Update parquet files (step 42 of 249)

spaces/17TheWord/RealESRGAN/README.md

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----
-title: Real ESRGAN
-emoji: 🏃
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
----
-
-# Configuration
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-`title`: _string_  
-Display title for the Space
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-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
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-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crack Kernel for Outlook PST Repair The Best Tool for Outlook Data File Recovery.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Ccleaner Full Crack HOT 2023.md

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-<li>Download the Adobe AIR installer file from the official website or another trusted source.</li>
-<li>Double-click the downloaded file to launch the installer.</li>
-<li>Follow the onscreen instructions to complete the installation.</li>
-<li>If prompted, enter your administrator password.</li>
-</ol>
-<p>Note: If the installation fails, see <a href="">Troubleshoot AIR installation | Mac OS</a>.</p>
- <h3>Installing Adobe AIR on Linux</h3>
-<p>To install Adobe AIR on Linux, follow these steps:</p>
-<ol>
-<li>Download the Adobe AIR installer file from the official website or another trusted source.</li>
-<li>Open a terminal window and navigate to the location of the downloaded file.</li>
-<li>Change the permissions on the file to allow execution: <code>chmod +x AdobeAIRInstaller.bin</code>.</li>
-<li>Run the installer: <code>./AdobeAIRInstaller.bin</code>.</li>
-<li>Follow the onscreen instructions to complete the installation.</li>
-</ol>
-<p>Note: If you encounter any issues, see <a href="">Install or update Adobe AIR | Linux</a>.</p>
- <h3>Installing Adobe AIR on Android</h3>
-<p>To install Adobe AIR on Android, follow these steps:</p>
-<ol>
-<li>Go to Google Play Store and search for < Adobe AIR</a>.</li>
-<li>Tap Install or Update, depending on your device.</li>
-<li>Follow the onscreen instructions to complete the installation.</li>
-</ol>
-<p>Note: If you encounter any issues, see <a href="">Install or update Adobe AIR | Android devices</a>.</p>
- <h3>Installing Adobe AIR on iOS</h3>
-<p>To install Adobe AIR on iOS, follow these steps:</p>
-<ol>
-<li>Go to App Store and search for Adobe AIR.</li>
-<li>Tap Get or Update, depending on your device.</li>
-<li>Follow the onscreen instructions to complete the installation.</li>
-</ol>
-<p>Note: If you encounter any issues, see <a href="">Install or update Adobe AIR | iOS devices</a>.</p>
- <h2>How to update Adobe AIR to the latest version</h2>
-<p>Keeping Adobe AIR up to date is important for ensuring the security and performance of your applications. There are two ways to update Adobe AIR: manually or automatically.</p>
- <h3>Checking for updates manually</h3>
-<p>To check for updates manually, follow these steps:</p>
-<ul>
-<li>On Windows, go to Start > All Programs > Adobe AIR > Check for Updates.</li>
-<li>On Mac, go to Applications > Utilities > Adobe AIR Application Installer > Check for Updates.</li>
-<li>On Linux, go to Applications > System Tools > Adobe AIR Application Installer > Check for Updates.</li>
-<li>On Android, go to Settings > Apps > Adobe AIR > Check for Updates.</li>
-<li>On iOS, go to Settings > General > Software Update.</li>
-</ul>
-<p>If there is a new version available, follow the prompts to download and install it.</p>
- <h3>Enabling automatic updates</h3>
-<p>To enable automatic updates, follow these steps:</p>
-<ul>
-<li>On Windows, go to Start > All Programs > Adobe AIR > Settings Manager. Click the Updates tab and select Allow Adobe to install updates (recommended).</li>
-<li>On Mac, go to Applications > Utilities > Adobe AIR Settings Manager. Click the Updates tab and select Allow Adobe to install updates (recommended).</li>
-<li>On Linux, go to Applications > System Tools > Adobe AIR Settings Manager. Click the Updates tab and select Allow Adobe to install updates (recommended).</li>
-<li>On Android, go to Settings > Apps > Adobe AIR. Tap the menu icon and select Auto-update.</li>
-<li>On iOS, go to Settings > iTunes & App Store. Turn on Updates under Automatic Downloads.</li>
-</ul>
-<p>This way, Adobe AIR will check for updates periodically and install them automatically when available.</p>
- <h2>How to use Adobe AIR applications</h2>
-<p>Adobe AIR applications are web applications that can run on your device without a browser. They have the file extension .air or .apk (for Android) or .ipa (for iOS). To use Adobe AIR applications, you need to find and install them first, and then run and manage them on your device.</p>
- <h3>Finding and installing AIR applications</h3>
-<p>To find and install AIR applications, you can use one of the following methods:</p>
-<ul>
-<li>Browse the official <a href="">Adobe AIR Marketplace</a>, where you can find hundreds of free and paid applications in various categories.</li>
-<li>Browse other online sources that offer AIR applications, such as <a href="">Google Play Store</a>, <a href="">App Store</a>, <a href="">Amazon Appstore</a>, etc. Make sure you download from trusted and reputable sources only.</li>
-<li>Download an AIR application file from a website or a link provided by the developer. Make sure you scan the file for viruses and malware before opening it.</li>
-<li>Create your own AIR application using one of the development tools and environments mentioned earlier.</li>
-</ul>
-<p>To install an AIR application, you need to have Adobe AIR installed on your device first. Then, depending on your device and operating system, you can use one of the following methods:</p>
-<ul>
-<li>If you download an AIR application from a website or a link, double-click the file to launch the installer. Follow the onscreen instructions to complete the installation.</li>
-<li>If you download an AIR application from an online source that offers web installation badges (such as the Adobe AIR Marketplace), click the badge to launch the installer. Follow the onscreen instructions to complete the installation.</li>
-<li>If you download an AIR application from an online source that offers custom installers (such as Google Play Store or App Store), open the installer file and follow the onscreen instructions to complete the installation.</li>
-<li>If you create your own AIR application using a development tool or environment, export it as an installer file and then open it on your device. Follow the onscreen instructions to complete the installation.</li> <li>If you download an AIR application from an online source that offers captive runtime (such as Amazon Appstore), open the application file and follow the onscreen instructions to complete the installation.</li>
-</ul>
- <h3>Running and managing AIR applications</h3>
-<p>To run and manage AIR applications, you can use one of the following methods:</p>
-<ul>
-<li>If you install an AIR application on your desktop, you can find it in your Start menu (Windows), Applications folder (Mac), or Applications menu (Linux). Double-click the application icon to launch it.</li>
-<li>If you install an AIR application on your mobile device, you can find it in your app drawer or home screen. Tap the application icon to launch it.</li>
-<li>If you want to uninstall, update, or change the settings of an AIR application, you can use the Adobe AIR Settings Manager. On Windows, go to Start > All Programs > Adobe AIR > Settings Manager. On Mac, go to Applications > Utilities > Adobe AIR Settings Manager. On Linux, go to Applications > System Tools > Adobe AIR Settings Manager. On Android, go to Settings > Apps > Adobe AIR. On iOS, go to Settings > General > Usage > Manage Storage > Adobe AIR.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Adobe AIR is a powerful and versatile runtime that allows you to run rich web applications and games on your device without a browser. It offers many features and benefits for both developers and users, such as cross-platform compatibility, native device access, offline mode, high performance, DRM support, extensions support, etc. To use Adobe AIR applications, you need to download and install Adobe AIR on your device first, and then find and install your favorite AIR applications from various sources. You also need to keep Adobe AIR updated to the latest version for security and performance reasons. You can use the Adobe AIR Settings Manager to manage your AIR applications and change their settings.</p>
- <h3>Summary of the main points</h3>
-<ul>
-<li>Adobe AIR is a cross-platform runtime that allows you to run web applications and games on your device without a browser.</li>
-<li>Adobe AIR offers many features and benefits for both developers and users, such as cross-platform compatibility, native device access, offline mode, high performance, DRM support, extensions support, etc.</li>
-<li>To use Adobe AIR applications, you need to download and install Adobe AIR on your device first, and then find and install your favorite AIR applications from various sources.</li>
-<li>You also need to keep Adobe AIR updated to the latest version for security and performance reasons.</li>
-<li>You can use the Adobe AIR Settings Manager to manage your AIR applications and change their settings.</li>
-</ul>
- <h3>Call to action</h3>
-<p>If you are interested in using Adobe AIR applications or creating your own ones, you can visit the official website: <a href="">Adobe - Adobe AIR</a>. There, you can find more information about Adobe AIR, download the latest version of the runtime, browse the marketplace for existing applications, access the documentation and tutorials for developers, join the community forums for support and feedback, etc. You can also follow Adobe AIR on social media platforms such as <a href="">Facebook</a>, <a href="">Twitter</a>, <a href="">YouTube</a>, etc. for news and updates.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Adobe AIR:</p>
-<ol>
-<li><b>Is Adobe AIR free?</b><br>Yes, Adobe AIR is free for both developers and users. You can download and use it without any charge or license fee.</li>
-<li><b>Is Adobe AIR safe?</b><br>Yes, Adobe AIR is safe as long as you download it from the official website or another trusted source. You should also scan any application file before installing it on your device. You can also check the digital signature of any application by right-clicking or control-clicking on it and selecting Properties (Windows) or Get Info (Mac).</li>
-<li><b>Is Adobe AIR still supported?</b><br>Yes, Adobe AIR is still supported by Adobe. The latest version of Adobe AIR is 33.1.1.533 (as of June 2023), which was released on May 18th 2023. You can check for updates regularly or enable automatic updates to keep your runtime up to date.</li>
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-<h1>How to Download and Run Checkers for Java</h1>
-<p>Checkers is a classic board game that involves moving pieces diagonally across a grid of squares, capturing the opponent's pieces by jumping over them, and reaching the other side of the board to become a king. Checkers is also known as draughts in some countries, and it has many variations and rules. Checkers is a fun and easy game that can be played by anyone, anywhere.</p>
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-<p>In this article, we will show you how to download and run checkers for Java on your Windows system. We will also provide you with some tips and tricks for playing checkers for Java. Let's get started!</p>
- <h2>Checkers Rules and Gameplay</h2>
-<p>Before we download and run checkers for Java, let's review the basic rules and gameplay of checkers. Here are some key points:</p>
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-<li>Checkers is played on an 8x8 board with 64 squares of alternating colors (dark and light).</li>
-<li>Each player has 12 pieces (also called men or checkers) of one color (black or white).</li>
-<li>The pieces are placed on the dark squares in the first three rows closest to each player.</li>
-<li>The player with the black pieces moves first, then the players alternate turns.</li>
-<li>A piece can only move one diagonal space forward (toward the opponent's side) to an empty square.</li>
-<li>If a piece is next to an opponent's piece and there is an empty square behind it, the piece can jump over the opponent's piece and capture it. The captured piece is removed from the board.</li>
-<li>A piece can make multiple jumps in one turn if possible.</li>
-<li>If a piece reaches the last row on the opponent's side (also called the king row), it becomes a king. A king can move in both directions (forward and backward) and jump over any piece in its way.</li>
-<li>The game ends when one player has no more pieces left or cannot make any valid moves. The player with more pieces left or who made the last move wins the game.</li>
-</ul>
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-<p>Now that we know how to play checkers, let's learn more about Java. Java is a programming language that was created by James Gosling at Sun Microsystems in 1995. <h2>Java Programming Language</h2>
-<p>Now that we know how to play checkers, let's learn more about Java. Java is a programming language that was created by James Gosling at Sun Microsystems in 1995. It is a high-level, object-oriented, and general-purpose language that can run on different platforms and devices. Java is widely used for developing applications such as web servers, mobile apps, games, and software tools.</p>
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-<li>Java is easy to learn. Java has a simple and clear syntax that is based on C and C++. It also has many built-in libraries and frameworks that provide ready-made solutions for common problems. Java follows the principle of "write once, run anywhere", which means that the same code can work on different platforms without any changes.</li>
-<li>Java is statically typed. This means that the data types of variables are checked at compile time, which helps to avoid errors and bugs at runtime. Java also supports type inference, which allows the compiler to infer the data types of variables without explicit declaration.</li>
-<li>Java has expert leadership. Java is maintained and developed by Oracle Corporation, which is a leading software company that provides support and updates for Java. Oracle also collaborates with other organizations and communities to ensure the quality and security of Java.</li>
-</ul>
- <h2>How to Install Java on Windows</h2>
-<p>If you want to download and run checkers for Java on your Windows system, you need to install Java first. Here are the steps to install Java on Windows:</p>
-<ol>
-<li>Download the JDK installer. Go to the [Oracle Java Downloads page](^1^) and click Accept License Agreement. Under the Download menu, click the x64 Installer download link that corresponds to your version of Windows. Save the file jdk-20.interim.update.patch_windows-x64_bin.exe to your computer.</li>
-<li>Run the downloaded file. Double-click the downloaded file to start the installation. Click Yes in the User Account Control prompt. The installation wizard will appear on your screen.</li>
-<li>Configure the installation wizard. Click Next to proceed to the next step. Choose the destination folder for the Java installation files or stick to the default path. Click Next to proceed. Wait for the wizard to finish the installation process until the Successfully Installed message appears. Click Close to exit the wizard.</li>
-<li>Set environmental variables in Java. Open the Start menu and search for environment variables. Select the Edit the system environment variables result. In the System Properties window, under the Advanced tab, click Environment Variables... Under the System variables category, select the Path variable and click Edit... Click the New button and enter the path to the Java bin directory: `C:\Program Files\Java\jdk-20\bin`. Click OK to save the changes.</li>
-</ol>
- <h2>How to Download and Run Checkers for Java</h2>
-<p>After installing Java on your Windows system, you can download and run checkers for Java. Here are the steps to do so:</p>
-<ol>
-<li>Download checkers for Java source code. Go to [GitHub](^2^) and find the repository named DevonMcGrath/Java-Checkers. Click on the green Code button, then click on Download ZIP button, then save it on your computer.</li>
-<li>Extract checkers for Java source code files from ZIP file into a folder named CheckersForJava on your computer.</li>
-<li>Compile checkers for Java source code files into class files using javac command in Command Prompt. Open Command Prompt by typing cmd in Start menu search bar <li>Compile checkers for Java source code files into class files using javac command in Command Prompt. Open Command Prompt by typing cmd in Start menu search bar and press Enter. Navigate to the CheckersForJava folder by typing cd followed by the path to the folder, for example: `cd C:\Users\YourName\Downloads\CheckersForJava`. Press Enter. Type javac followed by the name of the main source code file, which is Checkers.java, for example: `javac Checkers.java`. Press Enter. This will compile all the source code files into class files and store them in the same folder.</li>
-<li>Run checkers for Java class files using java command in Command Prompt. In the same Command Prompt window, type java followed by the name of the main class file, which is Checkers, for example: `java Checkers`. Press Enter. This will launch the checkers for Java application in a new window.</li>
-<li>Enjoy playing checkers for Java. You can choose to play against the computer or another human player, either online or offline. You can also adjust the game settings, such as the board size, the difficulty level, the game mode, the theme, the sound, and the statistics. You can also pause, resume, restart, or quit the game at any time.</li>
-</ol>
- <h2>Tips and Tricks for Playing Checkers for Java</h2>
-<p>Now that you know how to download and run checkers for Java, here are some tips and tricks for playing checkers for Java:</p>
-<ul>
-<li>Practice makes perfect. The more you play checkers, the more you will improve your skills and strategies. You can practice against the computer or another human player, either online or offline. You can also choose different difficulty levels and game modes to challenge yourself.</li>
-<li>Think ahead. Checkers is a game of planning and foresight. You should always try to anticipate your opponent's moves and counter them with your own. You should also try to control the center of the board and create opportunities for multiple jumps.</li>
-<li>Protect your pieces. You should avoid leaving your pieces vulnerable to capture by your opponent. You should also try to protect your king pieces, as they are more powerful and versatile than regular pieces.</li>
-<li>Use your king pieces wisely. King pieces can move in both directions and jump over any piece in their way. You should use your king pieces to attack your opponent's pieces, especially their king pieces. You should also use your king pieces to block your opponent's moves and prevent them from reaching the king row.</li>
-<li>Customize your game settings. Checkers for Java allows you to customize your game settings according to your preferences. You can change the board size, the difficulty level, the game mode, the theme, the sound, and the statistics. You can also save and load your game progress at any time.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download and run checkers for Java on your Windows system. We have also provided you with some tips and tricks for playing checkers for Java. Checkers for Java is a free and open-source application that allows you to play checkers against the computer or another human player, either online or offline. Checkers for Java has many features and options, such as different board sizes, difficulty levels, game modes, themes, sounds, and statistics.</p>
-<p>If you are looking for a fun and easy game that can be played by anyone, anywhere, you should try checkers for Java. Checkers is a classic board game that involves moving pieces diagonally across a grid of squares, capturing the opponent's pieces by jumping over them, and reaching the other side of the board to become a king. Checkers is also known as draughts in some countries, and it has many variations and rules.</p>
-<p>We hope you enjoyed this article and learned something new. Thank you for reading!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions related to checkers for Java:</p>
-<ol>
-<li><b>What are some other games that I can play with Java?</b></li>
-<p>There are many other games that you can play with Java, such as chess, sudoku, minesweeper, snake, tetris, pacman, pong, tic-tac-toe, hangman, and many others. You can find many free and open-source Java games online or create your own using Java programming language.</p>
-<li><b>How can I update my Java version?</b></li>
-<p>You can update your Java version by visiting [Oracle Java Downloads page] and downloading and installing the <p>You can update your Java version by visiting [Oracle Java Downloads page] and downloading and installing the latest version of Java for your system. You can also check for updates automatically by opening the Java Control Panel and clicking on the Update tab. You can also uninstall older versions of Java from your system to avoid security risks and performance issues.</p>
-<li><b>How can I play checkers for Java online with another human player?</b></li>
-<p>You can play checkers for Java online with another human player by choosing the Online mode in the game settings. You will need to enter your name and a server address to connect to. You can either join an existing game or create a new game and wait for another player to join. You can also chat with your opponent during the game using the Chat button.</p>
-<li><b>How can I change the theme of checkers for Java?</b></li>
-<p>You can change the theme of checkers for Java by choosing the Theme option in the game settings. You can choose from different themes, such as Classic, Wood, Metal, Marble, and Neon. You can also change the color of the board and the pieces according to your preference.</p>
-<li><b>How can I view my statistics in checkers for Java?</b></li>
-<p>You can view your statistics in checkers for Java by choosing the Statistics option in the game settings. You can see your total number of games played, won, lost, and drawn, as well as your win percentage and rating. You can also see your best and worst moves, your longest and shortest games, and your average moves per game.</p>
-<li><b>How can I report a bug or suggest a feature in checkers for Java?</b></li>
-<p>You can report a bug or suggest a feature in checkers for Java by visiting [GitHub] and finding the repository named DevonMcGrath/Java-Checkers. Click on the Issues tab, then click on the New issue button. Fill out the title and description of your issue or suggestion, then click on Submit new issue button. The developer will review your feedback and respond accordingly.</p>
-</ol></p> 197e85843d<br />
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-<h1>Download Love 020 Dramacool: A Guide to Watch the Hit Chinese Drama Online</h1>
-<p>If you are a fan of Chinese dramas, you might have heard of Love 020, a romantic comedy series that has taken the internet by storm. But how can you watch this amazing show online? And how can you download it for offline viewing? In this article, we will tell you everything you need to know about downloading Love 020 on Dramacool, one of the best websites to watch Asian dramas for free.</p>
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-<p>Love 020 is a 2016 Chinese drama based on the web novel "A Slight Smile Is Very Charming" by Gu Man. It revolves around the love story of a first-year and a final year student who fell in love with each other while playing an online video game. It follows the couple as they overcome different challenges and numerous obstacles in their online and offline worlds.</p>
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-<p>Little does she know that Yixiao Naihe is actually Xiao Nai (Yang Yang), her senior in college and the most popular student on campus. He is a gaming expert, a basketball star, an academic genius, and a successful entrepreneur. He falls in love with Wei Wei at first sight when he sees her playing the game in an internet cafe. He decides to pursue her both online and offline, using his skills and charm.</p>
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-<p>The cast of Love 020 consists of some of the most talented and popular actors and actresses in China. Here are some of them:</p>
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-<li>Zheng Shuang as Bei Wei Wei / Lu Wei Wei Wei: She is the female lead of the drama. She is beautiful, intelligent, and kind. She is a computer science major and an online gaming expert. She becomes Xiao Nai's online wife and real-life girlfriend.</li>
-<li>Bai Yu as Cao Guang / Zhen Shao Xiang: He is Xiao Nai's rival in love and business. He is also a computer science major and a gaming company CEO. He likes Wei Wei and tries to win her over.</li>
-<li>Mao Xiao Tong as Er Xi / Yao Yao: She is Wei Wei's best friend and roommate. She is a literature major and an online game fan. She is bubbly, cheerful, and loyal.</li>
-<li>Zhang Bin Bin as KO / Yu Ban Shan: He is Xiao Nai's best friend and business partner. He is a computer science major and a gaming genius. He is cool, calm, and witty.</li>
-<li>Niu Jun Feng as Hao Mei / Qiu Yong Hou: He is Xiao Nai's friend and colleague. He is a computer science major and a gaming programmer. He is cute, naive, and funny.</li>
-<li>Zheng Ye Cheng as Zhen Shui Wu Xiang / Yu Gong: He is Wei Wei's ex-online husband and Cao Guang's friend. He is a computer science major and a gaming developer. He is arrogant, selfish, and jealous.</li>
-</ul>
-<h3>The popularity of Love 020</h3>
-<p>Love 020 is one of the most popular and successful Chinese dramas of all time. It has received rave reviews from critics and audiences alike, for its sweet romance, hilarious comedy, thrilling action, and stunning visuals. It has also won several awards, such as the Best Foreign TV Series at the Seoul International Drama Awards in 2017.</p>
-<p>Love 020 has also gained a huge fan base both in China and abroad, especially among the young generation who can relate to the online gaming culture and the campus life. It has been viewed over 24 billion times on various online platforms, making it one of the most watched Chinese dramas ever. It has also been adapted into a movie, a spin-off series, and a Thai remake.</p>
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-<h2>Why watch Love 020 on Dramacool?</h2>
-<p>If you are interested in watching Love 020 online, you might be wondering where to find it. There are many websites that offer Asian dramas for streaming or downloading, but not all of them are reliable or safe. Some of them might have low-quality videos, annoying ads, broken links, or even viruses. That's why we recommend you to watch Love 020 on Dramacool, one of the best websites to watch Asian dramas for free.</p>
-<h3>The benefits of Dramacool</h3>
-<p>Dramacool is a website that provides a large collection of Asian dramas, movies, shows, and anime from various countries, such as China, Korea, Japan, Taiwan, Thailand, and more. You can watch them online or download them for offline viewing. Here are some of the benefits of using Dramacool:</p>
-<ul>
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-</ul> <h3>The features of Dramacool</h3>
-<p>Dramacool is not only a website that provides a lot of Asian dramas, but also a website that offers a lot of features to enhance your viewing experience. Here are some of the features of Dramacool:</p>
-<ul>
-<li>It has multiple servers: You can choose from different servers to watch or download your favorite dramas on Dramacool. You can switch to another server if one is not working or slow.</li>
-<li>It has multiple languages: You can watch your favorite dramas on Dramacool with subtitles in various languages, such as English, Spanish, French, Arabic, and more. You can also change the font size, color, and style of the subtitles.</li>
-<li>It has multiple devices: You can watch your favorite dramas on Dramacool on any device, such as a computer, a laptop, a tablet, or a smartphone. You can also cast them to your TV or Chromecast.</li>
-<li>It has multiple genres: You can find your favorite dramas on Dramacool in different genres, such as romance, comedy, action, thriller, horror, fantasy, historical, and more. You can also filter them by ratings, reviews, or recommendations.</li>
-</ul>
-<h3>The drawbacks of Dramacool</h3>
-<p>Dramacool is a great website to watch Asian dramas for free, but it is not perfect. It also has some drawbacks that you should be aware of before using it. Here are some of the drawbacks of Dramacool:</p>
-<ul>
-<li>It is illegal: You should know that watching or downloading dramas on Dramacool is illegal, as it violates the copyright laws and the intellectual property rights of the original creators and distributors. You might face legal consequences or penalties if you are caught using it.</li>
-<li>It is risky: You should also know that watching or downloading dramas on Dramacool is risky, as it might expose your device or data to malware, viruses, spyware, or hackers. You might lose your personal information or damage your device if you are not careful.</li>
-<li>It is unreliable: You should also know that watching or downloading dramas on Dramacool is unreliable, as it might have broken links, missing episodes, wrong subtitles, low-quality videos, or annoying ads. You might not enjoy your viewing experience if you encounter these problems.</li>
-</ul>
-<h2>How to download Love 020 on Dramacool?</h2>
-<p>If you still want to watch Love 020 on Dramacool despite its drawbacks, you should follow these steps to download it safely and easily:</p>
-<h3>Step 1: Visit the official website of Dramacool</h3>
-<p>The first step is to visit the official website of Dramacool at https://www.dramacool9.co/. You can use any browser or device to access it. However, you should make sure that you have a good internet connection and a reliable antivirus software installed on your device.</p>
-<h3>Step 2: Search for Love 020 in the search bar</h3>
-<p>The second step is to search for Love 020 in the search bar at the top right corner of the website. You can type in "Love 020" or "Just One Smile Is Very Alluring" (the alternative title of the drama) and hit enter. You will see a list of results related to the drama.</p>
-<h3>Step 3: Choose the episode you want to download</h3>
-<p>The third step is to choose the episode you want to download from the list of results. You can click on the title or the image of the episode to open it. You will see a video player with some options below it.</p> <h3>Step 4: Click on the download button and select the quality and format</h3>
-<p>The fourth step is to click on the download button below the video player. You will see a pop-up window with some options to choose from. You can select the quality and format of the video you want to download, such as HD, SD, MP4, or MKV. You can also see the size and duration of the video.</p>
-<h3>Step 5: Enjoy watching Love 020 offline</h3>
-<p>The fifth and final step is to enjoy watching Love 020 offline. You can click on the download link or scan the QR code to start downloading the video to your device. You can also copy and paste the link to your download manager or browser. Once the download is complete, you can watch Love 020 anytime and anywhere you want.</p>
-<h2>Conclusion</h2>
-<p>Love 020 is a wonderful Chinese drama that you should not miss. It has a captivating plot, a charming cast, and a beautiful soundtrack. It will make you laugh, cry, and swoon over the adorable couple. If you want to watch Love 020 online, you can use Dramacool, a free website that offers a lot of Asian dramas. However, you should also be aware of the drawbacks of using Dramacool, such as its illegality, riskiness, and unreliability. If you want to download Love 020 on Dramacool, you can follow the steps we have provided in this article. We hope you enjoy watching Love 020 on Dramacool!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about downloading Love 020 on Dramacool:</p>
-<ul>
-<li>Q: Is it safe to download Love 020 on Dramacool?</li>
-<li>A: It depends on how careful you are when using Dramacool. You should always use a reliable antivirus software and a VPN service to protect your device and data from malware, viruses, spyware, or hackers. You should also avoid clicking on any suspicious links or ads that might redirect you to harmful websites.</li>
-<li>Q: Is it legal to download Love 020 on Dramacool?</li>
-<li>A: No, it is not legal to download Love 020 on Dramacool. You are violating the copyright laws and the intellectual property rights of the original creators and distributors of the drama. You might face legal consequences or penalties if you are caught using Dramacool.</li>
-<li>Q: How many episodes are there in Love 020?</li>
-<li>A: There are 30 episodes in Love 020, each lasting about 45 minutes. You can watch them all on Dramacool for free.</li>
-<li>Q: Where can I find the subtitles for Love 020?</li>
-<li>A: You can find the subtitles for Love 020 on Dramacool in various languages, such as English, Spanish, French, Arabic, and more. You can also change the font size, color, and style of the subtitles according to your preference.</li>
-<li>Q: What are some other websites to watch or download Love 020?</li>
-<li>A: Some other websites to watch or download Love 020 are Kissasian, Viki, Netflix, WeTV, iQiyi, and more. However, some of them might require a subscription or registration fee to access their content.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />

spaces/1toTree/lora_test/ppdiffusers/schedulers/scheduling_repaint.py

@@ -1,321 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import paddle
-import paddle.nn.functional as F
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from .scheduling_utils import SchedulerMixin
-
-
-@dataclass
-class RePaintSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from
-             the current timestep. `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: paddle.Tensor
-    pred_original_sample: paddle.Tensor
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-
-    def alpha_bar(time_step):
-        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return paddle.to_tensor(betas, dtype="float32")
-
-
-class RePaintScheduler(SchedulerMixin, ConfigMixin):
-    """
-    RePaint is a schedule for DDPM inpainting inside a given mask.
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        eta (`float`):
-            The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 -0.0 is DDIM and
-            1.0 is DDPM scheduler respectively.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        variance_type (`str`):
-            options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
-            `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
-        clip_sample (`bool`, default `True`):
-            option to clip predicted sample between -1 and 1 for numerical stability.
-
-    """
-
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        eta: float = 0.0,
-        trained_betas: Optional[np.ndarray] = None,
-        clip_sample: bool = True,
-    ):
-        if trained_betas is not None:
-            self.betas = paddle.to_tensor(trained_betas)
-        elif beta_schedule == "linear":
-            self.betas = paddle.linspace(beta_start, beta_end, num_train_timesteps, dtype="float32")
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = paddle.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype="float32") ** 2
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        elif beta_schedule == "sigmoid":
-            # GeoDiff sigmoid schedule
-            betas = paddle.linspace(-6, 6, num_train_timesteps)
-            self.betas = F.sigmoid(betas) * (beta_end - beta_start) + beta_start
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = paddle.cumprod(self.alphas, 0)
-        self.one = paddle.to_tensor(1.0)
-
-        self.final_alpha_cumprod = paddle.to_tensor(1.0)
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-        # setable values
-        self.num_inference_steps = None
-        self.timesteps = paddle.to_tensor(np.arange(0, num_train_timesteps)[::-1].copy())
-
-        self.eta = eta
-
-    def scale_model_input(self, sample: paddle.Tensor, timestep: Optional[int] = None) -> paddle.Tensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`paddle.Tensor`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `paddle.Tensor`: scaled input sample
-        """
-        return sample
-
-    def set_timesteps(
-        self,
-        num_inference_steps: int,
-        jump_length: int = 10,
-        jump_n_sample: int = 10,
-    ):
-        num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
-        self.num_inference_steps = num_inference_steps
-
-        timesteps = []
-
-        jumps = {}
-        for j in range(0, num_inference_steps - jump_length, jump_length):
-            jumps[j] = jump_n_sample - 1
-
-        t = num_inference_steps
-        while t >= 1:
-            t = t - 1
-            timesteps.append(t)
-
-            if jumps.get(t, 0) > 0:
-                jumps[t] = jumps[t] - 1
-                for _ in range(jump_length):
-                    t = t + 1
-                    timesteps.append(t)
-
-        timesteps = np.array(timesteps) * (self.config.num_train_timesteps // self.num_inference_steps)
-        self.timesteps = paddle.to_tensor(timesteps)
-
-    def _get_variance(self, t):
-        prev_timestep = t - self.config.num_train_timesteps // self.num_inference_steps
-
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        # For t > 0, compute predicted variance βt (see formula (6) and (7) from
-        # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
-        # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
-        # variance to pred_sample
-        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
-        # without eta.
-        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
-        variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-
-        return variance
-
-    def step(
-        self,
-        model_output: paddle.Tensor,
-        timestep: int,
-        sample: paddle.Tensor,
-        original_image: paddle.Tensor,
-        mask: paddle.Tensor,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-        return_dict: bool = True,
-    ) -> Union[RePaintSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`paddle.Tensor`): direct output from learned
-                diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`paddle.Tensor`):
-                current instance of sample being created by diffusion process.
-            original_image (`paddle.Tensor`):
-                the original image to inpaint on.
-            mask (`paddle.Tensor`):
-                the mask where 0.0 values define which part of the original image to inpaint (change).
-            generator (`paddle.Generator`, *optional*): random number generator.
-            return_dict (`bool`): option for returning tuple rather than
-                DDPMSchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_utils.RePaintSchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.RePaintSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-
-        """
-        t = timestep
-        prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
-
-        # 1. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[t]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-        beta_prod_t = 1 - alpha_prod_t
-
-        # 2. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-        pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
-
-        # 3. Clip "predicted x_0"
-        if self.config.clip_sample:
-            pred_original_sample = paddle.clip(pred_original_sample, -1, 1)
-
-        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
-        # substitute formula (7) in the algorithm coming from DDPM paper
-        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
-        # DDIM schedule gives the same results as DDPM with eta = 1.0
-        # Noise is being reused in 7. and 8., but no impact on quality has
-        # been observed.
-
-        # 5. Add noise
-        noise = paddle.randn(model_output.shape, dtype=model_output.dtype, generator=generator)
-        std_dev_t = self.eta * self._get_variance(timestep) ** 0.5
-
-        variance = 0
-        if t > 0 and self.eta > 0:
-            variance = std_dev_t * noise
-
-        # 6. compute "direction pointing to x_t" of formula (12)
-        # from https://arxiv.org/pdf/2010.02502.pdf
-        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
-
-        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        prev_unknown_part = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
-
-        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
-        prev_known_part = (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
-
-        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
-        pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part
-
-        if not return_dict:
-            return (
-                pred_prev_sample,
-                pred_original_sample,
-            )
-
-        return RePaintSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
-
-    def undo_step(self, sample, timestep, generator=None):
-        n = self.config.num_train_timesteps // self.num_inference_steps
-
-        for i in range(n):
-            beta = self.betas[timestep + i]
-            noise = paddle.randn(sample.shape, dtype=sample.dtype, generator=generator)
-
-            # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
-            sample = (1 - beta) ** 0.5 * sample + beta**0.5 * noise
-
-        return sample
-
-    def add_noise(
-        self,
-        original_samples: paddle.Tensor,
-        noise: paddle.Tensor,
-        timesteps: paddle.Tensor,
-    ) -> paddle.Tensor:
-        raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.")
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/commons/conformer/espnet_transformer_attn.py

@@ -1,186 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Multi-Head Attention layer definition."""
-
-import math
-
-import numpy
-import torch
-from torch import nn
-
-
-class MultiHeadedAttention(nn.Module):
-    """Multi-Head Attention layer.
-    Args:
-        n_head (int): The number of heads.
-        n_feat (int): The number of features.
-        dropout_rate (float): Dropout rate.
-    """
-
-    def __init__(self, n_head, n_feat, dropout_rate):
-        """Construct an MultiHeadedAttention object."""
-        super(MultiHeadedAttention, self).__init__()
-        assert n_feat % n_head == 0
-        # We assume d_v always equals d_k
-        self.d_k = n_feat // n_head
-        self.h = n_head
-        self.linear_q = nn.Linear(n_feat, n_feat)
-        self.linear_k = nn.Linear(n_feat, n_feat)
-        self.linear_v = nn.Linear(n_feat, n_feat)
-        self.linear_out = nn.Linear(n_feat, n_feat)
-        self.attn = None
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def forward_qkv(self, query, key, value):
-        """Transform query, key and value.
-        Args:
-            query (torch.Tensor): Query tensor (#batch, time1, size).
-            key (torch.Tensor): Key tensor (#batch, time2, size).
-            value (torch.Tensor): Value tensor (#batch, time2, size).
-        Returns:
-            torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
-            torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
-            torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
-        """
-        n_batch = query.size(0)
-        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
-        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
-        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
-        q = q.transpose(1, 2)  # (batch, head, time1, d_k)
-        k = k.transpose(1, 2)  # (batch, head, time2, d_k)
-        v = v.transpose(1, 2)  # (batch, head, time2, d_k)
-
-        return q, k, v
-
-    def forward_attention(self, value, scores, mask):
-        """Compute attention context vector.
-        Args:
-            value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
-            scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
-            mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
-        Returns:
-            torch.Tensor: Transformed value (#batch, time1, d_model)
-                weighted by the attention score (#batch, time1, time2).
-        """
-        n_batch = value.size(0)
-        if mask is not None:
-            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
-            min_value = float(
-                numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
-            )
-            scores = scores.masked_fill(mask, min_value)
-            self.attn = torch.softmax(scores, dim=-1).masked_fill(
-                mask, 0.0
-            )  # (batch, head, time1, time2)
-        else:
-            self.attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
-
-        p_attn = self.dropout(self.attn)
-        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
-        x = (
-            x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
-        )  # (batch, time1, d_model)
-
-        return self.linear_out(x)  # (batch, time1, d_model)
-
-    def forward(self, query, key, value, mask):
-        """Compute scaled dot product attention.
-        Args:
-            query (torch.Tensor): Query tensor (#batch, time1, size).
-            key (torch.Tensor): Key tensor (#batch, time2, size).
-            value (torch.Tensor): Value tensor (#batch, time2, size).
-            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
-                (#batch, time1, time2).
-        Returns:
-            torch.Tensor: Output tensor (#batch, time1, d_model).
-        """
-        q, k, v = self.forward_qkv(query, key, value)
-        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
-        return self.forward_attention(v, scores, mask)
-
-
-class RelPositionMultiHeadedAttention(MultiHeadedAttention):
-    """Multi-Head Attention layer with relative position encoding.
-    Paper: https://arxiv.org/abs/1901.02860
-    Args:
-        n_head (int): The number of heads.
-        n_feat (int): The number of features.
-        dropout_rate (float): Dropout rate.
-    """
-
-    def __init__(self, n_head, n_feat, dropout_rate):
-        """Construct an RelPositionMultiHeadedAttention object."""
-        super().__init__(n_head, n_feat, dropout_rate)
-        # linear transformation for positional ecoding
-        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
-        # these two learnable bias are used in matrix c and matrix d
-        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
-        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
-        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
-        torch.nn.init.xavier_uniform_(self.pos_bias_u)
-        torch.nn.init.xavier_uniform_(self.pos_bias_v)
-
-    def rel_shift(self, x, zero_triu=False):
-        """Compute relative positinal encoding.
-        Args:
-            x (torch.Tensor): Input tensor (batch, time, size).
-            zero_triu (bool): If true, return the lower triangular part of the matrix.
-        Returns:
-            torch.Tensor: Output tensor.
-        """
-        zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
-        x_padded = torch.cat([zero_pad, x], dim=-1)
-
-        x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
-        x = x_padded[:, :, 1:].view_as(x)
-
-        if zero_triu:
-            ones = torch.ones((x.size(2), x.size(3)))
-            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
-
-        return x
-
-    def forward(self, query, key, value, pos_emb, mask):
-        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
-        Args:
-            query (torch.Tensor): Query tensor (#batch, time1, size).
-            key (torch.Tensor): Key tensor (#batch, time2, size).
-            value (torch.Tensor): Value tensor (#batch, time2, size).
-            pos_emb (torch.Tensor): Positional embedding tensor (#batch, time2, size).
-            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
-                (#batch, time1, time2).
-        Returns:
-            torch.Tensor: Output tensor (#batch, time1, d_model).
-        """
-        q, k, v = self.forward_qkv(query, key, value)
-        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
-
-        n_batch_pos = pos_emb.size(0)
-        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
-        p = p.transpose(1, 2)  # (batch, head, time1, d_k)
-
-        # (batch, head, time1, d_k)
-        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
-        # (batch, head, time1, d_k)
-        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
-
-        # compute attention score
-        # first compute matrix a and matrix c
-        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
-        # (batch, head, time1, time2)
-        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
-
-        # compute matrix b and matrix d
-        # (batch, head, time1, time2)
-        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
-        matrix_bd = self.rel_shift(matrix_bd)
-
-        scores = (matrix_ac + matrix_bd) / math.sqrt(
-            self.d_k
-        )  # (batch, head, time1, time2)
-
-        return self.forward_attention(v, scores, mask)

spaces/AP123/CerealBoxMaker/README.md

@@ -1,13 +0,0 @@
----
-title: CerealBoxMaker
-emoji: 🥛
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ASJMO/freegpt/client/js/sidebar-toggler.js

@@ -1,34 +0,0 @@
-const sidebar = document.querySelector(".sidebar");
-const menuButton = document.querySelector(".menu-button");
-
-function toggleSidebar(event) {
-    if (sidebar.classList.contains("shown")) {
-        hideSidebar(event.target);
-    } else {
-        showSidebar(event.target);
-    }
-    window.scrollTo(0, 0);
-}
-
-function showSidebar(target) {
-    sidebar.classList.add("shown");
-    target.classList.add("rotated");
-    document.body.style.overflow = "hidden";
-}
-
-function hideSidebar(target) {
-    sidebar.classList.remove("shown");
-    target.classList.remove("rotated");
-    document.body.style.overflow = "auto";
-}
-
-menuButton.addEventListener("click", toggleSidebar);
-
-document.body.addEventListener('click', function(event) {
-    if (event.target.matches('.conversation-title')) {
-        const menuButtonStyle = window.getComputedStyle(menuButton);
-        if (menuButtonStyle.display !== 'none') {
-            hideSidebar(menuButton);
-        }
-    }
-});

spaces/AashishKumar/Restaurant_voice_chatbot/README.md

@@ -1,13 +0,0 @@
----
-title: Restaurant Voice Chatbot
-emoji: 💩
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/GPTalk.py

