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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Construct 2 License File Crack What You Need to Know Before Downloading.md

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-<h1>How to Crack Construct 2 License File and Enjoy All Features</h1>
- <p>If you are a game developer or a hobbyist who wants to create your own games without coding, you might have heard of Construct 2. Construct 2 is a powerful game development tool that lets you create games for various platforms using a simple drag-and-drop interface. However, if you want to access all the features of Construct 2, you need to buy a license that can cost up to $399. That's a lot of money for some people who just want to have fun with game creation. Fortunately, there is a way to crack Construct 2 license file and enjoy all the features for free. In this article, we will show you how to do that, as well as some tips and tricks to use Construct 2 effectively after cracking the license file.</p>
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- <h2>What is Construct 2 and Why You Need a License</h2>
- <h3>Construct 2: A Powerful Game Development Tool</h3>
- <p>Construct 2 is a game development tool created by Scirra, a company based in the UK. It allows you to create games for various platforms, such as Windows, Mac, Linux, Android, iOS, HTML5, Facebook, and more. You can create games using a simple drag-and-drop interface, where you can add objects, behaviors, events, and actions without writing any code. You can also use plugins and extensions to add more functionality and customization to your games. Construct 2 has a built-in preview mode that lets you test your games instantly on any device.</p>
- <h3>The Benefits of Having a License for Construct 2</h3>
- <p>While you can download and use Construct 2 for free, there are some limitations that come with the free edition. For example, you can only create up to 100 events per project, you can't use any third-party plugins or extensions, you can't export your games to Android or iOS, and you have to display a splash screen that says "Made with Construct 2" when your games start. If you want to remove these limitations and access all the features of Construct 2, you need to buy a license. There are three types of licenses available:</p>
- <ul>
-<li>Personal License: This license costs $129 and allows you to create unlimited events per project, use third-party plugins and extensions, export your games to Android or iOS using Cordova, and remove the splash screen. However, this license is only for personal use and non-commercial projects.</li>
-<li>Business License: This license costs $399 and allows you to do everything that the personal license does, plus use your games for commercial purposes. However, this license is only for individuals or small businesses with less than $5000 in annual revenue.</li>
-<li>Educational License: This license costs $49 per seat and allows you to use Construct 2 for educational purposes in schools or universities. However, this license does not allow you to export your games or use them for commercial purposes.</li>
-</ul>
- <p>As you can see, buying a license for Construct 2 can be quite expensive depending on your needs and goals. That's why some people resort to cracking the license file and enjoying all the features for free.</p>
- <h2>How to Crack Construct 2 License File for Free</h2>
- <h3>The Risks of Cracking Construct 2 License File</h3>
- <p>Before we show you how to crack Construct 2 license file, we have to warn you about the risks involved in doing so. First of all, cracking the license file is illegal and unethical. You are violating the terms and conditions of Scirra and depriving them of their rightful income. You are also exposing yourself to potential legal actions from Scirra if they find out that you are using a cracked license file. Secondly, cracking the license file can be dangerous for your computer and your games. You might download a cracked license file that contains malware or viruses that can harm your computer or steal your personal information. You might also encounter errors or bugs in your games that are caused by the cracked license file. Thirdly, cracking the license file can be unfair for other game developers who paid for their licenses legitimately. You are gaining an unfair advantage over them by accessing all the features of Construct 2 without paying anything.</p>
- <p>Therefore, we do not recommend or endorse cracking the license file for Construct 2. We are only providing this information for educational purposes only. If you decide to crack the license file anyway, you are doing so at your own risk and responsibility.</p>
- <h3>The Steps to Crack Construct 2 License File</h3>
- <p>If you still want to crack Construct 2 license file despite the risks involved, here are the steps that you need to follow:</p>
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- <h4>Download and Install Construct 2</h4>
- <p>The first step is to download and install Construct 2 from the official website: https://www.scirra.com/construct2/releases. You can choose any version that you want, but we recommend using the latest stable release (r277 at the time of writing). Make sure that you install it in a folder that is easy to access (such as C:\Program Files\Construct 2).</p>
- <h4>Download a Cracked License File</h4>
- <p>The next step is to download a cracked license file from an online source. There are many websites that offer cracked license files for various software applications, including Construct 2. However, not all of them are reliable or safe. Some of them might contain malware or viruses that can harm your computer or steal your personal information. Some of them might also provide outdated or invalid license files that won't work with your version of Construct 2.</p>
- <p>Therefore, you have to be careful when choosing where to download a cracked license file from. We suggest using one of these sources:</p>
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-<li>Nexus Gamez: This is an itch.io page that offers cracked license files for various versions of Construct 2 (from r251 to r277). You can download them from here: https://nexus-gamez.itch.io/construct-2-r277-cracked.</li>
-<li>Virus CX: This is a YouTube channel that offers cracked license files for various versions of Construct 2 (from r251 to r280). You can watch their videos and find the download links in the description or comments section.</li>
-<li>Newcodern: This is another YouTube channel that offers cracked license files for various versions of Construct 2 (from r239 to r279). You can watch their videos and find the download links in the description or comments section.</li>
-</ul>
- <p>Once you have downloaded a cracked license file from one of these sources (or any other source that you trust), make sure that it has the name "c2license.txt" and save it in a folder that is easy to access (such as C:\Users\YourName\Downloads).</p>
- <h4>Copy and Paste the License File to the Construct 2 Folder</h4>
- <p>The final step is to copy and paste the cracked license file (c2license.txt) from where you saved it (such as C:\Users\YourName\Downloads) to where you installed Construct 2 (such as C:\Program Files\Construct 2). If there is already an existing c2license.txt file in the Construct 2 folder (which means that you have already used another non-working license before), delete it first before pasting the new one.</p>
- <h4>Restart Construct 2 and Enjoy All Features</h4>
- <p>Now that you have copied and pasted the cracked license file (c2license.txt) to the Construct 2 folder (such as C:\Program Files\Construct 2), all you have to do is restart Construct 2 and enjoy all its features without any limitations. You should see a message saying "License activated" when you open Construct 2.</p>
- <h5>Note:</h5>
-<h2>How to Use Construct 2 Effectively After Cracking the License File</h2>
- <p>Now that you have cracked the license file for Construct 2 and unlocked all its features, you might be wondering how to use it effectively for your game development projects. Here are some of the best features of Construct 2 that you can use after cracking the license file, as well as some tips and tricks to make your games stand out.</p>
- <h3>The Best Features of Construct 2 for Game Development</h3>
- <p>Construct 2 has many features that make it a powerful and versatile game development tool. Some of the best features that you can use after cracking the license file are:</p>
- <ul>
-<li>Multiplatform Export: You can export your games to various platforms, such as Windows, Mac, Linux, Android, iOS, HTML5, Facebook, and more. You can also use third-party tools and services to enhance your games for different platforms, such as Cocoon.io, PhoneGap, Intel XDK, Steamworks, and more.</li>
-<li>Third-Party Plugins and Extensions: You can use third-party plugins and extensions to add more functionality and customization to your games. There are hundreds of plugins and extensions available for Construct 2, such as AdMob, Firebase, Google Play Games, Photon Cloud, Spriter, Q3D, and more. You can find them on the official Scirra Store or on other websites and forums.</li>
-<li>Advanced Event System: You can create complex logic and gameplay without writing any code using the advanced event system of Construct 2. You can use variables, functions, arrays, dictionaries, families, groups, sub-events, loops, conditions, actions, expressions, and more to create your own game logic. You can also use behaviors to add common features to your objects, such as platform movement, physics, pathfinding, drag-and-drop, and more.</li>
-<li>Visual Effects and Animations: You can add visual effects and animations to your games using the built-in features of Construct 2. You can use effects such as blur, glow, tint, warp, pixelate, noise, and more to enhance your graphics. You can also use animations to make your objects move and change appearance. You can create animations using frames or spritesheets or import them from external sources.</li>
-<li>Audio and Music: You can add audio and music to your games using the built-in audio system of Construct 2. You can import audio files in various formats (such as WAV, OGG, MP3) or generate them using the built-in sound generator. You can also control the volume, pitch, looping, panning, fading, and more of your audio files.</li>
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- <h3>The Tips and Tricks to Make Your Games Stand Out</h3>
- <h3>The Tips and Tricks to Make Your Games Stand Out</h3>
- <p>Besides using the best features of Construct 2 for game development, you also need to apply some tips and tricks to make your games stand out from the crowd. Here are some of them:</p>
- <ul>
-<li>Plan Your Game: Before you start creating your game, you should have a clear idea of what you want to achieve. You should plan your game concept, genre, theme, story, characters, gameplay, graphics, sound, and more. You should also do some research on your target audience and market. Having a plan will help you stay focused and organized throughout your game development process.</li>
-<li>Use Templates and Tutorials: If you are new to Construct 2 or game development in general, you can use templates and tutorials to learn the basics and get started quickly. Construct 2 comes with many templates and examples that you can use as a reference or modify to suit your needs. You can also find many tutorials online that cover various topics and aspects of game development using Construct 2.</li>
-<li>Test Your Game: Testing your game is essential to ensure that it works properly and meets your expectations. You should test your game regularly and thoroughly on different devices and platforms. You should also get feedback from other people, such as friends, family, or beta testers. Testing your game will help you identify and fix any errors, bugs, or issues that might affect your game quality or performance.</li>
-<li>Optimize Your Game: Optimizing your game is important to improve its speed, efficiency, and compatibility. You should optimize your game by reducing the size of your assets (such as images, sounds, fonts), using efficient events and actions, avoiding unnecessary objects and effects, using layers and layouts wisely, and more. Optimizing your game will help you reduce loading times, save memory and bandwidth, and increase frame rate.</li>
-<li>Publish Your Game: Publishing your game is the final step to share it with the world and reach your potential players. You should publish your game to the platforms that suit your goals and audience. You should also promote your game using various methods and channels, such as social media, blogs, forums, websites, ads, and more. Publishing and promoting your game will help you increase its visibility, popularity, and revenue.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Construct 2 is a powerful game development tool that lets you create games for various platforms without coding. However, if you want to access all its features, you need to buy a license that can be expensive. That's why some people crack Construct 2 license file and enjoy all its features for free. In this article, we showed you how to crack Construct 2 license file for free using a simple method. We also showed you some of the best features of Construct 2 that you can use after cracking the license file, as well as some tips and tricks to make your games stand out.</p>
- <p>Moreover, cracking the license file can be dangerous for your computer and your games. You might download a cracked license file that contains malware or viruses that can harm your computer or steal your personal information. You might also encounter errors or bugs in your games that are caused by the cracked license file. Furthermore, cracking the license file can be unfair for other game developers who paid for their licenses legitimately. You are gaining an unfair advantage over them by accessing all the features of Construct 2 without paying anything.</p>
- <p>Therefore, we do not recommend or endorse cracking the license file for Construct 2. We are only providing this information for educational purposes only. If you decide to crack the license file anyway, you are doing so at your own risk and responsibility.</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about cracking Construct 2 license file:</p>
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-<li>Q: Is cracking Construct 2 license file legal?<br>
-A: No, cracking Construct 2 license file is illegal and unethical. You are violating the terms and conditions of Scirra and depriving them of their rightful income. You are also exposing yourself to potential legal actions from Scirra if they find out that you are using a cracked license file.</li>
-<li>Q: Is cracking Construct 2 license file safe?<br>
-A: No, cracking Construct 2 license file can be dangerous for your computer and your games. You might download a cracked license file that contains malware or viruses that can harm your computer or steal your personal information. You might also encounter errors or bugs in your games that are caused by the cracked license file.</li>
-<li>Q: Is cracking Construct 2 license file fair?<br>
-A: No, cracking Construct 2 license file can be unfair for other game developers who paid for their licenses legitimately. You are gaining an unfair advantage over them by accessing all the features of Construct 2 without paying anything.</li>
-<li>Q: How can I crack Construct 2 license file?<br>
-A: To crack Construct 2 license file, you need to download and install Construct 2 from the official website, download a cracked license file from an online source, copy and paste the license file to the Construct 2 folder, and restart Construct 2.</li>
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-A: To use Construct 2 effectively after cracking the license file, you need to use the best features of Construct 2 for game development, such as multiplatform export, third-party plugins and extensions, advanced event system, visual effects and animations, and audio and music. You also need to apply some tips and tricks to make your games stand out, such as planning your game, using templates and tutorials, testing your game, optimizing your game, and publishing your game.</li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Culegere matematica petrica pdf O culegere completa de matematica pentru clasa 1-6.md

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- <p><strong>Culegere Matematica Petrica</strong> is a series of math books for grades 1-8, published by Sigma Publishing House in Bucharest, Romania. The books are based on the national math curriculum and cover all the topics and objectives required for each grade level. The books are divided into chapters, each containing a summary of the main concepts, followed by a large number of exercises, problems and tests of different difficulty levels. The books also include answers and solutions for all the questions.</p>
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-<h2>Why download Evangelisches Gesangbuch Pdf for free?</h2>
-<p>Evangelisches Gesangbuch is a valuable resource for anyone who wants to learn more about Protestant hymnody and liturgy. However, buying a physical copy of the hymnal can be expensive or inconvenient for some people. That is why downloading Evangelisches Gesangbuch Pdf for free can be a good option for many reasons.</p>
-<h3>The benefits of having a digital copy of the hymnal</h3>
-<p>Having a digital copy of Evangelisches Gesangbuch Pdf can offer you several advantages over having a physical copy. For example:</p>
-<ul>
-<li>You can access it anytime and anywhere on your computer or mobile device.</li>
-<li>You can save space and money by not having to buy or store a bulky book.</li>
-<li>You can search for any hymn or text by keywords or numbers.</li>
-<li>You can zoom in or out to adjust the font size or layout.</li>
-<li>You can bookmark your favorite hymns or pages for easy reference.</li>
-<li>You can copy or paste any text or image from the pdf file.</li>
-<li>You can print any page or section you want.</li>
-</ul>
-<h3>The challenges and risks of downloading the hymnal for free</h3>
-<p>However, downloading Evangelisches Gesangbuch Pdf for free also comes with some challenges and risks that you should be aware of. For example:</p>
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-<li>You may not be able to find a reliable or legal source for downloading the pdf file.</li>
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-<li>You may experience some technical issues or errors with the pdf file such as poor quality, missing pages, incorrect formatting, etc.</li>
-</ul>
-<h3>The best sources and methods for downloading the hymnal for free</h3>
-<p>To avoid these challenges and risks, you should be careful and selective when choosing where and how to download Evangelisches Gesangbuch Pdf for free. Here are some tips and resources that can help you:</p>
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-<li>Check if your church or library has a digital subscription or license for accessing Evangelisches Gesangbuch Pdf online or offline. If so, you can use their login credentials or ask them for permission to download it.</li>
-<li>Look for reputable websites or platforms that offer free or low-cost downloads of Evangelisches Gesangbuch Pdf legally and safely. For example: <a href="https://www.evangeliums.net/lieder/evangelisches_gesangbuch.html">Evangeliums.net</a>, <a href="https://www.ebook.de/de/category/66667/evangelische_gesangbuecher.html">Ebook.de</a>, <a href="https://www.amazon.de/Evangelische-Gesangb%C3%BCcher/b?ie=UTF8&node=340533031">Amazon.de</a>, etc.</li>
-<li>Use a reliable antivirus software or browser extension that can scan and protect your device from any malicious downloads.</li>
-<li>Read the terms and conditions carefully before downloading any pdf file. Make sure you understand your rights and responsibilities as a user.</li>
-<li>Respect the intellectual property rights of the authors and publishers of Evangelisches Gesangbuch. Do not distribute or reproduce the pdf file without their consent or acknowledgment.</li>
- </ul>
- <h2>How to use Evangelisches Gesangbuch Pdf effectively?</h2>
- <p>Once you have downloaded Evangelisches Gesangbuch Pdf for free successfully, you may wonder how to use it effectively. Here are some suggestions:</p>
- <h3>The features and functions of the pdf format</h3>
- <p>The pdf format is one of the most popular and widely used formats for digital documents. It has many features and functions that can enhance your reading and learning experience. For example:</p>
- <ul>
- <li>You can open and view the pdf file with any compatible software or application, such as Adobe Acrobat Reader, Google Chrome, Microsoft Edge, etc.</li>
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- <li>You can edit, modify, or convert the pdf file by using a specialized software or application, such as Adobe Acrobat Pro, PDFelement, Smallpdf, etc. However, may need to pay for some of these features or functions.</li>
- </ul>
- <h3>The tips and tricks for using the pdf reader and editor</h3>
- <p>To make the most out of the pdf reader and editor, you can use some tips and tricks that can save you time and effort. For example:</p>
- <ul>
- <li>You can use keyboard shortcuts to perform common tasks faster and easier, such as zoom in (Ctrl++), zoom out (Ctrl+-), copy (Ctrl+C), paste (Ctrl+V), undo (Ctrl+Z), redo (Ctrl+Y), etc.</li>
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- </ul>
- <h3>The ways to share and print the pdf file</h3>
- <p>Finally, you may want to share or print the pdf file of Evangelisches Gesangbuch for various purposes. Here are some ways to do that:</p>
- <ul>
- <li>You can share the pdf file by attaching it to an email, uploading it to a cloud service, posting it on a social media platform, or sending it via a messaging app. However, you should always ask for permission and give credit to the original source before sharing the pdf file.</li>
- <li>You can print the pdf file by using a printer that is connected to your device or network. You can choose the print settings that suit your needs, such as the number of copies, the page range, the orientation, the paper size, etc. However, you should always respect the copyright laws and ethical principles before printing the pdf file.</li>
- </ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Evangelisches Gesangbuch Pdf is a great resource for anyone who wants to learn more about Protestant hymnody and liturgy. It is a hymnal that contains more than 600 hymns, songs, psalms, canticles, prayers, and liturgical texts that cover various themes and occasions of Christian worship. It is also a symbol of the ecumenical spirit and cooperation among the Protestant churches in Germany, Austria, and Switzerland. By downloading Evangelisches Gesangbuch Pdf for free, you can enjoy the benefits of having a digital copy of the hymnal that you can access anytime and anywhere on your device. However, you should also be aware of the challenges and risks of downloading the hymnal for free and follow some tips and resources for finding and downloading the pdf file safely and legally. Moreover, you should also know how to use Evangelisches Gesangbuch Pdf effectively by using the features and functions of the pdf format, the tips and tricks for using the pdf reader and editor, and the ways to share and print the pdf file. We hope this article has helped you understand more about Evangelisches Gesangbuch Pdf and how to download it for free.</p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about Evangelisches Gesangbuch Pdf:</p>
-<ol>
-<li>Q: How many versions or editions of Evangelisches Gesangbuch are there?<br>
-A: There are 14 regional versions or editions of Evangelisches Gesangbuch that correspond to each region or denomination in Germany, Austria, and Switzerland. Each version or edition has a different color and number on its cover.</li>
-<li>Q: How can I find out which version or edition of Evangelisches Gesangbuch I need?<br>
-A: You can find out which version or edition of Evangelisches Gesangbuch you need by checking with your church or denomination. You can also look at the list of regions or denominations on <a href="https://www.evangelisch.de/inhalte/149383/01-12-2018/evangelisches-gesangbuch-die-regionalausgaben">this website</a>.</li>
-<li>Q: How can I buy a physical copy of Evangelisches Gesangbuch?<br>
-A: You can buy a physical copy of Evangelisches Gesangbuch from various online or offline bookstores or publishers. For example: <a href="https://www.gottesdienstinstitut.org/shop/gesangbuecher/">Gottesdienstinstitut.org</a>, <a href="https://www.buchhandel.de/buch/Evangelisches-Gesangbuch-Ausgabe-fuer-die-Evangelisch-Lutherische-Kirche-in-Bayern-9783872143020">Buchhandel.de</a>, <a href="https://www.v-r.de/de/evangelisches_gesangbuch/t-0/1011134/">V-r.de</a>, etc.</li>
-<li>Q: How can I update my pdf file of Evangelisches Gesangbuch?<br>
-A: You can update your pdf file of Evangelisches Gesangbuch by downloading the latest version or edition from a reliable or legal source. You can also check for any updates or corrections on <a href="https://www.evangelisch.de/inhalte/149383/01-12-2018/evangelisches-gesangbuch-die-regionalausgaben">this website</a>.</li>
-<li>Q: How can I contact the authors or publishers of Evangelisches Gesangbuch?<br>
-A: You can contact the authors or publishers of Evangelisches Gesangbuch by visiting their official websites or sending them an email. For example: <a href="https://www.evlka.de/">Evlka.de</a>, <a href="https://www.evang.at/">Evang.at</a>, <a href="https://www.ref.ch/">Ref.ch</a>, etc.</li>
-</ol>
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spaces/1phancelerku/anime-remove-background/Driver Simulator The Best Way to Practice Driving Online.md

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-<h1>Driver Simulator Games: A Review and Comparison</h1>
-<p>Have you ever wondered what it would be like to drive a car, a truck, a bus, or even a plane in different scenarios and conditions? Do you want to learn how to drive better, safer, and more efficiently? Do you enjoy the thrill and challenge of racing, drifting, or exploring different roads and tracks? If you answered yes to any of these questions, then you might be interested in trying out a driver simulator game.</p>
-<p>A driver simulator game is a software that simulates the experience of driving a vehicle in a virtual environment. You can control the vehicle using a keyboard, a mouse, a joystick, a steering wheel, or other devices. You can see the road, the traffic, the weather, and other elements on your screen. You can hear the engine, the brakes, the horn, and other sounds through your speakers or headphones. You can feel the vibration, the acceleration, the deceleration, and other forces through your seat or your motion platform.</p>
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-<p>Driver simulator games can be used for various purposes. Some people play them for fun and entertainment. They enjoy the realism, the graphics, the physics, and the content of these games. They like to race against other players online, to drift around corners, to explore different maps and locations, or to customize their vehicles. Some people play them for education and research. They want to learn more about driving rules, traffic laws, vehicle dynamics, road safety, or human factors. They use these games to test their skills, to measure their performance, to collect data, or to conduct experiments. Some people play them for training and practice. They want to improve their driving abilities, knowledge, and confidence. They use these games to train for specific situations, to practice for real-world driving tests, to refresh their memory, or to prepare for emergencies.</p>
-<p>Whatever your reason for playing a driver simulator game is, you will benefit from it in many ways. In this article, we will review some of the benefits of driver simulator games. We will also compare some of the features of different driver simulator games. Finally, we will give you our reviews of some of the best driver simulator games available in 2023.</p>
- <h2>Benefits of driver simulator games</h2>
-<p>Driver simulator games offer various advantages compared to real vehicles or other types of games. Here are some of them:</p>
-<ul>
-<li><strong>Controllability</strong>: You can control the behavior of virtual traffic, weather conditions, road layout, vehicle settings, and other factors as a function of your needs or preferences. You can also pause, rewind, replay, or skip any part of the simulation.</li>
-<li><strong>Reproducibility</strong>: You can repeat the same scenario as many times as you want with consistent results. You can also compare your performance with others or with yourself over time.</li>
-<li><strong>Standardization</strong>: You can use the same simulation environment for all participants or groups. You can also follow the same protocol or procedure for each session.</li>
-<li><strong>Safety</strong>: You can experience risky or dangerous situations without putting yourself or others at risk. You can also avoid injuries or damages to yourself or your property.</li>
-<li><strong>Cost-effectiveness</strong>: You can save money on fuel consumption - <strong>Accessibility</strong>: You can access a wide range of vehicles, roads, and scenarios that you might not have in real life. You can also play these games anytime and anywhere you want.</li>
-</ul>
-<p>These benefits make driver simulator games a valuable tool for enhancing your driving skills, knowledge, and safety. They can also make your driving experience more enjoyable and satisfying.</p>
- <h2>Features of driver simulator games</h2>
-<p>Driver simulator games vary in terms of their quality, realism, and complexity. Some of the main features that you should look for when choosing a driver simulator game are:</p>
-<ul>
-<li><strong>Realism</strong>: How closely does the game mimic the real world? Does it include realistic graphics, sounds, physics, traffic, weather, and other elements? Does it account for human factors such as perception, attention, memory, decision making, and emotions?</li>
-<li><strong>Graphics</strong>: How detailed and clear are the images and animations in the game? Do they create a sense of immersion and presence? Do they run smoothly and without glitches?</li>
-<li><strong>Physics</strong>: How accurately does the game model the behavior and interaction of the vehicle, the road, and the environment? Does it consider factors such as speed, acceleration, braking, steering, traction, suspension, aerodynamics, and damage?</li>
-<li><strong>Content</strong>: How much variety and diversity does the game offer in terms of vehicles, roads, scenarios, and modes? Does it include different types of vehicles such as cars, trucks, buses, motorcycles, or planes? Does it include different types of roads such as highways, city streets, rural roads, or off-road tracks? Does it include different types of scenarios such as racing, drifting, parking, or exploring? Does it include different modes such as single-player, multiplayer, online, or offline?</li>
-<li><strong>Gameplay</strong>: How easy and intuitive is the game to play? Does it have a user-friendly interface and controls? Does it have a clear and consistent feedback system? Does it have a fair and balanced difficulty level? Does it have a fun and engaging storyline and objectives?</li>
-</ul>
-<p>These features determine the quality and enjoyment of your driver simulator game. You should choose a game that suits your preferences, goals, and expectations.</p>
- <h2>Comparison of driver simulator games</h2>
-<p>To help you decide which driver simulator game to play, we have compared some of the most popular and realistic driver simulator games in 2023. We have rated them on a scale of 1 to 5 stars based on their realism, graphics, physics, content, and gameplay. We have also summarized their pros and cons in a table below.</p>
- <table>
-<tr>
-<th>Game</th>
-<th>Realism</th>
-<th>Graphics</th>
-<th>Physics</th>
-<th>Content</th>
-<th>Gameplay</th>
-<th>Total</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td><a href="">Gran Turismo 7</a></td>
-<td>★★★★★</td>
-<td>★★★★★</td>
-<td>★★★★★</td>
-<td>★★★★☆</td>
-<td>★★★★☆</td>
-<td>23/25</td>
-<td>- Stunning visuals<br>- Realistic physics<br>- Licensed vehicles<br>- Career mode<br>- Online features</td>
-<td>- Limited tracks<br>- Long loading times<br>- High system requirements<br>- Expensive DLCs<br>- Occasional bugs</td>
-</tr>
-<tr>
-<td><a href="">Euro Truck Simulator 2</a></td>
-<td>★★★★☆</td>
-<td>★★★☆☆</td>
-<td>★★★★☆</td>
-<td>★★★★★</td>
-<td>★★★★☆</td>
-<td>20/25</td>
-<td>- Immersive simulation<br>- Diverse content<br>- Customizable trucks<br>- Mod support<br>- Multiplayer mode</td>
-<td>- Dated graphics<br>- Repetitive gameplay<br>- Unrealistic AI<br>- Complex controls<br>- Steep learning curve</td>
-</tr>
-<tr>
-<td><a href="">City Car Driving</a></td>
-<td>★★★☆☆</td>
-<td>★★☆☆☆</td>
-<td>★★★☆☆</td <td>★★★★☆</td>
-<td>★★★☆☆</td>
-<td>18/25</td>
-<td>- Educational value<br>- Realistic traffic<br>- Driving scenarios<br>- Weather effects<br>- VR support</td>
-<td>- Low-quality graphics<br>- Limited vehicles<br>- Boring content<br>- Poor sound effects<br>- Expensive price</td>
-</tr>
-<tr>
-<td><a href="">Forza Horizon 5</a></td>
-<td>★★★☆☆</td>
-<td>★★★★★</td>
-<td>★★★☆☆</td>
-<td>★★★★★</td>
-<td>★★★★★</td>
-<td>21/25</td>
-<td>- Gorgeous graphics<br>- Open-world exploration<br>- Diverse vehicles<br>- Fun gameplay<br>- Social features</td>
-<td>- Arcade physics<br>- Unrealistic scenarios<br>- Frequent updates<br>- Microtransactions<br>- Online dependency</td>
-</tr>
-<tr>
-<td><a href="">Flight Simulator 2023</a></td>
-<td>★★★★★</td <td>★★★★★</td>
-<td>★★★★★</td>
-<td>★★★★☆</td>
-<td>★★★★☆</td>
-<td>23/25</td>
-<td>- Amazing realism<br>- Stunning scenery<br>- Real-time weather<br>- Live traffic<br>- Flight lessons</td>
-<td>- High hardware demands<br>- Long installation time<br>- Limited aircraft<br>- Complex controls<br>- Occasional glitches</td>
-</tr>
-</table>
- <h2>Reviews of driver simulator games</h2>
-<p>In this section, we will give you our detailed reviews of some of the best driver simulator games in 2023. We will highlight their strengths and weaknesses, and give you our recommendations.</p>
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- <h3>Gran Turismo 7</h3>
-<p>Gran Turismo 7 is the latest installment in the legendary racing simulator series by Polyphony Digital. It is exclusive to PlayStation 5 and features over 400 licensed vehicles, 28 tracks, and a revamped career mode. It also supports online multiplayer, VR mode, and ray tracing technology.</p>
-<p>The game is praised for its stunning visuals, realistic physics, and authentic sound effects. The vehicles look and feel like their real counterparts, and the tracks are faithfully recreated from real locations. The game also offers a variety of customization options, such as tuning, painting, and decals. The career mode is engaging and challenging, and the online features are smooth and fun.</p>
-<p>The game is criticized for its limited track selection, long loading times, and high system requirements. The game also has some expensive DLCs that add more content, but some players feel that they should have been included in the base game. The game also has some occasional bugs and glitches that can affect the gameplay.</p>
-<p>Overall, Gran Turismo 7 is a must-have for racing enthusiasts who own a PlayStation 5. It is one of the most realistic and beautiful racing simulators ever made, and it offers hours of entertainment and challenge. However, it is not perfect, and it might not appeal to casual gamers or those who prefer arcade-style racing games.</p>
- <h3>Euro Truck Simulator 2</h3>
-<p>Euro Truck Simulator 2 is a truck driving simulator by SCS Software. It is available for Windows, Mac, and Linux, and features over 70 European cities, 15 countries, and hundreds of roads. It also supports modding, multiplayer mode, and VR mode.</p>
-<p>The game is praised for its immersive simulation, diverse content, and customizable trucks. The game lets you drive various types of trucks across Europe, delivering cargo, earning money, and expanding your business. The game also lets you modify your trucks with different parts, colors, and accessories. The modding community is active and provides many additional content such as maps, vehicles, skins, and more. The multiplayer mode is fun and social, and the VR mode is realistic and thrilling.</p <p>The game is criticized for its dated graphics, repetitive gameplay, and unrealistic AI. The game does not have the best visuals, and some of the environments look bland and boring. The game can also get monotonous after a while, as the missions are similar and the routes are long. The game also has some issues with the AI traffic, such as erratic behavior, collisions, and traffic jams.</p>
-<p>Overall, Euro Truck Simulator 2 is a great game for truck lovers who want to experience the life of a truck driver. It is a relaxing and rewarding game that offers a lot of variety and customization. However, it is not a game for everyone, and it might not appeal to those who prefer fast-paced or action-packed games.</p>
- <h3>City Car Driving</h3>
-<p>City Car Driving is a car driving simulator by Forward Development. It is available for Windows and features over 25 vehicles, 16 maps, and 24 scenarios. It also supports VR mode and modding.</p>
-<p>The game is praised for its educational value, realistic traffic, and driving scenarios. The game is designed to help you learn how to drive in different situations and conditions, such as city traffic, country roads, night driving, rain, snow, fog, etc. The game also includes a traffic rules mode, where you have to follow the traffic laws and signs of different countries. The game also has a variety of scenarios, such as parking, overtaking, emergency braking, etc. The game also supports VR mode, which enhances the immersion and realism of the game.</p>
-<p>The game is criticized for its low-quality graphics, limited vehicles, and boring content. The game does not have the best graphics, and some of the models and textures look outdated and low-resolution. The game also has a small selection of vehicles, mostly sedans and hatchbacks. The game also lacks content in terms of maps, modes, and objectives. The game can get dull and tedious after a while.</p>
-<p>Overall, City Car Driving is a good game for beginners who want to learn how to drive or improve their driving skills. It is a realistic and challenging game that teaches you the basics of driving in various situations. However, it is not a very entertaining or exciting game, and it might not appeal to those who want more action or variety in their games.</p>
- <h3>Forza Horizon 5</h3>
-<p>Forza Horizon 5 is an open-world racing simulator by Playground Games. It is available for Windows and Xbox Series X/S and features over 500 vehicles , and a stunning recreation of Mexico. It also supports online multiplayer, cross-play, and ray tracing technology.</p>
-<p>The game is praised for its gorgeous graphics, open-world exploration, and diverse vehicles. The game showcases the beauty and diversity of Mexico, with its vibrant cities, lush forests, arid deserts, snowy mountains, and ancient ruins. The game also offers a huge variety of vehicles, from supercars to off-road trucks, from motorcycles to planes. The game also has a fun and engaging gameplay, with dynamic seasons, events, challenges, and rewards.</p>
-<p>The game is criticized for its arcade physics, unrealistic scenarios, and frequent updates. The game does not have the most realistic physics, and some of the vehicles and tracks feel too easy or too hard to drive. The game also has some unrealistic scenarios, such as driving through a volcano, a sandstorm, or a tornado. The game also has frequent updates that add more content, but also require more storage space and internet bandwidth.</p>
-<p>Overall, Forza Horizon 5 is a fantastic game for racing fans who want to experience the thrill and joy of driving in a beautiful and diverse world. It is one of the most fun and enjoyable racing simulators ever made, and it offers hours of entertainment and challenge. However, it is not a very realistic or serious game, and it might not appeal to those who prefer more simulation or realism in their games.</p>
- <h3>Flight Simulator 2023</h3>
-<p>Flight Simulator 2023 is a flight simulator by Asobo Studio. It is available for Windows and Xbox Series X/S and features over 40 aircraft , and a realistic representation of the entire Earth. It also supports online multiplayer, VR mode, and modding.</p>
-<p>The game is praised for its amazing realism, stunning scenery, and real-time weather. The game uses satellite imagery, 3D mapping, and artificial intelligence to create a detailed and accurate model of the Earth. The game also uses real-time data from various sources to simulate the weather, the traffic, and the wildlife. The game also offers a variety of aircraft, from light planes to jets, from helicopters to gliders. The game also supports VR mode, which enhances the immersion and realism of the game.</p>
-<p>The game is criticized for its high hardware demands, long installation time, and limited aircraft. The game requires a powerful PC or console, a fast internet connection, and a large storage space to run smoothly and without issues. The game also takes a long time to install and update, which can be frustrating for some players. The game also has a small selection of aircraft, mostly civilian and commercial ones. The game also lacks some features such as combat, missions, or challenges.</p>
-<p>Overall, Flight Simulator 2023 is an incredible game for aviation enthusiasts who want to experience the wonder and beauty of flying in a realistic and immersive way. It is one of the most advanced and impressive flight simulators ever made, and it offers hours of exploration and discovery. However, it is not a very accessible or casual game, and it might not appeal to those who prefer more action or variety in their games.</p>
- <h2>Conclusion</h2>
-<p>Driver simulator games are software that simulate the experience of driving a vehicle in a virtual environment. They can be used for entertainment, education, research, or training purposes. They can also improve your driving skills, knowledge, and safety.</p>
-<p>Driver simulator games vary in terms of their quality, realism, and complexity. Some of the main features that you should look for when choosing a driver simulator game are realism, graphics, physics, content, and gameplay.</p>
-<p>We have compared and reviewed some of the best driver simulator games available in 2023. We have rated them on a scale of 1 to 5 stars based on their realism, graphics, physics, content , and gameplay. We have also summarized their pros and cons in a table and given our detailed reviews of each game.</p>
-<p>Some of the best driver simulator games in 2023 are Gran Turismo 7, Euro Truck Simulator 2, City Car Driving, Forza Horizon 5, and Flight Simulator 2023. Each game has its own strengths and weaknesses, and you should choose the one that suits your preferences, goals, and expectations.</p>
-<p>Driver simulator games are a great way to enjoy the thrill and challenge of driving in a safe and convenient way. They can also help you learn more about driving rules, traffic laws, vehicle dynamics, road safety, or human factors. They can also help you improve your driving abilities, knowledge, and confidence.</p>
-<p>We hope that this article has helped you understand more about driver simulator games and how to choose the best one for you. Happy driving!</p>
- <h2>FAQs</h2>
-<p>Here are some common questions and answers about driver simulator games:</p>
-<ul>
-<li><strong>What is the difference between a driver simulator game and a racing game?</strong><br>A driver simulator game is a software that simulates the experience of driving a vehicle in a virtual environment. A racing game is a software that focuses on the competitive aspect of driving a vehicle in a virtual environment. Driver simulator games tend to be more realistic, complex, and educational than racing games. Racing games tend to be more arcade-style, simple, and entertaining than driver simulator games.</li>
-<li><strong>What are the benefits of playing driver simulator games?</strong><br>Driver simulator games can offer various benefits such as controllability, reproducibility, standardization, safety, cost-effectiveness, and accessibility. They can also improve your driving skills, knowledge, and safety.</li>
-<li><strong>What are the features of driver simulator games?</strong><br>Driver simulator games vary in terms of their quality, realism, and complexity. Some of the main features that you should look for when choosing a driver simulator game are realism, graphics, physics, content, and gameplay.</li>
-<li><strong>What are some of the best driver simulator games in 2023?</strong><br>Some of the best driver simulator games in 2023 are Gran Turismo 7, Euro Truck Simulator 2, City Car Driving, Forza Horizon 5, and Flight Simulator 2023. Each game has its own strengths and weaknesses, and you should choose the one that suits your preferences, goals , and expectations.</li>
-<li><strong>How can I play driver simulator games?</strong><br>You can play driver simulator games on various platforms such as PC, console, mobile, or VR. You can also use different devices such as keyboard, mouse, joystick, steering wheel, or motion platform to control the vehicle. You can also play driver simulator games online or offline, alone or with others.</li>
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spaces/30SecondsToMoon/30SecondsToMoon/app.py

