60;
-}
-
-#float_display {
-    position: absolute;
-    max-height: 30px;
-}
-/* user_info */
-#user_info {
-    white-space: nowrap;
-    position: absolute; left: 8em; top: .2em;
-    z-index: var(--layer-2);
-    box-shadow: var(--block-shadow);
-    border: none; border-radius: var(--block-label-radius);
-    background: var(--color-accent);
-    padding: var(--block-label-padding);
-    font-size: var(--block-label-text-size); line-height: var(--line-sm);
-    width: auto; min-height: 30px!important;
-    opacity: 1;
-    transition: opacity 0.3s ease-in-out;
-}
-#user_info .wrap {
-    opacity: 0;
-}
-#user_info p {
-    color: white;
-    font-weight: var(--block-label-text-weight);
-}
-#user_info.hideK {
-    opacity: 0;
-    transition: opacity 1s ease-in-out;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: ui-monospace, "SF Mono", "SFMono-Regular", "Menlo", "Consolas", "Liberation Mono", "Microsoft Yahei UI", "Microsoft Yahei", monospace;
-    /* Windows下中文的monospace会fallback为新宋体，实在太丑，这里折中使用微软雅黑 */
-    color: var(--body-text-color-subdued);
-}
-
-#status_display {
-    transition: all 0.6s;
-}
-#chuanhu_chatbot {
-    transition: height 0.3s ease;
-}
-
-/* usage_display */
-.insert_block {
-    position: relative;
-    margin: 0;
-    padding: .5em 1em;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius);
-    background: var(--block-background-fill);
-    width: 100%;
-    line-height: var(--line-sm);
-    min-height: 2em;
-}
-#usage_display p, #usage_display span {
-    margin: 0;
-    font-size: .85em;
-    color: var(--body-text-color-subdued);
-}
-.progress-bar {
-    background-color: var(--input-background-fill);;
-    margin: .5em 0 !important;
-    height: 20px;
-    border-radius: 10px;
-    overflow: hidden;
-}
-.progress {
-    background-color: var(--block-title-background-fill);
-    height: 100%;
-    border-radius: 10px;
-    text-align: right;
-    transition: width 0.5s ease-in-out;
-}
-.progress-text {
-    /* color: white; */
-    color: var(--color-accent) !important;
-    font-size: 1em !important;
-    font-weight: bold;
-    padding-right: 10px;
-    line-height: 20px;
-}
-
-.apSwitch {
-    top: 2px;
-    display: inline-block;
-    height: 24px;
-    position: relative;
-    width: 48px;
-    border-radius: 12px;
-}
-.apSwitch input {
-    display: none !important;
-}
-.apSlider {
-    background-color: var(--neutral-200);
-    bottom: 0;
-    cursor: pointer;
-    left: 0;
-    position: absolute;
-    right: 0;
-    top: 0;
-    transition: .4s;
-    font-size: 18px;
-    border-radius: 12px;
-}
-.apSlider::before {
-    bottom: -1.5px;
-    left: 1px;
-    position: absolute;
-    transition: .4s;
-    content: "🌞";
-}
-input:checked + .apSlider {
-    background-color: var(--primary-600);
-}
-input:checked + .apSlider::before {
-    transform: translateX(23px);
-    content:"🌚";
-}
-
-/* Override Slider Styles (for webkit browsers like Safari and Chrome)
- * 好希望这份提案能早日实现 https://github.com/w3c/csswg-drafts/issues/4410
- * 进度滑块在各个平台还是太不统一了
- */
-input[type="range"] {
-    -webkit-appearance: none;
-    height: 4px;
-    background: var(--input-background-fill);
-    border-radius: 5px;
-    background-image: linear-gradient(var(--primary-500),var(--primary-500));
-    background-size: 0% 100%;
-    background-repeat: no-repeat;
-}
-input[type="range"]::-webkit-slider-thumb {
-    -webkit-appearance: none;
-    height: 20px;
-    width: 20px;
-    border-radius: 50%;
-    border: solid 0.5px #ddd;
-    background-color: white;
-    cursor: ew-resize;
-    box-shadow: var(--input-shadow);
-    transition: background-color .1s ease;
-}
-input[type="range"]::-webkit-slider-thumb:hover {
-    background: var(--neutral-50);
-}
-input[type=range]::-webkit-slider-runnable-track {
-    -webkit-appearance: none;
-    box-shadow: none;
-    border: none;
-    background: transparent;
-}
-
-#submit_btn, #cancel_btn {
-    height: 42px !important;
-}
-#submit_btn::before {
-    content: url("data:image/svg+xml, %3Csvg width='21px' height='20px' viewBox='0 0 21 20' version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E %3Cg id='page' stroke='none' stroke-width='1' fill='none' fill-rule='evenodd'%3E %3Cg id='send' transform='translate(0.435849, 0.088463)' fill='%23FFFFFF' fill-rule='nonzero'%3E %3Cpath d='M0.579148261,0.0428666046 C0.301105539,-0.0961547561 -0.036517765,0.122307382 0.0032026237,0.420210298 L1.4927172,18.1553639 C1.5125774,18.4334066 1.79062012,18.5922882 2.04880264,18.4929872 L8.24518329,15.8913017 L11.6412765,19.7441794 C11.8597387,19.9825018 12.2370824,19.8832008 12.3165231,19.5852979 L13.9450591,13.4882182 L19.7839562,11.0255541 C20.0619989,10.8865327 20.0818591,10.4694687 19.7839562,10.3105871 L0.579148261,0.0428666046 Z M11.6138902,17.0883151 L9.85385903,14.7195502 L0.718169621,0.618812241 L12.69945,12.9346347 L11.6138902,17.0883151 Z' id='shape'%3E%3C/path%3E %3C/g%3E %3C/g%3E %3C/svg%3E");
-    height: 21px;
-}
-#cancel_btn::before {
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-    height: 21px;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色（默认） */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-background-color-light) !important;
-    color: var(--chatbot-color-light) !important;
-}
-[data-testid = "bot"] {
-    background-color: var(--message-bot-background-color-light) !important;
-}
-[data-testid = "user"] {
-    background-color: var(--message-user-background-color-light) !important;
-}
-/* 暗色 */
-.dark #chuanhu_chatbot {
-    background-color: var(--chatbot-background-color-dark) !important;
-    color: var(--chatbot-color-dark) !important;
-}
-.dark [data-testid = "bot"] {
-    background-color: var(--message-bot-background-color-dark) !important;
-}
-.dark [data-testid = "user"] {
-    background-color: var(--message-user-background-color-dark) !important;
-}
-
-/* 屏幕宽度大于等于500px的设备 */
-/* update on 2023.4.8: 高度的细致调整已写入JavaScript */
-@media screen and (min-width: 500px) {
-    #chuanhu_chatbot {
-        height: calc(100vh - 200px);
-    }
-    #chuanhu_chatbot .wrap {
-        max-height: calc(100vh - 200px - var(--line-sm)*1rem - 2*var(--block-label-margin) );
-    }
-}
-/* 屏幕宽度小于500px的设备 */
-@media screen and (max-width: 499px) {
-    #chuanhu_chatbot {
-        height: calc(100vh - 140px);
-    }
-    #chuanhu_chatbot .wrap {
-        max-height: calc(100vh - 140px - var(--line-sm)*1rem - 2*var(--block-label-margin) );
-    }
-    [data-testid = "bot"] {
-        max-width: 95% !important;
-    }
-    #app_title h1{
-        letter-spacing: -1px; font-size: 22px;
-    }
-}
-#chuanhu_chatbot .wrap {
-    overflow-x: hidden;
-}
-/* 对话气泡 */
-.message {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
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-    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: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
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-    border-bottom-right-radius: 0 !important;
-}
-
-.message p {
-    margin-top: 0.6em !important;
-    margin-bottom: 0.6em !important;
-}
-.message p:first-child { margin-top: 0 !important; }
-.message p:last-of-type { margin-bottom: 0 !important; }
-
-.message .md-message {
-    display: block;
-    padding: 0 !important;
-}
-.message .raw-message {
-    display: block;
-    padding: 0 !important;
-    white-space: pre-wrap;
-}
-.raw-message.hideM, .md-message.hideM {
-    display: none;
-}
-
-/* custom buttons */
-.chuanhu-btn {
-    border-radius: 5px;
-    /* background-color: #E6E6E6 !important; */
-    color: rgba(120, 120, 120, 0.64) !important;
-    padding: 4px !important;
-    position: absolute;
-    right: -22px;
-    cursor: pointer !important;
-    transition: color .2s ease, background-color .2s ease;
-}
-.chuanhu-btn:hover {
-    background-color: rgba(167, 167, 167, 0.25) !important;
-    color: unset !important;
-}
-.chuanhu-btn:active {
-    background-color: rgba(167, 167, 167, 0.5) !important;
-}
-.chuanhu-btn:focus {
-    outline: none;
-}
-.copy-bot-btn {
-    /* top: 18px; */
-    bottom: 0;
-}
-.toggle-md-btn {
-    /* top: 0; */
-    bottom: 20px;
-}
-.copy-code-btn {
-    position: relative;
-    float: right;
-    font-size: 1em;
-    cursor: pointer;
-}
-
-.message-wrap>div img{
-    border-radius: 10px !important;
-}
-
-/* history message */
-.wrap>.history-message {
-    padding: 10px !important;
-}
-.history-message {
-    /* padding: 0 !important; */
-    opacity: 80%;
-    display: flex;
-    flex-direction: column;
-}
-.history-message>.history-message {
-    padding: 0 !important;
-}
-.history-message>.message-wrap {
-    padding: 0 !important;
-    margin-bottom: 16px;
-}
-.history-message>.message {
-    margin-bottom: 16px;
-}
-.wrap>.history-message::after {
-    content: "";
-    display: block;
-    height: 2px;
-    background-color: var(--body-text-color-subdued);
-    margin-bottom: 10px;
-    margin-top: -10px;
-    clear: both;
-}
-.wrap>.history-message>:last-child::after {
-    content: "仅供查看";
-    display: block;
-    text-align: center;
-    color: var(--body-text-color-subdued);
-    font-size: 0.8em;
-}
-
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-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);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 0.6em 2em 1em 0.2em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-.message pre {
-    padding: 0 !important;
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
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-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
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-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
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-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
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-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/scedlatioru/img-to-music/app.py b/spaces/scedlatioru/img-to-music/app.py
deleted file mode 100644
index 6fe764aa6ac7777137ac18718e8878e7bfcb81eb..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/app.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import time
-import base64
-import gradio as gr
-from sentence_transformers import SentenceTransformer
-
-import httpx
-import json
-
-import os
-import requests
-import urllib
-
-from os import path
-from pydub import AudioSegment
-
-#img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator")
-img_to_text = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")
-
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-def get_prompts(uploaded_image, track_duration, gen_intensity, gen_mode):
-  print("calling clip interrogator")
-  #prompt = img_to_text(uploaded_image, "ViT-L (best for Stable Diffusion 1.*)", "fast", fn_index=1)[0]
-  prompt = img_to_text(uploaded_image, 'fast', 4, fn_index=1)[0]
-  print(prompt)
-  music_result = generate_track_by_prompt(prompt, track_duration, gen_intensity, gen_mode)
-  print(music_result)
-  return music_result[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat
-
-minilm = SentenceTransformer('all-MiniLM-L6-v2')
-mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
-
-
-def get_track_by_tags(tags, pat, duration, gen_intensity, gen_mode, maxit=20):
-    
-    r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
-                   json={
-                       "method": "RecordTrackTTM",
-                       "params": {
-                           "pat": pat,
-                           "duration": duration,
-                           "format": "wav",
-                           "intensity":gen_intensity,
-                           "tags": tags,
-                           "mode": gen_mode
-                       }
-                   })
-
-    rdata = json.loads(r.text)
-    assert rdata['status'] == 1, rdata['error']['text']
-    trackurl = rdata['data']['tasks'][0]['download_link']
-
-    print('Generating track ', end='')
-    for i in range(maxit):
-        r = httpx.get(trackurl)
-        if r.status_code == 200:
-            return trackurl
-        time.sleep(1)
-
-
-def generate_track_by_prompt(prompt, duration, gen_intensity, gen_mode):
-    try:
-        pat = get_pat("prodia@prodia.com")
-        _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0]
-        result = get_track_by_tags(tags, pat, int(duration), gen_intensity, gen_mode)
-        print(result)
-        return result, ",".join(tags), "Success"
-    except Exception as e:
-        return None, "", str(e)
-
-def convert_mp3_to_wav(mp3_filepath):
- 
-  url = mp3_filepath
-  save_as = "file.mp3"
-  
-  data = urllib.request.urlopen(url)
-
-  f = open(save_as,'wb')
-  f.write(data.read())
-  f.close()
-  
-  wave_file="file.wav"
-  
-  sound = AudioSegment.from_mp3(save_as)
-  sound.export(wave_file, format="wav")
-  
-  return wave_file
-
-article = """
-    
-    <div class="footer">
-        <p>
-         
-        Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates 🤗
-        </p>
-    </div>
-    
-    <div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;margin-bottom: 30px;">
-        <p style="font-size: 0.8em;margin-bottom: 4px;">You may also like: </p>
-        <div id="may-like" style="display: flex;flex-wrap: wrap;align-items: center;height: 20px;">
-            <svg height="20" width="122" style="margin-left:4px;margin-bottom: 6px;">       
-                 <a href="https://huggingface.co/spaces/fffiloni/spectrogram-to-music" target="_blank">
-                    <image href="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue" src="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue.png" height="20"/>
-                 </a>
-            </svg>
-        </div>
-    </div>
-
-    
-"""
-
-with gr.Blocks(css="style.css") as demo:
-    with gr.Column(elem_id="col-container"):
-        
-        gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-                <div
-                style="
-                    display: inline-flex;
-                    align-items: center;
-                    gap: 0.8rem;
-                    font-size: 1.75rem;
-                "
-                >
-                <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-                    Image to Music
-                </h1>
-                </div>
-                <p style="margin-bottom: 10px; font-size: 94%">
-                Sends an image in to <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator" target="_blank">CLIP Interrogator</a>
-                to generate a text prompt which is then run through 
-                <a href="https://huggingface.co/Mubert" target="_blank">Mubert</a> text-to-music to generate music from the input image!
-                </p>
-            </div>""")
-    
-        input_img = gr.Image(type="filepath", elem_id="input-img")
-        music_output = gr.Audio(label="Result", type="filepath", elem_id="music-output").style(height="5rem")
-        
-        with gr.Group(elem_id="share-btn-container"):
-            community_icon = gr.HTML(community_icon_html, visible=False)
-            loading_icon = gr.HTML(loading_icon_html, visible=False)
-            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
-        with gr.Accordion(label="Music Generation Options", open=False):
-            track_duration = gr.Slider(minimum=20, maximum=120, value=30, step=5, label="Track duration", elem_id="duration-inp")
-            with gr.Row():
-                gen_intensity = gr.Dropdown(choices=["low", "medium", "high"], value="medium", label="Intensity")
-                gen_mode = gr.Radio(label="mode", choices=["track", "loop"], value="track")
-        
-        generate = gr.Button("Generate Music from Image")
-
-        gr.HTML(article)
-    
-    generate.click(get_prompts, inputs=[input_img,track_duration,gen_intensity,gen_mode], outputs=[music_output, share_button, community_icon, loading_icon], api_name="i2m")
-    share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=32, concurrency_count=20).launch()
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/Crack De Fifa 2008 No Cd __LINK__.md b/spaces/scedlatioru/img-to-music/example/Crack De Fifa 2008 No Cd __LINK__.md
deleted file mode 100644
index a8318056598622dd311ca8e909a52cac7dc42c0d..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Crack De Fifa 2008 No Cd __LINK__.md	
+++ /dev/null
@@ -1,74 +0,0 @@
-<h2>crack de fifa 2008 no cd</h2><br /><p><b><b>Download Zip</b> &#9913;&#9913;&#9913; <a href="https://gohhs.com/2uEzsu">https://gohhs.com/2uEzsu</a></b></p><br /><br />
-
-All the cracks and game fixes are free for everyone. We will update this page with new Fifa 08 cracks daily. If you want to download crack for your game, then you're in the right place. Here you can find the latest and updated list of the latest cracked games for PC. If your game is in the list, you can download it directly from the download page. You can check all the latest game cheats, game keys, hacks, game trainer and patches for every game listed below. Just check the list of supported games and follow the download instructions.
-
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-
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-
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-
-F 4fefd39f24<br />
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-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Regnum Online Ximerin Hack.md b/spaces/scedlatioru/img-to-music/example/Regnum Online Ximerin Hack.md
deleted file mode 100644
index 8a8a837a3e267ce2e111fcc636fad13195d3c617..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Regnum Online Ximerin Hack.md	
+++ /dev/null
@@ -1,43 +0,0 @@
-
-<h1>How to Get Free Ximerin in Regnum Online</h1>
-<p>Ximerin is the premium currency used in Regnum Online, a free-to-play MMORPG that features three realms, epic battles, and diverse landscapes. Ximerin can be used to buy various items such as mounts, upgrades, consumables, dyes, and customization options. However, Ximerin is not easy to obtain, as it requires real money or a gift from another player.</p>
-<p>Fortunately, there is a way to get free Ximerin without spending a dime or relying on others. This method involves using a hack tool that can generate unlimited amounts of Ximerin for your account. The hack tool is safe, easy to use, and undetectable by the game's anti-cheat system. Here is how you can use it:</p>
-<h2>Regnum Online Ximerin Hack</h2><br /><p><b><b>Download Zip</b> &#10027; <a href="https://gohhs.com/2uEzG6">https://gohhs.com/2uEzG6</a></b></p><br /><br />
-<ol>
-<li>Download the hack tool from this link: <a href="https://gwbc.org/wp-content/uploads/2022/07/Regnum_Online_Ximerin_Hack.pdf">Regnum Online Ximerin Hack</a>. This is the only working and updated hack tool for Regnum Online as of 2023.</li>
-<li>Run the hack tool and enter your username and server. Make sure you are logged out of the game before using the hack tool.</li>
-<li>Select the amount of Ximerin you want to generate. You can choose from 4000 to 56000 Ximerin per use.</li>
-<li>Click on the "Generate" button and wait for the hack tool to process your request.</li>
-<li>Log in to your account and check your Ximerin balance. You should see the amount you requested added to your account.</li>
-<li>Enjoy your free Ximerin and use it to buy whatever you want in the game!</li>
-</ol>
-<p>Note: The hack tool can be used once every 24 hours per account. Do not abuse it or you may risk getting banned by the game's moderators. Use it wisely and responsibly.</p>
-<p>Disclaimer: This article is for educational purposes only. We do not condone hacking or cheating in any way. Use this hack tool at your own risk. We are not responsible for any consequences that may arise from using it.</p>
-
-<h2>Why You Need Ximerin in Regnum Online</h2>
-<p>Ximerin is not just a luxury in Regnum Online, it is also a necessity. There are many benefits of having Ximerin in your account, such as:</p>
-<ul>
-<li>You can buy mounts that will increase your speed and mobility in the game. Mounts are essential for traveling across the vast lands of Regnum, especially when you need to join a war or a raid. Mounts also have different appearances that can suit your personal style and preference.</li>
-<li>You can buy upgrades that will enhance your equipment and skills. Upgrades include lucky boxes that contain random items, engrave sockets that allow you to embed magic gems into your armor and weapons, and skill reset scrolls that let you redistribute your discipline points.</li>
-<li>You can buy consumables that will give you various advantages in combat and exploration. Consumables include potions that restore your health and mana, scrolls that teleport you to different locations, and fireworks that create spectacular effects in the sky.</li>
-<li>You can buy dyes that will change the color of your armor and weapons. Dyes are a great way to customize your appearance and express your personality. You can mix and match different colors to create unique combinations.</li>
-<li>You can buy customization options that will modify your character's face, hair, skin, and voice. Customization options are a fun way to alter your character's look and sound. You can choose from different presets or use sliders to adjust the details.</li>
-</ul>
-<p>As you can see, Ximerin is a valuable resource that can improve your gaming experience in Regnum Online. However, Ximerin is not cheap, and you may not have enough money or time to buy it regularly. That's why you need to use the hack tool that we mentioned earlier.</p>
-
-<h2>How to Use Ximerin Wisely in Regnum Online</h2>
-<p>Now that you have free Ximerin in your account, you may be tempted to spend it all at once. However, we advise you to use it wisely and responsibly. Here are some tips on how to use Ximerin effectively in Regnum Online:</p>
-<ul>
-<li>Buy only what you need. Don't waste your Ximerin on items that you don't really use or want. Focus on the items that will help you progress in the game and enjoy it more.</li>
-<li>Compare prices and quality. Don't buy the first item that you see. Check out different merchants and compare their prices and quality. You may find better deals or higher quality items elsewhere.</li>
-<li>Save some for later. Don't spend all your Ximerin at once. Save some for future needs or emergencies. You never know when you might need some extra Ximerin for a special occasion or a rare opportunity.</li>
-<li>Share with others. Don't be greedy or selfish with your Ximerin. Share some with your friends or guildmates who may need it more than you. You can also gift Ximerin to other players as a sign of gratitude or friendship.</li>
-</ul>
-<p>By following these tips, you can make the most out of your free Ximerin and enjoy Regnum Online even more.</p>
-
-<h2>Conclusion</h2>
-<p>In conclusion, Ximerin is an important currency in Regnum Online that can enhance your gameplay and satisfaction. However, Ximerin is hard to obtain with real money or gifts from other players. That's why we recommend using the hack tool that we provided in this article. The hack tool can generate unlimited amounts of free Ximerin for your account safely and easily. All you have to do is follow the simple steps that we explained above.</p>
-<p></p>
-<p>So what are you waiting for? Download the hack tool now and get free Ximerin in Regnum Online today!</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sczhou/CodeFormer/CodeFormer/basicsr/data/data_util.py b/spaces/sczhou/CodeFormer/CodeFormer/basicsr/data/data_util.py
deleted file mode 100644
index 63b1bce8e089485182c962e830a163d6d0059da8..0000000000000000000000000000000000000000
--- a/spaces/sczhou/CodeFormer/CodeFormer/basicsr/data/data_util.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import cv2
-import numpy as np
-import torch
-from os import path as osp
-from torch.nn import functional as F
-
-from basicsr.data.transforms import mod_crop
-from basicsr.utils import img2tensor, scandir
-
-
-def read_img_seq(path, require_mod_crop=False, scale=1):
-    """Read a sequence of images from a given folder path.
-
-    Args:
-        path (list[str] | str): List of image paths or image folder path.
-        require_mod_crop (bool): Require mod crop for each image.
-            Default: False.
-        scale (int): Scale factor for mod_crop. Default: 1.
-
-    Returns:
-        Tensor: size (t, c, h, w), RGB, [0, 1].
-    """
-    if isinstance(path, list):
-        img_paths = path
-    else:
-        img_paths = sorted(list(scandir(path, full_path=True)))
-    imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
-    if require_mod_crop:
-        imgs = [mod_crop(img, scale) for img in imgs]
-    imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
-    imgs = torch.stack(imgs, dim=0)
-    return imgs
-
-
-def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'):
-    """Generate an index list for reading `num_frames` frames from a sequence
-    of images.
-
-    Args:
-        crt_idx (int): Current center index.
-        max_frame_num (int): Max number of the sequence of images (from 1).
-        num_frames (int): Reading num_frames frames.
-        padding (str): Padding mode, one of
-            'replicate' | 'reflection' | 'reflection_circle' | 'circle'
-            Examples: current_idx = 0, num_frames = 5
-            The generated frame indices under different padding mode:
-            replicate: [0, 0, 0, 1, 2]
-            reflection: [2, 1, 0, 1, 2]
-            reflection_circle: [4, 3, 0, 1, 2]
-            circle: [3, 4, 0, 1, 2]
-
-    Returns:
-        list[int]: A list of indices.
-    """
-    assert num_frames % 2 == 1, 'num_frames should be an odd number.'
-    assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.'
-
-    max_frame_num = max_frame_num - 1  # start from 0
-    num_pad = num_frames // 2
-
-    indices = []
-    for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
-        if i < 0:
-            if padding == 'replicate':
-                pad_idx = 0
-            elif padding == 'reflection':
-                pad_idx = -i
-            elif padding == 'reflection_circle':
-                pad_idx = crt_idx + num_pad - i
-            else:
-                pad_idx = num_frames + i
-        elif i > max_frame_num:
-            if padding == 'replicate':
-                pad_idx = max_frame_num
-            elif padding == 'reflection':
-                pad_idx = max_frame_num * 2 - i
-            elif padding == 'reflection_circle':
-                pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
-            else:
-                pad_idx = i - num_frames
-        else:
-            pad_idx = i
-        indices.append(pad_idx)
-    return indices
-
-
-def paired_paths_from_lmdb(folders, keys):
-    """Generate paired paths from lmdb files.
-
-    Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:
-
-    lq.lmdb
-    ├── data.mdb
-    ├── lock.mdb
-    ├── meta_info.txt
-
-    The data.mdb and lock.mdb are standard lmdb files and you can refer to
-    https://lmdb.readthedocs.io/en/release/ for more details.
-
-    The meta_info.txt is a specified txt file to record the meta information
-    of our datasets. It will be automatically created when preparing
-    datasets by our provided dataset tools.
-    Each line in the txt file records
-    1)image name (with extension),
-    2)image shape,
-    3)compression level, separated by a white space.
-    Example: `baboon.png (120,125,3) 1`
-
-    We use the image name without extension as the lmdb key.
-    Note that we use the same key for the corresponding lq and gt images.
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-            Note that this key is different from lmdb keys.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
-        raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb '
-                         f'formats. But received {input_key}: {input_folder}; '
-                         f'{gt_key}: {gt_folder}')
-    # ensure that the two meta_info files are the same
-    with open(osp.join(input_folder, 'meta_info.txt')) as fin:
-        input_lmdb_keys = [line.split('.')[0] for line in fin]
-    with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
-        gt_lmdb_keys = [line.split('.')[0] for line in fin]
-    if set(input_lmdb_keys) != set(gt_lmdb_keys):
-        raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.')
-    else:
-        paths = []
-        for lmdb_key in sorted(input_lmdb_keys):
-            paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)]))
-        return paths
-
-
-def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl):
-    """Generate paired paths from an meta information file.
-
-    Each line in the meta information file contains the image names and
-    image shape (usually for gt), separated by a white space.
-
-    Example of an meta information file:
-    ```
-    0001_s001.png (480,480,3)
-    0001_s002.png (480,480,3)
-    ```
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-        meta_info_file (str): Path to the meta information file.
-        filename_tmpl (str): Template for each filename. Note that the
-            template excludes the file extension. Usually the filename_tmpl is
-            for files in the input folder.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    with open(meta_info_file, 'r') as fin:
-        gt_names = [line.split(' ')[0] for line in fin]
-
-    paths = []
-    for gt_name in gt_names:
-        basename, ext = osp.splitext(osp.basename(gt_name))
-        input_name = f'{filename_tmpl.format(basename)}{ext}'
-        input_path = osp.join(input_folder, input_name)
-        gt_path = osp.join(gt_folder, gt_name)
-        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
-    return paths
-
-
-def paired_paths_from_folder(folders, keys, filename_tmpl):
-    """Generate paired paths from folders.
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-        filename_tmpl (str): Template for each filename. Note that the
-            template excludes the file extension. Usually the filename_tmpl is
-            for files in the input folder.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    input_paths = list(scandir(input_folder))
-    gt_paths = list(scandir(gt_folder))
-    assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: '
-                                               f'{len(input_paths)}, {len(gt_paths)}.')
-    paths = []
-    for gt_path in gt_paths:
-        basename, ext = osp.splitext(osp.basename(gt_path))
-        input_name = f'{filename_tmpl.format(basename)}{ext}'
-        input_path = osp.join(input_folder, input_name)
-        assert input_name in input_paths, (f'{input_name} is not in ' f'{input_key}_paths.')
-        gt_path = osp.join(gt_folder, gt_path)
-        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
-    return paths
-
-
-def paths_from_folder(folder):
-    """Generate paths from folder.
-
-    Args:
-        folder (str): Folder path.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-
-    paths = list(scandir(folder))
-    paths = [osp.join(folder, path) for path in paths]
-    return paths
-
-
-def paths_from_lmdb(folder):
-    """Generate paths from lmdb.
-
-    Args:
-        folder (str): Folder path.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    if not folder.endswith('.lmdb'):
-        raise ValueError(f'Folder {folder}folder should in lmdb format.')
-    with open(osp.join(folder, 'meta_info.txt')) as fin:
-        paths = [line.split('.')[0] for line in fin]
-    return paths
-
-
-def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
-    """Generate Gaussian kernel used in `duf_downsample`.
-
-    Args:
-        kernel_size (int): Kernel size. Default: 13.
-        sigma (float): Sigma of the Gaussian kernel. Default: 1.6.
-
-    Returns:
-        np.array: The Gaussian kernel.
-    """
-    from scipy.ndimage import filters as filters
-    kernel = np.zeros((kernel_size, kernel_size))
-    # set element at the middle to one, a dirac delta
-    kernel[kernel_size // 2, kernel_size // 2] = 1
-    # gaussian-smooth the dirac, resulting in a gaussian filter
-    return filters.gaussian_filter(kernel, sigma)
-
-
-def duf_downsample(x, kernel_size=13, scale=4):
-    """Downsamping with Gaussian kernel used in the DUF official code.
-
-    Args:
-        x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
-        kernel_size (int): Kernel size. Default: 13.
-        scale (int): Downsampling factor. Supported scale: (2, 3, 4).
-            Default: 4.
-
-    Returns:
-        Tensor: DUF downsampled frames.
-    """
-    assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.'
-
-    squeeze_flag = False
-    if x.ndim == 4:
-        squeeze_flag = True
-        x = x.unsqueeze(0)
-    b, t, c, h, w = x.size()
-    x = x.view(-1, 1, h, w)
-    pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
-    x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')
-
-    gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
-    gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0)
-    x = F.conv2d(x, gaussian_filter, stride=scale)
-    x = x[:, :, 2:-2, 2:-2]
-    x = x.view(b, t, c, x.size(2), x.size(3))
-    if squeeze_flag:
-        x = x.squeeze(0)
-    return x
diff --git a/spaces/sdhsdhk/bingo111/tailwind.config.js b/spaces/sdhsdhk/bingo111/tailwind.config.js
deleted file mode 100644
index 03da3c3c45be6983b9f5ffa6df5f1fd0870e9636..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingo111/tailwind.config.js
+++ /dev/null
@@ -1,48 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/ui/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  "darkMode": "class",
-  theme: {
-    extend: {
-      colors: {
-        'primary-blue': 'rgb(var(--color-primary-blue) / <alpha-value>)',
-        secondary: 'rgb(var(--color-secondary) / <alpha-value>)',
-        'primary-background': 'rgb(var(--primary-background) / <alpha-value>)',
-        'primary-text': 'rgb(var(--primary-text) / <alpha-value>)',
-        'secondary-text': 'rgb(var(--secondary-text) / <alpha-value>)',
-        'light-text': 'rgb(var(--light-text) / <alpha-value>)',
-        'primary-border': 'rgb(var(--primary-border) / <alpha-value>)',
-      },
-      keyframes: {
-        slideDownAndFade: {
-          from: { opacity: 0, transform: 'translateY(-2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideLeftAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-        slideUpAndFade: {
-          from: { opacity: 0, transform: 'translateY(2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideRightAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-      },
-      animation: {
-        slideDownAndFade: 'slideDownAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideLeftAndFade: 'slideLeftAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideUpAndFade: 'slideUpAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideRightAndFade: 'slideRightAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-      },
-    },
-  },
-  plugins: [require('@headlessui/tailwindcss'), require('tailwind-scrollbar')],
-}
diff --git a/spaces/sgxz/bingo/src/components/settings.tsx b/spaces/sgxz/bingo/src/components/settings.tsx
deleted file mode 100644
index 45ba6044ff9cbe584f62292a49ea2ace9acc1f48..0000000000000000000000000000000000000000
--- a/spaces/sgxz/bingo/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
-  Dialog,
-  DialogContent,
-  DialogDescription,
-  DialogFooter,
-  DialogHeader,
-  DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  const [loc, setLoc] = useAtom(hashAtom)
-  const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
-  const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
-  const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-
-  if (loc === 'settings') {
-    return (
-      <Dialog open onOpenChange={() => setLoc('')} modal>
-        <DialogContent>
-          <DialogHeader>
-            <DialogTitle>设置你的用户信息</DialogTitle>
-            <DialogDescription>
-              请使用 Edge 浏览器
-              <ExternalLink
-                href="https://www.bing.com/turing/captcha/challenge"
-              >
-                打开并登录 Bing
-              </ExternalLink>
-              ，然后再打开
-              <ExternalLink href="https://www.bing.com/turing/captcha/challenge">Challenge 接口</ExternalLink>
-              右键 》检查。打开开发者工具，在网络里面找到 Create 接口 》右键复制》复制为 cURL(bash)，粘贴到此处，然后保存。
-              <div className="h-2" />
-              图文示例：
-              <ExternalLink href="https://github.com/weaigc/bingo#如何获取%20BING_HEADER">如何获取 BING_HEADER</ExternalLink>
-            </DialogDescription>
-          </DialogHeader>
-          <div className="flex gap-4">
-
-          </div>
-          <Input
-            value={curlValue}
-            placeholder="在此填写用户信息，格式: curl 'https://www.bing.com/turing/captcha/challenge' ..."
-            onChange={e => setCurlValue(e.target.value)}
-          />
-          <div className="flex gap-2">
-            身份信息仅用于画图（推荐）
-            <Switch
-              checked={imageOnly}
-              className={`${imageOnly ? 'bg-blue-600' : 'bg-gray-200'} relative inline-flex h-6 w-11 items-center rounded-full`}
-              onChange={(checked: boolean) => setImageOnly(checked)}
-            >
-              <span
-                className={`${imageOnly ? 'translate-x-6' : 'translate-x-1'} inline-block h-4 w-4 transform rounded-full bg-white transition`}
-              />
-            </Switch>
-          </div>
-
-          <Button variant="ghost" className="bg-[#F5F5F5] hover:bg-[#F2F2F2]" onClick={() => copyToClipboard(btoa(curlValue))}>
-            转成 BING_HEADER 并复制
-          </Button>
-
-          <DialogFooter className="items-center">
-            <Button
-              variant="secondary"
-              className="bg-[#c7f3ff] hover:bg-[#fdc7ff]"
-              onClick={() => {
-                let headerValue = curlValue
-                if (headerValue) {
-                  try {
-                    headerValue = atob(headerValue)
-                  } catch (e) { }
-                  if (!/^\s*curl ['"]https:\/\/www\.bing\.com\/turing\/captcha\/challenge['"]/.test(headerValue)) {
-                    toast.error('格式不正确')
-                    return
-                  }
-                  const maxAge = 86400 * 30
-                  encodeHeadersToCookie(headerValue).forEach(cookie => document.cookie = `${cookie}; Max-Age=${maxAge}; Path=/; SameSite=None; Secure`)
-                } else {
-                  [...ChunkKeys, 'BING_COOKIE', 'BING_UA', 'BING_IP'].forEach(key => setCookie(key, ''))
-                }
-                setCookie('IMAGE_ONLY', imageOnly ? '1' : '0')
-
-                toast.success('保存成功')
-                setLoc('')
-                setTimeout(() => {
-                  location.href = './'
-                }, 2000)
-              }}
-            >
-              保存
-            </Button>
-          </DialogFooter>
-        </DialogContent>
-      </Dialog>
-    )
-  } else if (loc === 'voice') {
-    return (
-      <Dialog open onOpenChange={() => setLoc('')} modal>
-        <DialogContent>
-          <DialogHeader>
-            <DialogTitle>语音设置</DialogTitle>
-            <DialogDescription>
-              目前仅支持 PC 端 Edge 及 Chrome 浏览器
-            </DialogDescription>
-          </DialogHeader>
-
-          <div className="flex gap-2">
-            启用语音回答
-            <Switch
-              checked={enableTTS}
-              className={`${enableTTS ? 'bg-blue-600' : 'bg-gray-200'} relative inline-flex h-6 w-11 items-center rounded-full`}
-              onChange={(checked: boolean) => setEnableTTS(checked)}
-            >
-              <span
-                className={`${enableTTS ? 'translate-x-6' : 'translate-x-1'} inline-block h-4 w-4 transform rounded-full bg-white transition`}
-              />
-            </Switch>
-          </div>
-
-          <DialogFooter className="items-center">
-            <Button
-              variant="secondary"
-              onClick={() => {
-                toast.success('保存成功')
-                setLoc('')
-                setTimeout(() => {
-                  location.href = './'
-                }, 2000)
-              }}
-            >
-              保存
-            </Button>
-          </DialogFooter>
-        </DialogContent>
-      </Dialog>
-    )
-  }
-  return null
-}
diff --git a/spaces/shabnam91/Sanskrit-TTS/model_modules/transforms.py b/spaces/shabnam91/Sanskrit-TTS/model_modules/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/shabnam91/Sanskrit-TTS/model_modules/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/shaolin123/soulteary-Chinese-Llama-2-7b-ggml-q4/README.md b/spaces/shaolin123/soulteary-Chinese-Llama-2-7b-ggml-q4/README.md
deleted file mode 100644
index 05f645a09dc8f0eed5773c0d77d287c524bea82c..0000000000000000000000000000000000000000
--- a/spaces/shaolin123/soulteary-Chinese-Llama-2-7b-ggml-q4/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Soulteary Chinese Llama 2 7b Ggml Q4
-emoji: 🐢
-colorFrom: red
-colorTo: green
-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/shencc/gpt/docs/README_JP.md b/spaces/shencc/gpt/docs/README_JP.md
deleted file mode 100644
index 9fc6dbe595657894c9f6f449c50f6f681d762329..0000000000000000000000000000000000000000
--- a/spaces/shencc/gpt/docs/README_JP.md
+++ /dev/null
@@ -1,302 +0,0 @@
-> **Note**
->
-> このReadmeファイルは、このプロジェクトのmarkdown翻訳プラグインによって自動的に生成されたもので、100%正確ではない可能性があります。
->
-
-# <img src="logo.png" width="40" > ChatGPT 学術最適化
-
-**このプロジェクトが好きだったら、スターをつけてください。もし、より使いやすい学術用のショートカットキーまたはファンクションプラグインを発明した場合は、issueを発行するかpull requestを作成してください。また、このプロジェクト自体によって翻訳されたREADMEは[英語説明書|](docs/README_EN.md)[日本語説明書|](docs/README_JP.md)[ロシア語説明書|](docs/README_RS.md)[フランス語説明書](docs/README_FR.md)もあります。**
-
-> **注意事項**
->
-> 1. **赤色**のラベルが付いているファンクションプラグイン（ボタン）のみファイルを読み込めます。一部のプラグインはプラグインエリアのドロップダウンメニューにあります。新しいプラグインのPRを歓迎いたします！
->
-> 2. このプロジェクトの各ファイルの機能は`self_analysis.md`（自己解析レポート）で詳しく説明されています。バージョンが追加されると、関連するファンクションプラグインをクリックして、GPTを呼び出して自己解析レポートを再生成することができます。一般的な質問は`wiki`にまとめられています。(`https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98`)
-
-
-<div align="center">
-    
-機能 | 説明
---- | ---
-ワンクリック整形 | 論文の文法エラーを一括で正確に修正できます。
-ワンクリック日英翻訳 | 日英翻訳には、ワンクリックで対応できます。
-ワンクリックコード説明 | コードの正しい表示と説明が可能です。
-[カスタムショートカットキー](https://www.bilibili.com/video/BV14s4y1E7jN) | カスタムショートカットキーをサポートします。
-[プロキシサーバーの設定](https://www.bilibili.com/video/BV1rc411W7Dr) | プロキシサーバーの設定をサポートします。
-モジュラーデザイン | カスタム高階関数プラグインと[関数プラグイン]、プラグイン[ホット更新]のサポートが可能です。詳細は[こちら](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[自己プログラム解析](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン][ワンクリック理解](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)このプロジェクトのソースコード
-[プログラム解析機能](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン] ワンクリックで別のPython/C/C++/Java/Lua/...プロジェクトツリーを解析できます。
-論文読解 | [関数プラグイン] LaTeX論文の全文をワンクリックで解読し、要約を生成します。
-LaTeX全文翻訳、整形 | [関数プラグイン] ワンクリックでLaTeX論文を翻訳または整形できます。
-注釈生成 | [関数プラグイン] ワンクリックで関数の注釈を大量に生成できます。
-チャット分析レポート生成 | [関数プラグイン] 実行後、まとめレポートを自動生成します。
-[arxivヘルパー](https://www.bilibili.com/video/BV1LM4y1279X) | [関数プラグイン] 入力したarxivの記事URLで要約をワンクリック翻訳+PDFダウンロードができます。
-[PDF論文全文翻訳機能](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] PDF論文タイトルと要約を抽出し、全文を翻訳します（マルチスレッド）。
-[Google Scholar Integratorヘルパー](https://www.bilibili.com/video/BV19L411U7ia) | [関数プラグイン] 任意のGoogle Scholar検索ページURLを指定すると、gptが興味深い記事を選択します。
-数式/画像/テーブル表示 | 数式のTex形式とレンダリング形式を同時に表示できます。数式、コードのハイライトをサポートしています。
-マルチスレッド関数プラグインサポート | ChatGPTをマルチスレッドで呼び出すことができ、大量のテキストやプログラムを簡単に処理できます。
-ダークグラジオ[テーマ](https://github.com/binary-husky/chatgpt_academic/issues/173)の起動 | 「/?__dark-theme=true」というURLをブラウザに追加することで、ダークテーマに切り替えることができます。
-[多数のLLMモデル](https://www.bilibili.com/video/BV1wT411p7yf)をサポート、[API2D](https://api2d.com/)インターフェースをサポート | GPT3.5、GPT4、[清華ChatGLM](https://github.com/THUDM/ChatGLM-6B)による同時サポートは、とても素晴らしいですね！
-huggingface免科学上网[オンライン版](https://huggingface.co/spaces/qingxu98/gpt-academic) | huggingfaceにログイン後、[このスペース](https://huggingface.co/spaces/qingxu98/gpt-academic)をコピーしてください。
-...... | ......
-
-
-</div>
-
-
-- 新しいインターフェース（config.pyのLAYOUTオプションを変更するだけで、「左右レイアウト」と「上下レイアウト」を切り替えることができます）
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
-</div>
-
-
-- すべてのボタンは、functional.pyを読み込んで動的に生成されます。カスタム機能を自由に追加して、クリップボードを解放します
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
-</div>
-
-- 色を修正/修正
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
-</div>
-
-- 出力に数式が含まれている場合、TeX形式とレンダリング形式の両方が表示され、コピーと読み取りが容易になります
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
-</div>
-
-- プロジェクトのコードを見るのが面倒？chatgptに整備されたプロジェクトを直接与えましょう
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
-</div>
-
-- 多数の大規模言語モデルの混合呼び出し(ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
-</div>
-
-多数の大規模言語モデルの混合呼び出し[huggingfaceテスト版](https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta)(huggigface版はchatglmをサポートしていません)
-
-
----
-
-## インストール-方法1：直接運転 (Windows、LinuxまたはMacOS)
-
-1. プロジェクトをダウンロードします。
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. API_KEYとプロキシ設定を構成する
-
-`config.py`で、海外のProxyとOpenAI API KEYを構成して説明します。
-```
-1.あなたが中国にいる場合、OpenAI APIをスムーズに使用するには海外プロキシを設定する必要があります。構成の詳細については、config.py（1.その中のUSE_PROXYをTrueに変更し、2.手順に従ってプロキシを変更する）を詳細に読んでください。
-2. OpenAI API KEYを構成する。OpenAIのウェブサイトでAPI KEYを取得してください。一旦API KEYを手に入れると、config.pyファイルで設定するだけです。
-3.プロキシネットワークに関連する問題(ネットワークタイムアウト、プロキシが動作しない）をhttps://github.com/binary-husky/chatgpt_academic/issues/1にまとめました。
-```
-(P.S. プログラム実行時にconfig.pyの隣にconfig_private.pyという名前のプライバシー設定ファイルを作成し、同じ名前の設定を上書きするconfig_private.pyが存在するかどうかを優先的に確認します。そのため、私たちの構成読み取りロジックを理解できる場合は、config.pyの隣にconfig_private.pyという名前の新しい設定ファイルを作成し、その中のconfig.pyから設定を移動してください。config_private.pyはgitで保守されていないため、プライバシー情報をより安全にすることができます。)
-
-3. 依存関係をインストールします。
-```sh
-# 選択肢があります。
-python -m pip install -r requirements.txt
-
-
-# (選択肢2) もしAnacondaを使用する場合、手順は同様です：
-# (選択肢2.1) conda create -n gptac_venv python=3.11
-# (選択肢2.2) conda activate gptac_venv
-# (選択肢2.3) python -m pip install -r requirements.txt
-
-# 注: 公式のpipソースまたはAlibabaのpipソースを使用してください。 別のpipソース（例：一部の大学のpip）は問題が発生する可能性があります。 一時的なソースの切り替え方法： 
-# python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-```
-
-もしあなたが清華ChatGLMをサポートする必要がある場合、さらに多くの依存関係をインストールする必要があります（Pythonに慣れない方やコンピューターの設定が十分でない方は、試みないことをお勧めします）：
-```sh
-python -m pip install -r request_llm/requirements_chatglm.txt
-```
-
-4. 実行
-```sh
-python main.py
-```
-
-5. 関数プラグインのテスト
-```
-- Pythonプロジェクト分析のテスト
-    入力欄に `./crazy_functions/test_project/python/dqn` と入力し、「Pythonプロジェクト全体の解析」をクリックします。
-- 自己コード解読のテスト
-    「[マルチスレッドデモ] このプロジェクト自体を解析します（ソースを翻訳して解読します）」をクリックします。
-- 実験的な機能テンプレート関数のテスト（GPTが「今日の歴史」に何が起こったかを回答することが求められます）。この関数をテンプレートとして使用して、より複雑な機能を実装できます。
-    「[関数プラグインテンプレートデモ] 今日の歴史」をクリックします。
-- 関数プラグインエリアのドロップダウンメニューには他にも選択肢があります。
-```
-
-## インストール方法2：Dockerを使用する（Linux）
-
-1. ChatGPTのみ（大多数の人にお勧めです）
-``` sh
-# プロジェクトのダウンロード
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-# 海外プロキシとOpenAI API KEYの設定
-config.pyを任意のテキストエディタで編集する
-# インストール
-docker build -t gpt-academic .