@@ -1,83 +0,0 @@
-from __future__ import annotations
-
-import secrets, time, json
-from aiohttp import ClientSession
-from typing import AsyncGenerator
-
-from .base_provider import AsyncGeneratorProvider
-from .helper import format_prompt
-
-
-class GPTalk(AsyncGeneratorProvider):
-    url = "https://gptalk.net"
-    supports_gpt_35_turbo = True
-    working = True
-    _auth = None
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> AsyncGenerator:
-        if not model:
-            model = "gpt-3.5-turbo"
-        timestamp = int(time.time())
-        headers = {
-            'authority': 'gptalk.net',
-            'accept': '*/*',
-            'accept-language': 'de-DE,de;q=0.9,en-DE;q=0.8,en;q=0.7,en-US;q=0.6,nl;q=0.5,zh-CN;q=0.4,zh-TW;q=0.3,zh;q=0.2',
-            'content-type': 'application/json',
-            'origin': 'https://gptalk.net',
-            'sec-ch-ua': '"Google Chrome";v="117", "Not;A=Brand";v="8", "Chromium";v="117"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"Linux"',
-            'sec-fetch-dest': 'empty',
-            'sec-fetch-mode': 'cors',
-            'sec-fetch-site': 'same-origin',
-            'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36',
-            'x-auth-appid': '2229',
-            'x-auth-openid': '',
-            'x-auth-platform': '',
-            'x-auth-timestamp': f"{timestamp}",
-        }
-        async with ClientSession(headers=headers) as session:
-            if not cls._auth or cls._auth["expires_at"] < timestamp:
-                data = {
-                    "fingerprint": secrets.token_hex(16).zfill(32),
-                    "platform": "fingerprint"
-                }
-                async with session.post(cls.url + "/api/chatgpt/user/login", json=data) as response:
-                    response.raise_for_status()
-                    cls._auth = (await response.json())["data"]
-            data = {
-                "content": format_prompt(messages),
-                "accept": "stream",
-                "from": 1,
-                "model": model,
-                "is_mobile": 0,
-                "user_agent": headers["user-agent"],
-                "is_open_ctx": 0,
-                "prompt": "",
-                "roid": 111,
-                "temperature": 0,
-                "ctx_msg_count": 3,
-                "created_at": timestamp
-            }
-            headers = {
-                'authorization': f'Bearer {cls._auth["token"]}',
-            }
-            async with session.post(cls.url + "/api/chatgpt/chatapi/text", json=data, headers=headers) as response:
-                response.raise_for_status()
-                token = (await response.json())["data"]["token"]
-            last_message = ""
-            async with session.get(cls.url + "/api/chatgpt/chatapi/stream", params={"token": token}) as response:
-                response.raise_for_status()
-                async for line in response.content:
-                    if line.startswith(b"data: "):
-                        if line.startswith(b"data: [DONE]"):
-                            break
-                        message = json.loads(line[6:-1])["content"]
-                        yield message[len(last_message):]
-                        last_message = message

spaces/AdamOswald1/finetuned_diffusion/README.md

@@ -1,14 +0,0 @@
----
-title: Finetuned Diffusion
-emoji: 🪄🖼️
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: true
-license: mit
-duplicated_from: anzorq/finetuned_diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/confirmdialog/ConfirmDialog.d.ts

@@ -1,127 +0,0 @@
-import Dialog from '../dialog/Dialog';
-import { GeneralCreateGameObjectCallbackType } from '../utils/build/GeneralCreateGameObjectCallbackType';
-import CreateBackground from '../utils/build/CreateBackground';
-import SimpleLabel from '../simplelabel/SimpleLabel';
-import CreateTextArea from '../utils/build/CreateTextArea';
-import Label from '../label/Label';
-
-export default ConfirmDialog;
-
-declare namespace ConfirmDialog {
-    type AlignTypes = number | 'left' | 'center' | 'right';
-
-    interface IConfigClick {
-        mode: 0 | 1 | 'pointerup' | 'pointerdown' | 'release' | 'press',
-        clickInterval?: number
-    }
-
-    interface IConfig {
-        x?: number,
-        y?: number,
-        width?: number,
-        height?: number,
-
-        space?: {
-            left?: number, right?: number, top?: number, bottom?: number,
-
-            title?: number,
-            titleLeft?: number,
-            titleRight?: number,
-
-            content?: number,
-            contentLeft?: number,
-            contentRight?: number,
-
-            actionsLeft?: number,
-            actionsRight?: number,
-            action?: number,
-
-            choices?: number,
-            choicesLeft?: number,
-            choicesRight?: number,
-            choice?: number,
-            choiceLine?: number,
-            choiceColumn?: number, choiceRow?: number,
-            choicesBackgroundLeft?: number,
-            choicesBackgroundRight?: number,
-            choicesBackgroundTop?: number,
-            choicesBackgroundBottom?: number,
-        };
-
-        background?: CreateBackground.IConfig,
-
-        title?: SimpleLabel.IConfig,
-
-        content?: SimpleLabel.IConfig | CreateTextArea.IConfig,
-
-        buttonMode?: 0 | 1 | 2;
-        button?: SimpleLabel.IConfig,
-        buttonA?: SimpleLabel.IConfig,
-        buttonB?: SimpleLabel.IConfig,
-
-        choicesType?: string,
-        choice?: SimpleLabel.IConfig,
-        choicesWidth?: number,
-        choicesHeight?: number,
-
-        proportion?: {
-            title?: number,
-            content?: number,
-            actions?: number,
-            choices?: number,
-        },
-
-        expand?: {
-            title?: boolean,
-            content?: boolean,
-            actions?: boolean,
-            choices?: boolean,
-        },
-
-        align?: {
-            title?: AlignTypes,
-            content?: AlignTypes,
-            actions?: AlignTypes,
-            choices?: AlignTypes,
-        },
-
-        click?: IConfigClick
-    }
-
-    interface IResetChoiceDisplayContentConfig extends Label.IResetDisplayContentConfig {
-        value?: any;
-    }
-
-    interface IResetDisplayContentConfig {
-        title?: string | Label.IResetDisplayContentConfig,
-
-        content?: string | Label.IResetDisplayContentConfig,
-
-        buttonA?: string | Label.IResetDisplayContentConfig,
-        buttonB?: string | Label.IResetDisplayContentConfig,
-
-        choices?: (string | IResetChoiceDisplayContentConfig)[]
-    }
-
-    interface ICreatorsConfig {
-        background?: GeneralCreateGameObjectCallbackType,
-        title?: SimpleLabel.ICreatorsConfig,
-        content?: SimpleLabel.ICreatorsConfig | CreateTextArea.ICreatorsConfig,
-        button?: SimpleLabel.ICreatorsConfig,
-        buttonA?: SimpleLabel.ICreatorsConfig,
-        buttonB?: SimpleLabel.ICreatorsConfig,
-        choice?: SimpleLabel.ICreatorsConfig,
-    }
-}
-
-declare class ConfirmDialog extends Dialog {
-    constructor(
-        scene: Phaser.Scene,
-        config?: ConfirmDialog.IConfig,
-        creators?: ConfirmDialog.ICreatorsConfig
-    );
-
-    resetDisplayContent(
-        config?: ConfirmDialog.IResetDisplayContentConfig
-    ): this;
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/confirmdialog/methods/CreateContent.js

@@ -1,32 +0,0 @@
-import CreateLabel from '../../utils/build/CreateLabel.js';
-import CreateTextArea from '../../utils/build/CreateTextArea.js'
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-var CreateContent = function (scene, config, creators) {
-    var type = GetValue(config, '$type');
-    if (type === undefined) {
-        if (config &&
-            (config.hasOwnProperty('slider') || config.hasOwnProperty('scroller'))
-        ) {
-            type = 'textarea';
-        }
-    }
-
-
-    var gameObject;
-    switch (type) {
-        case 'textarea':
-            gameObject = new CreateTextArea(scene, config, creators);
-            break;
-
-        default:
-            gameObject = new CreateLabel(scene, config, creators);
-            break;
-    }
-
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-
-export default CreateContent;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/CreateButtons.js

@@ -1,22 +0,0 @@
-var CreateButtons = function (scene, items, callback, scope) {
-    var item;
-    var buttons = [],
-        button;
-    if (items && callback) {
-        for (var i = 0, cnt = items.length; i < cnt; i++) {
-            item = items[i];
-            item.scene = scene;
-            if (scope) {
-                button = callback.call(scope, item, i, items);
-            } else {
-                button = callback(item, i, items);
-            }
-            item.scene = undefined;
-            buttons.push(button);
-        }
-    }
-
-    return buttons;
-}
-
-export default CreateButtons;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/methods/GetPage.js

@@ -1,10 +0,0 @@
-var GetPage = function (key) {
-    if (key === undefined) {
-        return null;
-    } else if (!this.sizerChildren.hasOwnProperty(key)) {
-        return null;
-    } else {
-        return this.sizerChildren[key];
-    }
-}
-export default GetPage;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/tabpages/TabPages.d.ts

@@ -1,74 +0,0 @@
-// import * as Phaser from 'phaser';
-import Sizer from '../sizer/Sizer';
-import Buttons from '../buttons/Buttons';
-import FixWidthButtons from '../fixwidthbuttons/FixWidthButtons';
-import Pages from '../pages/Pages';
-
-
-export default TabPages;
-
-declare namespace TabPages {
-    interface IConfig extends Sizer.IConfig {
-        background?: Phaser.GameObjects.GameObject,
-
-        tabPosition?: 'top' | 'bottom' | 'left' | 'right',
-        wrapTabs?: boolean,
-        tabs?: Buttons.IConfig | FixWidthButtons.IConfig,
-        pages?: Pages.IConfig,
-
-        expand?: {
-            tabs?: boolean
-        },
-
-        align?: {
-            tabs?: 'top' | 'bottom' | 'left' | 'right' | 'center'
-        }
-
-
-    }
-
-    interface IAddPageConfig {
-        key?: string,
-        tab: Phaser.GameObjects.GameObject,
-        page: Phaser.GameObjects.GameObject
-    }
-
-}
-
-declare class TabPages extends Sizer {
-    constructor(
-        scene: Phaser.Scene,
-        config?: TabPages.IConfig
-    );
-
-    getPageKey(index: number): string;
-    getPageIndex(key: string): number;
-
-    addPage(
-        key: string,
-        tabGameObject: Phaser.GameObjects.GameObject,
-        pageGameObject: Phaser.GameObjects.GameObject
-    ): this;
-
-    addPage(config: TabPages.IAddPageConfig): this;
-
-    removePage(
-        key: string,
-        destroyChild?: boolean
-    ): this;
-
-    swapPage(
-        key: string,
-        fadeInDuration?: number
-    ): this;
-    swapFirstPage(fadeInDuration?: number): this;
-    swapLastPage(fadeInDuration?: number): this;
-
-    currentKey: string;
-    readonly previousKey: string;
-    keys: string[];
-
-    getPage(key: string): Phaser.GameObjects.GameObject;
-    readonly currentPage: Phaser.GameObjects.GameObject;
-    readonly previousPage: Phaser.GameObjects.GameObject;
-}

spaces/AiMimicry/sovits-models/modules/losses.py

@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import modules.commons as commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-  #print(logs_p)
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l

spaces/AkitoP/umamusume_bert_vits2/bert/bert-base-japanese-v3/README.md

@@ -1,53 +0,0 @@
----
-license: apache-2.0
-datasets:
-- cc100
-- wikipedia
-language:
-- ja
-widget:
-- text: 東北大学で[MASK]の研究をしています。
----
-
-# BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
-
-This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
-
-This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
-Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
-
-The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
-
-## Model architecture
-
-The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
-
-## Training Data
-
-The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
-For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
-The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
-
-For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
-
-## Tokenization
-
-The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
-The vocabulary size is 32768.
-
-We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
-
-## Training
-
-We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
-For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
-
-For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
-
-## Licenses
-
-The pretrained models are distributed under the Apache License 2.0.
-
-## Acknowledgments
-
-This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.

spaces/AleksBlacky/Arxiv_paper_classifier/app.py

@@ -1,136 +0,0 @@
-import streamlit as st
-import transformers
-import pickle
-import seaborn as sns
-from pandas import DataFrame
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-st.markdown("# Hello, friend!")
-st.markdown(" This magic application going to help you with understanding of science paper topic! Cool? Yeah! ")
-
-try:
-    model_name_global = "allenai/scibert_scivocab_uncased"
-    tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
-    with open('./models/scibert/decode_dict.pkl', 'rb') as f:
-        decode_dict = pickle.load(f)
-except ValueError:
-    st.error("Load tokenizer or decode answer dict goes wrong! Pls contact author [email protected]")
-
-with st.form(key="my_form"):
-    st.markdown("### 🎈 Do you want a little magic?  ")
-    st.markdown(" Write your article title and abstract to textboxes bellow and I'll gues topic of your paper!  ")
-    ce, c2, c3 = st.columns([0.07, 7, 0.07])
-
-    with c2:
-        doc_title = st.text_area(
-            "Paste your abstract title below (1 to 50 words)",
-            height=210,
-        )
-
-        doc_abstract = st.text_area(
-            "Paste your abstract text below (1 to 500 words)",
-            height=410,
-        )
-
-        MAX_WORDS_TITLE, MAX_WORDS_ABSTRACT = 50, 500
-        import re
-
-        len_title = len(re.findall(r"\w+", doc_title))
-        len_abstract = len(re.findall(r"\w+", doc_abstract))
-
-        if len_title > MAX_WORDS_TITLE:
-            st.warning(
-                "⚠️ Your title contains "
-                + str(len_title)
-                + " words."
-                + " Only the first 50 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
-            )
-
-            doc_title = doc_title[:MAX_WORDS_TITLE]
-
-        if len_abstract > MAX_WORDS_ABSTRACT:
-            st.warning(
-                "⚠️ Your abstract contains "
-                + str(len_abstract)
-                + " words."
-                + " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
-            )
-
-            doc_abstract = doc_abstract[:MAX_WORDS_ABSTRACT]
-
-        submit_button = st.form_submit_button(label="✨ Let's play, try it!")
-
-if not submit_button:
-    st.stop()
-
-if len_title < 1:
-    st.error("Article without any words in title? Pls give me correct title!")
-    st.stop()
-
-if len_abstract < 1:
-    st.error("Article without any words in abstract? Pls give me correct abstract!")
-    st.stop()
-
-
-#  allow_output_mutation=True
-@st.cache(suppress_st_warning=True)
-def load_model():
-    st.write("Loading big model")
-    return AutoModelForSequenceClassification.from_pretrained("models/scibert/")
-
-
-def make_predict(tokens, decode_dict):
-
-    model_ = load_model()
-    outs = model_(tokens.input_ids)
-
-    probs = outs["logits"].softmax(dim=-1).tolist()[0]
-    topic_probs = {}
-    for i, p in enumerate(probs):
-        if p > 0.1:
-            topic_probs[decode_dict[i]] = p
-    return topic_probs
-
-
-model_local = "models/scibert/"
-
-title = doc_title
-abstract = doc_abstract
-try:
-    tokens = tokenizer_(title + abstract, return_tensors="pt")
-except ValueError:
-    st.error("Word parsing into tokens went wrong! Is input valid? If yes, pls contact author [email protected]")
-
-predicts = make_predict(tokens, decode_dict)
-
-st.markdown("## 🎈 Yor article probably about:  ")
-st.header("")
-
-df = (
-    DataFrame(predicts.items(), columns=["Topic", "Prob"])
-        .sort_values(by="Prob", ascending=False)
-        .reset_index(drop=True)
-)
-
-df.index += 1
-
-# Add styling
-cmGreen = sns.light_palette("green", as_cmap=True)
-cmRed = sns.light_palette("red", as_cmap=True)
-df = df.style.background_gradient(
-    cmap=cmGreen,
-    subset=[
-        "Prob",
-    ],
-)
-
-c1, c2, c3 = st.columns([1, 3, 1])
-
-format_dictionary = {
-    "Prob": "{:.1%}",
-}
-
-df = df.format(format_dictionary)
-
-with c2:
-    st.table(df)

spaces/Alex89912/ai-code-v1/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/codellama/CodeLlama-7b-hf").launch()

spaces/AlgoveraAI/ocean-marketplace/app.py

@@ -1,173 +0,0 @@
-import gradio as gr
-from ocean_lib.config import Config
-from ocean_lib.ocean.ocean import Ocean
-from ocean_lib.web3_internal.wallet import Wallet
-from ocean_lib.web3_internal.currency import pretty_ether_and_wei, to_wei
-from ocean_lib.web3_internal.constants import ZERO_ADDRESS
-from ocean_lib.common.agreements.service_types import ServiceTypes
-from PIL import Image
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-config = Config('config.ini')
-ocean = Ocean(config)
-
-def search(term="", did_in="", address="", buy_top_result=False):
-
-    if address:
-        wallet = Wallet(ocean.web3, private_key=address, transaction_timeout=20, block_confirmations=0)
-    
-    results = None
-    dids = None
-    data=None
-    if term and not did_in:
-        assets = ocean.assets.search(term)
-
-        results = []
-        datas = []
-        balances = []
-        dids = []
-        for i in range(len(assets)):
-            name = assets[i].values['_source']['service'][0]['attributes']['main']['name']
-            type_ = assets[i].values['_source']['service'][0]['attributes']['main']['type'].upper()
-            symbol = assets[i].values['_source']['dataTokenInfo']['symbol']
-            data_token_address = assets[i].values['_source']['dataTokenInfo']['address']
-            try:
-                description = assets[i].values['_source']['service'][0]['attributes']['additionalInformation']['description']
-            except:
-                description = "No description"
-            author = assets[i].values['_source']['service'][0]['attributes']['main']['author']
-            did = assets[i].values['_source']['id']
-            dids.append(did)
-            chain = assets[i].values['_source']['service'][1]['serviceEndpoint']
-            
-            if chain != 'https://provider.rinkeby.oceanprotocol.com':
-                continue
-            
-            if address:
-                data_token = ocean.get_data_token(data_token_address)
-                token_address = data_token.address
-                balances.append(pretty_ether_and_wei(data_token.balanceOf(wallet.address)))
-            else:
-                balances.append(0)
-            
-            img = Image.open('algovera-tile.png')
-
-            fig = plt.figure(figsize=(5,5))
-            plt.axis("off")
-            plt.imshow(img)
-            plt.text(20, 100, name[:22], size=20)
-            plt.text(20, 60, symbol)
-            plt.text(400, 40, type_)
-            plt.text(20, 140, author, size=12)
-            plt.text(20, 200, description[:50])
-            fig.tight_layout()
-            fig.canvas.draw()
-            data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
-            datas.append(data.reshape(fig.canvas.get_width_height()[::-1] + (3,)))
-            plt.close()
-            
-            results.append([dids[-1], datas[-1], balances[-1]])
-
-    
-    if did_in:
-        results = []
-        balances = []
-        datas = []
-        dids = []
-        
-        asset = ocean.assets.resolve(did_in)        
-        name = asset.as_dictionary()['service'][0]['attributes']['main']['name']
-        type_ = asset.as_dictionary()['service'][0]['attributes']['main']['type'].upper()
-        symbol = asset.as_dictionary()['dataTokenInfo']['symbol']
-        try:
-            description = asset.as_dictionary()['service'][0]['attributes']['additionalInformation']['description']
-        except:
-            description = "No description"
-        author = asset.as_dictionary()['service'][0]['attributes']['main']['author']
-        dids.append(did_in)
-        chain = asset.as_dictionary()['service'][1]['serviceEndpoint']
-        
-        if chain != 'https://provider.rinkeby.oceanprotocol.com':
-            pass
-        
-        if address:
-            data_token = ocean.get_data_token(asset.data_token_address)
-            token_address = data_token.address
-            balances.append(pretty_ether_and_wei(data_token.balanceOf(wallet.address)))
-        else:
-            balances.append(0)
-        
-        
-        
-        img = Image.open('algovera-tile.png')
-
-        fig = plt.figure(figsize=(5,5))
-        plt.axis("off")
-        plt.imshow(img)
-        plt.text(20, 100, name[:22], size=20)
-        plt.text(20, 60, symbol)
-        plt.text(400, 40, type_)
-        plt.text(20, 140, author, size=12)
-        plt.text(20, 200, description[:50])
-        fig.tight_layout()
-        fig.canvas.draw()
-        data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
-        datas.append(data.reshape(fig.canvas.get_width_height()[::-1] + (3,)))
-        plt.close()
-        
-        results.append([dids[-1], datas[-1], balances[-1]])
-        
-    if buy_top_result and address:
-        asset = ocean.assets.resolve(dids[0])
-        data_token = ocean.get_data_token(asset.data_token_address)
-        
-        service_type = asset.as_dictionary()['service'][1]['type']
-        compute_service = asset.get_service(service_type)
-        
-        owner_address = asset.as_dictionary()['publicKey'][0]['owner']
-        
-        logs = ocean.exchange.search_exchange_by_data_token(asset.data_token_address)
-        exchange_id = logs[0].args.exchangeId
-        
-        tx_result = ocean.exchange.buy_at_fixed_rate(to_wei(1), wallet, to_wei(5), exchange_id, asset.data_token_address, owner_address)
-        assert tx_result, "failed buying tokens"
-            
-        balance = pretty_ether_and_wei(data_token.balanceOf(wallet.address))
-    
-        results[0][2] = balance
-
-    return results 
-
-description = (
-    "This app can be used to search datasets and algorithms on the Ocean Marketplace. Enter a search term in the text box and the first result will be displayed as an image tile with description. " 
-)
-
-article = (
-    "<p style='text-align: center'>"
-    "<a href='https://market.oceanprotocol.com/' target='_blank'>1. Ocean Marketplace</a> | "
-    "<a href='https://docs.algovera.ai/blog/2022/01/04/Using%20the%20Ocean%20Marketplace%20with%20HuggingFace%20Apps,%20Algorithms%20and%20Datasets' target='_blank'>2. Blog about Ocean Protocol on HuggingFace</a> "
-    "</p>"
-)
-
-
-interface = gr.Interface(
-    search,
-    [
-        gr.inputs.Textbox(label="Search Datasets and Algorithms by name"),
-        gr.inputs.Textbox(label="Search Datasets and Algorithms by DID"),
-        gr.inputs.Textbox(label="Show Token Balance for Each (by Inputting Private Key)"),
-        "checkbox"
-
-    ],
-    [
-        gr.outputs.Carousel(["text", "image", "text"], label="Search Results"),
-    ],
-    title="Ocean Marketplace",
-    description=description,
-    article=article,
-    theme="huggingface",
-)
-
-interface.launch()

spaces/AllAideas/SegmentacionVideo/README.md

@@ -1,13 +0,0 @@
----
-title: SegmentacionVideo
-emoji: 🔥
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Aloento/9Nine-PITS/text/japanese.py

