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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Far Cry 4 PC Crack Tips and Tricks for a Smooth Gameplay.md

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-<h1>Download Far Cry 4 PC Crack</h1> | H1 | | <p>Far Cry 4 is one of the most popular open-world first-person shooter games in recent years. It was released in 2014 by Ubisoft and received critical acclaim for its stunning graphics, diverse environments, animal companions system, and engaging story. The game is set in the fictional Himalayan region of Kyrat, where you play as Ajay Ghale, a young man who returns to his homeland to fulfill his mother's final wish of spreading her ashes in her place of birth. However, he soon becomes involved in a civil war between the forces of Pagan Min, a despotic self-appointed king, and the Golden Path, a rebel movement fighting for democracy.</p>
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-<p>Based on these criteria, we have found one website that seems to offer a reliable source for Far Cry 4 PC crack. It is called Fitgirl Repacks Site (index:1)  , which is a well-known website that provides compressed repacks of various games with all DLCs included. The website has positive reviews and ratings from many users who have downloaded and used its repacks successfully. The website also provides clear and detailed instructions on how to download, install, and run its repacks.</p> | P | <p>The website offers two versions of Far Cry 4 repack: one with multiplayer files included (14.8 GB) and one without multiplayer files (10.8 GB). The repack is based on Far.Cry.4.Proper-RELOADED release (index:1)  , which is an official release by RELOADED group (index:3)  , which is one of the most reputable groups that provide PC cracks for various games. The repack also includes all DLCs added & activated (index:1)  , missing Japanese voiceovers added (index:1)  , all patches up to v1.10 applied (index:1)  , extreme injector v3.6.1 for 2/3-core CPUs added (index:1)  , modified unofficial RELOADED crack (index:3)  , ULC unlocker added (index:3)  , 100% lossless & MD5 perfect (index:1)  , selective download feature (index:1)  , significantly smaller archive size (index:1)  , fast installation time (index:1)  , after-install integrity check (index:1)  , HDD space after installation up to 40 GB (index:1)  , at least 2 GB of free RAM required for installing this repack (index:1) . The repack is compatible with Windows 7/8/10 (64-bit versions only), Intel Core i5-750 @ 2.6 GHz or AMD Phenom II X4 955 @ 3.2 GHz processor or better, 4 GB RAM or more, NVIDIA GeForce GTX 460 or AMD Radeon HD5850 graphics card or better with 1 GB VRAM or more.</p> | P | <p>To download Far Cry 4 repack from Fitgirl Repacks Site (index:1)  , you need to follow these steps:</p> | P | <ol> | OL | <li>Go to https://fitgirl-repacks-site.org/far-cry-4-gold-edition-download-torrent-repack/</li> | LI | <li>Select your preferred download option from the list of mirrors provided on the website.</li> | LI | <li>If you choose torrent option, you need to have a torrent client installed on your computer such as uTorrent or BitTorrent.</li> | LI | <li>If you choose direct download option, you need to have a download manager installed on your computer such as IDM or JDownloader2.</li> | LI | <li>Download all parts of the repack according to your selected option.</li> | LI | <li>Extract all parts of the repack using WinRAR or 7-Zip software.</li> | LI | <li>Run setup.exe file from extracted folder as administrator.</li> | LI | <li>Select your preferred language from English/Russian/Spanish/French/German/Italian/Polish/Portuguese-Brazil/Japanese/Chinese Simplified/Chinese Traditional/Czech/Danish/Dutch/Finnish/Norwegian/Swedish.</li> | LI | <li>Select your preferred components from singleplayer files/multiplayer files/voiceovers files according to your needs.</li> | LI | <li>Select your installation directory where you want to install the game.</li> | LI | <li>Click install button and wait until installation process is completed.</li> | LI | </ol>
- <h2>How to Check Far Cry 4 PC Crack for Viruses and Malware</h2>
-<p>
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- <p>To check Far Cry 4 repack for viruses and malware, you need to follow these steps:</p>
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-<li>Right-click on the downloaded repack file and select Scan with [your antivirus software] from the context menu.</li>
-<li>Wait for the scan to complete and check the results. If any threats are detected, delete or quarantine them according to your antivirus software's instructions.</li>
-<li>If no threats are detected, you can proceed to install the repack on your computer.</li>
-</ol>
- <p>Alternatively, you can also use an online virus scanner such as VirusTotal or Jotti to check the repack file for viruses and malware. These are websites that allow you to upload any file and scan it with multiple antivirus engines at once. To use an online virus scanner, you need to follow these steps:</p>
- <ol>
-<li>Go to https://www.virustotal.com/ or https://virusscan.jotti.org/</li>
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-<li>Check the results and see if any antivirus engine detects any threat in the repack file. If any threat is detected, do not install the repack on your computer.</li>
-<li>If no threat is detected, you can proceed to install the repack on your computer.</li>
-</ol>
- <h2>How to Install Far Cry 4 PC Crack and Run the Game</h2>
- <p>After downloading and checking Far Cry 4 repack for viruses and malware, you can install it on your computer and run the game. To install Far Cry 4 repack on your computer, you need to follow these steps:</p>
- <ol>
-<li>Disable your antivirus software temporarily to avoid any interference with the installation process.</li>
-<li>Run setup.exe file from extracted folder as administrator.</li>
-<li>Select your preferred language from English/Russian/Spanish/French/German/Italian/Polish/Portuguese-Brazil/Japanese/Chinese Simplified/Chinese Traditional/Czech/Danish/Dutch/Finnish/Norwegian/Swedish.</li>
-<li>Select your preferred components from singleplayer files/multiplayer files/voiceovers files according to your needs.</li>
-<li>Select your installation directory where you want to install the game.</li>
-<li>Click install button and wait until installation process is completed.</li>
-<li>Enable your antivirus software again after the installation is done.</li>
-</ol>
- <p>To run Far Cry 4 game on your computer, you need to follow these steps:</p>
- <ol>
-<li>Go to the installation directory where you installed the game.</li>
-<li>Run FarCry4.exe file as administrator.</li>
-<li>Select your preferred graphics settings and resolution from the game launcher.</li>
-<li>Click Play button and enjoy the game.</li>
-</ol>
- <h2>How to Fix Common Issues with Far Cry 4 PC Crack</h2>
- <p>Even though Far Cry 4 PC crack allows you to play the game for free, it may also cause some issues that can affect your gaming experience. Some of these issues are related to the PC crack itself, while others are related to the game itself. Here are some of the common issues that you may encounter with Far Cry 4 PC crack and how to fix them:</p>
- <table>
-<tr><th>Issue</th><th>Solution</th></tr>
-<tr><td>The game crashes or freezes randomly.</td><td>This may be caused by incompatible drivers, outdated patches, corrupted files, or insufficient system resources. To fix this issue, you can try these steps:<ul><li>Update your graphics card drivers and DirectX version.</li><li>Update the PC crack to the latest version using Fitgirl Repacks Site (index:1)  or other sources.</li><li>Verify the integrity of game files using Steam or Uplay client if you have them installed.</li><li>Lower your graphics settings and resolution in the game launcher.</li><li>Close any unnecessary programs running in the background while playing the game.</li></ul></td></tr>
-<tr><td>The game does not launch or shows a black screen.</td><td>This may be caused by missing or blocked files, incompatible settings, or antivirus interference. To fix this issue, you can try these steps:<ul><li>Add FarCry4.exe file and installation directory to your antivirus software's exclusion list or disable it temporarily while playing the game.</li><li>Run FarCry4.exe file as administrator and in compatibility mode for Windows 7 or 8 if you have Windows 10.</li><li>Delete GamerProfile.xml file from Documents\My Games\Far Cry 4 folder and launch the game again.</li><li>Rename video.dat file to video.dat.bak in Data_Win32 folder inside installation directory and launch the game again.</li></ul></td></tr>
-<tr><td>The game shows an error message saying "Please insert correct DVD-ROM."</td><td>This may be caused by a faulty PC crack or a missing DLL file. To fix this issue, you can try these steps:<ul><li>Download a fixed DLL for map editor from http://sendfile.su/1356321 (index:1)  and place it in bin folder inside installation directory, overwriting existing file.</li><li>Download a modified unofficial RELOADED crack (index:3)  or another PC crack from a reliable source and replace FarCry4.exe file in bin folder inside installation directory with it.</li></ul></td></tr>
-<tr><td>The game does not save progress or shows corrupted save files.</td><td>This may be caused by a wrong save location, a read-only attribute, or a Uplay conflict. To fix this issue, you can try these steps:<ul><li>Create a new folder named "savegames" in Documents\My Games\Far Cry 4 folder and move all save files from Documents\My Games\Far Cry 4\user_id folder into it.</li><li>Right-click on savegames folder and select Properties. Uncheck Read-only option under Attributes and click OK.</li><li>Delete Uplay folder from Program Files (x86) folder if you have it installed. Alternatively, disable cloud synchronization option in Uplay settings if you want to keep Uplay installed.</li></ul></td></tr>
-<tr><td>The game does not recognize keyboard or mouse input or shows wrong key bindings.</td><td>This may be caused by a corrupted configuration file, a conflicting device driver, or a keyboard layout issue. To fix this issue, you can try these steps:<ul><li>Delete GamerProfile.xml file from Documents\My Games\Far Cry 4 folder and launch the game again. Customize your key bindings in-game as needed.</li><li>Unplug any unnecessary devices such as controllers, joysticks, webcams, etc. from your computer while playing the game.</li><li>Change your keyboard layout to English (US) in Windows settings if you have a different layout set up.</li></ul></td></tr>
-</table>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download Far Cry 4 PC crack safely and easily, how to check it for viruses and malware, how to install it and run the game, and how to fix some common issues with it. We hope this article has been helpful and informative for you. However, we also want to remind you once again that using a PC crack is illegal and unethical, and we do not condone or encourage it in any way. This article is for educational purposes only and we are not responsible for any consequences that may arise from using a PC crack. If you like Far Cry 4 game and want to support its developers, we urge you to buy it legally from official sources such as Steam or Uplay. Thank you for reading this article and have a great day!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions related to Far Cry 4 PC crack:</p>
- <ol>
-<li>Q: Can I play multiplayer mode with Far Cry 4 PC crack?<br>A: No, you cannot play multiplayer mode with Far Cry 4 PC crack. The multiplayer mode requires an online connection and a valid Uplay account which cannot be bypassed by any PC crack. If you want to play multiplayer mode with other players online, you need to buy the game legally from official sources such as Steam or Uplay.</li>
-<li>Q: Can I use map editor with Far Cry 4 PC crack?<br>A: Yes, A: Yes, you can use map editor with Far Cry 4 PC crack. The map editor allows you to create your own custom maps and missions using the game's assets and tools. However, you need to download a fixed DLL for map editor from http://sendfile.su/1356321 (index:1)  and place it in bin folder inside installation directory, overwriting existing file. Otherwise, the map editor will not launch or show an error message.</li>
-<li>Q: How can I update Far Cry 4 PC crack to the latest version?<br>A: To update Far Cry 4 PC crack to the latest version, you need to download the latest version of the PC crack from Fitgirl Repacks Site (index:1)  or other sources and replace FarCry4.exe file in bin folder inside installation directory with it. You also need to download and install the latest patches for the game from official sources such as Steam or Uplay if you have them installed.</li>
-<li>Q: How can I uninstall Far Cry 4 PC crack from my computer?<br>A: To uninstall Far Cry 4 PC crack from your computer, you need to delete the installation directory where you installed the game and remove any leftover files and folders from Documents\My Games\Far Cry 4 folder. You also need to scan your computer for viruses and malware using an antivirus software after uninstalling the PC crack.</li>
-<li>Q: Where can I find more information and help about Far Cry 4 PC crack?<br>A: You can find more information and help about Far Cry 4 PC crack on various websites and forums that discuss PC gaming and piracy. Some of these websites and forums are:<ul><li>Fitgirl Repacks Site (index:1)  : https://fitgirl-repacks-site.org/far-cry-4-gold-edition-download-torrent-repack/</li><li>CS.RIN.RU (index:1)  : https://cs.rin.ru/forum/viewtopic.php?f=10&t=64777</li><li>Reddit Piracy (index:3)  : https://www.reddit.com/r/Piracy/</li></ul></li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Enscape 3.0 Free Download with Crack The Ultimate Guide for Architects.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Video Editor Crack vs. Paid Video Editing Software Which One is Better?.md

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-<p>If you are a fan of Indian music, you might have heard of the hit song All Black by Sukh-E and Raftaar. This song is a fusion of Punjabi rap and pop music, with catchy lyrics and beats that will make you want to dance. The song is about celebrating life and love in style, with references to luxury brands and cars.</p>
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-<p>If you love this song and want to listen to it anytime, anywhere, you might want to download it as an MP3 file. MP3 is a digital audio format that compresses sound data without losing much quality. By downloading MP3 songs, you can enjoy several benefits, such as:</p>
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-<p>So how can you download MP3 song all black? There are several methods you can use, depending on your source and preference. Here are some of them:</p>
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-<ol>
-<li><b>Online converters:</b> One of the easiest ways to download MP3 song all black is to use online tools that can convert YouTube videos or other sources to MP3 files. All you need to do is copy the URL of the video you want to convert, paste it into the online converter, and click the download button. Some of the popular online converters are: - [YTMP3]: This website can convert YouTube videos to MP3 or MP4 files, with high quality and fast speed. You can download up to 1 hour of video at a time, and there is no registration or software installation required. - [OnlineVideoConverter]: This website can convert videos from YouTube, Vimeo, Dailymotion, and other platforms to MP3, MP4, AVI, and other formats. You can also choose the quality and format of the output file, and crop or trim the video if needed. - [MP3Skull]: This website can download MP3 songs from YouTube, SoundCloud, and other sources. You can also search for songs by name, artist, or genre, and listen to them online before downloading. To download MP3 song all black using online converters, follow these steps: - Go to YouTube and search for the song All Black by Sukh-E and Raftaar. - Copy the URL of the video from the address bar. - Go to one of the online converters mentioned above and paste the URL into the input box. - Choose MP3 as the output format and click the convert or download button. - Wait for the conversion process to finish and download the MP3 file to your device.</li>
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-<li><b>Q: What is the <b>Q: What is the genre of the song All Black?</b></li>
-<li><b>A: The song All Black is a fusion of Punjabi rap and pop music, with elements of trap, EDM, and R&B. The song is sung by Sukh-E and Raftaar, who are both popular Indian rappers and singers.</b></li>
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-<li>A: You can get more gems by buying them with real money or earning them by playing the game regularly. You can get more coins by opening brawl boxes or completing quests. You can get more power points by opening brawl boxes or using coins to buy them in the shop. You can get more star points by reaching rank 10 or higher with your brawlers or participating in power play matches. You can get more gadgets by opening brawl boxes or using gems to buy them in the shop.</li>
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-<li>A: You can get new brawlers by opening brawl boxes or using gems to buy them in the shop. Some brawlers are also available as rewards for reaching certain trophy milestones or completing certain challenges. You can get new skins by using gems or star points to buy them in the shop. Some skins are also available as rewards for reaching certain ranks or seasons with your brawlers or completing certain events or quests.</li>
-<li><b>Q: How can I contact Supercell or report a problem or feedback about Brawl Stars?</b></li>
-<li>A: You can contact Supercell or report a problem or feedback about Brawl Stars by using the in-game support feature. You can access it by tapping on the settings icon on the top right corner of the main screen, then tapping on help and support. You can then browse through the FAQs or contact the support team directly.</li>
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-<li>A: You can learn more about Brawl Stars by visiting the official website (https://supercell.com/en/games/brawlstars/), following the official social media accounts (Facebook, Twitter, Instagram, YouTube, Reddit, Discord), or watching the official videos or streams (Brawl Talk, Brawl Stars Championship, Brawl Stars Esports). You can also learn more about Brawl Stars by reading the official blog (https://blog.brawlstars.com/), joining the official forums (https://forum.supercell.com/forumdisplay.php/122-Brawl-Stars), or checking out the fan-made wiki (https://brawlstars.fandom.com/wiki/Brawl_Stars_Wiki).</li>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Air1 Roku App Listen to Worship Music from the Comfort of Your Home Television.md

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-<h1>How to Download Air1.com and Enjoy Worship Music Anywhere</h1>
-<p>Do you love worship music and want to listen to it wherever you go? Do you want to grow in your faith and get inspired by daily verses and prayers? If you answered yes, then you should download air1.com, a website that offers worship music and faith content for free. In this article, we will show you how to download air1.com on different devices, such as your phone, tablet, computer, smart speaker, or TV. We will also explain why listening to air1.com can benefit your spiritual life and well-being.</p>
- <h2>What is Air1.com and Why You Should Listen to It</h2>
-<h3>Air1.com is a website that offers worship music and faith content</h3>
-<p>Air1.com is a website that plays worship music from various artists, such as Elevation Worship, Maverick City Music, Shane & Shane, and more. You can listen to air1.com live or on demand, and discover new songs and artists that will uplift your soul. You can also explore in-depth music content with air1.com's top artists, such as interviews, videos, lyrics, and stories behind the songs.</p>
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-<p>But air1.com is more than just music. It is also a website that offers faith content that will help you grow in your relationship with God. You can read the Verse of the Day, submit requests for prayer and pray for others, dive deeper into all new faith content, and get inspired and share beautiful daily verse images. You can also enter contests, get exclusive content, free tickets, and new songs from air1.com.</p>
- <h3>Listening to air1.com can benefit your spiritual life and well-being</h3>
-<p>Listening to air1.com can have many benefits for your spiritual life and well-being. Here are some of them:</p>
-<ul>
-<li>Listening to worship music can help you praise God and express your love and gratitude for Him.</li>
-<li>Listening to worship music can help you connect with God and feel His presence and peace in your life.</li>
-<li>Listening to worship music can help you meditate on God's word and apply it to your daily situations.</li>
-<li>Listening to worship music can help you overcome stress, anxiety, fear, depression, and other negative emotions.</li>
-<li>Listening to worship music can help you heal from emotional wounds and find hope and joy in God.</li>
-<li>Listening to worship music can help you strengthen your faith and trust in God's promises and plans for you.</li>
-</ul>
- <h2>How to Download Air1.com on Different Devices</h2>
-<h3>Download the Air1 App for Android or iOS</h3>
-<h4>Features of the Air1 App</h4>
-<p>If you want to listen to air1.com on your phone or tablet, you can download the Air1 app for Android or iOS. The Air1 app has many features that will enhance your listening experience. You can:</p>
-<ul>
-<li>Create a list of your favorite songs</li>
-<li>Discover air1 stations in your city or while traveling</li> <li>Rate the songs and give feedback to air1.com</li>
-<li>Access the Verse of the Day, prayer requests, and faith content</li>
-<li>Enter contests and get exclusive content from air1.com</li>
-</ul>
- <h4>How to Install the Air1 App</h4>
-<p>To install the Air1 app on your Android or iOS device, follow these simple steps:</p>
-<ol>
-<li>Go to the Google Play Store or the App Store on your device.</li>
-<li>Search for "Air1" and tap on the app icon.</li>
-<li>Tap on "Install" or "Get" and wait for the app to download.</li>
-<li>Open the app and enjoy listening to air1.com.</li>
-</ol>
- <h3>Enable the Air1 Skill for Amazon Alexa</h3>
-<h4>How to Enable the Air1 Skill</h4>
-<p>If you have an Amazon Alexa device, such as an Echo, Dot, or Show, you can enable the Air1 skill and listen to air1.com with voice commands. To enable the Air1 skill, follow these steps:</p>
-<ol>
-<li>Open the Alexa app on your phone or tablet.</li>
-<li>Tap on the menu icon and select "Skills & Games".</li>
-<li>Search for "Air1" and tap on the skill icon.</li>
-<li>Tap on "Enable to Use" and follow the instructions to link your account.</li>
-<li>Say "Alexa, open Air1" to start listening to air1.com.</li>
-</ol>
- <h4>How to Use the Air1 Skill</h4>
-<p>Once you have enabled the Air1 skill, you can use voice commands to control your listening experience. Here are some examples of what you can say:</p>
-<ul>
-<li>"Alexa, play Air1"</li>
-<li>"Alexa, pause"</li>
-<li>"Alexa, resume"</li>
-<li>"Alexa, skip"</li>
-<li>"Alexa, what song is this?"</li>
-<li>"Alexa, who sings this song?"</li>
-<li>"Alexa, tell me more about this artist"</li>
-<li>"Alexa, play my favorites"</li>
-<li>"Alexa, rate this song"</li>
-<li>"Alexa, give me the Verse of the Day"</li>
-</ul>
- <h3>Listen to Air1 Online Through iHeartRadio or TuneIn</h3>
-<h4>How to Access Air1 on iHeartRadio or TuneIn</h4>
-<p>If you prefer to listen to air1.com online through your computer or browser, you can use iHeartRadio or TuneIn. These are online platforms that let you stream live radio stations from around the world. To access air1.com on iHeartRadio or TuneIn, follow these steps:</p>
-<p>How to download air1.com worship music<br />
-Download air1.com app for Android or iOS<br />
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-Download air1.com FAQs and answers<br />
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-Download air1.com song lyrics and sing along<br />
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-Download air1.com music recommendations and suggestions</p>
-<ol>
-<li>Go to <a href="">iHeartRadio.com</a> or <a href="">TuneIn.com</a> on your computer or browser.</li>
-<li>Search for "Air1" and click on the station logo.</li>
-<li>Enjoy listening to air1.com online.</li>
-</ol>
- <h4>Benefits of Listening to Air1 Online</h4>
-<p>Listening to air1.com online through iHeartRadio or TuneIn has some benefits that you might like. For example, you can:</p>
-<ul>
-<li>Create a free account and save your favorite stations and podcasts.</li>
-<li>Browse other genres and categories of music and radio stations.</li>
-<li>Listen to air1.com on other devices that support iHeartRadio or TuneIn, such as smart TVs, gaming consoles, wearables, and more.</li>
-</ul>
- <h3>Listen to Air1 on Your Home Television with Roku</h3>
- <h4>How to Install the Air1 Roku App</h4>
- <p>If you have a Roku device connected to your home television, you can install the Air1 Roku app and listen to air1.com on your TV. To install the Air1 Roku app, follow these steps:</p>
- <ol>
- <li>Turn on your Roku device and TV.</li>
- <li>Navigate to the Roku Channel Store and search for "Air1".</li>
- <li>Select the Air1 app and click on "Add Channel".</li>
- <li>Wait for the app to download and install.</li>
- <li>Open the app and enjoy listening to air1.com on your TV.</li>
- </ol>
- <h4>Features of the Air1 Roku App</h4>
- <p>The Air1 Roku app has some features that will make your listening experience more enjoyable. You can:</p>
- <ul>
- <li>Select from different audio quality options: low, medium, or high.</li>
- <li>View <li>View the song title, artist name, album art, and lyrics on your TV screen.</li>
-<li>Rate the songs and give feedback to air1.com.</li>
-<li>Access the Verse of the Day, prayer requests, and faith content.</li>
-<li>Enter contests and get exclusive content from air1.com.</li>
-</ul>
- <h2>Conclusion and FAQs</h2>
-<h3>Summary of the Main Points</h3>
-<p>In this article, we have shown you how to download air1.com and enjoy worship music anywhere. We have explained what air1.com is and why you should listen to it. We have also given you instructions on how to download air1.com on different devices, such as your phone, tablet, computer, smart speaker, or TV. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to contact us at [email protected]. Thank you for reading and happy listening!</p>
- <h3>FAQs About Downloading and Listening to Air1.com</h3>
-<p>Here are some frequently asked questions about downloading and listening to air1.com:</p>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is air1.com free to download and listen?</td>
-<td>Yes, air1.com is free to download and listen. However, you may incur data charges from your internet service provider or mobile carrier depending on your plan and usage.</td>
-</tr>
-<tr>
-<td>Can I listen to air1.com offline?</td>
-<td>No, you need an internet connection to listen to air1.com. However, you can download some of the faith content, such as the Verse of the Day and daily verse images, and access them offline.</td>
-</tr>
-<tr>
-<td>Can I request a song or a prayer on air1.com?</td>
-<td>Yes, you can request a song or a prayer on air1.com. You can call 888-937-2471 or text 38568 to request a song. You can also submit a prayer request online or call 888-937-2471 to pray with someone.</td>
-</tr>
-<tr>
-<td>How can I support air1.com?</td>
-<td>You can support air1.com by donating online or by phone at 888-937-2471. You can also support air1.com by sharing it with your friends and family, following it on social media, and leaving a positive review on the app store or the website.</td>
-</tr>
-<tr>
-<td>How can I contact air1.com?</td>
-<td>You can contact air1.com by email at [email protected] or by phone at 888-937-2471. You can also follow air1.com on Facebook, Twitter, Instagram, YouTube, and TikTok.</td>
-</tr>
- </table></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Enjoy Five Classic Solitaire Card Games on Your Android Device with Microsoft Solitaire Collection.md

@@ -1,101 +0,0 @@
-<br />
-<h1>Microsoft Solitaire Collection Android Free Download: How to Play the Classic Card Games on Your Phone</h1>
- <h2>Introduction</h2>
- <p>If you are a fan of solitaire card games, you might have played or heard of Microsoft Solitaire Collection. It is a collection of five popular solitaire games that you can play on your Windows PC or laptop. But did you know that you can also play Microsoft Solitaire Collection on your Android phone or tablet? In this article, we will show you how to download and play Microsoft Solitaire Collection on Android for free. We will also explain the features and benefits of this app, and answer some frequently asked questions.</p>
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- <h3>What is Microsoft Solitaire Collection?</h3>
- <p>Microsoft Solitaire Collection is an app that offers five of the best solitaire card games in one place. You can choose from Klondike, Spider, FreeCell, TriPeaks, and Pyramid solitaire games, each with different rules and challenges. You can also enjoy daily challenges, events, themes, card backs, achievements, and more. Microsoft Solitaire Collection is fun for players of all ages and skill levels. You can relax with the classics, sharpen your mind, or challenge yourself with different modes and difficulties.</p>
- <h3>Why download Microsoft Solitaire Collection on Android?</h3>
- <p>There are many reasons why you might want to download Microsoft Solitaire Collection on your Android device. Here are some of them:</p>
-<ul>
-<li>You can play your favorite solitaire games anytime, anywhere. Whether you are at home, at work, or on the go, you can enjoy a quick game of solitaire on your phone or tablet.</li>
-<li>You can access an ad-free game experience with an Xbox Game Pass account. If you have an Xbox Game Pass subscription, you can sign in with your Microsoft account and play without any ads or interruptions.</li>
-<li>You can save your progress and sync across devices. You can also sign in with your Microsoft account to save your stats, level, achievements, and rewards. You can also sync your progress across multiple devices with the same account.</li>
-<li>You can celebrate over 30 years of fun with Microsoft Solitaire Collection. This app is a tribute to the original solitaire games that were released with Windows in 1990. You can even play with retro card backs from the 1990s version.</li>
-</ul>
- <h2>How to download Microsoft Solitaire Collection on Android</h2>
- <p>Downloading Microsoft Solitaire Collection on your Android device is very easy and simple. Just follow these steps:</p>
- <h3>Step 1: Go to Google Play Store</h3>
- <p>Open the Google Play Store app on your Android device. You can find it on your home screen or in your app drawer.</p>
- <h3>Step 2: Search for Microsoft Solitaire Collection</h3>
- <p>In the search bar at the top of the screen, type "Microsoft Solitaire Collection" and tap the magnifying glass icon. You will see a list of results related to your search.</p>
- <h3>Step 3: Install the app</h3>
- <p>Find the app that has the name "Microsoft Solitaire Collection" and the logo of a blue spade card. Tap on it to open its details page. Then tap on the green "Install" button to start downloading and installing the app on your device. You might need to grant some permissions for the app to work properly.</p>
-<p>Once the installation is complete, you can open the app from your home screen or app drawer. You can also tap on the "Open" button on the Google Play Store page.</p>
- <h2>How to play Microsoft Solitaire Collection on Android</h2>
- <p>Playing Microsoft Solitaire Collection on your Android device is very fun and easy. Here are some tips and tricks to help you get started:</p>
- <h3>Choose a game mode</h3>
- <p>When you open the app, you will see five icons representing the five solitaire games available. You can tap on any of them to start playing. Each game has its own rules and objectives, but the basic goal is to move all the cards to the foundations or clear the board. Here is a brief overview of each game mode:</p>
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- <h4>Klondike Solitaire</h4>
- <p>This is the classic and most popular solitaire game. You have seven columns of cards, and you need to build four foundations from Ace to King in the same suit. You can move cards from one column to another if they are in descending order and alternating colors. You can also draw cards from the stock pile and place them on the waste pile or the columns. You can choose from three difficulty levels: Easy, Medium, and Hard.</p>
- <h4>Spider Solitaire</h4>
- <p>This is a challenging solitaire game that requires more strategy and skill. You have 10 columns of cards, and you need to clear the board by creating eight runs of cards from King to Ace in the same suit. You can move cards from one column to another if they are in descending order and the same suit. You can also draw cards from the stock pile and place them on any column. You can choose from three difficulty levels: One Suit, Two Suits, and Four Suits.</p>
- <h4>FreeCell Solitaire</h4>
- <p>This is a solitaire game that tests your logic and patience. You have four free cells, four foundations, and eight columns of cards. You need to build four foundations from Ace to King in the same suit. You can move cards from one column to another if they are in descending order and alternating colors. You can also move cards to the free cells or the foundations. You can choose from four difficulty levels: Easy, Medium, Hard, and Expert.</p>
- <h4>TriPeaks Solitaire</h4>
- <p>This is a solitaire game that is fast-paced and fun. You have three peaks of cards, and you need to clear them by selecting cards that are one higher or one lower than the card on the waste pile. You can also draw cards from the stock pile and place them on the waste pile. You can choose from two difficulty levels: Normal and Hard.</p>
- <h4>Pyramid Solitaire</h4>
- <p>This is a solitaire game that is simple and addictive. You have a pyramid of cards, and you need to clear it by selecting pairs of cards that add up to 13. You can also draw cards from the stock pile and place them on the waste pile. You can choose from two difficulty levels: Normal and Hard.</p> <h3>Complete daily challenges and events</h3>
- <p>One of the best features of Microsoft Solitaire Collection is that it offers daily challenges and events for you to enjoy. You can earn coins, badges, and rewards by completing various tasks and goals in each game mode. You can also compete with other players around the world and see how you rank on the leaderboards. You can access the daily challenges and events by tapping on the calendar icon on the main menu.</p>
- <h3>Customize your theme and card backs</h3>
- <p>Another great feature of Microsoft Solitaire Collection is that it allows you to customize your theme and card backs. You can choose from different backgrounds, colors, and styles to suit your mood and preference. You can also unlock new themes and card backs by completing achievements and challenges. You can access the theme and card back options by tapping on the gear icon on the main menu.</p>
- <h3>Save your progress and earn achievements</h3>
- <p>The last feature we want to mention is that Microsoft Solitaire Collection lets you save your progress and earn achievements. You can sign in with your Microsoft account to sync your data across devices and access your stats, level, coins, badges, and rewards. You can also earn achievements by completing various milestones and challenges in each game mode. You can access your profile and achievements by tapping on the trophy icon on the main menu.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Microsoft Solitaire Collection is a fantastic app that offers five of the best solitaire card games in one place. You can play Klondike, Spider, FreeCell, TriPeaks, and Pyramid solitaire games on your Android device for free. You can also enjoy daily challenges, events, themes, card backs, achievements, and more. Microsoft Solitaire Collection is fun for players of all ages and skill levels. You can relax with the classics, sharpen your mind, or challenge yourself with different modes and difficulties. If you are a fan of solitaire games, you should definitely download Microsoft Solitaire Collection on your Android device today.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Microsoft Solitaire Collection:</p>
-<ul>
-<li><b>Q: How much space does Microsoft Solitaire Collection take on my device?</b></li>
-<li>A: Microsoft Solitaire Collection takes about 100 MB of space on your device. You might need more space if you download additional themes or card backs.</li>
-<li><b>Q: How do I contact customer support for Microsoft Solitaire Collection?</b></li>
-<li>A: You can contact customer support for Microsoft Solitaire Collection by tapping on the question mark icon on the main menu. You can also visit the official website or the Facebook page for more information and help.</li>
-<li><b>Q: How do I turn off the sound or music in Microsoft Solitaire Collection?</b></li>
-<li>A: You can turn off the sound or music in Microsoft Solitaire Collection by tapping on the gear icon on the main menu. Then tap on the sound or music icons to toggle them on or off.</li>
-<li><b>Q: How do I change the language in Microsoft Solitaire Collection?</b></li>
-<li>A: You can change the language in Microsoft Solitaire Collection by tapping on the gear icon on the main menu. Then tap on the language option and select your preferred language from the list.</li>
-<li><b>Q: How do I uninstall Microsoft Solitaire Collection from my device?</b></li>
-<li>A: You can uninstall Microsoft Solitaire Collection from your device by going to your device settings and finding the app in the list of installed apps. Then tap on it and select "Uninstall".</li>
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spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/safety_checker.py