@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

spaces/3laa2/Text2img/app.py

@@ -1,120 +0,0 @@
-import streamlit as st
-import cv2 as cv
-import time
-import torch
-from diffusers import StableDiffusionPipeline
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-
-def create_model(loc = "stabilityai/stable-diffusion-2-1-base", mch = 'cpu'):
-    pipe = StableDiffusionPipeline.from_pretrained(loc)
-    pipe = pipe.to(mch)
-    return pipe
-
-
-def tok_mod():
-  tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
-  tokenizer.add_special_tokens({'pad_token': '[PAD]'})
-  model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion-v2')
-  model.to('cpu')
-  return model,tokenizer
-
-
-t2i = st.title("""
-Txt2Img
-###### `CLICK "Create_Update_Model"` :
-- `FIRST RUN OF THE CODE`
-- `CHANGING MODEL`
-###### TO USE GPT PROMPTS GENERATOR CHECK `GPT PROMS` THEN CLICK `CREATE GPT MODEL`""")
-
-the_type = st.selectbox("Model",("stabilityai/stable-diffusion-2-1-base",
-                                      "CompVis/stable-diffusion-v1-4"))
-st.session_state.gate = False
-
-ma_1,_,ma_2 = st.columns([2,2,2])
-
-with ma_1 :
-  create = st.button("Create The Model")
-
-if create:
-    st.session_state.t2m_mod = create_model(loc=the_type)
-
-with ma_2 : 
-  gpt = st.checkbox("GPT PROMS")
-
-if gpt :
-    gen = st.button("Create GPT Model")
-    if gen:
-        st.session_state.mod,st.session_state.tok = tok_mod()
-
-    m1,m2,m3 = st.columns([1,1,3])
-    m4,m5 = st.columns(2)
-    prompt = st.text_input("GPT PROM",r'' )
-    with m1 :
-      temperature = st.slider("Temp",0.0,1.0,.9,.1)   
-    with m2 :        
-      top_k = st.slider("K",2,16,8,2)  
-    with m3 :                                               
-      max_length = st.slider("Length",10,100,80,1)                                        
-    with m4 :
-      repitition_penalty = st.slider("penality",1.0,5.0,1.2,1.0)                                
-    with m5 :
-      num_return_sequences=st.slider("Proms Num",1,10,5,1)
-
-    prom_gen = st.button("Generate Proms")
-
-    if prom_gen :
-        model, tokenizer = st.session_state.mod,st.session_state.tok
-        input_ids = tokenizer(prompt, return_tensors='pt').input_ids
-        output = model.generate(input_ids, do_sample=True, temperature=temperature, top_k=top_k, max_length=max_length,
-                                num_return_sequences=num_return_sequences, repetition_penalty=repitition_penalty,
-                                penalty_alpha=0.6, no_repeat_ngram_size=1, early_stopping=True)
-
-        st.session_state.PROMPTS = []
-        for i in range(len(output)):
-          st.session_state.PROMPTS.append(tokenizer.decode(output[i]))
-
-if 'PROMPTS' in st.session_state :
-    prom = st.selectbox("Proms",st.session_state.PROMPTS)
-
-else :
-    prom = st.text_input("# Prompt",'')
-
-
-
-                               
-c1,c2,c3 = st.columns([1,1,3])
-c4,c5 = st.columns(2)
-with c1:
-  bu_1 = st.text_input("Seed",'999')
-with c2:
-  bu_2 = st.text_input("Steps",'12')
-with c3:
-  bu_3 = st.text_input("Number of Images",'1')
-with c4:
-  sl_1 = st.slider("Width",128,1024,512,8)
-with c5:
-  sl_2 = st.slider("hight",128,1024,512,8)
-
-st.session_state.generator = torch.Generator("cpu").manual_seed(int(bu_1))
-
-create = st.button("Imagine")
-
-if create:
-    model = st.session_state.t2m_mod
-    generator = st.session_state.generator
-
-    if int(bu_3) == 1 :
-      IMG = model(prom, width=int(sl_1), height=int(sl_2),
-                    num_inference_steps=int(bu_2),
-                    generator=generator).images[0]
-      st.image(IMG)
-        
-    else :
-      PROMS = [prom]*int(bu_3)
-        
-      IMGS = model(PROMS, width=int(sl_1), height=int(sl_2),
-                     num_inference_steps=int(bu_2),
-                     generator=generator).images
-    
-      st.image(IMGS)

spaces/4Taps/SadTalker/src/facerender/modules/util.py

@@ -1,564 +0,0 @@
-from torch import nn
-
-import torch.nn.functional as F
-import torch
-
-from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d
-from src.facerender.sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d
-
-import torch.nn.utils.spectral_norm as spectral_norm
-
-
-def kp2gaussian(kp, spatial_size, kp_variance):
-    """
-    Transform a keypoint into gaussian like representation
-    """
-    mean = kp['value']
-
-    coordinate_grid = make_coordinate_grid(spatial_size, mean.type())
-    number_of_leading_dimensions = len(mean.shape) - 1
-    shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape
-    coordinate_grid = coordinate_grid.view(*shape)
-    repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1)
-    coordinate_grid = coordinate_grid.repeat(*repeats)
-
-    # Preprocess kp shape
-    shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3)
-    mean = mean.view(*shape)
-
-    mean_sub = (coordinate_grid - mean)
-
-    out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
-
-    return out
-
-def make_coordinate_grid_2d(spatial_size, type):
-    """
-    Create a meshgrid [-1,1] x [-1,1] of given spatial_size.
-    """
-    h, w = spatial_size
-    x = torch.arange(w).type(type)
-    y = torch.arange(h).type(type)
-
-    x = (2 * (x / (w - 1)) - 1)
-    y = (2 * (y / (h - 1)) - 1)
-
-    yy = y.view(-1, 1).repeat(1, w)
-    xx = x.view(1, -1).repeat(h, 1)
-
-    meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)
-
-    return meshed
-
-
-def make_coordinate_grid(spatial_size, type):
-    d, h, w = spatial_size
-    x = torch.arange(w).type(type)
-    y = torch.arange(h).type(type)
-    z = torch.arange(d).type(type)
-
-    x = (2 * (x / (w - 1)) - 1)
-    y = (2 * (y / (h - 1)) - 1)
-    z = (2 * (z / (d - 1)) - 1)
-   
-    yy = y.view(1, -1, 1).repeat(d, 1, w)
-    xx = x.view(1, 1, -1).repeat(d, h, 1)
-    zz = z.view(-1, 1, 1).repeat(1, h, w)
-
-    meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3)
-
-    return meshed
-
-
-class ResBottleneck(nn.Module):
-    def __init__(self, in_features, stride):
-        super(ResBottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features//4, kernel_size=1)
-        self.conv2 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features//4, kernel_size=3, padding=1, stride=stride)
-        self.conv3 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features, kernel_size=1)
-        self.norm1 = BatchNorm2d(in_features//4, affine=True)
-        self.norm2 = BatchNorm2d(in_features//4, affine=True)
-        self.norm3 = BatchNorm2d(in_features, affine=True)
-
-        self.stride = stride
-        if self.stride != 1:
-            self.skip = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=1, stride=stride)
-            self.norm4 = BatchNorm2d(in_features, affine=True)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.norm1(out)
-        out = F.relu(out)
-        out = self.conv2(out)
-        out = self.norm2(out)
-        out = F.relu(out)
-        out = self.conv3(out)
-        out = self.norm3(out)
-        if self.stride != 1:
-            x = self.skip(x)
-            x = self.norm4(x)
-        out += x
-        out = F.relu(out)
-        return out
-
-
-class ResBlock2d(nn.Module):
-    """
-    Res block, preserve spatial resolution.
-    """
-
-    def __init__(self, in_features, kernel_size, padding):
-        super(ResBlock2d, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.norm1 = BatchNorm2d(in_features, affine=True)
-        self.norm2 = BatchNorm2d(in_features, affine=True)
-
-    def forward(self, x):
-        out = self.norm1(x)
-        out = F.relu(out)
-        out = self.conv1(out)
-        out = self.norm2(out)
-        out = F.relu(out)
-        out = self.conv2(out)
-        out += x
-        return out
-
-
-class ResBlock3d(nn.Module):
-    """
-    Res block, preserve spatial resolution.
-    """
-
-    def __init__(self, in_features, kernel_size, padding):
-        super(ResBlock3d, self).__init__()
-        self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
-                               padding=padding)
-        self.norm1 = BatchNorm3d(in_features, affine=True)
-        self.norm2 = BatchNorm3d(in_features, affine=True)
-
-    def forward(self, x):
-        out = self.norm1(x)
-        out = F.relu(out)
-        out = self.conv1(out)
-        out = self.norm2(out)
-        out = F.relu(out)
-        out = self.conv2(out)
-        out += x
-        return out
-
-
-class UpBlock2d(nn.Module):
-    """
-    Upsampling block for use in decoder.
-    """
-
-    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
-        super(UpBlock2d, self).__init__()
-
-        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups)
-        self.norm = BatchNorm2d(out_features, affine=True)
-
-    def forward(self, x):
-        out = F.interpolate(x, scale_factor=2)
-        out = self.conv(out)
-        out = self.norm(out)
-        out = F.relu(out)
-        return out
-
-class UpBlock3d(nn.Module):
-    """
-    Upsampling block for use in decoder.
-    """
-
-    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
-        super(UpBlock3d, self).__init__()
-
-        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups)
-        self.norm = BatchNorm3d(out_features, affine=True)
-
-    def forward(self, x):
-        # out = F.interpolate(x, scale_factor=(1, 2, 2), mode='trilinear')
-        out = F.interpolate(x, scale_factor=(1, 2, 2))
-        out = self.conv(out)
-        out = self.norm(out)
-        out = F.relu(out)
-        return out
-
-
-class DownBlock2d(nn.Module):
-    """
-    Downsampling block for use in encoder.
-    """
-
-    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
-        super(DownBlock2d, self).__init__()
-        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups)
-        self.norm = BatchNorm2d(out_features, affine=True)
-        self.pool = nn.AvgPool2d(kernel_size=(2, 2))
-
-    def forward(self, x):
-        out = self.conv(x)
-        out = self.norm(out)
-        out = F.relu(out)
-        out = self.pool(out)
-        return out
-
-
-class DownBlock3d(nn.Module):
-    """
-    Downsampling block for use in encoder.
-    """
-
-    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
-        super(DownBlock3d, self).__init__()
-        '''
-        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups, stride=(1, 2, 2))
-        '''
-        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
-                              padding=padding, groups=groups)
-        self.norm = BatchNorm3d(out_features, affine=True)
-        self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2))
-
-    def forward(self, x):
-        out = self.conv(x)
-        out = self.norm(out)
-        out = F.relu(out)
-        out = self.pool(out)
-        return out
-
-
-class SameBlock2d(nn.Module):
-    """
-    Simple block, preserve spatial resolution.
-    """
-
-    def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False):
-        super(SameBlock2d, self).__init__()
-        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features,
-                              kernel_size=kernel_size, padding=padding, groups=groups)
-        self.norm = BatchNorm2d(out_features, affine=True)
-        if lrelu:
-            self.ac = nn.LeakyReLU()
-        else:
-            self.ac = nn.ReLU()
-
-    def forward(self, x):
-        out = self.conv(x)
-        out = self.norm(out)
-        out = self.ac(out)
-        return out
-
-
-class Encoder(nn.Module):
-    """
-    Hourglass Encoder
-    """
-
-    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
-        super(Encoder, self).__init__()
-
-        down_blocks = []
-        for i in range(num_blocks):
-            down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
-                                           min(max_features, block_expansion * (2 ** (i + 1))),
-                                           kernel_size=3, padding=1))
-        self.down_blocks = nn.ModuleList(down_blocks)
-
-    def forward(self, x):
-        outs = [x]
-        for down_block in self.down_blocks:
-            outs.append(down_block(outs[-1]))
-        return outs
-
-
-class Decoder(nn.Module):
-    """
-    Hourglass Decoder
-    """
-
-    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
-        super(Decoder, self).__init__()
-
-        up_blocks = []
-
-        for i in range(num_blocks)[::-1]:
-            in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))
-            out_filters = min(max_features, block_expansion * (2 ** i))
-            up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
-
-        self.up_blocks = nn.ModuleList(up_blocks)
-        # self.out_filters = block_expansion
-        self.out_filters = block_expansion + in_features
-
-        self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1)
-        self.norm = BatchNorm3d(self.out_filters, affine=True)
-
-    def forward(self, x):
-        out = x.pop()
-        # for up_block in self.up_blocks[:-1]:
-        for up_block in self.up_blocks:
-            out = up_block(out)
-            skip = x.pop()
-            out = torch.cat([out, skip], dim=1)
-        # out = self.up_blocks[-1](out)
-        out = self.conv(out)
-        out = self.norm(out)
-        out = F.relu(out)
-        return out
-
-
-class Hourglass(nn.Module):
-    """
-    Hourglass architecture.
-    """
-
-    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
-        super(Hourglass, self).__init__()
-        self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features)
-        self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features)
-        self.out_filters = self.decoder.out_filters
-
-    def forward(self, x):
-        return self.decoder(self.encoder(x))
-
-
-class KPHourglass(nn.Module):
-    """
-    Hourglass architecture.
-    """ 
-
-    def __init__(self, block_expansion, in_features, reshape_features, reshape_depth, num_blocks=3, max_features=256):
-        super(KPHourglass, self).__init__()
-        
-        self.down_blocks = nn.Sequential()
-        for i in range(num_blocks):
-            self.down_blocks.add_module('down'+ str(i), DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
-                                                                   min(max_features, block_expansion * (2 ** (i + 1))),
-                                                                   kernel_size=3, padding=1))
-
-        in_filters = min(max_features, block_expansion * (2 ** num_blocks))
-        self.conv = nn.Conv2d(in_channels=in_filters, out_channels=reshape_features, kernel_size=1)
-
-        self.up_blocks = nn.Sequential()
-        for i in range(num_blocks):
-            in_filters = min(max_features, block_expansion * (2 ** (num_blocks - i)))
-            out_filters = min(max_features, block_expansion * (2 ** (num_blocks - i - 1)))
-            self.up_blocks.add_module('up'+ str(i), UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
-
-        self.reshape_depth = reshape_depth
-        self.out_filters = out_filters
-
-    def forward(self, x):
-        out = self.down_blocks(x)
-        out = self.conv(out)
-        bs, c, h, w = out.shape
-        out = out.view(bs, c//self.reshape_depth, self.reshape_depth, h, w)
-        out = self.up_blocks(out)
-
-        return out
-        
-
-
-class AntiAliasInterpolation2d(nn.Module):
-    """
-    Band-limited downsampling, for better preservation of the input signal.
-    """
-    def __init__(self, channels, scale):
-        super(AntiAliasInterpolation2d, self).__init__()
-        sigma = (1 / scale - 1) / 2
-        kernel_size = 2 * round(sigma * 4) + 1
-        self.ka = kernel_size // 2
-        self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka
-
-        kernel_size = [kernel_size, kernel_size]
-        sigma = [sigma, sigma]
-        # The gaussian kernel is the product of the
-        # gaussian function of each dimension.
-        kernel = 1
-        meshgrids = torch.meshgrid(
-            [
-                torch.arange(size, dtype=torch.float32)
-                for size in kernel_size
-                ]
-        )
-        for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
-            mean = (size - 1) / 2
-            kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2))
-
-        # Make sure sum of values in gaussian kernel equals 1.
-        kernel = kernel / torch.sum(kernel)
-        # Reshape to depthwise convolutional weight
-        kernel = kernel.view(1, 1, *kernel.size())
-        kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
-
-        self.register_buffer('weight', kernel)
-        self.groups = channels
-        self.scale = scale
-        inv_scale = 1 / scale
-        self.int_inv_scale = int(inv_scale)
-
-    def forward(self, input):
-        if self.scale == 1.0:
-            return input
-
-        out = F.pad(input, (self.ka, self.kb, self.ka, self.kb))
-        out = F.conv2d(out, weight=self.weight, groups=self.groups)
-        out = out[:, :, ::self.int_inv_scale, ::self.int_inv_scale]
-
-        return out
-
-
-class SPADE(nn.Module):
-    def __init__(self, norm_nc, label_nc):
-        super().__init__()
-
-        self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
-        nhidden = 128
-
-        self.mlp_shared = nn.Sequential(
-            nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1),
-            nn.ReLU())
-        self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
-        self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
-
-    def forward(self, x, segmap):
-        normalized = self.param_free_norm(x)
-        segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
-        actv = self.mlp_shared(segmap)
-        gamma = self.mlp_gamma(actv)
-        beta = self.mlp_beta(actv)
-        out = normalized * (1 + gamma) + beta
-        return out
-    
-
-class SPADEResnetBlock(nn.Module):
-    def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1):
-        super().__init__()
-        # Attributes
-        self.learned_shortcut = (fin != fout)
-        fmiddle = min(fin, fout)
-        self.use_se = use_se
-        # create conv layers
-        self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation)
-        self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation)
-        if self.learned_shortcut:
-            self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)
-        # apply spectral norm if specified
-        if 'spectral' in norm_G:
-            self.conv_0 = spectral_norm(self.conv_0)
-            self.conv_1 = spectral_norm(self.conv_1)
-            if self.learned_shortcut:
-                self.conv_s = spectral_norm(self.conv_s)
-        # define normalization layers
-        self.norm_0 = SPADE(fin, label_nc)
-        self.norm_1 = SPADE(fmiddle, label_nc)
-        if self.learned_shortcut:
-            self.norm_s = SPADE(fin, label_nc)
-
-    def forward(self, x, seg1):
-        x_s = self.shortcut(x, seg1)
-        dx = self.conv_0(self.actvn(self.norm_0(x, seg1)))
-        dx = self.conv_1(self.actvn(self.norm_1(dx, seg1)))
-        out = x_s + dx
-        return out
-
-    def shortcut(self, x, seg1):
-        if self.learned_shortcut:
-            x_s = self.conv_s(self.norm_s(x, seg1))
-        else:
-            x_s = x
-        return x_s
-
-    def actvn(self, x):
-        return F.leaky_relu(x, 2e-1)
-
-class audio2image(nn.Module):
-    def __init__(self, generator, kp_extractor, he_estimator_video, he_estimator_audio, train_params):
-        super().__init__()
-        # Attributes
-        self.generator = generator
-        self.kp_extractor = kp_extractor
-        self.he_estimator_video = he_estimator_video
-        self.he_estimator_audio = he_estimator_audio
-        self.train_params = train_params
-
-    def headpose_pred_to_degree(self, pred):
-        device = pred.device
-        idx_tensor = [idx for idx in range(66)]
-        idx_tensor = torch.FloatTensor(idx_tensor).to(device)
-        pred = F.softmax(pred)
-        degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
-
-        return degree
-    
-    def get_rotation_matrix(self, yaw, pitch, roll):
-        yaw = yaw / 180 * 3.14
-        pitch = pitch / 180 * 3.14
-        roll = roll / 180 * 3.14
-
-        roll = roll.unsqueeze(1)
-        pitch = pitch.unsqueeze(1)
-        yaw = yaw.unsqueeze(1)
-
-        roll_mat = torch.cat([torch.ones_like(roll), torch.zeros_like(roll), torch.zeros_like(roll), 
-                          torch.zeros_like(roll), torch.cos(roll), -torch.sin(roll),
-                          torch.zeros_like(roll), torch.sin(roll), torch.cos(roll)], dim=1)
-        roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
-
-        pitch_mat = torch.cat([torch.cos(pitch), torch.zeros_like(pitch), torch.sin(pitch), 
-                           torch.zeros_like(pitch), torch.ones_like(pitch), torch.zeros_like(pitch),
-                           -torch.sin(pitch), torch.zeros_like(pitch), torch.cos(pitch)], dim=1)
-        pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
-
-        yaw_mat = torch.cat([torch.cos(yaw), -torch.sin(yaw), torch.zeros_like(yaw),  
-                         torch.sin(yaw), torch.cos(yaw), torch.zeros_like(yaw),
-                         torch.zeros_like(yaw), torch.zeros_like(yaw), torch.ones_like(yaw)], dim=1)
-        yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
-
-        rot_mat = torch.einsum('bij,bjk,bkm->bim', roll_mat, pitch_mat, yaw_mat)
-
-        return rot_mat
-
-    def keypoint_transformation(self, kp_canonical, he):
-        kp = kp_canonical['value']    # (bs, k, 3)
-        yaw, pitch, roll = he['yaw'], he['pitch'], he['roll']
-        t, exp = he['t'], he['exp']
-    
-        yaw = self.headpose_pred_to_degree(yaw)
-        pitch = self.headpose_pred_to_degree(pitch)
-        roll = self.headpose_pred_to_degree(roll)
-
-        rot_mat = self.get_rotation_matrix(yaw, pitch, roll)    # (bs, 3, 3)
-    
-        # keypoint rotation
-        kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
-
-    
-
-        # keypoint translation
-        t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1)
-        kp_t = kp_rotated + t
-
-        # add expression deviation 
-        exp = exp.view(exp.shape[0], -1, 3)
-        kp_transformed = kp_t + exp
-
-        return {'value': kp_transformed}
-
-    def forward(self, source_image, target_audio):
-        pose_source = self.he_estimator_video(source_image)
-        pose_generated = self.he_estimator_audio(target_audio)
-        kp_canonical = self.kp_extractor(source_image)
-        kp_source = self.keypoint_transformation(kp_canonical, pose_source)
-        kp_transformed_generated = self.keypoint_transformation(kp_canonical, pose_generated)
-        generated = self.generator(source_image, kp_source=kp_source, kp_driving=kp_transformed_generated)
-        return generated

spaces/AIConsultant/MusicGen/app_v2.py

@@ -1,1839 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-import glob
-import re
-from PIL import Image
-from pydub import AudioSegment
-from datetime import datetime
-
-import json
-import shutil
-import taglib
-import torch
-import torchaudio
-import gradio as gr
-import numpy as np
-import typing as tp
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import AudioGen, MusicGen, MultiBandDiffusion
-from audiocraft.utils import ui
-import random, string
-
-version = "2.0.0a"
-
-theme = gr.themes.Base(
-    primary_hue="lime",
-    secondary_hue="lime",
-    neutral_hue="neutral",
-).set(
-    button_primary_background_fill_hover='*primary_500',
-    button_primary_background_fill_hover_dark='*primary_500',
-    button_secondary_background_fill_hover='*primary_500',
-    button_secondary_background_fill_hover_dark='*primary_500'
-)
-
-MODEL = None  # Last used model
-MODELS = None
-UNLOAD_MODEL = False
-MOVE_TO_CPU = False
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-INTERRUPTING = False
-MBD = None
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def generate_random_string(length):
-    characters = string.ascii_letters + string.digits
-    return ''.join(random.choice(characters) for _ in range(length))
-
-
-def resize_video(input_path, output_path, target_width, target_height):
-    ffmpeg_cmd = [
-        'ffmpeg',
-        '-y',
-        '-i', input_path,
-        '-vf', f'scale={target_width}:{target_height}',
-        '-c:a', 'copy',
-        output_path
-    ]
-    sp.run(ffmpeg_cmd)
-
-
-def _call_nostderr(*args, **kwargs):
-    # Avoid ffmpeg vomiting on the logs.
-    kwargs['stderr'] = sp.DEVNULL
-    kwargs['stdout'] = sp.DEVNULL
-    _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(4)
-pool.__enter__()
-
-
-def interrupt():
-    global INTERRUPTING
-    INTERRUPTING = True
-
-
-class FileCleaner:
-    def __init__(self, file_lifetime: float = 3600):
-        self.file_lifetime = file_lifetime
-        self.files = []
-
-    def add(self, path: tp.Union[str, Path]):
-        self._cleanup()
-        self.files.append((time.time(), Path(path)))
-
-    def _cleanup(self):
-        now = time.time()
-        for time_added, path in list(self.files):
-            if now - time_added > self.file_lifetime:
-                if path.exists():
-                    path.unlink()
-                self.files.pop(0)
-            else:
-                break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
-    # Further remove some warnings.
-    be = time.time()
-    with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
-        height = kwargs.pop('height')
-        width = kwargs.pop('width')
-        if height < 256:
-            height = 256
-        if width < 256:
-            width = 256
-        waveform_video = gr.make_waveform(*args, **kwargs)
-        out = f"{generate_random_string(12)}.mp4"
-        image = kwargs.get('bg_image', None)
-        if image is None:
-            resize_video(waveform_video, out, 900, 300)
-        else:
-            resize_video(waveform_video, out, width, height)
-        print("Make a video took", time.time() - be)
-        return out
-
-
-def load_model(version='GrandaddyShmax/musicgen-melody', custom_model=None, base_model='GrandaddyShmax/musicgen-medium', gen_type="music"):
-    global MODEL, MODELS
-    print("Loading model", version)
-    if MODELS is None:
-        if version == 'GrandaddyShmax/musicgen-custom':
-            MODEL = MusicGen.get_pretrained(base_model)
-            file_path = os.path.abspath("models/" + str(custom_model) + ".pt")
-            MODEL.lm.load_state_dict(torch.load(file_path))
-        else:
-            if gen_type == "music":
-                MODEL = MusicGen.get_pretrained(version)
-            elif gen_type == "audio":
-                MODEL = AudioGen.get_pretrained(version)
-
-        return
-
-    else:
-        t1 = time.monotonic()
-        if MODEL is not None:
-            MODEL.to('cpu') # move to cache
-            print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
-            t1 = time.monotonic()
-        if version != 'GrandaddyShmax/musicgen-custom' and MODELS.get(version) is None:
-            print("Loading model %s from disk" % version)
-            if gen_type == "music":
-                result = MusicGen.get_pretrained(version)
-            elif gen_type == "audio":
-                result = AudioGen.get_pretrained(version)
-            MODELS[version] = result
-            print("Model loaded in %.2fs" % (time.monotonic() - t1))
-            MODEL = result
-            return
-        result = MODELS[version].to('cuda')
-        print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
-        MODEL = result
-
-def get_audio_info(audio_path):
-    if audio_path is not None:
-        if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
-            if not audio_path.name.endswith(".json"):
-                with taglib.File(audio_path.name, save_on_exit=False) as song:
-                    if 'COMMENT' not in song.tags:
-                        return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
-                    json_string = song.tags['COMMENT'][0]
-                    data = json.loads(json_string)
-                    global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
-                    bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
-                    key = str("\nKey: " + data['key']) if 'key' in data else ""
-                    scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
-                    prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
-                    duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
-                    overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
-                    seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
-                    audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
-                    input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
-                    channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
-                    sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
-                    gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
-                    model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
-                    custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
-                    base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
-                    decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
-                    topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
-                    topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
-                    temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
-                    cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
-                    version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
-                    info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
-                    if info == "":
-                        return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
-                    return info
-            else:
-                with open(audio_path.name) as json_file:
-                    data = json.load(json_file)
-                    #if 'global_prompt' not in data:
-                        #return "No tags found. Either the file is not generated by MusicGen+ V1.2.8a and higher or the tags are corrupted."
-                    global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
-                    bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
-                    key = str("\nKey: " + data['key']) if 'key' in data else ""
-                    scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
-                    prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
-                    duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
-                    overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
-                    seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
-                    audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
-                    input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
-                    channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
-                    sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
-                    gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
-                    model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
-                    custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
-                    base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
-                    decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
-                    topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
-                    topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
-                    temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
-                    cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
-                    version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
-                    info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
-                    if info == "":
-                        return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted."
-                    return info
-        else:
-            return "Only .wav ,.mp4 and .json files are supported"
-    else:
-        return None
-
-
-def info_to_params(audio_path):
-    if audio_path is not None:
-        if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
-            if not audio_path.name.endswith(".json"):
-                with taglib.File(audio_path.name, save_on_exit=False) as song:
-                    if 'COMMENT' not in song.tags:
-                        return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-                    json_string = song.tags['COMMENT'][0]
-                    data = json.loads(json_string)
-                    struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
-                    global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
-                    bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
-                    key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
-                    scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
-                    model = data['model'] if 'model' in data else "large"
-                    custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
-                    base_model = data['base_model'] if 'base_model' in data else "medium"
-                    decoder = data['decoder'] if 'decoder' in data else "Default"
-                    if 'texts' not in data:
-                        unique_prompts = 1
-                        text = ["", "", "", "", "", "", "", "", "", ""]
-                        repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-                    else:
-                        s = data['texts']
-                        s = re.findall(r"'(.*?)'", s)
-                        text = []
-                        repeat = []
-                        i = 0
-                        for elem in s:
-                            if elem.strip():
-                                if i == 0 or elem != s[i-1]:
-                                    text.append(elem)
-                                    repeat.append(1)
-                                else:
-                                    repeat[-1] += 1
-                            i += 1
-                        text.extend([""] * (10 - len(text)))
-                        repeat.extend([1] * (10 - len(repeat)))
-                        unique_prompts = len([t for t in text if t])
-                    audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
-                    duration = int(data['duration']) if 'duration' in data else 10
-                    topk = float(data['topk']) if 'topk' in data else 250
-                    topp = float(data['topp']) if 'topp' in data else 0
-                    temperature = float(data['temperature']) if 'temperature' in data else 1.0
-                    cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
-                    seed = int(data['seed']) if 'seed' in data else -1
-                    overlap = int(data['overlap']) if 'overlap' in data else 12
-                    channel = data['channel'] if 'channel' in data else "stereo"
-                    sr_select = data['sr_select'] if 'sr_select' in data else "48000"
-                    return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-            else:
-                with open(audio_path.name) as json_file:
-                    data = json.load(json_file)
-                    struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
-                    global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
-                    bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
-                    key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
-                    scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
-                    model = data['model'] if 'model' in data else "large"
-                    custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
-                    base_model = data['base_model'] if 'base_model' in data else "medium"
-                    decoder = data['decoder'] if 'decoder' in data else "Default"
-                    if 'texts' not in data:
-                        unique_prompts = 1
-                        text = ["", "", "", "", "", "", "", "", "", ""]
-                        repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-                    else:
-                        s = data['texts']
-                        s = re.findall(r"'(.*?)'", s)
-                        text = []
-                        repeat = []
-                        i = 0
-                        for elem in s:
-                            if elem.strip():
-                                if i == 0 or elem != s[i-1]:
-                                    text.append(elem)
-                                    repeat.append(1)
-                                else:
-                                    repeat[-1] += 1
-                            i += 1
-                        text.extend([""] * (10 - len(text)))
-                        repeat.extend([1] * (10 - len(repeat)))
-                        unique_prompts = len([t for t in text if t])
-                    audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
-                    duration = int(data['duration']) if 'duration' in data else 10
-                    topk = float(data['topk']) if 'topk' in data else 250
-                    topp = float(data['topp']) if 'topp' in data else 0
-                    temperature = float(data['temperature']) if 'temperature' in data else 1.0
-                    cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
-                    seed = int(data['seed']) if 'seed' in data else -1
-                    overlap = int(data['overlap']) if 'overlap' in data else 12
-                    channel = data['channel'] if 'channel' in data else "stereo"
-                    sr_select = data['sr_select'] if 'sr_select' in data else "48000"
-                    return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-        else:
-            return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-    else:
-        return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def info_to_params_a(audio_path):
-    if audio_path is not None:
-        if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
-            if not audio_path.name.endswith(".json"):
-                with taglib.File(audio_path.name, save_on_exit=False) as song:
-                    if 'COMMENT' not in song.tags:
-                        return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-                    json_string = song.tags['COMMENT'][0]
-                    data = json.loads(json_string)
-                    struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
-                    global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
-                    decoder = data['decoder'] if 'decoder' in data else "Default"
-                    if 'texts' not in data:
-                        unique_prompts = 1
-                        text = ["", "", "", "", "", "", "", "", "", ""]
-                        repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-                    else:
-                        s = data['texts']
-                        s = re.findall(r"'(.*?)'", s)
-                        text = []
-                        repeat = []
-                        i = 0
-                        for elem in s:
-                            if elem.strip():
-                                if i == 0 or elem != s[i-1]:
-                                    text.append(elem)
-                                    repeat.append(1)
-                                else:
-                                    repeat[-1] += 1
-                            i += 1
-                        text.extend([""] * (10 - len(text)))
-                        repeat.extend([1] * (10 - len(repeat)))
-                        unique_prompts = len([t for t in text if t])
-                    duration = int(data['duration']) if 'duration' in data else 10
-                    topk = float(data['topk']) if 'topk' in data else 250
-                    topp = float(data['topp']) if 'topp' in data else 0
-                    temperature = float(data['temperature']) if 'temperature' in data else 1.0
-                    cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
-                    seed = int(data['seed']) if 'seed' in data else -1
-                    overlap = int(data['overlap']) if 'overlap' in data else 12
-                    channel = data['channel'] if 'channel' in data else "stereo"
-                    sr_select = data['sr_select'] if 'sr_select' in data else "48000"
-                    return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-            else:
-                with open(audio_path.name) as json_file:
-                    data = json.load(json_file)
-                    struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
-                    global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
-                    decoder = data['decoder'] if 'decoder' in data else "Default"
-                    if 'texts' not in data:
-                        unique_prompts = 1
-                        text = ["", "", "", "", "", "", "", "", "", ""]
-                        repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-                    else:
-                        s = data['texts']
-                        s = re.findall(r"'(.*?)'", s)
-                        text = []
-                        repeat = []
-                        i = 0
-                        for elem in s:
-                            if elem.strip():
-                                if i == 0 or elem != s[i-1]:
-                                    text.append(elem)
-                                    repeat.append(1)
-                                else:
-                                    repeat[-1] += 1
-                            i += 1
-                        text.extend([""] * (10 - len(text)))
-                        repeat.extend([1] * (10 - len(repeat)))
-                        unique_prompts = len([t for t in text if t])
-                    duration = int(data['duration']) if 'duration' in data else 10
-                    topk = float(data['topk']) if 'topk' in data else 250
-                    topp = float(data['topp']) if 'topp' in data else 0
-                    temperature = float(data['temperature']) if 'temperature' in data else 1.0
-                    cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
-                    seed = int(data['seed']) if 'seed' in data else -1
-                    overlap = int(data['overlap']) if 'overlap' in data else 12
-                    channel = data['channel'] if 'channel' in data else "stereo"
-                    sr_select = data['sr_select'] if 'sr_select' in data else "48000"
-                    return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-                    
-        else:
-            return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-    else:
-        return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def make_pseudo_stereo (filename, sr_select, pan, delay):
-    if pan:
-        temp = AudioSegment.from_wav(filename)
-        if sr_select != "32000":
-            temp = temp.set_frame_rate(int(sr_select))
-        left = temp.pan(-0.5) - 5
-        right = temp.pan(0.6) - 5
-        temp = left.overlay(right, position=5)
-        temp.export(filename, format="wav")
-    if delay:     
-        waveform, sample_rate = torchaudio.load(filename) # load mono WAV file
-        delay_seconds = 0.01 # set delay 10ms
-        delay_samples = int(delay_seconds * sample_rate) # Calculating delay value in number of samples
-        stereo_waveform = torch.stack([waveform[0], torch.cat((torch.zeros(delay_samples), waveform[0][:-delay_samples]))]) # Generate a stereo file with original mono audio and delayed version
-        torchaudio.save(filename, stereo_waveform, sample_rate)
-    return
-
-
-def normalize_audio(audio_data):
-    audio_data = audio_data.astype(np.float32)
-    max_value = np.max(np.abs(audio_data))
-    audio_data /= max_value
-    return audio_data
-
-
-def load_diffusion():
-    global MBD
-    if MBD is None:
-        print("loading MBD")
-        MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-def unload_diffusion():
-    global MBD
-    if MBD is not None:
-        print("unloading MBD")
-        MBD = None
-
-
-def _do_predictions(gen_type, texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
-    if gen_type == "music":
-        maximum_size = 29.5
-    elif gen_type == "audio":
-        maximum_size = 9.5
-    cut_size = 0
-    input_length = 0
-    sampleP = None
-    if sample is not None:
-        globalSR, sampleM = sample[0], sample[1]
-        sampleM = normalize_audio(sampleM)
-        sampleM = torch.from_numpy(sampleM).t()
-        if sampleM.dim() == 1:
-            sampleM = sampleM.unsqueeze(0)
-        sample_length = sampleM.shape[sampleM.dim() - 1] / globalSR
-        if trim_start >= sample_length:
-            trim_start = sample_length - 0.5
-        if trim_end >= sample_length:
-            trim_end = sample_length - 0.5
-        if trim_start + trim_end >= sample_length:
-            tmp = sample_length - 0.5
-            trim_start = tmp / 2
-            trim_end = tmp / 2
-        sampleM = sampleM[..., int(globalSR * trim_start):int(globalSR * (sample_length - trim_end))]
-        sample_length = sample_length - (trim_start + trim_end)
-        if sample_length > maximum_size:
-            cut_size = sample_length - maximum_size
-            sampleP = sampleM[..., :int(globalSR * cut_size)]
-            sampleM = sampleM[..., int(globalSR * cut_size):]
-        if sample_length >= duration:
-            duration = sample_length + 0.5
-        input_length = sample_length
-    global MODEL
-    MODEL.set_generation_params(duration=(duration - cut_size), **gen_kwargs)
-    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies], [None if sample is None else (sample[0], sample[1].shape)])
-    be = time.time()
-    processed_melodies = []
-    if gen_type == "music":
-        target_sr = 32000
-    elif gen_type == "audio":
-        target_sr = 16000
-    target_ac = 1
-
-    for melody in melodies:
-        if melody is None:
-            processed_melodies.append(None)
-        else:
-            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
-            if melody.dim() == 1:
-                melody = melody[None]
-            melody = melody[..., :int(sr * duration)]
-            melody = convert_audio(melody, sr, target_sr, target_ac)
-            processed_melodies.append(melody)
-
-    if sample is not None:
-        if sampleP is None:
-            if gen_type == "music":
-                outputs = MODEL.generate_continuation(
-                    prompt=sampleM,
-                    prompt_sample_rate=globalSR,
-                    descriptions=texts,
-                    progress=progress,
-                    return_tokens=USE_DIFFUSION
-                )
-            elif gen_type == "audio":
-                outputs = MODEL.generate_continuation(
-                    prompt=sampleM,
-                    prompt_sample_rate=globalSR,
-                    descriptions=texts,
-                    progress=progress
-                )
-        else:
-            if sampleP.dim() > 1:
-                sampleP = convert_audio(sampleP, globalSR, target_sr, target_ac)
-            sampleP = sampleP.to(MODEL.device).float().unsqueeze(0)
-            if gen_type == "music":
-                outputs = MODEL.generate_continuation(
-                    prompt=sampleM,
-                    prompt_sample_rate=globalSR,
-                    descriptions=texts,
-                    progress=progress,
-                    return_tokens=USE_DIFFUSION
-                )
-            elif gen_type == "audio":
-                outputs = MODEL.generate_continuation(
-                    prompt=sampleM,
-                    prompt_sample_rate=globalSR,
-                    descriptions=texts,
-                    progress=progress
-                )
-            outputs = torch.cat([sampleP, outputs], 2)
-            
-    elif any(m is not None for m in processed_melodies):
-        if gen_type == "music":
-            outputs = MODEL.generate_with_chroma(
-                descriptions=texts,
-                melody_wavs=processed_melodies,
-                melody_sample_rate=target_sr,
-                progress=progress,
-                return_tokens=USE_DIFFUSION
-            )
-        elif gen_type == "audio":
-            outputs = MODEL.generate_with_chroma(
-                descriptions=texts,
-                melody_wavs=processed_melodies,
-                melody_sample_rate=target_sr,
-                progress=progress
-            )
-    else:
-        if gen_type == "music":
-            outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
-        elif gen_type == "audio":
-            outputs = MODEL.generate(texts, progress=progress)
-
-    if USE_DIFFUSION:
-        print("outputs: " + str(outputs))
-        outputs_diffusion = MBD.tokens_to_wav(outputs[1])
-        outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
-    outputs = outputs.detach().cpu().float()
-    backups = outputs
-    if channel == "stereo":
-        outputs = convert_audio(outputs, target_sr, int(sr_select), 2)
-    elif channel == "mono" and sr_select != "32000":
-        outputs = convert_audio(outputs, target_sr, int(sr_select), 1)
-    out_files = []
-    out_audios = []
-    out_backup = []
-    for output in outputs:
-        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-            audio_write(
-                file.name, output, (MODEL.sample_rate if channel == "stereo effect" else int(sr_select)), strategy="loudness",
-                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-
-            if channel == "stereo effect":
-                make_pseudo_stereo(file.name, sr_select, pan=True, delay=True);
-
-            out_files.append(pool.submit(make_waveform, file.name, bg_image=image, bg_color=background, bars_color=(bar1, bar2), fg_alpha=1.0, bar_count=75, height=height, width=width))
-            out_audios.append(file.name)
-            file_cleaner.add(file.name)
-            print(f'wav: {file.name}')
-    for backup in backups:
-        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-            audio_write(
-                file.name, backup, MODEL.sample_rate, strategy="loudness",
-                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-            out_backup.append(file.name)
-            file_cleaner.add(file.name)
-    res = [out_file.result() for out_file in out_files]
-    res_audio = out_audios
-    res_backup = out_backup
-    for file in res:
-        file_cleaner.add(file)
-        print(f'video: {file}')
-    print("batch finished", len(texts), time.time() - be)
-    print("Tempfiles currently stored: ", len(file_cleaner.files))
-    if MOVE_TO_CPU:
-        MODEL.to('cpu')
-    if UNLOAD_MODEL:
-        MODEL = None
-    torch.cuda.empty_cache()
-    torch.cuda.ipc_collect()
-    return res, res_audio, res_backup, input_length
-
-
-def predict_batched(texts, melodies):
-    max_text_length = 512
-    texts = [text[:max_text_length] for text in texts]
-    load_model('melody')
-    res = _do_predictions(texts, melodies, BATCHED_DURATION)
-    return res
-
-
-def add_tags(filename, tags): 
-    json_string = None
-
-    data = {
-        "global_prompt": tags[0],
-        "bpm": tags[1],
-        "key": tags[2],
-        "scale": tags[3],
-        "texts": tags[4],
-        "duration": tags[5],
-        "overlap": tags[6],
-        "seed": tags[7],
-        "audio_mode": tags[8],
-        "input_length": tags[9],
-        "channel": tags[10],
-        "sr_select": tags[11],
-        "model": tags[12],
-        "custom_model": tags[13],
-        "base_model": tags[14],
-        "decoder": tags[15],
-        "topk": tags[16],  
-        "topp": tags[17],
-        "temperature": tags[18],
-        "cfg_coef": tags[19],
-        "generator": tags[20],
-        "version": version
-        }
-
-    json_string = json.dumps(data)
-
-    if os.path.exists(filename):
-        with taglib.File(filename, save_on_exit=True) as song:
-            song.tags = {'COMMENT': json_string }
-
-    json_file = open(tags[7] + '.json', 'w')
-    json_file.write(json_string)
-    json_file.close()
-
-    return json_file.name;
-
-
-def save_outputs(mp4, wav_tmp, tags, gen_type):
-    # mp4: .mp4 file name in root running folder of app.py    
-    # wav_tmp: temporary wav file located in %TEMP% folder
-    # seed - used seed 
-    # exanple BgnJtr4Pn1AJ.mp4,  C:\Users\Alex\AppData\Local\Temp\tmp4ermrebs.wav,  195123182343465
-    # procedure read generated .mp4 and wav files, rename it by using seed as name, 
-    # and will store it to ./output/today_date/wav and  ./output/today_date/mp4 folders. 
-    # if file with same seed number already exist its make postfix in name like seed(n) 
-    # where is n - consiqunce number 1-2-3-4 and so on
-    # then we store generated mp4 and wav into destination folders.     
-
-    current_date = datetime.now().strftime("%Y%m%d")
-    wav_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'wav')
-    mp4_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'mp4')
-    json_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'json')
-    os.makedirs(wav_directory, exist_ok=True)
-    os.makedirs(mp4_directory, exist_ok=True)
-    os.makedirs(json_directory, exist_ok=True)
-
-    filename = str(tags[7]) + '.wav'
-    target = os.path.join(wav_directory, filename)
-    counter = 1
-    while os.path.exists(target):
-        filename = str(tags[7]) + f'({counter})' + '.wav'
-        target = os.path.join(wav_directory, filename)
-        counter += 1
-
-    shutil.copyfile(wav_tmp, target); # make copy of original file
-    json_file = add_tags(target, tags);
-    
-    wav_target=target;
-    target=target.replace('wav', 'mp4');
-    mp4_target=target;
-    
-    mp4=r'./' +mp4;    
-    shutil.copyfile(mp4, target); # make copy of original file  
-    _ = add_tags(target, tags);
-
-    target=target.replace('mp4', 'json'); # change the extension to json
-    json_target=target; # store the json target
-
-    with open(target, 'w') as f: # open a writable file object
-        shutil.copyfile(json_file, target); # make copy of original file
-    
-    os.remove(json_file)
-
-    return wav_target, mp4_target, json_target;
-
-
-def clear_cash():
-    # delete all temporary files genegated my system
-    current_date = datetime.now().date()
-    current_directory = os.getcwd()
-    files = glob.glob(os.path.join(current_directory, '*.mp4'))
-    for file in files:
-        creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
-        if creation_date == current_date:
-            os.remove(file)
-
-    temp_directory = os.environ.get('TEMP')
-    files = glob.glob(os.path.join(temp_directory, 'tmp*.mp4'))
-    for file in files:
-        creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
-        if creation_date == current_date:
-            os.remove(file)
-   
-    files = glob.glob(os.path.join(temp_directory, 'tmp*.wav'))
-    for file in files:
-        creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
-        if creation_date == current_date:
-            os.remove(file)
-
-    files = glob.glob(os.path.join(temp_directory, 'tmp*.png'))
-    for file in files:
-        creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
-        if creation_date == current_date:
-            os.remove(file)
-    return
-
-
-def s2t(seconds, seconds2):
-    # convert seconds to time format
-    # seconds - time in seconds
-    # return time in format 00:00
-    m, s = divmod(seconds, 60)
-    m2, s2 = divmod(seconds2, 60)
-    if seconds != 0 and seconds < seconds2:
-        s = s + 1
-    return ("%02d:%02d - %02d:%02d" % (m, s, m2, s2))
-
-
-def calc_time(gen_type, s, duration, overlap, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9):
-    # calculate the time of generation
-    # overlap - overlap in seconds
-    # d0-d9 - drag
-    # return time in seconds
-    d_amount = [int(d0), int(d1), int(d2), int(d3), int(d4), int(d5), int(d6), int(d7), int(d8), int(d9)]
-    calc = []
-    tracks = []
-    time = 0
-    s = s - 1
-    max_time = duration
-    max_limit = 0
-    if gen_type == "music":
-        max_limit = 30
-    elif gen_type == "audio":
-        max_limit = 10
-    track_add = max_limit - overlap
-    tracks.append(max_limit + ((d_amount[0] - 1) * track_add))
-    for i in range(1, 10):
-        tracks.append(d_amount[i] * track_add)
-    
-    if tracks[0] >= max_time or s == 0:
-        calc.append(s2t(time, max_time))
-        time = max_time
-    else:
-        calc.append(s2t(time, tracks[0]))
-        time = tracks[0]
-
-    for i in range(1, 10):
-        if time + tracks[i] >= max_time or i == s:
-            calc.append(s2t(time, max_time))
-            time = max_time
-        else:
-            calc.append(s2t(time, time + tracks[i]))
-            time = time + tracks[i]
-    
-    return calc[0], calc[1], calc[2], calc[3], calc[4], calc[5], calc[6], calc[7], calc[8], calc[9]
-
-
-def predict_full(gen_type, model, decoder, custom_model, base_model, prompt_amount, struc_prompt, bpm, key, scale, global_prompt, p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select, progress=gr.Progress()):
-    global INTERRUPTING
-    global USE_DIFFUSION
-    INTERRUPTING = False
-
-    if gen_type == "audio":
-        custom_model = None
-        base_model = "medium"
-
-    if temperature < 0:
-        raise gr.Error("Temperature must be >= 0.")
-    if topk < 0:
-        raise gr.Error("Topk must be non-negative.")
-    if topp < 0:
-        raise gr.Error("Topp must be non-negative.")
-
-    if trim_start < 0:
-        trim_start = 0
-    if trim_end < 0:
-        trim_end = 0
-
-    topk = int(topk)
-
-    if decoder == "MultiBand_Diffusion":
-        USE_DIFFUSION = True
-        load_diffusion()
-    else:
-        USE_DIFFUSION = False
-        unload_diffusion()
-
-    if gen_type == "music":
-        model_shrt = model
-        model = "GrandaddyShmax/musicgen-" + model
-    elif gen_type == "audio":
-        model_shrt = model
-        model = "GrandaddyShmax/audiogen-" + model
-    base_model_shrt = base_model
-    base_model = "GrandaddyShmax/musicgen-" + base_model
-
-    if MODEL is None or MODEL.name != (model):
-        load_model(model, custom_model, base_model, gen_type)
-    else:
-        if MOVE_TO_CPU:
-            MODEL.to('cuda')
-
-    if seed < 0:
-        seed = random.randint(0, 0xffff_ffff_ffff)
-    torch.manual_seed(seed)
-
-    def _progress(generated, to_generate):
-        progress((min(generated, to_generate), to_generate))
-        if INTERRUPTING:
-            raise gr.Error("Interrupted.")
-    MODEL.set_custom_progress_callback(_progress)
-
-    audio_mode = "none"
-    melody = None
-    sample = None
-    if audio:
-      audio_mode = mode
-      if mode == "sample":
-          sample = audio
-      elif mode == "melody":
-          melody = audio
-
-    base_model = "none" if model != "custom" else base_model
-    custom_model = "none" if model != "custom" else custom_model
-
-    text_cat = [p0, p1, p2, p3, p4, p5, p6, p7, p8, p9]
-    drag_cat = [d0, d1, d2, d3, d4, d5, d6, d7, d8, d9]
-    texts = []
-    raw_texts = []
-    ind = 0
-    ind2 = 0
-    while ind < prompt_amount:
-        for ind2 in range(int(drag_cat[ind])):
-            if not struc_prompt:
-                texts.append(text_cat[ind])
-                global_prompt = "none"
-                bpm = "none"
-                key = "none"
-                scale = "none"
-                raw_texts.append(text_cat[ind])
-            else:
-                if gen_type == "music":
-                    bpm_str = str(bpm) + " bpm"
-                    key_str = ", " + str(key) + " " + str(scale)
-                    global_str = (", " + str(global_prompt)) if str(global_prompt) != "" else ""
-                elif gen_type == "audio":
-                    bpm_str = ""
-                    key_str = ""
-                    global_str = (str(global_prompt)) if str(global_prompt) != "" else ""
-                texts_str = (", " + str(text_cat[ind])) if str(text_cat[ind]) != "" else ""
-                texts.append(bpm_str + key_str + global_str + texts_str)
-                raw_texts.append(text_cat[ind])
-        ind2 = 0
-        ind = ind + 1
-
-    outs, outs_audio, outs_backup, input_length = _do_predictions(
-        gen_type, [texts], [melody], sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=True,
-        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, extend_stride=MODEL.max_duration-overlap)
-    tags = [str(global_prompt), str(bpm), str(key), str(scale), str(raw_texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model_shrt), str(custom_model), str(base_model_shrt), str(decoder), str(topk), str(topp), str(temperature), str(cfg_coef), str(gen_type)]
-    wav_target, mp4_target, json_target = save_outputs(outs[0], outs_audio[0], tags, gen_type);
-    # Removes the temporary files.
-    for out in outs:
-        os.remove(out)
-    for out in outs_audio:
-        os.remove(out)
-
-    return mp4_target, wav_target, outs_backup[0], [mp4_target, wav_target, json_target], seed
-
-
-max_textboxes = 10
-
-
-def get_available_models():
-    return sorted([re.sub('.pt$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('.pt')])
-
-
-def toggle_audio_src(choice):
-    if choice == "mic":
-        return gr.update(source="microphone", value=None, label="Microphone")
-    else:
-        return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
-    with gr.Blocks(title='AudioCraft Plus', theme=theme) as interface:
-        gr.Markdown(
-            """
-            # AudioCraft Plus - v2.0.0a
-
-            ### An All-in-One AudioCraft WebUI
-
-            #### **Disclaimer:** This will not run on CPU only. Its best to clone this App and run on GPU instance!
-            **Alternatively**, you can run this for free on a google colab:  
-            https://colab.research.google.com/github/camenduru/MusicGen-colab/blob/main/MusicGen_ClownOfMadness_plus_colab.ipynb
-            
-            **Or**, run this locally on your PC:  
-            https://github.com/GrandaddyShmax/audiocraft_plus/tree/main
-
-            Thanks to: facebookresearch, Camenduru, rkfg, oobabooga, AlexHK and GrandaddyShmax
-            """
-        )
-        with gr.Tab("MusicGen"):
-            gr.Markdown(
-                """
-                ### MusicGen
-                """
-            )
-            with gr.Row():
-                with gr.Column():
-                    with gr.Tab("Generation"):
-                        with gr.Accordion("Structure Prompts", open=False):
-                            with gr.Column():
-                                with gr.Row():
-                                    struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
-                                    bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
-                                    key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
-                                    scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
-                                with gr.Row():
-                                    global_prompt = gr.Text(label="Global Prompt", interactive=True, scale=3)
-                        with gr.Row():
-                            s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
-                            #s_mode = gr.Radio(["segmentation", "batch"], value="segmentation", interactive=True, scale=1, label="Generation Mode")
-                        with gr.Column():
-                            textboxes = []
-                            prompts = []
-                            repeats = []
-                            calcs = []
-                            with gr.Row():
-                                text0 = gr.Text(label="Input Text", interactive=True, scale=4)
-                                prompts.append(text0)
-                                drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
-                                repeats.append(drag0)
-                                calc0 = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
-                                calcs.append(calc0)
-                            for i in range(max_textboxes):
-                                with gr.Row(visible=False) as t:
-                                    text = gr.Text(label="Input Text", interactive=True, scale=3)
-                                    repeat = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
-                                    calc = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
-                                textboxes.append(t)
-                                prompts.append(text)
-                                repeats.append(repeat)
-                                calcs.append(calc)
-                            to_calc = gr.Button("Calculate Timings", variant="secondary")
-                        with gr.Row():
-                            duration = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
-                        with gr.Row():
-                            overlap = gr.Slider(minimum=1, maximum=29, value=12, step=1, label="Overlap", interactive=True)
-                        with gr.Row():
-                            seed = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
-                            gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed], queue=False)
-                            reuse_seed = gr.Button('\u267b\ufe0f', scale=1)
-
-                    with gr.Tab("Audio"):
-                        with gr.Row():
-                            with gr.Column():
-                                input_type = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
-                                mode = gr.Radio(["melody", "sample"], label="Input Audio Mode (optional)", value="sample", interactive=True)
-                                with gr.Row():
-                                    trim_start = gr.Number(label="Trim Start", value=0, interactive=True)
-                                    trim_end = gr.Number(label="Trim End", value=0, interactive=True)
-                            audio = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
-                    with gr.Tab("Customization"):
-                        with gr.Row():
-                            with gr.Column():
-                                background = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
-                                bar1 = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
-                                bar2 = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
-                            with gr.Column():
-                                image = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
-                                with gr.Row():
-                                    height = gr.Number(label="Height", value=512, interactive=True)
-                                    width = gr.Number(label="Width", value=768, interactive=True)
-
-                    with gr.Tab("Settings"):
-                        with gr.Row():
-                            channel = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
-                            sr_select = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
-                        with gr.Row():
-                            model = gr.Radio(["melody", "small", "medium", "large", "custom"], label="Model", value="large", interactive=True, scale=1)
-                            with gr.Column():
-                                dropdown = gr.Dropdown(choices=get_available_models(), value=("No models found" if len(get_available_models()) < 1 else get_available_models()[0]), label='Custom Model (models folder)', elem_classes='slim-dropdown', interactive=True)
-                                ui.create_refresh_button(dropdown, lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
-                                basemodel = gr.Radio(["small", "medium", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
-                        with gr.Row():
-                            decoder = gr.Radio(["Default", "MultiBand_Diffusion"], label="Decoder", value="Default", interactive=True)
-                        with gr.Row():
-                            topk = gr.Number(label="Top-k", value=250, interactive=True)
-                            topp = gr.Number(label="Top-p", value=0, interactive=True)
-                            temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
-                            cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
-                    with gr.Row():
-                        submit = gr.Button("Generate", variant="primary")
-                        # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
-                        _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
-                with gr.Column() as c:
-                    with gr.Tab("Output"):
-                        output = gr.Video(label="Generated Music", scale=0)
-                        with gr.Row():
-                            audio_only = gr.Audio(type="numpy", label="Audio Only", interactive=False)
-                            backup_only = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
-                            send_audio = gr.Button("Send to Input Audio")
-                        seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
-                        download = gr.File(label="Generated Files", interactive=False)
-                    with gr.Tab("Wiki"):
-                        gr.Markdown(
-                            """
-                            - **[Generate (button)]:**  
-                            Generates the music with the given settings and prompts.
-
-                            - **[Interrupt (button)]:**  
-                            Stops the music generation as soon as it can, providing an incomplete output.
-
-                            ---
-
-                            ### Generation Tab:
-
-                            #### Structure Prompts:
-
-                            This feature helps reduce repetetive prompts by allowing you to set global prompts  
-                            that will be used for all prompt segments.
-
-                            - **[Structure Prompts (checkbox)]:**  
-                            Enable/Disable the structure prompts feature.
-
-                            - **[BPM (number)]:**  
-                            Beats per minute of the generated music.
-
-                            - **[Key (dropdown)]:**  
-                            The key of the generated music.
-
-                            - **[Scale (dropdown)]:**  
-                            The scale of the generated music.
-
-                            - **[Global Prompt (text)]:**  
-                            Here write the prompt that you wish to be used for all prompt segments.
-
-                            #### Multi-Prompt: 
-                            
-                            This feature allows you to control the music, adding variation to different time segments.  
-                            You have up to 10 prompt segments. the first prompt will always be 30s long  
-                            the other prompts will be [30s - overlap].  
-                            for example if the overlap is 10s, each prompt segment will be 20s.
-
-                            - **[Prompt Segments (number)]:**  
-                            Amount of unique prompt to generate throughout the music generation.
-
-                            - **[Prompt/Input Text (prompt)]:**  
-                            Here describe the music you wish the model to generate.
-
-                            - **[Repeat (number)]:**  
-                            Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
-                            - **[Time (text)]:**  
-                            The time of the prompt segment.
-
-                            - **[Calculate Timings (button)]:**  
-                            Calculates the timings of the prompt segments.
-
-                            - **[Duration (number)]:**  
-                            How long you want the generated music to be (in seconds).
-
-                            - **[Overlap (number)]:**  
-                            How much each new segment will reference the previous segment (in seconds).  
-                            For example, if you choose 20s: Each new segment after the first one will reference the previous segment 20s  
-                            and will generate only 10s of new music. The model can only process 30s of music.
-
-                            - **[Seed (number)]:**  
-                            Your generated music id. If you wish to generate the exact same music,  
-                            place the exact seed with the exact prompts  
-                            (This way you can also extend specific song that was generated short).
-
-                            - **[Random Seed (button)]:**  
-                            Gives "-1" as a seed, which counts as a random seed.
-
-                            - **[Copy Previous Seed (button)]:**  
-                            Copies the seed from the output seed (if you don't feel like doing it manualy).
-
-                            ---
-
-                            ### Audio Tab:
-
-                            - **[Input Type (selection)]:**  
-                            `File` mode allows you to upload an audio file to use as input  
-                            `Mic` mode allows you to use your microphone as input
-
-                            - **[Input Audio Mode (selection)]:**  
-                            `Melody` mode only works with the melody model: it conditions the music generation to reference the melody  
-                            `Sample` mode works with any model: it gives a music sample to the model to generate its continuation.
-
-                            - **[Trim Start and Trim End (numbers)]:**  
-                            `Trim Start` set how much you'd like to trim the input audio from the start  
-                            `Trim End` same as the above but from the end
-
-                            - **[Input Audio (audio file)]:**  
-                            Input here the audio you wish to use with "melody" or "sample" mode.
-
-                            ---
-
-                            ### Customization Tab:
-
-                            - **[Background Color (color)]:**  
-                            Works only if you don't upload image. Color of the background of the waveform.
-
-                            - **[Bar Color Start (color)]:**  
-                            First color of the waveform bars.
-
-                            - **[Bar Color End (color)]:**  
-                            Second color of the waveform bars.
-
-                            - **[Background Image (image)]:**  
-                            Background image that you wish to be attached to the generated video along with the waveform.
-
-                            - **[Height and Width (numbers)]:**  
-                            Output video resolution, only works with image.  
-                            (minimum height and width is 256).
-                            
-                            ---
-
-                            ### Settings Tab:
-
-                            - **[Output Audio Channels (selection)]:**  
-                            With this you can select the amount of channels that you wish for your output audio.  
-                            `mono` is a straightforward single channel audio  
-                            `stereo` is a dual channel audio but it will sound more or less like mono  
-                            `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
-                            - **[Output Audio Sample Rate (dropdown)]:**  
-                            The output audio sample rate, the model default is 32000.
-
-                            - **[Model (selection)]:**  
-                            Here you can choose which model you wish to use:  
-                            `melody` model is based on the medium model with a unique feature that lets you use melody conditioning  
-                            `small` model is trained on 300M parameters  
-                            `medium` model is trained on 1.5B parameters  
-                            `large` model is trained on 3.3B parameters  
-                            `custom` model runs the custom model that you provided.
-
-                            - **[Custom Model (selection)]:**  
-                            This dropdown will show you models that are placed in the `models` folder  
-                            you must select `custom` in the model options in order to use it.
-
-                            - **[Refresh (button)]:**  
-                            Refreshes the dropdown list for custom model.
-
-                            - **[Base Model (selection)]:**  
-                            Choose here the model that your custom model is based on.
-
-                            - **[Decoder (selection)]:**  
-                            Choose here the decoder that you wish to use:  
-                            `Default` is the default decoder  
-                            `MultiBand_Diffusion` is a decoder that uses diffusion to generate the audio.
-
-                            - **[Top-k (number)]:**  
-                            is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
-                            - **[Top-p (number)]:**  
-                            also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-                            
-                            - **[Temperature (number)]:**  
-                            is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
-                            - **[Classifier Free Guidance (number)]:**  
-                            refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
-                            """
-                        )
-        with gr.Tab("AudioGen"):
-            gr.Markdown(
-                """
-                ### AudioGen
-                """
-            )
-            with gr.Row():
-                with gr.Column():
-                    with gr.Tab("Generation"):
-                        with gr.Accordion("Structure Prompts", open=False):
-                            with gr.Row():
-                                struc_prompts_a = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
-                                global_prompt_a = gr.Text(label="Global Prompt", interactive=True, scale=3)
-                        with gr.Row():
-                            s_a = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
-                        with gr.Column():
-                            textboxes_a = []
-                            prompts_a = []
-                            repeats_a = []
-                            calcs_a = []
-                            with gr.Row():
-                                text0_a = gr.Text(label="Input Text", interactive=True, scale=4)
-                                prompts_a.append(text0_a)
-                                drag0_a = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
-                                repeats_a.append(drag0_a)
-                                calc0_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
-                                calcs_a.append(calc0_a)
-                            for i in range(max_textboxes):
-                                with gr.Row(visible=False) as t_a:
-                                    text_a = gr.Text(label="Input Text", interactive=True, scale=3)
-                                    repeat_a = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
-                                    calc_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
-                                textboxes_a.append(t_a)
-                                prompts_a.append(text_a)
-                                repeats_a.append(repeat_a)
-                                calcs_a.append(calc_a)
-                            to_calc_a = gr.Button("Calculate Timings", variant="secondary")
-                        with gr.Row():
-                            duration_a = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
-                        with gr.Row():
-                            overlap_a = gr.Slider(minimum=1, maximum=9, value=2, step=1, label="Overlap", interactive=True)
-                        with gr.Row():
-                            seed_a = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
-                            gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed_a], queue=False)
-                            reuse_seed_a = gr.Button('\u267b\ufe0f', scale=1)
-
-                    with gr.Tab("Audio"):
-                        with gr.Row():
-                            with gr.Column():
-                                input_type_a = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
-                                mode_a = gr.Radio(["sample"], label="Input Audio Mode (optional)", value="sample", interactive=False, visible=False)
-                                with gr.Row():
-                                    trim_start_a = gr.Number(label="Trim Start", value=0, interactive=True)
-                                    trim_end_a = gr.Number(label="Trim End", value=0, interactive=True)
-                            audio_a = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
-                    with gr.Tab("Customization"):
-                        with gr.Row():
-                            with gr.Column():
-                                background_a = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
-                                bar1_a = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
-                                bar2_a = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
-                            with gr.Column():
-                                image_a = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
-                                with gr.Row():
-                                    height_a = gr.Number(label="Height", value=512, interactive=True)
-                                    width_a = gr.Number(label="Width", value=768, interactive=True)
-
-                    with gr.Tab("Settings"):
-                        with gr.Row():
-                            channel_a = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
-                            sr_select_a = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
-                        with gr.Row():
-                            model_a = gr.Radio(["medium"], label="Model", value="medium", interactive=False, visible=False)
-                            decoder_a = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False, visible=False)
-                        with gr.Row():
-                            topk_a = gr.Number(label="Top-k", value=250, interactive=True)
-                            topp_a = gr.Number(label="Top-p", value=0, interactive=True)
-                            temperature_a = gr.Number(label="Temperature", value=1.0, interactive=True)
-                            cfg_coef_a = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
-                    with gr.Row():
-                        submit_a = gr.Button("Generate", variant="primary")
-                        _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
-                with gr.Column():
-                    with gr.Tab("Output"):
-                        output_a = gr.Video(label="Generated Audio", scale=0)
-                        with gr.Row():
-                            audio_only_a = gr.Audio(type="numpy", label="Audio Only", interactive=False)
-                            backup_only_a = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
-                            send_audio_a = gr.Button("Send to Input Audio")
-                        seed_used_a = gr.Number(label='Seed used', value=-1, interactive=False)
-                        download_a = gr.File(label="Generated Files", interactive=False)
-                    with gr.Tab("Wiki"):
-                        gr.Markdown(
-                            """
-                            - **[Generate (button)]:**  
-                            Generates the audio with the given settings and prompts.
-
-                            - **[Interrupt (button)]:**  
-                            Stops the audio generation as soon as it can, providing an incomplete output.
-
-                            ---
-
-                            ### Generation Tab:
-
-                            #### Structure Prompts:
-
-                            This feature helps reduce repetetive prompts by allowing you to set global prompts  
-                            that will be used for all prompt segments.
-
-                            - **[Structure Prompts (checkbox)]:**  
-                            Enable/Disable the structure prompts feature.
-
-                            - **[Global Prompt (text)]:**  
-                            Here write the prompt that you wish to be used for all prompt segments.
-
-                            #### Multi-Prompt: 
-                            
-                            This feature allows you to control the audio, adding variation to different time segments.  
-                            You have up to 10 prompt segments. the first prompt will always be 10s long  
-                            the other prompts will be [10s - overlap].  
-                            for example if the overlap is 2s, each prompt segment will be 8s.
-
-                            - **[Prompt Segments (number)]:**  
-                            Amount of unique prompt to generate throughout the audio generation.
-
-                            - **[Prompt/Input Text (prompt)]:**  
-                            Here describe the audio you wish the model to generate.
-
-                            - **[Repeat (number)]:**  
-                            Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
-                            - **[Time (text)]:**  
-                            The time of the prompt segment.
-
-                            - **[Calculate Timings (button)]:**  
-                            Calculates the timings of the prompt segments.
-
-                            - **[Duration (number)]:**  
-                            How long you want the generated audio to be (in seconds).
-
-                            - **[Overlap (number)]:**  
-                            How much each new segment will reference the previous segment (in seconds).  
-                            For example, if you choose 2s: Each new segment after the first one will reference the previous segment 2s  
-                            and will generate only 8s of new audio. The model can only process 10s of music.
-
-                            - **[Seed (number)]:**  
-                            Your generated audio id. If you wish to generate the exact same audio,  
-                            place the exact seed with the exact prompts  
-                            (This way you can also extend specific song that was generated short).
-
-                            - **[Random Seed (button)]:**  
-                            Gives "-1" as a seed, which counts as a random seed.
-
-                            - **[Copy Previous Seed (button)]:**  
-                            Copies the seed from the output seed (if you don't feel like doing it manualy).
-
-                            ---
-
-                            ### Audio Tab:
-
-                            - **[Input Type (selection)]:**  
-                            `File` mode allows you to upload an audio file to use as input  
-                            `Mic` mode allows you to use your microphone as input
-
-                            - **[Trim Start and Trim End (numbers)]:**  
-                            `Trim Start` set how much you'd like to trim the input audio from the start  
-                            `Trim End` same as the above but from the end
-
-                            - **[Input Audio (audio file)]:**  
-                            Input here the audio you wish to use.
-
-                            ---
-
-                            ### Customization Tab:
-
-                            - **[Background Color (color)]:**  
-                            Works only if you don't upload image. Color of the background of the waveform.
-
-                            - **[Bar Color Start (color)]:**  
-                            First color of the waveform bars.
-
-                            - **[Bar Color End (color)]:**  
-                            Second color of the waveform bars.
-
-                            - **[Background Image (image)]:**  
-                            Background image that you wish to be attached to the generated video along with the waveform.
-
-                            - **[Height and Width (numbers)]:**  
-                            Output video resolution, only works with image.  
-                            (minimum height and width is 256).
-                            
-                            ---
-
-                            ### Settings Tab:
-
-                            - **[Output Audio Channels (selection)]:**  
-                            With this you can select the amount of channels that you wish for your output audio.  
-                            `mono` is a straightforward single channel audio  
-                            `stereo` is a dual channel audio but it will sound more or less like mono  
-                            `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
-                            - **[Output Audio Sample Rate (dropdown)]:**  
-                            The output audio sample rate, the model default is 32000.
-
-                            - **[Top-k (number)]:**  
-                            is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
-                            - **[Top-p (number)]:**  
-                            also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-                            
-                            - **[Temperature (number)]:**  
-                            is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
-                            - **[Classifier Free Guidance (number)]:**  
-                            refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
-                            """
-                        )
-        with gr.Tab("Audio Info"):
-            gr.Markdown(
-                """
-                ### Audio Info
-                """
-            )
-            with gr.Row():
-                with gr.Column():
-                    in_audio = gr.File(type="file", label="Input Any Audio", interactive=True)
-                    with gr.Row():
-                        send_gen = gr.Button("Send to MusicGen", variant="primary")
-                        send_gen_a = gr.Button("Send to AudioGen", variant="primary")
-                with gr.Column():
-                    info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
-        with gr.Tab("Changelog"):
-            gr.Markdown(
-                            """
-                            ## Changelog:
-
-                            ### v2.0.0a
-
-                            - Forgot to move all the update to app.py from temp2.py... oops
-
-
-
-                            ### v2.0.0
-
-                            - Changed name from MusicGen+ to AudioCraft Plus
-                            
-                            - Complete overhaul of the repo "backend" with the latest changes from the main facebookresearch repo
-
-                            - Added a new decoder: MultiBand_Diffusion
-
-                            - Added AudioGen: a new tab for generating audio
-
-
-
-                            ### v1.2.8c
-
-                            - Implemented Reverse compatibility for audio info tab with previous versions
-
-
-
-                            ### v1.2.8b
-
-                            - Fixed the error when loading default models
-
-
-
-                            ### v1.2.8a
-
-                            - Adapted Audio info tab to work with the new structure prompts feature
-
-                            - Now custom models actually work, make sure you select the correct base model
-
-
-
-                            ### v1.2.8
-
-                            - Now you will also recieve json file with metadata of generated audio
-
-                            - Added error messages in Audio Info tab
-
-                            - Added structure prompts: you can select bpm, key and global prompt for all prompts
-
-                            - Added time display next to each prompt, can be calculated with "Calculate Timings" button
-
-
-
-                            ### v1.2.7
-
-                            - When sending generated audio to Input Audio, it will send a backup audio with default settings  
-                            (best for continuos generation)
-
-                            - Added Metadata to generated audio (Thanks to AlexHK ♥)
-
-                            - Added Audio Info tab that will display the metadata of the input audio
-
-                            - Added "send to Text2Audio" button in Audio Info tab
-
-                            - Generated audio is now stored in the "output" folder (Thanks to AlexHK ♥)
-
-                            - Added an output area with generated files and download buttons
-
-                            - Enhanced Stereo effect (Thanks to AlexHK ♥)
-
-
-
-                            ### v1.2.6
-
-                            - Added option to generate in stereo (instead of only mono)
-
-                            - Added dropdown for selecting output sample rate (model default is 32000)
-
-
-
-                            ### v1.2.5a
-
-                            - Added file cleaner (This comes from the main facebookresearch repo)
-
-                            - Reorganized a little, moved audio to a seperate tab
-
-
-
-                            ### v1.2.5
-
-                            - Gave a unique lime theme to the webui
-                            
-                            - Added additional output for audio only
-
-                            - Added button to send generated audio to Input Audio
-
-                            - Added option to trim Input Audio
-
-
-
-                            ### v1.2.4
-
-                            - Added mic input (This comes from the main facebookresearch repo)
-
-
-
-                            ### v1.2.3
-
-                            - Added option to change video size to fit the image you upload
-
-
-
-                            ### v1.2.2
-
-                            - Added Wiki, Changelog and About tabs
-
-
-
-                            ### v1.2.1
-
-                            - Added tabs and organized the entire interface
-
-                            - Added option to attach image to the output video
-
-                            - Added option to load fine-tuned models (Yet to be tested)
-
-
-
-                            ### v1.2.0
-
-                            - Added Multi-Prompt
-
-
-
-                            ### v1.1.3
-
-                            - Added customization options for generated waveform
-
-
-
-                            ### v1.1.2
-
-                            - Removed sample length limit: now you can input audio of any length as music sample
-
-
-
-                            ### v1.1.1
-
-                            - Improved music sample audio quality when using music continuation
-
-
-
-                            ### v1.1.0
-
-                            - Rebuilt the repo on top of the latest structure of the main MusicGen repo
-                            
-                            - Improved Music continuation feature
-
-
-
-                            ### v1.0.0 - Stable Version
-
-                            - Added Music continuation
-                            """
-                        )
-        with gr.Tab("About"):
-            gen_type = gr.Text(value="music", interactive=False, visible=False)
-            gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
-            gr.Markdown(
-                            """
-                            This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
-                            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-                            
-                            ## MusicGen+ is an extended version of the original MusicGen by facebookresearch. 
-                            
-                            ### Repo: https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
-
-                            ---
-                            
-                            ### This project was possible thanks to:
-
-                            #### GrandaddyShmax - https://github.com/GrandaddyShmax
-
-                            #### Camenduru - https://github.com/camenduru
-
-                            #### rkfg - https://github.com/rkfg
-
-                            #### oobabooga - https://github.com/oobabooga
-                            
-                            #### AlexHK - https://github.com/alanhk147
-                            """
-                        )
-
-        send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
-        reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
-        send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
-        submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
-        input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
-        to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
-
-        send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
-        reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
-        send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
-        submit_a.click(predict_full, inputs=[gen_type_a, model_a, decoder_a, dropdown, basemodel, s_a, struc_prompts_a, bpm, key, scale, global_prompt_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], audio_a, mode_a, trim_start_a, trim_end_a, duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, image_a, height_a, width_a, background_a, bar1_a, bar2_a, channel_a, sr_select_a], outputs=[output_a, audio_only_a, backup_only_a, download_a, seed_used_a])
-        input_type_a.change(toggle_audio_src, input_type_a, [audio_a], queue=False, show_progress=False)
-        to_calc_a.click(calc_time, inputs=[gen_type_a, s_a, duration_a, overlap_a, repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9]], outputs=[calcs_a[0], calcs_a[1], calcs_a[2], calcs_a[3], calcs_a[4], calcs_a[5], calcs_a[6], calcs_a[7], calcs_a[8], calcs_a[9]], queue=False)
-
-        in_audio.change(get_audio_info, in_audio, outputs=[info])
-
-        def variable_outputs(k):
-            k = int(k) - 1
-            return [gr.Textbox.update(visible=True)]*k + [gr.Textbox.update(visible=False)]*(max_textboxes-k)
-        def get_size(image):
-            if image is not None:
-                img = Image.open(image)
-                img_height = img.height
-                img_width = img.width
-                if (img_height%2) != 0:
-                    img_height = img_height + 1
-                if (img_width%2) != 0:
-                    img_width = img_width + 1
-                return img_height, img_width
-            else:
-                return 512, 768
-
-        image.change(get_size, image, outputs=[height, width])
-        image_a.change(get_size, image_a, outputs=[height_a, width_a])
-        s.change(variable_outputs, s, textboxes)
-        s_a.change(variable_outputs, s_a, textboxes_a)
-        interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            # MusicGen
-
-            This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
-            a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-            <br/>
-            <a href="https://huggingface.co/spaces/facebook/MusicGen?duplicate=true"
-                style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
-            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;"
-                src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            for longer sequences, more control and no queue.</p>
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Generate")
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-                audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
-        submit.click(predict_batched, inputs=[text, melody],
-                     outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_batched,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                ],
-            ],
-            inputs=[text, melody],
-            outputs=[output]
-        )
-        gr.Markdown("""
-        ### More details
-
-        The model will generate 12 seconds of audio based on the description you provided.
-        You can optionally provide a reference audio from which a broad melody will be extracted.
-        The model will then try to follow both the description and melody provided.
-        All samples are generated with the `melody` model.
-
-        You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
-        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-        for more details.
-        """)
-
-        demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--listen',
-        type=str,
-        default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
-        help='IP to listen on for connections to Gradio',
-    )
-    parser.add_argument(
-        '--username', type=str, default='', help='Username for authentication'
-    )
-    parser.add_argument(
-        '--password', type=str, default='', help='Password for authentication'
-    )
-    parser.add_argument(
-        '--server_port',
-        type=int,
-        default=0,
-        help='Port to run the server listener on',
-    )
-    parser.add_argument(
-        '--inbrowser', action='store_true', help='Open in browser'
-    )
-    parser.add_argument(
-        '--share', action='store_true', help='Share the gradio UI'
-    )
-    parser.add_argument(
-        '--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
-    )
-
-    parser.add_argument(
-        '--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
-    )
-
-    parser.add_argument(
-        '--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
-    )
-
-    args = parser.parse_args()
-    UNLOAD_MODEL = args.unload_model
-    MOVE_TO_CPU = args.unload_to_cpu
-    if args.cache:
-        MODELS = {}
-
-    launch_kwargs = {}
-    launch_kwargs['server_name'] = args.listen
-
-    if args.username and args.password:
-        launch_kwargs['auth'] = (args.username, args.password)
-    if args.server_port:
-        launch_kwargs['server_port'] = args.server_port
-    if args.inbrowser:
-        launch_kwargs['inbrowser'] = args.inbrowser
-    if args.share:
-        launch_kwargs['share'] = args.share
-
-    # Show the interface
-    if IS_BATCHED:
-        global USE_DIFFUSION
-        USE_DIFFUSION = False
-        ui_batched(launch_kwargs)
-    else:
-        ui_full(launch_kwargs)

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/models/source.py

@@ -1,538 +0,0 @@
-import torch
-import numpy as np
-import sys
-import torch.nn.functional as torch_nn_func
-
-
-class SineGen(torch.nn.Module):
-    """ Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(self, samp_rate, harmonic_num=0,
-                 sine_amp=0.1, noise_std=0.003,
-                 voiced_threshold=0,
-                 flag_for_pulse=False):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-        self.flag_for_pulse = flag_for_pulse
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def _f02sine(self, f0_values):
-        """ f0_values: (batchsize, length, dim)
-            where dim indicates fundamental tone and overtones
-        """
-        # convert to F0 in rad. The interger part n can be ignored
-        # because 2 * np.pi * n doesn't affect phase
-        rad_values = (f0_values / self.sampling_rate) % 1
-
-        # initial phase noise (no noise for fundamental component)
-        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
-                              device=f0_values.device)
-        rand_ini[:, 0] = 0
-        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-
-        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
-        if not self.flag_for_pulse:
-            # for normal case
-
-            # To prevent torch.cumsum numerical overflow,
-            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
-            # Buffer tmp_over_one_idx indicates the time step to add -1.
-            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
-            tmp_over_one = torch.cumsum(rad_values, 1) % 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] -
-                                tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-
-            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
-                              * 2 * np.pi)
-        else:
-            # If necessary, make sure that the first time step of every
-            # voiced segments is sin(pi) or cos(0)
-            # This is used for pulse-train generation
-
-            # identify the last time step in unvoiced segments
-            uv = self._f02uv(f0_values)
-            uv_1 = torch.roll(uv, shifts=-1, dims=1)
-            uv_1[:, -1, :] = 1
-            u_loc = (uv < 1) * (uv_1 > 0)
-
-            # get the instantanouse phase
-            tmp_cumsum = torch.cumsum(rad_values, dim=1)
-            # different batch needs to be processed differently
-            for idx in range(f0_values.shape[0]):
-                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
-                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
-                # stores the accumulation of i.phase within
-                # each voiced segments
-                tmp_cumsum[idx, :, :] = 0
-                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
-
-            # rad_values - tmp_cumsum: remove the accumulation of i.phase
-            # within the previous voiced segment.
-            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
-
-            # get the sines
-            sines = torch.cos(i_phase * 2 * np.pi)
-        return sines
-
-    def forward(self, f0):
-        """ sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
-                                 device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
-
-            # generate sine waveforms
-            sine_waves = self._f02sine(f0_buf) * self.sine_amp
-
-            # generate uv signal
-            # uv = torch.ones(f0.shape)
-            # uv = uv * (f0 > self.voiced_threshold)
-            uv = self._f02uv(f0)
-
-            # noise: for unvoiced should be similar to sine_amp
-            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
-            # .       for voiced regions is self.noise_std
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-
-            # first: set the unvoiced part to 0 by uv
-            # then: additive noise
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class PulseGen(torch.nn.Module):
-    """ Definition of Pulse train generator
-
-    There are many ways to implement pulse generator.
-    Here, PulseGen is based on SinGen. For a perfect
-    """
-    def __init__(self, samp_rate, pulse_amp = 0.1,
-                 noise_std = 0.003, voiced_threshold = 0):
-        super(PulseGen, self).__init__()
-        self.pulse_amp = pulse_amp
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-        self.noise_std = noise_std
-        self.l_sinegen = SineGen(self.sampling_rate, harmonic_num=0, \
-                                 sine_amp=self.pulse_amp, noise_std=0, \
-                                 voiced_threshold=self.voiced_threshold, \
-                                 flag_for_pulse=True)
-
-    def forward(self, f0):
-        """ Pulse train generator
-        pulse_train, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output pulse_train: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-
-        Note: self.l_sine doesn't make sure that the initial phase of
-        a voiced segment is np.pi, the first pulse in a voiced segment
-        may not be at the first time step within a voiced segment
-        """
-        with torch.no_grad():
-            sine_wav, uv, noise = self.l_sinegen(f0)
-
-            # sine without additive noise
-            pure_sine = sine_wav - noise
-
-            # step t corresponds to a pulse if
-            # sine[t] > sine[t+1] & sine[t] > sine[t-1]
-            # & sine[t-1], sine[t+1], and sine[t] are voiced
-            # or
-            # sine[t] is voiced, sine[t-1] is unvoiced
-            # we use torch.roll to simulate sine[t+1] and sine[t-1]
-            sine_1 = torch.roll(pure_sine, shifts=1, dims=1)
-            uv_1 = torch.roll(uv, shifts=1, dims=1)
-            uv_1[:, 0, :] = 0
-            sine_2 = torch.roll(pure_sine, shifts=-1, dims=1)
-            uv_2 = torch.roll(uv, shifts=-1, dims=1)
-            uv_2[:, -1, :] = 0
-
-            loc = (pure_sine > sine_1) * (pure_sine > sine_2) \
-                  * (uv_1 > 0) * (uv_2 > 0) * (uv > 0) \
-                  + (uv_1 < 1) * (uv > 0)
-
-            # pulse train without noise
-            pulse_train = pure_sine * loc
-
-            # additive noise to pulse train
-            # note that noise from sinegen is zero in voiced regions
-            pulse_noise = torch.randn_like(pure_sine) * self.noise_std
-
-            # with additive noise on pulse, and unvoiced regions
-            pulse_train += pulse_noise * loc + pulse_noise * (1 - uv)
-        return pulse_train, sine_wav, uv, pulse_noise
-
-
-class SignalsConv1d(torch.nn.Module):
-    """ Filtering input signal with time invariant filter
-    Note: FIRFilter conducted filtering given fixed FIR weight
-          SignalsConv1d convolves two signals
-    Note: this is based on torch.nn.functional.conv1d
-
-    """
-
-    def __init__(self):
-        super(SignalsConv1d, self).__init__()
-
-    def forward(self, signal, system_ir):
-        """ output = forward(signal, system_ir)
-
-        signal:    (batchsize, length1, dim)
-        system_ir: (length2, dim)
-
-        output:    (batchsize, length1, dim)
-        """
-        if signal.shape[-1] != system_ir.shape[-1]:
-            print("Error: SignalsConv1d expects shape:")
-            print("signal    (batchsize, length1, dim)")
-            print("system_id (batchsize, length2, dim)")
-            print("But received signal: {:s}".format(str(signal.shape)))
-            print(" system_ir: {:s}".format(str(system_ir.shape)))
-            sys.exit(1)
-        padding_length = system_ir.shape[0] - 1
-        groups = signal.shape[-1]
-
-        # pad signal on the left
-        signal_pad = torch_nn_func.pad(signal.permute(0, 2, 1), \
-                                       (padding_length, 0))
-        # prepare system impulse response as (dim, 1, length2)
-        # also flip the impulse response
-        ir = torch.flip(system_ir.unsqueeze(1).permute(2, 1, 0), \
-                        dims=[2])
-        # convolute
-        output = torch_nn_func.conv1d(signal_pad, ir, groups=groups)
-        return output.permute(0, 2, 1)
-
-
-class CyclicNoiseGen_v1(torch.nn.Module):
-    """ CyclicnoiseGen_v1
-    Cyclic noise with a single parameter of beta.
-    Pytorch v1 implementation assumes f_t is also fixed
-    """
-
-    def __init__(self, samp_rate,
-                 noise_std=0.003, voiced_threshold=0):
-        super(CyclicNoiseGen_v1, self).__init__()
-        self.samp_rate = samp_rate
-        self.noise_std = noise_std
-        self.voiced_threshold = voiced_threshold
-
-        self.l_pulse = PulseGen(samp_rate, pulse_amp=1.0,
-                                noise_std=noise_std,
-                                voiced_threshold=voiced_threshold)
-        self.l_conv = SignalsConv1d()
-
-    def noise_decay(self, beta, f0mean):
-        """ decayed_noise = noise_decay(beta, f0mean)
-        decayed_noise =  n[t]exp(-t * f_mean / beta / samp_rate)
-
-        beta: (dim=1) or (batchsize=1, 1, dim=1)
-        f0mean (batchsize=1, 1, dim=1)
-
-        decayed_noise (batchsize=1, length, dim=1)
-        """
-        with torch.no_grad():
-            # exp(-1.0 n / T) < 0.01 => n > -log(0.01)*T = 4.60*T
-            # truncate the noise when decayed by -40 dB
-            length = 4.6 * self.samp_rate / f0mean
-            length = length.int()
-            time_idx = torch.arange(0, length, device=beta.device)
-            time_idx = time_idx.unsqueeze(0).unsqueeze(2)
-            time_idx = time_idx.repeat(beta.shape[0], 1, beta.shape[2])
-
-        noise = torch.randn(time_idx.shape, device=beta.device)
-
-        # due to Pytorch implementation, use f0_mean as the f0 factor
-        decay = torch.exp(-time_idx * f0mean / beta / self.samp_rate)
-        return noise * self.noise_std * decay
-
-    def forward(self, f0s, beta):
-        """ Producde cyclic-noise
-        """
-        # pulse train
-        pulse_train, sine_wav, uv, noise = self.l_pulse(f0s)
-        pure_pulse = pulse_train - noise
-
-        # decayed_noise (length, dim=1)
-        if (uv < 1).all():
-            # all unvoiced
-            cyc_noise = torch.zeros_like(sine_wav)
-        else:
-            f0mean = f0s[uv > 0].mean()
-
-            decayed_noise = self.noise_decay(beta, f0mean)[0, :, :]
-            # convolute
-            cyc_noise = self.l_conv(pure_pulse, decayed_noise)
-
-        # add noise in invoiced segments
-        cyc_noise = cyc_noise + noise * (1.0 - uv)
-        return cyc_noise, pulse_train, sine_wav, uv, noise
-
-
-class SineGen(torch.nn.Module):
-    """ Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(self, samp_rate, harmonic_num=0,
-                 sine_amp=0.1, noise_std=0.003,
-                 voiced_threshold=0,
-                 flag_for_pulse=False):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-        self.flag_for_pulse = flag_for_pulse
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def _f02sine(self, f0_values):
-        """ f0_values: (batchsize, length, dim)
-            where dim indicates fundamental tone and overtones
-        """
-        # convert to F0 in rad. The interger part n can be ignored
-        # because 2 * np.pi * n doesn't affect phase
-        rad_values = (f0_values / self.sampling_rate) % 1
-
-        # initial phase noise (no noise for fundamental component)
-        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
-                              device=f0_values.device)
-        rand_ini[:, 0] = 0
-        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-
-        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
-        if not self.flag_for_pulse:
-            # for normal case
-
-            # To prevent torch.cumsum numerical overflow,
-            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
-            # Buffer tmp_over_one_idx indicates the time step to add -1.
-            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
-            tmp_over_one = torch.cumsum(rad_values, 1) % 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] -
-                                tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-
-            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
-                              * 2 * np.pi)
-        else:
-            # If necessary, make sure that the first time step of every
-            # voiced segments is sin(pi) or cos(0)
-            # This is used for pulse-train generation
-
-            # identify the last time step in unvoiced segments
-            uv = self._f02uv(f0_values)
-            uv_1 = torch.roll(uv, shifts=-1, dims=1)
-            uv_1[:, -1, :] = 1
-            u_loc = (uv < 1) * (uv_1 > 0)
-
-            # get the instantanouse phase
-            tmp_cumsum = torch.cumsum(rad_values, dim=1)
-            # different batch needs to be processed differently
-            for idx in range(f0_values.shape[0]):
-                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
-                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
-                # stores the accumulation of i.phase within
-                # each voiced segments
-                tmp_cumsum[idx, :, :] = 0
-                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
-
-            # rad_values - tmp_cumsum: remove the accumulation of i.phase
-            # within the previous voiced segment.
-            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
-
-            # get the sines
-            sines = torch.cos(i_phase * 2 * np.pi)
-        return sines
-
-    def forward(self, f0):
-        """ sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, \
-                                 device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
-
-            # generate sine waveforms
-            sine_waves = self._f02sine(f0_buf) * self.sine_amp
-
-            # generate uv signal
-            # uv = torch.ones(f0.shape)
-            # uv = uv * (f0 > self.voiced_threshold)
-            uv = self._f02uv(f0)
-
-            # noise: for unvoiced should be similar to sine_amp
-            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
-            # .       for voiced regions is self.noise_std
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-
-            # first: set the unvoiced part to 0 by uv
-            # then: additive noise
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleCycNoise_v1(torch.nn.Module):
-    """ SourceModuleCycNoise_v1
-    SourceModule(sampling_rate, noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-
-    noise_std: std of Gaussian noise (default: 0.003)
-    voiced_threshold: threshold to set U/V given F0 (default: 0)
-
-    cyc, noise, uv = SourceModuleCycNoise_v1(F0_upsampled, beta)
-    F0_upsampled (batchsize, length, 1)
-    beta (1)
-    cyc (batchsize, length, 1)
-    noise (batchsize, length, 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(self, sampling_rate, noise_std=0.003, voiced_threshod=0):
-        super(SourceModuleCycNoise_v1, self).__init__()
-        self.sampling_rate = sampling_rate
-        self.noise_std = noise_std
-        self.l_cyc_gen = CyclicNoiseGen_v1(sampling_rate, noise_std,
-                                           voiced_threshod)
-
-    def forward(self, f0_upsamped, beta):
-        """
-        cyc, noise, uv = SourceModuleCycNoise_v1(F0, beta)
-        F0_upsampled (batchsize, length, 1)
-        beta (1)
-        cyc (batchsize, length, 1)
-        noise (batchsize, length, 1)
-        uv (batchsize, length, 1)
-        """
-        # source for harmonic branch
-        cyc, pulse, sine, uv, add_noi = self.l_cyc_gen(f0_upsamped, beta)
-
-        # source for noise branch, in the same shape as uv
-        noise = torch.randn_like(uv) * self.noise_std / 3
-        return cyc, noise, uv
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """ SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
-                                 sine_amp, add_noise_std, voiced_threshod)
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x):
-        """
-        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-        F0_sampled (batchsize, length, 1)
-        Sine_source (batchsize, length, 1)
-        noise_source (batchsize, length 1)
-        """
-        # source for harmonic branch
-        sine_wavs, uv, _ = self.l_sin_gen(x)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-
-        # source for noise branch, in the same shape as uv
-        noise = torch.randn_like(uv) * self.sine_amp / 3
-        return sine_merge, noise, uv
-
-
-if __name__ == '__main__':
-    source = SourceModuleCycNoise_v1(24000)
-    x = torch.randn(16, 25600, 1)
-
-