-# 実行
-docker run --rm -it --net=host gpt-academic
-
-# 関数プラグインのテスト
-## 関数プラグインテンプレート関数のテスト（GPTが「今日の歴史」に何が起こったかを回答することが求められます）。この関数をテンプレートとして使用して、より複雑な機能を実装できます。
-「[関数プラグインテンプレートデモ] 今日の歴史」をクリックします。
-## Latexプロジェクトの要約を書くテスト
-入力欄に./crazy_functions/test_project/latex/attentionと入力し、「テックス論文を読んで要約を書く」をクリックします。
-## Pythonプロジェクト分析のテスト
-入力欄に./crazy_functions/test_project/python/dqnと入力し、[Pythonプロジェクトの全解析]をクリックします。
-
-関数プラグインエリアのドロップダウンメニューには他にも選択肢があります。
-```
-
-2. ChatGPT + ChatGLM（Dockerに非常に詳しい人+十分なコンピューター設定が必要）
-
-
-
-```sh
-# Dockerfileの編集
-cd docs && nano Dockerfile+ChatGLM
-# ビルド方法
-docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
-# 実行方法 (1) 直接実行: 
-docker run --rm -it --net=host --gpus=all gpt-academic
-# 実行方法 (2) コンテナに入って調整する:
-docker run --rm -it --net=host --gpus=all gpt-academic bash
-```
-
-## インストール方法3：その他のデプロイ方法
-
-1. クラウドサーバーデプロイ
-[デプロイwiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-2. WSL2を使用 (Windows Subsystem for Linux)
-[デプロイwiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-
-## インストール-プロキシ設定
-1. 通常の方法
-[プロキシを設定する](https://github.com/binary-husky/chatgpt_academic/issues/1)
-
-2. 初心者向けチュートリアル
-[初心者向けチュートリアル](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
-
-
----
-
-## カスタムボタンの追加(学術ショートカットキー)
-
-`core_functional.py`を任意のテキストエディタで開き、以下のエントリーを追加し、プログラムを再起動してください。(ボタンが追加されて表示される場合、前置詞と後置詞はホット編集がサポートされているため、プログラムを再起動せずに即座に有効になります。)
-
-例:
-```
-"超级英译中": {
-    # 前置詞 - あなたの要求を説明するために使用されます。翻訳、コードの説明、編集など。
-    "Prefix": "以下のコンテンツを中国語に翻訳して、マークダウンテーブルを使用して専門用語を説明してください。\n\n", 
-    
-    # 後置詞 - プレフィックスと共に使用すると、入力内容を引用符で囲むことができます。
-    "Suffix": "",
-},
-```
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
-</div>
-
-
----
-
-## いくつかの機能の例
-
-### 画像表示：
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/228737599-bf0a9d9c-1808-4f43-ae15-dfcc7af0f295.png" width="800" >
-</div>
-
-
-### プログラムが自己解析できる場合：
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="800" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936618-9b487e4b-ab5b-4b6e-84c6-16942102e917.png" width="800" >
-</div>
-
-### 他のPython/Cppプロジェクトの解析：
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="800" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="800" >
-</div>
-
-### Latex論文の一括読解と要約生成
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227504406-86ab97cd-f208-41c3-8e4a-7000e51cf980.png" width="800" >
-</div>
-
-### 自動報告生成
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
-</div>
-
-### モジュール化された機能デザイン
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-
-### ソースコードの英語翻訳
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229720562-fe6c3508-6142-4635-a83d-21eb3669baee.png" height="400" >
-</div>
-
-## Todo およびバージョン計画:
-- version 3.2+ (todo): 関数プラグインがより多くのパラメーターインターフェースをサポートするようになります。
-- version 3.1: 複数のgptモデルを同時にクエリし、api2dをサポートし、複数のapikeyの負荷分散をサポートします。
-- version 3.0: chatglmおよび他の小型llmのサポート
-- version 2.6: プラグイン構造を再構成し、相互作用性を高め、より多くのプラグインを追加しました。
-- version 2.5: 自己更新。総括的な大規模プロジェクトのソースコードをまとめた場合、テキストが長すぎる、トークンがオーバーフローする問題を解決します。
-- version 2.4: (1)PDF全文翻訳機能を追加。(2)入力エリアの位置を切り替える機能を追加。(3)垂直レイアウトオプションを追加。(4)マルチスレッド関数プラグインの最適化。
-- version 2.3: 多スレッドの相互作用性を向上させました。
-- version 2.2: 関数プラグインでホットリロードをサポート
-- version 2.1: 折りたたみ式レイアウト
-- version 2.0: モジュール化された関数プラグインを導入
-- version 1.0: 基本機能
-
-## 参考および学習
-
-
-以下は中国語のマークダウンファイルです。日本語に翻訳してください。既存のマークダウンコマンドを変更しないでください：
-
-```
-多くの優秀なプロジェクトの設計を参考にしています。主なものは以下の通りです：
-
-# 参考プロジェクト1：ChuanhuChatGPTから多くのテクニックを借用
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# 参考プロジェクト2：清華ChatGLM-6B：
-https://github.com/THUDM/ChatGLM-6B
-```
-
diff --git a/spaces/shgao/MDT/diffusion/diffusion_utils.py b/spaces/shgao/MDT/diffusion/diffusion_utils.py
deleted file mode 100644
index e493a6a3ecb91e553a53cc7eadee5cc0d1753060..0000000000000000000000000000000000000000
--- a/spaces/shgao/MDT/diffusion/diffusion_utils.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Modified from OpenAI's diffusion repos
-#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
-#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
-#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-
-import torch as th
-import numpy as np
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    Compute the KL divergence between two gaussians.
-    Shapes are automatically broadcasted, so batches can be compared to
-    scalars, among other use cases.
-    """
-    tensor = None
-    for obj in (mean1, logvar1, mean2, logvar2):
-        if isinstance(obj, th.Tensor):
-            tensor = obj
-            break
-    assert tensor is not None, "at least one argument must be a Tensor"
-
-    # Force variances to be Tensors. Broadcasting helps convert scalars to
-    # Tensors, but it does not work for th.exp().
-    logvar1, logvar2 = [
-        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
-        for x in (logvar1, logvar2)
-    ]
-
-    return 0.5 * (
-        -1.0
-        + logvar2
-        - logvar1
-        + th.exp(logvar1 - logvar2)
-        + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
-    )
-
-
-def approx_standard_normal_cdf(x):
-    """
-    A fast approximation of the cumulative distribution function of the
-    standard normal.
-    """
-    return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
-
-
-def continuous_gaussian_log_likelihood(x, *, means, log_scales):
-    """
-    Compute the log-likelihood of a continuous Gaussian distribution.
-    :param x: the targets
-    :param means: the Gaussian mean Tensor.
-    :param log_scales: the Gaussian log stddev Tensor.
-    :return: a tensor like x of log probabilities (in nats).
-    """
-    centered_x = x - means
-    inv_stdv = th.exp(-log_scales)
-    normalized_x = centered_x * inv_stdv
-    log_probs = th.distributions.Normal(th.zeros_like(x), th.ones_like(x)).log_prob(normalized_x)
-    return log_probs
-
-
-def discretized_gaussian_log_likelihood(x, *, means, log_scales):
-    """
-    Compute the log-likelihood of a Gaussian distribution discretizing to a
-    given image.
-    :param x: the target images. It is assumed that this was uint8 values,
-              rescaled to the range [-1, 1].
-    :param means: the Gaussian mean Tensor.
-    :param log_scales: the Gaussian log stddev Tensor.
-    :return: a tensor like x of log probabilities (in nats).
-    """
-    assert x.shape == means.shape == log_scales.shape
-    centered_x = x - means
-    inv_stdv = th.exp(-log_scales)
-    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
-    cdf_plus = approx_standard_normal_cdf(plus_in)
-    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
-    cdf_min = approx_standard_normal_cdf(min_in)
-    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
-    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
-    cdf_delta = cdf_plus - cdf_min
-    log_probs = th.where(
-        x < -0.999,
-        log_cdf_plus,
-        th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
-    )
-    assert log_probs.shape == x.shape
-    return log_probs
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download PUBG Mobile Hack APK 2023 The Best Way to Play PUBG Mobile with Auto Aim Bot No Recoil and Anti Ban.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download PUBG Mobile Hack APK 2023 The Best Way to Play PUBG Mobile with Auto Aim Bot No Recoil and Anti Ban.md
deleted file mode 100644
index 8252dc2a65ad9addf08e3e68c5bb85d1c9923552..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download PUBG Mobile Hack APK 2023 The Best Way to Play PUBG Mobile with Auto Aim Bot No Recoil and Anti Ban.md	
+++ /dev/null
@@ -1,91 +0,0 @@
-
-<h1>PUBG Hack APK Download 2023: Everything You Need to Know</h1>
-<p>If you are a fan of battle royale games, you must have heard of PUBG, one of the most popular and successful games in this genre. PUBG, which stands for PlayerUnknown's Battlegrounds, is a game that pits up to 100 players against each other on a large map, where they have to scavenge for weapons, items, and vehicles, and fight to be the last one standing.</p>
-<p>PUBG has a mobile version called PUBG Mobile, which is a scaled-down version of the game that can be played on smartphones and tablets. PUBG Mobile has millions of players around the world, who enjoy the thrill and excitement of the game on their devices.</p>
-<h2>pubg hack apk download 2023</h2><br /><p><b><b>Download</b> >> <a href="https://ssurll.com/2uNTXJ">https://ssurll.com/2uNTXJ</a></b></p><br /><br />
-<p>However, not everyone is satisfied with the original version of PUBG Mobile. Some players want to have an edge over their opponents, or simply enjoy the game more by unlocking all the features and items that are otherwise restricted or paid. That's why some people use a modified version of PUBG Mobile called PUBG Hack APK.</p>
-<p>In this article, we will tell you everything you need to know about PUBG Hack APK, including what it is, why people use it, what features it offers, and how to download and install it on your Android device. Read on to find out more.</p>
- <h2>What is PUBG and PUBG Mobile?</h2>
-<h3>PUBG: A Battle Royale Sensation</h3>
-<p>PUBG is a game that was released in 2017 by a company called PUBG Corporation, which is a subsidiary of Krafton, a South Korean video game company. The game was inspired by a mod for another game called ARMA 2, which was created by a player named Brendan Greene, who later became the lead designer of PUBG.</p>
-<p>PUBG is a game that belongs to the battle royale genre, which is a type of game that involves a large number of players who are dropped into a map with no weapons or items, and have to find them while avoiding or killing other players. The map gradually shrinks over time, forcing the players to move closer to each other and increasing the chances of encounters. The last player or team alive wins the game.</p>
-<p>PUBG became a huge hit among gamers, who loved the realistic graphics, the intense gameplay, and the variety of modes and maps that the game offered. The game also became popular among streamers and esports enthusiasts, who showcased their skills and strategies on platforms like Twitch and YouTube. As of 2021, PUBG has sold over 70 million copies worldwide, making it one of the best-selling games of all time.</p>
- <h3>PUBG Mobile: A Portable Version of the Game</h3>
-<p>PUBG Mobile is a version of PUBG that was released in 2018 for mobile devices. It was developed by Tencent Games, a Chinese video game company that also owns Riot Games, Epic Games, and Supercell. PUBG Mobile is designed to run on smartphones and tablets, with lower graphics quality and smaller maps than the original version.</p>
-<p>PUBG Mobile has many features that make it appealing to mobile gamers , such as: - It is free to download and play, unlike the original version which costs around $30. - It has a smaller file size, which makes it easier to install and update. - It has more game modes, such as arcade, classic, and arena, which offer different challenges and experiences. - It has more customization options, such as skins, outfits, emotes, and vehicles, which can be purchased with in-game currency or real money. - It has more social features, such as voice chat, friends list, clans, and events, which allow players to communicate and cooperate with each other. PUBG Mobile has also become a huge success among mobile gamers, who enjoy the convenience and accessibility of playing the game on their devices. The game has over 1 billion downloads on Google Play Store and App Store, making it one of the most downloaded games of all time. The game also has a large and active fan base, who participate in tournaments, contests, and community events. <h2>What is PUBG Hack APK?</h2>
-<h3>PUBG Hack APK: A Modified Version of PUBG Mobile</h3>
-<p>PUBG Hack APK is a modified version of PUBG Mobile that has been altered by some developers or hackers to give the players some advantages or extra features that are not available in the original version. PUBG Hack APK is also known as PUBG Mod APK or PUBG Mobile Mod APK.</p>
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-<p>PUBG Hack APK is not an official version of the game, and it is not endorsed or supported by PUBG Corporation or Tencent Games. It is an illegal and unauthorized version of the game that violates the terms of service and the intellectual property rights of the original developers. Therefore, using PUBG Hack APK can result in legal consequences or penalties.</p>
- <h3>Why Do People Use PUBG Hack APK?</h3>
-<p>Some people use PUBG Hack APK for various reasons, such as: - They want to have an unfair advantage over their opponents, such as aiming better, seeing through walls, or having unlimited resources. - They want to have more fun and enjoyment by exploring new features and options that are not available in the original version. - They want to save money and time by unlocking all the items and features that are otherwise paid or hard to get. - They want to experiment with different settings and configurations that are not possible in the original version. However, using PUBG Hack APK also comes with some risks and drawbacks, such as: - They can get banned by the game's anti-cheat system, which can detect the use of modified files or scripts. - They can get infected by malware or viruses that are hidden in the APK file or the source website. - They can get scammed by fake or fraudulent websites that claim to offer PUBG Hack APK but instead steal their personal or financial information. - They can ruin the game's balance and integrity by disrupting the fair play and competition among other players.</p>
- <h2>What are the Features of PUBG Hack APK?</h2>
-<h3>Auto Aim Bot: Aim and Shoot with Ease</h3>
-<p>One of the most common features of PUBG Hack APK is the auto aim bot, which is a feature that automatically aims and shoots at the enemies for the player. This feature can help the player to eliminate their opponents faster and easier, without having to manually aim and fire their weapon.</p>
-<p>The auto aim bot can be customized according to the player's preference, such as: - The sensitivity level, which determines how fast and accurate the aim bot is. - The target selection, which determines which enemy to aim at first based on their distance, health, or visibility. - The shooting mode, which determines when to shoot at the enemy based on their movement, position, or status.</p>
- <h3>Wallhack: See Through Walls and Objects</h3>
-<p>Another popular feature of PUBG Hack APK is the wallhack, which is a feature that allows the player to see through walls and objects. This feature can help the player to spot their enemies easier and faster, without having to expose themselves or search for them.</p>
-<p>The wallhack can also be customized according to the player's preference, such as: - The color mode, which determines how to highlight the enemies on the screen based on their team color or health bar color. - The distance mode, which determines how far away the enemies can be seen through walls and objects. - The object mode, which determines which objects can be seen through or not, such as trees, rocks, buildings, or vehicles.</p>
- <h3>Unlimited UC: Get Unlimited In-Game Currency</h3>
-<p>A feature that many players desire is the unlimited UC (Unknown Cash), which is the in-game currency of PUBG Mobile. UC can be used to buy various items and features in the game shop , such as: - Skins, which are cosmetic items that change the appearance of the weapons, vehicles, or characters. - Outfits, which are cosmetic items that change the clothing and accessories of the characters. - Emotes, which are gestures or expressions that the characters can perform in the game. - Crates, which are boxes that contain random items or rewards. - Royale Pass, which is a subscription service that gives access to exclusive missions, rewards, and features. Normally, UC can be obtained by spending real money or completing certain tasks or events in the game. However, with PUBG Hack APK, the player can get unlimited UC for free, without having to spend any money or time. This feature can help the player to buy and enjoy all the items and features that they want in the game. <h3>Anti Ban: Avoid Getting Banned by the Game</h3>
-<p>A feature that is essential for PUBG Hack APK users is the anti ban, which is a feature that prevents the player from getting banned by the game's anti-cheat system. The anti-cheat system is a mechanism that detects and punishes the players who use cheats or hacks in the game, such as modified files, scripts, or third-party applications.</p>
-<p>The anti-cheat system can ban the players in different ways, such as: - Temporary ban, which suspends the player's account for a certain period of time, ranging from a few hours to a few days. - Permanent ban, which deletes the player's account and blocks their device from accessing the game ever again. - Hardware ban, which blacklists the player's device ID and prevents them from creating or using any other account on the same device.</p>
- <p>The anti ban feature of PUBG Hack APK can protect the player from getting banned by the anti-cheat system, by hiding or masking their cheat activities from the game's detection. This feature can help the player to enjoy using PUBG Hack APK without worrying about losing their account or device.</p>
- <h3>No Recoil: Shoot with Accuracy and Stability</h3>
-<p>A feature that can improve the player's performance in PUBG Hack APK is the no recoil, which is a feature that eliminates or reduces the recoil of the weapons. Recoil is a phenomenon that occurs when a weapon is fired, causing it to kick back and move upwards or sideways. Recoil can affect the accuracy and stability of the weapon, making it harder to aim and shoot at the target.</p>
-<p>The no recoil feature of PUBG Hack APK can counteract or cancel out the recoil of the weapons, making them stay steady and stable when fired. This feature can help the player to shoot with more precision and consistency, especially at long distances or with high-powered weapons.</p>
- <h3>No Root: Install and Play without Rooting Your Device</h3>
-<p>A feature that can make PUBG Hack APK more convenient and accessible is the no root, which is a feature that allows the player to install and play PUBG Hack APK without rooting their device. Rooting is a process that gives the user full control and access to their device's system and settings, allowing them to modify or customize their device according to their preference.</p>
-<p>However, rooting also has some disadvantages, such as: - It can void the warranty of the device, making it ineligible for any service or repair from the manufacturer or provider. - It can expose the device to security risks, such as malware or viruses that can harm or steal data from the device. - It can cause compatibility issues, such as apps or games not working properly or crashing on the device.</p>
- <p>The no root feature of PUBG Hack APK can enable the player to install and play PUBG Hack APK without rooting their device, making it safer and easier to use. This feature can help the player to avoid the hassle and risk of rooting their device, and still enjoy the benefits of PUBG Hack APK. <h2>How to Download and Install PUBG Hack APK on Android?</h2>
-<h3>Step 1: Download the APK File from a Trusted Source</h3>
-<p>The first step to download and install PUBG Hack APK on your Android device is to find and download the APK file from a trusted source. An APK file is a file format that contains the installation package of an Android app or game. You can find many websites that offer PUBG Hack APK for download, but you have to be careful and choose a reliable and safe one.</p>
-<p>Some of the factors that you should consider when choosing a source for PUBG Hack APK are: - The reputation and credibility of the website, which can be checked by reading reviews, ratings, or feedback from other users. - The security and protection of the website, which can be checked by looking for signs of encryption, such as HTTPS or a padlock icon in the address bar. - The quality and authenticity of the APK file, which can be checked by looking for details such as the file size, the version number, the update date, or the developer name.</p>
- <p>Once you have found a trustworthy source for PUBG Hack APK, you can download the APK file to your device by following these steps: - Tap on the download link or button on the website. - Wait for the download to start and finish. - Locate the downloaded APK file in your device's storage, usually in the Downloads folder.</p>
- <h3>Step 2: Enable Unknown Sources on Your Device Settings</h3>
-<p>The next step to install PUBG Hack APK on your Android device is to enable unknown sources on your device settings. Unknown sources are sources that are not verified or authorized by Google Play Store, such as websites or third-party apps. By default, Android devices do not allow the installation of apps or games from unknown sources, for security reasons.</p>
-<p>To enable unknown sources on your device settings, you have to follow these steps: - Go to your device's Settings app. - Tap on Security or Privacy. - Find and tap on Unknown Sources or Install Unknown Apps. - Toggle on the switch or check the box to allow installation from unknown sources.</p>
- <h3>Step 3: Install the APK File and Launch the Game</h3>
-<p>The final step to install PUBG Hack APK on your Android device is to install the APK file and launch the game. To install the APK file, you have to follow these steps: - Tap on the downloaded APK file in your device's storage. - Tap on Install when prompted. - Wait for the installation to complete. - Tap on Open or Launch when prompted.</p>
- <p>To launch the game, you have to follow these steps: - Find and tap on the PUBG Hack APK icon on your device's home screen or app drawer. - Wait for the game to load and start. - Log in with your account or create a new one if needed. - Enjoy playing PUBG Hack APK with all its features.</p>
- <h2>Conclusion</h2>
-<p>PUBG Hack APK is a modified version of PUBG Mobile that offers some advantages and extra features that are not available in the original version. Some of these features are auto aim bot, wallhack, unlimited UC, anti ban, no recoil, and no root. These features can help the player to have more fun and enjoyment by unlocking all the items and features that they want in the game.</p>
-<p>However, using PUBG Hack APK also has some risks and drawbacks, such as getting banned by the game's anti-cheat system, getting infected by malware or viruses, getting scammed by fake or fraudulent websites, or ruining the game's balance and integrity. Therefore, using PUBG Hack APK is not recommended or advised by us, as it violates the terms of service and the intellectual property rights of the original developers.</p>
-<p>If you want to play PUBG Mobile legally and safely, you should download and install it from Google Play Store or App Store, where you can find the official and updated version of the game, along with many other apps and games that you can enjoy. We hope that this article has given you some useful information and insights about PUBG Hack APK, and that you have learned something new today. Thank you for reading and have a great day! <h2>FAQs</h2>
-<p>Here are some frequently asked questions about PUBG Hack APK that you might be interested in:</p>
- <h3>Q: Is PUBG Hack APK safe to use?</h3>
-<p>A: No, PUBG Hack APK is not safe to use, as it can expose your device to security risks, such as malware or viruses, or legal consequences, such as getting banned by the game's anti-cheat system. It is better to use the original version of PUBG Mobile from Google Play Store or App Store, which is safe and secure.</p>
- <h3>Q: Is PUBG Hack APK free to download and play?</h3>
-<p>A: Yes, PUBG Hack APK is free to download and play, as it does not require any payment or subscription to access its features. However, this also means that it does not have any quality assurance or support from the original developers, and that it may contain bugs or errors that can affect your gaming experience.</p>
- <h3>Q: How can I update PUBG Hack APK to the latest version?</h3>
-<p>A: To update PUBG Hack APK to the latest version, you have to find and download the updated APK file from a trusted source, and then install it on your device. However, this may not always work, as the updated version may not be compatible with your device or the previous version of PUBG Hack APK. It is better to use the original version of PUBG Mobile from Google Play Store or App Store, which updates automatically and regularly.</p>
- <h3>Q: Can I play PUBG Hack APK with my friends who use the original version of PUBG Mobile?</h3>
-<p>A: No, you cannot play PUBG Hack APK with your friends who use the original version of PUBG Mobile, as they are not compatible with each other. If you try to join a match with them, you will either get an error message or get kicked out of the game. It is better to use the original version of PUBG Mobile from Google Play Store or App Store, which allows you to play with your friends and other players online.</p>
- <h3>Q: Can I use PUBG Hack APK on iOS devices?</h3>
-<p>A: No, you cannot use PUBG Hack APK on iOS devices, as it is only designed for Android devices. If you want to play PUBG Mobile on iOS devices, you have to download and install it from App Store, which is the official and authorized source for iOS apps and games.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Real Boxing 2 APK and Enter the World of Boxing.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Real Boxing 2 APK and Enter the World of Boxing.md
deleted file mode 100644
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-<h1>Real Boxing 2 APK Download: A Guide for Boxing Fans</h1>
-<p>If you are a fan of boxing games, you might want to check out Real Boxing 2, a mobile fighting game that lets you enter the ring and face dangerous opponents in realistic and immersive matches. In this article, we will tell you everything you need to know about Real Boxing 2, including how to download the APK file, what are the features of the game, and what are some tips and tricks to help you become a boxing champion.</p>
- <h2>What is Real Boxing 2?</h2>
-<p>Real Boxing 2 is a sports simulation game developed by Vivid Games, a Polish studio that specializes in creating high-quality games for mobile devices. Real Boxing 2 is the sequel to Real Boxing, a game that won several awards and received positive reviews from critics and players alike. Real Boxing 2 aims to improve on its predecessor by offering more content, better graphics, and more options for customization.</p>
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- <h3>A realistic and immersive boxing game for mobile devices</h3>
-<p>Real Boxing 2 is designed to provide a realistic and immersive boxing experience on your mobile device. The game uses Unreal Engine 4, a powerful engine that delivers stunning graphics, smooth animations, and dynamic lighting effects. The game also features detailed character models, realistic physics, and lifelike sound effects. You can feel every punch, every jab, every hook, and every uppercut as you fight your way to glory.</p>
- <h3>A sequel to the award-winning Real Boxing</h3>
-<p>Real Boxing 2 is a sequel to Real Boxing, a game that was released in 2012 and became one of the most popular boxing games on mobile devices. Real Boxing was praised for its realistic graphics, gameplay, and controls. It also featured a career mode, a multiplayer mode, and various mini-games. Real Boxing 2 builds on the success of Real Boxing by adding more features, modes, characters, and challenges.</p>
- <h3>A free-to-play game with optional in-app purchases</h3>
-<p>Real Boxing 2 is a free-to-play game that you can download and play without spending any money. However, if you want to enhance your gaming experience, you can also make optional in-app purchases using real currency. You can buy coins and diamonds, which are the two main currencies in the game. You can use them to buy cards, gear, fighters, boosts, and more. You can also buy VIP membership, which gives you access to exclusive benefits like extra rewards, discounts, and premium content.</p>
- <h2>How to download Real Boxing 2 APK?</h2>
-<p>APK stands for Android Package Kit, which is a file format that contains all the necessary files and data to install and run an Android application on your device. APK files are useful when you want to install an app that is not available on the official app store, or when you want to access a newer or older version of an app. However, you should be careful when downloading APK files from unknown sources, as they may contain malware or viruses that can harm your device.</p>
- <h3>Download from the official website or Google Play Store</h3>
-<p>The easiest and safest way to download Real Boxing 2 APK is to visit the official website of the game, which is https://realboxing2.com/. There, you can find the latest version of the game, as well as information about the features, updates, and support. You can also download the game from the Google Play Store, which is the official app store for Android devices. You can search for Real Boxing 2 on the store, or use this link: https://play.google.com/store/apps/details?id=com.vividgames.realboxing2. Both methods will ensure that you get a verified and secure APK file that is compatible with your device.</p>
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- <h3>Download from third-party sources like APKCombo or Aptoide</h3>
-<p>If you want to download Real Boxing 2 APK from other sources, you can use third-party websites or apps that offer APK files for various Android applications. Some of the popular ones are APKCombo, Aptoide, APKPure, and APKMirror. These websites and apps allow you to download different versions of Real Boxing 2 APK, as well as other games and apps that may not be available on the official app store. However, you should be careful when using these sources, as they may not be verified or secure. You should always check the reviews, ratings, and permissions of the APK file before downloading it. You should also scan the file with an antivirus software before installing it.</p>
- <h3>Install the APK file on your device and grant permissions</h3>
-<p>Once you have downloaded the Real Boxing 2 APK file from your preferred source, you need to install it on your device. To do this, you need to enable the installation of apps from unknown sources on your device settings. This will allow you to install apps that are not from the official app store. You can follow these steps to enable this option:</p>
-<ul>
-<li>Go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Alternatively, go to Settings > Apps & Notifications > Special App Access > Install Unknown Apps and select the browser or app that you used to download the APK file.</li>
-<li>Grant permission to install apps from unknown sources.</li>
-</ul>
-<p>After enabling this option, you can locate the Real Boxing 2 APK file on your device storage and tap on it to start the installation process. You may need to grant some permissions to the app, such as access to your storage, camera, microphone, etc. Once the installation is complete, you can launch the game and enjoy it.</p>
- <h2>What are the features of Real Boxing 2?</h2>
-<p>Real Boxing 2 is a game that offers a lot of features and content for boxing fans and gamers alike. Here are some of the main features of the game:</p>
- <h3>Stunning graphics powered by Unreal Engine 4</h3>
-<p>Real Boxing 2 uses Unreal Engine 4, one of the most advanced game engines in the world. This engine allows the game to deliver stunning graphics, smooth animations, and dynamic lighting effects. The game also features detailed character models, realistic physics, and lifelike sound effects. You can feel every punch, every jab, every hook, and every uppercut as you fight your way to glory.</p>
- <h3>Various game modes to challenge yourself and others</h3>
-<p>Real Boxing 2 offers various game modes that will keep you entertained and challenged. You can play the career mode, where you can create your own fighter and take him from a rookie to a legend. You can fight against different opponents, earn money and fame, and unlock new gear and skills. You can also play the arcade mode, where you can fight against random opponents in quick matches. You can also play the underground mode, where you can fight against illegal fighters in dark arenas. You can also play the story mode, where you can follow the story of Rocky Balboa, one of the most iconic characters in boxing history. You can relive his epic fights against Apollo Creed, Clubber Lang, Ivan Drago, and more.</p>
- <h3>Unique boss battles with special rewards</h3>
-<p>Real Boxing 2 also features unique boss battles that will test your skills and endurance. You can fight against legendary boxers like Mike Tyson, Floyd Mayweather Jr., Manny Pacquiao , and more. These boss battles are challenging and rewarding, as they offer special rewards like coins, diamonds, cards, and gear. You can also unlock new boss battles by completing certain tasks and achievements.</p>
- <h3>Customizable fighters and gear</h3>
-<p>Real Boxing 2 allows you to customize your fighter and gear to suit your preferences and style. You can choose from hundreds of options for your fighter's appearance, such as hair, skin, eyes, tattoos, scars, etc. You can also choose from hundreds of options for your fighter's gear, such as gloves, shorts, shoes, belts, etc. You can also upgrade your gear to improve its stats and effects. You can also collect cards that give you different bonuses and abilities.</p>
- <h3>Online multiplayer and social features</h3>
-<p>Real Boxing 2 also offers online multiplayer and social features that let you compete and interact with other players around the world. You can join or create a club, where you can chat, share tips, and challenge other club members. You can also participate in tournaments, where you can fight against other players in different brackets and win prizes. You can also play in real-time PvP matches, where you can test your skills against other players in live fights. You can also use the chat feature to communicate with your opponent or send them emojis. You can also connect your game to Facebook or Google Play Games to save your progress, sync your data, and invite your friends to play with you.</p>
- <h2>What are some tips and tricks for Real Boxing 2?</h2>
-<p>Real Boxing 2 is a game that requires skill, strategy, and practice to master. Here are some tips and tricks that can help you improve your performance and win more matches:</p>
- <h3>Train your fighter in the gym and upgrade your stats</h3>
-<p>One of the most important things to do in Real Boxing 2 is to train your fighter in the gym and upgrade your stats. The gym is where you can improve your fighter's attributes, such as strength, speed, stamina, health, etc. You can train your fighter by playing mini-games that test your reflexes, timing, and accuracy. You can also upgrade your stats by spending coins or diamonds. The higher your stats are, the better your fighter will perform in the ring.</p>
- <h3>Use cards and power-ups to boost your performance</h3>
-<p>Another way to enhance your performance in Real Boxing 2 is to use cards and power-ups that give you different advantages and effects. Cards are items that you can collect or buy in the game. They have different rarities and types, such as attack, defense, special, etc. You can equip up to four cards on your fighter before each match. Each card has a different effect, such as increasing your damage, reducing your opponent's damage, healing yourself, stunning your opponent, etc. You can also use power-ups during a match by tapping on the icons on the screen. Power-ups are temporary boosts that can help you turn the tide of the battle. They include things like adrenaline, rage, shield, etc.</p>
- <h3>Master the controls and combos for different punches</h3>
-<p>Another tip for Real Boxing 2 is to master the controls and combos for different punches. The game has simple and intuitive controls that let you control your fighter's movements and actions. You can use the virtual joystick on the left side of the screen to move around the ring. You can use the buttons on the right side of the screen to punch, block , and dodge. You can also swipe on the screen to perform different punches, such as jabs, hooks, uppercuts, and body shots. You can also combine different punches to create combos that deal more damage and stun your opponent. You should learn the timing and range of each punch and combo, and use them according to the situation and your opponent's behavior.</p>
- <h3>Dodge, block, and counter your opponent's attacks</h3>
-<p>Another tip for Real Boxing 2 is to dodge, block, and counter your opponent's attacks. Defense is as important as offense in boxing, and you should be able to avoid or reduce the damage from your opponent's punches. You can use the dodge button to move your head and evade incoming punches. You can also use the block button to raise your guard and protect yourself from punches. However, both dodging and blocking will consume your stamina, which is the green bar below your health bar. If your stamina runs out, you will be more vulnerable and slower. You should also try to counter your opponent's attacks by timing your punches right after they miss or hit your guard. This will deal more damage and surprise your opponent.</p>
- <h3>Choose your fighting style and strategy</h3>
-<p>Another tip for Real Boxing 2 is to choose your fighting style and strategy. The game allows you to customize your fighter's attributes, skills, gear, and cards to suit your preferences and style. You can choose from different fighting styles, such as brawler, boxer-puncher, counter-puncher, out-boxer, etc. Each style has its own strengths and weaknesses, and you should choose the one that matches your personality and skills. You should also choose a strategy for each match, depending on your opponent's style, stats, and behavior. You should analyze your opponent's strengths and weaknesses, and exploit them accordingly. You should also adapt your strategy during the match, as your opponent may change their tactics or use power-ups.</p>
- <h2>Conclusion</h2>
-<p>Real Boxing 2 is a fun and addictive boxing game for mobile devices that offers realistic graphics, gameplay, and customization options. You can download the APK file from various sources and enjoy the game for free. The game also offers various game modes, features, and challenges that will keep you entertained and challenged. You can improve your skills, fight against different opponents, and become a boxing legend.</p>
- <h4>FAQs</h4>
-<p>Here are some frequently asked questions about Real Boxing 2:</p>
-<ul>
-<li><b>Q: How do I unlock new fighters in Real Boxing 2?</b></li>
-<li>A: You can unlock new fighters by completing certain tasks or achievements in the game. For example, you can unlock Rocky Balboa by completing the story mode. You can also unlock new fighters by buying them with coins or diamonds in the shop.</li>
-<li><b>Q: How do I get more coins and diamonds in Real Boxing 2?</b></li>
-<li>A: You can get more coins and diamonds by playing the game and completing matches, tasks, achievements, etc. You can also get more coins and diamonds by watching ads or participating in events or promotions. You can also buy more coins and diamonds with real money through in-app purchases.</li>
-<li><b>Q: How do I level up my fighter in Real Boxing 2?</b></li>
-<li>A: You can level up your fighter by earning experience points (XP) from playing the game and completing matches, tasks , achievements, etc. You can also level up your fighter by spending coins or diamonds to upgrade your stats. The higher your level is, the more skills and gear you can unlock and use.</li>
-<li><b>Q: How do I play with my friends in Real Boxing 2?</b></li>
-<li>A: You can play with your friends in Real Boxing 2 by connecting your game to Facebook or Google Play Games. This will allow you to invite your friends to join your club, chat with them, and challenge them to friendly matches. You can also play with your friends in real-time PvP matches, where you can fight against each other in live fights.</li>
-<li><b>Q: How do I contact the support team of Real Boxing 2?</b></li>
-<li>A: You can contact the support team of Real Boxing 2 by visiting the official website of the game, which is https://realboxing2.com/. There, you can find the contact form, where you can fill in your details and your issue or feedback. You can also contact the support team by sending an email to support@vividgames.com. You can also visit the official Facebook page of the game, which is https://www.facebook.com/realboxing/, where you can find the latest news, updates, and community posts.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Songs from Game of Thrones The Official and Unofficial Albums.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Songs from Game of Thrones The Official and Unofficial Albums.md
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-<br />
-<h1>How to Download Game of Thrones Songs for Free</h1>
-<p>Game of Thrones is one of the most popular and acclaimed TV shows of all time. The epic fantasy series has captivated millions of fans around the world with its thrilling plot, stunning visuals, and memorable characters. But what makes Game of Thrones even more immersive and enjoyable is its amazing music. The soundtrack composed by Ramin Djawadi features a variety of songs that evoke different emotions, themes, and cultures. From the iconic main theme to the haunting "Light of the Seven" to the catchy "The Bear and the Maiden Fair", there is a song for every mood and occasion.</p>
-<p>If you are a fan of Game of Thrones music, you might want to download some of your favorite songs and listen to them anytime, anywhere. But how can you do that without spending any money? Luckily, there are several ways to download Game of Thrones songs for free from various online sources. In this article, we will show you how to download Game of Thrones songs from Spotify, YouTube, and other websites. We will also provide you with some tips and tricks to ensure that you get the best quality and experience.</p>
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- <h2>How to Download Game of Thrones Songs from Spotify</h2>
-<h3>What is Spotify and why it is a good option for Game of Thrones fans</h3>
-<p>Spotify is one of the most popular and widely used music streaming services in the world. It offers access to millions of songs, podcasts, playlists, and radio stations. You can listen to music on Spotify for free with ads, or upgrade to a premium subscription for ad-free listening, offline mode, better sound quality, and more features.</p>
-<p>Spotify is a great option for Game of Thrones fans because it has an official playlist that contains all the songs from the show's eight seasons. The playlist is called "Game of Thrones Soundtrack (All Seasons)" and it has over 100 songs and more than six hours of music. You can find this playlist by searching for it on Spotify or by clicking on this link.</p>
- <h3>How to create a Spotify account and access the Game of Thrones soundtrack playlist</h3>
-<p>To listen to music on Spotify, you need to create an account first. You can sign up with your email address, Facebook account, or Apple ID. You can also use your Google account if you have an Android device. Once you have created an account, you can log in to Spotify on your computer, smartphone, tablet, or smart TV.</p> <p>After logging in to Spotify, you can access the Game of Thrones soundtrack playlist by following these steps:</p>
-<ol>
-<li>Click on the search icon on the bottom left corner of the app or the top left corner of the website.</li>
-<li>Type "Game of Thrones Soundtrack (All Seasons)" in the search bar and hit enter.</li>
-<li>Select the playlist with the same name from the results. It should have a picture of the Iron Throne as the cover art.</li>
-<li>Click on the play button to start listening to the songs. You can also shuffle, repeat, skip, or add songs to your own playlist.</li>
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-<p>While Spotify allows you to listen to music online, it does not let you download songs for free. You need to have a premium subscription to download songs and listen to them offline. However, there is a way to download Game of Thrones songs from Spotify for free using a third-party tool. A third-party tool is a software or website that can convert Spotify songs to MP3 or other formats and save them on your device. There are many such tools available online, but you need to be careful and choose a reliable and safe one.</p>
-<p>One of the best third-party tools for downloading Game of Thrones songs from Spotify is AudKit Spotify Music Converter. This is a professional and powerful tool that can download any song, playlist, album, or podcast from Spotify and convert them to MP3, AAC, WAV, FLAC, or other formats. It can also preserve the original quality and metadata of the songs, such as title, artist, album, genre, etc. It has a user-friendly interface and a fast conversion speed. You can use it on both Windows and Mac computers.</p>
-<p>To download Game of Thrones songs from Spotify using AudKit Spotify Music Converter, you need to follow these steps:</p>
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-<li>Download and install AudKit Spotify Music Converter on your computer from its official website. Launch it and it will automatically open Spotify as well.</li>
-<li>Find the Game of Thrones soundtrack playlist on Spotify and drag and drop it to the AudKit interface. You can also copy and paste the playlist link to the search bar of AudKit and click on the "+" button.</li>
-<li>Choose the output format, quality, bitrate, sample rate, and other parameters according to your preference. You can access these settings by clicking on the "Menu" icon on the top right corner and then selecting "Preferences".</li>
-<li>Click on the "Convert" button to start downloading and converting the songs. You can check the progress and status of each song on the AudKit interface.</li>
-<li>After the conversion is done, you can find the downloaded songs in the output folder. You can also open it by clicking on the "History" icon on the top right corner and then selecting "Open Output Folder".</li>
-</ol>
-<p>Now you have successfully downloaded Game of Thrones songs from Spotify for free using AudKit Spotify Music Converter. You can transfer them to any device or player and enjoy them offline.</p>
- <h2>How to Download Game of Thrones Songs from YouTube</h2>
-<h3>What is YouTube and why it is another good option for Game of Thrones fans</h3>
-<p>YouTube is another popular and widely used online platform that offers a variety of videos, including music videos, trailers, clips, interviews, documentaries, and more. You can watch videos on YouTube for free with ads, or subscribe to YouTube Premium for ad-free watching, offline mode, background play, and more features.</p>
-<p>YouTube is another good option for Game of Thrones fans because it has many videos related to Game of Thrones music. You can find official music videos, fan-made videos, covers, remixes, mashups, tributes, parodies, and more. You can also find videos that explain the meaning and symbolism behind some of the songs. You can discover new and interesting perspectives on Game of Thrones music by watching these videos.</p>
- <h3>How to find Game of Thrones songs on YouTube using keywords and filters</h3>
-<p>To find Game of Thrones songs on YouTube, you need to use keywords and filters that will help you narrow down your search results. Keywords are words or phrases that describe what you are looking for. Filters are options that let you sort or refine your search results by various criteria. Here are some tips on how to use keywords and filters effectively:</p>
-<ul>
-<li>Use specific keywords that match your query. For example, if you want to find a video of "The Rains of Castamere", type that exact phrase in the search bar instead of just "Game of Thrones song".</li>
-<li>Use quotation marks around your keywords if you want to find an exact match. For example, if you want to find a video of "The Rains of Castamere" sung by The National, type "The Rains of Castamere" "The National" in the search bar.</li>
-<li>Use the plus (+) sign to add keywords that you want to include in your search results. For example, if you want to find a video of "The Rains of Castamere" with lyrics, type "The Rains of Castamere" +lyrics in the search bar.</li>
-<li>Use the minus (-) sign to exclude keywords that you don't want in your search results. For example, if you want to find a video of "The Rains of Castamere" without spoilers, type "The Rains of Castamere" -spoilers in the search bar.</li>
-<li>Use the filters option to sort or refine your search results by various criteria, such as upload date, duration, type, quality, features, etc. You can access the filters option by clicking on the three horizontal lines next to the search bar and then selecting "Filters".</li>
-</ul>
-<p>By using keywords and filters, you can find Game of Thrones songs on YouTube that suit your preferences and needs.</p>
- <h3>How to download Game of Thrones songs from YouTube using an online converter or downloader</h3>
-<p>While YouTube allows you to watch videos online, it does not let you download videos for free. You need to have a YouTube Premium subscription to download videos and watch them offline. However, there is a way to download Game of Thrones songs from YouTube for free using an online converter or downloader. An online converter or downloader is a website that can convert YouTube videos to MP3 or other formats and save them on your device. There are many such websites available online, but you need to be careful and choose a reliable and safe one.</p>
-<p>One of the best online converters or downloaders for downloading Game of Thrones songs from YouTube is Y2mate. This is a simple and fast website that can download any YouTube video or audio in various formats and qualities. It does not require any registration or installation. You can use it on any device or browser.</p>
-<p>To download Game of Thrones songs from YouTube using Y2mate, you need to follow these steps:</p>
-<ol>
-<li>Find the YouTube video that contains the Game of Thrones song that you want to download and copy its URL from the address bar.</li>
-<li>Go to Y2mate's website and paste the URL in the search box. Click on the "Start" button.</li>
-<li>Choose the output format and quality that you want from the list of options. You can choose MP3, MP4, M4A, WEBM, etc. You can also choose the audio or video quality from 64kbps to 320kbps or 144p to 1080p.</li>
-<li>Click on the "Download" button next to the option that you have chosen. A new tab will open with a preview of the file.</li>
-<li>Right-click on the preview and select "Save as" or "Save link as". Choose a location and name for the file and click on "Save".</li>
-</ol>
-<p>Now you have successfully downloaded Game of Thrones songs from YouTube for free using Y2mate. You can transfer them to any device or player and enjoy them offline.</p>
- <h2>How to Download Game of Thrones Songs from Other Websites</h2>
-<h3>What are some other websites that offer Game of Thrones songs for free download</h3>
-<p>Besides Spotify and YouTube, there are some other websites that offer Game of Thrones songs for free download. These websites usually host MP3 files of various songs from different sources and genres. You can find Game of Thrones songs on these websites by searching for them by name, artist, album, or category. Some examples of these websites are:</p>
-<table>
-<tr><th>Website</th><th>Description</th></tr>
-<tr><td>MP3Juices</td><td>A free MP3 search engine that allows you to search for songs from multiple sources and download them with one click.</td></tr>
-<tr><td>ZippyAudio</td><td>A free online music streaming and downloading service that offers high-quality MP3 files of various songs.</td></tr>
-<tr><td>eMP3 Downloads</td><td>A free MP3 music download site that provides access to millions of songs from different genres and artists.</td></tr>
-<tr><td>BeeMP3</td><td>A free MP3 music download site that has a large database of songs from various sources and categories.</td></tr>
-<tr><td>SongsLover</td><td>A free music download site that offers latest and old songs from different genres and artists.</td></tr>
-</table>
-<p>These are some of the websites that offer Game of Thrones songs for free download. However, you should be aware that these websites may not be legal, safe, or reliable. They may contain viruses, malware, pop-ups, ads, or other unwanted content. They may also violate the copyright or intellectual property rights of the creators of Game of Thrones music. Therefore, you should use these websites at your own risk and discretion.</p>
- <h3>How to check the quality and safety of the websites before downloading</h3>
-<p>Before downloading Game of Thrones songs from any website, you should check the quality and safety of the website first. This will help you avoid any potential problems or issues that may arise from downloading. Here are some tips on how to check the quality and safety of the websites before downloading:</p>
-<ul>
-<li>Check the URL of the website and make sure it is secure and valid. A secure website will have a padlock icon and "https" at the beginning of its URL. A valid website will have a clear and relevant domain name and extension.</li>
-<li>Check the reviews and ratings of the website and see what other users have to say about it. You can use online tools such as Trustpilot, Sitejabber, or WOT to find out the reputation and feedback of the website.</li>
-<li>Check the content and layout of the website and see if it is professional and user-friendly. A good website will have a clear and organized design, easy navigation, accurate information, and relevant content.</li>
-<li>Check the download options and see if they are compatible and convenient for you. A good website will offer multiple formats, qualities, and sources for downloading. It will also provide clear instructions and previews for downloading.</li>
-</ul>
-<p>By checking the quality and safety of the websites before downloading, you can ensure that you get the best experience and results from downloading Game of Thrones songs.</p>
- <h3>How to download Game of Thrones songs from other websites using a browser extension or software</h3>
-<p>Another way to download Game of Thrones songs from other websites is to use a browser extension or software. A browser extension or software is a program that can enhance the functionality of your browser or device. It can help you download Game of Thrones songs from other websites by adding a download button, capturing the audio stream, or converting the file format. There are many such programs available online, but you need to be careful and choose a reliable and safe one.</p>
-<p>One of the best browser extensions or software for downloading Game of Thrones songs from other websites is Video DownloadHelper. This is a versatile and powerful program that can download any video or audio from any website. It can also convert, edit, or merge the files as per your needs. It works on both Chrome and Firefox browsers. You can use it on both Windows and Mac computers.</p>
-<p>To download Game of Thrones songs from other websites using Video DownloadHelper, you need to follow these steps:</p>
-<ol>
-<li>Download and install Video DownloadHelper on your browser from its official website. Launch it and you will see a three-colored icon on the top right corner of your browser.</li>
-<li>Find the website that contains the Game of Thrones song that you want to download and open it on your browser.</li>
-<li>Play the song on the website and you will see that the Video DownloadHelper icon will become active and animated.</li>
-<li>Click on the icon and you will see a list of options for downloading. Choose the option that suits your preference and click on it.</li>