@@ -1,131 +0,0 @@
-import re
-
-import pyopenjtalk
-from unidecode import unidecode
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-  r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-  r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('u', 'ɯ'),
-  ('ʧ', 'tʃ'),
-  ('j', 'dʑ'),
-  ('y', 'j'),
-  ('ni', 'n^i'),
-  ('nj', 'n^'),
-  ('hi', 'çi'),
-  ('hj', 'ç'),
-  ('f', 'ɸ'),
-  ('I', 'i*'),
-  ('U', 'ɯ*'),
-  ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-  (r'Q([↑↓]*[kg])', r'k#\1'),
-  (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-  (r'Q([↑↓]*[sʃ])', r's\1'),
-  (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-  (r'N([↑↓]*[pbm])', r'm\1'),
-  (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-  (r'N([↑↓]*[tdn])', r'n\1'),
-  (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-  for regex, replacement in _symbols_to_japanese:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def japanese_to_romaji_with_accent(text):
-  """
-  Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html
-  """
-
-  text = symbols_to_japanese(text)
-  sentences = re.split(_japanese_marks, text)
-  marks = re.findall(_japanese_marks, text)
-  text = ''
-
-  for i, sentence in enumerate(sentences):
-
-    if re.match(_japanese_characters, sentence):
-
-      if text != '':
-        text += ' '
-
-      labels = pyopenjtalk.extract_fullcontext(sentence)
-
-      for n, label in enumerate(labels):
-        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-
-        if phoneme not in ['sil', 'pau']:
-          text += phoneme.replace('ch', 'ʧ').replace('sh', 'ʃ').replace('cl', 'Q')
-        else:
-          continue
-
-        # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-        a3 = int(re.search(r"\+(\d+)/", label).group(1))
-
-        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-          a2_next = -1
-        else:
-          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-
-        # Accent phrase boundary
-        if a3 == 1 and a2_next == 1:
-          text += ' '
-        # Falling
-        elif a1 == 0 and a2_next == a2 + 1:
-          text += '↓'
-        # Rising
-        elif a2 == 1 and a2_next == 2:
-          text += '↑'
-
-    if i < len(marks):
-      text += unidecode(marks[i]).replace(' ', '')
-
-  return text
-
-
-def get_real_sokuon(text):
-  for regex, replacement in _real_sokuon:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def get_real_hatsuon(text):
-  for regex, replacement in _real_hatsuon:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def japanese_to_ipa(text):
-  text = japanese_to_romaji_with_accent(text).replace('...', '…')
-  text = get_real_sokuon(text)
-  text = get_real_hatsuon(text)
-
-  for regex, replacement in _romaji_to_ipa:
-    text = re.sub(regex, replacement, text)
-
-  return text

spaces/Alpaca233/ChatPDF-GUI/gpt_reader/pdf_reader.py

@@ -1,121 +0,0 @@
-from PyPDF2 import PdfReader
-import openai
-from .prompt import BASE_POINTS, READING_PROMT_V2
-from .paper import Paper
-from .model_interface import OpenAIModel
-
-
-# Setting the API key to use the OpenAI API
-class PaperReader:
-
-    """
-    A class for summarizing research papers using the OpenAI API.
-
-    Attributes:
-        openai_key (str): The API key to use the OpenAI API.
-        token_length (int): The length of text to send to the API at a time.
-        model (str): The GPT model to use for summarization.
-        points_to_focus (str): The key points to focus on while summarizing.
-        verbose (bool): A flag to enable/disable verbose logging.
-
-    """
-
-    def __init__(self, openai_key, token_length=4000, model="gpt-3.5-turbo",
-                 points_to_focus=BASE_POINTS, verbose=False):
-
-        # Setting the API key to use the OpenAI API
-        openai.api_key = openai_key
-
-        # Initializing prompts for the conversation
-        self.init_prompt = READING_PROMT_V2.format(points_to_focus)
-
-        self.summary_prompt = 'You are a researcher helper bot. Now you need to read the summaries of a research paper.'
-        self.messages = []  # Initializing the conversation messages
-        self.summary_msg = []  # Initializing the summary messages
-        self.token_len = token_length  # Setting the token length to use
-        self.keep_round = 2  # Rounds of previous dialogues to keep in conversation
-        self.model = model  # Setting the GPT model to use
-        self.verbose = verbose  # Flag to enable/disable verbose logging
-        self.model = OpenAIModel(api_key=openai_key, model=model)
-
-    def drop_conversation(self, msg):
-        # This method is used to drop previous messages from the conversation and keep only recent ones
-        if len(msg) >= (self.keep_round + 1) * 2 + 1:
-            new_msg = [msg[0]]
-            for i in range(3, len(msg)):
-                new_msg.append(msg[i])
-            return new_msg
-        else:
-            return msg
-
-    def send_msg(self, msg):
-        return self.model.send_msg(msg)
-
-    def _chat(self, message):
-        # This method is used to send a message and get a response from the OpenAI API
-
-        # Adding the user message to the conversation messages
-        self.messages.append({"role": "user", "content": message})
-        # Sending the messages to the API and getting the response
-        response = self.send_msg(self.messages)
-        # Adding the system response to the conversation messages
-        self.messages.append({"role": "system", "content": response})
-        # Dropping previous conversation messages to keep the conversation history short
-        self.messages = self.drop_conversation(self.messages)
-        # Returning the system response
-        return response
-
-    def summarize(self, paper: Paper):
-        # This method is used to summarize a given research paper
-
-        # Adding the initial prompt to the conversation messages
-        self.messages = [
-            {"role": "system", "content": self.init_prompt},
-        ]
-        # Adding the summary prompt to the summary messages
-        self.summary_msg = [{"role": "system", "content": self.summary_prompt}]
-
-         # Reading and summarizing each part of the research paper
-        for (page_idx, part_idx, text) in paper.iter_pages():
-            print('page: {}, part: {}'.format(page_idx, part_idx))
-            # Sending the text to the API and getting the response
-            summary = self._chat('now I send you page {}, part {}：{}'.format(page_idx, part_idx, text))
-            # Logging the summary if verbose logging is enabled
-            if self.verbose:
-                print(summary)
-            # Adding the summary of the part to the summary messages
-            self.summary_msg.append({"role": "user", "content": '{}'.format(summary)})
-
-        # Adding a prompt for the user to summarize the whole paper to the summary messages
-        self.summary_msg.append({"role": "user", "content": 'Now please make a summary of the whole paper'})
-        # Sending the summary messages to the API and getting the response
-        result = self.send_msg(self.summary_msg)
-        # Returning the summary of the whole paper
-        return result
-
-    def read_pdf_and_summarize(self, pdf_path):
-        # This method is used to read a research paper from a PDF file and summarize it
-        
-        # Creating a PdfReader object to read the PDF file
-        pdf_reader = PdfReader(pdf_path)
-        paper = Paper(pdf_reader)
-        # Summarizing the full text of the research paper and returning the summary
-        print('reading pdf finished')
-        summary = self.summarize(paper)
-        return summary
-
-    def get_summary_of_each_part(self):
-        # This method is used to get the summary of each part of the research paper
-        return self.summary_msg
-
-    def question(self, question):
-        # This method is used to ask a question after summarizing a paper
-
-        # Adding the question to the summary messages
-        self.summary_msg.append({"role": "user", "content": question})
-        # Sending the summary messages to the API and getting the response
-        response = self.send_msg(self.summary_msg)
-        # Adding the system response to the summary messages
-        self.summary_msg.append({"role": "system", "content": response})
-        # Returning the system response
-        return response

spaces/Alpaca233/SadTalker/src/utils/face_enhancer.py

@@ -1,123 +0,0 @@
-import os
-import torch 
-
-from gfpgan import GFPGANer
-
-from tqdm import tqdm
-
-from src.utils.videoio import load_video_to_cv2
-
-import cv2
-
-
-class GeneratorWithLen(object):
-    """ From https://stackoverflow.com/a/7460929 """
-
-    def __init__(self, gen, length):
-        self.gen = gen
-        self.length = length
-
-    def __len__(self):
-        return self.length
-
-    def __iter__(self):
-        return self.gen
-
-def enhancer_list(images, method='gfpgan', bg_upsampler='realesrgan'):
-    gen = enhancer_generator_no_len(images, method=method, bg_upsampler=bg_upsampler)
-    return list(gen)
-
-def enhancer_generator_with_len(images, method='gfpgan', bg_upsampler='realesrgan'):
-    """ Provide a generator with a __len__ method so that it can passed to functions that
-    call len()"""
-
-    if os.path.isfile(images): # handle video to images
-        # TODO: Create a generator version of load_video_to_cv2
-        images = load_video_to_cv2(images)
-
-    gen = enhancer_generator_no_len(images, method=method, bg_upsampler=bg_upsampler)
-    gen_with_len = GeneratorWithLen(gen, len(images))
-    return gen_with_len
-
-def enhancer_generator_no_len(images, method='gfpgan', bg_upsampler='realesrgan'):
-    """ Provide a generator function so that all of the enhanced images don't need
-    to be stored in memory at the same time. This can save tons of RAM compared to
-    the enhancer function. """
-
-    print('face enhancer....')
-    if not isinstance(images, list) and os.path.isfile(images): # handle video to images
-        images = load_video_to_cv2(images)
-
-    # ------------------------ set up GFPGAN restorer ------------------------
-    if  method == 'gfpgan':
-        arch = 'clean'
-        channel_multiplier = 2
-        model_name = 'GFPGANv1.4'
-        url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth'
-    elif method == 'RestoreFormer':
-        arch = 'RestoreFormer'
-        channel_multiplier = 2
-        model_name = 'RestoreFormer'
-        url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth'
-    elif method == 'codeformer': # TODO:
-        arch = 'CodeFormer'
-        channel_multiplier = 2
-        model_name = 'CodeFormer'
-        url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-    else:
-        raise ValueError(f'Wrong model version {method}.')
-
-
-    # ------------------------ set up background upsampler ------------------------
-    if bg_upsampler == 'realesrgan':
-        if not torch.cuda.is_available():  # CPU
-            import warnings
-            warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. '
-                          'If you really want to use it, please modify the corresponding codes.')
-            bg_upsampler = None
-        else:
-            from basicsr.archs.rrdbnet_arch import RRDBNet
-            from realesrgan import RealESRGANer
-            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-            bg_upsampler = RealESRGANer(
-                scale=2,
-                model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
-                model=model,
-                tile=400,
-                tile_pad=10,
-                pre_pad=0,
-                half=True)  # need to set False in CPU mode
-    else:
-        bg_upsampler = None
-
-    # determine model paths
-    model_path = os.path.join('gfpgan/weights', model_name + '.pth')
-    
-    if not os.path.isfile(model_path):
-        model_path = os.path.join('checkpoints', model_name + '.pth')
-    
-    if not os.path.isfile(model_path):
-        # download pre-trained models from url
-        model_path = url
-
-    restorer = GFPGANer(
-        model_path=model_path,
-        upscale=2,
-        arch=arch,
-        channel_multiplier=channel_multiplier,
-        bg_upsampler=bg_upsampler)
-
-    # ------------------------ restore ------------------------
-    for idx in tqdm(range(len(images)), 'Face Enhancer:'):
-        
-        img = cv2.cvtColor(images[idx], cv2.COLOR_RGB2BGR)
-        
-        # restore faces and background if necessary
-        cropped_faces, restored_faces, r_img = restorer.enhance(
-            img,
-            has_aligned=False,
-            only_center_face=False,
-            paste_back=True)
-        
-        r_img = cv2.cvtColor(r_img, cv2.COLOR_BGR2RGB)
-        yield r_img

spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/libJPG/jpge.cpp

@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <[email protected]>
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include <stdlib.h>
-#include <string.h>
-#if PLATFORM_WINDOWS
-#include <malloc.h>
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES]  =
-{
-  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
-  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
-  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
-  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
-  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
-  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
-  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
-  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
-  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
-  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-
-// Low-level helper functions.
-template <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }
-
-static void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)
-{
-  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }
-}
-
-// Forward DCT - DCT derived from jfdctint.
-#define CONST_BITS  13
-#define ROW_BITS    2
-#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))
-#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))
-#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \
-  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \
-  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \
-  int32 u1 = DCT_MUL(t12 + t13, 4433); \
-  s2 = u1 + DCT_MUL(t13, 6270); \
-  s6 = u1 + DCT_MUL(t12, -15137); \
-  u1 = t4 + t7; \
-  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \
-  int32 z5 = DCT_MUL(u3 + u4, 9633); \
-  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \
-  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \
-  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \
-  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \
-  u3 += z5; u4 += z5; \
-  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;
-
-static void DCT2D(int32 *p)
-{
-  int32 c, *q = p;
-  for (c = 7; c >= 0; c--, q += 8)
-  {
-    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);
-    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);
-  }
-  for (q = p, c = 7; c >= 0; c--, q++)
-  {
-    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);
-    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);
-  }
-}
-
-struct sym_freq { uint m_key, m_sym_index; };
-
-// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.
-static inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)
-{
-  const uint cMaxPasses = 4;
-  uint32 hist[256 * cMaxPasses]; clear_obj(hist);
-  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }
-  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;
-  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;
-  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)
-  {
-    const uint32* pHist = &hist[pass << 8];
-    uint offsets[256], cur_ofs = 0;
-    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }
-    for (uint i = 0; i < num_syms; i++)
-      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];
-    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;
-  }
-  return pCur_syms;
-}
-
-// calculate_minimum_redundancy() originally written by: Alistair Moffat, [email protected], Jyrki Katajainen, [email protected], November 1996.
-static void calculate_minimum_redundancy(sym_freq *A, int n)
-{
-  int root, leaf, next, avbl, used, dpth;
-  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }
-  A[0].m_key += A[1].m_key; root = 0; leaf = 2;
-  for (next=1; next < n-1; next++)
-  {
-    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;
-    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;
-  }
-  A[n-2].m_key = 0;
-  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;
-  avbl = 1; used = dpth = 0; root = n-2; next = n-1;
-  while (avbl>0)
-  {
-    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }
-    while (avbl>used) { A[next--].m_key = dpth; avbl--; }
-    avbl = 2*used; dpth++; used = 0;
-  }
-}
-
-// Limits canonical Huffman code table's max code size to max_code_size.
-static void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)
-{
-  if (code_list_len <= 1) return;
-
-  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];
-
-  uint32 total = 0;
-  for (int i = max_code_size; i > 0; i--)
-    total += (((uint32)pNum_codes[i]) << (max_code_size - i));
-
-  while (total != (1UL << max_code_size))
-  {
-    pNum_codes[max_code_size]--;
-    for (int i = max_code_size - 1; i > 0; i--)
-    {
-      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }
-    }
-    total--;
-  }
-}
-
-// Generates an optimized offman table.
-void jpeg_encoder::optimize_huffman_table(int table_num, int table_len)
-{
-  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];
-  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's
-  int num_used_syms = 1;
-  const uint32 *pSym_count = &m_huff_count[table_num][0];
-  for (int i = 0; i < table_len; i++)
-    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }
-  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);
-  calculate_minimum_redundancy(pSyms, num_used_syms);
-
-  // Count the # of symbols of each code size.
-  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);
-  for (int i = 0; i < num_used_syms; i++)
-    num_codes[pSyms[i].m_key]++;
-
-  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)
-  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);
-
-  // Compute m_huff_bits array, which contains the # of symbols per code size.
-  clear_obj(m_huff_bits[table_num]);
-  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)
-    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);
-
-  // Remove the dummy symbol added above, which must be in largest bucket.
-  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)
-  {
-    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }
-  }
-
-  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).
-  for (int i = num_used_syms - 1; i >= 1; i--)
-    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);
-}
-
-// JPEG marker generation.
-void jpeg_encoder::emit_byte(uint8 i)
-{
-  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);
-}
-
-void jpeg_encoder::emit_word(uint i)
-{
-  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));
-}
-
-void jpeg_encoder::emit_marker(int marker)
-{
-  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));
-}
-
-// Emit JFIF marker
-void jpeg_encoder::emit_jfif_app0()
-{
-  emit_marker(M_APP0);
-  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);
-  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII "JFIF" */
-  emit_byte(0);
-  emit_byte(1);      /* Major version */
-  emit_byte(1);      /* Minor version */
-  emit_byte(0);      /* Density unit */
-  emit_word(1);
-  emit_word(1);
-  emit_byte(0);      /* No thumbnail image */
-  emit_byte(0);
-}
-
-// Emit quantization tables
-void jpeg_encoder::emit_dqt()
-{
-  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)
-  {
-    emit_marker(M_DQT);
-    emit_word(64 + 1 + 2);
-    emit_byte(static_cast<uint8>(i));
-    for (int j = 0; j < 64; j++)
-      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));
-  }
-}
-
-// Emit start of frame marker
-void jpeg_encoder::emit_sof()
-{
-  emit_marker(M_SOF0);                           /* baseline */
-  emit_word(3 * m_num_components + 2 + 5 + 1);
-  emit_byte(8);                                  /* precision */
-  emit_word(m_image_y);
-  emit_word(m_image_x);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */
-    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */
-    emit_byte(i > 0);                                   /* quant. table num */
-  }
-}
-
-// Emit Huffman table.
-void jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)
-{
-  emit_marker(M_DHT);
-
-  int length = 0;
-  for (int i = 1; i <= 16; i++)
-    length += bits[i];
-
-  emit_word(length + 2 + 1 + 16);
-  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));
-
-  for (int i = 1; i <= 16; i++)
-    emit_byte(bits[i]);
-
-  for (int i = 0; i < length; i++)
-    emit_byte(val[i]);
-}
-
-// Emit all Huffman tables.
-void jpeg_encoder::emit_dhts()
-{
-  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);
-  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);
-  if (m_num_components == 3)
-  {
-    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);
-    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);
-  }
-}
-
-// emit start of scan
-void jpeg_encoder::emit_sos()
-{
-  emit_marker(M_SOS);
-  emit_word(2 * m_num_components + 2 + 1 + 3);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));
-    if (i == 0)
-      emit_byte((0 << 4) + 0);
-    else
-      emit_byte((1 << 4) + 1);
-  }
-  emit_byte(0);     /* spectral selection */
-  emit_byte(63);
-  emit_byte(0);
-}
-
-// Emit all markers at beginning of image file.
-void jpeg_encoder::emit_markers()
-{
-  emit_marker(M_SOI);
-  emit_jfif_app0();
-  emit_dqt();
-  emit_sof();
-  emit_dhts();
-  emit_sos();
-}
-
-// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.
-void jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)
-{
-  int i, l, last_p, si;
-  uint8 huff_size[257];
-  uint huff_code[257];
-  uint code;
-
-  int p = 0;
-  for (l = 1; l <= 16; l++)
-    for (i = 1; i <= bits[l]; i++)
-      huff_size[p++] = (char)l;
-
-  huff_size[p] = 0; last_p = p; // write sentinel
-
-  code = 0; si = huff_size[0]; p = 0;
-
-  while (huff_size[p])
-  {
-    while (huff_size[p] == si)
-      huff_code[p++] = code++;
-    code <<= 1;
-    si++;
-  }
-
-  memset(codes, 0, sizeof(codes[0])*256);
-  memset(code_sizes, 0, sizeof(code_sizes[0])*256);
-  for (p = 0; p < last_p; p++)
-  {
-    codes[val[p]]      = huff_code[p];
-    code_sizes[val[p]] = huff_size[p];
-  }
-}
-
-// Quantization table generation.
-void jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)
-{
-  int32 q;
-  if (m_params.m_quality < 50)
-    q = 5000 / m_params.m_quality;
-  else
-    q = 200 - m_params.m_quality * 2;
-  for (int i = 0; i < 64; i++)
-  {
-    int32 j = *pSrc++; j = (j * q + 50L) / 100L;
-    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);
-  }
-}
-
-// Higher-level methods.
-void jpeg_encoder::first_pass_init()
-{
-  m_bit_buffer = 0; m_bits_in = 0;
-  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));
-  m_mcu_y_ofs = 0;
-  m_pass_num = 1;
-}
-
-bool jpeg_encoder::second_pass_init()
-{
-  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);
-  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);
-  if (m_num_components > 1)
-  {
-    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);
-    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);
-  }
-  first_pass_init();
-  emit_markers();
-  m_pass_num = 2;
-  return true;
-}
-
-bool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)
-{
-  m_num_components = 3;
-  switch (m_params.m_subsampling)
-  {
-    case Y_ONLY:
-    {
-      m_num_components = 1;
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H1V1:
-    {
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H2V1:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 8;
-      break;
-    }
-    case H2V2:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 16;
-    }
-  }
-
-  m_image_x        = p_x_res; m_image_y = p_y_res;
-  m_image_bpp      = src_channels;
-  m_image_bpl      = m_image_x * src_channels;
-  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));
-  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));
-  m_image_bpl_xlt  = m_image_x * m_num_components;
-  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;
-  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;
-
-  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;
-  for (int i = 1; i < m_mcu_y; i++)
-    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;
-
-  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);
-  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);
-
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-  m_pOut_buf = m_out_buf;
-
-  if (m_params.m_two_pass_flag)
-  {
-    clear_obj(m_huff_count);
-    first_pass_init();
-  }
-  else
-  {
-    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);
-    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);
-    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);
-    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);
-    if (!second_pass_init()) return false;   // in effect, skip over the first pass
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-void jpeg_encoder::load_block_8_8_grey(int x)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[i] + x;
-    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;
-    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_8_8(int x, int y, int c)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (8 * 3)) + c;
-  y <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[y + i] + x;
-    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;
-    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_16_8(int x, int c)
-{
-  uint8 *pSrc1, *pSrc2;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  int a = 0, b = 2;
-  for (int i = 0; i < 16; i += 2, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pSrc2 = m_mcu_lines[i + 1] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;
-    int temp = a; a = b; b = temp;
-  }
-}
-
-void jpeg_encoder::load_block_16_8_8(int x, int c)
-{
-  uint8 *pSrc1;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;
-  }
-}
-
-void jpeg_encoder::load_quantized_coefficients(int component_num)
-{
-  int32 *q = m_quantization_tables[component_num > 0];
-  int16 *pDst = m_coefficient_array;
-  for (int i = 0; i < 64; i++)
-  {
-    sample_array_t j = m_sample_array[s_zag[i]];
-    if (j < 0)
-    {
-      if ((j = -j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>(-(j / *q));
-    }
-    else
-    {
-      if ((j = j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>((j / *q));
-    }
-    q++;
-  }
-}
-
-void jpeg_encoder::flush_output_buffer()
-{
-  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)
-    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);
-  m_pOut_buf = m_out_buf;
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-}
-
-void jpeg_encoder::put_bits(uint bits, uint len)
-{
-  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));
-  while (m_bits_in >= 8)
-  {
-    uint8 c;
-    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }
-    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));
-    if (c == 0xFF) JPGE_PUT_BYTE(0);
-    m_bit_buffer <<= 8;
-    m_bits_in -= 8;
-  }
-}
-
-void jpeg_encoder::code_coefficients_pass_one(int component_num)
-{
-  if (component_num >= 3) return; // just to shut up static analysis
-  int i, run_len, nbits, temp1;
-  int16 *src = m_coefficient_array;
-  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];
-
-  temp1 = src[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = src[0];
-  if (temp1 < 0) temp1 = -temp1;
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  dc_count[nbits]++;
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        ac_count[0xF0]++;
-        run_len -= 16;
-      }
-      if (temp1 < 0) temp1 = -temp1;
-      nbits = 1;
-      while (temp1 >>= 1) nbits++;
-      ac_count[(run_len << 4) + nbits]++;
-      run_len = 0;
-    }
-  }
-  if (run_len) ac_count[0]++;
-}
-
-void jpeg_encoder::code_coefficients_pass_two(int component_num)
-{
-  int i, j, run_len, nbits, temp1, temp2;
-  int16 *pSrc = m_coefficient_array;
-  uint *codes[2];
-  uint8 *code_sizes[2];
-
-  if (component_num == 0)
-  {
-    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];
-    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];
-  }
-  else
-  {
-    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];
-    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];
-  }
-
-  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = pSrc[0];
-
-  if (temp1 < 0)
-  {
-    temp1 = -temp1; temp2--;
-  }
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  put_bits(codes[0][nbits], code_sizes[0][nbits]);
-  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);
-
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);
-        run_len -= 16;
-      }
-      if ((temp2 = temp1) < 0)
-      {
-        temp1 = -temp1;
-        temp2--;
-      }
-      nbits = 1;
-      while (temp1 >>= 1)
-        nbits++;
-      j = (run_len << 4) + nbits;
-      put_bits(codes[1][j], code_sizes[1][j]);
-      put_bits(temp2 & ((1 << nbits) - 1), nbits);
-      run_len = 0;
-    }
-  }
-  if (run_len)
-    put_bits(codes[1][0], code_sizes[1][0]);
-}
-
-void jpeg_encoder::code_block(int component_num)
-{
-  DCT2D(m_sample_array);
-  load_quantized_coefficients(component_num);
-  if (m_pass_num == 1)
-    code_coefficients_pass_one(component_num);
-  else
-    code_coefficients_pass_two(component_num);
-}
-
-void jpeg_encoder::process_mcu_row()
-{
-  if (m_num_components == 1)
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8_grey(i); code_block(0);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);
-      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);
-    }
-  }
-}
-
-bool jpeg_encoder::terminate_pass_one()
-{
-  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);
-  if (m_num_components > 1)
-  {
-    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);
-  }
-  return second_pass_init();
-}
-
-bool jpeg_encoder::terminate_pass_two()
-{
-  put_bits(0x7F, 7);
-  flush_output_buffer();
-  emit_marker(M_EOI);
-  m_pass_num++; // purposely bump up m_pass_num, for debugging
-  return true;
-}
-
-bool jpeg_encoder::process_end_of_image()
-{
-  if (m_mcu_y_ofs)
-  {
-    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis
-    {
-      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)
-        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);
-    }
-
-    process_mcu_row();
-  }
-
-  if (m_pass_num == 1)
-    return terminate_pass_one();
-  else
-    return terminate_pass_two();
-}
-
-void jpeg_encoder::load_mcu(const void *pSrc)
-{
-  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);
-
-  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst
-
-  if (m_num_components == 1)
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_Y(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_Y(pDst, Psrc, m_image_x);
-    else
-      memcpy(pDst, Psrc, m_image_x);
-  }
-  else
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_YCC(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_YCC(pDst, Psrc, m_image_x);
-    else
-      Y_to_YCC(pDst, Psrc, m_image_x);
-  }
-
-  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16
-  if (m_num_components == 1)
-    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);
-  else
-  {
-    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];
-    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;
-    for (int i = m_image_x; i < m_image_x_mcu; i++)
-    {
-      *q++ = y; *q++ = cb; *q++ = cr;
-    }
-  }
-
-  if (++m_mcu_y_ofs == m_mcu_y)
-  {
-    process_mcu_row();
-    m_mcu_y_ofs = 0;
-  }
-}
-
-void jpeg_encoder::clear()
-{
-  m_mcu_lines[0] = NULL;
-  m_pass_num = 0;
-  m_all_stream_writes_succeeded = true;
-}
-
-jpeg_encoder::jpeg_encoder()
-{
-  clear();
-}
-
-jpeg_encoder::~jpeg_encoder()
-{
-  deinit();
-}
-
-bool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)
-{
-  deinit();
-  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;
-  m_pStream = pStream;
-  m_params = comp_params;
-  return jpg_open(width, height, src_channels);
-}
-
-void jpeg_encoder::deinit()
-{
-  jpge_free(m_mcu_lines[0]);
-  clear();
-}
-
-bool jpeg_encoder::process_scanline(const void* pScanline)
-{
-  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;
-  if (m_all_stream_writes_succeeded)
-  {
-    if (!pScanline)
-    {
-      if (!process_end_of_image()) return false;
-    }
-    else
-    {
-      load_mcu(pScanline);
-    }
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-// Higher level wrappers/examples (optional).
-#include <stdio.h>
-
-class cfile_stream : public output_stream
-{
-   cfile_stream(const cfile_stream &);
-   cfile_stream &operator= (const cfile_stream &);
-
-   FILE* m_pFile;
-   bool m_bStatus;
-
-public:
-   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }
-
-   virtual ~cfile_stream()
-   {
-      close();
-   }
-
-   bool open(const char *pFilename)
-   {
-      close();
-#if defined(_MSC_VER)
-      if (fopen_s(&m_pFile, pFilename, "wb") != 0)
-	  {
-		  return false;
-	  }
-#else
-      m_pFile = fopen(pFilename, "wb");
-#endif
-      m_bStatus = (m_pFile != NULL);
-      return m_bStatus;
-   }
-
-   bool close()
-   {
-      if (m_pFile)
-      {
-         if (fclose(m_pFile) == EOF)
-         {
-            m_bStatus = false;
-         }
-         m_pFile = NULL;
-      }
-      return m_bStatus;
-   }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);
-      return m_bStatus;
-   }
-
-   uint get_size() const
-   {
-      return m_pFile ? ftell(m_pFile) : 0;
-   }
-};
-
-// Writes JPEG image to file.
-bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-  cfile_stream dst_stream;
-  if (!dst_stream.open(pFilename))
-    return false;
-
-  jpge::jpeg_encoder dst_image;
-  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-    return false;
-
-  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-  {
-    for (int64_t i = 0; i < height; i++)
-    {
-		// i, width, and num_channels are all 64bit
-       const uint8* pBuf = pImage_data + i * width * num_channels;
-       if (!dst_image.process_scanline(pBuf))
-          return false;
-    }
-    if (!dst_image.process_scanline(NULL))
-       return false;
-  }
-
-  dst_image.deinit();
-
-  return dst_stream.close();
-}
-
-class memory_stream : public output_stream
-{
-   memory_stream(const memory_stream &);
-   memory_stream &operator= (const memory_stream &);
-
-   uint8 *m_pBuf;
-   uint64_t m_buf_size, m_buf_ofs;
-
-public:
-   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }
-
-   virtual ~memory_stream() { }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      uint64_t buf_remaining = m_buf_size - m_buf_ofs;
-      if ((uint64_t)len > buf_remaining)
-         return false;
-      memcpy(m_pBuf + m_buf_ofs, pBuf, len);
-      m_buf_ofs += len;
-      return true;
-   }
-
-   uint64_t get_size() const
-   {
-      return m_buf_ofs;
-   }
-};
-
-bool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-   if ((!pDstBuf) || (!buf_size))
-      return false;
-
-   memory_stream dst_stream(pDstBuf, buf_size);
-
-   buf_size = 0;
-
-   jpge::jpeg_encoder dst_image;
-   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-      return false;
-
-   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-   {
-     for (int64_t i = 0; i < height; i++)
-     {
-        const uint8* pScanline = pImage_data + i * width * num_channels;
-        if (!dst_image.process_scanline(pScanline))
-           return false;
-     }
-     if (!dst_image.process_scanline(NULL))
-        return false;
-   }
-
-   dst_image.deinit();
-
-   buf_size = dst_stream.get_size();
-   return true;
-}
-
-} // namespace jpge

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py

@@ -1,589 +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.
-
-import warnings
-from functools import partial
-from typing import Dict, List, Optional, Union
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-from flax.core.frozen_dict import FrozenDict
-from flax.jax_utils import unreplicate
-from flax.training.common_utils import shard
-from packaging import version
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
-
-from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
-from ...schedulers import (
-    FlaxDDIMScheduler,
-    FlaxDPMSolverMultistepScheduler,
-    FlaxLMSDiscreteScheduler,
-    FlaxPNDMScheduler,
-)
-from ...utils import PIL_INTERPOLATION, deprecate, logging, replace_example_docstring
-from ..pipeline_flax_utils import FlaxDiffusionPipeline
-from . import FlaxStableDiffusionPipelineOutput
-from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-# Set to True to use python for loop instead of jax.fori_loop for easier debugging
-DEBUG = False
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import jax
-        >>> import numpy as np
-        >>> from flax.jax_utils import replicate
-        >>> from flax.training.common_utils import shard
-        >>> import PIL
-        >>> import requests
-        >>> from io import BytesIO
-        >>> from diffusers import FlaxStableDiffusionInpaintPipeline
-
-
-        >>> def download_image(url):
-        ...     response = requests.get(url)
-        ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
-
-
-        >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-        >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-
-        >>> init_image = download_image(img_url).resize((512, 512))
-        >>> mask_image = download_image(mask_url).resize((512, 512))
-
-        >>> pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(
-        ...     "xvjiarui/stable-diffusion-2-inpainting"
-        ... )
-
-        >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
-        >>> prng_seed = jax.random.PRNGKey(0)
-        >>> num_inference_steps = 50
-
-        >>> num_samples = jax.device_count()
-        >>> prompt = num_samples * [prompt]
-        >>> init_image = num_samples * [init_image]
-        >>> mask_image = num_samples * [mask_image]
-        >>> prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(
-        ...     prompt, init_image, mask_image
-        ... )
-        # shard inputs and rng
-
-        >>> params = replicate(params)
-        >>> prng_seed = jax.random.split(prng_seed, jax.device_count())
-        >>> prompt_ids = shard(prompt_ids)
-        >>> processed_masked_images = shard(processed_masked_images)
-        >>> processed_masks = shard(processed_masks)
-
-        >>> images = pipeline(
-        ...     prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True
-        ... ).images
-        >>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-        ```
-"""
-
-
-class FlaxStableDiffusionInpaintPipeline(FlaxDiffusionPipeline):
-    r"""
-    Flax-based pipeline for text-guided image inpainting using Stable Diffusion.
-
-    <Tip warning={true}>
-
-    🧪 This is an experimental feature!
-
-    </Tip>
-
-    This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        vae ([`FlaxAutoencoderKL`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        text_encoder ([`~transformers.FlaxCLIPTextModel`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer ([`~transformers.CLIPTokenizer`]):
-            A `CLIPTokenizer` to tokenize text.
-        unet ([`FlaxUNet2DConditionModel`]):
-            A `FlaxUNet2DConditionModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
-            [`FlaxDPMSolverMultistepScheduler`].
-        safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: FlaxAutoencoderKL,
-        text_encoder: FlaxCLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: FlaxUNet2DConditionModel,
-        scheduler: Union[
-            FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
-        ],
-        safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        dtype: jnp.dtype = jnp.float32,
-    ):
-        super().__init__()
-        self.dtype = dtype
-
-        if safety_checker is None:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-
-    def prepare_inputs(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[Image.Image, List[Image.Image]],
-        mask: Union[Image.Image, List[Image.Image]],
-    ):
-        if not isinstance(prompt, (str, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if not isinstance(image, (Image.Image, list)):
-            raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
-
-        if isinstance(image, Image.Image):
-            image = [image]
-
-        if not isinstance(mask, (Image.Image, list)):
-            raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
-
-        if isinstance(mask, Image.Image):
-            mask = [mask]
-
-        processed_images = jnp.concatenate([preprocess_image(img, jnp.float32) for img in image])
-        processed_masks = jnp.concatenate([preprocess_mask(m, jnp.float32) for m in mask])
-        # processed_masks[processed_masks < 0.5] = 0
-        processed_masks = processed_masks.at[processed_masks < 0.5].set(0)
-        # processed_masks[processed_masks >= 0.5] = 1
-        processed_masks = processed_masks.at[processed_masks >= 0.5].set(1)
-
-        processed_masked_images = processed_images * (processed_masks < 0.5)
-
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="np",
-        )
-        return text_input.input_ids, processed_masked_images, processed_masks
-
-    def _get_has_nsfw_concepts(self, features, params):
-        has_nsfw_concepts = self.safety_checker(features, params)
-        return has_nsfw_concepts
-
-    def _run_safety_checker(self, images, safety_model_params, jit=False):
-        # safety_model_params should already be replicated when jit is True
-        pil_images = [Image.fromarray(image) for image in images]
-        features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
-
-        if jit:
-            features = shard(features)
-            has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
-            has_nsfw_concepts = unshard(has_nsfw_concepts)
-            safety_model_params = unreplicate(safety_model_params)
-        else:
-            has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
-
-        images_was_copied = False
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                if not images_was_copied:
-                    images_was_copied = True
-                    images = images.copy()
-
-                images[idx] = np.zeros(images[idx].shape, dtype=np.uint8)  # black image
-
-            if any(has_nsfw_concepts):
-                warnings.warn(
-                    "Potential NSFW content was detected in one or more images. A black image will be returned"
-                    " instead. Try again with a different prompt and/or seed."
-                )
-
-        return images, has_nsfw_concepts
-
-    def _generate(
-        self,
-        prompt_ids: jnp.array,
-        mask: jnp.array,
-        masked_image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        num_inference_steps: int,
-        height: int,
-        width: int,
-        guidance_scale: float,
-        latents: Optional[jnp.array] = None,
-        neg_prompt_ids: Optional[jnp.array] = None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # get prompt text embeddings
-        prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
-
-        # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
-        # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
-        batch_size = prompt_ids.shape[0]
-
-        max_length = prompt_ids.shape[-1]
-
-        if neg_prompt_ids is None:
-            uncond_input = self.tokenizer(
-                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
-            ).input_ids
-        else:
-            uncond_input = neg_prompt_ids
-        negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
-        context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
-
-        latents_shape = (
-            batch_size,
-            self.vae.config.latent_channels,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-        if latents is None:
-            latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=self.dtype)
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-
-        prng_seed, mask_prng_seed = jax.random.split(prng_seed)
-
-        masked_image_latent_dist = self.vae.apply(
-            {"params": params["vae"]}, masked_image, method=self.vae.encode
-        ).latent_dist
-        masked_image_latents = masked_image_latent_dist.sample(key=mask_prng_seed).transpose((0, 3, 1, 2))
-        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
-        del mask_prng_seed
-
-        mask = jax.image.resize(mask, (*mask.shape[:-2], *masked_image_latents.shape[-2:]), method="nearest")
-
-        # 8. Check that sizes of mask, masked image and latents match
-        num_channels_latents = self.vae.config.latent_channels
-        num_channels_mask = mask.shape[1]
-        num_channels_masked_image = masked_image_latents.shape[1]
-        if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
-            raise ValueError(
-                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
-                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
-                f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
-                " `pipeline.unet` or your `mask_image` or `image` input."
-            )
-
-        def loop_body(step, args):
-            latents, mask, masked_image_latents, scheduler_state = args
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            latents_input = jnp.concatenate([latents] * 2)
-            mask_input = jnp.concatenate([mask] * 2)
-            masked_image_latents_input = jnp.concatenate([masked_image_latents] * 2)
-
-            t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
-            timestep = jnp.broadcast_to(t, latents_input.shape[0])
-
-            latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
-            # concat latents, mask, masked_image_latents in the channel dimension
-            latents_input = jnp.concatenate([latents_input, mask_input, masked_image_latents_input], axis=1)
-
-            # predict the noise residual
-            noise_pred = self.unet.apply(
-                {"params": params["unet"]},
-                jnp.array(latents_input),
-                jnp.array(timestep, dtype=jnp.int32),
-                encoder_hidden_states=context,
-            ).sample
-            # perform guidance
-            noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
-            noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
-            return latents, mask, masked_image_latents, scheduler_state
-
-        scheduler_state = self.scheduler.set_timesteps(
-            params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape
-        )
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * params["scheduler"].init_noise_sigma
-
-        if DEBUG:
-            # run with python for loop
-            for i in range(num_inference_steps):
-                latents, mask, masked_image_latents, scheduler_state = loop_body(
-                    i, (latents, mask, masked_image_latents, scheduler_state)
-                )
-        else:
-            latents, _, _, _ = jax.lax.fori_loop(
-                0, num_inference_steps, loop_body, (latents, mask, masked_image_latents, scheduler_state)
-            )
-
-        # scale and decode the image latents with vae
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
-
-        image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
-        return image
-
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt_ids: jnp.array,
-        mask: jnp.array,
-        masked_image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        num_inference_steps: int = 50,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        guidance_scale: Union[float, jnp.array] = 7.5,
-        latents: jnp.array = None,
-        neg_prompt_ids: jnp.array = None,
-        return_dict: bool = True,
-        jit: bool = False,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide image generation.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter is modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            latents (`jnp.array`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                array is generated by sampling using the supplied random `generator`.
-            jit (`bool`, defaults to `False`):
-                Whether to run `pmap` versions of the generation and safety scoring functions.
-
-                    <Tip warning={true}>
-
-                    This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
-                    future release.
-
-                    </Tip>
-
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
-                a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is
-                returned, otherwise a `tuple` is returned where the first element is a list with the generated images
-                and the second element is a list of `bool`s indicating whether the corresponding generated image
-                contains "not-safe-for-work" (nsfw) content.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        masked_image = jax.image.resize(masked_image, (*masked_image.shape[:-2], height, width), method="bicubic")
-        mask = jax.image.resize(mask, (*mask.shape[:-2], height, width), method="nearest")
-
-        if isinstance(guidance_scale, float):
-            # Convert to a tensor so each device gets a copy. Follow the prompt_ids for
-            # shape information, as they may be sharded (when `jit` is `True`), or not.
-            guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
-            if len(prompt_ids.shape) > 2:
-                # Assume sharded
-                guidance_scale = guidance_scale[:, None]
-
-        if jit:
-            images = _p_generate(
-                self,
-                prompt_ids,
-                mask,
-                masked_image,
-                params,
-                prng_seed,
-                num_inference_steps,
-                height,
-                width,
-                guidance_scale,
-                latents,
-                neg_prompt_ids,
-            )
-        else:
-            images = self._generate(
-                prompt_ids,
-                mask,
-                masked_image,
-                params,
-                prng_seed,
-                num_inference_steps,
-                height,
-                width,
-                guidance_scale,
-                latents,
-                neg_prompt_ids,
-            )
-
-        if self.safety_checker is not None:
-            safety_params = params["safety_checker"]
-            images_uint8_casted = (images * 255).round().astype("uint8")
-            num_devices, batch_size = images.shape[:2]
-
-            images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
-            images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
-            images = np.asarray(images)
-
-            # block images
-            if any(has_nsfw_concept):
-                for i, is_nsfw in enumerate(has_nsfw_concept):
-                    if is_nsfw:
-                        images[i] = np.asarray(images_uint8_casted[i])
-
-            images = images.reshape(num_devices, batch_size, height, width, 3)
-        else:
-            images = np.asarray(images)
-            has_nsfw_concept = False
-
-        if not return_dict:
-            return (images, has_nsfw_concept)
-
-        return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
-
-
-# Static argnums are pipe, num_inference_steps, height, width. A change would trigger recompilation.
-# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
-@partial(
-    jax.pmap,
-    in_axes=(None, 0, 0, 0, 0, 0, None, None, None, 0, 0, 0),
-    static_broadcasted_argnums=(0, 6, 7, 8),
-)
-def _p_generate(
-    pipe,
-    prompt_ids,
-    mask,
-    masked_image,
-    params,
-    prng_seed,
-    num_inference_steps,
-    height,
-    width,
-    guidance_scale,
-    latents,
-    neg_prompt_ids,
-):
-    return pipe._generate(
-        prompt_ids,
-        mask,
-        masked_image,
-        params,
-        prng_seed,
-        num_inference_steps,
-        height,
-        width,
-        guidance_scale,
-        latents,
-        neg_prompt_ids,
-    )
-
-
-@partial(jax.pmap, static_broadcasted_argnums=(0,))
-def _p_get_has_nsfw_concepts(pipe, features, params):
-    return pipe._get_has_nsfw_concepts(features, params)
-
-
-def unshard(x: jnp.ndarray):
-    # einops.rearrange(x, 'd b ... -> (d b) ...')
-    num_devices, batch_size = x.shape[:2]
-    rest = x.shape[2:]
-    return x.reshape(num_devices * batch_size, *rest)
-
-
-def preprocess_image(image, dtype):
-    w, h = image.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = jnp.array(image).astype(dtype) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    return 2.0 * image - 1.0
-
-
-def preprocess_mask(mask, dtype):
-    w, h = mask.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    mask = mask.resize((w, h))
-    mask = jnp.array(mask.convert("L")).astype(dtype) / 255.0
-    mask = jnp.expand_dims(mask, axis=(0, 1))
-
-    return mask

