Update parquet files (step 96 of 249)

spaces/101-5/gpt4free/testing/binghuan/README.md

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-https://github.com/xtekky/gpt4free/issues/40#issuecomment-1630946450
-flow chat process is realy like real Bing (create conversation,listern to websocket and more)
-so i just use code Bing Provider from https://gitler.moe/g4f/gpt4free/ version and replace API endpoint and some conversationstyles and work fine
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-but bing dont realy support multi/continues conversation (using prompt template from original Provider : def convert(messages) : https://github.com/xtekky/gpt4free/blob/e594500c4e7a8443e9b3f4af755c72f42dae83f0/g4f/Provider/Providers/Bing.py#L322)
-
-also i have problem with emoji encoding idk how to fix that

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cimatron E12 Crack Serial Download The Benefits and Risks of Using a Cracked Version.md

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-<h1>Cimatron E12 Crack Serial Download: How to Get It and Why You Need It</h1>
- <p>If you are looking for a powerful and versatile CAD/CAM software for mold, die, and tool design and manufacturing, you might have heard of Cimatron E12. This software is one of the most popular and widely used solutions in the industry, offering a comprehensive set of features and benefits for various applications. However, you might also know that Cimatron E12 is not cheap, and you might not be able to afford it or justify its cost. That's why you might be interested in finding a way to get Cimatron E12 crack serial download, which can allow you to use the software for free without any limitations. In this article, we will explain what Cimatron E12 is, what a crack serial is and why you need it, and how to download and install Cimatron E12 crack serial safely and easily. Let's get started!</p>
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- <p>Cimatron E12 is a CAD/CAM software that provides a complete solution for mold, die, and tool design and manufacturing. It enables you to design complex parts and assemblies, create high-quality molds and dies, optimize machining processes, and manage your projects efficiently. With Cimatron E12, you can benefit from:</p>
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- <p>To run Cimatron E12 smoothly on your computer, you need to meet the following minimum system requirements:</p>
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-<tr><th>Operating system</th><th>Windows 7/8/10 (64-bit)</th></tr>
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-<tr><td>Memory</td><td>8 GB RAM or higher</td></tr>
-<tr><td>Graphics card</td><td>NVIDIA Quadro or AMD FirePro with 2 GB VRAM or higher</td></tr>
-<tr><td>Hard disk space</td><td>20 GB or higher</td></tr>
-<tr><td>Internet connection</td><td>Required for activation and updates</td></tr>
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- <p>Cimatron E12 is compatible with various file formats, such as IGES, STEP, DXF/DWG, STL, Parasolid, CATIA V4/V5/V6/3DEXPERIENCE, SolidWorks, Solid Edge, NX, Creo, Inventor, and more. You can import and export files easily using the built-in translators.</p>
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- <p>A crack serial is a program or code that can bypass the security measures of a software and unlock its full features without paying for it. In other words, a crack serial can make a software think that it has been activated legally with a valid license key. By using a crack serial for Cimatron E12, you can enjoy all the benefits of the software without spending any money.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cyprus Patch Football Manager 2008.md

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-<h1>How to Install Cyprus Patch for Football Manager 2008</h1>
-<p>If you are a fan of Football Manager 2008, you might want to spice up your game with some extra leagues and teams. One of the most popular patches for FM 2008 is the Cyprus Patch, which adds the Cypriot First and Second Division, as well as the Cup and Super Cup competitions. In this article, we will show you how to download and install the Cyprus Patch for Football Manager 2008.</p>
-<h2>Step 1: Download the Patch</h2>
-<p>The first thing you need to do is to download the patch file from one of the following mirrors[^2^]:</p>
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-<p>Make sure you download the correct version of the patch for your version of Football Manager 2008. The patch is compatible with both PC and Mac versions of the game.</p>
-<h2>Step 2: Extract the Patch</h2>
-<p>Once you have downloaded the patch file, you need to extract it using a program like WinRAR or 7-Zip. You should get a folder called "Cyprus Patch 08" with two subfolders: "graphics" and "editor data".</p>
-<h2>Step 3: Copy the Patch Files</h2>
-<p>Now you need to copy the patch files to your Football Manager 2008 folder. Depending on your operating system and installation location, this folder might be different. Here are some common paths:</p>
-<ul>
-<li>C:\Program Files\Sports Interactive\Football Manager 2008\ (Windows)</li>
-<li>C:\Program Files (x86)\Sports Interactive\Football Manager 2008\ (Windows 64-bit)</li>
-<li>/Users/[username]/Library/Application Support/Sports Interactive/Football Manager 2008/ (Mac)</li>
-</ul>
-<p>You need to copy the "graphics" folder from the patch to the "graphics" folder in your FM 2008 folder. If you don't have a "graphics" folder in your FM 2008 folder, you can create one.</p>
-<p>You also need to copy the "editor data" folder from the patch to the "editor data" folder in your FM 2008 folder. If you don't have an "editor data" folder in your FM 2008 folder, you can create one.</p>
-<h2>Step 4: Start a New Game</h2>
-<p>Now you are ready to start a new game with the Cyprus Patch. Launch Football Manager 2008 and click on "New Game". In the database selection screen, make sure you tick the box next to "Cyprus Patch 08". You can also choose other databases and custom files if you want.</p>
-<p>Then proceed with the game setup as usual. You should be able to select Cyprus as a playable nation and choose from its clubs and leagues. Enjoy!</p>
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-<h2>Step 5: Customize Your Game</h2>
-<p>If you want to make your game more realistic and immersive, you can also download some extra files to enhance the Cyprus Patch. For example, you can download logos, kits, faces, and stadiums for the Cypriot teams and players. You can find these files on various websites and forums dedicated to Football Manager 2008.</p>
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-<h2>Step 6: Have Fun!</h2>
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-<h4>Step 2: Complete Google sign-in to access the Play Store</h4>
-<p>After installing BlueStacks, launch it and sign in with your Google account. This will allow you to access the Google Play Store from BlueStacks.</p>
-<h4>Step 3: Look for Car Simulator 2 in the search bar and click to install</h4 <p>Enter Car Simulator 2 in the search bar at the top right corner of the BlueStacks home screen. You will see the game icon in the search results. Click on it and then click on "Install" to start downloading and installing Car Simulator 2 on your PC.</p>
-<h4>Step 4: Click the Car Simulator 2 icon on the home screen to start playing</h4>
-<p>Once the installation is complete, you will see the Car Simulator 2 icon on the BlueStacks home screen. Click on it and enjoy playing Car Simulator 2 on your laptop!</p>
-<h3>Option 2: Use GameLoop emulator</h3>
-<p>GameLoop is another popular and reliable Android emulator that is specially designed for gaming. It has a smooth and stable performance, high compatibility, low latency, and many optimization features. Here are the steps to download and play Car Simulator 2 on your laptop using GameLoop:</p>
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-<p>Go to [GameLoop website] and click on "Download". Once the download is complete, run the exe file and follow the instructions to install GameLoop on your PC.</p>
-<h4>Step 2: Open GameLoop and search for Car Simulator 2</h4>
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spaces/1phancelerku/anime-remove-background/Cube Rubik Solver APK The Ultimate Guide to Mastering the 3x3 Puzzle.md

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-<h1>Cube Rubik Solver APK: How to Solve the Rubik's Cube with Your Android Device</h1>
- <h2>Introduction</h2>
- <p>Have you ever wondered how to solve the Rubik's Cube, the world's most famous and challenging puzzle? If you have, you are not alone. Millions of people around the world have tried to crack this colorful cube, but only a few have succeeded. Some people spend hours, days, or even years trying to figure out the right moves, while others give up in frustration.</p>
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- <p>But what if we told you that there is an easier way to solve the Rubik's Cube, using only your Android device? Yes, you read that right. Thanks to technology, you can now download and install a Cube Rubik Solver APK, an application that will guide you step by step to solve your cube in minutes or even seconds. Sounds amazing, right?</p>
- <p>In this article, we will explain what a Rubik's Cube is and why it is so popular, what a Cube Rubik Solver APK is and how it works, and how to download and install one on your device. We will also compare some of the most popular Cube Rubik Solver APKs available on the market, and give you some tips on how to use them effectively. By the end of this article, you will be able to solve any Rubik's Cube with ease and impress your friends and family.</p>
- <h2>What is a Rubik's Cube and why is it so popular?</h2>
- <h3>What is a Rubik's Cube?</h3>
- <p>A Rubik's Cube is a three-dimensional puzzle that consists of six faces, each divided into nine smaller squares of one of six colors: white, yellow, red, blue, green, and orange. The goal of the puzzle is to twist and turn the cube until each face has only one color.</p>
- <p>The Rubik's Cube was invented in 1974 by Ernő Rubik, a Hungarian professor of architecture and design. He originally created it as a model to demonstrate three-dimensional geometry, but soon realized that it could also be used as a toy. He patented his invention in 1975 and named it the "Magic Cube".</p>
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- <p>The Magic Cube was first sold in Hungary in 1977, but it was not until 1980 that it became an international sensation. That year, it was renamed the "Rubik's Cube" and licensed by Ideal Toy Corp., an American company that marketed it worldwide. The Rubik's Cube quickly became a best-selling toy, winning several awards and breaking records. By 1982, more than 100 million cubes had been sold.</p>
- <h3>Why is the Rubik's Cube so popular?</h3>
- <p>The Rubik's Cube is not only a toy, but also a cultural icon. It has inspired countless books, movies, songs, games, art works, competitions, and even algorithms. It has been featured in museums, exhibitions, and festivals. It has been used as a symbol of intelligence, creativity, innovation, and problem-solving.</p>
- <p>But it has 43 quintillion possible combinations, making it extremely difficult to solve. It challenges the mind and the patience of anyone who tries it.</p>
- <p>Secondly, it is universal and timeless. It can be enjoyed by anyone, regardless of age, gender, culture, or language. It does not require batteries, electricity, or internet connection. It can be played anywhere, anytime, and with anyone. It never goes out of style or becomes obsolete.</p>
- <p>Thirdly, it is fun and rewarding. It provides a sense of accomplishment and satisfaction when solved. It stimulates the brain and improves memory, concentration, logic, and spatial awareness. It also fosters creativity and curiosity, as there are many ways to approach and solve it.</p>
- <h2>What is a Cube Rubik Solver APK and how does it work?</h2>
- <h3>What is a Cube Rubik Solver APK?</h3>
- <p>A Cube Rubik Solver APK is an application that can be downloaded and installed on an Android device, such as a smartphone or a tablet. It is designed to help users solve the Rubik's Cube by providing them with step-by-step instructions and animations.</p>
- <p>An APK (Android Package Kit) is a file format that contains all the elements needed to run an application on an Android device. It is similar to an EXE file for Windows or a DMG file for Mac. An APK file can be obtained from various sources, such as official app stores, third-party websites, or direct links.</p>
- <h3>How does a Cube Rubik Solver APK work?</h3>
- <p>A Cube Rubik Solver APK works by using the device's camera to scan the scrambled cube and analyze its colors and positions. Then, it applies a mathematical algorithm to find the optimal solution for the cube. Finally, it displays the solution on the screen in the form of text instructions and 3D animations that show how to rotate the cube.</p>
- <p>The user can choose between different modes of solving the cube, such as beginner, intermediate, advanced, or expert. The user can also adjust the speed and difficulty of the solution, as well as the color scheme and orientation of the cube.</p>
- <h2>Benefits of using a Cube Rubik Solver APK</h2>
- <p>Using a Cube Rubik Solver APK has many benefits for users who want to solve the Rubik's Cube. Some of these benefits are:</p>
- <ul>
-<li>It saves time and effort. Instead of spending hours or days trying to figure out the cube by trial and error, users can solve it in minutes or seconds with the help of the app.</li>
-<li>It boosts confidence and motivation. Users can feel proud and happy when they solve the cube with ease and speed. They can also challenge themselves to improve their skills and beat their own records.</li>
-<li>It enhances learning and understanding. Users can learn the logic and principles behind the cube's movements and patterns. They can also understand how the app's algorithm works and how it finds the best solution.</li>
-<li>It increases fun and enjoyment. Users can have fun playing with the cube and watching the app's animations. They can also share their achievements with their friends and family or compete with other users online.</li>
-<h2>How to download and install a Cube Rubik Solver APK</h2>
- <p>If you want to use a Cube Rubik Solver APK on your Android device, you need to download and install it first. Here are the steps you need to follow:</p>
- <h3>Step 1: Find a reliable source for the APK file</h3>
- <p>The first thing you need to do is to find a trustworthy website that offers the APK file of the Cube Rubik Solver app you want to use. There are many websites that claim to provide free and safe APK files, but some of them may contain viruses, malware, or spyware that can harm your device or steal your personal information.</p>
- <p>Therefore, you need to be careful and do some research before downloading any APK file from an unknown source. You can check the reviews, ratings, comments, and feedback of other users who have downloaded the same file. You can also use antivirus software or online tools to scan the file for any potential threats.</p>
- <p>Some of the reputable websites that offer Cube Rubik Solver APK files are:</p>
- <ul>
-<li>[APKPure]: This website provides a large collection of APK files for various apps and games, including Cube Rubik Solver apps. It also updates its files regularly and verifies their security and quality.</li>
-<li>[APKMirror]: This website is another popular source for APK files, especially for apps that are not available on the official app stores. It also ensures that its files are safe and authentic.</li>
-<li>[Uptodown]: This website is a global platform that offers APK files for thousands of apps and games in different languages and regions. It also checks its files for viruses and malware.</li>
-</ul>
- <h3>Step 2: Enable unknown sources on your device settings</h3>
- <p>The next thing you need to do is to allow your device to install apps from unknown sources. This is because most Android devices have a default setting that prevents them from installing apps that are not downloaded from the official app stores, such as Google Play Store or Amazon Appstore.</p>
- <p>To enable unknown sources on your device settings, you need to follow these steps:</p>
- <ol>
-<li>Go to your device's Settings menu and tap on Security or Privacy.</li>
-<li>Find the option that says Unknown Sources or Install Unknown Apps and toggle it on.</li>
-<li>A warning message may appear, asking you to confirm your action. Tap on OK or Allow to proceed.</li>
-</ol>
- <p>Note: The exact steps may vary depending on your device model and Android version. You can also disable this option after installing the app if you want to.</p>
- <h3>Step 3: Download and install the APK file</h3>
- <p>The final thing you need to do is to download and install the APK file of the Cube Rubik Solver app you want to use. To do this, you need to follow these steps:</p>
- <ol>
-<li>Open your device's browser and go to the website where you found the APK file.</li>
-<li>Find the download button or link and tap on it. The file will start downloading automatically.</li>
-<li>Once the download is complete, go to your device's Downloads folder and find the APK file.</li>
-<li>Tap on the file and follow the installation instructions on the screen.</li>
-<li>Wait for the installation process to finish. You may see a message that says App Installed or Done.</li>
-<li>Tap on Open or Launch to start using the app.</li>
-</ol>
- <h2>How to use a Cube Rubik Solver APK</h2>
- <p>Now that you have downloaded and installed a Cube Rubik Solver APK on your Android device, you may be wondering how to use it to solve your Rubik's Cube. Don't worry, it's very easy and intuitive. Here are the steps you need to follow:</p>
- <h3>Step 1: Scan your scrambled cube with your device camera</h3>
- <p>The first thing you need to do is to scan your scrambled cube with your device camera. To do this, you need to follow these steps:</p>
- <ol>
-<li>Open the Cube Rubik Solver app on your device.</li>
-<li>Hold your cube in front of your device camera, making sure that the entire face is visible and well-lit.</li>
-<li>The app will automatically detect the colors and positions of the squares on the face.</li>
-<li>Repeat this process for all six faces of the cube, following the app's instructions on which face to scan next.</li>
-<li>The app will show you a 3D model of your scanned cube on the screen. You can rotate it and zoom in or out to check if it matches your real cube.</li>
-<li>If you notice any errors or discrepancies, you can tap on the Edit button and manually adjust the colors and positions of the squares.</li>
-<li>Once you are satisfied with the scanned cube, tap on the Solve button and wait for the app to find the solution.</li>
-</ol>
- <p>Note: The scanning process may vary depending on the app you are using. Some apps may require you to scan only one face at a time, while others may allow you to scan multiple faces at once. Some apps may also have different color schemes or orientations for the cube. You can check the app's settings or help section for more details.</p>
- <h3>Step 2: Choose a solution mode and follow the instructions</h3>
- <p>The next thing you need to do is to choose a solution mode and follow the instructions. To do this, you need to follow these steps:</p>
- <ol>
-<li>The app will show you several options for solving the cube, such as beginner, intermediate, advanced, or expert. You can choose the one that suits your skill level and preference.</li>
-<li>The app will also show you how many moves and how much time it will take to solve the cube in each mode. You can compare them and select the one that meets your goals.</li>
-<li>The app will then display the solution on the screen in the form of text instructions and 3D animations. The text instructions will tell you which face to rotate and in which direction, using standard notation such as R for right, L for left, U for up, D for down, F for front, B for back, and ' for counterclockwise. The 3D animations will show you how the cube changes after each move.</li>
-<li>You can follow the instructions and animations on your device screen and perform the same moves on your real cube. You can also pause, resume, rewind, or fast-forward the solution as needed.</li>
-<li>The app will keep track of your progress and tell you when you have solved the cube.</li>
-</ol>
- <p>Note: The solution mode and instructions may vary depending on the app you are using. Some apps may have different modes or levels of difficulty, such as easy, normal, hard, or expert. Some apps may also have different notations or formats for the instructions, such as arrows, symbols, or colors. You can check the app's settings or help section for more details.</p>
- <h3>Step 3: Enjoy solving your cube in minutes or seconds</h3>
- <p>The final thing you need to do is to enjoy solving your cube in minutes or seconds. To do this, you need to follow these steps:</p>
- <ol>
-<li>Congratulate yourself for solving the Rubik's Cube with ease and speed. You have just accomplished something that many people find impossible or extremely difficult.</li>
-<li>Feel free to share your achievement with your friends and family or post it on social media. You can also take a screenshot or a video of your solved cube and your solution time.</li>
-<li>If you want to challenge yourself further, you can try to solve the cube faster or with fewer moves. You can also try different types or sizes of cubes, such as 2x2x2, 4x4x4, 5x5x5, or even 7x7x7.</li>
-<li>If you want to learn more about the Rubik's Cube and its history, theory, methods, algorithms, competitions, and culture, you can visit some of these websites:</li>
-<ul>
-<li>[World Cube Association]: This is the official organization that governs Rubik's Cube competitions and records. It also provides information on events, rankings, regulations, and news.</li>
-<li>[Speedsolving.com]: This is a community website for speedcubers and puzzle enthusiasts. It features forums, articles, tutorials, resources, and tools.</li>
-<li>[Ruwix.com]: This is a website dedicated to the Rubik's Cube and other twisty puzzles. It offers online solvers, simulators, timers, guides, and trivia.</li>
-</ul>
-</ol>
- <p>You have now learned how to use a Cube Rubik Solver APK to solve the Rubik's Cube with your Android device. We hope you enjoyed this article and found it useful and informative. If you have any questions or feedback, please feel free to leave a comment below. Happy cubing!</p>
- <h2>Comparison of some popular Cube Rubik Solver APKs</h2>
- <p>As we mentioned earlier, there are many Cube Rubik Solver APKs available on the market, each with its own features and advantages. To help you choose the best one for your needs and preferences, we have compared some of the most popular ones in terms of their ratings, downloads, size, and functionality. Here is a table that summarizes our comparison:</p>
- <table>
-<tr>
-<th>Cube Rubik Solver APK</th>
-<th>Rating</th>
-<th>Downloads</th>
-<th>Size</th>
-<th>Functionality</th>
-</tr>
-<tr>
-<td>AZ Rubik's Cube Solver</td>
-<td>4.5/5</td>
-<td>1M+</td>
-<td>8.9 MB</td>
-<td>- Supports 2x2x2 to 7x7x7 cubes<br>- Offers beginner to expert modes<br>- Allows manual or automatic scanning<br>- Shows text and 3D animations<br>- Has customizable settings and themes<br>- Includes a timer and a leaderboard</td>
-</tr>
-<tr>
-<td>Rubik's Solver</td>
-<td>4.4/5</td>
-<td>500K+</td>
-<td>6.8 MB</td>
-<td>- Supports 3x3x3 cubes only<br>- Offers beginner to advanced modes<br>- Requires manual scanning<br>- Shows text and 2D animations<br>- Has simple settings and interface<br>- Includes a timer and a history</td>
-</tr>
-<tr>
-<td>Online Rubik's Cube Solver</td>
-<td>4.3/5</td>
-<td>100K+</td>
-<td>4.1 MB</td>
-<td>- Supports 2x2x2 to 6x6x6 cubes<br>- Offers easy to expert modes<br>- Allows manual or automatic scanning<br>- Shows text and 3D animations<br>- Has adjustable settings and colors<br>- Includes a timer and a statistics</td>
-</tr>
-</table>
- <p>Note: The information in this table is based on the data available at the time of writing this article. It may change or vary depending on the updates or changes made by the app developers or providers.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In this article, we have covered the following topics:</p>
- <ul>
-<li>What is a Rubik's Cube and why is it so popular?</li>
-<li>What is a Cube Rubik Solver APK and how does it work?</li>
-<li>Benefits of using a Cube Rubik Solver APK</li>
-<li>How to download and install a Cube Rubik Solver APK</li>
-<li>How to use a Cube Rubik Solver APK</li>
-<li>Comparison of some popular Cube Rubik Solver APKs</li>
-</ul>
- <p>We have also provided you with some tips, resources, and examples to help you solve the Rubik's Cube with ease and speed using your Android device.</p>
- <h3>Call to action and final remarks</h3>
- <p>If you are interested in trying out a Cube Rubik Solver APK, we recommend you to download one of the apps we have compared in this article and follow our instructions on how to use it. You can also explore other apps that may suit your needs and preferences better.</p>
- <p>We hope you enjoyed this article and found it useful and informative. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you.</p>
- <p>Thank you for reading this article and happy cubing!</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
- <p>Here are some of the most common questions that people ask about Cube Rubik Solver APKs:</p>
- <h3>Q: Is using a Cube Rubik Solver APK cheating?</h3>
- <p>A: No, using a A: No, using a Cube Rubik Solver APK is not cheating. It is a tool that can help you learn and improve your skills in solving the Rubik's Cube. It can also provide you with fun and entertainment. However, if you are participating in a competition or a challenge, you should not use the app, as it may be considered unfair or dishonest by the organizers or the other participants.</p>
- <h3>Q: How accurate and reliable are Cube Rubik Solver APKs?</h3>
- <p>A: Cube Rubik Solver APKs are generally accurate and reliable, as they use mathematical algorithms and formulas to find the optimal solution for the cube. However, some factors may affect their accuracy and reliability, such as the quality of the device camera, the lighting conditions, the color recognition, and the scanning process. Therefore, you should always check the scanned cube and the solution on the screen before following them on your real cube. You should also make sure that the app is updated and compatible with your device.</p>
- <h3>Q: Are Cube Rubik Solver APKs safe and secure?</h3>
- <p>A: Cube Rubik Solver APKs are usually safe and secure, as they do not require any special permissions or access to your device's data or functions. However, as with any APK file, you should always download and install them from reputable and trustworthy sources, such as official app stores or websites. You should also scan them for any viruses, malware, or spyware before installing them on your device. You should also read the app's privacy policy and terms of service to understand how it collects, uses, and protects your personal information.</p>
- <h3>Q: Do Cube Rubik Solver APKs work offline?</h3>
- <p>A: Most Cube Rubik Solver APKs work offline, as they do not require any internet connection to scan the cube or find the solution. However, some apps may require an internet connection for some features or functions, such as downloading updates, accessing online resources, or sharing your results. You should check the app's description or settings to see if it works offline or not.</p>
- <h3>Q: Can I use Cube Rubik Solver APKs on other devices besides Android?</h3>
- <p>A: No, Cube Rubik Solver APKs are designed to work only on Android devices, such as smartphones or tablets. They are not compatible with other devices or operating systems, such as iOS, Windows, Mac, or Linux. However, there may be other apps or websites that offer similar services for other devices or platforms. You can search for them online or ask for recommendations from other users.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Draw Bridge Puzzle APK and Test Your Drawing Skills.md

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-</blockquote>
- <h2>How to Download and Install Draw Bridge Puzzle APK?</h2>
-<p>If you are ready to join the fun and challenge of Draw Bridge Puzzle APK, then follow these simple steps to download and install the game on your Android device:</p>
- <h3>The steps and requirements to get the game on your device</h3>
-<ol>
-<li>Make sure that your device meets the minimum requirements for the game, which are: Android 4.4 or higher, 50 MB of free storage space, and an internet connection.</li>
-<li>Go to the official website of Draw Bridge Puzzle APK, which is [Draw Bridge Puzzle APK], or scan the QR code below with your device's camera.</li>
-<li>Click on the download button and wait for the game file to be downloaded on your device.</li>
-<li>Once the download is complete, locate the game 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 for the game to run properly.</li>
-<li>After the installation is done, you can launch the game from your device's home screen or app drawer.</li>
-</ol>
- <h4>The safety and security of the game file</h4>
-<p>You might be wondering if Draw Bridge Puzzle APK is safe and secure to download and install on your device. The answer is yes, it is. The game file is scanned by various antivirus programs and verified by Google Play Protect, which ensures that it is free from any malware, viruses, or harmful code. The game also respects your privacy and does not collect or share any personal or sensitive information from your device.</p>
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-<p>In conclusion, Draw Bridge Puzzle APK is a fun and challenging game that will test your logic and creativity while providing you with hours of entertainment and relaxation. The game has hundreds of levels with different scenarios and obstacles, simple and colorful graphics, smooth animations, relaxing music, various modes, settings, cars, skins, leaderboards, achievements, hints, ads, and more. The game is easy to play but hard to master, so you will never get bored or frustrated. The game is also safe and secure to download and install on your Android device, as it is scanned by antivirus programs and verified by Google Play Protect. So what are you waiting for? Download Draw Bridge Puzzle APK today and enjoy drawing bridges to solve puzzles!</p>
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-<ul>
-<li><strong>Q: How much does Draw Bridge Puzzle APK cost?</strong></li>
-<li>A: Draw Bridge Puzzle APK is free to download and play. However, it contains some optional in-app purchases that can enhance your gaming experience, such as removing ads, buying hints, or unlocking cars and skins.</li>
-<li><strong>Q: How can I contact the developer of Draw Bridge Puzzle APK?</strong></li>
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spaces/1phancelerku/anime-remove-background/Download Last Pirate Island Survival MOD APK Terbaru and Experience a Unique Survival Game.md

@@ -1,110 +0,0 @@
-
-<h1>Download Last Pirate Island Survival Mod Apk Terbaru: A Guide for Android Users</h1>
- <p>Do you love pirate games? Do you enjoy survival and adventure games? If you answered yes to both questions, then you should try Last Pirate Island Survival, a game that combines both genres in an immersive and realistic way. In this game, you will be stranded on a deserted island, where you will have to fight for your life against zombies, wild animals, and other pirates. You will also have to craft weapons, build shelters, and explore the island for resources and secrets. Sounds exciting, right?</p>
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- <p>But wait, there's more. You can also download the mod apk version of the game, which will give you access to unlimited money, free craft, god mode, and other features that will make your gameplay more fun and easy. In this article, we will tell you everything you need to know about Last Pirate Island Survival and how to download the mod apk terbaru version for your Android device. Let's get started!</p>
- <h2>What is Last Pirate Island Survival?</h2>
- <p>Last Pirate Island Survival is a survival and pirate game developed by RetroStyle Games UA. It was released in 2019 and has since gained over 10 million downloads on Google Play Store. The game is set in a post-apocalyptic world, where a deadly virus has turned most people into zombies. You are one of the few survivors who managed to escape to an island, where you hope to find a safe haven. However, you soon realize that the island is not as peaceful as it seems. You will have to face many dangers and enemies, such as:</p>
- <ul>
-<li>Zombies: These are the most common threat on the island. They are fast, aggressive, and hungry for flesh. You will need to use your weapons and skills to fend them off or avoid them.</li>
-<li>Wild animals: The island is home to various animals, such as wolves, bears, crocodiles, and sharks. Some of them are friendly and can be tamed as pets, while others are hostile and will attack you on sight.</li>
-<li>Other pirates: You are not the only pirate on the island. There are other groups of pirates who want to take over the island and loot its treasures. You will have to fight them or ally with them depending on your strategy.</li>
-</ul>
- <h3>Features of the game</h3>
- <p>Last Pirate Island Survival has many features that make it a unique and enjoyable game. Some of them are:</p>
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-<li>Realistic graphics and sound effects: The game has stunning 3D graphics that create a realistic atmosphere of the island. You can see the details of the environment, such as the trees, rocks, water, and weather. The sound effects also add to the immersion, such as the waves crashing, the wind blowing, and the zombies groaning.</li>
-<li>Crafting and building system: The game allows you to craft various items and weapons using the resources you find on the island. You can make swords, axes, bows, guns, bombs, and more. You can also build shelters, traps, fences, and other structures to protect yourself from enemies and weather.</li>
-<li>Exploration and discovery: The game has a large open world map that you can explore freely. You can find hidden caves, shipwrecks, treasure chests, and other secrets on the island. You can also interact with different objects and NPCs on the island.</li>
-<li>Pet system: The game lets you tame some of the animals on the island as your pets. You can feed them, play with them, and use them as your companions in combat or exploration.</li>
-</ul>
- <h3>Challenges and tips</h3>
- <p>Last Pirate Island Survival is not an easy game. It has many challenges that will test your skills and patience. Some of them are:</p>
- <ul>
-<li>Hunger and thirst: You will have to monitor your hunger and thirst levels constantly. If they drop too low, you will lose health and stamina. You will have to find food and water sources on the island, such as fruits, vegetables, fish, and rainwater. You can also cook your food using a fire or a stove.</li>
-<li>Health and stamina: You will also have to watch your health and stamina levels. If you get injured or exhausted, you will need to heal yourself using bandages, potions, or resting. You can also improve your health and stamina by leveling up your skills and attributes.</li>
-<li>Enemies and combat: You will have to face many enemies on the island, such as zombies, animals, and pirates. You will need to use your weapons and tactics to defeat them or escape from them. You can also use stealth, traps, and explosives to gain an advantage in combat.</li>
-</ul>
- <p>Here are some tips that will help you survive and thrive on the island:</p>
- <ul>
-<li>Gather resources: The island has many resources that you can collect and use for crafting and building. You can chop trees, mine rocks, hunt animals, fish in the sea, and loot chests. You can also trade with other pirates or raid their camps for more resources.</li>
-<li>Upgrade your equipment: You can upgrade your weapons, armor, and tools using the crafting system. You can also find or buy better equipment from other pirates or merchants. Upgrading your equipment will increase your damage, defense, and durability.</li>
-<li>Explore the island: The island has many secrets and surprises that you can discover by exploring it. You can find hidden locations, quests, items, and events that will enrich your gameplay. You can also learn more about the island's history and lore by reading notes, books, and journals.</li>
-<li>Customize your character: You can customize your character's appearance, skills, and attributes using the game's options. You can choose your gender, hair style, skin color, clothes, and accessories. You can also level up your skills and attributes by gaining experience points from various activities.</li>
-</ul>
- <h2>Why download the mod apk version?</h2>
- <p>If you want to enjoy Last Pirate Island Survival without any limitations or restrictions, you should download the mod apk version of the game. The mod apk version is a modified version of the game that has some extra features that are not available in the original version. Some of these features are:</p>
- <h3>Benefits of the mod apk</h3>
- <p>The mod apk version of Last Pirate Island Survival has many benefits that will make your gameplay more fun and easy. Some of them are:</p>
- <ul>
-<li>Unlimited money: The mod apk version gives you unlimited money that you can use to buy anything you want in the game. You can buy weapons, armor, tools, items, pets, and more without worrying about running out of money.</li>
-<li>Free craft: The mod apk version allows you to craft anything you want without needing any resources or materials. You can craft weapons, armor, tools, items, buildings, and more without having to gather any resources.</li>
-<li>God mode: The mod apk version enables you to activate god mode that makes you invincible in the game. You can survive any attack from enemies or hazards without losing any health or stamina.</li>
-<li>No ads: The mod apk version removes all the ads that appear in the game. You can play the game without any interruptions or distractions from annoying ads.</li>
-</ul>
- <h3>How to download and install the mod apk</h3>
- <p>If you want to download and install the mod apk version of Last Pirate Island Survival on your Android device, you will need to follow these simple steps:</p>
- <ol>
-<li>Download the mod apk file from a reliable source on the internet. You can search for "download last pirate island survival mod apk terbaru" on Google or any other search engine.</li>
-<li>Enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store.</li>
-<li>Locate the downloaded mod apk file on your device storage and tap on it to install it.</li>
-<li>Wait for the installation process to finish and then launch the game from your app drawer or home screen.</li>
-<li>Enjoy playing Last Pirate Island Survival with unlimited money, free craft, god mode, and no ads!</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Last Pirate Island Survival is a survival and pirate game that offers a lot of fun and excitement for Android users. You can experience a realistic and immersive gameplay on a deserted island full of dangers and secrets. You can also download the mod apk version of the game that gives you access to unlimited money, free craft, god mode, and no ads. If you are looking for a game that combines survival and adventure with pirate themes, then you should try Last Pirate Island Survival today!</p>
- <h3>Summary of the main points</h3> <p>In this article, we have covered the following main points:</p>
- <ul>
-<li>Last Pirate Island Survival is a survival and pirate game that lets you explore, craft, build, and fight on a deserted island.</li>
-<li>The game has realistic graphics, sound effects, and gameplay that create an immersive and challenging experience.</li>
-<li>The game has many features, such as crafting, building, exploration, discovery, pet system, customization, and more.</li>
-<li>The game has many challenges, such as hunger, thirst, health, stamina, enemies, and combat.</li>
-<li>The game also has a mod apk version that gives you unlimited money, free craft, god mode, and no ads.</li>
-<li>The mod apk version can be downloaded and installed easily on your Android device by following some simple steps.</li>
-</ul>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Last Pirate Island Survival and its mod apk version:</p>
- <ol>
-<li>Q: Is Last Pirate Island Survival free to play?</li>
-<li>A: Yes, Last Pirate Island Survival is free to play. However, it contains some in-app purchases that can enhance your gameplay. You can also download the mod apk version that gives you unlimited money for free.</li>
-<li>Q: Is Last Pirate Island Survival online or offline?</li>
-<li>A: Last Pirate Island Survival is an offline game. You can play it without an internet connection. However, some features may require an internet connection, such as updates, cloud save, and social media integration.</li>
-<li>Q: Is Last Pirate Island Survival safe to download and install?</li>
-<li>A: Yes, Last Pirate Island Survival is safe to download and install. The game does not contain any viruses or malware that can harm your device. However, you should always download the game from a trusted source and enable unknown sources on your device settings before installing it.</li>
-<li>Q: What are the minimum requirements to play Last Pirate Island Survival?</li>
-<li>A: The minimum requirements to play Last Pirate Island Survival are:</li>
-<ul>
-<li>Android version: 4.4 or higher</li>
-<li>RAM: 2 GB or higher</li>
-<li>Storage space: 200 MB or higher</li>
-</ul>
-<li>Q: How can I contact the developers of Last Pirate Island Survival?</li>
-<li>A: You can contact the developers of Last Pirate Island Survival by sending them an email at [email protected] or visiting their website at https://retrostylegames.com/.</li>
-</ol></p> 197e85843d<br />
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spaces/4Taps/SadTalker/src/face3d/data/template_dataset.py