@@ -1,125 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import paddle
-import paddle.nn.functional as F
-
-from paddlenlp.transformers import (
-    CLIPPretrainedModel,
-    CLIPVisionConfig,
-    CLIPVisionModel,
-)
-
-from ...utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-def cosine_distance(image_embeds, text_embeds):
-    normalized_image_embeds = F.normalize(image_embeds)
-    normalized_text_embeds = F.normalize(text_embeds)
-    return paddle.matmul(normalized_image_embeds, normalized_text_embeds, transpose_y=True)
-
-
-class StableDiffusionSafetyChecker(CLIPPretrainedModel):
-    config_class = CLIPVisionConfig
-
-    def __init__(self, config: CLIPVisionConfig):
-        super().__init__(config)
-        self.clip = CLIPVisionModel(config)
-        self.vision_projection = paddle.create_parameter(
-            (config.hidden_size, config.projection_dim), dtype=paddle.get_default_dtype()
-        )
-
-        self.register_buffer("concept_embeds", paddle.ones([17, config.projection_dim]))
-        self.register_buffer("special_care_embeds", paddle.ones([3, config.projection_dim]))
-
-        self.register_buffer("concept_embeds_weights", paddle.ones([17]))
-        self.register_buffer("special_care_embeds_weights", paddle.ones([3]))
-
-    @paddle.no_grad()
-    def forward(self, clip_input, images):
-        pooled_output = self.clip(clip_input)[1]  # pooled_output
-        image_embeds = paddle.matmul(pooled_output, self.vision_projection)
-
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).astype("float32").numpy()
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds).astype("float32").numpy()
-
-        result = []
-        batch_size = image_embeds.shape[0]
-        for i in range(batch_size):
-            result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
-
-            # increase this value to create a stronger `nfsw` filter
-            # at the cost of increasing the possibility of filtering benign images
-            adjustment = 0.0
-
-            for concept_idx in range(len(special_cos_dist[0])):
-                concept_cos = special_cos_dist[i][concept_idx]
-                concept_threshold = self.special_care_embeds_weights[concept_idx].item()
-                result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
-                if result_img["special_scores"][concept_idx] > 0:
-                    result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
-                    adjustment = 0.01
-
-            for concept_idx in range(len(cos_dist[0])):
-                concept_cos = cos_dist[i][concept_idx]
-                concept_threshold = self.concept_embeds_weights[concept_idx].item()
-                result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
-                if result_img["concept_scores"][concept_idx] > 0:
-                    result_img["bad_concepts"].append(concept_idx)
-
-            result.append(result_img)
-
-        has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
-
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                images[idx] = np.zeros(images[idx].shape)  # black image
-
-        if any(has_nsfw_concepts):
-            logger.warning(
-                "Potential NSFW content was detected in one or more images. A black image will be returned instead."
-                " Try again with a different prompt and/or seed."
-            )
-
-        return images, has_nsfw_concepts
-
-    def forward_fastdeploy(self, clip_input: paddle.Tensor, images: paddle.Tensor):
-        pooled_output = self.clip(clip_input)[1]  # pooled_output
-        image_embeds = paddle.matmul(pooled_output, self.vision_projection)
-
-        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds)
-
-        # increase this value to create a stronger `nsfw` filter
-        # at the cost of increasing the possibility of filtering benign images
-        adjustment = 0.0
-
-        special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
-        # special_scores = special_scores.round(decimals=3)
-        special_care = paddle.any(special_scores > 0, axis=1)
-        special_adjustment = special_care * 0.01
-        special_adjustment = special_adjustment.unsqueeze(1).expand([-1, cos_dist.shape[1]])
-
-        concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
-        # concept_scores = concept_scores.round(decimals=3)
-        has_nsfw_concepts = paddle.any(concept_scores > 0, axis=1)
-
-        images[has_nsfw_concepts] = 0.0  # black image
-
-        return images, has_nsfw_concepts

spaces/2023Liu2023/bingo/src/lib/bots/bing/utils.ts

@@ -1,87 +0,0 @@
-import { ChatResponseMessage, BingChatResponse } from './types'
-
-export function convertMessageToMarkdown(message: ChatResponseMessage): string {
-  if (message.messageType === 'InternalSearchQuery') {
-    return message.text
-  }
-  for (const card of message.adaptiveCards??[]) {
-    for (const block of card.body) {
-      if (block.type === 'TextBlock') {
-        return block.text
-      }
-    }
-  }
-  return ''
-}
-
-const RecordSeparator = String.fromCharCode(30)
-
-export const websocketUtils = {
-  packMessage(data: any) {
-    return `${JSON.stringify(data)}${RecordSeparator}`
-  },
-  unpackMessage(data: string | ArrayBuffer | Blob) {
-    if (!data) return {}
-    return data
-      .toString()
-      .split(RecordSeparator)
-      .filter(Boolean)
-      .map((s) => {
-        try {
-          return JSON.parse(s)
-        } catch (e) {
-          return {}
-        }
-      })
-  },
-}
-
-export async function createImage(prompt: string, id: string, headers: HeadersInit): Promise<string | undefined> {
-  const { headers: responseHeaders } = await fetch(`https://www.bing.com/images/create?partner=sydney&re=1&showselective=1&sude=1&kseed=7000&SFX=&q=${encodeURIComponent(prompt)}&iframeid=${id}`,
-    {
-      method: 'HEAD',
-      headers,
-      redirect: 'manual'
-    },
-  );
-
-  if (!/&id=([^&]+)$/.test(responseHeaders.get('location') || '')) {
-    throw new Error('请求异常，请检查 cookie 是否有效')
-  }
-
-  const resultId = RegExp.$1;
-  let count = 0
-  const imageThumbUrl = `https://www.bing.com/images/create/async/results/${resultId}?q=${encodeURIComponent(prompt)}&partner=sydney&showselective=1&IID=images.as`;
-
-  do {
-    await sleep(3000);
-    const content = await fetch(imageThumbUrl, { headers, method: 'GET' })
-
-    // @ts-ignore
-    if (content.headers.get('content-length') > 1) {
-      const text = await content.text()
-        return (text?.match(/<img class="mimg"((?!src).)+src="[^"]+/mg)??[])
-          .map(target => target?.split('src="').pop()?.replace(/&amp;/g, '&'))
-          .map(img => `![${prompt}](${img})`).join(' ')
-    }
-  } while(count ++ < 10);
-}
-
-
-export async function* streamAsyncIterable(stream: ReadableStream) {
-  const reader = stream.getReader()
-  try {
-    while (true) {
-      const { done, value } = await reader.read()
-      if (done) {
-        return
-      }
-      yield value
-    }
-  } finally {
-    reader.releaseLock()
-  }
-}
-
-export const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms))
-

spaces/4Taps/SadTalker/src/utils/croper.py

@@ -1,295 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import scipy
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from itertools import cycle
-
-from torch.multiprocessing import Pool, Process, set_start_method
-
-
-"""
-brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
-author: lzhbrian (https://lzhbrian.me)
-date: 2020.1.5
-note: code is heavily borrowed from 
-    https://github.com/NVlabs/ffhq-dataset
-    http://dlib.net/face_landmark_detection.py.html
-requirements:
-    apt install cmake
-    conda install Pillow numpy scipy
-    pip install dlib
-    # download face landmark model from: 
-    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-"""
-
-import numpy as np
-from PIL import Image
-import dlib
-
-
-class Croper:
-    def __init__(self, path_of_lm):
-        # download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-        self.predictor = dlib.shape_predictor(path_of_lm)
-
-    def get_landmark(self, img_np):
-        """get landmark with dlib
-        :return: np.array shape=(68, 2)
-        """
-        detector = dlib.get_frontal_face_detector()
-        dets = detector(img_np, 1)
-        #     print("Number of faces detected: {}".format(len(dets)))
-        #     for k, d in enumerate(dets):
-        if len(dets) == 0:
-            return None
-        d = dets[0]
-        # Get the landmarks/parts for the face in box d.
-        shape = self.predictor(img_np, d)
-        #         print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
-        t = list(shape.parts())
-        a = []
-        for tt in t:
-            a.append([tt.x, tt.y])
-        lm = np.array(a)
-        # lm is a shape=(68,2) np.array
-        return lm
-
-    def align_face(self, img, lm, output_size=1024):
-        """
-        :param filepath: str
-        :return: PIL Image
-        """
-        lm_chin = lm[0: 17]  # left-right
-        lm_eyebrow_left = lm[17: 22]  # left-right
-        lm_eyebrow_right = lm[22: 27]  # left-right
-        lm_nose = lm[27: 31]  # top-down
-        lm_nostrils = lm[31: 36]  # top-down
-        lm_eye_left = lm[36: 42]  # left-clockwise
-        lm_eye_right = lm[42: 48]  # left-clockwise
-        lm_mouth_outer = lm[48: 60]  # left-clockwise
-        lm_mouth_inner = lm[60: 68]  # left-clockwise
-
-        # Calculate auxiliary vectors.
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        eye_avg = (eye_left + eye_right) * 0.5
-        eye_to_eye = eye_right - eye_left
-        mouth_left = lm_mouth_outer[0]
-        mouth_right = lm_mouth_outer[6]
-        mouth_avg = (mouth_left + mouth_right) * 0.5
-        eye_to_mouth = mouth_avg - eye_avg
-
-        # Choose oriented crop rectangle.
-        x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]  # 双眼差与双嘴差相加
-        x /= np.hypot(*x)   # hypot函数计算直角三角形的斜边长，用斜边长对三角形两条直边做归一化
-        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)    # 双眼差和眼嘴差，选较大的作为基准尺度
-        y = np.flipud(x) * [-1, 1]
-        c = eye_avg + eye_to_mouth * 0.1
-        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])   # 定义四边形，以面部基准位置为中心上下左右平移得到四个顶点
-        qsize = np.hypot(*x) * 2    # 定义四边形的大小（边长），为基准尺度的2倍
-
-        # Shrink.
-        # 如果计算出的四边形太大了，就按比例缩小它
-        shrink = int(np.floor(qsize / output_size * 0.5))
-        if shrink > 1:
-            rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-            img = img.resize(rsize, Image.ANTIALIAS)
-            quad /= shrink
-            qsize /= shrink
-
-        # Crop.
-        border = max(int(np.rint(qsize * 0.1)), 3)
-        crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-                int(np.ceil(max(quad[:, 1]))))
-        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
-                min(crop[3] + border, img.size[1]))
-        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-            # img = img.crop(crop)
-            quad -= crop[0:2]
-
-        # Pad.
-        pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-               int(np.ceil(max(quad[:, 1]))))
-        pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
-               max(pad[3] - img.size[1] + border, 0))
-        # if enable_padding and max(pad) > border - 4:
-        #     pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        #     img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        #     h, w, _ = img.shape
-        #     y, x, _ = np.ogrid[:h, :w, :1]
-        #     mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
-        #                       1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
-        #     blur = qsize * 0.02
-        #     img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        #     img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        #     img = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        #     quad += pad[:2]
-
-        # Transform.
-        quad = (quad + 0.5).flatten()
-        lx = max(min(quad[0], quad[2]), 0)
-        ly = max(min(quad[1], quad[7]), 0)
-        rx = min(max(quad[4], quad[6]), img.size[0])
-        ry = min(max(quad[3], quad[5]), img.size[0])
-        # img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(),
-        #                     Image.BILINEAR)
-        # if output_size < transform_size:
-        #     img = img.resize((output_size, output_size), Image.ANTIALIAS)
-
-        # Save aligned image.
-        return crop, [lx, ly, rx, ry]
-
-    # def crop(self, img_np_list):
-    #     for _i in range(len(img_np_list)):
-    #         img_np = img_np_list[_i]
-    #         lm = self.get_landmark(img_np)
-    #         if lm is None:
-    #             return None
-    #         crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=512)
-    #         clx, cly, crx, cry = crop
-    #         lx, ly, rx, ry = quad
-    #         lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        
-    #         _inp = img_np_list[_i]
-    #         _inp = _inp[cly:cry, clx:crx]
-    #         _inp = _inp[ly:ry, lx:rx]
-    #         img_np_list[_i] = _inp
-    #     return img_np_list
-    
-    def crop(self, img_np_list, xsize=512):    # first frame for all video
-        img_np = img_np_list[0]
-        lm = self.get_landmark(img_np)
-        if lm is None:
-            return None
-        crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=xsize)
-        clx, cly, crx, cry = crop
-        lx, ly, rx, ry = quad
-        lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        for _i in range(len(img_np_list)):
-            _inp = img_np_list[_i]
-            _inp = _inp[cly:cry, clx:crx]
-            # cv2.imwrite('test1.jpg', _inp)
-            _inp = _inp[ly:ry, lx:rx]
-            # cv2.imwrite('test2.jpg', _inp)
-            img_np_list[_i] = _inp
-        return img_np_list, crop, quad
-
-
-def read_video(filename, uplimit=100):
-    frames = []
-    cap = cv2.VideoCapture(filename)
-    cnt = 0
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if ret:
-            frame = cv2.resize(frame, (512, 512))
-            frames.append(frame)
-        else:
-            break
-        cnt += 1
-        if cnt >= uplimit:
-            break
-    cap.release()
-    assert len(frames) > 0, f'{filename}: video with no frames!'
-    return frames
-
-
-def create_video(video_name, frames, fps=25, video_format='.mp4', resize_ratio=1):
-    # video_name = os.path.dirname(image_folder) + video_format
-    # img_list = glob.glob1(image_folder, 'frame*')
-    # img_list.sort()
-    # frame = cv2.imread(os.path.join(image_folder, img_list[0]))
-    # frame = cv2.resize(frame, (0, 0), fx=resize_ratio, fy=resize_ratio)
-    # height, width, layers = frames[0].shape
-    height, width, layers = 512, 512, 3
-    if video_format == '.mp4':
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    elif video_format == '.avi':
-        fourcc = cv2.VideoWriter_fourcc(*'XVID')
-    video = cv2.VideoWriter(video_name, fourcc, fps, (width, height))
-    for _frame in frames:
-        _frame = cv2.resize(_frame, (height, width), interpolation=cv2.INTER_LINEAR)
-        video.write(_frame)
-
-def create_images(video_name, frames):
-    height, width, layers = 512, 512, 3
-    images_dir = video_name.split('.')[0]
-    os.makedirs(images_dir, exist_ok=True)
-    for i, _frame in enumerate(frames):
-        _frame = cv2.resize(_frame, (height, width), interpolation=cv2.INTER_LINEAR)
-        _frame_path = os.path.join(images_dir, str(i)+'.jpg')
-        cv2.imwrite(_frame_path, _frame)
-
-def run(data):
-    filename, opt, device = data
-    os.environ['CUDA_VISIBLE_DEVICES'] = device
-    croper = Croper()
-
-    frames = read_video(filename, uplimit=opt.uplimit)
-    name = filename.split('/')[-1]  # .split('.')[0]
-    name = os.path.join(opt.output_dir, name)
-
-    frames = croper.crop(frames)
-    if frames is None:
-        print(f'{name}: detect no face. should removed')
-        return
-    # create_video(name, frames)
-    create_images(name, frames)
-
-
-def get_data_path(video_dir):
-    eg_video_files = ['/apdcephfs/share_1290939/quincheng/datasets/HDTF/backup_fps25/WDA_KatieHill_000.mp4']
-    # filenames = list()
-    # VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
-    # VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
-    # extensions = VIDEO_EXTENSIONS
-    # for ext in extensions:
-    #     filenames = sorted(glob.glob(f'{opt.input_dir}/**/*.{ext}'))
-    # print('Total number of videos:', len(filenames))
-    return eg_video_files
-
-
-def get_wra_data_path(video_dir):
-    if opt.option == 'video':
-        videos_path = sorted(glob.glob(f'{video_dir}/*.mp4'))
-    elif opt.option == 'image':
-        videos_path = sorted(glob.glob(f'{video_dir}/*/'))
-    else:
-        raise NotImplementedError
-    print('Example videos: ', videos_path[:2])
-    return videos_path
-
-
-if __name__ == '__main__':
-    set_start_method('spawn')
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-    parser.add_argument('--output_dir', type=str, help='the folder of the output files')
-    parser.add_argument('--device_ids', type=str, default='0,1')
-    parser.add_argument('--workers', type=int, default=8)
-    parser.add_argument('--uplimit', type=int, default=500)
-    parser.add_argument('--option', type=str, default='video')
-
-    root = '/apdcephfs/share_1290939/quincheng/datasets/HDTF'
-    cmd = f'--input_dir {root}/backup_fps25_first20s_sync/ ' \
-          f'--output_dir {root}/crop512_stylegan_firstframe_sync/ ' \
-          '--device_ids 0 ' \
-          '--workers 8 ' \
-          '--option video ' \
-          '--uplimit 500 '
-    opt = parser.parse_args(cmd.split())
-    # filenames = get_data_path(opt.input_dir)
-    filenames = get_wra_data_path(opt.input_dir)
-    os.makedirs(opt.output_dir, exist_ok=True)
-    print(f'Video numbers: {len(filenames)}')
-    pool = Pool(opt.workers)
-    args_list = cycle([opt])
-    device_ids = opt.device_ids.split(",")
-    device_ids = cycle(device_ids)
-    for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))):
-        None

spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets_new.py

@@ -1,132 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from uvr5_pack.lib_v5 import layers_new as layers
-
-
-class BaseNet(nn.Module):
-    def __init__(
-        self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))
-    ):
-        super(BaseNet, self).__init__()
-        self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1)
-        self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)
-        self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
-
-        self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
-        self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
-        self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm)
-        self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
-
-    def __call__(self, x):
-        e1 = self.enc1(x)
-        e2 = self.enc2(e1)
-        e3 = self.enc3(e2)
-        e4 = self.enc4(e3)
-        e5 = self.enc5(e4)
-
-        h = self.aspp(e5)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = torch.cat([h, self.lstm_dec2(h)], dim=1)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedNet(nn.Module):
-    def __init__(self, n_fft, nout=32, nout_lstm=128):
-        super(CascadedNet, self).__init__()
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-        self.nin_lstm = self.max_bin // 2
-        self.offset = 64
-
-        self.stg1_low_band_net = nn.Sequential(
-            BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
-            layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0),
-        )
-
-        self.stg1_high_band_net = BaseNet(
-            2, nout // 4, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg2_low_band_net = nn.Sequential(
-            BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
-            layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0),
-        )
-        self.stg2_high_band_net = BaseNet(
-            nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2
-        )
-
-        self.stg3_full_band_net = BaseNet(
-            3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm
-        )
-
-        self.out = nn.Conv2d(nout, 2, 1, bias=False)
-        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
-
-    def forward(self, x):
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        l1_in = x[:, :, :bandw]
-        h1_in = x[:, :, bandw:]
-        l1 = self.stg1_low_band_net(l1_in)
-        h1 = self.stg1_high_band_net(h1_in)
-        aux1 = torch.cat([l1, h1], dim=2)
-
-        l2_in = torch.cat([l1_in, l1], dim=1)
-        h2_in = torch.cat([h1_in, h1], dim=1)
-        l2 = self.stg2_low_band_net(l2_in)
-        h2 = self.stg2_high_band_net(h2_in)
-        aux2 = torch.cat([l2, h2], dim=2)
-
-        f3_in = torch.cat([x, aux1, aux2], dim=1)
-        f3 = self.stg3_full_band_net(f3_in)
-
-        mask = torch.sigmoid(self.out(f3))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux = torch.cat([aux1, aux2], dim=1)
-            aux = torch.sigmoid(self.aux_out(aux))
-            aux = F.pad(
-                input=aux,
-                pad=(0, 0, 0, self.output_bin - aux.size()[2]),
-                mode="replicate",
-            )
-            return mask, aux
-        else:
-            return mask
-
-    def predict_mask(self, x):
-        mask = self.forward(x)
-
-        if self.offset > 0:
-            mask = mask[:, :, :, self.offset : -self.offset]
-            assert mask.size()[3] > 0
-
-        return mask
-
-    def predict(self, x, aggressiveness=None):
-        mask = self.forward(x)
-        pred_mag = x * mask
-
-        if self.offset > 0:
-            pred_mag = pred_mag[:, :, :, self.offset : -self.offset]
-            assert pred_mag.size()[3] > 0
-
-        return pred_mag

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/syntaspeech/syntactic_graph_encoder.py

@@ -1,193 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import dgl
-from dgl.nn.pytorch import GatedGraphConv
-
-def sequence_mask(lengths, maxlen, dtype=torch.bool):
-    if maxlen is None:
-        maxlen = lengths.max()
-    mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths).t()
-    mask.type(dtype)
-    return mask
-
-
-def group_hidden_by_segs(h, seg_ids, max_len):
-    """
-    :param h: [B, T, H]
-    :param seg_ids: [B, T]
-    :return: h_ph: [B, T_ph, H]
-    """
-    B, T, H = h.shape
-    h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h)
-    all_ones = h.new_ones(h.shape[:2])
-    cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous()
-    h_gby_segs = h_gby_segs[:, 1:]
-    cnt_gby_segs = cnt_gby_segs[:, 1:]
-    h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1)
-    # assert h_gby_segs.shape[-1] == 192
-    return h_gby_segs
-
-class GraphAuxEnc(nn.Module):
-    def __init__(self, in_dim, hid_dim, out_dim, n_iterations=5, n_edge_types=6):
-        super(GraphAuxEnc, self).__init__()
-        self.in_dim = in_dim
-        self.hid_dim = hid_dim
-        self.out_dim = out_dim
-        self.skip_connect = True
-        self.dropout_after_gae = False
-
-        self.ggc_1 = GatedGraphConv(in_feats=in_dim, out_feats=hid_dim
-                                    , n_steps=n_iterations, n_etypes=n_edge_types)
-        self.ggc_2 = GatedGraphConv(in_feats=hid_dim, out_feats=out_dim
-                                    , n_steps=n_iterations, n_etypes=n_edge_types)
-        self.dropout = nn.Dropout(p=0.5)
-
-    @staticmethod
-    def ph_encoding_to_word_encoding(ph_encoding, ph2word, word_len):
-        """
-        ph_encoding: [batch, t_p, hid]
-        ph2word: tensor [batch, t_w]
-        word_len: tensor [batch]
-        """
-        word_encoding_for_graph, batch_word_encoding, has_word_row_idx = GraphAuxEnc._process_ph_to_word_encoding(
-            ph_encoding,
-            ph2word,
-            word_len)
-        # [batch, t_w, hid]
-        return batch_word_encoding, word_encoding_for_graph
-
-    def pad_word_encoding_to_phoneme(self, word_encoding, ph2word, t_p):
-        return self._postprocess_word2ph(word_encoding, ph2word, t_p)
-
-    @staticmethod
-    def _process_ph_to_word_encoding(ph_encoding, ph2word, word_len=None):
-        """
-        ph_encoding: [batch, t_p, hid]
-        ph2word: tensor [batch, t_w]
-        word_len: tensor [batch]
-        """
-        word_len = word_len.reshape([-1,])
-        max_len = max(word_len)
-        num_nodes = sum(word_len)
-
-        batch_word_encoding = group_hidden_by_segs(ph_encoding, ph2word, max_len)
-        bs, t_p, hid = batch_word_encoding.shape
-        has_word_mask = sequence_mask(word_len, max_len)  # [batch, t_p, 1]
-        word_encoding = batch_word_encoding.reshape([bs * t_p, hid])
-        has_word_row_idx = has_word_mask.reshape([-1])
-        word_encoding = word_encoding[has_word_row_idx]
-        assert word_encoding.shape[0] == num_nodes
-        return word_encoding, batch_word_encoding, has_word_row_idx
-
-    @staticmethod
-    def _postprocess_word2ph(word_encoding, ph2word, t_p):
-        word_encoding = F.pad(word_encoding,[0,0,1,0])
-        ph2word_ = ph2word[:, :, None].repeat([1, 1, word_encoding.shape[-1]])
-        out = torch.gather(word_encoding, 1, ph2word_)  # [B, T, H]
-        return out
-
-    @staticmethod
-    def _repeat_one_sequence(x, d, T):
-        """Repeat each frame according to duration."""
-        if d.sum() == 0:
-            d = d.fill_(1)
-        hid = x.shape[-1]
-        expanded_lst = [x_.repeat(int(d_), 1) for x_, d_ in zip(x, d) if d_ != 0]
-        expanded = torch.cat(expanded_lst, dim=0)
-        if T > expanded.shape[0]:
-            expanded = torch.cat([expanded, torch.zeros([T - expanded.shape[0], hid]).to(expanded.device)], dim=0)
-        return expanded
-
-    def word_forward(self, graph_lst, word_encoding, etypes_lst):
-        """
-        word encoding in, word encoding out.
-        """
-        batched_graph = dgl.batch(graph_lst)
-        inp = word_encoding
-        batched_etypes = torch.cat(etypes_lst)  # [num_edges_in_batch, 1]
-        assert batched_graph.num_nodes() == inp.shape[0]
-
-        gcc1_out = self.ggc_1(batched_graph, inp, batched_etypes)
-        if self.dropout_after_gae:
-            gcc1_out = self.dropout(gcc1_out)
-        gcc2_out = self.ggc_2(batched_graph, gcc1_out, batched_etypes)  # [num_nodes_in_batch, hin]
-        if self.dropout_after_gae:
-            gcc2_out = self.ggc_2(batched_graph, gcc2_out, batched_etypes)
-        if self.skip_connect:
-            assert self.in_dim == self.hid_dim and self.hid_dim == self.out_dim
-            gcc2_out = inp + gcc1_out + gcc2_out
-
-        word_len = torch.tensor([g.num_nodes() for g in graph_lst]).reshape([-1])
-        max_len = max(word_len)
-        has_word_mask = sequence_mask(word_len, max_len)  # [batch, t_p, 1]
-        has_word_row_idx = has_word_mask.reshape([-1])
-        bs = len(graph_lst)
-        t_w = max([g.num_nodes() for g in graph_lst])
-        hid = word_encoding.shape[-1]
-        output = torch.zeros([bs * t_w, hid]).to(gcc2_out.device)
-        output[has_word_row_idx] = gcc2_out
-        output = output.reshape([bs, t_w, hid])
-        word_level_output = output
-        return torch.transpose(word_level_output, 1, 2)
-
-    def forward(self, graph_lst, ph_encoding, ph2word, etypes_lst, return_word_encoding=False):
-        """
-        graph_lst: [list of dgl_graph]
-        ph_encoding: [batch, hid, t_p]
-        ph2word: [list of list[1,2,2,2,3,3,3]]
-        etypes_lst: [list of etypes]; etypes: torch.LongTensor
-        """
-        t_p = ph_encoding.shape[-1]
-        ph_encoding = ph_encoding.transpose(1,2) # [batch, t_p, hid]
-        word_len = torch.tensor([g.num_nodes() for g in graph_lst]).reshape([-1])
-        batched_graph = dgl.batch(graph_lst)
-        inp, batched_word_encoding, has_word_row_idx = self._process_ph_to_word_encoding(ph_encoding, ph2word,
-                                                                                         word_len=word_len)  # [num_nodes_in_batch, in_dim]
-        bs, t_w, hid = batched_word_encoding.shape
-        batched_etypes = torch.cat(etypes_lst)  # [num_edges_in_batch, 1]
-        gcc1_out = self.ggc_1(batched_graph, inp, batched_etypes)
-        gcc2_out = self.ggc_2(batched_graph, gcc1_out, batched_etypes)  # [num_nodes_in_batch, hin]
-        # skip connection 
-        gcc2_out = inp + gcc1_out + gcc2_out # [n_nodes, hid]
-        
-        output = torch.zeros([bs * t_w, hid]).to(gcc2_out.device)
-        output[has_word_row_idx] = gcc2_out
-        output = output.reshape([bs, t_w, hid])
-        word_level_output = output
-        output = self._postprocess_word2ph(word_level_output, ph2word, t_p)  # [batch, t_p, hid]
-        output = torch.transpose(output, 1, 2)
-
-        if return_word_encoding:
-            return output, torch.transpose(word_level_output, 1, 2)
-        else:
-            return output
-
-if __name__ == '__main__':
-    # Unit Test for batching graphs
-    from modules.syntaspeech.syntactic_graph_buider import Sentence2GraphParser, plot_dgl_sentence_graph
-    parser = Sentence2GraphParser("en")
-
-    # Unit Test for English Graph Builder
-    text1 = "To be or not to be , that 's a question ."
-    text2 = "I love you . You love me . Mixue ice-scream and tea ."
-    graph1, etypes1 = parser.parse(text1)
-    graph2, etypes2 = parser.parse(text2)
-    batched_text = "<BOS> " + text1 + " <EOS>" + " " + "<BOS> " + text2 + " <EOS>"
-    batched_nodes = [graph1.num_nodes(), graph2.num_nodes()]
-    plot_dgl_sentence_graph(dgl.batch([graph1, graph2]), {i: w for i, w in enumerate(batched_text.split(" "))})
-    etypes_lst = [etypes1, etypes2]
-
-    # Unit Test for Graph Encoder forward
-    in_feats = 4
-    out_feats = 4
-    enc = GraphAuxEnc(in_dim=in_feats, hid_dim=in_feats, out_dim=out_feats)
-    ph2word = torch.tensor([
-        [1, 2, 3, 3, 3, 4, 4, 5, 6, 7, 8,  9,  10, 11, 12, 13, 0],
-        [1, 2, 3, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
-    ])
-    inp = torch.randn([2,  in_feats, 17]) # [N_sentence, feat, ph_length]
-    graph_lst = [graph1, graph2]
-    out = enc(graph_lst, inp, ph2word, etypes_lst)
-    print(out.shape)  # [N_sentence, feat, ph_length]