spaces/Andy1621/uniformer_image_detection/configs/free_anchor/README.md

@@ -1,27 +0,0 @@
-# FreeAnchor: Learning to Match Anchors for Visual Object Detection
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@inproceedings{zhang2019freeanchor,
-  title   =  {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection},
-  author  =  {Zhang, Xiaosong and Wan, Fang and Liu, Chang and Ji, Rongrong and Ye, Qixiang},
-  booktitle =  {Neural Information Processing Systems},
-  year    =  {2019}
-}
-```
-
-## Results and Models
-
-| Backbone | Style   | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-|:--------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
-| R-50     | pytorch | 1x      | 4.9      | 18.4 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130_095625.log.json) |
-| R-101       | pytorch | 1x   | 6.8      | 14.9 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130_100723.log.json) |
-| X-101-32x4d | pytorch | 1x   | 8.1      | 11.1 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130_095627.log.json) |
-
-**Notes:**
-
-- We use 8 GPUs with 2 images/GPU.
-- For more settings and models, please refer to the [official repo](https://github.com/zhangxiaosong18/FreeAnchor).

spaces/Andy1621/uniformer_image_segmentation/configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py

@@ -1,9 +0,0 @@
-_base_ = '../fcn/fcn_r101-d8_512x512_160k_ade20k.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=128,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/scatter_points.py

@@ -1,135 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import nn
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext',
-    ['dynamic_point_to_voxel_forward', 'dynamic_point_to_voxel_backward'])
-
-
-class _DynamicScatter(Function):
-
-    @staticmethod
-    def forward(ctx, feats, coors, reduce_type='max'):
-        """convert kitti points(N, >=3) to voxels.
-
-        Args:
-            feats (torch.Tensor): [N, C]. Points features to be reduced
-                into voxels.
-            coors (torch.Tensor): [N, ndim]. Corresponding voxel coordinates
-                (specifically multi-dim voxel index) of each points.
-            reduce_type (str, optional): Reduce op. support 'max', 'sum' and
-                'mean'. Default: 'max'.
-
-        Returns:
-            voxel_feats (torch.Tensor): [M, C]. Reduced features, input
-                features that shares the same voxel coordinates are reduced to
-                one row.
-            voxel_coors (torch.Tensor): [M, ndim]. Voxel coordinates.
-        """
-        results = ext_module.dynamic_point_to_voxel_forward(
-            feats, coors, reduce_type)
-        (voxel_feats, voxel_coors, point2voxel_map,
-         voxel_points_count) = results
-        ctx.reduce_type = reduce_type
-        ctx.save_for_backward(feats, voxel_feats, point2voxel_map,
-                              voxel_points_count)
-        ctx.mark_non_differentiable(voxel_coors)
-        return voxel_feats, voxel_coors
-
-    @staticmethod
-    def backward(ctx, grad_voxel_feats, grad_voxel_coors=None):
-        (feats, voxel_feats, point2voxel_map,
-         voxel_points_count) = ctx.saved_tensors
-        grad_feats = torch.zeros_like(feats)
-        # TODO: whether to use index put or use cuda_backward
-        # To use index put, need point to voxel index
-        ext_module.dynamic_point_to_voxel_backward(
-            grad_feats, grad_voxel_feats.contiguous(), feats, voxel_feats,
-            point2voxel_map, voxel_points_count, ctx.reduce_type)
-        return grad_feats, None, None
-
-
-dynamic_scatter = _DynamicScatter.apply
-
-
-class DynamicScatter(nn.Module):
-    """Scatters points into voxels, used in the voxel encoder with dynamic
-    voxelization.
-
-    Note:
-        The CPU and GPU implementation get the same output, but have numerical
-        difference after summation and division (e.g., 5e-7).
-
-    Args:
-        voxel_size (list): list [x, y, z] size of three dimension.
-        point_cloud_range (list): The coordinate range of points, [x_min,
-            y_min, z_min, x_max, y_max, z_max].
-        average_points (bool): whether to use avg pooling to scatter points
-            into voxel.
-    """
-
-    def __init__(self, voxel_size, point_cloud_range, average_points: bool):
-        super().__init__()
-
-        self.voxel_size = voxel_size
-        self.point_cloud_range = point_cloud_range
-        self.average_points = average_points
-
-    def forward_single(self, points, coors):
-        """Scatters points into voxels.
-
-        Args:
-            points (torch.Tensor): Points to be reduced into voxels.
-            coors (torch.Tensor): Corresponding voxel coordinates (specifically
-                multi-dim voxel index) of each points.
-
-        Returns:
-            voxel_feats (torch.Tensor): Reduced features, input features that
-                shares the same voxel coordinates are reduced to one row.
-            voxel_coors (torch.Tensor): Voxel coordinates.
-        """
-        reduce = 'mean' if self.average_points else 'max'
-        return dynamic_scatter(points.contiguous(), coors.contiguous(), reduce)
-
-    def forward(self, points, coors):
-        """Scatters points/features into voxels.
-
-        Args:
-            points (torch.Tensor): Points to be reduced into voxels.
-            coors (torch.Tensor): Corresponding voxel coordinates (specifically
-                multi-dim voxel index) of each points.
-
-        Returns:
-            voxel_feats (torch.Tensor): Reduced features, input features that
-                shares the same voxel coordinates are reduced to one row.
-            voxel_coors (torch.Tensor): Voxel coordinates.
-        """
-        if coors.size(-1) == 3:
-            return self.forward_single(points, coors)
-        else:
-            batch_size = coors[-1, 0] + 1
-            voxels, voxel_coors = [], []
-            for i in range(batch_size):
-                inds = torch.where(coors[:, 0] == i)
-                voxel, voxel_coor = self.forward_single(
-                    points[inds], coors[inds][:, 1:])
-                coor_pad = nn.functional.pad(
-                    voxel_coor, (1, 0), mode='constant', value=i)
-                voxel_coors.append(coor_pad)
-                voxels.append(voxel)
-            features = torch.cat(voxels, dim=0)
-            feature_coors = torch.cat(voxel_coors, dim=0)
-
-            return features, feature_coors
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += 'voxel_size=' + str(self.voxel_size)
-        s += ', point_cloud_range=' + str(self.point_cloud_range)
-        s += ', average_points=' + str(self.average_points)
-        s += ')'
-        return s

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

@@ -1,88 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#   Shy Shalom - original C code
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .charsetgroupprober import CharSetGroupProber
-from .hebrewprober import HebrewProber
-from .langbulgarianmodel import ISO_8859_5_BULGARIAN_MODEL, WINDOWS_1251_BULGARIAN_MODEL
-from .langgreekmodel import ISO_8859_7_GREEK_MODEL, WINDOWS_1253_GREEK_MODEL
-from .langhebrewmodel import WINDOWS_1255_HEBREW_MODEL
-
-# from .langhungarianmodel import (ISO_8859_2_HUNGARIAN_MODEL,
-#                                  WINDOWS_1250_HUNGARIAN_MODEL)
-from .langrussianmodel import (
-    IBM855_RUSSIAN_MODEL,
-    IBM866_RUSSIAN_MODEL,
-    ISO_8859_5_RUSSIAN_MODEL,
-    KOI8_R_RUSSIAN_MODEL,
-    MACCYRILLIC_RUSSIAN_MODEL,
-    WINDOWS_1251_RUSSIAN_MODEL,
-)
-from .langthaimodel import TIS_620_THAI_MODEL
-from .langturkishmodel import ISO_8859_9_TURKISH_MODEL
-from .sbcharsetprober import SingleByteCharSetProber
-
-
-class SBCSGroupProber(CharSetGroupProber):
-    def __init__(self) -> None:
-        super().__init__()
-        hebrew_prober = HebrewProber()
-        logical_hebrew_prober = SingleByteCharSetProber(
-            WINDOWS_1255_HEBREW_MODEL, is_reversed=False, name_prober=hebrew_prober
-        )
-        # TODO: See if using ISO-8859-8 Hebrew model works better here, since
-        #       it's actually the visual one
-        visual_hebrew_prober = SingleByteCharSetProber(
-            WINDOWS_1255_HEBREW_MODEL, is_reversed=True, name_prober=hebrew_prober
-        )
-        hebrew_prober.set_model_probers(logical_hebrew_prober, visual_hebrew_prober)
-        # TODO: ORDER MATTERS HERE. I changed the order vs what was in master
-        #       and several tests failed that did not before. Some thought
-        #       should be put into the ordering, and we should consider making
-        #       order not matter here, because that is very counter-intuitive.
-        self.probers = [
-            SingleByteCharSetProber(WINDOWS_1251_RUSSIAN_MODEL),
-            SingleByteCharSetProber(KOI8_R_RUSSIAN_MODEL),
-            SingleByteCharSetProber(ISO_8859_5_RUSSIAN_MODEL),
-            SingleByteCharSetProber(MACCYRILLIC_RUSSIAN_MODEL),
-            SingleByteCharSetProber(IBM866_RUSSIAN_MODEL),
-            SingleByteCharSetProber(IBM855_RUSSIAN_MODEL),
-            SingleByteCharSetProber(ISO_8859_7_GREEK_MODEL),
-            SingleByteCharSetProber(WINDOWS_1253_GREEK_MODEL),
-            SingleByteCharSetProber(ISO_8859_5_BULGARIAN_MODEL),
-            SingleByteCharSetProber(WINDOWS_1251_BULGARIAN_MODEL),
-            # TODO: Restore Hungarian encodings (iso-8859-2 and windows-1250)
-            #       after we retrain model.
-            # SingleByteCharSetProber(ISO_8859_2_HUNGARIAN_MODEL),
-            # SingleByteCharSetProber(WINDOWS_1250_HUNGARIAN_MODEL),
-            SingleByteCharSetProber(TIS_620_THAI_MODEL),
-            SingleByteCharSetProber(ISO_8859_9_TURKISH_MODEL),
-            hebrew_prober,
-            logical_hebrew_prober,
-            visual_hebrew_prober,
-        ]
-        self.reset()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/formatters/bbcode.py

@@ -1,108 +0,0 @@
-"""
-    pygments.formatters.bbcode
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    BBcode formatter.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.util import get_bool_opt
-
-__all__ = ['BBCodeFormatter']
-
-
-class BBCodeFormatter(Formatter):
-    """
-    Format tokens with BBcodes. These formatting codes are used by many
-    bulletin boards, so you can highlight your sourcecode with pygments before
-    posting it there.
-
-    This formatter has no support for background colors and borders, as there
-    are no common BBcode tags for that.
-
-    Some board systems (e.g. phpBB) don't support colors in their [code] tag,
-    so you can't use the highlighting together with that tag.
-    Text in a [code] tag usually is shown with a monospace font (which this
-    formatter can do with the ``monofont`` option) and no spaces (which you
-    need for indentation) are removed.
-
-    Additional options accepted:
-
-    `style`
-        The style to use, can be a string or a Style subclass (default:
-        ``'default'``).
-
-    `codetag`
-        If set to true, put the output into ``[code]`` tags (default:
-        ``false``)
-
-    `monofont`
-        If set to true, add a tag to show the code with a monospace font
-        (default: ``false``).
-    """
-    name = 'BBCode'
-    aliases = ['bbcode', 'bb']
-    filenames = []
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self._code = get_bool_opt(options, 'codetag', False)
-        self._mono = get_bool_opt(options, 'monofont', False)
-
-        self.styles = {}
-        self._make_styles()
-
-    def _make_styles(self):
-        for ttype, ndef in self.style:
-            start = end = ''
-            if ndef['color']:
-                start += '[color=#%s]' % ndef['color']
-                end = '[/color]' + end
-            if ndef['bold']:
-                start += '[b]'
-                end = '[/b]' + end
-            if ndef['italic']:
-                start += '[i]'
-                end = '[/i]' + end
-            if ndef['underline']:
-                start += '[u]'
-                end = '[/u]' + end
-            # there are no common BBcodes for background-color and border
-
-            self.styles[ttype] = start, end
-
-    def format_unencoded(self, tokensource, outfile):
-        if self._code:
-            outfile.write('[code]')
-        if self._mono:
-            outfile.write('[font=monospace]')
-
-        lastval = ''
-        lasttype = None
-
-        for ttype, value in tokensource:
-            while ttype not in self.styles:
-                ttype = ttype.parent
-            if ttype == lasttype:
-                lastval += value
-            else:
-                if lastval:
-                    start, end = self.styles[lasttype]
-                    outfile.write(''.join((start, lastval, end)))
-                lastval = value
-                lasttype = ttype
-
-        if lastval:
-            start, end = self.styles[lasttype]
-            outfile.write(''.join((start, lastval, end)))
-
-        if self._mono:
-            outfile.write('[/font]')
-        if self._code:
-            outfile.write('[/code]')
-        if self._code or self._mono:
-            outfile.write('\n')