@@ -1,75 +0,0 @@
-"""Dataset class template
-
-This module provides a template for users to implement custom datasets.
-You can specify '--dataset_mode template' to use this dataset.
-The class name should be consistent with both the filename and its dataset_mode option.
-The filename should be <dataset_mode>_dataset.py
-The class name should be <Dataset_mode>Dataset.py
-You need to implement the following functions:
-    -- <modify_commandline_options>:　Add dataset-specific options and rewrite default values for existing options.
-    -- <__init__>: Initialize this dataset class.
-    -- <__getitem__>: Return a data point and its metadata information.
-    -- <__len__>: Return the number of images.
-"""
-from data.base_dataset import BaseDataset, get_transform
-# from data.image_folder import make_dataset
-# from PIL import Image
-
-
-class TemplateDataset(BaseDataset):
-    """A template dataset class for you to implement custom datasets."""
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new dataset-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        parser.add_argument('--new_dataset_option', type=float, default=1.0, help='new dataset option')
-        parser.set_defaults(max_dataset_size=10, new_dataset_option=2.0)  # specify dataset-specific default values
-        return parser
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-
-        A few things can be done here.
-        - save the options (have been done in BaseDataset)
-        - get image paths and meta information of the dataset.
-        - define the image transformation.
-        """
-        # save the option and dataset root
-        BaseDataset.__init__(self, opt)
-        # get the image paths of your dataset;
-        self.image_paths = []  # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root
-        # define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function
-        self.transform = get_transform(opt)
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index -- a random integer for data indexing
-
-        Returns:
-            a dictionary of data with their names. It usually contains the data itself and its metadata information.
-
-        Step 1: get a random image path: e.g., path = self.image_paths[index]
-        Step 2: load your data from the disk: e.g., image = Image.open(path).convert('RGB').
-        Step 3: convert your data to a PyTorch tensor. You can use helpder functions such as self.transform. e.g., data = self.transform(image)
-        Step 4: return a data point as a dictionary.
-        """
-        path = 'temp'    # needs to be a string
-        data_A = None    # needs to be a tensor
-        data_B = None    # needs to be a tensor
-        return {'data_A': data_A, 'data_B': data_B, 'path': path}
-
-    def __len__(self):
-        """Return the total number of images."""
-        return len(self.image_paths)

spaces/7hao/bingo/src/lib/bots/bing/types.ts

@@ -1,259 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
-  Creative = 'Creative',
-  Balanced = 'Balanced',
-  Precise = 'Precise'
-}
-
-export enum ErrorCode {
-  CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
-  BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
-  BING_FORBIDDEN = 'BING_FORBIDDEN',
-  BING_CAPTCHA = 'BING_CAPTCHA',
-  THROTTLE_LIMIT = 'THROTTLE_LIMIT',
-  NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
-  UNKOWN_ERROR = 'UNKOWN_ERROR',
-  NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
-  code: ErrorCode
-  constructor(message: string, code: ErrorCode) {
-    super(message)
-    this.code = code
-  }
-}
-
-export type ChatMessageModel = {
-  id: string
-  author: Author
-  text: string
-  error?: ChatError
-  throttling?: Throttling
-  sourceAttributions?: SourceAttribution[]
-  suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
-  messages: ChatMessageModel[]
-}
-
-export type Event =
-  | {
-      type: 'UPDATE_ANSWER'
-      data: {
-        text: string
-        spokenText?: string
-        sourceAttributions?: SourceAttribution[]
-        suggestedResponses?: SuggestedResponse[]
-        throttling?: Throttling
-      }
-    }
-  | {
-      type: 'DONE'
-    }
-  | {
-      type: 'ERROR'
-      error: ChatError
-    }
-
-export interface SendMessageParams<T> {
-  prompt: string
-  imageUrl?: string
-  options: T
-  onEvent: (event: Event) => void
-  signal?: AbortSignal
-}
-
-export interface ConversationResponse {
-  conversationId: string
-  clientId: string
-  conversationSignature: string
-  result: {
-    value: string
-    message?: string
-  }
-}
-
-export interface Telemetry {
-  metrics?: null
-  startTime: string
-}
-
-export interface ChatUpdateArgument {
-  messages?: ChatResponseMessage[]
-  throttling?: Throttling
-  requestId: string
-  result: null
-}
-
-export type ChatUpdateCompleteResponse = {
-  type: 2
-  invocationId: string
-  item: ChatResponseItem
-} | {
-  type: 1
-  target: string
-  arguments: ChatUpdateArgument[]
-} | {
-  type: 3
-  invocationId: string
-} | {
-  type: 6 | 7
-}
-
-export interface ChatRequestResult {
-  value: string
-  serviceVersion: string
-  error?: string
-}
-
-export interface ChatResponseItem {
-  messages: ChatResponseMessage[]
-  firstNewMessageIndex: number
-  suggestedResponses: null
-  conversationId: string
-  requestId: string
-  conversationExpiryTime: string
-  telemetry: Telemetry
-  result: ChatRequestResult
-  throttling: Throttling
-}
-export enum InvocationEventType {
-  Invocation = 1,
-  StreamItem = 2,
-  Completion = 3,
-  StreamInvocation = 4,
-  CancelInvocation = 5,
-  Ping = 6,
-  Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
-  conversationId: string
-  clientId: string
-  conversationSignature: string
-  invocationId: number
-  conversationStyle: BingConversationStyle
-  prompt: string
-  imageUrl?: string
-}
-
-export interface BingChatResponse {
-  conversationSignature: string
-  conversationId: string
-  clientId: string
-  invocationId: number
-  conversationExpiryTime: Date
-  response: string
-  details: ChatResponseMessage
-}
-
-export interface Throttling {
-  maxNumLongDocSummaryUserMessagesInConversation: number
-  maxNumUserMessagesInConversation: number
-  numLongDocSummaryUserMessagesInConversation: number
-  numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
-  text: string
-  spokenText?: string
-  author: string
-  createdAt: Date
-  timestamp: Date
-  messageId: string
-  requestId: string
-  offense: string
-  adaptiveCards: AdaptiveCard[]
-  sourceAttributions: SourceAttribution[]
-  feedback: Feedback
-  contentOrigin: string
-  messageType?: string
-  contentType?: string
-  privacy: null
-  suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
-  type: string
-  version: string
-  body: Body[]
-}
-
-export interface Body {
-  type: string
-  text: string
-  wrap: boolean
-  size?: string
-}
-
-export interface Feedback {
-  tag: null
-  updatedOn: null
-  type: string
-}
-
-export interface SourceAttribution {
-  providerDisplayName: string
-  seeMoreUrl: string
-  searchQuery: string
-}
-
-export interface SuggestedResponse {
-  text: string
-  author?: Author
-  createdAt?: Date
-  timestamp?: Date
-  messageId?: string
-  messageType?: string
-  offense?: string
-  feedback?: Feedback
-  contentOrigin?: string
-  privacy?: null
-}
-
-export interface KBlobRequest {
-  knowledgeRequest: KnowledgeRequestContext
-  imageBase64?: string
-}
-
-export interface KBlobResponse {
-  blobId: string
-  processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
-  imageInfo:        ImageInfo;
-  knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
-  url?: string;
-}
-
-export interface KnowledgeRequest {
-  invokedSkills:            string[];
-  subscriptionId:           string;
-  invokedSkillsRequestData: InvokedSkillsRequestData;
-  convoData:                ConvoData;
-}
-
-export interface ConvoData {
-  convoid:   string;
-  convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
-  enableFaceBlur: boolean;
-}
-
-export interface FileItem {
-  url: string;
-  status?: 'loading' | 'error' | 'loaded'
-}

spaces/AIFILMS/generate_human_motion/VQ-Trans/VQ_eval.py

@@ -1,95 +0,0 @@
-import os
-import json
-
-import torch
-from torch.utils.tensorboard import SummaryWriter
-import numpy as np
-import models.vqvae as vqvae
-import options.option_vq as option_vq
-import utils.utils_model as utils_model
-from dataset import dataset_TM_eval
-import utils.eval_trans as eval_trans
-from options.get_eval_option import get_opt
-from models.evaluator_wrapper import EvaluatorModelWrapper
-import warnings
-warnings.filterwarnings('ignore')
-import numpy as np
-##### ---- Exp dirs ---- #####
-args = option_vq.get_args_parser()
-torch.manual_seed(args.seed)
-
-args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
-os.makedirs(args.out_dir, exist_ok = True)
-
-##### ---- Logger ---- #####
-logger = utils_model.get_logger(args.out_dir)
-writer = SummaryWriter(args.out_dir)
-logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
-
-
-from utils.word_vectorizer import WordVectorizer
-w_vectorizer = WordVectorizer('./glove', 'our_vab')
-
-
-dataset_opt_path = 'checkpoints/kit/Comp_v6_KLD005/opt.txt' if args.dataname == 'kit' else 'checkpoints/t2m/Comp_v6_KLD005/opt.txt'
-
-wrapper_opt = get_opt(dataset_opt_path, torch.device('cuda'))
-eval_wrapper = EvaluatorModelWrapper(wrapper_opt)
-
-
-##### ---- Dataloader ---- #####
-args.nb_joints = 21 if args.dataname == 'kit' else 22
-
-val_loader = dataset_TM_eval.DATALoader(args.dataname, True, 32, w_vectorizer, unit_length=2**args.down_t)
-
-##### ---- Network ---- #####
-net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers
-                       args.nb_code,
-                       args.code_dim,
-                       args.output_emb_width,
-                       args.down_t,
-                       args.stride_t,
-                       args.width,
-                       args.depth,
-                       args.dilation_growth_rate,
-                       args.vq_act,
-                       args.vq_norm)
-
-if args.resume_pth : 
-    logger.info('loading checkpoint from {}'.format(args.resume_pth))
-    ckpt = torch.load(args.resume_pth, map_location='cpu')
-    net.load_state_dict(ckpt['net'], strict=True)
-net.train()
-net.cuda()
-
-fid = []
-div = []
-top1 = []
-top2 = []
-top3 = []
-matching = []
-repeat_time = 20
-for i in range(repeat_time):
-    best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, writer, logger = eval_trans.evaluation_vqvae(args.out_dir, val_loader, net, logger, writer, 0, best_fid=1000, best_iter=0, best_div=100, best_top1=0, best_top2=0, best_top3=0, best_matching=100, eval_wrapper=eval_wrapper, draw=False, save=False, savenpy=(i==0))
-    fid.append(best_fid)
-    div.append(best_div)
-    top1.append(best_top1)
-    top2.append(best_top2)
-    top3.append(best_top3)
-    matching.append(best_matching)
-print('final result:')
-print('fid: ', sum(fid)/repeat_time)
-print('div: ', sum(div)/repeat_time)
-print('top1: ', sum(top1)/repeat_time)
-print('top2: ', sum(top2)/repeat_time)
-print('top3: ', sum(top3)/repeat_time)
-print('matching: ', sum(matching)/repeat_time)
-
-fid = np.array(fid)
-div = np.array(div)
-top1 = np.array(top1)
-top2 = np.array(top2)
-top3 = np.array(top3)
-matching = np.array(matching)
-msg_final = f"FID. {np.mean(fid):.3f}, conf. {np.std(fid)*1.96/np.sqrt(repeat_time):.3f}, Diversity. {np.mean(div):.3f}, conf. {np.std(div)*1.96/np.sqrt(repeat_time):.3f}, TOP1. {np.mean(top1):.3f}, conf. {np.std(top1)*1.96/np.sqrt(repeat_time):.3f}, TOP2. {np.mean(top2):.3f}, conf. {np.std(top2)*1.96/np.sqrt(repeat_time):.3f}, TOP3. {np.mean(top3):.3f}, conf. {np.std(top3)*1.96/np.sqrt(repeat_time):.3f}, Matching. {np.mean(matching):.3f}, conf. {np.std(matching)*1.96/np.sqrt(repeat_time):.3f}"
-logger.info(msg_final)