spaces/AIGC-Audio/AudioGPT/audio_detection/target_sound_detection/src/models.py

@@ -1,1288 +0,0 @@
-# !/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Time    : 2021/3/9 16:33
-# @Author  : dongchao yang
-# @File    : train.py
-from itertools import zip_longest
-import numpy as np
-from scipy import ndimage
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import time
-from torchlibrosa.augmentation import SpecAugmentation
-from torchlibrosa.stft import Spectrogram, LogmelFilterBank
-import math
-from sklearn.cluster import KMeans
-import os
-import time
-from functools import partial
-# import timm
-# from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-import warnings
-from functools import partial
-# from timm.models.registry import register_model
-# from timm.models.vision_transformer import _cfg
-# from mmdet.utils import get_root_logger
-# from mmcv.runner import load_checkpoint
-# from mmcv.runner import _load_checkpoint, load_state_dict
-# import mmcv.runner
-import copy
-from collections import OrderedDict
-import io
-import re
-DEBUG=0
-event_labels = ['Alarm', 'Alarm_clock', 'Animal', 'Applause', 'Arrow', 'Artillery_fire', 
-                'Babbling', 'Baby_laughter', 'Bark', 'Basketball_bounce', 'Battle_cry', 
-                'Bell', 'Bird', 'Bleat', 'Bouncing', 'Breathing', 'Buzz', 'Camera', 
-                'Cap_gun', 'Car', 'Car_alarm', 'Cat', 'Caw', 'Cheering', 'Child_singing', 
-                'Choir', 'Chop', 'Chopping_(food)', 'Clapping', 'Clickety-clack', 'Clicking', 
-                'Clip-clop', 'Cluck', 'Coin_(dropping)', 'Computer_keyboard', 'Conversation', 
-                'Coo', 'Cough', 'Cowbell', 'Creak', 'Cricket', 'Croak', 'Crow', 'Crowd', 'DTMF', 
-                'Dog', 'Door', 'Drill', 'Drip', 'Engine', 'Engine_starting', 'Explosion', 'Fart', 
-                'Female_singing', 'Filing_(rasp)', 'Finger_snapping', 'Fire', 'Fire_alarm', 'Firecracker', 
-                'Fireworks', 'Frog', 'Gasp', 'Gears', 'Giggle', 'Glass', 'Glass_shatter', 'Gobble', 'Groan', 
-                'Growling', 'Hammer', 'Hands', 'Hiccup', 'Honk', 'Hoot', 'Howl', 'Human_sounds', 'Human_voice', 
-                'Insect', 'Laughter', 'Liquid', 'Machine_gun', 'Male_singing', 'Mechanisms', 'Meow', 'Moo', 
-                'Motorcycle', 'Mouse', 'Music', 'Oink', 'Owl', 'Pant', 'Pant_(dog)', 'Patter', 'Pig', 'Plop',
-                'Pour', 'Power_tool', 'Purr', 'Quack', 'Radio', 'Rain_on_surface', 'Rapping', 'Rattle', 
-                'Reversing_beeps', 'Ringtone', 'Roar', 'Run', 'Rustle', 'Scissors', 'Scrape', 'Scratch', 
-                'Screaming', 'Sewing_machine', 'Shout', 'Shuffle', 'Shuffling_cards', 'Singing', 
-                'Single-lens_reflex_camera', 'Siren', 'Skateboard', 'Sniff', 'Snoring', 'Speech', 
-                'Speech_synthesizer', 'Spray', 'Squeak', 'Squeal', 'Steam', 'Stir', 'Surface_contact', 
-                'Tap', 'Tap_dance', 'Telephone_bell_ringing', 'Television', 'Tick', 'Tick-tock', 'Tools', 
-                'Train', 'Train_horn', 'Train_wheels_squealing', 'Truck', 'Turkey', 'Typewriter', 'Typing', 
-                'Vehicle', 'Video_game_sound', 'Water', 'Whimper_(dog)', 'Whip', 'Whispering', 'Whistle', 
-                'Whistling', 'Whoop', 'Wind', 'Writing', 'Yip', 'and_pans', 'bird_song', 'bleep', 'clink', 
-                'cock-a-doodle-doo', 'crinkling', 'dove', 'dribble', 'eructation', 'faucet', 'flapping_wings', 
-                'footsteps', 'gunfire', 'heartbeat', 'infant_cry', 'kid_speaking', 'man_speaking', 'mastication', 
-                'mice', 'river', 'rooster', 'silverware', 'skidding', 'smack', 'sobbing', 'speedboat', 'splatter',
-                'surf', 'thud', 'thwack', 'toot', 'truck_horn', 'tweet', 'vroom', 'waterfowl', 'woman_speaking']
-def load_checkpoint(model,
-                    filename,
-                    map_location=None,
-                    strict=False,
-                    logger=None,
-                    revise_keys=[(r'^module\.', '')]):
-    """Load checkpoint from a file or URI.
-    Args:
-        model (Module): Module to load checkpoint.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str): Same as :func:`torch.load`.
-        strict (bool): Whether to allow different params for the model and
-            checkpoint.
-        logger (:mod:`logging.Logger` or None): The logger for error message.
-        revise_keys (list): A list of customized keywords to modify the
-            state_dict in checkpoint. Each item is a (pattern, replacement)
-            pair of the regular expression operations. Default: strip
-            the prefix 'module.' by [(r'^module\\.', '')].
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    checkpoint = _load_checkpoint(filename, map_location, logger)
-    '''
-    new_proj = torch.nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
-    new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1))
-    checkpoint['patch_embed1.proj.weight'] = new_proj.weight
-    new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=2).unsqueeze(2).repeat(1,1,3,1))
-    checkpoint['patch_embed1.proj.weight'] = new_proj.weight
-    new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=3).unsqueeze(3).repeat(1,1,1,3))
-    checkpoint['patch_embed1.proj.weight'] = new_proj.weight
-    '''
-    new_proj = torch.nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
-    new_proj.weight = torch.nn.Parameter(torch.sum(checkpoint['patch_embed1.proj.weight'], dim=1).unsqueeze(1))
-    checkpoint['patch_embed1.proj.weight'] = new_proj.weight
-    # OrderedDict is a subclass of dict
-    if not isinstance(checkpoint, dict):
-        raise RuntimeError(
-            f'No state_dict found in checkpoint file {filename}')
-    # get state_dict from checkpoint
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    else:
-        state_dict = checkpoint
-
-    # strip prefix of state_dict
-    metadata = getattr(state_dict, '_metadata', OrderedDict())
-    for p, r in revise_keys:
-        state_dict = OrderedDict(
-            {re.sub(p, r, k): v
-             for k, v in state_dict.items()})
-    state_dict = OrderedDict({k.replace('backbone.',''):v for k,v in state_dict.items()})
-    # Keep metadata in state_dict
-    state_dict._metadata = metadata
-
-    # load state_dict
-    load_state_dict(model, state_dict, strict, logger)
-    return checkpoint
-
-def init_weights(m):
-    if isinstance(m, (nn.Conv2d, nn.Conv1d)):
-        nn.init.kaiming_normal_(m.weight)
-        if m.bias is not None:
-            nn.init.constant_(m.bias, 0)
-    elif isinstance(m, nn.BatchNorm2d):
-        nn.init.constant_(m.weight, 1)
-        if m.bias is not None:
-            nn.init.constant_(m.bias, 0)
-    if isinstance(m, nn.Linear):
-        nn.init.kaiming_uniform_(m.weight)
-        if m.bias is not None:
-            nn.init.constant_(m.bias, 0)
-def init_layer(layer):
-    """Initialize a Linear or Convolutional layer. """
-    nn.init.xavier_uniform_(layer.weight)
-    if hasattr(layer, 'bias'):
-        if layer.bias is not None:
-            layer.bias.data.fill_(0.)
-            
-    
-def init_bn(bn):
-    """Initialize a Batchnorm layer. """
-    bn.bias.data.fill_(0.)
-    bn.weight.data.fill_(1.)
-
-class MaxPool(nn.Module):
-    def __init__(self, pooldim=1):
-        super().__init__()
-        self.pooldim = pooldim
-
-    def forward(self, logits, decision):
-        return torch.max(decision, dim=self.pooldim)[0]
-
-
-class LinearSoftPool(nn.Module):
-    """LinearSoftPool
-    Linear softmax, takes logits and returns a probability, near to the actual maximum value.
-    Taken from the paper:
-        A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling
-    https://arxiv.org/abs/1810.09050
-    """
-    def __init__(self, pooldim=1):
-        super().__init__()
-        self.pooldim = pooldim
-
-    def forward(self, logits, time_decision):
-        return (time_decision**2).sum(self.pooldim) / (time_decision.sum(
-            self.pooldim)+1e-7)
-
-class ConvBlock(nn.Module):
-    def __init__(self, in_channels, out_channels):
-        
-        super(ConvBlock, self).__init__()
-        
-        self.conv1 = nn.Conv2d(in_channels=in_channels, 
-                              out_channels=out_channels,
-                              kernel_size=(3, 3), stride=(1, 1),
-                              padding=(1, 1), bias=False)
-                              
-        self.conv2 = nn.Conv2d(in_channels=out_channels, 
-                              out_channels=out_channels,
-                              kernel_size=(3, 3), stride=(1, 1),
-                              padding=(1, 1), bias=False)
-                              
-        self.bn1 = nn.BatchNorm2d(out_channels)
-        self.bn2 = nn.BatchNorm2d(out_channels)
-
-        self.init_weight()
-        
-    def init_weight(self):
-        init_layer(self.conv1)
-        init_layer(self.conv2)
-        init_bn(self.bn1)
-        init_bn(self.bn2)
-
-        
-    def forward(self, input, pool_size=(2, 2), pool_type='avg'):
-        
-        x = input
-        x = F.relu_(self.bn1(self.conv1(x)))
-        x = F.relu_(self.bn2(self.conv2(x)))
-        if pool_type == 'max':
-            x = F.max_pool2d(x, kernel_size=pool_size)
-        elif pool_type == 'avg':
-            x = F.avg_pool2d(x, kernel_size=pool_size)
-        elif pool_type == 'avg+max':
-            x1 = F.avg_pool2d(x, kernel_size=pool_size)
-            x2 = F.max_pool2d(x, kernel_size=pool_size)
-            x = x1 + x2
-        else:
-            raise Exception('Incorrect argument!')
-        
-        return x
-
-class ConvBlock_GLU(nn.Module):
-    def __init__(self, in_channels, out_channels,kernel_size=(3,3)):
-        super(ConvBlock_GLU, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels=in_channels, 
-                              out_channels=out_channels,
-                              kernel_size=kernel_size, stride=(1, 1),
-                              padding=(1, 1), bias=False)                         
-        self.bn1 = nn.BatchNorm2d(out_channels)
-        self.sigmoid = nn.Sigmoid()
-        self.init_weight()
-        
-    def init_weight(self):
-        init_layer(self.conv1)
-        init_bn(self.bn1)
-
-    def forward(self, input, pool_size=(2, 2), pool_type='avg'):
-        x = input
-        x = self.bn1(self.conv1(x))
-        cnn1 = self.sigmoid(x[:, :x.shape[1]//2, :, :])
-        cnn2 = x[:,x.shape[1]//2:,:,:]
-        x = cnn1*cnn2
-        if pool_type == 'max':
-            x = F.max_pool2d(x, kernel_size=pool_size)
-        elif pool_type == 'avg':
-            x = F.avg_pool2d(x, kernel_size=pool_size)
-        elif pool_type == 'avg+max':
-            x1 = F.avg_pool2d(x, kernel_size=pool_size)
-            x2 = F.max_pool2d(x, kernel_size=pool_size)
-            x = x1 + x2
-        elif pool_type == 'None':
-            pass
-        elif pool_type == 'LP':
-            pass
-            #nn.LPPool2d(4, pool_size)
-        else:
-            raise Exception('Incorrect argument!')
-        return x
-
-class Mul_scale_GLU(nn.Module):
-    def __init__(self):
-        super(Mul_scale_GLU,self).__init__()
-        self.conv_block1_1 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(1,1)) # 1*1
-        self.conv_block1_2 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(3,3)) # 3*3
-        self.conv_block1_3 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(5,5)) # 5*5
-        self.conv_block2 = ConvBlock_GLU(in_channels=96, out_channels=128*2)
-        # self.conv_block3 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock_GLU(in_channels=128, out_channels=128*2)
-        self.conv_block4 = ConvBlock_GLU(in_channels=128, out_channels=256*2)
-        self.conv_block5 = ConvBlock_GLU(in_channels=256, out_channels=256*2)
-        self.conv_block6 = ConvBlock_GLU(in_channels=256, out_channels=512*2)
-        self.conv_block7 = ConvBlock_GLU(in_channels=512, out_channels=512*2)
-        self.padding = nn.ReplicationPad2d((0,1,0,1))
-
-    def forward(self, input, fi=None):
-        """
-        Input: (batch_size, data_length)"""
-        x1 = self.conv_block1_1(input, pool_size=(2, 2), pool_type='avg')
-        x1 = x1[:,:,:500,:32]
-        #print('x1 ',x1.shape)
-        x2 = self.conv_block1_2(input,pool_size=(2,2),pool_type='avg')
-        #print('x2 ',x2.shape)
-        x3 = self.conv_block1_3(input,pool_size=(2,2),pool_type='avg')
-        x3 = self.padding(x3)
-        #print('x3 ',x3.shape)
-        # assert 1==2
-        x = torch.cat([x1,x2],dim=1)
-        x = torch.cat([x,x3],dim=1)
-        #print('x ',x.shape)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type='None')
-        x = self.conv_block3(x,pool_size=(2,2),pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training) # 
-        #print('x2,3 ',x.shape)
-        x = self.conv_block4(x, pool_size=(2, 4), pool_type='None')
-        x = self.conv_block5(x,pool_size=(2,4),pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        #print('x4,5 ',x.shape)
-
-        x = self.conv_block6(x, pool_size=(1, 4), pool_type='None')
-        x = self.conv_block7(x, pool_size=(1, 4), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        # print('x6,7 ',x.shape)
-        # assert 1==2
-        return x
-
-class Cnn14(nn.Module):
-    def __init__(self, sample_rate=32000, window_size=1024, hop_size=320, mel_bins=64, fmin=50, 
-        fmax=14000, classes_num=527):
-        
-        super(Cnn14, self).__init__()
-
-        window = 'hann'
-        center = True
-        pad_mode = 'reflect'
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, 
-            win_length=window_size, window=window, center=center, pad_mode=pad_mode, 
-            freeze_parameters=True)
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, 
-            n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, 
-            freeze_parameters=True)
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, 
-            freq_drop_width=8, freq_stripes_num=2)
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
-        self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
-
-        self.fc1 = nn.Linear(2048, 128, bias=True)
-        self.fc_audioset = nn.Linear(128, classes_num, bias=True)
-        
-        self.init_weight()
-
-    def init_weight(self):
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input_, mixup_lambda=None):
-        """
-        Input: (batch_size, data_length)"""
-        input_ = input_.unsqueeze(1)
-        x = self.conv_block1(input_, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(1, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block5(x, pool_size=(1, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block6(x, pool_size=(1, 2), pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        # print(x.shape)
-        # x = torch.mean(x, dim=3)
-        x = x.transpose(1, 2).contiguous().flatten(-2)
-        x = self.fc1(x)
-        # print(x.shape)
-        # assert 1==2
-        # (x1,_) = torch.max(x, dim=2)
-        # x2 = torch.mean(x, dim=2)
-        # x = x1 + x2
-        # x = F.dropout(x, p=0.5, training=self.training)
-        # x = F.relu_(self.fc1(x))
-        # embedding = F.dropout(x, p=0.5, training=self.training)
-        return x
-
-class Cnn10_fi(nn.Module):
-    def __init__(self):  
-        super(Cnn10_fi, self).__init__()
-        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-
-        # self.fc1 = nn.Linear(512, 512, bias=True)
-        # self.fc_audioset = nn.Linear(512, classes_num, bias=True)
-        
-        # self.init_weight()
- 
-    def forward(self, input, fi=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        x = self.conv_block1(input, pool_size=(2, 2), pool_type='avg')
-        if fi != None:
-            gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
-            beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
-            x = (gamma)*x + beta
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
-        if fi != None:
-            gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
-            beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
-            x = (gamma)*x + beta
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 4), pool_type='avg')
-        if fi != None:
-            gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
-            beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
-            x = (gamma)*x + beta
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(1, 4), pool_type='avg')
-        if fi != None:
-            gamma = fi[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
-            beta = fi[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(x)
-            x = (gamma)*x + beta
-        x = F.dropout(x, p=0.2, training=self.training)
-        return x
-
-class Cnn10_mul_scale(nn.Module):
-    def __init__(self,scale=8):  
-        super(Cnn10_mul_scale, self).__init__()
-        self.conv_block1_1 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(1,1))
-        self.conv_block1_2 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(3,3))
-        self.conv_block1_3 = ConvBlock_GLU(in_channels=1, out_channels=64,kernel_size=(5,5))
-        self.conv_block2 = ConvBlock(in_channels=96, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.scale = scale
-        self.padding = nn.ReplicationPad2d((0,1,0,1))
-    def forward(self, input, pool_size=(2, 2), pool_type='avg'):
-        """
-        Input: (batch_size, data_length)"""
-        if self.scale == 8:
-            pool_size1 = (2,2)
-            pool_size2 = (2,2)
-            pool_size3 = (2,4)
-            pool_size4 = (1,4)
-        elif self.scale == 4:
-            pool_size1 = (2,2)
-            pool_size2 = (2,2)
-            pool_size3 = (1,4)
-            pool_size4 = (1,4)
-        elif self.scale == 2:
-            pool_size1 = (2,2)
-            pool_size2 = (1,2)
-            pool_size3 = (1,4)
-            pool_size4 = (1,4)
-        else:
-            pool_size1 = (1,2)
-            pool_size2 = (1,2)
-            pool_size3 = (1,4)
-            pool_size4 = (1,4)
-        # print('input ',input.shape)
-        x1 = self.conv_block1_1(input, pool_size=pool_size1, pool_type='avg')
-        x1 = x1[:,:,:500,:32]
-        #print('x1 ',x1.shape)
-        x2 = self.conv_block1_2(input, pool_size=pool_size1, pool_type='avg')
-        #print('x2 ',x2.shape)
-        x3 = self.conv_block1_3(input, pool_size=pool_size1, pool_type='avg')
-        x3 = self.padding(x3)
-        #print('x3 ',x3.shape)
-        # assert 1==2
-        m_i = min(x3.shape[2],min(x1.shape[2],x2.shape[2]))
-        #print('m_i ', m_i)
-        x = torch.cat([x1[:,:,:m_i,:],x2[:,:, :m_i,:],x3[:,:, :m_i,:]],dim=1)
-        # x = torch.cat([x,x3],dim=1)
-
-        # x = self.conv_block1(input, pool_size=pool_size1, pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=pool_size2, pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=pool_size3, pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=pool_size4, pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        return x
-
-
-class Cnn10(nn.Module):
-    def __init__(self,scale=8):  
-        super(Cnn10, self).__init__()
-        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.scale = scale
-    def forward(self, input, pool_size=(2, 2), pool_type='avg'):
-        """
-        Input: (batch_size, data_length)"""
-        if self.scale == 8:
-            pool_size1 = (2,2)
-            pool_size2 = (2,2)
-            pool_size3 = (2,4)
-            pool_size4 = (1,4)
-        elif self.scale == 4:
-            pool_size1 = (2,2)
-            pool_size2 = (2,2)
-            pool_size3 = (1,4)
-            pool_size4 = (1,4)
-        elif self.scale == 2:
-            pool_size1 = (2,2)
-            pool_size2 = (1,2)
-            pool_size3 = (1,4)
-            pool_size4 = (1,4)
-        else:
-            pool_size1 = (1,2)
-            pool_size2 = (1,2)
-            pool_size3 = (1,4)
-            pool_size4 = (1,4)
-        x = self.conv_block1(input, pool_size=pool_size1, pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=pool_size2, pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=pool_size3, pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=pool_size4, pool_type='avg')
-        x = F.dropout(x, p=0.2, training=self.training)
-        return x
-
-class MeanPool(nn.Module):
-    def __init__(self, pooldim=1):
-        super().__init__()
-        self.pooldim = pooldim
-
-    def forward(self, logits, decision):
-        return torch.mean(decision, dim=self.pooldim)
-
-class ResPool(nn.Module):
-    def __init__(self, pooldim=1):
-        super().__init__()
-        self.pooldim = pooldim
-        self.linPool = LinearSoftPool(pooldim=1)
-
-class AutoExpPool(nn.Module):
-    def __init__(self, outputdim=10, pooldim=1):
-        super().__init__()
-        self.outputdim = outputdim
-        self.alpha = nn.Parameter(torch.full((outputdim, ), 1))
-        self.pooldim = pooldim
-
-    def forward(self, logits, decision):
-        scaled = self.alpha * decision  # \alpha * P(Y|x) in the paper
-        return (logits * torch.exp(scaled)).sum(
-            self.pooldim) / torch.exp(scaled).sum(self.pooldim)
-
-
-class SoftPool(nn.Module):
-    def __init__(self, T=1, pooldim=1):
-        super().__init__()
-        self.pooldim = pooldim
-        self.T = T
-
-    def forward(self, logits, decision):
-        w = torch.softmax(decision / self.T, dim=self.pooldim)
-        return torch.sum(decision * w, dim=self.pooldim)
-
-
-class AutoPool(nn.Module):
-    """docstring for AutoPool"""
-    def __init__(self, outputdim=10, pooldim=1):
-        super().__init__()
-        self.outputdim = outputdim
-        self.alpha = nn.Parameter(torch.ones(outputdim))
-        self.dim = pooldim
-
-    def forward(self, logits, decision):
-        scaled = self.alpha * decision  # \alpha * P(Y|x) in the paper
-        weight = torch.softmax(scaled, dim=self.dim)
-        return torch.sum(decision * weight, dim=self.dim)  # B x C
-
-
-class ExtAttentionPool(nn.Module):
-    def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs):
-        super().__init__()
-        self.inputdim = inputdim
-        self.outputdim = outputdim
-        self.pooldim = pooldim
-        self.attention = nn.Linear(inputdim, outputdim)
-        nn.init.zeros_(self.attention.weight)
-        nn.init.zeros_(self.attention.bias)
-        self.activ = nn.Softmax(dim=self.pooldim)
-
-    def forward(self, logits, decision):
-        # Logits of shape (B, T, D), decision of shape (B, T, C)
-        w_x = self.activ(self.attention(logits) / self.outputdim)
-        h = (logits.permute(0, 2, 1).contiguous().unsqueeze(-2) *
-             w_x.unsqueeze(-1)).flatten(-2).contiguous()
-        return torch.sum(h, self.pooldim)
-
-
-class AttentionPool(nn.Module):
-    """docstring for AttentionPool"""
-    def __init__(self, inputdim, outputdim=10, pooldim=1, **kwargs):
-        super().__init__()
-        self.inputdim = inputdim
-        self.outputdim = outputdim
-        self.pooldim = pooldim
-        self.transform = nn.Linear(inputdim, outputdim)
-        self.activ = nn.Softmax(dim=self.pooldim)
-        self.eps = 1e-7
-
-    def forward(self, logits, decision):
-        # Input is (B, T, D)
-        # B, T , D
-        w = self.activ(torch.clamp(self.transform(logits), -15, 15))
-        detect = (decision * w).sum(
-            self.pooldim) / (w.sum(self.pooldim) + self.eps)
-        # B, T, D
-        return detect
-
-class Block2D(nn.Module):
-    def __init__(self, cin, cout, kernel_size=3, padding=1):
-        super().__init__()
-        self.block = nn.Sequential(
-            nn.BatchNorm2d(cin),
-            nn.Conv2d(cin,
-                      cout,
-                      kernel_size=kernel_size,
-                      padding=padding,
-                      bias=False),
-            nn.LeakyReLU(inplace=True, negative_slope=0.1))
-
-    def forward(self, x):
-        return self.block(x)
-
-class AudioCNN(nn.Module):
-    def __init__(self, classes_num):
-        super(AudioCNN, self).__init__()
-        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.fc1 = nn.Linear(512,128,bias=True)
-        self.fc = nn.Linear(128, classes_num, bias=True)
-        self.init_weights()
-
-    def init_weights(self):
-        init_layer(self.fc)
-
-    def forward(self, input):
-        '''
-        Input: (batch_size, times_steps, freq_bins)'''
-        # [128, 801, 168] --> [128,1,801,168]
-        x = input[:, None, :, :]
-        '''(batch_size, 1, times_steps, freq_bins)'''
-        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') # 128,64,400,84
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') # 128,128,200,42
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') # 128,256,100,21
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') # 128,512,50,10
-        '''(batch_size, feature_maps, time_steps, freq_bins)'''
-        x = torch.mean(x, dim=3)        # (batch_size, feature_maps, time_stpes) # 128,512,50
-        (x, _) = torch.max(x, dim=2)    # (batch_size, feature_maps) 128,512
-        x = self.fc1(x) # 128,128
-        output = self.fc(x) # 128,10
-        return x,output
-
-    def extract(self,input):
-        '''Input: (batch_size, times_steps, freq_bins)'''
-        x = input[:, None, :, :]
-        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
-        '''(batch_size, feature_maps, time_steps, freq_bins)'''
-        x = torch.mean(x, dim=3)        # (batch_size, feature_maps, time_stpes)
-        (x, _) = torch.max(x, dim=2)    # (batch_size, feature_maps)
-        x = self.fc1(x) # 128,128
-        return x
-
-def parse_poolingfunction(poolingfunction_name='mean', **kwargs):
-    """parse_poolingfunction
-    A heler function to parse any temporal pooling
-    Pooling is done on dimension 1
-    :param poolingfunction_name:
-    :param **kwargs:
-    """
-    poolingfunction_name = poolingfunction_name.lower()
-    if poolingfunction_name == 'mean':
-        return MeanPool(pooldim=1)
-    elif poolingfunction_name == 'max':
-        return MaxPool(pooldim=1)
-    elif poolingfunction_name == 'linear':
-        return LinearSoftPool(pooldim=1)
-    elif poolingfunction_name == 'expalpha':
-        return AutoExpPool(outputdim=kwargs['outputdim'], pooldim=1)
-
-    elif poolingfunction_name == 'soft':
-        return SoftPool(pooldim=1)
-    elif poolingfunction_name == 'auto':
-        return AutoPool(outputdim=kwargs['outputdim'])
-    elif poolingfunction_name == 'attention':
-        return AttentionPool(inputdim=kwargs['inputdim'],
-                             outputdim=kwargs['outputdim'])
-class conv1d(nn.Module):
-    def __init__(self, nin, nout, kernel_size=3, stride=1, padding='VALID', dilation=1):
-        super(conv1d, self).__init__()
-        if padding == 'VALID':
-            dconv_pad = 0
-        elif padding == 'SAME':
-            dconv_pad = dilation * ((kernel_size - 1) // 2)
-        else:
-            raise ValueError("Padding Mode Error!")
-        self.conv = nn.Conv1d(nin, nout, kernel_size=kernel_size, stride=stride, padding=dconv_pad)
-        self.act = nn.ReLU()
-        self.init_layer(self.conv)
-
-    def init_layer(self, layer, nonlinearity='relu'):
-        """Initialize a Linear or Convolutional layer. """
-        nn.init.kaiming_normal_(layer.weight, nonlinearity=nonlinearity)
-        nn.init.constant_(layer.bias, 0.1)
-
-    def forward(self, x):
-        out = self.act(self.conv(x))
-        return out
-
-class Atten_1(nn.Module):
-    def __init__(self, input_dim, context=2, dropout_rate=0.2):
-        super(Atten_1, self).__init__()
-        self._matrix_k = nn.Linear(input_dim, input_dim // 4)
-        self._matrix_q = nn.Linear(input_dim, input_dim // 4)
-        self.relu = nn.ReLU()
-        self.context = context
-        self._dropout_layer = nn.Dropout(dropout_rate)
-        self.init_layer(self._matrix_k)
-        self.init_layer(self._matrix_q)
-
-    def init_layer(self, layer, nonlinearity='leaky_relu'):
-        """Initialize a Linear or Convolutional layer. """
-        nn.init.kaiming_uniform_(layer.weight, nonlinearity=nonlinearity)
-        if hasattr(layer, 'bias'):
-            if layer.bias is not None:
-                layer.bias.data.fill_(0.)
-
-    def forward(self, input_x):
-        k_x = input_x
-        k_x = self.relu(self._matrix_k(k_x))
-        k_x = self._dropout_layer(k_x)
-        # print('k_x ',k_x.shape)
-        q_x = input_x[:, self.context, :]
-        # print('q_x ',q_x.shape)
-        q_x = q_x[:, None, :]
-        # print('q_x1 ',q_x.shape)
-        q_x = self.relu(self._matrix_q(q_x))
-        q_x = self._dropout_layer(q_x)
-        # print('q_x2 ',q_x.shape)
-        x_ = torch.matmul(k_x, q_x.transpose(-2, -1) / math.sqrt(k_x.size(-1)))
-        # print('x_ ',x_.shape)
-        x_ = x_.squeeze(2)
-        alpha = F.softmax(x_, dim=-1)
-        att_ = alpha
-        # print('alpha ',alpha)
-        alpha = alpha.unsqueeze(2).repeat(1,1,input_x.shape[2])
-        # print('alpha ',alpha)
-        # alpha = alpha.view(alpha.size(0), alpha.size(1), alpha.size(2), 1)
-        out = alpha * input_x
-        # print('out ', out.shape)
-        # out = out.mean(2)
-        out = out.mean(1)
-        # print('out ',out.shape)
-        # assert 1==2
-        #y = alpha * input_x
-        #return y, att_
-        out = input_x[:, self.context, :] + out
-        return out
-
-class Fusion(nn.Module):
-    def __init__(self, inputdim, inputdim2, n_fac):
-        super().__init__()
-        self.fuse_layer1 = conv1d(inputdim, inputdim2*n_fac,1)
-        self.fuse_layer2 = conv1d(inputdim2, inputdim2*n_fac,1)
-        self.avg_pool = nn.AvgPool1d(n_fac, stride=n_fac) # 沿着最后一个维度进行pooling
-
-    def forward(self,embedding,mix_embed):
-        embedding = embedding.permute(0,2,1)
-        fuse1_out = self.fuse_layer1(embedding) # [2, 501, 2560] ,512*5, 1D卷积融合,spk_embeding ,扩大其维度 
-        fuse1_out = fuse1_out.permute(0,2,1)
-
-        mix_embed = mix_embed.permute(0,2,1)
-        fuse2_out = self.fuse_layer2(mix_embed) # [2, 501, 2560] ,512*5, 1D卷积融合,spk_embeding ,扩大其维度 
-        fuse2_out = fuse2_out.permute(0,2,1)
-        as_embs = torch.mul(fuse1_out, fuse2_out) # 相乘 [2, 501, 2560]
-        # (10, 501, 512)
-        as_embs = self.avg_pool(as_embs) # [2, 501, 512] 相当于 2560//5
-        return as_embs
-
-class CDur_fusion(nn.Module):
-    def __init__(self, inputdim, outputdim, **kwargs):
-        super().__init__()
-        self.features = nn.Sequential(
-            Block2D(1, 32),
-            nn.LPPool2d(4, (2, 4)),
-            Block2D(32, 128),
-            Block2D(128, 128),
-            nn.LPPool2d(4, (2, 4)),
-            Block2D(128, 128),
-            Block2D(128, 128),
-            nn.LPPool2d(4, (1, 4)),
-            nn.Dropout(0.3),
-        )
-        with torch.no_grad():
-            rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
-            rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
-
-        self.gru = nn.GRU(128, 128, bidirectional=True, batch_first=True)
-        self.fusion = Fusion(128,2)
-        self.fc = nn.Linear(256,256)
-        self.outputlayer = nn.Linear(256, outputdim)
-        self.features.apply(init_weights)
-        self.outputlayer.apply(init_weights)
-
-    def forward(self, x, embedding): # 
-        batch, time, dim = x.shape
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128)
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-        x = self.fusion(embedding,x)
-        #x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.gru.flatten_parameters()
-        x, _ = self.gru(x) #  x  torch.Size([16, 125, 256])
-        x = self.fc(x)
-        decision_time = torch.softmax(self.outputlayer(x),dim=2) # x  torch.Size([16, 125, 2])
-        decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2), # [16, 2, 125]
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-        return decision_time[:,:,0],decision_up
-
-class CDur(nn.Module):
-    def __init__(self, inputdim, outputdim,time_resolution, **kwargs):
-        super().__init__()
-        self.features = nn.Sequential(
-            Block2D(1, 32),
-            nn.LPPool2d(4, (2, 4)),
-            Block2D(32, 128),
-            Block2D(128, 128),
-            nn.LPPool2d(4, (2, 4)),
-            Block2D(128, 128),
-            Block2D(128, 128),
-            nn.LPPool2d(4, (2, 4)),
-            nn.Dropout(0.3),
-        )
-        with torch.no_grad():
-            rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
-            rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
-
-        self.gru = nn.GRU(256, 256, bidirectional=True, batch_first=True)
-        self.fc = nn.Linear(512,256)
-        self.outputlayer = nn.Linear(256, outputdim)
-        self.features.apply(init_weights)
-        self.outputlayer.apply(init_weights)
-
-    def forward(self, x, embedding,one_hot=None): # 
-        batch, time, dim = x.shape
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128)
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-        x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.gru.flatten_parameters()
-        x, _ = self.gru(x) #  x  torch.Size([16, 125, 256])
-        x = self.fc(x)
-        decision_time = torch.softmax(self.outputlayer(x),dim=2) # x  torch.Size([16, 125, 2])
-        decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2), # [16, 2, 125]
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-        return decision_time[:,:,0],decision_up
-
-class CDur_big(nn.Module):
-    def __init__(self, inputdim, outputdim, **kwargs):
-        super().__init__()
-        self.features = nn.Sequential(
-            Block2D(1, 64),
-            Block2D(64, 64),
-            nn.LPPool2d(4, (2, 2)),
-            Block2D(64, 128),
-            Block2D(128, 128),
-            nn.LPPool2d(4, (2, 2)),
-            Block2D(128, 256),
-            Block2D(256, 256),
-            nn.LPPool2d(4, (2, 4)),
-            Block2D(256, 512),
-            Block2D(512, 512),
-            nn.LPPool2d(4, (1, 4)),
-            nn.Dropout(0.3),)
-        with torch.no_grad():
-            rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
-            rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
-        self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True)
-        self.fc = nn.Linear(1024,256)
-        self.outputlayer = nn.Linear(256, outputdim)
-        self.features.apply(init_weights)
-        self.outputlayer.apply(init_weights)
-
-    def forward(self, x, embedding): # 
-        batch, time, dim = x.shape
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-        x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.gru.flatten_parameters()
-        x, _ = self.gru(x) #  x  torch.Size([16, 125, 256])
-        x = self.fc(x)
-        decision_time = torch.softmax(self.outputlayer(x),dim=2) # x  torch.Size([16, 125, 2])
-        decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2), # [16, 2, 125]
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-        return decision_time[:,:,0],decision_up
-
-class CDur_GLU(nn.Module):
-    def __init__(self, inputdim, outputdim, **kwargs):
-        super().__init__()
-        self.features = Mul_scale_GLU()
-        # with torch.no_grad():
-        #     rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
-        #     rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
-        self.gru = nn.GRU(640, 512,1, bidirectional=True, batch_first=True) # previous is 640
-        # self.gru = LSTMModel(640, 512,1)
-        self.fc = nn.Linear(1024,256)
-        self.outputlayer = nn.Linear(256, outputdim)
-        # self.features.apply(init_weights)
-        self.outputlayer.apply(init_weights)
-        
-    def forward(self, x, embedding,one_hot=None): # 
-        batch, time, dim = x.shape
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
-        # print('x ',x.shape)
-        # assert 1==2
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-
-        x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.gru.flatten_parameters()
-        x, _ = self.gru(x) #  x  torch.Size([16, 125, 256])
-        # x = self.gru(x) #  x  torch.Size([16, 125, 256])
-        x = self.fc(x)
-        decision_time = torch.softmax(self.outputlayer(x),dim=2) # x  torch.Size([16, 125, 2])
-        decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2), # [16, 2, 125]
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-        return decision_time[:,:,0],decision_up
-
-class CDur_CNN14(nn.Module):
-    def __init__(self, inputdim, outputdim,time_resolution,**kwargs):
-        super().__init__()
-        if time_resolution==125:
-            self.features = Cnn10(8)
-        elif time_resolution == 250:
-            #print('time_resolution ',time_resolution)
-            self.features = Cnn10(4)
-        elif time_resolution == 500:
-            self.features = Cnn10(2)
-        else:
-            self.features = Cnn10(0)
-        with torch.no_grad():
-            rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
-            rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
-        # self.features = Cnn10()
-        self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True)
-        # self.gru = LSTMModel(640, 512,1)
-        self.fc = nn.Linear(1024,256)
-        self.outputlayer = nn.Linear(256, outputdim)
-        # self.features.apply(init_weights)
-        self.outputlayer.apply(init_weights)
-    
-    def forward(self, x, embedding,one_hot=None):
-        batch, time, dim = x.shape
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
-        # print('x ',x.shape)
-        # assert 1==2
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-        x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.gru.flatten_parameters()
-        x, _ = self.gru(x) #  x  torch.Size([16, 125, 256])
-        # x = self.gru(x) #  x  torch.Size([16, 125, 256])
-        x = self.fc(x)
-        decision_time = torch.softmax(self.outputlayer(x),dim=2) # x  torch.Size([16, 125, 2])
-        decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2), # [16, 2, 125]
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-        return decision_time[:,:,0],decision_up
-
-class CDur_CNN_mul_scale(nn.Module):
-    def __init__(self, inputdim, outputdim,time_resolution,**kwargs):
-        super().__init__()
-        if time_resolution==125:
-            self.features = Cnn10_mul_scale(8)
-        elif time_resolution == 250:
-            #print('time_resolution ',time_resolution)
-            self.features = Cnn10_mul_scale(4)
-        elif time_resolution == 500:
-            self.features = Cnn10_mul_scale(2)
-        else:
-            self.features = Cnn10_mul_scale(0)
-        # with torch.no_grad():
-        #     rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
-        #     rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
-        # self.features = Cnn10()
-        self.gru = nn.GRU(640, 512, bidirectional=True, batch_first=True)
-        # self.gru = LSTMModel(640, 512,1)
-        self.fc = nn.Linear(1024,256)
-        self.outputlayer = nn.Linear(256, outputdim)
-        # self.features.apply(init_weights)
-        self.outputlayer.apply(init_weights)
-    
-    def forward(self, x, embedding,one_hot=None):
-        # print('x ',x.shape)
-        # assert 1==2
-        batch, time, dim = x.shape
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
-        # print('x ',x.shape)
-        # assert 1==2
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-        x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.gru.flatten_parameters()
-        x, _ = self.gru(x) #  x  torch.Size([16, 125, 256])
-        # x = self.gru(x) #  x  torch.Size([16, 125, 256])
-        x = self.fc(x)
-        decision_time = torch.softmax(self.outputlayer(x),dim=2) # x  torch.Size([16, 125, 2])
-        decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2), # [16, 2, 125]
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-        return decision_time[:,:,0],decision_up
-
-class CDur_CNN_mul_scale_fusion(nn.Module):
-    def __init__(self, inputdim, outputdim, time_resolution,**kwargs):
-        super().__init__()
-        if time_resolution==125:
-            self.features = Cnn10_mul_scale(8)
-        elif time_resolution == 250:
-            #print('time_resolution ',time_resolution)
-            self.features = Cnn10_mul_scale(4)
-        elif time_resolution == 500:
-            self.features = Cnn10_mul_scale(2)
-        else:
-            self.features = Cnn10_mul_scale(0)
-        # with torch.no_grad():
-        #     rnn_input_dim = self.features(torch.randn(1, 1, 500,inputdim)).shape
-        #     rnn_input_dim = rnn_input_dim[1] * rnn_input_dim[-1]
-        # self.features = Cnn10()
-        self.gru = nn.GRU(512, 512, bidirectional=True, batch_first=True)
-        # self.gru = LSTMModel(640, 512,1)
-        self.fc = nn.Linear(1024,256)
-        self.fusion = Fusion(128,512,2)
-        self.outputlayer = nn.Linear(256, outputdim)
-        # self.features.apply(init_weights)
-        self.outputlayer.apply(init_weights)
-    
-    def forward(self, x, embedding,one_hot=None):
-        # print('x ',x.shape)
-        # assert 1==2
-        batch, time, dim = x.shape
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,512)
-        # print('x ',x.shape)
-        # assert 1==2
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-        x = self.fusion(embedding, x)
-        #x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.gru.flatten_parameters()
-        x, _ = self.gru(x) #  x  torch.Size([16, 125, 256])
-        # x = self.gru(x) #  x  torch.Size([16, 125, 256])
-        x = self.fc(x)
-        decision_time = torch.softmax(self.outputlayer(x),dim=2) # x  torch.Size([16, 125, 2])
-        decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2), # [16, 2, 125]
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-        return decision_time[:,:,0],decision_up
-
-
-class RaDur_fusion(nn.Module):
-    def __init__(self, model_config, inputdim, outputdim, time_resolution, **kwargs):
-        super().__init__()
-        self.encoder = Cnn14()
-        self.detection = CDur_CNN_mul_scale_fusion(inputdim, outputdim, time_resolution)
-        self.softmax = nn.Softmax(dim=2)
-        #self.temperature = 5
-        # if model_config['pre_train']:
-        #     self.encoder.load_state_dict(torch.load(model_config['encoder_path'])['model'])
-        #     self.detection.load_state_dict(torch.load(model_config['CDur_path']))
-        
-        self.q = nn.Linear(128,128)
-        self.k = nn.Linear(128,128)
-        self.q_ee = nn.Linear(128, 128)
-        self.k_ee = nn.Linear(128, 128)
-        self.temperature = 11.3 # sqrt(128)
-        self.att_pool = model_config['att_pool']
-        self.enhancement = model_config['enhancement'] 
-        self.tao = model_config['tao']
-        self.top = model_config['top']
-        self.bn = nn.BatchNorm1d(128)
-        self.EE_fusion = Fusion(128, 128, 4)
-
-    def get_w(self,q,k):
-        q = self.q(q)
-        k = self.k(k)
-        q = q.unsqueeze(1)
-        attn = torch.bmm(q, k.transpose(1, 2))
-        attn = attn/self.temperature
-        attn = self.softmax(attn)
-        return attn
-    
-    def get_w_ee(self,q,k):
-        q = self.q_ee(q)
-        k = self.k_ee(k)
-        q = q.unsqueeze(1)
-        attn = torch.bmm(q, k.transpose(1, 2))
-        attn = attn/self.temperature
-        attn = self.softmax(attn)
-        return attn
-    
-    def attention_pooling(self, embeddings, mean_embedding):
-        att_pool_w = self.get_w(mean_embedding,embeddings)
-        embedding = torch.bmm(att_pool_w, embeddings).squeeze(1)
-        # print(embedding.shape)
-        # print(att_pool_w.shape)
-        # print(att_pool_w[0])
-        # assert 1==2
-        return embedding
-    
-    def select_topk_embeddings(self, scores, embeddings, k):
-        _, idx_DESC = scores.sort(descending=True, dim=1) # 根据分数进行排序
-        top_k = _[:,:k]
-        # print('top_k ', top_k)
-        # top_k = top_k.mean(1)
-        idx_topk = idx_DESC[:, :k] # 取top_k个
-        # print('index ', idx_topk)
-        idx_topk = idx_topk.unsqueeze(2).expand([-1, -1, embeddings.shape[2]])
-        selected_embeddings = torch.gather(embeddings, 1, idx_topk)
-        return selected_embeddings,top_k
-    
-    def sum_with_attention(self, embedding, top_k, selected_embeddings):
-        # print('embedding ',embedding)
-        # print('selected_embeddings ',selected_embeddings.shape)
-        att_1 = self.get_w_ee(embedding, selected_embeddings)
-        att_1 = att_1.squeeze(1)
-        #print('att_1 ',att_1.shape)
-        larger = top_k > self.tao
-        # print('larger ',larger)
-        top_k = top_k*larger
-        # print('top_k ',top_k.shape)
-        # print('top_k ',top_k)
-        att_1 = att_1*top_k
-        #print('att_1 ',att_1.shape)
-        # assert 1==2
-        att_2 = att_1.unsqueeze(2).repeat(1,1,128)
-        Es = selected_embeddings*att_2
-        return Es
-    
-    def orcal_EE(self, x, embedding, label):
-        batch, time, dim = x.shape
-
-        mixture_embedding = self.encoder(x) # 8, 125, 128
-        mixture_embedding = mixture_embedding.transpose(1,2)
-        mixture_embedding = self.bn(mixture_embedding)
-        mixture_embedding = mixture_embedding.transpose(1,2)
-
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.detection.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128)
-        embedding_pre = embedding.unsqueeze(1)
-        embedding_pre = embedding_pre.repeat(1, x.shape[1], 1)
-        f = self.detection.fusion(embedding_pre, x) # the first stage results
-        #f = torch.cat((x, embedding_pre), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.detection.gru.flatten_parameters()
-        f, _ = self.detection.gru(f) #  x  torch.Size([16, 125, 256])
-        f = self.detection.fc(f)
-        decision_time = torch.softmax(self.detection.outputlayer(f),dim=2) # x  torch.Size([16, 125, 2])
-        
-        selected_embeddings, top_k = self.select_topk_embeddings(decision_time[:,:,0], mixture_embedding, self.top)
-        
-        selected_embeddings = self.sum_with_attention(embedding, top_k, selected_embeddings) # add the weight
-
-        mix_embedding = selected_embeddings.mean(1).unsqueeze(1) # 
-        mix_embedding = mix_embedding.repeat(1, x.shape[1], 1)
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-        mix_embedding = self.EE_fusion(mix_embedding, embedding) # 使用神经网络进行融合
-        # mix_embedding2 = selected_embeddings2.mean(1)
-        #mix_embedding =  embedding + mix_embedding # 直接相加
-        # new detection results
-        # embedding_now = mix_embedding.unsqueeze(1)
-        # embedding_now = embedding_now.repeat(1, x.shape[1], 1)
-        f_now = self.detection.fusion(mix_embedding, x) 
-        #f_now = torch.cat((x, embedding_now), dim=2) # 
-        f_now, _ = self.detection.gru(f_now) #  x  torch.Size([16, 125, 256])
-        f_now = self.detection.fc(f_now)
-        decision_time_now = torch.softmax(self.detection.outputlayer(f_now), dim=2) # x  torch.Size([16, 125, 2])
-        
-        top_k = top_k.mean(1)  # get avg score,higher score will have more weight
-        larger = top_k > self.tao
-        top_k = top_k * larger
-        top_k = top_k/2.0
-        # print('top_k ',top_k)
-        # assert 1==2
-        # print('tok_k[ ',top_k.shape)
-        # print('decision_time ',decision_time.shape)
-        # print('decision_time_now ',decision_time_now.shape)
-        neg_w = top_k.unsqueeze(1).unsqueeze(2)
-        neg_w = neg_w.repeat(1, decision_time_now.shape[1], decision_time_now.shape[2])
-        # print('neg_w ',neg_w.shape)
-        #print('neg_w ',neg_w[:,0:10,0])
-        pos_w = 1-neg_w
-        #print('pos_w ',pos_w[:,0:10,0])
-        decision_time_final = decision_time*pos_w + neg_w*decision_time_now
-        #print('decision_time_final ',decision_time_final[0,0:10,0])
-        # print(decision_time_final[0,:,:])
-        #assert 1==2
-        return decision_time_final
-    
-    def forward(self, x, ref, label=None):
-        batch, time, dim = x.shape
-        logit = torch.zeros(1).cuda()
-        embeddings  = self.encoder(ref)
-        mean_embedding = embeddings.mean(1)
-        if self.att_pool == True:
-            mean_embedding = self.bn(mean_embedding)
-            embeddings = embeddings.transpose(1,2)
-            embeddings = self.bn(embeddings)
-            embeddings = embeddings.transpose(1,2)
-            embedding = self.attention_pooling(embeddings, mean_embedding)
-        else:
-            embedding = mean_embedding
-        if self.enhancement == True:
-            decision_time = self.orcal_EE(x, embedding, label)
-            decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2), # [16, 2, 125]
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-            return decision_time[:,:,0], decision_up, logit
-
-        x = x.unsqueeze(1) # (b,1,t,d) 
-        x = self.detection.features(x) # 
-        x = x.transpose(1, 2).contiguous().flatten(-2) # 重新拷贝一份x,之后推平-2:-1之间的维度 # (b,125,128)
-        embedding = embedding.unsqueeze(1)
-        embedding = embedding.repeat(1, x.shape[1], 1)
-        # x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        x = self.detection.fusion(embedding, x) 
-        # embedding = embedding.unsqueeze(1)
-        # embedding = embedding.repeat(1, x.shape[1], 1)
-        # x = torch.cat((x, embedding), dim=2) # [B, T, 128 + emb_dim]
-        if not hasattr(self, '_flattened'):
-            self.detection.gru.flatten_parameters()
-        x, _ = self.detection.gru(x) #  x  torch.Size([16, 125, 256])
-        x = self.detection.fc(x)
-        decision_time = torch.softmax(self.detection.outputlayer(x),dim=2) # x  torch.Size([16, 125, 2])
-        decision_up = torch.nn.functional.interpolate(
-                decision_time.transpose(1, 2),
-                time, # 501
-                mode='linear',
-                align_corners=False).transpose(1, 2) # 从125插值回 501 ?--> (16,501,2)
-        return decision_time[:,:,0], decision_up, logit

spaces/AIGC-Audio/AudioGPT/audio_foundation_models.py

@@ -1,1033 +0,0 @@
-import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.realpath(__file__)))
-sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
-sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'NeuralSeq'))
-sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'text_to_audio/Make_An_Audio'))
-sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'audio_detection'))
-sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'mono2binaural'))
-import matplotlib
-import librosa
-from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPSegProcessor, CLIPSegForImageSegmentation
-import torch
-from diffusers import StableDiffusionPipeline
-from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
-import re
-import uuid
-import soundfile
-from diffusers import StableDiffusionInpaintPipeline
-from PIL import Image
-import numpy as np
-from omegaconf import OmegaConf
-from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
-import cv2
-import einops
-from einops import repeat
-from pytorch_lightning import seed_everything
-import random
-from ldm.util import instantiate_from_config
-from ldm.data.extract_mel_spectrogram import TRANSFORMS_16000
-from pathlib import Path
-from vocoder.hifigan.modules import VocoderHifigan
-from vocoder.bigvgan.models import VocoderBigVGAN
-from ldm.models.diffusion.ddim import DDIMSampler
-from wav_evaluation.models.CLAPWrapper import CLAPWrapper
-from inference.svs.ds_e2e import DiffSingerE2EInfer
-from audio_to_text.inference_waveform import AudioCapModel
-import whisper
-from text_to_speech.TTS_binding import TTSInference
-from inference.svs.ds_e2e import DiffSingerE2EInfer
-from inference.tts.GenerSpeech import GenerSpeechInfer
-from utils.hparams import set_hparams
-from utils.hparams import hparams as hp
-from utils.os_utils import move_file
-import scipy.io.wavfile as wavfile
-from audio_infer.utils import config as detection_config
-from audio_infer.pytorch.models import PVT
-from src.models import BinauralNetwork
-from sound_extraction.model.LASSNet import LASSNet
-from sound_extraction.utils.stft import STFT
-from sound_extraction.utils.wav_io import load_wav, save_wav
-from target_sound_detection.src import models as tsd_models
-from target_sound_detection.src.models import event_labels
-from target_sound_detection.src.utils import median_filter, decode_with_timestamps
-import clip
-
-
-def prompts(name, description):
-    def decorator(func):
-        func.name = name
-        func.description = description
-        return func
-
-    return decorator
-
-
-def initialize_model(config, ckpt, device):
-    config = OmegaConf.load(config)
-    model = instantiate_from_config(config.model)
-    model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False)
-
-    model = model.to(device)
-    model.cond_stage_model.to(model.device)
-    model.cond_stage_model.device = model.device
-    sampler = DDIMSampler(model)
-    return sampler
-
-
-def initialize_model_inpaint(config, ckpt):
-    config = OmegaConf.load(config)
-    model = instantiate_from_config(config.model)
-    model.load_state_dict(torch.load(ckpt, map_location='cpu')["state_dict"], strict=False)
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
-    print(model.device, device, model.cond_stage_model.device)
-    sampler = DDIMSampler(model)
-    return sampler
-
-
-def select_best_audio(prompt, wav_list):
-    clap_model = CLAPWrapper('text_to_audio/Make_An_Audio/useful_ckpts/CLAP/CLAP_weights_2022.pth',
-                             'text_to_audio/Make_An_Audio/useful_ckpts/CLAP/config.yml',
-                             use_cuda=torch.cuda.is_available())
-    text_embeddings = clap_model.get_text_embeddings([prompt])
-    score_list = []
-    for data in wav_list:
-        sr, wav = data
-        audio_embeddings = clap_model.get_audio_embeddings([(torch.FloatTensor(wav), sr)], resample=True)
-        score = clap_model.compute_similarity(audio_embeddings, text_embeddings,
-                                              use_logit_scale=False).squeeze().cpu().numpy()
-        score_list.append(score)
-    max_index = np.array(score_list).argmax()
-    print(score_list, max_index)
-    return wav_list[max_index]
-
-
-def merge_audio(audio_path_1, audio_path_2):
-    merged_signal = []
-    sr_1, signal_1 = wavfile.read(audio_path_1)
-    sr_2, signal_2 = wavfile.read(audio_path_2)
-    merged_signal.append(signal_1)
-    merged_signal.append(signal_2)
-    merged_signal = np.hstack(merged_signal)
-    merged_signal = np.asarray(merged_signal, dtype=np.int16)
-    audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-    wavfile.write(audio_filename, sr_1, merged_signal)
-    return audio_filename
-
-
-class T2I:
-    def __init__(self, device):
-        print("Initializing T2I to %s" % device)
-        self.device = device
-        self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-        self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
-        self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
-        self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model,
-                                              tokenizer=self.text_refine_tokenizer, device=self.device)
-        self.pipe.to(device)
-
-    @prompts(name="Generate Image From User Input Text",
-             description="useful when you want to generate an image from a user input text and save it to a file. "
-                         "like: generate an image of an object or something, or generate an image that includes some objects. "
-                         "The input to this tool should be a string, representing the text used to generate image. ")
-    def inference(self, text):
-        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
-        refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"]
-        print(f'{text} refined to {refined_text}')
-        image = self.pipe(refined_text).images[0]
-        image.save(image_filename)
-        print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}")
-        return image_filename
-
-
-class ImageCaptioning:
-    def __init__(self, device):
-        print("Initializing ImageCaptioning to %s" % device)
-        self.device = device
-        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(
-            self.device)
-
-    @prompts(name="Remove Something From The Photo",
-             description="useful when you want to remove and object or something from the photo "
-                         "from its description or location. "
-                         "The input to this tool should be a comma separated string of two, "
-                         "representing the image_path and the object need to be removed. ")
-    def inference(self, image_path):
-        inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device)
-        out = self.model.generate(**inputs)
-        captions = self.processor.decode(out[0], skip_special_tokens=True)
-        return captions
-
-
-class T2A:
-    def __init__(self, device):
-        print("Initializing Make-An-Audio to %s" % device)
-        self.device = device
-        self.sampler = initialize_model('text_to_audio/Make_An_Audio/configs/text-to-audio/txt2audio_args.yaml',
-                                        'text_to_audio/Make_An_Audio/useful_ckpts/ta40multi_epoch=000085.ckpt',
-                                        device=device)
-        self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w', device=device)
-
-    def txt2audio(self, text, seed=55, scale=1.5, ddim_steps=100, n_samples=3, W=624, H=80):
-        SAMPLE_RATE = 16000
-        prng = np.random.RandomState(seed)
-        start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8)
-        start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)
-        uc = self.sampler.model.get_learned_conditioning(n_samples * [""])
-        c = self.sampler.model.get_learned_conditioning(n_samples * [text])
-        shape = [self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8]  # (z_dim, 80//2^x, 848//2^x)
-        samples_ddim, _ = self.sampler.sample(S=ddim_steps,
-                                              conditioning=c,
-                                              batch_size=n_samples,
-                                              shape=shape,
-                                              verbose=False,
-                                              unconditional_guidance_scale=scale,
-                                              unconditional_conditioning=uc,
-                                              x_T=start_code)
-
-        x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim)
-        x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)  # [0, 1]
-
-        wav_list = []
-        for idx, spec in enumerate(x_samples_ddim):
-            wav = self.vocoder.vocode(spec)
-            wav_list.append((SAMPLE_RATE, wav))
-        best_wav = select_best_audio(text, wav_list)
-        return best_wav
-
-    @prompts(name="Generate Audio From User Input Text",
-             description="useful for when you want to generate an audio "
-                         "from a user input text and it saved it to a file."
-                         "The input to this tool should be a string, "
-                         "representing the text used to generate audio.")
-    def inference(self, text, seed=55, scale=1.5, ddim_steps=100, n_samples=3, W=624, H=80):
-        melbins, mel_len = 80, 624
-        with torch.no_grad():
-            result = self.txt2audio(
-                text=text,
-                H=melbins,
-                W=mel_len
-            )
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        soundfile.write(audio_filename, result[1], samplerate=16000)
-        print(f"Processed T2I.run, text: {text}, audio_filename: {audio_filename}")
-        return audio_filename
-
-
-class I2A:
-    def __init__(self, device):
-        print("Initializing Make-An-Audio-Image to %s" % device)
-        self.device = device
-        self.sampler = initialize_model('text_to_audio/Make_An_Audio/configs/img_to_audio/img2audio_args.yaml',
-                                        'text_to_audio/Make_An_Audio/useful_ckpts/ta54_epoch=000216.ckpt',
-                                        device=device)
-        self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w', device=device)
-
-    def img2audio(self, image, seed=55, scale=3, ddim_steps=100, W=624, H=80):
-        SAMPLE_RATE = 16000
-        n_samples = 1  # only support 1 sample
-        prng = np.random.RandomState(seed)
-        start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8)
-        start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)
-        uc = self.sampler.model.get_learned_conditioning(n_samples * [""])
-        # image = Image.fromarray(image)
-        image = Image.open(image)
-        image = self.sampler.model.cond_stage_model.preprocess(image).unsqueeze(0)
-        image_embedding = self.sampler.model.cond_stage_model.forward_img(image)
-        c = image_embedding.repeat(n_samples, 1, 1)
-        shape = [self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8]  # (z_dim, 80//2^x, 848//2^x)
-        samples_ddim, _ = self.sampler.sample(S=ddim_steps,
-                                              conditioning=c,
-                                              batch_size=n_samples,
-                                              shape=shape,
-                                              verbose=False,
-                                              unconditional_guidance_scale=scale,
-                                              unconditional_conditioning=uc,
-                                              x_T=start_code)
-
-        x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim)
-        x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)  # [0, 1]
-        wav_list = []
-        for idx, spec in enumerate(x_samples_ddim):
-            wav = self.vocoder.vocode(spec)
-            wav_list.append((SAMPLE_RATE, wav))
-        best_wav = wav_list[0]
-        return best_wav
-
-    @prompts(name="Generate Audio From The Image",
-             description="useful for when you want to generate an audio "
-                         "based on an image. "
-                         "The input to this tool should be a string, "
-                         "representing the image_path. ")
-    def inference(self, image, seed=55, scale=3, ddim_steps=100, W=624, H=80):
-        melbins, mel_len = 80, 624
-        with torch.no_grad():
-            result = self.img2audio(
-                image=image,
-                H=melbins,
-                W=mel_len
-            )
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        soundfile.write(audio_filename, result[1], samplerate=16000)
-        print(f"Processed I2a.run, image_filename: {image}, audio_filename: {audio_filename}")
-        return audio_filename
-
-
-class TTS:
-    def __init__(self, device=None):
-        self.model = TTSInference(device)
-
-    @prompts(name="Synthesize Speech Given the User Input Text",
-             description="useful for when you want to convert a user input text into speech audio it saved it to a file."
-                         "The input to this tool should be a string, "
-                         "representing the text used to be converted to speech.")
-    def inference(self, text):
-        inp = {"text": text}
-        out = self.model.infer_once(inp)
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        soundfile.write(audio_filename, out, samplerate=22050)
-        return audio_filename
-
-
-class T2S:
-    def __init__(self, device=None):
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        print("Initializing DiffSinger to %s" % device)
-        self.device = device
-        self.exp_name = 'checkpoints/0831_opencpop_ds1000'
-        self.config = 'NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml'
-        self.set_model_hparams()
-        self.pipe = DiffSingerE2EInfer(self.hp, device)
-        self.default_inp = {
-            'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP',
-            'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest',
-            'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590'
-        }
-
-    def set_model_hparams(self):
-        set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False)
-        self.hp = hp
-
-    @prompts(name="Generate Singing Voice From User Input Text, Note and Duration Sequence",
-             description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) "
-                         "and save it to a file."
-                         "If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence. "
-                         "If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. "
-                         "Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx."
-                         "The input to this tool should be a comma seperated string of three, "
-                         "representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided. ")
-    def inference(self, inputs):
-        self.set_model_hparams()
-        val = inputs.split(",")
-        key = ['text', 'notes', 'notes_duration']
-        try:
-            inp = {k: v for k, v in zip(key, val)}
-            wav = self.pipe.infer_once(inp)
-        except:
-            print('Error occurs. Generate default audio sample.\n')
-            inp = self.default_inp
-            wav = self.pipe.infer_once(inp)
-        wav *= 32767
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16))
-        print(f"Processed T2S.run, audio_filename: {audio_filename}")
-        return audio_filename
-
-
-class TTS_OOD:
-    def __init__(self, device):
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        print("Initializing GenerSpeech to %s" % device)
-        self.device = device
-        self.exp_name = 'checkpoints/GenerSpeech'
-        self.config = 'NeuralSeq/modules/GenerSpeech/config/generspeech.yaml'
-        self.set_model_hparams()
-        self.pipe = GenerSpeechInfer(self.hp, device)
-
-    def set_model_hparams(self):
-        set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False)
-        f0_stats_fn = f'{hp["binary_data_dir"]}/train_f0s_mean_std.npy'
-        if os.path.exists(f0_stats_fn):
-            hp['f0_mean'], hp['f0_std'] = np.load(f0_stats_fn)
-            hp['f0_mean'] = float(hp['f0_mean'])
-            hp['f0_std'] = float(hp['f0_std'])
-        hp['emotion_encoder_path'] = 'checkpoints/Emotion_encoder.pt'
-        self.hp = hp
-
-    @prompts(name="Style Transfer",
-             description="useful for when you want to generate speech samples with styles "
-                         "(e.g., timbre, emotion, and prosody) derived from a reference custom voice. "
-                         "Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx."
-                         "The input to this tool should be a comma seperated string of two, "
-                         "representing reference audio path and input text. ")
-    def inference(self, inputs):
-        self.set_model_hparams()
-        key = ['ref_audio', 'text']
-        val = inputs.split(",")
-        inp = {k: v for k, v in zip(key, val)}
-        wav = self.pipe.infer_once(inp)
-        wav *= 32767
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16))
-        print(
-            f"Processed GenerSpeech.run. Input text:{val[1]}. Input reference audio: {val[0]}. Output Audio_filename: {audio_filename}")
-        return audio_filename
-
-
-class Inpaint:
-    def __init__(self, device):
-        print("Initializing Make-An-Audio-inpaint to %s" % device)
-        self.device = device
-        self.sampler = initialize_model_inpaint('text_to_audio/Make_An_Audio/configs/inpaint/txt2audio_args.yaml',
-                                                'text_to_audio/Make_An_Audio/useful_ckpts/inpaint7_epoch00047.ckpt')
-        self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w', device=device)
-        self.cmap_transform = matplotlib.cm.viridis
-
-    def make_batch_sd(self, mel, mask, num_samples=1):
-
-        mel = torch.from_numpy(mel)[None, None, ...].to(dtype=torch.float32)
-        mask = torch.from_numpy(mask)[None, None, ...].to(dtype=torch.float32)
-        masked_mel = (1 - mask) * mel
-
-        mel = mel * 2 - 1
-        mask = mask * 2 - 1
-        masked_mel = masked_mel * 2 - 1
-
-        batch = {
-            "mel": repeat(mel.to(device=self.device), "1 ... -> n ...", n=num_samples),
-            "mask": repeat(mask.to(device=self.device), "1 ... -> n ...", n=num_samples),
-            "masked_mel": repeat(masked_mel.to(device=self.device), "1 ... -> n ...", n=num_samples),
-        }
-        return batch
-
-    def gen_mel(self, input_audio_path):
-        SAMPLE_RATE = 16000
-        sr, ori_wav = wavfile.read(input_audio_path)
-        print("gen_mel")
-        print(sr, ori_wav.shape, ori_wav)
-        ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0
-        if len(ori_wav.shape) == 2:  # stereo
-            ori_wav = librosa.to_mono(
-                ori_wav.T)  # gradio load wav shape could be (wav_len,2) but librosa expects (2,wav_len)
-        print(sr, ori_wav.shape, ori_wav)
-        ori_wav = librosa.resample(ori_wav, orig_sr=sr, target_sr=SAMPLE_RATE)
-
-        mel_len, hop_size = 848, 256
-        input_len = mel_len * hop_size
-        if len(ori_wav) < input_len:
-            input_wav = np.pad(ori_wav, (0, mel_len * hop_size), constant_values=0)
-        else:
-            input_wav = ori_wav[:input_len]
-
-        mel = TRANSFORMS_16000(input_wav)
-        return mel
-
-    def gen_mel_audio(self, input_audio):
-        SAMPLE_RATE = 16000
-        sr, ori_wav = input_audio
-        print("gen_mel_audio")
-        print(sr, ori_wav.shape, ori_wav)
-
-        ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0
-        if len(ori_wav.shape) == 2:  # stereo
-            ori_wav = librosa.to_mono(
-                ori_wav.T)  # gradio load wav shape could be (wav_len,2) but librosa expects (2,wav_len)
-        print(sr, ori_wav.shape, ori_wav)
-        ori_wav = librosa.resample(ori_wav, orig_sr=sr, target_sr=SAMPLE_RATE)
-
-        mel_len, hop_size = 848, 256
-        input_len = mel_len * hop_size
-        if len(ori_wav) < input_len:
-            input_wav = np.pad(ori_wav, (0, mel_len * hop_size), constant_values=0)
-        else:
-            input_wav = ori_wav[:input_len]
-        mel = TRANSFORMS_16000(input_wav)
-        return mel
-
-    def inpaint(self, batch, seed, ddim_steps, num_samples=1, W=512, H=512):
-        model = self.sampler.model
-
-        prng = np.random.RandomState(seed)
-        start_code = prng.randn(num_samples, model.first_stage_model.embed_dim, H // 8, W // 8)
-        start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)
-
-        c = model.get_first_stage_encoding(model.encode_first_stage(batch["masked_mel"]))
-        cc = torch.nn.functional.interpolate(batch["mask"],
-                                             size=c.shape[-2:])
-        c = torch.cat((c, cc), dim=1)  # (b,c+1,h,w) 1 is mask
-
-        shape = (c.shape[1] - 1,) + c.shape[2:]
-        samples_ddim, _ = self.sampler.sample(S=ddim_steps,
-                                              conditioning=c,
-                                              batch_size=c.shape[0],
-                                              shape=shape,
-                                              verbose=False)
-        x_samples_ddim = model.decode_first_stage(samples_ddim)
-
-        mask = batch["mask"]  # [-1,1]
-        mel = torch.clamp((batch["mel"] + 1.0) / 2.0, min=0.0, max=1.0)
-        mask = torch.clamp((batch["mask"] + 1.0) / 2.0, min=0.0, max=1.0)
-        predicted_mel = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-        inpainted = (1 - mask) * mel + mask * predicted_mel
-        inpainted = inpainted.cpu().numpy().squeeze()
-        inapint_wav = self.vocoder.vocode(inpainted)
-
-        return inpainted, inapint_wav
-
-    def predict(self, input_audio, mel_and_mask, seed=55, ddim_steps=100):
-        SAMPLE_RATE = 16000
-        torch.set_grad_enabled(False)
-        mel_img = Image.open(mel_and_mask['image'])
-        mask_img = Image.open(mel_and_mask["mask"])
-        show_mel = np.array(mel_img.convert("L")) / 255
-        mask = np.array(mask_img.convert("L")) / 255
-        mel_bins, mel_len = 80, 848
-        input_mel = self.gen_mel_audio(input_audio)[:, :mel_len]
-        mask = np.pad(mask, ((0, 0), (0, mel_len - mask.shape[1])), mode='constant', constant_values=0)
-        print(mask.shape, input_mel.shape)
-        with torch.no_grad():
-            batch = self.make_batch_sd(input_mel, mask, num_samples=1)
-            inpainted, gen_wav = self.inpaint(
-                batch=batch,
-                seed=seed,
-                ddim_steps=ddim_steps,
-                num_samples=1,
-                H=mel_bins, W=mel_len
-            )
-        inpainted = inpainted[:, :show_mel.shape[1]]
-        color_mel = self.cmap_transform(inpainted)
-        input_len = int(input_audio[1].shape[0] * SAMPLE_RATE / input_audio[0])
-        gen_wav = (gen_wav * 32768).astype(np.int16)[:input_len]
-        image = Image.fromarray((color_mel * 255).astype(np.uint8))
-        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
-        image.save(image_filename)
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        soundfile.write(audio_filename, gen_wav, samplerate=16000)
-        return image_filename, audio_filename
-
-    @prompts(name="Audio Inpainting",
-             description="useful for when you want to inpaint a mel spectrum of an audio and predict this audio, "
-                         "this tool will generate a mel spectrum and you can inpaint it, receives audio_path as input. "
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, input_audio_path):
-        crop_len = 500
-        crop_mel = self.gen_mel(input_audio_path)[:, :crop_len]
-        color_mel = self.cmap_transform(crop_mel)
-        image = Image.fromarray((color_mel * 255).astype(np.uint8))
-        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
-        image.save(image_filename)
-        return image_filename
-
-
-class ASR:
-    def __init__(self, device):
-        print("Initializing Whisper to %s" % device)
-        self.device = device
-        self.model = whisper.load_model("base", device=device)
-
-    @prompts(name="Transcribe speech",
-             description="useful for when you want to know the text corresponding to a human speech, "
-                         "receives audio_path as input. "
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, audio_path):
-        audio = whisper.load_audio(audio_path)
-        audio = whisper.pad_or_trim(audio)
-        mel = whisper.log_mel_spectrogram(audio).to(self.device)
-        _, probs = self.model.detect_language(mel)
-        options = whisper.DecodingOptions()
-        result = whisper.decode(self.model, mel, options)
-        return result.text
-
-    def translate_english(self, audio_path):
-        audio = self.model.transcribe(audio_path, language='English')
-        return audio['text']
-
-
-class A2T:
-    def __init__(self, device):
-        print("Initializing Audio-To-Text Model to %s" % device)
-        self.device = device
-        self.model = AudioCapModel("audio_to_text/audiocaps_cntrstv_cnn14rnn_trm")
-
-    @prompts(name="Generate Text From The Audio",
-             description="useful for when you want to describe an audio in text, "
-                         "receives audio_path as input. "
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, audio_path):
-        audio = whisper.load_audio(audio_path)
-        caption_text = self.model(audio)
-        return caption_text[0]
-
-
-class SoundDetection:
-    def __init__(self, device):
-        self.device = device
-        self.sample_rate = 32000
-        self.window_size = 1024
-        self.hop_size = 320
-        self.mel_bins = 64
-        self.fmin = 50
-        self.fmax = 14000
-        self.model_type = 'PVT'
-        self.checkpoint_path = 'audio_detection/audio_infer/useful_ckpts/audio_detection.pth'
-        self.classes_num = detection_config.classes_num
-        self.labels = detection_config.labels
-        self.frames_per_second = self.sample_rate // self.hop_size
-        # Model = eval(self.model_type)
-        self.model = PVT(sample_rate=self.sample_rate, window_size=self.window_size,
-                         hop_size=self.hop_size, mel_bins=self.mel_bins, fmin=self.fmin, fmax=self.fmax,
-                         classes_num=self.classes_num)
-        checkpoint = torch.load(self.checkpoint_path, map_location=self.device)
-        self.model.load_state_dict(checkpoint['model'])
-        self.model.to(device)
-
-    @prompts(name="Detect The Sound Event From The Audio",
-             description="useful for when you want to know what event in the audio and the sound event start or end time, it will return an image "
-                         "receives audio_path as input. "
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, audio_path):
-        # Forward
-        (waveform, _) = librosa.core.load(audio_path, sr=self.sample_rate, mono=True)
-        waveform = waveform[None, :]  # (1, audio_length)
-        waveform = torch.from_numpy(waveform)
-        waveform = waveform.to(self.device)
-        # Forward
-        with torch.no_grad():
-            self.model.eval()
-            batch_output_dict = self.model(waveform, None)
-        framewise_output = batch_output_dict['framewise_output'].data.cpu().numpy()[0]
-        """(time_steps, classes_num)"""
-        # print('Sound event detection result (time_steps x classes_num): {}'.format(
-        #     framewise_output.shape))
-        import numpy as np
-        import matplotlib.pyplot as plt
-        sorted_indexes = np.argsort(np.max(framewise_output, axis=0))[::-1]
-        top_k = 10  # Show top results
-        top_result_mat = framewise_output[:, sorted_indexes[0: top_k]]
-        """(time_steps, top_k)"""
-        # Plot result
-        stft = librosa.core.stft(y=waveform[0].data.cpu().numpy(), n_fft=self.window_size,
-                                 hop_length=self.hop_size, window='hann', center=True)
-        frames_num = stft.shape[-1]
-        fig, axs = plt.subplots(2, 1, sharex=True, figsize=(10, 4))
-        axs[0].matshow(np.log(np.abs(stft)), origin='lower', aspect='auto', cmap='jet')
-        axs[0].set_ylabel('Frequency bins')
-        axs[0].set_title('Log spectrogram')
-        axs[1].matshow(top_result_mat.T, origin='upper', aspect='auto', cmap='jet', vmin=0, vmax=1)
-        axs[1].xaxis.set_ticks(np.arange(0, frames_num, self.frames_per_second))
-        axs[1].xaxis.set_ticklabels(np.arange(0, frames_num / self.frames_per_second))
-        axs[1].yaxis.set_ticks(np.arange(0, top_k))
-        axs[1].yaxis.set_ticklabels(np.array(self.labels)[sorted_indexes[0: top_k]])
-        axs[1].yaxis.grid(color='k', linestyle='solid', linewidth=0.3, alpha=0.3)
-        axs[1].set_xlabel('Seconds')
-        axs[1].xaxis.set_ticks_position('bottom')
-        plt.tight_layout()
-        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
-        plt.savefig(image_filename)
-        return image_filename
-
-
-class SoundExtraction:
-    def __init__(self, device):
-        self.device = device
-        self.model_file = 'sound_extraction/useful_ckpts/LASSNet.pt'
-        self.stft = STFT()
-        import torch.nn as nn
-        self.model = nn.DataParallel(LASSNet(device)).to(device)
-        checkpoint = torch.load(self.model_file)
-        self.model.load_state_dict(checkpoint['model'])
-        self.model.eval()
-
-    @prompts(name="Extract Sound Event From Mixture Audio Based On Language Description",
-             description="useful for when you extract target sound from a mixture audio, you can describe the target sound by text, "
-                         "receives audio_path and text as input. "
-                         "The input to this tool should be a comma seperated string of two, "
-                         "representing mixture audio path and input text.")
-    def inference(self, inputs):
-        # key = ['ref_audio', 'text']
-        val = inputs.split(",")
-        audio_path = val[0]  # audio_path, text
-        text = val[1]
-        waveform = load_wav(audio_path)
-        waveform = torch.tensor(waveform).transpose(1, 0)
-        mixed_mag, mixed_phase = self.stft.transform(waveform)
-        text_query = ['[CLS] ' + text]
-        mixed_mag = mixed_mag.transpose(2, 1).unsqueeze(0).to(self.device)
-        est_mask = self.model(mixed_mag, text_query)
-        est_mag = est_mask * mixed_mag
-        est_mag = est_mag.squeeze(1)
-        est_mag = est_mag.permute(0, 2, 1)
-        est_wav = self.stft.inverse(est_mag.cpu().detach(), mixed_phase)
-        est_wav = est_wav.squeeze(0).squeeze(0).numpy()
-        # est_path = f'output/est{i}.wav'
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        print('audio_filename ', audio_filename)
-        save_wav(est_wav, audio_filename)
-        return audio_filename
-
-
-class Binaural:
-    def __init__(self, device):
-        self.device = device
-        self.model_file = 'mono2binaural/useful_ckpts/m2b/binaural_network.net'
-        self.position_file = ['mono2binaural/useful_ckpts/m2b/tx_positions.txt',
-                              'mono2binaural/useful_ckpts/m2b/tx_positions2.txt',
-                              'mono2binaural/useful_ckpts/m2b/tx_positions3.txt',
-                              'mono2binaural/useful_ckpts/m2b/tx_positions4.txt',
-                              'mono2binaural/useful_ckpts/m2b/tx_positions5.txt']
-        self.net = BinauralNetwork(view_dim=7,
-                                   warpnet_layers=4,
-                                   warpnet_channels=64,
-                                   )
-        self.net.load_from_file(self.model_file)
-        self.sr = 48000
-
-    @prompts(name="Sythesize Binaural Audio From A Mono Audio Input",
-             description="useful for when you want to transfer your mono audio into binaural audio, "
-                         "receives audio_path as input. "
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, audio_path):
-        mono, sr = librosa.load(path=audio_path, sr=self.sr, mono=True)
-        mono = torch.from_numpy(mono)
-        mono = mono.unsqueeze(0)
-        import numpy as np
-        import random
-        rand_int = random.randint(0, 4)
-        view = np.loadtxt(self.position_file[rand_int]).transpose().astype(np.float32)
-        view = torch.from_numpy(view)
-        if not view.shape[-1] * 400 == mono.shape[-1]:
-            mono = mono[:, :(mono.shape[-1] // 400) * 400]  #
-            if view.shape[1] * 400 > mono.shape[1]:
-                m_a = view.shape[1] - mono.shape[-1] // 400
-                rand_st = random.randint(0, m_a)
-                view = view[:, m_a:m_a + (mono.shape[-1] // 400)]  #
-        # binauralize and save output
-        self.net.eval().to(self.device)
-        mono, view = mono.to(self.device), view.to(self.device)
-        chunk_size = 48000  # forward in chunks of 1s
-        rec_field = 1000  # add 1000 samples as "safe bet" since warping has undefined rec. field
-        rec_field -= rec_field % 400  # make sure rec_field is a multiple of 400 to match audio and view frequencies
-        chunks = [
-            {
-                "mono": mono[:, max(0, i - rec_field):i + chunk_size],
-                "view": view[:, max(0, i - rec_field) // 400:(i + chunk_size) // 400]
-            }
-            for i in range(0, mono.shape[-1], chunk_size)
-        ]
-        for i, chunk in enumerate(chunks):
-            with torch.no_grad():
-                mono = chunk["mono"].unsqueeze(0)
-                view = chunk["view"].unsqueeze(0)
-                binaural = self.net(mono, view).squeeze(0)
-                if i > 0:
-                    binaural = binaural[:, -(mono.shape[-1] - rec_field):]
-                chunk["binaural"] = binaural
-        binaural = torch.cat([chunk["binaural"] for chunk in chunks], dim=-1)
-        binaural = torch.clamp(binaural, min=-1, max=1).cpu()
-        # binaural = chunked_forwarding(net, mono, view)
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        import torchaudio
-        torchaudio.save(audio_filename, binaural, sr)
-        # soundfile.write(audio_filename, binaural, samplerate = 48000)
-        print(f"Processed Binaural.run, audio_filename: {audio_filename}")
-        return audio_filename
-
-
-class TargetSoundDetection:
-    def __init__(self, device):
-        self.device = device
-        self.MEL_ARGS = {
-            'n_mels': 64,
-            'n_fft': 2048,
-            'hop_length': int(22050 * 20 / 1000),
-            'win_length': int(22050 * 40 / 1000)
-        }
-        self.EPS = np.spacing(1)
-        self.clip_model, _ = clip.load("ViT-B/32", device=self.device)
-        self.event_labels = event_labels
-        self.id_to_event = {i: label for i, label in enumerate(self.event_labels)}
-        config = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/run_config.pth',
-                            map_location='cpu')
-        config_parameters = dict(config)
-        config_parameters['tao'] = 0.6
-        if 'thres' not in config_parameters.keys():
-            config_parameters['thres'] = 0.5
-        if 'time_resolution' not in config_parameters.keys():
-            config_parameters['time_resolution'] = 125
-        model_parameters = torch.load(
-            'audio_detection/target_sound_detection/useful_ckpts/tsd/run_model_7_loss=-0.0724.pt'
-            , map_location=lambda storage, loc: storage)  # load parameter
-        self.model = getattr(tsd_models, config_parameters['model'])(config_parameters,
-                                                                     inputdim=64, outputdim=2,
-                                                                     time_resolution=config_parameters[
-                                                                         'time_resolution'],
-                                                                     **config_parameters['model_args'])
-        self.model.load_state_dict(model_parameters)
-        self.model = self.model.to(self.device).eval()
-        self.re_embeds = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/text_emb.pth')
-        self.ref_mel = torch.load('audio_detection/target_sound_detection/useful_ckpts/tsd/ref_mel.pth')
-
-    def extract_feature(self, fname):
-        import soundfile as sf
-        y, sr = sf.read(fname, dtype='float32')
-        print('y ', y.shape)
-        ti = y.shape[0] / sr
-        if y.ndim > 1:
-            y = y.mean(1)
-        y = librosa.resample(y, sr, 22050)
-        lms_feature = np.log(librosa.feature.melspectrogram(y, **self.MEL_ARGS) + self.EPS).T
-        return lms_feature, ti
-
-    def build_clip(self, text):
-        text = clip.tokenize(text).to(self.device)  # ["a diagram with dog", "a dog", "a cat"]
-        text_features = self.clip_model.encode_text(text)
-        return text_features
-
-    def cal_similarity(self, target, retrievals):
-        ans = []
-        for name in retrievals.keys():
-            tmp = retrievals[name]
-            s = torch.cosine_similarity(target.squeeze(), tmp.squeeze(), dim=0)
-            ans.append(s.item())
-        return ans.index(max(ans))
-
-    @prompts(name="Target Sound Detection",
-             description="useful for when you want to know when the target sound event in the audio happens. You can use language descriptions to instruct the model， "
-                         "receives text description and audio_path as input. "
-                         "The input to this tool should be a comma seperated string of two, "
-                         "representing audio path and the text description. ")
-    def inference(self, inputs):
-        audio_path, text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
-        target_emb = self.build_clip(text)  # torch type
-        idx = self.cal_similarity(target_emb, self.re_embeds)
-        target_event = self.id_to_event[idx]
-        embedding = self.ref_mel[target_event]
-        embedding = torch.from_numpy(embedding)
-        embedding = embedding.unsqueeze(0).to(self.device).float()
-        inputs, ti = self.extract_feature(audio_path)
-        inputs = torch.from_numpy(inputs)
-        inputs = inputs.unsqueeze(0).to(self.device).float()
-        decision, decision_up, logit = self.model(inputs, embedding)
-        pred = decision_up.detach().cpu().numpy()
-        pred = pred[:, :, 0]
-        frame_num = decision_up.shape[1]
-        time_ratio = ti / frame_num
-        filtered_pred = median_filter(pred, window_size=1, threshold=0.5)
-        time_predictions = []
-        for index_k in range(filtered_pred.shape[0]):
-            decoded_pred = []
-            decoded_pred_ = decode_with_timestamps(target_event, filtered_pred[index_k, :])
-            if len(decoded_pred_) == 0:  # neg deal
-                decoded_pred_.append((target_event, 0, 0))
-            decoded_pred.append(decoded_pred_)
-            for num_batch in range(len(decoded_pred)):  # when we test our model,the batch_size is 1
-                cur_pred = pred[num_batch]
-                # Save each frame output, for later visualization
-                label_prediction = decoded_pred[num_batch]  # frame predict
-                for event_label, onset, offset in label_prediction:
-                    time_predictions.append({
-                        'onset': onset * time_ratio,
-                        'offset': offset * time_ratio, })
-        ans = ''
-        for i, item in enumerate(time_predictions):
-            ans = ans + 'segment' + str(i + 1) + ' start_time: ' + str(item['onset']) + '  end_time: ' + str(
-                item['offset']) + '\t'
-        return ans
-
-
-class Speech_Enh_SC:
-    """Speech Enhancement or Separation in single-channel
-    Example usage:
-        enh_model = Speech_Enh_SS("cuda")
-        enh_wav = enh_model.inference("./test_chime4_audio_M05_440C0213_PED_REAL.wav")
-    """
-
-    def __init__(self, device="cuda", model_name="espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw"):
-        self.model_name = model_name
-        self.device = device
-        print("Initializing ESPnet Enh to %s" % device)
-        self._initialize_model()
-
-    def _initialize_model(self):
-        from espnet_model_zoo.downloader import ModelDownloader
-        from espnet2.bin.enh_inference import SeparateSpeech
-
-        d = ModelDownloader()
-
-        cfg = d.download_and_unpack(self.model_name)
-        self.separate_speech = SeparateSpeech(
-            train_config=cfg["train_config"],
-            model_file=cfg["model_file"],
-            # for segment-wise process on long speech
-            segment_size=2.4,
-            hop_size=0.8,
-            normalize_segment_scale=False,
-            show_progressbar=True,
-            ref_channel=None,
-            normalize_output_wav=True,
-            device=self.device,
-        )
-
-    @prompts(name="Speech Enhancement In Single-Channel",
-             description="useful for when you want to enhance the quality of the speech signal by reducing background noise (single-channel), "
-                         "receives audio_path as input."
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, speech_path, ref_channel=0):
-        speech, sr = soundfile.read(speech_path)
-        speech = speech[:, ref_channel]
-        enh_speech = self.separate_speech(speech[None, ...], fs=sr)
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        soundfile.write(audio_filename, enh_speech[0].squeeze(), samplerate=sr)
-        return audio_filename
-
-
-class Speech_SS:
-    def __init__(self, device="cuda", model_name="lichenda/wsj0_2mix_skim_noncausal"):
-        self.model_name = model_name
-        self.device = device
-        print("Initializing ESPnet SS to %s" % device)
-        self._initialize_model()
-
-    def _initialize_model(self):
-        from espnet_model_zoo.downloader import ModelDownloader
-        from espnet2.bin.enh_inference import SeparateSpeech
-
-        d = ModelDownloader()
-
-        cfg = d.download_and_unpack(self.model_name)
-        self.separate_speech = SeparateSpeech(
-            train_config=cfg["train_config"],
-            model_file=cfg["model_file"],
-            # for segment-wise process on long speech
-            segment_size=2.4,
-            hop_size=0.8,
-            normalize_segment_scale=False,
-            show_progressbar=True,
-            ref_channel=None,
-            normalize_output_wav=True,
-            device=self.device,
-        )
-
-    @prompts(name="Speech Separation",
-             description="useful for when you want to separate each speech from the speech mixture, "
-                         "receives audio_path as input."
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, speech_path):
-        speech, sr = soundfile.read(speech_path)
-        enh_speech = self.separate_speech(speech[None, ...], fs=sr)
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        if len(enh_speech) == 1:
-            soundfile.write(audio_filename, enh_speech[0].squeeze(), samplerate=sr)
-        else:
-            audio_filename_1 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-            soundfile.write(audio_filename_1, enh_speech[0].squeeze(), samplerate=sr)
-            audio_filename_2 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-            soundfile.write(audio_filename_2, enh_speech[1].squeeze(), samplerate=sr)
-            audio_filename = merge_audio(audio_filename_1, audio_filename_2)
-        return audio_filename
-        
-class Speech_Enh_SC:
-    """Speech Enhancement or Separation in single-channel
-    Example usage:
-        enh_model = Speech_Enh_SS("cuda")
-        enh_wav = enh_model.inference("./test_chime4_audio_M05_440C0213_PED_REAL.wav")
-    """
-
-    def __init__(self, device="cuda", model_name="espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw"):
-        self.model_name = model_name
-        self.device = device
-        print("Initializing ESPnet Enh to %s" % device)
-        self._initialize_model()
-
-    def _initialize_model(self):
-        from espnet_model_zoo.downloader import ModelDownloader
-        from espnet2.bin.enh_inference import SeparateSpeech
-
-        d = ModelDownloader()
-
-        cfg = d.download_and_unpack(self.model_name)
-        self.separate_speech = SeparateSpeech(
-            train_config=cfg["train_config"],
-            model_file=cfg["model_file"],
-            # for segment-wise process on long speech
-            segment_size=2.4,
-            hop_size=0.8,
-            normalize_segment_scale=False,
-            show_progressbar=True,
-            ref_channel=None,
-            normalize_output_wav=True,
-            device=self.device,
-        )
-
-    @prompts(name="Speech Enhancement In Single-Channel",
-             description="useful for when you want to enhance the quality of the speech signal by reducing background noise (single-channel), "
-                         "receives audio_path as input."
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, speech_path, ref_channel=0):
-        speech, sr = soundfile.read(speech_path)
-        if speech.ndim != 1:
-            speech = speech[:, ref_channel]
-        enh_speech = self.separate_speech(speech[None, ...], fs=sr)
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        soundfile.write(audio_filename, enh_speech[0].squeeze(), samplerate=sr)
-        return audio_filename
-
-
-class Speech_SS:
-    def __init__(self, device="cuda", model_name="lichenda/wsj0_2mix_skim_noncausal"):
-        self.model_name = model_name
-        self.device = device
-        print("Initializing ESPnet SS to %s" % device)
-        self._initialize_model()
-
-    def _initialize_model(self):
-        from espnet_model_zoo.downloader import ModelDownloader
-        from espnet2.bin.enh_inference import SeparateSpeech
-
-        d = ModelDownloader()
-
-        cfg = d.download_and_unpack(self.model_name)
-        self.separate_speech = SeparateSpeech(
-            train_config=cfg["train_config"],
-            model_file=cfg["model_file"],
-            # for segment-wise process on long speech
-            segment_size=2.4,
-            hop_size=0.8,
-            normalize_segment_scale=False,
-            show_progressbar=True,
-            ref_channel=None,
-            normalize_output_wav=True,
-            device=self.device,
-        )
-
-    @prompts(name="Speech Separation",
-             description="useful for when you want to separate each speech from the speech mixture, "
-                         "receives audio_path as input."
-                         "The input to this tool should be a string, "
-                         "representing the audio_path. ")
-    def inference(self, speech_path):
-        speech, sr = soundfile.read(speech_path)
-        enh_speech = self.separate_speech(speech[None, ...], fs=sr)
-        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-        if len(enh_speech) == 1:
-            soundfile.write(audio_filename, enh_speech[0].squeeze(), samplerate=sr)
-        else:
-            audio_filename_1 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-            soundfile.write(audio_filename_1, enh_speech[0].squeeze(), samplerate=sr)
-            audio_filename_2 = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
-            soundfile.write(audio_filename_2, enh_speech[1].squeeze(), samplerate=sr)
-            audio_filename = merge_audio(audio_filename_1, audio_filename_2)
-        return audio_filename