-<li>A new tab will open with a preview of the file. Click on the "Download" button at the bottom right corner to start downloading.</li>
-<li>After the download is complete, you can find the file in your default download folder. You can also open it by clicking on the "Open File" button at the bottom left corner.</li>
-</ol>
-<p>Now you have successfully downloaded Game of Thrones songs from other websites using Video DownloadHelper. You can transfer them to any device or player and enjoy them offline.</p>
- <h2>Conclusion</h2>
-<p>Game of Thrones songs are some of the best and most memorable music pieces in TV history. They add to the richness and depth of the show and its characters. If you are a fan of Game of Thrones music, you might want to download some of your favorite songs and listen to them anytime, anywhere. In this article, we have shown you how to download Game of Thrones songs for free from various online sources, such as Spotify, YouTube, and other websites. We have also provided you with some tips and tricks to ensure that you get the best quality and experience.</p>
-<p>We hope that this article has been helpful and informative for you. Now you can enjoy the music of Game of Thrones without any hassle or cost. Whether you want to relive the epic moments of the show, feel the emotions of the characters, or simply appreciate the beauty and creativity of the music, you can do so with ease and convenience.</p>
-<p>So, what are you waiting for? Go ahead and try out the methods that we have suggested and download Game of Thrones songs for free. You will not regret it!</p>
- <h2>FAQs</h2>
-<h3>Q1: What are the best songs from Game of Thrones?</h3>
-<p>A1: This is a subjective question that depends on your personal taste and preference. However, some of the most popular and acclaimed songs from Game of Thrones are:</p>
-<ul>
-<li>The Main Theme: This is the song that plays during the opening credits of every episode. It is a powerful and majestic piece that sets the tone and mood for the show.</li>
-<li>The Rains of Castamere: This is the song that is associated with House Lannister and their ruthless ambition. It is a dark and ominous piece that foreshadows the infamous Red Wedding.</li>
-<li>Light of the Seven: This is the song that plays during the season six finale, when Cersei Lannister blows up the Great Sept of Baelor with wildfire. It is a haunting and dramatic piece that builds up to a shocking climax.</li>
-<li>The Night King: This is the song that plays during the season eight episode "The Long Night", when Arya Stark kills the Night King and ends the threat of the White Walkers. It is a tense and emotional piece that contrasts the coldness of death and the warmth of life.</li>
-<li>Jenny of Oldstones: This is the song that Podrick Payne sings during the season eight episode "A Knight of the Seven Kingdoms", when the characters prepare for the Battle of Winterfell. It is a melancholic and nostalgic piece that reflects on love and loss.</li>
-</ul>
- <h3>Q2: How can I listen to Game of Thrones songs offline?</h3>
-<p>A2: You can listen to Game of Thrones songs offline by downloading them from various online sources, such as Spotify, YouTube, or other websites. You can use third-party tools, such as AudKit Spotify Music Converter, Y2mate, or Video DownloadHelper, to download Game of Thrones songs for free in different formats and qualities. You can then transfer them to any device or player and listen to them offline.</p>
- <h3>Q3: Are there any legal issues with downloading Game of Thrones songs for free?</h3>
-<p>A3: There may be some legal issues with downloading Game of Thrones songs for free, depending on your location and jurisdiction. Downloading Game of Thrones songs for free may violate the copyright or intellectual property rights of the creators of Game of Thrones music. Downloading Game of Thrones songs for free may also expose you to viruses, malware, pop-ups, ads, or other unwanted content. Therefore, you should use caution and discretion when downloading Game of Thrones songs for free. You should also respect and support the creators of Game of Thrones music by purchasing their music legally or subscribing to their official platforms.</p>
- <h3>Q4: Where can I find more information about Game of Thrones music and composers?</h3>
-<p>A4: You can find more information about Game of Thrones music and composers by visiting their official websites, social media accounts, or Wikipedia pages. Here are some links that you can check out:</p>
-<ul>
-<li>Ramin Djawadi: He is the main composer of Game of Thrones music. He has also composed music for other movies and shows, such as Iron Man, Pacific Rim, Westworld, and The Mandalorian. You can visit his website, Facebook, Twitter, or Wikipedia page for more information.</li>
-<li>Djawadi's collaborators: He has also worked with other composers, musicians, singers, and bands to create Game of Thrones music. Some of them are Matt Bellamy, Serj Tankian, The National, Florence + The Machine, Sigur Rós, Of Monsters and Men, etc. You can search for their names on Google or YouTube to find out more about them.</li>
-<li>Game of Thrones Wiki: This is a fan-made website that contains detailed information about everything related to Game of Thrones, including its music. You can visit its music category to learn more about the songs, albums, themes, motifs, instruments, etc.</li>
-</ul>
- <h3>Q5: How can I support the creators of Game of Thrones music?</h3>
-<p>A5: You can support the creators of Game of Thrones music by purchasing their music legally or subscribing to their official platforms. You can also show your appreciation and feedback by following them on social media, leaving comments, ratings, or reviews, sharing their music with others, or attending their live events or concerts. You can also donate to their charities or causes if they have any.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/models/megatron_t5/__init__.py b/spaces/skf15963/summary/fengshen/models/megatron_t5/__init__.py
deleted file mode 100644
index 84f78136331c5ef4975697bc6a77910bba7429bd..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/models/megatron_t5/__init__.py
+++ /dev/null
@@ -1,49 +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 typing import TYPE_CHECKING
-
-from transformers.file_utils import _LazyModule, is_torch_available
-
-
-_import_structure = {
-    "configuration_megatron_t5": ["T5Config"],
-    "tokenization_megatron_t5": ["T5Tokenizer"],
-}
-
-if is_torch_available():
-    _import_structure["modeling_megatron_t5"] = [
-        "T5Model",
-        "T5EncoderModel",
-        "T5ForConditionalGeneration"
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_megatron_t5 import T5Config
-    from .tokenization_megatron_t5 import T5Tokenizer
-
-    if is_torch_available():
-        from .modeling_megatron_t5 import (
-            T5Model,
-            T5EncoderModel,
-            T5ForConditionalGeneration
-        )
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(
-        __name__, globals()["__file__"], _import_structure)
diff --git a/spaces/sklearn-docs/classification/README.md b/spaces/sklearn-docs/classification/README.md
deleted file mode 100644
index bd034c1be2fa804b1331e71c7bf9a2a65808d656..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/classification/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Classification
-emoji: 🌲 
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sparanoid/milky-green-sovits-4/hubert/hubert_model_onnx.py b/spaces/sparanoid/milky-green-sovits-4/hubert/hubert_model_onnx.py
deleted file mode 100644
index d18f3c2a0fc29592a573a9780308d38f059640b9..0000000000000000000000000000000000000000
--- a/spaces/sparanoid/milky-green-sovits-4/hubert/hubert_model_onnx.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-    def forward(self, x):
-        return self.units(x)
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/feature_utils.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/feature_utils.py
deleted file mode 100644
index f80bc4569768fac181133cdc8f76d1230e03bff6..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/simple_kmeans/feature_utils.py
+++ /dev/null
@@ -1,66 +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 logging
-import os
-import sys
-
-import tqdm
-from npy_append_array import NpyAppendArray
-
-
-logging.basicConfig(
-    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=os.environ.get("LOGLEVEL", "INFO").upper(),
-    stream=sys.stdout,
-)
-logger = logging.getLogger("feature_utils")
-
-
-def get_shard_range(tot, nshard, rank):
-    assert rank < nshard and rank >= 0, f"invaid rank/nshard {rank}/{nshard}"
-    start = round(tot / nshard * rank)
-    end = round(tot / nshard * (rank + 1))
-    assert start < end, f"start={start}, end={end}"
-    logger.info(
-        f"rank {rank} of {nshard}, process {end-start} "
-        f"({start}-{end}) out of {tot}"
-    )
-    return start, end
-
-
-def get_path_iterator(tsv, nshard, rank):
-    with open(tsv, "r") as f:
-        root = f.readline().rstrip()
-        lines = [line.rstrip() for line in f]
-        start, end = get_shard_range(len(lines), nshard, rank)
-        lines = lines[start:end]
-        def iterate():
-            for line in lines:
-                subpath, nsample = line.split("\t")
-                yield f"{root}/{subpath}", int(nsample)
-    return iterate, len(lines)
-
-
-def dump_feature(reader, generator, num, split, nshard, rank, feat_dir):
-    iterator = generator()
-
-    feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy"
-    leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len"
-
-    os.makedirs(feat_dir, exist_ok=True)
-    if os.path.exists(feat_path):
-        os.remove(feat_path)
-
-    feat_f = NpyAppendArray(feat_path)
-    with open(leng_path, "w") as leng_f:
-        for path, nsample in tqdm.tqdm(iterator, total=num):
-            feat = reader.get_feats(path, nsample)
-            feat_f.append(feat.cpu().numpy())
-            leng_f.write(f"{len(feat)}\n")
-    logger.info("finished successfully")
-
-
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/laser/laser_src/laser_lstm.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/laser/laser_src/laser_lstm.py
deleted file mode 100644
index 10df90e002d5a7dd74a571dbc3b328c130c57a0a..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/laser/laser_src/laser_lstm.py
+++ /dev/null
@@ -1,585 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from fairseq import options, utils
-
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqIncrementalDecoder,
-    FairseqEncoderDecoderModel,
-    register_model,
-    register_model_architecture,
-)
-
-
-@register_model("laser_lstm")
-class LSTMModel(FairseqEncoderDecoderModel):
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens=None,
-        tgt_tokens=None,
-        tgt_lengths=None,
-        target_language_id=None,
-        dataset_name="",
-    ):
-        assert target_language_id is not None
-
-        src_encoder_out = self.encoder(src_tokens, src_lengths, dataset_name)
-        return self.decoder(
-            prev_output_tokens, src_encoder_out, lang_id=target_language_id
-        )
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        parser.add_argument(
-            "--dropout",
-            default=0.1,
-            type=float,
-            metavar="D",
-            help="dropout probability",
-        )
-        parser.add_argument(
-            "--encoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="encoder embedding dimension",
-        )
-        parser.add_argument(
-            "--encoder-embed-path",
-            default=None,
-            type=str,
-            metavar="STR",
-            help="path to pre-trained encoder embedding",
-        )
-        parser.add_argument(
-            "--encoder-hidden-size", type=int, metavar="N", help="encoder hidden size"
-        )
-        parser.add_argument(
-            "--encoder-layers", type=int, metavar="N", help="number of encoder layers"
-        )
-        parser.add_argument(
-            "--encoder-bidirectional",
-            action="store_true",
-            help="make all layers of encoder bidirectional",
-        )
-        parser.add_argument(
-            "--decoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder embedding dimension",
-        )
-        parser.add_argument(
-            "--decoder-embed-path",
-            default=None,
-            type=str,
-            metavar="STR",
-            help="path to pre-trained decoder embedding",
-        )
-        parser.add_argument(
-            "--decoder-hidden-size", type=int, metavar="N", help="decoder hidden size"
-        )
-        parser.add_argument(
-            "--decoder-layers", type=int, metavar="N", help="number of decoder layers"
-        )
-        parser.add_argument(
-            "--decoder-out-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder output embedding dimension",
-        )
-        parser.add_argument(
-            "--decoder-zero-init",
-            type=str,
-            metavar="BOOL",
-            help="initialize the decoder hidden/cell state to zero",
-        )
-        parser.add_argument(
-            "--decoder-lang-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder language embedding dimension",
-        )
-        parser.add_argument(
-            "--fixed-embeddings",
-            action="store_true",
-            help="keep embeddings fixed (ENCODER ONLY)",
-        )  # TODO Also apply to decoder embeddings?
-
-        # Granular dropout settings (if not specified these default to --dropout)
-        parser.add_argument(
-            "--encoder-dropout-in",
-            type=float,
-            metavar="D",
-            help="dropout probability for encoder input embedding",
-        )
-        parser.add_argument(
-            "--encoder-dropout-out",
-            type=float,
-            metavar="D",
-            help="dropout probability for encoder output",
-        )
-        parser.add_argument(
-            "--decoder-dropout-in",
-            type=float,
-            metavar="D",
-            help="dropout probability for decoder input embedding",
-        )
-        parser.add_argument(
-            "--decoder-dropout-out",
-            type=float,
-            metavar="D",
-            help="dropout probability for decoder output",
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        # make sure that all args are properly defaulted (in case there are any new ones)
-        base_architecture(args)
-
-        def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
-            num_embeddings = len(dictionary)
-            padding_idx = dictionary.pad()
-            embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
-            embed_dict = utils.parse_embedding(embed_path)
-            utils.print_embed_overlap(embed_dict, dictionary)
-            return utils.load_embedding(embed_dict, dictionary, embed_tokens)
-
-        pretrained_encoder_embed = None
-        if args.encoder_embed_path:
-            pretrained_encoder_embed = load_pretrained_embedding_from_file(
-                args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim
-            )
-        pretrained_decoder_embed = None
-        if args.decoder_embed_path:
-            pretrained_decoder_embed = load_pretrained_embedding_from_file(
-                args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim
-            )
-
-        num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0
-
-        encoder = LSTMEncoder(
-            dictionary=task.source_dictionary,
-            embed_dim=args.encoder_embed_dim,
-            hidden_size=args.encoder_hidden_size,
-            num_layers=args.encoder_layers,
-            dropout_in=args.encoder_dropout_in,
-            dropout_out=args.encoder_dropout_out,
-            bidirectional=args.encoder_bidirectional,
-            pretrained_embed=pretrained_encoder_embed,
-            fixed_embeddings=args.fixed_embeddings,
-        )
-        decoder = LSTMDecoder(
-            dictionary=task.target_dictionary,
-            embed_dim=args.decoder_embed_dim,
-            hidden_size=args.decoder_hidden_size,
-            out_embed_dim=args.decoder_out_embed_dim,
-            num_layers=args.decoder_layers,
-            dropout_in=args.decoder_dropout_in,
-            dropout_out=args.decoder_dropout_out,
-            zero_init=options.eval_bool(args.decoder_zero_init),
-            encoder_embed_dim=args.encoder_embed_dim,
-            encoder_output_units=encoder.output_units,
-            pretrained_embed=pretrained_decoder_embed,
-            num_langs=num_langs,
-            lang_embed_dim=args.decoder_lang_embed_dim,
-        )
-        return cls(encoder, decoder)
-
-
-class LSTMEncoder(FairseqEncoder):
-    """LSTM encoder."""
-
-    def __init__(
-        self,
-        dictionary,
-        embed_dim=512,
-        hidden_size=512,
-        num_layers=1,
-        dropout_in=0.1,
-        dropout_out=0.1,
-        bidirectional=False,
-        left_pad=True,
-        pretrained_embed=None,
-        padding_value=0.0,
-        fixed_embeddings=False,
-    ):
-        super().__init__(dictionary)
-        self.num_layers = num_layers
-        self.dropout_in = dropout_in
-        self.dropout_out = dropout_out
-        self.bidirectional = bidirectional
-        self.hidden_size = hidden_size
-
-        num_embeddings = len(dictionary)
-        self.padding_idx = dictionary.pad()
-        if pretrained_embed is None:
-            self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
-        else:
-            self.embed_tokens = pretrained_embed
-        if fixed_embeddings:
-            self.embed_tokens.weight.requires_grad = False
-
-        self.lstm = LSTM(
-            input_size=embed_dim,
-            hidden_size=hidden_size,
-            num_layers=num_layers,
-            dropout=self.dropout_out if num_layers > 1 else 0.0,
-            bidirectional=bidirectional,
-        )
-        self.left_pad = left_pad
-        self.padding_value = padding_value
-
-        self.output_units = hidden_size
-        if bidirectional:
-            self.output_units *= 2
-
-    def forward(self, src_tokens, src_lengths, dataset_name):
-        if self.left_pad:
-            # convert left-padding to right-padding
-            src_tokens = utils.convert_padding_direction(
-                src_tokens,
-                self.padding_idx,
-                left_to_right=True,
-            )
-
-        bsz, seqlen = src_tokens.size()
-
-        # embed tokens
-        x = self.embed_tokens(src_tokens)
-        x = F.dropout(x, p=self.dropout_in, training=self.training)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        # pack embedded source tokens into a PackedSequence
-        try:
-            packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data.tolist())
-        except BaseException:
-            raise Exception(f"Packing failed in dataset {dataset_name}")
-
-        # apply LSTM
-        if self.bidirectional:
-            state_size = 2 * self.num_layers, bsz, self.hidden_size
-        else:
-            state_size = self.num_layers, bsz, self.hidden_size
-        h0 = x.data.new(*state_size).zero_()
-        c0 = x.data.new(*state_size).zero_()
-        packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0))
-
-        # unpack outputs and apply dropout
-        x, _ = nn.utils.rnn.pad_packed_sequence(
-            packed_outs, padding_value=self.padding_value
-        )
-        x = F.dropout(x, p=self.dropout_out, training=self.training)
-        assert list(x.size()) == [seqlen, bsz, self.output_units]
-
-        if self.bidirectional:
-
-            def combine_bidir(outs):
-                return torch.cat(
-                    [
-                        torch.cat([outs[2 * i], outs[2 * i + 1]], dim=0).view(
-                            1, bsz, self.output_units
-                        )
-                        for i in range(self.num_layers)
-                    ],
-                    dim=0,
-                )
-
-            final_hiddens = combine_bidir(final_hiddens)
-            final_cells = combine_bidir(final_cells)
-
-        encoder_padding_mask = src_tokens.eq(self.padding_idx).t()
-
-        # Set padded outputs to -inf so they are not selected by max-pooling
-        padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1)
-        if padding_mask.any():
-            x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x)
-
-        # Build the sentence embedding by max-pooling over the encoder outputs
-        sentemb = x.max(dim=0)[0]
-
-        return {
-            "sentemb": sentemb,
-            "encoder_out": (x, final_hiddens, final_cells),
-            "encoder_padding_mask": encoder_padding_mask
-            if encoder_padding_mask.any()
-            else None,
-        }
-
-    def reorder_encoder_out(self, encoder_out_dict, new_order):
-        encoder_out_dict["sentemb"] = encoder_out_dict["sentemb"].index_select(
-            0, new_order
-        )
-        encoder_out_dict["encoder_out"] = tuple(
-            eo.index_select(1, new_order) for eo in encoder_out_dict["encoder_out"]
-        )
-        if encoder_out_dict["encoder_padding_mask"] is not None:
-            encoder_out_dict["encoder_padding_mask"] = encoder_out_dict[
-                "encoder_padding_mask"
-            ].index_select(1, new_order)
-        return encoder_out_dict
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        return int(1e5)  # an arbitrary large number
-
-
-class LSTMDecoder(FairseqIncrementalDecoder):
-    """LSTM decoder."""
-
-    def __init__(
-        self,
-        dictionary,
-        embed_dim=512,
-        hidden_size=512,
-        out_embed_dim=512,
-        num_layers=1,
-        dropout_in=0.1,
-        dropout_out=0.1,
-        zero_init=False,
-        encoder_embed_dim=512,
-        encoder_output_units=512,
-        pretrained_embed=None,
-        num_langs=1,
-        lang_embed_dim=0,
-    ):
-        super().__init__(dictionary)
-        self.dropout_in = dropout_in
-        self.dropout_out = dropout_out
-        self.hidden_size = hidden_size
-
-        num_embeddings = len(dictionary)
-        padding_idx = dictionary.pad()
-        if pretrained_embed is None:
-            self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
-        else:
-            self.embed_tokens = pretrained_embed
-
-        self.layers = nn.ModuleList(
-            [
-                LSTMCell(
-                    input_size=encoder_output_units + embed_dim + lang_embed_dim
-                    if layer == 0
-                    else hidden_size,
-                    hidden_size=hidden_size,
-                )
-                for layer in range(num_layers)
-            ]
-        )
-        if hidden_size != out_embed_dim:
-            self.additional_fc = Linear(hidden_size, out_embed_dim)
-        self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)
-
-        if zero_init:
-            self.sentemb2init = None
-        else:
-            self.sentemb2init = Linear(
-                encoder_output_units, 2 * num_layers * hidden_size
-            )
-
-        if lang_embed_dim == 0:
-            self.embed_lang = None
-        else:
-            self.embed_lang = nn.Embedding(num_langs, lang_embed_dim)
-            nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1)
-
-    def forward(
-        self, prev_output_tokens, encoder_out_dict, incremental_state=None, lang_id=0
-    ):
-        sentemb = encoder_out_dict["sentemb"]
-        encoder_out = encoder_out_dict["encoder_out"]
-
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-        bsz, seqlen = prev_output_tokens.size()
-
-        # get outputs from encoder
-        encoder_outs, _, _ = encoder_out[:3]
-        srclen = encoder_outs.size(0)
-
-        # embed tokens
-        x = self.embed_tokens(prev_output_tokens)
-        x = F.dropout(x, p=self.dropout_in, training=self.training)
-
-        # embed language identifier
-        if self.embed_lang is not None:
-            lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id)
-            langemb = self.embed_lang(lang_ids)
-            # TODO Should we dropout here???
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        # initialize previous states (or get from cache during incremental generation)
-        cached_state = utils.get_incremental_state(
-            self, incremental_state, "cached_state"
-        )
-        if cached_state is not None:
-            prev_hiddens, prev_cells, input_feed = cached_state
-        else:
-            num_layers = len(self.layers)
-            if self.sentemb2init is None:
-                prev_hiddens = [
-                    x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers)
-                ]
-                prev_cells = [
-                    x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers)
-                ]
-            else:
-                init = self.sentemb2init(sentemb)
-                prev_hiddens = [
-                    init[:, (2 * i) * self.hidden_size : (2 * i + 1) * self.hidden_size]
-                    for i in range(num_layers)
-                ]
-                prev_cells = [
-                    init[
-                        :,
-                        (2 * i + 1) * self.hidden_size : (2 * i + 2) * self.hidden_size,
-                    ]
-                    for i in range(num_layers)
-                ]
-            input_feed = x.data.new(bsz, self.hidden_size).zero_()
-
-        attn_scores = x.data.new(srclen, seqlen, bsz).zero_()
-        outs = []
-        for j in range(seqlen):
-            if self.embed_lang is None:
-                input = torch.cat((x[j, :, :], sentemb), dim=1)
-            else:
-                input = torch.cat((x[j, :, :], sentemb, langemb), dim=1)
-
-            for i, rnn in enumerate(self.layers):
-                # recurrent cell
-                hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i]))
-
-                # hidden state becomes the input to the next layer
-                input = F.dropout(hidden, p=self.dropout_out, training=self.training)
-
-                # save state for next time step
-                prev_hiddens[i] = hidden
-                prev_cells[i] = cell
-
-            out = hidden
-            out = F.dropout(out, p=self.dropout_out, training=self.training)
-
-            # input feeding
-            input_feed = out
-
-            # save final output
-            outs.append(out)
-
-        # cache previous states (no-op except during incremental generation)
-        utils.set_incremental_state(
-            self,
-            incremental_state,
-            "cached_state",
-            (prev_hiddens, prev_cells, input_feed),
-        )
-
-        # collect outputs across time steps
-        x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(1, 0)
-
-        # srclen x tgtlen x bsz -> bsz x tgtlen x srclen
-        attn_scores = attn_scores.transpose(0, 2)
-
-        # project back to size of vocabulary
-        if hasattr(self, "additional_fc"):
-            x = self.additional_fc(x)
-            x = F.dropout(x, p=self.dropout_out, training=self.training)
-        x = self.fc_out(x)
-
-        return x, attn_scores
-
-    def reorder_incremental_state(self, incremental_state, new_order):
-        super().reorder_incremental_state(incremental_state, new_order)
-        cached_state = utils.get_incremental_state(
-            self, incremental_state, "cached_state"
-        )
-        if cached_state is None:
-            return
-
-        def reorder_state(state):
-            if isinstance(state, list):
-                return [reorder_state(state_i) for state_i in state]
-            return state.index_select(0, new_order)
-
-        new_state = tuple(map(reorder_state, cached_state))
-        utils.set_incremental_state(self, incremental_state, "cached_state", new_state)
-
-    def max_positions(self):
-        """Maximum output length supported by the decoder."""
-        return int(1e5)  # an arbitrary large number
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.uniform_(m.weight, -0.1, 0.1)
-    nn.init.constant_(m.weight[padding_idx], 0)
-    return m
-
-
-def LSTM(input_size, hidden_size, **kwargs):
-    m = nn.LSTM(input_size, hidden_size, **kwargs)
-    for name, param in m.named_parameters():
-        if "weight" in name or "bias" in name:
-            param.data.uniform_(-0.1, 0.1)
-    return m
-
-
-def LSTMCell(input_size, hidden_size, **kwargs):
-    m = nn.LSTMCell(input_size, hidden_size, **kwargs)
-    for name, param in m.named_parameters():
-        if "weight" in name or "bias" in name:
-            param.data.uniform_(-0.1, 0.1)
-    return m
-
-
-def Linear(in_features, out_features, bias=True, dropout=0):
-    """Weight-normalized Linear layer (input: N x T x C)"""
-    m = nn.Linear(in_features, out_features, bias=bias)
-    m.weight.data.uniform_(-0.1, 0.1)
-    if bias:
-        m.bias.data.uniform_(-0.1, 0.1)
-    return m
-
-
-@register_model_architecture("laser_lstm", "laser_lstm")
-def base_architecture(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_hidden_size = getattr(
-        args, "encoder_hidden_size", args.encoder_embed_dim
-    )
-    args.encoder_layers = getattr(args, "encoder_layers", 1)
-    args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False)
-    args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout)
-    args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_hidden_size = getattr(
-        args, "decoder_hidden_size", args.decoder_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 1)
-    args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
-    args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout)
-    args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
-    args.decoder_zero_init = getattr(args, "decoder_zero_init", "0")
-    args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0)
-    args.fixed_embeddings = getattr(args, "fixed_embeddings", False)
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/adadelta.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/adadelta.py
deleted file mode 100644
index f1a21549770f0904a6a40a42ff7eb52811f1bfbe..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/adadelta.py
+++ /dev/null
@@ -1,47 +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.optim
-
-from . import LegacyFairseqOptimizer, register_optimizer
-
-
-@register_optimizer("adadelta")
-class Adadelta(LegacyFairseqOptimizer):
-    def __init__(self, args, params):
-        super().__init__(args)
-        self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config)
-
-    @staticmethod
-    def add_args(parser):
-        """Add optimizer-specific arguments to the parser."""
-        # fmt: off
-        parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO',
-                            help='coefficient used for computing a running average of squared gradients')
-        parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS',
-                            help='term added to the denominator to improve numerical stability')
-        parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
-                            help='weight decay')
-        parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps')
-        # fmt: on
-
-    @property
-    def optimizer_config(self):
-        """
-        Return a kwarg dictionary that will be used to override optimizer
-        args stored in checkpoints. This allows us to load a checkpoint and
-        resume training using a different set of optimizer args, e.g., with a
-        different learning rate.
-        """
-        return {
-            "lr": self.args.lr[0],
-            "rho": self.args.adadelta_rho,
-            "eps": self.args.adadelta_eps,
-            "weight_decay": self.args.weight_decay,
-        }
-
-    @property
-    def supports_flat_params(self):
-        return True
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Avast Full Virus Scan Stays At 0l.md b/spaces/stomexserde/gpt4-ui/Examples/Avast Full Virus Scan Stays At 0l.md
deleted file mode 100644
index ae1bd1fb0c0a3f6b70c58b29e9182d6bb2792faa..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Avast Full Virus Scan Stays At 0l.md	
+++ /dev/null
@@ -1,153 +0,0 @@
-
-<h1>Avast Full Virus Scan Stays at 0%: How to Fix It and Why You Should Use Avast Antivirus</h1>
- <p>Avast antivirus is one of the most popular and trusted antivirus solutions in the world. It protects your devices from viruses, malware, ransomware, phishing, and other online threats. It also offers a range of advanced features, such as network security, data shredder, webcam shield, sandbox, and more.</p>
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- <p>However, like any software, Avast antivirus can sometimes encounter problems that affect its performance and functionality. One of the most common issues that users face is when the full virus scan stays at 0% for a long time or never completes. This can be frustrating and worrying, as it means that your device is not fully scanned and protected from potential threats.</p>
- <p>Fortunately, there are some possible causes and solutions for this issue that you can try to fix it yourself. In this article, we will explain why Avast full virus scan gets stuck at 0%, how to fix it, and why you should use Avast antivirus for your device's security.</p>
- <h2>Possible Causes of Avast Full Virus Scan Stuck at 0%</h2>
- <p>There are several factors that can cause Avast full virus scan to stay at 0% or freeze during the process. Here are some of the most common ones:</p>
- <h3>Anti-rootkit check</h3>
- <p>Anti-rootkit is a feature that scans your device for any hidden or malicious software that can compromise your system's security. However, this feature can also slow down or freeze the scan progress, as it requires a lot of resources and time to complete. This can make it seem like the scan is stuck at 0%, even though it is actually running in the background.</p>
- <p>One possible solution for this issue is to disable the anti-rootkit option in the scan settings. This can speed up the scan process and prevent it from getting stuck. However, this also means that you will not be able to detect and remove any rootkits from your device, which can pose a serious risk to your security.</p>
- <h3>Corrupted or outdated Avast files</h3>
- <p>Another possible cause of Avast full virus scan stuck at 0% is when some of the Avast files are corrupted or outdated. This can happen due to various reasons, such as improper installation, disk errors, malware infection, or software conflicts. Corrupted or outdated Avast files can interfere with the scan process and cause errors or glitches that prevent it from completing.</p>
-<p></p>
- <p>One possible solution for this issue is to repair or update Avast antivirus using the Avast setup window. This can fix any damaged or missing files and ensure that your Avast antivirus is up to date and working properly. However, this also means that you will need to restart your device and reconnect to the internet, which can be inconvenient and time-consuming.</p>
- <h3>Conflicting software or settings</h3>
- <p>A third possible cause of Avast full virus scan stuck at 0% is when there are other software or settings on your device that conflict with Avast antivirus. This can happen due to various reasons, such as incompatible programs, firewall settings, proxy servers, VPNs, or other antivirus software. Conflicting software or settings can block or interrupt the scan process and cause it to stop or fail.</p>
- <p>One possible solution for this issue is to uninstall or disable any conflicting software or settings on your device. This can eliminate any interference and allow the scan process to run smoothly and successfully. However, this also means that you will lose some of the features or functions of the conflicting software or settings, which can affect your device's performance or functionality.</p>
- <h2>How to Fix Avast Full Virus Scan Stuck at 0%</h2>
- <p>Now that you know some of the possible causes of Avast full virus scan stuck at 0%, let's see how you can fix it using some of the possible solutions we mentioned above. Here are some step-by-step instructions on how to do so:</p>
- <h3>Disable anti-rootkit option in scan settings</h3>
- <p>To disable the anti-rootkit option in scan settings, follow these steps:</p>
- <ol>
-<li>Open Avast antivirus and go to Menu > Settings > Protection > Virus Scans.</li>
-<li>Select Full Virus Scan from the list of scans and click on Customize.</li>
-<li>Go to the Sensitivity tab and uncheck the box next to Scan for rootkits.</li>
-<li>Click on OK to save the changes and close the window.</li>
-<li>Run the full virus scan again and see if it works.</li>
-</ol>
- <p>The pros of this solution are that it can speed up the scan process and prevent it from getting stuck at 0%. The cons are that it can leave your device vulnerable to rootkits and other hidden malware.</p>
- <h3>Repair or update Avast antivirus</h3>
- <p>To repair or update Avast antivirus using the Avast setup window, follow these steps:</p>
- <ol>
-<li>Go to Control Panel > Programs > Programs and Features and find Avast antivirus in the list of installed programs.</li>
-<li>Right-click on Avast antivirus and select Change from the menu.</li>
-<li>Select Repair from the options and click on Next.</li>
-<li>Wait for the repair process to finish and click on Done.</li>
-<li>If prompted, restart your device and reconnect to the internet.</li>
-<li>If you want to update Avast antivirus, open Avast antivirus and go to Menu > Settings > General > Update and click on Check for updates under Program.</li>
-<li>Wait for the update process to finish and click on Restart Now if prompted.</li>
-<li>Run the full virus scan again and see if it works.</li>
-</ol>
- <p>The pros of this solution are that it can fix any corrupted or outdated Avast files and ensure that your Avast antivirus is up to date and working properly. The cons are that it can require a restart and an internet connection, which can be inconvenient and time-consuming.</p>
- <h3>Uninstall or disable any conflicting software or settings</h3>
- <p>To uninstall or disable any conflicting software or settings on your device, follow these steps:</p>
- <ol>
-<li>Go to Control Panel > Programs > Programs and Features and find any software that may conflict with Avast antivirus, such as other antivirus software, firewall software, proxy software, VPN software, etc.</li>
-<li>Right-click on the software and select Uninstall from the menu.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-<li>If prompted, restart your device and reconnect to the internet.</li>
-<li>If you want to disable any settings that may conflict with Avast antivirus, such as Windows Defender Firewall, proxy settings, VPN settings, etc., go to Control Panel > System and Security and find the relevant settings.</li>
-<li>Click on the settings and follow the instructions on the screen to turn them off or modify them.</li>
-<li>Run the full virus scan again and see if it works.</li>
-</ol>
- <p>The pros of this solution are that it can eliminate any interference and allow the scan process to run smoothly and successfully. The cons are that it can affect your device's performance or functionality and expose it to other risks or threats.</p>
- <h2>Why You Should Use Avast Antivirus</h2>
- <p>Now that you know how to fix Avast full virus scan stuck at 0%, let's see why you should use Avast antivirus for your device's security. Here are some of the features and benefits of Avast antivirus that make it one of the best antivirus solutions in the market:</p>
- <h3>Features and benefits of Avast antivirus</h3>
- <p>Avast antivirus offers a range of advanced features that protect your devices from various online threats and enhance your online experience. Some of these features are:</p>
- <ul>
-<li>Malware protection: Avast antivirus detects and removes viruses, malware, ransomware, spyware, adware, trojans, worms, and other malicious software from your device. It also blocks malicious websites, downloads, and email attachments that can infect your device.</li>
-<li>Network security: Avast antivirus scans your network for any vulnerabilities or intrusions and alerts you of any suspicious activity. It also protects your network from hackers, botnets, DDoS attacks, and other network threats. It also secures your Wi-Fi connection and prevents anyone from accessing your network without your permission.</li>
-<li>Data shredder: Avast antivirus permanently deletes any sensitive or unwanted files from your device and prevents anyone from recovering them. It also overwrites the deleted files with random data to ensure that they are completely erased. This feature is useful for protecting your privacy and identity online.</li>
-<li>Webcam shield: Avast antivirus prevents any unauthorized applications or websites from accessing your webcam and spying on you. It also notifies you of any attempts to access your webcam and lets you choose which applications or websites can use it. This feature is useful for protecting your personal and professional life online.</li>
-<li>Sandbox: Avast antivirus allows you to run any suspicious or unknown files or applications in a safe and isolated environment without affecting your device. It also analyzes the behavior of the files or applications and determines if they are safe or malicious. This feature is useful for testing new or untrusted software without risking your security.</li>
-</ul>
- <p>Avast antivirus also offers a free and a premium version that differ in their features and benefits. The free version provides basic protection for your device, while the premium version provides advanced protection and additional features. Here is a table that compares the free and premium versions of Avast antivirus and their features:</p>
- <table>
-<tr>
-<th>Feature</th>
-<th>Free Version</th>
-<th>Premium Version</th>
-</tr>
-<tr>
-<td>Malware protection</td>
-<td>Yes</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Network security</td>
-<td>No</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Data shredder</td>
-<td>No</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Webcam shield</td>
-<td>No</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Sandbox</td>
-<td>No</td>
-<td>Yes</td>
-</tr>
-<tr>
-<td>Price</td>
-<td>$0 per year</td>
-<td>$69.99 per year</td>
-</tr> </table>
- <p>As you can see, the premium version of Avast antivirus offers more features and benefits than the free version. However, the free version is still a good option for basic protection and security. You can choose the version that suits your needs and budget best.</p>
- <h3>Customer reviews and ratings of Avast antivirus</h3>
- <p>Avast antivirus is not only popular and trusted by millions of users worldwide, but also by experts and critics in the industry. It has received positive reviews and ratings from reputable sources, such as Forbes, CyberNews, PCMag, etc. Here are some examples of what they have to say about Avast antivirus:</p>
- <ul>
-<li>"Avast Free Antivirus offers antivirus protection that earns excellent scores in our hands-on tests and independent lab tests. As for bonus features, it offers much more than many competing commercial products, including a network security scanner, a software updater, and more."</li>
-<li>"Avast is one of the best antivirus software options on the market. It’s easy to use, has a lot of features, and provides great protection for your devices. Whether you choose the free or the premium version, you can rest assured that your security is in good hands."</li>
-<li>"Avast is a solid choice for anyone looking for a free antivirus. There are no major weaknesses in the package, either: it's easy to use, has accurate malware detection rates, comes with plenty of extra features and tools, and doesn't have any annoying ads or pop-ups."</li>
-</ul>
- <p>However, Avast antivirus is not perfect and has some drawbacks that you should be aware of. Some of these are:</p>
- <ul>
-<li>"Avast's malware protection is good, but not flawless. In our tests, it missed some potentially unwanted applications (PUAs) that other antivirus software caught. It also had some false positives, which means it flagged some safe files as malicious."</li>
-<li>"Avast's premium version is quite expensive compared to some of its competitors. It costs $69.99 per year for one device, which is more than double the price of some other premium antivirus software. It also doesn't offer any discounts or bundles for multiple devices or years."</li>
-<li>"Avast's privacy policy is not very transparent or user-friendly. It collects a lot of data from its users, such as browsing history, device information, location data, etc. It also shares some of this data with third parties, such as advertisers, partners, or affiliates."</li>
-</ul>
- <p>Therefore, you should weigh the pros and cons of Avast antivirus before deciding to use it or not. You should also read the terms and conditions and the privacy policy carefully before agreeing to them.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Avast full virus scan stuck at 0% is a common issue that can affect your device's security and performance. However, there are some possible causes and solutions for this issue that you can try to fix it yourself. Some of these are disabling the anti-rootkit option in scan settings, repairing or updating Avast antivirus, or uninstalling or disabling any conflicting software or settings.</p>
- <p>Avast antivirus is one of the best antivirus solutions in the market that offers a range of advanced features and benefits for your device's security and online experience. However, it also has some drawbacks that you should be aware of before using it. You can choose the free or the premium version of Avast antivirus depending on your needs and budget.</p>
- <p>If you want to try Avast antivirus for yourself or need more help with any issues with the full virus scan, you can visit their official website or contact their customer support team. They will be happy to assist you with any questions or concerns you may have.</p>
- <h2>Frequently Asked Questions</h2>
- <p>Here are some frequently asked questions about Avast full virus scan stuck at 0% and Avast antivirus:</p>
- <ol>
-<li><b>How long does Avast full virus scan take?</b></li>
-<p>The duration of Avast full virus scan depends on various factors, such as the size and number of files on your device, the speed and performance of your device, the settings and options you choose for the scan, etc. Generally speaking, a full virus scan can take anywhere from a few minutes to several hours to complete.</p>
-<li><b>How often should I run Avast full virus scan?</b></li>
-<p>The frequency of running Avast full virus scan depends on your personal preference and needs. However, it is recommended to run a full virus scan at least once a month to ensure that your device is fully scanned and protected from any potential threats.</p>
-<li><b>What is the difference between Avast full virus scan and smart scan?</ <b>What is the difference between Avast full virus scan and smart scan?</b></li>
-<p>Avast full virus scan and smart scan are two different types of scans that Avast antivirus offers. The full virus scan scans your entire device for any viruses, malware, or other threats, while the smart scan scans only the most critical areas and issues on your device, such as browser add-ons, network threats, performance issues, etc. The full virus scan is more thorough and comprehensive, but also more time-consuming and resource-intensive, while the smart scan is faster and lighter, but also less detailed and complete.</p>
-<li><b>How can I get Avast premium for free?</b></li>
-<p>Avast premium is the paid version of Avast antivirus that offers more features and benefits than the free version. However, you can get Avast premium for free for a limited time by using the free trial option. To do so, follow these steps:</p>
- <ol>
-<li>Open Avast antivirus and go to Menu > My Subscriptions.</li>
-<li>Click on Start free trial next to Avast Premium Security.</li>
-<li>Enter your email address and click on Continue.</li>
-<li>Wait for the confirmation email and click on the link to activate your free trial.</li>
-<li>Enjoy Avast premium for free for 30 days.</li>
-</ol>
- <p>Note that after the 30-day period, you will be automatically charged for the annual subscription of Avast premium unless you cancel it before the expiration date.</p>
-<li><b>Is Avast antivirus safe and reliable?</b></li>
-<p>Avast antivirus is safe and reliable in terms of protecting your devices from various online threats and enhancing your online experience. It has a high detection rate and a low false positive rate, which means it can catch most of the malicious software and avoid flagging safe files as malicious. It also has a low impact on your device's performance and speed, which means it won't slow down your device or cause any crashes or errors. It also has a user-friendly interface and a responsive customer support team, which means it is easy to use and get help from.</p>
-<li><b>What are some alternatives to Avast antivirus?</b></li>
-<p>If you are not satisfied with Avast antivirus or want to try something else, there are some alternatives that you can consider. Some of these are:</p>
- <ul>
-<li>Norton Antivirus: Norton antivirus is another popular and trusted antivirus solution that offers a range of features and benefits, such as malware protection, cloud backup, password manager, parental control, VPN, etc. It also has a high detection rate and a low impact on your device's performance. However, it is also quite expensive compared to some of its competitors.</li>
-<li>Bitdefender Antivirus: Bitdefender antivirus is another reputable and reliable antivirus solution that offers a range of features and benefits, such as malware protection, network security, ransomware protection, webcam protection, VPN, etc. It also has a high detection rate and a low impact on your device's performance. However, it can be difficult to install and configure for some users.</li>
-<li>Kaspersky Antivirus: Kaspersky antivirus is another well-known and respected antivirus solution that offers a range of features and benefits, such as malware protection, network security, password manager, file encryption, VPN, etc. It also has a high detection rate and a low impact on your device's performance. However, it can be involved in some privacy and security controversies due to its Russian origin.</li>
-</ul>
- <p>You can compare these alternatives with Avast antivirus based on their features, benefits, prices, reviews, ratings, etc. and choose the one that suits your needs and preferences best.</p> b2dd77e56b<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Dow Rallies More Than 300 Points As U.S.-China Trade Talks Progress.md b/spaces/stomexserde/gpt4-ui/Examples/Dow Rallies More Than 300 Points As U.S.-China Trade Talks Progress.md
deleted file mode 100644
index 8b9c9454d02f1c89cae463400f1c8a32e90b1108..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Dow Rallies More Than 300 Points As U.S.-China Trade Talks Progress.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
-<h1>Dow rallies more than 300 points as U.S.-China trade talks progress</h1>
-<p>The Dow Jones Industrial Average surged more than 300 points on Tuesday, as investors cheered the positive signs of progress in the ongoing trade negotiations between the U.S. and China.</p>
-<p>The blue-chip index closed at 35,611.65, up 1.08%, or 381.37 points, its best day since October 11. The S&P 500 also gained 1.04%, or 49.33 points, to finish at 4,798.01, while the Nasdaq Composite rose 0.85%, or 139.95 points, to end at 16,601.34.</p>
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-<p>"The market is optimistic that we're going to see some sort of a breakthrough in the trade talks," said Ryan Detrick, chief market strategist at LPL Financial. "This would be a huge positive for the global economy and for corporate earnings."</p>
-<p>Among the sectors that benefited from the trade optimism were industrials, materials, energy and consumer discretionary, which are more exposed to international trade and demand. Some of the biggest gainers in the Dow were Caterpillar, Boeing, Chevron and Nike.</p>
-<p>On the other hand, some of the defensive sectors that tend to perform well in times of uncertainty, such as utilities, health care and consumer staples, lagged behind.</p>
-<p>The market also shrugged off the weaker-than-expected retail sales data for December, which showed a decline of 1.9% from the previous month, compared to the consensus estimate of a 0.1% drop. The data reflected the impact of the omicron variant of Covid-19 on consumer spending and confidence during the holiday season.</p>
-<p>However, analysts said that the retail sales weakness was likely temporary and would not derail the economic recovery.</p>
-<p></p>
-<p>"We think that this is a transitory dip in consumer spending due to omicron," said Michelle Meyer, chief U.S. economist at Bank of America. "We expect a rebound in January as omicron cases peak and consumers resume their normal activities."</p>
-<p>The market also looked ahead to the earnings season, which kicked off this week with some of the major banks reporting their fourth-quarter results. JPMorgan Chase, Bank of America and Goldman Sachs beat analysts' expectations on both revenue and earnings, while Citigroup and Wells Fargo missed on revenue but beat on earnings.</p>
-<p>The earnings season will continue on Wednesday with reports from Morgan Stanley, Netflix, UnitedHealth Group and Procter & Gamble.</p>
-
-<p>The trade deal between the U.S. and China would mark a significant breakthrough in the long-standing dispute that has weighed on the global economy and markets for more than three years. The trade war started in 2018, when the Trump administration imposed tariffs on billions of dollars worth of Chinese goods, citing unfair trade practices and intellectual property theft. China retaliated with its own tariffs and restrictions on U.S. goods and services.</p>
-<p>The trade tensions escalated in 2019 and 2020, with both sides imposing more tariffs and sanctions on each other's companies and officials. The trade war also affected other countries and regions, such as the European Union, Japan and Canada, which were caught in the crossfire of the dispute.</p>
-<p>In January 2020, the U.S. and China signed a phase one trade deal, which paused the escalation of tariffs and committed China to increase its purchases of U.S. agricultural products, energy and manufactured goods. However, the deal did not address some of the core issues of the conflict, such as technology transfer, industrial subsidies and market access.</p>
-<p>The phase one deal was also disrupted by the Covid-19 pandemic, which originated in China and spread across the world, causing unprecedented health and economic crises. The pandemic also worsened the diplomatic relations between the U.S. and China, as both sides blamed each other for the outbreak and its consequences.</p>
-<p>The trade talks resumed in November 2021, after President Joe Biden and Chinese President Xi Jinping held a virtual summit and agreed to work together on areas of common interest, such as climate change, public health and nuclear proliferation. The two leaders also instructed their trade negotiators to find ways to ease some of the tariffs and restrictions that have hurt both economies.</p> cec2833e83<br />
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-<br />
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diff --git a/spaces/sub314xxl/MusicGen-Continuation/tests/common_utils/temp_utils.py b/spaces/sub314xxl/MusicGen-Continuation/tests/common_utils/temp_utils.py
deleted file mode 100644
index d1e0367e979c8b9fea65472c373916d956ad5aaa..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen-Continuation/tests/common_utils/temp_utils.py
+++ /dev/null
@@ -1,56 +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 os
-import tempfile
-
-
-class TempDirMixin:
-    """Mixin to provide easy access to temp dir.