spaces/AIFILMS/generate_human_motion/pyrender/pyrender/renderer.py

@@ -1,1339 +0,0 @@
-"""PBR renderer for Python.
-
-Author: Matthew Matl
-"""
-import sys
-
-import numpy as np
-import PIL
-
-from .constants import (RenderFlags, TextAlign, GLTF, BufFlags, TexFlags,
-                        ProgramFlags, DEFAULT_Z_FAR, DEFAULT_Z_NEAR,
-                        SHADOW_TEX_SZ, MAX_N_LIGHTS)
-from .shader_program import ShaderProgramCache
-from .material import MetallicRoughnessMaterial, SpecularGlossinessMaterial
-from .light import PointLight, SpotLight, DirectionalLight
-from .font import FontCache
-from .utils import format_color_vector
-
-from OpenGL.GL import *
-
-
-class Renderer(object):
-    """Class for handling all rendering operations on a scene.
-
-    Note
-    ----
-    This renderer relies on the existence of an OpenGL context and
-    does not create one on its own.
-
-    Parameters
-    ----------
-    viewport_width : int
-        Width of the viewport in pixels.
-    viewport_height : int
-        Width of the viewport height in pixels.
-    point_size : float, optional
-        Size of points in pixels. Defaults to 1.0.
-    """
-
-    def __init__(self, viewport_width, viewport_height, point_size=1.0):
-        self.dpscale = 1
-        # Scaling needed on retina displays
-        if sys.platform == 'darwin':
-            self.dpscale = 2
-
-        self.viewport_width = viewport_width
-        self.viewport_height = viewport_height
-        self.point_size = point_size
-
-        # Optional framebuffer for offscreen renders
-        self._main_fb = None
-        self._main_cb = None
-        self._main_db = None
-        self._main_fb_ms = None
-        self._main_cb_ms = None
-        self._main_db_ms = None
-        self._main_fb_dims = (None, None)
-        self._shadow_fb = None
-        self._latest_znear = DEFAULT_Z_NEAR
-        self._latest_zfar = DEFAULT_Z_FAR
-
-        # Shader Program Cache
-        self._program_cache = ShaderProgramCache()
-        self._font_cache = FontCache()
-        self._meshes = set()
-        self._mesh_textures = set()
-        self._shadow_textures = set()
-        self._texture_alloc_idx = 0
-
-    @property
-    def viewport_width(self):
-        """int : The width of the main viewport, in pixels.
-        """
-        return self._viewport_width
-
-    @viewport_width.setter
-    def viewport_width(self, value):
-        self._viewport_width = self.dpscale * value
-
-    @property
-    def viewport_height(self):
-        """int : The height of the main viewport, in pixels.
-        """
-        return self._viewport_height
-
-    @viewport_height.setter
-    def viewport_height(self, value):
-        self._viewport_height = self.dpscale * value
-
-    @property
-    def point_size(self):
-        """float : The size of screen-space points, in pixels.
-        """
-        return self._point_size
-
-    @point_size.setter
-    def point_size(self, value):
-        self._point_size = float(value)
-
-    def render(self, scene, flags, seg_node_map=None):
-        """Render a scene with the given set of flags.
-
-        Parameters
-        ----------
-        scene : :class:`Scene`
-            A scene to render.
-        flags : int
-            A specification from :class:`.RenderFlags`.
-        seg_node_map : dict
-            A map from :class:`.Node` objects to (3,) colors for each.
-            If specified along with flags set to :attr:`.RenderFlags.SEG`,
-            the color image will be a segmentation image.
-
-        Returns
-        -------
-        color_im : (h, w, 3) uint8 or (h, w, 4) uint8
-            If :attr:`RenderFlags.OFFSCREEN` is set, the color buffer. This is
-            normally an RGB buffer, but if :attr:`.RenderFlags.RGBA` is set,
-            the buffer will be a full RGBA buffer.
-        depth_im : (h, w) float32
-            If :attr:`RenderFlags.OFFSCREEN` is set, the depth buffer
-            in linear units.
-        """
-        # Update context with meshes and textures
-        self._update_context(scene, flags)
-
-        # Render necessary shadow maps
-        if not bool(flags & RenderFlags.DEPTH_ONLY or flags & RenderFlags.SEG):
-            for ln in scene.light_nodes:
-                take_pass = False
-                if (isinstance(ln.light, DirectionalLight) and
-                        bool(flags & RenderFlags.SHADOWS_DIRECTIONAL)):
-                    take_pass = True
-                elif (isinstance(ln.light, SpotLight) and
-                        bool(flags & RenderFlags.SHADOWS_SPOT)):
-                    take_pass = True
-                elif (isinstance(ln.light, PointLight) and
-                        bool(flags & RenderFlags.SHADOWS_POINT)):
-                    take_pass = True
-                if take_pass:
-                    self._shadow_mapping_pass(scene, ln, flags)
-
-        # Make forward pass
-        retval = self._forward_pass(scene, flags, seg_node_map=seg_node_map)
-
-        # If necessary, make normals pass
-        if flags & (RenderFlags.VERTEX_NORMALS | RenderFlags.FACE_NORMALS):
-            self._normals_pass(scene, flags)
-
-        # Update camera settings for retrieving depth buffers
-        self._latest_znear = scene.main_camera_node.camera.znear
-        self._latest_zfar = scene.main_camera_node.camera.zfar
-
-        return retval
-
-    def render_text(self, text, x, y, font_name='OpenSans-Regular',
-                    font_pt=40, color=None, scale=1.0,
-                    align=TextAlign.BOTTOM_LEFT):
-        """Render text into the current viewport.
-
-        Note
-        ----
-        This cannot be done into an offscreen buffer.
-
-        Parameters
-        ----------
-        text : str
-            The text to render.
-        x : int
-            Horizontal pixel location of text.
-        y : int
-            Vertical pixel location of text.
-        font_name : str
-            Name of font, from the ``pyrender/fonts`` folder, or
-            a path to a ``.ttf`` file.
-        font_pt : int
-            Height of the text, in font points.
-        color : (4,) float
-            The color of the text. Default is black.
-        scale : int
-            Scaling factor for text.
-        align : int
-            One of the :class:`TextAlign` options which specifies where the
-            ``x`` and ``y`` parameters lie on the text. For example,
-            :attr:`TextAlign.BOTTOM_LEFT` means that ``x`` and ``y`` indicate
-            the position of the bottom-left corner of the textbox.
-        """
-        x *= self.dpscale
-        y *= self.dpscale
-        font_pt *= self.dpscale
-
-        if color is None:
-            color = np.array([0.0, 0.0, 0.0, 1.0])
-        else:
-            color = format_color_vector(color, 4)
-
-        # Set up viewport for render
-        self._configure_forward_pass_viewport(0)
-
-        # Load font
-        font = self._font_cache.get_font(font_name, font_pt)
-        if not font._in_context():
-            font._add_to_context()
-
-        # Load program
-        program = self._get_text_program()
-        program._bind()
-
-        # Set uniforms
-        p = np.eye(4)
-        p[0,0] = 2.0 / self.viewport_width
-        p[0,3] = -1.0
-        p[1,1] = 2.0 / self.viewport_height
-        p[1,3] = -1.0
-        program.set_uniform('projection', p)
-        program.set_uniform('text_color', color)
-
-        # Draw text
-        font.render_string(text, x, y, scale, align)
-
-    def read_color_buf(self):
-        """Read and return the current viewport's color buffer.
-
-        Alpha cannot be computed for an on-screen buffer.
-
-        Returns
-        -------
-        color_im : (h, w, 3) uint8
-            The color buffer in RGB byte format.
-        """
-        # Extract color image from frame buffer
-        width, height = self.viewport_width, self.viewport_height
-        glBindFramebuffer(GL_READ_FRAMEBUFFER, 0)
-        glReadBuffer(GL_FRONT)
-        color_buf = glReadPixels(0, 0, width, height, GL_RGB, GL_UNSIGNED_BYTE)
-
-        # Re-format them into numpy arrays
-        color_im = np.frombuffer(color_buf, dtype=np.uint8)
-        color_im = color_im.reshape((height, width, 3))
-        color_im = np.flip(color_im, axis=0)
-
-        # Resize for macos if needed
-        if sys.platform == 'darwin':
-            color_im = self._resize_image(color_im, True)
-
-        return color_im
-
-    def read_depth_buf(self):
-        """Read and return the current viewport's color buffer.
-
-        Returns
-        -------
-        depth_im : (h, w) float32
-            The depth buffer in linear units.
-        """
-        width, height = self.viewport_width, self.viewport_height
-        glBindFramebuffer(GL_READ_FRAMEBUFFER, 0)
-        glReadBuffer(GL_FRONT)
-        depth_buf = glReadPixels(
-            0, 0, width, height, GL_DEPTH_COMPONENT, GL_FLOAT
-        )
-
-        depth_im = np.frombuffer(depth_buf, dtype=np.float32)
-        depth_im = depth_im.reshape((height, width))
-        depth_im = np.flip(depth_im, axis=0)
-
-        inf_inds = (depth_im == 1.0)
-        depth_im = 2.0 * depth_im - 1.0
-        z_near, z_far = self._latest_znear, self._latest_zfar
-        noninf = np.logical_not(inf_inds)
-        if z_far is None:
-            depth_im[noninf] = 2 * z_near / (1.0 - depth_im[noninf])
-        else:
-            depth_im[noninf] = ((2.0 * z_near * z_far) /
-                                (z_far + z_near - depth_im[noninf] *
-                                (z_far - z_near)))
-        depth_im[inf_inds] = 0.0
-
-        # Resize for macos if needed
-        if sys.platform == 'darwin':
-            depth_im = self._resize_image(depth_im)
-
-        return depth_im
-
-    def delete(self):
-        """Free all allocated OpenGL resources.
-        """
-        # Free shaders
-        self._program_cache.clear()
-
-        # Free fonts
-        self._font_cache.clear()
-
-        # Free meshes
-        for mesh in self._meshes:
-            for p in mesh.primitives:
-                p.delete()
-
-        # Free textures
-        for mesh_texture in self._mesh_textures:
-            mesh_texture.delete()
-
-        for shadow_texture in self._shadow_textures:
-            shadow_texture.delete()
-
-        self._meshes = set()
-        self._mesh_textures = set()
-        self._shadow_textures = set()
-        self._texture_alloc_idx = 0
-
-        self._delete_main_framebuffer()
-        self._delete_shadow_framebuffer()
-
-    def __del__(self):
-        try:
-            self.delete()
-        except Exception:
-            pass
-
-    ###########################################################################
-    # Rendering passes
-    ###########################################################################
-
-    def _forward_pass(self, scene, flags, seg_node_map=None):
-        # Set up viewport for render
-        self._configure_forward_pass_viewport(flags)
-
-        # Clear it
-        if bool(flags & RenderFlags.SEG):
-            glClearColor(0.0, 0.0, 0.0, 1.0)
-            if seg_node_map is None:
-                seg_node_map = {}
-        else:
-            glClearColor(*scene.bg_color)
-
-        glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
-
-        if not bool(flags & RenderFlags.SEG):
-            glEnable(GL_MULTISAMPLE)
-        else:
-            glDisable(GL_MULTISAMPLE)
-
-        # Set up camera matrices
-        V, P = self._get_camera_matrices(scene)
-
-        program = None
-        # Now, render each object in sorted order
-        for node in self._sorted_mesh_nodes(scene):
-            mesh = node.mesh
-
-            # Skip the mesh if it's not visible
-            if not mesh.is_visible:
-                continue
-
-            # If SEG, set color
-            if bool(flags & RenderFlags.SEG):
-                if node not in seg_node_map:
-                    continue
-                color = seg_node_map[node]
-                if not isinstance(color, (list, tuple, np.ndarray)):
-                    color = np.repeat(color, 3)
-                else:
-                    color = np.asanyarray(color)
-                color = color / 255.0
-
-            for primitive in mesh.primitives:
-
-                # First, get and bind the appropriate program
-                program = self._get_primitive_program(
-                    primitive, flags, ProgramFlags.USE_MATERIAL
-                )
-                program._bind()
-
-                # Set the camera uniforms
-                program.set_uniform('V', V)
-                program.set_uniform('P', P)
-                program.set_uniform(
-                    'cam_pos', scene.get_pose(scene.main_camera_node)[:3,3]
-                )
-                if bool(flags & RenderFlags.SEG):
-                    program.set_uniform('color', color)
-
-                # Next, bind the lighting
-                if not (flags & RenderFlags.DEPTH_ONLY or flags & RenderFlags.FLAT or
-                        flags & RenderFlags.SEG):
-                    self._bind_lighting(scene, program, node, flags)
-
-                # Finally, bind and draw the primitive
-                self._bind_and_draw_primitive(
-                    primitive=primitive,
-                    pose=scene.get_pose(node),
-                    program=program,
-                    flags=flags
-                )
-                self._reset_active_textures()
-
-        # Unbind the shader and flush the output
-        if program is not None:
-            program._unbind()
-        glFlush()
-
-        # If doing offscreen render, copy result from framebuffer and return
-        if flags & RenderFlags.OFFSCREEN:
-            return self._read_main_framebuffer(scene, flags)
-        else:
-            return
-
-    def _shadow_mapping_pass(self, scene, light_node, flags):
-        light = light_node.light
-
-        # Set up viewport for render
-        self._configure_shadow_mapping_viewport(light, flags)
-
-        # Set up camera matrices
-        V, P = self._get_light_cam_matrices(scene, light_node, flags)
-
-        # Now, render each object in sorted order
-        for node in self._sorted_mesh_nodes(scene):
-            mesh = node.mesh
-
-            # Skip the mesh if it's not visible
-            if not mesh.is_visible:
-                continue
-
-            for primitive in mesh.primitives:
-
-                # First, get and bind the appropriate program
-                program = self._get_primitive_program(
-                    primitive, flags, ProgramFlags.NONE
-                )
-                program._bind()
-
-                # Set the camera uniforms
-                program.set_uniform('V', V)
-                program.set_uniform('P', P)
-                program.set_uniform(
-                    'cam_pos', scene.get_pose(scene.main_camera_node)[:3,3]
-                )
-
-                # Finally, bind and draw the primitive
-                self._bind_and_draw_primitive(
-                    primitive=primitive,
-                    pose=scene.get_pose(node),
-                    program=program,
-                    flags=RenderFlags.DEPTH_ONLY
-                )
-                self._reset_active_textures()
-
-        # Unbind the shader and flush the output
-        if program is not None:
-            program._unbind()
-        glFlush()
-
-    def _normals_pass(self, scene, flags):
-        # Set up viewport for render
-        self._configure_forward_pass_viewport(flags)
-        program = None
-
-        # Set up camera matrices
-        V, P = self._get_camera_matrices(scene)
-
-        # Now, render each object in sorted order
-        for node in self._sorted_mesh_nodes(scene):
-            mesh = node.mesh
-
-            # Skip the mesh if it's not visible
-            if not mesh.is_visible:
-                continue
-
-            for primitive in mesh.primitives:
-
-                # Skip objects that don't have normals
-                if not primitive.buf_flags & BufFlags.NORMAL:
-                    continue
-
-                # First, get and bind the appropriate program
-                pf = ProgramFlags.NONE
-                if flags & RenderFlags.VERTEX_NORMALS:
-                    pf = pf | ProgramFlags.VERTEX_NORMALS
-                if flags & RenderFlags.FACE_NORMALS:
-                    pf = pf | ProgramFlags.FACE_NORMALS
-                program = self._get_primitive_program(primitive, flags, pf)
-                program._bind()
-
-                # Set the camera uniforms
-                program.set_uniform('V', V)
-                program.set_uniform('P', P)
-                program.set_uniform('normal_magnitude', 0.05 * primitive.scale)
-                program.set_uniform(
-                    'normal_color', np.array([0.1, 0.1, 1.0, 1.0])
-                )
-
-                # Finally, bind and draw the primitive
-                self._bind_and_draw_primitive(
-                    primitive=primitive,
-                    pose=scene.get_pose(node),
-                    program=program,
-                    flags=RenderFlags.DEPTH_ONLY
-                )
-                self._reset_active_textures()
-
-        # Unbind the shader and flush the output
-        if program is not None:
-            program._unbind()
-        glFlush()
-
-    ###########################################################################
-    # Handlers for binding uniforms and drawing primitives
-    ###########################################################################
-
-    def _bind_and_draw_primitive(self, primitive, pose, program, flags):
-        # Set model pose matrix
-        program.set_uniform('M', pose)
-
-        # Bind mesh buffers
-        primitive._bind()
-
-        # Bind mesh material
-        if not (flags & RenderFlags.DEPTH_ONLY or flags & RenderFlags.SEG):
-            material = primitive.material
-
-            # Bind textures
-            tf = material.tex_flags
-            if tf & TexFlags.NORMAL:
-                self._bind_texture(material.normalTexture,
-                                   'material.normal_texture', program)
-            if tf & TexFlags.OCCLUSION:
-                self._bind_texture(material.occlusionTexture,
-                                   'material.occlusion_texture', program)
-            if tf & TexFlags.EMISSIVE:
-                self._bind_texture(material.emissiveTexture,
-                                   'material.emissive_texture', program)
-            if tf & TexFlags.BASE_COLOR:
-                self._bind_texture(material.baseColorTexture,
-                                   'material.base_color_texture', program)
-            if tf & TexFlags.METALLIC_ROUGHNESS:
-                self._bind_texture(material.metallicRoughnessTexture,
-                                   'material.metallic_roughness_texture',
-                                   program)
-            if tf & TexFlags.DIFFUSE:
-                self._bind_texture(material.diffuseTexture,
-                                   'material.diffuse_texture', program)
-            if tf & TexFlags.SPECULAR_GLOSSINESS:
-                self._bind_texture(material.specularGlossinessTexture,
-                                   'material.specular_glossiness_texture',
-                                   program)
-
-            # Bind other uniforms
-            b = 'material.{}'
-            program.set_uniform(b.format('emissive_factor'),
-                                material.emissiveFactor)
-            if isinstance(material, MetallicRoughnessMaterial):
-                program.set_uniform(b.format('base_color_factor'),
-                                    material.baseColorFactor)
-                program.set_uniform(b.format('metallic_factor'),
-                                    material.metallicFactor)
-                program.set_uniform(b.format('roughness_factor'),
-                                    material.roughnessFactor)
-            elif isinstance(material, SpecularGlossinessMaterial):
-                program.set_uniform(b.format('diffuse_factor'),
-                                    material.diffuseFactor)
-                program.set_uniform(b.format('specular_factor'),
-                                    material.specularFactor)
-                program.set_uniform(b.format('glossiness_factor'),
-                                    material.glossinessFactor)
-
-            # Set blending options
-            if material.alphaMode == 'BLEND':
-                glEnable(GL_BLEND)
-                glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
-            else:
-                glEnable(GL_BLEND)
-                glBlendFunc(GL_ONE, GL_ZERO)
-
-            # Set wireframe mode
-            wf = material.wireframe
-            if flags & RenderFlags.FLIP_WIREFRAME:
-                wf = not wf
-            if (flags & RenderFlags.ALL_WIREFRAME) or wf:
-                glPolygonMode(GL_FRONT_AND_BACK, GL_LINE)
-            else:
-                glPolygonMode(GL_FRONT_AND_BACK, GL_FILL)
-
-            # Set culling mode
-            if material.doubleSided or flags & RenderFlags.SKIP_CULL_FACES:
-                glDisable(GL_CULL_FACE)
-            else:
-                glEnable(GL_CULL_FACE)
-                glCullFace(GL_BACK)
-        else:
-            glEnable(GL_CULL_FACE)
-            glEnable(GL_BLEND)
-            glCullFace(GL_BACK)
-            glBlendFunc(GL_ONE, GL_ZERO)
-            glPolygonMode(GL_FRONT_AND_BACK, GL_FILL)
-
-        # Set point size if needed
-        glDisable(GL_PROGRAM_POINT_SIZE)
-        if primitive.mode == GLTF.POINTS:
-            glEnable(GL_PROGRAM_POINT_SIZE)
-            glPointSize(self.point_size)
-
-        # Render mesh
-        n_instances = 1
-        if primitive.poses is not None:
-            n_instances = len(primitive.poses)
-
-        if primitive.indices is not None:
-            glDrawElementsInstanced(
-                primitive.mode, primitive.indices.size, GL_UNSIGNED_INT,
-                ctypes.c_void_p(0), n_instances
-            )
-        else:
-            glDrawArraysInstanced(
-                primitive.mode, 0, len(primitive.positions), n_instances
-            )
-
-        # Unbind mesh buffers
-        primitive._unbind()
-
-    def _bind_lighting(self, scene, program, node, flags):
-        """Bind all lighting uniform values for a scene.
-        """
-        max_n_lights = self._compute_max_n_lights(flags)
-
-        n_d = min(len(scene.directional_light_nodes), max_n_lights[0])
-        n_s = min(len(scene.spot_light_nodes), max_n_lights[1])
-        n_p = min(len(scene.point_light_nodes), max_n_lights[2])
-        program.set_uniform('ambient_light', scene.ambient_light)
-        program.set_uniform('n_directional_lights', n_d)
-        program.set_uniform('n_spot_lights', n_s)
-        program.set_uniform('n_point_lights', n_p)
-        plc = 0
-        slc = 0
-        dlc = 0
-
-        light_nodes = scene.light_nodes
-        if (len(scene.directional_light_nodes) > max_n_lights[0] or
-                len(scene.spot_light_nodes) > max_n_lights[1] or
-                len(scene.point_light_nodes) > max_n_lights[2]):
-            light_nodes = self._sorted_nodes_by_distance(
-                scene, scene.light_nodes, node
-            )
-
-        for n in light_nodes:
-            light = n.light
-            pose = scene.get_pose(n)
-            position = pose[:3,3]
-            direction = -pose[:3,2]
-
-            if isinstance(light, PointLight):
-                if plc == max_n_lights[2]:
-                    continue
-                b = 'point_lights[{}].'.format(plc)
-                plc += 1
-                shadow = bool(flags & RenderFlags.SHADOWS_POINT)
-                program.set_uniform(b + 'position', position)
-            elif isinstance(light, SpotLight):
-                if slc == max_n_lights[1]:
-                    continue
-                b = 'spot_lights[{}].'.format(slc)
-                slc += 1
-                shadow = bool(flags & RenderFlags.SHADOWS_SPOT)
-                las = 1.0 / max(0.001, np.cos(light.innerConeAngle) -
-                                np.cos(light.outerConeAngle))
-                lao = -np.cos(light.outerConeAngle) * las
-                program.set_uniform(b + 'direction', direction)
-                program.set_uniform(b + 'position', position)
-                program.set_uniform(b + 'light_angle_scale', las)
-                program.set_uniform(b + 'light_angle_offset', lao)
-            else:
-                if dlc == max_n_lights[0]:
-                    continue
-                b = 'directional_lights[{}].'.format(dlc)
-                dlc += 1
-                shadow = bool(flags & RenderFlags.SHADOWS_DIRECTIONAL)
-                program.set_uniform(b + 'direction', direction)
-
-            program.set_uniform(b + 'color', light.color)
-            program.set_uniform(b + 'intensity', light.intensity)
-            # if light.range is not None:
-            #     program.set_uniform(b + 'range', light.range)
-            # else:
-            #     program.set_uniform(b + 'range', 0)
-
-            if shadow:
-                self._bind_texture(light.shadow_texture,
-                                   b + 'shadow_map', program)
-                if not isinstance(light, PointLight):
-                    V, P = self._get_light_cam_matrices(scene, n, flags)
-                    program.set_uniform(b + 'light_matrix', P.dot(V))
-                else:
-                    raise NotImplementedError(
-                        'Point light shadows not implemented'
-                    )
-
-    def _sorted_mesh_nodes(self, scene):
-        cam_loc = scene.get_pose(scene.main_camera_node)[:3,3]
-        solid_nodes = []
-        trans_nodes = []
-        for node in scene.mesh_nodes:
-            mesh = node.mesh
-            if mesh.is_transparent:
-                trans_nodes.append(node)
-            else:
-                solid_nodes.append(node)
-
-        # TODO BETTER SORTING METHOD
-        trans_nodes.sort(
-            key=lambda n: -np.linalg.norm(scene.get_pose(n)[:3,3] - cam_loc)
-        )
-        solid_nodes.sort(
-            key=lambda n: -np.linalg.norm(scene.get_pose(n)[:3,3] - cam_loc)
-        )
-
-        return solid_nodes + trans_nodes
-
-    def _sorted_nodes_by_distance(self, scene, nodes, compare_node):
-        nodes = list(nodes)
-        compare_posn = scene.get_pose(compare_node)[:3,3]
-        nodes.sort(key=lambda n: np.linalg.norm(
-            scene.get_pose(n)[:3,3] - compare_posn)
-        )
-        return nodes
-
-    ###########################################################################
-    # Context Management
-    ###########################################################################
-
-    def _update_context(self, scene, flags):
-
-        # Update meshes
-        scene_meshes = scene.meshes
-
-        # Add new meshes to context
-        for mesh in scene_meshes - self._meshes:
-            for p in mesh.primitives:
-                p._add_to_context()
-
-        # Remove old meshes from context
-        for mesh in self._meshes - scene_meshes:
-            for p in mesh.primitives:
-                p.delete()
-
-        self._meshes = scene_meshes.copy()
-
-        # Update mesh textures
-        mesh_textures = set()
-        for m in scene_meshes:
-            for p in m.primitives:
-                mesh_textures |= p.material.textures
-
-        # Add new textures to context
-        for texture in mesh_textures - self._mesh_textures:
-            texture._add_to_context()
-
-        # Remove old textures from context
-        for texture in self._mesh_textures - mesh_textures:
-            texture.delete()
-
-        self._mesh_textures = mesh_textures.copy()
-
-        shadow_textures = set()
-        for l in scene.lights:
-            # Create if needed
-            active = False
-            if (isinstance(l, DirectionalLight) and
-                    flags & RenderFlags.SHADOWS_DIRECTIONAL):
-                active = True
-            elif (isinstance(l, PointLight) and
-                    flags & RenderFlags.SHADOWS_POINT):
-                active = True
-            elif isinstance(l, SpotLight) and flags & RenderFlags.SHADOWS_SPOT:
-                active = True
-
-            if active and l.shadow_texture is None:
-                l._generate_shadow_texture()
-            if l.shadow_texture is not None:
-                shadow_textures.add(l.shadow_texture)
-
-        # Add new textures to context
-        for texture in shadow_textures - self._shadow_textures:
-            texture._add_to_context()
-
-        # Remove old textures from context
-        for texture in self._shadow_textures - shadow_textures:
-            texture.delete()
-
-        self._shadow_textures = shadow_textures.copy()
-
-    ###########################################################################
-    # Texture Management
-    ###########################################################################
-
-    def _bind_texture(self, texture, uniform_name, program):
-        """Bind a texture to an active texture unit and return
-        the texture unit index that was used.
-        """
-        tex_id = self._get_next_active_texture()
-        glActiveTexture(GL_TEXTURE0 + tex_id)
-        texture._bind()
-        program.set_uniform(uniform_name, tex_id)
-
-    def _get_next_active_texture(self):
-        val = self._texture_alloc_idx
-        self._texture_alloc_idx += 1
-        return val
-
-    def _reset_active_textures(self):
-        self._texture_alloc_idx = 0
-
-    ###########################################################################
-    # Camera Matrix Management
-    ###########################################################################
-
-    def _get_camera_matrices(self, scene):
-        main_camera_node = scene.main_camera_node
-        if main_camera_node is None:
-            raise ValueError('Cannot render scene without a camera')
-        P = main_camera_node.camera.get_projection_matrix(
-            width=self.viewport_width, height=self.viewport_height
-        )
-        pose = scene.get_pose(main_camera_node)
-        V = np.linalg.inv(pose)  # V maps from world to camera
-        return V, P
-
-    def _get_light_cam_matrices(self, scene, light_node, flags):
-        light = light_node.light
-        pose = scene.get_pose(light_node).copy()
-        s = scene.scale
-        camera = light._get_shadow_camera(s)
-        P = camera.get_projection_matrix()
-        if isinstance(light, DirectionalLight):
-            direction = -pose[:3,2]
-            c = scene.centroid
-            loc = c - direction * s
-            pose[:3,3] = loc
-        V = np.linalg.inv(pose)  # V maps from world to camera
-        return V, P
-
-    ###########################################################################
-    # Shader Program Management
-    ###########################################################################
-
-    def _get_text_program(self):
-        program = self._program_cache.get_program(
-            vertex_shader='text.vert',
-            fragment_shader='text.frag'
-        )
-
-        if not program._in_context():
-            program._add_to_context()
-
-        return program
-
-    def _compute_max_n_lights(self, flags):
-        max_n_lights = [MAX_N_LIGHTS, MAX_N_LIGHTS, MAX_N_LIGHTS]
-        n_tex_units = glGetIntegerv(GL_MAX_TEXTURE_IMAGE_UNITS)
-
-        # Reserved texture units: 6
-        #   Normal Map
-        #   Occlusion Map
-        #   Emissive Map
-        #   Base Color or Diffuse Map
-        #   MR or SG Map
-        #   Environment cubemap
-
-        n_reserved_textures = 6
-        n_available_textures = n_tex_units - n_reserved_textures
-
-        # Distribute textures evenly among lights with shadows, with
-        # a preference for directional lights
-        n_shadow_types = 0
-        if flags & RenderFlags.SHADOWS_DIRECTIONAL:
-            n_shadow_types += 1
-        if flags & RenderFlags.SHADOWS_SPOT:
-            n_shadow_types += 1
-        if flags & RenderFlags.SHADOWS_POINT:
-            n_shadow_types += 1
-
-        if n_shadow_types > 0:
-            tex_per_light = n_available_textures // n_shadow_types
-
-            if flags & RenderFlags.SHADOWS_DIRECTIONAL:
-                max_n_lights[0] = (
-                    tex_per_light +
-                    (n_available_textures - tex_per_light * n_shadow_types)
-                )
-            if flags & RenderFlags.SHADOWS_SPOT:
-                max_n_lights[1] = tex_per_light
-            if flags & RenderFlags.SHADOWS_POINT:
-                max_n_lights[2] = tex_per_light
-
-        return max_n_lights
-
-    def _get_primitive_program(self, primitive, flags, program_flags):
-        vertex_shader = None
-        fragment_shader = None
-        geometry_shader = None
-        defines = {}
-
-        if (bool(program_flags & ProgramFlags.USE_MATERIAL) and
-                not flags & RenderFlags.DEPTH_ONLY and
-                not flags & RenderFlags.FLAT and
-                not flags & RenderFlags.SEG):
-            vertex_shader = 'mesh.vert'
-            fragment_shader = 'mesh.frag'
-        elif bool(program_flags & (ProgramFlags.VERTEX_NORMALS |
-                                   ProgramFlags.FACE_NORMALS)):
-            vertex_shader = 'vertex_normals.vert'
-            if primitive.mode == GLTF.POINTS:
-                geometry_shader = 'vertex_normals_pc.geom'
-            else:
-                geometry_shader = 'vertex_normals.geom'
-            fragment_shader = 'vertex_normals.frag'
-        elif flags & RenderFlags.FLAT:
-            vertex_shader = 'flat.vert'
-            fragment_shader = 'flat.frag'
-        elif flags & RenderFlags.SEG:
-            vertex_shader = 'segmentation.vert'
-            fragment_shader = 'segmentation.frag'
-        else:
-            vertex_shader = 'mesh_depth.vert'
-            fragment_shader = 'mesh_depth.frag'
-
-        # Set up vertex buffer DEFINES
-        bf = primitive.buf_flags
-        buf_idx = 1
-        if bf & BufFlags.NORMAL:
-            defines['NORMAL_LOC'] = buf_idx
-            buf_idx += 1
-        if bf & BufFlags.TANGENT:
-            defines['TANGENT_LOC'] = buf_idx
-            buf_idx += 1
-        if bf & BufFlags.TEXCOORD_0:
-            defines['TEXCOORD_0_LOC'] = buf_idx
-            buf_idx += 1
-        if bf & BufFlags.TEXCOORD_1:
-            defines['TEXCOORD_1_LOC'] = buf_idx
-            buf_idx += 1
-        if bf & BufFlags.COLOR_0:
-            defines['COLOR_0_LOC'] = buf_idx
-            buf_idx += 1
-        if bf & BufFlags.JOINTS_0:
-            defines['JOINTS_0_LOC'] = buf_idx
-            buf_idx += 1
-        if bf & BufFlags.WEIGHTS_0:
-            defines['WEIGHTS_0_LOC'] = buf_idx
-            buf_idx += 1
-        defines['INST_M_LOC'] = buf_idx
-
-        # Set up shadow mapping defines
-        if flags & RenderFlags.SHADOWS_DIRECTIONAL:
-            defines['DIRECTIONAL_LIGHT_SHADOWS'] = 1
-        if flags & RenderFlags.SHADOWS_SPOT:
-            defines['SPOT_LIGHT_SHADOWS'] = 1
-        if flags & RenderFlags.SHADOWS_POINT:
-            defines['POINT_LIGHT_SHADOWS'] = 1
-        max_n_lights = self._compute_max_n_lights(flags)
-        defines['MAX_DIRECTIONAL_LIGHTS'] = max_n_lights[0]
-        defines['MAX_SPOT_LIGHTS'] = max_n_lights[1]
-        defines['MAX_POINT_LIGHTS'] = max_n_lights[2]
-
-        # Set up vertex normal defines
-        if program_flags & ProgramFlags.VERTEX_NORMALS:
-            defines['VERTEX_NORMALS'] = 1
-        if program_flags & ProgramFlags.FACE_NORMALS:
-            defines['FACE_NORMALS'] = 1
-
-        # Set up material texture defines
-        if bool(program_flags & ProgramFlags.USE_MATERIAL):
-            tf = primitive.material.tex_flags
-            if tf & TexFlags.NORMAL:
-                defines['HAS_NORMAL_TEX'] = 1
-            if tf & TexFlags.OCCLUSION:
-                defines['HAS_OCCLUSION_TEX'] = 1
-            if tf & TexFlags.EMISSIVE:
-                defines['HAS_EMISSIVE_TEX'] = 1
-            if tf & TexFlags.BASE_COLOR:
-                defines['HAS_BASE_COLOR_TEX'] = 1
-            if tf & TexFlags.METALLIC_ROUGHNESS:
-                defines['HAS_METALLIC_ROUGHNESS_TEX'] = 1
-            if tf & TexFlags.DIFFUSE:
-                defines['HAS_DIFFUSE_TEX'] = 1
-            if tf & TexFlags.SPECULAR_GLOSSINESS:
-                defines['HAS_SPECULAR_GLOSSINESS_TEX'] = 1
-            if isinstance(primitive.material, MetallicRoughnessMaterial):
-                defines['USE_METALLIC_MATERIAL'] = 1
-            elif isinstance(primitive.material, SpecularGlossinessMaterial):
-                defines['USE_GLOSSY_MATERIAL'] = 1
-
-        program = self._program_cache.get_program(
-            vertex_shader=vertex_shader,
-            fragment_shader=fragment_shader,
-            geometry_shader=geometry_shader,
-            defines=defines
-        )
-
-        if not program._in_context():
-            program._add_to_context()
-
-        return program
-
-    ###########################################################################
-    # Viewport Management
-    ###########################################################################
-
-    def _configure_forward_pass_viewport(self, flags):
-
-        # If using offscreen render, bind main framebuffer
-        if flags & RenderFlags.OFFSCREEN:
-            self._configure_main_framebuffer()
-            glBindFramebuffer(GL_DRAW_FRAMEBUFFER, self._main_fb_ms)
-        else:
-            glBindFramebuffer(GL_DRAW_FRAMEBUFFER, 0)
-
-        glViewport(0, 0, self.viewport_width, self.viewport_height)
-        glEnable(GL_DEPTH_TEST)
-        glDepthMask(GL_TRUE)
-        glDepthFunc(GL_LESS)
-        glDepthRange(0.0, 1.0)
-
-    def _configure_shadow_mapping_viewport(self, light, flags):
-        self._configure_shadow_framebuffer()
-        glBindFramebuffer(GL_FRAMEBUFFER, self._shadow_fb)
-        light.shadow_texture._bind()
-        light.shadow_texture._bind_as_depth_attachment()
-        glActiveTexture(GL_TEXTURE0)
-        light.shadow_texture._bind()
-        glDrawBuffer(GL_NONE)
-        glReadBuffer(GL_NONE)
-
-        glClear(GL_DEPTH_BUFFER_BIT)
-        glViewport(0, 0, SHADOW_TEX_SZ, SHADOW_TEX_SZ)
-        glEnable(GL_DEPTH_TEST)
-        glDepthMask(GL_TRUE)
-        glDepthFunc(GL_LESS)
-        glDepthRange(0.0, 1.0)
-        glDisable(GL_CULL_FACE)
-        glDisable(GL_BLEND)
-
-    ###########################################################################
-    # Framebuffer Management
-    ###########################################################################
-
-    def _configure_shadow_framebuffer(self):
-        if self._shadow_fb is None:
-            self._shadow_fb = glGenFramebuffers(1)
-
-    def _delete_shadow_framebuffer(self):
-        if self._shadow_fb is not None:
-            glDeleteFramebuffers(1, [self._shadow_fb])
-
-    def _configure_main_framebuffer(self):
-        # If mismatch with prior framebuffer, delete it
-        if (self._main_fb is not None and
-                self.viewport_width != self._main_fb_dims[0] or
-                self.viewport_height != self._main_fb_dims[1]):
-            self._delete_main_framebuffer()
-
-        # If framebuffer doesn't exist, create it
-        if self._main_fb is None:
-            # Generate standard buffer
-            self._main_cb, self._main_db = glGenRenderbuffers(2)
-
-            glBindRenderbuffer(GL_RENDERBUFFER, self._main_cb)
-            glRenderbufferStorage(
-                GL_RENDERBUFFER, GL_RGBA,
-                self.viewport_width, self.viewport_height
-            )
-
-            glBindRenderbuffer(GL_RENDERBUFFER, self._main_db)
-            glRenderbufferStorage(
-                GL_RENDERBUFFER, GL_DEPTH_COMPONENT24,
-                self.viewport_width, self.viewport_height
-            )
-
-            self._main_fb = glGenFramebuffers(1)
-            glBindFramebuffer(GL_DRAW_FRAMEBUFFER, self._main_fb)
-            glFramebufferRenderbuffer(
-                GL_DRAW_FRAMEBUFFER, GL_COLOR_ATTACHMENT0,
-                GL_RENDERBUFFER, self._main_cb
-            )
-            glFramebufferRenderbuffer(
-                GL_DRAW_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
-                GL_RENDERBUFFER, self._main_db
-            )
-
-            # Generate multisample buffer
-            self._main_cb_ms, self._main_db_ms = glGenRenderbuffers(2)
-            glBindRenderbuffer(GL_RENDERBUFFER, self._main_cb_ms)
-            # glRenderbufferStorageMultisample(
-            #     GL_RENDERBUFFER, 4, GL_RGBA,
-            #     self.viewport_width, self.viewport_height
-            # )
-            # glBindRenderbuffer(GL_RENDERBUFFER, self._main_db_ms)
-            # glRenderbufferStorageMultisample(
-            #     GL_RENDERBUFFER, 4, GL_DEPTH_COMPONENT24,
-            #     self.viewport_width, self.viewport_height
-            # )
-            # 增加这一行
-            num_samples = min(glGetIntegerv(GL_MAX_SAMPLES), 4) # No more than GL_MAX_SAMPLES
-
-            # 其实就是把 4 替换成 num_samples，其余不变
-            glRenderbufferStorageMultisample(GL_RENDERBUFFER, num_samples, GL_RGBA, self.viewport_width, self.viewport_height)
-
-            glBindRenderbuffer(GL_RENDERBUFFER, self._main_db_ms) # 这行不变
-
-            # 这一行也是将 4 替换成 num_samples
-            glRenderbufferStorageMultisample(GL_RENDERBUFFER, num_samples, GL_DEPTH_COMPONENT24, self.viewport_width, self.viewport_height)
-            
-            self._main_fb_ms = glGenFramebuffers(1)
-            glBindFramebuffer(GL_DRAW_FRAMEBUFFER, self._main_fb_ms)
-            glFramebufferRenderbuffer(
-                GL_DRAW_FRAMEBUFFER, GL_COLOR_ATTACHMENT0,
-                GL_RENDERBUFFER, self._main_cb_ms
-            )
-            glFramebufferRenderbuffer(
-                GL_DRAW_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
-                GL_RENDERBUFFER, self._main_db_ms
-            )
-
-            self._main_fb_dims = (self.viewport_width, self.viewport_height)
-
-    def _delete_main_framebuffer(self):
-        if self._main_fb is not None:
-            glDeleteFramebuffers(2, [self._main_fb, self._main_fb_ms])
-        if self._main_cb is not None:
-            glDeleteRenderbuffers(2, [self._main_cb, self._main_cb_ms])
-        if self._main_db is not None:
-            glDeleteRenderbuffers(2, [self._main_db, self._main_db_ms])
-
-        self._main_fb = None
-        self._main_cb = None
-        self._main_db = None
-        self._main_fb_ms = None
-        self._main_cb_ms = None
-        self._main_db_ms = None
-        self._main_fb_dims = (None, None)
-
-    def _read_main_framebuffer(self, scene, flags):
-        width, height = self._main_fb_dims[0], self._main_fb_dims[1]
-
-        # Bind framebuffer and blit buffers
-        glBindFramebuffer(GL_READ_FRAMEBUFFER, self._main_fb_ms)
-        glBindFramebuffer(GL_DRAW_FRAMEBUFFER, self._main_fb)
-        glBlitFramebuffer(
-            0, 0, width, height, 0, 0, width, height,
-            GL_COLOR_BUFFER_BIT, GL_LINEAR
-        )
-        glBlitFramebuffer(
-            0, 0, width, height, 0, 0, width, height,
-            GL_DEPTH_BUFFER_BIT, GL_NEAREST
-        )
-        glBindFramebuffer(GL_READ_FRAMEBUFFER, self._main_fb)
-
-        # Read depth
-        depth_buf = glReadPixels(
-            0, 0, width, height, GL_DEPTH_COMPONENT, GL_FLOAT
-        )
-        depth_im = np.frombuffer(depth_buf, dtype=np.float32)
-        depth_im = depth_im.reshape((height, width))
-        depth_im = np.flip(depth_im, axis=0)
-        inf_inds = (depth_im == 1.0)
-        depth_im = 2.0 * depth_im - 1.0
-        z_near = scene.main_camera_node.camera.znear
-        z_far = scene.main_camera_node.camera.zfar
-        noninf = np.logical_not(inf_inds)
-        if z_far is None:
-            depth_im[noninf] = 2 * z_near / (1.0 - depth_im[noninf])
-        else:
-            depth_im[noninf] = ((2.0 * z_near * z_far) /
-                                (z_far + z_near - depth_im[noninf] *
-                                (z_far - z_near)))
-        depth_im[inf_inds] = 0.0
-
-        # Resize for macos if needed
-        if sys.platform == 'darwin':
-            depth_im = self._resize_image(depth_im)
-
-        if flags & RenderFlags.DEPTH_ONLY:
-            return depth_im
-
-        # Read color
-        if flags & RenderFlags.RGBA:
-            color_buf = glReadPixels(
-                0, 0, width, height, GL_RGBA, GL_UNSIGNED_BYTE
-            )
-            color_im = np.frombuffer(color_buf, dtype=np.uint8)
-            color_im = color_im.reshape((height, width, 4))
-        else:
-            color_buf = glReadPixels(
-                0, 0, width, height, GL_RGB, GL_UNSIGNED_BYTE
-            )
-            color_im = np.frombuffer(color_buf, dtype=np.uint8)
-            color_im = color_im.reshape((height, width, 3))
-        color_im = np.flip(color_im, axis=0)
-
-        # Resize for macos if needed
-        if sys.platform == 'darwin':
-            color_im = self._resize_image(color_im, True)
-
-        return color_im, depth_im
-
-    def _resize_image(self, value, antialias=False):
-        """If needed, rescale the render for MacOS."""
-        img = PIL.Image.fromarray(value)
-        resample = PIL.Image.NEAREST
-        if antialias:
-            resample = PIL.Image.BILINEAR
-        size = (self.viewport_width // self.dpscale,
-                self.viewport_height // self.dpscale)
-        img = img.resize(size, resample=resample)
-        return np.array(img)
-
-    ###########################################################################
-    # Shadowmap Debugging
-    ###########################################################################
-
-    def _forward_pass_no_reset(self, scene, flags):
-        # Set up camera matrices
-        V, P = self._get_camera_matrices(scene)
-
-        # Now, render each object in sorted order
-        for node in self._sorted_mesh_nodes(scene):
-            mesh = node.mesh
-
-            # Skip the mesh if it's not visible
-            if not mesh.is_visible:
-                continue
-
-            for primitive in mesh.primitives:
-
-                # First, get and bind the appropriate program
-                program = self._get_primitive_program(
-                    primitive, flags, ProgramFlags.USE_MATERIAL
-                )
-                program._bind()
-
-                # Set the camera uniforms
-                program.set_uniform('V', V)
-                program.set_uniform('P', P)
-                program.set_uniform(
-                    'cam_pos', scene.get_pose(scene.main_camera_node)[:3,3]
-                )
-
-                # Next, bind the lighting
-                if not flags & RenderFlags.DEPTH_ONLY and not flags & RenderFlags.FLAT:
-                    self._bind_lighting(scene, program, node, flags)
-
-                # Finally, bind and draw the primitive
-                self._bind_and_draw_primitive(
-                    primitive=primitive,
-                    pose=scene.get_pose(node),
-                    program=program,
-                    flags=flags
-                )
-                self._reset_active_textures()
-
-        # Unbind the shader and flush the output
-        if program is not None:
-            program._unbind()
-        glFlush()
-
-    def _render_light_shadowmaps(self, scene, light_nodes, flags, tile=False):
-        glBindFramebuffer(GL_DRAW_FRAMEBUFFER, 0)
-        glClearColor(*scene.bg_color)
-        glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
-        glEnable(GL_DEPTH_TEST)
-        glDepthMask(GL_TRUE)
-        glDepthFunc(GL_LESS)
-        glDepthRange(0.0, 1.0)
-
-        w = self.viewport_width
-        h = self.viewport_height
-
-        num_nodes = len(light_nodes)
-        viewport_dims = {
-            (0, 2): [0, h // 2, w // 2, h],
-            (1, 2): [w // 2, h // 2, w, h],
-            (0, 3): [0, h // 2, w // 2, h],
-            (1, 3): [w // 2, h // 2, w, h],
-            (2, 3): [0, 0, w // 2, h // 2],
-            (0, 4): [0, h // 2, w // 2, h],
-            (1, 4): [w // 2, h // 2, w, h],
-            (2, 4): [0, 0, w // 2, h // 2],
-            (3, 4): [w // 2, 0, w, h // 2]
-        }
-
-        if tile:
-            for i, ln in enumerate(light_nodes):
-                light = ln.light
-
-                if light.shadow_texture is None:
-                    raise ValueError('Light does not have a shadow texture')
-
-                glViewport(*viewport_dims[(i, num_nodes + 1)])
-
-                program = self._get_debug_quad_program()
-                program._bind()
-                self._bind_texture(light.shadow_texture, 'depthMap', program)
-                self._render_debug_quad()
-                self._reset_active_textures()
-                glFlush()
-            i += 1
-            glViewport(*viewport_dims[(i, num_nodes + 1)])
-            self._forward_pass_no_reset(scene, flags)
-        else:
-            for i, ln in enumerate(light_nodes):
-                light = ln.light
-
-                if light.shadow_texture is None:
-                    raise ValueError('Light does not have a shadow texture')
-
-                glViewport(0, 0, self.viewport_width, self.viewport_height)
-
-                program = self._get_debug_quad_program()
-                program._bind()
-                self._bind_texture(light.shadow_texture, 'depthMap', program)
-                self._render_debug_quad()
-                self._reset_active_textures()
-                glFlush()
-                return
-
-    def _get_debug_quad_program(self):
-        program = self._program_cache.get_program(
-            vertex_shader='debug_quad.vert',
-            fragment_shader='debug_quad.frag'
-        )
-        if not program._in_context():
-            program._add_to_context()
-        return program
-
-    def _render_debug_quad(self):
-        x = glGenVertexArrays(1)
-        glBindVertexArray(x)
-        glDrawArrays(GL_TRIANGLES, 0, 6)
-        glBindVertexArray(0)
-        glDeleteVertexArrays(1, [x])

spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/ps_adv_mlm.py

@@ -1,233 +0,0 @@
-import torch
-from torch import nn
-from tasks.tts.ps_adv import PortaSpeechAdvTask, FastSpeechTask
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.utils.nn.seq_utils import group_hidden_by_segs
-
-
-class PortaSpeechAdvMLMTask(PortaSpeechAdvTask):
-
-    def build_scheduler(self, optimizer):
-        return [
-            FastSpeechTask.build_scheduler(self, optimizer[0]), # Generator Scheduler
-            torch.optim.lr_scheduler.StepLR(optimizer=optimizer[1], # Discriminator Scheduler
-                **hparams["discriminator_scheduler_params"]),
-        ]
-
-    def on_before_optimization(self, opt_idx):
-        if opt_idx in [0, 2]:
-            nn.utils.clip_grad_norm_(self.dp_params, hparams['clip_grad_norm'])
-            if self.use_bert:
-                nn.utils.clip_grad_norm_(self.bert_params, hparams['clip_grad_norm'])
-                nn.utils.clip_grad_norm_(self.gen_params_except_bert_and_dp, hparams['clip_grad_norm'])
-            else:
-                nn.utils.clip_grad_norm_(self.gen_params_except_dp, hparams['clip_grad_norm'])
-        else:
-            nn.utils.clip_grad_norm_(self.disc_params, hparams["clip_grad_norm"])
-
-    def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx):
-        if self.scheduler is not None:
-            self.scheduler[0].step(self.global_step // hparams['accumulate_grad_batches'])
-            self.scheduler[1].step(self.global_step // hparams['accumulate_grad_batches'])
-
-
-    def _training_step(self, sample, batch_idx, optimizer_idx):
-        loss_output = {}
-        loss_weights = {}
-        disc_start = self.global_step >= hparams["disc_start_steps"] and hparams['lambda_mel_adv'] > 0
-        if optimizer_idx == 0:
-            #######################
-            #      Generator      #
-            #######################
-            loss_output, model_out = self.run_model(sample, infer=False)
-            self.model_out_gt = self.model_out = \
-                {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)}
-            if disc_start:
-                mel_p = model_out['mel_out']
-                if hasattr(self.model, 'out2mel'):
-                    mel_p = self.model.out2mel(mel_p)
-                o_ = self.mel_disc(mel_p)
-                p_, pc_ = o_['y'], o_['y_c']
-                if p_ is not None:
-                    loss_output['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size()))
-                    loss_weights['a'] = hparams['lambda_mel_adv']
-                if pc_ is not None:
-                    loss_output['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size()))
-                    loss_weights['ac'] = hparams['lambda_mel_adv']
-            else:
-                return None
-
-            loss_output2, model_out2 = self.run_contrastive_learning(sample)
-            loss_output.update(loss_output2)
-            model_out.update(model_out2)
-            
-        elif optimizer_idx == 1:
-            #######################
-            #    Discriminator    #
-            #######################
-            if disc_start and self.global_step % hparams['disc_interval'] == 0:
-                model_out = self.model_out_gt
-                mel_g = sample['mels']
-                mel_p = model_out['mel_out']
-                o = self.mel_disc(mel_g)
-                p, pc = o['y'], o['y_c']
-                o_ = self.mel_disc(mel_p)
-                p_, pc_ = o_['y'], o_['y_c']
-                if p_ is not None:
-                    loss_output["r"] = self.mse_loss_fn(p, p.new_ones(p.size()))
-                    loss_output["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size()))
-                if pc_ is not None:
-                    loss_output["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size()))
-                    loss_output["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size()))
-
-        total_loss = sum([loss_weights.get(k, 1) * v for k, v in loss_output.items() if isinstance(v, torch.Tensor) and v.requires_grad])
-        loss_output['batch_size'] = sample['txt_tokens'].size()[0]
-        return total_loss, loss_output
-
-    def run_contrastive_learning(self, sample):
-        losses = {}
-        outputs = {}
-
-        bert = self.model.encoder.bert.bert
-        bert_for_mlm = self.model.encoder.bert
-        pooler = self.model.encoder.pooler
-        sim = self.model.encoder.sim
-        tokenizer = self.model.encoder.tokenizer
-        ph_encoder = self.model.encoder
-
-        if hparams['lambda_cl'] > 0:
-            if hparams.get("cl_version", "v1") == "v1":
-                cl_feats = sample['cl_feats']
-                bs, _, t = cl_feats['cl_input_ids'].shape
-                cl_input_ids = cl_feats['cl_input_ids'].reshape([bs*2, t])
-                cl_attention_mask = cl_feats['cl_attention_mask'].reshape([bs*2, t])
-                cl_token_type_ids = cl_feats['cl_token_type_ids'].reshape([bs*2, t])
-                cl_output = bert(cl_input_ids, attention_mask=cl_attention_mask,token_type_ids=cl_token_type_ids,)
-                pooler_output = pooler(cl_attention_mask, cl_output)
-                pooler_output = pooler_output.reshape([bs, 2, -1])
-                z1, z2 = pooler_output[:,0], pooler_output[:,1]
-
-                cos_sim = sim(z1.unsqueeze(1), z2.unsqueeze(0))
-                labels = torch.arange(cos_sim.size(0)).long().to(z1.device)
-                ce_fn = nn.CrossEntropyLoss()
-                cl_loss = ce_fn(cos_sim, labels)
-                losses['cl_v'] = cl_loss.detach()
-                losses['cl'] = cl_loss * hparams['lambda_cl']
-            elif hparams['cl_version'] == "v2":
-                # use the output of ph encoder as sentence embedding
-                cl_feats = sample['cl_feats']
-                bs, _, t = cl_feats['cl_input_ids'].shape
-                cl_input_ids = cl_feats['cl_input_ids'].reshape([bs*2, t])
-                cl_attention_mask = cl_feats['cl_attention_mask'].reshape([bs*2, t])
-                cl_token_type_ids = cl_feats['cl_token_type_ids'].reshape([bs*2, t])
-                txt_tokens = sample['txt_tokens']
-                bert_feats = sample['bert_feats']
-                src_nonpadding = (txt_tokens > 0).float()[:, :, None]
-                ph_encoder_out1 = ph_encoder(txt_tokens, bert_feats=bert_feats, ph2word=sample['ph2word']) * src_nonpadding
-                ph_encoder_out2 = ph_encoder(txt_tokens, bert_feats=bert_feats, ph2word=sample['ph2word']) * src_nonpadding
-                # word_encoding1 = group_hidden_by_segs(ph_encoder_out1, sample['ph2word'], sample['ph2word'].max().item())
-                # word_encoding2 = group_hidden_by_segs(ph_encoder_out2, sample['ph2word'], sample['ph2word'].max().item())
-                z1 = ((ph_encoder_out1 * src_nonpadding).sum(1) / src_nonpadding.sum(1))
-                z2 = ((ph_encoder_out2 * src_nonpadding).sum(1) / src_nonpadding.sum(1))
-
-                cos_sim = sim(z1.unsqueeze(1), z2.unsqueeze(0))
-                labels = torch.arange(cos_sim.size(0)).long().to(z1.device)
-                ce_fn = nn.CrossEntropyLoss()
-                cl_loss = ce_fn(cos_sim, labels)
-                losses['cl_v'] = cl_loss.detach()
-                losses['cl'] = cl_loss * hparams['lambda_cl']
-            elif hparams['cl_version'] == "v3":
-                # use the word-level contrastive learning
-                cl_feats = sample['cl_feats']
-                bs, _, t = cl_feats['cl_input_ids'].shape
-                cl_input_ids = cl_feats['cl_input_ids'].reshape([bs*2, t])
-                cl_attention_mask = cl_feats['cl_attention_mask'].reshape([bs*2, t])
-                cl_token_type_ids = cl_feats['cl_token_type_ids'].reshape([bs*2, t])
-                cl_output = bert(cl_input_ids, attention_mask=cl_attention_mask,token_type_ids=cl_token_type_ids,)
-                cl_output = cl_output.last_hidden_state.reshape([-1, 768]) # [bs*2,t_w,768] ==> [bs*2*t_w, 768]
-                cl_word_out = cl_output[cl_attention_mask.reshape([-1]).bool()] # [num_word*2, 768]
-                cl_word_out = cl_word_out.view([-1, 2, 768])
-                z1_total, z2_total = cl_word_out[:,0], cl_word_out[:,1] # [num_word, 768]
-                ce_fn = nn.CrossEntropyLoss()
-                start_idx = 0
-                lengths = cl_attention_mask.sum(-1)
-                cl_loss_accu = 0
-                for i in range(bs):
-                    length = lengths[i]
-                    z1 = z1_total[start_idx:start_idx + length]
-                    z2 = z2_total[start_idx:start_idx + length]
-                    start_idx += length
-                    cos_sim = sim(z1.unsqueeze(1), z2.unsqueeze(0))
-                    labels = torch.arange(cos_sim.size(0)).long().to(z1.device)
-                    cl_loss_accu += ce_fn(cos_sim, labels) * length
-                cl_loss = cl_loss_accu / lengths.sum()
-                losses['cl_v'] = cl_loss.detach()
-                losses['cl'] = cl_loss * hparams['lambda_cl']
-            elif hparams['cl_version'] == "v4":
-                # with Wiki dataset
-                cl_feats = sample['cl_feats']
-                bs, _, t = cl_feats['cl_input_ids'].shape
-                cl_input_ids = cl_feats['cl_input_ids'].reshape([bs*2, t])
-                cl_attention_mask = cl_feats['cl_attention_mask'].reshape([bs*2, t])
-                cl_token_type_ids = cl_feats['cl_token_type_ids'].reshape([bs*2, t])
-                cl_output = bert(cl_input_ids, attention_mask=cl_attention_mask,token_type_ids=cl_token_type_ids,)
-                pooler_output = pooler(cl_attention_mask, cl_output)
-                pooler_output = pooler_output.reshape([bs, 2, -1])
-                z1, z2 = pooler_output[:,0], pooler_output[:,1]
-
-                cos_sim = sim(z1.unsqueeze(1), z2.unsqueeze(0))
-                labels = torch.arange(cos_sim.size(0)).long().to(z1.device)
-                ce_fn = nn.CrossEntropyLoss()
-                cl_loss = ce_fn(cos_sim, labels)
-                losses['cl_v'] = cl_loss.detach()
-                losses['cl'] = cl_loss * hparams['lambda_cl']
-            elif hparams['cl_version'] == "v5":
-                # with NLI dataset
-                cl_feats = sample['cl_feats']
-                cl_input_ids = cl_feats['sent0']['cl_input_ids']
-                cl_attention_mask = cl_feats['sent0']['cl_attention_mask']
-                cl_token_type_ids = cl_feats['sent0']['cl_token_type_ids']
-                cl_output = bert(cl_input_ids, attention_mask=cl_attention_mask,token_type_ids=cl_token_type_ids,)
-                z1 = pooler_output_sent0 = pooler(cl_attention_mask, cl_output)
-
-                cl_input_ids = cl_feats['sent1']['cl_input_ids']
-                cl_attention_mask = cl_feats['sent1']['cl_attention_mask']
-                cl_token_type_ids = cl_feats['sent1']['cl_token_type_ids']
-                cl_output = bert(cl_input_ids, attention_mask=cl_attention_mask,token_type_ids=cl_token_type_ids,)
-                z2 = pooler_output_sent1 = pooler(cl_attention_mask, cl_output)
-
-                cl_input_ids = cl_feats['hard_neg']['cl_input_ids']
-                cl_attention_mask = cl_feats['hard_neg']['cl_attention_mask']
-                cl_token_type_ids = cl_feats['hard_neg']['cl_token_type_ids']
-                cl_output = bert(cl_input_ids, attention_mask=cl_attention_mask,token_type_ids=cl_token_type_ids,)
-                z3 = pooler_output_neg = pooler(cl_attention_mask, cl_output)
-
-                cos_sim = sim(z1.unsqueeze(1), z2.unsqueeze(0))
-                z1_z3_cos = sim(z1.unsqueeze(1), z3.unsqueeze(0))
-                cos_sim = torch.cat([cos_sim, z1_z3_cos], 1) # [n_sent, n_sent * 2]
-                labels = torch.arange(cos_sim.size(0)).long().to(cos_sim.device) # [n_sent, ]
-                ce_fn = nn.CrossEntropyLoss()
-                cl_loss = ce_fn(cos_sim, labels)
-                losses['cl_v'] = cl_loss.detach()
-                losses['cl'] = cl_loss * hparams['lambda_cl']
-            else:
-                raise NotImplementedError()
-
-        if hparams['lambda_mlm'] > 0:
-            cl_feats = sample['cl_feats']
-            mlm_input_ids = cl_feats['mlm_input_ids']
-            bs, t = mlm_input_ids.shape
-            mlm_input_ids = mlm_input_ids.view((-1, mlm_input_ids.size(-1)))
-            mlm_labels = cl_feats['mlm_labels']
-            mlm_labels = mlm_labels.view(-1, mlm_labels.size(-1))
-            mlm_attention_mask = cl_feats['mlm_attention_mask']
-
-            prediction_scores = bert_for_mlm(mlm_input_ids, mlm_attention_mask).logits
-            ce_fn = nn.CrossEntropyLoss(reduction="none")
-            mlm_loss = ce_fn(prediction_scores.view(-1, tokenizer.vocab_size), mlm_labels.view(-1))
-            mlm_loss = mlm_loss[mlm_labels.view(-1)>=0].mean()
-            losses['mlm'] = mlm_loss * hparams['lambda_mlm']
-            losses['mlm_v'] = mlm_loss.detach()
-
-        return losses, outputs
-        

spaces/Aashiue/speech_to_text/app.py

@@ -1,25 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
-transcribe = pipeline("automatic-speech-recognition")
-
-model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
-
-tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-one-to-many-mmt", src_lang="en_XX")
-def speech_to_text(audio):
-    text = transcribe(audio)["text"]
-
-    model_inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-    generated_tokens = model.generate(
-    **model_inputs,
-    forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"]
-    )
-
-    translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-
-    return translation
-    
-gr.Interface(
-    fn=speech_to_text, 
-    inputs=gr.Audio(source="microphone", type="filepath"), 
-    outputs="text").launch()

spaces/AchyuthGamer/OpenGPT/client/css/message-input.css

@@ -1,27 +0,0 @@
-#message-input {
-	margin-right: 30px;
-	height: 64px;
-}
-
-#message-input::-webkit-scrollbar {
-	width: 5px;
-}
-
-#message-input::-webkit-scrollbar-track {
-	background: #f1f1f1;
-}
-
-#message-input::-webkit-scrollbar-thumb {
-	background: #c7a2ff;
-}
-
-#message-input::-webkit-scrollbar-thumb:hover {
-	background: #8b3dff;
-}
-
-@media screen and (max-width: 360px) {
-	#message-input {
-		margin: 0;
-	}
-}
-