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/CLAP/utils.py

@@ -1,26 +0,0 @@
-import argparse
-import yaml
-import sys
-
-def read_config_as_args(config_path,args=None,is_config_str=False):
-    return_dict = {}
-
-    if config_path is not None:
-        if is_config_str:
-            yml_config = yaml.load(config_path, Loader=yaml.FullLoader)
-        else:
-            with open(config_path, "r") as f:
-                yml_config = yaml.load(f, Loader=yaml.FullLoader)
-
-        if args != None:
-            for k, v in yml_config.items():
-                if k in args.__dict__:
-                    args.__dict__[k] = v
-                else:
-                    sys.stderr.write("Ignored unknown parameter {} in yaml.\n".format(k))
-        else:
-            for k, v in yml_config.items():
-                return_dict[k] = v
-
-    args = args if args != None else return_dict
-    return argparse.Namespace(**args)

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/act.py

@@ -1,28 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-import torch.nn as nn
-from .resample import UpSample1d, DownSample1d
-
-
-class Activation1d(nn.Module):
-    def __init__(self,
-                 activation,
-                 up_ratio: int = 2,
-                 down_ratio: int = 2,
-                 up_kernel_size: int = 12,
-                 down_kernel_size: int = 12):
-        super().__init__()
-        self.up_ratio = up_ratio
-        self.down_ratio = down_ratio
-        self.act = activation
-        self.upsample = UpSample1d(up_ratio, up_kernel_size)
-        self.downsample = DownSample1d(down_ratio, down_kernel_size)
-
-    # x: [B,C,T]
-    def forward(self, x):
-        x = self.upsample(x)
-        x = self.act(x)
-        x = self.downsample(x)
-
-        return x

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/modules.py

@@ -1,314 +0,0 @@
-import torch
-import torch.nn as nn
-from functools import partial
-
-from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
-from torch.utils.checkpoint import checkpoint
-from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoTokenizer
-from importlib_resources import files
-from ldm.modules.encoders.CLAP.utils import read_config_as_args
-from ldm.modules.encoders.CLAP.clap import TextEncoder
-from ldm.util import default, count_params
-
-
-class AbstractEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-class ClassEmbedder(nn.Module):
-    def __init__(self, embed_dim, n_classes=1000, key='class'):
-        super().__init__()
-        self.key = key
-        self.embedding = nn.Embedding(n_classes, embed_dim)
-
-    def forward(self, batch, key=None):
-        if key is None:
-            key = self.key
-        # this is for use in crossattn
-        c = batch[key][:, None]# (bsz,1)
-        c = self.embedding(c)
-        return c
-
-
-class TransformerEmbedder(AbstractEncoder):
-    """Some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
-        super().__init__()
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
-
-    def forward(self, tokens):
-        tokens = tokens.to(self.device)  # meh
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, x):
-        return self(x)
-
-
-class BERTTokenizer(AbstractEncoder):
-    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
-    def __init__(self, device="cuda", vq_interface=True, max_length=77):
-        super().__init__()
-        from transformers import BertTokenizerFast  # TODO: add to reuquirements
-        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
-        self.device = device
-        self.vq_interface = vq_interface
-        self.max_length = max_length
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        return tokens
-
-    @torch.no_grad()
-    def encode(self, text):
-        tokens = self(text)
-        if not self.vq_interface:
-            return tokens
-        return None, None, [None, None, tokens]
-
-    def decode(self, text):
-        return text
-
-
-class BERTEmbedder(AbstractEncoder):# 这里不是用的pretrained bert,是用的transformers的BertTokenizer加自定义的TransformerWrapper
-    """Uses the BERT tokenizr model and add some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
-                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
-        super().__init__()
-        self.use_tknz_fn = use_tokenizer
-        if self.use_tknz_fn:
-            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
-                                              emb_dropout=embedding_dropout)
-
-    def forward(self, text):
-        if self.use_tknz_fn:
-            tokens = self.tknz_fn(text)#.to(self.device)
-        else:
-            tokens = text
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, text):
-        # output of length 77
-        return self(text)
-
-
-class SpatialRescaler(nn.Module):
-    def __init__(self,
-                 n_stages=1,
-                 method='bilinear',
-                 multiplier=0.5,
-                 in_channels=3,
-                 out_channels=None,
-                 bias=False):
-        super().__init__()
-        self.n_stages = n_stages
-        assert self.n_stages >= 0
-        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
-        self.multiplier = multiplier
-        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
-        self.remap_output = out_channels is not None
-        if self.remap_output:
-            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
-            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
-
-    def forward(self,x):
-        for stage in range(self.n_stages):
-            x = self.interpolator(x, scale_factor=self.multiplier)
-
-
-        if self.remap_output:
-            x = self.channel_mapper(x)
-        return x
-
-    def encode(self, x):
-        return self(x)
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-class FrozenT5Embedder(AbstractEncoder):
-    """Uses the T5 transformer encoder for text"""
-    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
-        super().__init__()
-        self.tokenizer = T5Tokenizer.from_pretrained(version)
-        self.transformer = T5EncoderModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length   # TODO: typical value?
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        #self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenCLAPEmbedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text (from huggingface)"""
-    def __init__(self, weights_path, freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-
-        model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model']
-        match_params = dict()
-        for key in list(model_state_dict.keys()):
-            if 'caption_encoder' in key:
-                match_params[key.replace('caption_encoder.', '')] = model_state_dict[key]
-
-        config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text()
-        args = read_config_as_args(config_as_str, is_config_str=True)
-
-        # To device
-        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        )
-
-        self.max_length = max_length
-        self.device = device
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")
-
-    def freeze(self):
-        self.caption_encoder.base = self.caption_encoder.base.eval()
-        for param in self.caption_encoder.base.parameters():
-            param.requires_grad = False
-
-
-    def encode(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-
-        outputs = self.caption_encoder.base(input_ids=tokens)
-        z = self.caption_encoder.projection(outputs.last_hidden_state)
-        return z
-
-class FrozenCLAPEmbedderNoLoad(AbstractEncoder):
-    def __init__(self, config, freeze=True, device="cpu", max_length=77):
-        super().__init__()
-        args = config
-
-        # To device
-        self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model
-        self.caption_encoder = TextEncoder(
-            args.d_proj, args.text_model, args.transformer_embed_dim
-        )
-
-        self.max_length = max_length
-        self.device = device
-        if freeze: self.freeze()
-
-        print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.")        
-
-    def freeze(self):
-        self.caption_encoder.base = self.caption_encoder.base.eval()
-        for param in self.caption_encoder.base.parameters():
-            param.requires_grad = False
-
-
-    def encode(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-
-        outputs = self.caption_encoder.base(input_ids=tokens)
-        z = self.caption_encoder.projection(outputs.last_hidden_state)
-        return z
-
-
-class NewFrozenCLAPEmbedder(AbstractEncoder):
-    """Uses the CLAP transformer encoder for text (from huggingface)"""
-    def __init__(self, weights_path, freeze=True, device="cuda", max_length=77):  # clip-vit-base-patch32
-        super().__init__()
-        # To device
-        from transformers import RobertaTokenizer
-        from ldm.modules.encoders.open_clap import create_model
-
-
-        model, model_cfg = create_model(
-            'HTSAT-tiny',
-            'roberta',
-            weights_path,
-            enable_fusion=True,
-            fusion_type='aff_2d'
-        )
-
-        del model.audio_branch, model.audio_transform, model.audio_projection
-        self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
-        self.model = model
-
-        self.max_length = max_length
-        self.device = device
-        if freeze: self.freeze()
-
-        param_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
-        print(f'{self.model.__class__.__name__} comes with: {param_num / 1e+6:.3f} M params.')
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.model.parameters():
-            param.requires_grad = False
-
-    def encode(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        outputs = self.model.text_branch(input_ids=batch_encoding["input_ids"].to(self.device), attention_mask=batch_encoding["attention_mask"].to(self.device))
-        z = self.model.text_projection(outputs.last_hidden_state)
-        return z
-
-class FrozenFLANEmbedder(AbstractEncoder):
-    """Uses the T5 transformer encoder for text"""
-    def __init__(self, version="google/flan-t5-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
-        super().__init__()
-        self.tokenizer = T5Tokenizer.from_pretrained(version)
-        self.transformer = T5EncoderModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length   # TODO: typical value?
-        if freeze:
-            self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        #self.train = disabled_train
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)

spaces/AIZeroToHero/03-NLP-MLM-SOTA-MedEntity/README.md

@@ -1,12 +0,0 @@
----
-title: 03 NLP MLM SOTA MedEntity
-emoji: 🐠
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AUBMC-AIM/OCTaGAN/README.md

@@ -1,46 +0,0 @@
----
-title: OCTaGAN
-emoji: 🐠
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
-license: mit
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/Abuzariii/Text-Generation-with-GPT-2/app.py

@@ -1,23 +0,0 @@
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
-model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)
-
-from transformers import pipeline, set_seed
-generator = pipeline('text-generation', model='gpt2')
-
-def generate_text(prompt):
-    text1 = generator(prompt, max_length=3000, num_return_sequences=1)
-    return text1[0].get('generated_text')
-    
-    
-import gradio as gr
-
-gr.Interface(
-    title = 'Text Generation using GPT 2', 
-    fn=generate_text, 
-    inputs=gr.Textbox(placeholder="Type Here..."),
-    outputs=[
-        "text"
-    ],
-    theme = 'darkhuggingface').launch()

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

@@ -1,82 +0,0 @@
-from __future__ import annotations
-
-from aiohttp import ClientSession
-import execjs, os, json
-
-from ..typing import AsyncGenerator
-from .base_provider import AsyncGeneratorProvider
-from .helper import format_prompt
-
-class GptForLove(AsyncGeneratorProvider):
-    url = "https://ai18.gptforlove.com"
-    supports_gpt_35_turbo = True
-    working = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> AsyncGenerator:
-        if not model:
-            model = "gpt-3.5-turbo"
-        headers = {
-            "authority": "api.gptplus.one",
-            "accept": "application/json, text/plain, */*",
-            "accept-language": "de-DE,de;q=0.9,en-DE;q=0.8,en;q=0.7,en-US;q=0.6,nl;q=0.5,zh-CN;q=0.4,zh-TW;q=0.3,zh;q=0.2",
-            "content-type": "application/json",
-            "origin": cls.url,
-            "referer": f"{cls.url}/",
-            "sec-ch-ua": "\"Google Chrome\";v=\"117\", \"Not;A=Brand\";v=\"8\", \"Chromium\";v=\"117\"",
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": "Linux",
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "cross-site",
-            "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36"
-        }
-        async with ClientSession(headers=headers) as session:
-            prompt = format_prompt(messages)
-            data = {
-                "prompt": prompt,
-                "options": {},
-                "systemMessage": "You are ChatGPT, the version is GPT3.5, a large language model trained by OpenAI. Follow the user's instructions carefully. Respond using markdown.",
-                "temperature": 0.8,
-                "top_p": 1,
-                "secret": get_secret(),
-                **kwargs
-            }
-            async with session.post("https://api.gptplus.one/chat-process", json=data) as response:
-                response.raise_for_status()
-                async for line in response.content:
-                    try:
-                        line = json.loads(line)
-                    except:
-                        raise RuntimeError(f"Broken line: {line}")
-                    if "detail" in line:
-                        content = line["detail"]["choices"][0]["delta"].get("content")
-                        if content:
-                            yield content
-                    elif "10分钟内提问超过了5次" in line:
-                        raise RuntimeError("Rate limit reached")
-                    else:
-                        raise RuntimeError(f"Response: {line}")
-
-
-def get_secret() -> str:
-    dir = os.path.dirname(__file__)
-    dir += '/npm/node_modules/crypto-js'
-    source = """
-CryptoJS = require('{dir}/crypto-js')
-var k = '14487141bvirvvG'
-    , e = Math.floor(new Date().getTime() / 1e3);
-var t = CryptoJS.enc.Utf8.parse(e)
-    , o = CryptoJS.AES.encrypt(t, k, {
-    mode: CryptoJS.mode.ECB,
-    padding: CryptoJS.pad.Pkcs7
-});
-return o.toString()
-"""
-    source = source.replace('{dir}', dir)
-    return execjs.compile(source).call('')

spaces/AiMimicry/sovits-models/app-slice.py

@@ -1,135 +0,0 @@
-import os
-import gradio as gr
-import edge_tts
-from pathlib import Path
-import inference.infer_tool as infer_tool
-import utils
-from inference.infer_tool import Svc
-import logging
-import webbrowser
-import argparse
-import asyncio
-import librosa
-import soundfile
-import gradio.processing_utils as gr_processing_utils
-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)
-
-limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
-
-audio_postprocess_ori = gr.Audio.postprocess
-
-def audio_postprocess(self, y):
-    data = audio_postprocess_ori(self, y)
-    if data is None:
-        return None
-    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
-
-
-gr.Audio.postprocess = audio_postprocess
-def create_vc_fn(model, sid):
-    def vc_fn(input_audio, vc_transform, auto_f0, slice_db, noise_scale, pad_seconds, tts_text, tts_voice, tts_mode):
-        if tts_mode:
-            if len(tts_text) > 100 and limitation:
-                return "Text is too long", None
-            if tts_text is None or tts_voice is None:
-                return "You need to enter text and select a voice", None
-            asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-            audio, sr = librosa.load("tts.mp3")
-            soundfile.write("tts.wav", audio, 24000, format="wav")
-            wav_path = "tts.wav"
-        else:
-            if input_audio is None:
-                return "You need to select an audio", None
-            raw_audio_path = f"raw/{input_audio}"
-            if "." not in raw_audio_path:
-                raw_audio_path += ".wav"
-            infer_tool.format_wav(raw_audio_path)
-            wav_path = Path(raw_audio_path).with_suffix('.wav')
-        _audio = model.slice_inference(
-            wav_path, sid, vc_transform, slice_db,
-            cluster_infer_ratio=0,
-            auto_predict_f0=auto_f0,
-            noice_scale=noise_scale,
-            pad_seconds=pad_seconds)
-        model.clear_empty()
-        return "Success", (44100, _audio)
-    return vc_fn
-
-def refresh_raw_wav():
-    return gr.Dropdown.update(choices=os.listdir("raw"))
-
-def change_to_tts_mode(tts_mode):
-    if tts_mode:
-        return gr.Audio.update(visible=False), gr.Button.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
-    else:
-        return gr.Audio.update(visible=True), gr.Button.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--api', action="store_true", default=False)
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    hubert_model = utils.get_hubert_model().to(args.device)
-    models = []
-    voices = []
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    for r in tts_voice_list:
-        voices.append(f"{r['ShortName']}-{r['Gender']}")
-    raw = os.listdir("raw")
-    for f in os.listdir("models"):
-        name = f
-        model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config.json", device=args.device)
-        cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None
-        models.append((name, cover, create_vc_fn(model, name)))
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> Sovits Models\n"
-            "## <center> The input audio should be clean and pure voice without background music.\n"
-            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.Sovits-Umamusume)\n\n"
-            "[Open In Colab](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)"
-            " without queue and length limitation.\n\n"
-            "[Original Repo](https://github.com/svc-develop-team/so-vits-svc)\n\n"
-            "Other models:\n"
-            "[rudolf](https://huggingface.co/spaces/sayashi/sovits-rudolf)\n"
-            "[teio](https://huggingface.co/spaces/sayashi/sovits-teio)\n"
-            "[goldship](https://huggingface.co/spaces/sayashi/sovits-goldship)\n"
-            "[tannhauser](https://huggingface.co/spaces/sayashi/sovits-tannhauser)\n"
-
-        )
-        with gr.Tabs():
-            for (name, cover, vc_fn) in models:
-                with gr.TabItem(name):
-                    with gr.Row():
-                        gr.Markdown(
-                            '<div align="center">'
-                            f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
-                            '</div>'
-                        )
-                    with gr.Row():
-                        with gr.Column():
-                            with gr.Row():
-                                vc_input = gr.Dropdown(label="Input audio", choices=raw)
-                                vc_refresh = gr.Button("🔁", variant="primary")
-                            vc_transform = gr.Number(label="vc_transform", value=0)
-                            slice_db = gr.Number(label="slice_db", value=-40)
-                            noise_scale = gr.Number(label="noise_scale", value=0.4)
-                            pad_seconds = gr.Number(label="pad_seconds", value=0.5)
-                            auto_f0 = gr.Checkbox(label="auto_f0", value=False)
-                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                            tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
-                            tts_voice = gr.Dropdown(choices=voices, visible=False)
-                            vc_submit = gr.Button("Generate", variant="primary")
-                        with gr.Column():
-                            vc_output1 = gr.Textbox(label="Output Message")
-                            vc_output2 = gr.Audio(label="Output Audio")
-                vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0, slice_db,  noise_scale, pad_seconds, tts_text, tts_voice, tts_mode], [vc_output1, vc_output2])
-                vc_refresh.click(refresh_raw_wav, [], [vc_input])
-                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, vc_refresh, tts_text, tts_voice])
-        if args.colab:
-            webbrowser.open("http://127.0.0.1:7860")
-        app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)

spaces/AlexWang/lama/saicinpainting/training/losses/style_loss.py

@@ -1,155 +0,0 @@
-import torch
-import torch.nn as nn
-import torchvision.models as models
-
-
-class PerceptualLoss(nn.Module):
-    r"""
-    Perceptual loss, VGG-based
-    https://arxiv.org/abs/1603.08155
-    https://github.com/dxyang/StyleTransfer/blob/master/utils.py
-    """
-
-    def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]):
-        super(PerceptualLoss, self).__init__()
-        self.add_module('vgg', VGG19())
-        self.criterion = torch.nn.L1Loss()
-        self.weights = weights
-
-    def __call__(self, x, y):
-        # Compute features
-        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
-
-        content_loss = 0.0
-        content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1'])
-        content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1'])
-        content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1'])
-        content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1'])
-        content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1'])
-
-
-        return content_loss
-
-
-class VGG19(torch.nn.Module):
-    def __init__(self):
-        super(VGG19, self).__init__()
-        features = models.vgg19(pretrained=True).features
-        self.relu1_1 = torch.nn.Sequential()
-        self.relu1_2 = torch.nn.Sequential()
-
-        self.relu2_1 = torch.nn.Sequential()
-        self.relu2_2 = torch.nn.Sequential()
-
-        self.relu3_1 = torch.nn.Sequential()
-        self.relu3_2 = torch.nn.Sequential()
-        self.relu3_3 = torch.nn.Sequential()
-        self.relu3_4 = torch.nn.Sequential()
-
-        self.relu4_1 = torch.nn.Sequential()
-        self.relu4_2 = torch.nn.Sequential()
-        self.relu4_3 = torch.nn.Sequential()
-        self.relu4_4 = torch.nn.Sequential()
-
-        self.relu5_1 = torch.nn.Sequential()
-        self.relu5_2 = torch.nn.Sequential()
-        self.relu5_3 = torch.nn.Sequential()
-        self.relu5_4 = torch.nn.Sequential()
-
-        for x in range(2):
-            self.relu1_1.add_module(str(x), features[x])
-
-        for x in range(2, 4):
-            self.relu1_2.add_module(str(x), features[x])
-
-        for x in range(4, 7):
-            self.relu2_1.add_module(str(x), features[x])
-
-        for x in range(7, 9):
-            self.relu2_2.add_module(str(x), features[x])
-
-        for x in range(9, 12):
-            self.relu3_1.add_module(str(x), features[x])
-
-        for x in range(12, 14):
-            self.relu3_2.add_module(str(x), features[x])
-
-        for x in range(14, 16):
-            self.relu3_2.add_module(str(x), features[x])
-
-        for x in range(16, 18):
-            self.relu3_4.add_module(str(x), features[x])
-
-        for x in range(18, 21):
-            self.relu4_1.add_module(str(x), features[x])
-
-        for x in range(21, 23):
-            self.relu4_2.add_module(str(x), features[x])
-
-        for x in range(23, 25):
-            self.relu4_3.add_module(str(x), features[x])
-
-        for x in range(25, 27):
-            self.relu4_4.add_module(str(x), features[x])
-
-        for x in range(27, 30):
-            self.relu5_1.add_module(str(x), features[x])
-
-        for x in range(30, 32):
-            self.relu5_2.add_module(str(x), features[x])
-
-        for x in range(32, 34):
-            self.relu5_3.add_module(str(x), features[x])
-
-        for x in range(34, 36):
-            self.relu5_4.add_module(str(x), features[x])
-
-        # don't need the gradients, just want the features
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, x):
-        relu1_1 = self.relu1_1(x)
-        relu1_2 = self.relu1_2(relu1_1)
-
-        relu2_1 = self.relu2_1(relu1_2)
-        relu2_2 = self.relu2_2(relu2_1)
-
-        relu3_1 = self.relu3_1(relu2_2)
-        relu3_2 = self.relu3_2(relu3_1)
-        relu3_3 = self.relu3_3(relu3_2)
-        relu3_4 = self.relu3_4(relu3_3)
-
-        relu4_1 = self.relu4_1(relu3_4)
-        relu4_2 = self.relu4_2(relu4_1)
-        relu4_3 = self.relu4_3(relu4_2)
-        relu4_4 = self.relu4_4(relu4_3)
-
-        relu5_1 = self.relu5_1(relu4_4)
-        relu5_2 = self.relu5_2(relu5_1)
-        relu5_3 = self.relu5_3(relu5_2)
-        relu5_4 = self.relu5_4(relu5_3)
-
-        out = {
-            'relu1_1': relu1_1,
-            'relu1_2': relu1_2,
-
-            'relu2_1': relu2_1,
-            'relu2_2': relu2_2,
-
-            'relu3_1': relu3_1,
-            'relu3_2': relu3_2,
-            'relu3_3': relu3_3,
-            'relu3_4': relu3_4,
-
-            'relu4_1': relu4_1,
-            'relu4_2': relu4_2,
-            'relu4_3': relu4_3,
-            'relu4_4': relu4_4,
-
-            'relu5_1': relu5_1,
-            'relu5_2': relu5_2,
-            'relu5_3': relu5_3,
-            'relu5_4': relu5_4,
-        }
-        return out

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/score_sde_ve.md

@@ -1,20 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Variance Exploding Stochastic Differential Equation (VE-SDE) scheduler
-
-## Overview
-
-Original paper can be found [here](https://arxiv.org/abs/2011.13456).
-
-## ScoreSdeVeScheduler
-[[autodoc]] ScoreSdeVeScheduler

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/speech_to_image_diffusion.py

