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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Anytone At 5555 V3 Software 14.md

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-<h1>Anytone At 5555 V3 Software 14: A Review</h1>
- <h2>Introduction</h2>
- <p>If you are looking for a reliable and versatile software for your Anytone AT-5555 PLUS 10M mobile transceiver, you might want to check out the Anytone At 5555 V3 Software 14. This software is the latest official release from Anytone, a leading manufacturer of radios and accessories. In this article, we will review the features, benefits, and installation process of this software, and answer some frequently asked questions about it.</p>
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- <h3>What is Anytone At 5555 V3 Software 14?</h3>
- <p>Anytone At 5555 V3 Software 14 is a software that allows you to program and control your Anytone AT-5555 PLUS radio. It is designed for Windows operating systems, but it can also be used on Mac OSX and Linux with a virtual machine. The software enables you to access all the features and options of your radio, such as 2TONE, 5TONE, MSK, and more. It also supports CHIRP programming software, which is a free and open-source tool that allows you to easily copy and paste frequencies, import files from other radios, and edit channels.</p>
- <h3>Why do you need Anytone At 5555 V3 Software 14?</h3>
- <p>You need Anytone At 5555 V3 Software 14 if you want to get the most out of your Anytone AT-5555 PLUS radio. This software will help you to customize your radio settings according to your preferences and needs. It will also help you to update your radio firmware to the latest version, which can improve the performance and stability of your device. Moreover, it will allow you to use CHIRP programming software, which can make your frequency programming and editing much easier and faster.</p>
- <h3>How to download and install Anytone At 5555 V3 Software 14?</h3>
- <p>To download and install Anytone At 5555 V3 Software 14, you need to follow these steps:</p>
- <ol>
-<li>Go to <a href="https://www.anytone.net/download">https://www.anytone.net/download</a> and find the zip file for D878UV PLUS V3.02N official release.</li>
-<li>Download the zip file and extract it to a folder on your computer.</li>
-<li>Connect your Anytone AT-5555 PLUS radio to your computer with a USB cable.</li>
-<li>Run the setup.exe file in the folder and follow the instructions on the screen.</li>
-<li>When the installation is complete, launch the software and enjoy.</li>
-</ol>
- <h2>Features of Anytone At 5555 V3 Software 14</h2>
- <h3>RX noise reduction option</h3>
- <p>One of the features of Anytone At 5555 V3 Software 14 is the RX noise reduction option. This option allows you to reduce the background noise in your radio reception by using an extra PCB inside the radio. This can enhance the clarity and quality of your communication, especially in noisy environments.</p>
- <h3>Access to all features and options such as 2TONE, 5TONE, MSK, and more</h3>
- <p>Another feature of Anytone At 5555 V3 Software 14 is that it gives you access to all the features and options of your radio, such as 2TONE, 5TONE, MSK, and more. These are different modes of signaling that can be used for various purposes, such as selective calling, group calling, emergency calling, etc. You can use these modes to communicate with other radios that support them.</p>
- <h3>Compatibility with CHIRP programming software</h3>
- <p>A third feature of Anytone At 5555 V3 Software 14 is that it is compatible with CHIRP programming software. CHIRP is a free and open-source software that allows you to program your radio with ease. You can use CHIRP to copy and paste frequencies from other sources, import files from other radios, edit channels with a spreadsheet-like interface, etc. You can also use CHIRP on Windows, Mac OSX, or Linux operating systems.</p>
- <h2>Benefits of Anytone At 5555 V3 Software 14</h2>
- <h3>Improved performance and stability of your radio</h3>
- <p>One of the benefits of using Anytone At 5555 V3 Software 14 is that it can improve the performance and stability of your radio. By updating your radio firmware to the latest version, you can fix some bugs and glitches that might affect your device. You can also optimize your radio settings to suit your needs and preferences.</p>
- <h3>Easier frequency programming and editing</h3>
- <h2>Benefits of Anytone At 5555 V3 Software 14</h2>
- <h3>Improved performance and stability of your radio</h3>
- <p>One of the benefits of using Anytone At 5555 V3 Software 14 is that it can improve the performance and stability of your radio. By updating your radio firmware to the latest version, you can fix some bugs and glitches that might affect your device. You can also optimize your radio settings to suit your needs and preferences.</p>
- <h3>Easier frequency programming and editing</h3>
- <p>Another benefit of using Anytone At 5555 V3 Software 14 is that it makes your frequency programming and editing easier and faster. You can use the software to access all the features and options of your radio, such as 2TONE, 5TONE, MSK, and more. You can also use CHIRP programming software, which is a free and open-source tool that allows you to copy and paste frequencies from other sources, import files from other radios, edit channels with a spreadsheet-like interface, etc.</p>
-<p>How to update Anytone At 5555 V3 Software 14<br />
-Anytone At 5555 V3 Software 14 download link<br />
-Anytone At 5555 V3 Software 14 user manual<br />
-Anytone At 5555 V3 Software 14 review and rating<br />
-Anytone At 5555 V3 Software 14 troubleshooting guide<br />
-Anytone At 5555 V3 Software 14 compatible devices<br />
-Anytone At 5555 V3 Software 14 features and specifications<br />
-Anytone At 5555 V3 Software 14 installation instructions<br />
-Anytone At 5555 V3 Software 14 warranty and support<br />
-Anytone At 5555 V3 Software 14 price and availability<br />
-Anytone At 5555 V3 Software 14 vs other software versions<br />
-Anytone At 5555 V3 Software 14 best practices and tips<br />
-Anytone At 5555 V3 Software 14 feedback and testimonials<br />
-Anytone At 5555 V3 Software 14 pros and cons<br />
-Anytone At 5555 V3 Software 14 alternatives and comparisons<br />
-Anytone At 5555 V3 Software 14 benefits and advantages<br />
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-How to uninstall Anytone At 5555 V3 Software 14<br />
-How to backup and restore Anytone At 5555 V3 Software 14<br />
-How to reset and factory default Anytone At 5555 V3 Software 14<br />
-How to connect and sync Anytone At 5555 V3 Software 14 with other devices<br />
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-How to troubleshoot and fix common issues with Anytone At <br />
-How to troubleshoot and fix common issues with Anytone At </p>
- <h3>Support for Windows, Mac OSX, and Linux operating systems</h3>
- <p>A third benefit of using Anytone At 5555 V3 Software 14 is that it supports Windows, Mac OSX, and Linux operating systems. You can use the software on your preferred operating system without any hassle. If you are using Mac OSX or Linux, you can use a virtual machine to run the software. This way, you can enjoy the same functionality and compatibility as Windows users.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, Anytone At 5555 V3 Software 14 is a reliable and versatile software for your Anytone AT-5555 PLUS 10M mobile transceiver. It allows you to program and control your radio with ease. It has features such as RX noise reduction option, access to all features and options such as 2TONE, 5TONE, MSK, and more, and compatibility with CHIRP programming software. It also has benefits such as improved performance and stability of your radio, easier frequency programming and editing, and support for Windows, Mac OSX, and Linux operating systems.</p>
- <h3>Call to action</h3>
- <p>If you are interested in getting Anytone At 5555 V3 Software 14 for your radio, you can download it from <a href="https://www.anytone.net/download">https://www.anytone.net/download</a>. You can also find more information about Anytone products and services on their website. If you have any questions or feedback about the software or the radio, you can contact Anytone customer support or join their online community. Don't miss this opportunity to upgrade your radio with Anytone At 5555 V3 Software 14.</p>
- <h2>FAQs</h2>
- <h4>What is the difference between Anytone At 5555 V3 Software 14 and Anytone At 5555N II?</h4>
- <p>Anytone At 5555 V3 Software 14 is the latest official release for Anytone AT-5555 PLUS radio. Anytone At 5555N II is an upgraded version of Anytone AT-5555N radio. They are different models of radios with different features and specifications.</p>
- <h4>How much does Anytone At 5555 V3 Software 14 cost?</h4>
- <p>Anytone At 5555 V3 Software 14 is free to download from <a href="https://www.anytone.net/download">https://www.anytone.net/download</a>. You only need to pay for the Anytone AT-5555 PLUS radio itself.</p>
- <h4>How do I use CHIRP programming software with Anytone At 5555 V3 Software 14?</h4>
- <h4>How do I use CHIRP programming software with Anytone At 5555 V3 Software 14?</h4>
- <p>To use CHIRP programming software with Anytone At 5555 V3 Software 14, you need to follow these steps:</p>
- <ol>
-<li>Download and install CHIRP programming software from <a href="https://chirp.danplanet.com/">https://chirp.danplanet.com/</a>.</li>
-<li>Connect your Anytone AT-5555 PLUS radio to your computer with a USB cable.</li>
-<li>Launch CHIRP and select your radio model and port from the menu.</li>
-<li>Click on Radio > Download From Radio to read the current settings from your radio.</li>
-<li>Edit the channels and settings as you wish using the spreadsheet-like interface.</li>
-<li>Click on Radio > Upload To Radio to write the new settings to your radio.</li>
-</ol>
- <h4>What are some of the reviews of Anytone At 5555 V3 Software 14?</h4>
- <p>Some of the reviews of Anytone At 5555 V3 Software 14 are:</p>
- <ul>
-<li>"Great little radio ( Little in comparison to my Yaesu FTDX 101MP). This radio is more of size comparison to the RCI 2950 DX, Radio works great and covers all three bands 10 meters, 11 meters and the 12 meter ham bands. Other operators say I sound excellent on the air and it is quite easy to use and has the CTCSS (PL) encoder/Decoder as well for repeater access, also has Only six memories, why ONLY six is beyond me! I am sure there literally hundreds of usable repeaters on the FM portion of the Ham band. I had it in my car for a short time but found it is a bit large for my little car ( A ford ecosport). I took it out and put my Yaesu FT891 back in. I miss the other ham band capabilities." - KelliePicklerFan on Amazon.com</li>
-<li>"Radio is good, same board as the stryker 955 v2, it does have a overly sensitive receiver that could be a problem if you live in a noisy area." - Mark P. on Amazon.com</li>
-<li>"This rig is terrific for the price. As others have noted, it uses the same new board as the newest generation of the Stryker 955, a fabulous radio. It's like getting the performance of the Stryker for just a bit more than half price. Transmit audio is excellent on all modes just using the stock mic. The radio is rock stable on sideband. There is no drifting. Mine arrived spot on frequency from the factory. The receiver is excellent also, very smooth sounding on all modes. My only complaint is the use of a RJ-45 jack for the mic. Anytone cheaped out on that. Yes, I know that many ham rigs use them. I have many and hate it on those rigs as well. They are cheap and the mic connectors break easily. Anytone had plenty of room to fit a standard 4 or 6 pin jack, making it easier to wire up amplified mics. Aside from that one issue, which can be remedied with an adapter, this is THE rig to get for the mobile or a nice base setup with a power supply. Great job Anytone." - Mike on Amazon.com</li>
-</ul>
- <h4>What are some of the alternatives to Anytone At 5555 V3 Software 14?</h4>
- <p>Some of the alternatives to Anytone At 5555 V3 Software 14 are:</p>
- <ul>
-<li>Stryker SR-955HPC Software: This software is for Stryker SR-955HPC radio, which is similar to Anytone AT-5555 PLUS radio in features and specifications. It also supports CHIRP programming software.</li>
-<li>President Lincoln II+ Software: This software is for President Lincoln II+ radio, which is another popular 10M mobile transceiver with similar features and specifications as Anytone AT-5555 PLUS radio.</li>
-<li>Ranger RCI-2950DX Software: This software is for Ranger RCI-2950DX radio, which is another multi-mode rig that covers 10M, 11M, and 12M bands with similar features and specifications as Anytone AT-5555 PLUS radio.</li>
-</ul>
- <h4>Where can I buy Anytone AT-5555 PLUS radio?</h4>
- <p>You can buy Anytone AT-5555 PLUS radio from various online platforms such as Amazon.com, eBay.com, Moonrakeronline.com, etc. You can also find local dealers or distributors of Anytone products in your area by visiting <a href="https://www.anytone.net/contact">https://www.anytone.net/contact</a>.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/AutoCAD 2010 xforce keygen 32 bit Free download links and reviews for AutoCAD 2010 keygen.md

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-<h1>Anno 1602 No CD Crack Download German</h1>
-<p>If you are a fan of strategy games, you might have heard of <strong>Anno 1602</strong>, a classic game that lets you build your own colony in the New World. However, if you want to play this game on your PC without having to insert the CD every time, you might need a <strong>no CD crack</strong>. In this article, we will show you what a no CD crack is, why you might need it, how to use it, and where to find it. We will also give you some tips and tricks for playing Anno 1602 with a no CD crack without any problems.</p>
- <h2>What is Anno 1602?</h2>
-<p>Anno 1602 is a real-time strategy game that was released in 1998 by Sunflowers Interactive Entertainment Software. The game is set in the 17th century, when European explorers and settlers were discovering and colonizing new lands in America. The game allows you to create your own civilization by managing resources, building structures, trading with other nations, and engaging in warfare. The game features a single-player campaign mode, a sandbox mode, and a multiplayer mode. The game is also known as <em>1602 A.D.</em> in North America and <em>Anno: Create a New World</em> on Nintendo DS.</p>
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- <h3>Why do you need a no CD crack?</h3>
-<p>A no CD crack is a modified version of the game's executable file that allows you to run the game without having to insert the original CD in your drive. This can be useful for several reasons:</p>
-<ul>
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-<p>However, there are also some drawbacks of using a no CD crack:</p>
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-<ul>
-<li>You might violate the game's license agreement or copyright laws by using an unauthorized copy.</li>
-<li>You might expose your PC to viruses or malware by downloading files from untrusted sources.</li>
-<li>You might encounter compatibility or stability issues with your game or system by using an outdated or incompatible crack.</li>
-<li>You might miss out on some features or updates that require the original CD.</li>
-</ul>
- <h4>How to use a no CD crack for Anno 1602?</h4>
-<p>Using a no CD crack for Anno 1602 is not very difficult, but you need to follow some steps carefully:</p>
-<ol>
-<li>Make sure you have installed the game on your PC from the original CD.</li>
-<li>Make sure you have backed up your game files before applying any changes.</li>
-<li>Download a no CD crack file from a reliable and safe source (we will provide some suggestions later).</li>
-<li>Extract the file (usually a .zip or .rar archive) using a program like WinRAR or 7-Zip.</li>
-<li>Copy the extracted file (usually a .exe file) and paste it into your game directory (usually C:\Program Files\Anno 1602 or C:\Program Files (x86)\Anno 1602).</li>
-<li>Replace the existing file when prompted (you might need administrator privileges).</li>
-<li>Run the game as usual from your desktop shortcut or start menu.</li>
-</ol>
- <h2>Where to find a no CD crack for Anno 1602?</h2>
-<p>There are many websites that offer no CD cracks for various games, but not all of them are trustworthy or safe. Some of them might contain viruses, malware, spyware, adware, or other unwanted programs that can harm your PC or steal your personal information. Therefore, you should always be careful when downloading files from unknown sources and scan them with an antivirus program before opening them. Here are some of the most reliable and safe sources for downloading a no CD crack for Anno 1602:</p>
- <h3>MegaGames</h3>
-<p>MegaGames is one of the oldest and most popular websites for downloading game fixes, patches, trainers, mods, cheats, and cracks. It has a large database of games and files that are updated regularly. It also has a user-friendly interface and a rating system that helps you find the best files for your needs. You can download a no CD crack for Anno 1602 from MegaGames here:</p>
- <a href="https://megagames.com/fixes/anno-1602-3">https://megagames.com/fixes/anno-1602-3</a>
- <h4>Pros and cons of MegaGames</h4>
-<p>MegaGames has many advantages over other websites:</p>
-<ul>
-<li>It has a high reputation and credibility among gamers and developers.</li>
-<li>It has a large collection of games and files that are tested and verified.</li>
-<li>It has a fast and secure download system that does not require registration or payment.</li>
-<li>It has an active community and forum where you can get help or feedback.</li>
-</ul>
-<p>However, MegaGames also has some disadvantages:</p>
-<ul>
-<li>It has some annoying ads and pop-ups that might interfere with your browsing experience.</li>
-<li>It has some outdated or broken links that might lead you to dead ends or errors.</li>
-<li>It has some files that might not work properly with your game or system version.</li>
-</ul>
- <h4>How to download and install MegaGames crack</h4>
-<p>To download and install MegaGames crack for Anno 1602, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://megagames.com/fixes/anno-1602-3">https://megagames.com/fixes/anno-1602-3</a>.</li>
-<li>Click on "Download" under "Anno 1602 GER".</li>
-<li>Select one of the available mirrors (preferably one with high speed).</li>
-<li>Save the file (anno_ger.zip) on your PC.</li>
-<li>Extract the file using WinRAR or 7-Zip.</li>
-<li>Copy both files (crack.exe and anno.crk) into your game directory (C:\Program Files\Anno 1602 or C:\Program Files (x86)\Anno 1602).</li>
-<li>Run crack.exe from DOS mode (you can use CMD or PowerShell).</li>
-<li>Type "crack anno.crk" without quotes and press Enter.</li>
-<li>The program will patch your game executable file (1602 .exe) and remove the CD/MOVIE and SOUND check.</li>
-<li>Enjoy the game without the CD.</li>
-</ol>
- <h3>Other alternatives</h3>
-<p>If you are not satisfied with MegaGames or you want to try some other options, here are some other websites that offer no CD cracks for Anno 1602:</p>
- <h4>GameCopyWorld</h4>
-<p>GameCopyWorld is another well-known website for downloading game fixes, patches, trainers, mods, cheats, and cracks. It has a similar database and interface as MegaGames, but it also offers some additional features such as game manuals, covers, wallpapers, and music. You can download a no CD crack for Anno 1602 from GameCopyWorld here:</p>
- <a href="https://www.gamecopyworld.com/games/pc_anno_1602.shtml">https://www.gamecopyworld.com/games/pc_anno_1602.shtml</a>
- <h4>GameBurnWorld</h4>
-<p>GameBurnWorld is a smaller website that specializes in game fixes, patches, trainers, mods, cheats, and cracks. It has a more simple and minimalist design, but it also has a decent collection of games and files that are updated frequently. You can download a no CD crack for Anno 1602 from GameBurnWorld here:</p>
- <a href="http://www.gameburnworld.com/gp/gamefixes/anno1602.shtml">http://www.gameburnworld.com/gp/gamefixes/anno1602.shtml</a>
- <h2>Tips and tricks for playing Anno 1602 with a no CD crack</h2>
-<p>Now that you have downloaded and installed a no CD crack for Anno 1602, you might want to know some tips and tricks for playing the game without any issues. Here are some of them:</p>
- <h3>How to backup your game files</h3>
-<p>Before applying any changes to your game files, such as using a no CD crack or installing a patch or mod, it is always recommended to backup your original files in case something goes wrong or you want to restore them later. To backup your game files, follow these steps:</p>
-<ol>
-<li>Create a new folder on your PC where you want to store your backup files.</li>
-<li>Go to your game directory (C:\Program Files\Anno 1602 or C:\Program Files (x86)\Anno 1602) and select all the files and folders.</li>
-<li>Copy them and paste them into your backup folder.</li>
-<li>Rename your backup folder as you wish (for example, Anno 1602 Original).</li>
-</ol>
- <h3>How to run the game in compatibility mode</h3>
-<p>Anno 1602 is an old game that was designed for Windows 95/98/ME. Therefore, it might not run properly on newer versions of Windows such as Windows 10. To fix any potential compatibility problems, you can try running the game in compatibility mode. To do so, follow these steps:</p>
-<ol>
-<li>Right-click on your game executable file (1602.exe) and select Properties.</li>
-<li>Go to the Compatibility tab and check the box that says "Run this program in compatibility mode for:".</li>
-<li>Select the version of Windows that you want to use (for example, Windows XP Service Pack 3).</li>
-<li>Click Apply and OK.</li>
-<li>Run the game as usual.</li>
-</ol>
- <h3>How to update your game to the latest version</h3>
-<p>Anno 1602 has received several updates since its release that have improved its performance and added new features. However, some of these updates might require the original CD to work. To avoid this problem, you can use a no CD patch that updates your game to the latest version without needing the CD. To do so, follow these steps:</p>
-<ol>
-<li>Download a no CD patch for Anno 1602 from one of the sources mentioned above (for example, MegaGames).</li>
-<li>Extract the file (usually a .zip or .rar archive) using WinRAR or 7-Zip.</li>
-<li>Copy the extracted file (usually a .exe file) and paste it into your game directory (C:\Program Files\Anno 1602 or C:\Program Files (x86)\Anno 1602).</li>
-<li>Replace the existing file when prompted (you might need administrator privileges).</li>
-<li>Run the patch as usual from your desktop shortcut or start menu.</li>
-<li>The patch will update your game to the latest version (usually v1.05) without needing the CD.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Anno 1602 is a great game that deserves to be played by anyone who loves strategy games. However, if you don't want to deal with the hassle of inserting the CD every time you want to play it, you can use a no CD crack that allows you to run the game without it. In this article, we have shown you what a no CD crack is, why you might need it, how to use it, and where to find it. We have also given you some tips and tricks for playing Anno 1602 with a no CD crack without any issues. We hope you have found this article helpful and informative. Now go ahead and enjoy Anno 1602 without any limitations!</p>
- <h3>Frequently Asked Questions</h3>
-<p>Here are some of the most common questions that people ask about Anno 1602 and no CD cracks:</p>
- <ol>
-<li><strong>Is using a no CD crack illegal?</strong></li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ayitha Ezhuthu movie full movie in tamil hd 1080p A masterpiece by Mani Ratnam.md

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-<h1>Ayitha Ezhuthu: A Political Thriller That Changed Tamil Cinema</h1>
- <h2>Introduction</h2>
- <p>Tamil cinema is known for its rich variety of genres, themes and styles. From romance to comedy, from action to drama, from fantasy to realism, Tamil movies have something for everyone. But one genre that has been relatively less explored in Tamil cinema is political thriller. Political thrillers are movies that deal with issues of power, corruption, justice and violence in the context of politics and society. They often feature complex plots, suspenseful twists, moral dilemmas and social commentary.</p>
- <p>One of the most acclaimed and influential political thrillers in Tamil cinema is Ayitha Ezhuthu (2004), written and directed by Mani Ratnam. The movie is inspired by the Mexican film Amores Perros (2000), which tells three interconnected stories through a car accident. Ayitha Ezhuthu also uses a similar narrative device, but sets it in Chennai and focuses on three different men who are involved in a shooting incident on a bridge. The movie explores how their lives are changed by this event and how they are connected to each other and to the larger political scenario.</p>
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- <h3>What is Ayitha Ezhuthu?</h3>
- <p>Ayitha Ezhuthu is a Tamil word that means "three dots". It is also the name of a letter in the Tamil alphabet, ஃ, which is used as a diacritic mark to modify the sound of other letters. The title of the movie refers to the three main characters, who are represented by three different colors: red, blue and green. The movie also uses these colors to create a distinct visual style and mood for each story.</p>
- <h3>Who are the main characters and actors?</h3>
- <p>The three main characters of Ayitha Ezhuthu are:</p>
- <ul>
-<li>Michael Vasanth (played by Suriya), a charismatic student leader who wants to enter politics and fight against corruption. He is in love with Geetha (played by Esha Deol), his neighbor and childhood friend. He represents the color red, which symbolizes passion, courage and revolution.</li>
-<li>Inba Sekar (played by Madhavan), a violent goon who works for Selvanayagam (played by Bharathiraja), a powerful politician who wants to eliminate his rivals. He is married to Sashi (played by Meera Jasmine), whom he abuses and neglects. He represents the color blue, which symbolizes violence, sadness and oppression.</li>
-<li>Arjun Balakrishnan (played by Siddharth), a carefree and spoiled son of an IAS officer who wants to migrate to the US for a better future. He falls in love with Meera (played by Trisha Krishnan), a radio jockey who inspires him to change his outlook on life. He represents the color green, which symbolizes youth, hope and change.</li>
-</ul>
- <p>The movie also features other supporting actors such as Karthi, Suchitra, R.S. Shivaji, Sindhu Shyam, Sriman and T.S. Suresh.</p>
- <h3>What is the plot of the movie?</h3>
- <p>The plot of Ayitha Ezhuthu revolves around a shooting incident that takes place on Napier Bridge in Chennai. The incident involves Michael, Inba and Arjun, who are strangers to each other but whose lives are intertwined by fate. The movie follows their stories before and after the incident and shows how they are affected by it.</p>
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- <p>The movie begins with Inba shooting Michael on his bike, resulting in him falling off the bridge into the water below. This is witnessed by Arjun, who was chasing Meera after proposing to her on the road. The movie then goes into a flashback mode and shows how each character reached that point.</p>
- <p>Michael is an influential student leader who wants to contest in college elections and challenge Selvanayagam's dominance in politics. He faces opposition from Inba, who is hired by Selvanayagam to intimidate him and his supporters. Michael also has to deal with his relationship with Geetha, who wants him to stay away from politics for his safety.</p>
- <p>Inba is a ruthless goon who works for Selvanayagam as his hitman. He has no qualms about killing or hurting anyone for money or power. He has a troubled marriage with Sashi, whom he beats regularly and forces her to abort their child. He also has a rivalry with Guna (played by Karthi), his brother-in-law who works for another politician.</p>
- <p>Arjun is a rich and spoiled brat who has no aim or ambition in life. He wants to go to the US for higher studies but fails to get admission due to his poor grades. He meets Meera at a pub and falls in love with her at first sight. He tries to woo her with his charm and money but she rejects him initially.</p>
- <p>The movie then returns to the present day and shows how the shooting incident affects each character's life. Michael survives the fall but loses his memory temporarily. He recovers with Geetha's help but faces threats from Selvanayagam's men who want to finish him off. He decides to fight back and expose Selvanayagam's corruption.</p>
- <p>Inba escapes from the scene but is chased by Guna's men who want revenge for killing their boss. He also faces pressure from Sashi who wants him to leave his criminal life and start afresh elsewhere. He realizes his mistakes but finds it hard to change his ways.</p>
- <p>Arjun saves Meera from being hit by Inba's car during the chase. He takes her to his house where he confesses his love for her again. She accepts him after seeing his genuine concern for her. He also decides to stay back in India and join Michael's campaign against Selvanayagam.</p>
- <p>The movie ends with a climax where Michael confronts Selvanayagam at his office while Inba tries to stop him from killing him. Arjun arrives with Meera and helps Michael escape from Inba's attack. Inba shoots at Michael but misses him and hits Selvanayagam instead, killing him instantly. Inba then surrenders himself to the police while Michael celebrates his victory with Geetha and Arjun celebrates his love with Meera.</p>
- <h2>Analysis</h2>
- <h3>How does the movie portray different aspects of politics and society?</h3>
- <p>Ayitha Ezhuthu is a movie that explores various aspects of politics and society in contemporary India. It shows how politics affects different people from different backgrounds and how they react to it differently.</p>
- <p>The movie portrays politics as a complex and corrupt system that is dominated by powerful people who use violence, money and influence to manipulate others for their own interests. It also shows how politics can be used as a tool for positive change if people have courage, integrity and vision.</p>
- <p>The movie also depicts society as a diverse and dynamic entity that consists of different classes, cultures, ideologies and aspirations. It shows how society can be divided by conflicts, prejudices, inequalities and injustices but also united by common goals, values and hopes.</p>
- <h3>How does the movie use the three dots motif to connect the stories?</h3>
- <p>Ayitha Ezhuthu uses the three dots motif as a symbolic device to connect the stories of its three main characters. The three dots represent three different perspectives, personalities and paths that converge at one point: the shooting incident on Napier Bridge.</p>
- <p>The three dots also represent three different choices that each character makes: Michael chooses to fight for justice; Inba chooses to surrender to fate; Arjun chooses to change for love.</p>
- <p>The three dots also represent three different outcomes that each character faces: Michael succeeds in his mission; Inba fails in his ambition; Arjun finds his purpose.</p>
- <h3> Here is the continuation of the article. <h3>How does the movie challenge the stereotypes and expectations of Tamil cinema?</h3>
- <p>Ayitha Ezhuthu is a movie that challenges the stereotypes and expectations of Tamil cinema in many ways. It breaks away from the conventional formula of hero-centric, masala-oriented, melodramatic and escapist movies that are often seen in Tamil cinema. Instead, it offers a realistic, multi-layered, thought-provoking and engaging movie that deals with contemporary issues and themes.</p>
- <p>The movie also challenges the stereotypes and expectations of the characters and actors. It shows the characters as complex and flawed human beings who have their own strengths and weaknesses, motivations and conflicts, choices and consequences. It also shows the actors in different and unconventional roles that showcase their versatility and talent.</p>
- <p>For example, Suriya plays a role of a student leader who is not a typical hero who fights with his fists but with his words and ideas. Madhavan plays a role of a villain who is not a caricatured evil-doer but a conflicted and tragic character who has a backstory and a redemption arc. Siddharth plays a role of a lover boy who is not a cheesy romantic but a mature and responsible partner who supports his girlfriend's dreams.</p>
- <h2>Reception</h2>
- <h3>How did the critics and audience react to the movie?</h3>
- <p>Ayitha Ezhuthu received mostly positive reviews from the critics and audience. The movie was praised for its screenplay, direction, performances, music, cinematography and editing. The movie was also appreciated for its bold and innovative approach to storytelling, its social relevance and its message.</p>
- <p>The movie was also compared with its Hindi version, Yuva, which was released on the same day. Many critics and viewers felt that Ayitha Ezhuthu was superior to Yuva in terms of its authenticity, coherence, depth and impact. Some also felt that Ayitha Ezhuthu had better casting, acting and chemistry than Yuva.</p>
- <p>However, the movie also faced some criticism from some quarters. Some critics and viewers felt that the movie was too slow-paced, too complex, too preachy or too unrealistic. Some also felt that the movie had some flaws in its logic, continuity and climax.</p>
- <h3>What were the awards and accolades that the movie received?</h3>
- <p>Ayitha Ezhuthu received several awards and accolades for its excellence in various aspects of filmmaking. The movie won one Filmfare Award South for Best Music Director (A.R. Rahman) and one Tamil Nadu State Film Award for Best Film (Second Prize). The movie was also nominated for six Filmfare Awards South for Best Film, Best Director (Mani Ratnam), Best Actor (Suriya), Best Supporting Actor (Madhavan), Best Supporting Actress (Meera Jasmine) and Best Lyricist (Vairamuthu).</p>
- <p>The movie also received recognition from various other prestigious platforms such as National Film Awards, International Indian Film Academy Awards, Zee Cine Awards, Screen Awards, Stardust Awards and Vijay Awards.</p>
- <h3>What was the impact of the movie on Tamil cinema and culture?</h3>
- <p>Ayitha Ezhuthu had a significant impact on Tamil cinema and culture. The movie inspired many filmmakers to experiment with different genres, styles and techniques of storytelling. The movie also influenced many actors to take up challenging and diverse roles that showcase their range and potential.</p>
- <p>The movie also had an impact on Tamil society and politics. The movie raised awareness about various issues such as corruption, violence, education, youth empowerment and social change. The movie also motivated many young people to participate in politics and activism.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, Ayitha Ezhuthu is a political thriller that changed Tamil cinema by offering a realistic, multi-layered, thought-provoking and engaging movie that deals with contemporary issues and themes. The movie follows three different men who are involved in a shooting incident on a bridge and shows how their lives are changed by it. The movie explores various aspects of politics and society through their stories. The movie also challenges the stereotypes and expectations of Tamil cinema by breaking away from the conventional formula of hero-centric, masala-oriented, melodramatic and escapist movies. The movie received mostly positive reviews from the critics and audience for its screenplay, direction, performances, music, cinematography and editing. The movie also received several awards and accolades for its excellence in various aspects of filmmaking. The movie also had a significant impact on Tamil cinema and culture by inspiring many filmmakers and actors to experiment with different genres, styles and techniques of storytelling. The movie also influenced many young people to participate in politics and activism.</p>
- <h3>Personal opinion and recommendation</h3>
- <p>Personally, I think Ayitha Ezhuthu is one of the best movies ever made in Tamil cinema. I think it is a masterpiece that showcases Mani Ratnam's brilliance as a writer and director. I think it is a movie that has everything: drama, action, romance, comedy, suspense, thrill, emotion, message and entertainment. I think it is a movie that makes you think, feel and act.</p>
- <p>I would highly recommend Ayitha Ezhuthu to anyone who loves movies. I think it is a movie that everyone should watch at least once in their lifetime. I think it is a movie that will stay with you forever.</p>
- **FAQs** <code>
-Q: Where can I watch Ayitha Ezhuthu online? A: You can watch Ayitha Ezhuthu online on platforms such as Amazon Prime Video or Hotstar. Q: Is Ayitha Ezhuthu based on a true story? A: No, Ayitha Ezhuthu is not based on a true story. It is inspired by the Mexican film Amores Perros (2000), which tells three interconnected stories through a car accident. Q: What is the meaning of Ayitha Ezhuthu? A: Ayitha Ezhuthu means "three dots" in Tamil. It is also the name of a letter in the Tamil alphabet, ஃ , which is used as a diacritic mark to modify the sound of other letters. Q: Who composed the music for Ayitha Ezhuthu? A: A.R. Rahman composed the music for Ayitha Ezhuthu. He won one Filmfare Award South for Best Music Director for his work. Q: What are some other movies like Ayitha Ezhuthu? A: Some other movies like Ayitha Ezhuthu are Yuva (2004), which is the Hindi version of Ayitha Ezhuthu; Vettaiyaadu Vilaiyaadu (2006), which is another political thriller by Mani Ratnam; Ko (2011), which is another political thriller featuring Suriya; Sarkar (2018), which is another political thriller featuring Vijay; Kaappaan (2019), which is another political thriller featuring Suriya. </code>
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-<h1>Jhené Aiko's Blue Dream: A Song Review</h1>
-<p>If you are looking for a song that can transport you to a blissful state of love and happiness, you might want to check out Jhené Aiko's Blue Dream. This song is one of the bonus tracks from her debut album Souled Out, which was released in 2014. In this article, we will review the song and explore its lyrics, music, and meaning.</p>
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-<h3>Who is Jhené Aiko?</h3>
-<p>Jhené Aiko is an American singer and songwriter who was born in Los Angeles, California. She started her music career as a teenager, appearing on several R&B and hip-hop tracks. She gained more recognition after collaborating with artists like Drake, Big Sean, and Kendrick Lamar. She is known for her smooth and soulful voice, as well as her poetic and introspective lyrics. Some of her popular songs include The Worst, Sativa, Triggered, and B.S.</p>
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-<p>Blue Dream is a song that expresses Jhené Aiko's love and admiration for someone who opened her eyes to the beauty of life. She describes how this person made her see the truth in things and be in a dreamlike state where nothing else matters. She also talks about how this feeling is something that is blissful and mystical, and how she does not want to wake up from it. The song title refers to a type of cannabis strain that is known for its euphoric and relaxing effects.</p>
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-<h3>The lyrics</h3>
-<h4>The meaning of the verses</h4>
-<p>The first verse sets the scene for the song, as Jhené Aiko recalls a time when she was with her lover on the coast. She says that he opened her eyes to the beauty of the ocean and the sky, which symbolize the vastness and depth of their love. She also says that she was blinded before, implying that she was unaware or unhappy with her life until he came along. She then asks him to be hers, showing her desire and commitment.</p>
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-<p>The chorus repeats the main theme of the song, which is Jhené Aiko's love for her lover. She says that she does not want to wake up because she is in love with all that he is. She also says that he makes her see the truth in things, meaning that he helps her see things clearly and realistically. She then says that he is the remedy for everything, implying that he heals and soothes her from any pain or problems. She also says that he is the truth itself, meaning that he is honest and genuine with her. Finally, she says that nothing else can take her so far, meaning that no one else can make her feel as happy and fulfilled as he does.</p>
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-<p>The bridge emphasizes the blissful and mystical nature of Jhené Aiko's love for her lover. She says that her afternoon dream is when the world is sleeping, meaning that she feels like they are in their own world where nothing else matters. She also says that she is still thinking of her blue dream, meaning that she is still in love and content with him. She also says that she is in love with all that he is, echoing the chorus. She then says that he is the truth in things, meaning that he is the essence and reality of everything. She also says that he is the remedy for everything, repeating the chorus. Finally, she says that nothing else can take her so far, ending the song with the same line as the chorus.</p>
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-<p>Blue Dream is a song that belongs to the genre of neo-soul, which is a subgenre of R&B that incorporates elements of jazz, funk, hip-hop, and electronic music. The song has a smooth and laid-back style, with a slow tempo and a minimalistic production. The song also has a psychedelic and dreamy vibe, with a soft and ambient sound.</p>
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-<h2>Conclusion</h2>
-<h3>Why is Blue Dream a great song?</h3>
-<p>Blue Dream is a great song because it showcases Jhené Aiko's talent and artistry as a singer and songwriter. The song is beautifully written and composed, with poetic and meaningful lyrics, and soothing and enchanting music. The song is also emotionally engaging and relatable, as it expresses Jhené Aiko's love for her lover in a genuine and heartfelt way. The song is a testament to Jhené Aiko's ability to create soulful and captivating songs that touch the listener's soul.</p>
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-<p>If you want to listen to or download Blue Dream, you have several options available. You can stream the song on various music platforms such as Spotify, Apple Music, YouTube Music, or SoundCloud. You can also purchase or download the song from online stores such as iTunes, Amazon Music, or Google Play Music. Alternatively, you can watch the official lyric video of the song on YouTube, or listen to it on Jhené Aiko's official website.</p>
- <table>
-<tr><th>Platform</th><th>Link</th></tr>
-<tr><td>Spotify</td><td><a href="">Jhené Aiko - Blue Dream</a></td></tr>
-<tr><td>Apple Music</td><td><a href="">Jhené Aiko - Blue Dream</a></td></tr>
-<tr><td>YouTube Music</td><td><a href="">Jhené Aiko - Blue Dream</a></td></tr>
-<tr><td>SoundCloud</td><td><a href="">Jhené Aiko - Blue Dream</a></td></tr>
-<tr><td>iTunes</td><td><a href="">Jhené Aiko - Blue Dream</a></td></tr>
-<tr><td>Amazon Music</td><td><a href="">Jhené Aiko - Blue Dream</a></td></tr>
-<tr><td>Google Play Music</td><td><a href="">Jhené Aiko - Blue Dream</a></td></tr>
-<tr><td>YouTube</td><td><a href="">Jhené Aiko - Blue Dream (Lyric Video ))</a></td></tr>
-<tr><td>Jhené Aiko's website</td><td><a href="">Jhené Aiko - Blue Dream</a></td></tr>
-</table>
- <p>We hope you enjoyed this article and learned more about Jhené Aiko's Blue Dream. If you have any questions or comments, feel free to leave them below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<h3>What is the name of Jhené Aiko's debut album?</h3>
-<p>Jhené Aiko's debut album is called Souled Out, which was released in 2014. The album features 14 tracks, including Blue Dream, which is one of the bonus tracks.</p>
-<h3>Who produced Blue Dream?</h3>
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spaces/1phancelerku/anime-remove-background/Black Adam English Subtitles How to Download and Enjoy the Epic Movie.md

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-<h1>Black Adam Movie English Subtitles Download: How to Watch the DC Superhero Film Online</h1>
- <p>If you are a fan of DC Comics and superhero movies, you might be interested in watching <strong>Black Adam</strong>, the latest film in the DC Extended Universe (DCEU). But what if you are not a native English speaker, or you have trouble understanding the dialogue or accents in the movie? In that case, you might need to download English subtitles for Black Adam, so you can enjoy the film without missing any important details. In this article, we will tell you everything you need to know about Black Adam movie English subtitles download, including what the movie is about, why you need subtitles, how to download them, and how to watch the movie online with subtitles.</p>
- <h2>What is Black Adam about?</h2>
- <p>Black Adam is a spin-off of <em>Shazam!</em> (2019), another DCEU film that introduced the magical superhero Shazam, who is powered by the ancient wizard of the same name. Black Adam is Shazam's arch-nemesis, who was also given the powers of the wizard, but became corrupted and tried to conquer the world. He was banished by Shazam and returned to Earth after 5,000 years, seeking revenge and justice. He is an anti-hero who clashes with the Justice Society of America, a team of superheroes that includes Hawkman, Doctor Fate, Atom Smasher, and Cyclone.</p>
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- <h3>The plot of Black Adam</h3>
- <p>The movie begins in 2600 BC, in the fictional kingdom of Kahndaq, where Teth-Adam (Dwayne Johnson) was born. He was enslaved by the evil King Ahk-Ton, who created the Crown of Sabbac to attain great power. Teth-Adam led a revolt against the king, and was given the powers of Shazam by the Council of Wizards. He became Kahndaq's champion and killed Ahk-Ton, but he also became ruthless and tyrannical. Shazam intervened and imprisoned Teth-Adam in a magic tomb.</p>
- <p>In the present day, Kahndaq is oppressed by Intergang, a criminal organization that wants to find the Crown of Sabbac. Adrianna Tomaz (Sarah Shahi), an archaeologist and resistance fighter, tries to locate the crown with her brother Karim (Mohammed Amer) and their colleagues Samir (James Cusati-Moyer) and Ishmael (Marwan Kenzari). They accidentally free Teth-Adam from his tomb, who vows to liberate Kahndaq from Intergang and restore his glory.</p>
- <p>Meanwhile, the Justice Society of America (JSA), a group of superheroes that works for the US government, learns about Teth-Adam's return and decides to stop him. The JSA consists of Hawkman (Aldis Hodge), Doctor Fate (Pierce Brosnan), Atom Smasher (Noah Centineo), and Cyclone (Quintessa Swindell). They confront Teth-Adam in Kahndaq, but he proves to be too powerful for them. He also reveals that he is Karim's father and Adrianna's husband, who were separated from him when he was imprisoned.</p>
- <p>Teth-Adam eventually finds the Crown of Sabbac, which grants him even more power. He declares himself as Black Adam, the ruler of Kahndaq. He also offers Karim and Adrianna to join him, but they refuse, saying that he has become a monster. They join forces with the JSA to stop him from using the crown to destroy the world. A final battle ensues, where Black Adam faces Shazam (Zachary Levi), who arrives to help the JSA. The fate of Kahndaq and the world hangs in the balance.</p>
- <h3>The cast of Black Adam</h3>
- <p>The movie features a star-studded cast of actors, who bring the comic book characters to life. Here are some of the main cast members and their roles:</p>
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- <ul>
-<li><strong>Dwayne Johnson</strong> as <strong>Teth-Adam / Black Adam</strong>: The main protagonist and anti-hero of the movie, who is an ancient warrior with the powers of Shazam. He is driven by a sense of justice and vengeance, but also has a dark and violent side.</li>
-<li><strong>Sarah Shahi</strong> as <strong>Adrianna Tomaz / Isis</strong>: The main female lead and love interest of Black Adam, who is an archaeologist and resistance fighter in Kahndaq. She is also the mother of Karim, who is Black Adam's son.</li>
-<li><strong>Aldis Hodge</strong> as <strong>Carter Hall / Hawkman</strong>: The leader of the JSA, who is a reincarnated warrior with the ability to fly and wield a mystical mace. He has a history with Black Adam, as they were enemies in their past lives.</li>
-<li><strong>Pierce Brosnan</strong> as <strong>Kent Nelson / Doctor Fate</strong>: A member of the JSA, who is a powerful sorcerer and the host of Nabu, an ancient spirit of order. He wears the Helmet of Fate, which grants him various magical abilities.</li>
-<li><strong>Noah Centineo</strong> as <strong>Al Rothstein / Atom Smasher</strong>: A member of the JSA, who is a young and cocky superhero with the ability to manipulate his size and strength. He idolizes Black Adam, but also questions his methods.</li>
-<li><strong>Quintessa Swindell</strong> as <strong>Maxine Hunkel / Cyclone</strong>: A member of the JSA, who is a cheerful and optimistic superheroine with the ability to control wind and sound. She is the granddaughter of Ma Hunkel, the original Red Tornado.</li>
-<li><strong>Zachary Levi</strong> as <strong>Billy Batson / Shazam</strong>: The main hero of <em>Shazam!</em>, who is a teenage boy with the ability to transform into an adult superhero with the powers of Shazam. He is Black Adam's arch-nemesis and ally of the JSA.</li>
-<li><strong>Mohammed Amer</strong> as <strong>Karim Tomaz / Osiris</strong>: The son of Black Adam and Adrianna, who is unaware of his true heritage. He is a rebellious and adventurous teenager, who joins his mother in fighting against Intergang.</li>
-<li><strong>James Cusati-Moyer</strong> as <strong>Samir</strong>: A colleague and friend of Adrianna, who is an expert in ancient languages and artifacts. He helps her in finding the Crown of Sabbac.</li>
-<li><strong>Marwan Kenzari</strong> as <strong>Ishmael / Sabbac</strong>: The main antagonist of the movie, who is the leader of Intergang in Kahndaq. He is a ruthless and ambitious criminal, who wants to use the Crown of Sabbac to gain immense power.</li>
-</ul>
- <h3>The release date of Black Adam</h3>
- <p>The movie was originally scheduled to be released on December 22, 2021, but it was delayed due to the COVID-19 pandemic. The new release date is July 29, 2022. The movie will be distributed by Warner Bros. Pictures and will be available in theaters and on HBO Max (for 31 days after theatrical release).</p>
- <h2>Why do you need subtitles for Black Adam?</h2>
- <p>If you are not a native English speaker, or you have difficulty understanding some parts of the movie, you might want to download subtitles for Black Adam. Subtitles are text versions of the dialogue or narration that appear on the screen, usually at the bottom. They can help you follow along with what is happening in the movie, and also improve your language skills. Here are some reasons why you might need subtitles for Black Adam:</p>
- <h3>The benefits of subtitles for non-native speakers</h3>
- <p>If English is not your first language, subtitles can help you in many ways:</p>
- <ul>
-<li><em>They can enhance your comprehension.</em> Subtitles can help you understand the plot, the characters, the emotions, and the jokes in the movie. They can also clarify any words or phrases that you might not know or hear clearly.</li>
-<li><em>They can improve your vocabulary and grammar.</em> Subtitles can expose you to new words and expressions that you might not encounter in your everyday life. They can also show you how sentences are formed and punctuated in English.</li>
-<li><em>They can boost your listening and speaking skills.</em> Subtitles can help you practice your pronunciation and intonation by mimicking the actors' voices. They can also help you improve your listening comprehension by matching the sounds with the written words.</li>
-</ul>
- <h3>The challenges of subtitles for different languages and dialects</h3>
- <p>However, subtitles are not perfect, and they might have some limitations or drawbacks depending on the language and dialect of the movie. Here are some challenges that you might face when using subtitles for Black Adam:</p>
- <ul>
-<li><em>They might not be accurate or complete.</em> Subtitles are usually created by human translators or automated software, which might make mistakes or omit some information. For example, subtitles might not capture the nuances, idioms, slang, or humor of the original dialogue. They might also skip some words or sentences that are not essential for the plot, but might add some flavor or context to the movie.</li>
-<li><em>They might not match the speed or timing of the dialogue.</em> Subtitles are usually synchronized with the audio of the movie, but sometimes they might be delayed or ahead of the speech. This might cause confusion or distraction for the viewers, who have to read and listen at the same time. Subtitles might also appear too fast or too slow for some viewers, depending on their reading level and preference.</li>
-<li><em>They might not suit the style or tone of the movie.</em> Subtitles are usually written in a standard or formal way, which might not reflect the personality or mood of the characters or the movie. For example, subtitles might not convey the sarcasm, irony, anger, or excitement of the dialogue. They might also use different fonts, colors, or sizes that might clash with the aesthetics of the movie.</li>
-</ul>
- <h3>The availability of subtitles for Black Adam</h3>
- <p>The good news is that subtitles for Black Adam are widely available online, both officially and unofficially. You can find subtitles in various languages and formats, such as SRT, SSA, ASS, SUB, IDX, etc. Here are some ways to access subtitles for Black Adam:</p>
- <ul>
-<li><em>You can check the official sources.</em> The movie itself might have subtitles embedded in it, either as a default option or as a selectable feature. You can also look for subtitles on the official website of the movie, or on the streaming platforms that host the movie, such as HBO Max. These sources are likely to have high-quality and reliable subtitles that match the movie.</li>
-<li><em>You can search for fan-made subtitles.</em> There are many websites and communities that offer subtitles created by fans or volunteers, such as Subscene, OpenSubtitles, YIFY Subtitles, etc. These sources might have more variety and diversity of subtitles in terms of language and style. However, they might also have lower quality and accuracy of subtitles, and some of them might contain viruses or malware.</li>
-</ul>
- <h2>How to download subtitles for Black Adam?</h2>
- <p>If you want to download subtitles for Black Adam, you need to be careful and responsible. Downloading subtitles is not illegal per se, but it might involve some legal and ethical issues depending on the source and use of the subtitles. Here are some things to consider before downloading subtitles for Black Adam:</p>
- <h3>The legal and ethical issues of downloading subtitles</h3>
- <p>Downloading subtitles is a form of file sharing, which might infringe on the intellectual property rights of the creators and owners of the movie and the subtitles. You might also violate the terms and conditions of the streaming platforms or websites that provide the movie and the subtitles. Here are some legal and ethical issues that you might encounter when downloading subtitles for Black Adam:</p>
- <ul>
-<li><em>You might be breaking the law.</em> Depending on the jurisdiction and the laws of your country, downloading subtitles might be considered as piracy, which is a criminal offense that can result in fines or imprisonment. You might also be liable for civil damages if you infringe on the copyrights or trademarks of the movie and the subtitles.</li>
-<li><em>You might be harming the industry.</em> Downloading subtitles might reduce the revenue and profit of the movie and the subtitles, which can affect the livelihood and creativity of the filmmakers, actors, writers, translators, and other workers involved in the production and distribution of the movie and the subtitles. You might also discourage the creation and availability of more movies and subtitles in the future.</li>
-<li><em>You might be disrespecting the culture.</em> Downloading subtitles might undermine the artistic and cultural value of the movie and the subtitles, which can reflect the vision, identity, and expression of the original creators and speakers of the movie and the subtitles. You might also miss out on some of the subtleties, nuances, and meanings of the movie and the subtitles that are lost or altered in translation.</li>
-</ul>
- <p>Therefore, before downloading subtitles for Black Adam, you should ask yourself these questions:</p>
- <ul>
-<li><em>Is it legal?</em> Check the laws and regulations of your country regarding downloading subtitles, and make sure you are not violating any of them.</li>
-<li><em>Is it ethical?</em> Consider the impact and consequences of downloading subtitles on the movie industry, the subtitle community, and the movie culture, and make sure you are not harming or offending any of them.</li>
-<li><em>Is it necessary?</em> Evaluate your needs and preferences for downloading subtitles, and make sure you are not doing it for frivolous or selfish reasons.</li>
-</ul>
- <h3>The best sources and websites for downloading subtitles</h3>
- <p>If you decide to download subtitles for Black Adam, you should choose your sources and websites carefully. You should look for reputable and reliable sources and websites that offer high-quality and accurate subtitles that match the movie. You should also avoid sources and websites that offer low-quality or fake subtitles that might contain errors, spoilers, viruses, or malware. Here are some criteria to look for when choosing sources and websites for downloading subtitles:</p>
- <ul>
-<li><em>They have a good reputation and rating.</em> Check the reviews and feedback of other users who have downloaded subtitles from these sources and websites, and see if they are satisfied with their experience. You can also look for ratings or rankings from trusted websites or organizations that evaluate subtitle sources and websites based on their quality, reliability, security, etc.</li>
-<li><em>They have a large and diverse collection of subtitles.</em> Look for sources and websites that offer a wide range of subtitles in different languages, formats, styles, etc. You can also look for sources and websites that offer subtitles that are compatible with the movie, such as the version, the quality, the resolution, etc.</li>
-<li><em>They have a clear and easy interface and process.</em> Look for sources and websites that have a user-friendly and intuitive design and layout, that allow you to search, browse, select, and download subtitles with ease and convenience. You can also look for sources and websites that have clear and detailed instructions and guidelines on how to download subtitles.</li>
-<li><em>They have a secure and safe system and policy.</em> Look for sources and websites that have a strong and reliable security and privacy system and policy, that protect your device and data from any potential threats or risks. You can also look for sources and websites that have a fair and transparent terms and conditions and disclaimer on their service and content.</li>
-</ul>
- <p>Based on these criteria, here are some of the best sources and websites for downloading subtitles for Black Adam:</p>
- <table>
-<tr>
-<th>Source / Website</th>
-<th>Description</th>
-</tr>
-<tr>
-<td><a href="">Subscene</a></td>
-<td>A popular and trusted website that offers subtitles in various languages, formats, styles, etc. It has a large and active community of subtitle creators and users, who upload, download, rate, comment, and request subtitles. It also has a simple and easy interface and process for downloading subtitles.</td>
-</tr>
-<tr>
-<td><a href="">OpenSubtitles</a></td>
-<td>A well-known and reputable website that offers subtitles in multiple languages, formats, styles, etc. It has a huge and diverse collection of subtitles for movies, TV shows, documentaries, etc. It also has a clear and detailed interface and process for downloading subtitles.</td>
-</tr>
-<tr>
-<td><a href="">YIFY Subtitles</a></td>
-<td>A dedicated and reliable website that offers subtitles for YIFY movies, which are high-quality movies with small file sizes. It has a wide range of subtitles in different languages, formats, styles, etc. It also has a user-friendly and convenient interface and process for downloading subtitles.</td>
-</tr>
-<tr>
-<td><a href="">Podnapisi</a></td>
-<td>A professional and quality website that offers subtitles in various languages, formats, styles, etc. It has a sophisticated and advanced system for creating, editing, syncing, translating, and downloading subtitles. It also has a clear and easy interface and process for downloading subtitles.</td>
-</tr>
-<tr>
-<td><a href="">Addic7ed</a></td>
-<td>A specialized and quality website that offers subtitles for TV shows, movies, web series, etc. It has a dedicated and passionate team of subtitle creators and users, who work together to provide accurate and timely subtitles. It also has a simple and easy interface and process for downloading subtitles.</td>
-</tr>
-</table>
- <h3>The steps and tips for downloading subtitles</h3>
- <p>If you have chosen your source and website for downloading subtitles for Black Adam, you can follow these general steps and tips for downloading subtitles:</p>
- <ol>
-<li><em>Search for the movie and the subtitle language.</em> Enter the name of the movie and the language of the subtitle that you want to download in the search box of the website. You can also use filters or categories to narrow down your search results.</li>
-<li><em>Select the subtitle file that matches the movie.</em> Choose the subtitle file that has the same version, quality, resolution, etc. as the movie that you have or want to watch. You can also check the ratings, comments, or previews of the subtitle file to see if it is good or not.</li>
-<li><em>Download the subtitle file to your device.</em> Click on the download button or link of the subtitle file, and save it to your device. You might need to unzip or extract the subtitle file if it is compressed or archived.</li>
-<li><em>Rename and move the subtitle file to the same folder as the movie.</em> Rename the subtitle file to have the same name as the movie file, except for the extension. For example, if your movie file is called Black.Adam.2022.1080p.BluRay.x264.YIFY.mp4, your subtitle file should be called Black.Adam.2022.1080p.BluRay.x264.YIFY.srt. Then, move the subtitle file to the same folder or location as the movie file.</li>
-<li><em>Play the movie with subtitles using a media player.</em> Open the movie file with a media player that supports subtitles, such as VLC, MPC-HC, KMPlayer, etc. The subtitles should appear automatically on the screen. If not, you can manually enable them by clicking on the subtitle button or menu of the media player.</li>
-</ol>
- <p>Here are some tips to make your subtitle downloading experience better:</p>
- <ul>
-<li><em>Use a VPN or proxy service.</em> If you are downloading subtitles from sources or websites that are blocked or restricted in your country or region, you might need to use a VPN (virtual private network) or proxy service to access them. A VPN or proxy service can hide your IP address and location, and allow you to browse anonymously and securely.</li>
-<li><em>Use a malware scanner or antivirus software.</em> If you are downloading subtitles from sources or websites that are not verified or trusted, you might need to use a malware scanner or antivirus software to scan and protect your device and data from any potential threats or risks. A malware scanner or antivirus software can detect and remove any viruses, malware, spyware, etc. that might be hidden in the subtitle files.</li>
-<li><em>Use a subtitle editor or converter.</em> If you are downloading subtitles that are not compatible or suitable for your movie or media player, you might need to use a subtitle editor or converter to edit or convert them. A subtitle editor or converter can help you adjust the timing, format, style, language, etc. of the subtitles to make them fit your needs and preferences.</li>
-</ul>
- <h2>How to watch Black Adam online with subtitles?</h2>
- <p>If you have downloaded subtitles for Black Adam, you can watch the movie online with subtitles using various streaming platforms and devices. Streaming platforms are online services that allow you to watch movies and other content on demand, while devices are gadgets or tools that enable you to access and play the streaming platforms. Here are some ways to watch Black Adam online with subtitles:</p>
- <h3>The streaming platforms and devices that support subtitles</h3>
- <p>There are many streaming platforms and devices that support subtitles, but not all of them are compatible or available for Black Adam. You need to check the compatibility and availability of the streaming platforms and devices for Black Adam before choosing them. Here are some of the most popular and common streaming platforms and devices that support subtitles:</p>
- <table>
-<tr>
-<th>Streaming Platform</th>
-<th>Device</th>
-<th>Description</th>
-</tr>
-<tr>
-<td><a href="">HBO Max</a></td>
-<td>Smart TV, laptop, desktop, tablet, smartphone, game console, etc.</td>
-<td>The official and exclusive streaming platform for Black Adam, which offers the movie in HD quality and with subtitles in various languages. You need to subscribe to HBO Max to watch the movie, which costs $14.99 per month or $99.99 per year.</td>
-</tr>
-<tr>
-<td><a href="">Amazon Prime Video</a></td>
-<td>Smart TV, laptop, desktop, tablet, smartphone, game console, etc.</td>
-<td>A popular and widely available streaming platform that offers a large collection of movies and other content, including Black Adam. You need to rent or buy the movie on Amazon Prime Video to watch it, which costs $5.99 to $19.99 depending on the quality and format. You can also watch the movie with subtitles in various languages.</td>
-</tr>
-<tr>
-<td><a href="">Netflix</a></td>
-<td>Smart TV, laptop, desktop, tablet, smartphone, game console, etc.</td>
-<td>A well-known and global streaming platform that offers a huge variety of movies and other content, but not Black Adam. However, you can use a VPN or proxy service to access Netflix from other regions or countries that might have Black Adam available. You need to subscribe to Netflix to watch the movie, which costs $8.99 to $17.99 per month depending on the plan. You can also watch the movie with subtitles in various languages.</td>
-</tr>
-<tr>
-<td><a href="">YouTube</a></td>
-<td>Laptop, desktop, tablet, smartphone, game console, etc.</td>
-<td>A free and universal streaming platform that offers a vast amount of movies and other content, but not Black Adam. However, you can use a VPN or proxy service to access YouTube from other regions or countries that might have Black Adam available. You can watch the movie for free or for a fee depending on the uploader and the quality. You can also watch the movie with subtitles in various languages.</td>
-</tr>
-</table>
- <h3>The settings and options for enabling subtitles</h3>
- <p>If you have chosen your streaming platform and device for watching Black Adam online, you need to enable subtitles on them. Enabling subtitles is usually a simple and easy process, but it might vary depending on the streaming platform and device that you use. Here are some general steps and tips for enabling subtitles:</p>
- <ol>
-<li><em>Launch the streaming platform and play the movie.</em> Open the streaming platform that you want to use on your device, and search for Black Adam. Then, click on the play button or link to start watching the movie.</li>
-<li><em>Access the subtitle menu or button.</em> Look for the subtitle menu or button on the screen, which might be labeled as CC, Subtitles, Captions, etc. It might be located on the bottom, top, or side of the screen, or hidden under a settings or options icon. Click on the subtitle menu or button to open it.</li>
-<li><em>Select the subtitle language and format.</em> Choose the subtitle language that you want to use from the list of available languages. You might also be able to choose the subtitle format, such as font, color, size, position, etc. from the list of available options. Click on the subtitle language and format that you prefer to apply them.</li>
-<li><em>Enjoy the movie with subtitles.</em> The subtitles should appear on the screen according to your selection. You can adjust or change them at any time by accessing the subtitle menu or button again. You can also turn them off if you don't need them anymore.</li>
-</ol>
- <p>Here are some tips to make your subtitle watching experience better:</p>
- <ul>
-<li><em>Choose a subtitle language that matches your level and goal.</em> If you want to improve your English skills, you might want to choose English subtitles that match your level of proficiency and comprehension. If you want to learn a new language, you might want to choose subtitles in that language that match your goal and interest.</li>
-<li><em>Choose a subtitle format that suits your preference and comfort.</em> If you want to read the subtitles easily and clearly, you might want to choose a subtitle format that has a high contrast and visibility with the background and the movie. If you want to avoid distraction or clutter on the screen, you might want to choose a subtitle format that has a low profile and minimalism with the movie.</li>
-<li><em>Sync the subtitles with the audio and video of the movie.</em> If you notice that the subtitles are not in sync with the audio or video of the movie, you might want to adjust the timing or speed of the subtitles to match them. You can do this by using the subtitle menu or button, or by using a subtitle editor or converter.</li>
-<li><em>Compare and contrast the subtitles with the audio and video of the movie.</em> If you want to enhance your learning and enjoyment of the movie, you might want to compare and contrast the subtitles with the audio and video of the movie. You can do this by paying attention to the differences and similarities between the written and spoken words, the expressions and emotions of the actors, the context and culture of the movie, etc.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Black Adam is a movie that you might want to watch online with subtitles, especially if you are not a native English speaker, or you have trouble understanding some parts of the movie. Subtitles can help you comprehend, enjoy, and learn from the movie, but they also have some challenges and limitations. Therefore, you need to be careful and responsible when downloading and using subtitles for Black Adam. You also need to choose the best sources and websites for downloading subtitles, and the best streaming platforms and devices for watching the movie online with subtitles. By following these tips and steps, you can have a great subtitle watching experience with Black Adam.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions (FAQs) about Black Adam movie English subtitles download:</p>
- <ol>
-<li><strong>Q: Is Black Adam a sequel or a prequel to Shazam?</strong></li>
-<li><strong>A: Black Adam is neither a sequel nor a prequel to Shazam. It is a spin-off that takes place in the same universe as Shazam, but focuses on a different character and story. However, Black Adam and Shazam are connected by their origin and powers, and they might meet in a future crossover movie.</strong></li>
-<li><strong>Q: How long is Black Adam?</strong></li>
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spaces/1phancelerku/anime-remove-background/Challenge Opponents from Around the World in F1 Mobile Racing 2021 The Best F1 Multiplayer Game.md

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-<h1>Download F1 Mobile Racing 2021: The Official Game of the FIA Formula One World Championship</h1>
-<p>If you are a fan of Formula One, you will love F1 Mobile Racing 2021, the official free-to-play game of the 2021 FIA Formula One World Championship. This game lets you experience the thrill of racing against real players from around the world, as well as the official teams, drivers, and circuits of this season. You can also customize your own F1 car and upgrade it with new performance parts to dominate the grid. With stunning audio and visual quality, regular content updates, and exciting events, F1 Mobile Racing 2021 is the ultimate F1 game for your mobile device. In this article, we will show you how to download and play this amazing game.</p>
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-<p>F1 Mobile Racing 2021 is available for both iOS and Android devices. Here are some things you need to know before downloading the game:</p>
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-<li><b>Requirements and compatibility for iOS and Android devices</b>: F1 Mobile Racing 2021 requires iOS 12.0 or later and Android 6.0 or later to run. The game also requires a stable internet connection and at least 2.5 GB of free storage space. The game is compatible with most devices, but some older or low-end devices may experience performance issues or crashes. You can check the list of supported devices on the game's official website.</li>
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-    <li>Open the App Store or Google Play Store on your device and search for "F1 Mobile Racing 2021".</li>
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-<li><b>Game modes: Career, Duels, Events, and more</b>: F1 Mobile Racing 2021 offers various game modes for different levels of challenge and fun. You can choose from:     <ul>
-    <li>Career: This is where you start your journey as an F1 driver. You can create your own team, customize your car, and compete in different seasons and championships. You can also unlock new performance parts, liveries, helmets, stickers, and more as you progress.</li>
-    <li>Duels: This is where you race against other players in real-time PvP matches. You can choose from different race types, such as Sprint, Grid Start, Qualifying, or Endurance. You can also earn trophies, XP, credits, and rewards based on your performance.</li>
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-    <li>Other modes: You can also try out other modes, such as Time Trial, Practice, or Test Drive. These modes allow you to practice your skills, test your car's performance, or just have fun without any pressure.</li>
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-    <li>Follow the racing line: The racing line is a colored line that shows you the optimal path to take on each corner. It changes from green to yellow to red depending on your speed and braking point. Try to follow the racing line as much as possible to avoid losing time or crashing.</li>
-    <li>Use DRS and ERS wisely: DRS (Drag Reduction System) and ERS (Energy Recovery System) are two features that can boost your speed and performance. DRS allows you to open a flap on your rear wing to reduce drag and increase top speed on certain straights. ERS allows you to harvest energy from braking and use it to boost your engine power on demand. You can activate DRS and ERS by tapping on their icons on the screen. However , you need to use them strategically, as they have limited availability and can affect your car's handling and fuel consumption.</li>
-    <li>Manage your tyres and brakes: Your tyres and brakes are essential for your car's performance and safety. However, they can wear out and overheat over time, affecting your grip and braking. You need to monitor your tyre and brake temperatures and adjust your driving style accordingly. You can also choose different tyre compounds and brake modes to suit different tracks and conditions.</li>
-    <li>Learn from the best: You can watch replays of your own races or other players' races to learn from their mistakes and successes. You can also follow the tips and tutorials in the game to improve your skills and knowledge.</li>
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-<li><b>Progression and rewards system</b>: F1 Mobile Racing 2021 has a rewarding progression system that lets you level up, earn credits, unlock new items, and more. Here are some ways you can progress and earn rewards in the game:     <ul>
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-    <li>Open crates: Crates are boxes that contain random items, such as performance parts, liveries, helmets, stickers, or credits. You can earn crates by winning races, completing missions, or participating in events. You can also buy crates with real money or watch ads to get free crates.</li>
-    <li>Join the F1 Pass: The F1 Pass is a subscription service that gives you access to exclusive benefits and rewards. For example, you can get more XP, credits, crates, performance parts, liveries, helmets, stickers, and more. You can also get access to premium events and content. You can choose from different F1 Pass plans depending on your budget and preference.</li>
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spaces/1phancelerku/anime-remove-background/Enjoy Instagram with No Ads No Seen and More - Download Instagram MOD APK (286.0.0.20.69) for Android.md

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-<p>Instagram is one of the most popular social media platforms in the world, with over one billion active users. It allows you to share photos and videos with your followers, discover new content and creators, shop for products, chat with friends, and more. But what if you want to unlock some extra features that are not available in the official app? That's where Instagram mod apk comes in.</p>
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-<h1>FIFA apk dayı: A Guide to the Ultimate Mobile Soccer Experience</h1>
- <p>If you are a fan of soccer games, you might have heard of FIFA Mobile, the official mobile game of the FIFA World Cup 2022™. But did you know that there is a modded version of this game that offers more features and benefits? It's called FIFA apk dayı, and it's one of the most popular soccer games for Android devices. In this article, we will tell you everything you need to know about FIFA apk dayı, including what it is, how to download and install it, how to play it, and some tips and tricks to help you win more matches.</p>
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-<li>Learn new skills and tactics with The Academy mode. Play through various drills and challenges to improve your gameplay.</li>
-</ul>
- <h2>How to download and install FIFA apk dayı?</h2>
- <h3>Requirements and precautions</h3>
- <p>Before you download and install FIFA apk dayı, you need to make sure that your device meets the following requirements:</p>
- <ul>
-<li>Android version 4.4 or higher</li>
-<li>At least 1 GB of RAM</li>
-<li>At least 1.5 GB of free storage space</li>
-<li>A stable internet connection</li>
-</ul>
- <p>You also need to take some precautions before installing the game:</p>
- <ul>
-<li>Backup your data from the original FIFA Mobile game if you have it installed. You can use Google Play Games or any other cloud service to do this.</li>
-<li>Enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store.</li>
-<li <h3>Steps to download and install</h3>
- <p>Once you have met the requirements and taken the precautions, you can follow these steps to download and install FIFA apk dayı:</p>
- <ol>
-<li>Go to the official website of FIFA apk dayı at [fifaapkdayi.com] and click on the download button.</li>
-<li>Wait for the download to finish and locate the apk file in your device's file manager.</li>
-<li>Tap on the apk file and follow the instructions to install the game.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
- <h2>How to play FIFA apk dayı?</h2>
- <h3>Build your Ultimate Team with star players</h3>
- <p>The main mode of FIFA apk dayı is Ultimate Team, where you can create your own custom squad with your favorite players. You can choose from over 15,000 soccer stars from over 600 teams, including legends like Pelé, Maradona, Ronaldo, Messi, and more. You can also customize your team's kits, badges, and formations. To get new players, you can use coins and gems to buy packs or trade with other players in the market. You can also upgrade your players' skills and attributes by training them or using special items.</p>
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- <h3>Relive the FIFA World Cup 2022™ mode</h3>
- <p>If you want to experience the thrill of the world's biggest soccer tournament, you can play the FIFA World Cup 2022™ mode in FIFA apk dayı. This mode lets you replay the official tournament brackets with any of the 32 qualified nations or rewrite history with 15 non-qualified nations. You can also play through the qualifying stages and earn rewards along the way. The mode features authentic stadiums, kits, balls, and teams from the FIFA World Cup 2022™.</p>
- <h3>Compete in various pvp modes and events</h3>
- <p>If you want to test your skills against other players, you can play in various pvp modes and events in FIFA apk dayı. Some of the modes and events are:</p>
- <ul>
-<li>Head-to-Head: Play real-time matches against other players in a 11v11 format. Use your own tactics and strategies to outsmart your opponent.</li>
-<li>VS Attack: Play fast-paced matches against other players in a 4-minute turn-based format. Score as many goals as you can while defending your own goal.</li>
-<li>Manager Mode: Play as a manager and control your team's tactics, substitutions, and formations. Watch the match unfold and make adjustments as needed.</li>
-<li>Tournaments: Play in weekly tournaments and earn rewards based on your performance. Climb up the leaderboards and compete with the best.</li>
-<li>Seasons: Play in different seasons and leagues based on your team's rating. Earn points and prizes as you progress through the divisions.</li>
-<li>Events: Play in special events based on real-life soccer scenarios. Complete objectives and challenges to earn rewards.</li>
-</ul>
- <h2>Tips and tricks for FIFA apk dayı</h2>
- <h3>Use a combination of tap and button controls</h3>
- <p>FIFA apk dayı offers two types of controls for playing the game: tap and button. Tap controls allow you to tap on the screen to pass, shoot, dribble, and tackle. Button controls allow you to use virtual buttons on the screen to perform these actions. You can also use gestures to perform advanced moves like skill moves, finesse shots, lob passes, etc. You can switch between tap and button controls anytime during the game by tapping on the settings icon. You can also customize your button layout and size in the settings menu.</p>
- <h3>Choose the best tactics and formations for your team</h3>
- <p>FIFA apk dayı gives you the option to choose from different tactics and formations for your team. Tactics affect how your team plays on the pitch, such as attacking style, defensive style, width, depth, etc. Formations affect how your players are positioned on the pitch, such as 4-4-2, 4-3-3, 3-5-2, etc. You can change your tactics and formations before or during a match by tapping on the settings icon. You can also create your own custom tactics and formations in the settings menu.</p>
- <h3>Train your players and improve their chemistry</h3>
- <p>FIFA apk dayı allows you to train your players and improve their chemistry. Training your players increases their skills and attributes, making them perform better on the pitch. You can train your players by using training items or coins in the Ultimate Team menu. Improving your chemistry increases your team's overall rating and performance, making them play better together. You can improve your chemistry by using players from the same nation, league, team, or position. You can check your chemistry by looking at the green, yellow, or red lines connecting your players in the Ultimate Team menu.</p>
- <h2>Conclusion</h2>
- <p>FIFA apk dayı is a modded version of FIFA Mobile 2023 that offers more features and benefits than the original game. It allows you to build your dream team with star players, relive the FIFA World Cup 2022™ mode, compete in various pvp modes and events, and enjoy realistic soccer simulation. You can download and install FIFA apk dayı from its official website and play it on your Android device. You can also use some tips and tricks to improve your gameplay and win more matches. FIFA apk dayı is a must-have game for any soccer fan who wants to have the ultimate mobile soccer experience.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about FIFA apk dayı:</p>
- <ol>
-<li>Is FIFA apk dayı safe to download and install?</li>
-<p>Yes, FIFA apk dayı is safe to download and install, as long as you get it from its official website. However, you should always backup your data from the original FIFA Mobile game before installing the modded version, as it may overwrite or delete your data.</p>
-<li>Is FIFA apk dayı compatible with other devices?</li>
-<p>FIFA apk dayı is only compatible with Android devices that meet the requirements mentioned above. It is not compatible with iOS devices or other platforms.</p>
-<li>Is FIFA apk dayı legal to play?</li>
-<p>FIFA apk dayı is not an official product of EA Sports or FIFA, and it is not endorsed or supported by them. It is a fan-made mod that violates the terms of service of the original game. Therefore, playing FIFA apk dayı may result in a ban or suspension from the original game or other consequences. Play at your own risk.</p>
-<li>How can I update FIFA apk dayı?</li>
-<p>FIFA apk dayı is updated regularly by its developer to fix bugs, add new features, and keep up with the latest updates of the original game. You can check for updates on its official website or follow its social media accounts for announcements. To update FIFA apk dayı, you need to download and install the latest version of the apk file from its website.</p>
-<li>How can I contact the developer of FIFA apk dayı?</li>
-<p>You can contact the developer of FIFA apk dayı by sending an email to [[email protected]] or by visiting their Facebook page at [facebook.com/fifaapkdayi]. You can also leave a comment or a review on their website or social media accounts.</p>
-</ol></p> 401be4b1e0<br />
-<br />
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spaces/801artistry/RVC801/tools/app.py

@@ -1,148 +0,0 @@
-import logging
-import os
-
-# os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
-import gradio as gr
-from dotenv import load_dotenv
-
-from configs.config import Config
-from i18n import I18nAuto
-from infer.modules.vc.pipeline import Pipeline
-VC = Pipeline
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-logger = logging.getLogger(__name__)
-
-i18n = I18nAuto()
-#(i18n)
-
-load_dotenv()
-config = Config()
-vc = VC(config)
-
-weight_root = os.getenv("weight_root")
-weight_uvr5_root = os.getenv("weight_uvr5_root")
-index_root = os.getenv("index_root")
-names = []
-hubert_model = None
-for name in os.listdir(weight_root):
-    if name.endswith(".pth"):
-        names.append(name)
-index_paths = []
-for root, dirs, files in os.walk(index_root, topdown=False):
-    for name in files:
-        if name.endswith(".index") and "trained" not in name:
-            index_paths.append("%s/%s" % (root, name))
-
-
-app = gr.Blocks()
-with app:
-    with gr.Tabs():
-        with gr.TabItem("在线demo"):
-            gr.Markdown(
-                value="""
-                RVC 在线demo
-                """
-            )
-            sid = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
-            with gr.Column():
-                spk_item = gr.Slider(
-                    minimum=0,
-                    maximum=2333,
-                    step=1,
-                    label=i18n("请选择说话人id"),
-                    value=0,
-                    visible=False,
-                    interactive=True,
-                )
-            sid.change(fn=vc.get_vc, inputs=[sid], outputs=[spk_item])
-            gr.Markdown(
-                value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ")
-            )
-            vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
-            vc_transform0 = gr.Number(label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0)
-            f0method0 = gr.Radio(
-                label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
-                choices=["pm", "harvest", "crepe", "rmvpe"],
-                value="pm",
-                interactive=True,
-            )
-            filter_radius0 = gr.Slider(
-                minimum=0,
-                maximum=7,
-                label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
-                value=3,
-                step=1,
-                interactive=True,
-            )
-            with gr.Column():
-                file_index1 = gr.Textbox(
-                    label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
-                    value="",
-                    interactive=False,
-                    visible=False,
-                )
-            file_index2 = gr.Dropdown(
-                label=i18n("自动检测index路径,下拉式选择(dropdown)"),
-                choices=sorted(index_paths),
-                interactive=True,
-            )
-            index_rate1 = gr.Slider(
-                minimum=0,
-                maximum=1,
-                label=i18n("检索特征占比"),
-                value=0.88,
-                interactive=True,
-            )
-            resample_sr0 = gr.Slider(
-                minimum=0,
-                maximum=48000,
-                label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
-                value=0,
-                step=1,
-                interactive=True,
-            )
-            rms_mix_rate0 = gr.Slider(
-                minimum=0,
-                maximum=1,
-                label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
-                value=1,
-                interactive=True,
-            )
-            protect0 = gr.Slider(
-                minimum=0,
-                maximum=0.5,
-                label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
-                value=0.33,
-                step=0.01,
-                interactive=True,
-            )
-            f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
-            but0 = gr.Button(i18n("转换"), variant="primary")
-            vc_output1 = gr.Textbox(label=i18n("输出信息"))
-            vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
-            but0.click(
-                vc.vc_single,
-                [
-                    spk_item,
-                    vc_input3,
-                    vc_transform0,
-                    f0_file,
-                    f0method0,
-                    file_index1,
-                    file_index2,
-                    # file_big_npy1,
-                    index_rate1,
-                    filter_radius0,
-                    resample_sr0,
-                    rms_mix_rate0,
-                    protect0,
-                ],
-                [vc_output1, vc_output2],
-            )
-
-
-app.launch()

spaces/A00001/bingothoo/src/pages/api/blob.ts

@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { Readable } from 'node:stream'
-import { fetch } from '@/lib/isomorphic'
-
-const API_DOMAIN = 'https://www.bing.com'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { bcid } = req.query
-
-    const { headers, body } = await fetch(`${API_DOMAIN}/images/blob?bcid=${bcid}`,
-      {
-        method: 'GET',
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referrer-Policy": "origin-when-cross-origin",
-        },
-      },
-    )
-
-    res.writeHead(200, {
-      'Content-Length': headers.get('content-length')!,
-      'Content-Type': headers.get('content-type')!,
-    })
-    // @ts-ignore
-    return Readable.fromWeb(body!).pipe(res)
-  } catch (e) {
-    console.log('Error', e)
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}

spaces/AI-Hobbyist/Hoyo-RVC/infer/train-index -v2.py

@@ -1,44 +0,0 @@
-"""
-格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
-"""
-import faiss, numpy as np, os
-
-# ###########如果是原始特征要先写save
-inp_root = r"./logs/nene/3_feature768"
-npys = []
-listdir_res = list(os.listdir(inp_root))
-for name in sorted(listdir_res):
-    phone = np.load("%s/%s" % (inp_root, name))
-    npys.append(phone)
-big_npy = np.concatenate(npys, 0)
-big_npy_idx = np.arange(big_npy.shape[0])
-np.random.shuffle(big_npy_idx)
-big_npy = big_npy[big_npy_idx]
-print(big_npy.shape)  # (6196072, 192)#fp32#4.43G
-np.save("infer/big_src_feature_mi.npy", big_npy)
-
-##################train+add
-# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
-n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
-index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf)  # mi
-print("training")
-index_ivf = faiss.extract_index_ivf(index)  #
-index_ivf.nprobe = 1
-index.train(big_npy)
-faiss.write_index(
-    index, "infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf)
-)
-print("adding")
-batch_size_add = 8192
-for i in range(0, big_npy.shape[0], batch_size_add):
-    index.add(big_npy[i : i + batch_size_add])
-faiss.write_index(index, "infer/added_IVF%s_Flat_mi_baseline_src_feat.index" % (n_ivf))
-"""
-大小（都是FP32）
-big_src_feature 2.95G
-    (3098036, 256)
-big_emb         4.43G
-    (6196072, 192)
-big_emb双倍是因为求特征要repeat后再加pitch
-
-"""

spaces/AI-ZTH-03-23/8.Datasets-NER-Biomed-ClinicalTerms/app.py

@@ -1,268 +0,0 @@
-import gradio as gr
-import pandas as pd
-import json
-from collections import defaultdict
-
-# Create tokenizer for biomed model
-from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
-tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")    # https://huggingface.co/d4data/biomedical-ner-all?text=asthma
-model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
-pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-
-# Matplotlib for entity graph
-import matplotlib.pyplot as plt
-plt.switch_backend("Agg")
-
-# Load examples from JSON
-import os 
-
-# Load terminology datasets:
-basedir = os.path.dirname(__file__)
-#dataLOINC = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')
-#dataPanels = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-#dataSNOMED = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-#dataOMS = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-#dataICD10 = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv') 
-
-dataLOINC = pd.read_csv(f'LoincTableCore.csv')
-dataPanels = pd.read_csv(f'PanelsAndForms-ACW1208Labeled.csv')     
-dataSNOMED = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-dataOMS = pd.read_csv(f'SnomedOMS.csv')   
-dataICD10 = pd.read_csv(f'ICD10Diagnosis.csv')   
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-EXAMPLES = {}
-#with open(dir_path + "\\" + "examples.json", "r") as f:
-with open("examples.json", "r") as f:
-    example_json = json.load(f)
-    EXAMPLES = {x["text"]: x["label"] for x in example_json}
-
-def MatchLOINC(name):
-    #basedir = os.path.dirname(__file__)
-    pd.set_option("display.max_rows", None)
-    #data = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')    
-    data = dataLOINC
-    swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchLOINCPanelsandForms(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-    data = dataPanels
-    # Assessment Name:
-    #swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
-    # Assessment Question:
-    swith=data.loc[data['LoincName'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchSNOMED(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-    data = dataSNOMED
-    swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchOMS(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-    data = dataOMS
-    swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchICD10(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv')   
-    data = dataICD10
-    swith=data.loc[data['Description'].str.contains(name, case=False, na=False)]
-    return swith
-
-def SaveResult(text, outputfileName):
-    #try:
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    print("Saving: " + text + " to " + savePath)
-    from os.path import exists
-    file_exists = exists(savePath)
-    if file_exists:
-        with open(outputfileName, "a") as f: #append
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    else:
-        with open(outputfileName, "w") as f: #write
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    #except ValueError as err:
-    #    raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return
-
-def loadFile(filename):
-    try:
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-
-        print("Loading: " + loadPath)
-
-        from os.path import exists
-        file_exists = exists(loadPath)
-
-        if file_exists:
-            with open(loadPath, "r") as f: #read
-                contents = f.read()
-                print(contents)
-                return contents
-
-    except ValueError as err:
-        raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return ""
-
-def get_today_filename():
-    from datetime import datetime
-    date = datetime.now().strftime("%Y_%m_%d-%I.%M.%S.%p")
-    #print(f"filename_{date}")  'filename_2023_01_12-03-29-22_AM'
-    return f"MedNER_{date}.csv"
-
-def get_base(filename): 
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-        #print("Loading: " + loadPath)
-        return loadPath
-
-def group_by_entity(raw):
-    outputFile = get_base(get_today_filename())
-    out = defaultdict(int)
-
-    for ent in raw:
-        out[ent["entity_group"]] += 1
-        myEntityGroup = ent["entity_group"]
-        print("Found entity group type: " + myEntityGroup)
-
-        if (myEntityGroup in ['Sign_symptom', 'Detailed_description', 'History', 'Activity', 'Medication' ]):
-            eterm = ent["word"].replace('#','')
-            minlength = 3
-            if len(eterm) > minlength:
-                print("Found eterm: " + eterm)
-                eterm.replace("#","")
-                g1=MatchLOINC(eterm)
-                g2=MatchLOINCPanelsandForms(eterm)
-                g3=MatchSNOMED(eterm)
-                g4=MatchOMS(eterm)
-                g5=MatchICD10(eterm)
-                sAll = ""
-
-                print("Saving to output file " + outputFile)
-                # Create harmonisation output format of input to output code, name, Text
-
-                try: # 18 fields, output to labeled CSV dataset for results teaching on scored regret changes to action plan with data inputs
-                    col = "          1                            2            3         4            5                     6                    7                       8                   9              10                   11                         12       13               14                      15                  16                            17                    18                       19"
-                    
-                    #LOINC
-                    g11 = g1['LOINC_NUM'].to_string().replace(","," ").replace("\n"," ")
-                    g12 = g1['COMPONENT'].to_string().replace(","," ").replace("\n"," ")
-                    s1 = ("LOINC," + myEntityGroup + "," + eterm + ",questions of ," + g12 + "," + g11 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g11 != 'Series([]  )': SaveResult(s1, outputFile)
-
-                    #LOINC Panels
-                    g21 = g2['Loinc'].to_string().replace(","," ").replace("\n"," ")
-                    g22 = g2['LoincName'].to_string().replace(","," ").replace("\n"," ")
-                    g23 = g2['ParentLoinc'].to_string().replace(","," ").replace("\n"," ")
-                    g24 = g2['ParentName'].to_string().replace(","," ").replace("\n"," ")
-                    # s2 = ("LOINC Panel," + myEntityGroup + "," + eterm + ",name of ," + g22 + "," + g21 + ", and Parent codes of ," + g23 + ", with Parent names of ," + g24 + ", Label,Value  ")
-                    s2 = ("LOINC Panel," + myEntityGroup + "," + eterm + ",name of ," + g22 + "," + g21 + "," + g24 + ", and Parent codes of ," + g23 + "," + ", Label,Value  ")
-                    if g21 != 'Series([]  )': SaveResult(s2, outputFile)
-
-                    #SNOMED
-                    g31 = g3['conceptId'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    g32 = g3['term'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    s3 = ("SNOMED Concept," + myEntityGroup + "," + eterm + ",terms of ," + g32 + "," + g31 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g31 != 'Series([]  )': SaveResult(s3, outputFile)
-
-                    #OMS
-                    g41 = g4['Omaha Code'].to_string().replace(","," ").replace("\n"," ")
-                    g42 = g4['SNOMED CT concept ID'].to_string().replace(","," ").replace("\n"," ")
-                    g43 = g4['SNOMED CT'].to_string().replace(","," ").replace("\n"," ")
-                    g44 = g4['PR'].to_string().replace(","," ").replace("\n"," ")
-                    g45 = g4['S&S'].to_string().replace(","," ").replace("\n"," ")
-                    s4 = ("OMS," + myEntityGroup + "," + eterm + ",concepts of ," + g44 + "," + g45 + ", and SNOMED codes of ," + g43 + ", and OMS problem of ," + g42 + ", and OMS Sign Symptom of ," + g41)
-                    if g41 != 'Series([]  )': SaveResult(s4, outputFile)
-
-                    #ICD10
-                    g51 = g5['Code'].to_string().replace(","," ").replace("\n"," ")
-                    g52 = g5['Description'].to_string().replace(","," ").replace("\n"," ")
-                    s5 = ("ICD10," + myEntityGroup + "," + eterm + ",descriptions of ," + g52 + "," + g51 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g51 != 'Series([]  )': SaveResult(s5, outputFile)
-
-                except ValueError as err:
-                    raise ValueError("Error in group by entity \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return outputFile
-
-
-def plot_to_figure(grouped):
-    fig = plt.figure()
-    plt.bar(x=list(grouped.keys()), height=list(grouped.values()))
-    plt.margins(0.2)
-    plt.subplots_adjust(bottom=0.4)
-    plt.xticks(rotation=90)
-    return fig
-
-
-def ner(text):
-    raw = pipe(text)
-    ner_content = {
-        "text": text,
-        "entities": [
-            {
-                "entity": x["entity_group"],
-                "word": x["word"],
-                "score": x["score"],
-                "start": x["start"],
-                "end": x["end"],
-            }
-            for x in raw
-        ],
-    }
-    
-    outputFile = group_by_entity(raw)
-    label = EXAMPLES.get(text, "Unknown")
-    outputDataframe = pd.read_csv(outputFile)
-    return (ner_content, outputDataframe, outputFile)
-
-demo = gr.Blocks()
-with demo:
-    gr.Markdown(
-    """
-    # 🩺⚕️NLP Clinical Ontology Biomedical NER 
-    """
-    )
-    input = gr.Textbox(label="Note text", value="")
-
-    with gr.Tab("Biomedical Entity Recognition"):
-        output=[
-            gr.HighlightedText(label="NER", combine_adjacent=True),
-            #gr.JSON(label="Entity Counts"),
-            #gr.Label(label="Rating"),
-            #gr.Plot(label="Bar"),
-            gr.Dataframe(label="Dataframe"),
-            gr.File(label="File"),
-        ]
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-        
-    with gr.Tab("Clinical Terminology Resolution"):
-        with gr.Row(variant="compact"):
-            btnLOINC = gr.Button("LOINC")
-            btnPanels = gr.Button("Panels")
-            btnSNOMED = gr.Button("SNOMED")
-            btnOMS = gr.Button("OMS")
-            btnICD10 = gr.Button("ICD10")
-
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-#layout="vertical"
-demo.launch(debug=True)

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/optimizers/__init__.py

@@ -1,2 +0,0 @@
-from torch.optim import *  # NOQA
-from .radam import *  # NOQA

spaces/AIGText/GlyphControl/ldm/modules/diffusionmodules/openaimodel.py

@@ -1,790 +0,0 @@
-from abc import abstractmethod
-import math
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-from ldm.util import exists
-
-
-# dummy replace
-def convert_module_to_f16(x):
-    pass
-
-def convert_module_to_f32(x):
-    pass
-
-
-## go
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb, context=None):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-class TransposedUpsample(nn.Module):
-    'Learned 2x upsampling without padding'
-    def __init__(self, channels, out_channels=None, ks=5):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-
-        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
-
-    def forward(self,x):
-        return self.up(x)
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        #return pt_checkpoint(self._forward, x)  # pytorch
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        disable_self_attentions=None,
-        num_attention_blocks=None,
-        disable_middle_self_attn=False,
-        use_linear_in_transformer=False,
-    ):
-        super().__init__()
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            from omegaconf.listconfig import ListConfig
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        if isinstance(num_res_blocks, int):
-            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
-        else:
-            if len(num_res_blocks) != len(channel_mult):
-                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
-                                 "as a list/tuple (per-level) with the same length as channel_mult")
-            self.num_res_blocks = num_res_blocks
-        if disable_self_attentions is not None:
-            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
-            assert len(disable_self_attentions) == len(channel_mult)
-        if num_attention_blocks is not None:
-            assert len(num_attention_blocks) == len(self.num_res_blocks)
-            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
-            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
-                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
-                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
-                  f"attention will still not be set.")
-
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-        # time embedding 
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-        # class-related 
-        if self.num_classes is not None:
-            if isinstance(self.num_classes, int):
-                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-            elif self.num_classes == "continuous":
-                print("setting up linear c_adm embedding layer")
-                self.label_emb = nn.Linear(1, time_embed_dim)
-            else:
-                raise ValueError()
-        # input blocks
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for nr in range(self.num_res_blocks[level]):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint
-                            )
-                        )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        # output blocks
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(self.num_res_blocks[level] + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads_upsample,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint
-                            )
-                        )
-                if level and i == self.num_res_blocks[level]:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-        self.context_dim = context_dim
-        
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
-            emb = emb + self.label_emb(y) # class condition added to the time embedding
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb, context)
-            hs.append(h)
-        h = self.middle_block(h, emb, context)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            out = self.out(h)
-            # if True in np.isnan(out.detach().cpu().numpy()):
-            #     aa = 1
-            return out

spaces/AIZero2HeroBootcamp/ExperimentalChatGPTv1/app.py

@@ -1,442 +0,0 @@
-import streamlit as st
-import openai
-import os
-import base64
-import glob
-import json
-import mistune
-import pytz
-import math
-import requests
-import time
-import re
-import textract
-
-from datetime import datetime
-from openai import ChatCompletion
-from xml.etree import ElementTree as ET
-from bs4 import BeautifulSoup
-from collections import deque
-from audio_recorder_streamlit import audio_recorder
-
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from templates import css, bot_template, user_template
-
-
-
-def generate_filename(prompt, file_type):
-    central = pytz.timezone('US/Central')
-    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")  # Date and time DD-HHMM
-    safe_prompt = "".join(x for x in prompt if x.isalnum())[:90]  # Limit file name size and trim whitespace
-    return f"{safe_date_time}_{safe_prompt}.{file_type}"  # Return a safe file name
-
-
-def transcribe_audio(openai_key, file_path, model):
-    OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
-    headers = {
-        "Authorization": f"Bearer {openai_key}",
-    }
-    with open(file_path, 'rb') as f:
-        data = {'file': f}
-        response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
-    if response.status_code == 200:
-        st.write(response.json())
-        chatResponse = chat_with_model(response.json().get('text'), '') # *************************************
-        transcript = response.json().get('text')
-        #st.write('Responses:')
-        #st.write(chatResponse)
-        filename = generate_filename(transcript, 'txt')
-        create_file(filename, transcript, chatResponse)
-        return transcript
-    else:
-        st.write(response.json())
-        st.error("Error in API call.")
-        return None
-
-def save_and_play_audio(audio_recorder):
-    audio_bytes = audio_recorder()
-    if audio_bytes:
-        filename = generate_filename("Recording", "wav")
-        with open(filename, 'wb') as f:
-            f.write(audio_bytes)
-        st.audio(audio_bytes, format="audio/wav")
-        return filename
-    return None
-
-def create_file(filename, prompt, response):
-    if filename.endswith(".txt"):
-        with open(filename, 'w') as file:
-            file.write(f"{prompt}\n{response}")
-    elif filename.endswith(".htm"):
-        with open(filename, 'w') as file:
-            file.write(f"{prompt}   {response}")
-    elif filename.endswith(".md"):
-        with open(filename, 'w') as file:
-            file.write(f"{prompt}\n\n{response}")
-            
-def truncate_document(document, length):
-    return document[:length]
-def divide_document(document, max_length):
-    return [document[i:i+max_length] for i in range(0, len(document), max_length)]
-
-def get_table_download_link(file_path):
-    with open(file_path, 'r') as file:
-        try:
-            data = file.read()
-        except:
-            st.write('')
-            return file_path    
-    b64 = base64.b64encode(data.encode()).decode()  
-    file_name = os.path.basename(file_path)
-    ext = os.path.splitext(file_name)[1]  # get the file extension
-    if ext == '.txt':
-        mime_type = 'text/plain'
-    elif ext == '.py':
-        mime_type = 'text/plain'
-    elif ext == '.xlsx':
-        mime_type = 'text/plain'
-    elif ext == '.csv':
-        mime_type = 'text/plain'
-    elif ext == '.htm':
-        mime_type = 'text/html'
-    elif ext == '.md':
-        mime_type = 'text/markdown'
-    else:
-        mime_type = 'application/octet-stream'  # general binary data type
-    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
-    return href
-
-def CompressXML(xml_text):
-    root = ET.fromstring(xml_text)
-    for elem in list(root.iter()):
-        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
-            elem.parent.remove(elem)
-    return ET.tostring(root, encoding='unicode', method="xml")
-    
-def read_file_content(file,max_length):
-    if file.type == "application/json":
-        content = json.load(file)
-        return str(content)
-    elif file.type == "text/html" or file.type == "text/htm":
-        content = BeautifulSoup(file, "html.parser")
-        return content.text
-    elif file.type == "application/xml" or file.type == "text/xml":
-        tree = ET.parse(file)
-        root = tree.getroot()
-        xml = CompressXML(ET.tostring(root, encoding='unicode'))
-        return xml
-    elif file.type == "text/markdown" or file.type == "text/md":
-        md = mistune.create_markdown()
-        content = md(file.read().decode())
-        return content
-    elif file.type == "text/plain":
-        return file.getvalue().decode()
-    else:
-        return ""
-
-def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
-    model = model_choice
-    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
-    conversation.append({'role': 'user', 'content': prompt})
-    if len(document_section)>0:
-        conversation.append({'role': 'assistant', 'content': document_section})
-        
-    start_time = time.time()
-    report = []
-    res_box = st.empty()
-    collected_chunks = []
-    collected_messages = []
-
-    for chunk in openai.ChatCompletion.create(
-        model='gpt-3.5-turbo',
-        messages=conversation,
-        temperature=0.5,
-        stream=True  
-    ):
-        
-        collected_chunks.append(chunk)  # save the event response
-        chunk_message = chunk['choices'][0]['delta']  # extract the message
-        collected_messages.append(chunk_message)  # save the message
-        
-        content=chunk["choices"][0].get("delta",{}).get("content")
-        
-        try:
-            report.append(content)
-            if len(content) > 0:
-                result = "".join(report).strip()
-                #result = result.replace("\n", "")        
-                res_box.markdown(f'*{result}*') 
-        except:
-            st.write(' ')
-        
-    full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
-    st.write("Elapsed time:")
-    st.write(time.time() - start_time)
-    return full_reply_content
-
-def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
-    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
-    conversation.append({'role': 'user', 'content': prompt})
-    if len(file_content)>0:
-        conversation.append({'role': 'assistant', 'content': file_content})
-    response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
-    return response['choices'][0]['message']['content']
-
-def extract_mime_type(file):
-    # Check if the input is a string
-    if isinstance(file, str):
-        pattern = r"type='(.*?)'"
-        match = re.search(pattern, file)
-        if match:
-            return match.group(1)
-        else:
-            raise ValueError(f"Unable to extract MIME type from {file}")
-    # If it's not a string, assume it's a streamlit.UploadedFile object
-    elif isinstance(file, streamlit.UploadedFile):
-        return file.type
-    else:
-        raise TypeError("Input should be a string or a streamlit.UploadedFile object")
-
-from io import BytesIO
-import re
-
-def extract_file_extension(file):
-    # get the file name directly from the UploadedFile object
-    file_name = file.name
-    pattern = r".*?\.(.*?)$"
-    match = re.search(pattern, file_name)
-    if match:
-        return match.group(1)
-    else:
-        raise ValueError(f"Unable to extract file extension from {file_name}")
-
-def pdf2txt(docs):
-    text = ""
-    for file in docs:
-        file_extension = extract_file_extension(file)
-        # print the file extension
-        st.write(f"File type extension: {file_extension}")
-
-        # read the file according to its extension
-        try:
-            if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']:
-                text += file.getvalue().decode('utf-8')
-            elif file_extension.lower() == 'pdf':
-                from PyPDF2 import PdfReader
-                pdf = PdfReader(BytesIO(file.getvalue()))
-                for page in range(len(pdf.pages)):
-                    text += pdf.pages[page].extract_text() # new PyPDF2 syntax
-        except Exception as e:
-            st.write(f"Error processing file {file.name}: {e}")
-
-    return text
-
-def pdf2txt_old(pdf_docs):
-    st.write(pdf_docs)
-    for file in pdf_docs:
-        mime_type = extract_mime_type(file)
-        st.write(f"MIME type of file: {mime_type}")
-    
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-def txt2chunks(text):
-    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
-    return text_splitter.split_text(text)
-
-def vector_store(text_chunks):
-    key = os.getenv('OPENAI_API_KEY')
-    embeddings = OpenAIEmbeddings(openai_api_key=key)
-    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-
-def get_chain(vectorstore):
-    llm = ChatOpenAI()
-    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
-    return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
-
-def process_user_input(user_question):
-    response = st.session_state.conversation({'question': user_question})
-    st.session_state.chat_history = response['chat_history']
-    for i, message in enumerate(st.session_state.chat_history):
-        template = user_template if i % 2 == 0 else bot_template
-        st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
-        # Save file output from PDF query results
-        filename = generate_filename(user_question, 'txt')
-        create_file(filename, user_question, message.content)
-        
-        #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-def divide_prompt(prompt, max_length):
-    words = prompt.split()
-    chunks = []
-    current_chunk = []
-    current_length = 0
-    for word in words:
-        if len(word) + current_length <= max_length:
-            current_length += len(word) + 1  # Adding 1 to account for spaces
-            current_chunk.append(word)
-        else:
-            chunks.append(' '.join(current_chunk))
-            current_chunk = [word]
-            current_length = len(word)
-    chunks.append(' '.join(current_chunk))  # Append the final chunk
-    return chunks
-    
-def main():
-    # Sidebar and global
-    openai.api_key = os.getenv('OPENAI_API_KEY')
-    st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
-
-    # File type for output, model choice
-    menu = ["txt", "htm", "xlsx", "csv", "md", "py"]  #619
-    choice = st.sidebar.selectbox("Output File Type:", menu)
-    model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
-    
-    # Audio, transcribe, GPT:
-    filename = save_and_play_audio(audio_recorder)
-    if filename is not None:
-        transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
-        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-        filename=None # since transcription is finished next time just use the saved transcript
-
-    # prompt interfaces
-    user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
-
-    # file section interface for prompts against large documents as context
-    collength, colupload = st.columns([2,3])  # adjust the ratio as needed
-    with collength:
-        max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
-    with colupload:
-        uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx","csv","html", "htm", "md", "txt"])
-
-    # Document section chat
-    document_sections = deque()
-    document_responses = {}
-    if uploaded_file is not None:
-        file_content = read_file_content(uploaded_file, max_length)
-        document_sections.extend(divide_document(file_content, max_length))
-    if len(document_sections) > 0:
-        if st.button("👁️ View Upload"):
-            st.markdown("**Sections of the uploaded file:**")
-            for i, section in enumerate(list(document_sections)):
-                st.markdown(f"**Section {i+1}**\n{section}")
-        st.markdown("**Chat with the model:**")
-        for i, section in enumerate(list(document_sections)):
-            if i in document_responses:
-                st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
-            else:
-                if st.button(f"Chat about Section {i+1}"):
-                    st.write('Reasoning with your inputs...')
-                    response = chat_with_model(user_prompt, section, model_choice) # *************************************
-                    st.write('Response:')
-                    st.write(response)
-                    document_responses[i] = response
-                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
-                    create_file(filename, user_prompt, response)
-                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-    if st.button('💬 Chat'):
-        st.write('Reasoning with your inputs...')
-        
-        #response = chat_with_model(user_prompt, ''.join(list(document_sections,)), model_choice) # *************************************
-
-        # Divide the user_prompt into smaller sections
-        user_prompt_sections = divide_prompt(user_prompt, max_length)
-        full_response = ''
-        for prompt_section in user_prompt_sections:
-            # Process each section with the model
-            response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
-            full_response += response + '\n'  # Combine the responses
-        
-        #st.write('Response:')
-        #st.write(full_response)
-
-        response = full_response
-        st.write('Response:')
-        st.write(response)
-        
-        filename = generate_filename(user_prompt, choice)
-        create_file(filename, user_prompt, response)
-        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-    all_files = glob.glob("*.*")
-    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
-    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
-
-    # sidebar of files
-    file_contents=''
-    next_action=''
-    for file in all_files:
-        col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1])  # adjust the ratio as needed
-        with col1:
-            if st.button("🌐", key="md_"+file):  # md emoji button
-                with open(file, 'r') as f:
-                    file_contents = f.read()
-                    next_action='md'
-        with col2:
-            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
-        with col3:
-            if st.button("📂", key="open_"+file):  # open emoji button
-                with open(file, 'r') as f:
-                    file_contents = f.read()
-                    next_action='open'
-        with col4:
-            if st.button("🔍", key="read_"+file):  # search emoji button
-                with open(file, 'r') as f:
-                    file_contents = f.read()
-                    next_action='search'
-        with col5:
-            if st.button("🗑", key="delete_"+file):
-                os.remove(file)
-                st.experimental_rerun()
-                
-    if len(file_contents) > 0:
-        if next_action=='open':
-            file_content_area = st.text_area("File Contents:", file_contents, height=500)
-        if next_action=='md':
-            st.markdown(file_contents)
-        if next_action=='search':
-            file_content_area = st.text_area("File Contents:", file_contents, height=500)
-            st.write('Reasoning with your inputs...')
-            response = chat_with_model(user_prompt, file_contents, model_choice)
-            filename = generate_filename(file_contents, choice)
-            create_file(filename, file_contents, response)
-
-            st.experimental_rerun()
-            #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-                
-if __name__ == "__main__":
-    main()
-
-load_dotenv()
-st.write(css, unsafe_allow_html=True)
-
-st.header("Chat with documents :books:")
-user_question = st.text_input("Ask a question about your documents:")
-if user_question:
-    process_user_input(user_question)
-
-with st.sidebar:
-    st.subheader("Your documents")
-    docs = st.file_uploader("import documents", accept_multiple_files=True)
-    with st.spinner("Processing"):
-        raw = pdf2txt(docs)
-        if len(raw) > 0:
-            length = str(len(raw))
-            text_chunks = txt2chunks(raw)
-            vectorstore = vector_store(text_chunks)
-            st.session_state.conversation = get_chain(vectorstore)
-            st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
-            filename = generate_filename(raw, 'txt')
-            create_file(filename, raw, '')

spaces/Aabdelhamidaz/animals/README.md

@@ -1,13 +0,0 @@
----
-title: Animals
-emoji: 🐠
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Abhilashvj/planogram-compliance/README.md

@@ -1,166 +0,0 @@
----
-sdk: streamlit
-sdk_version: 1.10.0 # The latest supported version
-app_file: app.py
-pinned: false
-fullWidth: True
----
-## <div align="center">Planogram Scoring</div>
-<p>
-
-</p>
-- Train a Yolo Model on the available products in our data base to detect them on a shelf
-- https://wandb.ai/abhilash001vj/YOLOv5/runs/1v6yh7nk?workspace=user-abhilash001vj
-- Have the master planogram data captured as a matrix of products encoded as numbers (label encoding by looking the products names saved in a  list of all - the available product names )
-- Detect the products on real images from stores.
-- Arrange the detected products in the captured photograph to rows and columns 
-- Compare the product arrangement of captured photograph to the existing master planogram and produce the compliance score for correctly placed products
-
-</div>
-
-## <div align="center">YOLOv5</div>
-<p>
-YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
- open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
-</p>
-
-</div>
-
-## <div align="center">Documentation</div>
-
-See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
-
-## <div align="center">Quick Start Examples</div>
-
-<details open>
-<summary>Install</summary>
-
-[**Python>=3.6.0**](https://www.python.org/) is required with all
-[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
-[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
-<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
-
-```bash
-$ git clone https://github.com/ultralytics/yolov5
-$ cd yolov5
-$ pip install -r requirements.txt
-```
-
-</details>
-
-<details open>
-<summary>Inference</summary>
-
-Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
-from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
-
-```python
-import torch
-
-# Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom
-
-# Images
-img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
-
-# Inference
-results = model(img)
-
-# Results
-results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
-```
-
-</details>
-
-
-## <div align="center">Why YOLOv5</div>
-
-<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
-<details>
-  <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
-
-<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
-</details>
-<details>
-  <summary>Figure Notes (click to expand)</summary>
-
-* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size
-  32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
-* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
-* **Reproduce** by
-  `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-
-</details>
-
-### Pretrained Checkpoints
-
-[assets]: https://github.com/ultralytics/yolov5/releases
-
-|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPs<br><sup>640 (B)
-|---                    |---  |---      |---      |---      |---     |---|---   |---
-|[YOLOv5s][assets]      |640  |36.7     |36.7     |55.4     |**2.0** |   |7.3   |17.0
-|[YOLOv5m][assets]      |640  |44.5     |44.5     |63.1     |2.7     |   |21.4  |51.3
-|[YOLOv5l][assets]      |640  |48.2     |48.2     |66.9     |3.8     |   |47.0  |115.4
-|[YOLOv5x][assets]      |640  |**50.4** |**50.4** |**68.8** |6.1     |   |87.7  |218.8
-|                       |     |         |         |         |        |   |      |
-|[YOLOv5s6][assets]     |1280 |43.3     |43.3     |61.9     |**4.3** |   |12.7  |17.4
-|[YOLOv5m6][assets]     |1280 |50.5     |50.5     |68.7     |8.4     |   |35.9  |52.4
-|[YOLOv5l6][assets]     |1280 |53.4     |53.4     |71.1     |12.3    |   |77.2  |117.7
-|[YOLOv5x6][assets]     |1280 |**54.4** |**54.4** |**72.0** |22.4    |   |141.8 |222.9
-|                       |     |         |         |         |        |   |      |
-|[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8    |   |-     |-
-
-<details>
-  <summary>Table Notes (click to expand)</summary>
-
-* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results
-  denote val2017 accuracy.
-* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP**
-  by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-* Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a
-  GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and
-  includes FP16 inference, postprocessing and NMS. **Reproduce speed**
-  by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`
-* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
-* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale
-  augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
-
-</details>
-
-## <div align="center">Contribute</div>
-
-We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see
-our [Contributing Guide](CONTRIBUTING.md) to get started.
-
-## <div align="center">Contact</div>
-
-For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or
-professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
-
-<br>
-
-<div align="center">
-    <a href="https://github.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.linkedin.com/company/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://twitter.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://youtube.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.facebook.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.instagram.com/ultralytics/">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
-    </a>
-</div>

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/clock/Clock.js

@@ -1,67 +0,0 @@
-import Base from '../base/Base.js';
-import { Circle, Line } from '../utils/Geoms.js'
-
-const RadToDeg = Phaser.Math.RadToDeg;
-const WrapDegrees = Phaser.Math.Angle.WrapDegrees;
-const WrapRad = Phaser.Math.Angle.Wrap;
-const ShortestBetween = Phaser.Math.Angle.ShortestBetween;
-const DegToRad = Phaser.Math.DegToRad;
-const Rad270 = Phaser.Math.DegToRad(270);
-
-class Clock extends Base {
-    constructor(scene, config) {
-        super(scene, config);
-        this.type = 'rexSpinnerClock';
-
-        this.minuteHandAngle = 0;
-        this.hourHandAngle = 0;
-    }
-
-    buildShapes() {
-        this.addShape((new Circle()).setName('border'));
-        this.addShape((new Line()).setName('minuteHand'));
-        this.addShape((new Line()).setName('hourHand'));
-    }
-
-    updateShapes() {
-        var centerX = this.centerX;
-        var centerY = this.centerY;
-        var radius = this.radius;
-        var lineWidth = Math.ceil(radius / 25);
-        var borderRadius = radius - (lineWidth / 2);
-        var minuteHandLength = radius * 0.8;
-        var hourHandLength = radius * 0.5;
-
-        var prevMinuteHandAngle = this.minuteHandAngle;
-        this.minuteHandAngle = Math.PI * 2 * this.value;
-        var angle0 = WrapDegrees(RadToDeg(prevMinuteHandAngle));
-        var angle1 = WrapDegrees(RadToDeg(this.minuteHandAngle));
-        var deltaAngle = ShortestBetween(angle0, angle1);
-        this.hourHandAngle = WrapRad(this.hourHandAngle + (DegToRad(deltaAngle) / 12))
-
-        this.getShape('border')
-            .lineStyle(lineWidth, this.color)
-            .setRadius(borderRadius)
-            .setCenterPosition(centerX, centerY);
-
-        var angle = this.minuteHandAngle + Rad270;
-        this.getShape('minuteHand')
-            .lineStyle(lineWidth, this.color)
-            .setP0(centerX, centerY)
-            .setP1(
-                centerX + (Math.cos(angle) * minuteHandLength),
-                centerY + (Math.sin(angle) * minuteHandLength)
-            )
-
-        var angle = this.hourHandAngle + Rad270;
-        this.getShape('hourHand')
-            .lineStyle(lineWidth, this.color)
-            .setP0(centerX, centerY)
-            .setP1(
-                centerX + (Math.cos(angle) * hourHandLength),
-                centerY + (Math.sin(angle) * hourHandLength)
-            )
-    }
-}
-
-export default Clock;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/los/Los.d.ts

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

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

@@ -1,7 +0,0 @@
-// import * as Phaser from 'phaser';
-import Pan from "./Pan";
-
-export default function (
-    gameObject: Phaser.GameObjects.GameObject | Phaser.Scene,
-    config?: Pan.IConfig
-): Pan;

spaces/AkitoP/umamusume_bert_vits2/monotonic_align/core.py

@@ -1,46 +0,0 @@
-import numba
-
-
-@numba.jit(
-    numba.void(
-        numba.int32[:, :, ::1],
-        numba.float32[:, :, ::1],
-        numba.int32[::1],
-        numba.int32[::1],
-    ),
-    nopython=True,
-    nogil=True,
-)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-    b = paths.shape[0]
-    max_neg_val = -1e9
-    for i in range(int(b)):
-        path = paths[i]
-        value = values[i]
-        t_y = t_ys[i]
-        t_x = t_xs[i]
-
-        v_prev = v_cur = 0.0
-        index = t_x - 1
-
-        for y in range(t_y):
-            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-                if x == y:
-                    v_cur = max_neg_val
-                else:
-                    v_cur = value[y - 1, x]
-                if x == 0:
-                    if y == 0:
-                        v_prev = 0.0
-                    else:
-                        v_prev = max_neg_val
-                else:
-                    v_prev = value[y - 1, x - 1]
-                value[y, x] += max(v_prev, v_cur)
-
-        for y in range(t_y - 1, -1, -1):
-            path[y, index] = 1
-            if index != 0 and (
-                index == y or value[y - 1, index] < value[y - 1, index - 1]
-            ):
-                index = index - 1

spaces/AkshayKollimarala/MygenAI/app.py

@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

spaces/Aloento/9Nine-PITS/text/frontend/zh_normalization/chronology.py

@@ -1,134 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-
-from .num import DIGITS
-from .num import num2str
-from .num import verbalize_cardinal
-from .num import verbalize_digit
-
-
-def _time_num2str(num_string: str) -> str:
-  """A special case for verbalizing number in time."""
-  result = num2str(num_string.lstrip('0'))
-  if num_string.startswith('0'):
-    result = DIGITS['0'] + result
-  return result
-
-
-# 时刻表达式
-RE_TIME = re.compile(r'([0-1]?[0-9]|2[0-3])'
-                     r':([0-5][0-9])'
-                     r'(:([0-5][0-9]))?')
-
-# 时间范围，如8:30-12:30
-RE_TIME_RANGE = re.compile(r'([0-1]?[0-9]|2[0-3])'
-                           r':([0-5][0-9])'
-                           r'(:([0-5][0-9]))?'
-                           r'(~|-)'
-                           r'([0-1]?[0-9]|2[0-3])'
-                           r':([0-5][0-9])'
-                           r'(:([0-5][0-9]))?')
-
-
-def replace_time(match) -> str:
-  """
-  Args:
-      match (re.Match)
-  Returns:
-      str
-  """
-
-  is_range = len(match.groups()) > 5
-
-  hour = match.group(1)
-  minute = match.group(2)
-  second = match.group(4)
-
-  if is_range:
-    hour_2 = match.group(6)
-    minute_2 = match.group(7)
-    second_2 = match.group(9)
-
-  result = f"{num2str(hour)}点"
-  if minute.lstrip('0'):
-    if int(minute) == 30:
-      result += "半"
-    else:
-      result += f"{_time_num2str(minute)}分"
-  if second and second.lstrip('0'):
-    result += f"{_time_num2str(second)}秒"
-
-  if is_range:
-    result += "至"
-    result += f"{num2str(hour_2)}点"
-    if minute_2.lstrip('0'):
-      if int(minute) == 30:
-        result += "半"
-      else:
-        result += f"{_time_num2str(minute_2)}分"
-    if second_2 and second_2.lstrip('0'):
-      result += f"{_time_num2str(second_2)}秒"
-
-  return result
-
-
-RE_DATE = re.compile(r'(\d{4}|\d{2})年'
-                     r'((0?[1-9]|1[0-2])月)?'
-                     r'(((0?[1-9])|((1|2)[0-9])|30|31)([日号]))?')
-
-
-def replace_date(match) -> str:
-  """
-  Args:
-      match (re.Match)
-  Returns:
-      str
-  """
-  year = match.group(1)
-  month = match.group(3)
-  day = match.group(5)
-  result = ""
-  if year:
-    result += f"{verbalize_digit(year)}年"
-  if month:
-    result += f"{verbalize_cardinal(month)}月"
-  if day:
-    result += f"{verbalize_cardinal(day)}{match.group(9)}"
-  return result
-
-
-# 用 / 或者 - 分隔的 YY/MM/DD 或者 YY-MM-DD 日期
-RE_DATE2 = re.compile(
-  r'(\d{4})([- /.])(0[1-9]|1[012])\2(0[1-9]|[12][0-9]|3[01])')
-
-
-def replace_date2(match) -> str:
-  """
-  Args:
-      match (re.Match)
-  Returns:
-      str
-  """
-  year = match.group(1)
-  month = match.group(3)
-  day = match.group(4)
-  result = ""
-  if year:
-    result += f"{verbalize_digit(year)}年"
-  if month:
-    result += f"{verbalize_cardinal(month)}月"
-  if day:
-    result += f"{verbalize_cardinal(day)}日"
-  return result

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/dataset/blender_dataset.py

@@ -1,164 +0,0 @@
-import numpy as np
-import torch, cv2
-from torch.utils.data import Dataset
-import json
-from tqdm import tqdm
-import os
-from PIL import Image
-from torchvision import transforms as T
-
-from .ray_utils import *
-
-
-class BlenderDataset(Dataset):
-    def __init__(self, datadir, split='train', downsample=1.0, is_stack=False, N_vis=-1):
-        self.N_vis = N_vis
-        self.root_dir = datadir
-        self.split = split
-        self.is_stack = is_stack
-        self.img_wh = (int(800 / downsample), int(800 / downsample))
-        self.define_transforms()
-
-        self.scene_bbox = torch.tensor([[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]])
-        self.blender2opencv = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
-        self.read_meta()
-        self.define_proj_mat()
-
-        self.white_bg = True
-        self.near_far = [2.0, 6.0]
-
-        self.center = torch.mean(self.scene_bbox, axis=0).float().view(1, 1, 3)
-        self.radius = (self.scene_bbox[1] - self.center).float().view(1, 1, 3)
-        self.downsample = downsample
-
-    def read_depth(self, filename):
-        depth = np.array(read_pfm(filename)[0], dtype=np.float32)  # (800, 800)
-        return depth
-
-    def read_meta(self):
-
-        with open(os.path.join(self.root_dir, f"transforms_{self.split}.json"), 'r') as f:
-            self.meta = json.load(f)
-
-        w, h = self.img_wh
-        self.focal = 0.5 * 800 / np.tan(0.5 * self.meta['camera_angle_x'])  # original focal length
-        self.focal *= self.img_wh[0] / 800  # modify focal length to match size self.img_wh
-
-        # ray directions for all pixels, same for all images (same H, W, focal)
-        self.directions = get_ray_directions(h, w, [self.focal, self.focal])  # (h, w, 3)
-        self.directions = self.directions / torch.norm(self.directions, dim=-1, keepdim=True)
-        self.intrinsics = torch.tensor([[self.focal, 0, w / 2], [0, self.focal, h / 2], [0, 0, 1]]).float()
-
-        self.image_paths = []
-        self.poses = []
-        self.all_rays = []
-        self.all_rgbs = []
-        self.all_masks = []
-        self.all_depth = []
-        self.downsample = 1.0
-
-        img_eval_interval = 1 if self.N_vis < 0 else len(self.meta['frames']) // self.N_vis
-        idxs = list(range(0, len(self.meta['frames']), img_eval_interval))
-        for i in tqdm(idxs, desc=f'Loading data {self.split} ({len(idxs)})'):  # img_list:#
-
-            frame = self.meta['frames'][i]
-            pose = np.array(frame['transform_matrix']) @ self.blender2opencv
-            c2w = torch.FloatTensor(pose)
-            self.poses += [c2w]
-
-            image_path = os.path.join(self.root_dir, f"{frame['file_path']}.png")
-            self.image_paths += [image_path]
-            img = Image.open(image_path)
-
-            if self.downsample != 1.0:
-                img = img.resize(self.img_wh, Image.LANCZOS)
-            img = self.transform(img)  # (4, h, w)
-            img = img.view(4, -1).permute(1, 0)  # (h*w, 4) RGBA
-            self.all_masks.append(img[:, -1:].reshape(h, w, 1))  # (h, w, 1) A
-            img = img[:, :3] * img[:, -1:] + (1 - img[:, -1:])  # blend A to RGB
-            self.all_rgbs += [img]
-
-            rays_o, rays_d = get_rays(self.directions, c2w)  # both (h*w, 3)
-            self.all_rays += [torch.cat([rays_o, rays_d], 1)]  # (h*w, 6)
-
-        self.all_masks = torch.stack(self.all_masks)  # (n_frames, h, w, 1)
-        self.poses = torch.stack(self.poses)
-        all_rays = self.all_rays
-        all_rgbs = self.all_rgbs
-
-        self.all_rays = torch.cat(self.all_rays, 0)  # (len(self.meta['frames])*h*w,6)
-        self.all_rgbs = torch.cat(self.all_rgbs, 0)  # (len(self.meta['frames])*h*w,3)
-
-        if self.is_stack:
-            self.all_rays_stack = torch.stack(all_rays, 0).reshape(-1, *self.img_wh[::-1],
-                                                                   6)  # (len(self.meta['frames]),h,w,6)
-            avg_pool = torch.nn.AvgPool2d(4, ceil_mode=True)
-            self.ds_all_rays_stack = avg_pool(self.all_rays_stack.permute(0, 3, 1, 2)).permute(0, 2, 3,
-                                                                                               1)  # (len(self.meta['frames]),h/4,w/4,6)
-            self.all_rgbs_stack = torch.stack(all_rgbs, 0).reshape(-1, *self.img_wh[::-1],
-                                                                   3)  # (len(self.meta['frames]),h,w,3)
-
-    @torch.no_grad()
-    def prepare_feature_data(self, encoder, chunk=8):
-        '''
-        Prepare feature maps as training data.
-        '''
-        assert self.is_stack, 'Dataset should contain original stacked taining data!'
-        print('====> prepare_feature_data ...')
-
-        frames_num, h, w, _ = self.all_rgbs_stack.size()
-        features = []
-
-        for chunk_idx in range(frames_num // chunk + int(frames_num % chunk > 0)):
-            print(chunk_idx, frames_num // chunk + int(frames_num % chunk > 0))
-            rgbs_chunk = self.all_rgbs_stack[chunk_idx * chunk: (chunk_idx + 1) * chunk].cuda()
-            features_chunk = encoder(normalize_vgg(rgbs_chunk.permute(0, 3, 1, 2))).relu3_1
-            # resize to the size of rgb map so that rays can match
-            features_chunk = T.functional.resize(features_chunk, size=(h, w),
-                                                 interpolation=T.InterpolationMode.BILINEAR)
-
-            features.append(features_chunk.detach().cpu().requires_grad_(False))
-
-        self.all_features_stack = torch.cat(features).permute(0, 2, 3, 1)  # (len(self.meta['frames]),h,w,256)
-        self.all_features = self.all_features_stack.reshape(-1, 256)
-        print('prepare_feature_data Done!')
-
-    def define_transforms(self):
-        self.transform = T.ToTensor()
-
-    def define_proj_mat(self):
-        self.proj_mat = self.intrinsics.unsqueeze(0) @ torch.inverse(self.poses)[:, :3]
-
-    def world2ndc(self, points, lindisp=None):
-        device = points.device
-        return (points - self.center.to(device)) / self.radius.to(device)
-
-    def __len__(self):
-        return len(self.all_rgbs)
-
-    def __getitem__(self, idx):
-
-        if self.split == 'train':  # use data in the buffers
-            sample = {'rays': self.all_rays[idx],
-                      'rgbs': self.all_rgbs[idx]}
-
-        else:  # create data for each image separately
-
-            img = self.all_rgbs[idx]
-            rays = self.all_rays[idx]
-            mask = self.all_masks[idx]  # for quantity evaluation
-
-            sample = {'rays': rays,
-                      'rgbs': img,
-                      'mask': mask}
-        return sample
-
-if __name__ == '__main__':
-    train_loader = BlenderDataset('data/nerf_synthetic/lego')
-    for idx, data in enumerate(train_loader):
-        print(idx, data)
-        print("Ray: ", data['rays'])
-        print("Ray shape: ", data['rays'].shape)
-        print("RGB: ", data['rgbs'])
-        print("RGB shape: ", data['rgbs'].shape)
-        break

spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py

@@ -1,13 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/configs/retinanet/retinanet_r50_fpn_1x_coco.py

@@ -1,7 +0,0 @@
-_base_ = [
-    '../_base_/models/retinanet_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

spaces/Andy1621/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py

@@ -1,2 +0,0 @@
-_base_ = './ccnet_r50-d8_512x512_20k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py

@@ -1,4 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(type='ResNet', depth=101))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/datasets/custom.py

@@ -1,400 +0,0 @@
-import os
-import os.path as osp
-from collections import OrderedDict
-from functools import reduce
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-from annotator.uniformer.mmcv.utils import print_log
-from prettytable import PrettyTable
-from torch.utils.data import Dataset
-
-from annotator.uniformer.mmseg.core import eval_metrics
-from annotator.uniformer.mmseg.utils import get_root_logger
-from .builder import DATASETS
-from .pipelines import Compose
-
-
-@DATASETS.register_module()
-class CustomDataset(Dataset):
-    """Custom dataset for semantic segmentation. An example of file structure
-    is as followed.
-
-    .. code-block:: none
-
-        ├── data
-        │   ├── my_dataset
-        │   │   ├── img_dir
-        │   │   │   ├── train
-        │   │   │   │   ├── xxx{img_suffix}
-        │   │   │   │   ├── yyy{img_suffix}
-        │   │   │   │   ├── zzz{img_suffix}
-        │   │   │   ├── val
-        │   │   ├── ann_dir
-        │   │   │   ├── train
-        │   │   │   │   ├── xxx{seg_map_suffix}
-        │   │   │   │   ├── yyy{seg_map_suffix}
-        │   │   │   │   ├── zzz{seg_map_suffix}
-        │   │   │   ├── val
-
-    The img/gt_semantic_seg pair of CustomDataset should be of the same
-    except suffix. A valid img/gt_semantic_seg filename pair should be like
-    ``xxx{img_suffix}`` and ``xxx{seg_map_suffix}`` (extension is also included
-    in the suffix). If split is given, then ``xxx`` is specified in txt file.
-    Otherwise, all files in ``img_dir/``and ``ann_dir`` will be loaded.
-    Please refer to ``docs/tutorials/new_dataset.md`` for more details.
-
-
-    Args:
-        pipeline (list[dict]): Processing pipeline
-        img_dir (str): Path to image directory
-        img_suffix (str): Suffix of images. Default: '.jpg'
-        ann_dir (str, optional): Path to annotation directory. Default: None
-        seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
-        split (str, optional): Split txt file. If split is specified, only
-            file with suffix in the splits will be loaded. Otherwise, all
-            images in img_dir/ann_dir will be loaded. Default: None
-        data_root (str, optional): Data root for img_dir/ann_dir. Default:
-            None.
-        test_mode (bool): If test_mode=True, gt wouldn't be loaded.
-        ignore_index (int): The label index to be ignored. Default: 255
-        reduce_zero_label (bool): Whether to mark label zero as ignored.
-            Default: False
-        classes (str | Sequence[str], optional): Specify classes to load.
-            If is None, ``cls.CLASSES`` will be used. Default: None.
-        palette (Sequence[Sequence[int]]] | np.ndarray | None):
-            The palette of segmentation map. If None is given, and
-            self.PALETTE is None, random palette will be generated.
-            Default: None
-    """
-
-    CLASSES = None
-
-    PALETTE = None
-
-    def __init__(self,
-                 pipeline,
-                 img_dir,
-                 img_suffix='.jpg',
-                 ann_dir=None,
-                 seg_map_suffix='.png',
-                 split=None,
-                 data_root=None,
-                 test_mode=False,
-                 ignore_index=255,
-                 reduce_zero_label=False,
-                 classes=None,
-                 palette=None):
-        self.pipeline = Compose(pipeline)
-        self.img_dir = img_dir
-        self.img_suffix = img_suffix
-        self.ann_dir = ann_dir
-        self.seg_map_suffix = seg_map_suffix
-        self.split = split
-        self.data_root = data_root
-        self.test_mode = test_mode
-        self.ignore_index = ignore_index
-        self.reduce_zero_label = reduce_zero_label
-        self.label_map = None
-        self.CLASSES, self.PALETTE = self.get_classes_and_palette(
-            classes, palette)
-
-        # join paths if data_root is specified
-        if self.data_root is not None:
-            if not osp.isabs(self.img_dir):
-                self.img_dir = osp.join(self.data_root, self.img_dir)
-            if not (self.ann_dir is None or osp.isabs(self.ann_dir)):
-                self.ann_dir = osp.join(self.data_root, self.ann_dir)
-            if not (self.split is None or osp.isabs(self.split)):
-                self.split = osp.join(self.data_root, self.split)
-
-        # load annotations
-        self.img_infos = self.load_annotations(self.img_dir, self.img_suffix,
-                                               self.ann_dir,
-                                               self.seg_map_suffix, self.split)
-
-    def __len__(self):
-        """Total number of samples of data."""
-        return len(self.img_infos)
-
-    def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix,
-                         split):
-        """Load annotation from directory.
-
-        Args:
-            img_dir (str): Path to image directory
-            img_suffix (str): Suffix of images.
-            ann_dir (str|None): Path to annotation directory.
-            seg_map_suffix (str|None): Suffix of segmentation maps.
-            split (str|None): Split txt file. If split is specified, only file
-                with suffix in the splits will be loaded. Otherwise, all images
-                in img_dir/ann_dir will be loaded. Default: None
-
-        Returns:
-            list[dict]: All image info of dataset.
-        """
-
-        img_infos = []
-        if split is not None:
-            with open(split) as f:
-                for line in f:
-                    img_name = line.strip()
-                    img_info = dict(filename=img_name + img_suffix)
-                    if ann_dir is not None:
-                        seg_map = img_name + seg_map_suffix
-                        img_info['ann'] = dict(seg_map=seg_map)
-                    img_infos.append(img_info)
-        else:
-            for img in mmcv.scandir(img_dir, img_suffix, recursive=True):
-                img_info = dict(filename=img)
-                if ann_dir is not None:
-                    seg_map = img.replace(img_suffix, seg_map_suffix)
-                    img_info['ann'] = dict(seg_map=seg_map)
-                img_infos.append(img_info)
-
-        print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger())
-        return img_infos
-
-    def get_ann_info(self, idx):
-        """Get annotation by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Annotation info of specified index.
-        """
-
-        return self.img_infos[idx]['ann']
-
-    def pre_pipeline(self, results):
-        """Prepare results dict for pipeline."""
-        results['seg_fields'] = []
-        results['img_prefix'] = self.img_dir
-        results['seg_prefix'] = self.ann_dir
-        if self.custom_classes:
-            results['label_map'] = self.label_map
-
-    def __getitem__(self, idx):
-        """Get training/test data after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Training/test data (with annotation if `test_mode` is set
-                False).
-        """
-
-        if self.test_mode:
-            return self.prepare_test_img(idx)
-        else:
-            return self.prepare_train_img(idx)
-
-    def prepare_train_img(self, idx):
-        """Get training data and annotations after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Training data and annotation after pipeline with new keys
-                introduced by pipeline.
-        """
-
-        img_info = self.img_infos[idx]
-        ann_info = self.get_ann_info(idx)
-        results = dict(img_info=img_info, ann_info=ann_info)
-        self.pre_pipeline(results)
-        return self.pipeline(results)
-
-    def prepare_test_img(self, idx):
-        """Get testing data after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Testing data after pipeline with new keys introduced by
-                pipeline.
-        """
-
-        img_info = self.img_infos[idx]
-        results = dict(img_info=img_info)
-        self.pre_pipeline(results)
-        return self.pipeline(results)
-
-    def format_results(self, results, **kwargs):
-        """Place holder to format result to dataset specific output."""
-
-    def get_gt_seg_maps(self, efficient_test=False):
-        """Get ground truth segmentation maps for evaluation."""
-        gt_seg_maps = []
-        for img_info in self.img_infos:
-            seg_map = osp.join(self.ann_dir, img_info['ann']['seg_map'])
-            if efficient_test:
-                gt_seg_map = seg_map
-            else:
-                gt_seg_map = mmcv.imread(
-                    seg_map, flag='unchanged', backend='pillow')
-            gt_seg_maps.append(gt_seg_map)
-        return gt_seg_maps
-
-    def get_classes_and_palette(self, classes=None, palette=None):
-        """Get class names of current dataset.
-
-        Args:
-            classes (Sequence[str] | str | None): If classes is None, use
-                default CLASSES defined by builtin dataset. If classes is a
-                string, take it as a file name. The file contains the name of
-                classes where each line contains one class name. If classes is
-                a tuple or list, override the CLASSES defined by the dataset.
-            palette (Sequence[Sequence[int]]] | np.ndarray | None):
-                The palette of segmentation map. If None is given, random
-                palette will be generated. Default: None
-        """
-        if classes is None:
-            self.custom_classes = False
-            return self.CLASSES, self.PALETTE
-
-        self.custom_classes = True
-        if isinstance(classes, str):
-            # take it as a file path
-            class_names = mmcv.list_from_file(classes)
-        elif isinstance(classes, (tuple, list)):
-            class_names = classes
-        else:
-            raise ValueError(f'Unsupported type {type(classes)} of classes.')
-
-        if self.CLASSES:
-            if not set(classes).issubset(self.CLASSES):
-                raise ValueError('classes is not a subset of CLASSES.')
-
-            # dictionary, its keys are the old label ids and its values
-            # are the new label ids.
-            # used for changing pixel labels in load_annotations.
-            self.label_map = {}
-            for i, c in enumerate(self.CLASSES):
-                if c not in class_names:
-                    self.label_map[i] = -1
-                else:
-                    self.label_map[i] = classes.index(c)
-
-        palette = self.get_palette_for_custom_classes(class_names, palette)
-
-        return class_names, palette
-
-    def get_palette_for_custom_classes(self, class_names, palette=None):
-
-        if self.label_map is not None:
-            # return subset of palette
-            palette = []
-            for old_id, new_id in sorted(
-                    self.label_map.items(), key=lambda x: x[1]):
-                if new_id != -1:
-                    palette.append(self.PALETTE[old_id])
-            palette = type(self.PALETTE)(palette)
-
-        elif palette is None:
-            if self.PALETTE is None:
-                palette = np.random.randint(0, 255, size=(len(class_names), 3))
-            else:
-                palette = self.PALETTE
-
-        return palette
-
-    def evaluate(self,
-                 results,
-                 metric='mIoU',
-                 logger=None,
-                 efficient_test=False,
-                 **kwargs):
-        """Evaluate the dataset.
-
-        Args:
-            results (list): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated. 'mIoU',
-                'mDice' and 'mFscore' are supported.
-            logger (logging.Logger | None | str): Logger used for printing
-                related information during evaluation. Default: None.
-
-        Returns:
-            dict[str, float]: Default metrics.
-        """
-
-        if isinstance(metric, str):
-            metric = [metric]
-        allowed_metrics = ['mIoU', 'mDice', 'mFscore']
-        if not set(metric).issubset(set(allowed_metrics)):
-            raise KeyError('metric {} is not supported'.format(metric))
-        eval_results = {}
-        gt_seg_maps = self.get_gt_seg_maps(efficient_test)
-        if self.CLASSES is None:
-            num_classes = len(
-                reduce(np.union1d, [np.unique(_) for _ in gt_seg_maps]))
-        else:
-            num_classes = len(self.CLASSES)
-        ret_metrics = eval_metrics(
-            results,
-            gt_seg_maps,
-            num_classes,
-            self.ignore_index,
-            metric,
-            label_map=self.label_map,
-            reduce_zero_label=self.reduce_zero_label)
-
-        if self.CLASSES is None:
-            class_names = tuple(range(num_classes))
-        else:
-            class_names = self.CLASSES
-
-        # summary table
-        ret_metrics_summary = OrderedDict({
-            ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2)
-            for ret_metric, ret_metric_value in ret_metrics.items()
-        })
-
-        # each class table
-        ret_metrics.pop('aAcc', None)
-        ret_metrics_class = OrderedDict({
-            ret_metric: np.round(ret_metric_value * 100, 2)
-            for ret_metric, ret_metric_value in ret_metrics.items()
-        })
-        ret_metrics_class.update({'Class': class_names})
-        ret_metrics_class.move_to_end('Class', last=False)
-
-        # for logger
-        class_table_data = PrettyTable()
-        for key, val in ret_metrics_class.items():
-            class_table_data.add_column(key, val)
-
-        summary_table_data = PrettyTable()
-        for key, val in ret_metrics_summary.items():
-            if key == 'aAcc':
-                summary_table_data.add_column(key, [val])
-            else:
-                summary_table_data.add_column('m' + key, [val])
-
-        print_log('per class results:', logger)
-        print_log('\n' + class_table_data.get_string(), logger=logger)
-        print_log('Summary:', logger)
-        print_log('\n' + summary_table_data.get_string(), logger=logger)
-
-        # each metric dict
-        for key, value in ret_metrics_summary.items():
-            if key == 'aAcc':
-                eval_results[key] = value / 100.0
-            else:
-                eval_results['m' + key] = value / 100.0
-
-        ret_metrics_class.pop('Class', None)
-        for key, value in ret_metrics_class.items():
-            eval_results.update({
-                key + '.' + str(name): value[idx] / 100.0
-                for idx, name in enumerate(class_names)
-            })
-
-        if mmcv.is_list_of(results, str):
-            for file_name in results:
-                os.remove(file_name)
-        return eval_results

spaces/Artrajz/vits-simple-api/bert_vits2/text/__init__.py

@@ -1,17 +0,0 @@
-from bert_vits2.text.symbols import *
-from bert_vits2.text.bert_handler import BertHandler
-
-
-def cleaned_text_to_sequence(cleaned_text, tones, language, _symbol_to_id):
-    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-    """
-    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-    tone_start = language_tone_start_map[language]
-    tones = [i + tone_start for i in tones]
-    lang_id = language_id_map[language]
-    lang_ids = [lang_id for i in phones]
-    return phones, tones, lang_ids

spaces/Artrajz/vits-simple-api/vits/mel_processing.py

@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec

spaces/Artrajz/vits-simple-api/vits/text/shanghainese.py

@@ -1,80 +0,0 @@
-import os
-import re
-import cn2an
-import opencc
-import config
-from utils.download import download_and_verify
-
-URLS = [
-    "https://github.com/CjangCjengh/chinese-dialect-lexicons/releases/download/v1.0.3/chinese_dialects.7z",
-    "https://ghproxy.com/https://github.com/CjangCjengh/chinese-dialect-lexicons/releases/download/v1.0.3/chinese_dialects.7z",
-]
-TARGET_PATH = os.path.join(config.ABS_PATH, "vits/text/chinese_dialects.7z")
-EXTRACT_DESTINATION = os.path.join(config.ABS_PATH, "vits/text/chinese_dialect_lexicons/")
-EXPECTED_MD5 = None
-OPENCC_FILE_PATH = os.path.join(config.ABS_PATH, "vits/text/chinese_dialect_lexicons/zaonhe.json")
-
-if not os.path.exists(OPENCC_FILE_PATH):
-    success, message = download_and_verify(URLS, TARGET_PATH, EXPECTED_MD5, EXTRACT_DESTINATION)
-
-converter = opencc.OpenCC(OPENCC_FILE_PATH)
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ᴇ'),
-    ('B', 'bi'),
-    ('C', 'si'),
-    ('D', 'di'),
-    ('E', 'i'),
-    ('F', 'ᴇf'),
-    ('G', 'dʑi'),
-    ('H', 'ᴇtɕʰ'),
-    ('I', 'ᴀi'),
-    ('J', 'dʑᴇ'),
-    ('K', 'kʰᴇ'),
-    ('L', 'ᴇl'),
-    ('M', 'ᴇm'),
-    ('N', 'ᴇn'),
-    ('O', 'o'),
-    ('P', 'pʰi'),
-    ('Q', 'kʰiu'),
-    ('R', 'ᴀl'),
-    ('S', 'ᴇs'),
-    ('T', 'tʰi'),
-    ('U', 'ɦiu'),
-    ('V', 'vi'),
-    ('W', 'dᴀbɤliu'),
-    ('X', 'ᴇks'),
-    ('Y', 'uᴀi'),
-    ('Z', 'zᴇ')
-]]
-
-
-def _number_to_shanghainese(num):
-    num = cn2an.an2cn(num).replace('一十', '十').replace('二十', '廿').replace('二', '两')
-    return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num)
-
-
-def number_to_shanghainese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def shanghainese_to_ipa(text):
-    from vits.text.mandarin import symbols_to_chinese
-    text = symbols_to_chinese(text)
-    text = number_to_shanghainese(text.upper())
-    text = converter.convert(text).replace('-', '').replace('$', ' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group()) + ' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text

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

@@ -1,2151 +0,0 @@
-# This file is automatically generated by tools/idna-data
-
-__version__ = '15.0.0'
-scripts = {
-    'Greek': (
-        0x37000000374,
-        0x37500000378,
-        0x37a0000037e,
-        0x37f00000380,
-        0x38400000385,
-        0x38600000387,
-        0x3880000038b,
-        0x38c0000038d,
-        0x38e000003a2,
-        0x3a3000003e2,
-        0x3f000000400,
-        0x1d2600001d2b,
-        0x1d5d00001d62,
-        0x1d6600001d6b,
-        0x1dbf00001dc0,
-        0x1f0000001f16,
-        0x1f1800001f1e,
-        0x1f2000001f46,
-        0x1f4800001f4e,
-        0x1f5000001f58,
-        0x1f5900001f5a,
-        0x1f5b00001f5c,
-        0x1f5d00001f5e,
-        0x1f5f00001f7e,
-        0x1f8000001fb5,
-        0x1fb600001fc5,
-        0x1fc600001fd4,
-        0x1fd600001fdc,
-        0x1fdd00001ff0,
-        0x1ff200001ff5,
-        0x1ff600001fff,
-        0x212600002127,
-        0xab650000ab66,
-        0x101400001018f,
-        0x101a0000101a1,
-        0x1d2000001d246,
-    ),
-    'Han': (
-        0x2e8000002e9a,
-        0x2e9b00002ef4,
-        0x2f0000002fd6,
-        0x300500003006,
-        0x300700003008,
-        0x30210000302a,
-        0x30380000303c,
-        0x340000004dc0,
-        0x4e000000a000,
-        0xf9000000fa6e,
-        0xfa700000fada,
-        0x16fe200016fe4,
-        0x16ff000016ff2,
-        0x200000002a6e0,
-        0x2a7000002b73a,
-        0x2b7400002b81e,
-        0x2b8200002cea2,
-        0x2ceb00002ebe1,
-        0x2f8000002fa1e,
-        0x300000003134b,
-        0x31350000323b0,
-    ),
-    'Hebrew': (
-        0x591000005c8,
-        0x5d0000005eb,
-        0x5ef000005f5,
-        0xfb1d0000fb37,
-        0xfb380000fb3d,
-        0xfb3e0000fb3f,
-        0xfb400000fb42,
-        0xfb430000fb45,
-        0xfb460000fb50,
-    ),
-    'Hiragana': (
-        0x304100003097,
-        0x309d000030a0,
-        0x1b0010001b120,
-        0x1b1320001b133,
-        0x1b1500001b153,
-        0x1f2000001f201,
-    ),
-    'Katakana': (
-        0x30a1000030fb,
-        0x30fd00003100,
-        0x31f000003200,
-        0x32d0000032ff,
-        0x330000003358,
-        0xff660000ff70,
-        0xff710000ff9e,
-        0x1aff00001aff4,
-        0x1aff50001affc,
-        0x1affd0001afff,
-        0x1b0000001b001,
-        0x1b1200001b123,
-        0x1b1550001b156,
-        0x1b1640001b168,
-    ),
-}
-joining_types = {
-    0x600: 85,
-    0x601: 85,
-    0x602: 85,
-    0x603: 85,
-    0x604: 85,
-    0x605: 85,
-    0x608: 85,
-    0x60b: 85,
-    0x620: 68,
-    0x621: 85,
-    0x622: 82,
-    0x623: 82,
-    0x624: 82,
-    0x625: 82,
-    0x626: 68,
-    0x627: 82,
-    0x628: 68,
-    0x629: 82,
-    0x62a: 68,
-    0x62b: 68,
-    0x62c: 68,
-    0x62d: 68,
-    0x62e: 68,
-    0x62f: 82,
-    0x630: 82,
-    0x631: 82,
-    0x632: 82,
-    0x633: 68,
-    0x634: 68,
-    0x635: 68,
-    0x636: 68,
-    0x637: 68,
-    0x638: 68,
-    0x639: 68,
-    0x63a: 68,
-    0x63b: 68,
-    0x63c: 68,
-    0x63d: 68,
-    0x63e: 68,
-    0x63f: 68,
-    0x640: 67,
-    0x641: 68,
-    0x642: 68,
-    0x643: 68,
-    0x644: 68,
-    0x645: 68,
-    0x646: 68,
-    0x647: 68,
-    0x648: 82,
-    0x649: 68,
-    0x64a: 68,
-    0x66e: 68,
-    0x66f: 68,
-    0x671: 82,
-    0x672: 82,
-    0x673: 82,
-    0x674: 85,
-    0x675: 82,
-    0x676: 82,
-    0x677: 82,
-    0x678: 68,
-    0x679: 68,
-    0x67a: 68,
-    0x67b: 68,
-    0x67c: 68,
-    0x67d: 68,
-    0x67e: 68,
-    0x67f: 68,
-    0x680: 68,
-    0x681: 68,
-    0x682: 68,
-    0x683: 68,
-    0x684: 68,
-    0x685: 68,
-    0x686: 68,
-    0x687: 68,
-    0x688: 82,
-    0x689: 82,
-    0x68a: 82,
-    0x68b: 82,
-    0x68c: 82,
-    0x68d: 82,
-    0x68e: 82,
-    0x68f: 82,
-    0x690: 82,
-    0x691: 82,
-    0x692: 82,
-    0x693: 82,
-    0x694: 82,
-    0x695: 82,
-    0x696: 82,
-    0x697: 82,
-    0x698: 82,
-    0x699: 82,
-    0x69a: 68,
-    0x69b: 68,
-    0x69c: 68,
-    0x69d: 68,
-    0x69e: 68,
-    0x69f: 68,
-    0x6a0: 68,
-    0x6a1: 68,
-    0x6a2: 68,
-    0x6a3: 68,
-    0x6a4: 68,
-    0x6a5: 68,
-    0x6a6: 68,
-    0x6a7: 68,
-    0x6a8: 68,
-    0x6a9: 68,
-    0x6aa: 68,
-    0x6ab: 68,
-    0x6ac: 68,
-    0x6ad: 68,
-    0x6ae: 68,
-    0x6af: 68,
-    0x6b0: 68,
-    0x6b1: 68,
-    0x6b2: 68,
-    0x6b3: 68,
-    0x6b4: 68,
-    0x6b5: 68,
-    0x6b6: 68,
-    0x6b7: 68,
-    0x6b8: 68,
-    0x6b9: 68,
-    0x6ba: 68,
-    0x6bb: 68,
-    0x6bc: 68,
-    0x6bd: 68,
-    0x6be: 68,
-    0x6bf: 68,
-    0x6c0: 82,
-    0x6c1: 68,
-    0x6c2: 68,
-    0x6c3: 82,
-    0x6c4: 82,
-    0x6c5: 82,
-    0x6c6: 82,
-    0x6c7: 82,
-    0x6c8: 82,
-    0x6c9: 82,
-    0x6ca: 82,
-    0x6cb: 82,
-    0x6cc: 68,
-    0x6cd: 82,
-    0x6ce: 68,
-    0x6cf: 82,
-    0x6d0: 68,
-    0x6d1: 68,
-    0x6d2: 82,
-    0x6d3: 82,
-    0x6d5: 82,
-    0x6dd: 85,
-    0x6ee: 82,
-    0x6ef: 82,
-    0x6fa: 68,
-    0x6fb: 68,
-    0x6fc: 68,
-    0x6ff: 68,
-    0x70f: 84,
-    0x710: 82,
-    0x712: 68,
-    0x713: 68,
-    0x714: 68,
-    0x715: 82,
-    0x716: 82,
-    0x717: 82,
-    0x718: 82,
-    0x719: 82,
-    0x71a: 68,
-    0x71b: 68,
-    0x71c: 68,
-    0x71d: 68,
-    0x71e: 82,
-    0x71f: 68,
-    0x720: 68,
-    0x721: 68,
-    0x722: 68,
-    0x723: 68,
-    0x724: 68,
-    0x725: 68,
-    0x726: 68,
-    0x727: 68,
-    0x728: 82,
-    0x729: 68,
-    0x72a: 82,
-    0x72b: 68,
-    0x72c: 82,
-    0x72d: 68,
-    0x72e: 68,
-    0x72f: 82,
-    0x74d: 82,
-    0x74e: 68,
-    0x74f: 68,
-    0x750: 68,
-    0x751: 68,
-    0x752: 68,
-    0x753: 68,
-    0x754: 68,
-    0x755: 68,
-    0x756: 68,
-    0x757: 68,
-    0x758: 68,
-    0x759: 82,
-    0x75a: 82,
-    0x75b: 82,
-    0x75c: 68,
-    0x75d: 68,
-    0x75e: 68,
-    0x75f: 68,
-    0x760: 68,
-    0x761: 68,
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-    ),
-    'CONTEXTJ': (
-        0x200c0000200e,
-    ),
-    'CONTEXTO': (
-        0xb7000000b8,
-        0x37500000376,
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-        0x30fb000030fc,
-    ),
-}

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

@@ -1,133 +0,0 @@
-class AbstractProvider(object):
-    """Delegate class to provide the required interface for the resolver."""
-
-    def identify(self, requirement_or_candidate):
-        """Given a requirement, return an identifier for it.
-
-        This is used to identify a requirement, e.g. whether two requirements
-        should have their specifier parts merged.
-        """
-        raise NotImplementedError
-
-    def get_preference(
-        self,
-        identifier,
-        resolutions,
-        candidates,
-        information,
-        backtrack_causes,
-    ):
-        """Produce a sort key for given requirement based on preference.
-
-        The preference is defined as "I think this requirement should be
-        resolved first". The lower the return value is, the more preferred
-        this group of arguments is.
-
-        :param identifier: An identifier as returned by ``identify()``. This
-            identifies the dependency matches which should be returned.
-        :param resolutions: Mapping of candidates currently pinned by the
-            resolver. Each key is an identifier, and the value is a candidate.
-            The candidate may conflict with requirements from ``information``.
-        :param candidates: Mapping of each dependency's possible candidates.
-            Each value is an iterator of candidates.
-        :param information: Mapping of requirement information of each package.
-            Each value is an iterator of *requirement information*.
-        :param backtrack_causes: Sequence of requirement information that were
-            the requirements that caused the resolver to most recently backtrack.
-
-        A *requirement information* instance is a named tuple with two members:
-
-        * ``requirement`` specifies a requirement contributing to the current
-          list of candidates.
-        * ``parent`` specifies the candidate that provides (depended on) the
-          requirement, or ``None`` to indicate a root requirement.
-
-        The preference could depend on various issues, including (not
-        necessarily in this order):
-
-        * Is this package pinned in the current resolution result?
-        * How relaxed is the requirement? Stricter ones should probably be
-          worked on first? (I don't know, actually.)
-        * How many possibilities are there to satisfy this requirement? Those
-          with few left should likely be worked on first, I guess?
-        * Are there any known conflicts for this requirement? We should
-          probably work on those with the most known conflicts.
-
-        A sortable value should be returned (this will be used as the ``key``
-        parameter of the built-in sorting function). The smaller the value is,
-        the more preferred this requirement is (i.e. the sorting function
-        is called with ``reverse=False``).
-        """
-        raise NotImplementedError
-
-    def find_matches(self, identifier, requirements, incompatibilities):
-        """Find all possible candidates that satisfy the given constraints.
-
-        :param identifier: An identifier as returned by ``identify()``. This
-            identifies the dependency matches of which should be returned.
-        :param requirements: A mapping of requirements that all returned
-            candidates must satisfy. Each key is an identifier, and the value
-            an iterator of requirements for that dependency.
-        :param incompatibilities: A mapping of known incompatibilities of
-            each dependency. Each key is an identifier, and the value an
-            iterator of incompatibilities known to the resolver. All
-            incompatibilities *must* be excluded from the return value.
-
-        This should try to get candidates based on the requirements' types.
-        For VCS, local, and archive requirements, the one-and-only match is
-        returned, and for a "named" requirement, the index(es) should be
-        consulted to find concrete candidates for this requirement.
-
-        The return value should produce candidates ordered by preference; the
-        most preferred candidate should come first. The return type may be one
-        of the following:
-
-        * A callable that returns an iterator that yields candidates.
-        * An collection of candidates.
-        * An iterable of candidates. This will be consumed immediately into a
-          list of candidates.
-        """
-        raise NotImplementedError
-
-    def is_satisfied_by(self, requirement, candidate):
-        """Whether the given requirement can be satisfied by a candidate.
-
-        The candidate is guaranteed to have been generated from the
-        requirement.
-
-        A boolean should be returned to indicate whether ``candidate`` is a
-        viable solution to the requirement.
-        """
-        raise NotImplementedError
-
-    def get_dependencies(self, candidate):
-        """Get dependencies of a candidate.
-
-        This should return a collection of requirements that `candidate`
-        specifies as its dependencies.
-        """
-        raise NotImplementedError
-
-
-class AbstractResolver(object):
-    """The thing that performs the actual resolution work."""
-
-    base_exception = Exception
-
-    def __init__(self, provider, reporter):
-        self.provider = provider
-        self.reporter = reporter
-
-    def resolve(self, requirements, **kwargs):
-        """Take a collection of constraints, spit out the resolution result.
-
-        This returns a representation of the final resolution state, with one
-        guarenteed attribute ``mapping`` that contains resolved candidates as
-        values. The keys are their respective identifiers.
-
-        :param requirements: A collection of constraints.
-        :param kwargs: Additional keyword arguments that subclasses may accept.
-
-        :raises: ``self.base_exception`` or its subclass.
-        """
-        raise NotImplementedError

spaces/Audio-AGI/AudioSep/models/CLAP/training/lp_main.py

@@ -1,670 +0,0 @@
-from cmath import cos
-from inspect import getargs
-import logging
-import os
-import random
-from datetime import datetime
-import bisect
-import copy
-from sched import scheduler
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-from torch import optim
-from torch.cuda.amp import GradScaler
-import faulthandler
-import pathlib
-import argparse
-import time
-
-try:
-    import wandb
-except ImportError:
-    wandb = None
-
-try:
-    import torch.utils.tensorboard as tensorboard
-except ImportError:
-    tensorboard = None
-
-try:
-    import horovod.torch as hvd
-except ImportError:
-    hvd = None
-
-from open_clip import create_model_and_transforms, trace_model, create_model
-from training.data import get_data
-from training.params import parse_args
-from training.distributed import is_master, init_distributed_device, world_info_from_env
-from training.logger import setup_logging
-from training.scheduler import cosine_lr
-from training.lp_train import train_one_epoch, evaluate
-from open_clip.utils import get_tar_path_from_dataset_name, dataset_split, get_optimizer
-from open_clip.utils import load_p, load_class_label
-from open_clip.linear_probe import LinearProbe
-
-
-def maintain_ckpts(args, startidx, all_idx_len):
-    for i in reversed(range(startidx, all_idx_len)):
-        if os.path.exists(os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt")):
-            os.rename(
-                os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
-                os.path.join(args.checkpoint_path, f"epoch_top_{i+1}.pt"),
-            )
-    if os.path.exists(
-        os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt")
-    ):
-        os.remove(os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt"))
-    return
-
-
-def update_top_k_performance(
-    new_metrics_inputs, current_top_k_ckpt_metrics, args, ckpt, bignumbetter=True
-):
-    """
-    Record the top-k performance of the current epoch.
-    current_top_k_metrics is a dictionary of the form: {1: top_1_ckpt_measure, 2: top_2_ckpt_measure, ...}
-    """
-    if isinstance(new_metrics_inputs, (list, tuple)):
-        new_metrics_inputs = np.mean(new_metrics_inputs)
-        return update_top_k_performance(
-            new_metrics_inputs,
-            current_top_k_ckpt_metrics,
-            args=args,
-            ckpt=ckpt,
-            bignumbetter=bignumbetter,
-        )
-    elif isinstance(new_metrics_inputs, dict):
-        new_metrics_inputs = np.mean(list(new_metrics_inputs.values()))
-        return update_top_k_performance(
-            new_metrics_inputs,
-            current_top_k_ckpt_metrics,
-            args=args,
-            ckpt=ckpt,
-            bignumbetter=bignumbetter,
-        )
-    elif isinstance(new_metrics_inputs, (float, int)):
-        update_flag = {k: False for k in current_top_k_ckpt_metrics.keys()}
-        sorted_keys = sorted(current_top_k_ckpt_metrics.keys())
-        sorted_values = sorted(
-            current_top_k_ckpt_metrics.values(), reverse=bignumbetter
-        )
-        sorted_values_ = copy.deepcopy(sorted_values)
-        sorted_values.append(new_metrics_inputs)
-        sorted_values = sorted(sorted_values, reverse=bignumbetter)
-        sorted_values = sorted_values[:-1]
-
-        if sorted_values == sorted_values_:
-            return current_top_k_ckpt_metrics, new_metrics_inputs
-        else:
-            for i in range(len(sorted_keys)):
-                if current_top_k_ckpt_metrics[sorted_keys[i]] != sorted_values[i]:
-                    current_top_k_ckpt_metrics[sorted_keys[i]] = sorted_values[i]
-                    update_flag[sorted_keys[i]] = True
-            for i in range(len(update_flag)):
-                if update_flag[i]:
-                    maintain_ckpts(args, i, len(sorted_keys))
-                    torch.save(
-                        ckpt,
-                        os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
-                    )
-                    break
-            return current_top_k_ckpt_metrics, new_metrics_inputs
-
-
-# def updateifNone(a, b):
-#     a = b if None else a
-#     return a
-
-
-def is_pretrained_params(n):
-    return (
-        n.startswith("clap_model.transformer")
-        or n in ["clap_model.positional_embedding", "clap_model.text_projection"]
-        or n.startswith("clap_model.token_embedding")
-        or n.startswith("clap_model.ln_final")
-        or n.startswith("clap_model.logit_scale_t")
-    )
-
-
-def random_seed(seed=42, rank=0):
-    torch.manual_seed(seed + rank)
-    np.random.seed(seed + rank)
-    random.seed(seed + rank)
-
-
-def config_lp_optimizer(model, data, args):
-    # set wd-related params to 0 if use adam optimizer
-    if args.optimizer == "adam":
-        args.wd = 0
-        args.wd_pretrained = 0
-        args.wd_new = 0
-
-    in_clap = lambda n, p: n.startswith("clap_model")
-
-    named_parameters = list(model.named_parameters())
-
-    optimizer = {}
-    scheduler = {}
-
-    # freeze text encoder
-    text_freeze_parameters = [
-        p
-        for n, p in named_parameters
-        if n.startswith("clap_model.transformer")
-        or n in ["clap_model.positional_embedding", "clap_model.text_projection"]
-        or n.startswith("clap_model.token_embedding")
-        or n.startswith("clap_model.ln_final")
-    ]
-
-    if args.freeze_text:
-        logging.info("Freeze Text!!!!")
-        for k in text_freeze_parameters:
-            k.requires_grad = False
-
-    if not args.lp_freeze:
-        exclude = (
-            lambda n, p: p.ndim < 2
-            or "bn" in n
-            or "ln" in n
-            or "bias" in n
-            or "logit_scale" in n
-        )
-        include = lambda n, p: not exclude(n, p)
-
-        # (yusong): we do not split the learning rate anymore
-        # p for n, p in named_parameters if in_clap(n,p) and exclude(n, p) and p.requires_grad
-        gain_or_bias_params = [
-            p for n, p in named_parameters if exclude(n, p) and p.requires_grad
-        ]
-        # rest_params = [p for n, p in named_parameters if in_clap(n,p) and include(n, p) and p.requires_grad]
-        rest_params = [
-            p for n, p in named_parameters if include(n, p) and p.requires_grad
-        ]
-
-        if args.train_data is None:
-            optimizer = None
-            scheduler = None
-        else:
-            total_steps = data["train"].dataloader.num_batches * args.epochs
-
-            if args.split_opt:
-                for x in ["lr", "beta1", "beta2", "eps", "wd"]:
-                    for y in ["_new", "_pretrained"]:
-                        if getattr(args, x + y) is None:
-                            setattr(args, x + y, getattr(args, x))
-
-                gain_or_bias_pretrained_params = [
-                    p
-                    for n, p in named_parameters
-                    if (exclude(n, p) and p.requires_grad) and is_pretrained_params(n)
-                ]
-                rest_pretrained_params = [
-                    p
-                    for n, p in named_parameters
-                    if (include(n, p) and p.requires_grad) and is_pretrained_params(n)
-                ]
-                gain_or_bias_new_params = [
-                    p
-                    for n, p in named_parameters
-                    if (exclude(n, p) and p.requires_grad)
-                    and (not is_pretrained_params(n))
-                ]
-                rest_new_params = [
-                    p
-                    for n, p in named_parameters
-                    if (include(n, p) and p.requires_grad)
-                    and (not is_pretrained_params(n))
-                ]
-
-                pretrained_params_optimizer = get_optimizer(
-                    [
-                        {"params": gain_or_bias_pretrained_params, "weight_decay": 0.0},
-                        {
-                            "params": rest_pretrained_params,
-                            "weight_decay": args.wd_pretrained,
-                        },
-                    ],
-                    lr=args.lr_pretrained,
-                    betas=(args.beta1_pretrained, args.beta2_pretrained),
-                    eps=args.eps_pretrained,
-                    momentum=args.momentum_pretrained,
-                    optimizer_name=args.optimizer,
-                )
-                pretrained_params_scheduler = cosine_lr(
-                    pretrained_params_optimizer,
-                    args.lr_pretrained,
-                    args.warmup,
-                    total_steps,
-                )
-
-                new_params_optimizer = get_optimizer(
-                    [
-                        {"params": gain_or_bias_new_params, "weight_decay": 0.0},
-                        {"params": rest_new_params, "weight_decay": args.wd_new},
-                    ],
-                    lr=args.lr_new,
-                    betas=(args.beta1_new, args.beta2_new),
-                    eps=args.eps_new,
-                    momentum=args.momentum_new,
-                    optimizer_name=args.optimizer,
-                )
-                new_params_scheduler = cosine_lr(
-                    new_params_optimizer, args.lr_new, args.warmup, total_steps
-                )
-
-                optimizer["text"] = pretrained_params_optimizer
-                optimizer["audio"] = new_params_optimizer
-                scheduler["text"] = pretrained_params_scheduler
-                scheduler["audio"] = new_params_scheduler
-
-                if args.horovod:
-                    pretrained_params_optimizer = hvd.DistributedOptimizer(
-                        pretrained_params_optimizer,
-                        named_parameters=model.named_parameters(),
-                    )
-                    new_params_optimizer = hvd.DistributedOptimizer(
-                        new_params_optimizer, named_parameters=model.named_parameters()
-                    )
-                    hvd.broadcast_parameters(model.state_dict(), root_rank=0)
-                    hvd.broadcast_optimizer_state(
-                        pretrained_params_optimizer, root_rank=0
-                    )
-                    hvd.broadcast_optimizer_state(new_params_optimizer, root_rank=0)
-            else:
-
-                optimizer["clap"] = get_optimizer(
-                    [
-                        {"params": gain_or_bias_params, "weight_decay": 0.0},
-                        {"params": rest_params, "weight_decay": args.wd},
-                    ],
-                    lr=args.lr,
-                    betas=(args.beta1, args.beta2),
-                    eps=args.eps,
-                    momentum=args.momentum,
-                    optimizer_name=args.optimizer,
-                )
-                scheduler["clap"] = cosine_lr(
-                    optimizer["clap"], args.lr, args.warmup, total_steps
-                )
-
-                if args.horovod:
-                    optimizer["clap"] = hvd.DistributedOptimizer(
-                        optimizer["clap"], named_parameters=model.named_parameters()
-                    )
-                    hvd.broadcast_parameters(model.state_dict(), root_rank=0)
-                    hvd.broadcast_optimizer_state(optimizer["clap"], root_rank=0)
-
-    # linear probe optimizer
-    else:
-        lp_params = [
-            p for n, p in named_parameters if (not in_clap(n, p)) and p.requires_grad
-        ]
-        lp_optim = get_optimizer(
-            lp_params,
-            lr=args.lp_lr,
-            betas=(args.beta1, args.beta2),
-            eps=args.eps,
-            momentum=0.9,
-            optimizer_name=args.optimizer,
-        )
-        optimizer["lp"] = lp_optim
-
-    return optimizer, scheduler, text_freeze_parameters
-
-
-def main():
-    args = parse_args()
-
-    time.sleep(args.sleep)
-
-    # sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
-    args.amodel = args.amodel.replace("/", "-")
-    # download sizes.json file
-
-    # (yusong): the below two lines are for debug
-    # print("setting up faulthandler")
-    # faulthandler.register(10)
-
-    random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    torch.cuda.manual_seed(args.seed)
-    torch.cuda.manual_seed_all(args.seed)
-    np.random.seed(args.seed)
-    args.class_index_dict = load_class_label(args.class_label_path)
-
-    # get the name of the experiments
-    if args.name is None:
-        args.name = "-".join(
-            [
-                datetime.now().strftime("%Y_%m_%d-%H_%M_%S"),
-                f"linear_probe" f"model_{args.amodel}",
-                f"lr_{args.lr}",
-                f"b_{args.batch_size}",
-                f"j_{args.workers}",
-                f"p_{args.precision}",
-            ]
-        )
-
-    # discover initial world args early so we can log properly
-    args.distributed = False
-    args.local_rank, args.rank, args.world_size = world_info_from_env()
-
-    if args.remotedata and is_master(args):
-        for dataset_name in args.datasetnames:
-            for split in dataset_split[dataset_name]:
-                if not os.path.exists(f"./json_files/{dataset_name}/{split}"):
-                    os.makedirs(f"./json_files/{dataset_name}/{split}")
-                os.system(
-                    f"aws s3 cp s3://s-laion-audio/webdataset_tar/{dataset_name}/{split}/sizes.json ./json_files/{dataset_name}/{split}/sizes.json"
-                )
-
-    args.log_path = None
-    if is_master(args, local=args.log_local):
-        log_base_path = os.path.join(args.logs, args.name)
-        os.makedirs(log_base_path, exist_ok=True)
-        log_filename = f"out-{args.rank}" if args.log_local else "out.log"
-        args.log_path = os.path.join(log_base_path, log_filename)
-
-        # avoid log dir in same name:
-        postfix = 0
-        while os.path.exists(args.log_path):
-            postfix += 1
-            log_base_path_new = log_base_path + "-" + str(postfix)
-            os.makedirs(log_base_path_new, exist_ok=True)
-            log_filename = f"out-{args.rank}" if args.log_local else "out.log"
-            args.log_path = os.path.join(log_base_path_new, log_filename)
-            # print(
-            #     "Error. Experiment already exists. Use --name {} to specify a new experiment."
-            # )
-            # return -1
-
-    # Set logger
-    args.log_level = logging.DEBUG if args.debug else logging.INFO
-    setup_logging(args.log_path, args.log_level)
-
-    # fully initialize distributed device environment
-    device = init_distributed_device(args)
-
-    args.wandb = "wandb" in args.report_to or "all" in args.report_to
-    args.tensorboard = "tensorboard" in args.report_to or "all" in args.report_to
-    if is_master(args):
-        args.tensorboard_path = (
-            os.path.join(args.logs, args.name, "tensorboard")
-            if args.tensorboard
-            else ""
-        )
-        args.checkpoint_path = os.path.join(args.logs, args.name, "checkpoints")
-        for dirname in [args.tensorboard_path, args.checkpoint_path]:
-            if dirname:
-                os.makedirs(dirname, exist_ok=True)
-    else:
-        args.tensorboard_path = ""
-        args.checkpoint_path = ""
-
-    if args.copy_codebase:
-        copy_codebase(args)
-
-    assert args.precision in ["amp", "fp16", "fp32"]
-    if args.precision == "fp16":
-        logging.warning(
-            "It is recommended to use AMP mixed-precision instead of FP16. "
-            "FP16 support needs further verification and tuning, especially for train."
-        )
-
-    if args.horovod:
-        logging.info(
-            f"Running in horovod mode with multiple processes / nodes. Device: {args.device}."
-            f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
-        )
-    elif args.distributed:
-        logging.info(
-            f"Running in distributed mode with multiple processes. Device: {args.device}."
-            f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
-        )
-    else:
-        logging.info(f"Running with a single process. Device {args.device}.")
-
-    logging.info(f"openai cache dir: {os.path.expanduser(args.openai_model_cache_dir)}")
-
-    # Create CLAP model
-    clap_model, clap_model_cfg = create_model(
-        args.amodel,
-        args.tmodel,
-        args.pretrained,
-        precision=args.precision,
-        device=device,
-        jit=args.torchscript,
-        force_quick_gelu=args.force_quick_gelu,
-        openai_model_cache_dir=os.path.expanduser(args.openai_model_cache_dir),
-        skip_params=False,
-        pretrained_audio=args.pretrained_audio,
-        pretrained_text=args.pretrained_text,
-        enable_fusion=args.enable_fusion,
-        fusion_type=args.fusion_type,
-    )
-
-    args.lp_out_ch = len(list(args.class_index_dict.keys()))
-    # Linear Probe
-    logging.info(f"linear probe using mlp: {args.lp_mlp}")
-    logging.info(f"linear probe using freeze: {args.lp_freeze}")
-    logging.info(f"linear probe act layer: {args.lp_act}")
-    logging.info(f"linear probe out ch: {args.lp_out_ch}")
-    logging.info(f"linear probe learning rate (if applicable): {args.lp_lr}")
-    logging.info(f"linear probe loss func: {args.lp_loss}")
-    logging.info(f"linear probe lp_metrics: {args.lp_metrics}")
-
-    model = LinearProbe(
-        clap_model,
-        mlp=args.lp_mlp,
-        freeze=args.lp_freeze,
-        in_ch=512,
-        out_ch=args.lp_out_ch,
-        act=args.lp_act,
-    )  # in_ch is fixed (i.e., 512)
-    model = model.to(device)
-
-    if args.horovod:
-        with torch.no_grad():
-            for param in model.parameters():
-                param.set_(param.contiguous())
-
-    if args.trace:
-        model = trace_model(model, batch_size=args.batch_size, device=device)
-
-    if is_master(args):
-        logging.info("Linear Probe CLAP Model:")
-        logging.info(f"{str(clap_model)}")
-        logging.info("Params:")
-        params_file = os.path.join(args.logs, args.name, "params.txt")
-        with open(params_file, "w") as f:
-            for name in sorted(vars(args)):
-                val = getattr(args, name)
-                logging.info(f"  {name}: {val}")
-                f.write(f"{name}: {val}\n")
-
-    if args.distributed and not args.horovod:
-        if args.use_bn_sync:
-            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
-        ddp_args = {}
-        if args.ddp_static_graph:
-            # this doesn't exist in older PyTorch, arg only added if enabled
-            ddp_args["static_graph"] = True
-        model = torch.nn.parallel.DistributedDataParallel(
-            model, device_ids=[device], find_unused_parameters=True, **ddp_args
-        )
-
-    data = get_data(args, clap_model_cfg)
-    assert len(data), "At least one train or eval dataset must be specified."
-    if args.trace:
-        assert "train" not in data, "Cannot train with traced model"
-
-    optimizer, scheduler, text_freeze_parameters = config_lp_optimizer(
-        model, data, args
-    )
-
-    scaler = GradScaler() if args.precision == "amp" else None
-
-    # optionally resume from a checkpoint
-    start_epoch = 0
-    if args.resume is not None:
-        if os.path.isfile(args.resume):
-            checkpoint = torch.load(args.resume, map_location=device)
-            if "epoch" in checkpoint:
-                # resuming a train checkpoint w/ epoch and optimizer state
-                start_epoch = checkpoint["epoch"]
-                sd = checkpoint["state_dict"]
-                if not args.distributed and next(iter(sd.items()))[0].startswith(
-                    "module"
-                ):
-                    sd = {k[len("module.") :]: v for k, v in sd.items()}
-                model.load_state_dict(sd)
-                if args.split_opt:
-                    if optimizer is not None:
-                        for k, o_ in optimizer.items():
-                            o_.load_state_dict(checkpoint[k + "_" + "optimizer"])
-                if optimizer is not None:
-                    optimizer.load_state_dict(checkpoint["optimizer"])
-                if scaler is not None and "scaler" in checkpoint:
-                    scaler.load_state_dict(checkpoint["scaler"])
-                logging.info(
-                    f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})"
-                )
-            else:
-                # loading a bare (model only) checkpoint for fine-tune or evaluation
-                model.load_state_dict(checkpoint)
-                logging.info(
-                    f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})"
-                )
-            if args.freeze_text:
-                print("Freeze Text!!!!")
-                for k in text_freeze_parameters:
-                    k.requires_grad = False
-        else:
-            logging.info("=> no checkpoint found at '{}'".format(args.resume))
-
-    cudnn.benchmark = True
-    cudnn.deterministic = False
-
-    # determine if this worker should save logs and checkpoints. only do so if it is rank == 0
-    args.save_logs = args.logs and args.logs.lower() != "none" and is_master(args)
-    writer = None
-    if args.save_logs and args.tensorboard:
-        assert tensorboard is not None, "Please install tensorboard."
-        writer = tensorboard.SummaryWriter(args.tensorboard_path)
-
-    if args.wandb and is_master(args):
-        assert wandb is not None, "Please install wandb."
-        logging.debug("Starting wandb.")
-        args.train_sz = data["train"].dataloader.num_samples
-        if args.val_data is not None:
-            args.val_sz = data["val"].dataloader.num_samples
-        # you will have to configure this for your project!
-        wandb.init(
-            project="clap",
-            notes=args.wandb_notes,
-            name=args.wandb_notes,
-            tags=[],
-            config=vars(args),
-        )
-        if args.debug:
-            wandb.watch(model, log="all")
-        wandb.save(params_file)
-        logging.debug("Finished loading wandb.")
-
-    if "train" not in data:
-        evaluate(model, data, start_epoch, args, writer)
-        return
-    elif start_epoch == 0 and "val" in data and not args.no_eval:
-        evaluate(model, data, 0, args, writer)
-    if args.save_top_performance:
-        current_top_k_ckpt_metrics = {
-            i: 0 for i in range(args.save_top_performance)
-        }  # initialize the top-k metric for ckpts to 0
-
-    for epoch in range(start_epoch, args.epochs):
-        # freeze the text param after (include) args.freeze_text_after, this is -1 by default
-        if epoch == args.freeze_text_after:
-            print("Text pretrained parameters are freezed since this epoch.")
-            for k in text_freeze_parameters:
-                k.requires_grad = False
-        if is_master(args):
-            logging.info(f"Start epoch {epoch}")
-
-        train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, writer)
-        completed_epoch = epoch + 1
-
-        if (
-            any(v in data for v in ("val", "imagenet-val", "imagenet-v2"))
-            and not args.no_eval
-        ):
-            metrics = evaluate(model, data, completed_epoch, args, writer)
-            if args.save_top_performance:
-                top_k_dataset = args.top_k_checkpoint_select_dataset
-                top_k_metric = args.top_k_checkpoint_select_metric
-                filtered_metrics = [
-                    v
-                    for k, v in metrics.items()
-                    if top_k_metric in k and top_k_dataset in k
-                ]  # check all R@10 metrics (all dataset) and use it to update the ckpt
-        # Saving checkpoints.
-        if args.save_logs:
-            opt_dict = {
-                k + "_" + "optimizer": v.state_dict() for k, v in optimizer.items()
-            }
-            checkpoint_dict = {
-                "epoch": completed_epoch,
-                "name": args.name,
-                "state_dict": model.state_dict(),
-            }
-            checkpoint_dict.update(opt_dict)
-            if scaler is not None:
-                checkpoint_dict["scaler"] = scaler.state_dict()
-
-            if completed_epoch == args.epochs or (
-                args.save_frequency > 0 and (completed_epoch % args.save_frequency) == 0
-            ):
-                torch.save(
-                    checkpoint_dict,
-                    os.path.join(args.checkpoint_path, f"epoch_{completed_epoch}.pt"),
-                )
-            if args.save_most_recent:
-                torch.save(
-                    checkpoint_dict,
-                    os.path.join(args.checkpoint_path, f"epoch_latest.pt"),
-                )
-            if args.save_top_performance and not args.no_eval:
-                update_top_k_performance(
-                    filtered_metrics,
-                    current_top_k_ckpt_metrics,
-                    args,
-                    checkpoint_dict,
-                    bignumbetter=True,
-                )
-
-    if args.wandb and is_master(args):
-        wandb.finish()
-
-
-def copy_codebase(args):
-    from shutil import copytree, ignore_patterns
-
-    new_code_path = os.path.join(args.logs, args.name, "code")
-    if os.path.exists(new_code_path):
-        print(
-            f"Error. Experiment already exists at {new_code_path}. Use --name to specify a new experiment."
-        )
-        return -1
-    print(f"Copying codebase to {new_code_path}")
-    current_code_path = os.path.realpath(__file__)
-    for _ in range(3):
-        current_code_path = os.path.dirname(current_code_path)
-    copytree(
-        current_code_path, new_code_path, ignore=ignore_patterns("log", "logs", "wandb")
-    )
-    print("Done copying code.")
-    return 1
-
-
-if __name__ == "__main__":
-    main()

spaces/Awiny/Image2Paragraph/models/grit_src/grit/data/custom_dataset_dataloader.py

@@ -1,250 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Jialian Wu from https://github.com/facebookresearch/Detic/blob/main/detic/data/custom_dataset_dataloader.py
-import operator
-import torch
-import torch.utils.data
-from detectron2.utils.comm import get_world_size
-
-from detectron2.config import configurable
-from torch.utils.data.sampler import BatchSampler, Sampler
-from detectron2.data.common import DatasetFromList, MapDataset
-from detectron2.data.dataset_mapper import DatasetMapper
-from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader
-from detectron2.data.samplers import TrainingSampler
-from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram
-from detectron2.data.build import filter_images_with_only_crowd_annotations
-from detectron2.data.build import filter_images_with_few_keypoints
-from detectron2.data.build import check_metadata_consistency
-from detectron2.data.catalog import MetadataCatalog, DatasetCatalog
-from detectron2.utils import comm
-import itertools
-from typing import Optional
-
-
-def _custom_train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None):
-    sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
-    if 'MultiDataset' in sampler_name:
-        dataset_dicts = get_detection_dataset_dicts_with_source(
-            cfg.DATASETS.TRAIN,
-            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
-            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
-            if cfg.MODEL.KEYPOINT_ON else 0,
-            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
-        )
-    else:
-        dataset_dicts = get_detection_dataset_dicts(
-            cfg.DATASETS.TRAIN,
-            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
-            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
-            if cfg.MODEL.KEYPOINT_ON else 0,
-            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
-        )
-
-    if mapper is None:
-        mapper = DatasetMapper(cfg, True)
-
-    if sampler is not None:
-        pass
-    elif sampler_name == "TrainingSampler":
-        sampler = TrainingSampler(len(dataset))
-    elif sampler_name == "MultiDatasetSampler":
-        sampler = MultiDatasetSampler(
-            dataset_dicts,
-            dataset_ratio=cfg.DATALOADER.DATASET_RATIO,
-        )
-    else:
-        raise ValueError("Unknown training sampler: {}".format(sampler_name))
-
-    return {
-        "dataset": dataset_dicts,
-        "sampler": sampler,
-        "mapper": mapper,
-        "total_batch_size": cfg.SOLVER.IMS_PER_BATCH,
-        "num_workers": cfg.DATALOADER.NUM_WORKERS,
-        'dataset_bs': cfg.DATALOADER.DATASET_BS,
-        'num_datasets': len(cfg.DATASETS.TRAIN)
-    }
-
-
-@configurable(from_config=_custom_train_loader_from_config)
-def build_custom_train_loader(
-        dataset, *, mapper, sampler, 
-        total_batch_size=16,
-        num_workers=0,
-        num_datasets=1,
-        dataset_bs=1
-):
-
-    if isinstance(dataset, list):
-        dataset = DatasetFromList(dataset, copy=False)
-    if mapper is not None:
-        dataset = MapDataset(dataset, mapper)
-    if sampler is None:
-        sampler = TrainingSampler(len(dataset))
-    assert isinstance(sampler, torch.utils.data.sampler.Sampler)
-
-    return build_dataset_batch_data_loader(
-        dataset_bs,
-        dataset,
-        sampler,
-        total_batch_size,
-        num_datasets=num_datasets,
-        num_workers=num_workers,
-    )
-
-
-def build_dataset_batch_data_loader(
-    dataset_bs, dataset, sampler, total_batch_size, num_datasets, num_workers=0
-):
-
-    world_size = get_world_size()
-    assert (
-        total_batch_size > 0 and total_batch_size % world_size == 0
-    ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format(
-        total_batch_size, world_size
-    )
-
-    data_loader = torch.utils.data.DataLoader(
-        dataset,
-        sampler=sampler,
-        num_workers=num_workers,
-        batch_sampler=None,
-        collate_fn=operator.itemgetter(0),  # don't batch, but yield individual elements
-        worker_init_fn=worker_init_reset_seed,
-    )
-
-    if num_datasets > 1:
-        return MultiDatasets(data_loader, dataset_bs, num_datasets)
-    else:
-        return SingleDataset(data_loader, dataset_bs)
-
-
-def get_detection_dataset_dicts_with_source(
-    dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None
-):
-    assert len(dataset_names)
-    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names]
-    for dataset_name, dicts in zip(dataset_names, dataset_dicts):
-        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
-    
-    for source_id, (dataset_name, dicts) in \
-        enumerate(zip(dataset_names, dataset_dicts)):
-        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
-        for d in dicts:
-            d['dataset_source'] = source_id
-
-        if "annotations" in dicts[0]:
-            try:
-                class_names = MetadataCatalog.get(dataset_name).thing_classes
-                check_metadata_consistency("thing_classes", dataset_name)
-                print_instances_class_histogram(dicts, class_names)
-            except AttributeError:  # class names are not available for this dataset
-                pass
-
-    assert proposal_files is None
-
-    dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
-
-    has_instances = "annotations" in dataset_dicts[0]
-    if filter_empty and has_instances:
-        dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
-    if min_keypoints > 0 and has_instances:
-        dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
-
-    return dataset_dicts
-
-
-class MultiDatasetSampler(Sampler):
-    def __init__(
-        self, 
-        dataset_dicts, 
-        dataset_ratio,
-        seed: Optional[int] = None,
-    ):
-        sizes = [0 for _ in range(len(dataset_ratio))]
-        for d in dataset_dicts:
-            sizes[d['dataset_source']] += 1
-        print('dataset sizes', sizes)
-        self.sizes = sizes
-        assert len(dataset_ratio) == len(sizes), \
-            'length of dataset ratio {} should be equal to number if dataset {}'.format(
-                len(dataset_ratio), len(sizes)
-            )
-        if seed is None:
-            seed = comm.shared_random_seed()
-        self._seed = int(seed)
-        self._rank = comm.get_rank()
-        self._world_size = comm.get_world_size()
-        
-        self.dataset_ids = torch.tensor(
-            [d['dataset_source'] for d in dataset_dicts], dtype=torch.long)
-        self.dataset_ratio = dataset_ratio
-
-        dataset_weight = [torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) \
-            for i, (r, s) in enumerate(zip(dataset_ratio, sizes))]
-        dataset_weight = torch.cat(dataset_weight)
-
-        self.weights = dataset_weight
-        self.sample_epoch_size = len(self.weights)
-
-    def __iter__(self):
-        start = self._rank
-        yield from itertools.islice(
-            self._infinite_indices(), start, None, self._world_size)
-
-    def _infinite_indices(self):
-        g = torch.Generator()
-        g.manual_seed(self._seed)
-        while True:
-            if len(self.dataset_ratio) > 1:
-                # multiple datasets
-                ids = torch.multinomial(
-                    self.weights, self.sample_epoch_size, generator=g,
-                    replacement=True)
-                nums = [(self.dataset_ids[ids] == i).sum().int().item() \
-                    for i in range(len(self.sizes))]
-                yield from ids
-            else:
-                # single dataset
-                yield from torch.randperm(self.sizes[0], generator=g).tolist()
-
-
-class SingleDataset(torch.utils.data.IterableDataset):
-    def __init__(self, dataset, batch_sizes):
-        self.dataset = dataset
-        self.batch_sizes = batch_sizes
-        self._buckets = [[] for _ in range(2)]
-
-    def __iter__(self):
-        for d in self.dataset:
-            w, h = d["width"], d["height"]
-            aspect_ratio_bucket_id = 0 if w > h else 1
-            bucket_id = aspect_ratio_bucket_id
-            bucket = self._buckets[bucket_id]
-            bucket.append(d)
-            if len(bucket) == self.batch_sizes:
-                yield bucket[:]
-                del bucket[:]
-
-
-class MultiDatasets(torch.utils.data.IterableDataset):
-    def __init__(self, dataset, batch_sizes, num_datasets):
-        self.dataset = dataset
-        self.batch_sizes = batch_sizes
-        self._buckets = [[] for _ in range(2 * num_datasets)]
-        self.iter_idx = 0
-        self.num_datasets = num_datasets
-
-    def __iter__(self):
-        for d in self.dataset:
-            w, h = d["width"], d["height"]
-            aspect_ratio_bucket_id = 0 if w > h else 1
-            bucket_id = d['dataset_source'] * 2 + aspect_ratio_bucket_id
-            bucket = self._buckets[bucket_id]
-            if len(bucket) < self.batch_sizes:
-                bucket.append(d)
-            selected_dataset = self.iter_idx % self.num_datasets
-            if len(bucket) == self.batch_sizes and selected_dataset == d['dataset_source']:
-                self.iter_idx += 1
-                yield bucket[:]
-                del bucket[:]

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/.github/CODE_OF_CONDUCT.md

@@ -1,5 +0,0 @@
-# Code of Conduct
-
-Facebook has adopted a Code of Conduct that we expect project participants to adhere to.
-Please read the [full text](https://code.fb.com/codeofconduct/)
-so that you can understand what actions will and will not be tolerated.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/GETTING_STARTED.md

@@ -1,79 +0,0 @@
-## Getting Started with Detectron2
-
-This document provides a brief intro of the usage of builtin command-line tools in detectron2.
-
-For a tutorial that involves actual coding with the API,
-see our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
-which covers how to run inference with an
-existing model, and how to train a builtin model on a custom dataset.
-
-
-### Inference Demo with Pre-trained Models
-
-1. Pick a model and its config file from
-  [model zoo](MODEL_ZOO.md),
-  for example, `mask_rcnn_R_50_FPN_3x.yaml`.
-2. We provide `demo.py` that is able to demo builtin configs. Run it with:
-```
-cd demo/
-python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
-  --input input1.jpg input2.jpg \
-  [--other-options]
-  --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
-```
-The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
-This command will run the inference and show visualizations in an OpenCV window.
-
-For details of the command line arguments, see `demo.py -h` or look at its source code
-to understand its behavior. Some common arguments are:
-* To run __on your webcam__, replace `--input files` with `--webcam`.
-* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
-* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
-* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.
-
-
-### Training & Evaluation in Command Line
-
-We provide two scripts in "tools/plain_train_net.py" and "tools/train_net.py",
-that are made to train all the configs provided in detectron2. You may want to
-use it as a reference to write your own training script.
-
-Compared to "train_net.py", "plain_train_net.py" supports fewer default
-features. It also includes fewer abstraction, therefore is easier to add custom
-logic.
-
-To train a model with "train_net.py", first
-setup the corresponding datasets following
-[datasets/README.md](./datasets/README.md),
-then run:
-```
-cd tools/
-./train_net.py --num-gpus 8 \
-  --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
-```
-
-The configs are made for 8-GPU training.
-To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g.:
-```
-./train_net.py \
-  --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
-  --num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
-```
-
-To evaluate a model's performance, use
-```
-./train_net.py \
-  --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
-  --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
-```
-For more options, see `./train_net.py -h`.
-
-### Use Detectron2 APIs in Your Code
-
-See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
-to learn how to use detectron2 APIs to:
-1. run inference with an existing model
-2. train a builtin model on a custom dataset
-
-See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/main/projects)
-for more ways to build your project on detectron2.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/structures/test_imagelist.py

@@ -1,75 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import unittest
-from typing import List, Sequence, Tuple
-import torch
-
-from detectron2.structures import ImageList
-
-
-class TestImageList(unittest.TestCase):
-    def test_imagelist_padding_tracing(self):
-        # test that the trace does not contain hard-coded constant sizes
-        def to_imagelist(tensors: Sequence[torch.Tensor]):
-            image_list = ImageList.from_tensors(tensors, 4)
-            return image_list.tensor, image_list.image_sizes
-
-        def _tensor(*shape):
-            return torch.ones(shape, dtype=torch.float32)
-
-        # test CHW (inputs needs padding vs. no padding)
-        for shape in [(3, 10, 10), (3, 12, 12)]:
-            func = torch.jit.trace(to_imagelist, ([_tensor(*shape)],))
-            tensor, image_sizes = func([_tensor(3, 15, 20)])
-            self.assertEqual(tensor.shape, (1, 3, 16, 20), tensor.shape)
-            self.assertEqual(image_sizes[0].tolist(), [15, 20], image_sizes[0])
-
-        # test HW
-        func = torch.jit.trace(to_imagelist, ([_tensor(10, 10)],))
-        tensor, image_sizes = func([_tensor(15, 20)])
-        self.assertEqual(tensor.shape, (1, 16, 20), tensor.shape)
-        self.assertEqual(image_sizes[0].tolist(), [15, 20], image_sizes[0])
-
-        # test 2x CHW
-        func = torch.jit.trace(
-            to_imagelist,
-            ([_tensor(3, 16, 10), _tensor(3, 13, 11)],),
-        )
-        tensor, image_sizes = func([_tensor(3, 25, 20), _tensor(3, 10, 10)])
-        self.assertEqual(tensor.shape, (2, 3, 28, 20), tensor.shape)
-        self.assertEqual(image_sizes[0].tolist(), [25, 20], image_sizes[0])
-        self.assertEqual(image_sizes[1].tolist(), [10, 10], image_sizes[1])
-        # support calling with different spatial sizes, but not with different #images
-
-    def test_imagelist_scriptability(self):
-        image_nums = 2
-        image_tensor = torch.randn((image_nums, 10, 20), dtype=torch.float32)
-        image_shape = [(10, 20)] * image_nums
-
-        def f(image_tensor, image_shape: List[Tuple[int, int]]):
-            return ImageList(image_tensor, image_shape)
-
-        ret = f(image_tensor, image_shape)
-        ret_script = torch.jit.script(f)(image_tensor, image_shape)
-
-        self.assertEqual(len(ret), len(ret_script))
-        for i in range(image_nums):
-            self.assertTrue(torch.equal(ret[i], ret_script[i]))
-
-    def test_imagelist_from_tensors_scriptability(self):
-        image_tensor_0 = torch.randn(10, 20, dtype=torch.float32)
-        image_tensor_1 = torch.randn(12, 22, dtype=torch.float32)
-        inputs = [image_tensor_0, image_tensor_1]
-
-        def f(image_tensor: List[torch.Tensor]):
-            return ImageList.from_tensors(image_tensor, 10)
-
-        ret = f(inputs)
-        ret_script = torch.jit.script(f)(inputs)
-
-        self.assertEqual(len(ret), len(ret_script))
-        self.assertTrue(torch.equal(ret.tensor, ret_script.tensor))
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/Benson/text-generation/Examples/Descargar Apk Hroe De La Cuerda Mod.md

@@ -1,72 +0,0 @@
-<br />
-<h1>Cómo descargar e instalar el héroe de cuerda Mod APK en Android</h1>
-<p>Si estás buscando un divertido y lleno de acción juego de superhéroes, es posible que desee probar <strong>Rope Hero</strong>. Este es un juego de disparos en tercera persona en 3D con elementos RPG, donde juegas como un súper héroe azul que puede usar una súper cuerda para girar alrededor de la ciudad, luchar contra el crimen y personalizar a tu personaje. También puedes elegir ser un villano y causar caos en la ciudad, si ese es tu estilo. </p>
-<p>Sin embargo, si desea disfrutar de más características y opciones en el juego, tales como dinero ilimitado, nuevas armas, vehículos, pieles y misiones, es posible que desee descargar el <strong>Rope Hero mod APK</strong>. Esta es una versión modificada del juego original que te da acceso a más contenido y diversión. En este artículo, le mostraremos cómo descargar e instalar el Héroe de cuerda mod APK en su dispositivo Android en unos sencillos pasos. </p>
-<h2>descargar apk héroe de la cuerda mod</h2><br /><p><b><b>DOWNLOAD</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6LQf">https://bltlly.com/2v6LQf</a></b></p><br /><br />
-<h2>Qué es un archivo APK y cómo instalarlo en Android</h2>
-<p>Un archivo APK es un archivo de paquete que contiene todos los archivos y datos necesarios para que una aplicación Android se ejecute. Es similar a un archivo EXE para computadoras con Windows. Puede instalar un archivo APK en su dispositivo Android directamente desde su navegador o desde una aplicación de administrador de archivos. Sin embargo, antes de hacer eso, debe asegurarse de que su dispositivo permita aplicaciones o fuentes desconocidas. Esto significa que puede instalar aplicaciones que no son de Google Play Store.</p>
-<p>Para habilitar aplicaciones o fuentes desconocidas en tu dispositivo Android, sigue estos pasos:</p>
-<ol>
-<li>Ve a la configuración de tu dispositivo y toca Aplicaciones y notificaciones (o Aplicaciones en versiones anteriores de Android). </li>
-<li>Toque los tres puntos en la esquina superior derecha. </li>
-<li>Toque Acceso especial. </li>
-<li>Toca Instalar aplicaciones desconocidas. </li>
-<li>Toque Chrome (o cualquier navegador web que utilice). </li>
-<li>Mover Permitir desde esta fuente a la posición On. </li>
-</ol>
-<p>Ahora estás listo para instalar cualquier archivo APK en tu dispositivo Android. </p>
-
-<p>El siguiente paso es descargar el archivo APK mod Rope Hero de una fuente confiable. Solo debe descargar archivos APK de sitios web de confianza que monitorean y verifican sus archivos de malware y virus. Uno de los mejores sitios web para descargar archivos APK es <a href="( 1 )">APK Mirror</a>. Este sitio web alberga un montón de aplicaciones populares de Android y actualizaciones que se pueden descargar de forma gratuita. </p>
-<p>Para descargar el mod de héroe de cuerda APK de APK Mirror, siga estos pasos:</p>
-<ol>
-<li>Abra su navegador web y vaya a <a href="( 1 )">https://an1.com/5642-rope-hero-vice-town-mod-apk-free.html</a>. </li>
-<li>Desplácese hacia abajo hasta que vea un botón verde que diga Descargar (MOD). </li>
-<li>Toca el botón y espera a que comience la descarga. </li>
-<li>Puede ver algunas ventanas emergentes o advertencias que dicen "Este tipo de archivo puede dañar su dispositivo." Ignórelos y toque OK o Descargar de todos modos. </li>
-</ol>
-<p>El archivo mod APK de héroe de cuerda se descargará en la carpeta de descargas de su dispositivo. </p>
-<h2>Cómo instalar el héroe de cuerda mod APK en su dispositivo Android</h2>
-<p>El paso final es instalar el Héroe de cuerda mod APK en su dispositivo Android. Para hacer esto, necesita una aplicación de administrador de archivos que puede localizar y abrir el archivo APK. Si no tiene uno, puede descargarlo de Google Play, como <a href="( 2 )">Cx File Explorer</a> o <a href="( 3 )">Administrador de archivos</a>. </p>
-<p></p>
-<p>Para instalar el héroe de cuerda mod APK usando una aplicación de administrador de archivos, siga estos pasos:</p>
-<ol>
-<li>Abra su aplicación de administrador de archivos y vaya a la carpeta Descargas. </li>
-<li> Localizar y toque el archivo Rope Hero mod APK. Debe tener un nombre como rope-hero-vice-town-mod.apk. </li>
-<li>Es posible que vea una ventana emergente que dice "Para su seguridad, el teléfono no se le permite instalar aplicaciones desconocidas de esta fuente." Toca Configuración y mueve Permitir desde esta fuente a la posición On. </li>
-<li>Volver a la aplicación de administrador de archivos y toque el archivo de mod APK Rope Hero de nuevo. </li>
-
-<li>Una vez que la instalación se haya hecho, puede tocar Abrir para iniciar el juego o Listo para salir de la aplicación de administrador de archivos. </li>
-</ol>
-<p>Felicidades! Usted ha instalado con éxito el Héroe de cuerda mod APK en su dispositivo Android. </p>
-<h2>Cómo disfrutar de las características de la Cuerda Héroe mod APK</h2>
-<p>Ahora que ha instalado el Héroe de cuerda mod APK, se puede disfrutar de las características y beneficios de esta versión modificada del juego. Estas son algunas de las cosas que puedes hacer con el mod de héroe de cuerda APK:</p>
-<ul>
-<li>Puedes obtener dinero ilimitado para comprar nuevas armas, vehículos, pieles y mejoras para tu personaje. </li>
-<li>Puedes desbloquear nuevas misiones que no están disponibles en el juego original. </li>
-<li>Puedes usar nuevas armas y gadgets, como un jetpack, una pistola láser, un lanzallamas y un lanzagranadas. </li>
-<li>Puede conducir vehículos nuevos, como un tanque, un helicóptero, una motocicleta y un automóvil deportivo. </li>
-<li>Puedes personalizar tu personaje con nuevas pieles, como un hombre araña, un batman, un hulk y un ninja. </li>
-<li>Puedes explorar la ciudad e interactuar con diferentes objetos y personas. </li>
-<li>Puedes elegir ser un héroe o un villano y luchar contra otras pandillas, policías o superhéroes. </li>
-</ul>
-<p>Con el Héroe de cuerda mod APK, puede tener más diversión y emoción en este juego de superhéroes. También puede comparar su progreso y logros con otros jugadores en línea y compartir sus capturas de pantalla y videos en las redes sociales. </p>
-<h2>Conclusión: Resumir los principales puntos y beneficios de la descarga y la instalación de la cuerda del héroe mod APK</h2>
-
-<h2>Preguntas frecuentes: Responder a algunas preguntas comunes sobre el héroe de la cuerda y el mod APK</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Rope Hero y el mod APK:</p>
-<h3>Q: ¿Es Rope Hero libre para jugar? </h3>
-<p>A: Sí, Rope Hero es gratis. Puedes descargarlo desde Google Play o desde otros sitios web. Sin embargo, algunas características y elementos pueden requerir compras en la aplicación o ver anuncios. Si desea evitar eso, puede descargar el mod APK en su lugar. </p>
-<h3>Q: ¿Es seguro jugar Rope Hero? </h3>
-<p>A: Sí, Rope Hero es seguro jugar. No contiene ningún contenido dañino o malicioso. Sin embargo, solo debe descargarlo de fuentes confiables y escanearlo con una aplicación antivirus antes de instalarlo. Además, tenga cuidado con los permisos que otorga a la aplicación cuando la instala. </p>
-<h3>Q: ¿Es Rope Hero compatible con mi dispositivo? </h3>
-<p>A: Rope Hero es compatible con la mayoría de dispositivos Android que se ejecutan en Android 4.4 o superior. Sin embargo, algunos dispositivos pueden experimentar retrasos o fallos debido a problemas de memoria o rendimiento. Para mejorar tu experiencia de juego, debes cerrar otras aplicaciones que se ejecutan en segundo plano y limpiar tu caché antes de jugar. </p>
-<h3>P: ¿Cómo actualizo Rope Hero? </h3>
-<p>A: Si has descargado Rope Hero de Google Play, recibirás actualizaciones automáticas cada vez que haya una nueva versión disponible. Si lo descargaste de otro sitio web o instalaste el mod APK, tendrás que comprobar manualmente las actualizaciones y descargarlas tú mismo. También puede tener que desinstalar la versión anterior antes de instalar la nueva. </p>
-<h3>Q: ¿Cómo puedo desinstalar Rope Hero? </h3>
-<p>A: Si quieres desinstalar Rope Hero desde tu dispositivo, puedes hacerlo siguiendo estos pasos:</p>
-<ol>
-<li>Ve a la configuración de tu dispositivo y toca Aplicaciones y notificaciones (o Aplicaciones en versiones anteriores de Android). </li> <li>Encuentra y toca Rope Hero en la lista de aplicaciones. </li>
-<li>Pulse Desinstalar y confirme su elección. </li>
-</ol> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Gratis Juegos De Matemticas Para El Grado 2.md

@@ -1,55 +0,0 @@
-<br />
-<h1>Descargar Write It! Coreano: La mejor aplicación para aprender Hangul</h1>
-<p>¿Quieres aprender a escribir hangul coreano de una manera rápida, eficiente y divertida? Si es así, usted debe descargar Write It! Coreano, la primera aplicación de reconocimiento de escritura para el coreano. En este artículo, le diremos lo que escribir! Coreano es, qué características tiene, qué beneficios ofrece, y cómo descargarlo en su dispositivo. </p>
-<h2>descargar gratis juegos de matemáticas para el grado 2</h2><br /><p><b><b>DOWNLOAD</b> &#10022;&#10022;&#10022; <a href="https://bltlly.com/2v6JtU">https://bltlly.com/2v6JtU</a></b></p><br /><br />
-<h2>¿Qué es Write It! Coreano? </h2>
-<p>Write It! Korean es una aplicación que te enseña cómo escribir hangul coreano, el alfabeto de la lengua coreana. A diferencia de otras aplicaciones que solo te permiten rastrear o copiar los caracteres, Write It! Korean te permite escribirlos por ti mismo usando tu dedo o un lápiz. La aplicación reconoce su escritura y le da retroalimentación instantánea sobre su precisión y pronunciación. De esta manera, puedes aprender a escribir hangul correctamente y con confianza. </p>
-<h3>Características de Write It! Coreano</h3>
-<p>Write It! Korean tiene muchas características que lo convierten en la mejor aplicación para aprender hangul. Estos son algunos de ellos:</p>
-<h4>Reconocimiento de escritura</h4>
-<p>La aplicación utiliza una sofisticada tecnología de reconocimiento de escritura que puede detectar su escritura y evaluar su rendimiento. No tienes que preocuparte por quedarte atascado o tener que volver y hacer referencia a cómo escribir un personaje. La aplicación le guiará a través de cada golpe y le dirá si cometió algún error. También puede ajustar los niveles de sensibilidad y dificultad según su preferencia. </p>
-<p></p>
-<h4>Lecciones guiadas</h4>
-<p>La aplicación tiene lecciones de tamaño bocado que cubren todos los caracteres básicos y avanzados hangul. Puedes practicar la escritura con guías antes de ponerte a prueba, haciendo que el aprendizaje sea extremadamente rápido y libre de estrés. También puedes revisar tus lecciones anteriores y repetirlas tantas veces como quieras. </p>
-<h4>Seguimiento del progreso</h4>
-
-<h4>Modo sin conexión</h4>
-<p>La aplicación funciona sin conexión, por lo que puede escribir en cualquier lugar y en cualquier momento sin conexión a Internet. No tiene que preocuparse por el uso de datos o problemas de red. Puede aprender hangul a su propio ritmo y conveniencia. </p>
-<h3>Beneficios de escribir! Coreano</h3>
-<p>Write It! Korean ofrece muchos beneficios que hacen que valga la pena descargarlo. Estos son algunos de ellos:</p>
-<h4>Aprendizaje rápido y eficiente</h4>
-<p>La aplicación le ayuda a aprender hangul de una manera rápida y eficiente mediante el uso de reconocimiento de escritura y lecciones guiadas. Puedes memorizar los personajes fácil y rápidamente sin aburrirte o frustrarte. También puedes mejorar tus habilidades de pronunciación y ortografía escuchando el audio y leyendo la romanización. </p>
-<h4>Experiencia divertida y atractiva</h4>
-<p>La aplicación hace que el aprendizaje sea divertido y atractivo mediante el uso de gráficos coloridos, animaciones y sonidos. Puedes disfrutar escribiendo en diferentes fondos, usando diferentes bolígrafos y ganando diferentes insignias. También puedes jugar juegos y cuestionarios para probar tus conocimientos y divertirte. </p>
-<h4>Práctica flexible y conveniente</h4>
-<p>La aplicación le permite practicar hangul de forma flexible y conveniente trabajando sin conexión y teniendo ajustes ajustables. Puede escribir en cualquier lugar y en cualquier momento sin limitaciones ni distracciones. También puede personalizar la aplicación según sus necesidades y preferencias. </p>
-<h3>Cómo descargar Write It! Coreano</h3>
-<p>Si estás convencido de que Write It! Korean es la mejor aplicación para aprender hangul, aquí es cómo se puede descargar en su dispositivo:</p>
-<h4>Para dispositivos Android</h4>
-<ol>
-<li>Ir a la tienda de Google Play en su dispositivo. </li>
-<li>Buscar "Write It! Korean " y toque en el icono de la aplicación. </li>
-<li>Toque en el botón "Instalar" y espere a que la aplicación se descargue e instale en su dispositivo. </li>
-<li>Toque en el botón "Abrir" y empezar a escribir hangul! </li>
-</ol>
-<h4>Para dispositivos iOS</h4>
-<ol>
-<li>Ir a la App Store en su dispositivo. </li>
-
-<li>Toque en el botón "Obtener" e introduzca su ID de Apple y contraseña si se le solicita. </li>
-<li>Espere a que la aplicación se descargue e instale en su dispositivo. </li>
-<li>Toque en el icono de la aplicación y empezar a escribir hangul! </li>
-</ol>
-<h2>Conclusión</h2>
-<p>¡Escribe tu mensaje! El coreano es la mejor aplicación para aprender hangul porque te enseña a escribir personajes coreanos por ti mismo usando el reconocimiento de escritura y lecciones guiadas. También ofrece muchas características y beneficios que hacen que el aprendizaje sea rápido, eficiente, divertido, atractivo, flexible y conveniente. Puede descargar Write It! Korean en su dispositivo Android o iOS siguiendo los sencillos pasos anteriores. Entonces, ¿qué estás esperando? Download Write It! Coreano hoy y empezar a escribir hangul como un profesional! </p>
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Write It! Coreano:</p>
-<ul>
-<li><b>¿Cuánto cuesta Write It! Korean cost? </b><br>Write It! Korean es gratis para descargar y usar. Sin embargo, puede actualizar a la versión premium para desbloquear más funciones y eliminar anuncios. La versión premium cuesta $4.99 por mes o $29.99 por año. </li>
-<li><b>¿Cuántos caracteres hangul puedo aprender con Write It! Korean? </b><br>Write It! Coreano cubre los 40 caracteres básicos y 11 caracteres hangul avanzados. Puedes aprender a escribir cada carácter en diferentes sílabas y palabras. </li>
-<li><b>¿Puedo usar Write It! Korean para aprender otros aspectos del idioma coreano? </b><br>Write It! El coreano se centra en enseñarte a escribir hangul, pero también te ayuda a mejorar tu pronunciación, ortografía, vocabulario y gramática. Puede escuchar el audio, leer la romanización y ver la traducción de cada carácter, sílaba y palabra. </li>
-<li><b>¿Puedo usar Write It! Coreano con otras aplicaciones o recursos? </b><br>Sí, puede usar Write It! Coreano con otras aplicaciones o recursos que te enseñan a hablar, escuchar, leer o entender coreano. ¡Escríbelo! El coreano complementa tu aprendizaje ayudándote a dominar el aspecto de escritura del idioma. </li>
-
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Brasd99/SquadDetective/app.py

@@ -1,159 +0,0 @@
-import json
-import zipfile
-import numpy as np
-import cv2
-import os
-import gradio as gr
-from deepface import DeepFace
-from ultralytics import YOLO
-import urllib.request
-import asyncio
-
-with open('config.json', 'r') as f:
-    config = json.load(f)
-
-FACE_DIST_TRESH = config['FACE_DIST_TRESH']
-FACE_DET_TRESH = config['FACE_DET_TRESH']
-YOLO_WEIGHTS_URL = config['YOLO_WEIGHTS_URL']
-
-yolo_weights_filename = os.path.basename(YOLO_WEIGHTS_URL)
-
-if not os.path.exists(yolo_weights_filename):
-   urllib.request.urlretrieve(YOLO_WEIGHTS_URL, yolo_weights_filename)
-
-model = YOLO(yolo_weights_filename)
-
-async def find_distance(base_face, check_face):
-    result = await asyncio.to_thread(DeepFace.verify, base_face, check_face, enforce_detection=False)
-    return result['distance']
-
-def find_faces(image):
-  outputs = model(image)
-  faces = []
-  for box in outputs[0].boxes:
-    if float(box.conf) >= FACE_DET_TRESH:
-      x, y, w, h = [int(coord) for coord in box.xywh[0]]
-      x_center, y_center = x + w / 2, y + h / 2
-      x1 = int(x_center - w)
-      y1 = int(y_center - h)
-      crop_img = image[y1:y1+h, x1:x1+w]
-      faces.append(crop_img)
-  return faces
-  
-async def load_images_from_zip(zip_path):
-    images = []
-    loop = asyncio.get_running_loop()
-    
-    with zipfile.ZipFile(zip_path, 'r') as zip_file:
-        for file_name in zip_file.namelist():
-            with zip_file.open(file_name) as file:
-                img_bytes = await loop.run_in_executor(None, file.read)
-                img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), cv2.IMREAD_COLOR)
-                if img is not None:
-                    images.append(img)
-    return images
-
-def create_image(images):
-    table_width = 800
-    row_height = 100
-    margin = 10
-    text_margin = 20
-    id_col_width = 100
-
-    font = cv2.FONT_HERSHEY_SIMPLEX
-    font_scale = 0.5
-    color = (255, 255, 255)
-    thickness = 2
-
-    table_height = text_margin + margin + (row_height + margin) * len(images)
-
-    table = np.zeros((table_height, table_width, 3), np.uint8)
-
-    id_x = 10
-    img_x = id_col_width + 10
-    y = text_margin
-
-    cv2.putText(table, 'Image ID', (id_x, y), font, font_scale, color, thickness)
-    cv2.putText(table, 'Face', (img_x, y), font, font_scale, color, thickness)
-
-    y += margin
-
-    for i, img in enumerate(images):
-        height, width = img.shape[:2]
-        new_width = int(width * row_height / height)
-        if img_x + new_width > table_width:
-            new_width = table_width - img_x
-        img_resized = cv2.resize(img, (new_width, row_height))
-
-        cv2.putText(table, str(i), (id_x, y + margin), font, font_scale, color, thickness)
-        table[y:y+row_height, img_x:img_x+new_width] = img_resized
-
-        y += row_height + margin
-
-    for col in range(table.shape[1]-1, -1, -1):
-        if not np.any(table[:, col]):
-            continue
-        else:
-            break
-    table_cropped = table[:, :col+1+id_x]
-
-    return table_cropped
-
-async def process_photo_async(photo, input_avatars_faces):
-    not_found_faces = []
-    avatars_faces_count = len(input_avatars_faces)
-    input_faces = find_faces(photo)
-    for input_face in input_faces:
-        for i in range(avatars_faces_count):
-            distance = await find_distance(input_avatars_faces[i], input_face)
-            if distance <= FACE_DIST_TRESH:
-                break
-            elif i + 1 == avatars_faces_count:
-                not_found_faces.append(input_face)
-    return not_found_faces
-
-async def check_async(photos, input_avatars_faces, progress):
-    tasks = []
-    not_found_faces = []
-
-    for photo in photos:
-        task = asyncio.create_task(process_photo_async(photo, input_avatars_faces))
-        tasks.append(task)
-    
-    for i, task in enumerate(tasks):
-        result = await task
-        not_found_faces += result
-        progress((i+1)/len(tasks))
-
-    return not_found_faces
-
-def check(avatars_zip, photos_zip, progress=gr.Progress()):
-    avatars = asyncio.run(load_images_from_zip(avatars_zip.name))
-    avatars = [cv2.cvtColor(avatar, cv2.COLOR_RGB2BGR) for avatar in avatars]
-
-    photos = asyncio.run(load_images_from_zip(photos_zip.name))
-    photos = [cv2.cvtColor(photo, cv2.COLOR_RGB2BGR) for photo in photos]
-
-    input_avatars_faces = [find_faces(avatar) for avatar in avatars]
-    input_avatars_faces = [face for faces in input_avatars_faces for face in faces]
-
-    not_found_faces = asyncio.run(check_async(photos, input_avatars_faces, progress))
-
-    return create_image(not_found_faces)
-
-title = '<h1 style="text-align:center">SquadDetective</h1>'
-logo = '<center><img src="https://i.ibb.co/C0BH40g/logo.png" width="300" height="300" alt="SquadDetective logo"></center>'
-
-with gr.Blocks(theme='soft', title='SquadDetective') as blocks:
-    gr.HTML(title)
-    gr.HTML(logo)
-    gr.Markdown('**SquadDetective** is a service that helps sports teams to identify unclaimed players by comparing their faces to photos taken during matches. By using state-of-the-art facial recognition technology, this service can quickly and accurately match the faces of players in photos to a database of registered players, allowing teams to quickly identify any unclaimed players and take appropriate action. With **SquadDetective**, sports teams can ensure that all players are properly registered and eligible to play, helping to avoid potential penalties and other issues.')
-    with gr.Row():
-        avatars = gr.inputs.File(label='Avatar photos (zip)')
-        photos = gr.inputs.File(label='Photos to be processed (zip)')
-    inputs = [avatars, photos]
-    process_button = gr.Button('Process')
-    outputs=gr.outputs.Image(type='numpy', label='Report')
-    process_button.click(fn=check, inputs=inputs, outputs=outputs)
-
-blocks.queue(concurrency_count=1).launch()

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

@@ -1,78 +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.
- */
-
-#pragma once
-
-#include <thrust/detail/preprocessor.h>
-#include <thrust/detail/integer_traits.h>
-
-/**
- * Dispatch between 32-bit and 64-bit index based versions of the same algorithm
- * implementation. This version assumes that callables for both branches consist
- * of the same tokens, and is intended to be used with Thrust-style dispatch
- * interfaces, that always deduce the size type from the arguments.
- */
-#define THRUST_INDEX_TYPE_DISPATCH(status, call, count, arguments) \
-    if (count <= thrust::detail::integer_traits<thrust::detail::int32_t>::const_max) { \
-        thrust::detail::int32_t THRUST_PP_CAT2(count, _fixed) = count; \
-        status = call arguments; \
-    } \
-    else { \
-        thrust::detail::int64_t THRUST_PP_CAT2(count, _fixed) = count; \
-        status = call arguments; \
-    }
-
-/**
- * Dispatch between 32-bit and 64-bit index based versions of the same algorithm
- * implementation. This version assumes that callables for both branches consist
- * of the same tokens, and is intended to be used with Thrust-style dispatch
- * interfaces, that always deduce the size type from the arguments.
- *
- * This version of the macro supports providing two count variables, which is
- * necessary for set algorithms.
- */
-#define THRUST_DOUBLE_INDEX_TYPE_DISPATCH(status, call, count1, count2, arguments) \
-    if (count1 + count2 <= thrust::detail::integer_traits<thrust::detail::int32_t>::const_max) { \
-        thrust::detail::int32_t THRUST_PP_CAT2(count1, _fixed) = count1; \
-        thrust::detail::int32_t THRUST_PP_CAT2(count2, _fixed) = count2; \
-        status = call arguments; \
-    } \
-    else { \
-        thrust::detail::int64_t THRUST_PP_CAT2(count1, _fixed) = count1; \
-        thrust::detail::int64_t THRUST_PP_CAT2(count2, _fixed) = count2; \
-        status = call arguments; \
-    }
-/**
- * Dispatch between 32-bit and 64-bit index based versions of the same algorithm
- * implementation. This version allows using different token sequences for callables
- * in both branches, and is intended to be used with CUB-style dispatch interfaces,
- * where the "simple" interface always forces the size to be `int` (making it harder
- * for us to use), but the complex interface that we end up using doesn't actually
- * provide a way to fully deduce the type from just the call, making the size type
- * appear in the token sequence of the callable.
- *
- * See reduce_n_impl to see an example of how this is meant to be used.
- */
-#define THRUST_INDEX_TYPE_DISPATCH2(status, call_32, call_64, count, arguments) \
-    if (count <= thrust::detail::integer_traits<thrust::detail::int32_t>::const_max) { \
-        thrust::detail::int32_t THRUST_PP_CAT2(count, _fixed) = count; \
-        status = call_32 arguments; \
-    } \
-    else { \
-        thrust::detail::int64_t THRUST_PP_CAT2(count, _fixed) = count; \
-        status = call_64 arguments; \
-    }
-

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

@@ -1,41 +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.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the per_device_resource.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch per_device_resource
-
-#include <thrust/system/detail/sequential/per_device_resource.h>
-
-#if 0
-#include <thrust/system/cpp/detail/per_device_resource.h>
-#include <thrust/system/cuda/detail/per_device_resource.h>
-#include <thrust/system/omp/detail/per_device_resource.h>
-#include <thrust/system/tbb/detail/per_device_resource.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_PER_DEVICE_RESOURCE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/per_device_resource.h>
-#include __THRUST_HOST_SYSTEM_PER_DEVICE_RESOURCE_HEADER
-#undef __THRUST_HOST_SYSTEM_PER_DEVICE_RESOURCE_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_PER_DEVICE_RESOURCE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/per_device_resource.h>
-#include __THRUST_DEVICE_SYSTEM_PER_DEVICE_RESOURCE_HEADER
-#undef __THRUST_DEVICE_SYSTEM_PER_DEVICE_RESOURCE_HEADER
-

spaces/CVPR/lama-example/fetch_data/places_standard_test_val_gen_masks.sh

@@ -1,13 +0,0 @@
-mkdir -p places_standard_dataset/val/
-mkdir -p places_standard_dataset/visual_test/
-
-
-python3 bin/gen_mask_dataset.py \
-$(pwd)/configs/data_gen/random_thick_512.yaml \
-places_standard_dataset/val_hires/ \
-places_standard_dataset/val/
-
-python3 bin/gen_mask_dataset.py \
-$(pwd)/configs/data_gen/random_thick_512.yaml \
-places_standard_dataset/visual_test_hires/ \
-places_standard_dataset/visual_test/

spaces/CVPR/regionclip-demo/detectron2/engine/train_loop.py

@@ -1,408 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import logging
-import numpy as np
-import time
-import weakref
-from typing import Dict, List, Optional
-import torch
-from torch.nn.parallel import DataParallel, DistributedDataParallel
-
-import detectron2.utils.comm as comm
-from detectron2.utils.events import EventStorage, get_event_storage
-from detectron2.utils.logger import _log_api_usage
-
-__all__ = ["HookBase", "TrainerBase", "SimpleTrainer", "AMPTrainer"]
-
-
-class HookBase:
-    """
-    Base class for hooks that can be registered with :class:`TrainerBase`.
-
-    Each hook can implement 4 methods. The way they are called is demonstrated
-    in the following snippet:
-    ::
-        hook.before_train()
-        for iter in range(start_iter, max_iter):
-            hook.before_step()
-            trainer.run_step()
-            hook.after_step()
-        iter += 1
-        hook.after_train()
-
-    Notes:
-        1. In the hook method, users can access ``self.trainer`` to access more
-           properties about the context (e.g., model, current iteration, or config
-           if using :class:`DefaultTrainer`).
-
-        2. A hook that does something in :meth:`before_step` can often be
-           implemented equivalently in :meth:`after_step`.
-           If the hook takes non-trivial time, it is strongly recommended to
-           implement the hook in :meth:`after_step` instead of :meth:`before_step`.
-           The convention is that :meth:`before_step` should only take negligible time.
-
-           Following this convention will allow hooks that do care about the difference
-           between :meth:`before_step` and :meth:`after_step` (e.g., timer) to
-           function properly.
-
-    """
-
-    trainer: "TrainerBase" = None
-    """
-    A weak reference to the trainer object. Set by the trainer when the hook is registered.
-    """
-
-    def before_train(self):
-        """
-        Called before the first iteration.
-        """
-        pass
-
-    def after_train(self):
-        """
-        Called after the last iteration.
-        """
-        pass
-
-    def before_step(self):
-        """
-        Called before each iteration.
-        """
-        pass
-
-    def after_step(self):
-        """
-        Called after each iteration.
-        """
-        pass
-
-    def state_dict(self):
-        """
-        Hooks are stateless by default, but can be made checkpointable by
-        implementing `state_dict` and `load_state_dict`.
-        """
-        return {}
-
-
-class TrainerBase:
-    """
-    Base class for iterative trainer with hooks.
-
-    The only assumption we made here is: the training runs in a loop.
-    A subclass can implement what the loop is.
-    We made no assumptions about the existence of dataloader, optimizer, model, etc.
-
-    Attributes:
-        iter(int): the current iteration.
-
-        start_iter(int): The iteration to start with.
-            By convention the minimum possible value is 0.
-
-        max_iter(int): The iteration to end training.
-
-        storage(EventStorage): An EventStorage that's opened during the course of training.
-    """
-
-    def __init__(self) -> None:
-        self._hooks: List[HookBase] = []
-        self.iter: int = 0
-        self.start_iter: int = 0
-        self.max_iter: int
-        self.storage: EventStorage
-        _log_api_usage("trainer." + self.__class__.__name__)
-
-    def register_hooks(self, hooks: List[Optional[HookBase]]) -> None:
-        """
-        Register hooks to the trainer. The hooks are executed in the order
-        they are registered.
-
-        Args:
-            hooks (list[Optional[HookBase]]): list of hooks
-        """
-        hooks = [h for h in hooks if h is not None]
-        for h in hooks:
-            assert isinstance(h, HookBase)
-            # To avoid circular reference, hooks and trainer cannot own each other.
-            # This normally does not matter, but will cause memory leak if the
-            # involved objects contain __del__:
-            # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/
-            h.trainer = weakref.proxy(self)
-        self._hooks.extend(hooks)
-
-    def train(self, start_iter: int, max_iter: int):
-        """
-        Args:
-            start_iter, max_iter (int): See docs above
-        """
-        logger = logging.getLogger(__name__)
-        logger.info("Starting training from iteration {}".format(start_iter))
-
-        self.iter = self.start_iter = start_iter
-        self.max_iter = max_iter
-
-        with EventStorage(start_iter) as self.storage:
-            try:
-                self.before_train()
-                for self.iter in range(start_iter, max_iter):
-                    self.before_step()
-                    self.run_step()
-                    self.after_step()
-                # self.iter == max_iter can be used by `after_train` to
-                # tell whether the training successfully finished or failed
-                # due to exceptions.
-                self.iter += 1
-            except Exception:
-                logger.exception("Exception during training:")
-                raise
-            finally:
-                self.after_train()
-
-    def before_train(self):
-        for h in self._hooks:
-            h.before_train()
-
-    def after_train(self):
-        self.storage.iter = self.iter
-        for h in self._hooks:
-            h.after_train()
-
-    def before_step(self):
-        # Maintain the invariant that storage.iter == trainer.iter
-        # for the entire execution of each step
-        self.storage.iter = self.iter
-
-        for h in self._hooks:
-            h.before_step()
-
-    def after_step(self):
-        for h in self._hooks:
-            h.after_step()
-
-    def run_step(self):
-        raise NotImplementedError
-
-    def state_dict(self):
-        ret = {"iteration": self.iter}
-        hooks_state = {}
-        for h in self._hooks:
-            sd = h.state_dict()
-            if sd:
-                name = type(h).__qualname__
-                if name in hooks_state:
-                    # TODO handle repetitive stateful hooks
-                    continue
-                hooks_state[name] = sd
-        if hooks_state:
-            ret["hooks"] = hooks_state
-        return ret
-
-    def load_state_dict(self, state_dict):
-        logger = logging.getLogger(__name__)
-        self.iter = state_dict["iteration"]
-        for key, value in state_dict.get("hooks", {}).items():
-            for h in self._hooks:
-                try:
-                    name = type(h).__qualname__
-                except AttributeError:
-                    continue
-                if name == key:
-                    h.load_state_dict(value)
-                    break
-            else:
-                logger.warning(f"Cannot find the hook '{key}', its state_dict is ignored.")
-
-
-class SimpleTrainer(TrainerBase):
-    """
-    A simple trainer for the most common type of task:
-    single-cost single-optimizer single-data-source iterative optimization,
-    optionally using data-parallelism.
-    It assumes that every step, you:
-
-    1. Compute the loss with a data from the data_loader.
-    2. Compute the gradients with the above loss.
-    3. Update the model with the optimizer.
-
-    All other tasks during training (checkpointing, logging, evaluation, LR schedule)
-    are maintained by hooks, which can be registered by :meth:`TrainerBase.register_hooks`.
-
-    If you want to do anything fancier than this,
-    either subclass TrainerBase and implement your own `run_step`,
-    or write your own training loop.
-    """
-
-    def __init__(self, model, data_loader, optimizer):
-        """
-        Args:
-            model: a torch Module. Takes a data from data_loader and returns a
-                dict of losses.
-            data_loader: an iterable. Contains data to be used to call model.
-            optimizer: a torch optimizer.
-        """
-        super().__init__()
-
-        """
-        We set the model to training mode in the trainer.
-        However it's valid to train a model that's in eval mode.
-        If you want your model (or a submodule of it) to behave
-        like evaluation during training, you can overwrite its train() method.
-        """
-        model.train()
-
-        self.model = model
-        self.data_loader = data_loader
-        self._data_loader_iter = iter(data_loader)
-        self.optimizer = optimizer
-
-    def run_step(self):
-        """
-        Implement the standard training logic described above.
-        """
-        assert self.model.training, "[SimpleTrainer] model was changed to eval mode!"
-        start = time.perf_counter()
-        """
-        If you want to do something with the data, you can wrap the dataloader.
-        """
-        data = next(self._data_loader_iter)
-        data_time = time.perf_counter() - start
-
-        """
-        If you want to do something with the losses, you can wrap the model.
-        """
-        loss_dict = self.model(data)
-        if isinstance(loss_dict, torch.Tensor):
-            losses = loss_dict
-            loss_dict = {"total_loss": loss_dict}
-        else:
-            losses = sum(loss_dict.values())
-
-        """
-        If you need to accumulate gradients or do something similar, you can
-        wrap the optimizer with your custom `zero_grad()` method.
-        """
-        self.optimizer.zero_grad()
-        losses.backward()
-
-        self._write_metrics(loss_dict, data_time)
-
-        """
-        If you need gradient clipping/scaling or other processing, you can
-        wrap the optimizer with your custom `step()` method. But it is
-        suboptimal as explained in https://arxiv.org/abs/2006.15704 Sec 3.2.4
-        """
-        self.optimizer.step()
-
-    def _write_metrics(
-        self,
-        loss_dict: Dict[str, torch.Tensor],
-        data_time: float,
-        prefix: str = "",
-    ):
-        """
-        Args:
-            loss_dict (dict): dict of scalar losses
-            data_time (float): time taken by the dataloader iteration
-        """
-        metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}
-        metrics_dict["data_time"] = data_time
-
-        # Gather metrics among all workers for logging
-        # This assumes we do DDP-style training, which is currently the only
-        # supported method in detectron2.
-        all_metrics_dict = comm.gather(metrics_dict)
-
-        if comm.is_main_process():
-            storage = get_event_storage()
-
-            # data_time among workers can have high variance. The actual latency
-            # caused by data_time is the maximum among workers.
-            data_time = np.max([x.pop("data_time") for x in all_metrics_dict])
-            storage.put_scalar("data_time", data_time)
-
-            # average the rest metrics
-            metrics_dict = {
-                k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()
-            }
-            total_losses_reduced = sum(metrics_dict.values())
-            if not np.isfinite(total_losses_reduced):
-                raise FloatingPointError(
-                    f"Loss became infinite or NaN at iteration={self.iter}!\n"
-                    f"loss_dict = {metrics_dict}"
-                )
-
-            storage.put_scalar("{}total_loss".format(prefix), total_losses_reduced)
-            if len(metrics_dict) > 1:
-                storage.put_scalars(**metrics_dict)
-
-    def state_dict(self):
-        ret = super().state_dict()
-        ret["optimizer"] = self.optimizer.state_dict()
-        return ret
-
-    def load_state_dict(self, state_dict):
-        super().load_state_dict(state_dict)
-        self.optimizer.load_state_dict(state_dict["optimizer"])
-
-
-class AMPTrainer(SimpleTrainer):
-    """
-    Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision
-    in the training loop.
-    """
-
-    def __init__(self, model, data_loader, optimizer, grad_scaler=None):
-        """
-        Args:
-            model, data_loader, optimizer: same as in :class:`SimpleTrainer`.
-            grad_scaler: torch GradScaler to automatically scale gradients.
-        """
-        unsupported = "AMPTrainer does not support single-process multi-device training!"
-        if isinstance(model, DistributedDataParallel):
-            assert not (model.device_ids and len(model.device_ids) > 1), unsupported
-        assert not isinstance(model, DataParallel), unsupported
-
-        super().__init__(model, data_loader, optimizer)
-
-        if grad_scaler is None:
-            from torch.cuda.amp import GradScaler
-
-            grad_scaler = GradScaler()
-        self.grad_scaler = grad_scaler
-
-    def run_step(self):
-        """
-        Implement the AMP training logic.
-        """
-        assert self.model.training, "[AMPTrainer] model was changed to eval mode!"
-        assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!"
-        from torch.cuda.amp import autocast
-
-        start = time.perf_counter()
-        data = next(self._data_loader_iter)
-        data_time = time.perf_counter() - start
-
-        with autocast():
-            loss_dict = self.model(data)
-            if isinstance(loss_dict, torch.Tensor):
-                losses = loss_dict
-                loss_dict = {"total_loss": loss_dict}
-            else:
-                losses = sum(loss_dict.values())
-
-        self.optimizer.zero_grad()
-        self.grad_scaler.scale(losses).backward()
-
-        self._write_metrics(loss_dict, data_time)
-
-        self.grad_scaler.step(self.optimizer)
-        self.grad_scaler.update()
-
-    def state_dict(self):
-        ret = super().state_dict()
-        ret["grad_scaler"] = self.grad_scaler.state_dict()
-        return ret
-
-    def load_state_dict(self, state_dict):
-        super().load_state_dict(state_dict)
-        self.grad_scaler.load_state_dict(state_dict["grad_scaler"])