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/x_transformer.py

@@ -1,641 +0,0 @@
-"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-from functools import partial
-from inspect import isfunction
-from collections import namedtuple
-from einops import rearrange, repeat, reduce
-
-# constants
-
-DEFAULT_DIM_HEAD = 64
-
-Intermediates = namedtuple('Intermediates', [
-    'pre_softmax_attn',
-    'post_softmax_attn'
-])
-
-LayerIntermediates = namedtuple('Intermediates', [
-    'hiddens',
-    'attn_intermediates'
-])
-
-
-class AbsolutePositionalEmbedding(nn.Module):
-    def __init__(self, dim, max_seq_len):
-        super().__init__()
-        self.emb = nn.Embedding(max_seq_len, dim)
-        self.init_()
-
-    def init_(self):
-        nn.init.normal_(self.emb.weight, std=0.02)
-
-    def forward(self, x):
-        n = torch.arange(x.shape[1], device=x.device)
-        return self.emb(n)[None, :, :]
-
-
-class FixedPositionalEmbedding(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq)
-
-    def forward(self, x, seq_dim=1, offset=0):
-        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
-        sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
-        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
-        return emb[None, :, :]
-
-
-# helpers
-
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def always(val):
-    def inner(*args, **kwargs):
-        return val
-    return inner
-
-
-def not_equals(val):
-    def inner(x):
-        return x != val
-    return inner
-
-
-def equals(val):
-    def inner(x):
-        return x == val
-    return inner
-
-
-def max_neg_value(tensor):
-    return -torch.finfo(tensor.dtype).max
-
-
-# keyword argument helpers
-
-def pick_and_pop(keys, d):
-    values = list(map(lambda key: d.pop(key), keys))
-    return dict(zip(keys, values))
-
-
-def group_dict_by_key(cond, d):
-    return_val = [dict(), dict()]
-    for key in d.keys():
-        match = bool(cond(key))
-        ind = int(not match)
-        return_val[ind][key] = d[key]
-    return (*return_val,)
-
-
-def string_begins_with(prefix, str):
-    return str.startswith(prefix)
-
-
-def group_by_key_prefix(prefix, d):
-    return group_dict_by_key(partial(string_begins_with, prefix), d)
-
-
-def groupby_prefix_and_trim(prefix, d):
-    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
-    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
-    return kwargs_without_prefix, kwargs
-
-
-# classes
-class Scale(nn.Module):
-    def __init__(self, value, fn):
-        super().__init__()
-        self.value = value
-        self.fn = fn
-
-    def forward(self, x, **kwargs):
-        x, *rest = self.fn(x, **kwargs)
-        return (x * self.value, *rest)
-
-
-class Rezero(nn.Module):
-    def __init__(self, fn):
-        super().__init__()
-        self.fn = fn
-        self.g = nn.Parameter(torch.zeros(1))
-
-    def forward(self, x, **kwargs):
-        x, *rest = self.fn(x, **kwargs)
-        return (x * self.g, *rest)
-
-
-class ScaleNorm(nn.Module):
-    def __init__(self, dim, eps=1e-5):
-        super().__init__()
-        self.scale = dim ** -0.5
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(1))
-
-    def forward(self, x):
-        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
-        return x / norm.clamp(min=self.eps) * self.g
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim, eps=1e-8):
-        super().__init__()
-        self.scale = dim ** -0.5
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x):
-        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
-        return x / norm.clamp(min=self.eps) * self.g
-
-
-class Residual(nn.Module):
-    def forward(self, x, residual):
-        return x + residual
-
-
-class GRUGating(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.gru = nn.GRUCell(dim, dim)
-
-    def forward(self, x, residual):
-        gated_output = self.gru(
-            rearrange(x, 'b n d -> (b n) d'),
-            rearrange(residual, 'b n d -> (b n) d')
-        )
-
-        return gated_output.reshape_as(x)
-
-
-# feedforward
-
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-# attention.
-class Attention(nn.Module):
-    def __init__(
-            self,
-            dim,
-            dim_head=DEFAULT_DIM_HEAD,
-            heads=8,
-            causal=False,
-            mask=None,
-            talking_heads=False,
-            sparse_topk=None,
-            use_entmax15=False,
-            num_mem_kv=0,
-            dropout=0.,
-            on_attn=False
-    ):
-        super().__init__()
-        if use_entmax15:
-            raise NotImplementedError("Check out entmax activation instead of softmax activation!")
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-        self.causal = causal
-        self.mask = mask
-
-        inner_dim = dim_head * heads
-
-        self.to_q = nn.Linear(dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(dim, inner_dim, bias=False)
-        self.dropout = nn.Dropout(dropout)
-
-        # talking heads
-        self.talking_heads = talking_heads
-        if talking_heads:
-            self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
-            self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
-
-        # explicit topk sparse attention
-        self.sparse_topk = sparse_topk
-
-        # entmax
-        #self.attn_fn = entmax15 if use_entmax15 else F.softmax
-        self.attn_fn = F.softmax
-
-        # add memory key / values
-        self.num_mem_kv = num_mem_kv
-        if num_mem_kv > 0:
-            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
-            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
-
-        # attention on attention
-        self.attn_on_attn = on_attn
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
-
-    def forward(
-            self,
-            x,
-            context=None,
-            mask=None,
-            context_mask=None,
-            rel_pos=None,
-            sinusoidal_emb=None,
-            prev_attn=None,
-            mem=None
-    ):
-        b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
-        kv_input = default(context, x)
-
-        q_input = x
-        k_input = kv_input
-        v_input = kv_input
-
-        if exists(mem):
-            k_input = torch.cat((mem, k_input), dim=-2)
-            v_input = torch.cat((mem, v_input), dim=-2)
-
-        if exists(sinusoidal_emb):
-            # in shortformer, the query would start at a position offset depending on the past cached memory
-            offset = k_input.shape[-2] - q_input.shape[-2]
-            q_input = q_input + sinusoidal_emb(q_input, offset=offset)
-            k_input = k_input + sinusoidal_emb(k_input)
-
-        q = self.to_q(q_input)
-        k = self.to_k(k_input)
-        v = self.to_v(v_input)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
-
-        input_mask = None
-        if any(map(exists, (mask, context_mask))):
-            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
-            k_mask = q_mask if not exists(context) else context_mask
-            k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
-            q_mask = rearrange(q_mask, 'b i -> b () i ()')
-            k_mask = rearrange(k_mask, 'b j -> b () () j')
-            input_mask = q_mask * k_mask
-
-        if self.num_mem_kv > 0:
-            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
-            k = torch.cat((mem_k, k), dim=-2)
-            v = torch.cat((mem_v, v), dim=-2)
-            if exists(input_mask):
-                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
-
-        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
-        mask_value = max_neg_value(dots)
-
-        if exists(prev_attn):
-            dots = dots + prev_attn
-
-        pre_softmax_attn = dots
-
-        if talking_heads:
-            dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
-
-        if exists(rel_pos):
-            dots = rel_pos(dots)
-
-        if exists(input_mask):
-            dots.masked_fill_(~input_mask, mask_value)
-            del input_mask
-
-        if self.causal:
-            i, j = dots.shape[-2:]
-            r = torch.arange(i, device=device)
-            mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
-            mask = F.pad(mask, (j - i, 0), value=False)
-            dots.masked_fill_(mask, mask_value)
-            del mask
-
-        if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
-            top, _ = dots.topk(self.sparse_topk, dim=-1)
-            vk = top[..., -1].unsqueeze(-1).expand_as(dots)
-            mask = dots < vk
-            dots.masked_fill_(mask, mask_value)
-            del mask
-
-        attn = self.attn_fn(dots, dim=-1)
-        post_softmax_attn = attn
-
-        attn = self.dropout(attn)
-
-        if talking_heads:
-            attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
-
-        out = einsum('b h i j, b h j d -> b h i d', attn, v)
-        out = rearrange(out, 'b h n d -> b n (h d)')
-
-        intermediates = Intermediates(
-            pre_softmax_attn=pre_softmax_attn,
-            post_softmax_attn=post_softmax_attn
-        )
-
-        return self.to_out(out), intermediates
-
-
-class AttentionLayers(nn.Module):
-    def __init__(
-            self,
-            dim,
-            depth,
-            heads=8,
-            causal=False,
-            cross_attend=False,
-            only_cross=False,
-            use_scalenorm=False,
-            use_rmsnorm=False,
-            use_rezero=False,
-            rel_pos_num_buckets=32,
-            rel_pos_max_distance=128,
-            position_infused_attn=False,
-            custom_layers=None,
-            sandwich_coef=None,
-            par_ratio=None,
-            residual_attn=False,
-            cross_residual_attn=False,
-            macaron=False,
-            pre_norm=True,
-            gate_residual=False,
-            **kwargs
-    ):
-        super().__init__()
-        ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
-        attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
-
-        dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
-
-        self.dim = dim
-        self.depth = depth
-        self.layers = nn.ModuleList([])
-
-        self.has_pos_emb = position_infused_attn
-        self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
-        self.rotary_pos_emb = always(None)
-
-        assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
-        self.rel_pos = None
-
-        self.pre_norm = pre_norm
-
-        self.residual_attn = residual_attn
-        self.cross_residual_attn = cross_residual_attn
-
-        norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
-        norm_class = RMSNorm if use_rmsnorm else norm_class
-        norm_fn = partial(norm_class, dim)
-
-        norm_fn = nn.Identity if use_rezero else norm_fn
-        branch_fn = Rezero if use_rezero else None
-
-        if cross_attend and not only_cross:
-            default_block = ('a', 'c', 'f')
-        elif cross_attend and only_cross:
-            default_block = ('c', 'f')
-        else:
-            default_block = ('a', 'f')
-
-        if macaron:
-            default_block = ('f',) + default_block
-
-        if exists(custom_layers):
-            layer_types = custom_layers
-        elif exists(par_ratio):
-            par_depth = depth * len(default_block)
-            assert 1 < par_ratio <= par_depth, 'par ratio out of range'
-            default_block = tuple(filter(not_equals('f'), default_block))
-            par_attn = par_depth // par_ratio
-            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
-            par_width = (depth_cut + depth_cut // par_attn) // par_attn
-            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
-            par_block = default_block + ('f',) * (par_width - len(default_block))
-            par_head = par_block * par_attn
-            layer_types = par_head + ('f',) * (par_depth - len(par_head))
-        elif exists(sandwich_coef):
-            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
-            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
-        else:
-            layer_types = default_block * depth
-
-        self.layer_types = layer_types
-        self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
-
-        for layer_type in self.layer_types:
-            if layer_type == 'a':
-                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
-            elif layer_type == 'c':
-                layer = Attention(dim, heads=heads, **attn_kwargs)
-            elif layer_type == 'f':
-                layer = FeedForward(dim, **ff_kwargs)
-                layer = layer if not macaron else Scale(0.5, layer)
-            else:
-                raise Exception(f'invalid layer type {layer_type}')
-
-            if isinstance(layer, Attention) and exists(branch_fn):
-                layer = branch_fn(layer)
-
-            if gate_residual:
-                residual_fn = GRUGating(dim)
-            else:
-                residual_fn = Residual()
-
-            self.layers.append(nn.ModuleList([
-                norm_fn(),
-                layer,
-                residual_fn
-            ]))
-
-    def forward(
-            self,
-            x,
-            context=None,
-            mask=None,
-            context_mask=None,
-            mems=None,
-            return_hiddens=False
-    ):
-        hiddens = []
-        intermediates = []
-        prev_attn = None
-        prev_cross_attn = None
-
-        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
-
-        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
-            is_last = ind == (len(self.layers) - 1)
-
-            if layer_type == 'a':
-                hiddens.append(x)
-                layer_mem = mems.pop(0)
-
-            residual = x
-
-            if self.pre_norm:
-                x = norm(x)
-
-            if layer_type == 'a':
-                out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
-                                   prev_attn=prev_attn, mem=layer_mem)
-            elif layer_type == 'c':
-                out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
-            elif layer_type == 'f':
-                out = block(x)
-
-            x = residual_fn(out, residual)
-
-            if layer_type in ('a', 'c'):
-                intermediates.append(inter)
-
-            if layer_type == 'a' and self.residual_attn:
-                prev_attn = inter.pre_softmax_attn
-            elif layer_type == 'c' and self.cross_residual_attn:
-                prev_cross_attn = inter.pre_softmax_attn
-
-            if not self.pre_norm and not is_last:
-                x = norm(x)
-
-        if return_hiddens:
-            intermediates = LayerIntermediates(
-                hiddens=hiddens,
-                attn_intermediates=intermediates
-            )
-
-            return x, intermediates
-
-        return x
-
-
-class Encoder(AttentionLayers):
-    def __init__(self, **kwargs):
-        assert 'causal' not in kwargs, 'cannot set causality on encoder'
-        super().__init__(causal=False, **kwargs)
-
-
-
-class TransformerWrapper(nn.Module):
-    def __init__(
-            self,
-            *,
-            num_tokens,
-            max_seq_len,
-            attn_layers,
-            emb_dim=None,
-            max_mem_len=0.,
-            emb_dropout=0.,
-            num_memory_tokens=None,
-            tie_embedding=False,
-            use_pos_emb=True
-    ):
-        super().__init__()
-        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
-
-        dim = attn_layers.dim
-        emb_dim = default(emb_dim, dim)
-
-        self.max_seq_len = max_seq_len
-        self.max_mem_len = max_mem_len
-        self.num_tokens = num_tokens
-
-        self.token_emb = nn.Embedding(num_tokens, emb_dim)
-        self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
-                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
-        self.emb_dropout = nn.Dropout(emb_dropout)
-
-        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
-        self.attn_layers = attn_layers
-        self.norm = nn.LayerNorm(dim)
-
-        self.init_()
-
-        self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
-
-        # memory tokens (like [cls]) from Memory Transformers paper
-        num_memory_tokens = default(num_memory_tokens, 0)
-        self.num_memory_tokens = num_memory_tokens
-        if num_memory_tokens > 0:
-            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
-
-            # let funnel encoder know number of memory tokens, if specified
-            if hasattr(attn_layers, 'num_memory_tokens'):
-                attn_layers.num_memory_tokens = num_memory_tokens
-
-    def init_(self):
-        nn.init.normal_(self.token_emb.weight, std=0.02)
-
-    def forward(
-            self,
-            x,
-            return_embeddings=False,
-            mask=None,
-            return_mems=False,
-            return_attn=False,
-            mems=None,
-            **kwargs
-    ):
-        b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
-        x = self.token_emb(x)
-        x += self.pos_emb(x)
-        x = self.emb_dropout(x)
-
-        x = self.project_emb(x)
-
-        if num_mem > 0:
-            mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
-            x = torch.cat((mem, x), dim=1)
-
-            # auto-handle masking after appending memory tokens
-            if exists(mask):
-                mask = F.pad(mask, (num_mem, 0), value=True)
-
-        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
-        x = self.norm(x)
-
-        mem, x = x[:, :num_mem], x[:, num_mem:]
-
-        out = self.to_logits(x) if not return_embeddings else x
-
-        if return_mems:
-            hiddens = intermediates.hiddens
-            new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
-            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
-            return out, new_mems
-
-        if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
-            return out, attn_maps
-
-        return out
-

spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/qformer_causual.py

@@ -1,1169 +0,0 @@
-"""
- * Copyright (c) 2023, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
- * Based on huggingface code base
- * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
-"""
-
-import math
-import os
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple, Dict, Any
-
-import torch
-from torch import Tensor, device, dtype, nn
-import torch.utils.checkpoint
-from torch.nn import CrossEntropyLoss
-import torch.nn.functional as F
-import numpy as np
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
-    ModelOutput, )
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    NextSentencePredictorOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    apply_chunking_to_forward,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import logging
-from transformers.models.bert.configuration_bert import BertConfig
-
-#torch.set_printoptions(profile="full")
-logger = logging.get_logger(__name__)
-
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word and position embeddings."""
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-
-        self.config = config
-
-    def forward(
-        self,
-        input_ids=None,
-        position_ids=None,
-        query_embeds=None,
-        past_key_values_length=0,
-    ):
-        if input_ids is not None:
-            seq_length = input_ids.size()[1]
-        else:
-            seq_length = 0
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length:seq_length + past_key_values_length].clone()
-
-        if input_ids is not None:
-            embeddings = self.word_embeddings(input_ids)
-            if self.position_embedding_type == "absolute":
-                position_embeddings = self.position_embeddings(position_ids)
-                embeddings = embeddings + position_embeddings
-
-            if query_embeds is not None:
-                embeddings = torch.cat((query_embeds, embeddings), dim=1)
-                #print(query_embeds.shape, embeddings.shape)
-        else:
-            embeddings = query_embeds
-
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config, is_cross_attention):
-        super().__init__()
-        self.config = config
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError("The hidden size (%d) is not a multiple of the number of attention "
-                             "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        if is_cross_attention:
-            self.key = nn.Linear(config.encoder_width, self.all_head_size)
-            self.value = nn.Linear(config.encoder_width, self.all_head_size)
-        else:
-            self.key = nn.Linear(config.hidden_size, self.all_head_size)
-            self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if (self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query"):
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-        self.save_attention = False
-
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-
-    def get_attn_gradients(self):
-        return self.attn_gradients
-
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-
-    def get_attention_map(self):
-        return self.attention_map
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (
-            self.num_attention_heads,
-            self.attention_head_size,
-        )
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-        if is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            #print(key_layer.shape, value_layer.shape)
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-            #print(past_key_value[0].shape, key_layer.shape)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        mixed_query_layer = self.query(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        # if  past_key_value is not None:
-        #     print(query_layer.shape)
-
-        past_key_value = (key_layer, value_layer)
-        #print(key_layer.shape, value_layer.shape)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        #if is_cross_attention:
-        # if attention_scores.shape[2] == 32:
-        #     attention_scores_save = attention_scores[0].detach().cpu().numpy()
-        #     print(attention_scores_save.shape)
-        #     np.save('attention_scores_causal_text_child.npy', attention_scores_save)
-
-        if (self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query"):
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = (attention_scores + relative_position_scores_query + relative_position_scores_key)
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        if is_cross_attention and self.save_attention:
-            self.save_attention_map(attention_probs)
-            attention_probs.register_hook(self.save_attn_gradients)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs_dropped = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs_dropped = attention_probs_dropped * head_mask
-
-        context_layer = torch.matmul(attention_probs_dropped, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size, )
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = ((context_layer, attention_probs) if output_attentions else (context_layer, ))
-
-        outputs = outputs + (past_key_value, )
-        return outputs
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config, is_cross_attention=False):
-        super().__init__()
-        self.self = BertSelfAttention(config, is_cross_attention)
-        self.output = BertSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads,
-            self.self.num_attention_heads,
-            self.self.attention_head_size,
-            self.pruned_heads,
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = (self.self.attention_head_size * self.self.num_attention_heads)
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-
-        outputs = (attention_output, ) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, layer_num):
-        super().__init__()
-        self.config = config
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = BertAttention(config)
-        self.layer_num = layer_num
-        if (self.config.add_cross_attention and layer_num % self.config.cross_attention_freq == 0):
-            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
-            self.has_cross_attention = True
-        else:
-            self.has_cross_attention = False
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-        self.intermediate_query = BertIntermediate(config)
-        self.output_query = BertOutput(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-        query_length=0,
-    ):
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = (past_key_value[:2] if past_key_value is not None else None)
-        # if past_key_value is not None:
-        #     print(hidden_states.shape, attention_mask.shape)
-        #print(hidden_states.shape, attention_mask.shape)
-        # casual attention for query embeds with self attention
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        #print('attention_mask', attention_mask.shape)
-        # if attention_mask.shape[-1] == 77:
-        #     print('attention_mask', attention_mask[0])
-        attention_output = self_attention_outputs[0]
-        outputs = self_attention_outputs[1:-1]
-
-        present_key_value = self_attention_outputs[-1]
-        #print(present_key_value[0].shape)
-
-        if query_length > 0:
-            query_attention_output = attention_output[:, :query_length, :]
-
-            if self.has_cross_attention:
-                assert (encoder_hidden_states is not None), "encoder_hidden_states must be given for cross-attention layers"
-                #print(attention_mask.shape)
-                cross_attention_outputs = self.crossattention(
-                    query_attention_output,
-                    attention_mask,
-                    head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    output_attentions=output_attentions,
-                )
-                query_attention_output = cross_attention_outputs[0]
-                outputs = (outputs + cross_attention_outputs[1:-1])  # add cross attentions if we output attention weights
-
-            layer_output = apply_chunking_to_forward(
-                self.feed_forward_chunk_query,
-                self.chunk_size_feed_forward,
-                self.seq_len_dim,
-                query_attention_output,
-            )
-            if attention_output.shape[1] > query_length:
-                layer_output_text = apply_chunking_to_forward(
-                    self.feed_forward_chunk,
-                    self.chunk_size_feed_forward,
-                    self.seq_len_dim,
-                    attention_output[:, query_length:, :],
-                )
-                layer_output = torch.cat([layer_output, layer_output_text], dim=1)
-        else:
-            layer_output = apply_chunking_to_forward(
-                self.feed_forward_chunk,
-                self.chunk_size_feed_forward,
-                self.seq_len_dim,
-                attention_output,
-            )
-        outputs = (layer_output, ) + outputs
-
-        outputs = outputs + (present_key_value, )
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-    def feed_forward_chunk_query(self, attention_output):
-        intermediate_output = self.intermediate_query(attention_output)
-        layer_output = self.output_query(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([BertLayer(config, i) for i in range(config.num_hidden_layers)])
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-        query_length=0,
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = (() if output_attentions and self.config.add_cross_attention else None)
-
-        next_decoder_cache = () if use_cache else None
-
-        for i in range(self.config.num_hidden_layers):
-            layer_module = self.layer[i]
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states, )
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-            # if past_key_value is not None:
-            #     print(past_key_value[0].shape, past_key_value[1].shape)
-
-            if getattr(self.config, "gradient_checkpointing", False) and self.training:
-
-                if use_cache:
-                    logger.warn("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
-                    use_cache = False
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions, query_length)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                    query_length,
-                )
-                # if past_key_value is not None:
-                #     print(hidden_states.shape, attention_mask.shape)
-                #     print(len(past_key_value))
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1], )
-                #print(layer_outputs[-1][0].shape)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1], )
-                all_cross_attentions = all_cross_attentions + (layer_outputs[2], )
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states, )
-
-        if not return_dict:
-            return tuple(v for v in [
-                hidden_states,
-                next_decoder_cache,
-                all_hidden_states,
-                all_self_attentions,
-                all_cross_attentions,
-            ] if v is not None)
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = BertLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertConfig
-    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-
-class BertModel(BertPreTrainedModel):
-    """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
-    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
-    input to the forward pass.
-    """
-    def __init__(self, config, add_pooling_layer=False):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertEmbeddings(config)
-
-        self.encoder = BertEncoder(config)
-
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    def get_extended_attention_mask(
-        self,
-        attention_mask: Tensor,
-        input_shape: Tuple[int],
-        device: device,
-        is_decoder: bool,
-        is_casual: bool,
-        has_query: bool = False,
-    ) -> Tensor:
-        """
-        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
-
-        Arguments:
-            attention_mask (:obj:`torch.Tensor`):
-                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
-            input_shape (:obj:`Tuple[int]`):
-                The shape of the input to the model.
-            device: (:obj:`torch.device`):
-                The device of the input to the model.
-
-        Returns:
-            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
-        """
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        #print(attention_mask.dim())
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            # Provided a padding mask of dimensions [batch_size, seq_length]
-            # - if the model is a decoder, apply a causal mask in addition to the padding mask
-            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if is_decoder or is_casual:
-                batch_size, seq_length = input_shape
-                #print(input_shape)
-                if not is_decoder and seq_length > 32:
-                    query_length = 32
-                    text_length = seq_length - query_length
-                    query_ids = torch.arange(query_length, device=device)
-                    query_causal_mask = (query_ids[None, None, :].repeat(batch_size, query_length, 1) <= query_ids[None, :,
-                                                                                                                   None])
-                    causal_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
-                    causal_mask[:, :query_length, :query_length] = query_causal_mask
-                    # print(query_causal_mask.shape, causal_mask.shape)
-                    #print(causal_mask[0])
-
-                else:
-                    seq_ids = torch.arange(seq_length, device=device)
-                    causal_mask = (seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None])
-
-                # add a prefix ones mask to the causal mask
-                # causal and attention masks must have same type with pytorch version < 1.3
-                causal_mask = causal_mask.to(attention_mask.dtype)
-                # if is_decoder:
-                #     print(causal_mask.shape, attention_mask.shape)
-                #print(causal_mask.shape, attention_mask.shape)
-
-                if causal_mask.shape[1] < attention_mask.shape[1]:
-                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
-                    if has_query:  # UniLM style attention mask
-                        causal_mask = torch.cat(
-                            [
-                                torch.zeros(
-                                    (batch_size, prefix_seq_len, seq_length),
-                                    device=device,
-                                    dtype=causal_mask.dtype,
-                                ),
-                                causal_mask,
-                            ],
-                            axis=1,
-                        )
-                    causal_mask = torch.cat(
-                        [
-                            torch.ones(
-                                (batch_size, causal_mask.shape[1], prefix_seq_len),
-                                device=device,
-                                dtype=causal_mask.dtype,
-                            ),
-                            causal_mask,
-                        ],
-                        axis=-1,
-                    )
-                    #print(has_query, causal_mask.shape)
-                #print(causal_mask[0])
-                extended_attention_mask = (causal_mask[:, None, :, :] * attention_mask[:, None, None, :])
-                #print(extended_attention_mask[0])
-                #print('extended_attention_mask', extended_attention_mask.shape)
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-                #print(attention_mask.shape, extended_attention_mask.shape)
-        else:
-            raise ValueError("Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
-                input_shape, attention_mask.shape))
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        return extended_attention_mask
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        query_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=False,
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        output_attentions = (output_attentions if output_attentions is not None else self.config.output_attentions)
-        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
-        return_dict = (return_dict if return_dict is not None else self.config.use_return_dict)
-
-        # use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        if input_ids is None:
-            assert (query_embeds is not None), "You have to specify query_embeds when input_ids is None"
-
-        #if query_embeds is not None:
-        if query_embeds is not None and query_embeds.shape[1] == 32:
-            is_casual = True
-        else:
-            is_casual = False
-        past_key_values_length = (past_key_values[0][0].shape[2] -
-                                  self.config.query_length if past_key_values is not None else 0)
-
-        query_length = query_embeds.shape[1] if query_embeds is not None else 0
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            query_embeds=query_embeds,
-            past_key_values_length=past_key_values_length,
-        )
-
-        input_shape = embedding_output.size()[:-1]
-        batch_size, seq_length = input_shape
-        device = embedding_output.device
-
-        #print('attention_mask', attention_mask)
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-            #print(seq_length, past_key_values_length)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if is_decoder:
-            #print(attention_mask.shape, input_ids.shape)
-            extended_attention_mask = self.get_extended_attention_mask(
-                attention_mask,
-                input_ids.shape,
-                device,
-                is_decoder,
-                is_casual,
-                has_query=(query_embeds is not None),
-            )
-        else:
-            extended_attention_mask = self.get_extended_attention_mask(
-                attention_mask,
-                input_shape,
-                device,
-                is_decoder,
-                is_casual,
-            )
-        #print(is_decoder, extended_attention_mask.shape)
-        # if is_decoder:
-        #     print(extended_attention_mask[0,0,:,32:])
-        # if attention_mask is not None:
-        #     print(input_ids, embedding_output.shape, extended_attention_mask.shape)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if encoder_hidden_states is not None:
-            if type(encoder_hidden_states) == list:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
-            else:
-                (
-                    encoder_batch_size,
-                    encoder_sequence_length,
-                    _,
-                ) = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-
-            if type(encoder_attention_mask) == list:
-                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
-            elif encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-            else:
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-                #print(is_casual, extended_attention_mask.shape, encoder_attention_mask.shape, encoder_extended_attention_mask.shape)
-        else:
-            encoder_extended_attention_mask = None
-
-        # if input_ids is not None and query_embeds is not None:
-        #     print(extended_attention_mask.shape, encoder_extended_attention_mask.shape)
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-        #print(head_mask)
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            query_length=query_length,
-        )
-        # if is_decoder:
-        #     print(embedding_output.shape, attention_mask.shape, len(past_key_values))
-        #print(embedding_output.shape, extended_attention_mask.shape, encoder_hidden_states.shape, encoder_extended_attention_mask.shape)
-        #print(extended_attention_mask[0], encoder_extended_attention_mask[0])
-
-        #print(query_embeds.shape, encoder_hidden_states.shape)
-
-        sequence_output = encoder_outputs[0]
-        pooled_output = (self.pooler(sequence_output) if self.pooler is not None else None)
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-class BertLMHeadModel(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        query_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        past_key_values=None,
-        use_cache=True,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        return_logits=False,
-        is_decoder=True,
-        reduction="mean",
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
-            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
-            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        Returns:
-        Example::
-            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
-            >>> import torch
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-            >>> config = BertConfig.from_pretrained("bert-base-cased")
-            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
-            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-            >>> outputs = model(**inputs)
-            >>> prediction_logits = outputs.logits
-        """
-        return_dict = (return_dict if return_dict is not None else self.config.use_return_dict)
-        if labels is not None:
-            use_cache = False
-        if past_key_values is not None:
-            query_embeds = None
-        #print(len(past_key_values))
-        #print('attention_mask', attention_mask)
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            query_embeds=query_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-        )
-
-        sequence_output = outputs[0]
-        if query_embeds is not None:
-            sequence_output = outputs[0][:, query_embeds.shape[1]:, :]
-
-        prediction_scores = self.cls(sequence_output)
-
-        if return_logits:
-            return prediction_scores[:, :-1, :].contiguous()
-
-        lm_loss = None
-        if labels is not None:
-            # we are doing next-token prediction; shift prediction scores and input ids by one
-            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
-            labels = labels[:, 1:].contiguous()
-            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
-            lm_loss = loss_fct(
-                shifted_prediction_scores.view(-1, self.config.vocab_size),
-                labels.view(-1),
-            )
-            if reduction == "none":
-                lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)
-
-        if not return_dict:
-            output = (prediction_scores, ) + outputs[2:]
-            return ((lm_loss, ) + output) if lm_loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=lm_loss,
-            logits=prediction_scores,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, query_embeds, past=None, attention_mask=None, **model_kwargs):
-        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
-        if attention_mask is None:
-            attention_mask = input_ids.new_ones(input_ids.shape)
-        query_mask = input_ids.new_ones(query_embeds.shape[:-1])
-        attention_mask = torch.cat([query_mask, attention_mask], dim=-1)
-
-        # cut decoder_input_ids if past is used
-        if past is not None:
-            input_ids = input_ids[:, -1:]
-
-        return {
-            "input_ids": input_ids,
-            "query_embeds": query_embeds,
-            "attention_mask": attention_mask,
-            "past_key_values": past,
-            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
-            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
-            "is_decoder": True,
-        }
-
-    def _reorder_cache(self, past, beam_idx):
-        reordered_past = ()
-        for layer_past in past:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past), )
-        return reordered_past
-
-
-class BertForMaskedLM(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        query_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        return_logits=False,
-        is_decoder=False,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
-            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
-            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
-        """
-
-        return_dict = (return_dict if return_dict is not None else self.config.use_return_dict)
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            query_embeds=query_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-        )
-
-        if query_embeds is not None:
-            sequence_output = outputs[0][:, query_embeds.shape[1]:, :]
-        prediction_scores = self.cls(sequence_output)
-
-        if return_logits:
-            return prediction_scores
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()  # -100 index = padding token
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (prediction_scores, ) + outputs[2:]
-            return (((masked_lm_loss, ) + output) if masked_lm_loss is not None else output)
-
-        return MaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )

spaces/AIWaves/Software_Company/src/agents/Agent/__init__.py

@@ -1 +0,0 @@
-from .Agent import Agent

spaces/AP123/dreamgaussian/main.py

@@ -1,882 +0,0 @@
-import os
-import cv2
-import time
-import tqdm
-import numpy as np
-import dearpygui.dearpygui as dpg
-
-import torch
-import torch.nn.functional as F
-
-import rembg
-
-from cam_utils import orbit_camera, OrbitCamera
-from gs_renderer import Renderer, MiniCam
-
-from grid_put import mipmap_linear_grid_put_2d
-from mesh import Mesh, safe_normalize
-
-class GUI:
-    def __init__(self, opt):
-        self.opt = opt  # shared with the trainer's opt to support in-place modification of rendering parameters.
-        self.gui = opt.gui # enable gui
-        self.W = opt.W
-        self.H = opt.H
-        self.cam = OrbitCamera(opt.W, opt.H, r=opt.radius, fovy=opt.fovy)
-
-        self.mode = "image"
-        self.seed = "random"
-
-        self.buffer_image = np.ones((self.W, self.H, 3), dtype=np.float32)
-        self.need_update = True  # update buffer_image
-
-        # models
-        self.device = torch.device("cuda")
-        self.bg_remover = None
-
-        self.guidance_sd = None
-        self.guidance_zero123 = None
-
-        self.enable_sd = False
-        self.enable_zero123 = False
-
-        # renderer
-        self.renderer = Renderer(sh_degree=self.opt.sh_degree)
-        self.gaussain_scale_factor = 1
-
-        # input image
-        self.input_img = None
-        self.input_mask = None
-        self.input_img_torch = None
-        self.input_mask_torch = None
-        self.overlay_input_img = False
-        self.overlay_input_img_ratio = 0.5
-
-        # input text
-        self.prompt = ""
-        self.negative_prompt = ""
-
-        # training stuff
-        self.training = False
-        self.optimizer = None
-        self.step = 0
-        self.train_steps = 1  # steps per rendering loop
-        
-        # load input data from cmdline
-        if self.opt.input is not None:
-            self.load_input(self.opt.input)
-        
-        # override prompt from cmdline
-        if self.opt.prompt is not None:
-            self.prompt = self.opt.prompt
-
-        # override if provide a checkpoint
-        if self.opt.load is not None:
-            self.renderer.initialize(self.opt.load)            
-        else:
-            # initialize gaussians to a blob
-            self.renderer.initialize(num_pts=self.opt.num_pts)
-
-        if self.gui:
-            dpg.create_context()
-            self.register_dpg()
-            self.test_step()
-
-    def __del__(self):
-        if self.gui:
-            dpg.destroy_context()
-
-    def seed_everything(self):
-        try:
-            seed = int(self.seed)
-        except:
-            seed = np.random.randint(0, 1000000)
-
-        os.environ["PYTHONHASHSEED"] = str(seed)
-        np.random.seed(seed)
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = True
-
-        self.last_seed = seed
-
-    def prepare_train(self):
-
-        self.step = 0
-
-        # setup training
-        self.renderer.gaussians.training_setup(self.opt)
-        # do not do progressive sh-level
-        self.renderer.gaussians.active_sh_degree = self.renderer.gaussians.max_sh_degree
-        self.optimizer = self.renderer.gaussians.optimizer
-
-        # default camera
-        pose = orbit_camera(self.opt.elevation, 0, self.opt.radius)
-        self.fixed_cam = MiniCam(
-            pose,
-            self.opt.ref_size,
-            self.opt.ref_size,
-            self.cam.fovy,
-            self.cam.fovx,
-            self.cam.near,
-            self.cam.far,
-        )
-
-        self.enable_sd = self.opt.lambda_sd > 0 and self.prompt != ""
-        self.enable_zero123 = self.opt.lambda_zero123 > 0 and self.input_img is not None
-
-        # lazy load guidance model
-        if self.guidance_sd is None and self.enable_sd:
-            print(f"[INFO] loading SD...")
-            from guidance.sd_utils import StableDiffusion
-            self.guidance_sd = StableDiffusion(self.device)
-            print(f"[INFO] loaded SD!")
-
-        if self.guidance_zero123 is None and self.enable_zero123:
-            print(f"[INFO] loading zero123...")
-            from guidance.zero123_utils import Zero123
-            self.guidance_zero123 = Zero123(self.device)
-            print(f"[INFO] loaded zero123!")
-
-        # input image
-        if self.input_img is not None:
-            self.input_img_torch = torch.from_numpy(self.input_img).permute(2, 0, 1).unsqueeze(0).to(self.device)
-            self.input_img_torch = F.interpolate(self.input_img_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False)
-
-            self.input_mask_torch = torch.from_numpy(self.input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device)
-            self.input_mask_torch = F.interpolate(self.input_mask_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False)
-
-        # prepare embeddings
-        with torch.no_grad():
-
-            if self.enable_sd:
-                self.guidance_sd.get_text_embeds([self.prompt], [self.negative_prompt])
-
-            if self.enable_zero123:
-                self.guidance_zero123.get_img_embeds(self.input_img_torch)
-
-    def train_step(self):
-        starter = torch.cuda.Event(enable_timing=True)
-        ender = torch.cuda.Event(enable_timing=True)
-        starter.record()
-
-        for _ in range(self.train_steps):
-
-            self.step += 1
-            step_ratio = min(1, self.step / self.opt.iters)
-
-            # update lr
-            self.renderer.gaussians.update_learning_rate(self.step)
-
-            loss = 0
-
-            ### known view
-            if self.input_img_torch is not None:
-                cur_cam = self.fixed_cam
-                out = self.renderer.render(cur_cam)
-
-                # rgb loss
-                image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
-                loss = loss + 10000 * step_ratio * F.mse_loss(image, self.input_img_torch)
-
-                # mask loss
-                mask = out["alpha"].unsqueeze(0) # [1, 1, H, W] in [0, 1]
-                loss = loss + 1000 * step_ratio * F.mse_loss(mask, self.input_mask_torch)
-
-            ### novel view (manual batch)
-            render_resolution = 128 if step_ratio < 0.3 else (256 if step_ratio < 0.6 else 512)
-            images = []
-            vers, hors, radii = [], [], []
-            # avoid too large elevation (> 80 or < -80), and make sure it always cover [-30, 30]
-            min_ver = max(min(-30, -30 - self.opt.elevation), -80 - self.opt.elevation)
-            max_ver = min(max(30, 30 - self.opt.elevation), 80 - self.opt.elevation)
-            for _ in range(self.opt.batch_size):
-
-                # render random view
-                ver = np.random.randint(min_ver, max_ver)
-                hor = np.random.randint(-180, 180)
-                radius = 0
-
-                vers.append(ver)
-                hors.append(hor)
-                radii.append(radius)
-
-                pose = orbit_camera(self.opt.elevation + ver, hor, self.opt.radius + radius)
-
-                cur_cam = MiniCam(
-                    pose,
-                    render_resolution,
-                    render_resolution,
-                    self.cam.fovy,
-                    self.cam.fovx,
-                    self.cam.near,
-                    self.cam.far,
-                )
-
-                invert_bg_color = np.random.rand() > self.opt.invert_bg_prob
-                out = self.renderer.render(cur_cam, invert_bg_color=invert_bg_color)
-
-                image = out["image"].unsqueeze(0)# [1, 3, H, W] in [0, 1]
-                images.append(image)
-            
-            images = torch.cat(images, dim=0)
-
-            # import kiui
-            # kiui.lo(hor, ver)
-            # kiui.vis.plot_image(image)
-
-            # guidance loss
-            if self.enable_sd:
-                loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, step_ratio)
-
-            if self.enable_zero123:
-                loss = loss + self.opt.lambda_zero123 * self.guidance_zero123.train_step(images, vers, hors, radii, step_ratio)
-            
-            # optimize step
-            loss.backward()
-            self.optimizer.step()
-            self.optimizer.zero_grad()
-
-            # densify and prune
-            if self.step >= self.opt.density_start_iter and self.step <= self.opt.density_end_iter:
-                viewspace_point_tensor, visibility_filter, radii = out["viewspace_points"], out["visibility_filter"], out["radii"]
-                self.renderer.gaussians.max_radii2D[visibility_filter] = torch.max(self.renderer.gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
-                self.renderer.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)
-
-                if self.step % self.opt.densification_interval == 0:
-                    # size_threshold = 20 if self.step > self.opt.opacity_reset_interval else None
-                    self.renderer.gaussians.densify_and_prune(self.opt.densify_grad_threshold, min_opacity=0.01, extent=0.5, max_screen_size=1)
-                
-                if self.step % self.opt.opacity_reset_interval == 0:
-                    self.renderer.gaussians.reset_opacity()
-
-        ender.record()
-        torch.cuda.synchronize()
-        t = starter.elapsed_time(ender)
-
-        self.need_update = True
-
-        if self.gui:
-            dpg.set_value("_log_train_time", f"{t:.4f}ms")
-            dpg.set_value(
-                "_log_train_log",
-                f"step = {self.step: 5d} (+{self.train_steps: 2d}) loss = {loss.item():.4f}",
-            )
-
-        # dynamic train steps (no need for now)
-        # max allowed train time per-frame is 500 ms
-        # full_t = t / self.train_steps * 16
-        # train_steps = min(16, max(4, int(16 * 500 / full_t)))
-        # if train_steps > self.train_steps * 1.2 or train_steps < self.train_steps * 0.8:
-        #     self.train_steps = train_steps
-
-    @torch.no_grad()
-    def test_step(self):
-        # ignore if no need to update
-        if not self.need_update:
-            return
-
-        starter = torch.cuda.Event(enable_timing=True)
-        ender = torch.cuda.Event(enable_timing=True)
-        starter.record()
-
-        # should update image
-        if self.need_update:
-            # render image
-
-            cur_cam = MiniCam(
-                self.cam.pose,
-                self.W,
-                self.H,
-                self.cam.fovy,
-                self.cam.fovx,
-                self.cam.near,
-                self.cam.far,
-            )
-
-            out = self.renderer.render(cur_cam, self.gaussain_scale_factor)
-
-            buffer_image = out[self.mode]  # [3, H, W]
-
-            if self.mode in ['depth', 'alpha']:
-                buffer_image = buffer_image.repeat(3, 1, 1)
-                if self.mode == 'depth':
-                    buffer_image = (buffer_image - buffer_image.min()) / (buffer_image.max() - buffer_image.min() + 1e-20)
-
-            buffer_image = F.interpolate(
-                buffer_image.unsqueeze(0),
-                size=(self.H, self.W),
-                mode="bilinear",
-                align_corners=False,
-            ).squeeze(0)
-
-            self.buffer_image = (
-                buffer_image.permute(1, 2, 0)
-                .contiguous()
-                .clamp(0, 1)
-                .contiguous()
-                .detach()
-                .cpu()
-                .numpy()
-            )
-
-            # display input_image
-            if self.overlay_input_img and self.input_img is not None:
-                self.buffer_image = (
-                    self.buffer_image * (1 - self.overlay_input_img_ratio)
-                    + self.input_img * self.overlay_input_img_ratio
-                )
-
-            self.need_update = False
-
-        ender.record()
-        torch.cuda.synchronize()
-        t = starter.elapsed_time(ender)
-
-        if self.gui:
-            dpg.set_value("_log_infer_time", f"{t:.4f}ms ({int(1000/t)} FPS)")
-            dpg.set_value(
-                "_texture", self.buffer_image
-            )  # buffer must be contiguous, else seg fault!
-
-    
-    def load_input(self, file):
-        # load image
-        print(f'[INFO] load image from {file}...')
-        img = cv2.imread(file, cv2.IMREAD_UNCHANGED)
-        if img.shape[-1] == 3:
-            if self.bg_remover is None:
-                self.bg_remover = rembg.new_session()
-            img = rembg.remove(img, session=self.bg_remover)
-
-        img = cv2.resize(img, (self.W, self.H), interpolation=cv2.INTER_AREA)
-        img = img.astype(np.float32) / 255.0
-
-        self.input_mask = img[..., 3:]
-        # white bg
-        self.input_img = img[..., :3] * self.input_mask + (1 - self.input_mask)
-        # bgr to rgb
-        self.input_img = self.input_img[..., ::-1].copy()
-
-        # load prompt
-        file_prompt = file.replace("_rgba.png", "_caption.txt")
-        if os.path.exists(file_prompt):
-            print(f'[INFO] load prompt from {file_prompt}...')
-            with open(file_prompt, "r") as f:
-                self.prompt = f.read().strip()
-
-    @torch.no_grad()
-    def save_model(self, mode='geo', texture_size=1024):
-        os.makedirs(self.opt.outdir, exist_ok=True)
-        if mode == 'geo':
-            path = os.path.join(self.opt.outdir, self.opt.save_path + '_mesh.ply')
-            mesh = self.renderer.gaussians.extract_mesh(path, self.opt.density_thresh)
-            mesh.write_ply(path)
-
-        elif mode == 'geo+tex':
-            path = os.path.join(self.opt.outdir, self.opt.save_path + '_mesh.obj')
-            mesh = self.renderer.gaussians.extract_mesh(path, self.opt.density_thresh)
-
-            # perform texture extraction
-            print(f"[INFO] unwrap uv...")
-            h = w = texture_size
-            mesh.auto_uv()
-            mesh.auto_normal()
-
-            albedo = torch.zeros((h, w, 3), device=self.device, dtype=torch.float32)
-            cnt = torch.zeros((h, w, 1), device=self.device, dtype=torch.float32)
-
-            # self.prepare_train() # tmp fix for not loading 0123
-            # vers = [0]
-            # hors = [0]
-            vers = [0] * 8 + [-45] * 8 + [45] * 8 + [-89.9, 89.9]
-            hors = [0, 45, -45, 90, -90, 135, -135, 180] * 3 + [0, 0]
-
-            render_resolution = 512
-
-            import nvdiffrast.torch as dr
-
-            if not self.opt.gui or os.name == 'nt':
-                glctx = dr.RasterizeGLContext()
-            else:
-                glctx = dr.RasterizeCudaContext()
-
-            for ver, hor in zip(vers, hors):
-                # render image
-                pose = orbit_camera(ver, hor, self.cam.radius)
-
-                cur_cam = MiniCam(
-                    pose,
-                    render_resolution,
-                    render_resolution,
-                    self.cam.fovy,
-                    self.cam.fovx,
-                    self.cam.near,
-                    self.cam.far,
-                )
-                
-                cur_out = self.renderer.render(cur_cam)
-
-                rgbs = cur_out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
-
-                # enhance texture quality with zero123 [not working well]
-                # if self.opt.guidance_model == 'zero123':
-                #     rgbs = self.guidance.refine(rgbs, [ver], [hor], [0])
-                    # import kiui
-                    # kiui.vis.plot_image(rgbs)
-                    
-                # get coordinate in texture image
-                pose = torch.from_numpy(pose.astype(np.float32)).to(self.device)
-                proj = torch.from_numpy(self.cam.perspective.astype(np.float32)).to(self.device)
-
-                v_cam = torch.matmul(F.pad(mesh.v, pad=(0, 1), mode='constant', value=1.0), torch.inverse(pose).T).float().unsqueeze(0)
-                v_clip = v_cam @ proj.T
-                rast, rast_db = dr.rasterize(glctx, v_clip, mesh.f, (render_resolution, render_resolution))
-
-                depth, _ = dr.interpolate(-v_cam[..., [2]], rast, mesh.f) # [1, H, W, 1]
-                depth = depth.squeeze(0) # [H, W, 1]
-
-                alpha = (rast[0, ..., 3:] > 0).float()
-
-                uvs, _ = dr.interpolate(mesh.vt.unsqueeze(0), rast, mesh.ft)  # [1, 512, 512, 2] in [0, 1]
-
-                # use normal to produce a back-project mask
-                normal, _ = dr.interpolate(mesh.vn.unsqueeze(0).contiguous(), rast, mesh.fn)
-                normal = safe_normalize(normal[0])
-
-                # rotated normal (where [0, 0, 1] always faces camera)
-                rot_normal = normal @ pose[:3, :3]
-                viewcos = rot_normal[..., [2]]
-
-                mask = (alpha > 0) & (viewcos > 0.5)  # [H, W, 1]
-                mask = mask.view(-1)
-
-                uvs = uvs.view(-1, 2).clamp(0, 1)[mask]
-                rgbs = rgbs.view(3, -1).permute(1, 0)[mask].contiguous()
-                
-                # update texture image
-                cur_albedo, cur_cnt = mipmap_linear_grid_put_2d(
-                    h, w,
-                    uvs[..., [1, 0]] * 2 - 1,
-                    rgbs,
-                    min_resolution=256,
-                    return_count=True,
-                )
-                
-                # albedo += cur_albedo
-                # cnt += cur_cnt
-                mask = cnt.squeeze(-1) < 0.1
-                albedo[mask] += cur_albedo[mask]
-                cnt[mask] += cur_cnt[mask]
-
-            mask = cnt.squeeze(-1) > 0
-            albedo[mask] = albedo[mask] / cnt[mask].repeat(1, 3)
-
-            mask = mask.view(h, w)
-
-            albedo = albedo.detach().cpu().numpy()
-            mask = mask.detach().cpu().numpy()
-
-            # dilate texture
-            from sklearn.neighbors import NearestNeighbors
-            from scipy.ndimage import binary_dilation, binary_erosion
-
-            inpaint_region = binary_dilation(mask, iterations=32)
-            inpaint_region[mask] = 0
-
-            search_region = mask.copy()
-            not_search_region = binary_erosion(search_region, iterations=3)
-            search_region[not_search_region] = 0
-
-            search_coords = np.stack(np.nonzero(search_region), axis=-1)
-            inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1)
-
-            knn = NearestNeighbors(n_neighbors=1, algorithm="kd_tree").fit(
-                search_coords
-            )
-            _, indices = knn.kneighbors(inpaint_coords)
-
-            albedo[tuple(inpaint_coords.T)] = albedo[tuple(search_coords[indices[:, 0]].T)]
-
-            mesh.albedo = torch.from_numpy(albedo).to(self.device)
-            mesh.write(path)
-
-        else:
-            path = os.path.join(self.opt.outdir, self.opt.save_path + '_model.ply')
-            self.renderer.gaussians.save_ply(path)
-
-        print(f"[INFO] save model to {path}.")
-
-    def register_dpg(self):
-        ### register texture
-
-        with dpg.texture_registry(show=False):
-            dpg.add_raw_texture(
-                self.W,
-                self.H,
-                self.buffer_image,
-                format=dpg.mvFormat_Float_rgb,
-                tag="_texture",
-            )
-
-        ### register window
-
-        # the rendered image, as the primary window
-        with dpg.window(
-            tag="_primary_window",
-            width=self.W,
-            height=self.H,
-            pos=[0, 0],
-            no_move=True,
-            no_title_bar=True,
-            no_scrollbar=True,
-        ):
-            # add the texture
-            dpg.add_image("_texture")
-
-        # dpg.set_primary_window("_primary_window", True)
-
-        # control window
-        with dpg.window(
-            label="Control",
-            tag="_control_window",
-            width=600,
-            height=self.H,
-            pos=[self.W, 0],
-            no_move=True,
-            no_title_bar=True,
-        ):
-            # button theme
-            with dpg.theme() as theme_button:
-                with dpg.theme_component(dpg.mvButton):
-                    dpg.add_theme_color(dpg.mvThemeCol_Button, (23, 3, 18))
-                    dpg.add_theme_color(dpg.mvThemeCol_ButtonHovered, (51, 3, 47))
-                    dpg.add_theme_color(dpg.mvThemeCol_ButtonActive, (83, 18, 83))
-                    dpg.add_theme_style(dpg.mvStyleVar_FrameRounding, 5)
-                    dpg.add_theme_style(dpg.mvStyleVar_FramePadding, 3, 3)
-
-            # timer stuff
-            with dpg.group(horizontal=True):
-                dpg.add_text("Infer time: ")
-                dpg.add_text("no data", tag="_log_infer_time")
-
-            def callback_setattr(sender, app_data, user_data):
-                setattr(self, user_data, app_data)
-
-            # init stuff
-            with dpg.collapsing_header(label="Initialize", default_open=True):
-
-                # seed stuff
-                def callback_set_seed(sender, app_data):
-                    self.seed = app_data
-                    self.seed_everything()
-
-                dpg.add_input_text(
-                    label="seed",
-                    default_value=self.seed,
-                    on_enter=True,
-                    callback=callback_set_seed,
-                )
-
-                # input stuff
-                def callback_select_input(sender, app_data):
-                    # only one item
-                    for k, v in app_data["selections"].items():
-                        dpg.set_value("_log_input", k)
-                        self.load_input(v)
-
-                    self.need_update = True
-
-                with dpg.file_dialog(
-                    directory_selector=False,
-                    show=False,
-                    callback=callback_select_input,
-                    file_count=1,
-                    tag="file_dialog_tag",
-                    width=700,
-                    height=400,
-                ):
-                    dpg.add_file_extension("Images{.jpg,.jpeg,.png}")
-
-                with dpg.group(horizontal=True):
-                    dpg.add_button(
-                        label="input",
-                        callback=lambda: dpg.show_item("file_dialog_tag"),
-                    )
-                    dpg.add_text("", tag="_log_input")
-                
-                # overlay stuff
-                with dpg.group(horizontal=True):
-
-                    def callback_toggle_overlay_input_img(sender, app_data):
-                        self.overlay_input_img = not self.overlay_input_img
-                        self.need_update = True
-
-                    dpg.add_checkbox(
-                        label="overlay image",
-                        default_value=self.overlay_input_img,
-                        callback=callback_toggle_overlay_input_img,
-                    )
-
-                    def callback_set_overlay_input_img_ratio(sender, app_data):
-                        self.overlay_input_img_ratio = app_data
-                        self.need_update = True
-
-                    dpg.add_slider_float(
-                        label="ratio",
-                        min_value=0,
-                        max_value=1,
-                        format="%.1f",
-                        default_value=self.overlay_input_img_ratio,
-                        callback=callback_set_overlay_input_img_ratio,
-                    )
-
-                # prompt stuff
-            
-                dpg.add_input_text(
-                    label="prompt",
-                    default_value=self.prompt,
-                    callback=callback_setattr,
-                    user_data="prompt",
-                )
-
-                dpg.add_input_text(
-                    label="negative",
-                    default_value=self.negative_prompt,
-                    callback=callback_setattr,
-                    user_data="negative_prompt",
-                )
-
-                # save current model
-                with dpg.group(horizontal=True):
-                    dpg.add_text("Save: ")
-
-                    def callback_save(sender, app_data, user_data):
-                        self.save_model(mode=user_data)
-
-                    dpg.add_button(
-                        label="model",
-                        tag="_button_save_model",
-                        callback=callback_save,
-                        user_data='model',
-                    )
-                    dpg.bind_item_theme("_button_save_model", theme_button)
-
-                    dpg.add_button(
-                        label="geo",
-                        tag="_button_save_mesh",
-                        callback=callback_save,
-                        user_data='geo',
-                    )
-                    dpg.bind_item_theme("_button_save_mesh", theme_button)
-
-                    dpg.add_button(
-                        label="geo+tex",
-                        tag="_button_save_mesh_with_tex",
-                        callback=callback_save,
-                        user_data='geo+tex',
-                    )
-                    dpg.bind_item_theme("_button_save_mesh_with_tex", theme_button)
-
-                    dpg.add_input_text(
-                        label="",
-                        default_value=self.opt.save_path,
-                        callback=callback_setattr,
-                        user_data="save_path",
-                    )
-
-            # training stuff
-            with dpg.collapsing_header(label="Train", default_open=True):
-                # lr and train button
-                with dpg.group(horizontal=True):
-                    dpg.add_text("Train: ")
-
-                    def callback_train(sender, app_data):
-                        if self.training:
-                            self.training = False
-                            dpg.configure_item("_button_train", label="start")
-                        else:
-                            self.prepare_train()
-                            self.training = True
-                            dpg.configure_item("_button_train", label="stop")
-
-                    # dpg.add_button(
-                    #     label="init", tag="_button_init", callback=self.prepare_train
-                    # )
-                    # dpg.bind_item_theme("_button_init", theme_button)
-
-                    dpg.add_button(
-                        label="start", tag="_button_train", callback=callback_train
-                    )
-                    dpg.bind_item_theme("_button_train", theme_button)
-
-                with dpg.group(horizontal=True):
-                    dpg.add_text("", tag="_log_train_time")
-                    dpg.add_text("", tag="_log_train_log")
-
-            # rendering options
-            with dpg.collapsing_header(label="Rendering", default_open=True):
-                # mode combo
-                def callback_change_mode(sender, app_data):
-                    self.mode = app_data
-                    self.need_update = True
-
-                dpg.add_combo(
-                    ("image", "depth", "alpha"),
-                    label="mode",
-                    default_value=self.mode,
-                    callback=callback_change_mode,
-                )
-
-                # fov slider
-                def callback_set_fovy(sender, app_data):
-                    self.cam.fovy = np.deg2rad(app_data)
-                    self.need_update = True
-
-                dpg.add_slider_int(
-                    label="FoV (vertical)",
-                    min_value=1,
-                    max_value=120,
-                    format="%d deg",
-                    default_value=np.rad2deg(self.cam.fovy),
-                    callback=callback_set_fovy,
-                )
-
-                def callback_set_gaussain_scale(sender, app_data):
-                    self.gaussain_scale_factor = app_data
-                    self.need_update = True
-
-                dpg.add_slider_float(
-                    label="gaussain scale",
-                    min_value=0,
-                    max_value=1,
-                    format="%.2f",
-                    default_value=self.gaussain_scale_factor,
-                    callback=callback_set_gaussain_scale,
-                )
-
-        ### register camera handler
-
-        def callback_camera_drag_rotate_or_draw_mask(sender, app_data):
-            if not dpg.is_item_focused("_primary_window"):
-                return
-
-            dx = app_data[1]
-            dy = app_data[2]
-
-            self.cam.orbit(dx, dy)
-            self.need_update = True
-
-        def callback_camera_wheel_scale(sender, app_data):
-            if not dpg.is_item_focused("_primary_window"):
-                return
-
-            delta = app_data
-
-            self.cam.scale(delta)
-            self.need_update = True
-
-        def callback_camera_drag_pan(sender, app_data):
-            if not dpg.is_item_focused("_primary_window"):
-                return
-
-            dx = app_data[1]
-            dy = app_data[2]
-
-            self.cam.pan(dx, dy)
-            self.need_update = True
-
-        def callback_set_mouse_loc(sender, app_data):
-            if not dpg.is_item_focused("_primary_window"):
-                return
-
-            # just the pixel coordinate in image
-            self.mouse_loc = np.array(app_data)
-
-        with dpg.handler_registry():
-            # for camera moving
-            dpg.add_mouse_drag_handler(
-                button=dpg.mvMouseButton_Left,
-                callback=callback_camera_drag_rotate_or_draw_mask,
-            )
-            dpg.add_mouse_wheel_handler(callback=callback_camera_wheel_scale)
-            dpg.add_mouse_drag_handler(
-                button=dpg.mvMouseButton_Middle, callback=callback_camera_drag_pan
-            )
-
-        dpg.create_viewport(
-            title="Gaussian3D",
-            width=self.W + 600,
-            height=self.H + (45 if os.name == "nt" else 0),
-            resizable=False,
-        )
-
-        ### global theme
-        with dpg.theme() as theme_no_padding:
-            with dpg.theme_component(dpg.mvAll):
-                # set all padding to 0 to avoid scroll bar
-                dpg.add_theme_style(
-                    dpg.mvStyleVar_WindowPadding, 0, 0, category=dpg.mvThemeCat_Core
-                )
-                dpg.add_theme_style(
-                    dpg.mvStyleVar_FramePadding, 0, 0, category=dpg.mvThemeCat_Core
-                )
-                dpg.add_theme_style(
-                    dpg.mvStyleVar_CellPadding, 0, 0, category=dpg.mvThemeCat_Core
-                )
-
-        dpg.bind_item_theme("_primary_window", theme_no_padding)
-
-        dpg.setup_dearpygui()
-
-        ### register a larger font
-        # get it from: https://github.com/lxgw/LxgwWenKai/releases/download/v1.300/LXGWWenKai-Regular.ttf
-        if os.path.exists("LXGWWenKai-Regular.ttf"):
-            with dpg.font_registry():
-                with dpg.font("LXGWWenKai-Regular.ttf", 18) as default_font:
-                    dpg.bind_font(default_font)
-
-        # dpg.show_metrics()
-
-        dpg.show_viewport()
-
-    def render(self):
-        assert self.gui
-        while dpg.is_dearpygui_running():
-            # update texture every frame
-            if self.training:
-                self.train_step()
-            self.test_step()
-            dpg.render_dearpygui_frame()
-    
-    # no gui mode
-    def train(self, iters=500):
-        if iters > 0:
-            self.prepare_train()
-            for i in tqdm.trange(iters):
-                self.train_step()
-            # do a last prune
-            self.renderer.gaussians.prune(min_opacity=0.01, extent=1, max_screen_size=1)
-        # save
-        self.save_model(mode='model')
-        self.save_model(mode='geo+tex')
-        
-
-if __name__ == "__main__":
-    import argparse
-    from omegaconf import OmegaConf
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--config", required=True, help="path to the yaml config file")
-    args, extras = parser.parse_known_args()
-
-    # override default config from cli
-    opt = OmegaConf.merge(OmegaConf.load(args.config), OmegaConf.from_cli(extras))
-
-    gui = GUI(opt)
-
-    if opt.gui:
-        gui.render()
-    else:
-        gui.train(opt.iters)

spaces/AgentVerse/agentVerse/agentverse/memory/__init__.py

@@ -1,9 +0,0 @@
-from agentverse.registry import Registry
-
-memory_registry = Registry(name="MemoryRegistry")
-
-from .base import BaseMemory
-from .chat_history import ChatHistoryMemory
-from .summary import SummaryMemory
-from .sde_team import SdeTeamMemory
-from .vectorstore import VectorStoreMemory

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/states/MatchState.js

@@ -1,160 +0,0 @@
-import BaseState from './BaseState.js';
-import EliminateChess from '../actions/EliminateChess.js';
-import FallingAllChess from '../actions/FallingAllChess.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-const SetStruct = Phaser.Structs.Set;
-
-class State extends BaseState {
-    constructor(bejeweled, config) {
-        super(bejeweled, config);
-        // this.bejeweled = bejeweled;            // Bejeweled
-        // this.board = bejeweled.board;       // Bejeweled.board
-
-        this.totalMatchedLinesCount = 0;
-        this.eliminatedChessArray;
-
-        // Actions
-        // Eliminating action
-        this.eliminatingAction = GetValue(config, 'eliminatingAction', EliminateChess);
-        // on falling chess
-        this.fallingAction = GetValue(config, 'fallingAction', FallingAllChess);
-
-        var debug = GetValue(config, 'debug', false);
-        if (debug) {
-            this.on('statechange', this.printState, this);
-        }
-    }
-
-    shutdown() {
-        super.shutdown();
-
-        this.eliminatedChessArray = undefined;
-        // Actions
-        this.eliminatingAction = undefined;
-        this.fallingAction = undefined;
-        return this;
-    }
-
-    destroy() {
-        this.shutdown();
-        return this;
-    }
-
-    // START
-    enter_START() {
-        this.totalMatchedLinesCount = 0;
-
-        this.bejeweled.emit('match-start', this.board.board, this.bejeweled);
-
-        this.next();
-    }
-    next_START() {
-        return 'MATCH3';
-    }
-    // START
-
-    // MATCH3
-    enter_MATCH3() {
-        var matchedLines = this.board.getAllMatch();
-
-        this.bejeweled.emit('match', matchedLines, this.board.board, this.bejeweled);
-
-        var matchedLinesCount = matchedLines.length;
-        this.totalMatchedLinesCount += matchedLinesCount;
-        switch (matchedLinesCount) {
-            case 0:
-                this.eliminatedChessArray = [];
-                break;
-            case 1:
-                this.eliminatedChessArray = matchedLines[0].entries;
-                break;
-            default:
-                // Put all chess to a set
-                var newSet = new SetStruct();
-                for (var i = 0; i < matchedLinesCount; i++) {
-                    matchedLines[i].entries.forEach(function (value) {
-                        newSet.set(value);
-                    });
-                }
-                this.eliminatedChessArray = newSet.entries;
-                break;
-        }
-        this.next();
-    }
-    next_MATCH3() {
-        var nextState;
-        if (this.eliminatedChessArray.length === 0) {
-            nextState = 'END'
-        } else {
-            nextState = 'ELIMINATING';
-        }
-        return nextState;
-    }
-    // MATCH3
-
-    // ELIMINATING
-    enter_ELIMINATING() {
-        var board = this.board.board,
-            chessArray = this.eliminatedChessArray;
-
-        this.bejeweled.emit('eliminate', chessArray, board, this.bejeweled);
-
-        this.eliminatingAction(chessArray, board, this.bejeweled);
-
-        // Remove eliminated chess
-        chessArray.forEach(board.removeChess, board);
-
-        // To next state when all completed
-        this.next();
-    }
-    next_ELIMINATING() {
-        return 'FALLING';
-    }
-    exit_ELIMINATING() {
-        this.eliminatedChessArray = undefined;
-    }
-    // ELIMINATING
-
-    // FALLING
-    enter_FALLING() {
-        var board = this.board.board;
-
-        this.bejeweled.emit('fall', board, this.bejeweled);
-
-        this.fallingAction(board, this.bejeweled);
-
-        // To next state when all completed
-        this.next();
-    }
-    next_FALLING() {
-        return 'FILL';
-    }
-    // FALLING
-
-    // FILL
-    enter_FILL() {
-        this.board.fill(true); // Fill upper board only
-
-        this.bejeweled.emit('fill', this.board.board, this.bejeweled);
-
-        this.next();
-    }
-    next_FILL() {
-        return 'MATCH3';
-    }
-    // FILL
-
-    // END
-    enter_END() {
-        this.bejeweled.emit('match-end', this.board.board, this.bejeweled);
-
-        this.emit('complete');
-    }
-    // END
-
-    printState() {
-        console.log('Match state: ' + this.prevState + ' -> ' + this.state);
-    }
-}
-export default State;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/cube/Cube.d.ts

@@ -1,2 +0,0 @@
-import Base from '../base/Base';
-export default class Cube extends Base { }

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/Factory.d.ts

@@ -1,17 +0,0 @@
-import FixWidthSizer from './FixWidthSizer';
-
-
-export default function (
-    config?: FixWidthSizer.IConfig
-): FixWidthSizer;
-
-export default function (
-    x: number, y: number,
-    config?: FixWidthSizer.IConfig
-): FixWidthSizer;
-
-export default function (
-    x: number, y: number,
-    width: number, height: number,
-    config?: FixWidthSizer.IConfig
-): FixWidthSizer;

spaces/Amon1/ChatGPTForAcadamic/main.py

@@ -1,145 +0,0 @@
-import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
-import gradio as gr
-from predict import predict
-from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf
-
-# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
-proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT = \
-    get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT')
-
-# 如果WEB_PORT是-1, 则随机选取WEB端口
-PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
-if not AUTHENTICATION: AUTHENTICATION = None
-
-initial_prompt = "Serve me as a writing and programming assistant."
-title_html = "<h1 align=\"center\">ChatGPT 学术优化</h1>"
-description =  """代码开源和更新[地址🚀](https://github.com/binary-husky/chatgpt_academic)，感谢热情的[开发者们❤️](https://github.com/binary-husky/chatgpt_academic/graphs/contributors)"""
-
-# 问询记录, python 版本建议3.9+（越新越好）
-import logging
-os.makedirs("gpt_log", exist_ok=True)
-try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8")
-except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO)
-print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！")
-
-# 一些普通功能模块
-from functional import get_functionals
-functional = get_functionals()
-
-# 高级函数插件
-from functional_crazy import get_crazy_functionals
-crazy_fns = get_crazy_functionals()
-
-# 处理markdown文本格式的转变
-gr.Chatbot.postprocess = format_io
-
-# 做一些外观色彩上的调整
-from theme import adjust_theme, advanced_css
-set_theme = adjust_theme()
-
-cancel_handles = []
-with gr.Blocks(theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
-    gr.HTML(title_html)
-    with gr.Row().style(equal_height=True):
-        with gr.Column(scale=2):
-            chatbot = gr.Chatbot()
-            chatbot.style(height=CHATBOT_HEIGHT)
-            history = gr.State([])
-        with gr.Column(scale=1):
-            with gr.Row():
-                txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
-            with gr.Row():
-                submitBtn = gr.Button("提交", variant="primary")
-            with gr.Row():
-                resetBtn = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm")
-                stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm")
-            with gr.Row():
-                from check_proxy import check_proxy
-                status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {check_proxy(proxies)}")
-            with gr.Accordion("基础功能区", open=True) as area_basic_fn:
-                with gr.Row():
-                    for k in functional:
-                        variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
-                        functional[k]["Button"] = gr.Button(k, variant=variant)
-            with gr.Accordion("函数插件区", open=True) as area_crazy_fn:
-                with gr.Row():
-                    gr.Markdown("注意：以下“红颜色”标识的函数插件需从input区读取路径作为参数.")
-                with gr.Row():
-                    for k in crazy_fns:
-                        if not crazy_fns[k].get("AsButton", True): continue
-                        variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
-                        crazy_fns[k]["Button"] = gr.Button(k, variant=variant)
-                with gr.Row():
-                    with gr.Accordion("更多函数插件", open=True):
-                        dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)]
-                        with gr.Column(scale=1):
-                            dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False)
-                        with gr.Column(scale=1):
-                            switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary")
-                with gr.Row():
-                    with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up:
-                        file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple")
-            with gr.Accordion("展开SysPrompt & 交互界面布局 & Github地址", open=False):
-                system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
-                top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
-                temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
-                checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
-                gr.Markdown(description)
-    # 功能区显示开关与功能区的互动
-    def fn_area_visibility(a):
-        ret = {}
-        ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))})
-        ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))})
-        return ret
-    checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn] )
-    # 整理反复出现的控件句柄组合
-    input_combo = [txt, top_p, temperature, chatbot, history, system_prompt]
-    output_combo = [chatbot, history, status]
-    predict_args = dict(fn=predict, inputs=input_combo, outputs=output_combo)
-    empty_txt_args = dict(fn=lambda: "", inputs=[], outputs=[txt]) # 用于在提交后清空输入栏
-    # 提交按钮、重置按钮
-    cancel_handles.append(txt.submit(**predict_args)) #; txt.submit(**empty_txt_args) 在提交后清空输入栏
-    cancel_handles.append(submitBtn.click(**predict_args)) #; submitBtn.click(**empty_txt_args) 在提交后清空输入栏
-    resetBtn.click(lambda: ([], [], "已重置"), None, output_combo)
-    # 基础功能区的回调函数注册
-    for k in functional:
-        click_handle = functional[k]["Button"].click(predict, [*input_combo, gr.State(True), gr.State(k)], output_combo)
-        cancel_handles.append(click_handle)
-    # 文件上传区，接收文件后与chatbot的互动
-    file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt], [chatbot, txt])
-    # 函数插件-固定按钮区
-    for k in crazy_fns:
-        if not crazy_fns[k].get("AsButton", True): continue
-        click_handle = crazy_fns[k]["Button"].click(crazy_fns[k]["Function"], [*input_combo, gr.State(PORT)], output_combo)
-        click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
-        cancel_handles.append(click_handle)
-    # 函数插件-下拉菜单与随变按钮的互动
-    def on_dropdown_changed(k):
-        variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
-        return {switchy_bt: gr.update(value=k, variant=variant)}
-    dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt] )
-    # 随变按钮的回调函数注册
-    def route(k, *args, **kwargs):
-        if k in [r"打开插件列表", r"请先从插件列表中选择"]: return 
-        yield from crazy_fns[k]["Function"](*args, **kwargs)
-    click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
-    click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
-    # def expand_file_area(file_upload, area_file_up):
-    #     if len(file_upload)>0: return {area_file_up: gr.update(open=True)}
-    # click_handle.then(expand_file_area, [file_upload, area_file_up], [area_file_up])
-    cancel_handles.append(click_handle)
-    # 终止按钮的回调函数注册
-    stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
-
-# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
-def auto_opentab_delay():
-    import threading, webbrowser, time
-    print(f"如果浏览器没有自动打开，请复制并转到以下URL: http://localhost:{PORT}")
-    def open(): 
-        time.sleep(2)
-        webbrowser.open_new_tab(f"http://localhost:{PORT}")
-    threading.Thread(target=open, name="open-browser", daemon=True).start()
-
-auto_opentab_delay()
-demo.title = "ChatGPT 学术优化"
-demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/schedulers.md

@@ -1,329 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# 스케줄러
-
-diffusion 파이프라인은 diffusion 모델, 스케줄러 등의 컴포넌트들로 구성됩니다. 그리고 파이프라인 안의 일부 컴포넌트를 다른 컴포넌트로 교체하는 식의 커스터마이징 역시 가능합니다.  이와 같은 컴포넌트 커스터마이징의 가장 대표적인 예시가 바로 [스케줄러](../api/schedulers/overview.md)를 교체하는 것입니다.
-
-
-
-스케쥴러는 다음과 같이 diffusion 시스템의 전반적인 디노이징 프로세스를 정의합니다.
-
-- 디노이징 스텝을 얼마나 가져가야 할까?
-- 확률적으로(stochastic) 혹은 확정적으로(deterministic)?
-- 디노이징 된 샘플을 찾아내기 위해 어떤 알고리즘을 사용해야 할까?
-
-이러한 프로세스는 다소 난해하고, 디노이징 속도와 디노이징 퀄리티 사이의 트레이드 오프를 정의해야 하는 문제가 될 수 있습니다. 주어진 파이프라인에 어떤 스케줄러가 가장 적합한지를 정량적으로 판단하는 것은 매우 어려운 일입니다. 이로 인해 일단 해당 스케줄러를 직접 사용하여, 생성되는 이미지를 직접 눈으로 보며, 정성적으로 성능을 판단해보는 것이 추천되곤 합니다.
-
-
-
-
-
-## 파이프라인 불러오기
-
-먼저 스테이블 diffusion 파이프라인을 불러오도록 해보겠습니다. 물론 스테이블 diffusion을 사용하기 위해서는, 허깅페이스 허브에 등록된 사용자여야 하며, 관련 [라이센스](https://huggingface.co/runwayml/stable-diffusion-v1-5)에 동의해야 한다는 점을 잊지 말아주세요. 
-
-*역자 주: 다만, 현재 신규로 생성한 허깅페이스 계정에 대해서는 라이센스 동의를 요구하지 않는 것으로 보입니다!*
-
-```python
-from huggingface_hub import login
-from diffusers import DiffusionPipeline
-import torch
-
-# first we need to login with our access token
-login()
-
-# Now we can download the pipeline
-pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-```
-
-다음으로, GPU로 이동합니다.
-
-```python
-pipeline.to("cuda")
-```
-
-
-
-
-
-## 스케줄러 액세스
-
-스케줄러는 언제나 파이프라인의 컴포넌트로서 존재하며, 일반적으로 파이프라인 인스턴스 내에 `scheduler`라는 이름의 속성(property)으로 정의되어 있습니다. 
-
-```python
-pipeline.scheduler
-```
-
-**Output**:
-
-```
-PNDMScheduler {
-  "_class_name": "PNDMScheduler",
-  "_diffusers_version": "0.8.0.dev0",
-  "beta_end": 0.012,
-  "beta_schedule": "scaled_linear",
-  "beta_start": 0.00085,
-  "clip_sample": false,
-  "num_train_timesteps": 1000,
-  "set_alpha_to_one": false,
-  "skip_prk_steps": true,
-  "steps_offset": 1,
-  "trained_betas": null
-}
-```
-
-출력 결과를 통해, 우리는 해당 스케줄러가 [`PNDMScheduler`]의 인스턴스라는 것을 알 수 있습니다. 이제 [`PNDMScheduler`]와 다른 스케줄러들의 성능을 비교해보도록 하겠습니다. 먼저 테스트에 사용할 프롬프트를 다음과 같이 정의해보도록 하겠습니다. 
-
-```python
-prompt = "A photograph of an astronaut riding a horse on Mars, high resolution, high definition."
-```
-
-다음으로 유사한 이미지 생성을 보장하기 위해서, 다음과 같이 랜덤시드를 고정해주도록 하겠습니다. 
-
-```python
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_pndm.png" width="400"/>
-    <br>
-</p>
-
-
-
-
-## 스케줄러 교체하기
-
-다음으로 파이프라인의 스케줄러를 다른 스케줄러로 교체하는 방법에 대해 알아보겠습니다. 모든 스케줄러는 [`SchedulerMixin.compatibles`]라는 속성(property)을 갖고 있습니다. 해당 속성은 **호환 가능한** 스케줄러들에 대한 정보를 담고 있습니다. 
-
-```python
-pipeline.scheduler.compatibles
-```
-
-**Output**:
-
-```
-[diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,
- diffusers.schedulers.scheduling_ddim.DDIMScheduler,
- diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,
- diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,
- diffusers.schedulers.scheduling_pndm.PNDMScheduler,
- diffusers.schedulers.scheduling_ddpm.DDPMScheduler,
- diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler]
-```
-
-호환되는 스케줄러들을 살펴보면 아래와 같습니다.
-
-- [`LMSDiscreteScheduler`], 
-- [`DDIMScheduler`], 
-- [`DPMSolverMultistepScheduler`], 
-- [`EulerDiscreteScheduler`], 
-- [`PNDMScheduler`], 
-- [`DDPMScheduler`], 
-- [`EulerAncestralDiscreteScheduler`].
-
-앞서 정의했던 프롬프트를 사용해서 각각의 스케줄러들을 비교해보도록 하겠습니다.
-
-먼저 파이프라인 안의 스케줄러를 바꾸기 위해 [`ConfigMixin.config`] 속성과 [`ConfigMixin.from_config`] 메서드를 활용해보려고 합니다.
-
-
-
-```python
-pipeline.scheduler.config
-```
-
-**Output**:
-
-```
-FrozenDict([('num_train_timesteps', 1000),
-            ('beta_start', 0.00085),
-            ('beta_end', 0.012),
-            ('beta_schedule', 'scaled_linear'),
-            ('trained_betas', None),
-            ('skip_prk_steps', True),
-            ('set_alpha_to_one', False),
-            ('steps_offset', 1),
-            ('_class_name', 'PNDMScheduler'),
-            ('_diffusers_version', '0.8.0.dev0'),
-            ('clip_sample', False)])
-```
-
-기존 스케줄러의 config를 호환 가능한 다른 스케줄러에 이식하는 것 역시 가능합니다. 
-
-다음 예시는 기존 스케줄러(`pipeline.scheduler`)를 다른 종류의 스케줄러(`DDIMScheduler`)로 바꾸는 코드입니다. 기존 스케줄러가 갖고 있던 config를 `.from_config` 메서드의 인자로 전달하는 것을 확인할 수 있습니다.
-
-```python
-from diffusers import DDIMScheduler
-
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-```
-
-
-
-이제 파이프라인을 실행해서 두 스케줄러 사이의 생성된 이미지의 퀄리티를 비교해봅시다.
-
-```python
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_ddim.png" width="400"/>
-    <br>
-</p>
-
-
-
-
-## 스케줄러들 비교해보기
-
-지금까지는 [`PNDMScheduler`]와 [`DDIMScheduler`] 스케줄러를 실행해보았습니다. 아직 비교해볼 스케줄러들이 더 많이 남아있으니 계속 비교해보도록 하겠습니다.
-
-
-
-[`LMSDiscreteScheduler`]을 일반적으로 더 좋은 결과를 보여줍니다.
-
-```python
-from diffusers import LMSDiscreteScheduler
-
-pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_lms.png" width="400"/>
-    <br>
-</p>
-
-
-[`EulerDiscreteScheduler`]와 [`EulerAncestralDiscreteScheduler`] 고작 30번의 inference step만으로도 높은 퀄리티의 이미지를 생성하는 것을 알 수 있습니다.
-
-```python
-from diffusers import EulerDiscreteScheduler
-
-pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_discrete.png" width="400"/>
-    <br>
-</p>
-
-
-```python
-from diffusers import EulerAncestralDiscreteScheduler
-
-pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_ancestral.png" width="400"/>
-    <br>
-</p>
-
-
-지금 이 문서를 작성하는 현시점 기준에선, [`DPMSolverMultistepScheduler`]가 시간 대비 가장 좋은 품질의 이미지를 생성하는 것 같습니다. 20번 정도의 스텝만으로도 실행될 수 있습니다.
-
-
-
-```python
-from diffusers import DPMSolverMultistepScheduler
-
-pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-
-generator = torch.Generator(device="cuda").manual_seed(8)
-image = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]
-image
-```
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_dpm.png" width="400"/>
-    <br>
-</p>
-
-
-보시다시피 생성된 이미지들은 매우 비슷하고, 비슷한 퀄리티를 보이는 것 같습니다. 실제�� 어떤 스케줄러를 선택할 것인가는 종종 특정 이용 사례에 기반해서 결정되곤 합니다. 결국 여러 종류의 스케줄러를 직접 실행시켜보고 눈으로 직접 비교해서 판단하는 게 좋은 선택일 것 같습니다.
-
-
-
-## Flax에서 스케줄러 교체하기
-
-JAX/Flax 사용자인 경우 기본 파이프라인 스케줄러를 변경할 수도 있습니다. 다음은 Flax Stable Diffusion 파이프라인과 초고속 [DDPM-Solver++ 스케줄러를](../api/schedulers/multistep_dpm_solver) 사용하여 추론을 실행하는 방법에 대한 예시입니다 .
-
-```Python
-import jax
-import numpy as np
-from flax.jax_utils import replicate
-from flax.training.common_utils import shard
-
-from diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler
-
-model_id = "runwayml/stable-diffusion-v1-5"
-scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
-    model_id,
-    subfolder="scheduler"
-)
-pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
-    model_id,
-    scheduler=scheduler,
-    revision="bf16",
-    dtype=jax.numpy.bfloat16,
-)
-params["scheduler"] = scheduler_state
-
-# Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)
-prompt = "a photo of an astronaut riding a horse on mars"
-num_samples = jax.device_count()
-prompt_ids = pipeline.prepare_inputs([prompt] * num_samples)
-
-prng_seed = jax.random.PRNGKey(0)
-num_inference_steps = 25
-
-# shard inputs and rng
-params = replicate(params)
-prng_seed = jax.random.split(prng_seed, jax.device_count())
-prompt_ids = shard(prompt_ids)
-
-images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
-images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-```
-
-<Tip warning={true}>
-
-다음 Flax 스케줄러는 *아직* Flax Stable Diffusion 파이프라인과 호환되지 않습니다.
-
-- `FlaxLMSDiscreteScheduler`
-- `FlaxDDPMScheduler`
-
-</Tip>
-

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_kakao_brain_unclip_to_diffusers.py

@@ -1,1159 +0,0 @@
-import argparse
-import tempfile
-
-import torch
-from accelerate import load_checkpoint_and_dispatch
-from transformers import CLIPTextModelWithProjection, CLIPTokenizer
-
-from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel
-from diffusers.models.prior_transformer import PriorTransformer
-from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
-from diffusers.schedulers.scheduling_unclip import UnCLIPScheduler
-
-
-"""
-Example - From the diffusers root directory:
-
-Download weights:
-```sh
-$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt
-$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt
-$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt
-$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th
-```
-
-Convert the model:
-```sh
-$ python scripts/convert_kakao_brain_unclip_to_diffusers.py \
-      --decoder_checkpoint_path ./decoder-ckpt-step\=01000000-of-01000000.ckpt \
-      --super_res_unet_checkpoint_path ./improved-sr-ckpt-step\=1.2M.ckpt \
-      --prior_checkpoint_path ./prior-ckpt-step\=01000000-of-01000000.ckpt \
-      --clip_stat_path ./ViT-L-14_stats.th \
-      --dump_path <path where to save model>
-```
-"""
-
-
-# prior
-
-PRIOR_ORIGINAL_PREFIX = "model"
-
-# Uses default arguments
-PRIOR_CONFIG = {}
-
-
-def prior_model_from_original_config():
-    model = PriorTransformer(**PRIOR_CONFIG)
-
-    return model
-
-
-def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint, clip_stats_checkpoint):
-    diffusers_checkpoint = {}
-
-    # <original>.time_embed.0 -> <diffusers>.time_embedding.linear_1
-    diffusers_checkpoint.update(
-        {
-            "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.weight"],
-            "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.bias"],
-        }
-    )
-
-    # <original>.clip_img_proj -> <diffusers>.proj_in
-    diffusers_checkpoint.update(
-        {
-            "proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.weight"],
-            "proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.bias"],
-        }
-    )
-
-    # <original>.text_emb_proj -> <diffusers>.embedding_proj
-    diffusers_checkpoint.update(
-        {
-            "embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.weight"],
-            "embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.bias"],
-        }
-    )
-
-    # <original>.text_enc_proj -> <diffusers>.encoder_hidden_states_proj
-    diffusers_checkpoint.update(
-        {
-            "encoder_hidden_states_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.weight"],
-            "encoder_hidden_states_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.bias"],
-        }
-    )
-
-    # <original>.positional_embedding -> <diffusers>.positional_embedding
-    diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.positional_embedding"]})
-
-    # <original>.prd_emb -> <diffusers>.prd_embedding
-    diffusers_checkpoint.update({"prd_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.prd_emb"]})
-
-    # <original>.time_embed.2 -> <diffusers>.time_embedding.linear_2
-    diffusers_checkpoint.update(
-        {
-            "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.weight"],
-            "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.bias"],
-        }
-    )
-
-    # <original>.resblocks.<x> -> <diffusers>.transformer_blocks.<x>
-    for idx in range(len(model.transformer_blocks)):
-        diffusers_transformer_prefix = f"transformer_blocks.{idx}"
-        original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.transformer.resblocks.{idx}"
-
-        # <original>.attn -> <diffusers>.attn1
-        diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1"
-        original_attention_prefix = f"{original_transformer_prefix}.attn"
-        diffusers_checkpoint.update(
-            prior_attention_to_diffusers(
-                checkpoint,
-                diffusers_attention_prefix=diffusers_attention_prefix,
-                original_attention_prefix=original_attention_prefix,
-                attention_head_dim=model.attention_head_dim,
-            )
-        )
-
-        # <original>.mlp -> <diffusers>.ff
-        diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff"
-        original_ff_prefix = f"{original_transformer_prefix}.mlp"
-        diffusers_checkpoint.update(
-            prior_ff_to_diffusers(
-                checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix
-            )
-        )
-
-        # <original>.ln_1 -> <diffusers>.norm1
-        diffusers_checkpoint.update(
-            {
-                f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[
-                    f"{original_transformer_prefix}.ln_1.weight"
-                ],
-                f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"],
-            }
-        )
-
-        # <original>.ln_2 -> <diffusers>.norm3
-        diffusers_checkpoint.update(
-            {
-                f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[
-                    f"{original_transformer_prefix}.ln_2.weight"
-                ],
-                f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"],
-            }
-        )
-
-    # <original>.final_ln -> <diffusers>.norm_out
-    diffusers_checkpoint.update(
-        {
-            "norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.weight"],
-            "norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.bias"],
-        }
-    )
-
-    # <original>.out_proj -> <diffusers>.proj_to_clip_embeddings
-    diffusers_checkpoint.update(
-        {
-            "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.weight"],
-            "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.bias"],
-        }
-    )
-
-    # clip stats
-    clip_mean, clip_std = clip_stats_checkpoint
-    clip_mean = clip_mean[None, :]
-    clip_std = clip_std[None, :]
-
-    diffusers_checkpoint.update({"clip_mean": clip_mean, "clip_std": clip_std})
-
-    return diffusers_checkpoint
-
-
-def prior_attention_to_diffusers(
-    checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim
-):
-    diffusers_checkpoint = {}
-
-    # <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v}
-    [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(
-        weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"],
-        bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"],
-        split=3,
-        chunk_size=attention_head_dim,
-    )
-
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_attention_prefix}.to_q.weight": q_weight,
-            f"{diffusers_attention_prefix}.to_q.bias": q_bias,
-            f"{diffusers_attention_prefix}.to_k.weight": k_weight,
-            f"{diffusers_attention_prefix}.to_k.bias": k_bias,
-            f"{diffusers_attention_prefix}.to_v.weight": v_weight,
-            f"{diffusers_attention_prefix}.to_v.bias": v_bias,
-        }
-    )
-
-    # <original>.c_proj -> <diffusers>.to_out.0
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"],
-            f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix):
-    diffusers_checkpoint = {
-        # <original>.c_fc -> <diffusers>.net.0.proj
-        f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"],
-        f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"],
-        # <original>.c_proj -> <diffusers>.net.2
-        f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"],
-        f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"],
-    }
-
-    return diffusers_checkpoint
-
-
-# done prior
-
-
-# decoder
-
-DECODER_ORIGINAL_PREFIX = "model"
-
-# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can
-# update then.
-DECODER_CONFIG = {
-    "sample_size": 64,
-    "layers_per_block": 3,
-    "down_block_types": (
-        "ResnetDownsampleBlock2D",
-        "SimpleCrossAttnDownBlock2D",
-        "SimpleCrossAttnDownBlock2D",
-        "SimpleCrossAttnDownBlock2D",
-    ),
-    "up_block_types": (
-        "SimpleCrossAttnUpBlock2D",
-        "SimpleCrossAttnUpBlock2D",
-        "SimpleCrossAttnUpBlock2D",
-        "ResnetUpsampleBlock2D",
-    ),
-    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
-    "block_out_channels": (320, 640, 960, 1280),
-    "in_channels": 3,
-    "out_channels": 6,
-    "cross_attention_dim": 1536,
-    "class_embed_type": "identity",
-    "attention_head_dim": 64,
-    "resnet_time_scale_shift": "scale_shift",
-}
-
-
-def decoder_model_from_original_config():
-    model = UNet2DConditionModel(**DECODER_CONFIG)
-
-    return model
-
-
-def decoder_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    original_unet_prefix = DECODER_ORIGINAL_PREFIX
-    num_head_channels = DECODER_CONFIG["attention_head_dim"]
-
-    diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix))
-    diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix))
-
-    # <original>.input_blocks -> <diffusers>.down_blocks
-
-    original_down_block_idx = 1
-
-    for diffusers_down_block_idx in range(len(model.down_blocks)):
-        checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            diffusers_down_block_idx=diffusers_down_block_idx,
-            original_down_block_idx=original_down_block_idx,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=num_head_channels,
-        )
-
-        original_down_block_idx += num_original_down_blocks
-
-        diffusers_checkpoint.update(checkpoint_update)
-
-    # done <original>.input_blocks -> <diffusers>.down_blocks
-
-    diffusers_checkpoint.update(
-        unet_midblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=num_head_channels,
-        )
-    )
-
-    # <original>.output_blocks -> <diffusers>.up_blocks
-
-    original_up_block_idx = 0
-
-    for diffusers_up_block_idx in range(len(model.up_blocks)):
-        checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            diffusers_up_block_idx=diffusers_up_block_idx,
-            original_up_block_idx=original_up_block_idx,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=num_head_channels,
-        )
-
-        original_up_block_idx += num_original_up_blocks
-
-        diffusers_checkpoint.update(checkpoint_update)
-
-    # done <original>.output_blocks -> <diffusers>.up_blocks
-
-    diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix))
-    diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix))
-
-    return diffusers_checkpoint
-
-
-# done decoder
-
-# text proj
-
-
-def text_proj_from_original_config():
-    # From the conditional unet constructor where the dimension of the projected time embeddings is
-    # constructed
-    time_embed_dim = DECODER_CONFIG["block_out_channels"][0] * 4
-
-    cross_attention_dim = DECODER_CONFIG["cross_attention_dim"]
-
-    model = UnCLIPTextProjModel(time_embed_dim=time_embed_dim, cross_attention_dim=cross_attention_dim)
-
-    return model
-
-
-# Note that the input checkpoint is the original decoder checkpoint
-def text_proj_original_checkpoint_to_diffusers_checkpoint(checkpoint):
-    diffusers_checkpoint = {
-        # <original>.text_seq_proj.0 -> <diffusers>.encoder_hidden_states_proj
-        "encoder_hidden_states_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.0.weight"],
-        "encoder_hidden_states_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.0.bias"],
-        # <original>.text_seq_proj.1 -> <diffusers>.text_encoder_hidden_states_norm
-        "text_encoder_hidden_states_norm.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.1.weight"],
-        "text_encoder_hidden_states_norm.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.1.bias"],
-        # <original>.clip_tok_proj -> <diffusers>.clip_extra_context_tokens_proj
-        "clip_extra_context_tokens_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.clip_tok_proj.weight"],
-        "clip_extra_context_tokens_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.clip_tok_proj.bias"],
-        # <original>.text_feat_proj -> <diffusers>.embedding_proj
-        "embedding_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_feat_proj.weight"],
-        "embedding_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_feat_proj.bias"],
-        # <original>.cf_param -> <diffusers>.learned_classifier_free_guidance_embeddings
-        "learned_classifier_free_guidance_embeddings": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.cf_param"],
-        # <original>.clip_emb -> <diffusers>.clip_image_embeddings_project_to_time_embeddings
-        "clip_image_embeddings_project_to_time_embeddings.weight": checkpoint[
-            f"{DECODER_ORIGINAL_PREFIX}.clip_emb.weight"
-        ],
-        "clip_image_embeddings_project_to_time_embeddings.bias": checkpoint[
-            f"{DECODER_ORIGINAL_PREFIX}.clip_emb.bias"
-        ],
-    }
-
-    return diffusers_checkpoint
-
-
-# done text proj
-
-# super res unet first steps
-
-SUPER_RES_UNET_FIRST_STEPS_PREFIX = "model_first_steps"
-
-SUPER_RES_UNET_FIRST_STEPS_CONFIG = {
-    "sample_size": 256,
-    "layers_per_block": 3,
-    "down_block_types": (
-        "ResnetDownsampleBlock2D",
-        "ResnetDownsampleBlock2D",
-        "ResnetDownsampleBlock2D",
-        "ResnetDownsampleBlock2D",
-    ),
-    "up_block_types": (
-        "ResnetUpsampleBlock2D",
-        "ResnetUpsampleBlock2D",
-        "ResnetUpsampleBlock2D",
-        "ResnetUpsampleBlock2D",
-    ),
-    "block_out_channels": (320, 640, 960, 1280),
-    "in_channels": 6,
-    "out_channels": 3,
-    "add_attention": False,
-}
-
-
-def super_res_unet_first_steps_model_from_original_config():
-    model = UNet2DModel(**SUPER_RES_UNET_FIRST_STEPS_CONFIG)
-
-    return model
-
-
-def super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    original_unet_prefix = SUPER_RES_UNET_FIRST_STEPS_PREFIX
-
-    diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix))
-    diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix))
-
-    # <original>.input_blocks -> <diffusers>.down_blocks
-
-    original_down_block_idx = 1
-
-    for diffusers_down_block_idx in range(len(model.down_blocks)):
-        checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            diffusers_down_block_idx=diffusers_down_block_idx,
-            original_down_block_idx=original_down_block_idx,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=None,
-        )
-
-        original_down_block_idx += num_original_down_blocks
-
-        diffusers_checkpoint.update(checkpoint_update)
-
-    diffusers_checkpoint.update(
-        unet_midblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=None,
-        )
-    )
-
-    # <original>.output_blocks -> <diffusers>.up_blocks
-
-    original_up_block_idx = 0
-
-    for diffusers_up_block_idx in range(len(model.up_blocks)):
-        checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            diffusers_up_block_idx=diffusers_up_block_idx,
-            original_up_block_idx=original_up_block_idx,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=None,
-        )
-
-        original_up_block_idx += num_original_up_blocks
-
-        diffusers_checkpoint.update(checkpoint_update)
-
-    # done <original>.output_blocks -> <diffusers>.up_blocks
-
-    diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix))
-    diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix))
-
-    return diffusers_checkpoint
-
-
-# done super res unet first steps
-
-# super res unet last step
-
-SUPER_RES_UNET_LAST_STEP_PREFIX = "model_last_step"
-
-SUPER_RES_UNET_LAST_STEP_CONFIG = {
-    "sample_size": 256,
-    "layers_per_block": 3,
-    "down_block_types": (
-        "ResnetDownsampleBlock2D",
-        "ResnetDownsampleBlock2D",
-        "ResnetDownsampleBlock2D",
-        "ResnetDownsampleBlock2D",
-    ),
-    "up_block_types": (
-        "ResnetUpsampleBlock2D",
-        "ResnetUpsampleBlock2D",
-        "ResnetUpsampleBlock2D",
-        "ResnetUpsampleBlock2D",
-    ),
-    "block_out_channels": (320, 640, 960, 1280),
-    "in_channels": 6,
-    "out_channels": 3,
-    "add_attention": False,
-}
-
-
-def super_res_unet_last_step_model_from_original_config():
-    model = UNet2DModel(**SUPER_RES_UNET_LAST_STEP_CONFIG)
-
-    return model
-
-
-def super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    original_unet_prefix = SUPER_RES_UNET_LAST_STEP_PREFIX
-
-    diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix))
-    diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix))
-
-    # <original>.input_blocks -> <diffusers>.down_blocks
-
-    original_down_block_idx = 1
-
-    for diffusers_down_block_idx in range(len(model.down_blocks)):
-        checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            diffusers_down_block_idx=diffusers_down_block_idx,
-            original_down_block_idx=original_down_block_idx,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=None,
-        )
-
-        original_down_block_idx += num_original_down_blocks
-
-        diffusers_checkpoint.update(checkpoint_update)
-
-    diffusers_checkpoint.update(
-        unet_midblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=None,
-        )
-    )
-
-    # <original>.output_blocks -> <diffusers>.up_blocks
-
-    original_up_block_idx = 0
-
-    for diffusers_up_block_idx in range(len(model.up_blocks)):
-        checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint(
-            model,
-            checkpoint,
-            diffusers_up_block_idx=diffusers_up_block_idx,
-            original_up_block_idx=original_up_block_idx,
-            original_unet_prefix=original_unet_prefix,
-            num_head_channels=None,
-        )
-
-        original_up_block_idx += num_original_up_blocks
-
-        diffusers_checkpoint.update(checkpoint_update)
-
-    # done <original>.output_blocks -> <diffusers>.up_blocks
-
-    diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix))
-    diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix))
-
-    return diffusers_checkpoint
-
-
-# done super res unet last step
-
-
-# unet utils
-
-
-# <original>.time_embed -> <diffusers>.time_embedding
-def unet_time_embeddings(checkpoint, original_unet_prefix):
-    diffusers_checkpoint = {}
-
-    diffusers_checkpoint.update(
-        {
-            "time_embedding.linear_1.weight": checkpoint[f"{original_unet_prefix}.time_embed.0.weight"],
-            "time_embedding.linear_1.bias": checkpoint[f"{original_unet_prefix}.time_embed.0.bias"],
-            "time_embedding.linear_2.weight": checkpoint[f"{original_unet_prefix}.time_embed.2.weight"],
-            "time_embedding.linear_2.bias": checkpoint[f"{original_unet_prefix}.time_embed.2.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-# <original>.input_blocks.0 -> <diffusers>.conv_in
-def unet_conv_in(checkpoint, original_unet_prefix):
-    diffusers_checkpoint = {}
-
-    diffusers_checkpoint.update(
-        {
-            "conv_in.weight": checkpoint[f"{original_unet_prefix}.input_blocks.0.0.weight"],
-            "conv_in.bias": checkpoint[f"{original_unet_prefix}.input_blocks.0.0.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-# <original>.out.0 -> <diffusers>.conv_norm_out
-def unet_conv_norm_out(checkpoint, original_unet_prefix):
-    diffusers_checkpoint = {}
-
-    diffusers_checkpoint.update(
-        {
-            "conv_norm_out.weight": checkpoint[f"{original_unet_prefix}.out.0.weight"],
-            "conv_norm_out.bias": checkpoint[f"{original_unet_prefix}.out.0.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-# <original>.out.2 -> <diffusers>.conv_out
-def unet_conv_out(checkpoint, original_unet_prefix):
-    diffusers_checkpoint = {}
-
-    diffusers_checkpoint.update(
-        {
-            "conv_out.weight": checkpoint[f"{original_unet_prefix}.out.2.weight"],
-            "conv_out.bias": checkpoint[f"{original_unet_prefix}.out.2.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-# <original>.input_blocks -> <diffusers>.down_blocks
-def unet_downblock_to_diffusers_checkpoint(
-    model, checkpoint, *, diffusers_down_block_idx, original_down_block_idx, original_unet_prefix, num_head_channels
-):
-    diffusers_checkpoint = {}
-
-    diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.resnets"
-    original_down_block_prefix = f"{original_unet_prefix}.input_blocks"
-
-    down_block = model.down_blocks[diffusers_down_block_idx]
-
-    num_resnets = len(down_block.resnets)
-
-    if down_block.downsamplers is None:
-        downsampler = False
-    else:
-        assert len(down_block.downsamplers) == 1
-        downsampler = True
-        # The downsample block is also a resnet
-        num_resnets += 1
-
-    for resnet_idx_inc in range(num_resnets):
-        full_resnet_prefix = f"{original_down_block_prefix}.{original_down_block_idx + resnet_idx_inc}.0"
-
-        if downsampler and resnet_idx_inc == num_resnets - 1:
-            # this is a downsample block
-            full_diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.downsamplers.0"
-        else:
-            # this is a regular resnet block
-            full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}"
-
-        diffusers_checkpoint.update(
-            resnet_to_diffusers_checkpoint(
-                checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix
-            )
-        )
-
-    if hasattr(down_block, "attentions"):
-        num_attentions = len(down_block.attentions)
-        diffusers_attention_prefix = f"down_blocks.{diffusers_down_block_idx}.attentions"
-
-        for attention_idx_inc in range(num_attentions):
-            full_attention_prefix = f"{original_down_block_prefix}.{original_down_block_idx + attention_idx_inc}.1"
-            full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}"
-
-            diffusers_checkpoint.update(
-                attention_to_diffusers_checkpoint(
-                    checkpoint,
-                    attention_prefix=full_attention_prefix,
-                    diffusers_attention_prefix=full_diffusers_attention_prefix,
-                    num_head_channels=num_head_channels,
-                )
-            )
-
-    num_original_down_blocks = num_resnets
-
-    return diffusers_checkpoint, num_original_down_blocks
-
-
-# <original>.middle_block -> <diffusers>.mid_block
-def unet_midblock_to_diffusers_checkpoint(model, checkpoint, *, original_unet_prefix, num_head_channels):
-    diffusers_checkpoint = {}
-
-    # block 0
-
-    original_block_idx = 0
-
-    diffusers_checkpoint.update(
-        resnet_to_diffusers_checkpoint(
-            checkpoint,
-            diffusers_resnet_prefix="mid_block.resnets.0",
-            resnet_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}",
-        )
-    )
-
-    original_block_idx += 1
-
-    # optional block 1
-
-    if hasattr(model.mid_block, "attentions") and model.mid_block.attentions[0] is not None:
-        diffusers_checkpoint.update(
-            attention_to_diffusers_checkpoint(
-                checkpoint,
-                diffusers_attention_prefix="mid_block.attentions.0",
-                attention_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}",
-                num_head_channels=num_head_channels,
-            )
-        )
-        original_block_idx += 1
-
-    # block 1 or block 2
-
-    diffusers_checkpoint.update(
-        resnet_to_diffusers_checkpoint(
-            checkpoint,
-            diffusers_resnet_prefix="mid_block.resnets.1",
-            resnet_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}",
-        )
-    )
-
-    return diffusers_checkpoint
-
-
-# <original>.output_blocks -> <diffusers>.up_blocks
-def unet_upblock_to_diffusers_checkpoint(
-    model, checkpoint, *, diffusers_up_block_idx, original_up_block_idx, original_unet_prefix, num_head_channels
-):
-    diffusers_checkpoint = {}
-
-    diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.resnets"
-    original_up_block_prefix = f"{original_unet_prefix}.output_blocks"
-
-    up_block = model.up_blocks[diffusers_up_block_idx]
-
-    num_resnets = len(up_block.resnets)
-
-    if up_block.upsamplers is None:
-        upsampler = False
-    else:
-        assert len(up_block.upsamplers) == 1
-        upsampler = True
-        # The upsample block is also a resnet
-        num_resnets += 1
-
-    has_attentions = hasattr(up_block, "attentions")
-
-    for resnet_idx_inc in range(num_resnets):
-        if upsampler and resnet_idx_inc == num_resnets - 1:
-            # this is an upsample block
-            if has_attentions:
-                # There is a middle attention block that we skip
-                original_resnet_block_idx = 2
-            else:
-                original_resnet_block_idx = 1
-
-            # we add the `minus 1` because the last two resnets are stuck together in the same output block
-            full_resnet_prefix = (
-                f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc - 1}.{original_resnet_block_idx}"
-            )
-
-            full_diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.upsamplers.0"
-        else:
-            # this is a regular resnet block
-            full_resnet_prefix = f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc}.0"
-            full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}"
-
-        diffusers_checkpoint.update(
-            resnet_to_diffusers_checkpoint(
-                checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix
-            )
-        )
-
-    if has_attentions:
-        num_attentions = len(up_block.attentions)
-        diffusers_attention_prefix = f"up_blocks.{diffusers_up_block_idx}.attentions"
-
-        for attention_idx_inc in range(num_attentions):
-            full_attention_prefix = f"{original_up_block_prefix}.{original_up_block_idx + attention_idx_inc}.1"
-            full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}"
-
-            diffusers_checkpoint.update(
-                attention_to_diffusers_checkpoint(
-                    checkpoint,
-                    attention_prefix=full_attention_prefix,
-                    diffusers_attention_prefix=full_diffusers_attention_prefix,
-                    num_head_channels=num_head_channels,
-                )
-            )
-
-    num_original_down_blocks = num_resnets - 1 if upsampler else num_resnets
-
-    return diffusers_checkpoint, num_original_down_blocks
-
-
-def resnet_to_diffusers_checkpoint(checkpoint, *, diffusers_resnet_prefix, resnet_prefix):
-    diffusers_checkpoint = {
-        f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.in_layers.0.weight"],
-        f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.in_layers.0.bias"],
-        f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.in_layers.2.weight"],
-        f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.in_layers.2.bias"],
-        f"{diffusers_resnet_prefix}.time_emb_proj.weight": checkpoint[f"{resnet_prefix}.emb_layers.1.weight"],
-        f"{diffusers_resnet_prefix}.time_emb_proj.bias": checkpoint[f"{resnet_prefix}.emb_layers.1.bias"],
-        f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.out_layers.0.weight"],
-        f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.out_layers.0.bias"],
-        f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.out_layers.3.weight"],
-        f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.out_layers.3.bias"],
-    }
-
-    skip_connection_prefix = f"{resnet_prefix}.skip_connection"
-
-    if f"{skip_connection_prefix}.weight" in checkpoint:
-        diffusers_checkpoint.update(
-            {
-                f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{skip_connection_prefix}.weight"],
-                f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{skip_connection_prefix}.bias"],
-            }
-        )
-
-    return diffusers_checkpoint
-
-
-def attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix, num_head_channels):
-    diffusers_checkpoint = {}
-
-    # <original>.norm -> <diffusers>.group_norm
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"],
-            f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"],
-        }
-    )
-
-    # <original>.qkv -> <diffusers>.{query, key, value}
-    [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(
-        weight=checkpoint[f"{attention_prefix}.qkv.weight"][:, :, 0],
-        bias=checkpoint[f"{attention_prefix}.qkv.bias"],
-        split=3,
-        chunk_size=num_head_channels,
-    )
-
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_attention_prefix}.to_q.weight": q_weight,
-            f"{diffusers_attention_prefix}.to_q.bias": q_bias,
-            f"{diffusers_attention_prefix}.to_k.weight": k_weight,
-            f"{diffusers_attention_prefix}.to_k.bias": k_bias,
-            f"{diffusers_attention_prefix}.to_v.weight": v_weight,
-            f"{diffusers_attention_prefix}.to_v.bias": v_bias,
-        }
-    )
-
-    # <original>.encoder_kv -> <diffusers>.{context_key, context_value}
-    [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions(
-        weight=checkpoint[f"{attention_prefix}.encoder_kv.weight"][:, :, 0],
-        bias=checkpoint[f"{attention_prefix}.encoder_kv.bias"],
-        split=2,
-        chunk_size=num_head_channels,
-    )
-
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_attention_prefix}.add_k_proj.weight": encoder_k_weight,
-            f"{diffusers_attention_prefix}.add_k_proj.bias": encoder_k_bias,
-            f"{diffusers_attention_prefix}.add_v_proj.weight": encoder_v_weight,
-            f"{diffusers_attention_prefix}.add_v_proj.bias": encoder_v_bias,
-        }
-    )
-
-    # <original>.proj_out (1d conv) -> <diffusers>.proj_attn (linear)
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][
-                :, :, 0
-            ],
-            f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)
-def split_attentions(*, weight, bias, split, chunk_size):
-    weights = [None] * split
-    biases = [None] * split
-
-    weights_biases_idx = 0
-
-    for starting_row_index in range(0, weight.shape[0], chunk_size):
-        row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size)
-
-        weight_rows = weight[row_indices, :]
-        bias_rows = bias[row_indices]
-
-        if weights[weights_biases_idx] is None:
-            assert weights[weights_biases_idx] is None
-            weights[weights_biases_idx] = weight_rows
-            biases[weights_biases_idx] = bias_rows
-        else:
-            assert weights[weights_biases_idx] is not None
-            weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows])
-            biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows])
-
-        weights_biases_idx = (weights_biases_idx + 1) % split
-
-    return weights, biases
-
-
-# done unet utils
-
-
-# Driver functions
-
-
-def text_encoder():
-    print("loading CLIP text encoder")
-
-    clip_name = "openai/clip-vit-large-patch14"
-
-    # sets pad_value to 0
-    pad_token = "!"
-
-    tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map="auto")
-
-    assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0
-
-    text_encoder_model = CLIPTextModelWithProjection.from_pretrained(
-        clip_name,
-        # `CLIPTextModel` does not support device_map="auto"
-        # device_map="auto"
-    )
-
-    print("done loading CLIP text encoder")
-
-    return text_encoder_model, tokenizer_model
-
-
-def prior(*, args, checkpoint_map_location):
-    print("loading prior")
-
-    prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location)
-    prior_checkpoint = prior_checkpoint["state_dict"]
-
-    clip_stats_checkpoint = torch.load(args.clip_stat_path, map_location=checkpoint_map_location)
-
-    prior_model = prior_model_from_original_config()
-
-    prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint(
-        prior_model, prior_checkpoint, clip_stats_checkpoint
-    )
-
-    del prior_checkpoint
-    del clip_stats_checkpoint
-
-    load_checkpoint_to_model(prior_diffusers_checkpoint, prior_model, strict=True)
-
-    print("done loading prior")
-
-    return prior_model
-
-
-def decoder(*, args, checkpoint_map_location):
-    print("loading decoder")
-
-    decoder_checkpoint = torch.load(args.decoder_checkpoint_path, map_location=checkpoint_map_location)
-    decoder_checkpoint = decoder_checkpoint["state_dict"]
-
-    decoder_model = decoder_model_from_original_config()
-
-    decoder_diffusers_checkpoint = decoder_original_checkpoint_to_diffusers_checkpoint(
-        decoder_model, decoder_checkpoint
-    )
-
-    # text proj interlude
-
-    # The original decoder implementation includes a set of parameters that are used
-    # for creating the `encoder_hidden_states` which are what the U-net is conditioned
-    # on. The diffusers conditional unet directly takes the encoder_hidden_states. We pull
-    # the parameters into the UnCLIPTextProjModel class
-    text_proj_model = text_proj_from_original_config()
-
-    text_proj_checkpoint = text_proj_original_checkpoint_to_diffusers_checkpoint(decoder_checkpoint)
-
-    load_checkpoint_to_model(text_proj_checkpoint, text_proj_model, strict=True)
-
-    # done text proj interlude
-
-    del decoder_checkpoint
-
-    load_checkpoint_to_model(decoder_diffusers_checkpoint, decoder_model, strict=True)
-
-    print("done loading decoder")
-
-    return decoder_model, text_proj_model
-
-
-def super_res_unet(*, args, checkpoint_map_location):
-    print("loading super resolution unet")
-
-    super_res_checkpoint = torch.load(args.super_res_unet_checkpoint_path, map_location=checkpoint_map_location)
-    super_res_checkpoint = super_res_checkpoint["state_dict"]
-
-    # model_first_steps
-
-    super_res_first_model = super_res_unet_first_steps_model_from_original_config()
-
-    super_res_first_steps_checkpoint = super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint(
-        super_res_first_model, super_res_checkpoint
-    )
-
-    # model_last_step
-    super_res_last_model = super_res_unet_last_step_model_from_original_config()
-
-    super_res_last_step_checkpoint = super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint(
-        super_res_last_model, super_res_checkpoint
-    )
-
-    del super_res_checkpoint
-
-    load_checkpoint_to_model(super_res_first_steps_checkpoint, super_res_first_model, strict=True)
-
-    load_checkpoint_to_model(super_res_last_step_checkpoint, super_res_last_model, strict=True)
-
-    print("done loading super resolution unet")
-
-    return super_res_first_model, super_res_last_model
-
-
-def load_checkpoint_to_model(checkpoint, model, strict=False):
-    with tempfile.NamedTemporaryFile() as file:
-        torch.save(checkpoint, file.name)
-        del checkpoint
-        if strict:
-            model.load_state_dict(torch.load(file.name), strict=True)
-        else:
-            load_checkpoint_and_dispatch(model, file.name, device_map="auto")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
-
-    parser.add_argument(
-        "--prior_checkpoint_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the prior checkpoint to convert.",
-    )
-
-    parser.add_argument(
-        "--decoder_checkpoint_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the decoder checkpoint to convert.",
-    )
-
-    parser.add_argument(
-        "--super_res_unet_checkpoint_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the super resolution checkpoint to convert.",
-    )
-
-    parser.add_argument(
-        "--clip_stat_path", default=None, type=str, required=True, help="Path to the clip stats checkpoint to convert."
-    )
-
-    parser.add_argument(
-        "--checkpoint_load_device",
-        default="cpu",
-        type=str,
-        required=False,
-        help="The device passed to `map_location` when loading checkpoints.",
-    )
-
-    parser.add_argument(
-        "--debug",
-        default=None,
-        type=str,
-        required=False,
-        help="Only run a specific stage of the convert script. Used for debugging",
-    )
-
-    args = parser.parse_args()
-
-    print(f"loading checkpoints to {args.checkpoint_load_device}")
-
-    checkpoint_map_location = torch.device(args.checkpoint_load_device)
-
-    if args.debug is not None:
-        print(f"debug: only executing {args.debug}")
-
-    if args.debug is None:
-        text_encoder_model, tokenizer_model = text_encoder()
-
-        prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location)
-
-        decoder_model, text_proj_model = decoder(args=args, checkpoint_map_location=checkpoint_map_location)
-
-        super_res_first_model, super_res_last_model = super_res_unet(
-            args=args, checkpoint_map_location=checkpoint_map_location
-        )
-
-        prior_scheduler = UnCLIPScheduler(
-            variance_type="fixed_small_log",
-            prediction_type="sample",
-            num_train_timesteps=1000,
-            clip_sample_range=5.0,
-        )
-
-        decoder_scheduler = UnCLIPScheduler(
-            variance_type="learned_range",
-            prediction_type="epsilon",
-            num_train_timesteps=1000,
-        )
-
-        super_res_scheduler = UnCLIPScheduler(
-            variance_type="fixed_small_log",
-            prediction_type="epsilon",
-            num_train_timesteps=1000,
-        )
-
-        print(f"saving Kakao Brain unCLIP to {args.dump_path}")
-
-        pipe = UnCLIPPipeline(
-            prior=prior_model,
-            decoder=decoder_model,
-            text_proj=text_proj_model,
-            tokenizer=tokenizer_model,
-            text_encoder=text_encoder_model,
-            super_res_first=super_res_first_model,
-            super_res_last=super_res_last_model,
-            prior_scheduler=prior_scheduler,
-            decoder_scheduler=decoder_scheduler,
-            super_res_scheduler=super_res_scheduler,
-        )
-        pipe.save_pretrained(args.dump_path)
-
-        print("done writing Kakao Brain unCLIP")
-    elif args.debug == "text_encoder":
-        text_encoder_model, tokenizer_model = text_encoder()
-    elif args.debug == "prior":
-        prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location)
-    elif args.debug == "decoder":
-        decoder_model, text_proj_model = decoder(args=args, checkpoint_map_location=checkpoint_map_location)
-    elif args.debug == "super_res_unet":
-        super_res_first_model, super_res_last_model = super_res_unet(
-            args=args, checkpoint_map_location=checkpoint_map_location
-        )
-    else:
-        raise ValueError(f"unknown debug value : {args.debug}")

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py

@@ -1,108 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List, Optional, Tuple, Union
-
-import torch
-
-from ...models import UNet2DModel
-from ...schedulers import ScoreSdeVeScheduler
-from ...utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-class ScoreSdeVePipeline(DiffusionPipeline):
-    r"""
-    Pipeline for unconditional image generation.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Parameters:
-        unet ([`UNet2DModel`]):
-            A `UNet2DModel` to denoise the encoded image.
-        scheduler ([`ScoreSdeVeScheduler`]):
-            A `ScoreSdeVeScheduler` to be used in combination with `unet` to denoise the encoded image.
-    """
-    unet: UNet2DModel
-    scheduler: ScoreSdeVeScheduler
-
-    def __init__(self, unet: UNet2DModel, scheduler: ScoreSdeVeScheduler):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        num_inference_steps: int = 2000,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`torch.Generator`, `optional`):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            output_type (`str`, `optional`, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images.
-        """
-
-        img_size = self.unet.config.sample_size
-        shape = (batch_size, 3, img_size, img_size)
-
-        model = self.unet
-
-        sample = randn_tensor(shape, generator=generator) * self.scheduler.init_noise_sigma
-        sample = sample.to(self.device)
-
-        self.scheduler.set_timesteps(num_inference_steps)
-        self.scheduler.set_sigmas(num_inference_steps)
-
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
-            sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)
-
-            # correction step
-            for _ in range(self.scheduler.config.correct_steps):
-                model_output = self.unet(sample, sigma_t).sample
-                sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample
-
-            # prediction step
-            model_output = model(sample, sigma_t).sample
-            output = self.scheduler.step_pred(model_output, t, sample, generator=generator)
-
-            sample, sample_mean = output.prev_sample, output.prev_sample_mean
-
-        sample = sample_mean.clamp(0, 1)
-        sample = sample.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            sample = self.numpy_to_pil(sample)
-
-        if not return_dict:
-            return (sample,)
-
-        return ImagePipelineOutput(images=sample)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/altdiffusion/test_alt_diffusion.py

@@ -1,254 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-
-import numpy as np
-import torch
-from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
-
-from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNet2DConditionModel
-from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
-    RobertaSeriesConfig,
-    RobertaSeriesModelWithTransformation,
-)
-from diffusers.utils import slow, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
-
-
-enable_full_determinism()
-
-
-class AltDiffusionPipelineFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
-    pipeline_class = AltDiffusionPipeline
-    params = TEXT_TO_IMAGE_PARAMS
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-
-        # TODO: address the non-deterministic text encoder (fails for save-load tests)
-        # torch.manual_seed(0)
-        # text_encoder_config = RobertaSeriesConfig(
-        #     hidden_size=32,
-        #     project_dim=32,
-        #     intermediate_size=37,
-        #     layer_norm_eps=1e-05,
-        #     num_attention_heads=4,
-        #     num_hidden_layers=5,
-        #     vocab_size=5002,
-        # )
-        # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
-
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            projection_dim=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=5002,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-
-        tokenizer = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
-        tokenizer.model_max_length = 77
-
-        components = {
-            "unet": unet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-        }
-        return inputs
-
-    def test_attention_slicing_forward_pass(self):
-        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
-
-    def test_inference_batch_single_identical(self):
-        super().test_inference_batch_single_identical(expected_max_diff=3e-3)
-
-    def test_alt_diffusion_ddim(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-
-        components = self.get_dummy_components()
-        torch.manual_seed(0)
-        text_encoder_config = RobertaSeriesConfig(
-            hidden_size=32,
-            project_dim=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            vocab_size=5002,
-        )
-        # TODO: remove after fixing the non-deterministic text encoder
-        text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
-        components["text_encoder"] = text_encoder
-
-        alt_pipe = AltDiffusionPipeline(**components)
-        alt_pipe = alt_pipe.to(device)
-        alt_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        inputs["prompt"] = "A photo of an astronaut"
-        output = alt_pipe(**inputs)
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]
-        )
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_alt_diffusion_pndm(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-
-        components = self.get_dummy_components()
-        components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
-        torch.manual_seed(0)
-        text_encoder_config = RobertaSeriesConfig(
-            hidden_size=32,
-            project_dim=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            vocab_size=5002,
-        )
-        # TODO: remove after fixing the non-deterministic text encoder
-        text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
-        components["text_encoder"] = text_encoder
-        alt_pipe = AltDiffusionPipeline(**components)
-        alt_pipe = alt_pipe.to(device)
-        alt_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        output = alt_pipe(**inputs)
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]
-        )
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-
-@slow
-@require_torch_gpu
-class AltDiffusionPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_alt_diffusion(self):
-        # make sure here that pndm scheduler skips prk
-        alt_pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", safety_checker=None)
-        alt_pipe = alt_pipe.to(torch_device)
-        alt_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        output = alt_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np")
-
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_alt_diffusion_fast_ddim(self):
-        scheduler = DDIMScheduler.from_pretrained("BAAI/AltDiffusion", subfolder="scheduler")
-
-        alt_pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", scheduler=scheduler, safety_checker=None)
-        alt_pipe = alt_pipe.to(torch_device)
-        alt_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-
-        output = alt_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

spaces/Andy1621/uniformer_image_detection/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py

@@ -1,236 +0,0 @@
-_base_ = [
-    '../_base_/datasets/coco_instance.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-# model settings
-model = dict(
-    type='HybridTaskCascade',
-    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,
-        num_outs=5),
-    rpn_head=dict(
-        type='RPNHead',
-        in_channels=256,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            scales=[8],
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
-    roi_head=dict(
-        type='HybridTaskCascadeRoIHead',
-        interleaved=True,
-        mask_info_flow=True,
-        num_stages=3,
-        stage_loss_weights=[1, 0.5, 0.25],
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        bbox_head=[
-            dict(
-                type='Shared2FCBBoxHead',
-                in_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.1, 0.1, 0.2, 0.2]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                               loss_weight=1.0)),
-            dict(
-                type='Shared2FCBBoxHead',
-                in_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.05, 0.05, 0.1, 0.1]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                               loss_weight=1.0)),
-            dict(
-                type='Shared2FCBBoxHead',
-                in_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.033, 0.033, 0.067, 0.067]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
-        ],
-        mask_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        mask_head=[
-            dict(
-                type='HTCMaskHead',
-                with_conv_res=False,
-                num_convs=4,
-                in_channels=256,
-                conv_out_channels=256,
-                num_classes=80,
-                loss_mask=dict(
-                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
-            dict(
-                type='HTCMaskHead',
-                num_convs=4,
-                in_channels=256,
-                conv_out_channels=256,
-                num_classes=80,
-                loss_mask=dict(
-                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
-            dict(
-                type='HTCMaskHead',
-                num_convs=4,
-                in_channels=256,
-                conv_out_channels=256,
-                num_classes=80,
-                loss_mask=dict(
-                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
-        ]),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=0,
-            pos_weight=-1,
-            debug=False),
-        rpn_proposal=dict(
-            nms_pre=2000,
-            max_per_img=2000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=[
-            dict(
-                assigner=dict(
-                    type='MaxIoUAssigner',
-                    pos_iou_thr=0.5,
-                    neg_iou_thr=0.5,
-                    min_pos_iou=0.5,
-                    ignore_iof_thr=-1),
-                sampler=dict(
-                    type='RandomSampler',
-                    num=512,
-                    pos_fraction=0.25,
-                    neg_pos_ub=-1,
-                    add_gt_as_proposals=True),
-                mask_size=28,
-                pos_weight=-1,
-                debug=False),
-            dict(
-                assigner=dict(
-                    type='MaxIoUAssigner',
-                    pos_iou_thr=0.6,
-                    neg_iou_thr=0.6,
-                    min_pos_iou=0.6,
-                    ignore_iof_thr=-1),
-                sampler=dict(
-                    type='RandomSampler',
-                    num=512,
-                    pos_fraction=0.25,
-                    neg_pos_ub=-1,
-                    add_gt_as_proposals=True),
-                mask_size=28,
-                pos_weight=-1,
-                debug=False),
-            dict(
-                assigner=dict(
-                    type='MaxIoUAssigner',
-                    pos_iou_thr=0.7,
-                    neg_iou_thr=0.7,
-                    min_pos_iou=0.7,
-                    ignore_iof_thr=-1),
-                sampler=dict(
-                    type='RandomSampler',
-                    num=512,
-                    pos_fraction=0.25,
-                    neg_pos_ub=-1,
-                    add_gt_as_proposals=True),
-                mask_size=28,
-                pos_weight=-1,
-                debug=False)
-        ]),
-    test_cfg=dict(
-        rpn=dict(
-            nms_pre=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            score_thr=0.001,
-            nms=dict(type='nms', iou_threshold=0.5),
-            max_per_img=100,
-            mask_thr_binary=0.5)))
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip', flip_ratio=0.5),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/bbox_heads/bbox_head.py

@@ -1,483 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.runner import auto_fp16, force_fp32
-from torch.nn.modules.utils import _pair
-
-from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
-from mmdet.models.builder import HEADS, build_loss
-from mmdet.models.losses import accuracy
-
-
-@HEADS.register_module()
-class BBoxHead(nn.Module):
-    """Simplest RoI head, with only two fc layers for classification and
-    regression respectively."""
-
-    def __init__(self,
-                 with_avg_pool=False,
-                 with_cls=True,
-                 with_reg=True,
-                 roi_feat_size=7,
-                 in_channels=256,
-                 num_classes=80,
-                 bbox_coder=dict(
-                     type='DeltaXYWHBBoxCoder',
-                     clip_border=True,
-                     target_means=[0., 0., 0., 0.],
-                     target_stds=[0.1, 0.1, 0.2, 0.2]),
-                 reg_class_agnostic=False,
-                 reg_decoded_bbox=False,
-                 loss_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=False,
-                     loss_weight=1.0),
-                 loss_bbox=dict(
-                     type='SmoothL1Loss', beta=1.0, loss_weight=1.0)):
-        super(BBoxHead, self).__init__()
-        assert with_cls or with_reg
-        self.with_avg_pool = with_avg_pool
-        self.with_cls = with_cls
-        self.with_reg = with_reg
-        self.roi_feat_size = _pair(roi_feat_size)
-        self.roi_feat_area = self.roi_feat_size[0] * self.roi_feat_size[1]
-        self.in_channels = in_channels
-        self.num_classes = num_classes
-        self.reg_class_agnostic = reg_class_agnostic
-        self.reg_decoded_bbox = reg_decoded_bbox
-        self.fp16_enabled = False
-
-        self.bbox_coder = build_bbox_coder(bbox_coder)
-        self.loss_cls = build_loss(loss_cls)
-        self.loss_bbox = build_loss(loss_bbox)
-
-        in_channels = self.in_channels
-        if self.with_avg_pool:
-            self.avg_pool = nn.AvgPool2d(self.roi_feat_size)
-        else:
-            in_channels *= self.roi_feat_area
-        if self.with_cls:
-            # need to add background class
-            self.fc_cls = nn.Linear(in_channels, num_classes + 1)
-        if self.with_reg:
-            out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes
-            self.fc_reg = nn.Linear(in_channels, out_dim_reg)
-        self.debug_imgs = None
-
-    def init_weights(self):
-        # conv layers are already initialized by ConvModule
-        if self.with_cls:
-            nn.init.normal_(self.fc_cls.weight, 0, 0.01)
-            nn.init.constant_(self.fc_cls.bias, 0)
-        if self.with_reg:
-            nn.init.normal_(self.fc_reg.weight, 0, 0.001)
-            nn.init.constant_(self.fc_reg.bias, 0)
-
-    @auto_fp16()
-    def forward(self, x):
-        if self.with_avg_pool:
-            x = self.avg_pool(x)
-        x = x.view(x.size(0), -1)
-        cls_score = self.fc_cls(x) if self.with_cls else None
-        bbox_pred = self.fc_reg(x) if self.with_reg else None
-        return cls_score, bbox_pred
-
-    def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes,
-                           pos_gt_labels, cfg):
-        """Calculate the ground truth for proposals in the single image
-        according to the sampling results.
-
-        Args:
-            pos_bboxes (Tensor): Contains all the positive boxes,
-                has shape (num_pos, 4), the last dimension 4
-                represents [tl_x, tl_y, br_x, br_y].
-            neg_bboxes (Tensor): Contains all the negative boxes,
-                has shape (num_neg, 4), the last dimension 4
-                represents [tl_x, tl_y, br_x, br_y].
-            pos_gt_bboxes (Tensor): Contains all the gt_boxes,
-                has shape (num_gt, 4), the last dimension 4
-                represents [tl_x, tl_y, br_x, br_y].
-            pos_gt_labels (Tensor): Contains all the gt_labels,
-                has shape (num_gt).
-            cfg (obj:`ConfigDict`): `train_cfg` of R-CNN.
-
-        Returns:
-            Tuple[Tensor]: Ground truth for proposals
-            in a single image. Containing the following Tensors:
-
-                - labels(Tensor): Gt_labels for all proposals, has
-                  shape (num_proposals,).
-                - label_weights(Tensor): Labels_weights for all
-                  proposals, has shape (num_proposals,).
-                - bbox_targets(Tensor):Regression target for all
-                  proposals, has shape (num_proposals, 4), the
-                  last dimension 4 represents [tl_x, tl_y, br_x, br_y].
-                - bbox_weights(Tensor):Regression weights for all
-                  proposals, has shape (num_proposals, 4).
-        """
-        num_pos = pos_bboxes.size(0)
-        num_neg = neg_bboxes.size(0)
-        num_samples = num_pos + num_neg
-
-        # original implementation uses new_zeros since BG are set to be 0
-        # now use empty & fill because BG cat_id = num_classes,
-        # FG cat_id = [0, num_classes-1]
-        labels = pos_bboxes.new_full((num_samples, ),
-                                     self.num_classes,
-                                     dtype=torch.long)
-        label_weights = pos_bboxes.new_zeros(num_samples)
-        bbox_targets = pos_bboxes.new_zeros(num_samples, 4)
-        bbox_weights = pos_bboxes.new_zeros(num_samples, 4)
-        if num_pos > 0:
-            labels[:num_pos] = pos_gt_labels
-            pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight
-            label_weights[:num_pos] = pos_weight
-            if not self.reg_decoded_bbox:
-                pos_bbox_targets = self.bbox_coder.encode(
-                    pos_bboxes, pos_gt_bboxes)
-            else:
-                # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-                # is applied directly on the decoded bounding boxes, both
-                # the predicted boxes and regression targets should be with
-                # absolute coordinate format.
-                pos_bbox_targets = pos_gt_bboxes
-            bbox_targets[:num_pos, :] = pos_bbox_targets
-            bbox_weights[:num_pos, :] = 1
-        if num_neg > 0:
-            label_weights[-num_neg:] = 1.0
-
-        return labels, label_weights, bbox_targets, bbox_weights
-
-    def get_targets(self,
-                    sampling_results,
-                    gt_bboxes,
-                    gt_labels,
-                    rcnn_train_cfg,
-                    concat=True):
-        """Calculate the ground truth for all samples in a batch according to
-        the sampling_results.
-
-        Almost the same as the implementation in bbox_head, we passed
-        additional parameters pos_inds_list and neg_inds_list to
-        `_get_target_single` function.
-
-        Args:
-            sampling_results (List[obj:SamplingResults]): Assign results of
-                all images in a batch after sampling.
-            gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch,
-                each tensor has shape (num_gt, 4),  the last dimension 4
-                represents [tl_x, tl_y, br_x, br_y].
-            gt_labels (list[Tensor]): Gt_labels of all images in a batch,
-                each tensor has shape (num_gt,).
-            rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
-            concat (bool): Whether to concatenate the results of all
-                the images in a single batch.
-
-        Returns:
-            Tuple[Tensor]: Ground truth for proposals in a single image.
-            Containing the following list of Tensors:
-
-                - labels (list[Tensor],Tensor): Gt_labels for all
-                  proposals in a batch, each tensor in list has
-                  shape (num_proposals,) when `concat=False`, otherwise
-                  just a single tensor has shape (num_all_proposals,).
-                - label_weights (list[Tensor]): Labels_weights for
-                  all proposals in a batch, each tensor in list has
-                  shape (num_proposals,) when `concat=False`, otherwise
-                  just a single tensor has shape (num_all_proposals,).
-                - bbox_targets (list[Tensor],Tensor): Regression target
-                  for all proposals in a batch, each tensor in list
-                  has shape (num_proposals, 4) when `concat=False`,
-                  otherwise just a single tensor has shape
-                  (num_all_proposals, 4), the last dimension 4 represents
-                  [tl_x, tl_y, br_x, br_y].
-                - bbox_weights (list[tensor],Tensor): Regression weights for
-                  all proposals in a batch, each tensor in list has shape
-                  (num_proposals, 4) when `concat=False`, otherwise just a
-                  single tensor has shape (num_all_proposals, 4).
-        """
-        pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
-        neg_bboxes_list = [res.neg_bboxes for res in sampling_results]
-        pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
-        pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]
-        labels, label_weights, bbox_targets, bbox_weights = multi_apply(
-            self._get_target_single,
-            pos_bboxes_list,
-            neg_bboxes_list,
-            pos_gt_bboxes_list,
-            pos_gt_labels_list,
-            cfg=rcnn_train_cfg)
-
-        if concat:
-            labels = torch.cat(labels, 0)
-            label_weights = torch.cat(label_weights, 0)
-            bbox_targets = torch.cat(bbox_targets, 0)
-            bbox_weights = torch.cat(bbox_weights, 0)
-        return labels, label_weights, bbox_targets, bbox_weights
-
-    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
-    def loss(self,
-             cls_score,
-             bbox_pred,
-             rois,
-             labels,
-             label_weights,
-             bbox_targets,
-             bbox_weights,
-             reduction_override=None):
-        losses = dict()
-        if cls_score is not None:
-            avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
-            if cls_score.numel() > 0:
-                losses['loss_cls'] = self.loss_cls(
-                    cls_score,
-                    labels,
-                    label_weights,
-                    avg_factor=avg_factor,
-                    reduction_override=reduction_override)
-                losses['acc'] = accuracy(cls_score, labels)
-        if bbox_pred is not None:
-            bg_class_ind = self.num_classes
-            # 0~self.num_classes-1 are FG, self.num_classes is BG
-            pos_inds = (labels >= 0) & (labels < bg_class_ind)
-            # do not perform bounding box regression for BG anymore.
-            if pos_inds.any():
-                if self.reg_decoded_bbox:
-                    # When the regression loss (e.g. `IouLoss`,
-                    # `GIouLoss`, `DIouLoss`) is applied directly on
-                    # the decoded bounding boxes, it decodes the
-                    # already encoded coordinates to absolute format.
-                    bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred)
-                if self.reg_class_agnostic:
-                    pos_bbox_pred = bbox_pred.view(
-                        bbox_pred.size(0), 4)[pos_inds.type(torch.bool)]
-                else:
-                    pos_bbox_pred = bbox_pred.view(
-                        bbox_pred.size(0), -1,
-                        4)[pos_inds.type(torch.bool),
-                           labels[pos_inds.type(torch.bool)]]
-                losses['loss_bbox'] = self.loss_bbox(
-                    pos_bbox_pred,
-                    bbox_targets[pos_inds.type(torch.bool)],
-                    bbox_weights[pos_inds.type(torch.bool)],
-                    avg_factor=bbox_targets.size(0),
-                    reduction_override=reduction_override)
-            else:
-                losses['loss_bbox'] = bbox_pred[pos_inds].sum()
-        return losses
-
-    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
-    def get_bboxes(self,
-                   rois,
-                   cls_score,
-                   bbox_pred,
-                   img_shape,
-                   scale_factor,
-                   rescale=False,
-                   cfg=None):
-        """Transform network output for a batch into bbox predictions.
-
-        If the input rois has batch dimension, the function would be in
-        `batch_mode` and return is a tuple[list[Tensor], list[Tensor]],
-        otherwise, the return is a tuple[Tensor, Tensor].
-
-        Args:
-            rois (Tensor): Boxes to be transformed. Has shape (num_boxes, 5)
-               or (B, num_boxes, 5)
-            cls_score (list[Tensor] or Tensor): Box scores for
-               each scale level, each is a 4D-tensor, the channel number is
-               num_points * num_classes.
-            bbox_pred (Tensor, optional): Box energies / deltas for each scale
-                level, each is a 4D-tensor, the channel number is
-                num_classes * 4.
-            img_shape (Sequence[int] or torch.Tensor or Sequence[
-                Sequence[int]], optional): Maximum bounds for boxes, specifies
-                (H, W, C) or (H, W). If rois shape is (B, num_boxes, 4), then
-                the max_shape should be a Sequence[Sequence[int]]
-                and the length of max_shape should also be B.
-            scale_factor (tuple[ndarray] or ndarray): Scale factor of the
-               image arange as (w_scale, h_scale, w_scale, h_scale). In
-               `batch_mode`, the scale_factor shape is tuple[ndarray].
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None
-
-        Returns:
-            tuple[list[Tensor], list[Tensor]] or tuple[Tensor, Tensor]:
-                If the input has a batch dimension, the return value is
-                a tuple of the list. The first list contains the boxes of
-                the corresponding image in a batch, each tensor has the
-                shape (num_boxes, 5) and last dimension 5 represent
-                (tl_x, tl_y, br_x, br_y, score). Each Tensor in the second
-                list is the labels with shape (num_boxes, ). The length of
-                both lists should be equal to batch_size. Otherwise return
-                value is a tuple of two tensors, the first tensor is the
-                boxes with scores, the second tensor is the labels, both
-                have the same shape as the first case.
-        """
-        if isinstance(cls_score, list):
-            cls_score = sum(cls_score) / float(len(cls_score))
-
-        scores = F.softmax(
-            cls_score, dim=-1) if cls_score is not None else None
-
-        batch_mode = True
-        if rois.ndim == 2:
-            # e.g. AugTest, Cascade R-CNN, HTC, SCNet...
-            batch_mode = False
-
-            # add batch dimension
-            if scores is not None:
-                scores = scores.unsqueeze(0)
-            if bbox_pred is not None:
-                bbox_pred = bbox_pred.unsqueeze(0)
-            rois = rois.unsqueeze(0)
-
-        if bbox_pred is not None:
-            bboxes = self.bbox_coder.decode(
-                rois[..., 1:], bbox_pred, max_shape=img_shape)
-        else:
-            bboxes = rois[..., 1:].clone()
-            if img_shape is not None:
-                max_shape = bboxes.new_tensor(img_shape)[..., :2]
-                min_xy = bboxes.new_tensor(0)
-                max_xy = torch.cat(
-                    [max_shape] * 2, dim=-1).flip(-1).unsqueeze(-2)
-                bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
-                bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
-
-        if rescale and bboxes.size(-2) > 0:
-            if not isinstance(scale_factor, tuple):
-                scale_factor = tuple([scale_factor])
-            # B, 1, bboxes.size(-1)
-            scale_factor = bboxes.new_tensor(scale_factor).unsqueeze(1).repeat(
-                1, 1,
-                bboxes.size(-1) // 4)
-            bboxes /= scale_factor
-
-        det_bboxes = []
-        det_labels = []
-        for (bbox, score) in zip(bboxes, scores):
-            if cfg is not None:
-                det_bbox, det_label = multiclass_nms(bbox, score,
-                                                     cfg.score_thr, cfg.nms,
-                                                     cfg.max_per_img)
-            else:
-                det_bbox, det_label = bbox, score
-            det_bboxes.append(det_bbox)
-            det_labels.append(det_label)
-
-        if not batch_mode:
-            det_bboxes = det_bboxes[0]
-            det_labels = det_labels[0]
-        return det_bboxes, det_labels
-
-    @force_fp32(apply_to=('bbox_preds', ))
-    def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
-        """Refine bboxes during training.
-
-        Args:
-            rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
-                and bs is the sampled RoIs per image. The first column is
-                the image id and the next 4 columns are x1, y1, x2, y2.
-            labels (Tensor): Shape (n*bs, ).
-            bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class).
-            pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
-                is a gt bbox.
-            img_metas (list[dict]): Meta info of each image.
-
-        Returns:
-            list[Tensor]: Refined bboxes of each image in a mini-batch.
-
-        Example:
-            >>> # xdoctest: +REQUIRES(module:kwarray)
-            >>> import kwarray
-            >>> import numpy as np
-            >>> from mmdet.core.bbox.demodata import random_boxes
-            >>> self = BBoxHead(reg_class_agnostic=True)
-            >>> n_roi = 2
-            >>> n_img = 4
-            >>> scale = 512
-            >>> rng = np.random.RandomState(0)
-            >>> img_metas = [{'img_shape': (scale, scale)}
-            ...              for _ in range(n_img)]
-            >>> # Create rois in the expected format
-            >>> roi_boxes = random_boxes(n_roi, scale=scale, rng=rng)
-            >>> img_ids = torch.randint(0, n_img, (n_roi,))
-            >>> img_ids = img_ids.float()
-            >>> rois = torch.cat([img_ids[:, None], roi_boxes], dim=1)
-            >>> # Create other args
-            >>> labels = torch.randint(0, 2, (n_roi,)).long()
-            >>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
-            >>> # For each image, pretend random positive boxes are gts
-            >>> is_label_pos = (labels.numpy() > 0).astype(np.int)
-            >>> lbl_per_img = kwarray.group_items(is_label_pos,
-            ...                                   img_ids.numpy())
-            >>> pos_per_img = [sum(lbl_per_img.get(gid, []))
-            ...                for gid in range(n_img)]
-            >>> pos_is_gts = [
-            >>>     torch.randint(0, 2, (npos,)).byte().sort(
-            >>>         descending=True)[0]
-            >>>     for npos in pos_per_img
-            >>> ]
-            >>> bboxes_list = self.refine_bboxes(rois, labels, bbox_preds,
-            >>>                    pos_is_gts, img_metas)
-            >>> print(bboxes_list)
-        """
-        img_ids = rois[:, 0].long().unique(sorted=True)
-        assert img_ids.numel() <= len(img_metas)
-
-        bboxes_list = []
-        for i in range(len(img_metas)):
-            inds = torch.nonzero(
-                rois[:, 0] == i, as_tuple=False).squeeze(dim=1)
-            num_rois = inds.numel()
-
-            bboxes_ = rois[inds, 1:]
-            label_ = labels[inds]
-            bbox_pred_ = bbox_preds[inds]
-            img_meta_ = img_metas[i]
-            pos_is_gts_ = pos_is_gts[i]
-
-            bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
-                                           img_meta_)
-
-            # filter gt bboxes
-            pos_keep = 1 - pos_is_gts_
-            keep_inds = pos_is_gts_.new_ones(num_rois)
-            keep_inds[:len(pos_is_gts_)] = pos_keep
-
-            bboxes_list.append(bboxes[keep_inds.type(torch.bool)])
-
-        return bboxes_list
-
-    @force_fp32(apply_to=('bbox_pred', ))
-    def regress_by_class(self, rois, label, bbox_pred, img_meta):
-        """Regress the bbox for the predicted class. Used in Cascade R-CNN.
-
-        Args:
-            rois (Tensor): shape (n, 4) or (n, 5)
-            label (Tensor): shape (n, )
-            bbox_pred (Tensor): shape (n, 4*(#class)) or (n, 4)
-            img_meta (dict): Image meta info.
-
-        Returns:
-            Tensor: Regressed bboxes, the same shape as input rois.
-        """
-        assert rois.size(1) == 4 or rois.size(1) == 5, repr(rois.shape)
-
-        if not self.reg_class_agnostic:
-            label = label * 4
-            inds = torch.stack((label, label + 1, label + 2, label + 3), 1)
-            bbox_pred = torch.gather(bbox_pred, 1, inds)
-        assert bbox_pred.size(1) == 4
-
-        if rois.size(1) == 4:
-            new_rois = self.bbox_coder.decode(
-                rois, bbox_pred, max_shape=img_meta['img_shape'])
-        else:
-            bboxes = self.bbox_coder.decode(
-                rois[:, 1:], bbox_pred, max_shape=img_meta['img_shape'])
-            new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
-
-        return new_rois

spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py

@@ -1,2 +0,0 @@
-_base_ = './pspnet_r50-d8_512x512_40k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Anni123/AuRoRA/README.md

@@ -1,6 +0,0 @@
----
-title: Unified-Adapter
-app_file: app.py
-sdk: gradio
-sdk_version: 3.36.1
----

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/padding.py

@@ -1,36 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .registry import PADDING_LAYERS
-
-PADDING_LAYERS.register_module('zero', module=nn.ZeroPad2d)
-PADDING_LAYERS.register_module('reflect', module=nn.ReflectionPad2d)
-PADDING_LAYERS.register_module('replicate', module=nn.ReplicationPad2d)
-
-
-def build_padding_layer(cfg, *args, **kwargs):
-    """Build padding layer.
-
-    Args:
-        cfg (None or dict): The padding layer config, which should contain:
-            - type (str): Layer type.
-            - layer args: Args needed to instantiate a padding layer.
-
-    Returns:
-        nn.Module: Created padding layer.
-    """
-    if not isinstance(cfg, dict):
-        raise TypeError('cfg must be a dict')
-    if 'type' not in cfg:
-        raise KeyError('the cfg dict must contain the key "type"')
-
-    cfg_ = cfg.copy()
-    padding_type = cfg_.pop('type')
-    if padding_type not in PADDING_LAYERS:
-        raise KeyError(f'Unrecognized padding type {padding_type}.')
-    else:
-        padding_layer = PADDING_LAYERS.get(padding_type)
-
-    layer = padding_layer(*args, **kwargs, **cfg_)
-
-    return layer

spaces/Ariharasudhan/YoloV5/utils/google_app_engine/Dockerfile

@@ -1,25 +0,0 @@
-FROM gcr.io/google-appengine/python
-
-# Create a virtualenv for dependencies. This isolates these packages from
-# system-level packages.
-# Use -p python3 or -p python3.7 to select python version. Default is version 2.
-RUN virtualenv /env -p python3
-
-# Setting these environment variables are the same as running
-# source /env/bin/activate.
-ENV VIRTUAL_ENV /env
-ENV PATH /env/bin:$PATH
-
-RUN apt-get update && apt-get install -y python-opencv
-
-# Copy the application's requirements.txt and run pip to install all
-# dependencies into the virtualenv.
-ADD requirements.txt /app/requirements.txt
-RUN pip install -r /app/requirements.txt
-
-# Add the application source code.
-ADD . /app
-
-# Run a WSGI server to serve the application. gunicorn must be declared as
-# a dependency in requirements.txt.
-CMD gunicorn -b :$PORT main:app

spaces/Armored-Atom/gpt2/README.md

@@ -1,13 +0,0 @@
----
-title: Gpt2
-emoji: 🚀
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/backbone/position_encoding.py

@@ -1,186 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Various positional encodings for the transformer.
-"""
-import math
-
-import torch
-from torch import nn
-
-from groundingdino.util.misc import NestedTensor
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        mask = tensor_list.mask
-        assert mask is not None
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            # if os.environ.get("SHILONG_AMP", None) == '1':
-            #     eps = 1e-4
-            # else:
-            #     eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-class PositionEmbeddingSineHW(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(
-        self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
-    ):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperatureH = temperatureH
-        self.temperatureW = temperatureW
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        mask = tensor_list.mask
-        assert mask is not None
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-
-        # import ipdb; ipdb.set_trace()
-
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
-        pos_x = x_embed[:, :, :, None] / dim_tx
-
-        dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
-        pos_y = y_embed[:, :, :, None] / dim_ty
-
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-
-        # import ipdb; ipdb.set_trace()
-
-        return pos
-
-
-class PositionEmbeddingLearned(nn.Module):
-    """
-    Absolute pos embedding, learned.
-    """
-
-    def __init__(self, num_pos_feats=256):
-        super().__init__()
-        self.row_embed = nn.Embedding(50, num_pos_feats)
-        self.col_embed = nn.Embedding(50, num_pos_feats)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        nn.init.uniform_(self.row_embed.weight)
-        nn.init.uniform_(self.col_embed.weight)
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        h, w = x.shape[-2:]
-        i = torch.arange(w, device=x.device)
-        j = torch.arange(h, device=x.device)
-        x_emb = self.col_embed(i)
-        y_emb = self.row_embed(j)
-        pos = (
-            torch.cat(
-                [
-                    x_emb.unsqueeze(0).repeat(h, 1, 1),
-                    y_emb.unsqueeze(1).repeat(1, w, 1),
-                ],
-                dim=-1,
-            )
-            .permute(2, 0, 1)
-            .unsqueeze(0)
-            .repeat(x.shape[0], 1, 1, 1)
-        )
-        return pos
-
-
-def build_position_encoding(args):
-    N_steps = args.hidden_dim // 2
-    if args.position_embedding in ("v2", "sine"):
-        # TODO find a better way of exposing other arguments
-        position_embedding = PositionEmbeddingSineHW(
-            N_steps,
-            temperatureH=args.pe_temperatureH,
-            temperatureW=args.pe_temperatureW,
-            normalize=True,
-        )
-    elif args.position_embedding in ("v3", "learned"):
-        position_embedding = PositionEmbeddingLearned(N_steps)
-    else:
-        raise ValueError(f"not supported {args.position_embedding}")
-
-    return position_embedding

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/build_meta.py

@@ -1,511 +0,0 @@
-"""A PEP 517 interface to setuptools
-
-Previously, when a user or a command line tool (let's call it a "frontend")
-needed to make a request of setuptools to take a certain action, for
-example, generating a list of installation requirements, the frontend would
-would call "setup.py egg_info" or "setup.py bdist_wheel" on the command line.
-
-PEP 517 defines a different method of interfacing with setuptools. Rather
-than calling "setup.py" directly, the frontend should:
-
-  1. Set the current directory to the directory with a setup.py file
-  2. Import this module into a safe python interpreter (one in which
-     setuptools can potentially set global variables or crash hard).
-  3. Call one of the functions defined in PEP 517.
-
-What each function does is defined in PEP 517. However, here is a "casual"
-definition of the functions (this definition should not be relied on for
-bug reports or API stability):
-
-  - `build_wheel`: build a wheel in the folder and return the basename
-  - `get_requires_for_build_wheel`: get the `setup_requires` to build
-  - `prepare_metadata_for_build_wheel`: get the `install_requires`
-  - `build_sdist`: build an sdist in the folder and return the basename
-  - `get_requires_for_build_sdist`: get the `setup_requires` to build
-
-Again, this is not a formal definition! Just a "taste" of the module.
-"""
-
-import io
-import os
-import shlex
-import sys
-import tokenize
-import shutil
-import contextlib
-import tempfile
-import warnings
-from pathlib import Path
-from typing import Dict, Iterator, List, Optional, Union
-
-import setuptools
-import distutils
-from . import errors
-from ._path import same_path
-from ._reqs import parse_strings
-from ._deprecation_warning import SetuptoolsDeprecationWarning
-from distutils.util import strtobool
-
-
-__all__ = ['get_requires_for_build_sdist',
-           'get_requires_for_build_wheel',
-           'prepare_metadata_for_build_wheel',
-           'build_wheel',
-           'build_sdist',
-           'get_requires_for_build_editable',
-           'prepare_metadata_for_build_editable',
-           'build_editable',
-           '__legacy__',
-           'SetupRequirementsError']
-
-SETUPTOOLS_ENABLE_FEATURES = os.getenv("SETUPTOOLS_ENABLE_FEATURES", "").lower()
-LEGACY_EDITABLE = "legacy-editable" in SETUPTOOLS_ENABLE_FEATURES.replace("_", "-")
-
-
-class SetupRequirementsError(BaseException):
-    def __init__(self, specifiers):
-        self.specifiers = specifiers
-
-
-class Distribution(setuptools.dist.Distribution):
-    def fetch_build_eggs(self, specifiers):
-        specifier_list = list(parse_strings(specifiers))
-
-        raise SetupRequirementsError(specifier_list)
-
-    @classmethod
-    @contextlib.contextmanager
-    def patch(cls):
-        """
-        Replace
-        distutils.dist.Distribution with this class
-        for the duration of this context.
-        """
-        orig = distutils.core.Distribution
-        distutils.core.Distribution = cls
-        try:
-            yield
-        finally:
-            distutils.core.Distribution = orig
-
-
-@contextlib.contextmanager
-def no_install_setup_requires():
-    """Temporarily disable installing setup_requires
-
-    Under PEP 517, the backend reports build dependencies to the frontend,
-    and the frontend is responsible for ensuring they're installed.
-    So setuptools (acting as a backend) should not try to install them.
-    """
-    orig = setuptools._install_setup_requires
-    setuptools._install_setup_requires = lambda attrs: None
-    try:
-        yield
-    finally:
-        setuptools._install_setup_requires = orig
-
-
-def _get_immediate_subdirectories(a_dir):
-    return [name for name in os.listdir(a_dir)
-            if os.path.isdir(os.path.join(a_dir, name))]
-
-
-def _file_with_extension(directory, extension):
-    matching = (
-        f for f in os.listdir(directory)
-        if f.endswith(extension)
-    )
-    try:
-        file, = matching
-    except ValueError:
-        raise ValueError(
-            'No distribution was found. Ensure that `setup.py` '
-            'is not empty and that it calls `setup()`.')
-    return file
-
-
-def _open_setup_script(setup_script):
-    if not os.path.exists(setup_script):
-        # Supply a default setup.py
-        return io.StringIO(u"from setuptools import setup; setup()")
-
-    return getattr(tokenize, 'open', open)(setup_script)
-
-
-@contextlib.contextmanager
-def suppress_known_deprecation():
-    with warnings.catch_warnings():
-        warnings.filterwarnings('ignore', 'setup.py install is deprecated')
-        yield
-
-
-_ConfigSettings = Optional[Dict[str, Union[str, List[str], None]]]
-"""
-Currently the user can run::
-
-    pip install -e . --config-settings key=value
-    python -m build -C--key=value -C key=value
-
-- pip will pass both key and value as strings and overwriting repeated keys
-  (pypa/pip#11059).
-- build will accumulate values associated with repeated keys in a list.
-  It will also accept keys with no associated value.
-  This means that an option passed by build can be ``str | list[str] | None``.
-- PEP 517 specifies that ``config_settings`` is an optional dict.
-"""
-
-
-class _ConfigSettingsTranslator:
-    """Translate ``config_settings`` into distutils-style command arguments.
-    Only a limited number of options is currently supported.
-    """
-    # See pypa/setuptools#1928 pypa/setuptools#2491
-
-    def _get_config(self, key: str, config_settings: _ConfigSettings) -> List[str]:
-        """
-        Get the value of a specific key in ``config_settings`` as a list of strings.
-
-        >>> fn = _ConfigSettingsTranslator()._get_config
-        >>> fn("--global-option", None)
-        []
-        >>> fn("--global-option", {})
-        []
-        >>> fn("--global-option", {'--global-option': 'foo'})
-        ['foo']
-        >>> fn("--global-option", {'--global-option': ['foo']})
-        ['foo']
-        >>> fn("--global-option", {'--global-option': 'foo'})
-        ['foo']
-        >>> fn("--global-option", {'--global-option': 'foo bar'})
-        ['foo', 'bar']
-        """
-        cfg = config_settings or {}
-        opts = cfg.get(key) or []
-        return shlex.split(opts) if isinstance(opts, str) else opts
-
-    def _valid_global_options(self):
-        """Global options accepted by setuptools (e.g. quiet or verbose)."""
-        options = (opt[:2] for opt in setuptools.dist.Distribution.global_options)
-        return {flag for long_and_short in options for flag in long_and_short if flag}
-
-    def _global_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
-        """
-        Let the user specify ``verbose`` or ``quiet`` + escape hatch via
-        ``--global-option``.
-        Note: ``-v``, ``-vv``, ``-vvv`` have similar effects in setuptools,
-        so we just have to cover the basic scenario ``-v``.
-
-        >>> fn = _ConfigSettingsTranslator()._global_args
-        >>> list(fn(None))
-        []
-        >>> list(fn({"verbose": "False"}))
-        ['-q']
-        >>> list(fn({"verbose": "1"}))
-        ['-v']
-        >>> list(fn({"--verbose": None}))
-        ['-v']
-        >>> list(fn({"verbose": "true", "--global-option": "-q --no-user-cfg"}))
-        ['-v', '-q', '--no-user-cfg']
-        >>> list(fn({"--quiet": None}))
-        ['-q']
-        """
-        cfg = config_settings or {}
-        falsey = {"false", "no", "0", "off"}
-        if "verbose" in cfg or "--verbose" in cfg:
-            level = str(cfg.get("verbose") or cfg.get("--verbose") or "1")
-            yield ("-q" if level.lower() in falsey else "-v")
-        if "quiet" in cfg or "--quiet" in cfg:
-            level = str(cfg.get("quiet") or cfg.get("--quiet") or "1")
-            yield ("-v" if level.lower() in falsey else "-q")
-
-        valid = self._valid_global_options()
-        args = self._get_config("--global-option", config_settings)
-        yield from (arg for arg in args if arg.strip("-") in valid)
-
-    def __dist_info_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
-        """
-        The ``dist_info`` command accepts ``tag-date`` and ``tag-build``.
-
-        .. warning::
-           We cannot use this yet as it requires the ``sdist`` and ``bdist_wheel``
-           commands run in ``build_sdist`` and ``build_wheel`` to re-use the egg-info
-           directory created in ``prepare_metadata_for_build_wheel``.
-
-        >>> fn = _ConfigSettingsTranslator()._ConfigSettingsTranslator__dist_info_args
-        >>> list(fn(None))
-        []
-        >>> list(fn({"tag-date": "False"}))
-        ['--no-date']
-        >>> list(fn({"tag-date": None}))
-        ['--no-date']
-        >>> list(fn({"tag-date": "true", "tag-build": ".a"}))
-        ['--tag-date', '--tag-build', '.a']
-        """
-        cfg = config_settings or {}
-        if "tag-date" in cfg:
-            val = strtobool(str(cfg["tag-date"] or "false"))
-            yield ("--tag-date" if val else "--no-date")
-        if "tag-build" in cfg:
-            yield from ["--tag-build", str(cfg["tag-build"])]
-
-    def _editable_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
-        """
-        The ``editable_wheel`` command accepts ``editable-mode=strict``.
-
-        >>> fn = _ConfigSettingsTranslator()._editable_args
-        >>> list(fn(None))
-        []
-        >>> list(fn({"editable-mode": "strict"}))
-        ['--mode', 'strict']
-        """
-        cfg = config_settings or {}
-        mode = cfg.get("editable-mode") or cfg.get("editable_mode")
-        if not mode:
-            return
-        yield from ["--mode", str(mode)]
-
-    def _arbitrary_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
-        """
-        Users may expect to pass arbitrary lists of arguments to a command
-        via "--global-option" (example provided in PEP 517 of a "escape hatch").
-
-        >>> fn = _ConfigSettingsTranslator()._arbitrary_args
-        >>> list(fn(None))
-        []
-        >>> list(fn({}))
-        []
-        >>> list(fn({'--build-option': 'foo'}))
-        ['foo']
-        >>> list(fn({'--build-option': ['foo']}))
-        ['foo']
-        >>> list(fn({'--build-option': 'foo'}))
-        ['foo']
-        >>> list(fn({'--build-option': 'foo bar'}))
-        ['foo', 'bar']
-        >>> warnings.simplefilter('error', SetuptoolsDeprecationWarning)
-        >>> list(fn({'--global-option': 'foo'}))  # doctest: +IGNORE_EXCEPTION_DETAIL
-        Traceback (most recent call last):
-        SetuptoolsDeprecationWarning: ...arguments given via `--global-option`...
-        """
-        args = self._get_config("--global-option", config_settings)
-        global_opts = self._valid_global_options()
-        bad_args = []
-
-        for arg in args:
-            if arg.strip("-") not in global_opts:
-                bad_args.append(arg)
-                yield arg
-
-        yield from self._get_config("--build-option", config_settings)
-
-        if bad_args:
-            msg = f"""
-            The arguments {bad_args!r} were given via `--global-option`.
-            Please use `--build-option` instead,
-            `--global-option` is reserved to flags like `--verbose` or `--quiet`.
-            """
-            warnings.warn(msg, SetuptoolsDeprecationWarning)
-
-
-class _BuildMetaBackend(_ConfigSettingsTranslator):
-    def _get_build_requires(self, config_settings, requirements):
-        sys.argv = [
-            *sys.argv[:1],
-            *self._global_args(config_settings),
-            "egg_info",
-            *self._arbitrary_args(config_settings),
-        ]
-        try:
-            with Distribution.patch():
-                self.run_setup()
-        except SetupRequirementsError as e:
-            requirements += e.specifiers
-
-        return requirements
-
-    def run_setup(self, setup_script='setup.py'):
-        # Note that we can reuse our build directory between calls
-        # Correctness comes first, then optimization later
-        __file__ = setup_script
-        __name__ = '__main__'
-
-        with _open_setup_script(__file__) as f:
-            code = f.read().replace(r'\r\n', r'\n')
-
-        exec(code, locals())
-
-    def get_requires_for_build_wheel(self, config_settings=None):
-        return self._get_build_requires(config_settings, requirements=['wheel'])
-
-    def get_requires_for_build_sdist(self, config_settings=None):
-        return self._get_build_requires(config_settings, requirements=[])
-
-    def _bubble_up_info_directory(self, metadata_directory: str, suffix: str) -> str:
-        """
-        PEP 517 requires that the .dist-info directory be placed in the
-        metadata_directory. To comply, we MUST copy the directory to the root.
-
-        Returns the basename of the info directory, e.g. `proj-0.0.0.dist-info`.
-        """
-        info_dir = self._find_info_directory(metadata_directory, suffix)
-        if not same_path(info_dir.parent, metadata_directory):
-            shutil.move(str(info_dir), metadata_directory)
-            # PEP 517 allow other files and dirs to exist in metadata_directory
-        return info_dir.name
-
-    def _find_info_directory(self, metadata_directory: str, suffix: str) -> Path:
-        for parent, dirs, _ in os.walk(metadata_directory):
-            candidates = [f for f in dirs if f.endswith(suffix)]
-
-            if len(candidates) != 0 or len(dirs) != 1:
-                assert len(candidates) == 1, f"Multiple {suffix} directories found"
-                return Path(parent, candidates[0])
-
-        msg = f"No {suffix} directory found in {metadata_directory}"
-        raise errors.InternalError(msg)
-
-    def prepare_metadata_for_build_wheel(self, metadata_directory,
-                                         config_settings=None):
-        sys.argv = [
-            *sys.argv[:1],
-            *self._global_args(config_settings),
-            "dist_info",
-            "--output-dir", metadata_directory,
-            "--keep-egg-info",
-        ]
-        with no_install_setup_requires():
-            self.run_setup()
-
-        self._bubble_up_info_directory(metadata_directory, ".egg-info")
-        return self._bubble_up_info_directory(metadata_directory, ".dist-info")
-
-    def _build_with_temp_dir(self, setup_command, result_extension,
-                             result_directory, config_settings):
-        result_directory = os.path.abspath(result_directory)
-
-        # Build in a temporary directory, then copy to the target.
-        os.makedirs(result_directory, exist_ok=True)
-        with tempfile.TemporaryDirectory(dir=result_directory) as tmp_dist_dir:
-            sys.argv = [
-                *sys.argv[:1],
-                *self._global_args(config_settings),
-                *setup_command,
-                "--dist-dir", tmp_dist_dir,
-                *self._arbitrary_args(config_settings),
-            ]
-            with no_install_setup_requires():
-                self.run_setup()
-
-            result_basename = _file_with_extension(
-                tmp_dist_dir, result_extension)
-            result_path = os.path.join(result_directory, result_basename)
-            if os.path.exists(result_path):
-                # os.rename will fail overwriting on non-Unix.
-                os.remove(result_path)
-            os.rename(os.path.join(tmp_dist_dir, result_basename), result_path)
-
-        return result_basename
-
-    def build_wheel(self, wheel_directory, config_settings=None,
-                    metadata_directory=None):
-        with suppress_known_deprecation():
-            return self._build_with_temp_dir(['bdist_wheel'], '.whl',
-                                             wheel_directory, config_settings)
-
-    def build_sdist(self, sdist_directory, config_settings=None):
-        return self._build_with_temp_dir(['sdist', '--formats', 'gztar'],
-                                         '.tar.gz', sdist_directory,
-                                         config_settings)
-
-    def _get_dist_info_dir(self, metadata_directory: Optional[str]) -> Optional[str]:
-        if not metadata_directory:
-            return None
-        dist_info_candidates = list(Path(metadata_directory).glob("*.dist-info"))
-        assert len(dist_info_candidates) <= 1
-        return str(dist_info_candidates[0]) if dist_info_candidates else None
-
-    if not LEGACY_EDITABLE:
-
-        # PEP660 hooks:
-        # build_editable
-        # get_requires_for_build_editable
-        # prepare_metadata_for_build_editable
-        def build_editable(
-            self, wheel_directory, config_settings=None, metadata_directory=None
-        ):
-            # XXX can or should we hide our editable_wheel command normally?
-            info_dir = self._get_dist_info_dir(metadata_directory)
-            opts = ["--dist-info-dir", info_dir] if info_dir else []
-            cmd = ["editable_wheel", *opts, *self._editable_args(config_settings)]
-            with suppress_known_deprecation():
-                return self._build_with_temp_dir(
-                    cmd, ".whl", wheel_directory, config_settings
-                )
-
-        def get_requires_for_build_editable(self, config_settings=None):
-            return self.get_requires_for_build_wheel(config_settings)
-
-        def prepare_metadata_for_build_editable(self, metadata_directory,
-                                                config_settings=None):
-            return self.prepare_metadata_for_build_wheel(
-                metadata_directory, config_settings
-            )
-
-
-class _BuildMetaLegacyBackend(_BuildMetaBackend):
-    """Compatibility backend for setuptools
-
-    This is a version of setuptools.build_meta that endeavors
-    to maintain backwards
-    compatibility with pre-PEP 517 modes of invocation. It
-    exists as a temporary
-    bridge between the old packaging mechanism and the new
-    packaging mechanism,
-    and will eventually be removed.
-    """
-    def run_setup(self, setup_script='setup.py'):
-        # In order to maintain compatibility with scripts assuming that
-        # the setup.py script is in a directory on the PYTHONPATH, inject
-        # '' into sys.path. (pypa/setuptools#1642)
-        sys_path = list(sys.path)           # Save the original path
-
-        script_dir = os.path.dirname(os.path.abspath(setup_script))
-        if script_dir not in sys.path:
-            sys.path.insert(0, script_dir)
-
-        # Some setup.py scripts (e.g. in pygame and numpy) use sys.argv[0] to
-        # get the directory of the source code. They expect it to refer to the
-        # setup.py script.
-        sys_argv_0 = sys.argv[0]
-        sys.argv[0] = setup_script
-
-        try:
-            super(_BuildMetaLegacyBackend,
-                  self).run_setup(setup_script=setup_script)
-        finally:
-            # While PEP 517 frontends should be calling each hook in a fresh
-            # subprocess according to the standard (and thus it should not be
-            # strictly necessary to restore the old sys.path), we'll restore
-            # the original path so that the path manipulation does not persist
-            # within the hook after run_setup is called.
-            sys.path[:] = sys_path
-            sys.argv[0] = sys_argv_0
-
-
-# The primary backend
-_BACKEND = _BuildMetaBackend()
-
-get_requires_for_build_wheel = _BACKEND.get_requires_for_build_wheel
-get_requires_for_build_sdist = _BACKEND.get_requires_for_build_sdist
-prepare_metadata_for_build_wheel = _BACKEND.prepare_metadata_for_build_wheel
-build_wheel = _BACKEND.build_wheel
-build_sdist = _BACKEND.build_sdist
-
-if not LEGACY_EDITABLE:
-    get_requires_for_build_editable = _BACKEND.get_requires_for_build_editable
-    prepare_metadata_for_build_editable = _BACKEND.prepare_metadata_for_build_editable
-    build_editable = _BACKEND.build_editable
-
-
-# The legacy backend
-__legacy__ = _BuildMetaLegacyBackend()

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/__init__.py

@@ -1,17 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .build import build_backbone, BACKBONE_REGISTRY  # noqa F401 isort:skip
-
-from .backbone import Backbone
-from .fpn import FPN
-from .regnet import RegNet
-from .resnet import (
-    BasicStem,
-    ResNet,
-    ResNetBlockBase,
-    build_resnet_backbone,
-    make_stage,
-    BottleneckBlock,
-)
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
-# TODO can expose more resnet blocks after careful consideration

spaces/Benson/text-generation/Examples/8 Bola Piscina 5.12.0 Apk Descargar.md

@@ -1,109 +0,0 @@
-<br />
-<h1>8 Piscina de bolas 5.12.0 APK Descargar: Cómo jugar el juego de billar más grande del mundo en su dispositivo Android</h1>
- <p>Si eres un fan de los juegos de billar, debes haber oído hablar de <strong>8 Ball Pool</strong>, el juego de billar más popular y adictivo del mundo. Desarrollado por Miniclip, este juego le permite jugar en línea o fuera de línea con millones de jugadores de diferentes países, competir en torneos, ganar monedas y artículos exclusivos, y personalizar su señal y mesa. Si eres un principiante o un profesional, encontrarás 8 Ball Pool desafiante y divertido. </p>
-<h2>8 bola piscina 5.12.0 apk descargar</h2><br /><p><b><b>Download Zip</b> &#10031; <a href="https://bltlly.com/2v6JB8">https://bltlly.com/2v6JB8</a></b></p><br /><br />
- <p>En este artículo, te diremos todo lo que necesitas saber sobre <strong>8 Ball Pool 5.12.0 APK</strong>, la última versión del juego que fue lanzado el 20 de junio de 2023. Te mostraremos cómo descargarlo e instalarlo en tu dispositivo Android, cómo jugarlo online o offline, y cómo mejorar tus habilidades y ganar más partidos con algunos consejos y trucos. </p>
- <h2>¿Qué es la piscina de bolas 8? </h2>
- <h3>Una breve introducción al juego y sus características</h3>
- <p>8 Ball Pool es un juego de billar basado en física de billar 3D real, donde puedes jugar contra tus amigos u otros jugadores en línea en diferentes modos, como partidos 1-a-1, torneos o minijuegos. También puede jugar sin conexión en el modo de práctica o contra el ordenador. </p>
- <p>El juego tiene un sistema de niveles que te empareja con jugadores de nivel de habilidad similar, y un sistema de clasificación que muestra tu progreso en la clasificación global. También puede unirse a clubes y chatear con otros miembros, o crear su propio club e invitar a sus amigos. </p>
- <p>El juego tiene una variedad de pistas y mesas que se puede desbloquear o comprar con monedas o dinero en efectivo, las monedas del juego. También puede obtener monedas o dinero en efectivo girando la rueda, viendo videos, completando ofertas o comprándolas con dinero real. Las monedas y el dinero en efectivo se pueden usar para ingresar partidas de apuestas más altas, comprar artículos en la tienda de la piscina o actualizar sus señales. </p>
- <h3>La última versión 5.12.0 y lo nuevo en ella</h3>
-
- <ul>
-<li>Una nueva temporada con nuevas recompensas y desafíos</li>
-<li>Una nueva función que te permite jugar por el cambio y donar a la Global Gift Foundation</li>
-<li> Algunos ajustes y correcciones de errores que hacen el juego más suave y más estable</li>
-</ul>
- <p>Para disfrutar de estas nuevas características, es necesario descargar e instalar la última versión de 8 Ball Pool en su dispositivo Android. </p>
- <h2> Cómo descargar e instalar 8 bola piscina 5.12.0 APK en su dispositivo Android</h2>
- <h3>Los pasos para descargar e instalar el archivo APK desde una fuente de confianza</h3>
- <p>Si desea descargar e instalar 8 Ball Pool 5.12.0 APK en su dispositivo Android, es necesario seguir estos pasos:</p>
-<p></p>
- <ol>
-<li>Ir a un sitio web de confianza que proporciona archivos APK para aplicaciones de Android, tales como [Soft onic] o [APKPure]. </li>
-<li>Búsqueda de 8 Ball Pool 5.12.0 APK y descargarlo en su dispositivo. Asegúrese de que el tamaño del archivo es de aproximadamente 75 MB y el nombre del archivo es com.miniclip.eightballpool_5.12.0-2410_minAPI16(armeabi-v7a,x86)(nodpi)_apkmirror.com.apk. </li>
-<li>Antes de instalar el archivo APK, es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en el dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Busque el archivo APK descargado en su dispositivo y toque en él para iniciar el proceso de instalación. Siga las instrucciones de la pantalla y espere a que termine la instalación. </li>
-<li>Una vez que se realiza la instalación, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio y disfrutar de jugar 8 Ball Pool 5.12.0 APK en su dispositivo Android. </li>
-</ol>
- <h3>Los permisos y requisitos para la aplicación</h3>
- <p>Antes de instalar 8 Ball Pool 5.12.0 APK en su dispositivo Android, usted debe ser consciente de los permisos y requisitos para la aplicación. La aplicación requiere los siguientes permisos:</p>
- <ul>
-<li> Acceso al almacenamiento, cámara, micrófono y ubicación de su dispositivo</li>
-<li>Acceso a las conexiones de red de su dispositivo, conexiones Wi-Fi y configuración de Bluetooth</li>
-
-<li>Acceso a la vibración de su dispositivo y evitar que duerma</li>
-</ul>
- <p>La aplicación también requiere los siguientes requisitos:</p>
- <ul>
-<li>Un dispositivo Android con Android 4.4 o superior</li>
-<li>Una conexión a Internet (Wi-Fi o datos móviles)</li>
-<li>Una cuenta de Google para iniciar sesión y sincronizar tu progreso</li>
-<li>Una cuenta de Facebook para conectar con tus amigos y retarlos</li>
-</ul>
- <p>Debes conceder estos permisos y cumplir con estos requisitos para asegurar el correcto funcionamiento de la aplicación y disfrutar de sus características. </p>
- <h2>Cómo jugar al billar de 8 bolas en línea o fuera de línea</h2>
- <h3>Las reglas y objetivos básicos del juego</h3>
- <p>Las reglas y objetivos básicos de 8 Ball Pool son similares a los de los juegos de billar reales. El juego se juega en una mesa de billar con seis bolsillos, una bola blanca y 15 bolas de objetos (siete bolas de color sólido, siete bolas a rayas y una bola negra). </p>
- <p>El juego comienza con un tiro de ruptura, donde un jugador golpea la bola blanca con un palo de taco e intenta dispersar las bolas de objeto en la mesa. El jugador que embolsa una pelota en el tiro de descanso puede optar por jugar ya sea sólidos o rayas, dependiendo del tipo de pelota que embolsó. El otro jugador tiene que jugar el tipo opuesto. </p>
- <p>El objetivo del juego es meter todas tus bolas (ya sean sólidas o rayas) antes que tu oponente, y luego meter la bola negra (también conocida como la bola 8) en un bolsillo designado. Tienes que llamar al bolsillo por cada tiro que haces, excepto por el tiro de ruptura. Si te embolsas una pelota en un bolsillo diferente al que pagaste, o si te embolsas la pelota de tu oponente o la bola blanca, es una falta y pierdes tu turno. </p>
- <p>También puedes perder el juego si cometes cualquiera de estas faltas:</p>
- <ul>
-<li>Embolsar la bola 8 antes de limpiar sus bolas</li>
-<li>Embolsar la bola 8 en un bolsillo diferente al que llamaste</li>
-<li>Embolsar la bola 8 y la bola blanca en el mismo tiro</li>
-
-</ul>
- <p>El juego termina cuando un jugador se embolsa la bola 8 legalmente o cuando un jugador pierde o se desconecta del juego. </p>
- <h3>Los diferentes modos, tablas, señales y elementos disponibles en el juego</h3>
- <p>8 Ball Pool tiene diferentes modos que puedes jugar online o offline, como:</p>
- <ul>
-<li><strong>1-on-1 partidos:</strong> Puede jugar contra otro jugador en línea en un partido al azar o un partido de amigos. Puede elegir entre diferentes tablas con diferentes cuotas de entrada y recompensas. Cuanto mayor sea la cuota de entrada, mayor será la recompensa. </li>
-<li><strong>Torneos:</strong> Puedes jugar contra otros siete jugadores en línea en un formato eliminatorio. Tienes que ganar cuatro partidos seguidos para ganar el torneo y obtener un gran premio. </li>
-<li><strong>Minijuegos:</strong> Puedes jugar algunos minijuegos para ganar monedas, dinero, tacos u otros objetos. Algunos de los minijuegos son Spin & Win, Scratch & Win, Hi-Lo, Lucky Shot y Cajas sorpresa.</li>
-<li><strong>Modo de práctica:</strong> Puedes jugar sin conexión contra la computadora o por ti mismo para practicar tus habilidades y probar diferentes disparos. </li>
-</ul>
- <p>El juego también tiene una variedad de pistas y mesas que puedes desbloquear o comprar con monedas o dinero en efectivo. Cada señal tiene diferentes atributos, como poder, puntería, efectos y tiempo. También puedes actualizar tus señales para mejorar sus atributos. Cada mesa tiene diferentes diseños, tamaños y reglas. También puede personalizar su mesa con diferentes telas, patrones y calcomanías. </p>
- <p>El juego también tiene una tienda de billar donde puedes comprar objetos como monedas, dinero en efectivo, tacos, mesas, paquetes de chat, avatares y señales de suerte. También puedes obtener algunos artículos gratis viendo videos, completando ofertas o invitando a amigos. </p>
- <h3>Los consejos y trucos para mejorar tus habilidades y ganar más partidos</h3>
- <p>Si quieres mejorar tus habilidades y ganar más partidos en 8 Ball Pool, debes seguir estos consejos y trucos:</p>
- <ul>
-
-<li><strong>Planificar con antelación:</strong> El juego requiere estrategia y previsión. Debes planificar con anticipación y pensar en qué bolas quieres meter primero, qué bolsillos quieres usar y cómo quieres colocar la bola blanca para el siguiente tiro. También debes evitar dejar tiros fáciles para tu oponente o bloquear tus propias bolas. </li>
-<li><strong>Usa spin:</strong> El juego te permite aplicar spin a la bola cue tocando el icono de spin y moviéndolo. Puede utilizar el giro para cambiar la dirección de la bola blanca después de que golpea una bola de objeto o un cojín. Puedes usar el giro para evitar rasguños, salir de situaciones difíciles o preparar mejores fotos. </li>
-<li><strong>Practicar offline:</strong> El juego tiene un modo de práctica donde puedes jugar offline contra el ordenador o por ti mismo. Puede utilizar este modo para practicar sus habilidades y probar diferentes disparos sin arriesgar sus monedas o clasificación. También puede aprender de los movimientos y errores de la computadora. </li>
-<li><strong>Ver vídeos:</strong> El juego tiene una sección de vídeo donde puedes ver vídeos de partidos o tutoriales de otros jugadores. Puedes usar estos videos para aprender de sus estrategias, técnicas y consejos. También puedes obtener algunas monedas u objetos viendo algunos videos. </li>
-</ul>
- <h2>Conclusión</h2>
- <h3>Un resumen de los principales puntos y beneficios de jugar 8 Ball Pool 5.12.0 APK en su dispositivo Android</h3>
- <p>Para resumir, 8 Ball Pool es un juego de billar que te permite jugar online o offline con millones de jugadores de diferentes países, competir en torneos, ganar monedas y artículos exclusivos, y personalizar tu señal y mesa. Se basa en la física real del pool 3D y tiene gráficos y sonidos realistas. </p>
-
- <p>Para jugar 8 Ball Pool 5.12.0 APK en su dispositivo Android, es necesario descargar e instalar desde un sitio web de confianza que proporciona archivos APK para aplicaciones Android. También debe habilitar la instalación de aplicaciones de fuentes desconocidas en su dispositivo, conceder los permisos y cumplir los requisitos para la aplicación, e iniciar sesión con su cuenta de Google o Facebook. </p>
- <p>Jugar 8 Ball Pool 5.12.0 APK en su dispositivo Android tiene muchos beneficios, tales como:</p>
- <ul>
-<li> Usted puede disfrutar de jugar al billar en cualquier momento y en cualquier lugar con su dispositivo Android</li>
-<li>Puedes desafiar a tus amigos u otros jugadores en línea en diferentes modos</li>
-<li>Puedes mejorar tus habilidades y posicionarte en la clasificación global</li>
-<li>Puedes desbloquear o comprar varias pistas y tablas que se adapten a tu estilo</li>
-<li>Puedes ganar monedas y artículos exclusivos que mejoran tu juego</li>
-<li> Usted puede divertirse y relajarse con una experiencia de piscina realista</li>
-</ul>
- <h3>Una llamada a la acción para descargar y jugar el juego ahora</h3>
- <p>Si usted está interesado en jugar 8 Ball Pool 5.12.0 APK en su dispositivo Android, ¿qué estás esperando? Descárgalo e instálalo desde un sitio web de confianza que proporciona archivos APK para aplicaciones de Android, como [Softonic] o [APKPure]. Siga los pasos que hemos proporcionado anteriormente y disfrutar de jugar el juego de billar más grande del mundo en su dispositivo Android. Usted no se arrepentirá, como 8 Ball Pool 5.12.0 APK es un juego que le mantendrá entretenido y desafiado durante horas. También tendrá la oportunidad de jugar por el cambio y donar a la Global Gift Foundation, una organización benéfica que apoya a niños y familias necesitadas de todo el mundo. Así que, ¿qué estás esperando? Descargar y jugar 8 Ball Pool 5.12.0 APK ahora y unirse a los millones de jugadores que aman este juego. Usted tendrá una explosión! <h2>Preguntas frecuentes</h2>
- <h3>Algunas preguntas y respuestas comunes sobre el juego y el archivo APK</h3>
- <p>Aquí hay algunas preguntas y respuestas comunes que usted podría tener acerca de 8 Piscina de bolas 5.12.0 APK:</p>
- <ol>
-
-<p>Sí, 8 Ball Pool 5.12.0 APK es seguro para descargar e instalar, siempre y cuando lo obtenga de un sitio web de confianza que proporciona archivos APK para aplicaciones Android, como [Softonic] o [APKPure]. Estos sitios web escanean los archivos APK en busca de virus y malware antes de cargarlos, para que pueda estar seguro de que están limpios y seguros. </p>
-<li><strong>Es 8 bola piscina 5.12.0 APK libre para jugar? </strong></li>
-<p>Sí, 8 Ball Pool 5.12.0 APK es libre de jugar, pero también tiene algunas compras en la aplicación que se puede hacer con dinero real si desea obtener más monedas, dinero en efectivo, señales, o elementos en el juego. Sin embargo, estas compras son opcionales y no necesarias para disfrutar del juego. </p>
-<li><strong>¿Puedo jugar 8 bola piscina 5.12.0 APK fuera de línea? </strong></li>
-<p>Sí, puede jugar 8 Ball Pool 5.12.0 APK fuera de línea en el modo de práctica o contra el ordenador, pero no podrá jugar en línea con otros jugadores o acceder a algunas características que requieren una conexión a Internet, como torneos, mini-juegos o clubes. </p>
-<li><strong>¿Puedo jugar 8 bola piscina 5.12.0 APK en otros dispositivos? </strong></li>
-<p>Sí, puede jugar 8 Ball Pool 5.12.0 APK en otros dispositivos además de su dispositivo Android, como su PC, Mac, dispositivo iOS o teléfono de Windows. Solo tienes que descargar e instalar la versión apropiada del juego para tu dispositivo desde el sitio web oficial de Miniclip o la tienda de aplicaciones de tu dispositivo. </p>
-<li><strong>¿Cómo puedo contactar al equipo de soporte de 8 Ball Pool? </strong></li>
-<p>Si tiene algún problema o pregunta sobre 8 Ball Pool, puede ponerse en contacto con el equipo de soporte de Miniclip llenando un formulario en su sitio web o enviando un correo electrónico a [email protected]. También puede visitar su centro de ayuda o su página de Facebook para obtener más información y actualizaciones. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat/vite.config.ts

@@ -1,12 +0,0 @@
-import { sveltekit } from "@sveltejs/kit/vite";
-import { defineConfig } from "vite";
-import Icons from "unplugin-icons/vite";
-
-export default defineConfig({
-	plugins: [
-		sveltekit(),
-		Icons({
-			compiler: "svelte",
-		}),
-	],
-});

spaces/Big-Web/MMSD/app.py

@@ -1,79 +0,0 @@
-import banana_dev as banana
-import base64
-from io import BytesIO
-from PIL import Image
-import gradio as gr
-import os
-# import boto3
-
-# model_key = os.environ.get("model_key")
-# api_key = os.environ.get("api_key")
-# aws_access_key_id = os.environ.get("aws_access_key_id")
-# aws_secret_access_key = os.environ.get("aws_secret_access_key")
-
-# #Create a session using AWS credentials
-# session = boto3.Session(aws_access_key_id, aws_secret_access_key)
-
-# #Create an S3 resource object using the session
-# s3 = session.resource('s3')
-
-# #Select your bucket
-# bucket = s3.Bucket('bwlmonet')
-
-model_inputs = {
-  "endpoint": "txt2img",
-  "params": {
-    "prompt": "",
-    "negative_prompt": "",
-    "steps": 25,
-    "sampler_name": "Euler a",
-    "cfg_scale": 7.5,
-    "seed": 42,
-    "batch_size": 1,
-    "n_iter": 1,
-    "width": 768,
-    "height": 768,
-    "tiling": False
-  }
-}
-
-# for obj in bucket.objects.all():
-#   print(obj.key)
-
-def stable_diffusion_txt2img(prompt, api_key, model_key, model_inputs):
-  # Update the model_inputs with the provided prompt
-  model_inputs["params"]["prompt"] = prompt
-
-  # Run the model
-  out = banana.run(api_key, model_key, model_inputs)
-
-  # Process the output
-  image_byte_string = out["modelOutputs"][0]["images"]
-  image_encoded = image_byte_string[0].encode("utf-8")
-  image_bytes = BytesIO(base64.b64decode(image_encoded))
-  image = Image.open(image_bytes)
-
-  # Save image to S3
-  # key = f"{prompt}.png"
-  # image.save(key)
-  # with open(key, "rb") as data:
-  #   bucket.put_object(Key=key, Body=data)
-  
-  # for obj in bucket.objects.all():
-  #   print(obj.key)
-
-  return image
-
-# Gradio Interface
-def generator(prompt):
-  return stable_diffusion_txt2img(prompt, api_key, model_key, model_inputs), stable_diffusion_txt2img(prompt, api_key, model_key, model_inputs)
-
-with gr.Blocks() as demo:
-  prompt = gr.Textbox(label="Prompt")
-  submit = gr.Button(label="Generate")
-  image1 = gr.Image()
-  image2 = gr.Image()
-
-  submit.click(generator, inputs=[prompt], outputs=[image1, image2], api_name="mmsd")
-
-demo.launch()

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

@@ -1,111 +0,0 @@
-# Copyright 2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-
-import logging
-
-from boto3.compat import _warn_deprecated_python
-from boto3.session import Session
-
-__author__ = 'Amazon Web Services'
-__version__ = '1.26.132'
-
-
-# The default Boto3 session; autoloaded when needed.
-DEFAULT_SESSION = None
-
-
-def setup_default_session(**kwargs):
-    """
-    Set up a default session, passing through any parameters to the session
-    constructor. There is no need to call this unless you wish to pass custom
-    parameters, because a default session will be created for you.
-    """
-    global DEFAULT_SESSION
-    DEFAULT_SESSION = Session(**kwargs)
-
-
-def set_stream_logger(name='boto3', level=logging.DEBUG, format_string=None):
-    """
-    Add a stream handler for the given name and level to the logging module.
-    By default, this logs all boto3 messages to ``stdout``.
-
-        >>> import boto3
-        >>> boto3.set_stream_logger('boto3.resources', logging.INFO)
-
-    For debugging purposes a good choice is to set the stream logger to ``''``
-    which is equivalent to saying "log everything".
-
-    .. WARNING::
-       Be aware that when logging anything from ``'botocore'`` the full wire
-       trace will appear in your logs. If your payloads contain sensitive data
-       this should not be used in production.
-
-    :type name: string
-    :param name: Log name
-    :type level: int
-    :param level: Logging level, e.g. ``logging.INFO``
-    :type format_string: str
-    :param format_string: Log message format
-    """
-    if format_string is None:
-        format_string = "%(asctime)s %(name)s [%(levelname)s] %(message)s"
-
-    logger = logging.getLogger(name)
-    logger.setLevel(level)
-    handler = logging.StreamHandler()
-    handler.setLevel(level)
-    formatter = logging.Formatter(format_string)
-    handler.setFormatter(formatter)
-    logger.addHandler(handler)
-
-
-def _get_default_session():
-    """
-    Get the default session, creating one if needed.
-
-    :rtype: :py:class:`~boto3.session.Session`
-    :return: The default session
-    """
-    if DEFAULT_SESSION is None:
-        setup_default_session()
-    _warn_deprecated_python()
-
-    return DEFAULT_SESSION
-
-
-def client(*args, **kwargs):
-    """
-    Create a low-level service client by name using the default session.
-
-    See :py:meth:`boto3.session.Session.client`.
-    """
-    return _get_default_session().client(*args, **kwargs)
-
-
-def resource(*args, **kwargs):
-    """
-    Create a resource service client by name using the default session.
-
-    See :py:meth:`boto3.session.Session.resource`.
-    """
-    return _get_default_session().resource(*args, **kwargs)
-
-
-# Set up logging to ``/dev/null`` like a library is supposed to.
-# https://docs.python.org/3.3/howto/logging.html#configuring-logging-for-a-library
-class NullHandler(logging.Handler):
-    def emit(self, record):
-        pass
-
-
-logging.getLogger('boto3').addHandler(NullHandler())

spaces/CShorten/Last-Week-on-ArXiv/README.md

@@ -1,13 +0,0 @@
----
-title: Last Week On ArXiv
-emoji: 🐢
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.9
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

spaces/CVPR/BigDL-Nano_inference/README.md

@@ -1,12 +0,0 @@
----
-title: BigDL-Nano Inference Demo
-emoji: 🦄
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.13
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_rpn.py

@@ -1,228 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-import unittest
-import torch
-
-from detectron2.config import get_cfg
-from detectron2.modeling.backbone import build_backbone
-from detectron2.modeling.proposal_generator.build import build_proposal_generator
-from detectron2.modeling.proposal_generator.rpn_outputs import find_top_rpn_proposals
-from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
-from detectron2.utils.events import EventStorage
-
-logger = logging.getLogger(__name__)
-
-
-class RPNTest(unittest.TestCase):
-    def test_rpn(self):
-        torch.manual_seed(121)
-        cfg = get_cfg()
-        cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RPN"
-        cfg.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator"
-        cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1)
-        backbone = build_backbone(cfg)
-        proposal_generator = build_proposal_generator(cfg, backbone.output_shape())
-        num_images = 2
-        images_tensor = torch.rand(num_images, 20, 30)
-        image_sizes = [(10, 10), (20, 30)]
-        images = ImageList(images_tensor, image_sizes)
-        image_shape = (15, 15)
-        num_channels = 1024
-        features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
-        gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
-        gt_instances = Instances(image_shape)
-        gt_instances.gt_boxes = Boxes(gt_boxes)
-        with EventStorage():  # capture events in a new storage to discard them
-            proposals, proposal_losses = proposal_generator(
-                images, features, [gt_instances[0], gt_instances[1]]
-            )
-
-        expected_losses = {
-            "loss_rpn_cls": torch.tensor(0.0804563984),
-            "loss_rpn_loc": torch.tensor(0.0990132466),
-        }
-        for name in expected_losses.keys():
-            self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]))
-
-        expected_proposal_boxes = [
-            Boxes(torch.tensor([[0, 0, 10, 10], [7.3365392685, 0, 10, 10]])),
-            Boxes(
-                torch.tensor(
-                    [
-                        [0, 0, 30, 20],
-                        [0, 0, 16.7862777710, 13.1362524033],
-                        [0, 0, 30, 13.3173446655],
-                        [0, 0, 10.8602609634, 20],
-                        [7.7165775299, 0, 27.3875980377, 20],
-                    ]
-                )
-            ),
-        ]
-
-        expected_objectness_logits = [
-            torch.tensor([0.1225359365, -0.0133192837]),
-            torch.tensor([0.1415634006, 0.0989848152, 0.0565387346, -0.0072308783, -0.0428492837]),
-        ]
-
-        for proposal, expected_proposal_box, im_size, expected_objectness_logit in zip(
-            proposals, expected_proposal_boxes, image_sizes, expected_objectness_logits
-        ):
-            self.assertEqual(len(proposal), len(expected_proposal_box))
-            self.assertEqual(proposal.image_size, im_size)
-            self.assertTrue(
-                torch.allclose(proposal.proposal_boxes.tensor, expected_proposal_box.tensor)
-            )
-            self.assertTrue(torch.allclose(proposal.objectness_logits, expected_objectness_logit))
-
-    def test_rrpn(self):
-        torch.manual_seed(121)
-        cfg = get_cfg()
-        cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RRPN"
-        cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator"
-        cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
-        cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1]]
-        cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [[0, 60]]
-        cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1)
-        cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead"
-        backbone = build_backbone(cfg)
-        proposal_generator = build_proposal_generator(cfg, backbone.output_shape())
-        num_images = 2
-        images_tensor = torch.rand(num_images, 20, 30)
-        image_sizes = [(10, 10), (20, 30)]
-        images = ImageList(images_tensor, image_sizes)
-        image_shape = (15, 15)
-        num_channels = 1024
-        features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
-        gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32)
-        gt_instances = Instances(image_shape)
-        gt_instances.gt_boxes = RotatedBoxes(gt_boxes)
-        with EventStorage():  # capture events in a new storage to discard them
-            proposals, proposal_losses = proposal_generator(
-                images, features, [gt_instances[0], gt_instances[1]]
-            )
-
-        expected_losses = {
-            "loss_rpn_cls": torch.tensor(0.0432923734),
-            "loss_rpn_loc": torch.tensor(0.1552739739),
-        }
-        for name in expected_losses.keys():
-            self.assertTrue(torch.allclose(proposal_losses[name], expected_losses[name]))
-
-        expected_proposal_boxes = [
-            RotatedBoxes(
-                torch.tensor(
-                    [
-                        [0.60189795, 1.24095452, 61.98131943, 18.03621292, -4.07244873],
-                        [15.64940453, 1.69624567, 59.59749603, 16.34339333, 2.62692475],
-                        [-3.02982378, -2.69752932, 67.90952301, 59.62455750, 59.97010040],
-                        [16.71863365, 1.98309708, 35.61507797, 32.81484985, 62.92267227],
-                        [0.49432933, -7.92979717, 67.77606201, 62.93098450, -1.85656738],
-                        [8.00880814, 1.36017394, 121.81007385, 32.74150467, 50.44297409],
-                        [16.44299889, -4.82221127, 63.39775848, 61.22503662, 54.12270737],
-                        [5.00000000, 5.00000000, 10.00000000, 10.00000000, -0.76943970],
-                        [17.64130402, -0.98095351, 61.40377808, 16.28918839, 55.53118134],
-                        [0.13016054, 4.60568953, 35.80157471, 32.30180359, 62.52872086],
-                        [-4.26460743, 0.39604485, 124.30079651, 31.84611320, -1.58203125],
-                        [7.52815342, -0.91636634, 62.39784622, 15.45565224, 60.79549789],
-                    ]
-                )
-            ),
-            RotatedBoxes(
-                torch.tensor(
-                    [
-                        [0.07734215, 0.81635046, 65.33510590, 17.34688377, -1.51821899],
-                        [-3.41833067, -3.11320257, 64.17595673, 60.55617905, 58.27033234],
-                        [20.67383385, -6.16561556, 63.60531998, 62.52315903, 54.85546494],
-                        [15.00000000, 10.00000000, 30.00000000, 20.00000000, -0.18218994],
-                        [9.22646523, -6.84775209, 62.09895706, 65.46472931, -2.74307251],
-                        [15.00000000, 4.93451595, 30.00000000, 9.86903191, -0.60272217],
-                        [8.88342094, 2.65560246, 120.95362854, 32.45022202, 55.75970078],
-                        [16.39088631, 2.33887148, 34.78761292, 35.61492920, 60.81977463],
-                        [9.78298569, 10.00000000, 19.56597137, 20.00000000, -0.86660767],
-                        [1.28576660, 5.49873352, 34.93610382, 33.22600174, 60.51599884],
-                        [17.58912468, -1.63270092, 62.96052551, 16.45713997, 52.91245270],
-                        [5.64749718, -1.90428460, 62.37649155, 16.19474792, 61.09543991],
-                        [0.82255805, 2.34931135, 118.83985901, 32.83671188, 56.50753784],
-                        [-5.33874989, 1.64404404, 125.28501892, 33.35424042, -2.80731201],
-                    ]
-                )
-            ),
-        ]
-
-        expected_objectness_logits = [
-            torch.tensor(
-                [
-                    0.10111768,
-                    0.09112845,
-                    0.08466332,
-                    0.07589971,
-                    0.06650183,
-                    0.06350251,
-                    0.04299347,
-                    0.01864817,
-                    0.00986163,
-                    0.00078543,
-                    -0.04573630,
-                    -0.04799230,
-                ]
-            ),
-            torch.tensor(
-                [
-                    0.11373727,
-                    0.09377633,
-                    0.05281663,
-                    0.05143715,
-                    0.04040275,
-                    0.03250912,
-                    0.01307789,
-                    0.01177734,
-                    0.00038105,
-                    -0.00540255,
-                    -0.01194804,
-                    -0.01461012,
-                    -0.03061717,
-                    -0.03599222,
-                ]
-            ),
-        ]
-
-        torch.set_printoptions(precision=8, sci_mode=False)
-
-        for proposal, expected_proposal_box, im_size, expected_objectness_logit in zip(
-            proposals, expected_proposal_boxes, image_sizes, expected_objectness_logits
-        ):
-            self.assertEqual(len(proposal), len(expected_proposal_box))
-            self.assertEqual(proposal.image_size, im_size)
-            # It seems that there's some randomness in the result across different machines:
-            # This test can be run on a local machine for 100 times with exactly the same result,
-            # However, a different machine might produce slightly different results,
-            # thus the atol here.
-            err_msg = "computed proposal boxes = {}, expected {}".format(
-                proposal.proposal_boxes.tensor, expected_proposal_box.tensor
-            )
-            self.assertTrue(
-                torch.allclose(
-                    proposal.proposal_boxes.tensor, expected_proposal_box.tensor, atol=1e-5
-                ),
-                err_msg,
-            )
-
-            err_msg = "computed objectness logits = {}, expected {}".format(
-                proposal.objectness_logits, expected_objectness_logit
-            )
-            self.assertTrue(
-                torch.allclose(proposal.objectness_logits, expected_objectness_logit, atol=1e-5),
-                err_msg,
-            )
-
-    def test_rpn_proposals_inf(self):
-        N, Hi, Wi, A = 3, 3, 3, 3
-        proposals = [torch.rand(N, Hi * Wi * A, 4)]
-        pred_logits = [torch.rand(N, Hi * Wi * A)]
-        pred_logits[0][1][3:5].fill_(float("inf"))
-        images = ImageList.from_tensors([torch.rand(3, 10, 10)] * 3)
-        find_top_rpn_proposals(proposals, pred_logits, images, 0.5, 1000, 1000, 0, False)
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/CVPR/LIVE/pybind11/tests/test_constants_and_functions.py

@@ -1,40 +0,0 @@
-# -*- coding: utf-8 -*-
-from pybind11_tests import constants_and_functions as m
-
-
-def test_constants():
-    assert m.some_constant == 14
-
-
-def test_function_overloading():
-    assert m.test_function() == "test_function()"
-    assert m.test_function(7) == "test_function(7)"
-    assert m.test_function(m.MyEnum.EFirstEntry) == "test_function(enum=1)"
-    assert m.test_function(m.MyEnum.ESecondEntry) == "test_function(enum=2)"
-
-    assert m.test_function() == "test_function()"
-    assert m.test_function("abcd") == "test_function(char *)"
-    assert m.test_function(1, 1.0) == "test_function(int, float)"
-    assert m.test_function(1, 1.0) == "test_function(int, float)"
-    assert m.test_function(2.0, 2) == "test_function(float, int)"
-
-
-def test_bytes():
-    assert m.print_bytes(m.return_bytes()) == "bytes[1 0 2 0]"
-
-
-def test_exception_specifiers():
-    c = m.C()
-    assert c.m1(2) == 1
-    assert c.m2(3) == 1
-    assert c.m3(5) == 2
-    assert c.m4(7) == 3
-    assert c.m5(10) == 5
-    assert c.m6(14) == 8
-    assert c.m7(20) == 13
-    assert c.m8(29) == 21
-
-    assert m.f1(33) == 34
-    assert m.f2(53) == 55
-    assert m.f3(86) == 89
-    assert m.f4(140) == 144

spaces/CVPR/LIVE/thrust/thrust/detail/raw_pointer_cast.h

@@ -1,52 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits/pointer_traits.h>
-
-namespace thrust
-{
-
-template<typename Pointer>
-__host__ __device__
-typename thrust::detail::pointer_traits<Pointer>::raw_pointer
-raw_pointer_cast(Pointer ptr)
-{
-  return thrust::detail::pointer_traits<Pointer>::get(ptr);
-}
-
-template <typename ToPointer, typename FromPointer>
-__host__ __device__
-ToPointer
-reinterpret_pointer_cast(FromPointer ptr)
-{
-  typedef typename thrust::detail::pointer_element<ToPointer>::type to_element;
-  return ToPointer(reinterpret_cast<to_element*>(thrust::raw_pointer_cast(ptr)));
-}
-
-template <typename ToPointer, typename FromPointer>
-__host__ __device__
-ToPointer
-static_pointer_cast(FromPointer ptr)
-{
-  typedef typename thrust::detail::pointer_element<ToPointer>::type to_element;
-  return ToPointer(static_cast<to_element*>(thrust::raw_pointer_cast(ptr)));
-}
-
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/mismatch.h

@@ -1,117 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <thrust/system/cuda/config.h>
-#include <thrust/system/cuda/detail/execution_policy.h>
-#include <thrust/pair.h>
-#include <thrust/distance.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-template <class Derived,
-          class InputIt1,
-          class InputIt2,
-          class BinaryPred>
-pair<InputIt1, InputIt2> __host__ __device__
-mismatch(execution_policy<Derived>& policy,
-         InputIt1                   first1,
-         InputIt1                   last1,
-         InputIt2                   first2,
-         BinaryPred                 binary_pred);
-
-template <class Derived,
-          class InputIt1,
-          class InputIt2>
-pair<InputIt1, InputIt2> __host__ __device__
-mismatch(execution_policy<Derived>& policy,
-         InputIt1                   first1,
-         InputIt1                   last1,
-         InputIt2                   first2);
-} // namespace cuda_
-} // end namespace thrust
-
-#include <thrust/system/cuda/detail/find.h>
-
-namespace thrust
-{
-namespace cuda_cub {
-
-template <class Derived,
-          class InputIt1,
-          class InputIt2,
-          class BinaryPred>
-pair<InputIt1, InputIt2> __host__ __device__
-mismatch(execution_policy<Derived>& policy,
-         InputIt1                   first1,
-         InputIt1                   last1,
-         InputIt2                   first2,
-         BinaryPred                 binary_pred)
-{
-  typedef transform_pair_of_input_iterators_t<bool,
-                                              InputIt1,
-                                              InputIt2,
-                                              BinaryPred>
-      transform_t;
-
-  transform_t transform_first = transform_t(first1, first2, binary_pred);
-
-  transform_t result = cuda_cub::find_if_not(policy,
-                                          transform_first,
-                                          transform_first + thrust::distance(first1, last1),
-                                          identity());
-
-  return thrust::make_pair(first1 + thrust::distance(transform_first,result),
-                           first2 + thrust::distance(transform_first,result));
-}
-
-template <class Derived,
-          class InputIt1,
-          class InputIt2>
-pair<InputIt1, InputIt2> __host__ __device__
-mismatch(execution_policy<Derived>& policy,
-         InputIt1                   first1,
-         InputIt1                   last1,
-         InputIt2                   first2)
-{
-  typedef typename thrust::iterator_value<InputIt1>::type InputType1;
-  return cuda_cub::mismatch(policy,
-                         first1,
-                         last1,
-                         first2,
-                         equal_to<InputType1>());
-}
-
-
-
-} // namespace cuda_cub
-} // end namespace thrust
-#endif

spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/async/for_each.h

@@ -1,34 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-// The purpose of this header is to #include the async/for_each.h header of the
-// sequential, host, and device systems. It should be #included in any code
-// which uses ADL to dispatch async for_each.
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-//#include <thrust/system/detail/sequential/async/for_each.h>
-
-//#define __THRUST_HOST_SYSTEM_ASYNC_FOR_EACH_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/async/for_each.h>
-//#include __THRUST_HOST_SYSTEM_ASYNC_FOR_EACH_HEADER
-//#undef __THRUST_HOST_SYSTEM_ASYNC_FOR_EACH_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_ASYNC_FOR_EACH_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/async/for_each.h>
-#include __THRUST_DEVICE_SYSTEM_ASYNC_FOR_EACH_HEADER
-#undef __THRUST_DEVICE_SYSTEM_ASYNC_FOR_EACH_HEADER
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/unique.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 unique.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch unique
-
-#include <thrust/system/detail/sequential/unique.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/unique.h>
-#include <thrust/system/cuda/detail/unique.h>
-#include <thrust/system/omp/detail/unique.h>
-#include <thrust/system/tbb/detail/unique.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_UNIQUE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/unique.h>
-#include __THRUST_HOST_SYSTEM_UNIQUE_HEADER
-#undef __THRUST_HOST_SYSTEM_UNIQUE_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_UNIQUE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/unique.h>
-#include __THRUST_DEVICE_SYSTEM_UNIQUE_HEADER
-#undef __THRUST_DEVICE_SYSTEM_UNIQUE_HEADER
-

spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/set_operations.h

@@ -1,23 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system inherits set_operations
-#include <thrust/system/cpp/detail/set_operations.h>
-

spaces/CVPR/WALT/mmcv_custom/runner/checkpoint.py

@@ -1,85 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-import os.path as osp
-import time
-from tempfile import TemporaryDirectory
-
-import torch
-from torch.optim import Optimizer
-
-import mmcv
-from mmcv.parallel import is_module_wrapper
-from mmcv.runner.checkpoint import weights_to_cpu, get_state_dict
-
-try:
-    import apex
-except:
-    print('apex is not installed')
-
-
-def save_checkpoint(model, filename, optimizer=None, meta=None):
-    """Save checkpoint to file.
-
-    The checkpoint will have 4 fields: ``meta``, ``state_dict`` and
-    ``optimizer``, ``amp``. By default ``meta`` will contain version
-    and time info.
-
-    Args:
-        model (Module): Module whose params are to be saved.
-        filename (str): Checkpoint filename.
-        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
-        meta (dict, optional): Metadata to be saved in checkpoint.
-    """
-    if meta is None:
-        meta = {}
-    elif not isinstance(meta, dict):
-        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
-    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
-
-    if is_module_wrapper(model):
-        model = model.module
-
-    if hasattr(model, 'CLASSES') and model.CLASSES is not None:
-        # save class name to the meta
-        meta.update(CLASSES=model.CLASSES)
-
-    checkpoint = {
-        'meta': meta,
-        'state_dict': weights_to_cpu(get_state_dict(model))
-    }
-    # save optimizer state dict in the checkpoint
-    if isinstance(optimizer, Optimizer):
-        checkpoint['optimizer'] = optimizer.state_dict()
-    elif isinstance(optimizer, dict):
-        checkpoint['optimizer'] = {}
-        for name, optim in optimizer.items():
-            checkpoint['optimizer'][name] = optim.state_dict()
-
-    # save amp state dict in the checkpoint
-    checkpoint['amp'] = apex.amp.state_dict()
-
-    if filename.startswith('pavi://'):
-        try:
-            from pavi import modelcloud
-            from pavi.exception import NodeNotFoundError
-        except ImportError:
-            raise ImportError(
-                'Please install pavi to load checkpoint from modelcloud.')
-        model_path = filename[7:]
-        root = modelcloud.Folder()
-        model_dir, model_name = osp.split(model_path)
-        try:
-            model = modelcloud.get(model_dir)
-        except NodeNotFoundError:
-            model = root.create_training_model(model_dir)
-        with TemporaryDirectory() as tmp_dir:
-            checkpoint_file = osp.join(tmp_dir, model_name)
-            with open(checkpoint_file, 'wb') as f:
-                torch.save(checkpoint, f)
-                f.flush()
-            model.create_file(checkpoint_file, name=model_name)
-    else:
-        mmcv.mkdir_or_exist(osp.dirname(filename))
-        # immediately flush buffer
-        with open(filename, 'wb') as f:
-            torch.save(checkpoint, f)
-            f.flush()

spaces/CVPR/WALT/mmdet/core/bbox/assigners/__init__.py

@@ -1,16 +0,0 @@
-from .approx_max_iou_assigner import ApproxMaxIoUAssigner
-from .assign_result import AssignResult
-from .atss_assigner import ATSSAssigner
-from .base_assigner import BaseAssigner
-from .center_region_assigner import CenterRegionAssigner
-from .grid_assigner import GridAssigner
-from .hungarian_assigner import HungarianAssigner
-from .max_iou_assigner import MaxIoUAssigner
-from .point_assigner import PointAssigner
-from .region_assigner import RegionAssigner
-
-__all__ = [
-    'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult',
-    'PointAssigner', 'ATSSAssigner', 'CenterRegionAssigner', 'GridAssigner',
-    'HungarianAssigner', 'RegionAssigner'
-]