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Cromicle.py

@@ -1,50 +0,0 @@
-from __future__ import annotations
-
-from aiohttp import ClientSession
-from hashlib import sha256
-from typing import AsyncGenerator, Dict, List
-
-from .base_provider import AsyncGeneratorProvider
-from .helper import format_prompt
-
-
-class Cromicle(AsyncGeneratorProvider):
-    url: str = 'https://cromicle.top'
-    working: bool = True
-    supports_gpt_35_turbo: bool = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: List[Dict[str, str]],
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncGenerator[str, None]:
-        async with ClientSession(
-            headers=_create_header()
-        ) as session:
-            async with session.post(
-                f'{cls.url}/chat',
-                proxy=proxy,
-                json=_create_payload(format_prompt(messages))
-            ) as response:
-                response.raise_for_status()
-                async for stream in response.content.iter_any():
-                    if stream:
-                        yield stream.decode()
-
-
-def _create_header() -> Dict[str, str]:
-    return {
-        'accept': '*/*',
-        'content-type': 'application/json',
-    }
-
-
-def _create_payload(message: str) -> Dict[str, str]:
-    return {
-        'message': message,
-        'token': 'abc',
-        'hash': sha256('abc'.encode() + message.encode()).hexdigest()
-    }

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/ModalMethods.js

@@ -1,41 +0,0 @@
-import { Modal, ModalClose } from '../modal/Modal.js';
-import IsFunction from '../../../plugins/utils/object/IsFunction.js';
-
-export default {
-    // Override
-    // onCreateModalBehavior(self, config) { },
-
-    modal(config, onClose) {
-        if (IsFunction(config)) {
-            onClose = config;
-            config = undefined;
-        }
-
-        if (this._modalBehavior === undefined) {
-            if (this.onCreateModalBehavior) {
-                this.onCreateModalBehavior(this, config);
-            }
-            this._modalBehavior = Modal(this, config);
-        }
-
-        if (onClose) {
-            this._modalBehavior.once('close', onClose);
-        }
-
-        this._modalBehavior.requestOpen();
-
-        return this;
-    },
-
-    modalPromise(config) {
-        var self = this;
-        return new Promise(function (resolve, reject) {
-            self.modal(config, resolve);
-        });
-    },
-
-    modalClose(closeEventData) {
-        ModalClose(this, closeEventData);
-        return this;
-    }
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/perspectivecard/PerspectiveMethods.js

@@ -1,67 +0,0 @@
-const FaceIndexMap = ['front', 'back'];
-
-export default {
-    enterPerspectiveMode() {
-        if (this.isInPerspectiveMode) {
-            return this;
-        }
-
-        // Set card's visible to true
-        this.setChildVisible(this.perspectiveCard, true);
-        // Snapshot front and back children to card's faces
-        this.snapshotFace(0);
-        this.snapshotFace(1);
-        // Set front and back children's visible to false
-        this.setChildVisible(this.childrenMap.front, false);
-        this.setChildVisible(this.childrenMap.back, false);
-        // Reset size of card
-        this.perspectiveCard.setSize(this.width, this.height);
-
-        return this;
-    },
-
-    exitPerspectiveMode() {
-        if (!this.isInPerspectiveMode) {
-            return this;
-        }
-
-        // Set card's visible to false
-        this.setChildVisible(this.perspectiveCard, false);
-        // Set front or back children's visible to true, according to card's face
-        var isFrontFace = (this.perspectiveCard.face === 0);
-        this.setChildVisible(this.childrenMap.front, isFrontFace);
-        this.setChildVisible(this.childrenMap.back, !isFrontFace);
-
-        return this;
-    },
-
-    setSnapshotPadding(padding) {
-        this.snapshotPadding = padding;
-        return this;
-    },
-
-    snapshotFace(face) {
-        if (typeof (face) === 'number') {
-            face = FaceIndexMap[face];
-        }
-
-        var cardFace = this.perspectiveCard.faces[face];
-        var faceChild = this.childrenMap[face];
-
-        cardFace.rt.clear();
-
-        var faceChildVisibleSave = faceChild.visible;
-        faceChild.visible = true;
-
-        var gameObjects = (faceChild.isRexContainerLite) ? faceChild.getAllVisibleChildren() : faceChild;
-        cardFace.snapshot(
-            gameObjects,
-            { padding: this.snapshotPadding }
-        );
-
-        faceChild.visible = faceChildVisibleSave;
-
-        return this;
-    }
-
-}

spaces/AkitoP/umamusume_bert_vits2/modules.py

@@ -1,597 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
-
-
-class TransformerCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        n_layers,
-        n_heads,
-        p_dropout=0,
-        filter_channels=0,
-        mean_only=False,
-        wn_sharing_parameter=None,
-        gin_channels=0,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = (
-            Encoder(
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-                isflow=True,
-                gin_channels=gin_channels,
-            )
-            if wn_sharing_parameter is None
-            else wn_sharing_parameter
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x

spaces/AlanMars/QYL-AI-Space/locale/extract_locale.py

@@ -1,26 +0,0 @@
-import os
-import json
-import re
-
-# Define regular expression patterns
-pattern = r'i18n\((\"{3}.*?\"{3}|\".*?\")\)'
-
-# Load the .py file
-with open('app.py', 'r', encoding='utf-8') as f:
-    contents = f.read()
-
-# Load the .py files in the modules folder
-for filename in os.listdir("modules"):
-    if filename.endswith(".py"):
-        with open(os.path.join("modules", filename), "r", encoding="utf-8") as f:
-            contents += f.read()
-
-# Matching with regular expressions
-matches = re.findall(pattern, contents, re.DOTALL)
-
-# Convert to key/value pairs
-data = {match.strip('()"'): '' for match in matches}
-
-# Save as a JSON file
-with open('labels.json', 'w', encoding='utf-8') as f:
-    json.dump(data, f, ensure_ascii=False, indent=4)

spaces/AlekseyKorshuk/huggingartists/app.py

@@ -1,245 +0,0 @@
-import json
-import math
-import random
-import os
-import streamlit as st
-import lyricsgenius
-import transformers
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-
-
-st.set_page_config(page_title="HuggingArtists")
-
-
-st.title("HuggingArtists")
-st.sidebar.markdown(
-    """
-<style>
-.aligncenter {
-    text-align: center;
-}
-</style>
-<p class="aligncenter">
-    <img src="https://raw.githubusercontent.com/AlekseyKorshuk/huggingartists/master/img/logo.jpg" width="420" />
-</p>
-""",
-    unsafe_allow_html=True,
-)
-st.sidebar.markdown(
-    """
-<style>
-.aligncenter {
-    text-align: center;
-}
-</style>
-
-<p style='text-align: center'>
-<a href="https://github.com/AlekseyKorshuk/huggingartists" target="_blank">GitHub</a> | <a href="https://wandb.ai/huggingartists/huggingartists/reportlist" target="_blank">Project Report</a>
-</p>
-
-<p class="aligncenter">
-    <a href="https://github.com/AlekseyKorshuk/huggingartists" target="_blank"> 
-        <img src="https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social"/>
-    </a>
-</p>
-<p class="aligncenter">
-    <a href="https://t.me/joinchat/_CQ04KjcJ-4yZTky" target="_blank"> 
-        <img src="https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram"/>
-    </a>
-</p>
-<p class="aligncenter">
-  <a href="https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb" target="_blank"> 
-        <img src="https://colab.research.google.com/assets/colab-badge.svg"/>
-    </a>
-</p>
-    """,
-    unsafe_allow_html=True,
-)
-
-
-
-st.sidebar.header("Generation settings:")
-num_sequences = st.sidebar.number_input(
-    "Number of sequences to generate",
-    min_value=1,
-    value=5,
-    help="The amount of generated texts",
-)
-min_length = st.sidebar.number_input(
-    "Minimum length of the sequence",
-    min_value=1,
-    value=100,
-    help="The minimum length of the sequence to be generated",
-)
-max_length= st.sidebar.number_input(
-    "Maximum length of the sequence",
-    min_value=1,
-    value=160,
-    help="The maximum length of the sequence to be generated",
-)
-temperature = st.sidebar.slider(
-    "Temperature",
-    min_value=0.0,
-    max_value=3.0,
-    step=0.01,
-    value=1.0,
-    help="The value used to module the next token probabilities",
-)
-top_p = st.sidebar.slider(
-    "Top-P",
-    min_value=0.0,
-    max_value=1.0,
-    step=0.01,
-    value=0.95,
-    help="If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
-)
-
-top_k= st.sidebar.number_input(
-    "Top-K",
-    min_value=0,
-    value=50,
-    step=1,
-    help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
-)
-
-caption = (
-    "In [HuggingArtists](https://github.com/AlekseyKorshuk/huggingartist), we can generate lyrics by a specific artist. This was made by fine-tuning a pre-trained [HuggingFace Transformer](https://huggingface.co) on parsed datasets from [Genius](https://genius.com)."
-)
-st.markdown("[HuggingArtists](https://github.com/AlekseyKorshuk/huggingartist) - Train a model to generate lyrics 🎵")
-st.markdown(caption)
-
-st.subheader("Settings:")
-artist_name = st.text_input("Artist name:", "Eminem")
-start = st.text_input("Beginning of the song:", "But for me to rap like a computer")
-
-TOKEN = "q_JK_BFy9OMiG7fGTzL-nUto9JDv3iXI24aYRrQnkOvjSCSbY4BuFIindweRsr5I"
-genius = lyricsgenius.Genius(TOKEN)
-
-model_html = """
-
-<div class="inline-flex flex-col" style="line-height: 1.5;">
-    <div class="flex">
-        <div
-\t\t\tstyle="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;USER_PROFILE&#39;)">
-        </div>
-    </div>
-    <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
-    <div style="text-align: center; font-size: 16px; font-weight: 800">USER_NAME</div>
-    <a href="https://genius.com/artists/USER_HANDLE">
-    \t<div style="text-align: center; font-size: 14px;">@USER_HANDLE</div>
-    </a>
-</div>
-"""
-
-
-def post_process(output_sequences):
-    predictions = []
-    generated_sequences = []
-
-    max_repeat = 2
-
-    # decode prediction
-    for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
-        generated_sequence = generated_sequence.tolist()
-        text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True)
-        generated_sequences.append(text.strip())
-                    
-    for i, g in enumerate(generated_sequences):
-        res = str(g).replace('\n\n\n', '\n').replace('\n\n', '\n')
-        lines = res.split('\n')
-        # print(lines)
-        # i = max_repeat
-        # while i != len(lines):
-        #   remove_count = 0
-        #   for index in range(0, max_repeat):
-        #     # print(i - index - 1, i - index)
-        #     if lines[i - index - 1] == lines[i - index]:
-        #       remove_count += 1
-        #   if remove_count == max_repeat:
-        #     lines.pop(i)
-        #     i -= 1
-        #   else:
-        #     i += 1
-        predictions.append('\n'.join(lines))
-
-    return predictions
-
-if st.button("Run"):
-    model_name = None
-    with st.spinner(text=f"Searching for {artist_name } in Genius..."):
-        artist = genius.search_artist(artist_name, max_songs=0, get_full_info=False)
-        if artist is not None:
-            artist_dict = genius.artist(artist.id)['artist']
-            artist_url = str(artist_dict['url'])
-            model_name = artist_url[artist_url.rfind('/') + 1:].lower()
-            st.markdown(model_html.replace("USER_PROFILE",artist.image_url).replace("USER_NAME",artist.name).replace("USER_HANDLE",model_name), unsafe_allow_html=True)
-        else:
-            st.markdown(f"Could not find {artist_name}! Be sure that he/she exists in [Genius](https://genius.com/).")
-    if model_name is not None:
-        with st.spinner(text=f"Downloading the model of {artist_name }..."):
-            model = None
-            tokenizer = None
-            try:
-                tokenizer = AutoTokenizer.from_pretrained(f"huggingartists/{model_name}")
-                model = AutoModelForCausalLM.from_pretrained(f"huggingartists/{model_name}")
-            except Exception as ex:
-                # st.markdown(ex)
-                st.markdown(f"Model for this artist does not exist yet. Create it in just 5 min with [Colab Notebook](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb):")
-                st.markdown(
-                        """
-                    <style>
-                    .aligncenter {
-                        text-align: center;
-                    }
-                    </style>
-                    <p class="aligncenter">
-                      <a href="https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb" target="_blank"> 
-                            <img src="https://colab.research.google.com/assets/colab-badge.svg"/>
-                        </a>
-                    </p>
-                        """,
-                        unsafe_allow_html=True,
-                    )
-    if model is not None:
-        with st.spinner(text=f"Generating lyrics..."):
-            encoded_prompt = tokenizer(start, add_special_tokens=False, return_tensors="pt").input_ids
-            encoded_prompt = encoded_prompt.to(model.device)
-            # prediction
-            output_sequences = model.generate(
-                                    input_ids=encoded_prompt,
-                                    max_length=max_length,
-                                    min_length=min_length,
-                                    temperature=float(temperature),
-                                    top_p=float(top_p),
-                                    top_k=int(top_k),
-                                    do_sample=True,
-                                    repetition_penalty=1.0,
-                                    num_return_sequences=num_sequences
-                                    )
-            # Post-processing
-            predictions = post_process(output_sequences)
-            st.subheader("Results")
-            for prediction in predictions:
-                st.text(prediction)
-            st.subheader("Please star this repository and join my Telegram Channel:")
-            st.markdown(
-                        """
-                    <style>
-                    .aligncenter {
-                        text-align: center;
-                    }
-                    </style>
-                    <p class="aligncenter">
-                        <a href="https://github.com/AlekseyKorshuk/huggingartists" target="_blank"> 
-                            <img src="https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social"/>
-                        </a>
-                    </p>
-                    <p class="aligncenter">
-                        <a href="https://t.me/joinchat/_CQ04KjcJ-4yZTky" target="_blank"> 
-                            <img src="https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram"/>
-                        </a>
-                    </p>
-                        """,
-                        unsafe_allow_html=True,
-                    )            

spaces/AlekseyKorshuk/michellejieli-NSFW_text_classifier/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/michellejieli/NSFW_text_classifier").launch()