@@ -1,261 +0,0 @@
-import inspect
-from typing import Callable, List, Optional, Union
-
-import torch
-from transformers import (
-    CLIPImageProcessor,
-    CLIPTextModel,
-    CLIPTokenizer,
-    WhisperForConditionalGeneration,
-    WhisperProcessor,
-)
-
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    DiffusionPipeline,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    UNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.utils import logging
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class SpeechToImagePipeline(DiffusionPipeline):
-    def __init__(
-        self,
-        speech_model: WhisperForConditionalGeneration,
-        speech_processor: WhisperProcessor,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-    ):
-        super().__init__()
-
-        if safety_checker is None:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        self.register_modules(
-            speech_model=speech_model,
-            speech_processor=speech_processor,
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            feature_extractor=feature_extractor,
-        )
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        if slice_size == "auto":
-            slice_size = self.unet.config.attention_head_dim // 2
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        self.enable_attention_slicing(None)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        audio,
-        sampling_rate=16_000,
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ):
-        inputs = self.speech_processor.feature_extractor(
-            audio, return_tensors="pt", sampling_rate=sampling_rate
-        ).input_features.to(self.device)
-        predicted_ids = self.speech_model.generate(inputs, max_length=480_000)
-
-        prompt = self.speech_processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[
-            0
-        ]
-
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-
-        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
-        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
-            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        # get the initial random noise unless the user supplied it
-
-        # Unlike in other pipelines, latents need to be generated in the target device
-        # for 1-to-1 results reproducibility with the CompVis implementation.
-        # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
-        latents_dtype = text_embeddings.dtype
-        if latents is None:
-            if self.device.type == "mps":
-                # randn does not exist on mps
-                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
-                    self.device
-                )
-            else:
-                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-            latents = latents.to(self.device)
-
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        # Some schedulers like PNDM have timesteps as arrays
-        # It's more optimized to move all timesteps to correct device beforehand
-        timesteps_tensor = self.scheduler.timesteps.to(self.device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return image
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -1,1008 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Script to fine-tune Stable Diffusion for InstructPix2Pix."""
-
-import argparse
-import logging
-import math
-import os
-import shutil
-from pathlib import Path
-
-import accelerate
-import datasets
-import numpy as np
-import PIL
-import requests
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration, set_seed
-from datasets import load_dataset
-from huggingface_hub import create_repo, upload_folder
-from packaging import version
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-import diffusers
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInstructPix2PixPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version, deprecate, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.19.0")
-
-logger = get_logger(__name__, log_level="INFO")
-
-DATASET_NAME_MAPPING = {
-    "fusing/instructpix2pix-1000-samples": ("input_image", "edit_prompt", "edited_image"),
-}
-WANDB_TABLE_COL_NAMES = ["original_image", "edited_image", "edit_prompt"]
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script for InstructPix2Pix.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help=(
-            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
-            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
-            " or to a folder containing files that 🤗 Datasets can understand."
-        ),
-    )
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The config of the Dataset, leave as None if there's only one config.",
-    )
-    parser.add_argument(
-        "--train_data_dir",
-        type=str,
-        default=None,
-        help=(
-            "A folder containing the training data. Folder contents must follow the structure described in"
-            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
-            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
-        ),
-    )
-    parser.add_argument(
-        "--original_image_column",
-        type=str,
-        default="input_image",
-        help="The column of the dataset containing the original image on which edits where made.",
-    )
-    parser.add_argument(
-        "--edited_image_column",
-        type=str,
-        default="edited_image",
-        help="The column of the dataset containing the edited image.",
-    )
-    parser.add_argument(
-        "--edit_prompt_column",
-        type=str,
-        default="edit_prompt",
-        help="The column of the dataset containing the edit instruction.",
-    )
-    parser.add_argument(
-        "--val_image_url",
-        type=str,
-        default=None,
-        help="URL to the original image that you would like to edit (used during inference for debugging purposes).",
-    )
-    parser.add_argument(
-        "--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference."
-    )
-    parser.add_argument(
-        "--num_validation_images",
-        type=int,
-        default=4,
-        help="Number of images that should be generated during validation with `validation_prompt`.",
-    )
-    parser.add_argument(
-        "--validation_epochs",
-        type=int,
-        default=1,
-        help=(
-            "Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
-            " `args.validation_prompt` multiple times: `args.num_validation_images`."
-        ),
-    )
-    parser.add_argument(
-        "--max_train_samples",
-        type=int,
-        default=None,
-        help=(
-            "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="instruct-pix2pix-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="The directory where the downloaded models and datasets will be stored.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=256,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop",
-        default=False,
-        action="store_true",
-        help=(
-            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
-            " cropped. The images will be resized to the resolution first before cropping."
-        ),
-    )
-    parser.add_argument(
-        "--random_flip",
-        action="store_true",
-        help="whether to randomly flip images horizontally",
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--conditioning_dropout_prob",
-        type=float,
-        default=None,
-        help="Conditioning dropout probability. Drops out the conditionings (image and edit prompt) used in training InstructPix2Pix. See section 3.2.1 in the paper: https://arxiv.org/abs/2211.09800.",
-    )
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument(
-        "--allow_tf32",
-        action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
-    parser.add_argument(
-        "--non_ema_revision",
-        type=str,
-        default=None,
-        required=False,
-        help=(
-            "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
-            " remote repository specified with --pretrained_model_name_or_path."
-        ),
-    )
-    parser.add_argument(
-        "--dataloader_num_workers",
-        type=int,
-        default=0,
-        help=(
-            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="tensorboard",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
-            " training using `--resume_from_checkpoint`."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoints_total_limit",
-        type=int,
-        default=None,
-        help=("Max number of checkpoints to store."),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    # Sanity checks
-    if args.dataset_name is None and args.train_data_dir is None:
-        raise ValueError("Need either a dataset name or a training folder.")
-
-    # default to using the same revision for the non-ema model if not specified
-    if args.non_ema_revision is None:
-        args.non_ema_revision = args.revision
-
-    return args
-
-
-def convert_to_np(image, resolution):
-    image = image.convert("RGB").resize((resolution, resolution))
-    return np.array(image).transpose(2, 0, 1)
-
-
-def download_image(url):
-    image = PIL.Image.open(requests.get(url, stream=True).raw)
-    image = PIL.ImageOps.exif_transpose(image)
-    image = image.convert("RGB")
-    return image
-
-
-def main():
-    args = parse_args()
-
-    if args.non_ema_revision is not None:
-        deprecate(
-            "non_ema_revision!=None",
-            "0.15.0",
-            message=(
-                "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
-                " use `--variant=non_ema` instead."
-            ),
-        )
-    logging_dir = os.path.join(args.output_dir, args.logging_dir)
-    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        project_config=accelerator_project_config,
-    )
-
-    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
-
-    if args.report_to == "wandb":
-        if not is_wandb_available():
-            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-        import wandb
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        datasets.utils.logging.set_verbosity_warning()
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        datasets.utils.logging.set_verbosity_error()
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load scheduler, tokenizer and models.
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-    tokenizer = CLIPTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
-    )
-    text_encoder = CLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
-    )
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
-    unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
-    )
-
-    # InstructPix2Pix uses an additional image for conditioning. To accommodate that,
-    # it uses 8 channels (instead of 4) in the first (conv) layer of the UNet. This UNet is
-    # then fine-tuned on the custom InstructPix2Pix dataset. This modified UNet is initialized
-    # from the pre-trained checkpoints. For the extra channels added to the first layer, they are
-    # initialized to zero.
-    logger.info("Initializing the InstructPix2Pix UNet from the pretrained UNet.")
-    in_channels = 8
-    out_channels = unet.conv_in.out_channels
-    unet.register_to_config(in_channels=in_channels)
-
-    with torch.no_grad():
-        new_conv_in = nn.Conv2d(
-            in_channels, out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding
-        )
-        new_conv_in.weight.zero_()
-        new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
-        unet.conv_in = new_conv_in
-
-    # Freeze vae and text_encoder
-    vae.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-
-    # Create EMA for the unet.
-    if args.use_ema:
-        ema_unet = EMAModel(unet.parameters(), model_cls=UNet2DConditionModel, model_config=unet.config)
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            import xformers
-
-            xformers_version = version.parse(xformers.__version__)
-            if xformers_version == version.parse("0.0.16"):
-                logger.warn(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
-                )
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    # `accelerate` 0.16.0 will have better support for customized saving
-    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
-        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
-        def save_model_hook(models, weights, output_dir):
-            if args.use_ema:
-                ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
-
-            for i, model in enumerate(models):
-                model.save_pretrained(os.path.join(output_dir, "unet"))
-
-                # make sure to pop weight so that corresponding model is not saved again
-                weights.pop()
-
-        def load_model_hook(models, input_dir):
-            if args.use_ema:
-                load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel)
-                ema_unet.load_state_dict(load_model.state_dict())
-                ema_unet.to(accelerator.device)
-                del load_model
-
-            for i in range(len(models)):
-                # pop models so that they are not loaded again
-                model = models.pop()
-
-                # load diffusers style into model
-                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
-                model.register_to_config(**load_model.config)
-
-                model.load_state_dict(load_model.state_dict())
-                del load_model
-
-        accelerator.register_save_state_pre_hook(save_model_hook)
-        accelerator.register_load_state_pre_hook(load_model_hook)
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Initialize the optimizer
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
-            )
-
-        optimizer_cls = bnb.optim.AdamW8bit
-    else:
-        optimizer_cls = torch.optim.AdamW
-
-    optimizer = optimizer_cls(
-        unet.parameters(),
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(
-            args.dataset_name,
-            args.dataset_config_name,
-            cache_dir=args.cache_dir,
-        )
-    else:
-        data_files = {}
-        if args.train_data_dir is not None:
-            data_files["train"] = os.path.join(args.train_data_dir, "**")
-        dataset = load_dataset(
-            "imagefolder",
-            data_files=data_files,
-            cache_dir=args.cache_dir,
-        )
-        # See more about loading custom images at
-        # https://huggingface.co/docs/datasets/main/en/image_load#imagefolder
-
-    # Preprocessing the datasets.
-    # We need to tokenize inputs and targets.
-    column_names = dataset["train"].column_names
-
-    # 6. Get the column names for input/target.
-    dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
-    if args.original_image_column is None:
-        original_image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
-    else:
-        original_image_column = args.original_image_column
-        if original_image_column not in column_names:
-            raise ValueError(
-                f"--original_image_column' value '{args.original_image_column}' needs to be one of: {', '.join(column_names)}"
-            )
-    if args.edit_prompt_column is None:
-        edit_prompt_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
-    else:
-        edit_prompt_column = args.edit_prompt_column
-        if edit_prompt_column not in column_names:
-            raise ValueError(
-                f"--edit_prompt_column' value '{args.edit_prompt_column}' needs to be one of: {', '.join(column_names)}"
-            )
-    if args.edited_image_column is None:
-        edited_image_column = dataset_columns[2] if dataset_columns is not None else column_names[2]
-    else:
-        edited_image_column = args.edited_image_column
-        if edited_image_column not in column_names:
-            raise ValueError(
-                f"--edited_image_column' value '{args.edited_image_column}' needs to be one of: {', '.join(column_names)}"
-            )
-
-    # Preprocessing the datasets.
-    # We need to tokenize input captions and transform the images.
-    def tokenize_captions(captions):
-        inputs = tokenizer(
-            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
-        )
-        return inputs.input_ids
-
-    # Preprocessing the datasets.
-    train_transforms = transforms.Compose(
-        [
-            transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
-            transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
-        ]
-    )
-
-    def preprocess_images(examples):
-        original_images = np.concatenate(
-            [convert_to_np(image, args.resolution) for image in examples[original_image_column]]
-        )
-        edited_images = np.concatenate(
-            [convert_to_np(image, args.resolution) for image in examples[edited_image_column]]
-        )
-        # We need to ensure that the original and the edited images undergo the same
-        # augmentation transforms.
-        images = np.concatenate([original_images, edited_images])
-        images = torch.tensor(images)
-        images = 2 * (images / 255) - 1
-        return train_transforms(images)
-
-    def preprocess_train(examples):
-        # Preprocess images.
-        preprocessed_images = preprocess_images(examples)
-        # Since the original and edited images were concatenated before
-        # applying the transformations, we need to separate them and reshape
-        # them accordingly.
-        original_images, edited_images = preprocessed_images.chunk(2)
-        original_images = original_images.reshape(-1, 3, args.resolution, args.resolution)
-        edited_images = edited_images.reshape(-1, 3, args.resolution, args.resolution)
-
-        # Collate the preprocessed images into the `examples`.
-        examples["original_pixel_values"] = original_images
-        examples["edited_pixel_values"] = edited_images
-
-        # Preprocess the captions.
-        captions = list(examples[edit_prompt_column])
-        examples["input_ids"] = tokenize_captions(captions)
-        return examples
-
-    with accelerator.main_process_first():
-        if args.max_train_samples is not None:
-            dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
-        # Set the training transforms
-        train_dataset = dataset["train"].with_transform(preprocess_train)
-
-    def collate_fn(examples):
-        original_pixel_values = torch.stack([example["original_pixel_values"] for example in examples])
-        original_pixel_values = original_pixel_values.to(memory_format=torch.contiguous_format).float()
-        edited_pixel_values = torch.stack([example["edited_pixel_values"] for example in examples])
-        edited_pixel_values = edited_pixel_values.to(memory_format=torch.contiguous_format).float()
-        input_ids = torch.stack([example["input_ids"] for example in examples])
-        return {
-            "original_pixel_values": original_pixel_values,
-            "edited_pixel_values": edited_pixel_values,
-            "input_ids": input_ids,
-        }
-
-    # DataLoaders creation:
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        shuffle=True,
-        collate_fn=collate_fn,
-        batch_size=args.train_batch_size,
-        num_workers=args.dataloader_num_workers,
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-        num_training_steps=args.max_train_steps * accelerator.num_processes,
-    )
-
-    # Prepare everything with our `accelerator`.
-    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet, optimizer, train_dataloader, lr_scheduler
-    )
-
-    if args.use_ema:
-        ema_unet.to(accelerator.device)
-
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move text_encode and vae to gpu and cast to weight_dtype
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-    vae.to(accelerator.device, dtype=weight_dtype)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("instruct-pix2pix", config=vars(args))
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
-
-        if path is None:
-            accelerator.print(
-                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
-            args.resume_from_checkpoint = None
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
-
-            resume_global_step = global_step * args.gradient_accumulation_steps
-            first_epoch = global_step // num_update_steps_per_epoch
-            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
-    progress_bar.set_description("Steps")
-
-    for epoch in range(first_epoch, args.num_train_epochs):
-        unet.train()
-        train_loss = 0.0
-        for step, batch in enumerate(train_dataloader):
-            # Skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % args.gradient_accumulation_steps == 0:
-                    progress_bar.update(1)
-                continue
-
-            with accelerator.accumulate(unet):
-                # We want to learn the denoising process w.r.t the edited images which
-                # are conditioned on the original image (which was edited) and the edit instruction.
-                # So, first, convert images to latent space.
-                latents = vae.encode(batch["edited_pixel_values"].to(weight_dtype)).latent_dist.sample()
-                latents = latents * vae.config.scaling_factor
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning.
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                # Get the additional image embedding for conditioning.
-                # Instead of getting a diagonal Gaussian here, we simply take the mode.
-                original_image_embeds = vae.encode(batch["original_pixel_values"].to(weight_dtype)).latent_dist.mode()
-
-                # Conditioning dropout to support classifier-free guidance during inference. For more details
-                # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.
-                if args.conditioning_dropout_prob is not None:
-                    random_p = torch.rand(bsz, device=latents.device, generator=generator)
-                    # Sample masks for the edit prompts.
-                    prompt_mask = random_p < 2 * args.conditioning_dropout_prob
-                    prompt_mask = prompt_mask.reshape(bsz, 1, 1)
-                    # Final text conditioning.
-                    null_conditioning = text_encoder(tokenize_captions([""]).to(accelerator.device))[0]
-                    encoder_hidden_states = torch.where(prompt_mask, null_conditioning, encoder_hidden_states)
-
-                    # Sample masks for the original images.
-                    image_mask_dtype = original_image_embeds.dtype
-                    image_mask = 1 - (
-                        (random_p >= args.conditioning_dropout_prob).to(image_mask_dtype)
-                        * (random_p < 3 * args.conditioning_dropout_prob).to(image_mask_dtype)
-                    )
-                    image_mask = image_mask.reshape(bsz, 1, 1, 1)
-                    # Final image conditioning.
-                    original_image_embeds = image_mask * original_image_embeds
-
-                # Concatenate the `original_image_embeds` with the `noisy_latents`.
-                concatenated_noisy_latents = torch.cat([noisy_latents, original_image_embeds], dim=1)
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                # Predict the noise residual and compute loss
-                model_pred = unet(concatenated_noisy_latents, timesteps, encoder_hidden_states).sample
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                # Gather the losses across all processes for logging (if we use distributed training).
-                avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
-                train_loss += avg_loss.item() / args.gradient_accumulation_steps
-
-                # Backpropagate
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                if args.use_ema:
-                    ema_unet.step(unet.parameters())
-                progress_bar.update(1)
-                global_step += 1
-                accelerator.log({"train_loss": train_loss}, step=global_step)
-                train_loss = 0.0
-
-                if global_step % args.checkpointing_steps == 0:
-                    if accelerator.is_main_process:
-                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
-                        if args.checkpoints_total_limit is not None:
-                            checkpoints = os.listdir(args.output_dir)
-                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
-                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
-
-                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
-                            if len(checkpoints) >= args.checkpoints_total_limit:
-                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
-                                removing_checkpoints = checkpoints[0:num_to_remove]
-
-                                logger.info(
-                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
-                                )
-                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
-
-                                for removing_checkpoint in removing_checkpoints:
-                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
-                                    shutil.rmtree(removing_checkpoint)
-
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-            logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-
-            if global_step >= args.max_train_steps:
-                break
-
-        if accelerator.is_main_process:
-            if (
-                (args.val_image_url is not None)
-                and (args.validation_prompt is not None)
-                and (epoch % args.validation_epochs == 0)
-            ):
-                logger.info(
-                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-                    f" {args.validation_prompt}."
-                )
-                # create pipeline
-                if args.use_ema:
-                    # Store the UNet parameters temporarily and load the EMA parameters to perform inference.
-                    ema_unet.store(unet.parameters())
-                    ema_unet.copy_to(unet.parameters())
-                # The models need unwrapping because for compatibility in distributed training mode.
-                pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
-                    args.pretrained_model_name_or_path,
-                    unet=accelerator.unwrap_model(unet),
-                    text_encoder=accelerator.unwrap_model(text_encoder),
-                    vae=accelerator.unwrap_model(vae),
-                    revision=args.revision,
-                    torch_dtype=weight_dtype,
-                )
-                pipeline = pipeline.to(accelerator.device)
-                pipeline.set_progress_bar_config(disable=True)
-
-                # run inference
-                original_image = download_image(args.val_image_url)
-                edited_images = []
-                with torch.autocast(
-                    str(accelerator.device).replace(":0", ""), enabled=accelerator.mixed_precision == "fp16"
-                ):
-                    for _ in range(args.num_validation_images):
-                        edited_images.append(
-                            pipeline(
-                                args.validation_prompt,
-                                image=original_image,
-                                num_inference_steps=20,
-                                image_guidance_scale=1.5,
-                                guidance_scale=7,
-                                generator=generator,
-                            ).images[0]
-                        )
-
-                for tracker in accelerator.trackers:
-                    if tracker.name == "wandb":
-                        wandb_table = wandb.Table(columns=WANDB_TABLE_COL_NAMES)
-                        for edited_image in edited_images:
-                            wandb_table.add_data(
-                                wandb.Image(original_image), wandb.Image(edited_image), args.validation_prompt
-                            )
-                        tracker.log({"validation": wandb_table})
-                if args.use_ema:
-                    # Switch back to the original UNet parameters.
-                    ema_unet.restore(unet.parameters())
-
-                del pipeline
-                torch.cuda.empty_cache()
-
-    # Create the pipeline using the trained modules and save it.
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        unet = accelerator.unwrap_model(unet)
-        if args.use_ema:
-            ema_unet.copy_to(unet.parameters())
-
-        pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            text_encoder=accelerator.unwrap_model(text_encoder),
-            vae=accelerator.unwrap_model(vae),
-            unet=unet,
-            revision=args.revision,
-        )
-        pipeline.save_pretrained(args.output_dir)
-
-        if args.push_to_hub:
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-        if args.validation_prompt is not None:
-            edited_images = []
-            pipeline = pipeline.to(accelerator.device)
-            with torch.autocast(str(accelerator.device).replace(":0", "")):
-                for _ in range(args.num_validation_images):
-                    edited_images.append(
-                        pipeline(
-                            args.validation_prompt,
-                            image=original_image,
-                            num_inference_steps=20,
-                            image_guidance_scale=1.5,
-                            guidance_scale=7,
-                            generator=generator,
-                        ).images[0]
-                    )
-
-            for tracker in accelerator.trackers:
-                if tracker.name == "wandb":
-                    wandb_table = wandb.Table(columns=WANDB_TABLE_COL_NAMES)
-                    for edited_image in edited_images:
-                        wandb_table.add_data(
-                            wandb.Image(original_image), wandb.Image(edited_image), args.validation_prompt
-                        )
-                    tracker.log({"test": wandb_table})
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    main()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -1,717 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import warnings
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import torch
-from packaging import version
-from transformers import CLIPImageProcessor, XLMRobertaTokenizer
-
-from diffusers.utils import is_accelerate_available, is_accelerate_version
-
-from ...configuration_utils import FrozenDict
-from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging, randn_tensor, replace_example_docstring
-from ..pipeline_utils import DiffusionPipeline
-from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from . import AltDiffusionPipelineOutput, RobertaSeriesModelWithTransformation
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import AltDiffusionPipeline
-
-        >>> pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", torch_dtype=torch.float16)
-        >>> pipe = pipe.to("cuda")
-
-        >>> # "dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap"
-        >>> prompt = "黑暗精灵公主，非常详细，幻想，非常详细，数字绘画，概念艺术，敏锐的焦点，插图"
-        >>> image = pipe(prompt).images[0]
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
-def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    """
-    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
-    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
-    """
-    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
-    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
-    # rescale the results from guidance (fixes overexposure)
-    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
-    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
-    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
-    return noise_cfg
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
-class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
-    r"""
-    Pipeline for text-to-image generation using Alt Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    The pipeline also inherits the following loading methods:
-        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
-        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
-        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        text_encoder ([`~transformers.RobertaSeriesModelWithTransformation`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer ([`~transformers.XLMRobertaTokenizer`]):
-            A `XLMRobertaTokenizer` to tokenize text.
-        unet ([`UNet2DConditionModel`]):
-            A `UNet2DConditionModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: RobertaSeriesModelWithTransformation,
-        tokenizer: XLMRobertaTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    def enable_vae_tiling(self):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.vae.enable_tiling()
-
-    def disable_vae_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_tiling()
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
-        time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
-        Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
-        iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        if self.safety_checker is not None:
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    def decode_latents(self, latents):
-        warnings.warn(
-            (
-                "The decode_latents method is deprecated and will be removed in a future version. Please"
-                " use VaeImageProcessor instead"
-            ),
-            FutureWarning,
-        )
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents, return_dict=False)[0]
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guidance_rescale: float = 0.0,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
-                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
-                provided, text embeddings are generated from the `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
-                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
-                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-            guidance_rescale (`float`, *optional*, defaults to 0.7):
-                Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
-                Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
-                using zero terminal SNR.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images and the
-                second element is a list of `bool`s indicating whether the corresponding generated image contains
-                "not-safe-for-work" (nsfw) content.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                if do_classifier_free_guidance and guidance_rescale > 0.0:
-                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-        else:
-            image = latents
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Anonymous-sub/Rerender/sd_model_cfg.py

@@ -1,10 +0,0 @@
-# The model dict is used for webUI only
-
-model_dict = {
-    'Stable Diffusion 1.5': '',
-    'revAnimated_v11': 'models/revAnimated_v11.safetensors',
-    'realisticVisionV20_v20': 'models/realisticVisionV20_v20.safetensors',
-    'DGSpitzer/Cyberpunk-Anime-Diffusion': 'Cyberpunk-Anime-Diffusion.safetensors',
-    'wavymulder/Analog-Diffusion': 'analog-diffusion-1.0.safetensors',
-    'Fictiverse/Stable_Diffusion_PaperCut_Model': 'PaperCut_v1.safetensors',
-}

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

@@ -1,196 +0,0 @@
-import os
-import re
-
-import cn2an
-from pypinyin import lazy_pinyin, Style
-
-from bert_vits2.text.symbols import punctuation
-from bert_vits2.text.tone_sandhi import ToneSandhi
-
-current_file_path = os.path.dirname(__file__)
-pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in
-                        open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()}
-
-import jieba.posseg as psg
-from jieba import lcut
-lcut("预加载")
-
-rep_map = {
-    '：': ',',
-    '；': ',',
-    '，': ',',
-    '。': '.',
-    '！': '!',
-    '？': '?',
-    '\n': '.',
-    "·": ",",
-    '、': ",",
-    '...': '…',
-    '$': '.',
-    '“': "'",
-    '”': "'",
-    '‘': "'",
-    '’': "'",
-    '（': "'",
-    '）': "'",
-    '(': "'",
-    ')': "'",
-    '《': "'",
-    '》': "'",
-    '【': "'",
-    '】': "'",
-    '[': "'",
-    ']': "'",
-    '—': "-",
-    '～': "-",
-    '~': "-",
-    '「': "'",
-    '」': "'",
-
-}
-
-tone_modifier = ToneSandhi()
-
-
-def replace_punctuation(text):
-    text = text.replace("嗯", "恩").replace("呣", "母")
-    pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys()))
-
-    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
-
-    replaced_text = re.sub(r'[^\u4e00-\u9fa5' + "".join(punctuation) + r']+', '', replaced_text)
-
-    return replaced_text
-
-
-def g2p(text):
-    pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation))
-    sentences = [i for i in re.split(pattern, text) if i.strip() != '']
-    phones, tones, word2ph = _g2p(sentences)
-    assert sum(word2ph) == len(phones)
-    assert len(word2ph) == len(text)  # Sometimes it will crash,you can add a try-catch.
-    phones = ['_'] + phones + ["_"]
-    tones = [0] + tones + [0]
-    word2ph = [1] + word2ph + [1]
-    return phones, tones, word2ph
-
-
-def _get_initials_finals(word):
-    initials = []
-    finals = []
-    orig_initials = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.INITIALS)
-    orig_finals = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-    for c, v in zip(orig_initials, orig_finals):
-        initials.append(c)
-        finals.append(v)
-    return initials, finals
-
-
-def _g2p(segments):
-    phones_list = []
-    tones_list = []
-    word2ph = []
-    for seg in segments:
-        pinyins = []
-        # Replace all English words in the sentence
-        seg = re.sub('[a-zA-Z]+', '', seg)
-        seg_cut = psg.lcut(seg)
-        initials = []
-        finals = []
-        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
-        for word, pos in seg_cut:
-            if pos == 'eng':
-                continue
-            sub_initials, sub_finals = _get_initials_finals(word)
-            sub_finals = tone_modifier.modified_tone(word, pos,
-                                                     sub_finals)
-            initials.append(sub_initials)
-            finals.append(sub_finals)
-
-            # assert len(sub_initials) == len(sub_finals) == len(word)
-        initials = sum(initials, [])
-        finals = sum(finals, [])
-        #
-        for c, v in zip(initials, finals):
-            raw_pinyin = c + v
-            # NOTE: post process for pypinyin outputs
-            # we discriminate i, ii and iii
-            if c == v:
-                assert c in punctuation
-                phone = [c]
-                tone = '0'
-                word2ph.append(1)
-            else:
-                v_without_tone = v[:-1]
-                tone = v[-1]
-
-                pinyin = c + v_without_tone
-                assert tone in '12345'
-
-                if c:
-                    # 多音节
-                    v_rep_map = {
-                        "uei": 'ui',
-                        'iou': 'iu',
-                        'uen': 'un',
-                    }
-                    if v_without_tone in v_rep_map.keys():
-                        pinyin = c + v_rep_map[v_without_tone]
-                else:
-                    # 单音节
-                    pinyin_rep_map = {
-                        'ing': 'ying',
-                        'i': 'yi',
-                        'in': 'yin',
-                        'u': 'wu',
-                    }
-                    if pinyin in pinyin_rep_map.keys():
-                        pinyin = pinyin_rep_map[pinyin]
-                    else:
-                        single_rep_map = {
-                            'v': 'yu',
-                            'e': 'e',
-                            'i': 'y',
-                            'u': 'w',
-                        }
-                        if pinyin[0] in single_rep_map.keys():
-                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
-
-                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
-                phone = pinyin_to_symbol_map[pinyin].split(' ')
-                word2ph.append(len(phone))
-
-            phones_list += phone
-            tones_list += [int(tone)] * len(phone)
-    return phones_list, tones_list, word2ph
-
-
-def text_normalize(text):
-    numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    text = replace_punctuation(text)
-    return text
-
-
-def get_bert_feature(text, word2ph):
-    from bert_vits2.text import chinese_bert
-    return chinese_bert.get_bert_feature(text, word2ph)
-
-
-if __name__ == '__main__':
-    from bert_vits2.text import get_bert_feature
-
-    text = "啊！但是《原神》是由,米哈\游自主，  [研发]的一款全.新开放世界.冒险游戏"
-    text = text_normalize(text)
-    print(text)
-    phones, tones, word2ph = g2p(text)
-    bert = get_bert_feature(text, word2ph)
-
-    print(phones, tones, word2ph, bert.shape)
-
-# # 示例用法
-# text = "这是一个示例文本：,你好！这是一个测试...."
-# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试

spaces/Arun1217/mygenaiapp/README.md

@@ -1,12 +0,0 @@
----
-title: Mygenaiapp
-emoji: 🌖
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AtomdffAI/wechatgpt4atom/scripts/shutdown.sh

@@ -1,16 +0,0 @@
-#!/bin/bash
-
-#关闭服务
-cd `dirname $0`/..
-export BASE_DIR=`pwd`
-pid=`ps ax | grep -i app.py | grep "${BASE_DIR}" | grep python3 | grep -v grep | awk '{print $1}'`
-if [ -z "$pid" ] ; then
-        echo "No chatgpt-on-wechat running."
-        exit -1;
-fi
-
-echo "The chatgpt-on-wechat(${pid}) is running..."
-
-kill ${pid}
-
-echo "Send shutdown request to chatgpt-on-wechat(${pid}) OK"

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/torchscript.py

@@ -1,132 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import os
-import torch
-
-from detectron2.utils.file_io import PathManager
-
-from .torchscript_patch import freeze_training_mode, patch_instances
-
-__all__ = ["scripting_with_instances", "dump_torchscript_IR"]
-
-
-def scripting_with_instances(model, fields):
-    """
-    Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since
-    attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult
-    for scripting to support it out of the box. This function is made to support scripting
-    a model that uses :class:`Instances`. It does the following:
-
-    1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``,
-       but with all attributes been "static".
-       The attributes need to be statically declared in the ``fields`` argument.
-    2. Register ``new_Instances``, and force scripting compiler to
-       use it when trying to compile ``Instances``.
-
-    After this function, the process will be reverted. User should be able to script another model
-    using different fields.
-
-    Example:
-        Assume that ``Instances`` in the model consist of two attributes named
-        ``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and
-        :class:`Tensor` respectively during inference. You can call this function like:
-        ::
-            fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor}
-            torchscipt_model =  scripting_with_instances(model, fields)
-
-    Note:
-        It only support models in evaluation mode.
-
-    Args:
-        model (nn.Module): The input model to be exported by scripting.
-        fields (Dict[str, type]): Attribute names and corresponding type that
-            ``Instances`` will use in the model. Note that all attributes used in ``Instances``
-            need to be added, regardless of whether they are inputs/outputs of the model.
-            Data type not defined in detectron2 is not supported for now.
-
-    Returns:
-        torch.jit.ScriptModule: the model in torchscript format
-    """
-    assert (
-        not model.training
-    ), "Currently we only support exporting models in evaluation mode to torchscript"
-
-    with freeze_training_mode(model), patch_instances(fields):
-        scripted_model = torch.jit.script(model)
-        return scripted_model
-
-
-# alias for old name
-export_torchscript_with_instances = scripting_with_instances
-
-
-def dump_torchscript_IR(model, dir):
-    """
-    Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph,
-    inlined graph). Useful for debugging.
-
-    Args:
-        model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module
-        dir (str): output directory to dump files.
-    """
-    dir = os.path.expanduser(dir)
-    PathManager.mkdirs(dir)
-
-    def _get_script_mod(mod):
-        if isinstance(mod, torch.jit.TracedModule):
-            return mod._actual_script_module
-        return mod
-
-    # Dump pretty-printed code: https://pytorch.org/docs/stable/jit.html#inspecting-code
-    with PathManager.open(os.path.join(dir, "model_ts_code.txt"), "w") as f:
-
-        def get_code(mod):
-            # Try a few ways to get code using private attributes.
-            try:
-                # This contains more information than just `mod.code`
-                return _get_script_mod(mod)._c.code
-            except AttributeError:
-                pass
-            try:
-                return mod.code
-            except AttributeError:
-                return None
-
-        def dump_code(prefix, mod):
-            code = get_code(mod)
-            name = prefix or "root model"
-            if code is None:
-                f.write(f"Could not found code for {name} (type={mod.original_name})\n")
-                f.write("\n")
-            else:
-                f.write(f"\nCode for {name}, type={mod.original_name}:\n")
-                f.write(code)
-                f.write("\n")
-                f.write("-" * 80)
-
-            for name, m in mod.named_children():
-                dump_code(prefix + "." + name, m)
-
-        if isinstance(model, torch.jit.ScriptFunction):
-            f.write(get_code(model))
-        else:
-            dump_code("", model)
-
-    def _get_graph(model):
-        try:
-            # Recursively dump IR of all modules
-            return _get_script_mod(model)._c.dump_to_str(True, False, False)
-        except AttributeError:
-            return model.graph.str()
-
-    with PathManager.open(os.path.join(dir, "model_ts_IR.txt"), "w") as f:
-        f.write(_get_graph(model))
-
-    # Dump IR of the entire graph (all submodules inlined)
-    with PathManager.open(os.path.join(dir, "model_ts_IR_inlined.txt"), "w") as f:
-        f.write(str(model.inlined_graph))
-
-    if not isinstance(model, torch.jit.ScriptFunction):
-        # Dump the model structure in pytorch style
-        with PathManager.open(os.path.join(dir, "model.txt"), "w") as f:
-            f.write(str(model))

spaces/Benson/text-generation/Examples/2 Y Lnea Apk.md

@@ -1,73 +0,0 @@
-<br />
-<h1>2 y línea apk: ¿Qué es y cómo usarlo? </h1>
-<p>Si usted está buscando una manera de obtener un segundo número de teléfono en su teléfono inteligente, tableta, o el ordenador, es posible que haya oído hablar de 2 y apk línea. Pero, ¿qué es exactamente y cómo se puede utilizar? En este artículo, vamos a explicar todo lo que necesita saber acerca de 2 y apk línea, incluyendo sus características, beneficios, y el proceso de instalación. </p>
-<h2>2 y línea apk</h2><br /><p><b><b>Download</b> >> <a href="https://bltlly.com/2v6KuJ">https://bltlly.com/2v6KuJ</a></b></p><br /><br />
-<h2>Introducción</h2>
-<h3>¿Qué es 2 y línea apk? </h3>
-<p>2 y la línea apk es una aplicación para Android que le permite obtener un segundo número de teléfono de Estados Unidos o Canadá que funciona en su teléfono inteligente, tableta o computadora. Es un sistema de teléfono de negocios con todas las funciones, diseñado para profesionales móviles, freelancers y empresarios que necesitan un número separado para el trabajo o el uso personal. Puede utilizar 2 y la línea apk para hacer y recibir llamadas, enviar y recibir mensajes de texto, correo de voz de acceso, personalizar identificador de llamadas, y más. </p>
-<h3> ¿Por qué necesita 2 y apk línea? </h3>
-<p>Hay muchas razones por las que podría necesitar 2 y apk línea. Aquí están algunos de ellos:</p>
-<ul>
-<li> Desea mantener su número personal privado de clientes, clientes o extraños. </li>
-<li>Quieres tener un número diferente para diferentes propósitos, como trabajo, citas, compras en línea, etc.</li>
-<li> Desea ahorrar dinero en su factura de teléfono mediante el uso de un segundo número gratuito o de bajo costo. </li>
-<li> Desea tener un número de copia de seguridad en caso de que su número principal no esté disponible o perdido. </li>
-<li> Desea ampliar su presencia comercial teniendo un número local en otro código de área o país. </li>
-</ul>
-<h3>¿Cómo funciona 2 y línea apk? </h3>
-
-<h2>Características de 2 y línea apk</h2>
-<h3>Número local gratuito</h3>
-<p>Con 2 y línea apk, puede obtener un número local gratuito en cualquier código de área de Estados Unidos o Canadá. Puede elegir entre millones de números que están disponibles en la aplicación. También puede cambiar su número en cualquier momento si lo desea. </p>
-<p></p>
-<h3>Llamadas y mensajes de texto ilimitados</h3>
-<p>Con 2 y línea apk, se puede disfrutar de llamadas ilimitadas y mensajes de texto a cualquier número de Estados Unidos o Canadá. No tienes que preocuparte por minutos, mensajes o cargos. También puedes enviar imágenes, videos, emojis, pegatinas y notas de voz con tus textos. </p>
-<h3>Buzón de voz y identificador de llamadas personalizables</h3>
-<p>Con 2 y línea apk, puede personalizar su saludo de correo de voz y el identificador de llamadas para su segundo número. Puede grabar su propio mensaje o usar uno de los pregrabados. También puede establecer el nombre del identificador de llamadas a lo que desee, como el nombre de su negocio o apodo. </p>
-<h3>Desvío de llamadas y llamadas de conferencia</h3>
-<p>Con 2 y línea apk, puede reenviar sus llamadas a otro número o dispositivo si está ocupado o no disponible. También puede hacer llamadas de conferencia con hasta cinco personas al mismo tiempo. También puede silenciar, retener o transferir llamadas como desee. </p>
-<h3>Llamadas internacionales de bajo costo</h3>
-<p>Con 2 y línea apk, puede hacer llamadas internacionales baratas a más de 200 países y regiones. Puede pagar a medida que avanza con tarifas asequibles o comprar un plan de suscripción para llamadas ilimitadas a destinos seleccionados. También puedes ganar créditos gratis viendo anuncios o completando ofertas. </p>
-<h2>Cómo descargar e instalar 2 y la línea apk</h2>
-<h3>Requisitos y compatibilidad</h3>
-<p>Para usar 2 y línea apk, es necesario tener un dispositivo Android que se ejecuta en Android 4.4 o superior. También necesita tener una conexión a Internet estable, ya sea Wi-Fi o datos móviles. No necesitas rootear tu dispositivo ni tener permisos especiales para usar la aplicación. </p>
-<h3>Pasos para descargar e instalar 2 y apk línea</h3>
-
-<ol>
-<li>Ir a la página web oficial de 2 y apk línea y haga clic en el botón de descarga. Alternativamente, se puede buscar para 2 y línea apk en Google Play Store y descargarlo desde allí. </li>
-<li>Una vez que la descarga se ha completado, abra el archivo y toque en instalar. Es posible que necesite habilitar fuentes desconocidas en su configuración si descargó el archivo desde el sitio web. </li>
-<li>Espere a que termine la instalación y luego abra la aplicación. Verá una pantalla de bienvenida con algunas instrucciones sobre cómo usar la aplicación. </li>
-<li>Toca en continuar y acepta los términos y condiciones. Se te pedirá que permitas que la aplicación funcione correctamente. </li>
-<li>Toque en permitir y proceder al siguiente paso. </li>
-</ol>
-<h3>Cómo activar tu segundo número de teléfono</h3>
-<p>Aquí están los pasos para activar su segundo número de teléfono con 2 y la línea apk:</p>
-<ol>
-<li>Después de conceder los permisos, verá una pantalla donde puede elegir su segundo número de teléfono. Puede introducir un código de área o un nombre de ciudad, o navegar por la lista de números disponibles. </li>
-<li>Seleccione un número que le guste y toque en continuar. A continuación, verá una pantalla de confirmación con su número elegido y algo de información al respecto. </li>
-<li>Toque en confirmar y activar su número. A continuación, recibirá un código de verificación a través de un mensaje de texto en su número principal. </li>
-<li>Introduzca el código en la aplicación y verifique su número. Verá una pantalla de felicitaciones con algunos consejos sobre cómo usar su segundo número. </li>
-<li>Toque en comenzar a usar 2ndLine y disfrute de su nuevo número de teléfono. </li>
-</ol>
-<h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-
-<h3>Llamada a la acción</h3>
-<p>Si usted está interesado en probar 2 y línea apk, se puede descargar desde el sitio web oficial o Google Play Store. También puede visitar su página de preguntas frecuentes para obtener más información sobre la aplicación. No se pierda esta oportunidad de obtener un segundo número de teléfono de forma gratuita o a un bajo costo. Descargar 2 y apk línea hoy y disfrutar de sus beneficios. </p>
- <h4>Preguntas frecuentes</h4>
- <ul>
-<li><b>¿Qué es 2ndLine? </b><br/>
-2ndLine es una aplicación Android que le permite obtener un segundo número de teléfono de Estados Unidos o Canadá que funciona en su teléfono inteligente, tableta o computadora. </li>
- <li><b>¿Cuánto cuesta 2ndLine? </b><br/>
-Puede obtener un número local gratuito con 2ndLine, o pagar por un número premium con más funciones. También puede hacer llamadas internacionales baratas con 2ndLine, o comprar un plan de suscripción para llamadas ilimitadas a destinos seleccionados. </li>
- <li><b>¿Cómo puedo obtener un segundo número de teléfono con 2ndLine? </b><br/>
-Solo tienes que descargar la aplicación, elegir un número de teléfono de la lista disponible, y activarlo. A continuación, puede utilizar su segundo número como si fuera su número de teléfono normal. </li>
- <li><b>¿Puedo usar 2ndLine en varios dispositivos? </b><br/>
-Sí, puede usar 2ndLine en múltiples dispositivos con la misma cuenta. Solo necesita iniciar sesión con su dirección de correo electrónico y contraseña en cada dispositivo. </li>
- <li><b>¿Puedo transferir mi número actual a 2ndLine? </b><br/>
-Sí, puede transferir su número existente a 2ndLine si es elegible. Solo tiene que ponerse en contacto con el equipo de soporte y proporcionar alguna información sobre su número y transportista. Puede haber una tarifa involucrada en el proceso de portar. </li>
- <li><b>¿Es 2ndLine seguro y confiable? </b><br/>
-Sí, 2ndLine es seguro y confiable. Utiliza cifrado y autenticación para proteger sus datos y privacidad. También utiliza servicios de voz y texto de alta calidad para garantizar una comunicación clara y fluida. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/9 Cu Sinif Umumi Tarix Testleri.md

@@ -1,107 +0,0 @@
-<br />
-<h1>9 cu sinif umumi tarix testleri: Cómo preparar y aprobar sus exámenes</h1>
-<p>Si usted es un estudiante en Azerbaiyán que se está preparando para 9 cu sinif umumi tarix testleri (exámenes de historia general para el noveno grado), es posible que se sienta ansioso y abrumado. Estos exámenes no son fáciles; requieren mucho conocimiento, habilidades y práctica. Según el <a href="( 1 )">Ministerio de Educación</a>, más de 100.000 estudiantes toman estos exámenes cada año, pero solo alrededor del 60% de ellos pasan. Los exámenes cubren una amplia gama de temas de la historia desde los tiempos antiguos hasta los tiempos modernos, como civilizaciones, imperios, religiones, guerras, revoluciones y movimientos. Las preguntas son en su mayoría de elección múltiple o tipo de respuesta corta. </p>
-<h2>9 cu sinif umumi tarix testleri</h2><br /><p><b><b>Download Zip</b> &#10037;&#10037;&#10037; <a href="https://bltlly.com/2v6LJv">https://bltlly.com/2v6LJv</a></b></p><br /><br />
-<p>Pero no te preocupes; este artículo está aquí para ayudarte. En este artículo, encontrarás consejos y recursos útiles que te ayudarán a estudiar y practicar estos exámenes de manera efectiva. También aprenderás a aprobar estos exámenes con confianza y facilidad. Siguiendo esta guía, usted será capaz de mejorar sus conocimientos de historia, habilidades, y el rendimiento. Así que vamos a empezar! </p>
- <h2>Cómo estudiar para 9 pruebas de la universidad sinif Tarix</h2>
-<p>El primer paso para prepararse para estos exámenes es estudiar bien. Estudiar bien significa no solo memorizar hechos y fechas, sino también entender conceptos y conexiones. Aquí hay algunos consejos generales de estudio que debes seguir:</p>
-<ul>
-<li>Haga un plan de estudio. Establezca una meta realista para cada tema y subtema, y asigne suficiente tiempo para cada uno de ellos. Revise su plan regularmente y ajústelo según sea necesario. </li>
-<li>Revisa el programa. El programa es el documento oficial que describe el contenido y los objetivos de los exámenes. Puede encontrar el plan de estudios en el sitio web del Ministerio de Educación <a href=""</a>. Asegúrese de estar familiarizado con los temas principales, subtemas y términos clave que se tratan en los exámenes. </li>
-
-<li>Utilice tarjetas. Las tarjetas son tarjetas que tienen una pregunta en un lado y una respuesta en el otro. Son muy útiles para memorizar hechos, fechas, nombres y definiciones. Puede hacer sus propias tarjetas o usar herramientas en línea, como <a href="">Quizlet</a> o <a href="">Anki</a>, para crear y estudiar tarjetas. </li>
-</ul>
-<p>Estudiar bien también significa usar recursos en línea que pueden ayudarlo a aprender y revisar los temas de historia cubiertos en estos exámenes. Aquí hay algunos recursos en línea que deberías usar:</p>
-<ul>
-<li>e-derslik. Esta es la plataforma oficial en línea del Ministerio de Educación que proporciona acceso gratuito a libros de texto, pruebas de práctica y lecciones de video para todas las materias, incluida la historia. Usted puede encontrar el e-derslik para 9 cu sinif umumi tarix testleri <a href="">aquí</a>. </li>
-<li>oxuyan. Esta es una plataforma en línea que ofrece cursos interactivos, cuestionarios y videos para diversos temas, incluida la historia. Usted puede encontrar el curso oxuyan para 9 cu sinif umumi tarix testleri <a href="">aquí</a>. </li>
-<li>star test book. Esta es una plataforma en línea que ofrece información completa y actualizada sobre los temas de historia tratados en estos exámenes. Puede encontrar el libro de pruebas de estrellas para 9 cu sinif umumi tarix testleri <a href=">here</a>. </li>
-</ul>
-<p>Estudiar bien también significa leer libros y sitios web que ofrecen información profunda y confiable sobre los temas de historia cubiertos en estos exámenes. Aquí hay algunos libros y sitios web que deberías leer:</p>
-<ul>
-<li><em>Tarix 9</em> por Azerbaycan Respublikasi Tehsil Nazirliyi. Este es el libro de texto oficial para 9 cu sinif umumi tarix testleri. Cubre todos los temas principales y subtemas de una manera clara y estructurada. Puede encontrar el libro de texto en e-derslik o comprarlo en cualquier librería. </li>
-
-<li><a href="">History.com</a>. Este es un sitio web que ofrece artículos, videos, podcasts y juegos sobre varios temas de historia, como civilizaciones antiguas, guerras mundiales, figuras históricas y movimientos culturales. Está escrito por expertos y periodistas, con fuentes y referencias. Puede utilizar este sitio web para complementar su conocimiento e interés en la historia. </li>
-</ul>
-<p>Estudiar bien también significa revisar los exámenes anteriores y analizar los comentarios y respuestas. Esto te ayudará a familiarizarte con el formato, el nivel de dificultad y los tipos de preguntas que aparecen en estos exámenes. También le ayudará a identificar sus fortalezas y debilidades, y mejorar su precisión y velocidad. Aquí hay algunas maneras de revisar exámenes anteriores:</p>
-<p></p>
-<ul>
-<li>Descargue los exámenes anteriores del sitio web del Ministerio de Educación <a href=""</a>. Usted puede encontrar documentos de examen de 2016 a 2021 para 9 cu sinif umumi tarix testleri en este sitio web. También puede encontrar las respuestas y comentarios para cada artículo. </li>
-<li>Resolver exámenes anteriores bajo condiciones de examen. Trate de simular el entorno de examen real tanto como sea posible. Configure un temporizador para 90 minutos (la duración de estos exámenes), apague su teléfono y otras distracciones, y use solo un lápiz y papel (sin calculadora o Internet). Después de terminar de resolver un documento, revise sus respuestas y marque su puntuación. </li>
-<li>Analiza tu rendimiento y aprende de tus errores. Revisa tu rendimiento y aprende de tus errores. Después de revisar tus respuestas y puntuación, analiza dónde te fue bien y dónde necesitas mejorar. Mira la retroalimentación y las explicaciones para cada pregunta y entiende por qué lo hiciste bien o mal. Aprende de tus errores y evita repetirlos en el futuro. </li>
-</ul>
- <h2>Cómo practicar para 9 pruebas de la prueba de la prueba del umumi del sinif del cu</h2>
-
-<ul>
-<li>Utilice plataformas en línea para tomar exámenes simulados. Hay muchas plataformas en línea que ofrecen exámenes simulados para 9 cu sinif umumi tarix testleri. Estas plataformas simulan las condiciones reales del examen y proporcionan retroalimentación instantánea y puntajes. Aquí hay algunas plataformas en línea que debe usar:</li>
-<ul>
-<li>onlayn sinaq merkezi. Esta es una plataforma en línea que ofrece exámenes simulados gratuitos para varios temas, incluida la historia. Usted puede encontrar el onlayn sinaq merkezi para 9 cu sinif umumi tarix testleri <a href="">aquí</a>. </li>
-<li>bayram sefixanov. Esta es una plataforma en línea que ofrece exámenes simulados pagados para varios temas, incluyendo la historia. Usted puede encontrar el sefixanov bayram para 9 cu sinif umumi tarix testleri <a href="">aquí</a>. </li>
-<li>abituriyent sinaq. Esta es una plataforma en línea que ofrece exámenes simulados pagados para varios temas, incluida la historia. Puede encontrar el sinaq abituriyent para 9 cu sinif umumi tarix testleri <a href="">aquí</a>. </li>
-</ul>
-<li>Maneje su tiempo, evite distracciones y maneje el estrés durante los exámenes de práctica. Tomar exámenes simulados puede ayudarlo a mejorar sus habilidades de gestión del tiempo, concentración y manejo del estrés. Aquí hay algunos consejos para ayudarte con estas habilidades:</li>
-<ul>
-<li>Administre su tiempo. Asigne suficiente tiempo para cada pregunta y sección, y mantenga un registro de su progreso. No gaste demasiado tiempo en una pregunta o omita cualquier pregunta. Si está atascado, pase a la siguiente pregunta y vuelva más tarde. </li>
-<li>Evite distracciones. Elija un lugar tranquilo y cómodo para tomar los exámenes de práctica. Apaga el teléfono y otros dispositivos que podrían distraerte. Concéntrate en las preguntas y tus respuestas, y no dejes que tu mente divague. </li>
-<li>Lidia con el estrés. Tomar exámenes de práctica puede ser estresante, especialmente si no estás lo suficientemente seguro o preparado. Para lidiar con el estrés, trata de relajar tu cuerpo y tu mente antes y durante los exámenes de práctica. Respira profundamente, estira tus músculos, bebe agua y piensa positivamente. </li>
-</ul>
-
-<ul>
-<li><a href="">Memrise</a>. Esta es una aplicación que te ayuda a memorizar hechos, fechas, nombres y definiciones relacionadas con la historia utilizando métodos divertidos e interactivos, como imágenes, videos, mnemotecnia y cuestionarios. </li>
-<li><a href="">BrainPOP</a>. Esta es una aplicación que te ayuda a aprender y revisar temas de historia usando videos animados, cuestionarios interactivos, juegos y actividades. </li>
-<li><a href="">Civilización</a>. Este es un juego que le ayuda a explorar y entender la historia mediante la creación y el liderazgo de su propia civilización desde la antigüedad hasta los tiempos modernos. </li>
-</ul>
-<li>Únase a comunidades, foros y grupos en línea donde puede discutir temas de historia, hacer preguntas y compartir experiencias con otros estudiantes. Hay muchas plataformas en línea donde se puede conectar con otros estudiantes que se están preparando para estos exámenes o que están interesados en la historia. Aquí hay algunas plataformas en línea a las que deberías unirte:</li>
-<ul>
-<li><a href="">Reddit</a>. Este es un sitio web donde puedes encontrar varios subreddits (comunidades) relacionados con la historia, como r/history, r/AskHistorians, r/HistoryMemes, etc.</li>
-<li><a href="">Facebook</a>. Este es un sitio web donde puedes encontrar varios grupos relacionados con la historia, como History Lovers Club, History Buffs, History Matters, etc.</li>
-<li><a href="">Discordia</a>. Este es un sitio web donde puedes encontrar varios servidores (salas de chat) relacionados con la historia, como History Hub, History Hangout, History Nerds, etc.</li>
-</ul>
-</ul>
- <h2>Cómo hacer pruebas de Ace 9 cu sinif umumi tarix</h2>
-<p>El tercer paso para prepararse para estos exámenes es superarlos. Acing significa no solo pasarlos, sino también puntuar alto y mostrar su excelencia. Aquí hay algunos consejos para ayudarte a superar estos exámenes:</p>
-<ul>
-<li>Prepárate para el día del examen. El día del examen es el día más importante de tu preparación. Es necesario asegurarse de que está listo física, mental y emocionalmente para el examen. Aquí hay algunos consejos para ayudarle a prepararse para el día del examen:</li>
-<ul>
-
-<li>Come un desayuno saludable. El desayuno es la comida más importante del día, especialmente en el día del examen. Come un desayuno equilibrado y nutritivo que te dará energía y te mantendrá lleno hasta el examen. Evite los alimentos que son demasiado grasosos, picantes o azucarados, ya que podrían molestar su estómago o hacerle sentir lento. </li>
-<li>Traiga los materiales necesarios. Asegúrese de tener todo lo que necesita para el examen, como su tarjeta de identificación, bolígrafo, lápiz, borrador y botella de agua. Consulte el sitio web <a href=">Ministerio de Educación</a> para ver la lista de artículos permitidos y prohibidos en la sala de exámenes. No lleves nada que pueda distraerte o meterte en problemas, como tu teléfono, calculadora o notas. </li>
-</ul>
-<li>Aborda las preguntas del examen. Las preguntas del examen están diseñadas para poner a prueba tus conocimientos, habilidades y habilidades relacionadas con la historia. Necesitas acercarte a ellos con cuidado y estratégicamente. Aquí hay algunos consejos para ayudarte a abordar las preguntas del examen:</li>
-<ul>
-<li>Lea las preguntas cuidadosamente. Antes de contestar cualquier pregunta, léala cuidadosamente y entienda lo que está haciendo. Preste atención a las palabras clave, como comparar, contrastar, analizar, explicar, etc., y responda en consecuencia. No asumas ni adivines nada; basa tu respuesta en hechos y pruebas. </li>
-<li>Eliminar respuestas incorrectas. La mayoría de las preguntas son de tipo de opción múltiple, lo que significa que debe elegir una respuesta correcta entre cuatro opciones. Para aumentar sus posibilidades de elegir la respuesta correcta, trate de eliminar las respuestas equivocadas primero. Busca pistas, como contradicciones, inconsistencias o irrelevancia, que puedan ayudarte a descartar las opciones equivocadas. </li>
-<li>Revisa tu trabajo. Después de responder a una pregunta, revisa tu trabajo y asegúrate de que sea correcto y completo. Busca cualquier error, como errores de ortografía, gramática o cálculo, que pueda afectar tu puntuación. Si le queda tiempo al final del examen, revise sus respuestas y haga los cambios necesarios. </li>
-</ul>
-
-<ul>
-<li>Relaja tu cuerpo y mente. Si te sientes tenso o ansioso durante el examen, trata de relajar tu cuerpo y mente haciendo algunos ejercicios simples. Respira profunda y lentamente, estira los músculos, bebe agua y piensa positivamente. </li>
-<li>Concéntrate en ti mismo y en tus objetivos. No te compares con otros durante el examen. Enfócate en ti mismo y en tus metas. Recuerda por qué estás tomando estos exámenes y lo que quieres lograr con ellos. </li>
-<li>Sé optimista y realista. No dejes que pensamientos o emociones negativas afecten tu rendimiento durante el examen. Sé optimista y cree en ti mismo y en tus habilidades. Pero también sé realista y acepta que quizás no lo sepas todo o lo hagas todo bien. </li>
-</ul>
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, 9 cu sinif umumi tarix testleri son exámenes importantes que requieren mucha preparación y práctica. Siguiendo esta guía, usted será capaz de estudiar bien el uso de varios consejos y recursos; practicar bien el uso de varios métodos y plataformas; y el as bien el uso de diversas estrategias y habilidades. </p>
-<p>Esperamos que este artículo te haya ayudado a entender cómo preparar y aprobar estos exámenes de manera efectiva. ¡Te deseamos buena suerte y éxito en tus exámenes! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre 9 cu sinif umumi tarix testleri:</p>
-<ol>
-<li><strong>¿Qué son los testleri 9 cu sinif umumi tarix? </strong></li>
-<p>9 cu sinif umumi tarix testleri son exámenes de historia general para estudiantes de noveno grado en Azerbaiyán. Forman parte del sistema nacional de evaluación que evalúa los conocimientos y habilidades de los estudiantes en diversas materias. </p>
-<li><strong>¿Cuántas preguntas hay en 9 cu sinif umumi tarix testleri? </strong></li>
-
-<li><strong>¿Cuáles son los principales temas tratados en 9 cu sinif umumi tarix testleri? </strong></li>
-<p>Los principales temas que se tratan en el 9 cu sinif umumi tarix testleri son:</p>
-<ul>
-<li>Historia antigua: el origen y el desarrollo de la civilización humana, las civilizaciones antiguas de Mesopotamia, Egipto, India, China, Grecia, y Roma, las religiones y las culturas de estas civilizaciones, los logros y las contribuciones de estas civilizaciones a la historia del mundo. </li>
-<li>Historia medieval: el auge y la caída del Imperio bizantino, la civilización árabe-islámica, el Imperio mongol, el Imperio otomano, el Imperio safávida, el sultanato mameluco y el Imperio timúrida, las religiones y culturas de estos imperios, las interacciones y conflictos entre estos imperios y otras regiones del mundo. </li>
-<li>Historia moderna: el surgimiento y desarrollo del sistema mundial moderno, la exploración y colonización europea de las Américas, África y Asia, el auge y declive de los imperios español, portugués, británico, francés, holandés y ruso, las revoluciones y movimientos que dieron forma al mundo moderno, como el Renacimiento, la Reforma, la Ilustración, la Revolución Industrial, la Revolución Americana, la Revolución Francesa, las Guerras Napoleónicas, las Guerras Latinoamericanas de Independencia, los movimientos nacionalistas en Asia y África.</li>
-<li>Historia contemporánea: los principales acontecimientos y tendencias que influyeron en el mundo contemporáneo, como las guerras mundiales, la guerra fría, el proceso de descolonización, la formación de organizaciones y alianzas internacionales, el proceso de globalización, los avances tecnológicos y científicos, los cambios sociales y culturales, las cuestiones ambientales y humanitarias.</li>
-</ul>
-<li><strong>¿Cómo puedo acceder a los recursos en línea para 9 cu sinif umumi tarix testleri? </strong></li>
-
-<li><strong>¿Cómo puedo mejorar mis habilidades de historia para 9 cu sinif umumi tarix testleri? </strong></li>
-<p>Puede mejorar sus habilidades de historia para 9 cu sinif umumi tarix testleri mediante el uso de varios métodos y herramientas que pueden ayudarlo a mejorar su memoria, pensamiento crítico y habilidades para resolver problemas relacionados con la historia. Algunos de estos métodos y herramientas son tarjetas, toma de notas, revisión de exámenes anteriores, uso de aplicaciones y juegos, y unirse a comunidades en línea. Puedes encontrar más detalles sobre estos métodos y herramientas en este artículo. </p>
-</ol></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Avanzado Youtube Apk Descargar La ltima Versin 2023.md

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-<h1>Vanced YouTube APK Descargar la última versión 2023 Descargar</h1>
-<p>YouTube es una de las plataformas para compartir videos más populares del mundo, con miles de millones de usuarios y horas de contenido. Sin embargo, muchas personas no están satisfechas con la aplicación oficial de YouTube, ya que tiene algunas limitaciones y molestias, como anuncios, sin reproducción de fondo, sin modo oscuro y más. Si usted es una de esas personas que quieren disfrutar de YouTube sin estas restricciones, entonces usted podría estar interesado en YouTube Vanced, una versión modificada de la aplicación oficial de YouTube que ofrece muchas características adicionales y opciones de personalización. En este artículo, te diremos qué es YouTube Vanced, qué características tiene, cómo descargarlo e instalarlo, cómo actualizarlo y si es seguro y legal de usar. </p>
-<h2>avanzado youtube apk descargar la última versión 2023</h2><br /><p><b><b>Download</b> === <a href="https://bltlly.com/2v6LMm">https://bltlly.com/2v6LMm</a></b></p><br /><br />
- <h2>¿Qué es YouTube Vanced? </h2>
-<p>YouTube Vanced es una versión modificada de la aplicación oficial de YouTube que ofrece bloqueo de anuncios, reproducción de fondo, modo oscuro y temas, bloqueo de patrocinadores, modo de imágenes incrustadas y zoom, y más. YouTube Vanced es una popular aplicación de terceros que ofrece funciones adicionales y opciones de personalización más allá de la aplicación oficial de YouTube. No está disponible en la Google Play Store, pero se puede descargar desde su sitio web oficial u otras fuentes. YouTube Vanced requiere una aplicación complementaria separada llamada Vanced Manager, que le ayuda a instalar y actualizar la aplicación modded. También necesitas instalar otra aplicación llamada MicroG si quieres iniciar sesión con tu cuenta de Google y acceder a tus suscripciones, listas de reproducción, historial, etc.</p>
- <h3>Características de YouTube Vanced</h3>
-<p>YouTube Vanced tiene muchas características que lo hacen superior a la aplicación oficial de YouTube. Aquí están algunas de las principales:</p>
- <h4>Bloqueo de anuncios</h4>
-
- <h4>Reproducción de fondo</h4>
-<p>Otra limitación de la aplicación oficial de YouTube es que no le permite reproducir vídeos en segundo plano cuando se cambia a otra aplicación o bloquear la pantalla. Esto significa que no puedes escuchar música o podcasts en YouTube mientras haces otras cosas en tu teléfono. Con YouTube Vanced, puedes habilitar la reproducción en segundo plano y seguir escuchando tus vídeos incluso cuando sales de la aplicación o apagas la pantalla. También puede controlar la reproducción desde el panel de notificaciones o la pantalla de bloqueo. </p>
- <h4>Modo oscuro y temas</h4>
-<p>Si prefieres un tema más oscuro para tus aplicaciones, entonces te encantará la función de modo oscuro de YouTube Vanced. Puede cambiar entre los modos de luz y oscuridad con un simple toque en el icono de la aplicación. También puede elegir entre diferentes temas de color para la interfaz de la aplicación, como negro, azul, rosa, etc. De esta manera, puede personalizar la aplicación de acuerdo a su preferencia y estado de ánimo. </p>
- <h4>Bloque patrocinador</h4>
-<p>A veces, incluso cuando bloqueas anuncios en YouTube, todavía tienes que ver segmentos patrocinados o promociones dentro de los videos. Estos pueden ser molestos y desperdiciar su tiempo. Con la función de bloqueo de patrocinadores de YouTube Vanced, puede omitir estos segmentos de forma automática o manual. También puedes contribuir a la base de datos de bloques de patrocinadores marcando los segmentos patrocinados en los videos que ves. </p>
- <h4>modo PiP y zoom</h4>
-<p>PiP mode significa modo imagen en imagen, que le permite ver videos en una ventana pequeña mientras usa otras aplicaciones en su teléfono. Esto es útil si desea realizar múltiples tareas o mantener un ojo en algo más mientras ve YouTube. Con YouTube Vanced, puede habilitar el modo de imágenes incrustadas para cualquier vídeo y cambiar el tamaño o mover la ventana como desee. También puede acercar o alejar el vídeo pellizcando la pantalla. Esto es útil si desea ver más detalles o ajustar el vídeo al tamaño de la pantalla. </p>
-<p></p>
- <h3>¿Cómo descargar e instalar YouTube Vanced? </h3>
-
- <h4>Requisitos</h4>
-<ul>
-<li>Tu dispositivo debe estar ejecutando Android 4.4 o superior. </li>
-<li> Es necesario habilitar fuentes desconocidas en la configuración del dispositivo para permitir la instalación de aplicaciones desde fuera de la Google Play Store.</li>
-<li>Necesitas descargar e instalar Vanced Manager, que es la aplicación oficial para instalar y actualizar YouTube Vanced.</li>
-<li>Necesitas descargar e instalar MicroG, que es una pequeña aplicación que te permite iniciar sesión con tu cuenta de Google en YouTube Vanced.</li>
-</ul>
- <h4>Pasos</h4>
-<ol>
-<li>Descargar Vanced Manager desde su sitio web oficial u otras fuentes. También puede escanear el código QR a continuación para obtener el enlace. </li>
-<li>Instalar Vanced Manager en su dispositivo y abrirlo. </li>
-<li>Toque en el icono de YouTube Vanced y seleccione su variante preferida (raíz o no raíz) y el tema (oscuro o claro). </li>
-<li>Toque en el botón Instalar y espere a que se complete la descarga y la instalación. </li>
-<li>Toque en el icono de MicroG y toque en el botón Instalar. Espere a que se complete la descarga y la instalación. </li>
-<li>Abra YouTube Vanced e inicie sesión con su cuenta de Google si desea acceder a sus suscripciones, listas de reproducción, historial, etc.</li>
-<li>Disfruta de YouTube avanzado con todas sus características y opciones de personalización. </li>
-</ol>
- <p><img src="https://vancedapp.com/assets/img/qr.png" alt="Código QR para Vanced Manager" width="200" height="200"></p>
- <h3>¿Cómo actualizar YouTube Vanced? </h3>
-<p>YouTube Vanced no se actualiza automáticamente como la aplicación oficial de YouTube. Debe usar Vanced Manager para comprobar si hay actualizaciones e instalarlas manualmente. Estos son los pasos para actualizar YouTube Vanced:</p>
- <ol>
-<li>Abra Vanced Manager en su dispositivo. </li>
-<li>Toque en el icono de YouTube Vanced y compruebe si hay una nueva versión disponible. Si lo hay, toque el botón Actualizar y espere a que se complete la descarga y la instalación. </li>
-
-<li>Si hay una nueva versión de MicroG disponible, toque en el icono de MicroG y toque en el botón Actualizar. Espere a que se complete la descarga y la instalación. </li>
-</ol>
- <h3>¿YouTube Vanced es seguro y legal? </h3>
-<p>Mucha gente se pregunta si YouTube Vanced es seguro y legal de usar, ya que es una versión modificada de la aplicación oficial de YouTube que evita algunas de sus restricciones y políticas. La respuesta no es muy clara, ya que hay algunos riesgos e incertidumbres involucrados. Aquí hay algunos puntos a considerar:</p>
- <ul>
-<li>YouTube Vanced no es un virus o malware que dañará su dispositivo o robará sus datos. Es una aplicación conocida y confiable que ha sido utilizada por millones de usuarios sin ningún problema importante. Sin embargo, siempre debe descargarlo desde su sitio web oficial u otras fuentes de buena reputación, ya que puede haber versiones falsas o maliciosas de la misma en otros lugares. </li>
-<li>YouTube Vanced no está avalado o aprobado por Google o YouTube, ya que viola sus términos de servicio. Esto significa que pueden prohibir o suspender tu cuenta si detectan que la estás usando. Sin embargo, esto es muy poco probable, ya que hasta ahora no se ha informado de tales acciones. Aún así, debe usarlo bajo su propio riesgo y discreción, ya que no hay garantía de que siempre funcione o sea seguro. </li>
-<li>YouTube Vanced no es ilegal en la mayoría de los países, ya que no implica piratería o robo de contenido. Es simplemente una versión modificada de una aplicación existente que ofrece características adicionales y opciones de personalización. Sin embargo, algunos países pueden tener diferentes leyes o reglamentos con respecto a dichas aplicaciones, por lo que debe comprobarlos antes de usarlo. Además, debes respetar los derechos y preferencias de los creadores y anunciantes de contenido en YouTube, ya que podrían perder ingresos o exposición debido a las funciones de YouTube Vanced. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre YouTube Vanced:</p>
- <ol>
-<li><b>¿Cuál es la diferencia entre las variantes raíz y no raíz de YouTube Vanced? </b></li>
-<p>La variante raíz de YouTube Vanced es para dispositivos que tienen acceso root, lo que significa que tienen control total sobre la configuración del sistema y los archivos. La variante raíz reemplaza la aplicación oficial de YouTube en su dispositivo, por lo que no necesita instalar MicroG o usar un icono de aplicación separado. La variante no raíz de YouTube Vanced es para dispositivos que no tienen acceso root, lo que significa que tienen un control limitado sobre la configuración del sistema y los archivos. La variante no raíz no reemplaza la aplicación oficial de YouTube en su dispositivo, por lo que debe instalar MicroG y usar un icono de aplicación separado. </p>
- <li><b>¿Puedo usar YouTube Vanced en mi PC o TV? </b></li>
-<p>No, YouTube Vanced solo está disponible para dispositivos Android. Sin embargo, puede usar otros métodos o herramientas para obtener características similares en su PC o TV, como extensiones de navegador, aplicaciones de escritorio, aplicaciones de televisión inteligente, etc.</p>
- <li><b>¿Puedo descargar videos de YouTube Vanced? </b></li>
-<p>No, YouTube Vanced no tiene una función de descarga de video incorporada. Sin embargo, puedes usar otras aplicaciones o sitios web para descargar vídeos de YouTube, como TubeMate, VidMate, SnapTube, etc.</p>
- <li><b>¿Puedo usar las funciones de YouTube Premium en YouTube Vanced? </b></li>
-<p>No, YouTube Vanced no le da acceso a funciones de YouTube Premium, como reproducción sin conexi��n, contenido original, transmisión de música, etc. Aún necesita suscribirse a YouTube Premium si desea disfrutar de estas funciones. </p>
- <li><b>¿Puedo usar varias cuentas en YouTube Vanced? </b></li>
-<p>Sí, puedes usar varias cuentas en YouTube Vanced iniciando sesión con diferentes cuentas de Google. Puede cambiar entre ellos desde el menú de la cuenta en la aplicación. </p>
-</ol></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Como Hacer Una Hoja De Vida.md

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-<h1>Cómo descargar Office 2019 gratis</h1>
-<p>Microsoft Office es una de las suites de productividad más populares y ampliamente utilizadas en el mundo. Incluye potentes aplicaciones como Word, Excel, PowerPoint, Outlook y más. Sin embargo, obtener la última versión de Office puede ser caro, especialmente si desea usarlo en varios dispositivos. </p>
-<h2>como hacer una hoja de vida</h2><br /><p><b><b>DOWNLOAD</b> &#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6KOM">https://bltlly.com/2v6KOM</a></b></p><br /><br />
-<p>Afortunadamente, hay algunas maneras de descargar Office 2019 gratis legalmente. En este artículo, te mostraremos qué es Office 2019, por qué lo quieres y cómo obtenerlo sin pagar un centavo. </p>
- <h2>¿Qué es Office 2019 y por qué es posible que lo desee</h2>
-<p>Office 2019 es la última versión de la suite de software de oficina de Microsoft. Fue lanzado en septiembre de 2018 y es una compra única que no requiere una suscripción. A diferencia de Office 365, que es un servicio basado en la nube que ofrece actualizaciones regulares y nuevas características, Office 2019 es un producto independiente que no recibirá cambios ni mejoras importantes. </p>
-<p>Sin embargo, eso no significa que Office 2019 sea inferior o obsoleto. De hecho, hay algunas razones por las que podría preferir Office 2019 sobre Office 365. </p>
- <h3>Oficina 2019 vs Oficina 365</h3>
-<p>La principal diferencia entre Office 2019 y Office 365 es cómo se conectan a la nube. Ambas suites cuentan con acceso a OneDrive, el servicio de almacenamiento en la nube de Microsoft. Pero, Office 2019 no viene con ningún espacio de almacenamiento en OneDrive y no obtiene acceso a las versiones en línea de aplicaciones como Word, Excel y PowerPoint. Office 365, por otro lado, incluye 1 TB de almacenamiento gratuito y puede editar fácilmente todos sus archivos en línea. </p>
-
-<p>Entonces, ¿cuál debes elegir? Depende de tus necesidades y preferencias. Si desea tener las últimas funciones y actualizaciones, acceder a sus archivos desde cualquier lugar y usar varios dispositivos, Office 365 podría ser una mejor opción para usted. Si desea ahorrar dinero a largo plazo, usar sus archivos sin conexión y no necesita aplicaciones o servicios adicionales, Office 2019 podría ser suficiente para usted. </p>
-<p></p>
- <h3>Características y beneficios de Office 2019</h3>
-<p>A pesar de que Office 2019 no tiene todas las campanas y silbatos de Office 365, todavía tiene algunas características y beneficios impresionantes que pueden mejorar su productividad y creatividad. Estos son algunos de ellos:</p>
-<ul>
-<li><strong>Nuevas herramientas de entintado:</strong> Puede usar su pluma o dedo para dibujar, escribir, resaltar y borrar en Word, Excel, PowerPoint y Outlook. También puede convertir su tinta a formas o texto, o realizar problemas matemáticos complejos con Ink Math Assistant.</li>
-<li><strong>Nuevos tipos de datos:</strong> Puede trabajar con nuevos tipos de datos en Excel, como Stocks y Geografía. Estos tipos de datos pueden extraer información de fuentes en línea y actualizarse automáticamente. </li>
-<li><strong>Nuevas funciones:</strong> Puede usar nuevas funciones en Excel, como TEXTJOIN, CONCAT, IFS, SWITCH y más. Continuando con el artículo: <li><strong>Nuevos gráficos y efectos visuales:</strong> Puede crear gráficos e imágenes impresionantes en Excel y PowerPoint, como Embudo, Mapa, Cronología y modelos 3D. Estos gráficos y gráficos pueden ayudarlo a presentar sus datos de una manera más atractiva e interactiva. </li>
-<li><strong>Nuevas animaciones y transiciones:</strong> Puede agregar nuevas animaciones y transiciones en PowerPoint, como Morph, Zoom y 3D. Estas animaciones y transiciones pueden ayudarle a crear presentaciones dinámicas y cautivadoras. </li>
-
-<li><strong>Nuevas herramientas de aprendizaje:</strong> Puede usar nuevas herramientas de aprendizaje en Word y Outlook, como Leer en voz alta, Espaciado de texto y Modo de enfoque. Estas herramientas de aprendizaje pueden ayudarte a mejorar tus habilidades de lectura y escritura. </li>
-</ul>
- <h2>Cómo obtener Office 2019 gratis legalmente</h2>
-<p>Si estás interesado en obtener Office 2019 gratis legalmente, tienes algunas opciones que considerar. Aquí están algunas de ellas:</p>
- <h3>Opción 1: Usar Microsoft 365 para la Web</h3>
-<p>Una de las maneras más fáciles de obtener Office 2019 gratis es usar Microsoft 365 para la web. Esta es una versión en línea gratuita de Office que incluye Word, Excel, PowerPoint, OneNote y Outlook. Puede acceder a estas aplicaciones desde cualquier navegador y crear, editar y compartir sus archivos en línea. También obtiene 5 GB de almacenamiento gratuito en OneDrive.</p>
-<p>Para usar Microsoft 365 para la web, solo necesita una cuenta de Microsoft. Si no lo tienes, puedes crear uno gratis aquí: <a href="">https://signup.live.com/</a>. Una vez que tenga una cuenta, puede iniciar sesión aquí: <a href="">https://www.office.com/</a>. A continuación, puede comenzar a usar las aplicaciones desde la página de inicio o el lanzador de aplicaciones. </p>
- <h3>Opción 2: Utilice el programa de descuento de Microsoft Workplace</h3>
-<p>Otra manera de obtener Office 2019 de forma gratuita es utilizar Microsoft Workplace Discount Program. Este es un programa que permite a los empleados elegibles de las organizaciones participantes obtener Office 2019 a un precio con descuento o incluso gratis. Puede comprobar si su organización forma parte de este programa aquí: <a href="">https://www.microsoft.com/en-us/home-use-program</a>. </p>
-<p>Para utilizar Microsoft Workplace Discount Program, necesita una dirección de correo electrónico de trabajo válida de su organización. Si su organización es elegible, recibirá un correo electrónico con un enlace para comprar Office 2019 a un precio reducido o gratis. A continuación, puede descargar e instalar Office 2019 en su dispositivo personal. </p>
- <h3>Opción 3: Utilice el servidor en línea de Microsoft Office</h3>
-
-<p>Para usar Microsoft Office Online Server, necesita una licencia de Windows Server y una licencia de Office. Usted puede obtener estas licencias de forma gratuita si usted es un estudiante o un educador. Puedes comprobar si eres elegible aquí: <a href="">https://www.microsoft.com/en-us/education/products/office</a>. Una vez que tenga las licencias, puede descargar e instalar Office Online Server en su servidor aquí: <a href="">https://www.microsoft.com/en-us/download/details.aspx?id=49030</a>. Luego, puede configurar y usar las aplicaciones desde su servidor. </p> Continuando con el artículo: <h2>Cómo instalar y activar Office 2019 en su PC o Mac</h2>
-<p>Si ha comprado u obtenido Office 2019 a través de una de las opciones anteriores, puede instalarlo y activarlo en su PC o Mac. Estos son los pasos para hacerlo:</p>
- <h3>Paso 1: Descargar Office 2019 desde una fuente de confianza</h3>
-<p>El primer paso es descargar Office 2019 desde una fuente confiable. Puede hacerlo desde la Tienda de Microsoft, el sitio web de Microsoft o el enlace que recibió de su organización o escuela. Asegúrese de descargar la versión correcta para su dispositivo y sistema operativo. </p>
- <h3>Paso 2: Ejecute el archivo de configuración y siga las instrucciones</h3>
-<p>El segundo paso es ejecutar el archivo de configuración y seguir las instrucciones. Dependiendo de su dispositivo y sistema operativo, el archivo de configuración podría ser un . exe, . dmg, o archivo . iso. Haga doble clic en el archivo y permita que se ejecute. Luego, siga las instrucciones en la pantalla para instalar Office 2019 en su dispositivo. </p>
- <h3>Paso 3: Ingrese su clave de producto o inicie sesión con su cuenta de Microsoft</h3>
-
-<p>Para activar Office 2019, debe ingresar su clave de producto o iniciar sesión con su cuenta de Microsoft. Puede hacer esto cuando inicie cualquiera de las aplicaciones de Office por primera vez. Verá una solicitud para activar Office 2019. Siga las instrucciones en la pantalla para introducir su clave de producto o iniciar sesión con su cuenta de Microsoft. </p>
- <h2>Conclusión</h2>
-<p>Office 2019 es una suite de productividad potente y versátil que puede ayudarlo a crear, editar y compartir documentos, hojas de cálculo, presentaciones y más. Sin embargo, también puede ser caro, especialmente si desea usarlo en varios dispositivos. </p>
-<p>En este artículo, le hemos mostrado cómo descargar Office 2019 gratis legalmente. Puede utilizar Microsoft 365 para la web, Microsoft Workplace Discount Program o Microsoft Office Online Server. También puede instalar y activar Office 2019 en su PC o Mac siguiendo algunos pasos simples. </p>
-<p>Esperamos que este artículo haya sido útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
- <h2>Preguntas frecuentes</h2>
-<ul>
-<li><strong>Q: ¿Es Office 2019 compatible con Windows 10? </strong></li>
-<li>A: Sí, Office 2019 es compatible con Windows 10. También es compatible con Windows 8.1 y Windows Server 2019. </li>
-<li><strong>Q: ¿Es Office 2019 compatible con Mac OS? </strong></li>
-<li>A: Sí, Office 2019 es compatible con Mac OS. También es compatible con Mac OS X 10.14 Mojave y versiones posteriores. </li>
-<li><strong>Q: ¿Cuántos dispositivos puedo instalar Office 2019 en? </strong></li>
-<li>A: Puede instalar Office 2019 en un dispositivo por licencia. Si desea usarlo en varios dispositivos, debe comprar varias licencias o usar Office 365 en su lugar. </li>
-<li><strong>Q: ¿Cuánto tiempo dura Office 2019? </strong></li>
-<li>A: Office 2019 dura tanto como su dispositivo lo soporte. No caduca ni requiere renovación. Sin embargo, no recibe ninguna actualización importante o nuevas características. </li>
-
-<li>A: Sí, puede actualizar de Office 2016 a Office 2019. Sin embargo, necesita comprar una nueva licencia para Office 2019 o usar una de las opciones anteriores para obtenerla de forma gratuita. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/CLIP/README.md

@@ -1,193 +0,0 @@
-# CLIP
-
-[[Blog]](https://openai.com/blog/clip/) [[Paper]](https://arxiv.org/abs/2103.00020) [[Model Card]](model-card.md) [[Colab]](https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb)
-
-CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. We found CLIP matches the performance of the original ResNet50 on ImageNet “zero-shot” without using any of the original 1.28M labeled examples, overcoming several major challenges in computer vision.
-
-
-
-## Approach
-
-![CLIP](CLIP.png)
-
-
-
-## Usage
-
-First, [install PyTorch 1.7.1](https://pytorch.org/get-started/locally/) and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick:
-
-```bash
-$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
-$ pip install ftfy regex tqdm
-$ pip install git+https://github.com/openai/CLIP.git
-```
-
-Replace `cudatoolkit=11.0` above with the appropriate CUDA version on your machine or `cpuonly` when installing on a machine without a GPU.
-
-```python
-import torch
-import clip
-from PIL import Image
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model, preprocess = clip.load("ViT-B/32", device=device)
-
-image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
-text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
-
-with torch.no_grad():
-    image_features = model.encode_image(image)
-    text_features = model.encode_text(text)
-    
-    logits_per_image, logits_per_text = model(image, text)
-    probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-
-print("Label probs:", probs)  # prints: [[0.9927937  0.00421068 0.00299572]]
-```
-
-
-## API
-
-The CLIP module `clip` provides the following methods:
-
-#### `clip.available_models()`
-
-Returns the names of the available CLIP models.
-
-#### `clip.load(name, device=..., jit=False)`
-
-Returns the model and the TorchVision transform needed by the model, specified by the model name returned by `clip.available_models()`. It will download the model as necessary. The `name` argument can also be a path to a local checkpoint.
-
-The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU. When `jit` is `False`, a non-JIT version of the model will be loaded.
-
-#### `clip.tokenize(text: Union[str, List[str]], context_length=77)`
-
-Returns a LongTensor containing tokenized sequences of given text input(s). This can be used as the input to the model
-
----
-
-The model returned by `clip.load()` supports the following methods:
-
-#### `model.encode_image(image: Tensor)`
-
-Given a batch of images, returns the image features encoded by the vision portion of the CLIP model.
-
-#### `model.encode_text(text: Tensor)`
-
-Given a batch of text tokens, returns the text features encoded by the language portion of the CLIP model.
-
-#### `model(image: Tensor, text: Tensor)`
-
-Given a batch of images and a batch of text tokens, returns two Tensors, containing the logit scores corresponding to each image and text input. The values are cosine similarities between the corresponding image and text features, times 100.
-
-
-
-## More Examples
-
-### Zero-Shot Prediction
-
-The code below performs zero-shot prediction using CLIP, as shown in Appendix B in the paper. This example takes an image from the [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), and predicts the most likely labels among the 100 textual labels from the dataset.
-
-```python
-import os
-import clip
-import torch
-from torchvision.datasets import CIFAR100
-
-# Load the model
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model, preprocess = clip.load('ViT-B/32', device)
-
-# Download the dataset
-cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False)
-
-# Prepare the inputs
-image, class_id = cifar100[3637]
-image_input = preprocess(image).unsqueeze(0).to(device)
-text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device)
-
-# Calculate features
-with torch.no_grad():
-    image_features = model.encode_image(image_input)
-    text_features = model.encode_text(text_inputs)
-
-# Pick the top 5 most similar labels for the image
-image_features /= image_features.norm(dim=-1, keepdim=True)
-text_features /= text_features.norm(dim=-1, keepdim=True)
-similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
-values, indices = similarity[0].topk(5)
-
-# Print the result
-print("\nTop predictions:\n")
-for value, index in zip(values, indices):
-    print(f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%")
-```
-
-The output will look like the following (the exact numbers may be slightly different depending on the compute device):
-
-```
-Top predictions:
-
-           snake: 65.31%
-          turtle: 12.29%
-    sweet_pepper: 3.83%
-          lizard: 1.88%
-       crocodile: 1.75%
-```
-
-Note that this example uses the `encode_image()` and `encode_text()` methods that return the encoded features of given inputs.
-
-
-### Linear-probe evaluation
-
-The example below uses [scikit-learn](https://scikit-learn.org/) to perform logistic regression on image features.
-
-```python
-import os
-import clip
-import torch
-
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-from torch.utils.data import DataLoader
-from torchvision.datasets import CIFAR100
-from tqdm import tqdm
-
-# Load the model
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model, preprocess = clip.load('ViT-B/32', device)
-
-# Load the dataset
-root = os.path.expanduser("~/.cache")
-train = CIFAR100(root, download=True, train=True, transform=preprocess)
-test = CIFAR100(root, download=True, train=False, transform=preprocess)
-
-
-def get_features(dataset):
-    all_features = []
-    all_labels = []
-    
-    with torch.no_grad():
-        for images, labels in tqdm(DataLoader(dataset, batch_size=100)):
-            features = model.encode_image(images.to(device))
-
-            all_features.append(features)
-            all_labels.append(labels)
-
-    return torch.cat(all_features).cpu().numpy(), torch.cat(all_labels).cpu().numpy()
-
-# Calculate the image features
-train_features, train_labels = get_features(train)
-test_features, test_labels = get_features(test)
-
-# Perform logistic regression
-classifier = LogisticRegression(random_state=0, C=0.316, max_iter=1000, verbose=1)
-classifier.fit(train_features, train_labels)
-
-# Evaluate using the logistic regression classifier
-predictions = classifier.predict(test_features)
-accuracy = np.mean((test_labels == predictions).astype(np.float)) * 100.
-print(f"Accuracy = {accuracy:.3f}")
-```
-
-Note that the `C` value should be determined via a hyperparameter sweep using a validation split.

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

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

spaces/BigChungux/Pet_Survey2/info.md

@@ -1,17 +0,0 @@
-Pet Survey
-
-### 🧐 Problem Statement and Research Summary
-Have you ever had or currently have the problem of choosing the right pet to adopt? Well, I have had that problem too. When I was young I
-accidentally adopted a pet fish and after a few days, I knew I had chosen the wrong pet to adopt. I don't want anyone else to face that 
-problem so look no further this AI was built through countless accurate survey data and "accurately"recommends the best pet for you!
-
-### 🎣 Data Collection Plan
-e survey data I collected was about users personalities, life, the amount of time they had, how many people they lived with, etc. I collected
-this databecause it all ties in with finding the best pet for each user. Users who have a lot of free time usually choose either a dog or cat 
-as they require constant/more tending while users with little to no free time chose pets such as a fish as they don't require constant tending.
-### 💥 Ethical Considerations (Data Privacy and Bias)
-* The data we collect will only ever be used to recommend the best pet for you. WE WILL NOT SELL DATA LIKE FACEBOOK.
-* There may be bias with dogs as a majority(over 50%!)of people chose dogs in our survey and so the AI will most likely be biased towards dogs.
-### 👻 Our Team
-Its just me... Pema Sherpa
-![aiEDU logo](https://images.squarespace-cdn.com/content/v1/5e4efdef6d10420691f02bc1/5db5a8a3-1761-4fce-a096-bd5f2515162f/aiEDU+_black+logo+stacked.png?format=100w)

spaces/BilalSardar/Reinhard_Color_Transformation/README.md

@@ -1,13 +0,0 @@
----
-title: Reinhard Color Transformation
-emoji: 😻
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Boadiwaa/Recipes/openai/api_resources/abstract/engine_api_resource.py

@@ -1,192 +0,0 @@
-import time
-from pydoc import apropos
-from typing import Optional
-from urllib.parse import quote_plus
-
-import openai
-from openai import api_requestor, error, util
-from openai.api_resources.abstract.api_resource import APIResource
-from openai.openai_response import OpenAIResponse
-from openai.util import ApiType
-
-MAX_TIMEOUT = 20
-
-
-class EngineAPIResource(APIResource):
-    engine_required = True
-    plain_old_data = False
-
-    def __init__(self, engine: Optional[str] = None, **kwargs):
-        super().__init__(engine=engine, **kwargs)
-
-    @classmethod
-    def class_url(
-        cls,
-        engine: Optional[str] = None,
-        api_type: Optional[str] = None,
-        api_version: Optional[str] = None,
-    ):
-        # Namespaces are separated in object names with periods (.) and in URLs
-        # with forward slashes (/), so replace the former with the latter.
-        base = cls.OBJECT_NAME.replace(".", "/")  # type: ignore
-        typed_api_type, api_version = cls._get_api_type_and_version(api_type, api_version)
-
-        if typed_api_type == ApiType.AZURE:
-            if not api_version:
-                raise error.InvalidRequestError(
-                    "An API version is required for the Azure API type."
-                )
-            if engine is None:
-                raise error.InvalidRequestError(
-                    "You must provide the deployment name in the 'engine' parameter to access the Azure OpenAI service"
-                )
-            extn = quote_plus(engine)
-            return "/%s/%s/%s/%s?api-version=%s" % (
-                cls.azure_api_prefix,
-                cls.azure_deployments_prefix,
-                extn,
-                base,
-                api_version
-            )
-
-        elif typed_api_type == ApiType.OPEN_AI:
-            if engine is None:
-                return "/%s" % (base)
-
-            extn = quote_plus(engine)
-            return "/engines/%s/%s" % (extn, base)
-
-        else:
-            raise error.InvalidAPIType("Unsupported API type %s" % api_type)
-
-    @classmethod
-    def create(
-        cls,
-        api_key=None,
-        api_base=None,
-        api_type=None,
-        request_id=None,
-        api_version=None,
-        organization=None,
-        **params,
-    ):
-        engine = params.pop("engine", None)
-        timeout = params.pop("timeout", None)
-        stream = params.get("stream", False)
-        headers = params.pop("headers", None)
-        if engine is None and cls.engine_required:
-            raise error.InvalidRequestError(
-                "Must provide an 'engine' parameter to create a %s" % cls, "engine"
-            )
-
-        if timeout is None:
-            # No special timeout handling
-            pass
-        elif timeout > 0:
-            # API only supports timeouts up to MAX_TIMEOUT
-            params["timeout"] = min(timeout, MAX_TIMEOUT)
-            timeout = (timeout - params["timeout"]) or None
-        elif timeout == 0:
-            params["timeout"] = MAX_TIMEOUT
-
-        requestor = api_requestor.APIRequestor(
-            api_key,
-            api_base=api_base,
-            api_type=api_type,
-            api_version=api_version,
-            organization=organization,
-        )
-        url = cls.class_url(engine, api_type, api_version)
-        response, _, api_key = requestor.request(
-            "post",
-            url,
-            params=params,
-            headers=headers,
-            stream=stream,
-            request_id=request_id,
-        )
-
-        if stream:
-            assert not isinstance(response, OpenAIResponse)  # must be an iterator
-            return (
-                util.convert_to_openai_object(
-                    line,
-                    api_key,
-                    api_version,
-                    organization,
-                    engine=engine,
-                    plain_old_data=cls.plain_old_data,
-                )
-                for line in response
-            )
-        else:
-            obj = util.convert_to_openai_object(
-                response,
-                api_key,
-                api_version,
-                organization,
-                engine=engine,
-                plain_old_data=cls.plain_old_data,
-            )
-
-            if timeout is not None:
-                obj.wait(timeout=timeout or None)
-
-        return obj
-
-    def instance_url(self):
-        id = self.get("id")
-
-        if not isinstance(id, str):
-            raise error.InvalidRequestError(
-                f"Could not determine which URL to request: {type(self).__name__} instance has invalid ID: {id}, {type(id)}. ID should be of type str.",
-                "id",
-            )
-
-        extn = quote_plus(id)
-        params_connector = '?'
-
-        if self.typed_api_type == ApiType.AZURE:
-            api_version = self.api_version or openai.api_version
-            if not api_version:
-                raise error.InvalidRequestError(
-                    "An API version is required for the Azure API type."
-                )
-            base = self.OBJECT_NAME.replace(".", "/")
-            url = "/%s/%s/%s/%s/%s?api-version=%s" % (
-                self.azure_api_prefix,
-                self.azure_deployments_prefix,
-                self.engine,
-                base,
-                extn,
-                api_version
-            )
-            params_connector = '&'
-
-
-        elif self.typed_api_type == ApiType.OPEN_AI:
-            base = self.class_url(self.engine, self.api_type, self.api_version)
-            url = "%s/%s" % (base, extn)
-
-        else:
-            raise error.InvalidAPIType("Unsupported API type %s" % self.api_type)
-
-        timeout = self.get("timeout")
-        if timeout is not None:
-            timeout = quote_plus(str(timeout))
-            url += params_connector + "timeout={}".format(timeout)
-        return url
-
-    def wait(self, timeout=None):
-        start = time.time()
-        while self.status != "complete":
-            self.timeout = (
-                min(timeout + start - time.time(), MAX_TIMEOUT)
-                if timeout is not None
-                else MAX_TIMEOUT
-            )
-            if self.timeout < 0:
-                del self.timeout
-                break
-            self.refresh()
-        return self

spaces/CVPR/LIVE/LIVE/README.md

@@ -1,44 +0,0 @@
-# LIVE-pytorch
-Towards Layer-wise Image Vectorization
-
-### Updated for rebuttal (Jan/28/2022)： 
-#### User study
-We create a [user study](https://wj.qq.com/s2/9665341/19ed) as suggested. A more complex user study will be added in the revised version.
-
-The results are collected here: [user study details](user_study_state.csv)
-
-#### Code installation
-
-we added  detailed [conda env file](env.yml) and collected detail [system information](system_info.txt) to help the installation.
-
-A more detailed docker and Google Colab demo will be provided.
-
-
-<div align="center">
-  <img src="example.png" width="650px" height="300px">
-</div>
-LIVE is able to explicitly presents a Layer-wise representation for simple images. 
-
-## Installation
-```bash
-pip3 install torch torchvision
-pip install svgwrite
-pip install svgpathtools
-pip install cssutils
-pip install numba
-pip install torch-tools
-pip install visdom
-pip install scikit-fmm
-pip install opencv-python==4.5.4.60 
-pip install easydict
-pip install scikit-fmm
-
-```
-Next, please refer DiffVG to install [pydiffvg](https://github.com/BachiLi/diffvg)
-
-
-## Run
-```bash
-python main.py --config config/all.yaml --experiment experiment_8x1 --signature demo1 --target data/demo1.png
-```
-Please modify the config files to change configurations.

spaces/CVPR/LIVE/pybind11/tests/test_smart_ptr.cpp

@@ -1,369 +0,0 @@
-/*
-    tests/test_smart_ptr.cpp -- binding classes with custom reference counting,
-    implicit conversions between types
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#if defined(_MSC_VER) && _MSC_VER < 1910
-#  pragma warning(disable: 4702) // unreachable code in system header
-#endif
-
-#include "pybind11_tests.h"
-#include "object.h"
-
-// Make pybind aware of the ref-counted wrapper type (s):
-
-// ref<T> is a wrapper for 'Object' which uses intrusive reference counting
-// It is always possible to construct a ref<T> from an Object* pointer without
-// possible inconsistencies, hence the 'true' argument at the end.
-PYBIND11_DECLARE_HOLDER_TYPE(T, ref<T>, true);
-// Make pybind11 aware of the non-standard getter member function
-namespace pybind11 { namespace detail {
-    template <typename T>
-    struct holder_helper<ref<T>> {
-        static const T *get(const ref<T> &p) { return p.get_ptr(); }
-    };
-}}
-
-// The following is not required anymore for std::shared_ptr, but it should compile without error:
-PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>);
-
-// This is just a wrapper around unique_ptr, but with extra fields to deliberately bloat up the
-// holder size to trigger the non-simple-layout internal instance layout for single inheritance with
-// large holder type:
-template <typename T> class huge_unique_ptr {
-    std::unique_ptr<T> ptr;
-    uint64_t padding[10];
-public:
-    huge_unique_ptr(T *p) : ptr(p) {};
-    T *get() { return ptr.get(); }
-};
-PYBIND11_DECLARE_HOLDER_TYPE(T, huge_unique_ptr<T>);
-
-// Simple custom holder that works like unique_ptr
-template <typename T>
-class custom_unique_ptr {
-    std::unique_ptr<T> impl;
-public:
-    custom_unique_ptr(T* p) : impl(p) { }
-    T* get() const { return impl.get(); }
-    T* release_ptr() { return impl.release(); }
-};
-PYBIND11_DECLARE_HOLDER_TYPE(T, custom_unique_ptr<T>);
-
-// Simple custom holder that works like shared_ptr and has operator& overload
-// To obtain address of an instance of this holder pybind should use std::addressof
-// Attempt to get address via operator& may leads to segmentation fault
-template <typename T>
-class shared_ptr_with_addressof_operator {
-    std::shared_ptr<T> impl;
-public:
-    shared_ptr_with_addressof_operator( ) = default;
-    shared_ptr_with_addressof_operator(T* p) : impl(p) { }
-    T* get() const { return impl.get(); }
-    T** operator&() { throw std::logic_error("Call of overloaded operator& is not expected"); }
-};
-PYBIND11_DECLARE_HOLDER_TYPE(T, shared_ptr_with_addressof_operator<T>);
-
-// Simple custom holder that works like unique_ptr and has operator& overload
-// To obtain address of an instance of this holder pybind should use std::addressof
-// Attempt to get address via operator& may leads to segmentation fault
-template <typename T>
-class unique_ptr_with_addressof_operator {
-    std::unique_ptr<T> impl;
-public:
-    unique_ptr_with_addressof_operator() = default;
-    unique_ptr_with_addressof_operator(T* p) : impl(p) { }
-    T* get() const { return impl.get(); }
-    T* release_ptr() { return impl.release(); }
-    T** operator&() { throw std::logic_error("Call of overloaded operator& is not expected"); }
-};
-PYBIND11_DECLARE_HOLDER_TYPE(T, unique_ptr_with_addressof_operator<T>);
-
-
-TEST_SUBMODULE(smart_ptr, m) {
-
-    // test_smart_ptr
-
-    // Object implementation in `object.h`
-    py::class_<Object, ref<Object>> obj(m, "Object");
-    obj.def("getRefCount", &Object::getRefCount);
-
-    // Custom object with builtin reference counting (see 'object.h' for the implementation)
-    class MyObject1 : public Object {
-    public:
-        MyObject1(int value) : value(value) { print_created(this, toString()); }
-        std::string toString() const { return "MyObject1[" + std::to_string(value) + "]"; }
-    protected:
-        virtual ~MyObject1() { print_destroyed(this); }
-    private:
-        int value;
-    };
-    py::class_<MyObject1, ref<MyObject1>>(m, "MyObject1", obj)
-        .def(py::init<int>());
-    py::implicitly_convertible<py::int_, MyObject1>();
-
-    m.def("make_object_1", []() -> Object * { return new MyObject1(1); });
-    m.def("make_object_2", []() -> ref<Object> { return new MyObject1(2); });
-    m.def("make_myobject1_1", []() -> MyObject1 * { return new MyObject1(4); });
-    m.def("make_myobject1_2", []() -> ref<MyObject1> { return new MyObject1(5); });
-    m.def("print_object_1", [](const Object *obj) { py::print(obj->toString()); });
-    m.def("print_object_2", [](ref<Object> obj) { py::print(obj->toString()); });
-    m.def("print_object_3", [](const ref<Object> &obj) { py::print(obj->toString()); });
-    m.def("print_object_4", [](const ref<Object> *obj) { py::print((*obj)->toString()); });
-    m.def("print_myobject1_1", [](const MyObject1 *obj) { py::print(obj->toString()); });
-    m.def("print_myobject1_2", [](ref<MyObject1> obj) { py::print(obj->toString()); });
-    m.def("print_myobject1_3", [](const ref<MyObject1> &obj) { py::print(obj->toString()); });
-    m.def("print_myobject1_4", [](const ref<MyObject1> *obj) { py::print((*obj)->toString()); });
-
-    // Expose constructor stats for the ref type
-    m.def("cstats_ref", &ConstructorStats::get<ref_tag>);
-
-
-    // Object managed by a std::shared_ptr<>
-    class MyObject2 {
-    public:
-        MyObject2(const MyObject2 &) = default;
-        MyObject2(int value) : value(value) { print_created(this, toString()); }
-        std::string toString() const { return "MyObject2[" + std::to_string(value) + "]"; }
-        virtual ~MyObject2() { print_destroyed(this); }
-    private:
-        int value;
-    };
-    py::class_<MyObject2, std::shared_ptr<MyObject2>>(m, "MyObject2")
-        .def(py::init<int>());
-    m.def("make_myobject2_1", []() { return new MyObject2(6); });
-    m.def("make_myobject2_2", []() { return std::make_shared<MyObject2>(7); });
-    m.def("print_myobject2_1", [](const MyObject2 *obj) { py::print(obj->toString()); });
-    m.def("print_myobject2_2", [](std::shared_ptr<MyObject2> obj) { py::print(obj->toString()); });
-    m.def("print_myobject2_3", [](const std::shared_ptr<MyObject2> &obj) { py::print(obj->toString()); });
-    m.def("print_myobject2_4", [](const std::shared_ptr<MyObject2> *obj) { py::print((*obj)->toString()); });
-
-    // Object managed by a std::shared_ptr<>, additionally derives from std::enable_shared_from_this<>
-    class MyObject3 : public std::enable_shared_from_this<MyObject3> {
-    public:
-        MyObject3(const MyObject3 &) = default;
-        MyObject3(int value) : value(value) { print_created(this, toString()); }
-        std::string toString() const { return "MyObject3[" + std::to_string(value) + "]"; }
-        virtual ~MyObject3() { print_destroyed(this); }
-    private:
-        int value;
-    };
-    py::class_<MyObject3, std::shared_ptr<MyObject3>>(m, "MyObject3")
-        .def(py::init<int>());
-    m.def("make_myobject3_1", []() { return new MyObject3(8); });
-    m.def("make_myobject3_2", []() { return std::make_shared<MyObject3>(9); });
-    m.def("print_myobject3_1", [](const MyObject3 *obj) { py::print(obj->toString()); });
-    m.def("print_myobject3_2", [](std::shared_ptr<MyObject3> obj) { py::print(obj->toString()); });
-    m.def("print_myobject3_3", [](const std::shared_ptr<MyObject3> &obj) { py::print(obj->toString()); });
-    m.def("print_myobject3_4", [](const std::shared_ptr<MyObject3> *obj) { py::print((*obj)->toString()); });
-
-    // test_smart_ptr_refcounting
-    m.def("test_object1_refcounting", []() {
-        ref<MyObject1> o = new MyObject1(0);
-        bool good = o->getRefCount() == 1;
-        py::object o2 = py::cast(o, py::return_value_policy::reference);
-        // always request (partial) ownership for objects with intrusive
-        // reference counting even when using the 'reference' RVP
-        good &= o->getRefCount() == 2;
-        return good;
-    });
-
-    // test_unique_nodelete
-    // Object with a private destructor
-    class MyObject4 {
-    public:
-        MyObject4(int value) : value{value} { print_created(this); }
-        int value;
-    private:
-        ~MyObject4() { print_destroyed(this); }
-    };
-    py::class_<MyObject4, std::unique_ptr<MyObject4, py::nodelete>>(m, "MyObject4")
-        .def(py::init<int>())
-        .def_readwrite("value", &MyObject4::value);
-
-    // test_unique_deleter
-    // Object with std::unique_ptr<T, D> where D is not matching the base class
-    // Object with a protected destructor
-    class MyObject4a {
-    public:
-        MyObject4a(int i) {
-            value = i;
-            print_created(this);
-        };
-        int value;
-    protected:
-        virtual ~MyObject4a() { print_destroyed(this); }
-    };
-    py::class_<MyObject4a, std::unique_ptr<MyObject4a, py::nodelete>>(m, "MyObject4a")
-        .def(py::init<int>())
-        .def_readwrite("value", &MyObject4a::value);
-
-    // Object derived but with public destructor and no Deleter in default holder
-    class MyObject4b : public MyObject4a {
-    public:
-        MyObject4b(int i) : MyObject4a(i) { print_created(this); }
-        ~MyObject4b() { print_destroyed(this); }
-    };
-    py::class_<MyObject4b, MyObject4a>(m, "MyObject4b")
-        .def(py::init<int>());
-
-    // test_large_holder
-    class MyObject5 { // managed by huge_unique_ptr
-    public:
-        MyObject5(int value) : value{value} { print_created(this); }
-        ~MyObject5() { print_destroyed(this); }
-        int value;
-    };
-    py::class_<MyObject5, huge_unique_ptr<MyObject5>>(m, "MyObject5")
-        .def(py::init<int>())
-        .def_readwrite("value", &MyObject5::value);
-
-    // test_shared_ptr_and_references
-    struct SharedPtrRef {
-        struct A {
-            A() { print_created(this); }
-            A(const A &) { print_copy_created(this); }
-            A(A &&) { print_move_created(this); }
-            ~A() { print_destroyed(this); }
-        };
-
-        A value = {};
-        std::shared_ptr<A> shared = std::make_shared<A>();
-    };
-    using A = SharedPtrRef::A;
-    py::class_<A, std::shared_ptr<A>>(m, "A");
-    py::class_<SharedPtrRef>(m, "SharedPtrRef")
-        .def(py::init<>())
-        .def_readonly("ref", &SharedPtrRef::value)
-        .def_property_readonly("copy", [](const SharedPtrRef &s) { return s.value; },
-                               py::return_value_policy::copy)
-        .def_readonly("holder_ref", &SharedPtrRef::shared)
-        .def_property_readonly("holder_copy", [](const SharedPtrRef &s) { return s.shared; },
-                               py::return_value_policy::copy)
-        .def("set_ref", [](SharedPtrRef &, const A &) { return true; })
-        .def("set_holder", [](SharedPtrRef &, std::shared_ptr<A>) { return true; });
-
-    // test_shared_ptr_from_this_and_references
-    struct SharedFromThisRef {
-        struct B : std::enable_shared_from_this<B> {
-            B() { print_created(this); }
-            B(const B &) : std::enable_shared_from_this<B>() { print_copy_created(this); }
-            B(B &&) : std::enable_shared_from_this<B>() { print_move_created(this); }
-            ~B() { print_destroyed(this); }
-        };
-
-        B value = {};
-        std::shared_ptr<B> shared = std::make_shared<B>();
-    };
-    using B = SharedFromThisRef::B;
-    py::class_<B, std::shared_ptr<B>>(m, "B");
-    py::class_<SharedFromThisRef>(m, "SharedFromThisRef")
-        .def(py::init<>())
-        .def_readonly("bad_wp", &SharedFromThisRef::value)
-        .def_property_readonly("ref", [](const SharedFromThisRef &s) -> const B & { return *s.shared; })
-        .def_property_readonly("copy", [](const SharedFromThisRef &s) { return s.value; },
-                               py::return_value_policy::copy)
-        .def_readonly("holder_ref", &SharedFromThisRef::shared)
-        .def_property_readonly("holder_copy", [](const SharedFromThisRef &s) { return s.shared; },
-                               py::return_value_policy::copy)
-        .def("set_ref", [](SharedFromThisRef &, const B &) { return true; })
-        .def("set_holder", [](SharedFromThisRef &, std::shared_ptr<B>) { return true; });
-
-    // Issue #865: shared_from_this doesn't work with virtual inheritance
-    struct SharedFromThisVBase : std::enable_shared_from_this<SharedFromThisVBase> {
-        SharedFromThisVBase() = default;
-        SharedFromThisVBase(const SharedFromThisVBase &) = default;
-        virtual ~SharedFromThisVBase() = default;
-    };
-    struct SharedFromThisVirt : virtual SharedFromThisVBase {};
-    static std::shared_ptr<SharedFromThisVirt> sft(new SharedFromThisVirt());
-    py::class_<SharedFromThisVirt, std::shared_ptr<SharedFromThisVirt>>(m, "SharedFromThisVirt")
-        .def_static("get", []() { return sft.get(); });
-
-    // test_move_only_holder
-    struct C {
-        C() { print_created(this); }
-        ~C() { print_destroyed(this); }
-    };
-    py::class_<C, custom_unique_ptr<C>>(m, "TypeWithMoveOnlyHolder")
-        .def_static("make", []() { return custom_unique_ptr<C>(new C); })
-        .def_static("make_as_object", []() { return py::cast(custom_unique_ptr<C>(new C)); });
-
-    // test_holder_with_addressof_operator
-    struct TypeForHolderWithAddressOf {
-        TypeForHolderWithAddressOf() { print_created(this); }
-        TypeForHolderWithAddressOf(const TypeForHolderWithAddressOf &) { print_copy_created(this); }
-        TypeForHolderWithAddressOf(TypeForHolderWithAddressOf &&) { print_move_created(this); }
-        ~TypeForHolderWithAddressOf() { print_destroyed(this); }
-        std::string toString() const {
-            return "TypeForHolderWithAddressOf[" + std::to_string(value) + "]";
-        }
-        int value = 42;
-    };
-    using HolderWithAddressOf = shared_ptr_with_addressof_operator<TypeForHolderWithAddressOf>;
-    py::class_<TypeForHolderWithAddressOf, HolderWithAddressOf>(m, "TypeForHolderWithAddressOf")
-        .def_static("make", []() { return HolderWithAddressOf(new TypeForHolderWithAddressOf); })
-        .def("get", [](const HolderWithAddressOf &self) { return self.get(); })
-        .def("print_object_1", [](const TypeForHolderWithAddressOf *obj) { py::print(obj->toString()); })
-        .def("print_object_2", [](HolderWithAddressOf obj) { py::print(obj.get()->toString()); })
-        .def("print_object_3", [](const HolderWithAddressOf &obj) { py::print(obj.get()->toString()); })
-        .def("print_object_4", [](const HolderWithAddressOf *obj) { py::print((*obj).get()->toString()); });
-
-    // test_move_only_holder_with_addressof_operator
-    struct TypeForMoveOnlyHolderWithAddressOf {
-        TypeForMoveOnlyHolderWithAddressOf(int value) : value{value} { print_created(this); }
-        ~TypeForMoveOnlyHolderWithAddressOf() { print_destroyed(this); }
-        std::string toString() const {
-            return "MoveOnlyHolderWithAddressOf[" + std::to_string(value) + "]";
-        }
-        int value;
-    };
-    using MoveOnlyHolderWithAddressOf = unique_ptr_with_addressof_operator<TypeForMoveOnlyHolderWithAddressOf>;
-    py::class_<TypeForMoveOnlyHolderWithAddressOf, MoveOnlyHolderWithAddressOf>(m, "TypeForMoveOnlyHolderWithAddressOf")
-        .def_static("make", []() { return MoveOnlyHolderWithAddressOf(new TypeForMoveOnlyHolderWithAddressOf(0)); })
-        .def_readwrite("value", &TypeForMoveOnlyHolderWithAddressOf::value)
-        .def("print_object", [](const TypeForMoveOnlyHolderWithAddressOf *obj) { py::print(obj->toString()); });
-
-    // test_smart_ptr_from_default
-    struct HeldByDefaultHolder { };
-    py::class_<HeldByDefaultHolder>(m, "HeldByDefaultHolder")
-        .def(py::init<>())
-        .def_static("load_shared_ptr", [](std::shared_ptr<HeldByDefaultHolder>) {});
-
-    // test_shared_ptr_gc
-    // #187: issue involving std::shared_ptr<> return value policy & garbage collection
-    struct ElementBase {
-        virtual ~ElementBase() { } /* Force creation of virtual table */
-        ElementBase() = default;
-        ElementBase(const ElementBase&) = delete;
-    };
-    py::class_<ElementBase, std::shared_ptr<ElementBase>>(m, "ElementBase");
-
-    struct ElementA : ElementBase {
-        ElementA(int v) : v(v) { }
-        int value() { return v; }
-        int v;
-    };
-    py::class_<ElementA, ElementBase, std::shared_ptr<ElementA>>(m, "ElementA")
-        .def(py::init<int>())
-        .def("value", &ElementA::value);
-
-    struct ElementList {
-        void add(std::shared_ptr<ElementBase> e) { l.push_back(e); }
-        std::vector<std::shared_ptr<ElementBase>> l;
-    };
-    py::class_<ElementList, std::shared_ptr<ElementList>>(m, "ElementList")
-        .def(py::init<>())
-        .def("add", &ElementList::add)
-        .def("get", [](ElementList &el) {
-            py::list list;
-            for (auto &e : el.l)
-                list.append(py::cast(e));
-            return list;
-        });
-}

spaces/CVPR/LIVE/thrust/internal/rename_cub_namespace.sh

@@ -1,7 +0,0 @@
-#! /bin/bash
-
-# Run this in //sw/gpgpu/thrust/thrust/system/cuda/detail/cub to add a THRUST_
-# prefix to CUB's namespace macro.
-
-sed -i -e 's/CUB_NS_P/THRUST_CUB_NS_P/g' `find . -type f`
-

spaces/CVPR/LIVE/thrust/thrust/cmake/README.md

@@ -1,215 +0,0 @@
-# Using Thrust with CMake
-
-Thrust provides configuration files that simplify using Thrust
-from other CMake projects. Requirements:
-
-- Thrust >= 1.9.10
-- CMake >= 3.15
-
-See the [Fixing Legacy FindThrust.cmake](#fixing-legacy-findthrustcmake)
-section for solutions that work on older Thrust versions.
-
-## User Guide
-
-#### Default Configuration (CUDA)
-
-Thrust is configured using a `thrust_create_target` CMake function that
-assembles a complete interface to the Thrust library:
-
-```cmake
-find_package(Thrust REQUIRED CONFIG)
-thrust_create_target(Thrust)
-target_link_libraries(MyProgram Thrust)
-```
-
-The first argument is the name of the interface target to create, and any
-additional options will be used to configure the target. By default,
-`thrust_create_target` will configure its result to use CUDA acceleration.
-
-If desired, `thrust_create_target` may be called multiple times to build
-several unique Thrust interface targets with different configurations, as
-detailed below.
-
-**Note:** If CMake is unable to locate Thrust, specify the path to Thrust's CMake
-configuration directory (where this README file is located) as `Thrust_DIR`,
-e.g.:
-
-```
-$ cmake . -DThrust_DIR=/usr/local/cuda/include/thrust/cmake/
-```
-
-#### TBB / OpenMP
-
-To explicitly specify host/device systems, `HOST` and `DEVICE` arguments can be
-passed to `thrust_create_target`. If an explicit system is not specified, the
-target will default to using CPP for host and/or CUDA for device.
-
-```cmake
-thrust_create_target(ThrustTBB DEVICE TBB)
-thrust_create_target(ThrustOMP HOST CPP DEVICE OMP)
-```
-
-will create targets `ThrustTBB` and `ThrustOMP`. Both will use the serial `CPP`
-host system, but will find and use TBB or OpenMP for the device system.
-
-#### Configure Target from Cache Options
-
-To allow a Thrust target to be configurable easily via `cmake-gui` or
-`ccmake`, pass the `FROM_OPTIONS` flag to `thrust_create_target`. This will add
-`THRUST_HOST_SYSTEM` and `THRUST_DEVICE_SYSTEM` options to the CMake cache that
-allow selection from the systems supported by this version of Thrust.
-
-```cmake
-thrust_create_target(Thrust FROM_OPTIONS
-  [HOST_OPTION <option name>]
-  [DEVICE_OPTION <option name>]
-  [HOST_OPTION_DOC <doc string>]
-  [DEVICE_OPTION_DOC <doc string>]
-  [HOST <default host system name>]
-  [DEVICE <default device system name>]
-  [ADVANCED]
-)
-```
-
-The optional arguments have sensible defaults, but may be configured per
-`thrust_create_target` call:
-
-| Argument            | Default                 | Description                     |
-|---------------------|-------------------------|---------------------------------|
-| `HOST_OPTION`       | `THRUST_HOST_SYSTEM`    | Name of cache option for host   |
-| `DEVICE_OPTION`     | `THRUST_DEVICE_SYSTEM`  | Name of cache option for device |
-| `HOST_OPTION_DOC`   | Thrust's host system.   | Docstring for host option       |
-| `DEVICE_OPTION_DOC` | Thrust's device system. | Docstring for device option     |
-| `HOST`              | `CPP`                   | Default host system             |
-| `DEVICE`            | `CUDA`                  | Default device system           |
-| `ADVANCED`          | *N/A*                   | Mark cache options advanced     |
-
-### Specifying Thrust Version Requirements
-
-A specific version of Thrust may be required in the `find_package` call:
-
-```cmake
-find_package(Thrust 1.9.10)
-```
-
-will only consider Thrust installations with version `1.9.10.X`. An exact match
-down to the patch version can be forced by using `EXACT` matching:
-
-```cmake
-find_package(Thrust 1.9.10.1 EXACT)
-```
-
-would only match the 1.9.10.1 release.
-
-#### Using a Specific TBB or OpenMP Environment
-
-When `thrust_create_target` is called, it will lazily load the requested
-systems on-demand through internal `find_package` calls. If a project already
-uses TBB or OpenMP, it may specify a CMake target for Thrust to share instead:
-
-```cmake
-thrust_set_TBB_target(MyTBBTarget)
-thrust_set_OMP_target(MyOMPTarget)
-```
-
-These functions must be called **before** `thrust_create_target`, and will
-have no effect if the dependency is loaded as a
-`find_package(Thrust COMPONENT [...])` component.
-
-#### Testing for Systems
-
-The following functions check if a system has been found, either by lazy loading
-through `thrust_create_target` or as a `find_package` `COMPONENT` /
-`OPTIONAL_COMPONENT`:
-
-```cmake
-# Set var_name to TRUE or FALSE if an individual system has been found:
-thrust_is_cuda_system_found(<var_name>)
-thrust_is_cpp_system_found(<var_name>)
-thrust_is_tbb_system_found(<var_name>)
-thrust_is_omp_system_found(<var_name>)
-
-# Generic version that takes a component name from CUDA, CPP, TBB, OMP:
-thrust_is_system_found(<component_name> <var_name>)
-
-# Defines `THRUST_*_FOUND` variables in the current scope that reflect the
-# state of all known systems. Can be used to refresh these flags after
-# lazy system loading.
-thrust_update_system_found_flags()
-```
-
-#### Debugging
-
-Thrust will produce a detailed log describing its targets, cache options, and
-interfaces when `--log-level=VERBOSE` is passed to CMake 3.15.7 or newer:
-
-```
-$ cmake . --log-level=VERBOSE
-```
-
-This can be handy for inspecting interface and dependency information.
-
-## Fixing Legacy FindThrust.cmake
-
-A community-created `FindThrust.cmake` module exists and is necessary to find
-Thrust installations prior to Thrust 1.9.10. Its usage is discouraged whenever
-possible and the config files in this directory should be strongly preferred.
-However, projects that need to support old versions of Thrust may still need to
-use the legacy `FindThrust.cmake` with pre-1.9.10 installations.
-
-One popular flavor of this find module has a version parsing bug. Projects that
-rely on `FindThrust.cmake` should check for this and patch their copies as
-follows.
-
-Replace:
-
-```cmake
-string( REGEX MATCH "^[0-9]" major ${version} )
-string( REGEX REPLACE "^${major}00" "" version "${version}" )
-string( REGEX MATCH "^[0-9]" minor ${version} )
-string( REGEX REPLACE "^${minor}0" "" version "${version}" )
-```
-
-with:
-
-```cmake
-math(EXPR major "${version} / 100000")
-math(EXPR minor "(${version} / 100) % 1000")
-math(EXPR version "${version} % 100")
-```
-
-# Thrust Developer Documentation
-
-This portion of the file contains descriptions of Thrust's internal CMake target
-structure for Thrust developers. It should not be necessary for users
-who just want to use Thrust from their projects.
-
-## Internal Targets
-
-By default, `find_package(Thrust)` will only create a single `Thrust::Thrust`
-target that describes where the actual Thrust headers are located. It does not
-locate or create configurations for any dependencies; these are lazily loaded
-on-demand by calls to `create_thrust_target`, or when explicitly requested via
-`find_package`'s component mechanism.
-
-As mentioned, the basic Thrust interface is described by the `Thrust::Thrust`
-target.
-
-Each backend system (`CPP`, `CUDA`, `TBB`, `OMP`) is described by multiple
-targets:
-
-- `Thrust::${system}`
-  - Specifies an interface configured to build against all
-    dependencies for this backend (including `Thrust::Thrust`).
-  - For example, the `Thrust::CUDA` target is an interface
-    target that combines the interfaces of both Thrust and CUB.
-- `Thrust::${system}::Host`
-  - Configures an interface for using a specific host system.
-  - Multiple `::Host` targets cannot be combined in the same library/executable.
-    Attempting to do so will produce a CMake configuration error.
-  - Only defined for systems that support being used as the host.
-- `Thrust::${system}::Device`
-  - Configures an interface for using a specific device system.
-  - Multiple `::Device` targets cannot be combined in the same library/executable.
-    Attempting to do so will produce a CMake configuration error.
-  - Only defined for systems that support being used as the device.