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

@@ -1,308 +0,0 @@
-from typing import TYPE_CHECKING, Optional
-
-from .align import AlignMethod
-from .box import ROUNDED, Box
-from .cells import cell_len
-from .jupyter import JupyterMixin
-from .measure import Measurement, measure_renderables
-from .padding import Padding, PaddingDimensions
-from .segment import Segment
-from .style import Style, StyleType
-from .text import Text, TextType
-
-if TYPE_CHECKING:
-    from .console import Console, ConsoleOptions, RenderableType, RenderResult
-
-
-class Panel(JupyterMixin):
-    """A console renderable that draws a border around its contents.
-
-    Example:
-        >>> console.print(Panel("Hello, World!"))
-
-    Args:
-        renderable (RenderableType): A console renderable object.
-        box (Box, optional): A Box instance that defines the look of the border (see :ref:`appendix_box`.
-            Defaults to box.ROUNDED.
-        safe_box (bool, optional): Disable box characters that don't display on windows legacy terminal with *raster* fonts. Defaults to True.
-        expand (bool, optional): If True the panel will stretch to fill the console
-            width, otherwise it will be sized to fit the contents. Defaults to True.
-        style (str, optional): The style of the panel (border and contents). Defaults to "none".
-        border_style (str, optional): The style of the border. Defaults to "none".
-        width (Optional[int], optional): Optional width of panel. Defaults to None to auto-detect.
-        height (Optional[int], optional): Optional height of panel. Defaults to None to auto-detect.
-        padding (Optional[PaddingDimensions]): Optional padding around renderable. Defaults to 0.
-        highlight (bool, optional): Enable automatic highlighting of panel title (if str). Defaults to False.
-    """
-
-    def __init__(
-        self,
-        renderable: "RenderableType",
-        box: Box = ROUNDED,
-        *,
-        title: Optional[TextType] = None,
-        title_align: AlignMethod = "center",
-        subtitle: Optional[TextType] = None,
-        subtitle_align: AlignMethod = "center",
-        safe_box: Optional[bool] = None,
-        expand: bool = True,
-        style: StyleType = "none",
-        border_style: StyleType = "none",
-        width: Optional[int] = None,
-        height: Optional[int] = None,
-        padding: PaddingDimensions = (0, 1),
-        highlight: bool = False,
-    ) -> None:
-        self.renderable = renderable
-        self.box = box
-        self.title = title
-        self.title_align: AlignMethod = title_align
-        self.subtitle = subtitle
-        self.subtitle_align = subtitle_align
-        self.safe_box = safe_box
-        self.expand = expand
-        self.style = style
-        self.border_style = border_style
-        self.width = width
-        self.height = height
-        self.padding = padding
-        self.highlight = highlight
-
-    @classmethod
-    def fit(
-        cls,
-        renderable: "RenderableType",
-        box: Box = ROUNDED,
-        *,
-        title: Optional[TextType] = None,
-        title_align: AlignMethod = "center",
-        subtitle: Optional[TextType] = None,
-        subtitle_align: AlignMethod = "center",
-        safe_box: Optional[bool] = None,
-        style: StyleType = "none",
-        border_style: StyleType = "none",
-        width: Optional[int] = None,
-        padding: PaddingDimensions = (0, 1),
-    ) -> "Panel":
-        """An alternative constructor that sets expand=False."""
-        return cls(
-            renderable,
-            box,
-            title=title,
-            title_align=title_align,
-            subtitle=subtitle,
-            subtitle_align=subtitle_align,
-            safe_box=safe_box,
-            style=style,
-            border_style=border_style,
-            width=width,
-            padding=padding,
-            expand=False,
-        )
-
-    @property
-    def _title(self) -> Optional[Text]:
-        if self.title:
-            title_text = (
-                Text.from_markup(self.title)
-                if isinstance(self.title, str)
-                else self.title.copy()
-            )
-            title_text.end = ""
-            title_text.plain = title_text.plain.replace("\n", " ")
-            title_text.no_wrap = True
-            title_text.expand_tabs()
-            title_text.pad(1)
-            return title_text
-        return None
-
-    @property
-    def _subtitle(self) -> Optional[Text]:
-        if self.subtitle:
-            subtitle_text = (
-                Text.from_markup(self.subtitle)
-                if isinstance(self.subtitle, str)
-                else self.subtitle.copy()
-            )
-            subtitle_text.end = ""
-            subtitle_text.plain = subtitle_text.plain.replace("\n", " ")
-            subtitle_text.no_wrap = True
-            subtitle_text.expand_tabs()
-            subtitle_text.pad(1)
-            return subtitle_text
-        return None
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        _padding = Padding.unpack(self.padding)
-        renderable = (
-            Padding(self.renderable, _padding) if any(_padding) else self.renderable
-        )
-        style = console.get_style(self.style)
-        border_style = style + console.get_style(self.border_style)
-        width = (
-            options.max_width
-            if self.width is None
-            else min(options.max_width, self.width)
-        )
-
-        safe_box: bool = console.safe_box if self.safe_box is None else self.safe_box
-        box = self.box.substitute(options, safe=safe_box)
-
-        def align_text(
-            text: Text, width: int, align: str, character: str, style: Style
-        ) -> Text:
-            """Gets new aligned text.
-
-            Args:
-                text (Text): Title or subtitle text.
-                width (int): Desired width.
-                align (str): Alignment.
-                character (str): Character for alignment.
-                style (Style): Border style
-
-            Returns:
-                Text: New text instance
-            """
-            text = text.copy()
-            text.truncate(width)
-            excess_space = width - cell_len(text.plain)
-            if excess_space:
-                if align == "left":
-                    return Text.assemble(
-                        text,
-                        (character * excess_space, style),
-                        no_wrap=True,
-                        end="",
-                    )
-                elif align == "center":
-                    left = excess_space // 2
-                    return Text.assemble(
-                        (character * left, style),
-                        text,
-                        (character * (excess_space - left), style),
-                        no_wrap=True,
-                        end="",
-                    )
-                else:
-                    return Text.assemble(
-                        (character * excess_space, style),
-                        text,
-                        no_wrap=True,
-                        end="",
-                    )
-            return text
-
-        title_text = self._title
-        if title_text is not None:
-            title_text.stylize_before(border_style)
-
-        child_width = (
-            width - 2
-            if self.expand
-            else console.measure(
-                renderable, options=options.update_width(width - 2)
-            ).maximum
-        )
-        child_height = self.height or options.height or None
-        if child_height:
-            child_height -= 2
-        if title_text is not None:
-            child_width = min(
-                options.max_width - 2, max(child_width, title_text.cell_len + 2)
-            )
-
-        width = child_width + 2
-        child_options = options.update(
-            width=child_width, height=child_height, highlight=self.highlight
-        )
-        lines = console.render_lines(renderable, child_options, style=style)
-
-        line_start = Segment(box.mid_left, border_style)
-        line_end = Segment(f"{box.mid_right}", border_style)
-        new_line = Segment.line()
-        if title_text is None or width <= 4:
-            yield Segment(box.get_top([width - 2]), border_style)
-        else:
-            title_text = align_text(
-                title_text,
-                width - 4,
-                self.title_align,
-                box.top,
-                border_style,
-            )
-            yield Segment(box.top_left + box.top, border_style)
-            yield from console.render(title_text, child_options.update_width(width - 4))
-            yield Segment(box.top + box.top_right, border_style)
-
-        yield new_line
-        for line in lines:
-            yield line_start
-            yield from line
-            yield line_end
-            yield new_line
-
-        subtitle_text = self._subtitle
-        if subtitle_text is not None:
-            subtitle_text.stylize_before(border_style)
-
-        if subtitle_text is None or width <= 4:
-            yield Segment(box.get_bottom([width - 2]), border_style)
-        else:
-            subtitle_text = align_text(
-                subtitle_text,
-                width - 4,
-                self.subtitle_align,
-                box.bottom,
-                border_style,
-            )
-            yield Segment(box.bottom_left + box.bottom, border_style)
-            yield from console.render(
-                subtitle_text, child_options.update_width(width - 4)
-            )
-            yield Segment(box.bottom + box.bottom_right, border_style)
-
-        yield new_line
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        _title = self._title
-        _, right, _, left = Padding.unpack(self.padding)
-        padding = left + right
-        renderables = [self.renderable, _title] if _title else [self.renderable]
-
-        if self.width is None:
-            width = (
-                measure_renderables(
-                    console,
-                    options.update_width(options.max_width - padding - 2),
-                    renderables,
-                ).maximum
-                + padding
-                + 2
-            )
-        else:
-            width = self.width
-        return Measurement(width, width)
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from .console import Console
-
-    c = Console()
-
-    from .box import DOUBLE, ROUNDED
-    from .padding import Padding
-
-    p = Panel(
-        "Hello, World!",
-        title="rich.Panel",
-        style="white on blue",
-        box=DOUBLE,
-        padding=1,
-    )
-
-    c.print()
-    c.print(p)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/utils/colormap.py

@@ -1,140 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-An awesome colormap for really neat visualizations.
-Copied from Detectron, and removed gray colors.
-"""
-
-import numpy as np
-
-__all__ = ["colormap", "random_color"]
-
-# fmt: off
-# RGB:
-_COLORS = np.array(
-    [
-        0.000, 0.447, 0.741,
-        0.850, 0.325, 0.098,
-        0.929, 0.694, 0.125,
-        0.494, 0.184, 0.556,
-        0.466, 0.674, 0.188,
-        0.301, 0.745, 0.933,
-        0.635, 0.078, 0.184,
-        0.300, 0.300, 0.300,
-        0.600, 0.600, 0.600,
-        1.000, 0.000, 0.000,
-        1.000, 0.500, 0.000,
-        0.749, 0.749, 0.000,
-        0.000, 1.000, 0.000,
-        0.000, 0.000, 1.000,
-        0.667, 0.000, 1.000,
-        0.333, 0.333, 0.000,
-        0.333, 0.667, 0.000,
-        0.333, 1.000, 0.000,
-        0.667, 0.333, 0.000,
-        0.667, 0.667, 0.000,
-        0.667, 1.000, 0.000,
-        1.000, 0.333, 0.000,
-        1.000, 0.667, 0.000,
-        1.000, 1.000, 0.000,
-        0.000, 0.333, 0.500,
-        0.000, 0.667, 0.500,
-        0.000, 1.000, 0.500,
-        0.333, 0.000, 0.500,
-        0.333, 0.333, 0.500,
-        0.333, 0.667, 0.500,
-        0.333, 1.000, 0.500,
-        0.667, 0.000, 0.500,
-        0.667, 0.333, 0.500,
-        0.667, 0.667, 0.500,
-        0.667, 1.000, 0.500,
-        1.000, 0.000, 0.500,
-        1.000, 0.333, 0.500,
-        1.000, 0.667, 0.500,
-        1.000, 1.000, 0.500,
-        0.000, 0.333, 1.000,
-        0.000, 0.667, 1.000,
-        0.000, 1.000, 1.000,
-        0.333, 0.000, 1.000,
-        0.333, 0.333, 1.000,
-        0.333, 0.667, 1.000,
-        0.333, 1.000, 1.000,
-        0.667, 0.000, 1.000,
-        0.667, 0.333, 1.000,
-        0.667, 0.667, 1.000,
-        0.667, 1.000, 1.000,
-        1.000, 0.000, 1.000,
-        1.000, 0.333, 1.000,
-        1.000, 0.667, 1.000,
-        0.333, 0.000, 0.000,
-        0.500, 0.000, 0.000,
-        0.667, 0.000, 0.000,
-        0.833, 0.000, 0.000,
-        1.000, 0.000, 0.000,
-        0.000, 0.167, 0.000,
-        0.000, 0.333, 0.000,
-        0.000, 0.500, 0.000,
-        0.000, 0.667, 0.000,
-        0.000, 0.833, 0.000,
-        0.000, 1.000, 0.000,
-        0.000, 0.000, 0.167,
-        0.000, 0.000, 0.333,
-        0.000, 0.000, 0.500,
-        0.000, 0.000, 0.667,
-        0.000, 0.000, 0.833,
-        0.000, 0.000, 1.000,
-        0.000, 0.000, 0.000,
-        0.143, 0.143, 0.143,
-        0.857, 0.857, 0.857,
-        1.000, 1.000, 1.000
-    ]
-).astype(np.float32).reshape(-1, 3)
-# fmt: on
-
-
-def colormap(rgb=False, maximum=255):
-    """
-    Args:
-        rgb (bool): whether to return RGB colors or BGR colors.
-        maximum (int): either 255 or 1
-
-    Returns:
-        ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]
-    """
-    assert maximum in [255, 1], maximum
-    c = _COLORS * maximum
-    if not rgb:
-        c = c[:, ::-1]
-    return c
-
-
-def random_color(rgb=False, maximum=255):
-    """
-    Args:
-        rgb (bool): whether to return RGB colors or BGR colors.
-        maximum (int): either 255 or 1
-
-    Returns:
-        ndarray: a vector of 3 numbers
-    """
-    idx = np.random.randint(0, len(_COLORS))
-    ret = _COLORS[idx] * maximum
-    if not rgb:
-        ret = ret[::-1]
-    return ret
-
-
-if __name__ == "__main__":
-    import cv2
-
-    size = 100
-    H, W = 10, 10
-    canvas = np.random.rand(H * size, W * size, 3).astype("float32")
-    for h in range(H):
-        for w in range(W):
-            idx = h * W + w
-            if idx >= len(_COLORS):
-                break
-            canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx]
-    cv2.imshow("a", canvas)
-    cv2.waitKey(0)

spaces/Banbri/zcvzcv/src/components/ui/textarea.tsx

@@ -1,24 +0,0 @@
-import * as React from "react"
-
-import { cn } from "@/lib/utils"
-
-export interface TextareaProps
-  extends React.TextareaHTMLAttributes<HTMLTextAreaElement> {}
-
-const Textarea = React.forwardRef<HTMLTextAreaElement, TextareaProps>(
-  ({ className, ...props }, ref) => {
-    return (
-      <textarea
-        className={cn(
-          "flex min-h-[80px] w-full rounded-md border border-stone-200 border-stone-200 bg-transparent px-3 py-2 text-sm ring-offset-white placeholder:text-stone-500 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-400 focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50 dark:border-stone-800 dark:border-stone-800 dark:ring-offset-stone-950 dark:placeholder:text-stone-400 dark:focus-visible:ring-stone-800",
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Textarea.displayName = "Textarea"
-
-export { Textarea }

spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/layers.py

@@ -1,118 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle

spaces/Benson/text-generation/Examples/Cesta Batalla Sin Anuncios Mod Apk.md

@@ -1,35 +0,0 @@
-
-<h1>Batalla cesta sin anuncios Mod APK: Un juego de baloncesto divertido y adictivo</h1>
-<p>¿Te gustan los juegos de baloncesto? ¿Quieres experimentar la emoción de jugar uno a uno contra tus oponentes? ¿Quieres disfrutar de un juego suave y rápido sin anuncios ni limitaciones? Si respondió sí a cualquiera de estas preguntas, entonces usted debe tratar de Basket Battle No Ads Mod APK, una versión modificada del popular juego de baloncesto por DoubleTap Software. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, sus características y cómo descargarlo e instalarlo en tu dispositivo Android. </p>
-<h2>¿Qué es la batalla de la cesta? </h2>
-<p>Basket Battle es un divertido y adictivo juego de baloncesto que te permite jugar uno a uno contra diferentes oponentes en varias ubicaciones. Puedes elegir entre diferentes personajes, cada uno con sus propias habilidades y habilidades, y personalizarlos con diferentes trajes y accesorios. También puede desbloquear y actualizar diferentes pistas, desde la calle hasta la playa, y disfrutar de los gráficos realistas y animaciones. El juego tiene controles simples e intuitivos que te permiten moverte, saltar, disparar, encestar, bloquear y robar con facilidad. También puedes realizar increíbles combos y trucos para sumar más puntos e impresionar a tu oponente. </p>
-<h2>cesta batalla sin anuncios mod apk</h2><br /><p><b><b>DOWNLOAD</b> &#10038;&#10038;&#10038; <a href="https://bltlly.com/2v6Jqo">https://bltlly.com/2v6Jqo</a></b></p><br /><br />
-<h3>Características de la batalla de la cesta</h3>
-<h4>Controles simples e intuitivos</h4>
-<p>El juego tiene controles fáciles de aprender que lo hacen adecuado para cualquier persona que ama los juegos de baloncesto. Puedes usar el joystick virtual para mover a tu personaje y tocar los botones para disparar, clavar, bloquear o robar. También puede deslizar la pantalla para realizar movimientos especiales y combos. El juego tiene un modo tutorial que te enseña lo básico del juego y te ayuda a mejorar tus habilidades. </p>
-<h4>Varios modos de juego y desafíos</h4>
-
-<h4>Personaliza tus jugadores y canchas</h4>
-<p>El juego te permite personalizar a tus jugadores y canchas con varios artículos que puedes comprar con el dinero que ganas jugando. Puedes elegir entre diferentes personajes, cada uno con sus propias fortalezas y debilidades, y cambiar su apariencia con diferentes trajes, peinados, zapatos, accesorios y más. También puede desbloquear y mejorar diferentes canchas, cada una con su propio tema y ambiente, como la calle, el gimnasio, la playa, el parque y más. </p>
-<h4>Juega online o offline con amigos</h4>
-<p>El juego es compatible con los modos en línea y fuera de línea, por lo que puede jugar en cualquier momento y en cualquier lugar. Puedes jugar en línea con otros jugadores de todo el mundo, o sin conexión con tus amigos en el mismo dispositivo. También puedes chatear con otros jugadores en el lobby del juego, enviarles emojis o retarlos a una revancha. </p>
-<h2>¿Por qué descargar Batalla de cesta sin anuncios Mod APK? </h2>
-<h3>Beneficios de la versión modificada</h3>
-<h4>Dinero ilimitado para comprar lo que quieras</h4>
-<p>La versión modificada de Basket Battle te da dinero ilimitado que puedes usar para comprar lo que quieras en el juego. Puedes comprar todos los personajes, trajes, accesorios, canchas y mejoras que quieras sin preocuparte por el costo. También puedes usar el dinero para saltarte los anuncios que aparecen en el juego. </p>
-<h4>No hay anuncios molestos para interrumpir su juego</h4>
-<p>La versión modificada de Basket Battle elimina todos los anuncios que normalmente aparecen en el juego. Puede disfrutar de un juego suave e ininterrumpido sin tener que ver ningún anuncio o esperar a que se carguen. También puede guardar sus datos y la batería jugando el juego sin anuncios. </p>
-<h4>Libre y seguro de instalar y usar</h4>
-
-<h3>¿Cómo descargar e instalar la cesta de batalla sin anuncios Mod APK? </h3>
-<h4>Paso 1: Descargar el archivo APK de una fuente de confianza</h4>
-<p>El primer paso es descargar el archivo APK de Basket Battle No Ads Mod APK de una fuente de confianza. Puedes encontrar muchos sitios web que ofrecen la versión modificada del juego, pero debes tener cuidado y elegir uno confiable. Puede utilizar el siguiente enlace para descargar el archivo APK de nuestro sitio web, que es 100% seguro y verificado. </p>
-<h4>Paso 2: Habilitar fuentes desconocidas en el dispositivo</h4>
-<p>El segundo paso es habilitar fuentes desconocidas en su dispositivo. Esto es necesario porque la versión modificada de Basket Battle no está disponible en Google Play Store, y necesitas permitir que tu dispositivo instale aplicaciones de otras fuentes. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y enciéndala. </p>
-<p></p>
-<h4>Paso 3: Instalar el archivo APK y disfrutar del juego</h4>
-<p>El tercer y último paso es instalar el archivo APK y disfrutar del juego. Para hacer esto, busque el archivo APK que descargó en el paso 1, y toque en él. Siga las instrucciones en la pantalla para completar el proceso de instalación. Una vez hecho, se puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio, y empezar a jugar Basket Battle No Ads Mod APK.</p>
-<h2>Conclusión</h2>
-<p>Batalla de la cesta sin anuncios Mod APK es un juego de baloncesto divertido y adictivo que le permite jugar uno a uno contra diferentes oponentes en varios lugares. Puede personalizar sus jugadores y canchas con varios elementos, y disfrutar de un juego suave y rápido sin anuncios ni limitaciones. La versión modificada de Basket Battle te da dinero ilimitado para comprar lo que quieras en el juego, y elimina todos los anuncios que normalmente aparecen en el juego. Puede descargar e instalar Basket Battle No Ads Mod APK en su dispositivo Android de forma gratuita y segura siguiendo los pasos anteriores. Si te gustan los juegos de baloncesto, usted debe probar definitivamente Basket Battle No Ads Mod APK.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/taming-transformers/scripts/sample_conditional.py