-    """
-
-    temp_dir_ = None
-
-    @classmethod
-    def get_base_temp_dir(cls):
-        # If AUDIOCRAFT_TEST_DIR is set, use it instead of temporary directory.
-        # this is handy for debugging.
-        key = "AUDIOCRAFT_TEST_DIR"
-        if key in os.environ:
-            return os.environ[key]
-        if cls.temp_dir_ is None:
-            cls.temp_dir_ = tempfile.TemporaryDirectory()
-        return cls.temp_dir_.name
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.temp_dir_ is not None:
-            try:
-                cls.temp_dir_.cleanup()
-                cls.temp_dir_ = None
-            except PermissionError:
-                # On Windows there is a know issue with `shutil.rmtree`,
-                # which fails intermittenly.
-                # https://github.com/python/cpython/issues/74168
-                # Following the above thread, we ignore it.
-                pass
-        super().tearDownClass()
-
-    @property
-    def id(self):
-        return self.__class__.__name__
-
-    def get_temp_path(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-        return path
-
-    def get_temp_dir(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(path, exist_ok=True)
-        return path
diff --git a/spaces/sub314xxl/MusicGen-Continuation/tests/data/__init__.py b/spaces/sub314xxl/MusicGen-Continuation/tests/data/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen-Continuation/tests/data/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/sub314xxl/MusicGen/audiocraft/quantization/__init__.py b/spaces/sub314xxl/MusicGen/audiocraft/quantization/__init__.py
deleted file mode 100644
index 836d6eb518978480c6b95d6f29ce4f84a9428793..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen/audiocraft/quantization/__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 .vq import ResidualVectorQuantizer
-from .base import BaseQuantizer, DummyQuantizer, QuantizedResult
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Age Of Empires 2 No Cd Crack !!EXCLUSIVE!! Instructions.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Age Of Empires 2 No Cd Crack !!EXCLUSIVE!! Instructions.md
deleted file mode 100644
index a39659a9e1aa518f84c00cbd01f4163f913619c6..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Age Of Empires 2 No Cd Crack !!EXCLUSIVE!! Instructions.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rainlendar 2 12 Keygen 15.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rainlendar 2 12 Keygen 15.md
deleted file mode 100644
index 51426e818f2bfb90c38c7753fae8105540c58c64..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rainlendar 2 12 Keygen 15.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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-December 21, 2562 B.C. - Rainlendar - Customizable Desk Calendar, New Windows 10 Version, Same PC (1/2) - Rainlendar2 - Support - Kunena Forum. 2562–2562 BC- Rainlendar - Customizable desktop calendar, Windows 7 (x64) - Rainlendar - Support - Kunena Forum. 2562 BC - Rainlendar - Adding posts - Kunena Forum. 2562 BC - Rainlendar - Resizing Windows - Kunena Forum. 2562 BC - Rainlendar - Display date, time, calendar, date and time in the form of a calendar - Forum Kunena. 2562 BC - Rainlendar - Change Text Color - Kunena Forum. 8a78ff9644<br />
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diff --git a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/inference/framework.py b/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/inference/framework.py
deleted file mode 100644
index a0f1f992539506b5743cf57ea30c5c8efdb6263f..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/inference/framework.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from spiga.inference.config import ModelConfig
-from spiga.models.spiga import SPIGA
-import spiga.inference.pretreatment as pretreat
-import os
-import pkg_resources
-import copy
-import torch
-import numpy as np
-
-# Paths
-weights_path_dft = pkg_resources.resource_filename('spiga', 'models/weights')
-
-
-class SPIGAFramework:
-
-    def __init__(self, model_cfg: ModelConfig(), gpus=[0], load3DM=True):
-
-        # Parameters
-        self.model_cfg = model_cfg
-        self.gpus = gpus
-
-        # Pretreatment initialization
-        self.transforms = pretreat.get_transformers(self.model_cfg)
-
-        # SPIGA model
-        self.model_inputs = ['image', "model3d", "cam_matrix"]
-        self.model = SPIGA(num_landmarks=model_cfg.dataset.num_landmarks,
-                           num_edges=model_cfg.dataset.num_edges)
-
-        # Load weights and set model
-        weights_path = self.model_cfg.model_weights_path
-        if weights_path is None:
-            weights_path = weights_path_dft
-
-        if self.model_cfg.load_model_url:
-            model_state_dict = torch.hub.load_state_dict_from_url(self.model_cfg.model_weights_url,
-                                                                  model_dir=weights_path,
-                                                                  file_name=self.model_cfg.model_weights)
-        else:
-            weights_file = os.path.join(
-                weights_path, self.model_cfg.model_weights)
-            model_state_dict = torch.load(weights_file)
-
-        self.model.load_state_dict(model_state_dict)
-        # self.model = self.model.cuda(gpus[0])
-        self.model = self.model.cuda(
-            gpus[0]) if torch.cuda.is_available() else self.model
-        self.model.eval()
-        print('SPIGA model loaded!')
-
-        # Load 3D model and camera intrinsic matrix
-        if load3DM:
-            loader_3DM = pretreat.AddModel3D(model_cfg.dataset.ldm_ids,
-                                             ftmap_size=model_cfg.ftmap_size,
-                                             focal_ratio=model_cfg.focal_ratio,
-                                             totensor=True)
-            params_3DM = self._data2device(loader_3DM())
-            self.model3d = params_3DM['model3d']
-            self.cam_matrix = params_3DM['cam_matrix']
-
-    def inference(self, image, bboxes):
-        """
-        @param self:
-        @param image: Raw image
-        @param bboxes: List of bounding box founded on the image [[x,y,w,h],...]
-        @return: features dict {'landmarks': list with shape (num_bbox, num_landmarks, 2) and x,y referred to image size
-                                'headpose': list with shape (num_bbox, 6) euler->[:3], trl->[3:]
-        """
-        batch_crops, crop_bboxes = self.pretreat(image, bboxes)
-        outputs = self.net_forward(batch_crops)
-        features = self.postreatment(outputs, crop_bboxes, bboxes)
-        return features
-
-    def pretreat(self, image, bboxes):
-        crop_bboxes = []
-        crop_images = []
-        for bbox in bboxes:
-            sample = {'image': copy.deepcopy(image),
-                      'bbox': copy.deepcopy(bbox)}
-            sample_crop = self.transforms(sample)
-            crop_bboxes.append(sample_crop['bbox'])
-            crop_images.append(sample_crop['image'])
-
-        # Images to tensor and device
-        batch_images = torch.tensor(np.array(crop_images), dtype=torch.float)
-        batch_images = self._data2device(batch_images)
-        # Batch 3D model and camera intrinsic matrix
-        batch_model3D = self.model3d.unsqueeze(0).repeat(len(bboxes), 1, 1)
-        batch_cam_matrix = self.cam_matrix.unsqueeze(
-            0).repeat(len(bboxes), 1, 1)
-
-        # SPIGA inputs
-        model_inputs = [batch_images, batch_model3D, batch_cam_matrix]
-        return model_inputs, crop_bboxes
-
-    def net_forward(self, inputs):
-        outputs = self.model(inputs)
-        return outputs
-
-    def postreatment(self, output, crop_bboxes, bboxes):
-        features = {}
-        crop_bboxes = np.array(crop_bboxes)
-        bboxes = np.array(bboxes)
-
-        if 'Landmarks' in output.keys():
-            landmarks = output['Landmarks'][-1].cpu().detach().numpy()
-            landmarks = landmarks.transpose((1, 0, 2))
-            landmarks = landmarks*self.model_cfg.image_size
-            landmarks_norm = (
-                landmarks - crop_bboxes[:, 0:2]) / crop_bboxes[:, 2:4]
-            landmarks_out = (landmarks_norm * bboxes[:, 2:4]) + bboxes[:, 0:2]
-            landmarks_out = landmarks_out.transpose((1, 0, 2))
-            features['landmarks'] = landmarks_out.tolist()
-
-        # Pose output
-        if 'Pose' in output.keys():
-            pose = output['Pose'].cpu().detach().numpy()
-            features['headpose'] = pose.tolist()
-
-        return features
-
-    def select_inputs(self, batch):
-        inputs = []
-        for ft_name in self.model_inputs:
-            data = batch[ft_name]
-            inputs.append(self._data2device(data.type(torch.float)))
-        return inputs
-
-    def _data2device(self, data):
-        if isinstance(data, list):
-            data_var = data
-            for data_id, v_data in enumerate(data):
-                data_var[data_id] = self._data2device(v_data)
-        if isinstance(data, dict):
-            data_var = data
-            for k, v in data.items():
-                data[k] = self._data2device(v)
-        else:
-            with torch.no_grad():
-                if torch.cuda.is_available():
-                    data_var = data.cuda(
-                        device=self.gpus[0], non_blocking=True)
-                else:
-                    data_var = data
-        return data_var
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/exp/upernet_global_small/test_config_h32.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/exp/upernet_global_small/test_config_h32.py
deleted file mode 100644
index a31e3874f76f9f7b089ac8834d85df2441af9b0e..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/exp/upernet_global_small/test_config_h32.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = [
-    '../../configs/_base_/models/upernet_uniformer.py', 
-    '../../configs/_base_/datasets/ade20k.py',
-    '../../configs/_base_/default_runtime.py', 
-    '../../configs/_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    backbone=dict(
-        type='UniFormer',
-        embed_dim=[64, 128, 320, 512],
-        layers=[3, 4, 8, 3],
-        head_dim=64,
-        drop_path_rate=0.25,
-        windows=False,
-        hybrid=True,
-        window_size=32
-    ),
-    decode_head=dict(
-        in_channels=[64, 128, 320, 512],
-        num_classes=150
-    ),
-    auxiliary_head=dict(
-        in_channels=320,
-        num_classes=150
-    ))
-
-# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
-optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-
-lr_config = dict(_delete_=True, policy='poly',
-                 warmup='linear',
-                 warmup_iters=1500,
-                 warmup_ratio=1e-6,
-                 power=1.0, min_lr=0.0, by_epoch=False)
-
-data=dict(samples_per_gpu=2)
\ No newline at end of file
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/video/optflow.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/video/optflow.py
deleted file mode 100644
index 84160f8d6ef9fceb5a2f89e7481593109fc1905d..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/video/optflow.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import cv2
-import numpy as np
-
-from annotator.uniformer.mmcv.arraymisc import dequantize, quantize
-from annotator.uniformer.mmcv.image import imread, imwrite
-from annotator.uniformer.mmcv.utils import is_str
-
-
-def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
-    """Read an optical flow map.
-
-    Args:
-        flow_or_path (ndarray or str): A flow map or filepath.
-        quantize (bool): whether to read quantized pair, if set to True,
-            remaining args will be passed to :func:`dequantize_flow`.
-        concat_axis (int): The axis that dx and dy are concatenated,
-            can be either 0 or 1. Ignored if quantize is False.
-
-    Returns:
-        ndarray: Optical flow represented as a (h, w, 2) numpy array
-    """
-    if isinstance(flow_or_path, np.ndarray):
-        if (flow_or_path.ndim != 3) or (flow_or_path.shape[-1] != 2):
-            raise ValueError(f'Invalid flow with shape {flow_or_path.shape}')
-        return flow_or_path
-    elif not is_str(flow_or_path):
-        raise TypeError(f'"flow_or_path" must be a filename or numpy array, '
-                        f'not {type(flow_or_path)}')
-
-    if not quantize:
-        with open(flow_or_path, 'rb') as f:
-            try:
-                header = f.read(4).decode('utf-8')
-            except Exception:
-                raise IOError(f'Invalid flow file: {flow_or_path}')
-            else:
-                if header != 'PIEH':
-                    raise IOError(f'Invalid flow file: {flow_or_path}, '
-                                  'header does not contain PIEH')
-
-            w = np.fromfile(f, np.int32, 1).squeeze()
-            h = np.fromfile(f, np.int32, 1).squeeze()
-            flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
-    else:
-        assert concat_axis in [0, 1]
-        cat_flow = imread(flow_or_path, flag='unchanged')
-        if cat_flow.ndim != 2:
-            raise IOError(
-                f'{flow_or_path} is not a valid quantized flow file, '
-                f'its dimension is {cat_flow.ndim}.')
-        assert cat_flow.shape[concat_axis] % 2 == 0
-        dx, dy = np.split(cat_flow, 2, axis=concat_axis)
-        flow = dequantize_flow(dx, dy, *args, **kwargs)
-
-    return flow.astype(np.float32)
-
-
-def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
-    """Write optical flow to file.
-
-    If the flow is not quantized, it will be saved as a .flo file losslessly,
-    otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
-    will be concatenated horizontally into a single image if quantize is True.)
-
-    Args:
-        flow (ndarray): (h, w, 2) array of optical flow.
-        filename (str): Output filepath.
-        quantize (bool): Whether to quantize the flow and save it to 2 jpeg
-            images. If set to True, remaining args will be passed to
-            :func:`quantize_flow`.
-        concat_axis (int): The axis that dx and dy are concatenated,
-            can be either 0 or 1. Ignored if quantize is False.
-    """
-    if not quantize:
-        with open(filename, 'wb') as f:
-            f.write('PIEH'.encode('utf-8'))
-            np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
-            flow = flow.astype(np.float32)
-            flow.tofile(f)
-            f.flush()
-    else:
-        assert concat_axis in [0, 1]
-        dx, dy = quantize_flow(flow, *args, **kwargs)
-        dxdy = np.concatenate((dx, dy), axis=concat_axis)
-        imwrite(dxdy, filename)
-
-
-def quantize_flow(flow, max_val=0.02, norm=True):
-    """Quantize flow to [0, 255].
-
-    After this step, the size of flow will be much smaller, and can be
-    dumped as jpeg images.
-
-    Args:
-        flow (ndarray): (h, w, 2) array of optical flow.
-        max_val (float): Maximum value of flow, values beyond
-                        [-max_val, max_val] will be truncated.
-        norm (bool): Whether to divide flow values by image width/height.
-
-    Returns:
-        tuple[ndarray]: Quantized dx and dy.
-    """
-    h, w, _ = flow.shape
-    dx = flow[..., 0]
-    dy = flow[..., 1]
-    if norm:
-        dx = dx / w  # avoid inplace operations
-        dy = dy / h
-    # use 255 levels instead of 256 to make sure 0 is 0 after dequantization.
-    flow_comps = [
-        quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]
-    ]
-    return tuple(flow_comps)
-
-
-def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
-    """Recover from quantized flow.
-
-    Args:
-        dx (ndarray): Quantized dx.
-        dy (ndarray): Quantized dy.
-        max_val (float): Maximum value used when quantizing.
-        denorm (bool): Whether to multiply flow values with width/height.
-
-    Returns:
-        ndarray: Dequantized flow.
-    """
-    assert dx.shape == dy.shape
-    assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)
-
-    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]
-
-    if denorm:
-        dx *= dx.shape[1]
-        dy *= dx.shape[0]
-    flow = np.dstack((dx, dy))
-    return flow
-
-
-def flow_warp(img, flow, filling_value=0, interpolate_mode='nearest'):
-    """Use flow to warp img.
-
-    Args:
-        img (ndarray, float or uint8): Image to be warped.
-        flow (ndarray, float): Optical Flow.
-        filling_value (int): The missing pixels will be set with filling_value.
-        interpolate_mode (str): bilinear -> Bilinear Interpolation;
-                                nearest -> Nearest Neighbor.
-
-    Returns:
-        ndarray: Warped image with the same shape of img
-    """
-    warnings.warn('This function is just for prototyping and cannot '
-                  'guarantee the computational efficiency.')
-    assert flow.ndim == 3, 'Flow must be in 3D arrays.'
-    height = flow.shape[0]
-    width = flow.shape[1]
-    channels = img.shape[2]
-
-    output = np.ones(
-        (height, width, channels), dtype=img.dtype) * filling_value
-
-    grid = np.indices((height, width)).swapaxes(0, 1).swapaxes(1, 2)
-    dx = grid[:, :, 0] + flow[:, :, 1]
-    dy = grid[:, :, 1] + flow[:, :, 0]
-    sx = np.floor(dx).astype(int)
-    sy = np.floor(dy).astype(int)
-    valid = (sx >= 0) & (sx < height - 1) & (sy >= 0) & (sy < width - 1)
-
-    if interpolate_mode == 'nearest':
-        output[valid, :] = img[dx[valid].round().astype(int),
-                               dy[valid].round().astype(int), :]
-    elif interpolate_mode == 'bilinear':
-        # dirty walkround for integer positions
-        eps_ = 1e-6
-        dx, dy = dx + eps_, dy + eps_
-        left_top_ = img[np.floor(dx[valid]).astype(int),
-                        np.floor(dy[valid]).astype(int), :] * (
-                            np.ceil(dx[valid]) - dx[valid])[:, None] * (
-                                np.ceil(dy[valid]) - dy[valid])[:, None]
-        left_down_ = img[np.ceil(dx[valid]).astype(int),
-                         np.floor(dy[valid]).astype(int), :] * (
-                             dx[valid] - np.floor(dx[valid]))[:, None] * (
-                                 np.ceil(dy[valid]) - dy[valid])[:, None]
-        right_top_ = img[np.floor(dx[valid]).astype(int),
-                         np.ceil(dy[valid]).astype(int), :] * (
-                             np.ceil(dx[valid]) - dx[valid])[:, None] * (
-                                 dy[valid] - np.floor(dy[valid]))[:, None]
-        right_down_ = img[np.ceil(dx[valid]).astype(int),
-                          np.ceil(dy[valid]).astype(int), :] * (
-                              dx[valid] - np.floor(dx[valid]))[:, None] * (
-                                  dy[valid] - np.floor(dy[valid]))[:, None]
-        output[valid, :] = left_top_ + left_down_ + right_top_ + right_down_
-    else:
-        raise NotImplementedError(
-            'We only support interpolation modes of nearest and bilinear, '
-            f'but got {interpolate_mode}.')
-    return output.astype(img.dtype)
-
-
-def flow_from_bytes(content):
-    """Read dense optical flow from bytes.
-
-    .. note::
-        This load optical flow function works for FlyingChairs, FlyingThings3D,
-        Sintel, FlyingChairsOcc datasets, but cannot load the data from
-        ChairsSDHom.
-
-    Args:
-        content (bytes): Optical flow bytes got from files or other streams.
-
-    Returns:
-        ndarray: Loaded optical flow with the shape (H, W, 2).
-    """
-
-    # header in first 4 bytes
-    header = content[:4]
-    if header.decode('utf-8') != 'PIEH':
-        raise Exception('Flow file header does not contain PIEH')
-    # width in second 4 bytes
-    width = np.frombuffer(content[4:], np.int32, 1).squeeze()
-    # height in third 4 bytes
-    height = np.frombuffer(content[8:], np.int32, 1).squeeze()
-    # after first 12 bytes, all bytes are flow
-    flow = np.frombuffer(content[12:], np.float32, width * height * 2).reshape(
-        (height, width, 2))
-
-    return flow
-
-
-def sparse_flow_from_bytes(content):
-    """Read the optical flow in KITTI datasets from bytes.
-
-    This function is modified from RAFT load the `KITTI datasets
-    <https://github.com/princeton-vl/RAFT/blob/224320502d66c356d88e6c712f38129e60661e80/core/utils/frame_utils.py#L102>`_.
-
-    Args:
-        content (bytes): Optical flow bytes got from files or other streams.
-
-    Returns:
-        Tuple(ndarray, ndarray): Loaded optical flow with the shape (H, W, 2)
-            and flow valid mask with the shape (H, W).
-    """  # nopa
-
-    content = np.frombuffer(content, np.uint8)
-    flow = cv2.imdecode(content, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR)
-    flow = flow[:, :, ::-1].astype(np.float32)
-    # flow shape (H, W, 2) valid shape (H, W)
-    flow, valid = flow[:, :, :2], flow[:, :, 2]
-    flow = (flow - 2**15) / 64.0
-    return flow, valid
diff --git a/spaces/t13718236382/bingoGPT4/Dockerfile b/spaces/t13718236382/bingoGPT4/Dockerfile
deleted file mode 100644
index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000
--- a/spaces/t13718236382/bingoGPT4/Dockerfile
+++ /dev/null
@@ -1,36 +0,0 @@
-FROM node:18
-
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-
-RUN npm run build
-
-ENV PORT 7860
-EXPOSE 7860
-
-CMD npm start
diff --git a/spaces/tappyness1/error_analysis_obj_det/src/confusion_matrix.py b/spaces/tappyness1/error_analysis_obj_det/src/confusion_matrix.py
deleted file mode 100644
index e741e54d7222ff10143657fa0e128dfa412ca18f..0000000000000000000000000000000000000000
--- a/spaces/tappyness1/error_analysis_obj_det/src/confusion_matrix.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import numpy as np
-import pandas as pd
-
-
-def box_iou_calc(boxes1, boxes2):
-    # 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:
-        boxes1 (Array[N, 4])
-        boxes2 (Array[M, 4])
-    Returns:
-        iou (Array[N, M]): the NxM matrix containing the pairwise
-            IoU values for every element in boxes1 and boxes2
-
-    This implementation is taken from the above link and changed so that it only uses numpy.
-    """
-
-    def box_area(box):
-        # box = 4xn
-        return (box[2] - box[0]) * (box[3] - box[1])
-    
-
-    area1 = box_area(boxes1.T)
-    area2 = box_area(boxes2.T)
-
-    lt = np.maximum(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    rb = np.minimum(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    inter = np.prod(np.clip(rb - lt, a_min = 0, a_max = None), 2)
-    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)
-
-def mask_iou_calc(mask1, mask2):
-
-    # build function to take in two masks, compare them and see what their iou is.
-    # similar to above but in mask.
-
-    return
-
-
-class ConfusionMatrix:
-    def __init__(self, num_classes, CONF_THRESHOLD = 0.2, IOU_THRESHOLD = 0.5):
-        self.matrix = np.zeros((num_classes + 1, num_classes + 1))
-        self.num_classes = num_classes
-        self.CONF_THRESHOLD = CONF_THRESHOLD
-        self.IOU_THRESHOLD = IOU_THRESHOLD
-        self.got_tpfpfn = False
-    
-    def process_batch(self, detections, labels, return_matches=False):
-        '''
-        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
-        '''
-        detections = detections[detections[:, 4] > self.CONF_THRESHOLD]
-        gt_classes = labels[:, 0].astype(np.int16)
-        detection_classes = detections[:, 5].astype(np.int16)
-
-        all_ious = box_iou_calc(labels[:, 1:], detections[:, :4])
-        # print()
-        # print('=== all_ious ===')
-        # print(all_ious)
-        want_idx = np.where(all_ious > self.IOU_THRESHOLD)
-        # print('=== want_idx ===')
-        # print(want_idx)
-        # print()
-        
-        all_matches = []
-        for i in range(want_idx[0].shape[0]):
-            all_matches.append([want_idx[0][i], want_idx[1][i], all_ious[want_idx[0][i], want_idx[1][i]]])
-        
-        all_matches = np.array(all_matches)
-        if all_matches.shape[0] > 0: # if there is match
-            all_matches = all_matches[all_matches[:, 2].argsort()[::-1]]
-
-            all_matches = all_matches[np.unique(all_matches[:, 1], return_index = True)[1]]
-
-            all_matches = all_matches[all_matches[:, 2].argsort()[::-1]]
-
-            all_matches = all_matches[np.unique(all_matches[:, 0], return_index = True)[1]]
-        
-        for i, label in enumerate(labels):
-            if all_matches.shape[0] > 0 and all_matches[all_matches[:, 0] == i].shape[0] == 1:
-                gt_class = gt_classes[i]
-                detection_class = detection_classes[int(all_matches[all_matches[:, 0] == i, 1][0])]
-                self.matrix[(gt_class), detection_class] += 1
-            else:
-                gt_class = gt_classes[i]
-                self.matrix[(gt_class), self.num_classes] += 1
-        
-        for i, detection in enumerate(detections):
-            if all_matches.shape[0] and all_matches[all_matches[:, 1] == i].shape[0] == 0:
-                detection_class = detection_classes[i]
-                self.matrix[self.num_classes ,detection_class] += 1
-    
-        if return_matches:
-            return all_matches
-        
-    def get_tpfpfn(self):
-        self.tp = np.diag(self.matrix).sum()
-        fp = self.matrix.copy()
-        np.fill_diagonal(fp, 0)
-        self.fp = fp[:,:-1].sum()
-        self.fn = self.matrix[:-1, -1].sum()
-        self.got_tpfpfn = True
-
-    def get_PR(self):
-        if not self.got_tpfpfn:
-            self.get_tpfpfn()
-        # print (tp, fp, fn)
-        self.precision = self.tp / (self.tp+self.fp)
-        self.recall = self.tp/(self.tp+self.fn)
-
-    def return_matrix(self):
-        return self.matrix
-
-    def process_full_matrix(self):
-        """method to process matrix to something more readable
-        """
-        for idx, i in enumerate(self.matrix):
-            i[0] = idx
-        self.matrix = np.delete(self.matrix, 0, 0)
-            
-    def print_matrix_as_df(self):
-        """method to print out processed matrix
-        """
-        df = pd.DataFrame(self.matrix)
-        print (df.to_string(index=False))
-
-    # def print_matrix(self):
-    #     for i in range(self.num_classes + 1):
-    #         print(' '.join(map(str, self.matrix[i])))
-
-    def return_as_csv(self, csv_file_path):
-        """method to print out processed matrix
-        """
-        df = pd.DataFrame(self.matrix)
-        df.to_csv(csv_file_path, index = False)
-        print (f"saved to: {csv_file_path}")
-
-    def return_as_df(self):
-        """method to print out processed matrix
-        """
-        df = pd.DataFrame(self.matrix)
-        # df = df.set_index(0)
-        # df.set_index(0)
-        # print(df.columns)
-        return df
\ No newline at end of file
diff --git a/spaces/tcapelle/spacy_wandb/README.md b/spaces/tcapelle/spacy_wandb/README.md
deleted file mode 100644
index 7b7fa3835597f7809455851bd32a02f481a6b312..0000000000000000000000000000000000000000
--- a/spaces/tcapelle/spacy_wandb/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Spacy_wandb
-emoji: 🐨
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Adobe Premiere Elements 2020 V18.0 Multilingual Crack Download VERIFIED.md b/spaces/terfces0erbo/CollegeProjectV2/Adobe Premiere Elements 2020 V18.0 Multilingual Crack Download VERIFIED.md
deleted file mode 100644
index 7360665e1824e65e70531c7b08ecffa18ed42b33..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Adobe Premiere Elements 2020 V18.0 Multilingual Crack Download VERIFIED.md	
+++ /dev/null
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diff --git a/spaces/test12356/SUI-svc-3.0/mel_processing.py b/spaces/test12356/SUI-svc-3.0/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/test12356/SUI-svc-3.0/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/tialenAdioni/chat-gpt-api/logs/Adobe Dreamweaver CC 2017 V17.1.0.9583 (x64) Portable [TechTools Serial Key The Best Web Design Software for Windows.md b/spaces/tialenAdioni/chat-gpt-api/logs/Adobe Dreamweaver CC 2017 V17.1.0.9583 (x64) Portable [TechTools Serial Key The Best Web Design Software for Windows.md
deleted file mode 100644
index ae8ff77ce2bf4cd62cb66535d93a7a92c38a8be2..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Adobe Dreamweaver CC 2017 V17.1.0.9583 (x64) Portable [TechTools Serial Key The Best Web Design Software for Windows.md	
+++ /dev/null
@@ -1,151 +0,0 @@
-<br />
-<h1>Adobe Dreamweaver CC 2017 V17.1.0.9583 (x64) Portable [TechTools Serial Key</h1>
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- <h2>Introduction</h2>
- <h3>What is Adobe Dreamweaver CC 2017?</h3>
- <p>Adobe Dreamweaver CC 2017 is a web development software that allows you to create, edit, and manage web pages using a visual interface or a code editor. It is part of the Adobe Creative Cloud suite, which means you can access it from any device and sync your projects across multiple platforms. It also integrates with other Adobe products, such as Photoshop, Illustrator, and Animate, to enhance your web design workflow.</p>
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- <p>Adobe Dreamweaver CC 2017 has many features that make it one of the best web design software in the market. Some of these features are:</p>
- <ul>
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-</ul>
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- <p>Using Adobe Dreamweaver CC 2017 Portable is very similar to using any other version of Adobe Dreamweaver. You can create and edit web pages using either the visual design mode or the code editor mode. You can also preview your web pages in real time using the live preview mode or the responsive design mode. You can also work with multiple browsers and devices using the device preview mode or the browser lab mode.</p>
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- <ul>
-<li>Design: This mode lets you drag and drop elements onto your web page and see how they look</li>
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-<li>Split: This mode lets you see both design and code views side by side</li>
-<li>Live: This mode lets you see how your web page will look on different browsers and devices in real time</li>
-</ul>
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- <ul>
-<li>Device Preview: This mode lets you see how your web page will look on different devices such as smartphones, tablets, laptops, etc.</li>
-<li>Browser Lab: This mode lets you see how your web page will look on different browsers such as Chrome, Firefox, Safari, etc.</li>
-</ul>
- <p>To upload your files to your web server, go to Site > Manage Sites > New Site (or edit an existing site). Enter your site name, local site folder, remote server details (such as FTP host name, username, password), etc. Then click "Save". To upload your files, go to File > Put (or press Ctrl+Shift+U).</p>
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- <h2>Conclusion</h2>
- <p>In conclusion, Adobe Dreamweaver CC 2017 is a great web design software that can help you create stunning websites without writing a single line of code. It has many features that make it easy and fun to use. It also has a portable version that does not require installation and can be run from any USB drive or external hard drive.</p>
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- <ol>
-<li>Download the file from this link: https://www.techtools.net/adobe-dreamweaver-cc-2017-portable-techtools-serial-key/</li>
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-</ol>
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-<li>Work with multiple browsers and devices using the device preview mode or the browser lab mode</li>
-<li>Upload your files to your web server using the built-in FTP client</li>
-<li>Share your projects with other developers and get feedback using the collaboration tool</li>
-</ul>
- <p>I hope you found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy web designing!</p>
- <h3>Frequently Asked Questions</h3>
- <p>Here are some frequently asked questions about Adobe Dreamweaver CC 2017 Portable [TechTools Serial Key:</p>
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-<li>Memory: 2 GB RAM</li>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Cubase 9 Pro Free Download [BETTER].md b/spaces/tialenAdioni/chat-gpt-api/logs/Cubase 9 Pro Free Download [BETTER].md
deleted file mode 100644
index 3ea3910d689230557453ba70345747f9b47906cf..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Cubase 9 Pro Free Download [BETTER].md	
+++ /dev/null
@@ -1,35 +0,0 @@
-
-<h1>How to Download Cubase 9 Pro for Free and Start Making Music</h1>
-<p>Cubase 9 Pro is a professional music production software that offers a complete set of tools for composing, recording, editing, mixing, and mastering audio. Cubase 9 Pro is one of the most popular and powerful DAWs (Digital Audio Workstations) in the market, used by thousands of musicians, producers, and engineers worldwide.</p>
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-<ol>
-<li>Go to the official website of Steinberg at <a href="https://www.steinberg.net/cubase/trial/">https://www.steinberg.net/cubase/trial/</a> .</li>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Started with CSI SAFE 2016 in 3 Easy Steps.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Started with CSI SAFE 2016 in 3 Easy Steps.md
deleted file mode 100644
index 7487b0b3d7db2311e8f9b3d9cc8f2c2cbfece350..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get Started with CSI SAFE 2016 in 3 Easy Steps.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<br />
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-<p>CSI SAFE 2016 is a software application that allows you to perform analysis and design of concrete slabs and foundations. It can handle any type of slab geometry, loading, and reinforcement configuration. It can also provide you with detailed drawings, reports, and documentation of your design.</p>
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-</ol>
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-<p>The third step is to start using CSI SAFE 2016 for your concrete slab and foundation design projects. You can use the software as a standalone application or in combination with ETABS for completing analysis, design, and detailing of concrete floor plates created in ETABS.</p>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/Doctor, I Love You 2012 Download [2021] 720p Movie.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/Doctor, I Love You 2012 Download [2021] 720p Movie.md
deleted file mode 100644
index de8a1dd7bbb1163311b2ced25bddf836e8da0eb2..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/Doctor, I Love You 2012 Download [2021] 720p Movie.md	
+++ /dev/null
@@ -1,104 +0,0 @@
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Annelids Online Battle APK and Experience the Best of Worms Games.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Annelids Online Battle APK and Experience the Best of Worms Games.md
deleted file mode 100644
index deed8ea0a9f51935c5b83453414dbfb6682cb114..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Annelids Online Battle APK and Experience the Best of Worms Games.md	
+++ /dev/null
@@ -1,98 +0,0 @@
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-<p>Annelids Online Battle is a game that requires skill, strategy, and luck. Here are some tips and tricks that can help you improve your gameplay and win more matches:</p>
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-<p>Annelids Online Battle is a game that has many benefits and advantages for its players. Here are some of them:</p>
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-<li>It can be frustrating and challenging. You may encounter some difficulties and obstacles while playing Annelids Online Battle, such as lag, glitches, bugs, hackers, or unfair matches. You may also lose some matches or fail some missions, which can make you feel angry or disappointed.</li>
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-<p>Annelids Online Battle is a fun and addictive game that will keep you entertained for hours. You can control a worm and fight against other worms in a fully destructible underground world. You can play online with your friends or against other players from around the world, or offline with bots or local multiplayer. You can also customize your worm with different skins, hats, weapons, and abilities. The game has many features, game modes, and options that will make your gameplay more immersive and enjoyable. However, the game also has some drawbacks that you should be aware of, such as frustration, addiction, and health issues. Therefore, you should play the game responsibly and moderately, and balance it with your other activities and needs.</p>
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-<p>Here are some frequently asked questions about Annelids Online Battle:</p>
-<ol>
-<li>What are the minimum requirements to play Annelids Online Battle on Android?</li>
-<p>To play Annelids Online Battle on Android, you need to have a device that runs on Android 4.4 or higher, and has at least 1 GB of RAM and 100 MB of free storage space.</p>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Clash of Clans Hack Version for Android and iOS Get Unlimited Gems Gold Elixir and More.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Clash of Clans Hack Version for Android and iOS Get Unlimited Gems Gold Elixir and More.md
deleted file mode 100644
index 17271006df5c5f6d3cd7310b659c123f08ed07ec..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Clash of Clans Hack Version for Android and iOS Get Unlimited Gems Gold Elixir and More.md	
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-<p>Are you a fan of Clash of Clans, the popular strategy game that has millions of players around the world? Do you want to get unlimited gems, gold, elixir, dark elixir, and other resources in the game? Do you want to unlock all the troops, buildings, spells, and heroes in the game? If you answered yes to any of these questions, then you might be interested in downloading Clash of Clans hack version (unlimited everything).</p>
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-<p>In this article, we will tell you everything you need to know about Clash of Clans hack version (unlimited everything), including what it is, how to download it, how to play it, and what are the pros and cons of using it. We will also answer some frequently asked questions about this topic. So, without further ado, let's get started!</p>
- <h2>What is Clash of Clans?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Clash of Clans is a freemium mobile strategy game developed and published by Supercell, a Finnish game company. It was released in 2012 for iOS devices and in 2013 for Android devices. The game has been one of the most successful and popular games in the mobile gaming industry, with over 500 million downloads and billions of dollars in revenue.</p>
-<p>The game is set in a fantasy world where players build their own villages, train their own armies, join or create clans, and fight against other players or computer-controlled enemies. The game has various modes, such as single-player campaign, multiplayer battles, clan wars, clan games, seasonal events, special challenges, and more. The game also has various resources, such as gems, gold, elixir, dark elixir, builder gold, builder elixir, magic items, etc., that are used to upgrade buildings, troops, spells, heroes, etc.</p>
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- <h3>Why do people want to hack Clash of Clans?</h3>
-<p>Clash of Clans is a fun and addictive game that can keep you entertained for hours. However, it can also be frustrating and time-consuming at times. This is because the game requires a lot of patience, strategy, skill, and luck to progress and succeed. The game also has a lot of limitations and restrictions that can hinder your enjoyment. For example:</p>
-<ul>
-<li>You have to wait for long periods of time for your buildings, troops, spells, heroes, etc., to be upgraded or trained.</li>
-<li>You have to spend real money or complete tedious tasks to get more gems, which are the premium currency in the game.</li>
-<li>You have to face tough competition from other players who have higher levels or better equipment than you.</li>
-<li>You have to deal with annoying ads and pop-ups that interrupt your gameplay.</li>
-</ul>
-<p>These are some of the reasons why some people want to hack Clash of Clans. They want to get unlimited resources, unlock everything in the game, bypass the waiting time, avoid spending money or doing tasks, dominate other players or enemies easily, and enjoy the game without any hassle or interruption.</p>
- <h3>The risks and drawbacks of hacking Clash of Clans</h3>
-<p>While hacking Clash of Clans might sound tempting and exciting for some people, it also comes with a lot of risks and drawbacks that you should be aware of before you decide to do it. Here are some of them:</p>
-<ul>
-<li>You might get banned from the game or lose your account if the game detects that you are using a hack version or a modded APK file. This can happen anytime, even if you are careful or use a VPN. The game has a strict anti-cheat system that can detect any abnormal activity or behavior in your account.</li>
-<li>You might get infected with malware, viruses, spyware, or other harmful software if you download or install a hack version or a modded APK file from an untrusted or unknown source. This can damage your device, compromise your privacy, steal your personal information, or cause other problems.</li>
-<li>You might lose the fun and challenge of the game if you use a hack version or a modded APK file that gives you unlimited resources, unlocks everything, and makes everything easy. This can make the game boring, repetitive, and uninteresting for you and others.</li>
-<li>You might face ethical and moral issues if you use a hack version or a modded APK file that gives you an unfair advantage over other players or enemies. This can make the game unfair, unbalanced, and disrespectful for you and others.</li>
-</ul>
-<p>These are some of the risks and drawbacks of hacking Clash of Clans that you should consider before you do it. You should also respect the game developers and their hard work, as they have created an amazing game that deserves your support and appreciation.</p>
- <h2>How to download Clash of Clans hack version (unlimited everything)?</h2>
-<h3>The steps to download and install the modded APK file</h3>
-<p>If you still want to download Clash of Clans hack version (unlimited everything) despite the risks and drawbacks, then you will need to follow these steps:</p>
-<ol>
-<li>Find a reliable and reputable source that offers the modded APK file for Clash of Clans hack version (unlimited everything). You can search online for reviews, ratings, feedback, comments, etc., to find the best one. You can also ask your friends or other players for recommendations.</li>
-<li>Download the modded APK file from the source that you have chosen. Make sure that the file is compatible with your device and has the latest version of the game. You can also scan the file with an antivirus or anti-malware program to ensure that it is safe and clean.</li>
-<li>Enable the installation of apps from unknown sources on your device. You can do this by going to your device settings, security settings, and allowing unknown sources. This will allow you to install the modded APK file that is not from the official Google Play Store or App Store.</li>
-<li>Install the modded APK file on your device by tapping on it and following the instructions. You might need to uninstall the original Clash of Clans game first if you have it on your device. You might also need to grant some permissions to the modded APK file to access your device features.</li>
-<li>Launch the Clash of Clans hack version (unlimited everything) on your device and enjoy the game with unlimited resources, unlocked everything, and more.</li>
-</ol>
- <h3>The benefits and features of the hack version</h3>
-<p>By downloading and installing Clash of Clans hack version (unlimited everything), you will be able to enjoy these benefits and features:</p>
-<ul>
-<li>You will get unlimited gems, gold, elixir, dark elixir, builder gold, builder elixir, magic items, etc., that you can use to upgrade your buildings, troops, spells, heroes, etc., without any limit or restriction.</li>
-<li>You will unlock all the troops, buildings, spells, heroes, etc., that are available in the game, without any level or requirement. You will also be able to use them in any mode or situation without any cooldown or cost.</li>
-<li>You will be able to customize your village, army, clan, profile, etc., with any design or style that you want. You will also be able to change your name, clan name, clan badge, etc., without any limit or penalty.</li>
-<li>You will be able to play in any mode or situation without any difficulty or challenge. You will be able to win every battle and raid easily and quickly. You will also be able to get more rewards and achievements in the game.</li>
-<li>You will be able to enjoy the game without any ads or pop-ups that interrupt your gameplay. You will also be able to access all the features and functions of the game without any problem or error.</li>
-</ul>
- <h3>The limitations and challenges of the hack version</h3>
-<p>However, by downloading and installing Clash of Clans hack version (unlimited everything), you will also face these limitations and challenges that might affect your gameplay and experience:</p>
-<ul>
-<li>You might not be able to play online or with other players who are using the official or original version of the game. You might also not be able to join or create clans, participate in clan wars, clan games, seasonal events, special challenges, etc., that are part of the online features of the game.</li>
-<li>You might not be able to update the game or get the latest features and content that are added by the game developers. You might also miss out on the bug fixes, security patches, performance improvements, etc., that are provided by the game updates.</li>
-<li>You might not be able to backup or restore your game progress or data if you lose your device, change your device, uninstall the game, etc. You might also not be able to sync your game progress or data across different devices or platforms.</li>
-<li>You might not be able to get any support or assistance from the game developers or customer service if you encounter any problem or issue with the game. You might also not be able to report any bug, glitch, error, etc., that you find in the game.</li>
-<li>You might not be able to enjoy the game as much as you would with the official or original version of the game. You might also lose interest or motivation in the game after a while.</li>
-</ul>
- <h2>How to play Clash of Clans hack version (unlimited everything)?</h2>
-<h3>The tips and tricks to enjoy the game with unlimited resources</h3>
-<p>If you have downloaded and installed Clash of Clans hack version (unlimited everything) and you want to play it and have fun with it, then you can follow these tips and tricks:</p>
-<ul>
-<li>Experiment with different combinations of troops, buildings, spells, heroes, etc., that you have unlocked and see what works best for you. You can also try out different layouts and designs for your village and see how they affect your defense and offense.</li>
-<li>Explore the different modes and situations that are available in the game and see how they challenge your skills and strategy. You can also try to complete the single-player campaign, which has various levels and missions that test your abilities.</li>
-<li>Learn from other players who are using the hack version or the modded APK file and see how they play and what they do. You can also watch videos, tutorials, guides, etc., that show how to play Clash of Clans hack version (unlimited everything) effectively and efficiently.</li>
-<li>Have fun and enjoy the game without worrying about anything. You can also invite your friends or other players who are using the hack version or the modded APK file and play together or against each other.</li>
-</ul>
- <h3>The best strategies and tactics to win battles and raids</h3>
-<p>If you want to win battles and raids in Clash of Clans hack version (unlimited everything), then you can use these strategies and tactics:</p>
-<ul>
-<li>Use a balanced and diverse army that can deal with different types of enemies and obstacles. You can also use a combination of ground and air troops, as well as different types of spells and heroes, to maximize your damage and versatility.</li>
-<li>Use a scout or a spy before you attack an enemy village or base. This will allow you to see their layout, defenses, traps, resources, etc., and plan your attack accordingly. You can also use a dummy attack or a diversion to distract their attention or lure their troops away from their main defenses.</li>
-<li>Use a funneling technique to direct your troops to where you want them to go. This will prevent them from wandering off or getting distracted by other buildings or enemies. You can also use wall breakers, jump spells, earthquake spells, etc., to break through their walls and barriers.</li>
-<li>Use a surgical technique to target specific buildings or enemies that are important or dangerous. This will allow you to eliminate them quickly and efficiently. You can also use snipers, assassins, bombers, etc., to do this job.</li>
-</ul>
- <h3>The common mistakes and pitfalls to avoid in the game</h3>
-<p>If you want to avoid making mistakes and pitfalls in Clash of Clans hack version (unlimited everything), then you should avoid these things:</p>
-<ul>
-<li>Do not underestimate your enemies or overestimate yourself. Even if you have unlimited resources and unlocked everything in the game, you still need to be careful and smart when you attack or defend. You might still face strong opponents who can surprise you or counter you.</li>
-<li>Do not waste your resources or time on unnecessary things. Even if you have unlimited resources and no waiting time in the game, you still need to be efficient and effective when you upgrade or train. You might still need to prioritize certain things that are more important or useful than others.</li>
-<li>Do not neglect your everything in the game, you still need to respect and follow the rules and regulations of the game and the community. You might still face consequences or backlash if you cheat or abuse the game or other players.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>Clash of Clans is a great game that can provide you with hours of fun and entertainment. However, if you want to get unlimited resources, unlock everything, and bypass the limitations and restrictions of the game, then you might want to download Clash of Clans hack version (unlimited everything).</p>
-<p>In this article, we have explained what Clash of Clans hack version (unlimited everything) is, how to download it, how to play it, and what are the pros and cons of using it. We have also given you some tips and tricks, strategies and tactics, and mistakes and pitfalls to help you enjoy the game with the hack version.</p>
-<p>However, we also want to remind you that hacking Clash of Clans is not recommended or endorsed by us or by the game developers. It is a risky and unethical thing to do that can ruin your gameplay and experience. It can also get you banned from the game or infected with malware. Therefore, we advise you to play the game with the official or original version and support the game developers and their hard work.</p>
-<p>If you still want to download Clash of Clans hack version (unlimited everything), then you can do so at your own risk and responsibility. We hope that this article has been helpful and informative for you. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
-<h3>Five unique questions and answers related to the topic</h3>
-<ul>
-<li><b>Q: Is Clash of Clans hack version (unlimited everything) legal?</b></li>
-<li>A: No, Clash of Clans hack version (unlimited everything) is not legal. It is a violation of the terms of service and the intellectual property rights of the game developers. It is also a form of cheating that can get you banned from the game or prosecuted by the law.</li>
-<li><b>Q: Is Clash of Clans hack version (unlimited everything) safe?</b></li>
-<li>A: No, Clash of Clans hack version (unlimited everything) is not safe. It can expose your device to malware, viruses, spyware, or other harmful software that can damage your device or steal your personal information. It can also cause errors, glitches, crashes, or other problems in your game or device.</li>
-<li><b>Q: Is Clash of Clans hack version (unlimited everything) free?</b></li>
-<li>A: Yes, Clash of Clans hack version (unlimited everything) is free. You do not need to pay any money or do any tasks to download or install it. However, you might need to deal with ads or pop-ups that can annoy you or interrupt your gameplay.</li>
-<li><b>Q: Is Clash of Clans hack version (unlimited everything) compatible with my device?</b></li>
-<li>A: It depends on your device model, operating system, and specifications. Clash of Clans hack version (unlimited everything) might work on some devices but not on others. You need to check the compatibility and requirements of the modded APK file before you download or install it.</li>
-<li><b>Q: Is Clash of Clans hack version (unlimited everything) worth it?</b></li>
-<li>A: It depends on your preference and perspective. Clash of Clans hack version (unlimited everything) might be worth it for some people who want to get unlimited resources, unlock everything, and bypass the limitations and restrictions of the game. However, it might not be worth it for others who want to enjoy the game as it is meant to be played, without cheating or abusing it.</li>
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diff --git a/spaces/tioseFevbu/cartoon-converter/150-Gamehouse-Games-Collection-Free-Download-Full-16-PATCHED.md b/spaces/tioseFevbu/cartoon-converter/150-Gamehouse-Games-Collection-Free-Download-Full-16-PATCHED.md
deleted file mode 100644
index 8fee6065041e98a917534d483cb06ab86135f7ef..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/150-Gamehouse-Games-Collection-Free-Download-Full-16-PATCHED.md
+++ /dev/null
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-## 150 Gamehouse Games Collection Free Download Full 16
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-**CLICK HERE --->>> [https://tinourl.com/2txBqa](https://tinourl.com/2txBqa)**
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-# 150 Gamehouse Games Collection: A Treasure Trove of Retro Fun
-
- 
-
-If you are a fan of classic casual games, you might have heard of Gamehouse, a company that developed and published hundreds of games for PC and mobile platforms. Gamehouse games are known for their simple yet addictive gameplay, colorful graphics, and catchy soundtracks. Some of their most popular titles include Bejeweled, Zuma, Diner Dash, and Super Collapse.
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- 
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- 
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-
-1. Go to the Internet Archive page for the 150 Gamehouse games pack[^1^] [^2^]. Click on the RAR file link to download it.
-
-2. Extract the RAR file to a folder on your computer. You will see two subfolders: CD1 and CD2.
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-3. Go to the CD1 folder and double-click on the Setup icon. It will start installing the games.
-
-4. When it asks you to insert CD2, click on Browse and navigate to the CD2 folder. Select it and click OK.
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-5. Wait for the installation to finish. You will see a shortcut icon for the 150 Gamehouse games on your desktop.
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-
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-- The games are easy to play and suitable for all ages. You don't need any special skills or knowledge to enjoy them. You can play them anytime, anywhere, and for as long as you want.
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-- The games are nostalgic and retro. If you grew up playing Gamehouse games, you will love revisiting them and reliving your childhood memories. You can also share them with your friends and family and have fun together.
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/C3660 A3jk9s Mz 12415 T14.md b/spaces/tioseFevbu/cartoon-converter/scripts/C3660 A3jk9s Mz 12415 T14.md
deleted file mode 100644
index 4dc5037b1122696f8e45922c379f0d26c9b4064b..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/C3660 A3jk9s Mz 12415 T14.md	
+++ /dev/null
@@ -1,58 +0,0 @@
-<br />
-```
-<h1>How to Install Cisco IOS Image on GNS3</h1>
-<p>GNS3 is a network simulator that allows you to create virtual network topologies and test various network scenarios. One of the advantages of GNS3 is that it supports Cisco IOS images, which means you can emulate real Cisco routers and switches on your computer.</p>
-<h2>C3660 A3jk9s Mz 12415 T14</h2><br /><p><b><b>Download File</b> >>> <a href="https://urlcod.com/2uHxQo">https://urlcod.com/2uHxQo</a></b></p><br /><br />
-<p>In this article, we will show you how to download and install Cisco IOS image on GNS3 using the example of C3660 A3jk9s Mz 12415 T14, which is an advanced enterprise image for Cisco 3660 routers.</p>
-<h2>Step 1: Download Cisco IOS Image</h2>
-<p>The first step is to download the Cisco IOS image that you want to use on GNS3. You can find various Cisco IOS images on the official Cisco website or other sources. However, you need to have a valid Cisco account and a service contract to access some of the images.</p>
-<p>For this example, we will use C3660 A3jk9s Mz 12415 T14, which is available on the following link:</p>
-<p><a href="https://software.cisco.com/download/home/268437990/type/280805680/release/12.4.15T14">https://software.cisco.com/download/home/268437990/type/280805680/release/12.4.15T14</a></p>
-<p>You need to log in with your Cisco account and accept the terms and conditions to download the image. The file name is c3660-a3jk9s-mz.124-15.T14.bin and the size is about 32 MB.</p>
-<h2>Step 2: Extract Cisco IOS Image</h2>
-<p>The next step is to extract the Cisco IOS image from the .bin file. You can use any archive software such as WinRAR or 7-Zip to do this. Simply right-click on the .bin file and choose Extract Here or Extract to c3660-a3jk9s-mz.124-15.T14.</p>
-<p>You will get a folder with the same name as the .bin file, which contains a single file with the .image extension. This is the actual Cisco IOS image that you need to use on GNS3.</p>
-<h2>Step 3: Add Cisco IOS Image to GNS3</h2>
-<p>The final step is to add the Cisco IOS image to GNS3 and configure it for your network topology. To do this, follow these steps:</p>
-<p></p>
-<ol>
-<li>Open GNS3 and go to Edit > Preferences > Dynamips > IOS Routers.</li>
-<li>Click on New and select New Image in the wizard.</li>
-<li>Browse to the folder where you extracted the Cisco IOS image and select the .image file.</li>
-<li>Click on Next and choose a name and platform for your router. For this example, we will use c3660-a3jk9s as the name and c3660 as the platform.</li>
-<li>Click on Next and adjust the RAM and Idle-PC values for your router. You can use the default values or find the optimal values by clicking on Idle-PC finder.</li>
-<li>Click on Next and add any additional modules or interfaces that you want for your router.</li>
-<li>Click on Next and review your settings.</li>
-<li>Click on Finish and then OK to save your changes.</li>
-</ol>
-<p>You have successfully added the Cisco IOS image to GNS3. You can now drag and drop the router from the left panel to the workspace and start configuring it for your network topology.</p>
-```
-
-```
-<h2>Step 4: Connect Cisco IOS Devices on GNS3</h2>
-<p>Once you have added the Cisco IOS image to GNS3, you can create multiple instances of the same router model and connect them with various network devices and links. To do this, follow these steps:</p>
-<ol>
-<li>Drag and drop the router icon from the left panel to the workspace. You can rename the router by double-clicking on its name.</li>
-<li>Repeat step 1 to add more routers as needed.</li>
-<li>Select a network device from the left panel, such as a switch, a hub, or a cloud, and drag and drop it to the workspace.</li>
-<li>Select a link type from the left panel, such as Ethernet, Serial, or FastEthernet, and click on the source device and then on the destination device to create a link between them.</li>
-<li>Repeat steps 3 and 4 to connect all your devices as desired.</li>
-<li>Click on the Apply button to save your topology.</li>
-</ol>
-<p>You have successfully connected your Cisco IOS devices on GNS3. You can now start your devices and configure them using the console or telnet.</p>
-<h2>Step 5: Configure Cisco IOS Devices on GNS3</h2>
-<p>The last step is to configure your Cisco IOS devices on GNS3 according to your network requirements. To do this, follow these steps:</p>
-<ol>
-<li>Right-click on a router and select Start to power it on. You can also select Start all nodes from the toolbar to start all your devices at once.</li>
-<li>Right-click on a router and select Console to open a terminal window. You can also select Console connect to all nodes from the toolbar to open all terminal windows at once.</li>
-<li>Enter the enable command to enter privileged mode.</li>
-<li>Enter the configure terminal command to enter global configuration mode.</li>
-<li>Enter the commands that you want to configure your router, such as hostname, interface, ip address, routing protocol, etc.</li>
-<li>Enter the end command or press Ctrl+Z to exit global configuration mode.</li>
-<li>Enter the show running-config command or the show ip route command to verify your configuration.</li>
-<li>Repeat steps 1 to 7 for each router that you want to configure.</li>
-</ol>
-<p>You have successfully configured your Cisco IOS devices on GNS3. You can now test your network connectivity and functionality using various commands such as ping, traceroute, debug, etc.</p>
-```</p> 7196e7f11a<br />
-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/HOT NAP - La Racaille Sort Un Disque ((NEW)).md b/spaces/tioseFevbu/cartoon-converter/scripts/HOT NAP - La Racaille Sort Un Disque ((NEW)).md
deleted file mode 100644
index c0e6e759659c15424ce23d729b2d938ffdd54f0e..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/HOT NAP - La Racaille Sort Un Disque ((NEW)).md	
+++ /dev/null
@@ -1,30 +0,0 @@
-
-<h1>HOT NAP - La Racaille Sort Un Disque: A New Album from the French Punk Rock Band</h1>
-
-<p>If you are a fan of punk rock music, you might want to check out the new album from HOT NAP, a French band that has been making waves in the underground scene. The album, titled <em>La Racaille Sort Un Disque</em>, which translates to "The Scum Releases a Record", is a collection of 12 songs that showcase the band's raw energy, catchy melodies and rebellious attitude.</p>
-<h2>HOT NAP - La Racaille Sort Un Disque</h2><br /><p><b><b>Download Zip</b> &#10084;&#10084;&#10084; <a href="https://urlcod.com/2uHvT3">https://urlcod.com/2uHvT3</a></b></p><br /><br />
-
-<p>HOT NAP was formed in 2019 by four friends who shared a passion for punk rock and a disdain for the mainstream music industry. They decided to name themselves HOT NAP, which is a slang term for a short and intense sleep, as a way of expressing their desire to escape from the boring and oppressive reality. The band members are:</p>
-
-<ul>
-<li><strong>Max</strong>, the vocalist and guitarist, who writes most of the lyrics and delivers them with a raspy and powerful voice.</li>
-<li><strong>Lou</strong>, the bassist and backing vocalist, who adds depth and groove to the songs with her low and smooth tones.</li>
-<li><strong>Tom</strong>, the drummer and backing vocalist, who sets the pace and rhythm of the songs with his fast and furious beats.</li>
-<li><strong>Sam</strong>, the keyboardist and backing vocalist, who adds flair and diversity to the songs with his synth and piano sounds.</li>
-</ul>
-
-<p>The band's debut album, <em>La Racaille Sort Un Disque</em>, was recorded in their own home studio, with minimal equipment and budget. The band wanted to keep their sound as authentic and DIY as possible, without compromising on quality or creativity. The album features songs that deal with topics such as social injustice, personal freedom, love and hate, boredom and excitement, and more. Some of the highlights of the album are:</p>
-
-<ul>
-<li><strong>La Racaille</strong>, the opening track and the title song of the album, which is a fast and furious anthem that declares the band's identity as outsiders and rebels.</li>
-<li><strong>Je M'en Fous</strong>, which means "I Don't Care", which is a catchy and upbeat song that expresses the band's indifference to what others think or say about them.</li>
-<li><strong>On S'en Va</strong>, which means "We're Leaving", which is a melodic and emotional song that talks about leaving behind a place or a person that no longer makes you happy.</li>
-<li><strong>C'est La Vie</strong>, which means "That's Life", which is a sarcastic and ironic song that mocks the clichés and stereotypes that people use to justify their actions or situations.</li>
-<li><strong>Rien à Faire</strong>, which means "Nothing to Do", which is a slow and dark song that reflects on the boredom and emptiness of modern life.</li>
-<li><strong>Vive Le Rock</strong>, which means "Long Live Rock", which is a fun and energetic song that celebrates the power and joy of rock music.</li>
-</ul>
-
-<p>The album has received positive reviews from critics and fans alike, who praised the band's originality, talent and charisma. The album is available on various streaming platforms, such as Spotify, Apple Music, YouTube Music, etc. You can also follow the band on their social media accounts, such as Facebook, Instagram, Twitter, etc., where they post updates, photos, videos and more. If you are looking for some fresh and exciting punk rock music, you should definitely give HOT NAP - La Racaille Sort Un Disque a listen!</p>
-<p></p> 7b8c122e87<br />
-<br />
-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Jagged Edge The Greatest Hits Album Zip.md b/spaces/tioseFevbu/cartoon-converter/scripts/Jagged Edge The Greatest Hits Album Zip.md
deleted file mode 100644
index a961f6fff7ed006d9b159efcb02d3d4151f15089..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Jagged Edge The Greatest Hits Album Zip.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<br />
-<h1>Jagged Edge The Greatest Hits Album Zip: A Review</h1>
-<p>Jagged Edge is an American R&B vocal group that has been active since the 1990s. They are known for their smooth harmonies and romantic ballads, such as "Let's Get Married", "Promise", and "Where the Party At". The group consists of twin brothers Brian and Brandon Casey, Kyle Norman, and Richard Wingo.</p>
-<h2>Jagged Edge The Greatest Hits Album Zip</h2><br /><p><b><b>DOWNLOAD</b> ->>> <a href="https://urlcod.com/2uHxK1">https://urlcod.com/2uHxK1</a></b></p><br /><br />
-<p>In 2006, Jagged Edge released their first compilation album, titled <i>The Hits</i>. The album features 13 of their most popular songs, as well as two new tracks: "Good Luck Charm" and "So Amazing". The album was well received by fans and critics, who praised the group's consistency and longevity in the R&B genre.</p>
-<p>However, some fans were disappointed that the album did not include some of their other hits, such as "He Can't Love U", "Walked Outta Heaven", and "Gotta Be". Moreover, some fans were looking for a more comprehensive collection of their songs, spanning their entire career from their debut album <i>A Jagged Era</i> (1997) to their latest album at the time <i>Jagged Edge</i> (2006).</p>
-<p>That's why some fans decided to create their own unofficial compilation album, titled <i>Jagged Edge The Greatest Hits Album Zip</i>. This album is a zip file that contains 18 songs by Jagged Edge, covering all of their albums and some of their collaborations with other artists. The zip file can be downloaded from various websites on the internet[^1^] [^3^], but it is not authorized by the group or their record label.</p>
-<p>The tracklist of <i>Jagged Edge The Greatest Hits Album Zip</i> is as follows:</p>
-<p></p>
-<ol>
-<li>"Let's Get Married"</li>
-<li>"Promise"</li>
-<li>"Where the Party At" (featuring Nelly)</li>
-<li>"I Gotta Be"</li>
-<li>"Walked Outta Heaven"</li>
-<li>"He Can't Love U"</li>
-<li>"Good Luck Charm"</li>
-<li>"So Amazing"</li>
-<li>"What's It Like"</li>
-<li>"Season's Change" (featuring John Legend)</li>
-<li>"Lay You Down"</li>
-<li>"Put A Little Umph In It" (featuring Ashanti)</li>
-<li>"Girl It's Over"</li>
-<li>"Trade It All" (Fabolous featuring P. Diddy and Jagged Edge)</li>
-<li>"Let's Get Married (Remix)" (featuring Reverend Run)</li>
-<li>"Best Man"</li>
-<li>"Nasty Girl" (The Notorious B.I.G. featuring Jagged Edge, Avery Storm, Diddy and Nelly)</li>
-<li>"Hope"</li>
-</ol>
-<p><i>Jagged Edge The Greatest Hits Album Zip</i> is a fan-made project that aims to celebrate the music and legacy of Jagged Edge. However, it is not an official release and it may violate the copyrights of the group and their record label. Therefore, it is recommended that fans support the group by buying their official albums and merchandise.</p><p>Jagged Edge is one of the most successful R&B groups of their generation. They have sold over 8 million records worldwide and have earned four Grammy nominations. They have also collaborated with some of the biggest names in the music industry, such as Jermaine Dupri, Mariah Carey, Usher, and Alicia Keys.</p>
-<p>The group's sound is influenced by classic soul and contemporary R&B, with a touch of hip-hop and pop. They are known for their catchy hooks, smooth vocals, and heartfelt lyrics. Their songs often deal with themes of love, romance, and relationships.</p>
-<p>Jagged Edge has released nine studio albums so far, with their latest being <i>A Jagged Love Story</i> (2020). The album was released independently by the group after they parted ways with their longtime label So So Def. The album features 31 tracks and showcases the group's versatility and maturity.</p>
-<p>Jagged Edge has also been involved in various philanthropic and social causes. They have supported charities such as the Make-A-Wish Foundation, the Boys & Girls Clubs of America, and the American Cancer Society. They have also spoken out against domestic violence and racism.</p>
-<p>Jagged Edge is a group that has stayed true to their roots and their fans. They have maintained their relevance and quality in a changing musical landscape. They have proven themselves to be one of the greatest R&B groups of all time.</p> 7196e7f11a<br />
-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Kaledo Style-torrent-megaupload.torrent [PATCHED].md b/spaces/tioseFevbu/cartoon-converter/scripts/Kaledo Style-torrent-megaupload.torrent [PATCHED].md
deleted file mode 100644
index b6df6f5d6ef6b671013d2434bdff5e5a91b39f15..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Kaledo Style-torrent-megaupload.torrent [PATCHED].md	
+++ /dev/null
@@ -1,110 +0,0 @@
-<br />
-<h1>What is Kaledo Style-torrent-megaupload.torrent?</h1>
-<p>If you are interested in fashion design, you might have heard of Kaledo Style, a software that helps you create new styles and patterns for garments. But what is Kaledo Style-torrent-megaupload.torrent? Is it a legitimate way to get the software for free? Or is it a scam or a virus that could harm your computer? In this article, we will explain what Kaledo Style-torrent-megaupload.torrent is, how it works, and what are the risks and alternatives to using it.</p>
- <h2>What is Kaledo Style?</h2>
-<p>Kaledo Style is a software developed by Lectra, a company that specializes in providing solutions for the fashion industry. Kaledo Style is designed to help fashion designers create new styles and carry over bestsellers with ease. It allows you to sketch, draw, color, print, and share your designs with others. It also integrates with other Lectra software, such as Modaris for pattern making and Diamino for marker making.</p>
-<h2>Kaledo style-torrent-megaupload.torrent</h2><br /><p><b><b>Download</b> &#10026; <a href="https://urlcod.com/2uHx4k">https://urlcod.com/2uHx4k</a></b></p><br /><br />
- <h3>Features and benefits of Kaledo Style</h3>
-<p>Some of the features and benefits of Kaledo Style are:</p>
-<ul>
-<li>It has apparel-specific tools that let you draw garments with realistic details, such as seams, buttons, zippers, pockets, etc.</li>
-<li>It has a library of fabrics, prints, colors, trims, and accessories that you can use to customize your designs.</li>
-<li>It has a 3D simulation feature that lets you see how your designs look on different body shapes and sizes.</li>
-<li>It has a print module that lets you create professional-quality printouts of your designs with technical specifications.</li>
-<li>It has a collaboration feature that lets you share your designs with other users or export them to other formats.</li>
-</ul>
- <h3>How to use Kaledo Style</h3>
-<p>To use Kaledo Style, you need to have a computer that meets the minimum system requirements. You also need to have a license key or a dongle to activate the software. You can get the license key or the dongle from Lectra or from an authorized reseller. Once you have installed and activated the software, you can start creating your designs by following these steps:</p>
-<ol>
-<li>Open Kaledo Style and choose a project type (such as tops, dresses, pants, etc.).</li>
-<li>Select a template or create your own sketch from scratch.</li>
-<li>Add details, such as fabrics, prints, colors, trims, etc., using the tools on the toolbar.</li>
-<li>Adjust the fit and style of your design using the tools on the sidebar.</li>
-<li>Preview your design in 3D using the simulation feature.</li>
-<li>Print or share your design using the print or collaboration feature.</li>
-</ol>
- <h2>What is a torrent file?</h2>
-<p>A torrent file is a small file that contains information about a larger file or a group of files that can be downloaded from the internet. A torrent file does not contain the actual files themselves, but rather the metadata, such as the file name, size, type, and location. A torrent file also contains the information about the peers, or the users who are sharing the file or parts of it. A torrent file usually has the extension .torrent.</p>
- <h3>Advantages and disadvantages of torrenting</h3>
-<p>Torrenting is a method of downloading and uploading files using a peer-to-peer (P2P) network. This means that instead of downloading a file from a single server, you download it from multiple sources that have the same file or parts of it. Some of the advantages and disadvantages of torrenting are:</p>
-<table>
-<tr>
-<th>Advantages</th>
-<th>Disadvantages</th>
-</tr>
-<tr>
-<td>It can be faster and more efficient than downloading from a single source, especially if the file is large or popular.</td>
-<td>It can be illegal or unethical if the file is protected by copyright or other laws.</td>
-</tr>
-<tr>
-<td>It can be more resilient and reliable than downloading from a single source, as it does not depend on the availability or performance of a single server.</td>
-<td>It can be risky or harmful if the file is infected with malware or viruses.</td>
-</tr>
-<tr>
-<td>It can be more diverse and flexible than downloading from a single source, as it allows you to choose from different versions or formats of the file.</td>
-<td>It can be complicated or confusing if the file is not well-organized or labeled.</td>
-</tr>
-</table>
- <h3>How to download and open a torrent file</h3>
-<p>To download and open a torrent file, you need to have a torrent client, which is a software that enables you to connect to the P2P network and manage your downloads and uploads. Some of the popular torrent clients are uTorrent, BitTorrent, qBittorrent, and Vuze. Once you have installed a torrent client, you can follow these steps:</p>
-<ol>
-<li>Find a torrent file that you want to download from a torrent site, such as The Pirate Bay, Kickass Torrents, 1337x, etc. Make sure that the torrent file has a good reputation and feedback from other users.</li>
-<li>Download the torrent file to your computer or click on the magnet link, which is a URL that contains the information about the torrent file.</li>
-<li>Open the torrent file or the magnet link with your torrent client. The torrent client will start downloading the file or files from the P2P network.</li>
-<li>Wait for the download to finish. You can monitor the progress and speed of your download on your torrent client.</li>
-<li>Open the downloaded file or files with the appropriate software. For example, if you downloaded a video file, you can open it with a media player, such as VLC, Windows Media Player, etc.</li>
-</ol>
- <h2>What is megaupload?</h2>
-<p>Megaupload was a file hosting and sharing service that allowed users to upload and download files of various types and sizes. Megaupload was founded in 2005 by Kim Dotcom, a German-Finnish entrepreneur who later became notorious for his legal battles with the US government. Megaupload was one of the most popular and widely used file hosting services in the world, with over 180 million registered users and 50 million daily visitors at its peak.</p>
-<p></p>
- <h3>History and popularity of megaupload</h3>
-<p>Megaupload was launched in 2005 as a simple file hosting service that offered free and premium accounts. Users could upload files up to 1 GB in size for free or up to 10 GB for premium users. Users could also download files without any limit for free or with faster speeds for premium users. Megaupload also offered other services, such as Megavideo for streaming videos, Megapix for hosting images, Megalive for live broadcasting, and Megabox for music streaming.</p>
- <p>Megaupload gained popularity among users who wanted to share large files, such as movies, music, games, software, etc., without any hassle or restriction. Megaupload also attracted celebrities and artists who used it to promote their work or interact with their fans. For example, Kanye West, Alicia Keys, Will.i.am, Snoop Dogg, Kim Kardashian, and others appeared in a promotional video for Megaupload in 2011.</p>
- <h3>Legal issues and shutdown of megaupload</h3>
-<p>Megaupload faced legal issues and criticism from various authorities and organizations who accused it of facilitating piracy and infringing intellectual property rights. Megaupload claimed that it complied with the Digital Millennium Copyright Act (DMCA) and removed any illegal content upon request. However, some critics argued that Megaupload did not do enough to prevent or discourage piracy and that it profited from the traffic and advertising revenue generated by the users who uploaded and downloaded pirated content. On January 19, 2012, Megaupload was shut down by the US Department of Justice (DOJ) and the Federal Bureau of Investigation (FBI) as part of a criminal investigation. The DOJ and the FBI seized the domain names and servers of Megaupload and its affiliated sites, as well as the assets and properties of its owners and operators. The DOJ and the FBI also arrested and indicted Kim Dotcom and six other individuals on charges of racketeering, conspiracy, money laundering, and copyright infringement. The shutdown of Megaupload sparked a controversy and a backlash among its users and supporters, who protested against the US government's actions and claimed that they violated the rights and freedoms of internet users. Some hackers and activists also launched cyberattacks against the websites of the DOJ, the FBI, and other organizations that were involved in or supported the shutdown of Megaupload. <h2>How to get Kaledo Style-torrent-megaupload.torrent?</h2>
-<p>Kaledo Style-torrent-megaupload.torrent is a torrent file that supposedly contains a link to download Kaledo Style from megaupload. However, since megaupload is no longer operational, it is impossible to download Kaledo Style from this torrent file. Moreover, even if megaupload was still working, there is no guarantee that this torrent file is authentic or safe. It could be a fake or a malicious file that could harm your computer or expose you to legal risks.</p>
- <h3>Sources and links for Kaledo Style-torrent-megaupload.torrent</h3>
-<p>There are several websites that claim to offer Kaledo Style-torrent-megaupload.torrent for free download. However, most of these websites are unreliable or shady, as they may contain viruses, malware, spyware, adware, or other unwanted programs. They may also require you to complete surveys, register accounts, provide personal information, or pay fees to access the torrent file. Some examples of these websites are:</p>
-<ul>
-<li>https://www.picktorrent.com/torrents/kaledo-style-torrent-megaupload/</li>
-<li>https://www.torlock.com/all/torrents/kaledo-style.html</li>
-<li>https://www.torrentfunk.com/all/torrents/kaledo-style.html</li>
-<li>https://www.limetorrents.info/search/all/Kaledo-Style/</li>
-<li>https://www.torrentdownloads.me/search/?search=Kaledo+Style</li>
-</ul>
- <h3>Risks and precautions for Kaledo Style-torrent-megaupload.torrent</h3>
-<p>Downloading and using Kaledo Style-torrent-megaupload.torrent is not recommended for several reasons. Some of the risks and precautions for Kaledo Style-torrent-megaupload.torrent are:</p>
-<ul>
-<li>It is illegal and unethical to download and use Kaledo Style without paying for it or obtaining a license from Lectra. You could face legal consequences or penalties if you are caught or reported by Lectra or other authorities.</li>
-<li>It is unsafe and harmful to download and open Kaledo Style-torrent-megaupload.torrent from unknown or untrusted sources. You could infect your computer with viruses or malware that could damage your system, steal your data, or compromise your security.</li>
-<li>It is futile and pointless to download and use Kaledo Style-torrent-megaupload.torrent since megaupload is no longer available. You will not be able to access or download Kaledo Style from this torrent file.</li>
-<li>It is advisable and prudent to avoid Kaledo Style-torrent-megaupload.torrent altogether and look for other ways to get Kaledo Style legally and safely. You should also use a reliable antivirus software and a VPN service to protect your computer and your privacy when downloading or using any torrent file.</li>
-</ul>
- <h2>Alternatives to Kaledo Style-torrent-megaupload.torrent</h2>
-<p>If you want to get Kaledo Style without resorting to Kaledo Style-torrent-megaupload.torrent, there are some alternatives that you can consider. Some of the alternatives to Kaledo Style-torrent-megaupload.torrent are:</p>
- <h3>Official website and trial version of Kaledo Style</h3>
-<p>The best and most legitimate way to get Kaledo Style is to visit the official website of Lectra at https://www.lectra.com/en/fashion-design-software/kaledo-style. Here you can find more information about Kaledo Style, such as its features, benefits, system requirements, pricing, etc. You can also contact Lectra directly or find an authorized reseller near you to purchase a license or a dongle for Kaledo Style. You can also request a free trial version of Kaledo Style for a limited time to test its features and functionality before buying it.</p>
- <h3>Other fashion design software and online tools</h3>
-<p>If you are looking for other options to create and design fashion styles and patterns, there are some other software and online tools that you can try. Some of them are free or cheaper than Kaledo Style, but they may have fewer features or lower quality. Some of them are similar or compatible with Kaledo Style, but they may have different interfaces or requirements. Some of the other fashion design software and online tools are:</p>
-<ul>
-<li>CLO 3D: A 3D fashion design software that lets you create realistic garments and simulate their fit and movement on different models. It is compatible with Kaledo Style and other Lectra software. You can find more information at https://www.clo3d.com/.</li>
-<li>Adobe Illustrator: A vector graphics editor that lets you create and edit illustrations, logos, icons, and other graphics. It is widely used by fashion designers to sketch and draw their designs. You can find more information at https://www.adobe.com/products/illustrator.html.</li>
-<li>Fashion Design Studio: An online tool that lets you create and customize your own fashion designs using templates, fabrics, colors, prints, and accessories. You can also share your designs with others or print them out. You can find more information at https://www.fashion-design-studio.com/.</li>
-<li>SketchUp: A 3D modeling software that lets you create and visualize anything in 3D. It is not specifically designed for fashion design, but it can be used to create and render 3D models of garments and accessories. You can find more information at https://www.sketchup.com/.</li>
-<li>GIMP: A free and open source image editor that lets you edit, retouch, and manipulate images. It is not specifically designed for fashion design, but it can be used to create and modify prints, patterns, textures, and colors for your designs. You can find more information at https://www.gimp.org/.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Kaledo Style-torrent-megaupload.torrent is a torrent file that claims to offer a free download of Kaledo Style, a fashion design software, from megaupload, a file hosting service. However, this torrent file is not reliable or safe, as it is illegal, unethical, unsafe, harmful, futile, and pointless. It is better to avoid this torrent file and look for other ways to get Kaledo Style legally and safely, such as visiting the official website of Lectra or trying other fashion design software and online tools.</p>
- <h2>FAQs</h2>
-<h4>What is the difference between Kaledo Style and Kaledo Collection?</h4>
-<p>Kaledo Style is a software that helps you create individual styles and patterns for garments. Kaledo Collection is a software that helps you create collections and assortments of styles for different seasons, markets, or customers. Both software are part of the Kaledo suite by Lectra.</p>
- <h4>How much does Kaledo Style cost?</h4>
-<p>The price of Kaledo Style depends on the type of license or dongle that you choose, the number of users that you need, the duration of your subscription or contract, and the region or country where you are located. You can contact Lectra or an authorized reseller to get a quote for Kaledo Style.</p>
- <h4>Is Kaledo Style compatible with Windows 10?</h4>
-<p>Yes, Kaledo Style is compatible with Windows 10 as well as Windows 8.1 and Windows 7. However, you may need to update your drivers or settings to ensure optimal performance of Kaledo Style on Windows 10.</p>
- <h4>Can I use Kaledo Style on a Mac?</h4>
-<p>No, Kaledo Style is not compatible with Mac OS X or macOS. You can only use Kaledo Style on a Windows PC or laptop.</p>
- <h4>Can I import or export files from Kaledo Style to other software?</h4>
-<p>Yes, you can import or export files from Kaledo Style to other software using different formats. For example, you can import images from JPG, PNG, BMP, TIFF, etc., or export images to PDF, JPG, PNG, etc. You can also import or export vector graphics from AI (Adobe Illustrator), DXF (AutoCAD), etc., or export vector graphics to AI (Adobe Illustrator), DXF (AutoCAD), PLT (HPGL), etc.</p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/containers.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/containers.py
deleted file mode 100644
index e29cf368991ccb083b67cda8133e4635defbfe53..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/containers.py
+++ /dev/null
@@ -1,167 +0,0 @@
-from itertools import zip_longest
-from typing import (
-    Iterator,
-    Iterable,
-    List,
-    Optional,
-    Union,
-    overload,
-    TypeVar,
-    TYPE_CHECKING,
-)
-
-if TYPE_CHECKING:
-    from .console import (
-        Console,
-        ConsoleOptions,
-        JustifyMethod,
-        OverflowMethod,
-        RenderResult,
-        RenderableType,
-    )
-    from .text import Text
-
-from .cells import cell_len
-from .measure import Measurement
-
-T = TypeVar("T")
-
-
-class Renderables:
-    """A list subclass which renders its contents to the console."""
-
-    def __init__(
-        self, renderables: Optional[Iterable["RenderableType"]] = None
-    ) -> None:
-        self._renderables: List["RenderableType"] = (
-            list(renderables) if renderables is not None else []
-        )
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        """Console render method to insert line-breaks."""
-        yield from self._renderables
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        dimensions = [
-            Measurement.get(console, options, renderable)
-            for renderable in self._renderables
-        ]
-        if not dimensions:
-            return Measurement(1, 1)
-        _min = max(dimension.minimum for dimension in dimensions)
-        _max = max(dimension.maximum for dimension in dimensions)
-        return Measurement(_min, _max)
-
-    def append(self, renderable: "RenderableType") -> None:
-        self._renderables.append(renderable)
-
-    def __iter__(self) -> Iterable["RenderableType"]:
-        return iter(self._renderables)
-
-
-class Lines:
-    """A list subclass which can render to the console."""
-
-    def __init__(self, lines: Iterable["Text"] = ()) -> None:
-        self._lines: List["Text"] = list(lines)
-
-    def __repr__(self) -> str:
-        return f"Lines({self._lines!r})"
-
-    def __iter__(self) -> Iterator["Text"]:
-        return iter(self._lines)
-
-    @overload
-    def __getitem__(self, index: int) -> "Text":
-        ...
-
-    @overload
-    def __getitem__(self, index: slice) -> List["Text"]:
-        ...
-
-    def __getitem__(self, index: Union[slice, int]) -> Union["Text", List["Text"]]:
-        return self._lines[index]
-
-    def __setitem__(self, index: int, value: "Text") -> "Lines":
-        self._lines[index] = value
-        return self
-
-    def __len__(self) -> int:
-        return self._lines.__len__()
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        """Console render method to insert line-breaks."""
-        yield from self._lines
-
-    def append(self, line: "Text") -> None:
-        self._lines.append(line)
-
-    def extend(self, lines: Iterable["Text"]) -> None:
-        self._lines.extend(lines)
-
-    def pop(self, index: int = -1) -> "Text":
-        return self._lines.pop(index)
-
-    def justify(
-        self,
-        console: "Console",
-        width: int,
-        justify: "JustifyMethod" = "left",
-        overflow: "OverflowMethod" = "fold",
-    ) -> None:
-        """Justify and overflow text to a given width.
-
-        Args:
-            console (Console): Console instance.
-            width (int): Number of characters per line.
-            justify (str, optional): Default justify method for text: "left", "center", "full" or "right". Defaults to "left".
-            overflow (str, optional): Default overflow for text: "crop", "fold", or "ellipsis". Defaults to "fold".
-
-        """
-        from .text import Text
-
-        if justify == "left":
-            for line in self._lines:
-                line.truncate(width, overflow=overflow, pad=True)
-        elif justify == "center":
-            for line in self._lines:
-                line.rstrip()
-                line.truncate(width, overflow=overflow)
-                line.pad_left((width - cell_len(line.plain)) // 2)
-                line.pad_right(width - cell_len(line.plain))
-        elif justify == "right":
-            for line in self._lines:
-                line.rstrip()
-                line.truncate(width, overflow=overflow)
-                line.pad_left(width - cell_len(line.plain))
-        elif justify == "full":
-            for line_index, line in enumerate(self._lines):
-                if line_index == len(self._lines) - 1:
-                    break
-                words = line.split(" ")
-                words_size = sum(cell_len(word.plain) for word in words)
-                num_spaces = len(words) - 1
-                spaces = [1 for _ in range(num_spaces)]
-                index = 0
-                if spaces:
-                    while words_size + num_spaces < width:
-                        spaces[len(spaces) - index - 1] += 1
-                        num_spaces += 1
-                        index = (index + 1) % len(spaces)
-                tokens: List[Text] = []
-                for index, (word, next_word) in enumerate(
-                    zip_longest(words, words[1:])
-                ):
-                    tokens.append(word)
-                    if index < len(spaces):
-                        style = word.get_style_at_offset(console, -1)
-                        next_style = next_word.get_style_at_offset(console, 0)
-                        space_style = style if style == next_style else line.style
-                        tokens.append(Text(" " * spaces[index], style=space_style))
-                self[line_index] = Text("").join(tokens)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/command/build.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/command/build.py
deleted file mode 100644
index 3aa7fac71b2a0eaec02db9c2a0d2c79886456d6c..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/command/build.py
+++ /dev/null
@@ -1,152 +0,0 @@
-"""distutils.command.build
-
-Implements the Distutils 'build' command."""
-
-import sys, os
-from distutils.core import Command
-from distutils.errors import DistutilsOptionError
-from distutils.util import get_platform
-
-
-def show_compilers():
-    from distutils.ccompiler import show_compilers
-
-    show_compilers()
-
-
-class build(Command):
-
-    description = "build everything needed to install"
-
-    user_options = [
-        ('build-base=', 'b', "base directory for build library"),
-        ('build-purelib=', None, "build directory for platform-neutral distributions"),
-        ('build-platlib=', None, "build directory for platform-specific distributions"),
-        (
-            'build-lib=',
-            None,
-            "build directory for all distribution (defaults to either "
-            + "build-purelib or build-platlib",
-        ),
-        ('build-scripts=', None, "build directory for scripts"),
-        ('build-temp=', 't', "temporary build directory"),
-        (
-            'plat-name=',
-            'p',
-            "platform name to build for, if supported "
-            "(default: %s)" % get_platform(),
-        ),
-        ('compiler=', 'c', "specify the compiler type"),
-        ('parallel=', 'j', "number of parallel build jobs"),
-        ('debug', 'g', "compile extensions and libraries with debugging information"),
-        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
-        ('executable=', 'e', "specify final destination interpreter path (build.py)"),
-    ]
-
-    boolean_options = ['debug', 'force']
-
-    help_options = [
-        ('help-compiler', None, "list available compilers", show_compilers),
-    ]
-
-    def initialize_options(self):
-        self.build_base = 'build'
-        # these are decided only after 'build_base' has its final value
-        # (unless overridden by the user or client)
-        self.build_purelib = None
-        self.build_platlib = None
-        self.build_lib = None
-        self.build_temp = None
-        self.build_scripts = None
-        self.compiler = None
-        self.plat_name = None
-        self.debug = None
-        self.force = 0
-        self.executable = None
-        self.parallel = None
-
-    def finalize_options(self):
-        if self.plat_name is None:
-            self.plat_name = get_platform()
-        else:
-            # plat-name only supported for windows (other platforms are
-            # supported via ./configure flags, if at all).  Avoid misleading
-            # other platforms.
-            if os.name != 'nt':
-                raise DistutilsOptionError(
-                    "--plat-name only supported on Windows (try "
-                    "using './configure --help' on your platform)"
-                )
-
-        plat_specifier = ".%s-%s" % (self.plat_name, sys.implementation.cache_tag)
-
-        # Make it so Python 2.x and Python 2.x with --with-pydebug don't
-        # share the same build directories. Doing so confuses the build
-        # process for C modules
-        if hasattr(sys, 'gettotalrefcount'):
-            plat_specifier += '-pydebug'
-
-        # 'build_purelib' and 'build_platlib' just default to 'lib' and
-        # 'lib.<plat>' under the base build directory.  We only use one of
-        # them for a given distribution, though --
-        if self.build_purelib is None:
-            self.build_purelib = os.path.join(self.build_base, 'lib')
-        if self.build_platlib is None:
-            self.build_platlib = os.path.join(self.build_base, 'lib' + plat_specifier)
-
-        # 'build_lib' is the actual directory that we will use for this
-        # particular module distribution -- if user didn't supply it, pick
-        # one of 'build_purelib' or 'build_platlib'.
-        if self.build_lib is None:
-            if self.distribution.has_ext_modules():
-                self.build_lib = self.build_platlib
-            else:
-                self.build_lib = self.build_purelib
-
-        # 'build_temp' -- temporary directory for compiler turds,
-        # "build/temp.<plat>"
-        if self.build_temp is None:
-            self.build_temp = os.path.join(self.build_base, 'temp' + plat_specifier)
-        if self.build_scripts is None:
-            self.build_scripts = os.path.join(
-                self.build_base, 'scripts-%d.%d' % sys.version_info[:2]
-            )
-
-        if self.executable is None and sys.executable:
-            self.executable = os.path.normpath(sys.executable)
-
-        if isinstance(self.parallel, str):
-            try:
-                self.parallel = int(self.parallel)
-            except ValueError:
-                raise DistutilsOptionError("parallel should be an integer")
-
-    def run(self):
-        # Run all relevant sub-commands.  This will be some subset of:
-        #  - build_py      - pure Python modules
-        #  - build_clib    - standalone C libraries
-        #  - build_ext     - Python extensions
-        #  - build_scripts - (Python) scripts
-        for cmd_name in self.get_sub_commands():
-            self.run_command(cmd_name)
-
-    # -- Predicates for the sub-command list ---------------------------
-
-    def has_pure_modules(self):
-        return self.distribution.has_pure_modules()
-
-    def has_c_libraries(self):
-        return self.distribution.has_c_libraries()
-
-    def has_ext_modules(self):
-        return self.distribution.has_ext_modules()
-
-    def has_scripts(self):
-        return self.distribution.has_scripts()
-
-    sub_commands = [
-        ('build_py', has_pure_modules),
-        ('build_clib', has_c_libraries),
-        ('build_ext', has_ext_modules),
-        ('build_scripts', has_scripts),
-    ]
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/installer.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/installer.py
deleted file mode 100644
index b7096df14b4a15980ad138a3990d3e25aeb3bfe1..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/installer.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import glob
-import os
-import subprocess
-import sys
-import tempfile
-import warnings
-from distutils import log
-from distutils.errors import DistutilsError
-
-import pkg_resources
-from setuptools.wheel import Wheel
-from ._deprecation_warning import SetuptoolsDeprecationWarning
-
-
-def _fixup_find_links(find_links):
-    """Ensure find-links option end-up being a list of strings."""
-    if isinstance(find_links, str):
-        return find_links.split()
-    assert isinstance(find_links, (tuple, list))
-    return find_links
-
-
-def fetch_build_egg(dist, req):  # noqa: C901  # is too complex (16)  # FIXME
-    """Fetch an egg needed for building.
-
-    Use pip/wheel to fetch/build a wheel."""
-    warnings.warn(
-        "setuptools.installer is deprecated. Requirements should "
-        "be satisfied by a PEP 517 installer.",
-        SetuptoolsDeprecationWarning,
-    )
-    # Warn if wheel is not available
-    try:
-        pkg_resources.get_distribution('wheel')
-    except pkg_resources.DistributionNotFound:
-        dist.announce('WARNING: The wheel package is not available.', log.WARN)
-    # Ignore environment markers; if supplied, it is required.
-    req = strip_marker(req)
-    # Take easy_install options into account, but do not override relevant
-    # pip environment variables (like PIP_INDEX_URL or PIP_QUIET); they'll
-    # take precedence.
-    opts = dist.get_option_dict('easy_install')
-    if 'allow_hosts' in opts:
-        raise DistutilsError('the `allow-hosts` option is not supported '
-                             'when using pip to install requirements.')
-    quiet = 'PIP_QUIET' not in os.environ and 'PIP_VERBOSE' not in os.environ
-    if 'PIP_INDEX_URL' in os.environ:
-        index_url = None
-    elif 'index_url' in opts:
-        index_url = opts['index_url'][1]
-    else:
-        index_url = None
-    find_links = (
-        _fixup_find_links(opts['find_links'][1])[:] if 'find_links' in opts
-        else []
-    )
-    if dist.dependency_links:
-        find_links.extend(dist.dependency_links)
-    eggs_dir = os.path.realpath(dist.get_egg_cache_dir())
-    environment = pkg_resources.Environment()
-    for egg_dist in pkg_resources.find_distributions(eggs_dir):
-        if egg_dist in req and environment.can_add(egg_dist):
-            return egg_dist
-    with tempfile.TemporaryDirectory() as tmpdir:
-        cmd = [
-            sys.executable, '-m', 'pip',
-            '--disable-pip-version-check',
-            'wheel', '--no-deps',
-            '-w', tmpdir,
-        ]
-        if quiet:
-            cmd.append('--quiet')
-        if index_url is not None:
-            cmd.extend(('--index-url', index_url))
-        for link in find_links or []:
-            cmd.extend(('--find-links', link))
-        # If requirement is a PEP 508 direct URL, directly pass
-        # the URL to pip, as `req @ url` does not work on the
-        # command line.
-        cmd.append(req.url or str(req))
-        try:
-            subprocess.check_call(cmd)
-        except subprocess.CalledProcessError as e:
-            raise DistutilsError(str(e)) from e
-        wheel = Wheel(glob.glob(os.path.join(tmpdir, '*.whl'))[0])
-        dist_location = os.path.join(eggs_dir, wheel.egg_name())
-        wheel.install_as_egg(dist_location)
-        dist_metadata = pkg_resources.PathMetadata(
-            dist_location, os.path.join(dist_location, 'EGG-INFO'))
-        dist = pkg_resources.Distribution.from_filename(
-            dist_location, metadata=dist_metadata)
-        return dist
-
-
-def strip_marker(req):
-    """
-    Return a new requirement without the environment marker to avoid
-    calling pip with something like `babel; extra == "i18n"`, which
-    would always be ignored.
-    """
-    # create a copy to avoid mutating the input
-    req = pkg_resources.Requirement.parse(str(req))
-    req.marker = None
-    return req
diff --git a/spaces/tomofi/MMOCR/configs/_base_/recog_datasets/seg_toy_data.py b/spaces/tomofi/MMOCR/configs/_base_/recog_datasets/seg_toy_data.py
deleted file mode 100644
index 59e008cf50f16fdc122029c0809bbd155652d765..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/_base_/recog_datasets/seg_toy_data.py
+++ /dev/null
@@ -1,32 +0,0 @@
-prefix = 'tests/data/ocr_char_ann_toy_dataset/'
-
-train = dict(
-    type='OCRSegDataset',
-    img_prefix=f'{prefix}/imgs',
-    ann_file=f'{prefix}/instances_train.txt',
-    loader=dict(
-        type='HardDiskLoader',
-        repeat=100,
-        parser=dict(
-            type='LineJsonParser', keys=['file_name', 'annotations', 'text'])),
-    pipeline=None,
-    test_mode=True)
-
-test = dict(
-    type='OCRDataset',
-    img_prefix=f'{prefix}/imgs',
-    ann_file=f'{prefix}/instances_test.txt',
-    loader=dict(
-        type='HardDiskLoader',
-        repeat=1,
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=True)
-
-train_list = [train]
-
-test_list = [test]
diff --git a/spaces/tomofi/MMOCR/tools/deployment/onnx2tensorrt.py b/spaces/tomofi/MMOCR/tools/deployment/onnx2tensorrt.py
deleted file mode 100644
index 6decbcd0e7d0b7f440ffafa51414c9fc7b006650..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/tools/deployment/onnx2tensorrt.py
+++ /dev/null
@@ -1,294 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import os
-import os.path as osp
-import warnings
-from typing import Iterable
-
-import cv2
-import mmcv
-import numpy as np
-import torch
-from mmcv.parallel import collate
-from mmcv.tensorrt import is_tensorrt_plugin_loaded, onnx2trt, save_trt_engine
-from mmdet.datasets import replace_ImageToTensor
-from mmdet.datasets.pipelines import Compose
-
-from mmocr.core.deployment import (ONNXRuntimeDetector, ONNXRuntimeRecognizer,
-                                   TensorRTDetector, TensorRTRecognizer)
-from mmocr.datasets.pipelines.crop import crop_img  # noqa: F401
-from mmocr.utils import is_2dlist
-
-
-def get_GiB(x: int):
-    """return x GiB."""
-    return x * (1 << 30)
-
-
-def _prepare_input_img(imgs, test_pipeline: Iterable[dict]):
-    """Inference image(s) with the detector.
-
-    Args:
-        imgs (str/ndarray or list[str/ndarray] or tuple[str/ndarray]):
-            Either image files or loaded images.
-        test_pipeline (Iterable[dict]): Test pipline of configuration.
-    Returns:
-        result (dict): Predicted results.
-    """
-    if isinstance(imgs, (list, tuple)):
-        if not isinstance(imgs[0], (np.ndarray, str)):
-            raise AssertionError('imgs must be strings or numpy arrays')
-
-    elif isinstance(imgs, (np.ndarray, str)):
-        imgs = [imgs]
-    else:
-        raise AssertionError('imgs must be strings or numpy arrays')
-
-    test_pipeline = replace_ImageToTensor(test_pipeline)
-    test_pipeline = Compose(test_pipeline)
-
-    data = []
-    for img in imgs:
-        # prepare data
-        # add information into dict
-        datum = dict(img_info=dict(filename=img), img_prefix=None)
-
-        # build the data pipeline
-        datum = test_pipeline(datum)
-        # get tensor from list to stack for batch mode (text detection)
-        data.append(datum)
-
-    if isinstance(data[0]['img'], list) and len(data) > 1:
-        raise Exception('aug test does not support '
-                        f'inference with batch size '
-                        f'{len(data)}')
-
-    data = collate(data, samples_per_gpu=len(imgs))
-
-    # process img_metas
-    if isinstance(data['img_metas'], list):
-        data['img_metas'] = [
-            img_metas.data[0] for img_metas in data['img_metas']
-        ]
-    else:
-        data['img_metas'] = data['img_metas'].data
-
-    if isinstance(data['img'], list):
-        data['img'] = [img.data for img in data['img']]
-        if isinstance(data['img'][0], list):
-            data['img'] = [img[0] for img in data['img']]
-    else:
-        data['img'] = data['img'].data
-    return data
-
-
-def onnx2tensorrt(onnx_file: str,
-                  model_type: str,
-                  trt_file: str,
-                  config: dict,
-                  input_config: dict,
-                  fp16: bool = False,
-                  verify: bool = False,
-                  show: bool = False,
-                  workspace_size: int = 1,
-                  verbose: bool = False):
-    import tensorrt as trt
-    min_shape = input_config['min_shape']
-    max_shape = input_config['max_shape']
-    # create trt engine and wrapper
-    opt_shape_dict = {'input': [min_shape, min_shape, max_shape]}
-    max_workspace_size = get_GiB(workspace_size)
-    trt_engine = onnx2trt(
-        onnx_file,
-        opt_shape_dict,
-        log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
-        fp16_mode=fp16,
-        max_workspace_size=max_workspace_size)
-    save_dir, _ = osp.split(trt_file)
-    if save_dir:
-        os.makedirs(save_dir, exist_ok=True)
-    save_trt_engine(trt_engine, trt_file)
-    print(f'Successfully created TensorRT engine: {trt_file}')
-
-    if verify:
-        mm_inputs = _prepare_input_img(input_config['input_path'],
-                                       config.data.test.pipeline)
-
-        imgs = mm_inputs.pop('img')
-        img_metas = mm_inputs.pop('img_metas')
-
-        if isinstance(imgs, list):
-            imgs = imgs[0]
-
-        img_list = [img[None, :] for img in imgs]
-
-        # Get results from ONNXRuntime
-        if model_type == 'det':
-            onnx_model = ONNXRuntimeDetector(onnx_file, config, 0)
-        else:
-            onnx_model = ONNXRuntimeRecognizer(onnx_file, config, 0)
-        onnx_out = onnx_model.simple_test(
-            img_list[0], img_metas[0], rescale=True)
-
-        # Get results from TensorRT
-        if model_type == 'det':
-            trt_model = TensorRTDetector(trt_file, config, 0)
-        else:
-            trt_model = TensorRTRecognizer(trt_file, config, 0)
-        img_list[0] = img_list[0].to(torch.device('cuda:0'))
-        trt_out = trt_model.simple_test(
-            img_list[0], img_metas[0], rescale=True)
-
-        # compare results
-        same_diff = 'same'
-        if model_type == 'recog':
-            for onnx_result, trt_result in zip(onnx_out, trt_out):
-                if onnx_result['text'] != trt_result['text'] or \
-                     not np.allclose(
-                            np.array(onnx_result['score']),
-                            np.array(trt_result['score']),
-                            rtol=1e-4,
-                            atol=1e-4):
-                    same_diff = 'different'
-                    break
-        else:
-            for onnx_result, trt_result in zip(onnx_out[0]['boundary_result'],
-                                               trt_out[0]['boundary_result']):
-                if not np.allclose(
-                        np.array(onnx_result),
-                        np.array(trt_result),
-                        rtol=1e-4,
-                        atol=1e-4):
-                    same_diff = 'different'
-                    break
-        print('The outputs are {} between TensorRT and ONNX'.format(same_diff))
-
-        if show:
-            onnx_img = onnx_model.show_result(
-                input_config['input_path'],
-                onnx_out[0],
-                out_file='onnx.jpg',
-                show=False)
-            trt_img = trt_model.show_result(
-                input_config['input_path'],
-                trt_out[0],
-                out_file='tensorrt.jpg',
-                show=False)
-            if onnx_img is None:
-                onnx_img = cv2.imread(input_config['input_path'])
-            if trt_img is None:
-                trt_img = cv2.imread(input_config['input_path'])
-
-            cv2.imshow('TensorRT', trt_img)
-            cv2.imshow('ONNXRuntime', onnx_img)
-            cv2.waitKey()
-    return
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='Convert MMOCR models from ONNX to TensorRT')
-    parser.add_argument('model_config', help='Config file of the model')
-    parser.add_argument(
-        'model_type',
-        type=str,
-        help='Detection or recognition model to deploy.',
-        choices=['recog', 'det'])
-    parser.add_argument('image_path', type=str, help='Image for test')
-    parser.add_argument('onnx_file', help='Path to the input ONNX model')
-    parser.add_argument(
-        '--trt-file',
-        type=str,
-        help='Path to the output TensorRT engine',
-        default='tmp.trt')
-    parser.add_argument(
-        '--max-shape',
-        type=int,
-        nargs=4,
-        default=[1, 3, 400, 600],
-        help='Maximum shape of model input.')
-    parser.add_argument(
-        '--min-shape',
-        type=int,
-        nargs=4,
-        default=[1, 3, 400, 600],
-        help='Minimum shape of model input.')
-    parser.add_argument(
-        '--workspace-size',
-        type=int,
-        default=1,
-        help='Max workspace size in GiB.')
-    parser.add_argument('--fp16', action='store_true', help='Enable fp16 mode')
-    parser.add_argument(
-        '--verify',
-        action='store_true',
-        help='Whether Verify the outputs of ONNXRuntime and TensorRT.',
-        default=True)
-    parser.add_argument(
-        '--show',
-        action='store_true',
-        help='Whether visiualize outputs of ONNXRuntime and TensorRT.',
-        default=True)
-    parser.add_argument(
-        '--verbose',
-        action='store_true',
-        help='Whether to verbose logging messages while creating \
-                TensorRT engine.')
-    args = parser.parse_args()
-    return args
-
-
-if __name__ == '__main__':
-
-    assert is_tensorrt_plugin_loaded(), 'TensorRT plugin should be compiled.'
-    args = parse_args()
-
-    # Following strings of text style are from colorama package
-    bright_style, reset_style = '\x1b[1m', '\x1b[0m'
-    red_text, blue_text = '\x1b[31m', '\x1b[34m'
-    white_background = '\x1b[107m'
-
-    msg = white_background + bright_style + red_text
-    msg += 'DeprecationWarning: This tool will be deprecated in future. '
-    msg += blue_text + 'Welcome to use the unified model deployment toolbox '
-    msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
-    msg += reset_style
-    warnings.warn(msg)
-
-    # check arguments
-    assert osp.exists(args.model_config), 'Config {} not found.'.format(
-        args.model_config)
-    assert osp.exists(args.onnx_file), \
-        'ONNX model {} not found.'.format(args.onnx_file)
-    assert args.workspace_size >= 0, 'Workspace size less than 0.'
-    for max_value, min_value in zip(args.max_shape, args.min_shape):
-        assert max_value >= min_value, \
-            'max_shape should be larger than min shape'
-
-    input_config = {
-        'min_shape': args.min_shape,
-        'max_shape': args.max_shape,
-        'input_path': args.image_path
-    }
-
-    cfg = mmcv.Config.fromfile(args.model_config)
-    if cfg.data.test.get('pipeline', None) is None:
-        if is_2dlist(cfg.data.test.datasets):
-            cfg.data.test.pipeline = \
-                cfg.data.test.datasets[0][0].pipeline
-        else:
-            cfg.data.test.pipeline = \
-                cfg.data.test['datasets'][0].pipeline
-    if is_2dlist(cfg.data.test.pipeline):
-        cfg.data.test.pipeline = cfg.data.test.pipeline[0]
-    onnx2tensorrt(
-        args.onnx_file,
-        args.model_type,
-        args.trt_file,
-        cfg,
-        input_config,
-        fp16=args.fp16,
-        verify=args.verify,
-        show=args.show,
-        workspace_size=args.workspace_size,
-        verbose=args.verbose)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/dataset_wrappers.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/dataset_wrappers.py
deleted file mode 100644
index 55ad5cb60e581a96bdbd1fbbeebc2f46f8c4e899..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/dataset_wrappers.py
+++ /dev/null
@@ -1,282 +0,0 @@
-import bisect
-import math
-from collections import defaultdict
-
-import numpy as np
-from mmcv.utils import print_log
-from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
-
-from .builder import DATASETS
-from .coco import CocoDataset
-
-
-@DATASETS.register_module()
-class ConcatDataset(_ConcatDataset):
-    """A wrapper of concatenated dataset.
-
-    Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
-    concat the group flag for image aspect ratio.
-
-    Args:
-        datasets (list[:obj:`Dataset`]): A list of datasets.
-        separate_eval (bool): Whether to evaluate the results
-            separately if it is used as validation dataset.
-            Defaults to True.
-    """
-
-    def __init__(self, datasets, separate_eval=True):
-        super(ConcatDataset, self).__init__(datasets)
-        self.CLASSES = datasets[0].CLASSES
-        self.separate_eval = separate_eval
-        if not separate_eval:
-            if any([isinstance(ds, CocoDataset) for ds in datasets]):
-                raise NotImplementedError(
-                    'Evaluating concatenated CocoDataset as a whole is not'
-                    ' supported! Please set "separate_eval=True"')
-            elif len(set([type(ds) for ds in datasets])) != 1:
-                raise NotImplementedError(
-                    'All the datasets should have same types')
-
-        if hasattr(datasets[0], 'flag'):
-            flags = []
-            for i in range(0, len(datasets)):
-                flags.append(datasets[i].flag)
-            self.flag = np.concatenate(flags)
-
-    def get_cat_ids(self, idx):
-        """Get category ids of concatenated dataset by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            list[int]: All categories in the image of specified index.
-        """
-
-        if idx < 0:
-            if -idx > len(self):
-                raise ValueError(
-                    'absolute value of index should not exceed dataset length')
-            idx = len(self) + idx
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
-        if dataset_idx == 0:
-            sample_idx = idx
-        else:
-            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
-        return self.datasets[dataset_idx].get_cat_ids(sample_idx)
-
-    def evaluate(self, results, logger=None, **kwargs):
-        """Evaluate the results.
-
-        Args:
-            results (list[list | tuple]): Testing results of the dataset.
-            logger (logging.Logger | str | None): Logger used for printing
-                related information during evaluation. Default: None.
-
-        Returns:
-            dict[str: float]: AP results of the total dataset or each separate
-            dataset if `self.separate_eval=True`.
-        """
-        assert len(results) == self.cumulative_sizes[-1], \
-            ('Dataset and results have different sizes: '
-             f'{self.cumulative_sizes[-1]} v.s. {len(results)}')
-
-        # Check whether all the datasets support evaluation
-        for dataset in self.datasets:
-            assert hasattr(dataset, 'evaluate'), \
-                    f'{type(dataset)} does not implement evaluate function'
-
-        if self.separate_eval:
-            dataset_idx = -1
-            total_eval_results = dict()
-            for size, dataset in zip(self.cumulative_sizes, self.datasets):
-                start_idx = 0 if dataset_idx == -1 else \
-                    self.cumulative_sizes[dataset_idx]
-                end_idx = self.cumulative_sizes[dataset_idx + 1]
-
-                results_per_dataset = results[start_idx:end_idx]
-                print_log(
-                    f'\nEvaluateing {dataset.ann_file} with '
-                    f'{len(results_per_dataset)} images now',
-                    logger=logger)
-
-                eval_results_per_dataset = dataset.evaluate(
-                    results_per_dataset, logger=logger, **kwargs)
-                dataset_idx += 1
-                for k, v in eval_results_per_dataset.items():
-                    total_eval_results.update({f'{dataset_idx}_{k}': v})
-
-            return total_eval_results
-        elif any([isinstance(ds, CocoDataset) for ds in self.datasets]):
-            raise NotImplementedError(
-                'Evaluating concatenated CocoDataset as a whole is not'
-                ' supported! Please set "separate_eval=True"')
-        elif len(set([type(ds) for ds in self.datasets])) != 1:
-            raise NotImplementedError(
-                'All the datasets should have same types')
-        else:
-            original_data_infos = self.datasets[0].data_infos
-            self.datasets[0].data_infos = sum(
-                [dataset.data_infos for dataset in self.datasets], [])
-            eval_results = self.datasets[0].evaluate(
-                results, logger=logger, **kwargs)
-            self.datasets[0].data_infos = original_data_infos
-            return eval_results
-
-
-@DATASETS.register_module()
-class RepeatDataset(object):
-    """A wrapper of repeated dataset.
-
-    The length of repeated dataset will be `times` larger than the original
-    dataset. This is useful when the data loading time is long but the dataset
-    is small. Using RepeatDataset can reduce the data loading time between
-    epochs.
-
-    Args:
-        dataset (:obj:`Dataset`): The dataset to be repeated.
-        times (int): Repeat times.
-    """
-
-    def __init__(self, dataset, times):
-        self.dataset = dataset
-        self.times = times
-        self.CLASSES = dataset.CLASSES
-        if hasattr(self.dataset, 'flag'):
-            self.flag = np.tile(self.dataset.flag, times)
-
-        self._ori_len = len(self.dataset)
-
-    def __getitem__(self, idx):
-        return self.dataset[idx % self._ori_len]
-
-    def get_cat_ids(self, idx):
-        """Get category ids of repeat dataset by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            list[int]: All categories in the image of specified index.
-        """
-
-        return self.dataset.get_cat_ids(idx % self._ori_len)
-
-    def __len__(self):
-        """Length after repetition."""
-        return self.times * self._ori_len
-
-
-# Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa
-@DATASETS.register_module()
-class ClassBalancedDataset(object):
-    """A wrapper of repeated dataset with repeat factor.
-
-    Suitable for training on class imbalanced datasets like LVIS. Following
-    the sampling strategy in the `paper <https://arxiv.org/abs/1908.03195>`_,
-    in each epoch, an image may appear multiple times based on its
-    "repeat factor".
-    The repeat factor for an image is a function of the frequency the rarest
-    category labeled in that image. The "frequency of category c" in [0, 1]
-    is defined by the fraction of images in the training set (without repeats)
-    in which category c appears.
-    The dataset needs to instantiate :func:`self.get_cat_ids` to support
-    ClassBalancedDataset.
-
-    The repeat factor is computed as followed.
-
-    1. For each category c, compute the fraction # of images
-       that contain it: :math:`f(c)`
-    2. For each category c, compute the category-level repeat factor:
-       :math:`r(c) = max(1, sqrt(t/f(c)))`
-    3. For each image I, compute the image-level repeat factor:
-       :math:`r(I) = max_{c in I} r(c)`
-
-    Args:
-        dataset (:obj:`CustomDataset`): The dataset to be repeated.
-        oversample_thr (float): frequency threshold below which data is
-            repeated. For categories with ``f_c >= oversample_thr``, there is
-            no oversampling. For categories with ``f_c < oversample_thr``, the
-            degree of oversampling following the square-root inverse frequency
-            heuristic above.
-        filter_empty_gt (bool, optional): If set true, images without bounding
-            boxes will not be oversampled. Otherwise, they will be categorized
-            as the pure background class and involved into the oversampling.
-            Default: True.
-    """
-
-    def __init__(self, dataset, oversample_thr, filter_empty_gt=True):
-        self.dataset = dataset
-        self.oversample_thr = oversample_thr
-        self.filter_empty_gt = filter_empty_gt
-        self.CLASSES = dataset.CLASSES
-
-        repeat_factors = self._get_repeat_factors(dataset, oversample_thr)
-        repeat_indices = []
-        for dataset_idx, repeat_factor in enumerate(repeat_factors):
-            repeat_indices.extend([dataset_idx] * math.ceil(repeat_factor))
-        self.repeat_indices = repeat_indices
-
-        flags = []
-        if hasattr(self.dataset, 'flag'):
-            for flag, repeat_factor in zip(self.dataset.flag, repeat_factors):
-                flags.extend([flag] * int(math.ceil(repeat_factor)))
-            assert len(flags) == len(repeat_indices)
-        self.flag = np.asarray(flags, dtype=np.uint8)
-
-    def _get_repeat_factors(self, dataset, repeat_thr):
-        """Get repeat factor for each images in the dataset.
-
-        Args:
-            dataset (:obj:`CustomDataset`): The dataset
-            repeat_thr (float): The threshold of frequency. If an image
-                contains the categories whose frequency below the threshold,
-                it would be repeated.
-
-        Returns:
-            list[float]: The repeat factors for each images in the dataset.
-        """
-
-        # 1. For each category c, compute the fraction # of images
-        #   that contain it: f(c)
-        category_freq = defaultdict(int)
-        num_images = len(dataset)
-        for idx in range(num_images):
-            cat_ids = set(self.dataset.get_cat_ids(idx))
-            if len(cat_ids) == 0 and not self.filter_empty_gt:
-                cat_ids = set([len(self.CLASSES)])
-            for cat_id in cat_ids:
-                category_freq[cat_id] += 1
-        for k, v in category_freq.items():
-            category_freq[k] = v / num_images
-
-        # 2. For each category c, compute the category-level repeat factor:
-        #    r(c) = max(1, sqrt(t/f(c)))
-        category_repeat = {
-            cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
-            for cat_id, cat_freq in category_freq.items()
-        }
-
-        # 3. For each image I, compute the image-level repeat factor:
-        #    r(I) = max_{c in I} r(c)
-        repeat_factors = []
-        for idx in range(num_images):
-            cat_ids = set(self.dataset.get_cat_ids(idx))
-            if len(cat_ids) == 0 and not self.filter_empty_gt:
-                cat_ids = set([len(self.CLASSES)])
-            repeat_factor = 1
-            if len(cat_ids) > 0:
-                repeat_factor = max(
-                    {category_repeat[cat_id]
-                     for cat_id in cat_ids})
-            repeat_factors.append(repeat_factor)
-
-        return repeat_factors
-
-    def __getitem__(self, idx):
-        ori_index = self.repeat_indices[idx]
-        return self.dataset[ori_index]
-
-    def __len__(self):
-        """Length after repetition."""
-        return len(self.repeat_indices)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/dense_heads/fsaf_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/dense_heads/fsaf_head.py
deleted file mode 100644
index 6aa442da4e113a53827ffb016134d576d6192f77..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/dense_heads/fsaf_head.py
+++ /dev/null
@@ -1,432 +0,0 @@
-import numpy as np
-import torch
-from mmcv.runner import force_fp32
-
-from mmdet.core import (anchor_inside_flags, images_to_levels, multi_apply,
-                        unmap)
-from ..builder import HEADS
-from ..losses.accuracy import accuracy
-from ..losses.utils import weight_reduce_loss
-from .retina_head import RetinaHead
-
-
-@HEADS.register_module()
-class FSAFHead(RetinaHead):
-    """Anchor-free head used in `FSAF <https://arxiv.org/abs/1903.00621>`_.
-
-    The head contains two subnetworks. The first classifies anchor boxes and
-    the second regresses deltas for the anchors (num_anchors is 1 for anchor-
-    free methods)
-
-    Args:
-        *args: Same as its base class in :class:`RetinaHead`
-        score_threshold (float, optional): The score_threshold to calculate
-            positive recall. If given, prediction scores lower than this value
-            is counted as incorrect prediction. Default to None.
-        init_cfg (dict or list[dict], optional): Initialization config dict.
-            Default: None
-        **kwargs: Same as its base class in :class:`RetinaHead`
-
-    Example:
-        >>> import torch
-        >>> self = FSAFHead(11, 7)
-        >>> x = torch.rand(1, 7, 32, 32)
-        >>> cls_score, bbox_pred = self.forward_single(x)
-        >>> # Each anchor predicts a score for each class except background
-        >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors
-        >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors
-        >>> assert cls_per_anchor == self.num_classes
-        >>> assert box_per_anchor == 4
-    """
-
-    def __init__(self, *args, score_threshold=None, init_cfg=None, **kwargs):
-        # The positive bias in self.retina_reg conv is to prevent predicted \
-        #  bbox with 0 area
-        if init_cfg is None:
-            init_cfg = dict(
-                type='Normal',
-                layer='Conv2d',
-                std=0.01,
-                override=[
-                    dict(
-                        type='Normal',
-                        name='retina_cls',
-                        std=0.01,
-                        bias_prob=0.01),
-                    dict(
-                        type='Normal', name='retina_reg', std=0.01, bias=0.25)
-                ])
-        super().__init__(*args, init_cfg=init_cfg, **kwargs)
-        self.score_threshold = score_threshold
-
-    def forward_single(self, x):
-        """Forward feature map of a single scale level.
-
-        Args:
-            x (Tensor): Feature map of a single scale level.
-
-        Returns:
-            tuple (Tensor):
-                cls_score (Tensor): Box scores for each scale level
-                    Has shape (N, num_points * num_classes, H, W).
-                bbox_pred (Tensor): Box energies / deltas for each scale
-                    level with shape (N, num_points * 4, H, W).
-        """
-        cls_score, bbox_pred = super().forward_single(x)
-        # relu: TBLR encoder only accepts positive bbox_pred
-        return cls_score, self.relu(bbox_pred)
-
-    def _get_targets_single(self,
-                            flat_anchors,
-                            valid_flags,
-                            gt_bboxes,
-                            gt_bboxes_ignore,
-                            gt_labels,
-                            img_meta,
-                            label_channels=1,
-                            unmap_outputs=True):
-        """Compute regression and classification targets for anchors in a
-        single image.
-
-        Most of the codes are the same with the base class
-          :obj: `AnchorHead`, except that it also collects and returns
-          the matched gt index in the image (from 0 to num_gt-1). If the
-          anchor bbox is not matched to any gt, the corresponding value in
-          pos_gt_inds is -1.
-        """
-        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
-                                           img_meta['img_shape'][:2],
-                                           self.train_cfg.allowed_border)
-        if not inside_flags.any():
-            return (None, ) * 7
-        # Assign gt and sample anchors
-        anchors = flat_anchors[inside_flags.type(torch.bool), :]
-        assign_result = self.assigner.assign(
-            anchors, gt_bboxes, gt_bboxes_ignore,
-            None if self.sampling else gt_labels)
-
-        sampling_result = self.sampler.sample(assign_result, anchors,
-                                              gt_bboxes)
-
-        num_valid_anchors = anchors.shape[0]
-        bbox_targets = torch.zeros_like(anchors)
-        bbox_weights = torch.zeros_like(anchors)
-        labels = anchors.new_full((num_valid_anchors, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = anchors.new_zeros((num_valid_anchors, label_channels),
-                                          dtype=torch.float)
-        pos_gt_inds = anchors.new_full((num_valid_anchors, ),
-                                       -1,
-                                       dtype=torch.long)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-
-        if len(pos_inds) > 0:
-            if not self.reg_decoded_bbox:
-                pos_bbox_targets = self.bbox_coder.encode(
-                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
-            else:
-                # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-                # is applied directly on the decoded bounding boxes, both
-                # the predicted boxes and regression targets should be with
-                # absolute coordinate format.
-                pos_bbox_targets = sampling_result.pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1.0
-            # The assigned gt_index for each anchor. (0-based)
-            pos_gt_inds[pos_inds] = sampling_result.pos_assigned_gt_inds
-            if gt_labels is None:
-                # Only rpn gives gt_labels as None
-                # Foreground is the first class
-                labels[pos_inds] = 0
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if self.train_cfg.pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg.pos_weight
-
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # shadowed_labels is a tensor composed of tuples
-        #  (anchor_inds, class_label) that indicate those anchors lying in the
-        #  outer region of a gt or overlapped by another gt with a smaller
-        #  area.
-        #
-        # Therefore, only the shadowed labels are ignored for loss calculation.
-        # the key `shadowed_labels` is defined in :obj:`CenterRegionAssigner`
-        shadowed_labels = assign_result.get_extra_property('shadowed_labels')
-        if shadowed_labels is not None and shadowed_labels.numel():
-            if len(shadowed_labels.shape) == 2:
-                idx_, label_ = shadowed_labels[:, 0], shadowed_labels[:, 1]
-                assert (labels[idx_] != label_).all(), \
-                    'One label cannot be both positive and ignored'
-                label_weights[idx_, label_] = 0
-            else:
-                label_weights[shadowed_labels] = 0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_anchors.size(0)
-            labels = unmap(labels, num_total_anchors, inside_flags)
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
-            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-            pos_gt_inds = unmap(
-                pos_gt_inds, num_total_anchors, inside_flags, fill=-1)
-
-        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
-                neg_inds, sampling_result, pos_gt_inds)
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute loss of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_points * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_points * 4, H, W).
-            gt_bboxes (list[Tensor]): each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        for i in range(len(bbox_preds)):  # loop over fpn level
-            # avoid 0 area of the predicted bbox
-            bbox_preds[i] = bbox_preds[i].clamp(min=1e-4)
-        # TODO: It may directly use the base-class loss function.
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-        batch_size = len(gt_bboxes)
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         num_total_pos, num_total_neg,
-         pos_assigned_gt_inds_list) = cls_reg_targets
-
-        num_gts = np.array(list(map(len, gt_labels)))
-        num_total_samples = (
-            num_total_pos + num_total_neg if self.sampling else num_total_pos)
-        # anchor number of multi levels
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-        # concat all level anchors and flags to a single tensor
-        concat_anchor_list = []
-        for i in range(len(anchor_list)):
-            concat_anchor_list.append(torch.cat(anchor_list[i]))
-        all_anchor_list = images_to_levels(concat_anchor_list,
-                                           num_level_anchors)
-        losses_cls, losses_bbox = multi_apply(
-            self.loss_single,
-            cls_scores,
-            bbox_preds,
-            all_anchor_list,
-            labels_list,
-            label_weights_list,
-            bbox_targets_list,
-            bbox_weights_list,
-            num_total_samples=num_total_samples)
-
-        # `pos_assigned_gt_inds_list` (length: fpn_levels) stores the assigned
-        # gt index of each anchor bbox in each fpn level.
-        cum_num_gts = list(np.cumsum(num_gts))  # length of batch_size
-        for i, assign in enumerate(pos_assigned_gt_inds_list):
-            # loop over fpn levels
-            for j in range(1, batch_size):
-                # loop over batch size
-                # Convert gt indices in each img to those in the batch
-                assign[j][assign[j] >= 0] += int(cum_num_gts[j - 1])
-            pos_assigned_gt_inds_list[i] = assign.flatten()
-            labels_list[i] = labels_list[i].flatten()
-        num_gts = sum(map(len, gt_labels))  # total number of gt in the batch
-        # The unique label index of each gt in the batch
-        label_sequence = torch.arange(num_gts, device=device)
-        # Collect the average loss of each gt in each level
-        with torch.no_grad():
-            loss_levels, = multi_apply(
-                self.collect_loss_level_single,
-                losses_cls,
-                losses_bbox,
-                pos_assigned_gt_inds_list,
-                labels_seq=label_sequence)
-            # Shape: (fpn_levels, num_gts). Loss of each gt at each fpn level
-            loss_levels = torch.stack(loss_levels, dim=0)
-            # Locate the best fpn level for loss back-propagation
-            if loss_levels.numel() == 0:  # zero gt
-                argmin = loss_levels.new_empty((num_gts, ), dtype=torch.long)
-            else:
-                _, argmin = loss_levels.min(dim=0)
-
-        # Reweight the loss of each (anchor, label) pair, so that only those
-        #  at the best gt level are back-propagated.
-        losses_cls, losses_bbox, pos_inds = multi_apply(
-            self.reweight_loss_single,
-            losses_cls,
-            losses_bbox,
-            pos_assigned_gt_inds_list,
-            labels_list,
-            list(range(len(losses_cls))),
-            min_levels=argmin)
-        num_pos = torch.cat(pos_inds, 0).sum().float()
-        pos_recall = self.calculate_pos_recall(cls_scores, labels_list,
-                                               pos_inds)
-
-        if num_pos == 0:  # No gt
-            avg_factor = num_pos + float(num_total_neg)
-        else:
-            avg_factor = num_pos
-        for i in range(len(losses_cls)):
-            losses_cls[i] /= avg_factor
-            losses_bbox[i] /= avg_factor
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox=losses_bbox,
-            num_pos=num_pos / batch_size,
-            pos_recall=pos_recall)
-
-    def calculate_pos_recall(self, cls_scores, labels_list, pos_inds):
-        """Calculate positive recall with score threshold.
-
-        Args:
-            cls_scores (list[Tensor]): Classification scores at all fpn levels.
-                Each tensor is in shape (N, num_classes * num_anchors, H, W)
-            labels_list (list[Tensor]): The label that each anchor is assigned
-                to. Shape (N * H * W * num_anchors, )
-            pos_inds (list[Tensor]): List of bool tensors indicating whether
-                the anchor is assigned to a positive label.
-                Shape (N * H * W * num_anchors, )
-
-        Returns:
-            Tensor: A single float number indicating the positive recall.
-        """
-        with torch.no_grad():
-            num_class = self.num_classes
-            scores = [
-                cls.permute(0, 2, 3, 1).reshape(-1, num_class)[pos]
-                for cls, pos in zip(cls_scores, pos_inds)
-            ]
-            labels = [
-                label.reshape(-1)[pos]
-                for label, pos in zip(labels_list, pos_inds)
-            ]
-            scores = torch.cat(scores, dim=0)
-            labels = torch.cat(labels, dim=0)
-            if self.use_sigmoid_cls:
-                scores = scores.sigmoid()
-            else:
-                scores = scores.softmax(dim=1)
-
-            return accuracy(scores, labels, thresh=self.score_threshold)
-
-    def collect_loss_level_single(self, cls_loss, reg_loss, assigned_gt_inds,
-                                  labels_seq):
-        """Get the average loss in each FPN level w.r.t. each gt label.
-
-        Args:
-            cls_loss (Tensor): Classification loss of each feature map pixel,
-              shape (num_anchor, num_class)
-            reg_loss (Tensor): Regression loss of each feature map pixel,
-              shape (num_anchor, 4)
-            assigned_gt_inds (Tensor): It indicates which gt the prior is
-              assigned to (0-based, -1: no assignment). shape (num_anchor),
-            labels_seq: The rank of labels. shape (num_gt)
-
-        Returns:
-            shape: (num_gt), average loss of each gt in this level
-        """
-        if len(reg_loss.shape) == 2:  # iou loss has shape (num_prior, 4)
-            reg_loss = reg_loss.sum(dim=-1)  # sum loss in tblr dims
-        if len(cls_loss.shape) == 2:
-            cls_loss = cls_loss.sum(dim=-1)  # sum loss in class dims
-        loss = cls_loss + reg_loss
-        assert loss.size(0) == assigned_gt_inds.size(0)
-        # Default loss value is 1e6 for a layer where no anchor is positive
-        #  to ensure it will not be chosen to back-propagate gradient
-        losses_ = loss.new_full(labels_seq.shape, 1e6)
-        for i, l in enumerate(labels_seq):
-            match = assigned_gt_inds == l
-            if match.any():
-                losses_[i] = loss[match].mean()
-        return losses_,
-
-    def reweight_loss_single(self, cls_loss, reg_loss, assigned_gt_inds,
-                             labels, level, min_levels):
-        """Reweight loss values at each level.
-
-        Reassign loss values at each level by masking those where the
-        pre-calculated loss is too large. Then return the reduced losses.
-
-        Args:
-            cls_loss (Tensor): Element-wise classification loss.
-              Shape: (num_anchors, num_classes)
-            reg_loss (Tensor): Element-wise regression loss.
-              Shape: (num_anchors, 4)
-            assigned_gt_inds (Tensor): The gt indices that each anchor bbox
-              is assigned to. -1 denotes a negative anchor, otherwise it is the
-              gt index (0-based). Shape: (num_anchors, ),
-            labels (Tensor): Label assigned to anchors. Shape: (num_anchors, ).
-            level (int): The current level index in the pyramid
-              (0-4 for RetinaNet)
-            min_levels (Tensor): The best-matching level for each gt.
-              Shape: (num_gts, ),
-
-        Returns:
-            tuple:
-                - cls_loss: Reduced corrected classification loss. Scalar.
-                - reg_loss: Reduced corrected regression loss. Scalar.
-                - pos_flags (Tensor): Corrected bool tensor indicating the
-                  final positive anchors. Shape: (num_anchors, ).
-        """
-        loc_weight = torch.ones_like(reg_loss)
-        cls_weight = torch.ones_like(cls_loss)
-        pos_flags = assigned_gt_inds >= 0  # positive pixel flag
-        pos_indices = torch.nonzero(pos_flags, as_tuple=False).flatten()
-
-        if pos_flags.any():  # pos pixels exist
-            pos_assigned_gt_inds = assigned_gt_inds[pos_flags]
-            zeroing_indices = (min_levels[pos_assigned_gt_inds] != level)
-            neg_indices = pos_indices[zeroing_indices]
-
-            if neg_indices.numel():
-                pos_flags[neg_indices] = 0
-                loc_weight[neg_indices] = 0
-                # Only the weight corresponding to the label is
-                #  zeroed out if not selected
-                zeroing_labels = labels[neg_indices]
-                assert (zeroing_labels >= 0).all()
-                cls_weight[neg_indices, zeroing_labels] = 0
-
-        # Weighted loss for both cls and reg loss
-        cls_loss = weight_reduce_loss(cls_loss, cls_weight, reduction='sum')
-        reg_loss = weight_reduce_loss(reg_loss, loc_weight, reduction='sum')
-
-        return cls_loss, reg_loss, pos_flags
diff --git a/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/face_selector.py b/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/face_selector.py
deleted file mode 100644
index b6f4c66e07c46ce0f961acbd99289e421cd4e619..0000000000000000000000000000000000000000
--- a/spaces/tonyassi/video-face-swap/DeepFakeAI/uis/components/face_selector.py
+++ /dev/null
@@ -1,133 +0,0 @@
-from typing import List, Optional, Tuple, Any, Dict
-from time import sleep
-
-import cv2
-import gradio
-
-import DeepFakeAI.choices
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.capturer import get_video_frame
-from DeepFakeAI.face_analyser import get_many_faces
-from DeepFakeAI.face_reference import clear_face_reference
-from DeepFakeAI.typing import Frame, FaceRecognition
-from DeepFakeAI.uis import core as ui
-from DeepFakeAI.uis.typing import ComponentName, Update
-from DeepFakeAI.utilities import is_image, is_video
-
-FACE_RECOGNITION_DROPDOWN : Optional[gradio.Dropdown] = None
-REFERENCE_FACE_POSITION_GALLERY : Optional[gradio.Gallery] = None
-REFERENCE_FACE_DISTANCE_SLIDER : Optional[gradio.Slider] = None
-
-
-def render() -> None:
-	global FACE_RECOGNITION_DROPDOWN
-	global REFERENCE_FACE_POSITION_GALLERY
-	global REFERENCE_FACE_DISTANCE_SLIDER
-
-	with gradio.Box():
-		reference_face_gallery_args: Dict[str, Any] = {
-			'label': wording.get('reference_face_gallery_label'),
-			'height': 120,
-			'object_fit': 'cover',
-			'columns': 10,
-			'allow_preview': False,
-			'visible': 'reference' in DeepFakeAI.globals.face_recognition
-		}
-		if is_image(DeepFakeAI.globals.target_path):
-			reference_frame = cv2.imread(DeepFakeAI.globals.target_path)
-			reference_face_gallery_args['value'] = extract_gallery_frames(reference_frame)
-		if is_video(DeepFakeAI.globals.target_path):
-			reference_frame = get_video_frame(DeepFakeAI.globals.target_path, DeepFakeAI.globals.reference_frame_number)
-			reference_face_gallery_args['value'] = extract_gallery_frames(reference_frame)
-		FACE_RECOGNITION_DROPDOWN = gradio.Dropdown(
-			label = wording.get('face_recognition_dropdown_label'),
-			choices = DeepFakeAI.choices.face_recognition,
-			value = DeepFakeAI.globals.face_recognition
-		)
-		REFERENCE_FACE_POSITION_GALLERY = gradio.Gallery(**reference_face_gallery_args)
-		REFERENCE_FACE_DISTANCE_SLIDER = gradio.Slider(
-			label = wording.get('reference_face_distance_slider_label'),
-			value = DeepFakeAI.globals.reference_face_distance,
-			maximum = 3,
-			step = 0.05,
-			visible = 'reference' in DeepFakeAI.globals.face_recognition
-		)
-		ui.register_component('face_recognition_dropdown', FACE_RECOGNITION_DROPDOWN)
-		ui.register_component('reference_face_position_gallery', REFERENCE_FACE_POSITION_GALLERY)
-		ui.register_component('reference_face_distance_slider', REFERENCE_FACE_DISTANCE_SLIDER)
-
-
-def listen() -> None:
-	FACE_RECOGNITION_DROPDOWN.select(update_face_recognition, inputs = FACE_RECOGNITION_DROPDOWN, outputs = [ REFERENCE_FACE_POSITION_GALLERY, REFERENCE_FACE_DISTANCE_SLIDER ])
-	REFERENCE_FACE_POSITION_GALLERY.select(clear_and_update_face_reference_position)
-	REFERENCE_FACE_DISTANCE_SLIDER.change(update_reference_face_distance, inputs = REFERENCE_FACE_DISTANCE_SLIDER)
-	update_component_names : List[ComponentName] =\
-	[
-		'target_file',
-		'preview_frame_slider'
-	]
-	for component_name in update_component_names:
-		component = ui.get_component(component_name)
-		if component:
-			component.change(update_face_reference_position, outputs = REFERENCE_FACE_POSITION_GALLERY)
-	select_component_names : List[ComponentName] =\
-	[
-		'face_analyser_direction_dropdown',
-		'face_analyser_age_dropdown',
-		'face_analyser_gender_dropdown'
-	]
-	for component_name in select_component_names:
-		component = ui.get_component(component_name)
-		if component:
-			component.select(update_face_reference_position, outputs = REFERENCE_FACE_POSITION_GALLERY)
-
-
-def update_face_recognition(face_recognition : FaceRecognition) -> Tuple[Update, Update]:
-	if face_recognition == 'reference':
-		DeepFakeAI.globals.face_recognition = face_recognition
-		return gradio.update(visible = True), gradio.update(visible = True)
-	if face_recognition == 'many':
-		DeepFakeAI.globals.face_recognition = face_recognition
-		return gradio.update(visible = False), gradio.update(visible = False)
-
-
-def clear_and_update_face_reference_position(event: gradio.SelectData) -> Update:
-	clear_face_reference()
-	return update_face_reference_position(event.index)
-
-
-def update_face_reference_position(reference_face_position : int = 0) -> Update:
-	sleep(0.2)
-	gallery_frames = []
-	DeepFakeAI.globals.reference_face_position = reference_face_position
-	if is_image(DeepFakeAI.globals.target_path):
-		reference_frame = cv2.imread(DeepFakeAI.globals.target_path)
-		gallery_frames = extract_gallery_frames(reference_frame)
-	if is_video(DeepFakeAI.globals.target_path):
-		reference_frame = get_video_frame(DeepFakeAI.globals.target_path, DeepFakeAI.globals.reference_frame_number)
-		gallery_frames = extract_gallery_frames(reference_frame)
-	if gallery_frames:
-		return gradio.update(value = gallery_frames)
-	return gradio.update(value = None)
-
-
-def update_reference_face_distance(reference_face_distance : float) -> Update:
-	DeepFakeAI.globals.reference_face_distance = reference_face_distance
-	return gradio.update(value = reference_face_distance)
-
-
-def extract_gallery_frames(reference_frame : Frame) -> List[Frame]:
-	crop_frames = []
-	faces = get_many_faces(reference_frame)
-	for face in faces:
-		start_x, start_y, end_x, end_y = map(int, face['bbox'])
-		padding_x = int((end_x - start_x) * 0.25)
-		padding_y = int((end_y - start_y) * 0.25)
-		start_x = max(0, start_x - padding_x)
-		start_y = max(0, start_y - padding_y)
-		end_x = max(0, end_x + padding_x)
-		end_y = max(0, end_y + padding_y)
-		crop_frame = reference_frame[start_y:end_y, start_x:end_x]
-		crop_frames.append(ui.normalize_frame(crop_frame))
-	return crop_frames
diff --git a/spaces/triggah61/chingu-music/tests/common_utils/__init__.py b/spaces/triggah61/chingu-music/tests/common_utils/__init__.py
deleted file mode 100644
index 74ffcfef96fec35c99b2a1a053a61f44f7a8bbe9..0000000000000000000000000000000000000000
--- a/spaces/triggah61/chingu-music/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/trysem/image-matting-app/ppmatting/models/__init__.py b/spaces/trysem/image-matting-app/ppmatting/models/__init__.py
deleted file mode 100644
index d446649bc75b44f5ff3f9183e22f057f128b5fa2..0000000000000000000000000000000000000000
--- a/spaces/trysem/image-matting-app/ppmatting/models/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .backbone import *
-from .losses import *
-from .modnet import MODNet
-from .human_matting import HumanMatting
-from .dim import DIM
-from .ppmatting import PPMatting
-from .gca import GCABaseline, GCA
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Cambia el imei de tu blackberry con el radio lab tool 2.13.222.0 sin pagar nada.md b/spaces/usbethFlerru/sovits-modelsV2/example/Cambia el imei de tu blackberry con el radio lab tool 2.13.222.0 sin pagar nada.md
deleted file mode 100644
index bcb3053255d52eed8640a3d37b7be5c95f948930..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Cambia el imei de tu blackberry con el radio lab tool 2.13.222.0 sin pagar nada.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>descargar gratis blackberry radio lab tool 2.13.222.0 para cambiar imei</h2><br /><p><b><b>Download Zip</b> >>>>> <a href="https://urlcod.com/2uyUUt">https://urlcod.com/2uyUUt</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Crime Affairs Aur Badla The Story of a Woman Who Fought for Justice (PDF).md b/spaces/usbethFlerru/sovits-modelsV2/example/Crime Affairs Aur Badla The Story of a Woman Who Fought for Justice (PDF).md
deleted file mode 100644
index fd87bbf596029c3ab382ff302ef55a7ad760a139..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Crime Affairs Aur Badla The Story of a Woman Who Fought for Justice (PDF).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Crime Affairs Aur Badla book pdf free download</h2><br /><p><b><b>Download File</b> &hArr; <a href="https://urlcod.com/2uyVe1">https://urlcod.com/2uyVe1</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/uwnlp/guanaco-playground-tgi/README.md b/spaces/uwnlp/guanaco-playground-tgi/README.md
deleted file mode 100644
index 1db3aeed3577985afa02231943b02789f55b1721..0000000000000000000000000000000000000000
--- a/spaces/uwnlp/guanaco-playground-tgi/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Guanaco Playground Tgi
-emoji: 📊
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vaibhavarduino/anime-plus/e4e/editings/ganspace.py b/spaces/vaibhavarduino/anime-plus/e4e/editings/ganspace.py
deleted file mode 100644
index 0c286a421280c542e9776a75e64bb65409da8fc7..0000000000000000000000000000000000000000
--- a/spaces/vaibhavarduino/anime-plus/e4e/editings/ganspace.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch
-
-
-def edit(latents, pca, edit_directions):
-    edit_latents = []
-    for latent in latents:
-        for pca_idx, start, end, strength in edit_directions:
-            delta = get_delta(pca, latent, pca_idx, strength)
-            delta_padded = torch.zeros(latent.shape).to('cuda')
-            delta_padded[start:end] += delta.repeat(end - start, 1)
-            edit_latents.append(latent + delta_padded)
-    return torch.stack(edit_latents)
-
-
-def get_delta(pca, latent, idx, strength):
-    # pca: ganspace checkpoint. latent: (16, 512) w+
-    w_centered = latent - pca['mean'].to('cuda')
-    lat_comp = pca['comp'].to('cuda')
-    lat_std = pca['std'].to('cuda')
-    w_coord = torch.sum(w_centered[0].reshape(-1)*lat_comp[idx].reshape(-1)) / lat_std[idx]
-    delta = (strength - w_coord)*lat_comp[idx]*lat_std[idx]
-    return delta
diff --git a/spaces/victor/tesTETZTRZE/README.md b/spaces/victor/tesTETZTRZE/README.md
deleted file mode 100644
index 776091592f04d794513d684db8e352f2b688e513..0000000000000000000000000000000000000000
--- a/spaces/victor/tesTETZTRZE/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: TesTETZTRZE
-emoji: 🚀
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vinthony/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py b/spaces/vinthony/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py
deleted file mode 100644
index eb4e0d31f1aedf4590628d394e1606920fefb5c9..0000000000000000000000000000000000000000
--- a/spaces/vinthony/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "r18"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 25
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/vivlavida/generative-disco/Dockerfile b/spaces/vivlavida/generative-disco/Dockerfile
deleted file mode 100644
index 02e917e3adceea3a64657bd0cf7382b94723346a..0000000000000000000000000000000000000000
--- a/spaces/vivlavida/generative-disco/Dockerfile
+++ /dev/null
@@ -1,43 +0,0 @@
-
-FROM nvidia/cuda:11.7.1-base-ubuntu22.04
-ENV DEBIAN_FRONTEND noninteractive
-
-RUN apt-get update -y && apt-get upgrade -y \ 
- && apt-get install -y libgl1 libgl1-mesa-glx libglib2.0-0 wget git git-lfs python3-pip python-is-python3  snapd curl ca-certificates sudo zip unzip htop bzip2 libx11-6 build-essential libsndfile-dev software-properties-common \
- && rm -rf /var/lib/apt/lists/*
-
-RUN sudo add-apt-repository ppa:savoury1/ffmpeg4 && apt-get update -y && apt-get install -y ffmpeg
-
-RUN pip3 install --upgrade pip
-
-RUN git clone https://github.com/hellovivian/generative-disco.git && cd generative-disco && pip  install --ignore-installed stable_diffusion_videos numba youtube-dl librosa flask openai moviepy Pillow accelerate
-
-RUN adduser --disabled-password --gecos '' user && adduser user sudo
-#RUN mkdir /content && chown -R user:user /content
-#WORKDIR /content
-RUN chown -R user:user /generative-disco/static/
-
-USER user
-CMD /bin/bash
-
-RUN echo ffmpeg -version
-
-#RUN echo $PATH
-#RUN export PATH="$HOME/user/.local/bin:$PATH"; echo $PATH
-#RUN echo $PATH
-
-#RUN  pip install numba youtube-dl librosa flask openai moviepy Pillow accelerate \
-# && mkdir ./.cache 
-
-
-EXPOSE 7860
-WORKDIR generative-disco
-COPY . ./generative-disco
-
-
-CMD  [ "python",  "../generative-disco/flask_app.py"]
-
-
-#CMD ["python", "../generative-disco/flask_app.py", "--port", "7860", "--host", "0.0.0.0"]
-
-
diff --git "a/spaces/wangrongsheng/ChatImprovement/crazy_functions/\347\224\237\346\210\220\345\207\275\346\225\260\346\263\250\351\207\212.py" "b/spaces/wangrongsheng/ChatImprovement/crazy_functions/\347\224\237\346\210\220\345\207\275\346\225\260\346\263\250\351\207\212.py"
deleted file mode 100644
index 010600cb3b43044b927588cfe484cb41363235e3..0000000000000000000000000000000000000000
--- "a/spaces/wangrongsheng/ChatImprovement/crazy_functions/\347\224\237\346\210\220\345\207\275\346\225\260\346\263\250\351\207\212.py"
+++ /dev/null
@@ -1,57 +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 生成函数注释(file_manifest, project_folder, top_p, 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()
-
-        i_say = f'请对下面的程序文件做一个概述，并对文件中的所有函数生成注释，使用markdown表格输出结果，文件名是{os.path.relpath(fp, project_folder)}，文件内容是 ```{file_content}```'
-        i_say_show_user = f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        print('[1] yield chatbot, history')
-        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, temperature, history=[])   # 带超时倒计时
-
-            print('[2] end gpt req')
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            print('[3] yield chatbot, history')
-            yield chatbot, history, msg
-            print('[4] next')
-            if not fast_debug: time.sleep(2)
-
-    if not fast_debug: 
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield chatbot, history, msg
-
-
-
-@CatchException
-def 批量生成函数注释(txt, top_p, 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)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', 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.tex文件: {txt}")
-        yield chatbot, history, '正常'
-        return
-    yield from 生成函数注释(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
diff --git a/spaces/weiwandaixu/ChatGPT3.5/custom.css b/spaces/weiwandaixu/ChatGPT3.5/custom.css
deleted file mode 100644
index 5143eb138ea2469d8c457c71cb210fd3fb7cbe15..0000000000000000000000000000000000000000
--- a/spaces/weiwandaixu/ChatGPT3.5/custom.css
+++ /dev/null
@@ -1,162 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-color-light) !important;
-}
-[data-testid = "bot"] {
-    background-color: #FFFFFF !important;
-}
-[data-testid = "user"] {
-    background-color: #95EC69 !important;
-}
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    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: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-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);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/weizmannscience/MultiDiffusion/README.md b/spaces/weizmannscience/MultiDiffusion/README.md
deleted file mode 100644
index 1e10679805c2f293bf7c1f60e7de716749fe1b5a..0000000000000000000000000000000000000000
--- a/spaces/weizmannscience/MultiDiffusion/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: MultiDiffusion
-emoji: 🐢
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-From: https://arxiv.org/abs/2302.08113
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/wendys-llc/panoptic-segment-anything/GroundingDINO/groundingdino/util/get_tokenlizer.py b/spaces/wendys-llc/panoptic-segment-anything/GroundingDINO/groundingdino/util/get_tokenlizer.py
deleted file mode 100644
index f7dcf7e95f03f95b20546b26442a94225924618b..0000000000000000000000000000000000000000
--- a/spaces/wendys-llc/panoptic-segment-anything/GroundingDINO/groundingdino/util/get_tokenlizer.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-
-
-def get_tokenlizer(text_encoder_type):
-    if not isinstance(text_encoder_type, str):
-        # print("text_encoder_type is not a str")
-        if hasattr(text_encoder_type, "text_encoder_type"):
-            text_encoder_type = text_encoder_type.text_encoder_type
-        elif text_encoder_type.get("text_encoder_type", False):
-            text_encoder_type = text_encoder_type.get("text_encoder_type")
-        else:
-            raise ValueError(
-                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
-            )
-    print("final text_encoder_type: {}".format(text_encoder_type))
-
-    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
-    return tokenizer
-
-
-def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased":
-        return BertModel.from_pretrained(text_encoder_type)
-    if text_encoder_type == "roberta-base":
-        return RobertaModel.from_pretrained(text_encoder_type)
-    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))
diff --git a/spaces/xdecoder/Instruct-X-Decoder/tasks/open_pano.py b/spaces/xdecoder/Instruct-X-Decoder/tasks/open_pano.py
deleted file mode 100644
index 48a05f3ec5a0e78568cc4a47c6433b52a4330e8b..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Instruct-X-Decoder/tasks/open_pano.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# --------------------------------------------------------
-# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
-# Copyright (c) 2022 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Xueyan Zou (xueyan@cs.wisc.edu)
-# --------------------------------------------------------
-
-import torch
-import numpy as np
-from PIL import Image
-from torchvision import transforms
-from utils.visualizer import Visualizer
-from detectron2.utils.colormap import random_color
-from detectron2.data import MetadataCatalog
-
-
-t = []
-t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
-transform = transforms.Compose(t)
-metadata = MetadataCatalog.get('ade20k_panoptic_train')
-
-def open_panoseg(model, image, texts, inpainting_text, *args, **kwargs):
-    stuff_classes = [x.strip() for x in texts.split(';')[0].replace('stuff:','').split(',')]
-    thing_classes = [x.strip() for x in texts.split(';')[1].replace('thing:','').split(',')]
-    thing_colors = [random_color(rgb=True, maximum=255).astype(np.int32).tolist() for _ in range(len(thing_classes))]
-    stuff_colors = [random_color(rgb=True, maximum=255).astype(np.int32).tolist() for _ in range(len(stuff_classes))]
-    thing_dataset_id_to_contiguous_id = {x:x for x in range(len(thing_classes))}
-    stuff_dataset_id_to_contiguous_id = {x+len(thing_classes):x for x in range(len(stuff_classes))}
-
-    MetadataCatalog.get("demo").set(
-        thing_colors=thing_colors,
-        thing_classes=thing_classes,
-        thing_dataset_id_to_contiguous_id=thing_dataset_id_to_contiguous_id,
-        stuff_colors=stuff_colors,
-        stuff_classes=stuff_classes,
-        stuff_dataset_id_to_contiguous_id=stuff_dataset_id_to_contiguous_id,
-    )
-    model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(thing_classes + stuff_classes + ["background"], is_eval=True)
-    metadata = MetadataCatalog.get('demo')
-    model.model.metadata = metadata
-    model.model.sem_seg_head.num_classes = len(thing_classes + stuff_classes)
-
-    with torch.no_grad():
-        image_ori = transform(image)
-        width = image_ori.size[0]
-        height = image_ori.size[1]
-        image = transform(image_ori)
-        image = np.asarray(image)
-        images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
-
-        batch_inputs = [{'image': images, 'height': height, 'width': width}]
-        outputs = model.forward(batch_inputs)
-        visual = Visualizer(image_ori, metadata=metadata)
-
-        pano_seg = outputs[-1]['panoptic_seg'][0]
-        pano_seg_info = outputs[-1]['panoptic_seg'][1]
-
-        for i in range(len(pano_seg_info)):
-            if pano_seg_info[i]['category_id'] in metadata.thing_dataset_id_to_contiguous_id.keys():
-                pano_seg_info[i]['category_id'] = metadata.thing_dataset_id_to_contiguous_id[pano_seg_info[i]['category_id']]
-            else:
-                pano_seg_info[i]['isthing'] = False
-                pano_seg_info[i]['category_id'] = metadata.stuff_dataset_id_to_contiguous_id[pano_seg_info[i]['category_id']]
-
-        demo = visual.draw_panoptic_seg(pano_seg.cpu(), pano_seg_info) # rgb Image
-        res = demo.get_image()
-
-    MetadataCatalog.remove('demo')
-    torch.cuda.empty_cache()
-    return Image.fromarray(res), '', None
\ No newline at end of file
diff --git a/spaces/xercon/chat-with-docs/README.md b/spaces/xercon/chat-with-docs/README.md
deleted file mode 100644
index b8f7d52c9e044b39f3039daeb1be4c52400aaf64..0000000000000000000000000000000000000000
--- a/spaces/xercon/chat-with-docs/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-license: apache-2.0
-title: Chat with Document
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-emoji: 🚀
-colorFrom: green
-colorTo: yellow
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/xfys/yolov5_tracking/val_utils/tests/test_all_quick.py b/spaces/xfys/yolov5_tracking/val_utils/tests/test_all_quick.py
deleted file mode 100644
index 89cd3e8c1b2061473ef148f10e60ffd07074d3f9..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/tests/test_all_quick.py
+++ /dev/null
@@ -1,75 +0,0 @@
-""" Test to ensure that the code is working correctly.
-Should test ALL metrics across all datasets and splits currently supported.
-Only tests one tracker per dataset/split to give a quick test result.
-"""
-
-import sys
-import os
-import numpy as np
-from multiprocessing import freeze_support
-
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import trackeval  # noqa: E402
-
-# Fixes multiprocessing on windows, does nothing otherwise
-if __name__ == '__main__':
-    freeze_support()
-
-eval_config = {'USE_PARALLEL': False,
-               'NUM_PARALLEL_CORES': 8,
-               }
-evaluator = trackeval.Evaluator(eval_config)
-metrics_list = [trackeval.metrics.HOTA(), trackeval.metrics.CLEAR(), trackeval.metrics.Identity()]
-
-tests = [
-    {'DATASET': 'Kitti2DBox', 'SPLIT_TO_EVAL': 'training', 'TRACKERS_TO_EVAL': ['CIWT']},
-    {'DATASET': 'MotChallenge2DBox', 'BENCHMARK': 'MOT15', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['MPNTrack']},
-    {'DATASET': 'MotChallenge2DBox', 'BENCHMARK': 'MOT16', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['MPNTrack']},
-    {'DATASET': 'MotChallenge2DBox', 'BENCHMARK': 'MOT17', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['MPNTrack']},
-    {'DATASET': 'MotChallenge2DBox', 'BENCHMARK': 'MOT20', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['MPNTrack']},
-]
-
-for dataset_config in tests:
-
-    dataset_name = dataset_config.pop('DATASET')
-    if dataset_name == 'MotChallenge2DBox':
-        dataset_list = [trackeval.datasets.MotChallenge2DBox(dataset_config)]
-        file_loc = os.path.join('mot_challenge', dataset_config['BENCHMARK'] + '-' + dataset_config['SPLIT_TO_EVAL'])
-    elif dataset_name == 'Kitti2DBox':
-        dataset_list = [trackeval.datasets.Kitti2DBox(dataset_config)]
-        file_loc = os.path.join('kitti', 'kitti_2d_box_train')
-    else:
-        raise Exception('Dataset %s does not exist.' % dataset_name)
-
-    raw_results, messages = evaluator.evaluate(dataset_list, metrics_list)
-
-    classes = dataset_list[0].config['CLASSES_TO_EVAL']
-    tracker = dataset_config['TRACKERS_TO_EVAL'][0]
-    test_data_loc = os.path.join(os.path.dirname(__file__), '..', 'data', 'tests', file_loc)
-
-    for cls in classes:
-        results = {seq: raw_results[dataset_name][tracker][seq][cls] for seq in raw_results[dataset_name][tracker].keys()}
-        current_metrics_list = metrics_list + [trackeval.metrics.Count()]
-        metric_names = trackeval.utils.validate_metrics_list(current_metrics_list)
-
-        # Load expected results:
-        test_data = trackeval.utils.load_detail(os.path.join(test_data_loc, tracker, cls + '_detailed.csv'))
-
-        # Do checks
-        for seq in test_data.keys():
-            assert len(test_data[seq].keys()) > 250, len(test_data[seq].keys())
-
-            details = []
-            for metric, metric_name in zip(current_metrics_list, metric_names):
-                table_res = {seq_key: seq_value[metric_name] for seq_key, seq_value in results.items()}
-                details.append(metric.detailed_results(table_res))
-            res_fields = sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])
-            res_values = sum([list(s[seq].values()) for s in details], [])
-            res_dict = dict(zip(res_fields, res_values))
-
-            for field in test_data[seq].keys():
-                assert np.isclose(res_dict[field], test_data[seq][field]), seq + ': ' + cls + ': ' + field
-
-    print('Tracker %s tests passed' % tracker)
-print('All tests passed')
-
diff --git a/spaces/xp3857/Image_Restoration_Colorization/Global/detection.py b/spaces/xp3857/Image_Restoration_Colorization/Global/detection.py
deleted file mode 100644
index f23494a3855ace6255261a56237f67f3c8dc7294..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/Global/detection.py
+++ /dev/null
@@ -1,178 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import argparse
-import gc
-import json
-import os
-import time
-import warnings
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torchvision as tv
-from PIL import Image, ImageFile
-
-from detection_models import networks
-from detection_util.util import *
-
-warnings.filterwarnings("ignore", category=UserWarning)
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-
-def data_transforms(img, full_size, method=Image.BICUBIC):
-    if full_size == "full_size":
-        ow, oh = img.size
-        h = int(round(oh / 16) * 16)
-        w = int(round(ow / 16) * 16)
-        if (h == oh) and (w == ow):
-            return img
-        return img.resize((w, h), method)
-
-    elif full_size == "scale_256":
-        ow, oh = img.size
-        pw, ph = ow, oh
-        if ow < oh:
-            ow = 256
-            oh = ph / pw * 256
-        else:
-            oh = 256
-            ow = pw / ph * 256
-
-        h = int(round(oh / 16) * 16)
-        w = int(round(ow / 16) * 16)
-        if (h == ph) and (w == pw):
-            return img
-        return img.resize((w, h), method)
-
-
-def scale_tensor(img_tensor, default_scale=256):
-    _, _, w, h = img_tensor.shape
-    if w < h:
-        ow = default_scale
-        oh = h / w * default_scale
-    else:
-        oh = default_scale
-        ow = w / h * default_scale
-
-    oh = int(round(oh / 16) * 16)
-    ow = int(round(ow / 16) * 16)
-
-    return F.interpolate(img_tensor, [ow, oh], mode="bilinear")
-
-
-def blend_mask(img, mask):
-
-    np_img = np.array(img).astype("float")
-
-    return Image.fromarray((np_img * (1 - mask) + mask * 255.0).astype("uint8")).convert("RGB")
-
-
-def main(config):
-    print("initializing the dataloader")
-
-    model = networks.UNet(
-        in_channels=1,
-        out_channels=1,
-        depth=4,
-        conv_num=2,
-        wf=6,
-        padding=True,
-        batch_norm=True,
-        up_mode="upsample",
-        with_tanh=False,
-        sync_bn=True,
-        antialiasing=True,
-    )
-
-    ## load model
-    checkpoint_path = os.path.join(os.path.dirname(__file__), "checkpoints/detection/FT_Epoch_latest.pt")
-    checkpoint = torch.load(checkpoint_path, map_location="cpu")
-    model.load_state_dict(checkpoint["model_state"])
-    print("model weights loaded")
-
-    if config.GPU >= 0:
-        model.to(config.GPU)
-    else: 
-        model.cpu()
-    model.eval()
-
-    ## dataloader and transformation
-    print("directory of testing image: " + config.test_path)
-    imagelist = os.listdir(config.test_path)
-    imagelist.sort()
-    total_iter = 0
-
-    P_matrix = {}
-    save_url = os.path.join(config.output_dir)
-    mkdir_if_not(save_url)
-
-    input_dir = os.path.join(save_url, "input")
-    output_dir = os.path.join(save_url, "mask")
-    # blend_output_dir=os.path.join(save_url, 'blend_output')
-    mkdir_if_not(input_dir)
-    mkdir_if_not(output_dir)
-    # mkdir_if_not(blend_output_dir)
-
-    idx = 0
-
-    results = []
-    for image_name in imagelist:
-
-        idx += 1
-
-        print("processing", image_name)
-
-        scratch_file = os.path.join(config.test_path, image_name)
-        if not os.path.isfile(scratch_file):
-            print("Skipping non-file %s" % image_name)
-            continue
-        scratch_image = Image.open(scratch_file).convert("RGB")
-        w, h = scratch_image.size
-
-        transformed_image_PIL = data_transforms(scratch_image, config.input_size)
-        scratch_image = transformed_image_PIL.convert("L")
-        scratch_image = tv.transforms.ToTensor()(scratch_image)
-        scratch_image = tv.transforms.Normalize([0.5], [0.5])(scratch_image)
-        scratch_image = torch.unsqueeze(scratch_image, 0)
-        _, _, ow, oh = scratch_image.shape
-        scratch_image_scale = scale_tensor(scratch_image)
-
-        if config.GPU >= 0:
-            scratch_image_scale = scratch_image_scale.to(config.GPU)
-        else:
-            scratch_image_scale = scratch_image_scale.cpu()
-        with torch.no_grad():
-            P = torch.sigmoid(model(scratch_image_scale))
-
-        P = P.data.cpu()
-        P = F.interpolate(P, [ow, oh], mode="nearest")
-
-        tv.utils.save_image(
-            (P >= 0.4).float(),
-            os.path.join(
-                output_dir,
-                image_name[:-4] + ".png",
-            ),
-            nrow=1,
-            padding=0,
-            normalize=True,
-        )
-        transformed_image_PIL.save(os.path.join(input_dir, image_name[:-4] + ".png"))
-        gc.collect()
-        torch.cuda.empty_cache()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # parser.add_argument('--checkpoint_name', type=str, default="FT_Epoch_latest.pt", help='Checkpoint Name')
-
-    parser.add_argument("--GPU", type=int, default=0)
-    parser.add_argument("--test_path", type=str, default=".")
-    parser.add_argument("--output_dir", type=str, default=".")
-    parser.add_argument("--input_size", type=str, default="scale_256", help="resize_256|full_size|scale_256")
-    config = parser.parse_args()
-
-    main(config)
diff --git a/spaces/xwsm/gpt/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp b/spaces/xwsm/gpt/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
deleted file mode 100644
index c94575903bdf2eef71ecbe66382375552446e510..0000000000000000000000000000000000000000
--- a/spaces/xwsm/gpt/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
+++ /dev/null
@@ -1,17 +0,0 @@
-#include "libipc/pool_alloc.h"
-
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace mem {
-
-void* pool_alloc::alloc(std::size_t size) {
-    return async_pool_alloc::alloc(size);
-}
-
-void pool_alloc::free(void* p, std::size_t size) {
-    async_pool_alloc::free(p, size);
-}
-
-} // namespace mem
-} // namespace ipc
diff --git a/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/inference_realesrgan.py b/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/inference_realesrgan.py
deleted file mode 100644
index 057eda86bababbd4e2828c0943695e40a99737f4..0000000000000000000000000000000000000000
--- a/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/inference_realesrgan.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import argparse
-import cv2
-import glob
-import os
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.download_util import load_file_from_url
-
-from realesrgan import RealESRGANer
-from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-
-
-def main():
-    """Inference demo for Real-ESRGAN."""
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-i", "--input", type=str, default="inputs", help="Input image or folder"
-    )
-    parser.add_argument(
-        "-n",
-        "--model_name",
-        type=str,
-        default="RealESRGAN_x4plus",
-        help=(
-            "Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | "
-            "realesr-animevideov3 | realesr-general-x4v3"
-        ),
-    )
-    parser.add_argument(
-        "-o", "--output", type=str, default="results", help="Output folder"
-    )
-    parser.add_argument(
-        "-dn",
-        "--denoise_strength",
-        type=float,
-        default=0.5,
-        help=(
-            "Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. "
-            "Only used for the realesr-general-x4v3 model"
-        ),
-    )
-    parser.add_argument(
-        "-s",
-        "--outscale",
-        type=float,
-        default=4,
-        help="The final upsampling scale of the image",
-    )
-    parser.add_argument(
-        "--model_path",
-        type=str,
-        default=None,
-        help="[Option] Model path. Usually, you do not need to specify it",
-    )
-    parser.add_argument(
-        "--suffix", type=str, default="out", help="Suffix of the restored image"
-    )
-    parser.add_argument(
-        "-t",
-        "--tile",
-        type=int,
-        default=0,
-        help="Tile size, 0 for no tile during testing",
-    )
-    parser.add_argument("--tile_pad", type=int, default=10, help="Tile padding")
-    parser.add_argument(
-        "--pre_pad", type=int, default=0, help="Pre padding size at each border"
-    )
-    parser.add_argument(
-        "--face_enhance", action="store_true", help="Use GFPGAN to enhance face"
-    )
-    parser.add_argument(
-        "--fp32",
-        action="store_true",
-        help="Use fp32 precision during inference. Default: fp16 (half precision).",
-    )
-    parser.add_argument(
-        "--alpha_upsampler",
-        type=str,
-        default="realesrgan",
-        help="The upsampler for the alpha channels. Options: realesrgan | bicubic",
-    )
-    parser.add_argument(
-        "--ext",
-        type=str,
-        default="auto",
-        help="Image extension. Options: auto | jpg | png, auto means using the same extension as inputs",
-    )
-    parser.add_argument(
-        "-g",
-        "--gpu-id",
-        type=int,
-        default=None,
-        help="gpu device to use (default=None) can be 0,1,2 for multi-gpu",
-    )
-
-    args = parser.parse_args()
-
-    # determine models according to model names
-    args.model_name = args.model_name.split(".")[0]
-    if args.model_name == "RealESRGAN_x4plus":  # x4 RRDBNet model
-        model = RRDBNet(
-            num_in_ch=3,
-            num_out_ch=3,
-            num_feat=64,
-            num_block=23,
-            num_grow_ch=32,
-            scale=4,
-        )
-        netscale = 4
-        file_url = [
-            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
-        ]
-    elif args.model_name == "RealESRNet_x4plus":  # x4 RRDBNet model
-        model = RRDBNet(
-            num_in_ch=3,
-            num_out_ch=3,
-            num_feat=64,
-            num_block=23,
-            num_grow_ch=32,
-            scale=4,
-        )
-        netscale = 4
-        file_url = [
-            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth"
-        ]
-    elif (
-        args.model_name == "RealESRGAN_x4plus_anime_6B"
-    ):  # x4 RRDBNet model with 6 blocks
-        model = RRDBNet(
-            num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4
-        )
-        netscale = 4
-        file_url = [
-            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
-        ]
-    elif args.model_name == "RealESRGAN_x2plus":  # x2 RRDBNet model
-        model = RRDBNet(
-            num_in_ch=3,
-            num_out_ch=3,
-            num_feat=64,
-            num_block=23,
-            num_grow_ch=32,
-            scale=2,
-        )
-        netscale = 2
-        file_url = [
-            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth"
-        ]
-    elif args.model_name == "realesr-animevideov3":  # x4 VGG-style model (XS size)
-        model = SRVGGNetCompact(
-            num_in_ch=3,
-            num_out_ch=3,
-            num_feat=64,
-            num_conv=16,
-            upscale=4,
-            act_type="prelu",
-        )
-        netscale = 4
-        file_url = [
-            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth"
-        ]
-    elif args.model_name == "realesr-general-x4v3":  # x4 VGG-style model (S size)
-        model = SRVGGNetCompact(
-            num_in_ch=3,
-            num_out_ch=3,
-            num_feat=64,
-            num_conv=32,
-            upscale=4,
-            act_type="prelu",
-        )
-        netscale = 4
-        file_url = [
-            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
-            "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
-        ]
-
-    # determine model paths
-    if args.model_path is not None:
-        model_path = args.model_path
-    else:
-        model_path = os.path.join("weights", args.model_name + ".pth")
-        if not os.path.isfile(model_path):
-            ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-            for url in file_url:
-                # model_path will be updated
-                model_path = load_file_from_url(
-                    url=url,
-                    model_dir=os.path.join(ROOT_DIR, "weights"),
-                    progress=True,
-                    file_name=None,
-                )
-
-    # use dni to control the denoise strength
-    dni_weight = None
-    if args.model_name == "realesr-general-x4v3" and args.denoise_strength != 1:
-        wdn_model_path = model_path.replace(
-            "realesr-general-x4v3", "realesr-general-wdn-x4v3"
-        )
-        model_path = [model_path, wdn_model_path]
-        dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
-
-    # restorer
-    upsampler = RealESRGANer(
-        scale=netscale,
-        model_path=model_path,
-        dni_weight=dni_weight,
-        model=model,
-        tile=args.tile,
-        tile_pad=args.tile_pad,
-        pre_pad=args.pre_pad,
-        half=not args.fp32,
-        gpu_id=args.gpu_id,
-    )
-
-    if args.face_enhance:  # Use GFPGAN for face enhancement
-        from gfpgan import GFPGANer
-
-        face_enhancer = GFPGANer(
-            model_path="https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth",
-            upscale=args.outscale,
-            arch="clean",
-            channel_multiplier=2,
-            bg_upsampler=upsampler,
-        )
-    os.makedirs(args.output, exist_ok=True)
-
-    if os.path.isfile(args.input):
-        paths = [args.input]
-    else:
-        paths = sorted(glob.glob(os.path.join(args.input, "*")))
-
-    for idx, path in enumerate(paths):
-        imgname, extension = os.path.splitext(os.path.basename(path))
-        print("Testing", idx, imgname)
-
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-        if len(img.shape) == 3 and img.shape[2] == 4:
-            img_mode = "RGBA"
-        else:
-            img_mode = None
-
-        try:
-            if args.face_enhance:
-                _, _, output = face_enhancer.enhance(
-                    img, has_aligned=False, only_center_face=False, paste_back=True
-                )
-            else:
-                output, _ = upsampler.enhance(img, outscale=args.outscale)
-        except RuntimeError as error:
-            print("Error", error)
-            print(
-                "If you encounter CUDA out of memory, try to set --tile with a smaller number."
-            )
-        else:
-            if args.ext == "auto":
-                extension = extension[1:]
-            else:
-                extension = args.ext
-            if img_mode == "RGBA":  # RGBA images should be saved in png format
-                extension = "png"
-            if args.suffix == "":
-                save_path = os.path.join(args.output, f"{imgname}.{extension}")
-            else:
-                save_path = os.path.join(
-                    args.output, f"{imgname}_{args.suffix}.{extension}"
-                )
-            cv2.imwrite(save_path, output)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/yangogo/bingo/src/pages/api/blob.ts b/spaces/yangogo/bingo/src/pages/api/blob.ts
deleted file mode 100644
index fecd48031916b2284b8958892196e0a1ad420421..0000000000000000000000000000000000000000
--- a/spaces/yangogo/bingo/src/pages/api/blob.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { Readable } from 'node:stream'
-import { fetch } from '@/lib/isomorphic'
-
-const API_DOMAIN = 'https://www.bing.com'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { bcid } = req.query
-
-    const { headers, body } = await fetch(`${API_DOMAIN}/images/blob?bcid=${bcid}`,
-      {
-        method: 'GET',
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referrer-Policy": "origin-when-cross-origin",
-        },
-      },
-    )
-
-    res.writeHead(200, {
-      'Content-Length': headers.get('content-length')!,
-      'Content-Type': headers.get('content-type')!,
-    })
-    // @ts-ignore
-    return Readable.fromWeb(body!).pipe(res)
-  } catch (e) {
-    console.log('Error', e)
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/yaoshining/text-generation-webui/extensions/multimodal/README.md b/spaces/yaoshining/text-generation-webui/extensions/multimodal/README.md
deleted file mode 100644
index 0f515ae69f6b8680522df9854c515ea698710629..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/extensions/multimodal/README.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# Multimodal
-
-## Description
-
-Adds support for multimodality (text+images) to text-generation-webui.
-
-https://user-images.githubusercontent.com/3718215/233817203-69b57e77-0c55-4fd6-b742-3204bb13b8fc.mp4
-
-## Usage
-
-To run this extension, download a LLM that supports multimodality, and then start server.py with the appropriate `--multimodal-pipeline` argument. Examples:
-
-```
-python server.py --model wojtab_llava-7b-v0-4bit-128g --multimodal-pipeline llava-7b --chat
-python3 server.py --model wojtab_llava-13b-v0-4bit-128g --multimodal-pipeline llava-13b --chat
-python server.py --model anon8231489123_vicuna-13b-GPTQ-4bit-128g --multimodal-pipeline minigpt4-13b --chat
-python server.py --model llama-7b-4bit --multimodal-pipeline minigpt4-7b --chat
-```
-
-There is built-in support for LLaVA-v0-13B and LLaVA-v0-7b. To install `minigpt4`:
-
-- clone https://github.com/Wojtab/minigpt-4-pipeline into `extensions/multimodal/pipelines`
-- install the requirements.txt
-
-The same procedure should be used to install other pipelines, which can then be used with `--multimodal-pipeline [pipeline name]`. For additional multimodal pipelines refer to the compatibility section below.
-
-Do note, that each image takes up a considerable amount of tokens, so adjust `max_new_tokens` to be at most 1700 (recommended value is between 200 to 500), so the images don't get truncated.
-
-To send an image, just upload it to the extension field below chat, and send a prompt as always. The image will be added to the end of your message. If you wish to modify the placement, include a string `<image>` in your prompt.
-
-Additionally, there is *Embed all images, not only the last one* checkbox. It modifies the image embeddings, by default (if it's unchecked), all but the most recent images have their embeddings empty, so they are not fed to the network. It seems as if some multimodal networks consider the features in all images at the same time as if they were a single image. Due to this behavior, by default, the extension skips previous images. However, it can lead to sub-par generation on other pipelines. If you want to include all images, just tick this checkbox.
-
-## Compatibility
-As of now, the following multimodal pipelines are supported:
-|Pipeline|`--multimodal-pipeline`|Default LLM|LLM info(for the linked model)|Pipeline repository|
-|-|-|-|-|-|
-|[LLaVA 13B](https://github.com/haotian-liu/LLaVA)|`llava-13b`|[LLaVA 13B](https://huggingface.co/wojtab/llava-13b-v0-4bit-128g)|GPTQ 4-bit quant, old CUDA|built-in|
-|[LLaVA 7B](https://github.com/haotian-liu/LLaVA)|`llava-7b`|[LLaVA 7B](https://huggingface.co/wojtab/llava-7b-v0-4bit-128g)|GPTQ 4-bit quant, old CUDA|built-in|
-|[MiniGPT-4 7B](https://github.com/Vision-CAIR/MiniGPT-4)|`minigpt4-7b`|[Vicuna v0 7B](https://huggingface.co/TheBloke/vicuna-7B-GPTQ-4bit-128g)|GPTQ 4-bit quant, new format|[Wojtab/minigpt-4-pipeline](https://github.com/Wojtab/minigpt-4-pipeline)|
-|[MiniGPT-4 13B](https://github.com/Vision-CAIR/MiniGPT-4)|`minigpt4-13b`|[Vicuna v0 13B](https://huggingface.co/anon8231489123/vicuna-13b-GPTQ-4bit-128g)|GPTQ 4-bit quant, old CUDA|[Wojtab/minigpt-4-pipeline](https://github.com/Wojtab/minigpt-4-pipeline)|
-
-Some pipelines could support different LLMs but do note that while it might work, it isn't a supported configuration.
-
-DO NOT report bugs if you are using a different LLM.
-
-DO NOT report bugs with pipelines in this repository (unless they are built-in)
-
-## Extension config
-This extension uses the following parameters (from `settings.json`):
-|Parameter|Description|
-|---------|-----------|
-|`multimodal-vision_bits`|Number of bits to load vision models (CLIP/ViT) feature extractor in (most pipelines should support either 32 or 16, default=32)|
-|`multimodal-vision_device`|Torch device to run the feature extractor on, for example, `cpu` or `cuda:0`, by default `cuda:0` if available|
-|`multimodal-projector_bits`|Number of bits to load feature projector model(s) in (most pipelines should support either 32 or 16, default=32)|
-|`multimodal-projector_device`|Torch device to run the feature projector model(s) on, for example `cpu` or `cuda:0`, by default `cuda:0` if available|
-|`multimodal-add_all_images_to_prompt`|Default value of "Embed all images, not only the last one" checkbox|
-
-## Usage through API
-
-You can run the multimodal inference through API, by inputting the images to prompt. Images are embedded like so: `f'<img src="data:image/jpeg;base64,{img_str}">'`, where `img_str` is base-64 jpeg data. Note that you will need to launch `server.py` with the arguments `--api --extensions multimodal`. 
-
-Python example:
-
-```Python
-import base64
-import requests
-
-CONTEXT = "You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language. Follow the instructions carefully and explain your answers in detail.### Human: Hi!### Assistant: Hi there! How can I help you today?\n"
-
-with open('extreme_ironing.jpg', 'rb') as f:
-    img_str = base64.b64encode(f.read()).decode('utf-8')
-    prompt = CONTEXT + f'### Human: What is unusual about this image: \n<img src="data:image/jpeg;base64,{img_str}">### Assistant: '
-    print(requests.post('http://127.0.0.1:5000/api/v1/generate', json={'prompt': prompt, 'stopping_strings': ['\n###']}).json())
-```
-script output:
-```Python
-{'results': [{'text': "The unusual aspect of this image is that a man is standing on top of a yellow minivan while doing his laundry. He has set up a makeshift clothes line using the car's rooftop as an outdoor drying area. This scene is uncommon because people typically do their laundry indoors, in a dedicated space like a laundromat or a room in their home, rather than on top of a moving vehicle. Additionally, hanging clothes on the car could be potentially hazardous or illegal in some jurisdictions due to the risk of damaging the vehicle or causing accidents on the road.\n##"}]}
-```
-
-## For pipeline developers/technical description
-see [DOCS.md](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/multimodal/DOCS.md)
diff --git a/spaces/ybelkada/interfacegan_pp/models/stylegan_tf_official/dnnlib/submission/run_context.py b/spaces/ybelkada/interfacegan_pp/models/stylegan_tf_official/dnnlib/submission/run_context.py
deleted file mode 100644
index 932320e4735bde1b547ac6062b175601b7959547..0000000000000000000000000000000000000000
--- a/spaces/ybelkada/interfacegan_pp/models/stylegan_tf_official/dnnlib/submission/run_context.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Helpers for managing the run/training loop."""
-
-import datetime
-import json
-import os
-import pprint
-import time
-import types
-
-from typing import Any
-
-from . import submit
-
-
-class RunContext(object):
-    """Helper class for managing the run/training loop.
-
-    The context will hide the implementation details of a basic run/training loop.
-    It will set things up properly, tell if run should be stopped, and then cleans up.
-    User should call update periodically and use should_stop to determine if run should be stopped.
-
-    Args:
-        submit_config: The SubmitConfig that is used for the current run.
-        config_module: The whole config module that is used for the current run.
-        max_epoch: Optional cached value for the max_epoch variable used in update.
-    """
-
-    def __init__(self, submit_config: submit.SubmitConfig, config_module: types.ModuleType = None, max_epoch: Any = None):
-        self.submit_config = submit_config
-        self.should_stop_flag = False
-        self.has_closed = False
-        self.start_time = time.time()
-        self.last_update_time = time.time()
-        self.last_update_interval = 0.0
-        self.max_epoch = max_epoch
-
-        # pretty print the all the relevant content of the config module to a text file
-        if config_module is not None:
-            with open(os.path.join(submit_config.run_dir, "config.txt"), "w") as f:
-                filtered_dict = {k: v for k, v in config_module.__dict__.items() if not k.startswith("_") and not isinstance(v, (types.ModuleType, types.FunctionType, types.LambdaType, submit.SubmitConfig, type))}
-                pprint.pprint(filtered_dict, stream=f, indent=4, width=200, compact=False)
-
-        # write out details about the run to a text file
-        self.run_txt_data = {"task_name": submit_config.task_name, "host_name": submit_config.host_name, "start_time": datetime.datetime.now().isoformat(sep=" ")}
-        with open(os.path.join(submit_config.run_dir, "run.txt"), "w") as f:
-            pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False)
-
-    def __enter__(self) -> "RunContext":
-        return self
-
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-
-    def update(self, loss: Any = 0, cur_epoch: Any = 0, max_epoch: Any = None) -> None:
-        """Do general housekeeping and keep the state of the context up-to-date.
-        Should be called often enough but not in a tight loop."""
-        assert not self.has_closed
-
-        self.last_update_interval = time.time() - self.last_update_time
-        self.last_update_time = time.time()
-
-        if os.path.exists(os.path.join(self.submit_config.run_dir, "abort.txt")):
-            self.should_stop_flag = True
-
-        max_epoch_val = self.max_epoch if max_epoch is None else max_epoch
-
-    def should_stop(self) -> bool:
-        """Tell whether a stopping condition has been triggered one way or another."""
-        return self.should_stop_flag
-
-    def get_time_since_start(self) -> float:
-        """How much time has passed since the creation of the context."""
-        return time.time() - self.start_time
-
-    def get_time_since_last_update(self) -> float:
-        """How much time has passed since the last call to update."""
-        return time.time() - self.last_update_time
-
-    def get_last_update_interval(self) -> float:
-        """How much time passed between the previous two calls to update."""
-        return self.last_update_interval
-
-    def close(self) -> None:
-        """Close the context and clean up.
-        Should only be called once."""
-        if not self.has_closed:
-            # update the run.txt with stopping time
-            self.run_txt_data["stop_time"] = datetime.datetime.now().isoformat(sep=" ")
-            with open(os.path.join(self.submit_config.run_dir, "run.txt"), "w") as f:
-                pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False)
-
-            self.has_closed = True
diff --git a/spaces/yerfor/SyntaSpeech/modules/commons/conformer/espnet_positional_embedding.py b/spaces/yerfor/SyntaSpeech/modules/commons/conformer/espnet_positional_embedding.py
deleted file mode 100644
index 89b9b5549cc779d1ea67f052b1c99cad92365503..0000000000000000000000000000000000000000
--- a/spaces/yerfor/SyntaSpeech/modules/commons/conformer/espnet_positional_embedding.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import math
-import torch
-
-
-class PositionalEncoding(torch.nn.Module):
-    """Positional encoding.
-    Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_len (int): Maximum input length.
-        reverse (bool): Whether to reverse the input position.
-    """
-
-    def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
-        """Construct an PositionalEncoding object."""
-        super(PositionalEncoding, self).__init__()
-        self.d_model = d_model
-        self.reverse = reverse
-        self.xscale = math.sqrt(self.d_model)
-        self.dropout = torch.nn.Dropout(p=dropout_rate)
-        self.pe = None
-        self.extend_pe(torch.tensor(0.0).expand(1, max_len))
-
-    def extend_pe(self, x):
-        """Reset the positional encodings."""
-        if self.pe is not None:
-            if self.pe.size(1) >= x.size(1):
-                if self.pe.dtype != x.dtype or self.pe.device != x.device:
-                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
-                return
-        pe = torch.zeros(x.size(1), self.d_model)
-        if self.reverse:
-            position = torch.arange(
-                x.size(1) - 1, -1, -1.0, dtype=torch.float32
-            ).unsqueeze(1)
-        else:
-            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
-        div_term = torch.exp(
-            torch.arange(0, self.d_model, 2, dtype=torch.float32)
-            * -(math.log(10000.0) / self.d_model)
-        )
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(0)
-        self.pe = pe.to(device=x.device, dtype=x.dtype)
-
-    def forward(self, x: torch.Tensor):
-        """Add positional encoding.
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, `*`).
-        Returns:
-            torch.Tensor: Encoded tensor (batch, time, `*`).
-        """
-        self.extend_pe(x)
-        x = x * self.xscale + self.pe[:, : x.size(1)]
-        return self.dropout(x)
-
-
-class ScaledPositionalEncoding(PositionalEncoding):
-    """Scaled positional encoding module.
-    See Sec. 3.2  https://arxiv.org/abs/1809.08895
-    Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_len (int): Maximum input length.
-    """
-
-    def __init__(self, d_model, dropout_rate, max_len=5000):
-        """Initialize class."""
-        super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
-        self.alpha = torch.nn.Parameter(torch.tensor(1.0))
-
-    def reset_parameters(self):
-        """Reset parameters."""
-        self.alpha.data = torch.tensor(1.0)
-
-    def forward(self, x):
-        """Add positional encoding.
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, `*`).
-        Returns:
-            torch.Tensor: Encoded tensor (batch, time, `*`).
-        """
-        self.extend_pe(x)
-        x = x + self.alpha * self.pe[:, : x.size(1)]
-        return self.dropout(x)
-
-
-class RelPositionalEncoding(PositionalEncoding):
-    """Relative positional encoding module.
-    See : Appendix B in https://arxiv.org/abs/1901.02860
-    Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_len (int): Maximum input length.
-    """
-
-    def __init__(self, d_model, dropout_rate, max_len=5000):
-        """Initialize class."""
-        super().__init__(d_model, dropout_rate, max_len, reverse=True)
-
-    def forward(self, x):
-        """Compute positional encoding.
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, `*`).
-        Returns:
-            torch.Tensor: Encoded tensor (batch, time, `*`).
-            torch.Tensor: Positional embedding tensor (1, time, `*`).
-        """
-        self.extend_pe(x)
-        x = x * self.xscale
-        pos_emb = self.pe[:, : x.size(1)]
-        return self.dropout(x), self.dropout(pos_emb)
\ No newline at end of file
diff --git a/spaces/ygangang/CodeFormer/CodeFormer/basicsr/utils/img_util.py b/spaces/ygangang/CodeFormer/CodeFormer/basicsr/utils/img_util.py
deleted file mode 100644
index d409a132ff216e6943a276fb5d8cd5f410824883..0000000000000000000000000000000000000000
--- a/spaces/ygangang/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/ygangang/CodeFormer/CodeFormer/facelib/utils/face_utils.py b/spaces/ygangang/CodeFormer/CodeFormer/facelib/utils/face_utils.py
deleted file mode 100644
index f1474a2a4419b6b62fab8a919ef805b802556464..0000000000000000000000000000000000000000
--- a/spaces/ygangang/CodeFormer/CodeFormer/facelib/utils/face_utils.py
+++ /dev/null
@@ -1,248 +0,0 @@
-import cv2
-import numpy as np
-import torch
-
-
-def compute_increased_bbox(bbox, increase_area, preserve_aspect=True):
-    left, top, right, bot = bbox
-    width = right - left
-    height = bot - top
-
-    if preserve_aspect:
-        width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
-        height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
-    else:
-        width_increase = height_increase = increase_area
-    left = int(left - width_increase * width)
-    top = int(top - height_increase * height)
-    right = int(right + width_increase * width)
-    bot = int(bot + height_increase * height)
-    return (left, top, right, bot)
-
-
-def get_valid_bboxes(bboxes, h, w):
-    left = max(bboxes[0], 0)
-    top = max(bboxes[1], 0)
-    right = min(bboxes[2], w)
-    bottom = min(bboxes[3], h)
-    return (left, top, right, bottom)
-
-
-def align_crop_face_landmarks(img,
-                              landmarks,
-                              output_size,
-                              transform_size=None,
-                              enable_padding=True,
-                              return_inverse_affine=False,
-                              shrink_ratio=(1, 1)):
-    """Align and crop face with landmarks.
-
-    The output_size and transform_size are based on width. The height is
-    adjusted based on shrink_ratio_h/shring_ration_w.
-
-    Modified from:
-    https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
-
-    Args:
-        img (Numpy array): Input image.
-        landmarks (Numpy array): 5 or 68 or 98 landmarks.
-        output_size (int): Output face size.
-        transform_size (ing): Transform size. Usually the four time of
-            output_size.
-        enable_padding (float): Default: True.
-        shrink_ratio (float | tuple[float] | list[float]): Shring the whole
-            face for height and width (crop larger area). Default: (1, 1).
-
-    Returns:
-        (Numpy array): Cropped face.
-    """
-    lm_type = 'retinaface_5'  # Options: dlib_5, retinaface_5
-
-    if isinstance(shrink_ratio, (float, int)):
-        shrink_ratio = (shrink_ratio, shrink_ratio)
-    if transform_size is None:
-        transform_size = output_size * 4
-
-    # Parse landmarks
-    lm = np.array(landmarks)
-    if lm.shape[0] == 5 and lm_type == 'retinaface_5':
-        eye_left = lm[0]
-        eye_right = lm[1]
-        mouth_avg = (lm[3] + lm[4]) * 0.5
-    elif lm.shape[0] == 5 and lm_type == 'dlib_5':
-        lm_eye_left = lm[2:4]
-        lm_eye_right = lm[0:2]
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        mouth_avg = lm[4]
-    elif lm.shape[0] == 68:
-        lm_eye_left = lm[36:42]
-        lm_eye_right = lm[42:48]
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        mouth_avg = (lm[48] + lm[54]) * 0.5
-    elif lm.shape[0] == 98:
-        lm_eye_left = lm[60:68]
-        lm_eye_right = lm[68:76]
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        mouth_avg = (lm[76] + lm[82]) * 0.5
-
-    eye_avg = (eye_left + eye_right) * 0.5
-    eye_to_eye = eye_right - eye_left
-    eye_to_mouth = mouth_avg - eye_avg
-
-    # Get the oriented crop rectangle
-    # x: half width of the oriented crop rectangle
-    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
-    #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
-    # norm with the hypotenuse: get the direction
-    x /= np.hypot(*x)  # get the hypotenuse of a right triangle
-    rect_scale = 1  # TODO: you can edit it to get larger rect
-    x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
-    # y: half height of the oriented crop rectangle
-    y = np.flipud(x) * [-1, 1]
-
-    x *= shrink_ratio[1]  # width
-    y *= shrink_ratio[0]  # height
-
-    # c: center
-    c = eye_avg + eye_to_mouth * 0.1
-    # quad: (left_top, left_bottom, right_bottom, right_top)
-    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-    # qsize: side length of the square
-    qsize = np.hypot(*x) * 2
-
-    quad_ori = np.copy(quad)
-    # Shrink, for large face
-    # TODO: do we really need shrink
-    shrink = int(np.floor(qsize / output_size * 0.5))
-    if shrink > 1:
-        h, w = img.shape[0:2]
-        rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink)))
-        img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA)
-        quad /= shrink
-        qsize /= shrink
-
-    # Crop
-    h, w = img.shape[0:2]
-    border = max(int(np.rint(qsize * 0.1)), 3)
-    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-            int(np.ceil(max(quad[:, 1]))))
-    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h))
-    if crop[2] - crop[0] < w or crop[3] - crop[1] < h:
-        img = img[crop[1]:crop[3], crop[0]:crop[2], :]
-        quad -= crop[0:2]
-
-    # Pad
-    # pad: (width_left, height_top, width_right, height_bottom)
-    h, w = img.shape[0:2]
-    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-           int(np.ceil(max(quad[:, 1]))))
-    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0))
-    if enable_padding and max(pad) > border - 4:
-        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        h, w = img.shape[0:2]
-        y, x, _ = np.ogrid[:h, :w, :1]
-        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
-                                           np.float32(w - 1 - x) / pad[2]),
-                          1.0 - np.minimum(np.float32(y) / pad[1],
-                                           np.float32(h - 1 - y) / pad[3]))
-        blur = int(qsize * 0.02)
-        if blur % 2 == 0:
-            blur += 1
-        blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur))
-
-        img = img.astype('float32')
-        img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        img = np.clip(img, 0, 255)  # float32, [0, 255]
-        quad += pad[:2]
-
-    # Transform use cv2
-    h_ratio = shrink_ratio[0] / shrink_ratio[1]
-    dst_h, dst_w = int(transform_size * h_ratio), transform_size
-    template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
-    # use cv2.LMEDS method for the equivalence to skimage transform
-    # ref: https://blog.csdn.net/yichxi/article/details/115827338
-    affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0]
-    cropped_face = cv2.warpAffine(
-        img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132))  # gray
-
-    if output_size < transform_size:
-        cropped_face = cv2.resize(
-            cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR)
-
-    if return_inverse_affine:
-        dst_h, dst_w = int(output_size * h_ratio), output_size
-        template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
-        # use cv2.LMEDS method for the equivalence to skimage transform
-        # ref: https://blog.csdn.net/yichxi/article/details/115827338
-        affine_matrix = cv2.estimateAffinePartial2D(
-            quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0]
-        inverse_affine = cv2.invertAffineTransform(affine_matrix)
-    else:
-        inverse_affine = None
-    return cropped_face, inverse_affine
-
-
-def paste_face_back(img, face, inverse_affine):
-    h, w = img.shape[0:2]
-    face_h, face_w = face.shape[0:2]
-    inv_restored = cv2.warpAffine(face, inverse_affine, (w, h))
-    mask = np.ones((face_h, face_w, 3), dtype=np.float32)
-    inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h))
-    # remove the black borders
-    inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8))
-    inv_restored_remove_border = inv_mask_erosion * inv_restored
-    total_face_area = np.sum(inv_mask_erosion) // 3
-    # compute the fusion edge based on the area of face
-    w_edge = int(total_face_area**0.5) // 20
-    erosion_radius = w_edge * 2
-    inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
-    blur_size = w_edge * 2
-    inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
-    img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img
-    # float32, [0, 255]
-    return img
-
-
-if __name__ == '__main__':
-    import os
-
-    from facelib.detection import init_detection_model
-    from facelib.utils.face_restoration_helper import get_largest_face
-
-    img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png'
-    img_name = os.splitext(os.path.basename(img_path))[0]
-
-    # initialize model
-    det_net = init_detection_model('retinaface_resnet50', half=False)
-    img_ori = cv2.imread(img_path)
-    h, w = img_ori.shape[0:2]
-    # if larger than 800, scale it
-    scale = max(h / 800, w / 800)
-    if scale > 1:
-        img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR)
-
-    with torch.no_grad():
-        bboxes = det_net.detect_faces(img, 0.97)
-    if scale > 1:
-        bboxes *= scale  # the score is incorrect
-    bboxes = get_largest_face(bboxes, h, w)[0]
-
-    landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)])
-
-    cropped_face, inverse_affine = align_crop_face_landmarks(
-        img_ori,
-        landmarks,
-        output_size=512,
-        transform_size=None,
-        enable_padding=True,
-        return_inverse_affine=True,
-        shrink_ratio=(1, 1))
-
-    cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face)
-    img = paste_face_back(img_ori, cropped_face, inverse_affine)
-    cv2.imwrite(f'tmp/{img_name}_back.png', img)
diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/face_detect/utils/nms/__init__.py b/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/face_detect/utils/nms/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py
deleted file mode 100644
index 72c6b7a5c8939970bd0e1e4a3c1155695943b19a..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py
+++ /dev/null
@@ -1,35 +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.mask_rcnn_fpn import model
-from ..common.train import train
-
-from detectron2.config import LazyCall as L
-from detectron2.modeling.backbone import RegNet
-from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
-
-
-# Replace default ResNet with RegNetY-4GF from the DDS paper. Config source:
-# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnety/RegNetY-4.0GF_dds_8gpu.yaml#L4-L10  # noqa
-model.backbone.bottom_up = L(RegNet)(
-    stem_class=SimpleStem,
-    stem_width=32,
-    block_class=ResBottleneckBlock,
-    depth=22,
-    w_a=31.41,
-    w_0=96,
-    w_m=2.24,
-    group_width=64,
-    se_ratio=0.25,
-    freeze_at=2,
-    norm="FrozenBN",
-    out_features=["s1", "s2", "s3", "s4"],
-)
-model.pixel_std = [57.375, 57.120, 58.395]
-
-optimizer.weight_decay = 5e-5
-train.init_checkpoint = (
-    "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906838/RegNetY-4.0GF_dds_8gpu.pyth"
-)
-# RegNets benefit from enabling cudnn benchmark mode
-train.cudnn_benchmark = True
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py
deleted file mode 100644
index 9f15f840355571b6d02d5534fa8a9b6b8cb22c70..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py
+++ /dev/null
@@ -1,479 +0,0 @@
-import numpy as np
-import math
-from os.path import join
-import fvcore.nn.weight_init as weight_init
-import torch
-import torch.nn.functional as F
-from torch import nn
-import torch.utils.model_zoo as model_zoo
-
-from detectron2.modeling.backbone.resnet import (
-    BasicStem, BottleneckBlock, DeformBottleneckBlock)
-from detectron2.layers import (
-    Conv2d,
-    DeformConv,
-    FrozenBatchNorm2d,
-    ModulatedDeformConv,
-    ShapeSpec,
-    get_norm,
-)
-
-from detectron2.modeling.backbone.backbone import Backbone
-from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
-from detectron2.modeling.backbone.fpn import FPN
-
-__all__ = [
-    "BottleneckBlock",
-    "DeformBottleneckBlock",
-    "BasicStem",
-]
-
-DCNV1 = False
-
-HASH = {
-    34: 'ba72cf86',
-    60: '24839fc4',
-}
-
-def get_model_url(data, name, hash):
-    return join('http://dl.yf.io/dla/models', data, '{}-{}.pth'.format(name, hash))
-
-class BasicBlock(nn.Module):
-    def __init__(self, inplanes, planes, stride=1, dilation=1, norm='BN'):
-        super(BasicBlock, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3,
-                               stride=stride, padding=dilation,
-                               bias=False, dilation=dilation)
-        self.bn1 = get_norm(norm, 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 = get_norm(norm, 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 Bottleneck(nn.Module):
-    expansion = 2
-
-    def __init__(self, inplanes, planes, stride=1, dilation=1, norm='BN'):
-        super(Bottleneck, self).__init__()
-        expansion = Bottleneck.expansion
-        bottle_planes = planes // expansion
-        self.conv1 = nn.Conv2d(inplanes, bottle_planes,
-                               kernel_size=1, bias=False)
-        self.bn1 = get_norm(norm, bottle_planes)
-        self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3,
-                               stride=stride, padding=dilation,
-                               bias=False, dilation=dilation)
-        self.bn2 = get_norm(norm, bottle_planes)
-        self.conv3 = nn.Conv2d(bottle_planes, planes,
-                               kernel_size=1, bias=False)
-        self.bn3 = get_norm(norm, planes)
-        self.relu = nn.ReLU(inplace=True)
-        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 = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-class Root(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, residual, norm='BN'):
-        super(Root, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels, out_channels, 1,
-            stride=1, bias=False, padding=(kernel_size - 1) // 2)
-        self.bn = get_norm(norm, 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 Tree(nn.Module):
-    def __init__(self, levels, block, in_channels, out_channels, stride=1,
-                 level_root=False, root_dim=0, root_kernel_size=1,
-                 dilation=1, root_residual=False, norm='BN'):
-        super(Tree, self).__init__()
-        if root_dim == 0:
-            root_dim = 2 * out_channels
-        if level_root:
-            root_dim += in_channels
-        if levels == 1:
-            self.tree1 = block(in_channels, out_channels, stride,
-                               dilation=dilation, norm=norm)
-            self.tree2 = block(out_channels, out_channels, 1,
-                               dilation=dilation, norm=norm)
-        else:
-            self.tree1 = Tree(levels - 1, block, in_channels, out_channels,
-                              stride, root_dim=0,
-                              root_kernel_size=root_kernel_size,
-                              dilation=dilation, root_residual=root_residual, 
-                              norm=norm)
-            self.tree2 = Tree(levels - 1, block, out_channels, out_channels,
-                              root_dim=root_dim + out_channels,
-                              root_kernel_size=root_kernel_size,
-                              dilation=dilation, root_residual=root_residual, 
-                              norm=norm)
-        if levels == 1:
-            self.root = Root(root_dim, out_channels, root_kernel_size,
-                             root_residual, norm=norm)
-        self.level_root = level_root
-        self.root_dim = root_dim
-        self.downsample = None
-        self.project = None
-        self.levels = levels
-        if stride > 1:
-            self.downsample = nn.MaxPool2d(stride, stride=stride)
-        if in_channels != out_channels:
-            self.project = nn.Sequential(
-                nn.Conv2d(in_channels, out_channels,
-                          kernel_size=1, stride=1, bias=False),
-                get_norm(norm, out_channels)
-            )
-
-    def forward(self, x, residual=None, children=None):
-        children = [] if children is None else children
-        bottom = self.downsample(x) if self.downsample else x
-        residual = self.project(bottom) if self.project else 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, num_layers, levels, channels, 
-        block=BasicBlock, residual_root=False, norm='BN'):
-        """
-        Args:
-        """
-        super(DLA, self).__init__()
-        self.norm = norm
-        self.channels = channels
-        self.base_layer = nn.Sequential(
-            nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
-                      padding=3, bias=False),
-            get_norm(self.norm, 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)
-        self.level2 = Tree(levels[2], block, channels[1], channels[2], 2,
-                           level_root=False,
-                           root_residual=residual_root, norm=norm)
-        self.level3 = Tree(levels[3], block, channels[2], channels[3], 2,
-                           level_root=True, root_residual=residual_root, 
-                           norm=norm)
-        self.level4 = Tree(levels[4], block, channels[3], channels[4], 2,
-                           level_root=True, root_residual=residual_root, 
-                           norm=norm)
-        self.level5 = Tree(levels[5], block, channels[4], channels[5], 2,
-                           level_root=True, root_residual=residual_root, 
-                           norm=norm)
-        self.load_pretrained_model(
-            data='imagenet', name='dla{}'.format(num_layers), 
-            hash=HASH[num_layers])
-
-    def load_pretrained_model(self, data, name, hash):
-        model_url = get_model_url(data, name, hash)
-        model_weights = model_zoo.load_url(model_url)
-        num_classes = len(model_weights[list(model_weights.keys())[-1]])
-        self.fc = nn.Conv2d(
-            self.channels[-1], num_classes,
-            kernel_size=1, stride=1, padding=0, bias=True)
-        print('Loading pretrained')
-        self.load_state_dict(model_weights, strict=False)
-
-    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),
-                get_norm(self.norm, planes),
-                nn.ReLU(inplace=True)])
-            inplanes = planes
-        return nn.Sequential(*modules)
-
-    def forward(self, x):
-        y = []
-        x = self.base_layer(x)
-        for i in range(6):
-            x = getattr(self, 'level{}'.format(i))(x)
-            y.append(x)
-        return y
-
-
-def fill_up_weights(up):
-    w = up.weight.data
-    f = math.ceil(w.size(2) / 2)
-    c = (2 * f - 1 - f % 2) / (2. * f)
-    for i in range(w.size(2)):
-        for j in range(w.size(3)):
-            w[0, 0, i, j] = \
-                (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c))
-    for c in range(1, w.size(0)):
-        w[c, 0, :, :] = w[0, 0, :, :]
-
-
-class _DeformConv(nn.Module):
-    def __init__(self, chi, cho, norm='BN'):
-        super(_DeformConv, self).__init__()
-        self.actf = nn.Sequential(
-            get_norm(norm, cho),
-            nn.ReLU(inplace=True)
-        )
-        if DCNV1:
-            self.offset = Conv2d(
-                chi, 18, kernel_size=3, stride=1,
-                padding=1, dilation=1)
-            self.conv = DeformConv(
-                chi, cho, kernel_size=(3,3), stride=1, padding=1,
-                dilation=1, deformable_groups=1)
-        else:
-            self.offset = Conv2d(
-                chi, 27, kernel_size=3, stride=1,
-                padding=1, dilation=1)
-            self.conv = ModulatedDeformConv(
-                chi, cho, kernel_size=3, stride=1, padding=1,
-                dilation=1, deformable_groups=1)
-        nn.init.constant_(self.offset.weight, 0)
-        nn.init.constant_(self.offset.bias, 0)
-        
-    def forward(self, x):
-        if DCNV1:
-            offset = self.offset(x)
-            x = self.conv(x, offset)
-        else:
-            offset_mask = self.offset(x)
-            offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1)
-            offset = torch.cat((offset_x, offset_y), dim=1)
-            mask = mask.sigmoid()
-            x = self.conv(x, offset, mask)
-        x = self.actf(x)
-        return x
-
-
-class IDAUp(nn.Module):
-    def __init__(self, o, channels, up_f, norm='BN'):
-        super(IDAUp, self).__init__()
-        for i in range(1, len(channels)):
-            c = channels[i]
-            f = int(up_f[i])  
-            proj = _DeformConv(c, o, norm=norm)
-            node = _DeformConv(o, o, norm=norm)
-     
-            up = nn.ConvTranspose2d(o, o, f * 2, stride=f, 
-                                    padding=f // 2, output_padding=0,
-                                    groups=o, bias=False)
-            fill_up_weights(up)
-
-            setattr(self, 'proj_' + str(i), proj)
-            setattr(self, 'up_' + str(i), up)
-            setattr(self, 'node_' + str(i), node)
-                 
-        
-    def forward(self, layers, startp, endp):
-        for i in range(startp + 1, endp):
-            upsample = getattr(self, 'up_' + str(i - startp))
-            project = getattr(self, 'proj_' + str(i - startp))
-            layers[i] = upsample(project(layers[i]))
-            node = getattr(self, 'node_' + str(i - startp))
-            layers[i] = node(layers[i] + layers[i - 1])
-
-
-class DLAUp(nn.Module):
-    def __init__(self, startp, channels, scales, in_channels=None, norm='BN'):
-        super(DLAUp, self).__init__()
-        self.startp = startp
-        if in_channels is None:
-            in_channels = channels
-        self.channels = channels
-        channels = list(channels)
-        scales = np.array(scales, dtype=int)
-        for i in range(len(channels) - 1):
-            j = -i - 2
-            setattr(self, 'ida_{}'.format(i),
-                    IDAUp(channels[j], in_channels[j:],
-                          scales[j:] // scales[j], norm=norm))
-            scales[j + 1:] = scales[j]
-            in_channels[j + 1:] = [channels[j] for _ in channels[j + 1:]]
-
-    def forward(self, layers):
-        out = [layers[-1]] # start with 32
-        for i in range(len(layers) - self.startp - 1):
-            ida = getattr(self, 'ida_{}'.format(i))
-            ida(layers, len(layers) -i - 2, len(layers))
-            out.insert(0, layers[-1])
-        return out
-
-DLA_CONFIGS = {
-    34: ([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], BasicBlock),
-    60: ([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024], Bottleneck)
-}
-
-
-class DLASeg(Backbone):
-    def __init__(self, num_layers, out_features, use_dla_up=True, 
-        ms_output=False, norm='BN'):
-        super(DLASeg, self).__init__()
-        # depth = 34
-        levels, channels, Block = DLA_CONFIGS[num_layers]
-        self.base = DLA(num_layers=num_layers,
-            levels=levels, channels=channels, block=Block, norm=norm)
-        down_ratio = 4
-        self.first_level = int(np.log2(down_ratio))
-        self.ms_output = ms_output
-        self.last_level = 5 if not self.ms_output else 6
-        channels = self.base.channels
-        scales = [2 ** i for i in range(len(channels[self.first_level:]))]
-        self.use_dla_up = use_dla_up
-        if self.use_dla_up:
-            self.dla_up = DLAUp(
-                self.first_level, channels[self.first_level:], scales, 
-                norm=norm)
-        out_channel = channels[self.first_level]
-        if not self.ms_output: # stride 4 DLA
-            self.ida_up = IDAUp(
-                out_channel, channels[self.first_level:self.last_level], 
-                [2 ** i for i in range(self.last_level - self.first_level)], 
-                norm=norm)
-        self._out_features = out_features
-        self._out_feature_channels = {
-            'dla{}'.format(i): channels[i] for i in range(6)}
-        self._out_feature_strides = {
-            'dla{}'.format(i): 2 ** i for i in range(6)}
-        self._size_divisibility = 32
-
-    @property
-    def size_divisibility(self):
-        return self._size_divisibility
-
-    def forward(self, x):
-        x = self.base(x)
-        if self.use_dla_up:
-            x = self.dla_up(x)
-        if not self.ms_output: # stride 4 dla
-            y = []
-            for i in range(self.last_level - self.first_level):
-                y.append(x[i].clone())
-            self.ida_up(y, 0, len(y))
-            ret = {}
-            for i in range(self.last_level - self.first_level):
-                out_feature = 'dla{}'.format(i)
-                if out_feature in self._out_features:
-                    ret[out_feature] = y[i]
-        else:
-            ret = {}
-            st = self.first_level if self.use_dla_up else 0
-            for i in range(self.last_level - st):
-                out_feature = 'dla{}'.format(i + st)
-                if out_feature in self._out_features:
-                    ret[out_feature] = x[i]
-        
-        return ret
-
-
-@BACKBONE_REGISTRY.register()
-def build_dla_backbone(cfg, input_shape):
-    """
-    Create a ResNet instance from config.
-
-    Returns:
-        ResNet: a :class:`ResNet` instance.
-    """
-    return DLASeg(
-        out_features=cfg.MODEL.DLA.OUT_FEATURES, 
-        num_layers=cfg.MODEL.DLA.NUM_LAYERS,
-        use_dla_up=cfg.MODEL.DLA.USE_DLA_UP,
-        ms_output=cfg.MODEL.DLA.MS_OUTPUT,
-        norm=cfg.MODEL.DLA.NORM)
-
-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):
-        super().__init__()
-        self.num_levels = 2
-        self.in_feature = "dla5"
-        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_retinanet_dla_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_dla_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()['dla5'].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/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tools/analyze_model.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tools/analyze_model.py
deleted file mode 100644
index 8e38f8b71eb3b8d1e2b670e7f01a796ec2ea4b7e..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tools/analyze_model.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import logging
-import numpy as np
-from collections import Counter
-import tqdm
-from fvcore.nn import flop_count_table  # can also try flop_count_str
-
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
-from detectron2.data import build_detection_test_loader
-from detectron2.engine import default_argument_parser
-from detectron2.modeling import build_model
-from detectron2.utils.analysis import (
-    FlopCountAnalysis,
-    activation_count_operators,
-    parameter_count_table,
-)
-from detectron2.utils.logger import setup_logger
-
-logger = logging.getLogger("detectron2")
-
-
-def setup(args):
-    if args.config_file.endswith(".yaml"):
-        cfg = get_cfg()
-        cfg.merge_from_file(args.config_file)
-        cfg.DATALOADER.NUM_WORKERS = 0
-        cfg.merge_from_list(args.opts)
-        cfg.freeze()
-    else:
-        cfg = LazyConfig.load(args.config_file)
-        cfg = LazyConfig.apply_overrides(cfg, args.opts)
-    setup_logger(name="fvcore")
-    setup_logger()
-    return cfg
-
-
-def do_flop(cfg):
-    if isinstance(cfg, CfgNode):
-        data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
-        model = build_model(cfg)
-        DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
-    else:
-        data_loader = instantiate(cfg.dataloader.test)
-        model = instantiate(cfg.model)
-        model.to(cfg.train.device)
-        DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
-    model.eval()
-
-    counts = Counter()
-    total_flops = []
-    for idx, data in zip(tqdm.trange(args.num_inputs), data_loader):  # noqa
-        flops = FlopCountAnalysis(model, data)
-        if idx > 0:
-            flops.unsupported_ops_warnings(False).uncalled_modules_warnings(False)
-        counts += flops.by_operator()
-        total_flops.append(flops.total())
-
-    logger.info("Flops table computed from only one input sample:\n" + flop_count_table(flops))
-    logger.info(
-        "Average GFlops for each type of operators:\n"
-        + str([(k, v / (idx + 1) / 1e9) for k, v in counts.items()])
-    )
-    logger.info(
-        "Total GFlops: {:.1f}±{:.1f}".format(np.mean(total_flops) / 1e9, np.std(total_flops) / 1e9)
-    )
-
-
-def do_activation(cfg):
-    if isinstance(cfg, CfgNode):
-        data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
-        model = build_model(cfg)
-        DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
-    else:
-        data_loader = instantiate(cfg.dataloader.test)
-        model = instantiate(cfg.model)
-        model.to(cfg.train.device)
-        DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
-    model.eval()
-
-    counts = Counter()
-    total_activations = []
-    for idx, data in zip(tqdm.trange(args.num_inputs), data_loader):  # noqa
-        count = activation_count_operators(model, data)
-        counts += count
-        total_activations.append(sum(count.values()))
-    logger.info(
-        "(Million) Activations for Each Type of Operators:\n"
-        + str([(k, v / idx) for k, v in counts.items()])
-    )
-    logger.info(
-        "Total (Million) Activations: {}±{}".format(
-            np.mean(total_activations), np.std(total_activations)
-        )
-    )
-
-
-def do_parameter(cfg):
-    if isinstance(cfg, CfgNode):
-        model = build_model(cfg)
-    else:
-        model = instantiate(cfg.model)
-    logger.info("Parameter Count:\n" + parameter_count_table(model, max_depth=5))
-
-
-def do_structure(cfg):
-    if isinstance(cfg, CfgNode):
-        model = build_model(cfg)
-    else:
-        model = instantiate(cfg.model)
-    logger.info("Model Structure:\n" + str(model))
-
-
-if __name__ == "__main__":
-    parser = default_argument_parser(
-        epilog="""
-Examples:
-
-To show parameters of a model:
-$ ./analyze_model.py --tasks parameter \\
-    --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
-
-Flops and activations are data-dependent, therefore inputs and model weights
-are needed to count them:
-
-$ ./analyze_model.py --num-inputs 100 --tasks flop \\
-    --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \\
-    MODEL.WEIGHTS /path/to/model.pkl
-"""
-    )
-    parser.add_argument(
-        "--tasks",
-        choices=["flop", "activation", "parameter", "structure"],
-        required=True,
-        nargs="+",
-    )
-    parser.add_argument(
-        "-n",
-        "--num-inputs",
-        default=100,
-        type=int,
-        help="number of inputs used to compute statistics for flops/activations, "
-        "both are data dependent.",
-    )
-    args = parser.parse_args()
-    assert not args.eval_only
-    assert args.num_gpus == 1
-
-    cfg = setup(args)
-
-    for task in args.tasks:
-        {
-            "flop": do_flop,
-            "activation": do_activation,
-            "parameter": do_parameter,
-            "structure": do_structure,
-        }[task](cfg)
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tools/deploy/README.md b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tools/deploy/README.md
deleted file mode 100644
index e33cbeb54c003a5738da68c838fdaa4e0d218501..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tools/deploy/README.md
+++ /dev/null
@@ -1,66 +0,0 @@
-See [deployment tutorial](https://detectron2.readthedocs.io/tutorials/deployment.html)
-for some high-level background about deployment.
-
-This directory contains the following examples:
-
-1. An example script `export_model.py`
-   that exports a detectron2 model for deployment using different methods and formats.
-
-2. A C++ example that runs inference with Mask R-CNN model in TorchScript format.
-
-## Build
-Deployment depends on libtorch and OpenCV. Some require more dependencies:
-
-* Running TorchScript-format models produced by `--export-method=caffe2_tracing` requires libtorch
-  to be built with caffe2 enabled.
-* Running TorchScript-format models produced by `--export-method=tracing/scripting` requires libtorchvision (C++ library of torchvision).
-
-All methods are supported in one C++ file that requires all the above dependencies.
-Adjust it and remove code you don't need.
-As a reference, we provide a [Dockerfile](../../docker/deploy.Dockerfile) that installs all the above dependencies and builds the C++ example.
-
-## Use
-
-We show a few example commands to export and execute a Mask R-CNN model in C++.
-
-* `export-method=tracing, format=torchscript`:
-```
-./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
-    --output ./output --export-method tracing --format torchscript \
-    MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \
-    MODEL.DEVICE cuda
-
-./build/torchscript_mask_rcnn output/model.ts input.jpg tracing
-```
-
-* `export-method=scripting, format=torchscript`:
-```
-./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
-    --output ./output --export-method scripting --format torchscript \
-    MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \
-
-./build/torchscript_mask_rcnn output/model.ts input.jpg scripting
-```
-
-* `export-method=caffe2_tracing, format=torchscript`:
-
-```
-./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
-    --output ./output --export-method caffe2_tracing --format torchscript \
-    MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \
-
-./build/torchscript_mask_rcnn output/model.ts input.jpg caffe2_tracing
-```
-
-
-## Notes:
-
-1. Tracing/Caffe2-tracing requires valid weights & sample inputs.
-   Therefore the above commands require pre-trained models and [COCO dataset](https://detectron2.readthedocs.io/tutorials/builtin_datasets.html).
-   You can modify the script to obtain sample inputs in other ways instead of from COCO.
-
-2. `--run-eval` is implemented only for tracing mode
-   to evaluate the exported model using the dataset in the config.
-   It's recommended to always verify the accuracy in case the conversion is not successful.
-   Evaluation can be slow if model is exported to CPU or dataset is too large ("coco_2017_val_100" is a small subset of COCO useful for evaluation).
-   `caffe2_tracing` accuracy may be slightly different (within 0.1 AP) from original model due to numerical precisions between different runtime.
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/display-grid.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/display-grid.js
deleted file mode 100644
index 290ec8b01fbd20f08657eccce5002fd197311b74..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/display-grid.js
+++ /dev/null
@@ -1,21 +0,0 @@
-let Value = require('../value')
-
-class DisplayGrid extends Value {
-  constructor(name, prefixes) {
-    super(name, prefixes)
-    if (name === 'display-grid') {
-      this.name = 'grid'
-    }
-  }
-
-  /**
-   * Faster check for flex value
-   */
-  check(decl) {
-    return decl.prop === 'display' && decl.value === this.name
-  }
-}
-
-DisplayGrid.names = ['display-grid', 'inline-grid']
-
-module.exports = DisplayGrid
diff --git a/spaces/ysharma/LLaVA_v1/llava/eval/eval_science_qa.py b/spaces/ysharma/LLaVA_v1/llava/eval/eval_science_qa.py
deleted file mode 100644
index e1b3ce52fd6d922f247cc0c48409e88d5af3f204..0000000000000000000000000000000000000000
--- a/spaces/ysharma/LLaVA_v1/llava/eval/eval_science_qa.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import argparse
-import json
-import os
-import re
-import random
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--base-dir', type=str)
-    parser.add_argument('--result-file', type=str)
-    parser.add_argument('--output-file', type=str)
-    parser.add_argument('--output-result', type=str)
-    parser.add_argument('--split', type=str, default='test')
-    parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
-    return parser.parse_args()
-
-
-def convert_caps(results):
-    fakecaps = []
-    for result in results:
-        image_id = result['question_id']
-        caption = result['text']
-        fakecaps.append({"image_id": int(image_id), "caption": caption})
-    return fakecaps
-
-
-def get_pred_idx(prediction, choices, options):
-    """
-    Get the index (e.g. 2) from the prediction (e.g. 'C')
-    """
-    if prediction in options[:len(choices)]:
-        return options.index(prediction)
-    else:
-        return random.choice(range(len(choices)))
-
-
-if __name__ == "__main__":
-    args = get_args()
-
-    base_dir = args.base_dir
-    split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
-    problems = json.load(open(os.path.join(base_dir, "problems.json")))
-    predictions = [json.loads(line) for line in open(args.result_file)]
-    predictions = {pred['question_id']: pred for pred in predictions}
-    split_problems = {idx: problems[idx] for idx in split_indices}
-
-    results = {'correct': [], 'incorrect': []}
-    sqa_results = {}
-    sqa_results['acc'] = None
-    sqa_results['correct'] = None
-    sqa_results['count'] = None
-    sqa_results['results'] = {}
-    sqa_results['outputs'] = {}
-
-    for prob_id, prob in split_problems.items():
-        if prob_id not in predictions:
-            continue
-        pred = predictions[prob_id]
-        pred_text = pred['text']
-
-        pattern = re.compile(r'The answer is ([A-Z]).')
-        res = pattern.findall(pred_text)
-        if len(res) == 1:
-            answer = res[0]  # 'A', 'B', ...
-        else:
-            answer = "FAILED"
-
-        pred_idx = get_pred_idx(answer, prob['choices'], args.options)
-
-        analysis = {
-            'question_id': prob_id,
-            'parsed_ans': answer,
-            'ground_truth': args.options[prob['answer']],
-            'question': pred['prompt'],
-            'pred': pred_text,
-            'is_multimodal': '<image>' in pred['prompt'],
-        }
-
-        sqa_results['results'][prob_id] = get_pred_idx(answer, prob['choices'], args.options)
-        sqa_results['outputs'][prob_id] = pred_text
-
-        if pred_idx == prob['answer']:
-            results['correct'].append(analysis)
-        else:
-            results['incorrect'].append(analysis)
-
-    correct = len(results['correct'])
-    total = len(results['correct']) + len(results['incorrect'])
-    print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%')
-
-    sqa_results['acc'] = correct / total * 100
-    sqa_results['correct'] = correct
-    sqa_results['count'] = total
-
-    with open(args.output_file, 'w') as f:
-        json.dump(results, f, indent=2)
-    with open(args.output_result, 'w') as f:
-        json.dump(sqa_results, f, indent=2)
diff --git a/spaces/zee2221/Hyper_Bot/style.css b/spaces/zee2221/Hyper_Bot/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/zee2221/Hyper_Bot/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/zeno-ml/imagenette/Dockerfile b/spaces/zeno-ml/imagenette/Dockerfile
deleted file mode 100644
index b5a55ae3b5928ddc6dca732a5adb4b758c5e1512..0000000000000000000000000000000000000000
--- a/spaces/zeno-ml/imagenette/Dockerfile
+++ /dev/null
@@ -1,22 +0,0 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-
-FROM python:3.8
-
-RUN useradd -m -u 1000 user
-USER user
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-WORKDIR $HOME/app
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-ADD --chown=user ./.zeno_cache $HOME/app/.zeno_cache
-RUN chown user:user -R $HOME/app
-
-COPY ./requirements.txt /code/requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-
-CMD ["zeno", "config.toml"]
\ No newline at end of file
diff --git a/spaces/zhang-wei-jian/docker/node_modules/has-symbols/test/index.js b/spaces/zhang-wei-jian/docker/node_modules/has-symbols/test/index.js
deleted file mode 100644
index 352129ca356c8ce4ba2e5e6b78c092f401e0f2da..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/has-symbols/test/index.js
+++ /dev/null
@@ -1,22 +0,0 @@
-'use strict';
-
-var test = require('tape');
-var hasSymbols = require('../');
-var runSymbolTests = require('./tests');
-
-test('interface', function (t) {
-	t.equal(typeof hasSymbols, 'function', 'is a function');
-	t.equal(typeof hasSymbols(), 'boolean', 'returns a boolean');
-	t.end();
-});
-
-test('Symbols are supported', { skip: !hasSymbols() }, function (t) {
-	runSymbolTests(t);
-	t.end();
-});
-
-test('Symbols are not supported', { skip: hasSymbols() }, function (t) {
-	t.equal(typeof Symbol, 'undefined', 'global Symbol is undefined');
-	t.equal(typeof Object.getOwnPropertySymbols, 'undefined', 'Object.getOwnPropertySymbols does not exist');
-	t.end();
-});
diff --git a/spaces/zhtet/RegBotBeta/assets/prompts/__init__.py b/spaces/zhtet/RegBotBeta/assets/prompts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/zideliu/styledrop/open_clip/tokenizer.py b/spaces/zideliu/styledrop/open_clip/tokenizer.py
deleted file mode 100644
index 23fcfcbcb4ca051ba5bba7520918693001999282..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/open_clip/tokenizer.py
+++ /dev/null
@@ -1,214 +0,0 @@
-""" CLIP tokenizer
-
-Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-import gzip
-import html
-import os
-from functools import lru_cache
-from typing import Union, List
-
-import ftfy
-import regex as re
-import torch
-
-# https://stackoverflow.com/q/62691279
-import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-
-@lru_cache()
-def default_bpe():
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a significant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r'\s+', ' ', text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        if not special_tokens:
-            special_tokens = ['<start_of_text>', '<end_of_text>']
-        else:
-            special_tokens = ['<start_of_text>', '<end_of_text>'] + special_tokens
-        vocab.extend(special_tokens)
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {t:t for t in special_tokens}
-        special = "|".join(special_tokens)
-        self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-        self.vocab_size = len(self.encoder)
-        self.all_special_ids = [self.encoder[t] for t in special_tokens]
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text
-
-
-_tokenizer = SimpleTokenizer()
-
-def decode(output_ids: torch.Tensor):
-    output_ids = output_ids.cpu().numpy()
-    return _tokenizer.decode(output_ids)
-
-def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor:
-    """
-    Returns the tokenized representation of given input string(s)
-
-    Parameters
-    ----------
-    texts : Union[str, List[str]]
-        An input string or a list of input strings to tokenize
-    context_length : int
-        The context length to use; all CLIP models use 77 as the context length
-
-    Returns
-    -------
-    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
-    """
-    if isinstance(texts, str):
-        texts = [texts]
-
-    sot_token = _tokenizer.encoder["<start_of_text>"]
-    eot_token = _tokenizer.encoder["<end_of_text>"]
-    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
-    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-    for i, tokens in enumerate(all_tokens):
-        if len(tokens) > context_length:
-            tokens = tokens[:context_length]  # Truncate
-            tokens[-1] = eot_token
-        result[i, :len(tokens)] = torch.tensor(tokens)
-
-    return result
-
-
-class HFTokenizer:
-    """HuggingFace tokenizer wrapper"""
-
-    def __init__(self, tokenizer_name: str):
-        from transformers import AutoTokenizer
-        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
-
-    def save_pretrained(self, dest):
-        self.tokenizer.save_pretrained(dest)
-
-    def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor:
-        # same cleaning as for default tokenizer, except lowercasing
-        # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance
-        if isinstance(texts, str):
-            texts = [texts]
-        texts = [whitespace_clean(basic_clean(text)) for text in texts]
-        input_ids = self.tokenizer(
-            texts,
-            return_tensors='pt',
-            max_length=context_length,
-            padding='max_length',
-            truncation=True,
-        ).input_ids
-        return input_ids
diff --git a/spaces/zombieofCrypto/image_interpreter/README.md b/spaces/zombieofCrypto/image_interpreter/README.md
deleted file mode 100644
index 27a6d71678ca458f619bf4a6f9bfc9aea0d13a3e..0000000000000000000000000000000000000000
--- a/spaces/zombieofCrypto/image_interpreter/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: This AI Knows NBA
-emoji: 🚀
-colorFrom: black
-colorTo: red
-sdk: docker
-pinned: false
-duplicated_from: Panel-Org/panel-template
-app_file: https://huggingface.co/spaces/zombieofCrypto/image_interpreter
-license: llama2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file