spaces/Aloento/9Nine-PITS/models.py

@@ -1,1383 +0,0 @@
-# from https://github.com/jaywalnut310/vits
-# from https://github.com/ncsoft/avocodo
-import math
-
-import torch
-from torch import nn
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn import functional as F
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-import attentions
-import commons
-import modules
-from analysis import Pitch
-from commons import init_weights, get_padding
-from pqmf import PQMF
-
-
-# for Q option
-# from functions import vq, vq_st
-
-
-class StochasticDurationPredictor(nn.Module):
-
-  def __init__(self,
-               in_channels,
-               filter_channels,
-               kernel_size,
-               p_dropout,
-               n_flows=4,
-               gin_channels=0):
-    super().__init__()
-    # it needs to be removed from future version.
-    filter_channels = in_channels
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(
-        modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels,
-                                      kernel_size,
-                                      n_layers=3,
-                                      p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(
-        modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels,
-                                 kernel_size,
-                                 n_layers=3,
-                                 p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self,
-              x,
-              x_mask,
-              w=None,
-              g=None,
-              reverse=False,
-              noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(
-        device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1)
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum(
-        (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
-      logq = torch.sum(
-        -0.5 * (math.log(2 * math.pi) +
-                (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2 * math.pi) +
-                             (z ** 2)) * x_mask, [1, 2]) - logdet_tot
-      return nll + logq  # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(
-        device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-
-  def __init__(self,
-               in_channels,
-               filter_channels,
-               kernel_size,
-               p_dropout,
-               gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels,
-                            filter_channels,
-                            kernel_size,
-                            padding=kernel_size // 2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels,
-                            filter_channels,
-                            kernel_size,
-                            padding=kernel_size // 2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-
-  def __init__(self, n_vocab, out_channels, hidden_channels, filter_channels,
-               n_heads, n_layers, kernel_size, p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-    self.emb_t = nn.Embedding(6, hidden_channels)
-    nn.init.normal_(self.emb_t.weight, 0.0, hidden_channels ** -0.5)
-
-    self.encoder = attentions.Encoder(hidden_channels, filter_channels,
-                                      n_heads, n_layers, kernel_size,
-                                      p_dropout)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, t, x_lengths):
-    t_zero = (t == 0)
-    emb_t = self.emb_t(t)
-    emb_t[t_zero, :] = 0
-    x = (self.emb(x) + emb_t) * math.sqrt(
-      self.hidden_channels)  # [b, t, h]
-    # x = torch.transpose(x, 1, -1)  # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(1)),
-                             1).to(x.dtype)
-    # x = self.encoder(x * x_mask, x_mask)
-    x = torch.einsum('btd,but->bdt', x, x_mask)
-    x = self.encoder(x, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-
-  def __init__(self,
-               channels,
-               hidden_channels,
-               kernel_size,
-               dilation_rate,
-               n_layers,
-               n_flows=4,
-               gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(
-        modules.ResidualCouplingLayer(channels,
-                                      hidden_channels,
-                                      kernel_size,
-                                      dilation_rate,
-                                      n_layers,
-                                      gin_channels=gin_channels,
-                                      mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-
-  def __init__(self,
-               in_channels,
-               out_channels,
-               hidden_channels,
-               kernel_size,
-               dilation_rate,
-               n_layers,
-               gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels,
-                          kernel_size,
-                          dilation_rate,
-                          n_layers,
-                          gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)),
-                             1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(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()
-    self.conv_posts = 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))
-      if i >= len(self.ups) - 3:
-        self.conv_posts.append(
-          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):
-        xs = xs + self.resblocks[i * self.num_kernels + j](x) if xs is not None \
-          else self.resblocks[i * self.num_kernels + j](x)
-      x = xs / self.num_kernels
-    x = F.leaky_relu(x)
-    x = self.conv_posts[-1](x)
-    x = torch.tanh(x)
-
-    return x
-
-  def hier_forward(self, x, g=None):
-    outs = []
-    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):
-        xs = xs + self.resblocks[i * self.num_kernels + j](x) if xs is not None \
-          else self.resblocks[i * self.num_kernels + j](x)
-      x = xs / self.num_kernels
-      if i >= self.num_upsamples - 3:
-        _x = F.leaky_relu(x)
-        _x = self.conv_posts[i - self.num_upsamples + 3](_x)
-        _x = torch.tanh(_x)
-        outs.append(_x)
-    return outs
-
-  def remove_weight_norm(self):
-    print('Removing weight norm...')
-    for l in self.ups:
-      remove_weight_norm(l)
-    for l in self.resblocks:
-      l.remove_weight_norm()
-
-
-class DiscriminatorP(nn.Module):
-
-  def __init__(self,
-               period,
-               kernel_size=5,
-               stride=3,
-               use_spectral_norm=False):
-    super(DiscriminatorP, self).__init__()
-    self.period = period
-    self.use_spectral_norm = use_spectral_norm
-    norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-    self.convs = nn.ModuleList([
-      norm_f(
-        Conv2d(1,
-               32, (kernel_size, 1), (stride, 1),
-               padding=(get_padding(kernel_size, 1), 0))),
-      norm_f(
-        Conv2d(32,
-               128, (kernel_size, 1), (stride, 1),
-               padding=(get_padding(kernel_size, 1), 0))),
-      norm_f(
-        Conv2d(128,
-               512, (kernel_size, 1), (stride, 1),
-               padding=(get_padding(kernel_size, 1), 0))),
-      norm_f(
-        Conv2d(512,
-               1024, (kernel_size, 1), (stride, 1),
-               padding=(get_padding(kernel_size, 1), 0))),
-      norm_f(
-        Conv2d(1024,
-               1024, (kernel_size, 1),
-               1,
-               padding=(get_padding(kernel_size, 1), 0))),
-    ])
-    self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-  def forward(self, x):
-    fmap = []
-
-    # 1d to 2d
-    b, c, t = x.shape
-    if t % self.period != 0:  # pad first
-      n_pad = self.period - (t % self.period)
-      x = F.pad(x, (0, n_pad), "reflect")
-      t = t + n_pad
-    x = x.view(b, c, t // self.period, self.period)
-
-    for l in self.convs:
-      x = l(x)
-      x = F.leaky_relu(x, modules.LRELU_SLOPE)
-      fmap.append(x)
-    x = self.conv_post(x)
-    fmap.append(x)
-    x = torch.flatten(x, 1, -1)
-
-    return x, fmap
-
-
-class DiscriminatorS(nn.Module):
-
-  def __init__(self, use_spectral_norm=False):
-    super(DiscriminatorS, self).__init__()
-    norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-    self.convs = nn.ModuleList([
-      norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-      norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-      norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-      norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-      norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-      norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-    ])
-    self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-  def forward(self, x):
-    fmap = []
-
-    for l in self.convs:
-      x = l(x)
-      x = F.leaky_relu(x, modules.LRELU_SLOPE)
-      fmap.append(x)
-    x = self.conv_post(x)
-    fmap.append(x)
-    x = torch.flatten(x, 1, -1)
-
-    return x, fmap
-
-
-class MultiPeriodDiscriminator(nn.Module):
-
-  def __init__(self, use_spectral_norm=False):
-    super(MultiPeriodDiscriminator, self).__init__()
-    periods = [2, 3, 5, 7, 11]
-
-    discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-    discs = discs + \
-            [DiscriminatorP(i, use_spectral_norm=use_spectral_norm)
-             for i in periods]
-    self.discriminators = nn.ModuleList(discs)
-
-  def forward(self, y, y_hat):
-    y_d_rs = []
-    y_d_gs = []
-    fmap_rs = []
-    fmap_gs = []
-    for i, d in enumerate(self.discriminators):
-      y_d_r, fmap_r = d(y)
-      y_d_g, fmap_g = d(y_hat)
-      y_d_rs.append(y_d_r)
-      y_d_gs.append(y_d_g)
-      fmap_rs.append(fmap_r)
-      fmap_gs.append(fmap_g)
-
-    return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-##### Avocodo
-class CoMBDBlock(torch.nn.Module):
-
-  def __init__(
-      self,
-      h_u,  # List[int],
-      d_k,  # List[int],
-      d_s,  # List[int],
-      d_d,  # List[int],
-      d_g,  # List[int],
-      d_p,  # List[int],
-      op_f,  # int,
-      op_k,  # int,
-      op_g,  # int,
-      use_spectral_norm=False):
-    super(CoMBDBlock, self).__init__()
-    norm_f = weight_norm if use_spectral_norm is False else spectral_norm
-
-    self.convs = nn.ModuleList()
-    filters = [[1, h_u[0]]]
-    for i in range(len(h_u) - 1):
-      filters.append([h_u[i], h_u[i + 1]])
-    for _f, _k, _s, _d, _g, _p in zip(filters, d_k, d_s, d_d, d_g, d_p):
-      self.convs.append(
-        norm_f(
-          Conv1d(in_channels=_f[0],
-                 out_channels=_f[1],
-                 kernel_size=_k,
-                 stride=_s,
-                 dilation=_d,
-                 groups=_g,
-                 padding=_p)))
-    self.projection_conv = norm_f(
-      Conv1d(in_channels=filters[-1][1],
-             out_channels=op_f,
-             kernel_size=op_k,
-             groups=op_g))
-
-  def forward(self, x, b_y, b_y_hat):
-    fmap_r = []
-    fmap_g = []
-    for block in self.convs:
-      x = block(x)
-      x = F.leaky_relu(x, 0.2)
-      f_r, f_g = x.split([b_y, b_y_hat], dim=0)
-      fmap_r.append(f_r.tile([2, 1, 1]) if b_y < b_y_hat else f_r)
-      fmap_g.append(f_g)
-    x = self.projection_conv(x)
-    x_r, x_g = x.split([b_y, b_y_hat], dim=0)
-    return x_r.tile([2, 1, 1
-                     ]) if b_y < b_y_hat else x_r, x_g, fmap_r, fmap_g
-
-
-class CoMBD(torch.nn.Module):
-
-  def __init__(self, use_spectral_norm=False):
-    super(CoMBD, self).__init__()
-    self.pqmf_list = nn.ModuleList([
-      PQMF(4, 192, 0.13, 10.0),  # lv2
-      PQMF(2, 256, 0.25, 10.0)  # lv1
-    ])
-    combd_h_u = [[16, 64, 256, 1024, 1024, 1024] for _ in range(3)]
-    combd_d_k = [[7, 11, 11, 11, 11, 5], [11, 21, 21, 21, 21, 5],
-                 [15, 41, 41, 41, 41, 5]]
-    combd_d_s = [[1, 1, 4, 4, 4, 1] for _ in range(3)]
-    combd_d_d = [[1, 1, 1, 1, 1, 1] for _ in range(3)]
-    combd_d_g = [[1, 4, 16, 64, 256, 1] for _ in range(3)]
-
-    combd_d_p = [[3, 5, 5, 5, 5, 2], [5, 10, 10, 10, 10, 2],
-                 [7, 20, 20, 20, 20, 2]]
-    combd_op_f = [1, 1, 1]
-    combd_op_k = [3, 3, 3]
-    combd_op_g = [1, 1, 1]
-
-    self.blocks = nn.ModuleList()
-    for _h_u, _d_k, _d_s, _d_d, _d_g, _d_p, _op_f, _op_k, _op_g in zip(
-        combd_h_u,
-        combd_d_k,
-        combd_d_s,
-        combd_d_d,
-        combd_d_g,
-        combd_d_p,
-        combd_op_f,
-        combd_op_k,
-        combd_op_g,
-    ):
-      self.blocks.append(
-        CoMBDBlock(
-          _h_u,
-          _d_k,
-          _d_s,
-          _d_d,
-          _d_g,
-          _d_p,
-          _op_f,
-          _op_k,
-          _op_g,
-        ))
-
-  def _block_forward(self, ys, ys_hat, blocks):
-    outs_real = []
-    outs_fake = []
-    f_maps_real = []
-    f_maps_fake = []
-    for y, y_hat, block in zip(ys, ys_hat,
-                               blocks):  # y:B, y_hat: 2B if i!=-1 else B,B
-      b_y = y.shape[0]
-      b_y_hat = y_hat.shape[0]
-      cat_y = torch.cat([y, y_hat], dim=0)
-      out_real, out_fake, f_map_r, f_map_g = block(cat_y, b_y, b_y_hat)
-      outs_real.append(out_real)
-      outs_fake.append(out_fake)
-      f_maps_real.append(f_map_r)
-      f_maps_fake.append(f_map_g)
-    return outs_real, outs_fake, f_maps_real, f_maps_fake
-
-  def _pqmf_forward(self, ys, ys_hat):
-    # preprocess for multi_scale forward
-    multi_scale_inputs_hat = []
-    for pqmf_ in self.pqmf_list:
-      multi_scale_inputs_hat.append(pqmf_.analysis(ys_hat[-1])[:, :1, :])
-
-    # real
-    # for hierarchical forward
-    # outs_real_, f_maps_real_ = self._block_forward(
-    #    ys, self.blocks)
-
-    # for multi_scale forward
-    # outs_real, f_maps_real = self._block_forward(
-    #        ys[:-1], self.blocks[:-1], outs_real, f_maps_real)
-    # outs_real.extend(outs_real[:-1])
-    # f_maps_real.extend(f_maps_real[:-1])
-
-    # outs_real = [torch.cat([o,o], dim=0) if i!=len(outs_real_)-1 else o for i,o in enumerate(outs_real_)]
-    # f_maps_real = [[torch.cat([fmap,fmap], dim=0) if i!=len(f_maps_real_)-1 else fmap for fmap in fmaps ] \
-    #        for i,fmaps in enumerate(f_maps_real_)]
-
-    inputs_fake = [
-      torch.cat([y, multi_scale_inputs_hat[i]], dim=0)
-      if i != len(ys_hat) - 1 else y for i, y in enumerate(ys_hat)
-    ]
-    outs_real, outs_fake, f_maps_real, f_maps_fake = self._block_forward(
-      ys, inputs_fake, self.blocks)
-
-    # predicted
-    # for hierarchical forward
-    # outs_fake, f_maps_fake = self._block_forward(
-    #    inputs_fake, self.blocks)
-
-    # outs_real_, f_maps_real_ = self._block_forward(
-    #    ys, self.blocks)
-    # for multi_scale forward
-    # outs_fake, f_maps_fake = self._block_forward(
-    #    multi_scale_inputs_hat, self.blocks[:-1], outs_fake, f_maps_fake)
-
-    return outs_real, outs_fake, f_maps_real, f_maps_fake
-
-  def forward(self, ys, ys_hat):
-    outs_real, outs_fake, f_maps_real, f_maps_fake = self._pqmf_forward(
-      ys, ys_hat)
-    return outs_real, outs_fake, f_maps_real, f_maps_fake
-
-
-class MDC(torch.nn.Module):
-
-  def __init__(self,
-               in_channels,
-               out_channels,
-               strides,
-               kernel_size,
-               dilations,
-               use_spectral_norm=False):
-    super(MDC, self).__init__()
-    norm_f = weight_norm if not use_spectral_norm else spectral_norm
-    self.d_convs = nn.ModuleList()
-    for _k, _d in zip(kernel_size, dilations):
-      self.d_convs.append(
-        norm_f(
-          Conv1d(in_channels=in_channels,
-                 out_channels=out_channels,
-                 kernel_size=_k,
-                 dilation=_d,
-                 padding=get_padding(_k, _d))))
-    self.post_conv = norm_f(
-      Conv1d(in_channels=out_channels,
-             out_channels=out_channels,
-             kernel_size=3,
-             stride=strides,
-             padding=get_padding(_k, _d)))
-    self.softmax = torch.nn.Softmax(dim=-1)
-
-  def forward(self, x):
-    _out = None
-    for _l in self.d_convs:
-      _x = torch.unsqueeze(_l(x), -1)
-      _x = F.leaky_relu(_x, 0.2)
-      _out = torch.cat([_out, _x], axis=-1) if _out is not None \
-        else _x
-    x = torch.sum(_out, dim=-1)
-    x = self.post_conv(x)
-    x = F.leaky_relu(x, 0.2)  # @@
-
-    return x
-
-
-class SBDBlock(torch.nn.Module):
-
-  def __init__(self,
-               segment_dim,
-               strides,
-               filters,
-               kernel_size,
-               dilations,
-               use_spectral_norm=False):
-    super(SBDBlock, self).__init__()
-    norm_f = weight_norm if not use_spectral_norm else spectral_norm
-    self.convs = nn.ModuleList()
-    filters_in_out = [(segment_dim, filters[0])]
-    for i in range(len(filters) - 1):
-      filters_in_out.append([filters[i], filters[i + 1]])
-
-    for _s, _f, _k, _d in zip(strides, filters_in_out, kernel_size,
-                              dilations):
-      self.convs.append(
-        MDC(in_channels=_f[0],
-            out_channels=_f[1],
-            strides=_s,
-            kernel_size=_k,
-            dilations=_d,
-            use_spectral_norm=use_spectral_norm))
-    self.post_conv = norm_f(
-      Conv1d(in_channels=_f[1],
-             out_channels=1,
-             kernel_size=3,
-             stride=1,
-             padding=3 // 2))  # @@
-
-  def forward(self, x):
-    fmap_r = []
-    fmap_g = []
-    for _l in self.convs:
-      x = _l(x)
-      f_r, f_g = torch.chunk(x, 2, dim=0)
-      fmap_r.append(f_r)
-      fmap_g.append(f_g)
-    x = self.post_conv(x)  # @@
-    x_r, x_g = torch.chunk(x, 2, dim=0)
-    return x_r, x_g, fmap_r, fmap_g
-
-
-class MDCDConfig:
-
-  def __init__(self):
-    self.pqmf_params = [16, 256, 0.03, 10.0]
-    self.f_pqmf_params = [64, 256, 0.1, 9.0]
-    self.filters = [[64, 128, 256, 256, 256], [64, 128, 256, 256, 256],
-                    [64, 128, 256, 256, 256], [32, 64, 128, 128, 128]]
-    self.kernel_sizes = [[[7, 7, 7], [7, 7, 7], [7, 7, 7], [7, 7, 7],
-                          [7, 7, 7]],
-                         [[5, 5, 5], [5, 5, 5], [5, 5, 5], [5, 5, 5],
-                          [5, 5, 5]],
-                         [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3],
-                          [3, 3, 3]],
-                         [[5, 5, 5], [5, 5, 5], [5, 5, 5], [5, 5, 5],
-                          [5, 5, 5]]]
-    self.dilations = [[[5, 7, 11], [5, 7, 11], [5, 7, 11], [5, 7, 11],
-                       [5, 7, 11]],
-                      [[3, 5, 7], [3, 5, 7], [3, 5, 7], [3, 5, 7],
-                       [3, 5, 7]],
-                      [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3],
-                       [1, 2, 3]],
-                      [[1, 2, 3], [1, 2, 3], [1, 2, 3], [2, 3, 5],
-                       [2, 3, 5]]]
-    self.strides = [[1, 1, 3, 3, 1], [1, 1, 3, 3, 1], [1, 1, 3, 3, 1],
-                    [1, 1, 3, 3, 1]]
-    self.band_ranges = [[0, 6], [0, 11], [0, 16], [0, 64]]
-    self.transpose = [False, False, False, True]
-    self.segment_size = 8192
-
-
-class SBD(torch.nn.Module):
-
-  def __init__(self, use_spectral_norm=False):
-    super(SBD, self).__init__()
-    self.config = MDCDConfig()
-    self.pqmf = PQMF(*self.config.pqmf_params)
-    if True in self.config.transpose:
-      self.f_pqmf = PQMF(*self.config.f_pqmf_params)
-    else:
-      self.f_pqmf = None
-
-    self.discriminators = torch.nn.ModuleList()
-
-    for _f, _k, _d, _s, _br, _tr in zip(self.config.filters,
-                                        self.config.kernel_sizes,
-                                        self.config.dilations,
-                                        self.config.strides,
-                                        self.config.band_ranges,
-                                        self.config.transpose):
-      if _tr:
-        segment_dim = self.config.segment_size // _br[1] - _br[0]
-      else:
-        segment_dim = _br[1] - _br[0]
-
-      self.discriminators.append(
-        SBDBlock(segment_dim=segment_dim,
-                 filters=_f,
-                 kernel_size=_k,
-                 dilations=_d,
-                 strides=_s,
-                 use_spectral_norm=use_spectral_norm))
-
-  def forward(self, y, y_hat):
-    y_d_rs = []
-    y_d_gs = []
-    fmap_rs = []
-    fmap_gs = []
-    y_in = self.pqmf.analysis(y)
-    y_hat_in = self.pqmf.analysis(y_hat)
-    y_in_f = self.f_pqmf.analysis(y)
-    y_hat_in_f = self.f_pqmf.analysis(y_hat)
-
-    for d, br, tr in zip(self.discriminators, self.config.band_ranges,
-                         self.config.transpose):
-      if not tr:
-        _y_in = y_in[:, br[0]:br[1], :]
-        _y_hat_in = y_hat_in[:, br[0]:br[1], :]
-      else:
-        _y_in = y_in_f[:, br[0]:br[1], :]
-        _y_hat_in = y_hat_in_f[:, br[0]:br[1], :]
-        _y_in = torch.transpose(_y_in, 1, 2)
-        _y_hat_in = torch.transpose(_y_hat_in, 1, 2)
-      # y_d_r, fmap_r = d(_y_in)
-      # y_d_g, fmap_g = d(_y_hat_in)
-      cat_y = torch.cat([_y_in, _y_hat_in], dim=0)
-      y_d_r, y_d_g, fmap_r, fmap_g = d(cat_y)
-      y_d_rs.append(y_d_r)
-      fmap_rs.append(fmap_r)
-      y_d_gs.append(y_d_g)
-      fmap_gs.append(fmap_g)
-
-    return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class AvocodoDiscriminator(nn.Module):
-
-  def __init__(self, use_spectral_norm=False):
-    super(AvocodoDiscriminator, self).__init__()
-    self.combd = CoMBD(use_spectral_norm)
-    self.sbd = SBD(use_spectral_norm)
-
-  def forward(self, y, ys_hat):
-    ys = [
-      self.combd.pqmf_list[0].analysis(y)[:, :1],  # lv2
-      self.combd.pqmf_list[1].analysis(y)[:, :1],  # lv1
-      y
-    ]
-    y_c_rs, y_c_gs, fmap_c_rs, fmap_c_gs = self.combd(ys, ys_hat)
-    y_s_rs, y_s_gs, fmap_s_rs, fmap_s_gs = self.sbd(y, ys_hat[-1])
-    y_c_rs.extend(y_s_rs)
-    y_c_gs.extend(y_s_gs)
-    fmap_c_rs.extend(fmap_s_rs)
-    fmap_c_gs.extend(fmap_s_gs)
-    return y_c_rs, y_c_gs, fmap_c_rs, fmap_c_gs
-
-
-##### Avocodo
-
-
-class YingDecoder(nn.Module):
-
-  def __init__(self,
-               hidden_channels,
-               kernel_size,
-               dilation_rate,
-               n_layers,
-               yin_start,
-               yin_scope,
-               yin_shift_range,
-               gin_channels=0):
-    super().__init__()
-    self.in_channels = yin_scope
-    self.out_channels = yin_scope
-    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.yin_start = yin_start
-    self.yin_scope = yin_scope
-    self.yin_shift_range = yin_shift_range
-
-    self.pre = nn.Conv1d(self.in_channels, hidden_channels, 1)
-    self.dec = modules.WN(hidden_channels,
-                          kernel_size,
-                          dilation_rate,
-                          n_layers,
-                          gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, self.out_channels, 1)
-
-  def crop_scope(self, x, yin_start,
-                 scope_shift):  # x: tensor [B,C,T] #scope_shift: tensor [B]
-    return torch.stack([
-      x[i, yin_start + scope_shift[i]:yin_start + self.yin_scope +
-                                      scope_shift[i], :] for i in range(x.shape[0])
-    ],
-      dim=0)
-
-  def infer(self, z_yin, z_mask, g=None):
-    B = z_yin.shape[0]
-    scope_shift = torch.randint(-self.yin_shift_range,
-                                self.yin_shift_range, (B,),
-                                dtype=torch.int)
-    z_yin_crop = self.crop_scope(z_yin, self.yin_start, scope_shift)
-    x = self.pre(z_yin_crop) * z_mask
-    x = self.dec(x, z_mask, g=g)
-    yin_hat_crop = self.proj(x) * z_mask
-    return yin_hat_crop
-
-  def forward(self, z_yin, yin_gt, z_mask, g=None):
-    B = z_yin.shape[0]
-    scope_shift = torch.randint(-self.yin_shift_range,
-                                self.yin_shift_range, (B,),
-                                dtype=torch.int)
-    z_yin_crop = self.crop_scope(z_yin, self.yin_start, scope_shift)
-    yin_gt_shifted_crop = self.crop_scope(yin_gt, self.yin_start,
-                                          scope_shift)
-    yin_gt_crop = self.crop_scope(yin_gt, self.yin_start,
-                                  torch.zeros_like(scope_shift))
-    x = self.pre(z_yin_crop) * z_mask
-    x = self.dec(x, z_mask, g=g)
-    yin_hat_crop = self.proj(x) * z_mask
-    return yin_gt_crop, yin_gt_shifted_crop, yin_hat_crop, z_yin_crop, scope_shift
-
-
-# For Q option
-# class VQEmbedding(nn.Module):
-#
-#    def __init__(self, codebook_size,
-#                 code_channels):
-#        super().__init__()
-#        self.embedding = nn.Embedding(codebook_size, code_channels)
-#        self.embedding.weight.data.uniform_(-1. / codebook_size,
-#                                            1. / codebook_size)
-#
-#    def forward(self, z_e_x):
-#        z_e_x_ = z_e_x.permute(0, 2, 1).contiguous()
-#        latent_indices = vq(z_e_x_, self.embedding.weight)
-#        z_q = self.embedding(latent_indices).permute(0, 2, 1)
-#        return z_q
-#
-#    def straight_through(self, z_e_x):
-#        z_e_x_ = z_e_x.permute(0, 2, 1).contiguous()
-#        z_q_x_st_, indices = vq_st(z_e_x_, self.embedding.weight.detach())
-#        z_q_x_st = z_q_x_st_.permute(0, 2, 1).contiguous()
-#
-#        z_q_x_flatten = torch.index_select(self.embedding.weight,
-#                                           dim=0,
-#                                           index=indices)
-#        z_q_x_ = z_q_x_flatten.view_as(z_e_x_)
-#        z_q_x = z_q_x_.permute(0, 2, 1).contiguous()
-#        return z_q_x_st, z_q_x
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(
-      self,
-      n_vocab,
-      spec_channels,
-      segment_size,
-      midi_start,
-      midi_end,
-      octave_range,
-      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,
-      yin_channels,
-      yin_start,
-      yin_scope,
-      yin_shift_range,
-      n_speakers=0,
-      gin_channels=0,
-      use_sdp=True,
-      # codebook_size=256, #for Q option
-      **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.yin_channels = yin_channels
-    self.yin_start = yin_start
-    self.yin_scope = yin_scope
-
-    self.use_sdp = use_sdp
-    self.enc_p = TextEncoder(n_vocab, inter_channels, hidden_channels,
-                             filter_channels, n_heads, n_layers,
-                             kernel_size, p_dropout)
-    self.dec = Generator(
-      inter_channels - yin_channels +
-      yin_scope,
-      resblock,
-      resblock_kernel_sizes,
-      resblock_dilation_sizes,
-      upsample_rates,
-      upsample_initial_channel,
-      upsample_kernel_sizes,
-      gin_channels=gin_channels)
-
-    self.enc_spec = PosteriorEncoder(spec_channels,
-                                     inter_channels - yin_channels,
-                                     inter_channels - yin_channels,
-                                     5,
-                                     1,
-                                     16,
-                                     gin_channels=gin_channels)
-
-    self.enc_pitch = PosteriorEncoder(yin_channels,
-                                      yin_channels,
-                                      yin_channels,
-                                      5,
-                                      1,
-                                      16,
-                                      gin_channels=gin_channels)
-
-    self.flow = ResidualCouplingBlock(inter_channels,
-                                      hidden_channels,
-                                      5,
-                                      1,
-                                      4,
-                                      gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels,
-                                            192,
-                                            3,
-                                            0.5,
-                                            4,
-                                            gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels,
-                                  256,
-                                  3,
-                                  0.5,
-                                  gin_channels=gin_channels)
-
-    self.yin_dec = YingDecoder(yin_scope,
-                               5,
-                               1,
-                               4,
-                               yin_start,
-                               yin_scope,
-                               yin_shift_range,
-                               gin_channels=gin_channels)
-
-    # self.vq = VQEmbedding(codebook_size, inter_channels - yin_channels)#inter_channels // 2)
-    self.emb_g = nn.Embedding(self.n_speakers, gin_channels)
-
-    self.pitch = Pitch(midi_start=midi_start,
-                       midi_end=midi_end,
-                       octave_range=octave_range)
-
-  def crop_scope(
-      self,
-      x,
-      scope_shift=0):  # x: list #need to modify for non-scalar shift
-    return [
-      i[:, self.yin_start + scope_shift:self.yin_start + self.yin_scope +
-                                        scope_shift, :] for i in x
-    ]
-
-  def crop_scope_tensor(
-      self, x,
-      scope_shift):  # x: tensor [B,C,T] #scope_shift: tensor [B]
-    return torch.stack([
-      x[i, self.yin_start + scope_shift[i]:self.yin_start +
-                                           self.yin_scope + scope_shift[i], :] for i in range(x.shape[0])
-    ],
-      dim=0)
-
-  def yin_dec_infer(self, z_yin, z_mask, sid=None):
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-    return self.yin_dec.infer(z_yin, z_mask, g)
-
-  def forward(self,
-              x,
-              t,
-              x_lengths,
-              y,
-              y_lengths,
-              ying,
-              ying_lengths,
-              sid=None,
-              scope_shift=0):
-    x, m_p, logs_p, x_mask = self.enc_p(x, t, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-
-    z_spec, m_spec, logs_spec, spec_mask = self.enc_spec(y, y_lengths, g=g)
-
-    # for Q option
-    # z_spec_q_st, z_spec_q = self.vq.straight_through(z_spec)
-    # z_spec_q_st = z_spec_q_st * spec_mask
-    # z_spec_q = z_spec_q * spec_mask
-
-    z_yin, m_yin, logs_yin, yin_mask = self.enc_pitch(ying, y_lengths, g=g)
-    z_yin_crop, logs_yin_crop, m_yin_crop = self.crop_scope(
-      [z_yin, logs_yin, m_yin], scope_shift)
-
-    # yin dec loss
-    yin_gt_crop, yin_gt_shifted_crop, yin_dec_crop, z_yin_crop_shifted, scope_shift = self.yin_dec(
-      z_yin, ying, yin_mask, g)
-
-    z = torch.cat([z_spec, z_yin], dim=1)
-    logs_q = torch.cat([logs_spec, logs_yin], dim=1)
-    m_q = torch.cat([m_spec, m_yin], dim=1)
-    y_mask = spec_mask
-
-    z_p = self.flow(z, y_mask, g=g)
-
-    z_dec = torch.cat([z_spec, z_yin_crop], dim=1)
-
-    z_dec_shifted = torch.cat([z_spec.detach(), z_yin_crop_shifted], dim=1)
-    z_dec_ = torch.cat([z_dec, z_dec_shifted], dim=0)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p)  # [b, d, t]
-      # [b, 1, t_s]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1],
-                            keepdim=True)
-      # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s], z_p: [b,d,t]
-      # neg_cent2 = torch.matmul(-0.5 * (z_p**2).transpose(1, 2), s_p_sq_r)
-      neg_cent2 = torch.einsum('bdt, bds -> bts', -0.5 * (z_p ** 2),
-                               s_p_sq_r)
-      # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      # neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r))
-      neg_cent3 = torch.einsum('bdt, bds -> bts', z_p, (m_p * s_p_sq_r))
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1],
-                            keepdim=True)  # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(
-        y_mask, -1)
-      from monotonic_align import maximum_path
-      attn = maximum_path(neg_cent,
-                          attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum(
-        (logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask)  # for averaging
-
-    # expand prior
-    m_p = torch.einsum('bctn, bdn -> bdt', attn, m_p)
-    logs_p = torch.einsum('bctn, bdn -> bdt', attn, logs_p)
-
-    # z_slice, ids_slice = commons.rand_slice_segments(z_dec, y_lengths, self.segment_size)
-    # o = self.dec(z_slice, g=g)
-    z_slice, ids_slice = commons.rand_slice_segments_for_cat(
-      z_dec_, torch.cat([y_lengths, y_lengths], dim=0),
-      self.segment_size)
-    o_ = self.dec.hier_forward(z_slice, g=torch.cat([g, g], dim=0))
-    o = [torch.chunk(o_hier, 2, dim=0)[0] for o_hier in o_]
-
-    o_pad = F.pad(o_[-1], (768, 768 + (-o_[-1].shape[-1]) % 256 + 256 *
-                           (o_[-1].shape[-1] % 256 == 0)),
-                  mode='constant').squeeze(1)
-    yin_hat = self.pitch.yingram(o_pad)
-    yin_hat_crop = self.crop_scope([yin_hat])[0]
-    yin_hat_shifted = self.crop_scope_tensor(
-      torch.chunk(yin_hat, 2, dim=0)[0], scope_shift)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, o_, \
-      (z, z_p, m_p, logs_p, m_q, logs_q), \
-      (z_dec_), \
-      (z_spec, m_spec, logs_spec, spec_mask, z_yin, m_yin, logs_yin, yin_mask), \
-      (yin_gt_crop, yin_gt_shifted_crop, yin_dec_crop, yin_hat_crop, scope_shift, yin_hat_shifted)
-
-  def infer(self,
-            x,
-            t,
-            x_lengths,
-            sid=None,
-            noise_scale=1,
-            length_scale=1,
-            noise_scale_w=1.,
-            max_len=None,
-            scope_shift=0):  # need to fix #vector scope shift needed
-    x, m_p, logs_p, x_mask = self.enc_p(x, t, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x,
-                     x_mask,
-                     g=g,
-                     reverse=True,
-                     noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None),
-                             1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.einsum('bctn, bdn -> bdt', attn, m_p)
-    logs_p = torch.einsum('bctn, bdn -> bdt', attn, logs_p)
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    z_spec, z_yin = torch.split(z,
-                                self.inter_channels - self.yin_channels,
-                                dim=1)
-    z_yin_crop = self.crop_scope([z_yin], scope_shift)[0]
-    z_crop = torch.cat([z_spec, z_yin_crop], dim=1)
-    o = self.dec((z_crop * y_mask)[:, :, :max_len], g=g)
-    return o, attn, y_mask, (z_crop, z, z_p, m_p, logs_p)
-
-  def infer_pre_decoder(self,
-                        x,
-                        t,
-                        x_lengths,
-                        sid=None,
-                        noise_scale=1.,
-                        length_scale=1.,
-                        noise_scale_w=1.,
-                        max_len=None,
-                        scope_shift=0):
-    x, m_p, logs_p, x_mask = self.enc_p(x, t, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x,
-                     x_mask,
-                     g=g,
-                     reverse=True,
-                     noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None),
-                             1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.einsum('bctn, bdn -> bdt', attn, m_p)
-    logs_p = torch.einsum('bctn, bdn -> bdt', attn, logs_p)
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    z_spec, z_yin = torch.split(z,
-                                self.inter_channels - self.yin_channels,
-                                dim=1)
-    z_yin_crop = self.crop_scope([z_yin], scope_shift)[0]
-    z_crop = torch.cat([z_spec, z_yin_crop], dim=1)
-    decoder_inputs = z_crop * y_mask
-    return decoder_inputs, attn, y_mask, (z_crop, z, z_p, m_p, logs_p)
-
-  def infer_pre_lr(
-      self,
-      x,
-      t,
-      x_lengths,
-      sid=None,
-      length_scale=1,
-      noise_scale_w=1.,
-  ):
-    x, m_p, logs_p, x_mask = self.enc_p(x, t, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x,
-                     x_mask,
-                     g=g,
-                     reverse=True,
-                     noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    return w_ceil, x, m_p, logs_p, x_mask, g
-
-  def infer_lr(self, w_ceil, x, m_p, logs_p, x_mask):
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None),
-                             1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.einsum('bctn, bdn -> bdt', attn, m_p)
-    logs_p = torch.einsum('bctn, bdn -> bdt', attn, logs_p)
-    return m_p, logs_p, y_mask
-
-  def infer_post_lr_pre_decoder(self,
-                                m_p,
-                                logs_p,
-                                g,
-                                y_mask,
-                                noise_scale=1,
-                                scope_shift=0):
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    z_spec, z_yin = torch.split(z,
-                                self.inter_channels - self.yin_channels,
-                                dim=1)
-
-    z_yin_crop = self.crop_scope([z_yin], scope_shift)[0]
-    z_crop = torch.cat([z_spec, z_yin_crop], dim=1)
-    decoder_inputs = z_crop * y_mask
-
-    return decoder_inputs, y_mask, (z_crop, z, z_p, m_p, logs_p)
-
-  def infer_decode_chunk(self, decoder_inputs, sid=None):
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-    return self.dec(decoder_inputs, g=g)

spaces/Andy1621/uniformer_image_detection/configs/atss/atss_r50_fpn_1x_coco.py

@@ -1,62 +0,0 @@
-_base_ = [
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
-    type='ATSS',
-    pretrained='torchvision://resnet50',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        start_level=1,
-        add_extra_convs='on_output',
-        num_outs=5),
-    bbox_head=dict(
-        type='ATSSHead',
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=4,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            octave_base_scale=8,
-            scales_per_octave=1,
-            strides=[8, 16, 32, 64, 128]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.1, 0.1, 0.2, 0.2]),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
-        loss_centerness=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
-    # training and testing settings
-    train_cfg=dict(
-        assigner=dict(type='ATSSAssigner', topk=9),
-        allowed_border=-1,
-        pos_weight=-1,
-        debug=False),
-    test_cfg=dict(
-        nms_pre=1000,
-        min_bbox_size=0,
-        score_thr=0.05,
-        nms=dict(type='nms', iou_threshold=0.6),
-        max_per_img=100))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

spaces/Andy1621/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './dnl_r50-d8_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/css/html_instruct_style.css

@@ -1,64 +0,0 @@
-.message {
-    display: grid;
-    grid-template-columns: 60px 1fr;
-    padding-bottom: 25px;
-    font-size: 15px;
-    font-family: 'Noto Sans', Helvetica, Arial, sans-serif;
-    line-height: 22px;
-}
-
-.username {
-    display: none;
-}
-
-.message-body p {
-    font-size: 15px !important;
-    line-height: 22px !important;
-    margin-bottom: 1.25em !important;
-}
-
-.chat .message-body ul, .chat .message-body ol {
-    margin-bottom: 1.25em !important;
-}
-
-.dark .message-body p em {
-    color: rgb(198, 202, 214) !important;
-}
-
-.message-body p em {
-    color: rgb(110, 110, 110) !important;
-}
-
-.gradio-container .chat .assistant-message {
-    padding: 15px;
-    border-radius: 20px;
-    background-color: #0000000f;
-    margin-top: 9px !important;
-    margin-bottom: 18px !important;
-}
-
-.gradio-container .chat .user-message {
-    padding: 15px;
-    border-radius: 20px;
-    margin-bottom: 9px !important;
-}
-
-.gradio-container .chat .assistant-message:last-child, .gradio-container .chat .user-message:last-child {
-    margin-bottom: 0px !important;
-}
-
-.dark .chat .assistant-message {
-    background-color: #1f2937;
-}
-
-.dark .chat .user-message {
-    background-color: transparent;
-}
-
-code {
-    background-color: white !important;
-}
-
-.dark code {
-    background-color: #0e1321 !important;
-}

spaces/Arnx/MusicGenXvAKN/audiocraft/data/audio_utils.py

@@ -1,174 +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 sys
-import typing as tp
-
-import julius
-import torch
-import torchaudio
-
-
-def convert_audio_channels(wav: torch.Tensor, channels: int = 2) -> torch.Tensor:
-    """Convert audio to the given number of channels.
-
-    Args:
-        wav (torch.Tensor): Audio wave of shape [B, C, T].
-        channels (int): Expected number of channels as output.
-    Returns:
-        torch.Tensor: Downmixed or unchanged audio wave [B, C, T].
-    """
-    *shape, src_channels, length = wav.shape
-    if src_channels == channels:
-        pass
-    elif channels == 1:
-        # Case 1:
-        # The caller asked 1-channel audio, and the stream has multiple
-        # channels, downmix all channels.
-        wav = wav.mean(dim=-2, keepdim=True)
-    elif src_channels == 1:
-        # Case 2:
-        # The caller asked for multiple channels, but the input file has
-        # a single channel, replicate the audio over all channels.
-        wav = wav.expand(*shape, channels, length)
-    elif src_channels >= channels:
-        # Case 3:
-        # The caller asked for multiple channels, and the input file has
-        # more channels than requested. In that case return the first channels.
-        wav = wav[..., :channels, :]
-    else:
-        # Case 4: What is a reasonable choice here?
-        raise ValueError('The audio file has less channels than requested but is not mono.')
-    return wav
-
-
-def convert_audio(wav: torch.Tensor, from_rate: float,
-                  to_rate: float, to_channels: int) -> torch.Tensor:
-    """Convert audio to new sample rate and number of audio channels.
-    """
-    wav = julius.resample_frac(wav, int(from_rate), int(to_rate))
-    wav = convert_audio_channels(wav, to_channels)
-    return wav
-
-
-def normalize_loudness(wav: torch.Tensor, sample_rate: int, loudness_headroom_db: float = 14,
-                       loudness_compressor: bool = False, energy_floor: float = 2e-3):
-    """Normalize an input signal to a user loudness in dB LKFS.
-    Audio loudness is defined according to the ITU-R BS.1770-4 recommendation.
-
-    Args:
-        wav (torch.Tensor): Input multichannel audio data.
-        sample_rate (int): Sample rate.
-        loudness_headroom_db (float): Target loudness of the output in dB LUFS.
-        loudness_compressor (bool): Uses tanh for soft clipping.
-        energy_floor (float): anything below that RMS level will not be rescaled.
-    Returns:
-        output (torch.Tensor): Loudness normalized output data.
-    """
-    energy = wav.pow(2).mean().sqrt().item()
-    if energy < energy_floor:
-        return wav
-    transform = torchaudio.transforms.Loudness(sample_rate)
-    input_loudness_db = transform(wav).item()
-    # calculate the gain needed to scale to the desired loudness level
-    delta_loudness = -loudness_headroom_db - input_loudness_db
-    gain = 10.0 ** (delta_loudness / 20.0)
-    output = gain * wav
-    if loudness_compressor:
-        output = torch.tanh(output)
-    assert output.isfinite().all(), (input_loudness_db, wav.pow(2).mean().sqrt())
-    return output
-
-
-def _clip_wav(wav: torch.Tensor, log_clipping: bool = False, stem_name: tp.Optional[str] = None) -> None:
-    """Utility function to clip the audio with logging if specified."""
-    max_scale = wav.abs().max()
-    if log_clipping and max_scale > 1:
-        clamp_prob = (wav.abs() > 1).float().mean().item()
-        print(f"CLIPPING {stem_name or ''} happening with proba (a bit of clipping is okay):",
-              clamp_prob, "maximum scale: ", max_scale.item(), file=sys.stderr)
-    wav.clamp_(-1, 1)
-
-
-def normalize_audio(wav: torch.Tensor, normalize: bool = True,
-                    strategy: str = 'peak', peak_clip_headroom_db: float = 1,
-                    rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
-                    loudness_compressor: bool = False, log_clipping: bool = False,
-                    sample_rate: tp.Optional[int] = None,
-                    stem_name: tp.Optional[str] = None) -> torch.Tensor:
-    """Normalize the audio according to the prescribed strategy (see after).
-
-    Args:
-        wav (torch.Tensor): Audio data.
-        normalize (bool): if `True` (default), normalizes according to the prescribed
-            strategy (see after). If `False`, the strategy is only used in case clipping
-            would happen.
-        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
-            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
-            with extra headroom to avoid clipping. 'clip' just clips.
-        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
-        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
-            than the `peak_clip` one to avoid further clipping.
-        loudness_headroom_db (float): Target loudness for loudness normalization.
-        loudness_compressor (bool): If True, uses tanh based soft clipping.
-        log_clipping (bool): If True, basic logging on stderr when clipping still
-            occurs despite strategy (only for 'rms').
-        sample_rate (int): Sample rate for the audio data (required for loudness).
-        stem_name (Optional[str]): Stem name for clipping logging.
-    Returns:
-        torch.Tensor: Normalized audio.
-    """
-    scale_peak = 10 ** (-peak_clip_headroom_db / 20)
-    scale_rms = 10 ** (-rms_headroom_db / 20)
-    if strategy == 'peak':
-        rescaling = (scale_peak / wav.abs().max())
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-    elif strategy == 'clip':
-        wav = wav.clamp(-scale_peak, scale_peak)
-    elif strategy == 'rms':
-        mono = wav.mean(dim=0)
-        rescaling = scale_rms / mono.pow(2).mean().sqrt()
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    elif strategy == 'loudness':
-        assert sample_rate is not None, "Loudness normalization requires sample rate."
-        wav = normalize_loudness(wav, sample_rate, loudness_headroom_db, loudness_compressor)
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    else:
-        assert wav.abs().max() < 1
-        assert strategy == '' or strategy == 'none', f"Unexpected strategy: '{strategy}'"
-    return wav
-
-
-def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to float 32 bits PCM format.
-    """
-    if wav.dtype.is_floating_point:
-        return wav
-    else:
-        assert wav.dtype == torch.int16
-        return wav.float() / 2**15
-
-
-def i16_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to int 16 bits PCM format.
-
-    ..Warning:: There exist many formula for doing this convertion. None are perfect
-    due to the asymetry of the int16 range. One either have possible clipping, DC offset,
-    or inconsistancies with f32_pcm. If the given wav doesn't have enough headroom,
-    it is possible that `i16_pcm(f32_pcm)) != Identity`.
-    """
-    if wav.dtype.is_floating_point:
-        assert wav.abs().max() <= 1
-        candidate = (wav * 2 ** 15).round()
-        if candidate.max() >= 2 ** 15:  # clipping would occur
-            candidate = (wav * (2 ** 15 - 1)).round()
-        return candidate.short()
-    else:
-        assert wav.dtype == torch.int16
-        return wav

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

@@ -1,136 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import re
-from typing import FrozenSet, NewType, Tuple, Union, cast
-
-from .tags import Tag, parse_tag
-from .version import InvalidVersion, Version
-
-BuildTag = Union[Tuple[()], Tuple[int, str]]
-NormalizedName = NewType("NormalizedName", str)
-
-
-class InvalidWheelFilename(ValueError):
-    """
-    An invalid wheel filename was found, users should refer to PEP 427.
-    """
-
-
-class InvalidSdistFilename(ValueError):
-    """
-    An invalid sdist filename was found, users should refer to the packaging user guide.
-    """
-
-
-_canonicalize_regex = re.compile(r"[-_.]+")
-# PEP 427: The build number must start with a digit.
-_build_tag_regex = re.compile(r"(\d+)(.*)")
-
-
-def canonicalize_name(name: str) -> NormalizedName:
-    # This is taken from PEP 503.
-    value = _canonicalize_regex.sub("-", name).lower()
-    return cast(NormalizedName, value)
-
-
-def canonicalize_version(version: Union[Version, str]) -> str:
-    """
-    This is very similar to Version.__str__, but has one subtle difference
-    with the way it handles the release segment.
-    """
-    if isinstance(version, str):
-        try:
-            parsed = Version(version)
-        except InvalidVersion:
-            # Legacy versions cannot be normalized
-            return version
-    else:
-        parsed = version
-
-    parts = []
-
-    # Epoch
-    if parsed.epoch != 0:
-        parts.append(f"{parsed.epoch}!")
-
-    # Release segment
-    # NB: This strips trailing '.0's to normalize
-    parts.append(re.sub(r"(\.0)+$", "", ".".join(str(x) for x in parsed.release)))
-
-    # Pre-release
-    if parsed.pre is not None:
-        parts.append("".join(str(x) for x in parsed.pre))
-
-    # Post-release
-    if parsed.post is not None:
-        parts.append(f".post{parsed.post}")
-
-    # Development release
-    if parsed.dev is not None:
-        parts.append(f".dev{parsed.dev}")
-
-    # Local version segment
-    if parsed.local is not None:
-        parts.append(f"+{parsed.local}")
-
-    return "".join(parts)
-
-
-def parse_wheel_filename(
-    filename: str,
-) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
-    if not filename.endswith(".whl"):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (extension must be '.whl'): {filename}"
-        )
-
-    filename = filename[:-4]
-    dashes = filename.count("-")
-    if dashes not in (4, 5):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (wrong number of parts): {filename}"
-        )
-
-    parts = filename.split("-", dashes - 2)
-    name_part = parts[0]
-    # See PEP 427 for the rules on escaping the project name
-    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
-        raise InvalidWheelFilename(f"Invalid project name: {filename}")
-    name = canonicalize_name(name_part)
-    version = Version(parts[1])
-    if dashes == 5:
-        build_part = parts[2]
-        build_match = _build_tag_regex.match(build_part)
-        if build_match is None:
-            raise InvalidWheelFilename(
-                f"Invalid build number: {build_part} in '{filename}'"
-            )
-        build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
-    else:
-        build = ()
-    tags = parse_tag(parts[-1])
-    return (name, version, build, tags)
-
-
-def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
-    if filename.endswith(".tar.gz"):
-        file_stem = filename[: -len(".tar.gz")]
-    elif filename.endswith(".zip"):
-        file_stem = filename[: -len(".zip")]
-    else:
-        raise InvalidSdistFilename(
-            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
-            f" {filename}"
-        )
-
-    # We are requiring a PEP 440 version, which cannot contain dashes,
-    # so we split on the last dash.
-    name_part, sep, version_part = file_stem.rpartition("-")
-    if not sep:
-        raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
-
-    name = canonicalize_name(name_part)
-    version = Version(version_part)
-    return (name, version)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/readers.py

@@ -1,122 +0,0 @@
-import collections
-import pathlib
-import operator
-
-from . import abc
-
-from ._itertools import unique_everseen
-from ._compat import ZipPath
-
-
-def remove_duplicates(items):
-    return iter(collections.OrderedDict.fromkeys(items))
-
-
-class FileReader(abc.TraversableResources):
-    def __init__(self, loader):
-        self.path = pathlib.Path(loader.path).parent
-
-    def resource_path(self, resource):
-        """
-        Return the file system path to prevent
-        `resources.path()` from creating a temporary
-        copy.
-        """
-        return str(self.path.joinpath(resource))
-
-    def files(self):
-        return self.path
-
-
-class ZipReader(abc.TraversableResources):
-    def __init__(self, loader, module):
-        _, _, name = module.rpartition('.')
-        self.prefix = loader.prefix.replace('\\', '/') + name + '/'
-        self.archive = loader.archive
-
-    def open_resource(self, resource):
-        try:
-            return super().open_resource(resource)
-        except KeyError as exc:
-            raise FileNotFoundError(exc.args[0])
-
-    def is_resource(self, path):
-        # workaround for `zipfile.Path.is_file` returning true
-        # for non-existent paths.
-        target = self.files().joinpath(path)
-        return target.is_file() and target.exists()
-
-    def files(self):
-        return ZipPath(self.archive, self.prefix)
-
-
-class MultiplexedPath(abc.Traversable):
-    """
-    Given a series of Traversable objects, implement a merged
-    version of the interface across all objects. Useful for
-    namespace packages which may be multihomed at a single
-    name.
-    """
-
-    def __init__(self, *paths):
-        self._paths = list(map(pathlib.Path, remove_duplicates(paths)))
-        if not self._paths:
-            message = 'MultiplexedPath must contain at least one path'
-            raise FileNotFoundError(message)
-        if not all(path.is_dir() for path in self._paths):
-            raise NotADirectoryError('MultiplexedPath only supports directories')
-
-    def iterdir(self):
-        files = (file for path in self._paths for file in path.iterdir())
-        return unique_everseen(files, key=operator.attrgetter('name'))
-
-    def read_bytes(self):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    def read_text(self, *args, **kwargs):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    def is_dir(self):
-        return True
-
-    def is_file(self):
-        return False
-
-    def joinpath(self, child):
-        # first try to find child in current paths
-        for file in self.iterdir():
-            if file.name == child:
-                return file
-        # if it does not exist, construct it with the first path
-        return self._paths[0] / child
-
-    __truediv__ = joinpath
-
-    def open(self, *args, **kwargs):
-        raise FileNotFoundError(f'{self} is not a file')
-
-    @property
-    def name(self):
-        return self._paths[0].name
-
-    def __repr__(self):
-        paths = ', '.join(f"'{path}'" for path in self._paths)
-        return f'MultiplexedPath({paths})'
-
-
-class NamespaceReader(abc.TraversableResources):
-    def __init__(self, namespace_path):
-        if 'NamespacePath' not in str(namespace_path):
-            raise ValueError('Invalid path')
-        self.path = MultiplexedPath(*list(namespace_path))
-
-    def resource_path(self, resource):
-        """
-        Return the file system path to prevent
-        `resources.path()` from creating a temporary
-        copy.
-        """
-        return str(self.path.joinpath(resource))
-
-    def files(self):
-        return self.path

spaces/AutoBG/Auto-BoardGame/Model_Constants_Template.py

@@ -1,7 +0,0 @@
-def SEND_KEY():
-    KEY = ""
-    return KEY
-
-def SEND_MODEL():
-    OAI_MODEL = ""
-    return OAI_MODEL

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/__main__.py

@@ -1,31 +0,0 @@
-import os
-import sys
-import warnings
-
-# Remove '' and current working directory from the first entry
-# of sys.path, if present to avoid using current directory
-# in pip commands check, freeze, install, list and show,
-# when invoked as python -m pip <command>
-if sys.path[0] in ("", os.getcwd()):
-    sys.path.pop(0)
-
-# If we are running from a wheel, add the wheel to sys.path
-# This allows the usage python pip-*.whl/pip install pip-*.whl
-if __package__ == "":
-    # __file__ is pip-*.whl/pip/__main__.py
-    # first dirname call strips of '/__main__.py', second strips off '/pip'
-    # Resulting path is the name of the wheel itself
-    # Add that to sys.path so we can import pip
-    path = os.path.dirname(os.path.dirname(__file__))
-    sys.path.insert(0, path)
-
-if __name__ == "__main__":
-    # Work around the error reported in #9540, pending a proper fix.
-    # Note: It is essential the warning filter is set *before* importing
-    #       pip, as the deprecation happens at import time, not runtime.
-    warnings.filterwarnings(
-        "ignore", category=DeprecationWarning, module=".*packaging\\.version"
-    )
-    from pip._internal.cli.main import main as _main
-
-    sys.exit(_main())

spaces/BulatF/StreamlitSentiment/README.md

@@ -1,13 +0,0 @@
----
-title: StreamlitSentiment
-emoji: 🔥
-colorFrom: purple
-colorTo: purple
-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

spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/gather.h

@@ -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 fill 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 gather.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch gather
-
-#include <thrust/system/detail/sequential/gather.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/gather.h>
-#include <thrust/system/cuda/detail/gather.h>
-#include <thrust/system/omp/detail/gather.h>
-#include <thrust/system/tbb/detail/gather.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_GATHER_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/gather.h>
-#include __THRUST_HOST_SYSTEM_GATHER_HEADER
-#undef __THRUST_HOST_SYSTEM_GATHER_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_GATHER_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/gather.h>
-#include __THRUST_DEVICE_SYSTEM_GATHER_HEADER
-#undef __THRUST_DEVICE_SYSTEM_GATHER_HEADER
-

spaces/CVPR/Object-Detection-With-DETR-and-YOLOS/README.md

@@ -1,13 +0,0 @@
----
-title: Object Detection With DETR And YOLOS
-emoji: ⚡
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.19
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Text2Human/Text2Human/models/hierarchy_vqgan_model.py

@@ -1,374 +0,0 @@
-import math
-import sys
-from collections import OrderedDict
-
-sys.path.append('..')
-import lpips
-import torch
-import torch.nn.functional as F
-from torchvision.utils import save_image
-
-from models.archs.vqgan_arch import (Decoder, DecoderRes, Discriminator,
-                                     Encoder,
-                                     VectorQuantizerSpatialTextureAware,
-                                     VectorQuantizerTexture)
-from models.losses.vqgan_loss import (DiffAugment, adopt_weight,
-                                      calculate_adaptive_weight, hinge_d_loss)
-
-
-class HierarchyVQSpatialTextureAwareModel():
-
-    def __init__(self, opt):
-        self.opt = opt
-        self.device = torch.device('cuda')
-        self.top_encoder = Encoder(
-            ch=opt['top_ch'],
-            num_res_blocks=opt['top_num_res_blocks'],
-            attn_resolutions=opt['top_attn_resolutions'],
-            ch_mult=opt['top_ch_mult'],
-            in_channels=opt['top_in_channels'],
-            resolution=opt['top_resolution'],
-            z_channels=opt['top_z_channels'],
-            double_z=opt['top_double_z'],
-            dropout=opt['top_dropout']).to(self.device)
-        self.decoder = Decoder(
-            in_channels=opt['top_in_channels'],
-            resolution=opt['top_resolution'],
-            z_channels=opt['top_z_channels'],
-            ch=opt['top_ch'],
-            out_ch=opt['top_out_ch'],
-            num_res_blocks=opt['top_num_res_blocks'],
-            attn_resolutions=opt['top_attn_resolutions'],
-            ch_mult=opt['top_ch_mult'],
-            dropout=opt['top_dropout'],
-            resamp_with_conv=True,
-            give_pre_end=False).to(self.device)
-        self.top_quantize = VectorQuantizerTexture(
-            1024, opt['embed_dim'], beta=0.25).to(self.device)
-        self.top_quant_conv = torch.nn.Conv2d(opt["top_z_channels"],
-                                              opt['embed_dim'],
-                                              1).to(self.device)
-        self.top_post_quant_conv = torch.nn.Conv2d(opt['embed_dim'],
-                                                   opt["top_z_channels"],
-                                                   1).to(self.device)
-        self.load_top_pretrain_models()
-
-        self.bot_encoder = Encoder(
-            ch=opt['bot_ch'],
-            num_res_blocks=opt['bot_num_res_blocks'],
-            attn_resolutions=opt['bot_attn_resolutions'],
-            ch_mult=opt['bot_ch_mult'],
-            in_channels=opt['bot_in_channels'],
-            resolution=opt['bot_resolution'],
-            z_channels=opt['bot_z_channels'],
-            double_z=opt['bot_double_z'],
-            dropout=opt['bot_dropout']).to(self.device)
-        self.bot_decoder_res = DecoderRes(
-            in_channels=opt['bot_in_channels'],
-            resolution=opt['bot_resolution'],
-            z_channels=opt['bot_z_channels'],
-            ch=opt['bot_ch'],
-            num_res_blocks=opt['bot_num_res_blocks'],
-            ch_mult=opt['bot_ch_mult'],
-            dropout=opt['bot_dropout'],
-            give_pre_end=False).to(self.device)
-        self.bot_quantize = VectorQuantizerSpatialTextureAware(
-            opt['bot_n_embed'],
-            opt['embed_dim'],
-            beta=0.25,
-            spatial_size=opt['codebook_spatial_size']).to(self.device)
-        self.bot_quant_conv = torch.nn.Conv2d(opt["bot_z_channels"],
-                                              opt['embed_dim'],
-                                              1).to(self.device)
-        self.bot_post_quant_conv = torch.nn.Conv2d(opt['embed_dim'],
-                                                   opt["bot_z_channels"],
-                                                   1).to(self.device)
-
-        self.disc = Discriminator(
-            opt['n_channels'], opt['ndf'],
-            n_layers=opt['disc_layers']).to(self.device)
-        self.perceptual = lpips.LPIPS(net="vgg").to(self.device)
-        self.perceptual_weight = opt['perceptual_weight']
-        self.disc_start_step = opt['disc_start_step']
-        self.disc_weight_max = opt['disc_weight_max']
-        self.diff_aug = opt['diff_aug']
-        self.policy = "color,translation"
-
-        self.load_discriminator_models()
-
-        self.disc.train()
-
-        self.fix_decoder = opt['fix_decoder']
-
-        self.init_training_settings()
-
-    def load_top_pretrain_models(self):
-        # load pretrained vqgan for segmentation mask
-        top_vae_checkpoint = torch.load(self.opt['top_vae_path'])
-        self.top_encoder.load_state_dict(
-            top_vae_checkpoint['encoder'], strict=True)
-        self.decoder.load_state_dict(
-            top_vae_checkpoint['decoder'], strict=True)
-        self.top_quantize.load_state_dict(
-            top_vae_checkpoint['quantize'], strict=True)
-        self.top_quant_conv.load_state_dict(
-            top_vae_checkpoint['quant_conv'], strict=True)
-        self.top_post_quant_conv.load_state_dict(
-            top_vae_checkpoint['post_quant_conv'], strict=True)
-        self.top_encoder.eval()
-        self.top_quantize.eval()
-        self.top_quant_conv.eval()
-        self.top_post_quant_conv.eval()
-
-    def init_training_settings(self):
-        self.log_dict = OrderedDict()
-        self.configure_optimizers()
-
-    def configure_optimizers(self):
-        optim_params = []
-        for v in self.bot_encoder.parameters():
-            if v.requires_grad:
-                optim_params.append(v)
-        for v in self.bot_decoder_res.parameters():
-            if v.requires_grad:
-                optim_params.append(v)
-        for v in self.bot_quantize.parameters():
-            if v.requires_grad:
-                optim_params.append(v)
-        for v in self.bot_quant_conv.parameters():
-            if v.requires_grad:
-                optim_params.append(v)
-        for v in self.bot_post_quant_conv.parameters():
-            if v.requires_grad:
-                optim_params.append(v)
-        if not self.fix_decoder:
-            for name, v in self.decoder.named_parameters():
-                if v.requires_grad:
-                    if 'up.0' in name:
-                        optim_params.append(v)
-                    if 'up.1' in name:
-                        optim_params.append(v)
-                    if 'up.2' in name:
-                        optim_params.append(v)
-                    if 'up.3' in name:
-                        optim_params.append(v)
-
-        self.optimizer = torch.optim.Adam(optim_params, lr=self.opt['lr'])
-
-        self.disc_optimizer = torch.optim.Adam(
-            self.disc.parameters(), lr=self.opt['lr'])
-
-    def load_discriminator_models(self):
-        # load pretrained vqgan for segmentation mask
-        top_vae_checkpoint = torch.load(self.opt['top_vae_path'])
-        self.disc.load_state_dict(
-            top_vae_checkpoint['discriminator'], strict=True)
-
-    def save_network(self, save_path):
-        """Save networks.
-        """
-
-        save_dict = {}
-        save_dict['bot_encoder'] = self.bot_encoder.state_dict()
-        save_dict['bot_decoder_res'] = self.bot_decoder_res.state_dict()
-        save_dict['decoder'] = self.decoder.state_dict()
-        save_dict['bot_quantize'] = self.bot_quantize.state_dict()
-        save_dict['bot_quant_conv'] = self.bot_quant_conv.state_dict()
-        save_dict['bot_post_quant_conv'] = self.bot_post_quant_conv.state_dict(
-        )
-        save_dict['discriminator'] = self.disc.state_dict()
-        torch.save(save_dict, save_path)
-
-    def load_network(self):
-        checkpoint = torch.load(self.opt['pretrained_models'])
-        self.bot_encoder.load_state_dict(
-            checkpoint['bot_encoder'], strict=True)
-        self.bot_decoder_res.load_state_dict(
-            checkpoint['bot_decoder_res'], strict=True)
-        self.decoder.load_state_dict(checkpoint['decoder'], strict=True)
-        self.bot_quantize.load_state_dict(
-            checkpoint['bot_quantize'], strict=True)
-        self.bot_quant_conv.load_state_dict(
-            checkpoint['bot_quant_conv'], strict=True)
-        self.bot_post_quant_conv.load_state_dict(
-            checkpoint['bot_post_quant_conv'], strict=True)
-
-    def optimize_parameters(self, data, step):
-        self.bot_encoder.train()
-        self.bot_decoder_res.train()
-        if not self.fix_decoder:
-            self.decoder.train()
-        self.bot_quantize.train()
-        self.bot_quant_conv.train()
-        self.bot_post_quant_conv.train()
-
-        loss, d_loss = self.training_step(data, step)
-        self.optimizer.zero_grad()
-        loss.backward()
-        self.optimizer.step()
-
-        if step > self.disc_start_step:
-            self.disc_optimizer.zero_grad()
-            d_loss.backward()
-            self.disc_optimizer.step()
-
-    def top_encode(self, x, mask):
-        h = self.top_encoder(x)
-        h = self.top_quant_conv(h)
-        quant, _, _ = self.top_quantize(h, mask)
-        quant = self.top_post_quant_conv(quant)
-        return quant
-
-    def bot_encode(self, x, mask):
-        h = self.bot_encoder(x)
-        h = self.bot_quant_conv(h)
-        quant, emb_loss, info = self.bot_quantize(h, mask)
-        quant = self.bot_post_quant_conv(quant)
-        bot_dec_res = self.bot_decoder_res(quant)
-        return bot_dec_res, emb_loss, info
-
-    def decode(self, quant_top, bot_dec_res):
-        dec = self.decoder(quant_top, bot_h=bot_dec_res)
-        return dec
-
-    def forward_step(self, input, mask):
-        with torch.no_grad():
-            quant_top = self.top_encode(input, mask)
-        bot_dec_res, diff, _ = self.bot_encode(input, mask)
-        dec = self.decode(quant_top, bot_dec_res)
-        return dec, diff
-
-    def feed_data(self, data):
-        x = data['image'].float().to(self.device)
-        mask = data['texture_mask'].float().to(self.device)
-
-        return x, mask
-
-    def training_step(self, data, step):
-        x, mask = self.feed_data(data)
-        xrec, codebook_loss = self.forward_step(x, mask)
-
-        # get recon/perceptual loss
-        recon_loss = torch.abs(x.contiguous() - xrec.contiguous())
-        p_loss = self.perceptual(x.contiguous(), xrec.contiguous())
-        nll_loss = recon_loss + self.perceptual_weight * p_loss
-        nll_loss = torch.mean(nll_loss)
-
-        # augment for input to discriminator
-        if self.diff_aug:
-            xrec = DiffAugment(xrec, policy=self.policy)
-
-        # update generator
-        logits_fake = self.disc(xrec)
-        g_loss = -torch.mean(logits_fake)
-        last_layer = self.decoder.conv_out.weight
-        d_weight = calculate_adaptive_weight(nll_loss, g_loss, last_layer,
-                                             self.disc_weight_max)
-        d_weight *= adopt_weight(1, step, self.disc_start_step)
-        loss = nll_loss + d_weight * g_loss + codebook_loss
-
-        self.log_dict["loss"] = loss
-        self.log_dict["l1"] = recon_loss.mean().item()
-        self.log_dict["perceptual"] = p_loss.mean().item()
-        self.log_dict["nll_loss"] = nll_loss.item()
-        self.log_dict["g_loss"] = g_loss.item()
-        self.log_dict["d_weight"] = d_weight
-        self.log_dict["codebook_loss"] = codebook_loss.item()
-
-        if step > self.disc_start_step:
-            if self.diff_aug:
-                logits_real = self.disc(
-                    DiffAugment(x.contiguous().detach(), policy=self.policy))
-            else:
-                logits_real = self.disc(x.contiguous().detach())
-            logits_fake = self.disc(xrec.contiguous().detach(
-            ))  # detach so that generator isn"t also updated
-            d_loss = hinge_d_loss(logits_real, logits_fake)
-            self.log_dict["d_loss"] = d_loss
-        else:
-            d_loss = None
-
-        return loss, d_loss
-
-    @torch.no_grad()
-    def inference(self, data_loader, save_dir):
-        self.bot_encoder.eval()
-        self.bot_decoder_res.eval()
-        self.decoder.eval()
-        self.bot_quantize.eval()
-        self.bot_quant_conv.eval()
-        self.bot_post_quant_conv.eval()
-
-        loss_total = 0
-        num = 0
-
-        for _, data in enumerate(data_loader):
-            img_name = data['img_name'][0]
-            x, mask = self.feed_data(data)
-            xrec, _ = self.forward_step(x, mask)
-
-            recon_loss = torch.abs(x.contiguous() - xrec.contiguous())
-            p_loss = self.perceptual(x.contiguous(), xrec.contiguous())
-            nll_loss = recon_loss + self.perceptual_weight * p_loss
-            nll_loss = torch.mean(nll_loss)
-            loss_total += nll_loss
-
-            num += x.size(0)
-
-            if x.shape[1] > 3:
-                # colorize with random projection
-                assert xrec.shape[1] > 3
-                # convert logits to indices
-                xrec = torch.argmax(xrec, dim=1, keepdim=True)
-                xrec = F.one_hot(xrec, num_classes=x.shape[1])
-                xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float()
-                x = self.to_rgb(x)
-                xrec = self.to_rgb(xrec)
-
-            img_cat = torch.cat([x, xrec], dim=3).detach()
-            img_cat = ((img_cat + 1) / 2)
-            img_cat = img_cat.clamp_(0, 1)
-            save_image(
-                img_cat, f'{save_dir}/{img_name}.png', nrow=1, padding=4)
-
-        return (loss_total / num).item()
-
-    def get_current_log(self):
-        return self.log_dict
-
-    def update_learning_rate(self, epoch):
-        """Update learning rate.
-
-        Args:
-            current_iter (int): Current iteration.
-            warmup_iter (int): Warmup iter numbers. -1 for no warmup.
-                Default: -1.
-        """
-        lr = self.optimizer.param_groups[0]['lr']
-
-        if self.opt['lr_decay'] == 'step':
-            lr = self.opt['lr'] * (
-                self.opt['gamma']**(epoch // self.opt['step']))
-        elif self.opt['lr_decay'] == 'cos':
-            lr = self.opt['lr'] * (
-                1 + math.cos(math.pi * epoch / self.opt['num_epochs'])) / 2
-        elif self.opt['lr_decay'] == 'linear':
-            lr = self.opt['lr'] * (1 - epoch / self.opt['num_epochs'])
-        elif self.opt['lr_decay'] == 'linear2exp':
-            if epoch < self.opt['turning_point'] + 1:
-                # learning rate decay as 95%
-                # at the turning point (1 / 95% = 1.0526)
-                lr = self.opt['lr'] * (
-                    1 - epoch / int(self.opt['turning_point'] * 1.0526))
-            else:
-                lr *= self.opt['gamma']
-        elif self.opt['lr_decay'] == 'schedule':
-            if epoch in self.opt['schedule']:
-                lr *= self.opt['gamma']
-        else:
-            raise ValueError('Unknown lr mode {}'.format(self.opt['lr_decay']))
-        # set learning rate
-        for param_group in self.optimizer.param_groups:
-            param_group['lr'] = lr
-
-        return lr

spaces/CVPR/WALT/mmdet/datasets/lvis.py

@@ -1,742 +0,0 @@
-import itertools
-import logging
-import os.path as osp
-import tempfile
-from collections import OrderedDict
-
-import numpy as np
-from mmcv.utils import print_log
-from terminaltables import AsciiTable
-
-from .builder import DATASETS
-from .coco import CocoDataset
-
-
-@DATASETS.register_module()
-class LVISV05Dataset(CocoDataset):
-
-    CLASSES = (
-        'acorn', 'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock',
-        'alcohol', 'alligator', 'almond', 'ambulance', 'amplifier', 'anklet',
-        'antenna', 'apple', 'apple_juice', 'applesauce', 'apricot', 'apron',
-        'aquarium', 'armband', 'armchair', 'armoire', 'armor', 'artichoke',
-        'trash_can', 'ashtray', 'asparagus', 'atomizer', 'avocado', 'award',
-        'awning', 'ax', 'baby_buggy', 'basketball_backboard', 'backpack',
-        'handbag', 'suitcase', 'bagel', 'bagpipe', 'baguet', 'bait', 'ball',
-        'ballet_skirt', 'balloon', 'bamboo', 'banana', 'Band_Aid', 'bandage',
-        'bandanna', 'banjo', 'banner', 'barbell', 'barge', 'barrel',
-        'barrette', 'barrow', 'baseball_base', 'baseball', 'baseball_bat',
-        'baseball_cap', 'baseball_glove', 'basket', 'basketball_hoop',
-        'basketball', 'bass_horn', 'bat_(animal)', 'bath_mat', 'bath_towel',
-        'bathrobe', 'bathtub', 'batter_(food)', 'battery', 'beachball', 'bead',
-        'beaker', 'bean_curd', 'beanbag', 'beanie', 'bear', 'bed',
-        'bedspread', 'cow', 'beef_(food)', 'beeper', 'beer_bottle', 'beer_can',
-        'beetle', 'bell', 'bell_pepper', 'belt', 'belt_buckle', 'bench',
-        'beret', 'bib', 'Bible', 'bicycle', 'visor', 'binder', 'binoculars',
-        'bird', 'birdfeeder', 'birdbath', 'birdcage', 'birdhouse',
-        'birthday_cake', 'birthday_card', 'biscuit_(bread)', 'pirate_flag',
-        'black_sheep', 'blackboard', 'blanket', 'blazer', 'blender', 'blimp',
-        'blinker', 'blueberry', 'boar', 'gameboard', 'boat', 'bobbin',
-        'bobby_pin', 'boiled_egg', 'bolo_tie', 'deadbolt', 'bolt', 'bonnet',
-        'book', 'book_bag', 'bookcase', 'booklet', 'bookmark',
-        'boom_microphone', 'boot', 'bottle', 'bottle_opener', 'bouquet',
-        'bow_(weapon)', 'bow_(decorative_ribbons)', 'bow-tie', 'bowl',
-        'pipe_bowl', 'bowler_hat', 'bowling_ball', 'bowling_pin',
-        'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere',
-        'bread-bin', 'breechcloth', 'bridal_gown', 'briefcase',
-        'bristle_brush', 'broccoli', 'broach', 'broom', 'brownie',
-        'brussels_sprouts', 'bubble_gum', 'bucket', 'horse_buggy', 'bull',
-        'bulldog', 'bulldozer', 'bullet_train', 'bulletin_board',
-        'bulletproof_vest', 'bullhorn', 'corned_beef', 'bun', 'bunk_bed',
-        'buoy', 'burrito', 'bus_(vehicle)', 'business_card', 'butcher_knife',
-        'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car',
-        'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf',
-        'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)',
-        'can', 'can_opener', 'candelabrum', 'candle', 'candle_holder',
-        'candy_bar', 'candy_cane', 'walking_cane', 'canister', 'cannon',
-        'canoe', 'cantaloup', 'canteen', 'cap_(headwear)', 'bottle_cap',
-        'cape', 'cappuccino', 'car_(automobile)', 'railcar_(part_of_a_train)',
-        'elevator_car', 'car_battery', 'identity_card', 'card', 'cardigan',
-        'cargo_ship', 'carnation', 'horse_carriage', 'carrot', 'tote_bag',
-        'cart', 'carton', 'cash_register', 'casserole', 'cassette', 'cast',
-        'cat', 'cauliflower', 'caviar', 'cayenne_(spice)', 'CD_player',
-        'celery', 'cellular_telephone', 'chain_mail', 'chair', 'chaise_longue',
-        'champagne', 'chandelier', 'chap', 'checkbook', 'checkerboard',
-        'cherry', 'chessboard', 'chest_of_drawers_(furniture)',
-        'chicken_(animal)', 'chicken_wire', 'chickpea', 'Chihuahua',
-        'chili_(vegetable)', 'chime', 'chinaware', 'crisp_(potato_chip)',
-        'poker_chip', 'chocolate_bar', 'chocolate_cake', 'chocolate_milk',
-        'chocolate_mousse', 'choker', 'chopping_board', 'chopstick',
-        'Christmas_tree', 'slide', 'cider', 'cigar_box', 'cigarette',
-        'cigarette_case', 'cistern', 'clarinet', 'clasp', 'cleansing_agent',
-        'clementine', 'clip', 'clipboard', 'clock', 'clock_tower',
-        'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster', 'coat',
-        'coat_hanger', 'coatrack', 'cock', 'coconut', 'coffee_filter',
-        'coffee_maker', 'coffee_table', 'coffeepot', 'coil', 'coin',
-        'colander', 'coleslaw', 'coloring_material', 'combination_lock',
-        'pacifier', 'comic_book', 'computer_keyboard', 'concrete_mixer',
-        'cone', 'control', 'convertible_(automobile)', 'sofa_bed', 'cookie',
-        'cookie_jar', 'cooking_utensil', 'cooler_(for_food)',
-        'cork_(bottle_plug)', 'corkboard', 'corkscrew', 'edible_corn',
-        'cornbread', 'cornet', 'cornice', 'cornmeal', 'corset',
-        'romaine_lettuce', 'costume', 'cougar', 'coverall', 'cowbell',
-        'cowboy_hat', 'crab_(animal)', 'cracker', 'crape', 'crate', 'crayon',
-        'cream_pitcher', 'credit_card', 'crescent_roll', 'crib', 'crock_pot',
-        'crossbar', 'crouton', 'crow', 'crown', 'crucifix', 'cruise_ship',
-        'police_cruiser', 'crumb', 'crutch', 'cub_(animal)', 'cube',
-        'cucumber', 'cufflink', 'cup', 'trophy_cup', 'cupcake', 'hair_curler',
-        'curling_iron', 'curtain', 'cushion', 'custard', 'cutting_tool',
-        'cylinder', 'cymbal', 'dachshund', 'dagger', 'dartboard',
-        'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk',
-        'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux',
-        'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher',
-        'dishwasher_detergent', 'diskette', 'dispenser', 'Dixie_cup', 'dog',
-        'dog_collar', 'doll', 'dollar', 'dolphin', 'domestic_ass', 'eye_mask',
-        'doorbell', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly',
-        'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit',
-        'dresser', 'drill', 'drinking_fountain', 'drone', 'dropper',
-        'drum_(musical_instrument)', 'drumstick', 'duck', 'duckling',
-        'duct_tape', 'duffel_bag', 'dumbbell', 'dumpster', 'dustpan',
-        'Dutch_oven', 'eagle', 'earphone', 'earplug', 'earring', 'easel',
-        'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater',
-        'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk',
-        'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan',
-        'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)',
-        'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm',
-        'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace',
-        'fireplug', 'fish', 'fish_(food)', 'fishbowl', 'fishing_boat',
-        'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flash',
-        'flashlight', 'fleece', 'flip-flop_(sandal)', 'flipper_(footwear)',
-        'flower_arrangement', 'flute_glass', 'foal', 'folding_chair',
-        'food_processor', 'football_(American)', 'football_helmet',
-        'footstool', 'fork', 'forklift', 'freight_car', 'French_toast',
-        'freshener', 'frisbee', 'frog', 'fruit_juice', 'fruit_salad',
-        'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage',
-        'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic',
-        'gasmask', 'gazelle', 'gelatin', 'gemstone', 'giant_panda',
-        'gift_wrap', 'ginger', 'giraffe', 'cincture',
-        'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles',
-        'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose',
-        'gorilla', 'gourd', 'surgical_gown', 'grape', 'grasshopper', 'grater',
-        'gravestone', 'gravy_boat', 'green_bean', 'green_onion', 'griddle',
-        'grillroom', 'grinder_(tool)', 'grits', 'grizzly', 'grocery_bag',
-        'guacamole', 'guitar', 'gull', 'gun', 'hair_spray', 'hairbrush',
-        'hairnet', 'hairpin', 'ham', 'hamburger', 'hammer', 'hammock',
-        'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel',
-        'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw',
-        'hardback_book', 'harmonium', 'hat', 'hatbox', 'hatch', 'veil',
-        'headband', 'headboard', 'headlight', 'headscarf', 'headset',
-        'headstall_(for_horses)', 'hearing_aid', 'heart', 'heater',
-        'helicopter', 'helmet', 'heron', 'highchair', 'hinge', 'hippopotamus',
-        'hockey_stick', 'hog', 'home_plate_(baseball)', 'honey', 'fume_hood',
-        'hook', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce',
-        'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear',
-        'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate',
-        'ice_tea', 'igniter', 'incense', 'inhaler', 'iPod',
-        'iron_(for_clothing)', 'ironing_board', 'jacket', 'jam', 'jean',
-        'jeep', 'jelly_bean', 'jersey', 'jet_plane', 'jewelry', 'joystick',
-        'jumpsuit', 'kayak', 'keg', 'kennel', 'kettle', 'key', 'keycard',
-        'kilt', 'kimono', 'kitchen_sink', 'kitchen_table', 'kite', 'kitten',
-        'kiwi_fruit', 'knee_pad', 'knife', 'knight_(chess_piece)',
-        'knitting_needle', 'knob', 'knocker_(on_a_door)', 'koala', 'lab_coat',
-        'ladder', 'ladle', 'ladybug', 'lamb_(animal)', 'lamb-chop', 'lamp',
-        'lamppost', 'lampshade', 'lantern', 'lanyard', 'laptop_computer',
-        'lasagna', 'latch', 'lawn_mower', 'leather', 'legging_(clothing)',
-        'Lego', 'lemon', 'lemonade', 'lettuce', 'license_plate', 'life_buoy',
-        'life_jacket', 'lightbulb', 'lightning_rod', 'lime', 'limousine',
-        'linen_paper', 'lion', 'lip_balm', 'lipstick', 'liquor', 'lizard',
-        'Loafer_(type_of_shoe)', 'log', 'lollipop', 'lotion',
-        'speaker_(stero_equipment)', 'loveseat', 'machine_gun', 'magazine',
-        'magnet', 'mail_slot', 'mailbox_(at_home)', 'mallet', 'mammoth',
-        'mandarin_orange', 'manger', 'manhole', 'map', 'marker', 'martini',
-        'mascot', 'mashed_potato', 'masher', 'mask', 'mast',
-        'mat_(gym_equipment)', 'matchbox', 'mattress', 'measuring_cup',
-        'measuring_stick', 'meatball', 'medicine', 'melon', 'microphone',
-        'microscope', 'microwave_oven', 'milestone', 'milk', 'minivan',
-        'mint_candy', 'mirror', 'mitten', 'mixer_(kitchen_tool)', 'money',
-        'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor',
-        'motor_scooter', 'motor_vehicle', 'motorboat', 'motorcycle',
-        'mound_(baseball)', 'mouse_(animal_rodent)',
-        'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom',
-        'music_stool', 'musical_instrument', 'nailfile', 'nameplate', 'napkin',
-        'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newsstand',
-        'nightshirt', 'nosebag_(for_animals)', 'noseband_(for_animals)',
-        'notebook', 'notepad', 'nut', 'nutcracker', 'oar', 'octopus_(food)',
-        'octopus_(animal)', 'oil_lamp', 'olive_oil', 'omelet', 'onion',
-        'orange_(fruit)', 'orange_juice', 'oregano', 'ostrich', 'ottoman',
-        'overalls_(clothing)', 'owl', 'packet', 'inkpad', 'pad', 'paddle',
-        'padlock', 'paintbox', 'paintbrush', 'painting', 'pajamas', 'palette',
-        'pan_(for_cooking)', 'pan_(metal_container)', 'pancake', 'pantyhose',
-        'papaya', 'paperclip', 'paper_plate', 'paper_towel', 'paperback_book',
-        'paperweight', 'parachute', 'parakeet', 'parasail_(sports)',
-        'parchment', 'parka', 'parking_meter', 'parrot',
-        'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport',
-        'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter',
-        'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'pegboard',
-        'pelican', 'pen', 'pencil', 'pencil_box', 'pencil_sharpener',
-        'pendulum', 'penguin', 'pennant', 'penny_(coin)', 'pepper',
-        'pepper_mill', 'perfume', 'persimmon', 'baby', 'pet', 'petfood',
-        'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano',
-        'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow',
-        'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball',
-        'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)',
-        'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat',
-        'plate', 'platter', 'playing_card', 'playpen', 'pliers',
-        'plow_(farm_equipment)', 'pocket_watch', 'pocketknife',
-        'poker_(fire_stirring_tool)', 'pole', 'police_van', 'polo_shirt',
-        'poncho', 'pony', 'pool_table', 'pop_(soda)', 'portrait',
-        'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato',
-        'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'printer',
-        'projectile_(weapon)', 'projector', 'propeller', 'prune', 'pudding',
-        'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher', 'puppet',
-        'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit', 'race_car',
-        'racket', 'radar', 'radiator', 'radio_receiver', 'radish', 'raft',
-        'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat',
-        'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt',
-        'recliner', 'record_player', 'red_cabbage', 'reflector',
-        'remote_control', 'rhinoceros', 'rib_(food)', 'rifle', 'ring',
-        'river_boat', 'road_map', 'robe', 'rocking_chair', 'roller_skate',
-        'Rollerblade', 'rolling_pin', 'root_beer',
-        'router_(computer_equipment)', 'rubber_band', 'runner_(carpet)',
-        'plastic_bag', 'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag',
-        'safety_pin', 'sail', 'salad', 'salad_plate', 'salami',
-        'salmon_(fish)', 'salmon_(food)', 'salsa', 'saltshaker',
-        'sandal_(type_of_shoe)', 'sandwich', 'satchel', 'saucepan', 'saucer',
-        'sausage', 'sawhorse', 'saxophone', 'scale_(measuring_instrument)',
-        'scarecrow', 'scarf', 'school_bus', 'scissors', 'scoreboard',
-        'scrambled_eggs', 'scraper', 'scratcher', 'screwdriver',
-        'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane',
-        'seashell', 'seedling', 'serving_dish', 'sewing_machine', 'shaker',
-        'shampoo', 'shark', 'sharpener', 'Sharpie', 'shaver_(electric)',
-        'shaving_cream', 'shawl', 'shears', 'sheep', 'shepherd_dog',
-        'sherbert', 'shield', 'shirt', 'shoe', 'shopping_bag', 'shopping_cart',
-        'short_pants', 'shot_glass', 'shoulder_bag', 'shovel', 'shower_head',
-        'shower_curtain', 'shredder_(for_paper)', 'sieve', 'signboard', 'silo',
-        'sink', 'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka',
-        'ski_pole', 'skirt', 'sled', 'sleeping_bag', 'sling_(bandage)',
-        'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman',
-        'snowmobile', 'soap', 'soccer_ball', 'sock', 'soda_fountain',
-        'carbonated_water', 'sofa', 'softball', 'solar_array', 'sombrero',
-        'soup', 'soup_bowl', 'soupspoon', 'sour_cream', 'soya_milk',
-        'space_shuttle', 'sparkler_(fireworks)', 'spatula', 'spear',
-        'spectacles', 'spice_rack', 'spider', 'sponge', 'spoon', 'sportswear',
-        'spotlight', 'squirrel', 'stapler_(stapling_machine)', 'starfish',
-        'statue_(sculpture)', 'steak_(food)', 'steak_knife',
-        'steamer_(kitchen_appliance)', 'steering_wheel', 'stencil',
-        'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer',
-        'stirrup', 'stockings_(leg_wear)', 'stool', 'stop_sign', 'brake_light',
-        'stove', 'strainer', 'strap', 'straw_(for_drinking)', 'strawberry',
-        'street_sign', 'streetlight', 'string_cheese', 'stylus', 'subwoofer',
-        'sugar_bowl', 'sugarcane_(plant)', 'suit_(clothing)', 'sunflower',
-        'sunglasses', 'sunhat', 'sunscreen', 'surfboard', 'sushi', 'mop',
-        'sweat_pants', 'sweatband', 'sweater', 'sweatshirt', 'sweet_potato',
-        'swimsuit', 'sword', 'syringe', 'Tabasco_sauce', 'table-tennis_table',
-        'table', 'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag',
-        'taillight', 'tambourine', 'army_tank', 'tank_(storage_vessel)',
-        'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure',
-        'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup',
-        'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth',
-        'telephone_pole', 'telephoto_lens', 'television_camera',
-        'television_set', 'tennis_ball', 'tennis_racket', 'tequila',
-        'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread',
-        'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil',
-        'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven',
-        'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush',
-        'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel',
-        'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light',
-        'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline',
-        'tray', 'tree_house', 'trench_coat', 'triangle_(musical_instrument)',
-        'tricycle', 'tripod', 'trousers', 'truck', 'truffle_(chocolate)',
-        'trunk', 'vat', 'turban', 'turkey_(bird)', 'turkey_(food)', 'turnip',
-        'turtle', 'turtleneck_(clothing)', 'typewriter', 'umbrella',
-        'underwear', 'unicycle', 'urinal', 'urn', 'vacuum_cleaner', 'valve',
-        'vase', 'vending_machine', 'vent', 'videotape', 'vinegar', 'violin',
-        'vodka', 'volleyball', 'vulture', 'waffle', 'waffle_iron', 'wagon',
-        'wagon_wheel', 'walking_stick', 'wall_clock', 'wall_socket', 'wallet',
-        'walrus', 'wardrobe', 'wasabi', 'automatic_washer', 'watch',
-        'water_bottle', 'water_cooler', 'water_faucet', 'water_filter',
-        'water_heater', 'water_jug', 'water_gun', 'water_scooter', 'water_ski',
-        'water_tower', 'watering_can', 'watermelon', 'weathervane', 'webcam',
-        'wedding_cake', 'wedding_ring', 'wet_suit', 'wheel', 'wheelchair',
-        'whipped_cream', 'whiskey', 'whistle', 'wick', 'wig', 'wind_chime',
-        'windmill', 'window_box_(for_plants)', 'windshield_wiper', 'windsock',
-        'wine_bottle', 'wine_bucket', 'wineglass', 'wing_chair',
-        'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon', 'wreath',
-        'wrench', 'wristband', 'wristlet', 'yacht', 'yak', 'yogurt',
-        'yoke_(animal_equipment)', 'zebra', 'zucchini')
-
-    def load_annotations(self, ann_file):
-        """Load annotation from lvis style annotation file.
-
-        Args:
-            ann_file (str): Path of annotation file.
-
-        Returns:
-            list[dict]: Annotation info from LVIS api.
-        """
-
-        try:
-            import lvis
-            assert lvis.__version__ >= '10.5.3'
-            from lvis import LVIS
-        except AssertionError:
-            raise AssertionError('Incompatible version of lvis is installed. '
-                                 'Run pip uninstall lvis first. Then run pip '
-                                 'install mmlvis to install open-mmlab forked '
-                                 'lvis. ')
-        except ImportError:
-            raise ImportError('Package lvis is not installed. Please run pip '
-                              'install mmlvis to install open-mmlab forked '
-                              'lvis.')
-        self.coco = LVIS(ann_file)
-        self.cat_ids = self.coco.get_cat_ids()
-        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
-        self.img_ids = self.coco.get_img_ids()
-        data_infos = []
-        for i in self.img_ids:
-            info = self.coco.load_imgs([i])[0]
-            if info['file_name'].startswith('COCO'):
-                # Convert form the COCO 2014 file naming convention of
-                # COCO_[train/val/test]2014_000000000000.jpg to the 2017
-                # naming convention of 000000000000.jpg
-                # (LVIS v1 will fix this naming issue)
-                info['filename'] = info['file_name'][-16:]
-            else:
-                info['filename'] = info['file_name']
-            data_infos.append(info)
-        return data_infos
-
-    def evaluate(self,
-                 results,
-                 metric='bbox',
-                 logger=None,
-                 jsonfile_prefix=None,
-                 classwise=False,
-                 proposal_nums=(100, 300, 1000),
-                 iou_thrs=np.arange(0.5, 0.96, 0.05)):
-        """Evaluation in LVIS protocol.
-
-        Args:
-            results (list[list | tuple]): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated. Options are
-                'bbox', 'segm', 'proposal', 'proposal_fast'.
-            logger (logging.Logger | str | None): Logger used for printing
-                related information during evaluation. Default: None.
-            jsonfile_prefix (str | None):
-            classwise (bool): Whether to evaluating the AP for each class.
-            proposal_nums (Sequence[int]): Proposal number used for evaluating
-                recalls, such as recall@100, recall@1000.
-                Default: (100, 300, 1000).
-            iou_thrs (Sequence[float]): IoU threshold used for evaluating
-                recalls. If set to a list, the average recall of all IoUs will
-                also be computed. Default: 0.5.
-
-        Returns:
-            dict[str, float]: LVIS style metrics.
-        """
-
-        try:
-            import lvis
-            assert lvis.__version__ >= '10.5.3'
-            from lvis import LVISResults, LVISEval
-        except AssertionError:
-            raise AssertionError('Incompatible version of lvis is installed. '
-                                 'Run pip uninstall lvis first. Then run pip '
-                                 'install mmlvis to install open-mmlab forked '
-                                 'lvis. ')
-        except ImportError:
-            raise ImportError('Package lvis is not installed. Please run pip '
-                              'install mmlvis to install open-mmlab forked '
-                              'lvis.')
-        assert isinstance(results, list), 'results must be a list'
-        assert len(results) == len(self), (
-            'The length of results is not equal to the dataset len: {} != {}'.
-            format(len(results), len(self)))
-
-        metrics = metric if isinstance(metric, list) else [metric]
-        allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
-        for metric in metrics:
-            if metric not in allowed_metrics:
-                raise KeyError('metric {} is not supported'.format(metric))
-
-        if jsonfile_prefix is None:
-            tmp_dir = tempfile.TemporaryDirectory()
-            jsonfile_prefix = osp.join(tmp_dir.name, 'results')
-        else:
-            tmp_dir = None
-        result_files = self.results2json(results, jsonfile_prefix)
-
-        eval_results = OrderedDict()
-        # get original api
-        lvis_gt = self.coco
-        for metric in metrics:
-            msg = 'Evaluating {}...'.format(metric)
-            if logger is None:
-                msg = '\n' + msg
-            print_log(msg, logger=logger)
-
-            if metric == 'proposal_fast':
-                ar = self.fast_eval_recall(
-                    results, proposal_nums, iou_thrs, logger='silent')
-                log_msg = []
-                for i, num in enumerate(proposal_nums):
-                    eval_results['AR@{}'.format(num)] = ar[i]
-                    log_msg.append('\nAR@{}\t{:.4f}'.format(num, ar[i]))
-                log_msg = ''.join(log_msg)
-                print_log(log_msg, logger=logger)
-                continue
-
-            if metric not in result_files:
-                raise KeyError('{} is not in results'.format(metric))
-            try:
-                lvis_dt = LVISResults(lvis_gt, result_files[metric])
-            except IndexError:
-                print_log(
-                    'The testing results of the whole dataset is empty.',
-                    logger=logger,
-                    level=logging.ERROR)
-                break
-
-            iou_type = 'bbox' if metric == 'proposal' else metric
-            lvis_eval = LVISEval(lvis_gt, lvis_dt, iou_type)
-            lvis_eval.params.imgIds = self.img_ids
-            if metric == 'proposal':
-                lvis_eval.params.useCats = 0
-                lvis_eval.params.maxDets = list(proposal_nums)
-                lvis_eval.evaluate()
-                lvis_eval.accumulate()
-                lvis_eval.summarize()
-                for k, v in lvis_eval.get_results().items():
-                    if k.startswith('AR'):
-                        val = float('{:.3f}'.format(float(v)))
-                        eval_results[k] = val
-            else:
-                lvis_eval.evaluate()
-                lvis_eval.accumulate()
-                lvis_eval.summarize()
-                lvis_results = lvis_eval.get_results()
-                if classwise:  # Compute per-category AP
-                    # Compute per-category AP
-                    # from https://github.com/facebookresearch/detectron2/
-                    precisions = lvis_eval.eval['precision']
-                    # precision: (iou, recall, cls, area range, max dets)
-                    assert len(self.cat_ids) == precisions.shape[2]
-
-                    results_per_category = []
-                    for idx, catId in enumerate(self.cat_ids):
-                        # area range index 0: all area ranges
-                        # max dets index -1: typically 100 per image
-                        nm = self.coco.load_cats(catId)[0]
-                        precision = precisions[:, :, idx, 0, -1]
-                        precision = precision[precision > -1]
-                        if precision.size:
-                            ap = np.mean(precision)
-                        else:
-                            ap = float('nan')
-                        results_per_category.append(
-                            (f'{nm["name"]}', f'{float(ap):0.3f}'))
-
-                    num_columns = min(6, len(results_per_category) * 2)
-                    results_flatten = list(
-                        itertools.chain(*results_per_category))
-                    headers = ['category', 'AP'] * (num_columns // 2)
-                    results_2d = itertools.zip_longest(*[
-                        results_flatten[i::num_columns]
-                        for i in range(num_columns)
-                    ])
-                    table_data = [headers]
-                    table_data += [result for result in results_2d]
-                    table = AsciiTable(table_data)
-                    print_log('\n' + table.table, logger=logger)
-
-                for k, v in lvis_results.items():
-                    if k.startswith('AP'):
-                        key = '{}_{}'.format(metric, k)
-                        val = float('{:.3f}'.format(float(v)))
-                        eval_results[key] = val
-                ap_summary = ' '.join([
-                    '{}:{:.3f}'.format(k, float(v))
-                    for k, v in lvis_results.items() if k.startswith('AP')
-                ])
-                eval_results['{}_mAP_copypaste'.format(metric)] = ap_summary
-            lvis_eval.print_results()
-        if tmp_dir is not None:
-            tmp_dir.cleanup()
-        return eval_results
-
-
-LVISDataset = LVISV05Dataset
-DATASETS.register_module(name='LVISDataset', module=LVISDataset)
-
-
-@DATASETS.register_module()
-class LVISV1Dataset(LVISDataset):
-
-    CLASSES = (
-        'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock', 'alcohol',
-        'alligator', 'almond', 'ambulance', 'amplifier', 'anklet', 'antenna',
-        'apple', 'applesauce', 'apricot', 'apron', 'aquarium',
-        'arctic_(type_of_shoe)', 'armband', 'armchair', 'armoire', 'armor',
-        'artichoke', 'trash_can', 'ashtray', 'asparagus', 'atomizer',
-        'avocado', 'award', 'awning', 'ax', 'baboon', 'baby_buggy',
-        'basketball_backboard', 'backpack', 'handbag', 'suitcase', 'bagel',
-        'bagpipe', 'baguet', 'bait', 'ball', 'ballet_skirt', 'balloon',
-        'bamboo', 'banana', 'Band_Aid', 'bandage', 'bandanna', 'banjo',
-        'banner', 'barbell', 'barge', 'barrel', 'barrette', 'barrow',
-        'baseball_base', 'baseball', 'baseball_bat', 'baseball_cap',
-        'baseball_glove', 'basket', 'basketball', 'bass_horn', 'bat_(animal)',
-        'bath_mat', 'bath_towel', 'bathrobe', 'bathtub', 'batter_(food)',
-        'battery', 'beachball', 'bead', 'bean_curd', 'beanbag', 'beanie',
-        'bear', 'bed', 'bedpan', 'bedspread', 'cow', 'beef_(food)', 'beeper',
-        'beer_bottle', 'beer_can', 'beetle', 'bell', 'bell_pepper', 'belt',
-        'belt_buckle', 'bench', 'beret', 'bib', 'Bible', 'bicycle', 'visor',
-        'billboard', 'binder', 'binoculars', 'bird', 'birdfeeder', 'birdbath',
-        'birdcage', 'birdhouse', 'birthday_cake', 'birthday_card',
-        'pirate_flag', 'black_sheep', 'blackberry', 'blackboard', 'blanket',
-        'blazer', 'blender', 'blimp', 'blinker', 'blouse', 'blueberry',
-        'gameboard', 'boat', 'bob', 'bobbin', 'bobby_pin', 'boiled_egg',
-        'bolo_tie', 'deadbolt', 'bolt', 'bonnet', 'book', 'bookcase',
-        'booklet', 'bookmark', 'boom_microphone', 'boot', 'bottle',
-        'bottle_opener', 'bouquet', 'bow_(weapon)', 'bow_(decorative_ribbons)',
-        'bow-tie', 'bowl', 'pipe_bowl', 'bowler_hat', 'bowling_ball', 'box',
-        'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere',
-        'bread-bin', 'bread', 'breechcloth', 'bridal_gown', 'briefcase',
-        'broccoli', 'broach', 'broom', 'brownie', 'brussels_sprouts',
-        'bubble_gum', 'bucket', 'horse_buggy', 'bull', 'bulldog', 'bulldozer',
-        'bullet_train', 'bulletin_board', 'bulletproof_vest', 'bullhorn',
-        'bun', 'bunk_bed', 'buoy', 'burrito', 'bus_(vehicle)', 'business_card',
-        'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car',
-        'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf',
-        'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)',
-        'can', 'can_opener', 'candle', 'candle_holder', 'candy_bar',
-        'candy_cane', 'walking_cane', 'canister', 'canoe', 'cantaloup',
-        'canteen', 'cap_(headwear)', 'bottle_cap', 'cape', 'cappuccino',
-        'car_(automobile)', 'railcar_(part_of_a_train)', 'elevator_car',
-        'car_battery', 'identity_card', 'card', 'cardigan', 'cargo_ship',
-        'carnation', 'horse_carriage', 'carrot', 'tote_bag', 'cart', 'carton',
-        'cash_register', 'casserole', 'cassette', 'cast', 'cat', 'cauliflower',
-        'cayenne_(spice)', 'CD_player', 'celery', 'cellular_telephone',
-        'chain_mail', 'chair', 'chaise_longue', 'chalice', 'chandelier',
-        'chap', 'checkbook', 'checkerboard', 'cherry', 'chessboard',
-        'chicken_(animal)', 'chickpea', 'chili_(vegetable)', 'chime',
-        'chinaware', 'crisp_(potato_chip)', 'poker_chip', 'chocolate_bar',
-        'chocolate_cake', 'chocolate_milk', 'chocolate_mousse', 'choker',
-        'chopping_board', 'chopstick', 'Christmas_tree', 'slide', 'cider',
-        'cigar_box', 'cigarette', 'cigarette_case', 'cistern', 'clarinet',
-        'clasp', 'cleansing_agent', 'cleat_(for_securing_rope)', 'clementine',
-        'clip', 'clipboard', 'clippers_(for_plants)', 'cloak', 'clock',
-        'clock_tower', 'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster',
-        'coat', 'coat_hanger', 'coatrack', 'cock', 'cockroach',
-        'cocoa_(beverage)', 'coconut', 'coffee_maker', 'coffee_table',
-        'coffeepot', 'coil', 'coin', 'colander', 'coleslaw',
-        'coloring_material', 'combination_lock', 'pacifier', 'comic_book',
-        'compass', 'computer_keyboard', 'condiment', 'cone', 'control',
-        'convertible_(automobile)', 'sofa_bed', 'cooker', 'cookie',
-        'cooking_utensil', 'cooler_(for_food)', 'cork_(bottle_plug)',
-        'corkboard', 'corkscrew', 'edible_corn', 'cornbread', 'cornet',
-        'cornice', 'cornmeal', 'corset', 'costume', 'cougar', 'coverall',
-        'cowbell', 'cowboy_hat', 'crab_(animal)', 'crabmeat', 'cracker',
-        'crape', 'crate', 'crayon', 'cream_pitcher', 'crescent_roll', 'crib',
-        'crock_pot', 'crossbar', 'crouton', 'crow', 'crowbar', 'crown',
-        'crucifix', 'cruise_ship', 'police_cruiser', 'crumb', 'crutch',
-        'cub_(animal)', 'cube', 'cucumber', 'cufflink', 'cup', 'trophy_cup',
-        'cupboard', 'cupcake', 'hair_curler', 'curling_iron', 'curtain',
-        'cushion', 'cylinder', 'cymbal', 'dagger', 'dalmatian', 'dartboard',
-        'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk',
-        'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux',
-        'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher',
-        'dishwasher_detergent', 'dispenser', 'diving_board', 'Dixie_cup',
-        'dog', 'dog_collar', 'doll', 'dollar', 'dollhouse', 'dolphin',
-        'domestic_ass', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly',
-        'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit',
-        'dresser', 'drill', 'drone', 'dropper', 'drum_(musical_instrument)',
-        'drumstick', 'duck', 'duckling', 'duct_tape', 'duffel_bag', 'dumbbell',
-        'dumpster', 'dustpan', 'eagle', 'earphone', 'earplug', 'earring',
-        'easel', 'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater',
-        'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk',
-        'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan',
-        'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)',
-        'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm',
-        'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace',
-        'fireplug', 'first-aid_kit', 'fish', 'fish_(food)', 'fishbowl',
-        'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flap',
-        'flash', 'flashlight', 'fleece', 'flip-flop_(sandal)',
-        'flipper_(footwear)', 'flower_arrangement', 'flute_glass', 'foal',
-        'folding_chair', 'food_processor', 'football_(American)',
-        'football_helmet', 'footstool', 'fork', 'forklift', 'freight_car',
-        'French_toast', 'freshener', 'frisbee', 'frog', 'fruit_juice',
-        'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage',
-        'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic',
-        'gasmask', 'gazelle', 'gelatin', 'gemstone', 'generator',
-        'giant_panda', 'gift_wrap', 'ginger', 'giraffe', 'cincture',
-        'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles',
-        'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose',
-        'gorilla', 'gourd', 'grape', 'grater', 'gravestone', 'gravy_boat',
-        'green_bean', 'green_onion', 'griddle', 'grill', 'grits', 'grizzly',
-        'grocery_bag', 'guitar', 'gull', 'gun', 'hairbrush', 'hairnet',
-        'hairpin', 'halter_top', 'ham', 'hamburger', 'hammer', 'hammock',
-        'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel',
-        'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw',
-        'hardback_book', 'harmonium', 'hat', 'hatbox', 'veil', 'headband',
-        'headboard', 'headlight', 'headscarf', 'headset',
-        'headstall_(for_horses)', 'heart', 'heater', 'helicopter', 'helmet',
-        'heron', 'highchair', 'hinge', 'hippopotamus', 'hockey_stick', 'hog',
-        'home_plate_(baseball)', 'honey', 'fume_hood', 'hook', 'hookah',
-        'hornet', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce',
-        'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear',
-        'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate',
-        'igniter', 'inhaler', 'iPod', 'iron_(for_clothing)', 'ironing_board',
-        'jacket', 'jam', 'jar', 'jean', 'jeep', 'jelly_bean', 'jersey',
-        'jet_plane', 'jewel', 'jewelry', 'joystick', 'jumpsuit', 'kayak',
-        'keg', 'kennel', 'kettle', 'key', 'keycard', 'kilt', 'kimono',
-        'kitchen_sink', 'kitchen_table', 'kite', 'kitten', 'kiwi_fruit',
-        'knee_pad', 'knife', 'knitting_needle', 'knob', 'knocker_(on_a_door)',
-        'koala', 'lab_coat', 'ladder', 'ladle', 'ladybug', 'lamb_(animal)',
-        'lamb-chop', 'lamp', 'lamppost', 'lampshade', 'lantern', 'lanyard',
-        'laptop_computer', 'lasagna', 'latch', 'lawn_mower', 'leather',
-        'legging_(clothing)', 'Lego', 'legume', 'lemon', 'lemonade', 'lettuce',
-        'license_plate', 'life_buoy', 'life_jacket', 'lightbulb',
-        'lightning_rod', 'lime', 'limousine', 'lion', 'lip_balm', 'liquor',
-        'lizard', 'log', 'lollipop', 'speaker_(stero_equipment)', 'loveseat',
-        'machine_gun', 'magazine', 'magnet', 'mail_slot', 'mailbox_(at_home)',
-        'mallard', 'mallet', 'mammoth', 'manatee', 'mandarin_orange', 'manger',
-        'manhole', 'map', 'marker', 'martini', 'mascot', 'mashed_potato',
-        'masher', 'mask', 'mast', 'mat_(gym_equipment)', 'matchbox',
-        'mattress', 'measuring_cup', 'measuring_stick', 'meatball', 'medicine',
-        'melon', 'microphone', 'microscope', 'microwave_oven', 'milestone',
-        'milk', 'milk_can', 'milkshake', 'minivan', 'mint_candy', 'mirror',
-        'mitten', 'mixer_(kitchen_tool)', 'money',
-        'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor',
-        'motor_scooter', 'motor_vehicle', 'motorcycle', 'mound_(baseball)',
-        'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom',
-        'music_stool', 'musical_instrument', 'nailfile', 'napkin',
-        'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newspaper',
-        'newsstand', 'nightshirt', 'nosebag_(for_animals)',
-        'noseband_(for_animals)', 'notebook', 'notepad', 'nut', 'nutcracker',
-        'oar', 'octopus_(food)', 'octopus_(animal)', 'oil_lamp', 'olive_oil',
-        'omelet', 'onion', 'orange_(fruit)', 'orange_juice', 'ostrich',
-        'ottoman', 'oven', 'overalls_(clothing)', 'owl', 'packet', 'inkpad',
-        'pad', 'paddle', 'padlock', 'paintbrush', 'painting', 'pajamas',
-        'palette', 'pan_(for_cooking)', 'pan_(metal_container)', 'pancake',
-        'pantyhose', 'papaya', 'paper_plate', 'paper_towel', 'paperback_book',
-        'paperweight', 'parachute', 'parakeet', 'parasail_(sports)', 'parasol',
-        'parchment', 'parka', 'parking_meter', 'parrot',
-        'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport',
-        'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter',
-        'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'wooden_leg',
-        'pegboard', 'pelican', 'pen', 'pencil', 'pencil_box',
-        'pencil_sharpener', 'pendulum', 'penguin', 'pennant', 'penny_(coin)',
-        'pepper', 'pepper_mill', 'perfume', 'persimmon', 'person', 'pet',
-        'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano',
-        'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow',
-        'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball',
-        'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)',
-        'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat',
-        'plate', 'platter', 'playpen', 'pliers', 'plow_(farm_equipment)',
-        'plume', 'pocket_watch', 'pocketknife', 'poker_(fire_stirring_tool)',
-        'pole', 'polo_shirt', 'poncho', 'pony', 'pool_table', 'pop_(soda)',
-        'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato',
-        'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'pretzel',
-        'printer', 'projectile_(weapon)', 'projector', 'propeller', 'prune',
-        'pudding', 'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher',
-        'puppet', 'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit',
-        'race_car', 'racket', 'radar', 'radiator', 'radio_receiver', 'radish',
-        'raft', 'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat',
-        'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt',
-        'recliner', 'record_player', 'reflector', 'remote_control',
-        'rhinoceros', 'rib_(food)', 'rifle', 'ring', 'river_boat', 'road_map',
-        'robe', 'rocking_chair', 'rodent', 'roller_skate', 'Rollerblade',
-        'rolling_pin', 'root_beer', 'router_(computer_equipment)',
-        'rubber_band', 'runner_(carpet)', 'plastic_bag',
-        'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag', 'safety_pin',
-        'sail', 'salad', 'salad_plate', 'salami', 'salmon_(fish)',
-        'salmon_(food)', 'salsa', 'saltshaker', 'sandal_(type_of_shoe)',
-        'sandwich', 'satchel', 'saucepan', 'saucer', 'sausage', 'sawhorse',
-        'saxophone', 'scale_(measuring_instrument)', 'scarecrow', 'scarf',
-        'school_bus', 'scissors', 'scoreboard', 'scraper', 'screwdriver',
-        'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane',
-        'seashell', 'sewing_machine', 'shaker', 'shampoo', 'shark',
-        'sharpener', 'Sharpie', 'shaver_(electric)', 'shaving_cream', 'shawl',
-        'shears', 'sheep', 'shepherd_dog', 'sherbert', 'shield', 'shirt',
-        'shoe', 'shopping_bag', 'shopping_cart', 'short_pants', 'shot_glass',
-        'shoulder_bag', 'shovel', 'shower_head', 'shower_cap',
-        'shower_curtain', 'shredder_(for_paper)', 'signboard', 'silo', 'sink',
-        'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka', 'ski_pole',
-        'skirt', 'skullcap', 'sled', 'sleeping_bag', 'sling_(bandage)',
-        'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman',
-        'snowmobile', 'soap', 'soccer_ball', 'sock', 'sofa', 'softball',
-        'solar_array', 'sombrero', 'soup', 'soup_bowl', 'soupspoon',
-        'sour_cream', 'soya_milk', 'space_shuttle', 'sparkler_(fireworks)',
-        'spatula', 'spear', 'spectacles', 'spice_rack', 'spider', 'crawfish',
-        'sponge', 'spoon', 'sportswear', 'spotlight', 'squid_(food)',
-        'squirrel', 'stagecoach', 'stapler_(stapling_machine)', 'starfish',
-        'statue_(sculpture)', 'steak_(food)', 'steak_knife', 'steering_wheel',
-        'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer',
-        'stirrup', 'stool', 'stop_sign', 'brake_light', 'stove', 'strainer',
-        'strap', 'straw_(for_drinking)', 'strawberry', 'street_sign',
-        'streetlight', 'string_cheese', 'stylus', 'subwoofer', 'sugar_bowl',
-        'sugarcane_(plant)', 'suit_(clothing)', 'sunflower', 'sunglasses',
-        'sunhat', 'surfboard', 'sushi', 'mop', 'sweat_pants', 'sweatband',
-        'sweater', 'sweatshirt', 'sweet_potato', 'swimsuit', 'sword',
-        'syringe', 'Tabasco_sauce', 'table-tennis_table', 'table',
-        'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag', 'taillight',
-        'tambourine', 'army_tank', 'tank_(storage_vessel)',
-        'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure',
-        'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup',
-        'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth',
-        'telephone_pole', 'telephoto_lens', 'television_camera',
-        'television_set', 'tennis_ball', 'tennis_racket', 'tequila',
-        'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread',
-        'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil',
-        'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven',
-        'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush',
-        'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel',
-        'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light',
-        'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline',
-        'tray', 'trench_coat', 'triangle_(musical_instrument)', 'tricycle',
-        'tripod', 'trousers', 'truck', 'truffle_(chocolate)', 'trunk', 'vat',
-        'turban', 'turkey_(food)', 'turnip', 'turtle', 'turtleneck_(clothing)',
-        'typewriter', 'umbrella', 'underwear', 'unicycle', 'urinal', 'urn',
-        'vacuum_cleaner', 'vase', 'vending_machine', 'vent', 'vest',
-        'videotape', 'vinegar', 'violin', 'vodka', 'volleyball', 'vulture',
-        'waffle', 'waffle_iron', 'wagon', 'wagon_wheel', 'walking_stick',
-        'wall_clock', 'wall_socket', 'wallet', 'walrus', 'wardrobe',
-        'washbasin', 'automatic_washer', 'watch', 'water_bottle',
-        'water_cooler', 'water_faucet', 'water_heater', 'water_jug',
-        'water_gun', 'water_scooter', 'water_ski', 'water_tower',
-        'watering_can', 'watermelon', 'weathervane', 'webcam', 'wedding_cake',
-        'wedding_ring', 'wet_suit', 'wheel', 'wheelchair', 'whipped_cream',
-        'whistle', 'wig', 'wind_chime', 'windmill', 'window_box_(for_plants)',
-        'windshield_wiper', 'windsock', 'wine_bottle', 'wine_bucket',
-        'wineglass', 'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon',
-        'wreath', 'wrench', 'wristband', 'wristlet', 'yacht', 'yogurt',
-        'yoke_(animal_equipment)', 'zebra', 'zucchini')
-
-    def load_annotations(self, ann_file):
-        try:
-            import lvis
-            assert lvis.__version__ >= '10.5.3'
-            from lvis import LVIS
-        except AssertionError:
-            raise AssertionError('Incompatible version of lvis is installed. '
-                                 'Run pip uninstall lvis first. Then run pip '
-                                 'install mmlvis to install open-mmlab forked '
-                                 'lvis. ')
-        except ImportError:
-            raise ImportError('Package lvis is not installed. Please run pip '
-                              'install mmlvis to install open-mmlab forked '
-                              'lvis.')
-        self.coco = LVIS(ann_file)
-        self.cat_ids = self.coco.get_cat_ids()
-        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
-        self.img_ids = self.coco.get_img_ids()
-        data_infos = []
-        for i in self.img_ids:
-            info = self.coco.load_imgs([i])[0]
-            # coco_url is used in LVISv1 instead of file_name
-            # e.g. http://images.cocodataset.org/train2017/000000391895.jpg
-            # train/val split in specified in url
-            info['filename'] = info['coco_url'].replace(
-                'http://images.cocodataset.org/', '')
-            data_infos.append(info)
-        return data_infos

spaces/CVPR/WALT/mmdet/models/roi_heads/standard_roi_head.py

@@ -1,306 +0,0 @@
-import torch
-
-from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
-from ..builder import HEADS, build_head, build_roi_extractor
-from .base_roi_head import BaseRoIHead
-from .test_mixins import BBoxTestMixin, MaskTestMixin
-
-
-@HEADS.register_module()
-class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
-    """Simplest base roi head including one bbox head and one mask head."""
-
-    def init_assigner_sampler(self):
-        """Initialize assigner and sampler."""
-        self.bbox_assigner = None
-        self.bbox_sampler = None
-        if self.train_cfg:
-            self.bbox_assigner = build_assigner(self.train_cfg.assigner)
-            self.bbox_sampler = build_sampler(
-                self.train_cfg.sampler, context=self)
-
-    def init_bbox_head(self, bbox_roi_extractor, bbox_head):
-        """Initialize ``bbox_head``"""
-        self.bbox_roi_extractor = build_roi_extractor(bbox_roi_extractor)
-        self.bbox_head = build_head(bbox_head)
-
-    def init_mask_head(self, mask_roi_extractor, mask_head):
-        """Initialize ``mask_head``"""
-        if mask_roi_extractor is not None:
-            self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor)
-            self.share_roi_extractor = False
-        else:
-            self.share_roi_extractor = True
-            self.mask_roi_extractor = self.bbox_roi_extractor
-        self.mask_head = build_head(mask_head)
-
-    def init_gan_head(self, gan_roi_extractor, gan_head):
-        """Initialize ``mask_head``"""
-        if gan_roi_extractor is not None:
-            self.gan_roi_extractor = build_roi_extractor(gan_roi_extractor)
-            self.share_roi_extractor = False
-        else:
-            self.share_roi_extractor = True
-            self.gan_roi_extractor = self.bbox_roi_extractor
-        self.gan_head = build_head(gan_head)
-
-
-    def init_weights(self, pretrained):
-        """Initialize the weights in head.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        if self.with_shared_head:
-            self.shared_head.init_weights(pretrained=pretrained)
-        if self.with_bbox:
-            self.bbox_roi_extractor.init_weights()
-            self.bbox_head.init_weights()
-        if self.with_mask:
-            self.mask_head.init_weights()
-            if not self.share_roi_extractor:
-                self.mask_roi_extractor.init_weights()
-
-    def forward_dummy(self, x, proposals):
-        """Dummy forward function."""
-        # bbox head
-        outs = ()
-        rois = bbox2roi([proposals])
-        if self.with_bbox:
-            bbox_results = self._bbox_forward(x, rois)
-            outs = outs + (bbox_results['cls_score'],
-                           bbox_results['bbox_pred'])
-        # mask head
-        if self.with_mask:
-            mask_rois = rois[:100]
-            mask_results = self._mask_forward(x, mask_rois)
-            outs = outs + (mask_results['mask_pred'], )
-        return outs
-
-    def forward_train(self,
-                      x,
-                      img_metas,
-                      proposal_list,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None,
-                      gt_masks=None):
-        """
-        Args:
-            x (list[Tensor]): list of multi-level img features.
-            img_metas (list[dict]): list of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                `mmdet/datasets/pipelines/formatting.py:Collect`.
-            proposals (list[Tensors]): list of region proposals.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-            gt_masks (None | Tensor) : true segmentation masks for each box
-                used if the architecture supports a segmentation task.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of loss components
-        """
-        # assign gts and sample proposals
-        if self.with_bbox or self.with_mask:
-            num_imgs = len(img_metas)
-            if gt_bboxes_ignore is None:
-                gt_bboxes_ignore = [None for _ in range(num_imgs)]
-            sampling_results = []
-            for i in range(num_imgs):
-                assign_result = self.bbox_assigner.assign(
-                    proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
-                    gt_labels[i])
-                sampling_result = self.bbox_sampler.sample(
-                    assign_result,
-                    proposal_list[i],
-                    gt_bboxes[i],
-                    gt_labels[i],
-                    feats=[lvl_feat[i][None] for lvl_feat in x])
-                sampling_results.append(sampling_result)
-
-        losses = dict()
-        # bbox head forward and loss
-        if self.with_bbox:
-            bbox_results = self._bbox_forward_train(x, sampling_results,
-                                                    gt_bboxes, gt_labels,
-                                                    img_metas)
-            losses.update(bbox_results['loss_bbox'])
-
-        # mask head forward and loss
-        if self.with_mask:
-            mask_results = self._mask_forward_train(x, sampling_results,
-                                                    bbox_results['bbox_feats'],
-                                                    gt_masks, img_metas)
-            losses.update(mask_results['loss_mask'])
-
-        return losses
-
-    def _bbox_forward(self, x, rois):
-        """Box head forward function used in both training and testing."""
-        # TODO: a more flexible way to decide which feature maps to use
-        bbox_feats = self.bbox_roi_extractor(
-            x[:self.bbox_roi_extractor.num_inputs], rois)
-        if self.with_shared_head:
-            bbox_feats = self.shared_head(bbox_feats)
-        cls_score, bbox_pred = self.bbox_head(bbox_feats)
-
-        bbox_results = dict(
-            cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
-        return bbox_results
-
-    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
-                            img_metas):
-        """Run forward function and calculate loss for box head in training."""
-        rois = bbox2roi([res.bboxes for res in sampling_results])
-        bbox_results = self._bbox_forward(x, rois)
-
-        bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
-                                                  gt_labels, self.train_cfg)
-        loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
-                                        bbox_results['bbox_pred'], rois,
-                                        *bbox_targets)
-
-        bbox_results.update(loss_bbox=loss_bbox)
-        return bbox_results
-
-    def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
-                            img_metas):
-        """Run forward function and calculate loss for mask head in
-        training."""
-        if not self.share_roi_extractor:
-            pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
-            mask_results = self._mask_forward(x, pos_rois)
-        else:
-            pos_inds = []
-            device = bbox_feats.device
-            for res in sampling_results:
-                pos_inds.append(
-                    torch.ones(
-                        res.pos_bboxes.shape[0],
-                        device=device,
-                        dtype=torch.uint8))
-                pos_inds.append(
-                    torch.zeros(
-                        res.neg_bboxes.shape[0],
-                        device=device,
-                        dtype=torch.uint8))
-            pos_inds = torch.cat(pos_inds)
-
-            mask_results = self._mask_forward(
-                x, pos_inds=pos_inds, bbox_feats=bbox_feats)
-
-        mask_targets = self.mask_head.get_targets(sampling_results, gt_masks,
-                                                  self.train_cfg)
-        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
-        loss_mask = self.mask_head.loss(mask_results['mask_pred'],
-                                        mask_targets, pos_labels)
-
-        mask_results.update(loss_mask=loss_mask, mask_targets=mask_targets)
-        return mask_results
-
-    def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None):
-        """Mask head forward function used in both training and testing."""
-        assert ((rois is not None) ^
-                (pos_inds is not None and bbox_feats is not None))
-        if rois is not None:
-            mask_feats = self.mask_roi_extractor(
-                x[:self.mask_roi_extractor.num_inputs], rois)
-            if self.with_shared_head:
-                mask_feats = self.shared_head(mask_feats)
-        else:
-            assert bbox_feats is not None
-            mask_feats = bbox_feats[pos_inds]
-
-        mask_pred = self.mask_head(mask_feats)
-        mask_results = dict(mask_pred=mask_pred, mask_feats=mask_feats)
-        return mask_results
-
-    async def async_simple_test(self,
-                                x,
-                                proposal_list,
-                                img_metas,
-                                proposals=None,
-                                rescale=False):
-        """Async test without augmentation."""
-        assert self.with_bbox, 'Bbox head must be implemented.'
-
-        det_bboxes, det_labels = await self.async_test_bboxes(
-            x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
-        bbox_results = bbox2result(det_bboxes, det_labels,
-                                   self.bbox_head.num_classes)
-        if not self.with_mask:
-            return bbox_results
-        else:
-            segm_results = await self.async_test_mask(
-                x,
-                img_metas,
-                det_bboxes,
-                det_labels,
-                rescale=rescale,
-                mask_test_cfg=self.test_cfg.get('mask'))
-            return bbox_results, segm_results
-
-    def simple_test(self,
-                    x,
-                    proposal_list,
-                    img_metas,
-                    proposals=None,
-                    rescale=False):
-        """Test without augmentation."""
-        assert self.with_bbox, 'Bbox head must be implemented.'
-
-        det_bboxes, det_labels = self.simple_test_bboxes(
-            x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
-        if torch.onnx.is_in_onnx_export():
-            if self.with_mask:
-                segm_results = self.simple_test_mask(
-                    x, img_metas, det_bboxes, det_labels, rescale=rescale)
-                return det_bboxes, det_labels, segm_results
-            else:
-                return det_bboxes, det_labels
-
-        bbox_results = [
-            bbox2result(det_bboxes[i], det_labels[i],
-                        self.bbox_head.num_classes)
-            for i in range(len(det_bboxes))
-        ]
-
-        if not self.with_mask:
-            return bbox_results
-        else:
-            segm_results = self.simple_test_mask(
-                x, img_metas, det_bboxes, det_labels, rescale=rescale)
-            return list(zip(bbox_results, segm_results))
-
-    def aug_test(self, x, proposal_list, img_metas, rescale=False):
-        """Test with augmentations.
-
-        If rescale is False, then returned bboxes and masks will fit the scale
-        of imgs[0].
-        """
-        det_bboxes, det_labels = self.aug_test_bboxes(x, img_metas,
-                                                      proposal_list,
-                                                      self.test_cfg)
-
-        if rescale:
-            _det_bboxes = det_bboxes
-        else:
-            _det_bboxes = det_bboxes.clone()
-            _det_bboxes[:, :4] *= det_bboxes.new_tensor(
-                img_metas[0][0]['scale_factor'])
-        bbox_results = bbox2result(_det_bboxes, det_labels,
-                                   self.bbox_head.num_classes)
-
-        # det_bboxes always keep the original scale
-        if self.with_mask:
-            segm_results = self.aug_test_mask(x, img_metas, det_bboxes,
-                                              det_labels)
-            return [(bbox_results, segm_results)]
-        else:
-            return [bbox_results]

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py

@@ -1,413 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# 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/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py
-# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
-# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py
-# ------------------------------------------------------------------------------------------------
-
-import math
-import warnings
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.init import constant_, xavier_uniform_
-
-try:
-    from groundingdino import _C
-except:
-    warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")
-
-
-# helpers
-def _is_power_of_2(n):
-    if (not isinstance(n, int)) or (n < 0):
-        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
-    return (n & (n - 1) == 0) and n != 0
-
-
-class MultiScaleDeformableAttnFunction(Function):
-    @staticmethod
-    def forward(
-        ctx,
-        value,
-        value_spatial_shapes,
-        value_level_start_index,
-        sampling_locations,
-        attention_weights,
-        im2col_step,
-    ):
-        ctx.im2col_step = im2col_step
-        output = _C.ms_deform_attn_forward(
-            value,
-            value_spatial_shapes,
-            value_level_start_index,
-            sampling_locations,
-            attention_weights,
-            ctx.im2col_step,
-        )
-        ctx.save_for_backward(
-            value,
-            value_spatial_shapes,
-            value_level_start_index,
-            sampling_locations,
-            attention_weights,
-        )
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        (
-            value,
-            value_spatial_shapes,
-            value_level_start_index,
-            sampling_locations,
-            attention_weights,
-        ) = ctx.saved_tensors
-        grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward(
-            value,
-            value_spatial_shapes,
-            value_level_start_index,
-            sampling_locations,
-            attention_weights,
-            grad_output,
-            ctx.im2col_step,
-        )
-
-        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
-
-
-def multi_scale_deformable_attn_pytorch(
-    value: torch.Tensor,
-    value_spatial_shapes: torch.Tensor,
-    sampling_locations: torch.Tensor,
-    attention_weights: torch.Tensor,
-) -> torch.Tensor:
-
-    bs, _, num_heads, embed_dims = value.shape
-    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
-    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
-    sampling_grids = 2 * sampling_locations - 1
-    sampling_value_list = []
-    for level, (H_, W_) in enumerate(value_spatial_shapes):
-        # bs, H_*W_, num_heads, embed_dims ->
-        # bs, H_*W_, num_heads*embed_dims ->
-        # bs, num_heads*embed_dims, H_*W_ ->
-        # bs*num_heads, embed_dims, H_, W_
-        value_l_ = (
-            value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
-        )
-        # bs, num_queries, num_heads, num_points, 2 ->
-        # bs, num_heads, num_queries, num_points, 2 ->
-        # bs*num_heads, num_queries, num_points, 2
-        sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
-        # bs*num_heads, embed_dims, num_queries, num_points
-        sampling_value_l_ = F.grid_sample(
-            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
-        )
-        sampling_value_list.append(sampling_value_l_)
-    # (bs, num_queries, num_heads, num_levels, num_points) ->
-    # (bs, num_heads, num_queries, num_levels, num_points) ->
-    # (bs, num_heads, 1, num_queries, num_levels*num_points)
-    attention_weights = attention_weights.transpose(1, 2).reshape(
-        bs * num_heads, 1, num_queries, num_levels * num_points
-    )
-    output = (
-        (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
-        .sum(-1)
-        .view(bs, num_heads * embed_dims, num_queries)
-    )
-    return output.transpose(1, 2).contiguous()
-
-
-class MultiScaleDeformableAttention(nn.Module):
-    """Multi-Scale Deformable Attention Module used in Deformable-DETR
-
-    `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
-    <https://arxiv.org/pdf/2010.04159.pdf>`_.
-
-    Args:
-        embed_dim (int): The embedding dimension of Attention. Default: 256.
-        num_heads (int): The number of attention heads. Default: 8.
-        num_levels (int): The number of feature map used in Attention. Default: 4.
-        num_points (int): The number of sampling points for each query
-            in each head. Default: 4.
-        img2col_steps (int): The step used in image_to_column. Defualt: 64.
-            dropout (float): Dropout layer used in output. Default: 0.1.
-        batch_first (bool): if ``True``, then the input and output tensor will be
-            provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`
-    """
-
-    def __init__(
-        self,
-        embed_dim: int = 256,
-        num_heads: int = 8,
-        num_levels: int = 4,
-        num_points: int = 4,
-        img2col_step: int = 64,
-        batch_first: bool = False,
-    ):
-        super().__init__()
-        if embed_dim % num_heads != 0:
-            raise ValueError(
-                "embed_dim must be divisible by num_heads, but got {} and {}".format(
-                    embed_dim, num_heads
-                )
-            )
-        head_dim = embed_dim // num_heads
-
-        self.batch_first = batch_first
-
-        if not _is_power_of_2(head_dim):
-            warnings.warn(
-                """
-                You'd better set d_model in MSDeformAttn to make sure that
-                each dim of the attention head a power of 2, which is more efficient.
-                """
-            )
-
-        self.im2col_step = img2col_step
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.num_levels = num_levels
-        self.num_points = num_points
-        self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)
-        self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)
-        self.value_proj = nn.Linear(embed_dim, embed_dim)
-        self.output_proj = nn.Linear(embed_dim, embed_dim)
-
-        self.init_weights()
-
-    def _reset_parameters(self):
-        return self.init_weights()
-
-    def init_weights(self):
-        """
-        Default initialization for Parameters of Module.
-        """
-        constant_(self.sampling_offsets.weight.data, 0.0)
-        thetas = torch.arange(self.num_heads, dtype=torch.float32) * (
-            2.0 * math.pi / self.num_heads
-        )
-        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
-        grid_init = (
-            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
-            .view(self.num_heads, 1, 1, 2)
-            .repeat(1, self.num_levels, self.num_points, 1)
-        )
-        for i in range(self.num_points):
-            grid_init[:, :, i, :] *= i + 1
-        with torch.no_grad():
-            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
-        constant_(self.attention_weights.weight.data, 0.0)
-        constant_(self.attention_weights.bias.data, 0.0)
-        xavier_uniform_(self.value_proj.weight.data)
-        constant_(self.value_proj.bias.data, 0.0)
-        xavier_uniform_(self.output_proj.weight.data)
-        constant_(self.output_proj.bias.data, 0.0)
-
-    def freeze_sampling_offsets(self):
-        print("Freeze sampling offsets")
-        self.sampling_offsets.weight.requires_grad = False
-        self.sampling_offsets.bias.requires_grad = False
-
-    def freeze_attention_weights(self):
-        print("Freeze attention weights")
-        self.attention_weights.weight.requires_grad = False
-        self.attention_weights.bias.requires_grad = False
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: Optional[torch.Tensor] = None,
-        value: Optional[torch.Tensor] = None,
-        query_pos: Optional[torch.Tensor] = None,
-        key_padding_mask: Optional[torch.Tensor] = None,
-        reference_points: Optional[torch.Tensor] = None,
-        spatial_shapes: Optional[torch.Tensor] = None,
-        level_start_index: Optional[torch.Tensor] = None,
-        **kwargs
-    ) -> torch.Tensor:
-
-        """Forward Function of MultiScaleDeformableAttention
-
-        Args:
-            query (torch.Tensor): Query embeddings with shape
-                `(num_query, bs, embed_dim)`
-            key (torch.Tensor): Key embeddings with shape
-                `(num_key, bs, embed_dim)`
-            value (torch.Tensor): Value embeddings with shape
-                `(num_key, bs, embed_dim)`
-            query_pos (torch.Tensor): The position embedding for `query`. Default: None.
-            key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,
-                indicating which elements within `key` to be ignored in attention.
-            reference_points (torch.Tensor): The normalized reference points
-                with shape `(bs, num_query, num_levels, 2)`,
-                all elements is range in [0, 1], top-left (0, 0),
-                bottom-right (1, 1), including padding are.
-                or `(N, Length_{query}, num_levels, 4)`, add additional
-                two dimensions `(h, w)` to form reference boxes.
-            spatial_shapes (torch.Tensor): Spatial shape of features in different levels.
-                With shape `(num_levels, 2)`, last dimension represents `(h, w)`.
-            level_start_index (torch.Tensor): The start index of each level. A tensor with
-                shape `(num_levels, )` which can be represented as
-                `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`.
-
-        Returns:
-            torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`
-        """
-
-        if value is None:
-            value = query
-
-        if query_pos is not None:
-            query = query + query_pos
-
-        if not self.batch_first:
-            # change to (bs, num_query ,embed_dims)
-            query = query.permute(1, 0, 2)
-            value = value.permute(1, 0, 2)
-
-        bs, num_query, _ = query.shape
-        bs, num_value, _ = value.shape
-
-        assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
-
-        value = self.value_proj(value)
-        if key_padding_mask is not None:
-            value = value.masked_fill(key_padding_mask[..., None], float(0))
-        value = value.view(bs, num_value, self.num_heads, -1)
-        sampling_offsets = self.sampling_offsets(query).view(
-            bs, num_query, self.num_heads, self.num_levels, self.num_points, 2
-        )
-        attention_weights = self.attention_weights(query).view(
-            bs, num_query, self.num_heads, self.num_levels * self.num_points
-        )
-        attention_weights = attention_weights.softmax(-1)
-        attention_weights = attention_weights.view(
-            bs,
-            num_query,
-            self.num_heads,
-            self.num_levels,
-            self.num_points,
-        )
-
-        # bs, num_query, num_heads, num_levels, num_points, 2
-        if reference_points.shape[-1] == 2:
-            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
-            sampling_locations = (
-                reference_points[:, :, None, :, None, :]
-                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
-            )
-        elif reference_points.shape[-1] == 4:
-            sampling_locations = (
-                reference_points[:, :, None, :, None, :2]
-                + sampling_offsets
-                / self.num_points
-                * reference_points[:, :, None, :, None, 2:]
-                * 0.5
-            )
-        else:
-            raise ValueError(
-                "Last dim of reference_points must be 2 or 4, but get {} instead.".format(
-                    reference_points.shape[-1]
-                )
-            )
-    
-        if torch.cuda.is_available() and value.is_cuda:
-            halffloat = False
-            if value.dtype == torch.float16:
-                halffloat = True
-                value = value.float()
-                sampling_locations = sampling_locations.float()
-                attention_weights = attention_weights.float()
-
-            output = MultiScaleDeformableAttnFunction.apply(
-                value,
-                spatial_shapes,
-                level_start_index,
-                sampling_locations,
-                attention_weights,
-                self.im2col_step,
-            )
-
-            if halffloat:
-                output = output.half()
-        else:
-            output = multi_scale_deformable_attn_pytorch(
-                value, spatial_shapes, sampling_locations, attention_weights
-            )
-
-        output = self.output_proj(output)
-
-        if not self.batch_first:
-            output = output.permute(1, 0, 2)
-
-        return output
-
-
-def create_dummy_class(klass, dependency, message=""):
-    """
-    When a dependency of a class is not available, create a dummy class which throws ImportError
-    when used.
-
-    Args:
-        klass (str): name of the class.
-        dependency (str): name of the dependency.
-        message: extra message to print
-    Returns:
-        class: a class object
-    """
-    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
-    if message:
-        err = err + " " + message
-
-    class _DummyMetaClass(type):
-        # throw error on class attribute access
-        def __getattr__(_, __):  # noqa: B902
-            raise ImportError(err)
-
-    class _Dummy(object, metaclass=_DummyMetaClass):
-        # throw error on constructor
-        def __init__(self, *args, **kwargs):
-            raise ImportError(err)
-
-    return _Dummy
-
-
-def create_dummy_func(func, dependency, message=""):
-    """
-    When a dependency of a function is not available, create a dummy function which throws
-    ImportError when used.
-
-    Args:
-        func (str): name of the function.
-        dependency (str or list[str]): name(s) of the dependency.
-        message: extra message to print
-    Returns:
-        function: a function object
-    """
-    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func)
-    if message:
-        err = err + " " + message
-
-    if isinstance(dependency, (list, tuple)):
-        dependency = ",".join(dependency)
-
-    def _dummy(*args, **kwargs):
-        raise ImportError(err)
-
-    return _dummy

spaces/ChristopherMarais/Andrew_AI-BB_classification-beta/README.md

@@ -1,12 +0,0 @@
----
-title: Andrew_AI
-emoji: 🐳
-colorFrom: yellow
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
-app_port: 7860
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CognitiveLabs/GPT-auto-webscraping/AssistantService.py

@@ -1,22 +0,0 @@
-from langchain.chat_models import ChatOpenAI
-from chains.output_format.base import chain_output_format
-from chains.code_generator.base import chain_code_generator
-import os
-
-class GPTAssistant():
-    def __init__(self,api_key:str):
-        os.environ['OPENAI_API_KEY'] = api_key
-        self.llm = ChatOpenAI(temperature=0, model_name='gpt-3.5-turbo-16k', request_timeout=120, client=None)
-
-    def chain_response_format(self, html_content):
-        # prompt templates
-        output_format_chain = chain_output_format(self.llm)
-
-        # chain
-        return output_format_chain.run(html_content=html_content)
-    
-    def chain_code_generator(self, output_format, html_content):
-        # Prompt templates
-        script_chain = chain_code_generator(self.llm)
-
-        return script_chain.run(output_format=output_format, html_content=html_content)

spaces/Cong723/gpt-academic-public/crazy_functions/读文章写摘要.py

@@ -1,67 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt=system_prompt)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, 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 from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    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 from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

spaces/Cpp4App/Cpp4App/SEM/paragraph_bayesian.py

@@ -1,52 +0,0 @@
-import csv
-import joblib
-
-from sklearn.naive_bayes import MultinomialNB
-
-from SEM.text_preprocessing import pre_process_title
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-
-
-def readtrain():
-    with open('SEM/training_data/title.csv', 'rt') as csvfile:
-        reader = csv.reader(csvfile)
-        column1 = [row for row in reader]
-    content_train = [i[0] for i in column1[1:]]
-    opinion_train = [i[1] for i in column1[1:]]
-    train = [content_train, opinion_train]
-    return train
-
-def segmentWord(cont):
-    c = []
-    for i in cont:
-        clean_text = pre_process_title(i)
-        c.append(clean_text)
-    return c
-
-train = readtrain()
-content = segmentWord(train[1])
-
-textMark = train[0]
-
-train_content = content[:]
-# test_content = content[450:508]
-train_textMark = textMark[:]
-# test_textMark = textMark[450:508]
-
-tf = TfidfVectorizer(max_df=0.5)
-
-train_features = tf.fit_transform(train_content)
-
-load_pretrain_model = True
-
-if not load_pretrain_model:
-
-    clf = MultinomialNB(alpha=0.1)
-    clf.fit(train_features,train_textMark)
-
-    joblib.dump(clf, 'SEM/model/para_model.pkl')
-else:
-    clf = joblib.load('SEM/model/para_model.pkl')
-
-

spaces/DEEMOSTECH/ChatAvatar/README.md

@@ -1,12 +0,0 @@
----
-title: Hyperhuman Hf
-emoji: ⚡
-colorFrom: indigo
-colorTo: pink
-sdk: static
-pinned: false
----
-
-This this the [Paper](https://arxiv.org/abs/2304.03117)
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/IcnsImagePlugin.py

@@ -1,399 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# macOS icns file decoder, based on icns.py by Bob Ippolito.
-#
-# history:
-# 2004-10-09 fl   Turned into a PIL plugin; removed 2.3 dependencies.
-# 2020-04-04      Allow saving on all operating systems.
-#
-# Copyright (c) 2004 by Bob Ippolito.
-# Copyright (c) 2004 by Secret Labs.
-# Copyright (c) 2004 by Fredrik Lundh.
-# Copyright (c) 2014 by Alastair Houghton.
-# Copyright (c) 2020 by Pan Jing.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-import os
-import struct
-import sys
-
-from . import Image, ImageFile, PngImagePlugin, features
-
-enable_jpeg2k = features.check_codec("jpg_2000")
-if enable_jpeg2k:
-    from . import Jpeg2KImagePlugin
-
-MAGIC = b"icns"
-HEADERSIZE = 8
-
-
-def nextheader(fobj):
-    return struct.unpack(">4sI", fobj.read(HEADERSIZE))
-
-
-def read_32t(fobj, start_length, size):
-    # The 128x128 icon seems to have an extra header for some reason.
-    (start, length) = start_length
-    fobj.seek(start)
-    sig = fobj.read(4)
-    if sig != b"\x00\x00\x00\x00":
-        msg = "Unknown signature, expecting 0x00000000"
-        raise SyntaxError(msg)
-    return read_32(fobj, (start + 4, length - 4), size)
-
-
-def read_32(fobj, start_length, size):
-    """
-    Read a 32bit RGB icon resource.  Seems to be either uncompressed or
-    an RLE packbits-like scheme.
-    """
-    (start, length) = start_length
-    fobj.seek(start)
-    pixel_size = (size[0] * size[2], size[1] * size[2])
-    sizesq = pixel_size[0] * pixel_size[1]
-    if length == sizesq * 3:
-        # uncompressed ("RGBRGBGB")
-        indata = fobj.read(length)
-        im = Image.frombuffer("RGB", pixel_size, indata, "raw", "RGB", 0, 1)
-    else:
-        # decode image
-        im = Image.new("RGB", pixel_size, None)
-        for band_ix in range(3):
-            data = []
-            bytesleft = sizesq
-            while bytesleft > 0:
-                byte = fobj.read(1)
-                if not byte:
-                    break
-                byte = byte[0]
-                if byte & 0x80:
-                    blocksize = byte - 125
-                    byte = fobj.read(1)
-                    for i in range(blocksize):
-                        data.append(byte)
-                else:
-                    blocksize = byte + 1
-                    data.append(fobj.read(blocksize))
-                bytesleft -= blocksize
-                if bytesleft <= 0:
-                    break
-            if bytesleft != 0:
-                msg = f"Error reading channel [{repr(bytesleft)} left]"
-                raise SyntaxError(msg)
-            band = Image.frombuffer("L", pixel_size, b"".join(data), "raw", "L", 0, 1)
-            im.im.putband(band.im, band_ix)
-    return {"RGB": im}
-
-
-def read_mk(fobj, start_length, size):
-    # Alpha masks seem to be uncompressed
-    start = start_length[0]
-    fobj.seek(start)
-    pixel_size = (size[0] * size[2], size[1] * size[2])
-    sizesq = pixel_size[0] * pixel_size[1]
-    band = Image.frombuffer("L", pixel_size, fobj.read(sizesq), "raw", "L", 0, 1)
-    return {"A": band}
-
-
-def read_png_or_jpeg2000(fobj, start_length, size):
-    (start, length) = start_length
-    fobj.seek(start)
-    sig = fobj.read(12)
-    if sig[:8] == b"\x89PNG\x0d\x0a\x1a\x0a":
-        fobj.seek(start)
-        im = PngImagePlugin.PngImageFile(fobj)
-        Image._decompression_bomb_check(im.size)
-        return {"RGBA": im}
-    elif (
-        sig[:4] == b"\xff\x4f\xff\x51"
-        or sig[:4] == b"\x0d\x0a\x87\x0a"
-        or sig == b"\x00\x00\x00\x0cjP  \x0d\x0a\x87\x0a"
-    ):
-        if not enable_jpeg2k:
-            msg = (
-                "Unsupported icon subimage format (rebuild PIL "
-                "with JPEG 2000 support to fix this)"
-            )
-            raise ValueError(msg)
-        # j2k, jpc or j2c
-        fobj.seek(start)
-        jp2kstream = fobj.read(length)
-        f = io.BytesIO(jp2kstream)
-        im = Jpeg2KImagePlugin.Jpeg2KImageFile(f)
-        Image._decompression_bomb_check(im.size)
-        if im.mode != "RGBA":
-            im = im.convert("RGBA")
-        return {"RGBA": im}
-    else:
-        msg = "Unsupported icon subimage format"
-        raise ValueError(msg)
-
-
-class IcnsFile:
-    SIZES = {
-        (512, 512, 2): [(b"ic10", read_png_or_jpeg2000)],
-        (512, 512, 1): [(b"ic09", read_png_or_jpeg2000)],
-        (256, 256, 2): [(b"ic14", read_png_or_jpeg2000)],
-        (256, 256, 1): [(b"ic08", read_png_or_jpeg2000)],
-        (128, 128, 2): [(b"ic13", read_png_or_jpeg2000)],
-        (128, 128, 1): [
-            (b"ic07", read_png_or_jpeg2000),
-            (b"it32", read_32t),
-            (b"t8mk", read_mk),
-        ],
-        (64, 64, 1): [(b"icp6", read_png_or_jpeg2000)],
-        (32, 32, 2): [(b"ic12", read_png_or_jpeg2000)],
-        (48, 48, 1): [(b"ih32", read_32), (b"h8mk", read_mk)],
-        (32, 32, 1): [
-            (b"icp5", read_png_or_jpeg2000),
-            (b"il32", read_32),
-            (b"l8mk", read_mk),
-        ],
-        (16, 16, 2): [(b"ic11", read_png_or_jpeg2000)],
-        (16, 16, 1): [
-            (b"icp4", read_png_or_jpeg2000),
-            (b"is32", read_32),
-            (b"s8mk", read_mk),
-        ],
-    }
-
-    def __init__(self, fobj):
-        """
-        fobj is a file-like object as an icns resource
-        """
-        # signature : (start, length)
-        self.dct = dct = {}
-        self.fobj = fobj
-        sig, filesize = nextheader(fobj)
-        if not _accept(sig):
-            msg = "not an icns file"
-            raise SyntaxError(msg)
-        i = HEADERSIZE
-        while i < filesize:
-            sig, blocksize = nextheader(fobj)
-            if blocksize <= 0:
-                msg = "invalid block header"
-                raise SyntaxError(msg)
-            i += HEADERSIZE
-            blocksize -= HEADERSIZE
-            dct[sig] = (i, blocksize)
-            fobj.seek(blocksize, io.SEEK_CUR)
-            i += blocksize
-
-    def itersizes(self):
-        sizes = []
-        for size, fmts in self.SIZES.items():
-            for fmt, reader in fmts:
-                if fmt in self.dct:
-                    sizes.append(size)
-                    break
-        return sizes
-
-    def bestsize(self):
-        sizes = self.itersizes()
-        if not sizes:
-            msg = "No 32bit icon resources found"
-            raise SyntaxError(msg)
-        return max(sizes)
-
-    def dataforsize(self, size):
-        """
-        Get an icon resource as {channel: array}.  Note that
-        the arrays are bottom-up like windows bitmaps and will likely
-        need to be flipped or transposed in some way.
-        """
-        dct = {}
-        for code, reader in self.SIZES[size]:
-            desc = self.dct.get(code)
-            if desc is not None:
-                dct.update(reader(self.fobj, desc, size))
-        return dct
-
-    def getimage(self, size=None):
-        if size is None:
-            size = self.bestsize()
-        if len(size) == 2:
-            size = (size[0], size[1], 1)
-        channels = self.dataforsize(size)
-
-        im = channels.get("RGBA", None)
-        if im:
-            return im
-
-        im = channels.get("RGB").copy()
-        try:
-            im.putalpha(channels["A"])
-        except KeyError:
-            pass
-        return im
-
-
-##
-# Image plugin for Mac OS icons.
-
-
-class IcnsImageFile(ImageFile.ImageFile):
-    """
-    PIL image support for Mac OS .icns files.
-    Chooses the best resolution, but will possibly load
-    a different size image if you mutate the size attribute
-    before calling 'load'.
-
-    The info dictionary has a key 'sizes' that is a list
-    of sizes that the icns file has.
-    """
-
-    format = "ICNS"
-    format_description = "Mac OS icns resource"
-
-    def _open(self):
-        self.icns = IcnsFile(self.fp)
-        self.mode = "RGBA"
-        self.info["sizes"] = self.icns.itersizes()
-        self.best_size = self.icns.bestsize()
-        self.size = (
-            self.best_size[0] * self.best_size[2],
-            self.best_size[1] * self.best_size[2],
-        )
-
-    @property
-    def size(self):
-        return self._size
-
-    @size.setter
-    def size(self, value):
-        info_size = value
-        if info_size not in self.info["sizes"] and len(info_size) == 2:
-            info_size = (info_size[0], info_size[1], 1)
-        if (
-            info_size not in self.info["sizes"]
-            and len(info_size) == 3
-            and info_size[2] == 1
-        ):
-            simple_sizes = [
-                (size[0] * size[2], size[1] * size[2]) for size in self.info["sizes"]
-            ]
-            if value in simple_sizes:
-                info_size = self.info["sizes"][simple_sizes.index(value)]
-        if info_size not in self.info["sizes"]:
-            msg = "This is not one of the allowed sizes of this image"
-            raise ValueError(msg)
-        self._size = value
-
-    def load(self):
-        if len(self.size) == 3:
-            self.best_size = self.size
-            self.size = (
-                self.best_size[0] * self.best_size[2],
-                self.best_size[1] * self.best_size[2],
-            )
-
-        px = Image.Image.load(self)
-        if self.im is not None and self.im.size == self.size:
-            # Already loaded
-            return px
-        self.load_prepare()
-        # This is likely NOT the best way to do it, but whatever.
-        im = self.icns.getimage(self.best_size)
-
-        # If this is a PNG or JPEG 2000, it won't be loaded yet
-        px = im.load()
-
-        self.im = im.im
-        self.mode = im.mode
-        self.size = im.size
-
-        return px
-
-
-def _save(im, fp, filename):
-    """
-    Saves the image as a series of PNG files,
-    that are then combined into a .icns file.
-    """
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-    sizes = {
-        b"ic07": 128,
-        b"ic08": 256,
-        b"ic09": 512,
-        b"ic10": 1024,
-        b"ic11": 32,
-        b"ic12": 64,
-        b"ic13": 256,
-        b"ic14": 512,
-    }
-    provided_images = {im.width: im for im in im.encoderinfo.get("append_images", [])}
-    size_streams = {}
-    for size in set(sizes.values()):
-        image = (
-            provided_images[size]
-            if size in provided_images
-            else im.resize((size, size))
-        )
-
-        temp = io.BytesIO()
-        image.save(temp, "png")
-        size_streams[size] = temp.getvalue()
-
-    entries = []
-    for type, size in sizes.items():
-        stream = size_streams[size]
-        entries.append(
-            {"type": type, "size": HEADERSIZE + len(stream), "stream": stream}
-        )
-
-    # Header
-    fp.write(MAGIC)
-    file_length = HEADERSIZE  # Header
-    file_length += HEADERSIZE + 8 * len(entries)  # TOC
-    file_length += sum(entry["size"] for entry in entries)
-    fp.write(struct.pack(">i", file_length))
-
-    # TOC
-    fp.write(b"TOC ")
-    fp.write(struct.pack(">i", HEADERSIZE + len(entries) * HEADERSIZE))
-    for entry in entries:
-        fp.write(entry["type"])
-        fp.write(struct.pack(">i", entry["size"]))
-
-    # Data
-    for entry in entries:
-        fp.write(entry["type"])
-        fp.write(struct.pack(">i", entry["size"]))
-        fp.write(entry["stream"])
-
-    if hasattr(fp, "flush"):
-        fp.flush()
-
-
-def _accept(prefix):
-    return prefix[:4] == MAGIC
-
-
-Image.register_open(IcnsImageFile.format, IcnsImageFile, _accept)
-Image.register_extension(IcnsImageFile.format, ".icns")
-
-Image.register_save(IcnsImageFile.format, _save)
-Image.register_mime(IcnsImageFile.format, "image/icns")
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Syntax: python3 IcnsImagePlugin.py [file]")
-        sys.exit()
-
-    with open(sys.argv[1], "rb") as fp:
-        imf = IcnsImageFile(fp)
-        for size in imf.info["sizes"]:
-            imf.size = size
-            imf.save("out-%s-%s-%s.png" % size)
-        with Image.open(sys.argv[1]) as im:
-            im.save("out.png")
-        if sys.platform == "windows":
-            os.startfile("out.png")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/_core/_signals.py

@@ -1,26 +0,0 @@
-from __future__ import annotations
-
-from typing import AsyncIterator
-
-from ._compat import DeprecatedAsyncContextManager
-from ._eventloop import get_asynclib
-
-
-def open_signal_receiver(
-    *signals: int,
-) -> DeprecatedAsyncContextManager[AsyncIterator[int]]:
-    """
-    Start receiving operating system signals.
-
-    :param signals: signals to receive (e.g. ``signal.SIGINT``)
-    :return: an asynchronous context manager for an asynchronous iterator which yields signal
-        numbers
-
-    .. warning:: Windows does not support signals natively so it is best to avoid relying on this
-        in cross-platform applications.
-
-    .. warning:: On asyncio, this permanently replaces any previous signal handler for the given
-        signals, as set via :meth:`~asyncio.loop.add_signal_handler`.
-
-    """
-    return get_asynclib().open_signal_receiver(*signals)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/dsv-576afacd.js

@@ -1,6 +0,0 @@
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spaces/DataScienceEngineering/1-SimPhysics-HTML5/README.md

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----
-title: 🏖️PlayCanvas Simulation Vehicle Physics⛱️🌊 Live HTML5
-emoji: 1-Sim🌊
-colorFrom: green
-colorTo: gray
-sdk: static
-pinned: false
----
-
-Inspired by Danny Lange, VP AI and ML at Unity 
-Reference: https://youtu.be/YsEDv13W1RI?t=48 
-
-Quote on MLAgents: ... if you think about what I just said about evolution and that the creation of tools for intelligence yeah so you have the basic nature you have the 3d spatial environment you have gravity and you have inertia and the physics engine and now we throw in ml agents which is a machine learning system
-