@@ -1,355 +0,0 @@
-import argparse, os, sys, glob, math, time
-import torch
-import numpy as np
-from omegaconf import OmegaConf
-import streamlit as st
-from streamlit import caching
-from PIL import Image
-from main import instantiate_from_config, DataModuleFromConfig
-from torch.utils.data import DataLoader
-from torch.utils.data.dataloader import default_collate
-
-
-rescale = lambda x: (x + 1.) / 2.
-
-
-def bchw_to_st(x):
-    return rescale(x.detach().cpu().numpy().transpose(0,2,3,1))
-
-def save_img(xstart, fname):
-    I = (xstart.clip(0,1)[0]*255).astype(np.uint8)
-    Image.fromarray(I).save(fname)
-
-
-
-def get_interactive_image(resize=False):
-    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"])
-    if image is not None:
-        image = Image.open(image)
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-        print("upload image shape: {}".format(image.shape))
-        img = Image.fromarray(image)
-        if resize:
-            img = img.resize((256, 256))
-        image = np.array(img)
-        return image
-
-
-def single_image_to_torch(x, permute=True):
-    assert x is not None, "Please provide an image through the upload function"
-    x = np.array(x)
-    x = torch.FloatTensor(x/255.*2. - 1.)[None,...]
-    if permute:
-        x = x.permute(0, 3, 1, 2)
-    return x
-
-
-def pad_to_M(x, M):
-    hp = math.ceil(x.shape[2]/M)*M-x.shape[2]
-    wp = math.ceil(x.shape[3]/M)*M-x.shape[3]
-    x = torch.nn.functional.pad(x, (0,wp,0,hp,0,0,0,0))
-    return x
-
-@torch.no_grad()
-def run_conditional(model, dsets):
-    if len(dsets.datasets) > 1:
-        split = st.sidebar.radio("Split", sorted(dsets.datasets.keys()))
-        dset = dsets.datasets[split]
-    else:
-        dset = next(iter(dsets.datasets.values()))
-    batch_size = 1
-    start_index = st.sidebar.number_input("Example Index (Size: {})".format(len(dset)), value=0,
-                                          min_value=0,
-                                          max_value=len(dset)-batch_size)
-    indices = list(range(start_index, start_index+batch_size))
-
-    example = default_collate([dset[i] for i in indices])
-
-    x = model.get_input("image", example).to(model.device)
-
-    cond_key = model.cond_stage_key
-    c = model.get_input(cond_key, example).to(model.device)
-
-    scale_factor = st.sidebar.slider("Scale Factor", min_value=0.5, max_value=4.0, step=0.25, value=1.00)
-    if scale_factor != 1.0:
-        x = torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="bicubic")
-        c = torch.nn.functional.interpolate(c, scale_factor=scale_factor, mode="bicubic")
-
-    quant_z, z_indices = model.encode_to_z(x)
-    quant_c, c_indices = model.encode_to_c(c)
-
-    cshape = quant_z.shape
-
-    xrec = model.first_stage_model.decode(quant_z)
-    st.write("image: {}".format(x.shape))
-    st.image(bchw_to_st(x), clamp=True, output_format="PNG")
-    st.write("image reconstruction: {}".format(xrec.shape))
-    st.image(bchw_to_st(xrec), clamp=True, output_format="PNG")
-
-    if cond_key == "segmentation":
-        # get image from segmentation mask
-        num_classes = c.shape[1]
-        c = torch.argmax(c, dim=1, keepdim=True)
-        c = torch.nn.functional.one_hot(c, num_classes=num_classes)
-        c = c.squeeze(1).permute(0, 3, 1, 2).float()
-        c = model.cond_stage_model.to_rgb(c)
-
-    st.write(f"{cond_key}: {tuple(c.shape)}")
-    st.image(bchw_to_st(c), clamp=True, output_format="PNG")
-
-    idx = z_indices
-
-    half_sample = st.sidebar.checkbox("Image Completion", value=False)
-    if half_sample:
-        start = idx.shape[1]//2
-    else:
-        start = 0
-
-    idx[:,start:] = 0
-    idx = idx.reshape(cshape[0],cshape[2],cshape[3])
-    start_i = start//cshape[3]
-    start_j = start %cshape[3]
-
-    if not half_sample and quant_z.shape == quant_c.shape:
-        st.info("Setting idx to c_indices")
-        idx = c_indices.clone().reshape(cshape[0],cshape[2],cshape[3])
-
-    cidx = c_indices
-    cidx = cidx.reshape(quant_c.shape[0],quant_c.shape[2],quant_c.shape[3])
-
-    xstart = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape)
-    st.image(bchw_to_st(xstart), clamp=True, output_format="PNG")
-
-    temperature = st.number_input("Temperature", value=1.0)
-    top_k = st.number_input("Top k", value=100)
-    sample = st.checkbox("Sample", value=True)
-    update_every = st.number_input("Update every", value=75)
-
-    st.text(f"Sampling shape ({cshape[2]},{cshape[3]})")
-
-    animate = st.checkbox("animate")
-    if animate:
-        import imageio
-        outvid = "sampling.mp4"
-        writer = imageio.get_writer(outvid, fps=25)
-    elapsed_t = st.empty()
-    info = st.empty()
-    st.text("Sampled")
-    if st.button("Sample"):
-        output = st.empty()
-        start_t = time.time()
-        for i in range(start_i,cshape[2]-0):
-            if i <= 8:
-                local_i = i
-            elif cshape[2]-i < 8:
-                local_i = 16-(cshape[2]-i)
-            else:
-                local_i = 8
-            for j in range(start_j,cshape[3]-0):
-                if j <= 8:
-                    local_j = j
-                elif cshape[3]-j < 8:
-                    local_j = 16-(cshape[3]-j)
-                else:
-                    local_j = 8
-
-                i_start = i-local_i
-                i_end = i_start+16
-                j_start = j-local_j
-                j_end = j_start+16
-                elapsed_t.text(f"Time: {time.time() - start_t} seconds")
-                info.text(f"Step: ({i},{j}) | Local: ({local_i},{local_j}) | Crop: ({i_start}:{i_end},{j_start}:{j_end})")
-                patch = idx[:,i_start:i_end,j_start:j_end]
-                patch = patch.reshape(patch.shape[0],-1)
-                cpatch = cidx[:, i_start:i_end, j_start:j_end]
-                cpatch = cpatch.reshape(cpatch.shape[0], -1)
-                patch = torch.cat((cpatch, patch), dim=1)
-                logits,_ = model.transformer(patch[:,:-1])
-                logits = logits[:, -256:, :]
-                logits = logits.reshape(cshape[0],16,16,-1)
-                logits = logits[:,local_i,local_j,:]
-
-                logits = logits/temperature
-
-                if top_k is not None:
-                    logits = model.top_k_logits(logits, top_k)
-                # apply softmax to convert to probabilities
-                probs = torch.nn.functional.softmax(logits, dim=-1)
-                # sample from the distribution or take the most likely
-                if sample:
-                    ix = torch.multinomial(probs, num_samples=1)
-                else:
-                    _, ix = torch.topk(probs, k=1, dim=-1)
-                idx[:,i,j] = ix
-
-                if (i*cshape[3]+j)%update_every==0:
-                    xstart = model.decode_to_img(idx[:, :cshape[2], :cshape[3]], cshape,)
-
-                    xstart = bchw_to_st(xstart)
-                    output.image(xstart, clamp=True, output_format="PNG")
-
-                    if animate:
-                        writer.append_data((xstart[0]*255).clip(0, 255).astype(np.uint8))
-
-        xstart = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape)
-        xstart = bchw_to_st(xstart)
-        output.image(xstart, clamp=True, output_format="PNG")
-        #save_img(xstart, "full_res_sample.png")
-        if animate:
-            writer.close()
-            st.video(outvid)
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-r",
-        "--resume",
-        type=str,
-        nargs="?",
-        help="load from logdir or checkpoint in logdir",
-    )
-    parser.add_argument(
-        "-b",
-        "--base",
-        nargs="*",
-        metavar="base_config.yaml",
-        help="paths to base configs. Loaded from left-to-right. "
-        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
-        default=list(),
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        nargs="?",
-        metavar="single_config.yaml",
-        help="path to single config. If specified, base configs will be ignored "
-        "(except for the last one if left unspecified).",
-        const=True,
-        default="",
-    )
-    parser.add_argument(
-        "--ignore_base_data",
-        action="store_true",
-        help="Ignore data specification from base configs. Useful if you want "
-        "to specify a custom datasets on the command line.",
-    )
-    return parser
-
-
-def load_model_from_config(config, sd, gpu=True, eval_mode=True):
-    if "ckpt_path" in config.params:
-        st.warning("Deleting the restore-ckpt path from the config...")
-        config.params.ckpt_path = None
-    if "downsample_cond_size" in config.params:
-        st.warning("Deleting downsample-cond-size from the config and setting factor=0.5 instead...")
-        config.params.downsample_cond_size = -1
-        config.params["downsample_cond_factor"] = 0.5
-    try:
-        if "ckpt_path" in config.params.first_stage_config.params:
-            config.params.first_stage_config.params.ckpt_path = None
-            st.warning("Deleting the first-stage restore-ckpt path from the config...")
-        if "ckpt_path" in config.params.cond_stage_config.params:
-            config.params.cond_stage_config.params.ckpt_path = None
-            st.warning("Deleting the cond-stage restore-ckpt path from the config...")
-    except:
-        pass
-
-    model = instantiate_from_config(config)
-    if sd is not None:
-        missing, unexpected = model.load_state_dict(sd, strict=False)
-        st.info(f"Missing Keys in State Dict: {missing}")
-        st.info(f"Unexpected Keys in State Dict: {unexpected}")
-    if gpu:
-        model.cuda()
-    if eval_mode:
-        model.eval()
-    return {"model": model}
-
-
-def get_data(config):
-    # get data
-    data = instantiate_from_config(config.data)
-    data.prepare_data()
-    data.setup()
-    return data
-
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True)
-def load_model_and_dset(config, ckpt, gpu, eval_mode):
-    # get data
-    dsets = get_data(config)   # calls data.config ...
-
-    # now load the specified checkpoint
-    if ckpt:
-        pl_sd = torch.load(ckpt, map_location="cpu")
-        global_step = pl_sd["global_step"]
-    else:
-        pl_sd = {"state_dict": None}
-        global_step = None
-    model = load_model_from_config(config.model,
-                                   pl_sd["state_dict"],
-                                   gpu=gpu,
-                                   eval_mode=eval_mode)["model"]
-    return dsets, model, global_step
-
-
-if __name__ == "__main__":
-    sys.path.append(os.getcwd())
-
-    parser = get_parser()
-
-    opt, unknown = parser.parse_known_args()
-
-    ckpt = None
-    if opt.resume:
-        if not os.path.exists(opt.resume):
-            raise ValueError("Cannot find {}".format(opt.resume))
-        if os.path.isfile(opt.resume):
-            paths = opt.resume.split("/")
-            try:
-                idx = len(paths)-paths[::-1].index("logs")+1
-            except ValueError:
-                idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
-            logdir = "/".join(paths[:idx])
-            ckpt = opt.resume
-        else:
-            assert os.path.isdir(opt.resume), opt.resume
-            logdir = opt.resume.rstrip("/")
-            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
-        print(f"logdir:{logdir}")
-        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
-        opt.base = base_configs+opt.base
-
-    if opt.config:
-        if type(opt.config) == str:
-            opt.base = [opt.config]
-        else:
-            opt.base = [opt.base[-1]]
-
-    configs = [OmegaConf.load(cfg) for cfg in opt.base]
-    cli = OmegaConf.from_dotlist(unknown)
-    if opt.ignore_base_data:
-        for config in configs:
-            if hasattr(config, "data"): del config["data"]
-    config = OmegaConf.merge(*configs, cli)
-
-    st.sidebar.text(ckpt)
-    gs = st.sidebar.empty()
-    gs.text(f"Global step: ?")
-    st.sidebar.text("Options")
-    #gpu = st.sidebar.checkbox("GPU", value=True)
-    gpu = True
-    #eval_mode = st.sidebar.checkbox("Eval Mode", value=True)
-    eval_mode = True
-    #show_config = st.sidebar.checkbox("Show Config", value=False)
-    show_config = False
-    if show_config:
-        st.info("Checkpoint: {}".format(ckpt))
-        st.json(OmegaConf.to_container(config))
-
-    dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode)
-    gs.text(f"Global step: {global_step}")
-    run_conditional(model, dsets)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/_windows.py

@@ -1,72 +0,0 @@
-import sys
-from dataclasses import dataclass
-
-
-@dataclass
-class WindowsConsoleFeatures:
-    """Windows features available."""
-
-    vt: bool = False
-    """The console supports VT codes."""
-    truecolor: bool = False
-    """The console supports truecolor."""
-
-
-try:
-    import ctypes
-    from ctypes import LibraryLoader
-
-    if sys.platform == "win32":
-        windll = LibraryLoader(ctypes.WinDLL)
-    else:
-        windll = None
-        raise ImportError("Not windows")
-
-    from pip._vendor.rich._win32_console import (
-        ENABLE_VIRTUAL_TERMINAL_PROCESSING,
-        GetConsoleMode,
-        GetStdHandle,
-        LegacyWindowsError,
-    )
-
-except (AttributeError, ImportError, ValueError):
-
-    # Fallback if we can't load the Windows DLL
-    def get_windows_console_features() -> WindowsConsoleFeatures:
-        features = WindowsConsoleFeatures()
-        return features
-
-else:
-
-    def get_windows_console_features() -> WindowsConsoleFeatures:
-        """Get windows console features.
-
-        Returns:
-            WindowsConsoleFeatures: An instance of WindowsConsoleFeatures.
-        """
-        handle = GetStdHandle()
-        try:
-            console_mode = GetConsoleMode(handle)
-            success = True
-        except LegacyWindowsError:
-            console_mode = 0
-            success = False
-        vt = bool(success and console_mode & ENABLE_VIRTUAL_TERMINAL_PROCESSING)
-        truecolor = False
-        if vt:
-            win_version = sys.getwindowsversion()
-            truecolor = win_version.major > 10 or (
-                win_version.major == 10 and win_version.build >= 15063
-            )
-        features = WindowsConsoleFeatures(vt=vt, truecolor=truecolor)
-        return features
-
-
-if __name__ == "__main__":
-    import platform
-
-    features = get_windows_console_features()
-    from pip._vendor.rich import print
-
-    print(f'platform="{platform.system()}"')
-    print(repr(features))

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/after.py

@@ -1,51 +0,0 @@
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import typing
-
-from pip._vendor.tenacity import _utils
-
-if typing.TYPE_CHECKING:
-    import logging
-
-    from pip._vendor.tenacity import RetryCallState
-
-
-def after_nothing(retry_state: "RetryCallState") -> None:
-    """After call strategy that does nothing."""
-
-
-def after_log(
-    logger: "logging.Logger",
-    log_level: int,
-    sec_format: str = "%0.3f",
-) -> typing.Callable[["RetryCallState"], None]:
-    """After call strategy that logs to some logger the finished attempt."""
-
-    def log_it(retry_state: "RetryCallState") -> None:
-        if retry_state.fn is None:
-            # NOTE(sileht): can't really happen, but we must please mypy
-            fn_name = "<unknown>"
-        else:
-            fn_name = _utils.get_callback_name(retry_state.fn)
-        logger.log(
-            log_level,
-            f"Finished call to '{fn_name}' "
-            f"after {sec_format % retry_state.seconds_since_start}(s), "
-            f"this was the {_utils.to_ordinal(retry_state.attempt_number)} time calling it.",
-        )
-
-    return log_it

spaces/CVPR/LIVE/thrust/thrust/mr/new.h

@@ -1,88 +0,0 @@
-/*
- *  Copyright 2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file new.h
- *  \brief Global operator new-based memory resource.
- */
-
-#pragma once
-
-#include <thrust/mr/memory_resource.h>
-
-namespace thrust
-{
-namespace mr
-{
-
-/** \addtogroup memory_resources Memory Resources
- *  \ingroup memory_management_classes
- *  \{
- */
-
-/*! A memory resource that uses global operators new and delete to allocate and deallocate memory. Uses alignment-enabled
- *      overloads when available, otherwise uses regular overloads and implements alignment requirements by itself.
- */
-class new_delete_resource THRUST_FINAL : public memory_resource<>
-{
-public:
-    void * do_allocate(std::size_t bytes, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT) THRUST_OVERRIDE
-    {
-#if defined(__cpp_aligned_new)
-        return ::operator new(bytes, std::align_val_t(alignment));
-#else
-        // allocate memory for bytes, plus potential alignment correction,
-        // plus store of the correction offset
-        void * p = ::operator new(bytes + alignment + sizeof(std::size_t));
-        std::size_t ptr_int = reinterpret_cast<std::size_t>(p);
-        // calculate the offset, i.e. how many bytes of correction was necessary
-        // to get an aligned pointer
-        std::size_t offset = (ptr_int % alignment) ? (alignment - ptr_int % alignment) : 0;
-        // calculate the return pointer
-        char * ptr = static_cast<char *>(p) + offset;
-        // store the offset right after the actually returned value
-        std::size_t * offset_store = reinterpret_cast<std::size_t *>(ptr + bytes);
-        *offset_store = offset;
-        return static_cast<void *>(ptr);
-#endif
-    }
-
-    void do_deallocate(void * p, std::size_t bytes, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT) THRUST_OVERRIDE
-    {
-#if defined(__cpp_aligned_new)
-# if defined(__cpp_sized_deallocation)
-        ::operator delete(p, bytes, std::align_val_t(alignment));
-# else
-        (void)bytes;
-        ::operator delete(p, std::align_val_t(alignment));
-# endif
-#else
-        (void)alignment;
-        char * ptr = static_cast<char *>(p);
-        // calculate where the offset is stored
-        std::size_t * offset = reinterpret_cast<std::size_t *>(ptr + bytes);
-        // calculate the original pointer
-        p = static_cast<void *>(ptr - *offset);
-        ::operator delete(p);
-#endif
-    }
-};
-
-/*! \}
- */
-
-} // end mr
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/scan_by_key.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 this algorithm
-#include <thrust/system/cpp/detail/scan_by_key.h>
-

spaces/CVPR/regionclip-demo/detectron2/data/catalog.py

@@ -1,236 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import types
-from collections import UserDict
-from typing import List
-
-from detectron2.utils.logger import log_first_n
-
-__all__ = ["DatasetCatalog", "MetadataCatalog", "Metadata"]
-
-
-class _DatasetCatalog(UserDict):
-    """
-    A global dictionary that stores information about the datasets and how to obtain them.
-
-    It contains a mapping from strings
-    (which are names that identify a dataset, e.g. "coco_2014_train")
-    to a function which parses the dataset and returns the samples in the
-    format of `list[dict]`.
-
-    The returned dicts should be in Detectron2 Dataset format (See DATASETS.md for details)
-    if used with the data loader functionalities in `data/build.py,data/detection_transform.py`.
-
-    The purpose of having this catalog is to make it easy to choose
-    different datasets, by just using the strings in the config.
-    """
-
-    def register(self, name, func):
-        """
-        Args:
-            name (str): the name that identifies a dataset, e.g. "coco_2014_train".
-            func (callable): a callable which takes no arguments and returns a list of dicts.
-                It must return the same results if called multiple times.
-        """
-        assert callable(func), "You must register a function with `DatasetCatalog.register`!"
-        assert name not in self, "Dataset '{}' is already registered!".format(name)
-        self[name] = func
-
-    def get(self, name):
-        """
-        Call the registered function and return its results.
-
-        Args:
-            name (str): the name that identifies a dataset, e.g. "coco_2014_train".
-
-        Returns:
-            list[dict]: dataset annotations.
-        """
-        try:
-            f = self[name]
-        except KeyError as e:
-            raise KeyError(
-                "Dataset '{}' is not registered! Available datasets are: {}".format(
-                    name, ", ".join(list(self.keys()))
-                )
-            ) from e
-        return f()
-
-    def list(self) -> List[str]:
-        """
-        List all registered datasets.
-
-        Returns:
-            list[str]
-        """
-        return list(self.keys())
-
-    def remove(self, name):
-        """
-        Alias of ``pop``.
-        """
-        self.pop(name)
-
-    def __str__(self):
-        return "DatasetCatalog(registered datasets: {})".format(", ".join(self.keys()))
-
-    __repr__ = __str__
-
-
-DatasetCatalog = _DatasetCatalog()
-DatasetCatalog.__doc__ = (
-    _DatasetCatalog.__doc__
-    + """
-    .. automethod:: detectron2.data.catalog.DatasetCatalog.register
-    .. automethod:: detectron2.data.catalog.DatasetCatalog.get
-"""
-)
-
-
-class Metadata(types.SimpleNamespace):
-    """
-    A class that supports simple attribute setter/getter.
-    It is intended for storing metadata of a dataset and make it accessible globally.
-
-    Examples:
-    ::
-        # somewhere when you load the data:
-        MetadataCatalog.get("mydataset").thing_classes = ["person", "dog"]
-
-        # somewhere when you print statistics or visualize:
-        classes = MetadataCatalog.get("mydataset").thing_classes
-    """
-
-    # the name of the dataset
-    # set default to N/A so that `self.name` in the errors will not trigger getattr again
-    name: str = "N/A"
-
-    _RENAMED = {
-        "class_names": "thing_classes",
-        "dataset_id_to_contiguous_id": "thing_dataset_id_to_contiguous_id",
-        "stuff_class_names": "stuff_classes",
-    }
-
-    def __getattr__(self, key):
-        if key in self._RENAMED:
-            log_first_n(
-                logging.WARNING,
-                "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]),
-                n=10,
-            )
-            return getattr(self, self._RENAMED[key])
-
-        # "name" exists in every metadata
-        if len(self.__dict__) > 1:
-            raise AttributeError(
-                "Attribute '{}' does not exist in the metadata of dataset '{}'. Available "
-                "keys are {}.".format(key, self.name, str(self.__dict__.keys()))
-            )
-        else:
-            raise AttributeError(
-                f"Attribute '{key}' does not exist in the metadata of dataset '{self.name}': "
-                "metadata is empty."
-            )
-
-    def __setattr__(self, key, val):
-        if key in self._RENAMED:
-            log_first_n(
-                logging.WARNING,
-                "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]),
-                n=10,
-            )
-            setattr(self, self._RENAMED[key], val)
-
-        # Ensure that metadata of the same name stays consistent
-        try:
-            oldval = getattr(self, key)
-            assert oldval == val, (
-                "Attribute '{}' in the metadata of '{}' cannot be set "
-                "to a different value!\n{} != {}".format(key, self.name, oldval, val)
-            )
-        except AttributeError:
-            super().__setattr__(key, val)
-
-    def as_dict(self):
-        """
-        Returns all the metadata as a dict.
-        Note that modifications to the returned dict will not reflect on the Metadata object.
-        """
-        return copy.copy(self.__dict__)
-
-    def set(self, **kwargs):
-        """
-        Set multiple metadata with kwargs.
-        """
-        for k, v in kwargs.items():
-            setattr(self, k, v)
-        return self
-
-    def get(self, key, default=None):
-        """
-        Access an attribute and return its value if exists.
-        Otherwise return default.
-        """
-        try:
-            return getattr(self, key)
-        except AttributeError:
-            return default
-
-
-class _MetadataCatalog(UserDict):
-    """
-    MetadataCatalog is a global dictionary that provides access to
-    :class:`Metadata` of a given dataset.
-
-    The metadata associated with a certain name is a singleton: once created, the
-    metadata will stay alive and will be returned by future calls to ``get(name)``.
-
-    It's like global variables, so don't abuse it.
-    It's meant for storing knowledge that's constant and shared across the execution
-    of the program, e.g.: the class names in COCO.
-    """
-
-    def get(self, name):
-        """
-        Args:
-            name (str): name of a dataset (e.g. coco_2014_train).
-
-        Returns:
-            Metadata: The :class:`Metadata` instance associated with this name,
-            or create an empty one if none is available.
-        """
-        assert len(name)
-        r = super().get(name, None)
-        if r is None:
-            r = self[name] = Metadata(name=name)
-        return r
-
-    def list(self):
-        """
-        List all registered metadata.
-
-        Returns:
-            list[str]: keys (names of datasets) of all registered metadata
-        """
-        return list(self.keys())
-
-    def remove(self, name):
-        """
-        Alias of ``pop``.
-        """
-        self.pop(name)
-
-    def __str__(self):
-        return "MetadataCatalog(registered metadata: {})".format(", ".join(self.keys()))
-
-    __repr__ = __str__
-
-
-MetadataCatalog = _MetadataCatalog()
-MetadataCatalog.__doc__ = (
-    _MetadataCatalog.__doc__
-    + """
-    .. automethod:: detectron2.data.catalog.MetadataCatalog.get
-"""
-)

spaces/ChandraMohanNayal/AutoGPT/run.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
-    echo Installing missing packages...
-    pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."

spaces/ChevyWithAI/rvc-aicover/infer_pack/models.py

@@ -1,982 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y_lengths, ds
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            x, y_lengths, self.segment_size
-        )
-
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice
-
-    def infer(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o, o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap