Update parquet files (step 31 of 249)

spaces/1acneusushi/gradio-2dmoleculeeditor/Avatar Friday Patcher V1.1 __HOT__.md

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-## Avatar Friday Patcher V1.1
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-# Avatar Friday Patcher v1.1: How to Fix Common Issues and Enjoy the Game
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-If you are a fan of the Avatar franchise, you might have been eagerly waiting for the release of Avatar Friday, the new open-world RPG game based on the popular movie and TV series. However, some players have reported experiencing various issues with the game, such as crashes, glitches, low FPS, and missing features. Fortunately, there is a solution: Avatar Friday Patcher v1.1.
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-Avatar Friday Patcher v1.1 is a fan-made mod that aims to improve the performance and stability of the game, as well as add some missing features and enhancements. The patcher is easy to use and compatible with most versions of the game. Here are some of the benefits of using Avatar Friday Patcher v1.1:
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-- Fixes crashes and freezes that occur randomly or at certain points in the game.
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-- Optimizes the graphics settings and reduces the CPU and GPU load, resulting in higher FPS and smoother gameplay.
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-- Enables full-screen mode and custom resolutions, allowing you to play the game in your preferred display settings.
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-- Adds missing features such as subtitles, controller support, achievements, and cloud saves.
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-- Enhances the game's visuals and audio quality, making the world of Pandora more immersive and realistic.
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-- Fixes bugs and glitches that affect the gameplay, such as broken quests, missing items, clipping issues, and more.
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-- Explore new areas and locations that were not included in the original game, such as the Floating Mountains, the Tree of Souls, and the Hallelujah Mountains.
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-- Customize your avatar's appearance and skills, choosing from different races, genders, hairstyles, outfits, weapons, and abilities.
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-- Collect and craft new items and resources, such as plants, minerals, artifacts, and equipment.
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-- Ride and tame various creatures that inhabit Pandora, such as banshees, direhorses, thanators, and more.
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-Avatar Friday Patcher v1.1 is a mod that transforms Avatar Friday into a more complete and satisfying game. It is compatible with most of the other mods available for the game, so you can mix and match them to create your own unique experience. If you are looking for a way to enhance your Avatar Friday adventure, you should definitely give Avatar Friday Patcher v1.1 a try!
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spaces/1gistliPinn/ChatGPT4/Crystal-Cs4280-Cm-Ep-Sound-Card-Driver-FOR-WINDOWS-7181.md

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spaces/1gistliPinn/ChatGPT4/Examples/Fifa 11 World Cup Patch Update V1.rar How to Get the Most Out of Your Fifa 11 Game.md

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-<p>Then, from 21st November to 18th December, a new "live" World Cup mode will be updated during the group and knockout stages, letting you play a single-player tournament along with real-world fixtures and squads for each game. You can play any past game to rewrite history and better real-world England's inevitable disappointing result.</p>
-<p>EA Game has released FIFA 23 patch 1.04 details for PC, PS4, and Xbox One. According to the official <strong>Fifa 23 patch notes</strong>, the latest update added the <strong>FIFA World Cup 2022</strong> to the game. Apart from this, Fifa 23 update 1.04 also includes stability fixes.</p>
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spaces/1phancelerku/anime-remove-background/Blue 3 ft. Radio Weasel - Where You Are - Free MP3 and Lyrics Download.md

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-<h1>How to Download Blue 3's "Where You Are" as an MP3 File</h1>
-<p>If you love Ugandan music, you probably know Blue 3, the girl group that rose to fame in 2005 after winning a talent show. And you probably know their hit song "Where You Are", which features Radio and Weasel, another popular duo in the Ugandan music scene.</p>
-<p>"Where You Are" is a catchy and romantic song that blends Afrobeat, R&B, and dancehall genres. It has over 20,000 views on YouTube and has been praised by critics and fans alike.</p>
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-<p>But what if you want to listen to this song offline, without any interruptions or ads? What if you want to save some storage space on your device and still enjoy the high-quality sound of this song?</p>
-<p>The answer is simple: download "Where You Are" as an MP3 file.</p>
-<p>In this article, we'll show you how to do that in a few easy steps. We'll also give you some alternative sources where you can download this song as an MP3 file.</p>
-<p>So let's get started!</p>
- <h2>What is Blue 3 and "Where You Are"?</h2>
-<p>Blue 3 was a Ugandan girl group that consisted of Jackie Chandiru, Lillian Mbabazi, and Cindy Sanyu. They formed in 2005 after winning a talent show called Coca-Cola Popstars.</p>
-<p>The group released their debut album "Hitaji" in 2006, which featured songs like "Burrn", "Ndayila", and "Hitaji". They also collaborated with other Ugandan artists like Bebe Cool, Jose Chameleone, and Bobi Wine.</p>
-<p>"Where You Are" was one of their most successful songs, released in 2008. It featured Radio and Weasel, who were part of the Goodlyfe Crew at the time. The song was a love ballad that expressed the desire to be with someone no matter where they are.</p>
-<p>The song was well-received by both fans and critics, who praised its catchy melody, smooth vocals, and sweet lyrics. It also won several awards, including Song of the Year at the Pearl of Africa Music Awards in 2008.</p>
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- <h2>Why Download "Where You Are" as an MP3 File?</h2>
-<p>Downloading "Where You Are" as an MP3 file has many advantages over streaming it online or playing it from a CD. Here are some of them:</p>
-<ul>
-<li>You can listen to it offline, without any internet connection or data charges.</li>
-<li>You can avoid any ads or interruptions that may ruin your listening experience.</li>
-<li>You can save some storage space on your device, as MP3 files are smaller than video or audio files.</li>
-<li>You can transfer it to any device that supports MP3 playback, such as your smartphone, tablet, laptop, or MP3 player.</li>
-<li>You can enjoy the high-quality sound of the song, as MP3 files preserve the original audio quality.</li>
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-<p>As you can see, downloading "Where You Are" as an MP3 file is a smart and convenient way to enjoy this song anytime, anywhere.</p>
- <h2>How to Download "Where You Are" as an MP3 File from YouTube</h2>
-<p>One of the easiest ways to download "Where You Are" as an MP3 file is to use YouTube, where you can find the official video of the song. Here are the steps you need to follow:</p>
-<ol>
-<li>Go to YouTube and search for "Blue 3 Where You Are".</li>
-<li>Select the video that has the title "Blue 3 ft Radio & Weasel - Where You Are (Official Video)" and has over 20,000 views. This is the official video of the song.</li>
-<li>Copy the URL of the video from the address bar of your browser.</li>
-<li>Go to a website that can convert YouTube videos into MP3 files, such as ytmp3.cc, y2mate.com, or onlinevideoconverter.com.</li>
-<li>Paste the URL of the video into the input box of the website and click on "Convert" or "Download".</li>
-<li>Wait for a few seconds until the conversion is done and then click on "Download" or "Save" to save the MP3 file to your device.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded "Where You Are" as an MP3 file from YouTube. You can now play it on your device or transfer it to another device.</p>
- <h2>How to Download "Where You Are" as an MP3 File from Other Sources</h2>
-<p>If you don't want to use YouTube or you want to explore other sources where you can download "Where You Are" as an MP3 file, here are some options you can try:</p>
- <table>
-<tr>
-<th>Source</th>
-<th>How to Download</th>
-</tr>
-<tr>
-<td>SoundCloud</td>
-<td>Go to soundcloud.com and search for "Blue 3 Where You Are". Select the track that has the title "Blue 3 ft Radio & Weasel - Where You Are (Official Audio)" and has over 1,000 plays. This is the official audio of the song. Click on the "More" button below the track and then click on "Download file". Save the MP3 file to your device.</td>
-</tr>
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-<td>Spotify</td>
-<td>Go to spotify.com and sign up for a free account or log in if you already have one. Search for "Blue 3 Where You Are". Select the track that has the title "Where You Are (feat. Radio & Weasel)" and has over 10,000 streams. This is the official track of the song. Click on the "..." button next to the track and then click on "Save to Your Library". Go to your library and find the track under "Liked Songs". Click on the "..." button again and then click on "Download". Wait for the download to finish and then play the MP3 file on your device.</td>
-</tr>
-<tr>
-<td>iTunes</td>
-<td>Go to itunes.apple.com and search for "Blue 3 Where You Are". Select the track that has the title "Where You Are (feat. Radio & Weasel)" and has a price of $0.99. This is the official track of the song. Click on the "Buy" button and enter your payment details. After purchasing, go to your library and find the track under "Purchased". Click on the "Download" button and save the MP3 file to your device.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Blue 3's song "Where You Are" as an MP3 file from various sources. We have also explained why downloading this song as an MP3 file is a good idea.</p>
-<p>"Where You Are" is a beautiful song that deserves to be listened to over and over again. By downloading it as an MP3 file, you can enjoy it offline, without ads, and with high-quality sound.</p>
-<p>So what are you waiting for? Download "Where You Are" as an MP3 file today and enjoy this Ugandan masterpiece!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about downloading "Where You Are" as an MP3 file:</p>
- <h4>Q: Is it legal to download "Where You Are" as an MP3 file?</h4>
-<p>A: It depends on where you download it from and how you use it. If you download it from a source that has permission from the artists or the record label, or if you use it for personal and non-commercial purposes, then it is legal. However, if you download it from a source that does not have permission or if you use it for commercial or public purposes, then it is illegal. You should always respect the intellectual property rights of the creators and follow the terms and conditions of the source you download from.</p>
- <h4>Q: How can I play "Where You Are" as an MP3 file on my device?</h4>
-<p>A: Once you have downloaded "Where You Are" as an MP3 file, you can play it on any device that supports MP3 playback. For example, you can play it on your smartphone using the default music player app or any other app that can play MP3 files. You can also play it on your laptop or desktop computer using a program like Windows Media Player, VLC Media Player, or iTunes. You can also transfer it to an MP3 player or a USB drive and play it on any compatible device.</p>
- <h4>Q: How can I share "Where You Are" as an MP3 file with my friends?</h4>
-<p>A: If you want to share "Where You Are" as an MP3 file with your friends, you can do so in several ways. For example, you can send it to them via email, WhatsApp, Telegram, or any other messaging app. You can also upload it to a cloud service like Google Drive, Dropbox, or OneDrive and share the link with them. You can also burn it to a CD or copy it to a USB drive and give it to them physically. However, you should always make sure that you have permission from the artists or the record label before sharing their music with others.</p>
- <h4>Q: How can I support Blue 3 and their music?</h4>
-<p>A: If you love Blue 3 and their music, you can support them in various ways. For example, you can buy their albums or songs from official sources like iTunes, Spotify, or Amazon. You can also stream their music from legal platforms like YouTube, SoundCloud, or Deezer. You can also follow them on social media like Facebook, Twitter, or Instagram and show them some love and appreciation. You can also attend their concerts or events if they are available in your area. By supporting Blue 3 and their music, you are helping them to continue making amazing songs for their fans.</p>
- <h4>Q: Where can I find more information about Blue 3 and their music?</h4>
-<p>A: If you want to find more information about Blue 3 and their music, you can visit their official website at www.blue3music.com. There you can find their biography, discography, news, photos, videos, and contact details. You can also check out their Wikipedia page at https://en.wikipedia.org/wiki/Blue_3_(group) for more facts and history about them. You can also search for them on Google or any other search engine for more articles and reviews about them.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Geometry Dash Lite APK for Android 2.3 and Enjoy Rhythm-based Action Platforming!.md

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-<h1>Geometry Dash Lite: A Rhythm-Based Action Platformer for Android 2.3</h1>
-<p>If you are looking for a fun and challenging game that will test your reflexes and timing, you might want to try Geometry Dash Lite. Geometry Dash Lite is a free version of the popular game Geometry Dash, which is a rhythm-based action platformer that has millions of fans around the world. In this article, we will tell you what Geometry Dash Lite is, what features it offers, and how to download and install it on your Android device running version 2.3 or higher.</p>
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-<p>Your device must have Android version 2.3 or higher to run Geometry Dash Lite apk. You can check your device's Android version by going to Settings > About phone > Software information.</p>
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- <h4>Permissions</h4> <p>You also need to grant some permissions to Geometry Dash Lite apk to run properly on your device. The permissions are:</p>
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-<li>Modify or delete the contents of your USB storage: This allows the game to save your progress and settings on your device.</li>
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-<p>You can review and manage these permissions by going to Settings > Apps > Geometry Dash Lite > Permissions.</p>
- <h3>Steps to download and install Geometry Dash Lite apk</h3>
-<p>After you have checked the requirements, you can follow these steps to download and install Geometry Dash Lite apk on your device:</p>
- <h4>Download the apk file from a trusted source</h4>
-<p>The first step is to download the apk file of Geometry Dash Lite from a reliable and secure source. You can use your browser or a third-party app store to find and download the apk file. However, you need to be careful and avoid any malicious or fake links that might harm your device or steal your data. You can use this link to download the latest version of Geometry Dash Lite apk from APKPure, a trusted and verified app store.</p>
- <h4>Enable unknown sources in your device settings</h4>
-<p>The next step is to enable unknown sources in your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown sources and toggle it on. You might see a warning message that installing apps from unknown sources might be risky, but you can ignore it if you trust the source of the apk file.</p>
- <h4>Locate and install the apk file</h4>
-<p>The third step is to locate and install the apk file on your device. You can use a file manager app or your browser's downloads folder to find the apk file. Once you find it, tap on it and follow the instructions on the screen to install it. You might see a confirmation message that asks you if you want to install this app, just tap on Install and wait for the process to finish.</p>
- <h4>Launch and enjoy the game</h4>
-<p>The final step is to launch and enjoy the game. You can find the Geometry Dash Lite icon on your home screen or app drawer. Tap on it and start playing the game. You can adjust the settings, choose a level, customize your character, and have fun with the rhythm-based action platforming.</p>
- <h2>Conclusion</h2>
-<p>Geometry Dash Lite is a great game for anyone who loves music, action, and challenge. It is a free version of Geometry Dash, which has more features and content. However, Geometry Dash Lite still offers plenty of fun and excitement for casual and hardcore gamers alike. You can download and install Geometry Dash Lite apk on your Android device running version 2.3 or higher by following the steps we have explained in this article. We hope you enjoy playing Geometry Dash Lite and have a blast with the rhythm-based action platforming.</p>
- <h3>FAQs</h3>
-<ul>
-<li><b>What is the difference between Geometry Dash Lite and Geometry Dash?</b><br>
-Geometry Dash Lite is a free version of Geometry Dash, which is a paid game that costs $1.99. Geometry Dash Lite has fewer levels, soundtracks, achievements, and features than Geometry Dash. However, Geometry Dash Lite still offers plenty of fun and challenge for casual and hardcore gamers alike.</li>
-<li><b>Is Geometry Dash Lite safe to download and install?</b><br>
-Yes, Geometry Dash Lite is safe to download and install if you use a trusted and verified source like APKPure. However, you need to be careful and avoid any malicious or fake links that might harm your device or steal your data.</li>
-<li><b>Can I play Geometry Dash Lite offline?</b><br>
-Yes, you can play Geometry Dash Lite offline without an internet connection. However, you will need an internet connection to download additional data or access some online features like leaderboards or achievements.</li>
-<li><b>How can I unlock more icons and colors in Geometry Dash Lite?</b><br>
-You can unlock more icons and colors in Geometry Dash Lite by completing levels, collecting stars, completing achievements, or using secret coins. You can also use some codes in the vault to unlock some special icons.</li>
-<li><b>How can I create my own levels in Geometry Dash Lite?</b><br>
-You cannot create your own levels in Geometry Dash Lite as this feature is only available in Geometry Dash. However, you can play some user-created levels in challenge mode by tapping on the dice icon in the main menu.</li>
-</ul>
- <p></</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Euphoria Season 1 Download Where to Find the Full Episodes Online.md

@@ -1,131 +0,0 @@
-
-<h1>Download Euphoria Season 1 Reddit: How to Watch the Hit HBO Series Online</h1>
- <p>If you are looking for a way to download Euphoria season 1 reddit, you are not alone. Euphoria is one of the most popular and acclaimed shows of recent years, and many people want to watch it online. But how can you download Euphoria season 1 reddit safely and legally? And what are the pros and cons of doing so? In this article, we will answer these questions and more.</p>
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- <h2>What is Euphoria and why should you watch it?</h2>
- <p>Euphoria is a drama series that follows a group of high-school students as they navigate a minefield of drugs, sex, identity, trauma, social media, love and friendship in today's increasingly unstable world. The show stars Zendaya as Rue, a 17-year-old drug addict who falls in love with Jules, a transgender girl played by Hunter Schafer. The show also features other talented actors such as Sydney Sweeney, Maude Apatow, Jacob Elordi, Alexa Demie, Barbie Ferreira, Algee Smith, Storm Reid, Angus Cloud, Eric Dane, Nika King and Colman Domingo.</p>
- <h3>A brief summary of Euphoria season 1</h3>
- <p>Euphoria season 1 consists of eight episodes that aired on HBO from June to August 2019. The season also has two special episodes that were released in December 2020 and January 2021. Here is a brief summary of what happens in each episode:</p>
- <ul>
-<li>Episode 1: Pilot. Rue returns home from rehab with no plans to stay clean. She meets Jules at a party where she is harassed by Nate, a local jock and bully.</li>
-<li>Episode 2: Stuntin' Like My Daddy. Rue tries to get clean for Jules but struggles to put the past behind her. Jules hooks up with an older man who turns out to be Nate's father.</li>
-<li>Episode 3: Made You Look. Kat starts camming; Jules falls for a boy online; Rue is confronted at NA; Cassie visits McKay at college.</li>
-<li>Episode 4: Shook Ones Pt. II. Rue and Jules attend a carnival where they encounter Nate and his father. Nate blackmails Jules with her nude pictures.</li>
-<li>Episode 5: '03 Bonnie and Clyde. Maddy and Nate must deal with a police investigation; Rue and Jules each reflect on their relationship.</li>
-<li>Episode 6: The Next Episode. On Halloween, Rue worries about her reliance on Jules when she starts acting strange.</li>
-<li>Episode 7: The Trials and Tribulations of Trying to Pee While Depressed. Rue gets depressed and watches 22 straight episodes of a British reality show. Jules visits an old friend.</li>
-<li>Episode 8: And Salt the Earth Behind You. In the season finale, Rue suggests to Jules that they run away together. Jules leaves but Rue stays behind and relapses.</li>
-<li <p>Episode 9: Trouble Don't Last Always. This is the first special episode that focuses on Rue as she celebrates Christmas with her family and talks to her sponsor Ali.</p>
-<p>Episode 10: F*ck Anyone Who's Not A Sea Blob. This is the second special episode that focuses on Jules as she reflects on her year and attends a therapy session.</p>
- <h3>The main characters and their stories</h3>
- <p>Euphoria features a diverse and complex cast of characters, each with their own struggles and secrets. Here are some of the main characters and their stories:</p>
- <ul>
-<li>Rue Bennett (Zendaya): The protagonist and narrator of the show, Rue is a troubled teenager who suffers from bipolar disorder, anxiety, depression and drug addiction. She is in love with Jules but also fears losing her.</li>
-<li>Jules Vaughn (Hunter Schafer): The deuteragonist and Rue's love interest, Jules is a transgender girl who moved to town with her father after her mother abandoned her. She is adventurous, optimistic and fearless, but also vulnerable and lonely.</li>
-<li>Nate Jacobs (Jacob Elordi): The main antagonist of the show, Nate is a violent and manipulative jock who has a toxic relationship with Maddy. He is also closeted and struggles with his sexuality and identity.</li>
-<li>Maddy Perez (Alexa Demie): Nate's girlfriend and Cassie's best friend, Maddy is a confident and outspoken cheerleader who likes to be the center of attention. She is often abused by Nate but still loves him.</li>
-<li>Kat Hernandez (Barbie Ferreira): Rue's childhood friend and Ethan's girlfriend, Kat is a smart and sarcastic girl who undergoes a transformation after losing her virginity. She becomes a cam girl and embraces her sexuality and body positivity.</li>
-<li>Cassie Howard (Sydney Sweeney): Maddy's best friend and McKay's girlfriend, Cassie is a sweet and naive girl who has a reputation for being promiscuous. She gets pregnant by McKay but decides to have an abortion.</li>
-<li>Christopher McKay (Algee Smith): Cassie's boyfriend and Nate's friend, McKay is a former football star who attends college on a scholarship. He is insecure about his future and his relationship with Cassie.</li>
-<li>Lexi Howard (Maude Apatow): Cassie's younger sister and Rue's friend, Lexi is a loyal and supportive girl who often feels overlooked by others. She has a crush on Rue but never acts on it.</li>
-<li>Fezco (Angus Cloud): Rue's drug dealer and friend, Fezco is a kind-hearted and protective guy who cares about Rue's well-being. He has a tense relationship with Nate and his father.</li>
-<li>Cal Jacobs (Eric Dane): Nate's father and Jules' lover, Cal is a successful businessman who leads a double life. He has sex with young trans women in motels while hiding his true self from his family.</li>
-</ul>
- <h3>The critical acclaim and awards</h3>
- <p>Euphoria has received widespread praise from critics and audiences alike for its realistic and unflinching portrayal of teenage life, its stunning cinematography and soundtrack, its diverse and talented cast, and its powerful performances by Zendaya and Hunter Schafer. The show has also won several awards, including:</p>
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- <ul>
-<li>The Primetime Emmy Award for Outstanding Lead Actress in a Drama Series for Zendaya in 2020</li>
-<li>The Satellite Award for Best Actress in a Drama / Genre Series for Zendaya in 2020</li>
-<li>The People's Choice Award for The Drama TV Star of 2020 for Zendaya in 2020</li>
-<li>The GLAAD Media Award for Outstanding Drama Series in 2020</li>
-<li>The Critics' Choice Television Award for Best Supporting Actress in a Drama Series for Hunter Schafer in 2021</li>
-</ul>
- <h2>How to download Euphoria season 1 reddit</h2>
- <p>If you want to watch Euphoria season 1 online, you have two options: the official ways or the unofficial ways. The official ways are the legal and authorized methods to stream the show from HBO or other platforms that have the rights to distribute it. The unofficial ways are the illegal or unauthorized methods to download the show from torrent sites, streaming sites or reddit links. Let's take a look at each option in more detail.</p>
- <h3>The official ways to stream Euphoria season 1 online</h3>
- <p>The official ways to stream Euphoria season 1 online are the safest and most reliable methods to watch the show without any hassle or risk. However, they may also require you to pay a subscription fee or have access to certain devices or regions. Here are some of the official ways to stream Euphoria season 1 online:</ <h4>HBO.com</h4>
- <p>The most obvious and direct way to stream Euphoria season 1 online is to visit the official website of HBO, the network that produces and airs the show. You can watch all the episodes of Euphoria season 1 on HBO.com, as well as the two special episodes and other bonus content. However, you will need to have an HBO subscription or a free trial to access the content. You can also use your HBO account to watch the show on other devices, such as your TV, smartphone, tablet or laptop, through the HBO app or HBO Max app.</p>
- <h4>Hulu</h4>
- <p>Another official way to stream Euphoria season 1 online is to use Hulu, a popular streaming service that offers a variety of TV shows and movies. You can watch all the episodes of Euphoria season 1 on Hulu, as well as the two special episodes and other bonus content. However, you will need to have a Hulu subscription or a free trial to access the content. You can also use your Hulu account to watch the show on other devices, such as your TV, smartphone, tablet or laptop, through the Hulu app.</p>
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- <p>The unofficial ways to download Euphoria season 1 reddit are the risky and illegal methods to watch the show without paying or following the rules. However, they may also provide you with free or cheap access to the show and allow you to watch it offline or share it with others. Here are some of the unofficial ways to download Euphoria season 1 reddit:</p> <h4>Torrent sites</h4>
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- <h4>Streaming sites</h4>
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- <p>Some of the benefits of downloading Euphoria season 1 reddit are:</p>
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-<li>Free or cheap access to the show: You can watch Euphoria season 1 reddit without paying a subscription fee or buying a DVD. You can also find discounts or deals on some platforms.</li>
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- <h2>Conclusion: Download Euphoria season 1 reddit at your own risk</h2>
- <p>Euphoria is a captivating and compelling show that explores the dark and complex realities of teenage life in today's world. The show has received rave reviews from critics and audiences alike for its stunning visuals, powerful performances and gripping stories. If you want to watch Euphoria season 1 online, you have two options: the official ways or the unofficial ways. The official ways are the legal and authorized methods to stream the show from HBO or other platforms that have the rights to distribute it. The unofficial ways are the illegal or unauthorized methods to download the show from torrent sites, streaming sites or reddit links. Each option has its pros and cons that you should weigh carefully before making your decision. Downloading Euphoria season season 1 reddit can be tempting, but it also comes with many risks and challenges. You should download Euphoria season 1 reddit at your own risk and responsibility. <p>Here are some FAQs that you may have about downloading Euphoria season 1 reddit:</p>
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-<p>Yes, Euphoria season 1 is worth watching. The show is a captivating and compelling drama that explores the dark and complex realities of teenage life in today's world. The show has received rave reviews from critics and audiences alike for its stunning visuals, powerful performances and gripping stories.</p>
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spaces/1toTree/lora_test/ppdiffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -1,108 +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.
-
-from typing import List, Optional, Tuple, Union
-
-import paddle
-
-from ...configuration_utils import FrozenDict
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...utils import deprecate
-
-
-class DDPMPipeline(DiffusionPipeline):
-    r"""
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular xxxx, etc.)
-
-    Parameters:
-        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
-            [`DDPMScheduler`], or [`DDIMScheduler`].
-    """
-
-    def __init__(self, unet, scheduler):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @paddle.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-        num_inference_steps: int = 1000,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`paddle.Generator`, *optional*):
-                One or a list of paddle generator(s) to make generation deterministic.
-            num_inference_steps (`int`, *optional*, defaults to 1000):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
-            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
-            generated images.
-        """
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.13.0", message, take_from=kwargs)
-
-        if predict_epsilon is not None:
-            new_config = dict(self.scheduler.config)
-            new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"
-            self.scheduler._internal_dict = FrozenDict(new_config)
-
-        # Sample gaussian noise to begin loop
-        if isinstance(self.unet.sample_size, int):
-            image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)
-        else:
-            image_shape = (batch_size, self.unet.in_channels, *self.unet.sample_size)
-
-        image = paddle.randn(image_shape, generator=generator)
-
-        # set step values
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            # 1. predict noise model_output
-            model_output = self.unet(image, t).sample
-
-            # 2. compute previous image: x_t -> x_t-1
-            image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
-
-        image = (image / 2 + 0.5).clip(0, 1)
-        image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/44brabal/runwayml-stable-diffusion-v1-5/README.md

@@ -1,12 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: 🏃
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.45.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/A00001/bingothoo/src/components/ui/badge.tsx

@@ -1,36 +0,0 @@
-import * as React from 'react'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const badgeVariants = cva(
-  'inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2',
-  {
-    variants: {
-      variant: {
-        default:
-          'border-transparent bg-primary text-primary-foreground hover:bg-primary/80',
-        secondary:
-          'border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        destructive:
-          'border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80',
-        outline: 'text-foreground'
-      }
-    },
-    defaultVariants: {
-      variant: 'default'
-    }
-  }
-)
-
-export interface BadgeProps
-  extends React.HTMLAttributes<HTMLDivElement>,
-    VariantProps<typeof badgeVariants> {}
-
-function Badge({ className, variant, ...props }: BadgeProps) {
-  return (
-    <div className={cn(badgeVariants({ variant }), className)} {...props} />
-  )
-}
-
-export { Badge, badgeVariants }

spaces/AIZ2H/02-Gradio-Art-From-Text-And-Images/app.py

@@ -1,224 +0,0 @@
-import os
-
-os.system("git clone --recursive https://github.com/JD-P/cloob-latent-diffusion")
-os.system("cd cloob-latent-diffusion;pip install omegaconf pillow pytorch-lightning einops wandb ftfy regex ./CLIP")
-
-import argparse
-from functools import partial
-from pathlib import Path
-import sys
-sys.path.append('./cloob-latent-diffusion')
-sys.path.append('./cloob-latent-diffusion/cloob-training')
-sys.path.append('./cloob-latent-diffusion/latent-diffusion')
-sys.path.append('./cloob-latent-diffusion/taming-transformers')
-sys.path.append('./cloob-latent-diffusion/v-diffusion-pytorch')
-from omegaconf import OmegaConf
-from PIL import Image
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torchvision import transforms
-from torchvision.transforms import functional as TF
-from tqdm import trange
-from CLIP import clip
-from cloob_training import model_pt, pretrained
-import ldm.models.autoencoder
-from diffusion import sampling, utils
-import train_latent_diffusion as train
-from huggingface_hub import hf_hub_url, cached_download
-import random
-
-# Download the model files
-checkpoint = cached_download(hf_hub_url("huggan/distill-ccld-wa", filename="model_student.ckpt"))
-ae_model_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.ckpt"))
-ae_config_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.yaml"))
-
-# Define a few utility functions
-
-
-def parse_prompt(prompt, default_weight=3.):
-    if prompt.startswith('http://') or prompt.startswith('https://'):
-        vals = prompt.rsplit(':', 2)
-        vals = [vals[0] + ':' + vals[1], *vals[2:]]
-    else:
-        vals = prompt.rsplit(':', 1)
-    vals = vals + ['', default_weight][len(vals):]
-    return vals[0], float(vals[1])
-
-
-def resize_and_center_crop(image, size):
-    fac = max(size[0] / image.size[0], size[1] / image.size[1])
-    image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS)
-    return TF.center_crop(image, size[::-1])
-
-
-# Load the models
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-print('Using device:', device)
-print('loading models')
-
-# autoencoder
-ae_config = OmegaConf.load(ae_config_path)
-ae_model = ldm.models.autoencoder.AutoencoderKL(**ae_config.model.params)
-ae_model.eval().requires_grad_(False).to(device)
-ae_model.load_state_dict(torch.load(ae_model_path))
-n_ch, side_y, side_x = 4, 32, 32
-
-# diffusion model
-model = train.DiffusionModel(192, [1,1,2,2], autoencoder_scale=torch.tensor(4.3084))
-model.load_state_dict(torch.load(checkpoint, map_location='cpu'))
-model = model.to(device).eval().requires_grad_(False)
-
-# CLOOB
-cloob_config = pretrained.get_config('cloob_laion_400m_vit_b_16_16_epochs')
-cloob = model_pt.get_pt_model(cloob_config)
-checkpoint = pretrained.download_checkpoint(cloob_config)
-cloob.load_state_dict(model_pt.get_pt_params(cloob_config, checkpoint))
-cloob.eval().requires_grad_(False).to(device)
-
-
-# The key function: returns a list of n PIL images
-def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15,
-             method='plms', eta=None):
-  zero_embed = torch.zeros([1, cloob.config['d_embed']], device=device)
-  target_embeds, weights = [zero_embed], []
-
-  for prompt in prompts:
-      txt, weight = parse_prompt(prompt)
-      target_embeds.append(cloob.text_encoder(cloob.tokenize(txt).to(device)).float())
-      weights.append(weight)
-
-  for prompt in images:
-      path, weight = parse_prompt(prompt)
-      img = Image.open(utils.fetch(path)).convert('RGB')
-      clip_size = cloob.config['image_encoder']['image_size']
-      img = resize_and_center_crop(img, (clip_size, clip_size))
-      batch = TF.to_tensor(img)[None].to(device)
-      embed = F.normalize(cloob.image_encoder(cloob.normalize(batch)).float(), dim=-1)
-      target_embeds.append(embed)
-      weights.append(weight)
-
-  weights = torch.tensor([1 - sum(weights), *weights], device=device)
-
-  torch.manual_seed(seed)
-
-  def cfg_model_fn(x, t):
-      n = x.shape[0]
-      n_conds = len(target_embeds)
-      x_in = x.repeat([n_conds, 1, 1, 1])
-      t_in = t.repeat([n_conds])
-      clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0)
-      vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]])
-      v = vs.mul(weights[:, None, None, None, None]).sum(0)
-      return v
-
-  def run(x, steps):
-      if method == 'ddpm':
-          return sampling.sample(cfg_model_fn, x, steps, 1., {})
-      if method == 'ddim':
-          return sampling.sample(cfg_model_fn, x, steps, eta, {})
-      if method == 'prk':
-          return sampling.prk_sample(cfg_model_fn, x, steps, {})
-      if method == 'plms':
-          return sampling.plms_sample(cfg_model_fn, x, steps, {})
-      if method == 'pie':
-          return sampling.pie_sample(cfg_model_fn, x, steps, {})
-      if method == 'plms2':
-          return sampling.plms2_sample(cfg_model_fn, x, steps, {})
-      assert False
-
-  batch_size = n
-  x = torch.randn([n, n_ch, side_y, side_x], device=device)
-  t = torch.linspace(1, 0, steps + 1, device=device)[:-1]
-  steps = utils.get_spliced_ddpm_cosine_schedule(t)
-  pil_ims = []
-  for i in trange(0, n, batch_size):
-      cur_batch_size = min(n - i, batch_size)
-      out_latents = run(x[i:i+cur_batch_size], steps)
-      outs = ae_model.decode(out_latents * torch.tensor(2.55).to(device))
-      for j, out in enumerate(outs):
-          pil_ims.append(utils.to_pil_image(out))
-
-  return pil_ims
-  
-  
-import gradio as gr
-
-def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'):
-  if seed == None :
-    seed = random.randint(0, 10000)
-  print( prompt, im_prompt, seed, n_steps)
-  prompts = [prompt]
-  im_prompts = []
-  if im_prompt != None:
-    im_prompts = [im_prompt]
-  pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method)
-  return pil_ims[0]
-
-iface = gr.Interface(fn=gen_ims, 
-  inputs=[#gr.inputs.Slider(minimum=1, maximum=1, step=1, default=1,label="Number of images"),
-          #gr.inputs.Slider(minimum=0, maximum=200, step=1, label='Random seed', default=0),
-          gr.inputs.Textbox(label="Text prompt"),
-          gr.inputs.Image(optional=True, label="Image prompt", type='filepath'),
-          #gr.inputs.Slider(minimum=10, maximum=35, step=1, default=15,label="Number of steps")
-          ], 
-  outputs=[gr.outputs.Image(type="pil", label="Generated Image")],
-  examples=[
-  ["Futurism, in the style of Wassily Kandinsky"], 
-  ["Art Nouveau, in the style of John Singer Sargent"], 
-  ["Surrealism, in the style of Edgar Degas"], 
-  ["Expressionism, in the style of Wassily Kandinsky"], 
-  ["Futurism, in the style of Egon Schiele"], 
-  ["Neoclassicism, in the style of Gustav Klimt"], 
-  ["Cubism, in the style of Gustav Klimt"], 
-  ["Op Art, in the style of Marc Chagall"], 
-  ["Romanticism, in the style of M.C. Escher"], 
-  ["Futurism, in the style of M.C. Escher"], 
-  ["Abstract Art, in the style of M.C. Escher"], 
-  ["Mannerism, in the style of Paul Klee"], 
-  ["Romanesque Art, in the style of Leonardo da Vinci"], 
-  ["High Renaissance, in the style of Rembrandt"], 
-  ["Magic Realism, in the style of Gustave Dore"], 
-  ["Realism, in the style of Jean-Michel Basquiat"], 
-  ["Art Nouveau, in the style of Paul Gauguin"], 
-  ["Avant-garde, in the style of Pierre-Auguste Renoir"], 
-  ["Baroque, in the style of Edward Hopper"], 
-  ["Post-Impressionism, in the style of Wassily Kandinsky"], 
-  ["Naturalism, in the style of Rene Magritte"], 
-  ["Constructivism, in the style of Paul Cezanne"], 
-  ["Abstract Expressionism, in the style of Henri Matisse"], 
-  ["Pop Art, in the style of Vincent van Gogh"], 
-  ["Futurism, in the style of Wassily Kandinsky"], 
-  ["Futurism, in the style of Zdzislaw Beksinski"], 
-  ['Surrealism, in the style of Salvador Dali'], 
-  ["Aaron Wacker, oil on canvas"],
-      ["abstract"],
-    ["landscape"],
-    ["portrait"],
-    ["sculpture"],
-    ["genre painting"],
-    ["installation"],
-    ["photo"],
-    ["figurative"],
-    ["illustration"],
-    ["still life"],
-    ["history painting"],
-    ["cityscape"],
-    ["marina"],
-    ["animal painting"],
-    ["design"],
-    ["calligraphy"],
-    ["symbolic painting"],
-    ["graffiti"],
-    ["performance"],
-    ["mythological painting"],
-    ["battle painting"],
-    ["self-portrait"],
-    ["Impressionism, oil on canvas"]
-  ],
-  title='Art Generator and Style Mixer from 🧠 Cloob and 🎨 WikiArt - Visual Art Encyclopedia:',
-  description="Trained on images from the [WikiArt](https://www.wikiart.org/) dataset, comprised of visual arts",
-  article = 'Model used is: [model card](https://huggingface.co/huggan/distill-ccld-wa)..'
-
-)
-iface.launch(enable_queue=True) # , debug=True for colab debugging

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/helpers/you.py

@@ -1,79 +0,0 @@
-import sys
-import json
-import urllib.parse
-
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-messages = config['messages']
-prompt = ''
-
-
-def transform(messages: list) -> list:
-    result = []
-    i = 0
-
-    while i < len(messages):
-        if messages[i]['role'] == 'user':
-            question = messages[i]['content']
-            i += 1
-
-            if i < len(messages) and messages[i]['role'] == 'assistant':
-                answer = messages[i]['content']
-                i += 1
-            else:
-                answer = ''
-
-            result.append({'question': question, 'answer': answer})
-
-        elif messages[i]['role'] == 'assistant':
-            result.append({'question': '', 'answer': messages[i]['content']})
-            i += 1
-
-        elif messages[i]['role'] == 'system':
-            result.append({'question': messages[i]['content'], 'answer': ''})
-            i += 1
-            
-    return result
-
-headers = {
-    'Content-Type': 'application/x-www-form-urlencoded',
-    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
-    'Sec-Fetch-Site': 'same-origin',
-    'Accept-Language': 'en-GB,en;q=0.9',
-    'Sec-Fetch-Mode': 'navigate',
-    'Host': 'you.com',
-    'Origin': 'https://you.com',
-    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.4 Safari/605.1.15',
-    'Referer': 'https://you.com/api/streamingSearch?q=nice&safeSearch=Moderate&onShoppingPage=false&mkt=&responseFilter=WebPages,Translations,TimeZone,Computation,RelatedSearches&domain=youchat&queryTraceId=7a6671f8-5881-404d-8ea3-c3f8301f85ba&chat=%5B%7B%22question%22%3A%22hi%22%2C%22answer%22%3A%22Hello!%20How%20can%20I%20assist%20you%20today%3F%22%7D%5D&chatId=7a6671f8-5881-404d-8ea3-c3f8301f85ba&__cf_chl_tk=ex2bw6vn5vbLsUm8J5rDYUC0Bjzc1XZqka6vUl6765A-1684108495-0-gaNycGzNDtA',
-    'Connection': 'keep-alive',
-    'Sec-Fetch-Dest': 'document',
-    'Priority': 'u=0, i',
-}
-
-if messages[-1]['role'] == 'user':
-    prompt = messages[-1]['content']
-    messages = messages[:-1]
-
-params = urllib.parse.urlencode({
-    'q': prompt,
-    'domain': 'youchat',
-    'chat': transform(messages)
-})
-
-def output(chunk):
-    if b'"youChatToken"' in chunk:
-        chunk_json = json.loads(chunk.decode().split('data: ')[1])
-
-        print(chunk_json['youChatToken'], flush=True, end = '')
-
-while True:
-    try:
-        response = requests.get(f'https://you.com/api/streamingSearch?{params}',
-                        headers=headers, content_callback=output, impersonate='safari15_5')
-        
-        exit(0)
-    
-    except Exception as e:
-        print('an error occured, retrying... |', e, flush=True)
-        continue

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/toggleswitchshape.d.ts

@@ -1,2 +0,0 @@
-import ToggleSwitchShape from './gameobjects/shape/toggleswitch/ToggleSwitchShape';
-export default ToggleSwitchShape;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/puff/Factory.js

@@ -1,13 +0,0 @@
-import Puff from './Puff.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('puff', function (config) {
-    var gameObject = new Puff(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.Spinner.Puff', Puff);
-
-export default Puff;

spaces/AiMimicry/sovits-models/hubert/hubert_model.py

@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/monotonic_align/setup.py

@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)

spaces/Alfasign/HuggingGPT-Lite/awesome_chat.py

@@ -1,933 +0,0 @@
-import base64
-import copy
-import datetime
-from io import BytesIO
-import io
-import os
-import random
-import time
-import traceback
-import uuid
-import requests
-import re
-import json
-import logging
-import argparse
-import yaml
-from PIL import Image, ImageDraw
-from diffusers.utils import load_image
-from pydub import AudioSegment
-import threading
-from queue import Queue
-from get_token_ids import get_token_ids_for_task_parsing, get_token_ids_for_choose_model, count_tokens, get_max_context_length
-from huggingface_hub.inference_api import InferenceApi
-from huggingface_hub.inference_api import ALL_TASKS
-from models_server import models, status
-from functools import partial
-from huggingface_hub import Repository
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default="config.yaml.dev")
-parser.add_argument("--mode", type=str, default="cli")
-args = parser.parse_args()
-
-if __name__ != "__main__":
-    args.config = "config.gradio.yaml"
-
-config = yaml.load(open(args.config, "r"), Loader=yaml.FullLoader)
-
-if not os.path.exists("logs"):
-    os.mkdir("logs")
-
-now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
-
-DATASET_REPO_URL = "https://huggingface.co/datasets/tricktreat/HuggingGPT_logs"
-LOG_HF_TOKEN = os.environ.get("LOG_HF_TOKEN")
-if LOG_HF_TOKEN:
-    repo = Repository(
-        local_dir="logs", clone_from=DATASET_REPO_URL, use_auth_token=LOG_HF_TOKEN
-    )
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.CRITICAL)
-
-handler = logging.StreamHandler()
-formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-handler.setFormatter(formatter)
-if not config["debug"]:
-    handler.setLevel(logging.INFO)
-logger.addHandler(handler)
-
-log_file = config["log_file"]
-if log_file:
-    log_file = log_file.replace("TIMESTAMP", now)
-    filehandler = logging.FileHandler(log_file)
-    filehandler.setLevel(logging.DEBUG)
-    filehandler.setFormatter(formatter)
-    logger.addHandler(filehandler)
-
-LLM = config["model"]
-use_completion = config["use_completion"]
-
-# consistent: wrong msra model name 
-LLM_encoding = LLM
-if LLM == "gpt-3.5-turbo":
-    LLM_encoding = "text-davinci-003"
-task_parsing_highlight_ids = get_token_ids_for_task_parsing(LLM_encoding)
-choose_model_highlight_ids = get_token_ids_for_choose_model(LLM_encoding)
-
-# ENDPOINT	MODEL NAME	
-# /v1/chat/completions	gpt-4, gpt-4-0314, gpt-4-32k, gpt-4-32k-0314, gpt-3.5-turbo, gpt-3.5-turbo-0301	
-# /v1/completions	text-davinci-003, text-davinci-002, text-curie-001, text-babbage-001, text-ada-001, davinci, curie, babbage, ada
-
-if use_completion:
-    api_name = "completions"
-else:
-    api_name = "chat/completions"
-
-if not config["dev"]:
-    if not config["openai"]["key"].startswith("sk-") and not config["openai"]["key"]=="gradio":
-        raise ValueError("Incrorrect OpenAI key. Please check your config.yaml file.")
-    OPENAI_KEY = config["openai"]["key"]
-    endpoint = f"https://api.openai.com/v1/{api_name}"
-    if OPENAI_KEY.startswith("sk-"):
-        HEADER = {
-            "Authorization": f"Bearer {OPENAI_KEY}"
-        }
-    else:
-        HEADER = None
-else:
-    endpoint = f"{config['local']['endpoint']}/v1/{api_name}"
-    HEADER = None
-
-PROXY = None
-if config["proxy"]:
-    PROXY = {
-        "https": config["proxy"],
-    }
-
-inference_mode = config["inference_mode"]
-
-parse_task_demos_or_presteps = open(config["demos_or_presteps"]["parse_task"], "r").read()
-choose_model_demos_or_presteps = open(config["demos_or_presteps"]["choose_model"], "r").read()
-response_results_demos_or_presteps = open(config["demos_or_presteps"]["response_results"], "r").read()
-
-parse_task_prompt = config["prompt"]["parse_task"]
-choose_model_prompt = config["prompt"]["choose_model"]
-response_results_prompt = config["prompt"]["response_results"]
-
-parse_task_tprompt = config["tprompt"]["parse_task"]
-choose_model_tprompt = config["tprompt"]["choose_model"]
-response_results_tprompt = config["tprompt"]["response_results"]
-
-MODELS = [json.loads(line) for line in open("data/p0_models.jsonl", "r").readlines()]
-MODELS_MAP = {}
-for model in MODELS:
-    tag = model["task"]
-    if tag not in MODELS_MAP:
-        MODELS_MAP[tag] = []
-    MODELS_MAP[tag].append(model)
-METADATAS = {}
-for model in MODELS:
-    METADATAS[model["id"]] = model
-
-def convert_chat_to_completion(data):
-    messages = data.pop('messages', [])
-    tprompt = ""
-    if messages[0]['role'] == "system":
-        tprompt = messages[0]['content']
-        messages = messages[1:]
-    final_prompt = ""
-    for message in messages:
-        if message['role'] == "user":
-            final_prompt += ("<im_start>"+ "user" + "\n" + message['content'] + "<im_end>\n")
-        elif message['role'] == "assistant":
-            final_prompt += ("<im_start>"+ "assistant" + "\n" + message['content'] + "<im_end>\n")
-        else:
-            final_prompt += ("<im_start>"+ "system" + "\n" + message['content'] + "<im_end>\n")
-    final_prompt = tprompt + final_prompt
-    final_prompt = final_prompt + "<im_start>assistant"
-    data["prompt"] = final_prompt
-    data['stop'] = data.get('stop', ["<im_end>"])
-    data['max_tokens'] = data.get('max_tokens', max(get_max_context_length(LLM) - count_tokens(LLM_encoding, final_prompt), 1))
-    return data
-
-def send_request(data):
-    global HEADER
-    openaikey = data.pop("openaikey")
-    if use_completion:
-        data = convert_chat_to_completion(data)
-    if openaikey and openaikey.startswith("sk-"):
-        HEADER = {
-            "Authorization": f"Bearer {openaikey}"
-        }
-    
-    response = requests.post(endpoint, json=data, headers=HEADER, proxies=PROXY)
-    logger.debug(response.text.strip())
-    if "choices" not in response.json():
-        return response.json()
-    if use_completion:
-        return response.json()["choices"][0]["text"].strip()
-    else:
-        return response.json()["choices"][0]["message"]["content"].strip()
-
-def replace_slot(text, entries):
-    for key, value in entries.items():
-        if not isinstance(value, str):
-            value = str(value)
-        text = text.replace("{{" + key +"}}", value.replace('"', "'").replace('\n', ""))
-    return text
-
-def find_json(s):
-    s = s.replace("\'", "\"")
-    start = s.find("{")
-    end = s.rfind("}")
-    res = s[start:end+1]
-    res = res.replace("\n", "")
-    return res
-
-def field_extract(s, field):
-    try:
-        field_rep = re.compile(f'{field}.*?:.*?"(.*?)"', re.IGNORECASE)
-        extracted = field_rep.search(s).group(1).replace("\"", "\'")
-    except:
-        field_rep = re.compile(f'{field}:\ *"(.*?)"', re.IGNORECASE)
-        extracted = field_rep.search(s).group(1).replace("\"", "\'")
-    return extracted
-
-def get_id_reason(choose_str):
-    reason = field_extract(choose_str, "reason")
-    id = field_extract(choose_str, "id")
-    choose = {"id": id, "reason": reason}
-    return id.strip(), reason.strip(), choose
-
-def record_case(success, **args):
-    if not success:
-        return
-    f = open(f"logs/log_success_{now}.jsonl", "a")
-    log = args
-    f.write(json.dumps(log) + "\n")
-    f.close()
-    if LOG_HF_TOKEN:
-        commit_url = repo.push_to_hub(blocking=False)
-
-def image_to_bytes(img_url):
-    img_byte = io.BytesIO()
-    type = img_url.split(".")[-1]
-    load_image(img_url).save(img_byte, format="png")
-    img_data = img_byte.getvalue()
-    return img_data
-
-def resource_has_dep(command):
-    args = command["args"]
-    for _, v in args.items():
-        if "<GENERATED>" in v:
-            return True
-    return False
-
-def fix_dep(tasks):
-    for task in tasks:
-        args = task["args"]
-        task["dep"] = []
-        for k, v in args.items():
-            if "<GENERATED>" in v:
-                dep_task_id = int(v.split("-")[1])
-                if dep_task_id not in task["dep"]:
-                    task["dep"].append(dep_task_id)
-        if len(task["dep"]) == 0:
-            task["dep"] = [-1]
-    return tasks
-
-def unfold(tasks):
-    flag_unfold_task = False
-    try:
-        for task in tasks:
-            for key, value in task["args"].items():
-                if "<GENERATED>" in value:
-                    generated_items = value.split(",")
-                    if len(generated_items) > 1:
-                        flag_unfold_task = True
-                        for item in generated_items:
-                            new_task = copy.deepcopy(task)
-                            dep_task_id = int(item.split("-")[1])
-                            new_task["dep"] = [dep_task_id]
-                            new_task["args"][key] = item
-                            tasks.append(new_task)
-                        tasks.remove(task)
-    except Exception as e:
-        print(e)
-        traceback.print_exc()
-        logger.debug("unfold task failed.")
-
-    if flag_unfold_task:
-        logger.debug(f"unfold tasks: {tasks}")
-        
-    return tasks
-
-def chitchat(messages, openaikey=None):
-    data = {
-        "model": LLM,
-        "messages": messages,
-        "openaikey": openaikey
-    }
-    return send_request(data)
-
-def parse_task(context, input, openaikey=None):
-    demos_or_presteps = parse_task_demos_or_presteps
-    messages = json.loads(demos_or_presteps)
-    messages.insert(0, {"role": "system", "content": parse_task_tprompt})
-
-    # cut chat logs
-    start = 0
-    while start <= len(context):
-        history = context[start:]
-        prompt = replace_slot(parse_task_prompt, {
-            "input": input,
-            "context": history 
-        })
-        messages.append({"role": "user", "content": prompt})
-        history_text = "<im_end>\nuser<im_start>".join([m["content"] for m in messages])
-        num = count_tokens(LLM_encoding, history_text)
-        if get_max_context_length(LLM) - num > 800:
-            break
-        messages.pop()
-        start += 2
-    
-    logger.debug(messages)
-    data = {
-        "model": LLM,
-        "messages": messages,
-        "temperature": 0,
-        "logit_bias": {item: config["logit_bias"]["parse_task"] for item in task_parsing_highlight_ids},
-        "openaikey": openaikey
-    }
-    return send_request(data)
-
-def choose_model(input, task, metas, openaikey = None):
-    prompt = replace_slot(choose_model_prompt, {
-        "input": input,
-        "task": task,
-        "metas": metas,
-    })
-    demos_or_presteps = replace_slot(choose_model_demos_or_presteps, {
-        "input": input,
-        "task": task,
-        "metas": metas
-    })
-    messages = json.loads(demos_or_presteps)
-    messages.insert(0, {"role": "system", "content": choose_model_tprompt})
-    messages.append({"role": "user", "content": prompt})
-    logger.debug(messages)
-    data = {
-        "model": LLM,
-        "messages": messages,
-        "temperature": 0,
-        "logit_bias": {item: config["logit_bias"]["choose_model"] for item in choose_model_highlight_ids}, # 5
-        "openaikey": openaikey
-    }
-    return send_request(data)
-
-
-def response_results(input, results, openaikey=None):
-    results = [v for k, v in sorted(results.items(), key=lambda item: item[0])]
-    prompt = replace_slot(response_results_prompt, {
-        "input": input,
-    })
-    demos_or_presteps = replace_slot(response_results_demos_or_presteps, {
-        "input": input,
-        "processes": results
-    })
-    messages = json.loads(demos_or_presteps)
-    messages.insert(0, {"role": "system", "content": response_results_tprompt})
-    messages.append({"role": "user", "content": prompt})
-    logger.debug(messages)
-    data = {
-        "model": LLM,
-        "messages": messages,
-        "temperature": 0,
-        "openaikey": openaikey
-    }
-    return send_request(data)
-
-def huggingface_model_inference(model_id, data, task, huggingfacetoken=None):
-    if huggingfacetoken is None:
-        HUGGINGFACE_HEADERS = {}
-    else:
-        HUGGINGFACE_HEADERS = {
-            "Authorization": f"Bearer {huggingfacetoken}",
-    }
-    task_url = f"https://api-inference.huggingface.co/models/{model_id}" # InferenceApi does not yet support some tasks
-    inference = InferenceApi(repo_id=model_id, token=huggingfacetoken)
-    
-    # NLP tasks
-    if task == "question-answering":
-        inputs = {"question": data["text"], "context": (data["context"] if "context" in data else "" )}
-        result = inference(inputs)
-    if task == "sentence-similarity":
-        inputs = {"source_sentence": data["text1"], "target_sentence": data["text2"]}
-        result = inference(inputs)
-    if task in ["text-classification",  "token-classification", "text2text-generation", "summarization", "translation", "conversational", "text-generation"]:
-        inputs = data["text"]
-        result = inference(inputs)
-    
-    # CV tasks
-    if task == "visual-question-answering" or task == "document-question-answering":
-        img_url = data["image"]
-        text = data["text"]
-        img_data = image_to_bytes(img_url)
-        img_base64 = base64.b64encode(img_data).decode("utf-8")
-        json_data = {}
-        json_data["inputs"] = {}
-        json_data["inputs"]["question"] = text
-        json_data["inputs"]["image"] = img_base64
-        result = requests.post(task_url, headers=HUGGINGFACE_HEADERS, json=json_data).json()
-        # result = inference(inputs) # not support
-
-    if task == "image-to-image":
-        img_url = data["image"]
-        img_data = image_to_bytes(img_url)
-        # result = inference(data=img_data) # not support
-        HUGGINGFACE_HEADERS["Content-Length"] = str(len(img_data))
-        r = requests.post(task_url, headers=HUGGINGFACE_HEADERS, data=img_data)
-        result = r.json()
-        if "path" in result:
-            result["generated image"] = result.pop("path")
-    
-    if task == "text-to-image":
-        inputs = data["text"]
-        img = inference(inputs)
-        name = str(uuid.uuid4())[:4]
-        img.save(f"public/images/{name}.png")
-        result = {}
-        result["generated image"] = f"/images/{name}.png"
-
-    if task == "image-segmentation":
-        img_url = data["image"]
-        img_data = image_to_bytes(img_url)
-        image = Image.open(BytesIO(img_data))
-        predicted = inference(data=img_data)
-        colors = []
-        for i in range(len(predicted)):
-            colors.append((random.randint(100, 255), random.randint(100, 255), random.randint(100, 255), 155))
-        for i, pred in enumerate(predicted):
-            label = pred["label"]
-            mask = pred.pop("mask").encode("utf-8")
-            mask = base64.b64decode(mask)
-            mask = Image.open(BytesIO(mask), mode='r')
-            mask = mask.convert('L')
-
-            layer = Image.new('RGBA', mask.size, colors[i])
-            image.paste(layer, (0, 0), mask)
-        name = str(uuid.uuid4())[:4]
-        image.save(f"public/images/{name}.jpg")
-        result = {}
-        result["generated image with segmentation mask"] = f"/images/{name}.jpg"
-        result["predicted"] = predicted
-
-    if task == "object-detection":
-        img_url = data["image"]
-        img_data = image_to_bytes(img_url)
-        predicted = inference(data=img_data)
-        image = Image.open(BytesIO(img_data))
-        draw = ImageDraw.Draw(image)
-        labels = list(item['label'] for item in predicted)
-        color_map = {}
-        for label in labels:
-            if label not in color_map:
-                color_map[label] = (random.randint(0, 255), random.randint(0, 100), random.randint(0, 255))
-        for label in predicted:
-            box = label["box"]
-            draw.rectangle(((box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])), outline=color_map[label["label"]], width=2)
-            draw.text((box["xmin"]+5, box["ymin"]-15), label["label"], fill=color_map[label["label"]])
-        name = str(uuid.uuid4())[:4]
-        image.save(f"public/images/{name}.jpg")
-        result = {}
-        result["generated image with predicted box"] = f"/images/{name}.jpg"
-        result["predicted"] = predicted
-
-    if task in ["image-classification"]:
-        img_url = data["image"]
-        img_data = image_to_bytes(img_url)
-        result = inference(data=img_data)
- 
-    if task == "image-to-text":
-        img_url = data["image"]
-        img_data = image_to_bytes(img_url)
-        HUGGINGFACE_HEADERS["Content-Length"] = str(len(img_data))
-        r = requests.post(task_url, headers=HUGGINGFACE_HEADERS, data=img_data)
-        result = {}
-        if "generated_text" in r.json()[0]:
-            result["generated text"] = r.json()[0].pop("generated_text")
-    
-    # AUDIO tasks
-    if task == "text-to-speech":
-        inputs = data["text"]
-        response = inference(inputs, raw_response=True)
-        # response = requests.post(task_url, headers=HUGGINGFACE_HEADERS, json={"inputs": text})
-        name = str(uuid.uuid4())[:4]
-        with open(f"public/audios/{name}.flac", "wb") as f:
-            f.write(response.content)
-        result = {"generated audio": f"/audios/{name}.flac"}
-    if task in ["automatic-speech-recognition", "audio-to-audio", "audio-classification"]:
-        audio_url = data["audio"]
-        audio_data = requests.get(audio_url, timeout=10).content
-        response = inference(data=audio_data, raw_response=True)
-        result = response.json()
-        if task == "audio-to-audio":
-            content = None
-            type = None
-            for k, v in result[0].items():
-                if k == "blob":
-                    content = base64.b64decode(v.encode("utf-8"))
-                if k == "content-type":
-                    type = "audio/flac".split("/")[-1]
-            audio = AudioSegment.from_file(BytesIO(content))
-            name = str(uuid.uuid4())[:4]
-            audio.export(f"public/audios/{name}.{type}", format=type)
-            result = {"generated audio": f"/audios/{name}.{type}"}
-    return result
-
-def local_model_inference(model_id, data, task):
-    inference = partial(models, model_id)
-    # contronlet
-    if model_id.startswith("lllyasviel/sd-controlnet-"):
-        img_url = data["image"]
-        text = data["text"]
-        results = inference({"img_url": img_url, "text": text})
-        if "path" in results:
-            results["generated image"] = results.pop("path")
-        return results
-    if model_id.endswith("-control"):
-        img_url = data["image"]
-        results = inference({"img_url": img_url})
-        if "path" in results:
-            results["generated image"] = results.pop("path")
-        return results
-        
-    if task == "text-to-video":
-        results = inference(data)
-        if "path" in results:
-            results["generated video"] = results.pop("path")
-        return results
-
-    # NLP tasks
-    if task == "question-answering" or task == "sentence-similarity":
-        results = inference(json=data)
-        return results
-    if task in ["text-classification",  "token-classification", "text2text-generation", "summarization", "translation", "conversational", "text-generation"]:
-        results = inference(json=data)
-        return results
-
-    # CV tasks
-    if task == "depth-estimation":
-        img_url = data["image"]
-        results = inference({"img_url": img_url})
-        if "path" in results:
-            results["generated depth image"] = results.pop("path")
-        return results
-    if task == "image-segmentation":
-        img_url = data["image"]
-        results = inference({"img_url": img_url})
-        results["generated image with segmentation mask"] = results.pop("path")
-        return results
-    if task == "image-to-image":
-        img_url = data["image"]
-        results = inference({"img_url": img_url})
-        if "path" in results:
-            results["generated image"] = results.pop("path")
-        return results
-    if task == "text-to-image":
-        results = inference(data)
-        if "path" in results:
-            results["generated image"] = results.pop("path")
-        return results
-    if task == "object-detection":
-        img_url = data["image"]
-        predicted = inference({"img_url": img_url})
-        if "error" in predicted:
-            return predicted
-        image = load_image(img_url)
-        draw = ImageDraw.Draw(image)
-        labels = list(item['label'] for item in predicted)
-        color_map = {}
-        for label in labels:
-            if label not in color_map:
-                color_map[label] = (random.randint(0, 255), random.randint(0, 100), random.randint(0, 255))
-        for label in predicted:
-            box = label["box"]
-            draw.rectangle(((box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])), outline=color_map[label["label"]], width=2)
-            draw.text((box["xmin"]+5, box["ymin"]-15), label["label"], fill=color_map[label["label"]])
-        name = str(uuid.uuid4())[:4]
-        image.save(f"public/images/{name}.jpg")
-        results = {}
-        results["generated image with predicted box"] = f"/images/{name}.jpg"
-        results["predicted"] = predicted
-        return results
-    if task in ["image-classification", "image-to-text", "document-question-answering", "visual-question-answering"]:
-        img_url = data["image"]
-        text = None
-        if "text" in data:
-            text = data["text"]
-        results = inference({"img_url": img_url, "text": text})
-        return results
-    # AUDIO tasks
-    if task == "text-to-speech":
-        results = inference(data)
-        if "path" in results:
-            results["generated audio"] = results.pop("path")
-        return results
-    if task in ["automatic-speech-recognition", "audio-to-audio", "audio-classification"]:
-        audio_url = data["audio"]
-        results = inference({"audio_url": audio_url})
-        return results
-
-
-def model_inference(model_id, data, hosted_on, task, huggingfacetoken=None):
-    if huggingfacetoken:
-        HUGGINGFACE_HEADERS = {
-            "Authorization": f"Bearer {huggingfacetoken}",
-        }
-    else:
-        HUGGINGFACE_HEADERS = None
-    if hosted_on == "unknown":
-        r = status(model_id)
-        logger.debug("Local Server Status: " + str(r))
-        if "loaded" in r and r["loaded"]:
-            hosted_on = "local"
-        else:
-            huggingfaceStatusUrl = f"https://api-inference.huggingface.co/status/{model_id}"
-            r = requests.get(huggingfaceStatusUrl, headers=HUGGINGFACE_HEADERS, proxies=PROXY)
-            logger.debug("Huggingface Status: " + str(r.json()))
-            if "loaded" in r and r["loaded"]:
-                hosted_on = "huggingface"
-    try:
-        if hosted_on == "local":
-            inference_result = local_model_inference(model_id, data, task)
-        elif hosted_on == "huggingface":
-            inference_result = huggingface_model_inference(model_id, data, task, huggingfacetoken)
-    except Exception as e:
-        print(e)
-        traceback.print_exc()
-        inference_result = {"error":{"message": str(e)}}
-    return inference_result
-
-
-def get_model_status(model_id, url, headers, queue = None):
-    endpoint_type = "huggingface" if "huggingface" in url else "local"
-    if "huggingface" in url:
-        r = requests.get(url, headers=headers, proxies=PROXY)
-    else:
-        r = status(model_id)
-    if "loaded" in r and r["loaded"]:
-        if queue:
-            queue.put((model_id, True, endpoint_type))
-        return True
-    else:
-        if queue:
-            queue.put((model_id, False, None))
-        return False
-
-def get_avaliable_models(candidates, topk=10, huggingfacetoken = None):
-    all_available_models = {"local": [], "huggingface": []}
-    threads = []
-    result_queue = Queue()
-    HUGGINGFACE_HEADERS = {
-        "Authorization": f"Bearer {huggingfacetoken}",
-    }
-    for candidate in candidates:
-        model_id = candidate["id"]
-
-        if inference_mode != "local":
-            huggingfaceStatusUrl = f"https://api-inference.huggingface.co/status/{model_id}"
-            thread = threading.Thread(target=get_model_status, args=(model_id, huggingfaceStatusUrl, HUGGINGFACE_HEADERS, result_queue))
-            threads.append(thread)
-            thread.start()
-        
-        if inference_mode != "huggingface" and config["local_deployment"] != "minimal":
-            thread = threading.Thread(target=get_model_status, args=(model_id, "", {}, result_queue))
-            threads.append(thread)
-            thread.start()
-        
-    result_count = len(threads)
-    while result_count:
-        model_id, status, endpoint_type = result_queue.get()
-        if status and model_id not in all_available_models:
-            all_available_models[endpoint_type].append(model_id)
-        if len(all_available_models["local"] + all_available_models["huggingface"]) >= topk:
-            break
-        result_count -= 1
-
-    for thread in threads:
-        thread.join()
-
-    return all_available_models
-
-def collect_result(command, choose, inference_result):
-    result = {"task": command}
-    result["inference result"] = inference_result
-    result["choose model result"] = choose
-    logger.debug(f"inference result: {inference_result}")
-    return result
-
-
-def run_task(input, command, results, openaikey = None, huggingfacetoken = None):
-    id = command["id"]
-    args = command["args"]
-    task = command["task"]
-    deps = command["dep"]
-    if deps[0] != -1:
-        dep_tasks = [results[dep] for dep in deps]
-    else:
-        dep_tasks = []
-    
-    logger.debug(f"Run task: {id} - {task}")
-    logger.debug("Deps: " + json.dumps(dep_tasks))
-
-    if deps[0] != -1:
-        if "image" in args and "<GENERATED>-" in args["image"]:
-            resource_id = int(args["image"].split("-")[1])
-            if "generated image" in results[resource_id]["inference result"]:
-                args["image"] = results[resource_id]["inference result"]["generated image"]
-        if "audio" in args and "<GENERATED>-" in args["audio"]:
-            resource_id = int(args["audio"].split("-")[1])
-            if "generated audio" in results[resource_id]["inference result"]:
-                args["audio"] = results[resource_id]["inference result"]["generated audio"]
-        if "text" in args and "<GENERATED>-" in args["text"]:
-            resource_id = int(args["text"].split("-")[1])
-            if "generated text" in results[resource_id]["inference result"]:
-                args["text"] = results[resource_id]["inference result"]["generated text"]
-
-    text = image = audio = None
-    for dep_task in dep_tasks:
-        if "generated text" in dep_task["inference result"]:
-            text = dep_task["inference result"]["generated text"]
-            logger.debug("Detect the generated text of dependency task (from results):" + text)
-        elif "text" in dep_task["task"]["args"]:
-            text = dep_task["task"]["args"]["text"]
-            logger.debug("Detect the text of dependency task (from args): " + text)
-        if "generated image" in dep_task["inference result"]:
-            image = dep_task["inference result"]["generated image"]
-            logger.debug("Detect the generated image of dependency task (from results): " + image)
-        elif "image" in dep_task["task"]["args"]:
-            image = dep_task["task"]["args"]["image"]
-            logger.debug("Detect the image of dependency task (from args): " + image)
-        if "generated audio" in dep_task["inference result"]:
-            audio = dep_task["inference result"]["generated audio"]
-            logger.debug("Detect the generated audio of dependency task (from results): " + audio)
-        elif "audio" in dep_task["task"]["args"]:
-            audio = dep_task["task"]["args"]["audio"]
-            logger.debug("Detect the audio of dependency task (from args): " + audio)
-
-    if "image" in args and "<GENERATED>" in args["image"]:
-        if image:
-            args["image"] = image
-    if "audio" in args and "<GENERATED>" in args["audio"]:
-        if audio:
-            args["audio"] = audio
-    if "text" in args and "<GENERATED>" in args["text"]:
-        if text:
-            args["text"] = text
-
-    for resource in ["image", "audio"]:
-        if resource in args and not args[resource].startswith("public/") and len(args[resource]) > 0 and not args[resource].startswith("http"):
-            args[resource] = f"public/{args[resource]}"
-    
-    if "-text-to-image" in command['task'] and "text" not in args:
-        logger.debug("control-text-to-image task, but text is empty, so we use control-generation instead.")
-        control = task.split("-")[0]
-        
-        if control == "seg":
-            task = "image-segmentation"
-            command['task'] = task
-        elif control == "depth":
-            task = "depth-estimation"
-            command['task'] = task
-        else:
-            task = f"{control}-control"
-
-    command["args"] = args
-    logger.debug(f"parsed task: {command}")
-
-    if task.endswith("-text-to-image") or task.endswith("-control"):
-        if inference_mode != "huggingface":
-            if task.endswith("-text-to-image"):
-                control = task.split("-")[0]
-                best_model_id = f"lllyasviel/sd-controlnet-{control}"
-            else:
-                best_model_id = task
-            hosted_on = "local"
-            reason = "ControlNet is the best model for this task."
-            choose = {"id": best_model_id, "reason": reason}
-            logger.debug(f"chosen model: {choose}")
-        else:
-            logger.warning(f"Task {command['task']} is not available. ControlNet need to be deployed locally.")
-            record_case(success=False, **{"input": input, "task": command, "reason": f"Task {command['task']} is not available. ControlNet need to be deployed locally.", "op":"message"})
-            inference_result = {"error": f"service related to ControlNet is not available."}
-            results[id] = collect_result(command, "", inference_result)
-            return False
-    elif task in ["summarization", "translation", "conversational", "text-generation", "text2text-generation"]: # ChatGPT Can do
-        best_model_id = "ChatGPT"
-        reason = "ChatGPT performs well on some NLP tasks as well."
-        choose = {"id": best_model_id, "reason": reason}
-        messages = [{
-            "role": "user",
-            "content": f"[ {input} ] contains a task in JSON format {command}, 'task' indicates the task type and 'args' indicates the arguments required for the task. Don't explain the task to me, just help me do it and give me the result. The result must be in text form without any urls."
-        }]
-        response = chitchat(messages, openaikey)
-        results[id] = collect_result(command, choose, {"response": response})
-        return True
-    else:
-        if task not in MODELS_MAP:
-            logger.warning(f"no available models on {task} task.")
-            record_case(success=False, **{"input": input, "task": command, "reason": f"task not support: {command['task']}", "op":"message"})
-            inference_result = {"error": f"{command['task']} not found in available tasks."}
-            results[id] = collect_result(command, "", inference_result)
-            return False
-
-        candidates = MODELS_MAP[task][:20]
-        all_avaliable_models = get_avaliable_models(candidates, config["num_candidate_models"], huggingfacetoken)
-        all_avaliable_model_ids = all_avaliable_models["local"] + all_avaliable_models["huggingface"]
-        logger.debug(f"avaliable models on {command['task']}: {all_avaliable_models}")
-
-        if len(all_avaliable_model_ids) == 0:
-            logger.warning(f"no available models on {command['task']}")
-            record_case(success=False, **{"input": input, "task": command, "reason": f"no available models: {command['task']}", "op":"message"})
-            inference_result = {"error": f"no available models on {command['task']} task."}
-            results[id] = collect_result(command, "", inference_result)
-            return False
-            
-        if len(all_avaliable_model_ids) == 1:
-            best_model_id = all_avaliable_model_ids[0]
-            hosted_on = "local" if best_model_id in all_avaliable_models["local"] else "huggingface"
-            reason = "Only one model available."
-            choose = {"id": best_model_id, "reason": reason}
-            logger.debug(f"chosen model: {choose}")
-        else:
-            cand_models_info = [
-                {
-                    "id": model["id"],
-                    "inference endpoint": all_avaliable_models.get(
-                        "local" if model["id"] in all_avaliable_models["local"] else "huggingface"
-                    ),
-                    "likes": model.get("likes"),
-                    "description": model.get("description", "")[:config["max_description_length"]],
-                    "language": model.get("language"),
-                    "tags": model.get("tags"),
-                }
-                for model in candidates
-                if model["id"] in all_avaliable_model_ids
-            ]
-
-            choose_str = choose_model(input, command, cand_models_info, openaikey)
-            logger.debug(f"chosen model: {choose_str}")
-            try:
-                choose = json.loads(choose_str)
-                reason = choose["reason"]
-                best_model_id = choose["id"]
-                hosted_on = "local" if best_model_id in all_avaliable_models["local"] else "huggingface"
-            except Exception as e:
-                logger.warning(f"the response [ {choose_str} ] is not a valid JSON, try to find the model id and reason in the response.")
-                choose_str = find_json(choose_str)
-                best_model_id, reason, choose  = get_id_reason(choose_str)
-                hosted_on = "local" if best_model_id in all_avaliable_models["local"] else "huggingface"
-    inference_result = model_inference(best_model_id, args, hosted_on, command['task'], huggingfacetoken)
-
-    if "error" in inference_result:
-        logger.warning(f"Inference error: {inference_result['error']}")
-        record_case(success=False, **{"input": input, "task": command, "reason": f"inference error: {inference_result['error']}", "op":"message"})
-        results[id] = collect_result(command, choose, inference_result)
-        return False
-    
-    results[id] = collect_result(command, choose, inference_result)
-    return True
-
-def chat_huggingface(messages, openaikey = None, huggingfacetoken = None, return_planning = False, return_results = False):
-    start = time.time()
-    context = messages[:-1]
-    input = messages[-1]["content"]
-    logger.info("*"*80)
-    logger.info(f"input: {input}")
-
-    task_str = parse_task(context, input, openaikey)
-    logger.info(task_str)
-
-    if "error" in task_str:
-        return str(task_str), {}
-    else:
-        task_str = task_str.strip()
-
-    try:
-        tasks = json.loads(task_str)
-    except Exception as e:
-        logger.debug(e)
-        response = chitchat(messages, openaikey)
-        record_case(success=False, **{"input": input, "task": task_str, "reason": "task parsing fail", "op":"chitchat"})
-        return response, {}
-
-    if task_str == "[]":  # using LLM response for empty task
-        record_case(success=False, **{"input": input, "task": [], "reason": "task parsing fail: empty", "op": "chitchat"})
-        response = chitchat(messages, openaikey)
-        return response, {}
-
-    if len(tasks)==1 and tasks[0]["task"] in ["summarization", "translation", "conversational", "text-generation", "text2text-generation"]:
-        record_case(success=True, **{"input": input, "task": tasks, "reason": "task parsing fail: empty", "op": "chitchat"})
-        response = chitchat(messages, openaikey)
-        best_model_id = "ChatGPT"
-        reason = "ChatGPT performs well on some NLP tasks as well."
-        choose = {"id": best_model_id, "reason": reason}
-        return response, collect_result(tasks[0], choose, {"response": response})
-    
-
-    tasks = unfold(tasks)
-    tasks = fix_dep(tasks)
-    logger.debug(tasks)
-    
-    if return_planning:
-        return tasks
-
-    results = {}
-    threads = []
-    tasks = tasks[:]
-    d = dict()
-    retry = 0
-    while True:
-        num_threads = len(threads)
-        for task in tasks:
-            dep = task["dep"]
-            # logger.debug(f"d.keys(): {d.keys()}, dep: {dep}")
-            for dep_id in dep:
-                if dep_id >= task["id"]:
-                    task["dep"] = [-1]
-                    dep = [-1]
-                    break
-            if len(list(set(dep).intersection(d.keys()))) == len(dep) or dep[0] == -1:
-                tasks.remove(task)
-                thread = threading.Thread(target=run_task, args=(input, task, d, openaikey, huggingfacetoken))
-                thread.start()
-                threads.append(thread)
-        if num_threads == len(threads):
-            time.sleep(0.5)
-            retry += 1
-        if retry > 80:
-            logger.debug("User has waited too long, Loop break.")
-            break
-        if len(tasks) == 0:
-            break
-    for thread in threads:
-        thread.join()
-    
-    results = d.copy()
-
-    logger.debug(results)
-    if return_results:
-        return results
-    
-    response = response_results(input, results, openaikey).strip()
-
-    end = time.time()
-    during = end - start
-
-    answer = {"message": response}
-    record_case(success=True, **{"input": input, "task": task_str, "results": results, "response": response, "during": during, "op":"response"})
-    logger.info(f"response: {response}")
-    return response, results

spaces/Amrrs/DragGan-Inversion/PTI/criteria/localitly_regulizer.py

@@ -1,65 +0,0 @@
-import torch
-import numpy as np
-from PTI.criteria import l2_loss
-from PTI.configs import hyperparameters
-from PTI.configs import global_config
-
-
-class Space_Regulizer:
-    def __init__(self, original_G, lpips_net):
-        self.original_G = original_G
-        self.morphing_regulizer_alpha = hyperparameters.regulizer_alpha
-        self.lpips_loss = lpips_net
-
-    def get_morphed_w_code(self, new_w_code, fixed_w):
-        interpolation_direction = new_w_code - fixed_w
-        interpolation_direction_norm = torch.norm(interpolation_direction, p=2)
-        direction_to_move = hyperparameters.regulizer_alpha * \
-            interpolation_direction / interpolation_direction_norm
-        result_w = fixed_w + direction_to_move
-        self.morphing_regulizer_alpha * fixed_w + \
-            (1 - self.morphing_regulizer_alpha) * new_w_code
-
-        return result_w
-
-    def get_image_from_ws(self, w_codes, G):
-        return torch.cat([G.synthesis(w_code, noise_mode='none', force_fp32=True) for w_code in w_codes])
-
-    def ball_holder_loss_lazy(self, new_G, num_of_sampled_latents, w_batch, use_wandb=False):
-        loss = 0.0
-
-        z_samples = np.random.randn(
-            num_of_sampled_latents, self.original_G.z_dim)
-        w_samples = self.original_G.mapping(torch.from_numpy(z_samples).to(global_config.device), None,
-                                            truncation_psi=0.5)
-        territory_indicator_ws = [self.get_morphed_w_code(
-            w_code.unsqueeze(0), w_batch) for w_code in w_samples]
-
-        for w_code in territory_indicator_ws:
-            new_img = new_G.synthesis(
-                w_code, noise_mode='none', force_fp32=True)
-            with torch.no_grad():
-                old_img = self.original_G.synthesis(
-                    w_code, noise_mode='none', force_fp32=True)
-
-            if hyperparameters.regulizer_l2_lambda > 0:
-                l2_loss_val = l2_loss.l2_loss(old_img, new_img)
-                if use_wandb:
-                    wandb.log({f'space_regulizer_l2_loss_val': l2_loss_val.detach().cpu()},
-                              step=global_config.training_step)
-                loss += l2_loss_val * hyperparameters.regulizer_l2_lambda
-
-            if hyperparameters.regulizer_lpips_lambda > 0:
-                loss_lpips = self.lpips_loss(old_img, new_img)
-                loss_lpips = torch.mean(torch.squeeze(loss_lpips))
-                if use_wandb:
-                    wandb.log({f'space_regulizer_lpips_loss_val': loss_lpips.detach().cpu()},
-                              step=global_config.training_step)
-                loss += loss_lpips * hyperparameters.regulizer_lpips_lambda
-
-        return loss / len(territory_indicator_ws)
-
-    def space_regulizer_loss(self, new_G, w_batch, use_wandb):
-        ret_val = self.ball_holder_loss_lazy(
-            new_G, hyperparameters.latent_ball_num_of_samples, w_batch, use_wandb)
-        return ret_val

spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/ops/filtered_lrelu.h

@@ -1,90 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct filtered_lrelu_kernel_params
-{
-    // These parameters decide which kernel to use.
-    int             up;         // upsampling ratio (1, 2, 4)
-    int             down;       // downsampling ratio (1, 2, 4)
-    int2            fuShape;    // [size, 1] | [size, size]
-    int2            fdShape;    // [size, 1] | [size, size]
-
-    int             _dummy;     // Alignment.
-
-    // Rest of the parameters.
-    const void*     x;          // Input tensor.
-    void*           y;          // Output tensor.
-    const void*     b;          // Bias tensor.
-    unsigned char*  s;          // Sign tensor in/out. NULL if unused.
-    const float*    fu;         // Upsampling filter.
-    const float*    fd;         // Downsampling filter.
-
-    int2            pad0;       // Left/top padding.
-    float           gain;       // Additional gain factor.
-    float           slope;      // Leaky ReLU slope on negative side.
-    float           clamp;      // Clamp after nonlinearity.
-    int             flip;       // Filter kernel flip for gradient computation.
-
-    int             tilesXdim;  // Original number of horizontal output tiles.
-    int             tilesXrep;  // Number of horizontal tiles per CTA.
-    int             blockZofs;  // Block z offset to support large minibatch, channel dimensions.
-
-    int4            xShape;     // [width, height, channel, batch]
-    int4            yShape;     // [width, height, channel, batch]
-    int2            sShape;     // [width, height] - width is in bytes. Contiguous. Zeros if unused.
-    int2            sOfs;       // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
-    int             swLimit;    // Active width of sign tensor in bytes.
-
-    longlong4       xStride;    // Strides of all tensors except signs, same component order as shapes.
-    longlong4       yStride;    //
-    int64_t         bStride;    //
-    longlong3       fuStride;   //
-    longlong3       fdStride;   //
-};
-
-struct filtered_lrelu_act_kernel_params
-{
-    void*           x;          // Input/output, modified in-place.
-    unsigned char*  s;          // Sign tensor in/out. NULL if unused.
-
-    float           gain;       // Additional gain factor.
-    float           slope;      // Leaky ReLU slope on negative side.
-    float           clamp;      // Clamp after nonlinearity.
-
-    int4            xShape;     // [width, height, channel, batch]
-    longlong4       xStride;    // Input/output tensor strides, same order as in shape.
-    int2            sShape;     // [width, height] - width is in elements. Contiguous. Zeros if unused.
-    int2            sOfs;       // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct filtered_lrelu_kernel_spec
-{
-    void*   setup;              // Function for filter kernel setup.
-    void*   exec;               // Function for main operation.
-    int2    tileOut;            // Width/height of launch tile.
-    int     numWarps;           // Number of warps per thread block, determines launch block size.
-    int     xrep;               // For processing multiple horizontal tiles per thread block.
-    int     dynamicSharedKB;    // How much dynamic shared memory the exec kernel wants.
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T, class index_t, bool signWrite, bool signRead> filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB);
-template <class T, bool signWrite, bool signRead> void* choose_filtered_lrelu_act_kernel(void);
-template <bool signWrite, bool signRead> cudaError_t copy_filters(cudaStream_t stream);
-
-//------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_controlnet_inpaint_img2img.py

@@ -1,1119 +0,0 @@
-# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
-
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import PIL.Image
-import torch
-import torch.nn.functional as F
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import (
-    PIL_INTERPOLATION,
-    is_accelerate_available,
-    is_accelerate_version,
-    randn_tensor,
-    replace_example_docstring,
-)
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import numpy as np
-        >>> import torch
-        >>> from PIL import Image
-        >>> from stable_diffusion_controlnet_inpaint_img2img import StableDiffusionControlNetInpaintImg2ImgPipeline
-
-        >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
-        >>> from diffusers import ControlNetModel, UniPCMultistepScheduler
-        >>> from diffusers.utils import load_image
-
-        >>> def ade_palette():
-                return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-                        [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-                        [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-                        [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-                        [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-                        [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-                        [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-                        [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-                        [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-                        [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-                        [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-                        [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-                        [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-                        [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-                        [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
-                        [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-                        [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
-                        [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-                        [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
-                        [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-                        [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
-                        [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-                        [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
-                        [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-                        [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
-                        [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-                        [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
-                        [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-                        [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
-                        [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-                        [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
-                        [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-                        [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
-                        [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-                        [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
-                        [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-                        [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
-                        [102, 255, 0], [92, 0, 255]]
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
-        >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
-
-        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)
-
-        >>> pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained(
-                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
-            )
-
-        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-        >>> pipe.enable_xformers_memory_efficient_attention()
-        >>> pipe.enable_model_cpu_offload()
-
-        >>> def image_to_seg(image):
-                pixel_values = image_processor(image, return_tensors="pt").pixel_values
-                with torch.no_grad():
-                    outputs = image_segmentor(pixel_values)
-                seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
-                color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)  # height, width, 3
-                palette = np.array(ade_palette())
-                for label, color in enumerate(palette):
-                    color_seg[seg == label, :] = color
-                color_seg = color_seg.astype(np.uint8)
-                seg_image = Image.fromarray(color_seg)
-                return seg_image
-
-        >>> image = load_image(
-                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-            )
-
-        >>> mask_image = load_image(
-                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-            )
-
-        >>> controlnet_conditioning_image = image_to_seg(image)
-
-        >>> image = pipe(
-                "Face of a yellow cat, high resolution, sitting on a park bench",
-                image,
-                mask_image,
-                controlnet_conditioning_image,
-                num_inference_steps=20,
-            ).images[0]
-
-        >>> image.save("out.png")
-        ```
-"""
-
-
-def prepare_image(image):
-    if isinstance(image, torch.Tensor):
-        # Batch single image
-        if image.ndim == 3:
-            image = image.unsqueeze(0)
-
-        image = image.to(dtype=torch.float32)
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-    return image
-
-
-def prepare_mask_image(mask_image):
-    if isinstance(mask_image, torch.Tensor):
-        if mask_image.ndim == 2:
-            # Batch and add channel dim for single mask
-            mask_image = mask_image.unsqueeze(0).unsqueeze(0)
-        elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
-            # Single mask, the 0'th dimension is considered to be
-            # the existing batch size of 1
-            mask_image = mask_image.unsqueeze(0)
-        elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
-            # Batch of mask, the 0'th dimension is considered to be
-            # the batching dimension
-            mask_image = mask_image.unsqueeze(1)
-
-        # Binarize mask
-        mask_image[mask_image < 0.5] = 0
-        mask_image[mask_image >= 0.5] = 1
-    else:
-        # preprocess mask
-        if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):
-            mask_image = [mask_image]
-
-        if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):
-            mask_image = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0)
-            mask_image = mask_image.astype(np.float32) / 255.0
-        elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):
-            mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
-
-        mask_image[mask_image < 0.5] = 0
-        mask_image[mask_image >= 0.5] = 1
-        mask_image = torch.from_numpy(mask_image)
-
-    return mask_image
-
-
-def prepare_controlnet_conditioning_image(
-    controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype
-):
-    if not isinstance(controlnet_conditioning_image, torch.Tensor):
-        if isinstance(controlnet_conditioning_image, PIL.Image.Image):
-            controlnet_conditioning_image = [controlnet_conditioning_image]
-
-        if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):
-            controlnet_conditioning_image = [
-                np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
-                for i in controlnet_conditioning_image
-            ]
-            controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)
-            controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0
-            controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)
-            controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)
-        elif isinstance(controlnet_conditioning_image[0], torch.Tensor):
-            controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)
-
-    image_batch_size = controlnet_conditioning_image.shape[0]
-
-    if image_batch_size == 1:
-        repeat_by = batch_size
-    else:
-        # image batch size is the same as prompt batch size
-        repeat_by = num_images_per_prompt
-
-    controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)
-
-    controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)
-
-    return controlnet_conditioning_image
-
-
-class StableDiffusionControlNetInpaintImg2ImgPipeline(DiffusionPipeline):
-    """
-    Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
-    """
-
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        controlnet: ControlNetModel,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        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 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 ."
-            )
-
-        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."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            controlnet=controlnet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        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 invoked, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        Note that offloading happens on a submodule basis. Memory savings are higher than with
-        `enable_model_cpu_offload`, but performance is lower.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:
-            cpu_offload(cpu_offloaded_model, device)
-
-        if self.safety_checker is not None:
-            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method 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 with
-        `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}")
-
-        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:
-            # the safety checker can offload the vae again
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # control net hook has be manually offloaded as it alternates with unet
-        cpu_offload_with_hook(self.controlnet, device)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    @property
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    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,
-    ):
-        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.
-        """
-        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:
-            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 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 = 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 not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    def decode_latents(self, latents):
-        latents = 1 / self.vae.config.scaling_factor * 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()
-        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,
-        image,
-        mask_image,
-        controlnet_conditioning_image,
-        height,
-        width,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        strength=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}."
-                )
-
-        controlnet_cond_image_is_pil = isinstance(controlnet_conditioning_image, PIL.Image.Image)
-        controlnet_cond_image_is_tensor = isinstance(controlnet_conditioning_image, torch.Tensor)
-        controlnet_cond_image_is_pil_list = isinstance(controlnet_conditioning_image, list) and isinstance(
-            controlnet_conditioning_image[0], PIL.Image.Image
-        )
-        controlnet_cond_image_is_tensor_list = isinstance(controlnet_conditioning_image, list) and isinstance(
-            controlnet_conditioning_image[0], torch.Tensor
-        )
-
-        if (
-            not controlnet_cond_image_is_pil
-            and not controlnet_cond_image_is_tensor
-            and not controlnet_cond_image_is_pil_list
-            and not controlnet_cond_image_is_tensor_list
-        ):
-            raise TypeError(
-                "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
-            )
-
-        if controlnet_cond_image_is_pil:
-            controlnet_cond_image_batch_size = 1
-        elif controlnet_cond_image_is_tensor:
-            controlnet_cond_image_batch_size = controlnet_conditioning_image.shape[0]
-        elif controlnet_cond_image_is_pil_list:
-            controlnet_cond_image_batch_size = len(controlnet_conditioning_image)
-        elif controlnet_cond_image_is_tensor_list:
-            controlnet_cond_image_batch_size = len(controlnet_conditioning_image)
-
-        if prompt is not None and isinstance(prompt, str):
-            prompt_batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            prompt_batch_size = len(prompt)
-        elif prompt_embeds is not None:
-            prompt_batch_size = prompt_embeds.shape[0]
-
-        if controlnet_cond_image_batch_size != 1 and controlnet_cond_image_batch_size != prompt_batch_size:
-            raise ValueError(
-                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {controlnet_cond_image_batch_size}, prompt batch size: {prompt_batch_size}"
-            )
-
-        if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor):
-            raise TypeError("if `image` is a tensor, `mask_image` must also be a tensor")
-
-        if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image):
-            raise TypeError("if `image` is a PIL image, `mask_image` must also be a PIL image")
-
-        if isinstance(image, torch.Tensor):
-            if image.ndim != 3 and image.ndim != 4:
-                raise ValueError("`image` must have 3 or 4 dimensions")
-
-            if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4:
-                raise ValueError("`mask_image` must have 2, 3, or 4 dimensions")
-
-            if image.ndim == 3:
-                image_batch_size = 1
-                image_channels, image_height, image_width = image.shape
-            elif image.ndim == 4:
-                image_batch_size, image_channels, image_height, image_width = image.shape
-
-            if mask_image.ndim == 2:
-                mask_image_batch_size = 1
-                mask_image_channels = 1
-                mask_image_height, mask_image_width = mask_image.shape
-            elif mask_image.ndim == 3:
-                mask_image_channels = 1
-                mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape
-            elif mask_image.ndim == 4:
-                mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape
-
-            if image_channels != 3:
-                raise ValueError("`image` must have 3 channels")
-
-            if mask_image_channels != 1:
-                raise ValueError("`mask_image` must have 1 channel")
-
-            if image_batch_size != mask_image_batch_size:
-                raise ValueError("`image` and `mask_image` mush have the same batch sizes")
-
-            if image_height != mask_image_height or image_width != mask_image_width:
-                raise ValueError("`image` and `mask_image` must have the same height and width dimensions")
-
-            if image.min() < -1 or image.max() > 1:
-                raise ValueError("`image` should be in range [-1, 1]")
-
-            if mask_image.min() < 0 or mask_image.max() > 1:
-                raise ValueError("`mask_image` should be in range [0, 1]")
-        else:
-            mask_image_channels = 1
-            image_channels = 3
-
-        single_image_latent_channels = self.vae.config.latent_channels
-
-        total_latent_channels = single_image_latent_channels * 2 + mask_image_channels
-
-        if total_latent_channels != self.unet.config.in_channels:
-            raise ValueError(
-                f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received"
-                f" non inpainting latent channels: {single_image_latent_channels},"
-                f" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}."
-                f" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs."
-            )
-
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
-        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
-            )
-
-        image = image.to(device=device, dtype=dtype)
-
-        batch_size = batch_size * num_images_per_prompt
-        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 isinstance(generator, list):
-            init_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
-            ]
-            init_latents = torch.cat(init_latents, dim=0)
-        else:
-            init_latents = self.vae.encode(image).latent_dist.sample(generator)
-
-        init_latents = self.vae.config.scaling_factor * init_latents
-
-        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
-            )
-        else:
-            init_latents = torch.cat([init_latents], dim=0)
-
-        shape = init_latents.shape
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        latents = init_latents
-
-        return latents
-
-    def prepare_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))
-        mask_image = mask_image.to(device=device, dtype=dtype)
-
-        # duplicate mask for each generation per prompt, using mps friendly method
-        if mask_image.shape[0] < batch_size:
-            if not batch_size % mask_image.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1)
-
-        mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image
-
-        mask_image_latents = mask_image
-
-        return mask_image_latents
-
-    def prepare_masked_image_latents(
-        self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
-    ):
-        masked_image = masked_image.to(device=device, dtype=dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            masked_image_latents = [
-                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            masked_image_latents = torch.cat(masked_image_latents, dim=0)
-        else:
-            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
-        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
-
-        # duplicate masked_image_latents for each generation per prompt, using mps friendly method
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
-
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-        return masked_image_latents
-
-    def _default_height_width(self, height, width, image):
-        if isinstance(image, list):
-            image = image[0]
-
-        if height is None:
-            if isinstance(image, PIL.Image.Image):
-                height = image.height
-            elif isinstance(image, torch.Tensor):
-                height = image.shape[3]
-
-            height = (height // 8) * 8  # round down to nearest multiple of 8
-
-        if width is None:
-            if isinstance(image, PIL.Image.Image):
-                width = image.width
-            elif isinstance(image, torch.Tensor):
-                width = image.shape[2]
-
-            width = (width // 8) * 8  # round down to nearest multiple of 8
-
-        return height, width
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[torch.Tensor, PIL.Image.Image] = None,
-        mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
-        controlnet_conditioning_image: Union[
-            torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]
-        ] = None,
-        strength: float = 0.8,
-        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,
-        controlnet_conditioning_scale: float = 1.0,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            image (`torch.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            mask_image (`torch.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
-                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
-                instead of 3, so the expected shape would be `(B, H, W, 1)`.
-            controlnet_conditioning_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
-                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
-                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. PIL.Image.Image` can
-                also be accepted as an image. The control image is automatically resized to fit the output image.
-            strength (`float`, *optional*):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            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):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            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`).
-            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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](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 will ge 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, *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.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                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 will be called. If not specified, the callback will be
-                called at every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-            controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
-                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
-                to the residual in the original unet.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height, width = self._default_height_width(height, width, controlnet_conditioning_image)
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            image,
-            mask_image,
-            controlnet_conditioning_image,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-            strength,
-        )
-
-        # 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
-        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,
-        )
-
-        # 4. Prepare mask, image, and controlnet_conditioning_image
-        image = prepare_image(image)
-
-        mask_image = prepare_mask_image(mask_image)
-
-        controlnet_conditioning_image = prepare_controlnet_conditioning_image(
-            controlnet_conditioning_image,
-            width,
-            height,
-            batch_size * num_images_per_prompt,
-            num_images_per_prompt,
-            device,
-            self.controlnet.dtype,
-        )
-
-        masked_image = image * (mask_image < 0.5)
-
-        # 5. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
-        # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size,
-            num_images_per_prompt,
-            prompt_embeds.dtype,
-            device,
-            generator,
-        )
-
-        mask_image_latents = self.prepare_mask_latents(
-            mask_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            do_classifier_free_guidance,
-        )
-
-        masked_image_latents = self.prepare_masked_image_latents(
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            do_classifier_free_guidance,
-        )
-
-        if do_classifier_free_guidance:
-            controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)
-
-        # 7. 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)
-
-        # 8. 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
-                non_inpainting_latent_model_input = (
-                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                )
-
-                non_inpainting_latent_model_input = self.scheduler.scale_model_input(
-                    non_inpainting_latent_model_input, t
-                )
-
-                inpainting_latent_model_input = torch.cat(
-                    [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1
-                )
-
-                down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    non_inpainting_latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    controlnet_cond=controlnet_conditioning_image,
-                    return_dict=False,
-                )
-
-                down_block_res_samples = [
-                    down_block_res_sample * controlnet_conditioning_scale
-                    for down_block_res_sample in down_block_res_samples
-                ]
-                mid_block_res_sample *= controlnet_conditioning_scale
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    inpainting_latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    down_block_additional_residuals=down_block_res_samples,
-                    mid_block_additional_residual=mid_block_res_sample,
-                ).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 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 we do sequential model offloading, let's offload unet and controlnet
-        # manually for max memory savings
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.unet.to("cpu")
-            self.controlnet.to("cpu")
-            torch.cuda.empty_cache()
-
-        if output_type == "latent":
-            image = latents
-            has_nsfw_concept = None
-        elif output_type == "pil":
-            # 8. Post-processing
-            image = self.decode_latents(latents)
-
-            # 9. Run safety checker
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-            # 10. Convert to PIL
-            image = self.numpy_to_pil(image)
-        else:
-            # 8. Post-processing
-            image = self.decode_latents(latents)
-
-            # 9. Run safety checker
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-        # 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 StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

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

@@ -1,16 +0,0 @@
-# Fast R-CNN
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@inproceedings{girshick2015fast,
-  title={Fast r-cnn},
-  author={Girshick, Ross},
-  booktitle={Proceedings of the IEEE international conference on computer vision},
-  year={2015}
-}
-```
-
-## Results and models

spaces/Andy1621/uniformer_image_detection/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py

@@ -1,4 +0,0 @@
-_base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)

spaces/Andy1621/uniformer_image_detection/configs/wider_face/ssd300_wider_face.py

@@ -1,18 +0,0 @@
-_base_ = [
-    '../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
-    '../_base_/default_runtime.py'
-]
-model = dict(bbox_head=dict(num_classes=1))
-# optimizer
-optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4)
-optimizer_config = dict()
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=0.001,
-    step=[16, 20])
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=24)
-log_config = dict(interval=1)

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/fast_rcnn.py

@@ -1,52 +0,0 @@
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class FastRCNN(TwoStageDetector):
-    """Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
-
-    def __init__(self,
-                 backbone,
-                 roi_head,
-                 train_cfg,
-                 test_cfg,
-                 neck=None,
-                 pretrained=None):
-        super(FastRCNN, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained)
-
-    def forward_test(self, imgs, img_metas, proposals, **kwargs):
-        """
-        Args:
-            imgs (List[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains all images in the batch.
-            img_metas (List[List[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch.
-            proposals (List[List[Tensor]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch. The Tensor should have a shape Px4, where
-                P is the number of proposals.
-        """
-        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
-            if not isinstance(var, list):
-                raise TypeError(f'{name} must be a list, but got {type(var)}')
-
-        num_augs = len(imgs)
-        if num_augs != len(img_metas):
-            raise ValueError(f'num of augmentations ({len(imgs)}) '
-                             f'!= num of image meta ({len(img_metas)})')
-
-        if num_augs == 1:
-            return self.simple_test(imgs[0], img_metas[0], proposals[0],
-                                    **kwargs)
-        else:
-            # TODO: support test-time augmentation
-            assert NotImplementedError

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/pisa_roi_head.py

@@ -1,159 +0,0 @@
-from mmdet.core import bbox2roi
-from ..builder import HEADS
-from ..losses.pisa_loss import carl_loss, isr_p
-from .standard_roi_head import StandardRoIHead
-
-
-@HEADS.register_module()
-class PISARoIHead(StandardRoIHead):
-    r"""The RoI head for `Prime Sample Attention in Object Detection
-    <https://arxiv.org/abs/1904.04821>`_."""
-
-    def forward_train(self,
-                      x,
-                      img_metas,
-                      proposal_list,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None,
-                      gt_masks=None):
-        """Forward function for training.
-
-        Args:
-            x (list[Tensor]): List of multi-level img features.
-            img_metas (list[dict]): List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                `mmdet/datasets/pipelines/formatting.py:Collect`.
-            proposals (list[Tensors]): List of region proposals.
-            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): Class indices corresponding to each box
-            gt_bboxes_ignore (list[Tensor], optional): Specify which bounding
-                boxes can be ignored when computing the loss.
-            gt_masks (None | Tensor) : True segmentation masks for each box
-                used if the architecture supports a segmentation task.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of loss components
-        """
-        # assign gts and sample proposals
-        if self.with_bbox or self.with_mask:
-            num_imgs = len(img_metas)
-            if gt_bboxes_ignore is None:
-                gt_bboxes_ignore = [None for _ in range(num_imgs)]
-            sampling_results = []
-            neg_label_weights = []
-            for i in range(num_imgs):
-                assign_result = self.bbox_assigner.assign(
-                    proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
-                    gt_labels[i])
-                sampling_result = self.bbox_sampler.sample(
-                    assign_result,
-                    proposal_list[i],
-                    gt_bboxes[i],
-                    gt_labels[i],
-                    feats=[lvl_feat[i][None] for lvl_feat in x])
-                # neg label weight is obtained by sampling when using ISR-N
-                neg_label_weight = None
-                if isinstance(sampling_result, tuple):
-                    sampling_result, neg_label_weight = sampling_result
-                sampling_results.append(sampling_result)
-                neg_label_weights.append(neg_label_weight)
-
-        losses = dict()
-        # bbox head forward and loss
-        if self.with_bbox:
-            bbox_results = self._bbox_forward_train(
-                x,
-                sampling_results,
-                gt_bboxes,
-                gt_labels,
-                img_metas,
-                neg_label_weights=neg_label_weights)
-            losses.update(bbox_results['loss_bbox'])
-
-        # mask head forward and loss
-        if self.with_mask:
-            mask_results = self._mask_forward_train(x, sampling_results,
-                                                    bbox_results['bbox_feats'],
-                                                    gt_masks, img_metas)
-            losses.update(mask_results['loss_mask'])
-
-        return losses
-
-    def _bbox_forward(self, x, rois):
-        """Box forward function used in both training and testing."""
-        # TODO: a more flexible way to decide which feature maps to use
-        bbox_feats = self.bbox_roi_extractor(
-            x[:self.bbox_roi_extractor.num_inputs], rois)
-        if self.with_shared_head:
-            bbox_feats = self.shared_head(bbox_feats)
-        cls_score, bbox_pred = self.bbox_head(bbox_feats)
-
-        bbox_results = dict(
-            cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
-        return bbox_results
-
-    def _bbox_forward_train(self,
-                            x,
-                            sampling_results,
-                            gt_bboxes,
-                            gt_labels,
-                            img_metas,
-                            neg_label_weights=None):
-        """Run forward function and calculate loss for box head in training."""
-        rois = bbox2roi([res.bboxes for res in sampling_results])
-
-        bbox_results = self._bbox_forward(x, rois)
-
-        bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
-                                                  gt_labels, self.train_cfg)
-
-        # neg_label_weights obtained by sampler is image-wise, mapping back to
-        # the corresponding location in label weights
-        if neg_label_weights[0] is not None:
-            label_weights = bbox_targets[1]
-            cur_num_rois = 0
-            for i in range(len(sampling_results)):
-                num_pos = sampling_results[i].pos_inds.size(0)
-                num_neg = sampling_results[i].neg_inds.size(0)
-                label_weights[cur_num_rois + num_pos:cur_num_rois + num_pos +
-                              num_neg] = neg_label_weights[i]
-                cur_num_rois += num_pos + num_neg
-
-        cls_score = bbox_results['cls_score']
-        bbox_pred = bbox_results['bbox_pred']
-
-        # Apply ISR-P
-        isr_cfg = self.train_cfg.get('isr', None)
-        if isr_cfg is not None:
-            bbox_targets = isr_p(
-                cls_score,
-                bbox_pred,
-                bbox_targets,
-                rois,
-                sampling_results,
-                self.bbox_head.loss_cls,
-                self.bbox_head.bbox_coder,
-                **isr_cfg,
-                num_class=self.bbox_head.num_classes)
-        loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, rois,
-                                        *bbox_targets)
-
-        # Add CARL Loss
-        carl_cfg = self.train_cfg.get('carl', None)
-        if carl_cfg is not None:
-            loss_carl = carl_loss(
-                cls_score,
-                bbox_targets[0],
-                bbox_pred,
-                bbox_targets[2],
-                self.bbox_head.loss_bbox,
-                **carl_cfg,
-                num_class=self.bbox_head.num_classes)
-            loss_bbox.update(loss_carl)
-
-        bbox_results.update(loss_bbox=loss_bbox)
-        return bbox_results

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/encnet_r50-d8.py

@@ -1,48 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='EncHead',
-        in_channels=[512, 1024, 2048],
-        in_index=(1, 2, 3),
-        channels=512,
-        num_codes=32,
-        use_se_loss=True,
-        add_lateral=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-        loss_se_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py

@@ -1,10 +0,0 @@
-_base_ = './fcn_hr18_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w48',
-    backbone=dict(
-        extra=dict(
-            stage2=dict(num_channels=(48, 96)),
-            stage3=dict(num_channels=(48, 96, 192)),
-            stage4=dict(num_channels=(48, 96, 192, 384)))),
-    decode_head=dict(
-        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

spaces/Aniquel/WizApp/README.md

@@ -1,13 +0,0 @@
----
-title: WizApp
-emoji: ⚡
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/RWKV-model.md

@@ -1,72 +0,0 @@
-> RWKV: RNN with Transformer-level LLM Performance
->
-> It combines the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state).
-
-https://github.com/BlinkDL/RWKV-LM
-
-https://github.com/BlinkDL/ChatRWKV
-
-## Using RWKV in the web UI
-
-### Hugging Face weights
-
-Simply download the weights from https://huggingface.co/RWKV and load them as you would for any other model.
-
-There is a bug in transformers==4.29.2 that prevents RWKV from being loaded in 8-bit mode. You can install the dev branch to solve this bug: `pip install git+https://github.com/huggingface/transformers`
-
-### Original .pth weights
-
-The instructions below are from before RWKV was supported in transformers, and they are kept for legacy purposes. The old implementation is possibly faster, but it lacks the full range of samplers that the transformers library offers.
-
-#### 0. Install the RWKV library
-
-```
-pip install rwkv
-```
-
-`0.7.3` was the last version that I tested. If you experience any issues, try ```pip install rwkv==0.7.3```.
-
-#### 1. Download the model
-
-It is available in different sizes:
-
-* https://huggingface.co/BlinkDL/rwkv-4-pile-3b/
-* https://huggingface.co/BlinkDL/rwkv-4-pile-7b/
-* https://huggingface.co/BlinkDL/rwkv-4-pile-14b/
-
-There are also older releases with smaller sizes like:
-
-* https://huggingface.co/BlinkDL/rwkv-4-pile-169m/resolve/main/RWKV-4-Pile-169M-20220807-8023.pth
-
-Download the chosen `.pth` and put it directly in the `models` folder. 
-
-#### 2. Download the tokenizer
-
-[20B_tokenizer.json](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/v2/20B_tokenizer.json)
-
-Also put it directly in the `models` folder. Make sure to not rename it. It should be called `20B_tokenizer.json`.
-
-#### 3. Launch the web UI
-
-No additional steps are required. Just launch it as you would with any other model.
-
-```
-python server.py --listen  --no-stream --model RWKV-4-Pile-169M-20220807-8023.pth
-```
-
-#### Setting a custom strategy
-
-It is possible to have very fine control over the offloading and precision for the model with the `--rwkv-strategy` flag. Possible values include:
-
-```
-"cpu fp32" # CPU mode
-"cuda fp16" # GPU mode with float16 precision
-"cuda fp16 *30 -> cpu fp32" # GPU+CPU offloading. The higher the number after *, the higher the GPU allocation.
-"cuda fp16i8" # GPU mode with 8-bit precision
-```
-
-See the README for the PyPl package for more details: https://pypi.org/project/rwkv/
-
-#### Compiling the CUDA kernel
-
-You can compile the CUDA kernel for the model with `--rwkv-cuda-on`. This should improve the performance a lot but I haven't been able to get it to work yet.

spaces/AnonAndDesu/Desu_Proxy/Dockerfile

@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
-    apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]

spaces/Anonymous-sub/Rerender/gmflow_module/gmflow/geometry.py

@@ -1,96 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-
-def coords_grid(b, h, w, homogeneous=False, device=None):
-    y, x = torch.meshgrid(torch.arange(h), torch.arange(w))  # [H, W]
-
-    stacks = [x, y]
-
-    if homogeneous:
-        ones = torch.ones_like(x)  # [H, W]
-        stacks.append(ones)
-
-    grid = torch.stack(stacks, dim=0).float()  # [2, H, W] or [3, H, W]
-
-    grid = grid[None].repeat(b, 1, 1, 1)  # [B, 2, H, W] or [B, 3, H, W]
-
-    if device is not None:
-        grid = grid.to(device)
-
-    return grid
-
-
-def generate_window_grid(h_min, h_max, w_min, w_max, len_h, len_w, device=None):
-    assert device is not None
-
-    x, y = torch.meshgrid([torch.linspace(w_min, w_max, len_w, device=device),
-                           torch.linspace(h_min, h_max, len_h, device=device)],
-                          )
-    grid = torch.stack((x, y), -1).transpose(0, 1).float()  # [H, W, 2]
-
-    return grid
-
-
-def normalize_coords(coords, h, w):
-    # coords: [B, H, W, 2]
-    c = torch.Tensor([(w - 1) / 2., (h - 1) / 2.]).float().to(coords.device)
-    return (coords - c) / c  # [-1, 1]
-
-
-def bilinear_sample(img, sample_coords, mode='bilinear', padding_mode='zeros', return_mask=False):
-    # img: [B, C, H, W]
-    # sample_coords: [B, 2, H, W] in image scale
-    if sample_coords.size(1) != 2:  # [B, H, W, 2]
-        sample_coords = sample_coords.permute(0, 3, 1, 2)
-
-    b, _, h, w = sample_coords.shape
-
-    # Normalize to [-1, 1]
-    x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1
-    y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1
-
-    grid = torch.stack([x_grid, y_grid], dim=-1)  # [B, H, W, 2]
-
-    img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True)
-
-    if return_mask:
-        mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1)  # [B, H, W]
-
-        return img, mask
-
-    return img
-
-
-def flow_warp(feature, flow, mask=False, padding_mode='zeros'):
-    b, c, h, w = feature.size()
-    assert flow.size(1) == 2
-
-    grid = coords_grid(b, h, w).to(flow.device) + flow  # [B, 2, H, W]
-
-    return bilinear_sample(feature, grid, padding_mode=padding_mode,
-                           return_mask=mask)
-
-
-def forward_backward_consistency_check(fwd_flow, bwd_flow,
-                                       alpha=0.01,
-                                       beta=0.5
-                                       ):
-    # fwd_flow, bwd_flow: [B, 2, H, W]
-    # alpha and beta values are following UnFlow (https://arxiv.org/abs/1711.07837)
-    assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4
-    assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2
-    flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1)  # [B, H, W]
-
-    warped_bwd_flow = flow_warp(bwd_flow, fwd_flow)  # [B, 2, H, W]
-    warped_fwd_flow = flow_warp(fwd_flow, bwd_flow)  # [B, 2, H, W]
-
-    diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1)  # [B, H, W]
-    diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1)
-
-    threshold = alpha * flow_mag + beta
-
-    fwd_occ = (diff_fwd > threshold).float()  # [B, H, W]
-    bwd_occ = (diff_bwd > threshold).float()
-
-    return fwd_occ, bwd_occ

spaces/Ariharasudhan/YoloV5/utils/aws/resume.py

@@ -1,40 +0,0 @@
-# Resume all interrupted trainings in yolov5/ dir including DDP trainings
-# Usage: $ python utils/aws/resume.py
-
-import os
-import sys
-from pathlib import Path
-
-import torch
-import yaml
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[2]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-
-port = 0  # --master_port
-path = Path('').resolve()
-for last in path.rglob('*/**/last.pt'):
-    ckpt = torch.load(last)
-    if ckpt['optimizer'] is None:
-        continue
-
-    # Load opt.yaml
-    with open(last.parent.parent / 'opt.yaml', errors='ignore') as f:
-        opt = yaml.safe_load(f)
-
-    # Get device count
-    d = opt['device'].split(',')  # devices
-    nd = len(d)  # number of devices
-    ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1)  # distributed data parallel
-
-    if ddp:  # multi-GPU
-        port += 1
-        cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}'
-    else:  # single-GPU
-        cmd = f'python train.py --resume {last}'
-
-    cmd += ' > /dev/null 2>&1 &'  # redirect output to dev/null and run in daemon thread
-    print(cmd)
-    os.system(cmd)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/build/wheel.py

@@ -1,37 +0,0 @@
-import logging
-import os
-from typing import Optional
-
-from pip._vendor.pyproject_hooks import BuildBackendHookCaller
-
-from pip._internal.utils.subprocess import runner_with_spinner_message
-
-logger = logging.getLogger(__name__)
-
-
-def build_wheel_pep517(
-    name: str,
-    backend: BuildBackendHookCaller,
-    metadata_directory: str,
-    tempd: str,
-) -> Optional[str]:
-    """Build one InstallRequirement using the PEP 517 build process.
-
-    Returns path to wheel if successfully built. Otherwise, returns None.
-    """
-    assert metadata_directory is not None
-    try:
-        logger.debug("Destination directory: %s", tempd)
-
-        runner = runner_with_spinner_message(
-            f"Building wheel for {name} (pyproject.toml)"
-        )
-        with backend.subprocess_runner(runner):
-            wheel_name = backend.build_wheel(
-                tempd,
-                metadata_directory=metadata_directory,
-            )
-    except Exception:
-        logger.error("Failed building wheel for %s", name)
-        return None
-    return os.path.join(tempd, wheel_name)

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

@@ -1,142 +0,0 @@
-"""
-    pygments.formatters
-    ~~~~~~~~~~~~~~~~~~~
-
-    Pygments formatters.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import sys
-import types
-from fnmatch import fnmatch
-from os.path import basename
-
-from pip._vendor.pygments.formatters._mapping import FORMATTERS
-from pip._vendor.pygments.plugin import find_plugin_formatters
-from pip._vendor.pygments.util import ClassNotFound
-
-__all__ = ['get_formatter_by_name', 'get_formatter_for_filename',
-           'get_all_formatters', 'load_formatter_from_file'] + list(FORMATTERS)
-
-_formatter_cache = {}  # classes by name
-
-def _load_formatters(module_name):
-    """Load a formatter (and all others in the module too)."""
-    mod = __import__(module_name, None, None, ['__all__'])
-    for formatter_name in mod.__all__:
-        cls = getattr(mod, formatter_name)
-        _formatter_cache[cls.name] = cls
-
-
-def get_all_formatters():
-    """Return a generator for all formatter classes."""
-    # NB: this returns formatter classes, not info like get_all_lexers().
-    for info in FORMATTERS.values():
-        if info[1] not in _formatter_cache:
-            _load_formatters(info[0])
-        yield _formatter_cache[info[1]]
-    for _, formatter in find_plugin_formatters():
-        yield formatter
-
-
-def find_formatter_class(alias):
-    """Lookup a formatter by alias.
-
-    Returns None if not found.
-    """
-    for module_name, name, aliases, _, _ in FORMATTERS.values():
-        if alias in aliases:
-            if name not in _formatter_cache:
-                _load_formatters(module_name)
-            return _formatter_cache[name]
-    for _, cls in find_plugin_formatters():
-        if alias in cls.aliases:
-            return cls
-
-
-def get_formatter_by_name(_alias, **options):
-    """Lookup and instantiate a formatter by alias.
-
-    Raises ClassNotFound if not found.
-    """
-    cls = find_formatter_class(_alias)
-    if cls is None:
-        raise ClassNotFound("no formatter found for name %r" % _alias)
-    return cls(**options)
-
-
-def load_formatter_from_file(filename, formattername="CustomFormatter",
-                             **options):
-    """Load a formatter from a file.
-
-    This method expects a file located relative to the current working
-    directory, which contains a class named CustomFormatter. By default,
-    it expects the Formatter to be named CustomFormatter; you can specify
-    your own class name as the second argument to this function.
-
-    Users should be very careful with the input, because this method
-    is equivalent to running eval on the input file.
-
-    Raises ClassNotFound if there are any problems importing the Formatter.
-
-    .. versionadded:: 2.2
-    """
-    try:
-        # This empty dict will contain the namespace for the exec'd file
-        custom_namespace = {}
-        with open(filename, 'rb') as f:
-            exec(f.read(), custom_namespace)
-        # Retrieve the class `formattername` from that namespace
-        if formattername not in custom_namespace:
-            raise ClassNotFound('no valid %s class found in %s' %
-                                (formattername, filename))
-        formatter_class = custom_namespace[formattername]
-        # And finally instantiate it with the options
-        return formatter_class(**options)
-    except OSError as err:
-        raise ClassNotFound('cannot read %s: %s' % (filename, err))
-    except ClassNotFound:
-        raise
-    except Exception as err:
-        raise ClassNotFound('error when loading custom formatter: %s' % err)
-
-
-def get_formatter_for_filename(fn, **options):
-    """Lookup and instantiate a formatter by filename pattern.
-
-    Raises ClassNotFound if not found.
-    """
-    fn = basename(fn)
-    for modname, name, _, filenames, _ in FORMATTERS.values():
-        for filename in filenames:
-            if fnmatch(fn, filename):
-                if name not in _formatter_cache:
-                    _load_formatters(modname)
-                return _formatter_cache[name](**options)
-    for cls in find_plugin_formatters():
-        for filename in cls.filenames:
-            if fnmatch(fn, filename):
-                return cls(**options)
-    raise ClassNotFound("no formatter found for file name %r" % fn)
-
-
-class _automodule(types.ModuleType):
-    """Automatically import formatters."""
-
-    def __getattr__(self, name):
-        info = FORMATTERS.get(name)
-        if info:
-            _load_formatters(info[0])
-            cls = _formatter_cache[info[1]]
-            setattr(self, name, cls)
-            return cls
-        raise AttributeError(name)
-
-
-oldmod = sys.modules[__name__]
-newmod = _automodule(__name__)
-newmod.__dict__.update(oldmod.__dict__)
-sys.modules[__name__] = newmod
-del newmod.newmod, newmod.oldmod, newmod.sys, newmod.types

spaces/AvaterClasher/Food_Classifier_Moni/model.py

@@ -1,36 +0,0 @@
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes:int=3, 
-                          seed:int=42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.efficientnet_b2(weights=weights)
-
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.3, inplace=True),
-        nn.Linear(in_features=1408, out_features=num_classes),
-    )
-    
-    return model, transforms

spaces/Awesimo/jojogan/e4e/criteria/moco_loss.py

@@ -1,71 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from configs.paths_config import model_paths
-
-
-class MocoLoss(nn.Module):
-
-    def __init__(self, opts):
-        super(MocoLoss, self).__init__()
-        print("Loading MOCO model from path: {}".format(model_paths["moco"]))
-        self.model = self.__load_model()
-        self.model.eval()
-        for param in self.model.parameters():
-            param.requires_grad = False
-
-    @staticmethod
-    def __load_model():
-        import torchvision.models as models
-        model = models.__dict__["resnet50"]()
-        # freeze all layers but the last fc
-        for name, param in model.named_parameters():
-            if name not in ['fc.weight', 'fc.bias']:
-                param.requires_grad = False
-        checkpoint = torch.load(model_paths['moco'], map_location="cpu")
-        state_dict = checkpoint['state_dict']
-        # rename moco pre-trained keys
-        for k in list(state_dict.keys()):
-            # retain only encoder_q up to before the embedding layer
-            if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
-                # remove prefix
-                state_dict[k[len("module.encoder_q."):]] = state_dict[k]
-            # delete renamed or unused k
-            del state_dict[k]
-        msg = model.load_state_dict(state_dict, strict=False)
-        assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}
-        # remove output layer
-        model = nn.Sequential(*list(model.children())[:-1]).cuda()
-        return model
-
-    def extract_feats(self, x):
-        x = F.interpolate(x, size=224)
-        x_feats = self.model(x)
-        x_feats = nn.functional.normalize(x_feats, dim=1)
-        x_feats = x_feats.squeeze()
-        return x_feats
-
-    def forward(self, y_hat, y, x):
-        n_samples = x.shape[0]
-        x_feats = self.extract_feats(x)
-        y_feats = self.extract_feats(y)
-        y_hat_feats = self.extract_feats(y_hat)
-        y_feats = y_feats.detach()
-        loss = 0
-        sim_improvement = 0
-        sim_logs = []
-        count = 0
-        for i in range(n_samples):
-            diff_target = y_hat_feats[i].dot(y_feats[i])
-            diff_input = y_hat_feats[i].dot(x_feats[i])
-            diff_views = y_feats[i].dot(x_feats[i])
-            sim_logs.append({'diff_target': float(diff_target),
-                             'diff_input': float(diff_input),
-                             'diff_views': float(diff_views)})
-            loss += 1 - diff_target
-            sim_diff = float(diff_target) - float(diff_views)
-            sim_improvement += sim_diff
-            count += 1
-
-        return loss / count, sim_improvement / count, sim_logs

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/rotated_coco_evaluation.py

@@ -1,207 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import json
-import numpy as np
-import os
-import torch
-from pycocotools.cocoeval import COCOeval, maskUtils
-
-from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
-from detectron2.utils.file_io import PathManager
-
-from .coco_evaluation import COCOEvaluator
-
-
-class RotatedCOCOeval(COCOeval):
-    @staticmethod
-    def is_rotated(box_list):
-        if type(box_list) == np.ndarray:
-            return box_list.shape[1] == 5
-        elif type(box_list) == list:
-            if box_list == []:  # cannot decide the box_dim
-                return False
-            return np.all(
-                np.array(
-                    [
-                        (len(obj) == 5) and ((type(obj) == list) or (type(obj) == np.ndarray))
-                        for obj in box_list
-                    ]
-                )
-            )
-        return False
-
-    @staticmethod
-    def boxlist_to_tensor(boxlist, output_box_dim):
-        if type(boxlist) == np.ndarray:
-            box_tensor = torch.from_numpy(boxlist)
-        elif type(boxlist) == list:
-            if boxlist == []:
-                return torch.zeros((0, output_box_dim), dtype=torch.float32)
-            else:
-                box_tensor = torch.FloatTensor(boxlist)
-        else:
-            raise Exception("Unrecognized boxlist type")
-
-        input_box_dim = box_tensor.shape[1]
-        if input_box_dim != output_box_dim:
-            if input_box_dim == 4 and output_box_dim == 5:
-                box_tensor = BoxMode.convert(box_tensor, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS)
-            else:
-                raise Exception(
-                    "Unable to convert from {}-dim box to {}-dim box".format(
-                        input_box_dim, output_box_dim
-                    )
-                )
-        return box_tensor
-
-    def compute_iou_dt_gt(self, dt, gt, is_crowd):
-        if self.is_rotated(dt) or self.is_rotated(gt):
-            # TODO: take is_crowd into consideration
-            assert all(c == 0 for c in is_crowd)
-            dt = RotatedBoxes(self.boxlist_to_tensor(dt, output_box_dim=5))
-            gt = RotatedBoxes(self.boxlist_to_tensor(gt, output_box_dim=5))
-            return pairwise_iou_rotated(dt, gt)
-        else:
-            # This is the same as the classical COCO evaluation
-            return maskUtils.iou(dt, gt, is_crowd)
-
-    def computeIoU(self, imgId, catId):
-        p = self.params
-        if p.useCats:
-            gt = self._gts[imgId, catId]
-            dt = self._dts[imgId, catId]
-        else:
-            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
-            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
-        if len(gt) == 0 and len(dt) == 0:
-            return []
-        inds = np.argsort([-d["score"] for d in dt], kind="mergesort")
-        dt = [dt[i] for i in inds]
-        if len(dt) > p.maxDets[-1]:
-            dt = dt[0 : p.maxDets[-1]]
-
-        assert p.iouType == "bbox", "unsupported iouType for iou computation"
-
-        g = [g["bbox"] for g in gt]
-        d = [d["bbox"] for d in dt]
-
-        # compute iou between each dt and gt region
-        iscrowd = [int(o["iscrowd"]) for o in gt]
-
-        # Note: this function is copied from cocoeval.py in cocoapi
-        # and the major difference is here.
-        ious = self.compute_iou_dt_gt(d, g, iscrowd)
-        return ious
-
-
-class RotatedCOCOEvaluator(COCOEvaluator):
-    """
-    Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs,
-    with rotated boxes support.
-    Note: this uses IOU only and does not consider angle differences.
-    """
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
-                It is a list of dict. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name", "image_id".
-            outputs: the outputs of a COCO model. It is a list of dicts with key
-                "instances" that contains :class:`Instances`.
-        """
-        for input, output in zip(inputs, outputs):
-            prediction = {"image_id": input["image_id"]}
-
-            if "instances" in output:
-                instances = output["instances"].to(self._cpu_device)
-
-                prediction["instances"] = self.instances_to_json(instances, input["image_id"])
-            if "proposals" in output:
-                prediction["proposals"] = output["proposals"].to(self._cpu_device)
-            self._predictions.append(prediction)
-
-    def instances_to_json(self, instances, img_id):
-        num_instance = len(instances)
-        if num_instance == 0:
-            return []
-
-        boxes = instances.pred_boxes.tensor.numpy()
-        if boxes.shape[1] == 4:
-            boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-        boxes = boxes.tolist()
-        scores = instances.scores.tolist()
-        classes = instances.pred_classes.tolist()
-
-        results = []
-        for k in range(num_instance):
-            result = {
-                "image_id": img_id,
-                "category_id": classes[k],
-                "bbox": boxes[k],
-                "score": scores[k],
-            }
-
-            results.append(result)
-        return results
-
-    def _eval_predictions(self, predictions, img_ids=None):  # img_ids: unused
-        """
-        Evaluate predictions on the given tasks.
-        Fill self._results with the metrics of the tasks.
-        """
-        self._logger.info("Preparing results for COCO format ...")
-        coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
-
-        # unmap the category ids for COCO
-        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
-            reverse_id_mapping = {
-                v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
-            }
-            for result in coco_results:
-                result["category_id"] = reverse_id_mapping[result["category_id"]]
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "coco_instances_results.json")
-            self._logger.info("Saving results to {}".format(file_path))
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(coco_results))
-                f.flush()
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating predictions ...")
-
-        assert self._tasks is None or set(self._tasks) == {
-            "bbox"
-        }, "[RotatedCOCOEvaluator] Only bbox evaluation is supported"
-        coco_eval = (
-            self._evaluate_predictions_on_coco(self._coco_api, coco_results)
-            if len(coco_results) > 0
-            else None  # cocoapi does not handle empty results very well
-        )
-
-        task = "bbox"
-        res = self._derive_coco_results(
-            coco_eval, task, class_names=self._metadata.get("thing_classes")
-        )
-        self._results[task] = res
-
-    def _evaluate_predictions_on_coco(self, coco_gt, coco_results):
-        """
-        Evaluate the coco results using COCOEval API.
-        """
-        assert len(coco_results) > 0
-
-        coco_dt = coco_gt.loadRes(coco_results)
-
-        # Only bbox is supported for now
-        coco_eval = RotatedCOCOeval(coco_gt, coco_dt, iouType="bbox")
-
-        coco_eval.evaluate()
-        coco_eval.accumulate()
-        coco_eval.summarize()
-
-        return coco_eval

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/visualize_json_results.py

@@ -1,90 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import argparse
-import json
-import numpy as np
-import os
-from collections import defaultdict
-import cv2
-import tqdm
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.structures import Boxes, BoxMode, Instances
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.logger import setup_logger
-from detectron2.utils.visualizer import Visualizer
-
-
-def create_instances(predictions, image_size):
-    ret = Instances(image_size)
-
-    score = np.asarray([x["score"] for x in predictions])
-    chosen = (score > args.conf_threshold).nonzero()[0]
-    score = score[chosen]
-    bbox = np.asarray([predictions[i]["bbox"] for i in chosen]).reshape(-1, 4)
-    bbox = BoxMode.convert(bbox, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
-
-    labels = np.asarray([dataset_id_map(predictions[i]["category_id"]) for i in chosen])
-
-    ret.scores = score
-    ret.pred_boxes = Boxes(bbox)
-    ret.pred_classes = labels
-
-    try:
-        ret.pred_masks = [predictions[i]["segmentation"] for i in chosen]
-    except KeyError:
-        pass
-    return ret
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="A script that visualizes the json predictions from COCO or LVIS dataset."
-    )
-    parser.add_argument("--input", required=True, help="JSON file produced by the model")
-    parser.add_argument("--output", required=True, help="output directory")
-    parser.add_argument("--dataset", help="name of the dataset", default="coco_2017_val")
-    parser.add_argument("--conf-threshold", default=0.5, type=float, help="confidence threshold")
-    args = parser.parse_args()
-
-    logger = setup_logger()
-
-    with PathManager.open(args.input, "r") as f:
-        predictions = json.load(f)
-
-    pred_by_image = defaultdict(list)
-    for p in predictions:
-        pred_by_image[p["image_id"]].append(p)
-
-    dicts = list(DatasetCatalog.get(args.dataset))
-    metadata = MetadataCatalog.get(args.dataset)
-    if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
-
-        def dataset_id_map(ds_id):
-            return metadata.thing_dataset_id_to_contiguous_id[ds_id]
-
-    elif "lvis" in args.dataset:
-        # LVIS results are in the same format as COCO results, but have a different
-        # mapping from dataset category id to contiguous category id in [0, #categories - 1]
-        def dataset_id_map(ds_id):
-            return ds_id - 1
-
-    else:
-        raise ValueError("Unsupported dataset: {}".format(args.dataset))
-
-    os.makedirs(args.output, exist_ok=True)
-
-    for dic in tqdm.tqdm(dicts):
-        img = cv2.imread(dic["file_name"], cv2.IMREAD_COLOR)[:, :, ::-1]
-        basename = os.path.basename(dic["file_name"])
-
-        predictions = create_instances(pred_by_image[dic["image_id"]], img.shape[:2])
-        vis = Visualizer(img, metadata)
-        vis_pred = vis.draw_instance_predictions(predictions).get_image()
-
-        vis = Visualizer(img, metadata)
-        vis_gt = vis.draw_dataset_dict(dic).get_image()
-
-        concat = np.concatenate((vis_pred, vis_gt), axis=1)
-        cv2.imwrite(os.path.join(args.output, basename), concat[:, :, ::-1])

spaces/Bart92/RVC_HF/tools/calc_rvc_model_similarity.py

@@ -1,96 +0,0 @@
-# This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py
-# Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models.
-import os
-import logging
-
-logger = logging.getLogger(__name__)
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def cal_cross_attn(to_q, to_k, to_v, rand_input):
-    hidden_dim, embed_dim = to_q.shape
-    attn_to_q = nn.Linear(hidden_dim, embed_dim, bias=False)
-    attn_to_k = nn.Linear(hidden_dim, embed_dim, bias=False)
-    attn_to_v = nn.Linear(hidden_dim, embed_dim, bias=False)
-    attn_to_q.load_state_dict({"weight": to_q})
-    attn_to_k.load_state_dict({"weight": to_k})
-    attn_to_v.load_state_dict({"weight": to_v})
-
-    return torch.einsum(
-        "ik, jk -> ik",
-        F.softmax(
-            torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)),
-            dim=-1,
-        ),
-        attn_to_v(rand_input),
-    )
-
-
-def model_hash(filename):
-    try:
-        with open(filename, "rb") as file:
-            import hashlib
-
-            m = hashlib.sha256()
-
-            file.seek(0x100000)
-            m.update(file.read(0x10000))
-            return m.hexdigest()[0:8]
-    except FileNotFoundError:
-        return "NOFILE"
-
-
-def eval(model, n, input):
-    qk = f"enc_p.encoder.attn_layers.{n}.conv_q.weight"
-    uk = f"enc_p.encoder.attn_layers.{n}.conv_k.weight"
-    vk = f"enc_p.encoder.attn_layers.{n}.conv_v.weight"
-    atoq, atok, atov = model[qk][:, :, 0], model[uk][:, :, 0], model[vk][:, :, 0]
-
-    attn = cal_cross_attn(atoq, atok, atov, input)
-    return attn
-
-
-def main(path, root):
-    torch.manual_seed(114514)
-    model_a = torch.load(path, map_location="cpu")["weight"]
-
-    logger.info("Query:\t\t%s\t%s" % (path, model_hash(path)))
-
-    map_attn_a = {}
-    map_rand_input = {}
-    for n in range(6):
-        hidden_dim, embed_dim, _ = model_a[
-            f"enc_p.encoder.attn_layers.{n}.conv_v.weight"
-        ].shape
-        rand_input = torch.randn([embed_dim, hidden_dim])
-
-        map_attn_a[n] = eval(model_a, n, rand_input)
-        map_rand_input[n] = rand_input
-
-    del model_a
-
-    for name in sorted(list(os.listdir(root))):
-        path = "%s/%s" % (root, name)
-        model_b = torch.load(path, map_location="cpu")["weight"]
-
-        sims = []
-        for n in range(6):
-            attn_a = map_attn_a[n]
-            attn_b = eval(model_b, n, map_rand_input[n])
-
-            sim = torch.mean(torch.cosine_similarity(attn_a, attn_b))
-            sims.append(sim)
-
-        logger.info(
-            "Reference:\t%s\t%s\t%s"
-            % (path, model_hash(path), f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%")
-        )
-
-
-if __name__ == "__main__":
-    query_path = r"assets\weights\mi v3.pth"
-    reference_root = r"assets\weights"
-    main(query_path, reference_root)

spaces/Benson/text-generation/Examples/Choo Choo Charles Juego Completo.md

@@ -1,47 +0,0 @@
-
-<h1>Choo Choo Charles: Un juego de terror de supervivencia con un tren de araña</h1>
-<p>Si usted está buscando un juego emocionante y aterrador que le mantendrá en el borde de su asiento, entonces es posible que desee echa un vistazo a Choo Choo Charles. Este es un juego de terror de supervivencia que te enfrenta a un tren de araña malvado llamado Charles, que te está cazando en una isla de mundo abierto. Tienes que usar tu propio tren, que puedes actualizar y personalizar, para luchar y sobrevivir. En este artículo, le diremos todo lo que necesita saber sobre Choo Choo Charles, incluyendo lo que es, cómo jugarlo y por qué debe jugarlo. </p>
-<h2>choo choo charles juego completo</h2><br /><p><b><b>Download Zip</b> ===> <a href="https://bltlly.com/2v6ICP">https://bltlly.com/2v6ICP</a></b></p><br /><br />
- <h2>¿Qué es Choo Choo Charles? </h2>
-<p>Choo Choo Charles es un juego de terror de supervivencia que fue lanzado el 9 de diciembre de 2022 por Two Star Games, un estudio de juegos independiente con sede en Canadá. El juego está disponible en Steam para Windows PC, y ha recibido críticas muy positivas de jugadores y críticos por igual. El juego está inspirado en el libro infantil Charlie the Choo-Choo, de Stephen King, de 2016, que se extrae de la serie King’s Dark Tower. </p>
- <h3>La historia y la configuración del juego</h3>
-<p>El juego tiene lugar en una isla que está habitada por un tren de araña monstruoso llamado Charles, que ha estado aterrorizando a la gente durante años. Usted es uno de los sobrevivientes que ha logrado encontrar un viejo tren que todavía funciona, y decide usarlo para explorar la isla y encontrar una manera de detener a Charles. En el camino, te encontrarás con otros supervivientes que te ofrecerán misiones, objetos e información a cambio de tu ayuda. También descubrirá los secretos y misterios de la isla y su historia. </p>
- <h3>La jugabilidad y características del juego</h3>
-
-<p>El juego también te permite actualizar tu tren con varias piezas y accesorios que puedes encontrar o comprar de otros supervivientes. Puede mejorar la velocidad de su tren, la durabilidad, la eficiencia del combustible, la capacidad de almacenamiento y más. También puede personalizar la apariencia de su tren con diferentes colores, calcomanías, banderas, cuernos, luces y más. Incluso puedes nombrar tu tren y darle una personalidad. </p>
-<p></p>
-<p>El juego tiene un ciclo día-noche y un sistema de clima dinámico que afectan el juego y la atmósfera. Durante el día, puedes ver más claramente y viajar más rápido, pero Charles también puede localizarte más fácilmente. Durante la noche, puedes esconderte mejor y escabullirte, pero Charles también puede sorprenderte más fácilmente. El clima también puede cambiar de soleado a lluvioso a brumoso a tormentoso, creando diferentes desafíos y oportunidades. </p>
- <h3>La recepción y comentarios del juego</h3>
-
-<p>Si usted está interesado en jugar Choo Choo Charles, aquí hay algunos consejos sobre cómo empezar y disfrutar del juego. </p>
- <h3>La línea de búsqueda principal y las misiones secundarias</h3>
-<p>El juego tiene una misión principal que sigue tu viaje para encontrar y detener a Charles. Tendrás que completar varios objetivos, como encontrar pistas, encontrar aliados, sabotear las pistas de Charles y enfrentar a Charles en batallas épicas. La principal línea de búsqueda te llevará a diferentes lugares de la isla, como ciudades, granjas, minas, bosques, montañas y más. </p>
-<p>El juego también tiene muchas misiones secundarias que puedes hacer para ganar recompensas adicionales, como dinero, objetos, piezas, armas e información. Puedes encontrar misiones secundarias hablando con otros sobrevivientes, explorando la isla o escuchando la radio. Algunas de las misiones secundarias incluyen ayudar a otros sobrevivientes con sus problemas, recolectar recursos, cazar animales, encontrar tesoros, destruir a los secuaces de Carlos, y más. </p>
- <h3>El tren mejora y armas</h3>
-<p>El juego te permite actualizar tu tren con varias piezas y accesorios que puedes encontrar o comprar de otros supervivientes. Puede mejorar la velocidad de su tren, la durabilidad, la eficiencia del combustible, la capacidad de almacenamiento y más. También puede personalizar la apariencia de su tren con diferentes colores, calcomanías, banderas, cuernos, luces y más. Incluso puedes nombrar tu tren y darle una personalidad. </p>
-<p>El juego también te permite equipar tu tren con diferentes armas que puedes usar para luchar contra Charles. Puedes elegir entre cañones, ametralladoras, cohetes, lanzallamas y más. Cada arma tiene sus propias ventajas y desventajas, como rango, daño, precisión, tiempo de recarga, capacidad de munición y más. Tienes que equilibrar tus armas según tu estilo de juego y estrategia. </p>
- <h3>Los consejos y trucos para sobrevivir Charles</h3>
-
-<p>Si todavía no está convencido de que Choo Choo Charles es un juego que vale la pena jugar, aquí hay algunas razones por las que debe darle una oportunidad:</p>
- <h3>El concepto único y original del juego</h3>
-<p>Choo Choo Charles es un juego que se destaca de la multitud con su concepto único y original. ¿Cuántos juegos puedes pensar que cuentan con un tren de araña gigante como el antagonista principal? El juego es una mezcla creativa e innovadora de géneros, como el terror de supervivencia, la acción-aventura, el mundo abierto y el sandbox. El juego ofrece una experiencia fresca y emocionante que no encontrarás en ningún otro lugar. </p>
- <h3>Los gráficos inmersivos y atmosféricos y el sonido del juego</h3>
-<p>Choo Choo Charles es un juego que te sumerge en su mundo con sus impresionantes gráficos y sonido. El juego tiene un estilo gráfico realista y detallado que muestra la belleza y la diversidad de la isla. El juego también tiene un sistema dinámico de iluminación y sombra que crea un efecto dramático y cinematográfico. El juego también tiene un excelente diseño de sonido que mejora el estado de ánimo y la atmósfera del juego. El juego cuenta con sonidos realistas y ambientales, como el viento, la lluvia, los pájaros, los animales y más. El juego también cuenta con una banda sonora aterradora y emocionante que acompaña tus encuentros con Charles.</p>
- <h3>La dificultad desafiante y gratificante y el valor de repetición del juego</h3>
-
- <h2>Conclusión</h2>
-<p>Choo Choo Charles es un juego de terror de supervivencia que te mantendrá enganchado con su concepto único y original, gráficos inmersivos y atmosféricos y sonido, dificultad desafiante y gratificante y valor de repetición. Si usted está buscando un juego emocionante y aterrador que le hará gritar, reír, llorar y animar, entonces usted debe jugar Choo Choo Charles.</p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Choo Choo Charles:</p>
- <h4>Q: ¿Cuánto dura el juego? </h4>
-<p>A: La duración del juego depende de cómo lo juegues, pero en promedio, se tarda unas 10 horas en completar la línea de misión principal. Sin embargo, hay muchas misiones secundarias y secretos por descubrir que pueden extender tu tiempo de juego. </p>
- <h4>Q: ¿Es el juego multijugador? </h4>
-<p>A: No, el juego es actualmente solo para un jugador. Sin embargo, los desarrolladores han declarado que podrían considerar agregar características multijugador en el futuro si hay suficiente demanda. </p>
- <h4>Q: ¿El juego da miedo? </h4>
-<p>A: Sí, el juego da mucho miedo. El juego tiene muchos sustos de salto, gore, violencia, suspenso, tensión y elementos de terror que te harán gritar o temblar. Sin embargo, el juego también tiene muchos elementos de humor, encanto, diversión y aventura que te harán sonreír o reír. </p>
- <h4>Q: ¿Es el juego adecuado para los niños? </h4>
-<p>A: No, el juego no es adecuado para niños. El juego tiene muchos contenidos maduros, como sangre, violencia, lenguaje y horror que no son apropiados para el público joven. El juego tiene una calificación de M para Maduro por la ESRB, lo que significa que es adecuado para edades de 17 años en adelante. </p>
- <h4>Q: ¿Dónde puedo comprar el juego? </h4>
-<p>A: Puedes comprar el juego en Steam para PC con Windows. El juego cuesta $19.99 USD, pero puedes obtenerlo por un precio reducido durante las ventas o promociones. También puedes buscar el juego en Steam para recibir notificaciones cuando esté a la venta o actualizado. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Cmo Descargar Llamada De Deber Warzone Mvil Apk.md

@@ -1,165 +0,0 @@
-
-<br>
- <tabla>
-<tr>
-<th>Tabla 2: Artículo con formato HTML</th>
-</tr>
-<tr>
-<td>
- <h1> Cómo descargar llamada de Duty Warzone móvil APK</h1>
-<p>¿Eres un fan de la franquicia Call of Duty y quieres experimentar la emoción de battle royale en tu dispositivo móvil? Si es así, entonces usted debe comprobar definitivamente Call of Duty Warzone móvil APK, la última adición a la popular serie FPS. Call of Duty Warzone Mobile APK es un juego independiente que le permite disfrutar de la experiencia completa de Warzone en su teléfono inteligente o tableta. Puedes jugar solo o formar equipo con tus amigos en un mapa masivo de Verdansk, donde tienes que saquear, disparar y sobrevivir contra hasta 150 jugadores. También puedes usar contratos, killstreaks y vehículos para ganar ventaja sobre tus enemigos. Call of Duty Warzone Mobile APK todavía no se ha lanzado oficialmente, pero usted puede pre-registrarse ahora para obtener acceso temprano y recompensas exclusivas. En este artículo, le mostraremos cómo descargar Call of Duty Warzone Mobile APK de varias fuentes y cómo jugar como un profesional. ¡Vamos a empezar! </p>
- <h2>Cómo Pre-Register para Call of Duty Warzone móvil APK</h2>
-<p>Una de las formas más fáciles de descargar Call of Duty Warzone APK móvil es pre-registrarse en la Google Play Store o el sitio web oficial. Al registrarte, estarás entre los primeros en ser notificado cuando el juego esté disponible para descargar. También recibirás algunas recompensas especiales, como pieles, armas y moneda del juego. Aquí es cómo pre-registrarse para Call of Duty Warzone móvil APK:</p>
-<h2>cómo descargar llamada de deber warzone móvil apk</h2><br /><p><b><b>Download Zip</b> &#9733; <a href="https://bltlly.com/2v6MDY">https://bltlly.com/2v6MDY</a></b></p><br /><br />
- <h3>Cómo pre-registrarse en Google Play Store</h3>
-<p>Para pre-registrarse en Google Play Store, siga estos pasos:</p>
-<ol>
-<li>Abra la aplicación Google Play Store en su dispositivo. </li>
-<li>Buscar "Call of Duty Warzone Mobile" o haga clic en este enlace. </li>
-<li>Toque en el botón "Pre-registro". </li>
-<li>Confirma tu pre-registro tocando en "OK". </li>
-<li>Verás un mensaje que dice "Estás registrado". </li>
-
-</ol>
-<p>Eso es todo! Usted ha pre-registrado con éxito para Call of Duty Warzone APK móvil en Google Play Store. Recibirás una notificación cuando el juego esté listo para descargar. </p>
- <h3>Cómo pre-registrarse en el sitio web oficial</h3>
-<p>Para pre-registrarse en el sitio web oficial, siga estos pasos:</p>
-<ol>
-<li>Abra su navegador y vaya a este enlace. </li>
-<li>Introduzca su dirección de correo electrónico y toque en "Enviar". </li>
-<li> Verá un mensaje que dice "Gracias por registrarse". </li>
-<li>También recibirá una confirmación por correo electrónico de Activision.</li>
-</ol>
-<p>Eso es todo! Usted ha pre-registrado con éxito para Call of Duty Warzone Mobile APK en el sitio web oficial. Recibirás un correo electrónico cuando el juego esté disponible para descargar. </p>
- <h3>¿Cuáles son los beneficios del prerregistro? </h3>
-<p>Al registrarse previamente para Call of Duty Warzone Mobile APK, disfrutará de algunos beneficios, tales como:</p>
-<p></p>
-<ul>
-<li>Usted estará entre los primeros en descargar y jugar el juego. </li>
-<li>Recibirás recompensas exclusivas, como pieles, armas y moneda del juego. </li>
-<li>Podrás participar en las pruebas beta y proporcionar comentarios para mejorar el juego. </li>
-<li>Podrás unirte a la comunidad y compartir tus pensamientos y experiencias con otros jugadores. </li>
-</ul>
-<p>Entonces, ¿qué estás esperando? ¡Regístrate ahora y prepárate para la mejor experiencia de batalla real en tu dispositivo móvil! </p>
- <h2>Cómo descargar Call of Duty Warzone móvil APK de otras fuentes</h2>
-<p>Si usted no quiere esperar a que el lanzamiento oficial de Call of Duty Warzone Mobile APK, también se puede descargar desde otras fuentes. Sin embargo, debe tener cuidado al descargar archivos APK de sitios web desconocidos o no confiables, ya que pueden contener malware o virus que pueden dañar su dispositivo. Le recomendamos que utilice fuentes confiables y confiables, como Uptodown o APKCombo. Aquí es cómo descargar Call of Duty Warzone móvil APK de estas fuentes:</p>
- <h3>Cómo descargar desde Uptodown</h3>
-
-<ol>
-<li>Abra su navegador y vaya a este enlace. </li>
-<li>Toque en el botón "Descargar". </li>
-<li> Verá una ventana emergente que le pide que elija un método de descarga. Toque en "Descargar APK". </li>
-<li>El archivo APK comenzará a descargarse automáticamente. </li>
-<li> Puede comprobar el progreso de la descarga en la barra de notificaciones. </li>
-</ol>
- <h3>Cómo descargar desde APKCombo</h3>
-<p>Para descargar desde APKCombo, siga estos pasos:</p>
-<ol>
-<li>Abra su navegador y vaya a este enlace. </li>
-<li>Toque en el botón "Descargar". </li>
-<li> Verá una ventana emergente que le pide que elija un método de descarga. Toque en "APK Downloader". </li>
-<li>Verá una lista de las versiones disponibles. Elija la última y toque en "Descargar". </li>
-<li>El archivo APK comenzará a descargarse automáticamente. </li>
-<li> Puede comprobar el progreso de la descarga en la barra de notificaciones. </li>
-</ol>
- <h3>Cómo instalar el archivo APK en tu dispositivo</h3>
-<p>Después de descargar el archivo APK desde cualquier fuente, debe instalarlo en su dispositivo. Para hacerlo, siga estos pasos:</p>
-<ol>
-<li>Ir a la configuración del dispositivo y habilitar "Fuentes desconocidas" o "Instalar aplicaciones desconocidas" opción. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store.</li>
-<li>Busque el archivo APK en su administrador de archivos o carpeta de descargas y toque en él. </li>
-<li> Verá una ventana emergente pidiéndole que confirme la instalación. Toque en "Instalar". </li>
-<li>El proceso de instalación tomará unos segundos. </li>
-<li>Una vez completada la instalación, verá un mensaje que dice "App instalado". </li>
-<li> Ahora puede abrir la aplicación y disfrutar del juego. </li>
-</ol>
- <h2>Cómo jugar Call of Duty Warzone móvil APK</h2>
-<p>Ahora que ha descargado e instalado Call of Duty Warzone Mobile APK, usted está listo para jugar el juego. Estos son algunos pasos para ayudarte a empezar:</p>
- <h3>Cómo crear una cuenta e iniciar sesión</h3>
-
-<ol>
-<li> Abra la aplicación y toque en "Registrarse" o "Iniciar sesión". </li>
-<li>Si tiene una cuenta de Activision, introduzca su correo electrónico y contraseña y toque en "Iniciar sesión". </li>
-<li>Si no tiene una cuenta de Activision, toque en "Crear cuenta" y complete los detalles necesarios. También puedes registrarte con tu cuenta de Facebook, Google o Apple. </li>
-<li>Aceptar los términos de servicio y la política de privacidad y toque en "Continuar". </li>
-<li> Verá un mensaje que dice "Cuenta creada" o "Iniciada sesión con éxito". </li>
-<li>Ahora puedes acceder al menú del juego y personalizar tu perfil. </li>
-</ol>
- <h3>Cómo personalizar la configuración y los controles</h3>
-<p>Antes de unirse a un partido, es posible que desee personalizar la configuración y los controles para adaptarse a sus preferencias. Puede ajustar varias opciones, como gráficos, sonido, sensibilidad, diseño y más. Así es como:</p>
-<ol>
-<li>Desde el menú del juego, toque en el icono del engranaje en la esquina superior derecha. </li>
-<li>Verá una lista de pestañas, como General, Gráficos, Audio, Controles, etc.</li>
-<li>Toque en cualquier pestaña y explore las opciones disponibles. </li>
-<li> Hacer cualquier cambio según su gusto y toque en "Aplicar" o "Guardar". </li>
-<li> También puede restablecer la configuración por defecto pulsando en "Restablecer". </li>
-</ol>
- <h3>Cómo unirse a un partido y jugar con tus amigos</h3>
-<p>Para unirte a un partido, puedes jugar solo o formar equipo con tus amigos. También puedes elegir entre diferentes modos, como Battle Royale, Plunder, Resurgence, etc. Así es como:</p>
-<ol>
-<li>Desde el menú del juego, toque en el icono de modo en la esquina superior izquierda. </li>
-<li>Verás una lista de modos, como Battle Royale, Plunder, Resurgence, etc.</li>
-<li>Toque en cualquier modo y seleccione sus opciones preferidas, como tamaño de escuadrón, tamaño de mapa, relleno o no, etc.</li>
-<li>Si quieres jugar con tus amigos, toca el icono de invitación en la esquina inferior derecha. </li>
-<li>Verás una lista de tus amigos que están en línea o fuera de línea. </li>
-<li> Toque en cualquier amigo y enviarles una invitación. </li>
-
-<li>Una vez que esté listo, toque en "Iniciar partido" o "Jugar" en el centro inferior. </li>
-<li>Serás emparejado con otros jugadores y entrarás al lobby del juego. </li>
-<li>Puedes chatear con tus compañeros de equipo usando chat de voz o texto. </li>
-<li> También puede cambiar su carga, operador, piel, arma, etc. tocando los iconos en la esquina inferior izquierda. </li>
-<li>Cuando comience la coincidencia, se desplegará desde un avión sobre Verdansk.</li>
-<li> Puede elegir dónde aterrizar abriendo su mapa y marcando una ubicación. </li>
-<li>También puedes seguir a tus compañeros de equipo o al líder del escuadrón tocando su nombre o icono. </li>
-<li>Una vez que aterrizas, tienes que saquear, disparar y sobrevivir contra otros jugadores y el círculo de gas. </li>
-<li>Puedes usar contratos, killstreaks y vehículos para obtener una ventaja sobre tus enemigos. </li>
-<li> También puede revivir a sus compañeros de equipo o comprarlos de nuevo en las estaciones de compra. </li>
-<li>El último equipo o jugador de pie gana el partido. </li>
-</ol>
- <h2> Consejos y trucos para Call of Duty Warzone móvil APK</h2>
-<p>Jugar Call of Duty Warzone APK móvil puede ser desafiante y divertido, pero también frustrante y competitivo. Para mejorar tu rendimiento y habilidades, necesitas practicar y aprender algunos consejos y trucos. Estos son algunos de ellos:</p>
- <h3>Cómo mejorar tu rendimiento y habilidades</h3>
-<p>Para mejorar tu rendimiento y habilidades, necesitas hacer lo siguiente:</p>
-<ul>
-<li>Elige la carga correcta, operador, piel, arma, etc. que se adapte a tu estilo de juego y estrategia. </li>
-<li>Ajusta tus ajustes y controles para optimizar tus gráficos, sonido, sensibilidad, diseño, etc.</li>
-<li> Practica tu puntería, movimiento y tácticas en el modo de entrenamiento o en el modo de práctica. </li>
-<li>Ver tutoriales, guías y vídeos de juego de otros jugadores o streamers. </li>
-<li>Aprende de tus errores y analiza tus estadísticas y repeticiones. </li>
-<li>Manténgase actualizado con las últimas noticias, actualizaciones y eventos del juego. </li>
-</ul>
-
-<p>Contratos, killstreaks, y los vehículos son algunas de las características que hacen Call of Duty Warzone móvil APK único y emocionante. Puedes usarlos para ganar ventaja sobre tus enemigos. Así es como:</p>
-<ul>
-<li>Los contratos son misiones que puedes encontrar y activar en el mapa. Te dan recompensas como dinero, botín, intel, etc. Hay diferentes tipos de contratos, como recompensa, carroñero, reconocimiento, más buscados, etc. Elige los que se adapten a tu situación y objetivo. </li>
-<li>Killstreaks son habilidades especiales que puedes usar una vez que tengas suficiente dinero o puntos. Incluyen ataques aéreos, UAV, ataques de racimo, etc. Puede comprarlos en las estaciones de compra o encontrarlos en cajas de botín. Úsalos sabiamente y estratégicamente para eliminar o distraer a tus enemigos. </li>
-<li>Los vehículos son modos de transporte que puede utilizar para moverse por el mapa más rápido y más seguro. Incluyen helicópteros, camiones, ATV, etc. Puedes encontrarlos en varios lugares o llamarlos desde las estaciones de compra. Tenga cuidado al usarlos mientras hacen ruido y atraen la atención. </li>
-</ul>
- <h3>Cómo sobrevivir en Verdansk y ganar la batalla real</h3>
-<p>Verdansk es el mapa principal de Call of Duty Warzone Mobile APK. Es un mapa enorme y diverso con varias ubicaciones, como el centro, el aeropuerto, el estadio, la prisión, etc. Para sobrevivir en Verdansk y ganar la batalla real, debe hacer lo siguiente:</p>
-<ul>
-<li>Elige un buen lugar de aterrizaje que tenga suficiente botín y cobertura. </li>
-<li>Saquea tanto como puedas pero no te vuelvas codicioso o distraído. </li>
-<li>Evite peleas y compromisos innecesarios a menos que tenga una clara ventaja u objetivo. </li>
-<li>Manténgase alerta y consciente de sus alrededores y enemigos. </li>
-<li>Usa el sistema de ping y el chat de voz para comunicarte con tus compañeros de equipo. </li>
-<li>Muévete con el círculo de gas y evita quedar atrapado fuera de él. </li>
-<li>Elige tus batallas sabiamente y sabe cuándo luchar o huir. </li>
-
-<li>No te olvides de revivir o comprar a tus compañeros de equipo si están abajo o muertos. </li>
-<li>Juega inteligente y divertirse! </li>
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, Call of Duty Warzone Mobile APK es un gran juego que ofrece una experiencia de batalla real emocionante e inmersiva en su dispositivo móvil. Puede descargarlo de varias fuentes o pre-registrarse ahora para obtener acceso temprano y recompensas exclusivas. También puedes jugar solo o con tus amigos en diferentes modos y mapas. También puede utilizar contratos, killstreaks y vehículos para mejorar su juego y estrategia. También puede mejorar su rendimiento y habilidades siguiendo algunos consejos y trucos. Si usted está buscando un juego divertido y desafiante que le mantendrá enganchado durante horas, entonces usted debe probar definitivamente Call of Duty Warzone móvil APK. ¡Descárgalo ahora y únete a la zona de guerra! <h2>FAQs</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Call of Duty Warzone móvil APK:</p>
- <h3>Q1: ¿Es Call of Duty Warzone móvil APK libre para jugar? </h3>
-<p>A1: Sí, Call of Duty Warzone Mobile APK es libre de jugar con compras opcionales en el juego. Puede descargarlo de varias fuentes o pre-registrarse ahora para obtener acceso temprano y recompensas exclusivas. </p>
- <h3>Q2: ¿Cuáles son las especificaciones mínimas del dispositivo para Call of Duty Warzone Mobile APK? </h3>
-<p>A2: Necesita un Adreno 618 o mejor GPU y 6GB RAM o más para jugar Call of Duty Warzone Mobile APK. También necesita una conexión a Internet estable y suficiente espacio de almacenamiento. </p>
- <h3>Q3: ¿Cuándo se lanzará oficialmente Call of Duty Warzone Mobile APK? </h3>
-<p>A3: La fecha oficial de lanzamiento de Call of Duty Warzone Mobile APK no se ha anunciado todavía, pero puede pre-registrarse ahora para recibir una notificación cuando esté disponible. También puede seguir las cuentas oficiales de las redes sociales o el sitio web para obtener las últimas noticias y actualizaciones. </p>
- <h3>Q4: ¿Puedo jugar Call of Duty Warzone móvil APK con jugadores en otras plataformas? </h3>
-
- <h3>Q5: ¿Puedo transferir mi progreso de Call of Duty Warzone en PC o consola a Call of Duty Warzone Mobile APK? </h3>
-<p>A5: Sí, puede transferir su progreso de Call of Duty Warzone en PC o consola a Call of Duty Warzone Mobile APK. Solo necesitas usar tu cuenta de Activision existente o crear una nueva para sincronizar tu progreso en todos los dispositivos. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/__init__.py

@@ -1,142 +0,0 @@
-"""
-    pygments.formatters
-    ~~~~~~~~~~~~~~~~~~~
-
-    Pygments formatters.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import sys
-import types
-from fnmatch import fnmatch
-from os.path import basename
-
-from pip._vendor.pygments.formatters._mapping import FORMATTERS
-from pip._vendor.pygments.plugin import find_plugin_formatters
-from pip._vendor.pygments.util import ClassNotFound
-
-__all__ = ['get_formatter_by_name', 'get_formatter_for_filename',
-           'get_all_formatters', 'load_formatter_from_file'] + list(FORMATTERS)
-
-_formatter_cache = {}  # classes by name
-
-def _load_formatters(module_name):
-    """Load a formatter (and all others in the module too)."""
-    mod = __import__(module_name, None, None, ['__all__'])
-    for formatter_name in mod.__all__:
-        cls = getattr(mod, formatter_name)
-        _formatter_cache[cls.name] = cls
-
-
-def get_all_formatters():
-    """Return a generator for all formatter classes."""
-    # NB: this returns formatter classes, not info like get_all_lexers().
-    for info in FORMATTERS.values():
-        if info[1] not in _formatter_cache:
-            _load_formatters(info[0])
-        yield _formatter_cache[info[1]]
-    for _, formatter in find_plugin_formatters():
-        yield formatter
-
-
-def find_formatter_class(alias):
-    """Lookup a formatter by alias.
-
-    Returns None if not found.
-    """
-    for module_name, name, aliases, _, _ in FORMATTERS.values():
-        if alias in aliases:
-            if name not in _formatter_cache:
-                _load_formatters(module_name)
-            return _formatter_cache[name]
-    for _, cls in find_plugin_formatters():
-        if alias in cls.aliases:
-            return cls
-
-
-def get_formatter_by_name(_alias, **options):
-    """Lookup and instantiate a formatter by alias.
-
-    Raises ClassNotFound if not found.
-    """
-    cls = find_formatter_class(_alias)
-    if cls is None:
-        raise ClassNotFound("no formatter found for name %r" % _alias)
-    return cls(**options)
-
-
-def load_formatter_from_file(filename, formattername="CustomFormatter",
-                             **options):
-    """Load a formatter from a file.
-
-    This method expects a file located relative to the current working
-    directory, which contains a class named CustomFormatter. By default,
-    it expects the Formatter to be named CustomFormatter; you can specify
-    your own class name as the second argument to this function.
-
-    Users should be very careful with the input, because this method
-    is equivalent to running eval on the input file.
-
-    Raises ClassNotFound if there are any problems importing the Formatter.
-
-    .. versionadded:: 2.2
-    """
-    try:
-        # This empty dict will contain the namespace for the exec'd file
-        custom_namespace = {}
-        with open(filename, 'rb') as f:
-            exec(f.read(), custom_namespace)
-        # Retrieve the class `formattername` from that namespace
-        if formattername not in custom_namespace:
-            raise ClassNotFound('no valid %s class found in %s' %
-                                (formattername, filename))
-        formatter_class = custom_namespace[formattername]
-        # And finally instantiate it with the options
-        return formatter_class(**options)
-    except OSError as err:
-        raise ClassNotFound('cannot read %s: %s' % (filename, err))
-    except ClassNotFound:
-        raise
-    except Exception as err:
-        raise ClassNotFound('error when loading custom formatter: %s' % err)
-
-
-def get_formatter_for_filename(fn, **options):
-    """Lookup and instantiate a formatter by filename pattern.
-
-    Raises ClassNotFound if not found.
-    """
-    fn = basename(fn)
-    for modname, name, _, filenames, _ in FORMATTERS.values():
-        for filename in filenames:
-            if fnmatch(fn, filename):
-                if name not in _formatter_cache:
-                    _load_formatters(modname)
-                return _formatter_cache[name](**options)
-    for cls in find_plugin_formatters():
-        for filename in cls.filenames:
-            if fnmatch(fn, filename):
-                return cls(**options)
-    raise ClassNotFound("no formatter found for file name %r" % fn)
-
-
-class _automodule(types.ModuleType):
-    """Automatically import formatters."""
-
-    def __getattr__(self, name):
-        info = FORMATTERS.get(name)
-        if info:
-            _load_formatters(info[0])
-            cls = _formatter_cache[info[1]]
-            setattr(self, name, cls)
-            return cls
-        raise AttributeError(name)
-
-
-oldmod = sys.modules[__name__]
-newmod = _automodule(__name__)
-newmod.__dict__.update(oldmod.__dict__)
-sys.modules[__name__] = newmod
-del newmod.newmod, newmod.oldmod, newmod.sys, newmod.types

spaces/CVH-vn1210/make_hair/minigpt4/datasets/builders/base_dataset_builder.py

@@ -1,235 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import logging
-import os
-import shutil
-import warnings
-
-from omegaconf import OmegaConf
-import torch.distributed as dist
-from torchvision.datasets.utils import download_url
-
-import minigpt4.common.utils as utils
-from minigpt4.common.dist_utils import is_dist_avail_and_initialized, is_main_process
-from minigpt4.common.registry import registry
-from minigpt4.processors.base_processor import BaseProcessor
-
-
-
-class BaseDatasetBuilder:
-    train_dataset_cls, eval_dataset_cls = None, None
-
-    def __init__(self, cfg=None):
-        super().__init__()
-
-        if cfg is None:
-            # help to create datasets from default config.
-            self.config = load_dataset_config(self.default_config_path())
-        elif isinstance(cfg, str):
-            self.config = load_dataset_config(cfg)
-        else:
-            # when called from task.build_dataset()
-            self.config = cfg
-
-        self.data_type = self.config.data_type
-
-        self.vis_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
-        self.text_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
-
-    def build_datasets(self):
-        # download, split, etc...
-        # only called on 1 GPU/TPU in distributed
-
-        if is_main_process():
-            self._download_data()
-
-        if is_dist_avail_and_initialized():
-            dist.barrier()
-
-        # at this point, all the annotations and image/videos should be all downloaded to the specified locations.
-        logging.info("Building datasets...")
-        datasets = self.build()  # dataset['train'/'val'/'test']
-
-        return datasets
-
-    def build_processors(self):
-        vis_proc_cfg = self.config.get("vis_processor")
-        txt_proc_cfg = self.config.get("text_processor")
-
-        if vis_proc_cfg is not None:
-            vis_train_cfg = vis_proc_cfg.get("train")
-            vis_eval_cfg = vis_proc_cfg.get("eval")
-
-            self.vis_processors["train"] = self._build_proc_from_cfg(vis_train_cfg)
-            self.vis_processors["eval"] = self._build_proc_from_cfg(vis_eval_cfg)
-
-        if txt_proc_cfg is not None:
-            txt_train_cfg = txt_proc_cfg.get("train")
-            txt_eval_cfg = txt_proc_cfg.get("eval")
-
-            self.text_processors["train"] = self._build_proc_from_cfg(txt_train_cfg)
-            self.text_processors["eval"] = self._build_proc_from_cfg(txt_eval_cfg)
-
-    @staticmethod
-    def _build_proc_from_cfg(cfg):
-        return (
-            registry.get_processor_class(cfg.name).from_config(cfg)
-            if cfg is not None
-            else None
-        )
-
-    @classmethod
-    def default_config_path(cls, type="default"):
-        return utils.get_abs_path(cls.DATASET_CONFIG_DICT[type])
-
-    def _download_data(self):
-        self._download_ann()
-        self._download_vis()
-
-    def _download_ann(self):
-        """
-        Download annotation files if necessary.
-        All the vision-language datasets should have annotations of unified format.
-
-        storage_path can be:
-          (1) relative/absolute: will be prefixed with env.cache_root to make full path if relative.
-          (2) basename/dirname: will be suffixed with base name of URL if dirname is provided.
-
-        Local annotation paths should be relative.
-        """
-        anns = self.config.build_info.annotations
-
-        splits = anns.keys()
-
-        cache_root = registry.get_path("cache_root")
-
-        for split in splits:
-            info = anns[split]
-
-            urls, storage_paths = info.get("url", None), info.storage
-
-            if isinstance(urls, str):
-                urls = [urls]
-            if isinstance(storage_paths, str):
-                storage_paths = [storage_paths]
-
-            assert len(urls) == len(storage_paths)
-
-            for url_or_filename, storage_path in zip(urls, storage_paths):
-                # if storage_path is relative, make it full by prefixing with cache_root.
-                if not os.path.isabs(storage_path):
-                    storage_path = os.path.join(cache_root, storage_path)
-
-                dirname = os.path.dirname(storage_path)
-                if not os.path.exists(dirname):
-                    os.makedirs(dirname)
-
-                if os.path.isfile(url_or_filename):
-                    src, dst = url_or_filename, storage_path
-                    if not os.path.exists(dst):
-                        shutil.copyfile(src=src, dst=dst)
-                    else:
-                        logging.info("Using existing file {}.".format(dst))
-                else:
-                    if os.path.isdir(storage_path):
-                        # if only dirname is provided, suffix with basename of URL.
-                        raise ValueError(
-                            "Expecting storage_path to be a file path, got directory {}".format(
-                                storage_path
-                            )
-                        )
-                    else:
-                        filename = os.path.basename(storage_path)
-
-                    download_url(url=url_or_filename, root=dirname, filename=filename)
-
-    def _download_vis(self):
-
-        storage_path = self.config.build_info.get(self.data_type).storage
-        storage_path = utils.get_cache_path(storage_path)
-
-        if not os.path.exists(storage_path):
-            warnings.warn(
-                f"""
-                The specified path {storage_path} for visual inputs does not exist.
-                Please provide a correct path to the visual inputs or
-                refer to datasets/download_scripts/README.md for downloading instructions.
-                """
-            )
-
-    def build(self):
-        """
-        Create by split datasets inheriting torch.utils.data.Datasets.
-
-        # build() can be dataset-specific. Overwrite to customize.
-        """
-        self.build_processors()
-
-        build_info = self.config.build_info
-
-        ann_info = build_info.annotations
-        vis_info = build_info.get(self.data_type)
-
-        datasets = dict()
-        for split in ann_info.keys():
-            if split not in ["train", "val", "test"]:
-                continue
-
-            is_train = split == "train"
-
-            # processors
-            vis_processor = (
-                self.vis_processors["train"]
-                if is_train
-                else self.vis_processors["eval"]
-            )
-            text_processor = (
-                self.text_processors["train"]
-                if is_train
-                else self.text_processors["eval"]
-            )
-
-            # annotation path
-            ann_paths = ann_info.get(split).storage
-            if isinstance(ann_paths, str):
-                ann_paths = [ann_paths]
-
-            abs_ann_paths = []
-            for ann_path in ann_paths:
-                if not os.path.isabs(ann_path):
-                    ann_path = utils.get_cache_path(ann_path)
-                abs_ann_paths.append(ann_path)
-            ann_paths = abs_ann_paths
-
-            # visual data storage path
-            vis_path = os.path.join(vis_info.storage, split)
-
-            if not os.path.isabs(vis_path):
-                # vis_path = os.path.join(utils.get_cache_path(), vis_path)
-                vis_path = utils.get_cache_path(vis_path)
-
-            if not os.path.exists(vis_path):
-                warnings.warn("storage path {} does not exist.".format(vis_path))
-
-            # create datasets
-            dataset_cls = self.train_dataset_cls if is_train else self.eval_dataset_cls
-            datasets[split] = dataset_cls(
-                vis_processor=vis_processor,
-                text_processor=text_processor,
-                ann_paths=ann_paths,
-                vis_root=vis_path,
-            )
-
-        return datasets
-
-
-def load_dataset_config(cfg_path):
-    cfg = OmegaConf.load(cfg_path).datasets
-    cfg = cfg[list(cfg.keys())[0]]
-
-    return cfg

spaces/CVPR/LIVE/scene.cpp

@@ -1,1035 +0,0 @@
-#include "scene.h"
-#include "aabb.h"
-#include "cuda_utils.h"
-#include "filter.h"
-#include "shape.h"
-#include <numeric>
-#include <algorithm>
-#include <cstring>
-#include <chrono>
-#include <cstddef>
-
-size_t align(size_t s) {
-    auto a = alignof(std::max_align_t);
-    return ((s + a - 1) / a) * a;
-}
-
-template <typename T>
-void allocate(bool use_gpu, T **p) {
-    if (use_gpu) {
-#ifdef __NVCC__
-        checkCuda(cudaMallocManaged(p, sizeof(T)));
-#else
-        throw std::runtime_error("diffvg not compiled with GPU");
-        assert(false);
-#endif
-    } else {
-        *p = (T*)malloc(sizeof(T));
-    }
-}
-
-template <typename T>
-void allocate(bool use_gpu, size_t size, T **p) {
-    if (use_gpu) {
-#ifdef __NVCC__
-        checkCuda(cudaMallocManaged(p, size * sizeof(T)));
-#else
-        throw std::runtime_error("diffvg not compiled with GPU");
-        assert(false);
-#endif
-    } else {
-        *p = (T*)malloc(size * sizeof(T));
-    }
-}
-
-void copy_and_init_shapes(Scene &scene,
-                          const std::vector<const Shape *> &shape_list) {
-    for (int shape_id = 0; shape_id < scene.num_shapes; shape_id++) {
-        switch (shape_list[shape_id]->type) {
-            case ShapeType::Circle: {
-                Circle *p = (Circle *)scene.shapes[shape_id].ptr;
-                const Circle *p_ = (const Circle*)(shape_list[shape_id]->ptr);
-                *p = *p_;
-                Circle *d_p = (Circle *)scene.d_shapes[shape_id].ptr;
-                d_p->radius = 0;
-                d_p->center = Vector2f{0, 0};
-                break;
-            } case ShapeType::Ellipse: {
-                Ellipse *p = (Ellipse *)scene.shapes[shape_id].ptr;
-                const Ellipse *p_ = (const Ellipse*)(shape_list[shape_id]->ptr);
-                *p = *p_;
-                Ellipse *d_p = (Ellipse *)scene.d_shapes[shape_id].ptr;
-                d_p->radius = Vector2f{0, 0};
-                d_p->center = Vector2f{0, 0};
-                break;
-            } case ShapeType::Path: {
-                Path *p = (Path *)scene.shapes[shape_id].ptr;
-                const Path *p_ = (const Path*)(shape_list[shape_id]->ptr);
-                p->num_points = p_->num_points;
-                p->num_base_points = p_->num_base_points;
-                for (int i = 0; i < p_->num_base_points; i++) {
-                    p->num_control_points[i] = p_->num_control_points[i];
-                }
-                for (int i = 0; i < 2 * p_->num_points; i++) {
-                    p->points[i] = p_->points[i];
-                }
-                p->is_closed = p_->is_closed;
-                p->use_distance_approx = p_->use_distance_approx;
-                Path *d_p = (Path *)scene.d_shapes[shape_id].ptr;
-                d_p->num_points = p_->num_points;
-                d_p->num_base_points = p_->num_base_points;
-                for (int i = 0; i < 2 * p_->num_points; i++) {
-                    d_p->points[i] = 0;
-                }
-                d_p->is_closed = p_->is_closed;
-                if (p_->thickness != nullptr) {
-                    for (int i = 0; i < p_->num_points; i++) {
-                        p->thickness[i] = p_->thickness[i];
-                        d_p->thickness[i] = 0;
-                    }
-                }
-                d_p->use_distance_approx = p_->use_distance_approx;
-                break;
-            } case ShapeType::Rect: {
-                Rect *p = (Rect *)scene.shapes[shape_id].ptr;
-                const Rect *p_ = (const Rect*)(shape_list[shape_id]->ptr);
-                *p = *p_;
-                Rect *d_p = (Rect *)scene.d_shapes[shape_id].ptr;
-                d_p->p_min = Vector2f{0, 0};
-                d_p->p_max = Vector2f{0, 0};
-                break;
-            } default: {
-                assert(false);
-                break;
-            }
-        }
-        scene.shapes[shape_id].type = shape_list[shape_id]->type;
-        scene.shapes[shape_id].stroke_width = shape_list[shape_id]->stroke_width;
-        scene.d_shapes[shape_id].type = shape_list[shape_id]->type;
-        scene.d_shapes[shape_id].stroke_width = 0;
-    }
-}
-
-std::vector<float>
-compute_shape_length(const std::vector<const Shape *> &shape_list) {
-    int num_shapes = (int)shape_list.size();
-    std::vector<float> shape_length_list(num_shapes, 0.f);
-    for (int shape_id = 0; shape_id < num_shapes; shape_id++) {
-        auto shape_length = 0.f;
-        switch (shape_list[shape_id]->type) {
-            case ShapeType::Circle: {
-                const Circle *p_ = (const Circle*)(shape_list[shape_id]->ptr);
-                shape_length += float(2.f * M_PI) * p_->radius;
-                break;
-            } case ShapeType::Ellipse: {
-                const Ellipse *p_ = (const Ellipse*)(shape_list[shape_id]->ptr);
-                // https://en.wikipedia.org/wiki/Ellipse#Circumference
-                // Ramanujan's ellipse circumference approximation
-                auto a = p_->radius.x;
-                auto b = p_->radius.y;
-                shape_length += float(M_PI) * (3 * (a + b) - sqrt((3 * a + b) * (a + 3 * b)));
-                break;
-            } case ShapeType::Path: {
-                const Path *p_ = (const Path*)(shape_list[shape_id]->ptr);
-                auto length = 0.f;
-                auto point_id = 0;
-                for (int i = 0; i < p_->num_base_points; i++) {
-                    if (p_->num_control_points[i] == 0) {
-                        // Straight line
-                        auto i0 = point_id;
-                        assert(i0 < p_->num_points);
-                        auto i1 = (i0 + 1) % p_->num_points;
-                        point_id += 1;
-                        auto p0 = Vector2f{p_->points[2 * i0], p_->points[2 * i0 + 1]};
-                        auto p1 = Vector2f{p_->points[2 * i1], p_->points[2 * i1 + 1]};
-                        length += distance(p1, p0);
-                    } else if (p_->num_control_points[i] == 1) {
-                        // Quadratic Bezier curve
-                        auto i0 = point_id;
-                        auto i1 = i0 + 1;
-                        auto i2 = (i0 + 2) % p_->num_points;
-                        point_id += 2;
-                        auto p0 = Vector2f{p_->points[2 * i0], p_->points[2 * i0 + 1]};
-                        auto p1 = Vector2f{p_->points[2 * i1], p_->points[2 * i1 + 1]};
-                        auto p2 = Vector2f{p_->points[2 * i2], p_->points[2 * i2 + 1]};
-                        auto eval = [&](float t) -> Vector2f {
-                            auto tt = 1 - t;
-                            return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2;
-                        };
-                        // We use 3-point samples to approximate the length
-                        auto v0 = p0;
-                        auto v1 = eval(0.5f);
-                        auto v2 = p2;
-                        length += distance(v1, v0) + distance(v1, v2);
-                    } else if (p_->num_control_points[i] == 2) {
-                        // Cubic Bezier curve
-                        auto i0 = point_id;
-                        auto i1 = i0 + 1;
-                        auto i2 = i0 + 2;
-                        auto i3 = (i0 + 3) % p_->num_points;
-                        point_id += 3;
-                        auto p0 = Vector2f{p_->points[2 * i0], p_->points[2 * i0 + 1]};
-                        auto p1 = Vector2f{p_->points[2 * i1], p_->points[2 * i1 + 1]};
-                        auto p2 = Vector2f{p_->points[2 * i2], p_->points[2 * i2 + 1]};
-                        auto p3 = Vector2f{p_->points[2 * i3], p_->points[2 * i3 + 1]};
-                        auto eval = [&](float t) -> Vector2f {
-                            auto tt = 1 - t;
-                            return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3;
-                        };
-                        // We use 4-point samples to approximate the length
-                        auto v0 = p0;
-                        auto v1 = eval(1.f/3.f);
-                        auto v2 = eval(2.f/3.f);
-                        auto v3 = p3;
-                        length += distance(v1, v0) + distance(v1, v2) + distance(v2, v3);
-                    } else {
-                        assert(false);
-                    }
-                }
-                assert(isfinite(length));
-                shape_length += length;
-                break;
-            } case ShapeType::Rect: {
-                const Rect *p_ = (const Rect*)(shape_list[shape_id]->ptr);
-                shape_length += 2 * (p_->p_max.x - p_->p_min.x + p_->p_max.y - p_->p_min.y);
-                break;
-            } default: {
-                assert(false);
-                break;
-            }
-        }
-        assert(isfinite(shape_length));
-        shape_length_list[shape_id] = shape_length;
-    }
-    return shape_length_list;
-}
-
-void build_shape_cdfs(Scene &scene,
-                      const std::vector<const ShapeGroup *> &shape_group_list,
-                      const std::vector<float> &shape_length_list) {
-    int sample_id = 0;
-    for (int shape_group_id = 0; shape_group_id < (int)shape_group_list.size(); shape_group_id++) {
-        const ShapeGroup *shape_group = shape_group_list[shape_group_id];
-        for (int i = 0; i < shape_group->num_shapes; i++) {
-            int shape_id = shape_group->shape_ids[i];
-            float length = shape_length_list[shape_id];
-            scene.sample_shape_id[sample_id] = shape_id;
-            if (sample_id == 0) {
-                scene.sample_shapes_cdf[sample_id] = length;
-            } else {
-                scene.sample_shapes_cdf[sample_id] = length +
-                    scene.sample_shapes_cdf[sample_id - 1];
-            }
-            assert(isfinite(length));
-            scene.sample_shapes_pmf[sample_id] = length;
-            scene.sample_group_id[sample_id] = shape_group_id;
-            sample_id++;
-        }
-    }
-    assert(sample_id == scene.num_total_shapes);
-    auto normalization = scene.sample_shapes_cdf[scene.num_total_shapes - 1];
-    if (normalization <= 0) {
-        char buf[256];
-        sprintf(buf, "The total length of the shape boundaries in the scene is equal or less than 0. Length = %f", normalization);
-        throw std::runtime_error(buf);
-    }
-    if (!isfinite(normalization)) {
-        char buf[256];
-        sprintf(buf, "The total length of the shape boundaries in the scene is not a number. Length = %f", normalization);
-        throw std::runtime_error(buf);
-    }
-    assert(normalization > 0);
-    for (int sample_id = 0; sample_id < scene.num_total_shapes; sample_id++) {
-        scene.sample_shapes_cdf[sample_id] /= normalization;
-        scene.sample_shapes_pmf[sample_id] /= normalization;
-    }
-}
-
-void build_path_cdfs(Scene &scene,
-                     const std::vector<const Shape *> &shape_list,
-                     const std::vector<float> &shape_length_list) {
-    for (int shape_id = 0; shape_id < scene.num_shapes; shape_id++) {
-        if (shape_list[shape_id]->type == ShapeType::Path) {
-            const Path &path = shape_list[shape_id]->as_path();
-            float *pmf = scene.path_length_pmf[shape_id];
-            float *cdf = scene.path_length_cdf[shape_id];
-            int *point_id_map = scene.path_point_id_map[shape_id];
-            auto path_length = shape_length_list[shape_id];
-            auto inv_length = 1.f / path_length;
-            auto point_id = 0;
-            for (int i = 0; i < path.num_base_points; i++) {
-                point_id_map[i] = point_id;
-                if (path.num_control_points[i] == 0) {
-                    // Straight line
-                    auto i0 = point_id;
-                    auto i1 = (i0 + 1) % path.num_points;
-                    point_id += 1;
-                    auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]};
-                    auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]};
-                    auto d = distance(p0, p1) * inv_length;
-                    pmf[i] = d;
-                    if (i == 0) {
-                        cdf[i] = d;
-                    } else {
-                        cdf[i] = d + cdf[i - 1];
-                    }
-                } else if (path.num_control_points[i] == 1) {
-                    // Quadratic Bezier curve
-                    auto i0 = point_id;
-                    auto i1 = i0 + 1;
-                    auto i2 = (i0 + 2) % path.num_points;
-                    point_id += 2;
-                    auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]};
-                    auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]};
-                    auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]};
-                    auto eval = [&](float t) -> Vector2f {
-                        auto tt = 1 - t;
-                        return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2;
-                    };
-                    // We use 3-point samples to approximate the length
-                    auto v0 = p0;
-                    auto v1 = eval(0.5f);
-                    auto v2 = p2;
-                    auto d = (distance(v0, v1) + distance(v1, v2)) * inv_length;
-                    pmf[i] = d;
-                    if (i == 0) {
-                        cdf[i] = d;
-                    } else {
-                        cdf[i] = d + cdf[i - 1];
-                    }
-                } else if (path.num_control_points[i] == 2) {
-                    // Cubic Bezier curve
-                    auto i0 = point_id;
-                    auto i1 = point_id + 1;
-                    auto i2 = point_id + 2;
-                    auto i3 = (point_id + 3) % path.num_points;
-                    point_id += 3;
-                    auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]};
-                    auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]};
-                    auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]};
-                    auto p3 = Vector2f{path.points[2 * i3], path.points[2 * i3 + 1]};
-                    auto eval = [&](float t) -> Vector2f {
-                        auto tt = 1 - t;
-                        return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3;
-                    };
-                    // We use 4-point samples to approximate the length
-                    auto v0 = p0;
-                    auto v1 = eval(1.f/3.f);
-                    auto v2 = eval(2.f/3.f);
-                    auto v3 = p3;
-                    auto d = (distance(v1, v0) + distance(v1, v2) + distance(v2, v3)) * inv_length;
-                    pmf[i] = d;
-                    if (i == 0) {
-                        cdf[i] = d;
-                    } else {
-                        cdf[i] = d + cdf[i - 1];
-                    }
-                } else {
-                    assert(false);
-                }
-            }
-        }
-    }
-}
-
-void copy_and_init_shape_groups(Scene &scene,
-                                const std::vector<const ShapeGroup *> &shape_group_list) {
-    for (int group_id = 0; group_id < scene.num_shape_groups; group_id++) {
-        const ShapeGroup *shape_group = shape_group_list[group_id];
-        auto copy_and_init_color = [&](const ColorType &color_type, void *color_ptr, void *target_ptr, void *d_target_ptr) {
-            switch (color_type) {
-                case ColorType::Constant: {
-                    Constant *c = (Constant*)target_ptr;
-                    Constant *d_c = (Constant*)d_target_ptr;
-                    const Constant *c_ = (const Constant*)color_ptr;
-                    *c = *c_;
-                    d_c->color = Vector4{0, 0, 0, 0};
-                    break;
-                } case ColorType::LinearGradient: {
-                    LinearGradient *c = (LinearGradient*)target_ptr;
-                    LinearGradient *d_c = (LinearGradient*)d_target_ptr;
-                    const LinearGradient *c_ = (const LinearGradient*)color_ptr;
-                    c->begin = c_->begin;
-                    c->end = c_->end;
-                    c->num_stops = c_->num_stops;
-                    for (int i = 0; i < c_->num_stops; i++) {
-                        c->stop_offsets[i] = c_->stop_offsets[i];
-                    }
-                    for (int i = 0; i < 4 * c_->num_stops; i++) {
-                        c->stop_colors[i] = c_->stop_colors[i];
-                    }
-                    d_c->begin = Vector2f{0, 0};
-                    d_c->end = Vector2f{0, 0};
-                    d_c->num_stops = c_->num_stops;
-                    for (int i = 0; i < c_->num_stops; i++) {
-                        d_c->stop_offsets[i] = 0;
-                    }
-                    for (int i = 0; i < 4 * c_->num_stops; i++) {
-                        d_c->stop_colors[i] = 0;
-                    }
-                    break;
-                } case ColorType::RadialGradient: {
-                    RadialGradient *c = (RadialGradient*)target_ptr;
-                    RadialGradient *d_c = (RadialGradient*)d_target_ptr;
-                    const RadialGradient *c_ = (const RadialGradient*)color_ptr;
-                    c->center = c_->center;
-                    c->radius = c_->radius;
-                    c->num_stops = c_->num_stops;
-                    for (int i = 0; i < c_->num_stops; i++) {
-                        c->stop_offsets[i] = c_->stop_offsets[i];
-                    }
-                    for (int i = 0; i < 4 * c_->num_stops; i++) {
-                        c->stop_colors[i] = c_->stop_colors[i];
-                    }
-                    d_c->center = Vector2f{0, 0};
-                    d_c->radius = Vector2f{0, 0};
-                    d_c->num_stops = c_->num_stops;
-                    for (int i = 0; i < c_->num_stops; i++) {
-                        d_c->stop_offsets[i] = 0;
-                    }
-                    for (int i = 0; i < 4 * c_->num_stops; i++) {
-                        d_c->stop_colors[i] = 0;
-                    }
-                    break;
-                } default: {
-                    assert(false);
-                }
-            }
-        };
-        for (int i = 0; i < shape_group->num_shapes; i++) {
-            scene.shape_groups[group_id].shape_ids[i] = shape_group->shape_ids[i];
-        }
-        scene.shape_groups[group_id].num_shapes = shape_group->num_shapes;
-        scene.shape_groups[group_id].use_even_odd_rule = shape_group->use_even_odd_rule;
-        scene.shape_groups[group_id].canvas_to_shape = shape_group->canvas_to_shape;
-        scene.shape_groups[group_id].shape_to_canvas = shape_group->shape_to_canvas;
-        scene.d_shape_groups[group_id].shape_ids = nullptr;
-        scene.d_shape_groups[group_id].num_shapes = shape_group->num_shapes;
-        scene.d_shape_groups[group_id].use_even_odd_rule = shape_group->use_even_odd_rule;
-        scene.d_shape_groups[group_id].canvas_to_shape = Matrix3x3f{};
-        scene.d_shape_groups[group_id].shape_to_canvas = Matrix3x3f{};
-
-        scene.shape_groups[group_id].fill_color_type = shape_group->fill_color_type;
-        scene.d_shape_groups[group_id].fill_color_type = shape_group->fill_color_type;
-        if (shape_group->fill_color != nullptr) {
-            copy_and_init_color(shape_group->fill_color_type,
-                                shape_group->fill_color,
-                                scene.shape_groups[group_id].fill_color,
-                                scene.d_shape_groups[group_id].fill_color);
-        }
-        scene.shape_groups[group_id].stroke_color_type = shape_group->stroke_color_type;
-        scene.d_shape_groups[group_id].stroke_color_type = shape_group->stroke_color_type;
-        if (shape_group->stroke_color != nullptr) {
-            copy_and_init_color(shape_group->stroke_color_type,
-                                shape_group->stroke_color,
-                                scene.shape_groups[group_id].stroke_color,
-                                scene.d_shape_groups[group_id].stroke_color);
-        }
-    }
-}
-
-DEVICE uint32_t morton2D(const Vector2f &p, int canvas_width, int canvas_height) {
-    auto scene_bounds = Vector2f{canvas_width, canvas_height};
-    auto pp = p / scene_bounds;
-    TVector2<uint32_t> pp_i{pp.x * 1023, pp.y * 1023};
-    return (expand_bits(pp_i.x) << 1u) |
-           (expand_bits(pp_i.y) << 0u);
-}
-
-template <bool sort>
-void build_bvh(const Scene &scene, BVHNode *nodes, int num_primitives) {
-    auto bvh_size = 2 * num_primitives - 1;
-    if (bvh_size > 1) {
-        if (sort) {
-            // Sort by Morton code
-            std::sort(nodes, nodes + num_primitives,
-                [&] (const BVHNode &n0, const BVHNode &n1) {
-                    auto p0 = 0.5f * (n0.box.p_min + n0.box.p_max);
-                    auto p1 = 0.5f * (n1.box.p_min + n1.box.p_max);
-                    auto m0 = morton2D(p0, scene.canvas_width, scene.canvas_height);
-                    auto m1 = morton2D(p1, scene.canvas_width, scene.canvas_height);
-                    return m0 < m1;
-            });
-        }
-        for (int i = num_primitives; i < bvh_size; i++) {
-            nodes[i] = BVHNode{-1, -1, AABB{}, 0.f};
-        }
-        int prev_beg = 0;
-        int prev_end = num_primitives;
-        // For handling odd number of nodes at a level
-        int leftover = prev_end % 2 == 0 ? -1 : prev_end - 1;
-        while (prev_end - prev_beg >= 1 || leftover != -1) {
-            int length = (prev_end - prev_beg) / 2;
-            if ((prev_end - prev_beg) % 2 == 1 && leftover != -1 &&
-                    leftover != prev_end - 1) {
-                length += 1;
-            }
-            for (int i = 0; i < length; i++) {
-                BVHNode node;
-                node.child0 = prev_beg + 2 * i;
-                node.child1 = prev_beg + 2 * i + 1;
-                if (node.child1 >= prev_end) {
-                    assert(leftover != -1);
-                    node.child1 = leftover;
-                    leftover = -1;
-                }
-                AABB child0_box = nodes[node.child0].box;
-                AABB child1_box = nodes[node.child1].box;
-                node.box = merge(child0_box, child1_box);
-                node.max_radius = std::max(nodes[node.child0].max_radius,
-                                           nodes[node.child1].max_radius);
-                nodes[prev_end + i] = node;
-            }
-            if (length == 1 && leftover == -1) {
-                break;
-            }
-            prev_beg = prev_end;
-            prev_end = prev_beg + length;
-            if (length % 2 == 1 && leftover == -1) {
-                leftover = prev_end - 1;
-            }
-        }
-    }
-    assert(nodes[2 * num_primitives - 2].child0 != -1);
-}
-
-void compute_bounding_boxes(Scene &scene,
-                            const std::vector<const Shape *> &shape_list,
-                            const std::vector<const ShapeGroup *> &shape_group_list) {
-    for (int shape_id = 0; shape_id < scene.num_shapes; shape_id++) {
-        switch (shape_list[shape_id]->type) {
-            case ShapeType::Circle: {
-                const Circle *p = (const Circle*)(shape_list[shape_id]->ptr);
-                scene.shapes_bbox[shape_id] = AABB{p->center - p->radius,
-                                                   p->center + p->radius};
-                break;
-            } case ShapeType::Ellipse: {
-                const Ellipse *p = (const Ellipse*)(shape_list[shape_id]->ptr);
-                scene.shapes_bbox[shape_id] = AABB{p->center - p->radius,
-                                                   p->center + p->radius};
-                break;
-            } case ShapeType::Path: {
-                const Path *p = (const Path*)(shape_list[shape_id]->ptr);
-                AABB box;
-                if (p->num_points > 0) {
-                    box = AABB{Vector2f{p->points[0], p->points[1]},
-                               Vector2f{p->points[0], p->points[1]}};
-                }
-                for (int i = 1; i < p->num_points; i++) {
-                    box = merge(box, Vector2f{p->points[2 * i], p->points[2 * i + 1]});
-                }
-                scene.shapes_bbox[shape_id] = box;
-                std::vector<AABB> boxes(p->num_base_points);
-                std::vector<float> thickness(p->num_base_points);
-                std::vector<int> first_point_id(p->num_base_points);
-                auto r = shape_list[shape_id]->stroke_width;
-                auto point_id = 0;
-                for (int i = 0; i < p->num_base_points; i++) {
-                    first_point_id[i] = point_id;
-                    if (p->num_control_points[i] == 0) {
-                        // Straight line
-                        auto i0 = point_id;
-                        auto i1 = (i0 + 1) % p->num_points;
-                        point_id += 1;
-                        auto p0 = Vector2f{p->points[2 * i0], p->points[2 * i0 + 1]};
-                        auto p1 = Vector2f{p->points[2 * i1], p->points[2 * i1 + 1]};
-                        boxes[i] = AABB();
-                        boxes[i] = merge(boxes[i], p0);
-                        boxes[i] = merge(boxes[i], p1);
-                        auto r0 = r;
-                        auto r1 = r;
-                        // override radius if path has thickness
-                        if (p->thickness != nullptr) {
-                            r0 = p->thickness[i0];
-                            r1 = p->thickness[i1];
-                        }
-                        thickness[i] = max(r0, r1);
-                    } else if (p->num_control_points[i] == 1) {
-                        // Quadratic Bezier curve
-                        auto i0 = point_id;
-                        auto i1 = i0 + 1;
-                        auto i2 = (i0 + 2) % p->num_points;
-                        point_id += 2;
-                        auto p0 = Vector2f{p->points[2 * i0], p->points[2 * i0 + 1]};
-                        auto p1 = Vector2f{p->points[2 * i1], p->points[2 * i1 + 1]};
-                        auto p2 = Vector2f{p->points[2 * i2], p->points[2 * i2 + 1]};
-                        boxes[i] = AABB();
-                        boxes[i] = merge(boxes[i], p0);
-                        boxes[i] = merge(boxes[i], p1);
-                        boxes[i] = merge(boxes[i], p2);
-                        auto r0 = r;
-                        auto r1 = r;
-                        auto r2 = r;
-                        // override radius if path has thickness
-                        if (p->thickness != nullptr) {
-                            r0 = p->thickness[i0];
-                            r1 = p->thickness[i1];
-                            r2 = p->thickness[i2];
-                        }
-                        thickness[i] = max(max(r0, r1), r2);
-                    } else if (p->num_control_points[i] == 2) {
-                        // Cubic Bezier curve
-                        auto i0 = point_id;
-                        auto i1 = i0 + 1;
-                        auto i2 = i0 + 2;
-                        auto i3 = (i0 + 3) % p->num_points;
-                        point_id += 3;
-                        auto p0 = Vector2f{p->points[2 * i0], p->points[2 * i0 + 1]};
-                        auto p1 = Vector2f{p->points[2 * i1], p->points[2 * i1 + 1]};
-                        auto p2 = Vector2f{p->points[2 * i2], p->points[2 * i2 + 1]};
-                        auto p3 = Vector2f{p->points[2 * i3], p->points[2 * i3 + 1]};
-                        boxes[i] = AABB();
-                        boxes[i] = merge(boxes[i], p0);
-                        boxes[i] = merge(boxes[i], p1);
-                        boxes[i] = merge(boxes[i], p2);
-                        boxes[i] = merge(boxes[i], p3);
-                        auto r0 = r;
-                        auto r1 = r;
-                        auto r2 = r;
-                        auto r3 = r;
-                        // override radius if path has thickness
-                        if (p->thickness != nullptr) {
-                            r0 = p->thickness[i0];
-                            r1 = p->thickness[i1];
-                            r2 = p->thickness[i2];
-                            r3 = p->thickness[i3];
-                        }
-                        thickness[i] = max(max(max(r0, r1), r2), r3);
-                    } else {
-                        assert(false);
-                    }
-                }
-                // Sort the boxes by y
-                std::vector<int> idx(boxes.size());
-                std::iota(idx.begin(), idx.end(), 0);
-                std::sort(idx.begin(), idx.end(), [&](int i0, int i1) {
-                    const AABB &b0 = boxes[i0];
-                    const AABB &b1 = boxes[i1];
-                    auto b0y = 0.5f * (b0.p_min.y + b0.p_max.y);
-                    auto b1y = 0.5f * (b1.p_min.y + b1.p_max.y);
-                    return b0y < b1y;
-                });
-                BVHNode *nodes = scene.path_bvhs[shape_id];
-                for (int i = 0; i < (int)idx.size(); i++) {
-                    nodes[i] = BVHNode{idx[i],
-                                       -(first_point_id[idx[i]]+1),
-                                       boxes[idx[i]],
-                                       thickness[idx[i]]};
-                }
-                build_bvh<false /*sort*/>(scene, nodes, boxes.size());
-                break;
-            } case ShapeType::Rect: {
-                const Rect *p = (const Rect*)(shape_list[shape_id]->ptr);
-                scene.shapes_bbox[shape_id] = AABB{p->p_min, p->p_max};
-                break;
-            } default: {
-                assert(false);
-                break;
-            }
-        }
-    }
-    
-    for (int shape_group_id = 0; shape_group_id < (int)shape_group_list.size(); shape_group_id++) {
-        const ShapeGroup *shape_group = shape_group_list[shape_group_id];
-        // Build a BVH for each shape group
-        BVHNode *nodes = scene.shape_groups_bvh_nodes[shape_group_id];
-        for (int i = 0; i < shape_group->num_shapes; i++) {
-            auto shape_id = shape_group->shape_ids[i];
-            auto r = shape_group->stroke_color == nullptr ? 0 : shape_list[shape_id]->stroke_width;
-            nodes[i] = BVHNode{shape_id,
-                               -1,
-                               scene.shapes_bbox[shape_id],
-                               r};
-        }
-        build_bvh<true /*sort*/>(scene, nodes, shape_group->num_shapes);
-    }
-
-    BVHNode *nodes = scene.bvh_nodes;
-    for (int shape_group_id = 0; shape_group_id < (int)shape_group_list.size(); shape_group_id++) {
-        const ShapeGroup *shape_group = shape_group_list[shape_group_id];
-        auto max_radius = shape_list[shape_group->shape_ids[0]]->stroke_width;
-        if (shape_list[shape_group->shape_ids[0]]->type == ShapeType::Path) {
-            const Path *p = (const Path*)(shape_list[shape_group->shape_ids[0]]->ptr);
-            if (p->thickness != nullptr) {
-                const BVHNode *nodes = scene.path_bvhs[shape_group->shape_ids[0]];
-                max_radius = nodes[0].max_radius;
-            }
-        }
-        for (int i = 1; i < shape_group->num_shapes; i++) {
-            auto shape_id = shape_group->shape_ids[i];
-            auto shape = shape_list[shape_id];
-            auto r = shape->stroke_width;
-            if (shape->type == ShapeType::Path) {
-                const Path *p = (const Path*)(shape_list[shape_id]->ptr);
-                if (p->thickness != nullptr) {
-                    const BVHNode *nodes = scene.path_bvhs[shape_id];
-                    r = nodes[0].max_radius;
-                }
-            }
-            max_radius = std::max(max_radius, r);
-        }
-        // Fetch group bbox from BVH
-        auto bbox = scene.shape_groups_bvh_nodes[shape_group_id][2 * shape_group->num_shapes - 2].box;
-        // Transform box from local to world space
-        nodes[shape_group_id].child0 = shape_group_id;
-        nodes[shape_group_id].child1 = -1;
-        nodes[shape_group_id].box = transform(shape_group->shape_to_canvas, bbox);
-        if (shape_group->stroke_color == nullptr) {
-            nodes[shape_group_id].max_radius = 0;
-        } else {
-            nodes[shape_group_id].max_radius = max_radius;
-        }
-    }
-    build_bvh<true /*sort*/>(scene, nodes, shape_group_list.size());
-}
-
-template <bool alloc_mode>
-size_t allocate_buffers(Scene &scene,
-                        const std::vector<const Shape *> &shape_list,
-                        const std::vector<const ShapeGroup *> &shape_group_list) {
-    auto num_shapes = shape_list.size();
-    auto num_shape_groups = shape_group_list.size();
-
-    size_t buffer_size = 0;
-    if (alloc_mode) scene.shapes = (Shape*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(Shape) * num_shapes);
-    if (alloc_mode) scene.d_shapes = (Shape*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(Shape) * num_shapes); 
-    if (alloc_mode) scene.shape_groups = (ShapeGroup*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(ShapeGroup) * num_shape_groups);
-    if (alloc_mode) scene.d_shape_groups = (ShapeGroup*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(ShapeGroup) * num_shape_groups);
-    if (alloc_mode) scene.sample_shapes_cdf = (float*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(float) * scene.num_total_shapes);
-    if (alloc_mode) scene.sample_shapes_pmf = (float*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(float) * scene.num_total_shapes);
-    if (alloc_mode) scene.sample_shape_id = (int*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(int) * scene.num_total_shapes);
-    if (alloc_mode) scene.sample_group_id = (int*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(int) * scene.num_total_shapes);
-    if (alloc_mode) scene.shapes_length = (float*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(float) * num_shapes);
-    if (alloc_mode) scene.path_length_cdf = (float**)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(float*) * num_shapes);
-    if (alloc_mode) scene.path_length_pmf = (float**)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(float*) * num_shapes);
-    if (alloc_mode) scene.path_point_id_map = (int**)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(int*) * num_shapes);
-    if (alloc_mode) scene.filter = (Filter*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(Filter));
-    if (alloc_mode) scene.d_filter = (DFilter*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(DFilter));
-    if (alloc_mode) scene.shapes_bbox = (AABB*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(AABB) * num_shapes);
-    if (alloc_mode) scene.path_bvhs = (BVHNode**)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(BVHNode*) * num_shapes);
-    if (alloc_mode) scene.shape_groups_bvh_nodes = (BVHNode**)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(BVHNode*) * num_shape_groups);
-    if (alloc_mode) scene.bvh_nodes = (BVHNode*)&scene.buffer[buffer_size];
-    buffer_size += align(sizeof(BVHNode) * (2 * num_shape_groups - 1));
-
-    if (alloc_mode) {
-        for (int i = 0; i < num_shapes; i++) {
-            scene.path_length_cdf[i] = nullptr;
-            scene.path_length_pmf[i] = nullptr;
-            scene.path_point_id_map[i] = nullptr;
-            scene.path_bvhs[i] = nullptr;
-        }
-    }
-
-    for (int shape_id = 0; shape_id < scene.num_shapes; shape_id++) {
-        switch (shape_list[shape_id]->type) {
-            case ShapeType::Circle: {
-                if (alloc_mode) scene.shapes[shape_id].ptr = (Circle*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(Circle)); // scene.shapes[shape_id].ptr
-                if (alloc_mode) scene.d_shapes[shape_id].ptr = (Circle*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(Circle)); // scene.d_shapes[shape_id].ptr
-                break;
-            } case ShapeType::Ellipse: {
-                if (alloc_mode) scene.shapes[shape_id].ptr = (Ellipse*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(Ellipse)); // scene.shapes[shape_id].ptr
-                if (alloc_mode) scene.d_shapes[shape_id].ptr = (Ellipse*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(Ellipse)); // scene.d_shapes[shape_id].ptr
-                break;
-            } case ShapeType::Path: {
-                if (alloc_mode) scene.shapes[shape_id].ptr = (Path*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(Path)); // scene.shapes[shape_id].ptr
-                if (alloc_mode) scene.d_shapes[shape_id].ptr = (Path*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(Path)); // scene.d_shapes[shape_id].ptr
-
-                const Path *p_ = (const Path*)(shape_list[shape_id]->ptr);
-                Path *p = nullptr, *d_p = nullptr;
-                if (alloc_mode) p = (Path*)scene.shapes[shape_id].ptr;
-                if (alloc_mode) d_p = (Path*)scene.d_shapes[shape_id].ptr; 
-                if (alloc_mode) p->num_control_points = (int*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(int) * p_->num_base_points); // p->num_control_points
-                if (alloc_mode) p->points = (float*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(float) * (2 * p_->num_points)); // p->points
-                if (alloc_mode) d_p->points = (float*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(float) * (2 * p_->num_points)); // d_p->points
-                if (p_->thickness != nullptr) {
-                    if (alloc_mode) p->thickness = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * p_->num_points); // p->thickness
-                    if (alloc_mode) d_p->thickness = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * p_->num_points); // d_p->thickness
-                } else {
-                    if (alloc_mode) p->thickness = nullptr;
-                    if (alloc_mode) d_p->thickness = nullptr;
-                }
-                if (alloc_mode) scene.path_length_pmf[shape_id] = (float*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(float) * p_->num_base_points); // scene.path_length_pmf
-                if (alloc_mode) scene.path_length_cdf[shape_id] = (float*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(float) * p_->num_base_points); // scene.path_length_cdf
-                if (alloc_mode) scene.path_point_id_map[shape_id] = (int*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(int) * p_->num_base_points); // scene.path_point_id_map
-                if (alloc_mode) scene.path_bvhs[shape_id] = (BVHNode*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(BVHNode) * (2 * p_->num_base_points - 1));
-                break;
-            } case ShapeType::Rect: {
-                if (alloc_mode) scene.shapes[shape_id].ptr = (Ellipse*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(Rect)); // scene.shapes[shape_id].ptr
-                if (alloc_mode) scene.d_shapes[shape_id].ptr = (Ellipse*)&scene.buffer[buffer_size];
-                buffer_size += align(sizeof(Rect)); // scene.d_shapes[shape_id].ptr
-                break;
-            } default: {
-                assert(false);
-                break;
-            }
-        }
-    }
-
-    for (int group_id = 0; group_id < scene.num_shape_groups; group_id++) {
-        const ShapeGroup *shape_group = shape_group_list[group_id];
-        if (shape_group->fill_color != nullptr) {
-            switch (shape_group->fill_color_type) {
-                case ColorType::Constant: {
-                    if (alloc_mode) scene.shape_groups[group_id].fill_color = (Constant*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(Constant)); // color
-                    if (alloc_mode) scene.d_shape_groups[group_id].fill_color = (Constant*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(Constant)); // d_color
-                    break;
-                } case ColorType::LinearGradient: {
-                    if (alloc_mode) scene.shape_groups[group_id].fill_color = (LinearGradient*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(LinearGradient)); // color
-                    if (alloc_mode) scene.d_shape_groups[group_id].fill_color = (LinearGradient*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(LinearGradient)); // d_color
-
-                    const LinearGradient *c_ = (const LinearGradient *)shape_group->fill_color;
-                    LinearGradient *c = nullptr, *d_c = nullptr;
-                    if (alloc_mode) c = (LinearGradient *)scene.shape_groups[group_id].fill_color;
-                    if (alloc_mode) d_c = (LinearGradient *)scene.d_shape_groups[group_id].fill_color;
-                    if (alloc_mode) c->stop_offsets = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * c_->num_stops); // c->stop_offsets
-                    if (alloc_mode) c->stop_colors = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * 4 * c_->num_stops); // c->stop_colors
-                    if (alloc_mode) d_c->stop_offsets = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * c_->num_stops); // d_c->stop_offsets
-                    if (alloc_mode) d_c->stop_colors = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * 4 * c_->num_stops); // d_c->stop_colors
-                    break;
-                } case ColorType::RadialGradient: {
-                    if (alloc_mode) scene.shape_groups[group_id].fill_color = (RadialGradient*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(RadialGradient)); // color
-                    if (alloc_mode) scene.d_shape_groups[group_id].fill_color = (RadialGradient*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(RadialGradient)); // d_color
-
-                    const RadialGradient *c_ = (const RadialGradient *)shape_group->fill_color;
-                    RadialGradient *c = nullptr, *d_c = nullptr;
-                    if (alloc_mode) c = (RadialGradient *)scene.shape_groups[group_id].fill_color;
-                    if (alloc_mode) d_c = (RadialGradient *)scene.d_shape_groups[group_id].fill_color;
-                    if (alloc_mode) c->stop_offsets = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * c_->num_stops); // c->stop_offsets
-                    if (alloc_mode) c->stop_colors = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * 4 * c_->num_stops); // c->stop_colors
-                    if (alloc_mode) d_c->stop_offsets = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * c_->num_stops); // d_c->stop_offsets
-                    if (alloc_mode) d_c->stop_colors = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * 4 * c_->num_stops); // d_c->stop_colors
-                    break;
-                } default: {
-                    assert(false);
-                }
-            }
-        } else {
-            if (alloc_mode) scene.shape_groups[group_id].fill_color = nullptr;
-            if (alloc_mode) scene.d_shape_groups[group_id].fill_color = nullptr;
-        }
-        if (shape_group->stroke_color != nullptr) {
-            switch (shape_group->stroke_color_type) {
-                case ColorType::Constant: {
-                    if (alloc_mode) scene.shape_groups[group_id].stroke_color = (Constant*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(Constant)); // color
-                    if (alloc_mode) scene.d_shape_groups[group_id].stroke_color = (Constant*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(Constant)); // d_color
-                    break;
-                } case ColorType::LinearGradient: {
-                    if (alloc_mode) scene.shape_groups[group_id].stroke_color = (LinearGradient*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(LinearGradient)); // color
-                    if (alloc_mode) scene.shape_groups[group_id].stroke_color = (LinearGradient*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(LinearGradient)); // d_color
-
-                    const LinearGradient *c_ = (const LinearGradient *)shape_group->stroke_color;
-                    LinearGradient *c = nullptr, *d_c = nullptr;
-                    if (alloc_mode) c = (LinearGradient *)scene.shape_groups[group_id].stroke_color;
-                    if (alloc_mode) d_c = (LinearGradient *)scene.d_shape_groups[group_id].stroke_color;
-                    if (alloc_mode) c->stop_offsets = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * c_->num_stops); // c->stop_offsets
-                    if (alloc_mode) c->stop_colors = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * 4 * c_->num_stops); // c->stop_colors
-                    if (alloc_mode) d_c->stop_offsets = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * c_->num_stops); // d_c->stop_offsets
-                    if (alloc_mode) d_c->stop_colors = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * 4 * c_->num_stops); // d_c->stop_colors
-                    break;
-                } case ColorType::RadialGradient: {
-                    if (alloc_mode) scene.shape_groups[group_id].stroke_color = (RadialGradient*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(RadialGradient)); // color
-                    if (alloc_mode) scene.shape_groups[group_id].stroke_color = (RadialGradient*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(RadialGradient)); // d_color
-
-                    const RadialGradient *c_ = (const RadialGradient *)shape_group->stroke_color;
-                    RadialGradient *c = nullptr, *d_c = nullptr;
-                    if (alloc_mode) c = (RadialGradient *)scene.shape_groups[group_id].stroke_color;
-                    if (alloc_mode) d_c = (RadialGradient *)scene.d_shape_groups[group_id].stroke_color;
-                    if (alloc_mode) c->stop_offsets = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * c_->num_stops); // c->stop_offsets
-                    if (alloc_mode) c->stop_colors = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * 4 * c_->num_stops); // c->stop_colors
-                    if (alloc_mode) d_c->stop_offsets = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * c_->num_stops); // d_c->stop_offsets
-                    if (alloc_mode) d_c->stop_colors = (float*)&scene.buffer[buffer_size];
-                    buffer_size += align(sizeof(float) * 4 * c_->num_stops); // d_c->stop_colors
-                    break;
-                } default: {
-                    assert(false);
-                }
-            }
-        } else {
-            if (alloc_mode) scene.shape_groups[group_id].stroke_color = nullptr;
-            if (alloc_mode) scene.d_shape_groups[group_id].stroke_color = nullptr;
-        }
-        if (alloc_mode) scene.shape_groups[group_id].shape_ids = (int*)&scene.buffer[buffer_size];
-        buffer_size += align(sizeof(int) * shape_group->num_shapes); // shape_group->shape_ids
-        if (alloc_mode) scene.shape_groups_bvh_nodes[group_id] = (BVHNode*)&scene.buffer[buffer_size];
-        buffer_size += align(sizeof(BVHNode) * (2 * shape_group->num_shapes - 1)); // scene.shape_groups_bvh_nodes[group_id]
-    }
-    return buffer_size;
-}
-
-Scene::Scene(int canvas_width,
-             int canvas_height,
-             const std::vector<const Shape *> &shape_list,
-             const std::vector<const ShapeGroup *> &shape_group_list,
-             const Filter &filter,
-             bool use_gpu,
-             int gpu_index)
-    : canvas_width(canvas_width),
-      canvas_height(canvas_height),
-      num_shapes(shape_list.size()),
-      num_shape_groups(shape_group_list.size()),
-      use_gpu(use_gpu),
-      gpu_index(gpu_index) {
-    if (num_shapes == 0) {
-        return;
-    }
-    // Shape group may reuse some of the shapes,
-    // record the total number of shapes.
-    int num_total_shapes = 0;
-    for (const ShapeGroup *sg : shape_group_list) {
-        num_total_shapes += sg->num_shapes;
-    }
-    this->num_total_shapes = num_total_shapes;
-
-    // Memory initialization
-#ifdef __NVCC__
-    int old_device_id = -1;
-#endif
-    if (use_gpu) {
-#ifdef __NVCC__
-        checkCuda(cudaGetDevice(&old_device_id));
-        if (gpu_index != -1) {
-            checkCuda(cudaSetDevice(gpu_index));
-        }
-#else
-        throw std::runtime_error("diffvg not compiled with GPU");
-        assert(false);
-#endif
-    }
-
-    size_t buffer_size = allocate_buffers<false /*alloc_mode*/>(*this, shape_list, shape_group_list);
-    // Allocate a huge buffer for everything
-    allocate<uint8_t>(use_gpu, buffer_size, &buffer);
-    // memset(buffer, 111, buffer_size);
-    // Actually distribute the buffer
-    allocate_buffers<true /*alloc_mode*/>(*this, shape_list, shape_group_list);
-    copy_and_init_shapes(*this, shape_list);
-    copy_and_init_shape_groups(*this, shape_group_list);
-
-    std::vector<float> shape_length_list = compute_shape_length(shape_list);
-    // Copy shape_length
-    if (use_gpu) {
-#ifdef __NVCC__
-        checkCuda(cudaMemcpy(this->shapes_length, &shape_length_list[0], num_shapes * sizeof(float), cudaMemcpyHostToDevice));
-#else
-        throw std::runtime_error("diffvg not compiled with GPU");
-        assert(false);
-#endif
-    } else {
-        memcpy(this->shapes_length, &shape_length_list[0], num_shapes * sizeof(float));
-    }
-    build_shape_cdfs(*this, shape_group_list, shape_length_list);
-    build_path_cdfs(*this, shape_list, shape_length_list);
-    compute_bounding_boxes(*this, shape_list, shape_group_list);
-
-    // Filter initialization
-    *(this->filter) = filter;
-    this->d_filter->radius = 0;
-
-    if (use_gpu) {
-#ifdef __NVCC__
-        if (old_device_id != -1) {
-            checkCuda(cudaSetDevice(old_device_id));
-        }
-#else
-        throw std::runtime_error("diffvg not compiled with GPU");
-        assert(false);
-#endif
-    }
-}
-
-Scene::~Scene() {
-    if (num_shapes == 0) {
-        return;
-    }
-    if (use_gpu) {
-#ifdef __NVCC__
-        int old_device_id = -1;
-        checkCuda(cudaGetDevice(&old_device_id));
-        if (gpu_index != -1) {
-            checkCuda(cudaSetDevice(gpu_index));
-        }
-
-        checkCuda(cudaFree(buffer));
-
-        checkCuda(cudaSetDevice(old_device_id));
-#else
-        // Don't throw because C++ don't want a destructor to throw.
-        std::cerr << "diffvg not compiled with GPU";
-        exit(1);
-#endif
-    } else {
-        free(buffer);
-    }
-}
-
-Shape Scene::get_d_shape(int shape_id) const {
-    return d_shapes[shape_id];
-}
-
-ShapeGroup Scene::get_d_shape_group(int group_id) const {
-    return d_shape_groups[group_id];
-}
-
-float Scene::get_d_filter_radius() const {
-    return d_filter->radius;
-}

spaces/CVPR/LIVE/thrust/thrust/system/cpp/execution_policy.h

@@ -1,157 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-/*! \file thrust/system/cpp/execution_policy.h
- *  \brief Execution policies for Thrust's standard C++ system.
- */
-
-#include <thrust/detail/config.h>
-
-// get the execution policies definitions first
-#include <thrust/system/cpp/detail/execution_policy.h>
-
-// get the definition of par
-#include <thrust/system/cpp/detail/par.h>
-
-// now get all the algorithm definitions
-
-#include <thrust/system/cpp/detail/adjacent_difference.h>
-#include <thrust/system/cpp/detail/assign_value.h>
-#include <thrust/system/cpp/detail/binary_search.h>
-#include <thrust/system/cpp/detail/copy.h>
-#include <thrust/system/cpp/detail/copy_if.h>
-#include <thrust/system/cpp/detail/count.h>
-#include <thrust/system/cpp/detail/equal.h>
-#include <thrust/system/cpp/detail/extrema.h>
-#include <thrust/system/cpp/detail/fill.h>
-#include <thrust/system/cpp/detail/find.h>
-#include <thrust/system/cpp/detail/for_each.h>
-#include <thrust/system/cpp/detail/gather.h>
-#include <thrust/system/cpp/detail/generate.h>
-#include <thrust/system/cpp/detail/get_value.h>
-#include <thrust/system/cpp/detail/inner_product.h>
-#include <thrust/system/cpp/detail/iter_swap.h>
-#include <thrust/system/cpp/detail/logical.h>
-#include <thrust/system/cpp/detail/malloc_and_free.h>
-#include <thrust/system/cpp/detail/merge.h>
-#include <thrust/system/cpp/detail/mismatch.h>
-#include <thrust/system/cpp/detail/partition.h>
-#include <thrust/system/cpp/detail/reduce.h>
-#include <thrust/system/cpp/detail/reduce_by_key.h>
-#include <thrust/system/cpp/detail/remove.h>
-#include <thrust/system/cpp/detail/replace.h>
-#include <thrust/system/cpp/detail/reverse.h>
-#include <thrust/system/cpp/detail/scan.h>
-#include <thrust/system/cpp/detail/scan_by_key.h>
-#include <thrust/system/cpp/detail/scatter.h>
-#include <thrust/system/cpp/detail/sequence.h>
-#include <thrust/system/cpp/detail/set_operations.h>
-#include <thrust/system/cpp/detail/sort.h>
-#include <thrust/system/cpp/detail/swap_ranges.h>
-#include <thrust/system/cpp/detail/tabulate.h>
-#include <thrust/system/cpp/detail/transform.h>
-#include <thrust/system/cpp/detail/transform_reduce.h>
-#include <thrust/system/cpp/detail/transform_scan.h>
-#include <thrust/system/cpp/detail/uninitialized_copy.h>
-#include <thrust/system/cpp/detail/uninitialized_fill.h>
-#include <thrust/system/cpp/detail/unique.h>
-#include <thrust/system/cpp/detail/unique_by_key.h>
-
-
-// define these entities here for the purpose of Doxygenating them
-// they are actually defined elsewhere
-#if 0
-namespace thrust
-{
-namespace system
-{
-namespace cpp
-{
-
-
-/*! \addtogroup execution_policies
- *  \{
- */
-
-
-/*! \p thrust::system::cpp::execution_policy is the base class for all Thrust parallel execution
- *  policies which are derived from Thrust's standard C++ backend system.
- */
-template<typename DerivedPolicy>
-struct execution_policy : thrust::execution_policy<DerivedPolicy>
-{};
-
-
-/*! \p thrust::system::cpp::tag is a type representing Thrust's standard C++ backend system in C++'s type system.
- *  Iterators "tagged" with a type which is convertible to \p cpp::tag assert that they may be
- *  "dispatched" to algorithm implementations in the \p cpp system.
- */
-struct tag : thrust::system::cpp::execution_policy<tag> { unspecified };
-
-
-/*! 
- *  \p thrust::system::cpp::par is the parallel execution policy associated with Thrust's standard
- *  C++ backend system.
- *
- *  Instead of relying on implicit algorithm dispatch through iterator system tags, users may
- *  directly target Thrust's C++ backend system by providing \p thrust::cpp::par as an algorithm
- *  parameter.
- *
- *  Explicit dispatch can be useful in avoiding the introduction of data copies into containers such
- *  as \p thrust::cpp::vector.
- *
- *  The type of \p thrust::cpp::par is implementation-defined.
- *
- *  The following code snippet demonstrates how to use \p thrust::cpp::par to explicitly dispatch an
- *  invocation of \p thrust::for_each to the standard C++ backend system:
- *
- *  \code
- *  #include <thrust/for_each.h>
- *  #include <thrust/system/cpp/execution_policy.h>
- *  #include <cstdio>
- *
- *  struct printf_functor
- *  {
- *    __host__ __device__
- *    void operator()(int x)
- *    {
- *      printf("%d\n", x);
- *    }
- *  };
- *  ...
- *  int vec[3];
- *  vec[0] = 0; vec[1] = 1; vec[2] = 2;
- *
- *  thrust::for_each(thrust::cpp::par, vec.begin(), vec.end(), printf_functor());
- *
- *  // 0 1 2 is printed to standard output in some unspecified order
- *  \endcode
- */
-static const unspecified par;
-
-
-/*! \}
- */
-
-
-} // end cpp
-} // end system
-} // end thrust
-#endif
-
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/uninitialized_fill.h

@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special unintialized_fill functions
-

spaces/Catspin/2_ai_chat/README.md

@@ -1,10 +0,0 @@
----
-title: Boxai
-emoji: 👀
-colorFrom: purple
-colorTo: yellow
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attrs/exceptions.py

@@ -1,3 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-from attr.exceptions import *  # noqa

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/click/_textwrap.py

@@ -1,49 +0,0 @@
-import textwrap
-import typing as t
-from contextlib import contextmanager
-
-
-class TextWrapper(textwrap.TextWrapper):
-    def _handle_long_word(
-        self,
-        reversed_chunks: t.List[str],
-        cur_line: t.List[str],
-        cur_len: int,
-        width: int,
-    ) -> None:
-        space_left = max(width - cur_len, 1)
-
-        if self.break_long_words:
-            last = reversed_chunks[-1]
-            cut = last[:space_left]
-            res = last[space_left:]
-            cur_line.append(cut)
-            reversed_chunks[-1] = res
-        elif not cur_line:
-            cur_line.append(reversed_chunks.pop())
-
-    @contextmanager
-    def extra_indent(self, indent: str) -> t.Iterator[None]:
-        old_initial_indent = self.initial_indent
-        old_subsequent_indent = self.subsequent_indent
-        self.initial_indent += indent
-        self.subsequent_indent += indent
-
-        try:
-            yield
-        finally:
-            self.initial_indent = old_initial_indent
-            self.subsequent_indent = old_subsequent_indent
-
-    def indent_only(self, text: str) -> str:
-        rv = []
-
-        for idx, line in enumerate(text.splitlines()):
-            indent = self.initial_indent
-
-            if idx > 0:
-                indent = self.subsequent_indent
-
-            rv.append(f"{indent}{line}")
-
-        return "\n".join(rv)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/implementations/libarchive.py

@@ -1,217 +0,0 @@
-from __future__ import absolute_import, division, print_function
-
-from contextlib import contextmanager
-from ctypes import (
-    CFUNCTYPE,
-    POINTER,
-    c_int,
-    c_longlong,
-    c_void_p,
-    cast,
-    create_string_buffer,
-)
-
-import libarchive
-import libarchive.ffi as ffi
-
-from fsspec import open_files
-from fsspec.archive import AbstractArchiveFileSystem
-from fsspec.implementations.memory import MemoryFile
-from fsspec.utils import DEFAULT_BLOCK_SIZE
-
-# Libarchive requires seekable files or memory only for certain archive
-# types. However, since we read the directory first to cache the contents
-# and also allow random access to any file, the file-like object needs
-# to be seekable no matter what.
-
-# Seek call-backs (not provided in the libarchive python wrapper)
-SEEK_CALLBACK = CFUNCTYPE(c_longlong, c_int, c_void_p, c_longlong, c_int)
-read_set_seek_callback = ffi.ffi(
-    "read_set_seek_callback", [ffi.c_archive_p, SEEK_CALLBACK], c_int, ffi.check_int
-)
-new_api = hasattr(ffi, "NO_OPEN_CB")
-
-
-@contextmanager
-def custom_reader(file, format_name="all", filter_name="all", block_size=ffi.page_size):
-    """Read an archive from a seekable file-like object.
-
-    The `file` object must support the standard `readinto` and 'seek' methods.
-    """
-    buf = create_string_buffer(block_size)
-    buf_p = cast(buf, c_void_p)
-
-    def read_func(archive_p, context, ptrptr):
-        # readinto the buffer, returns number of bytes read
-        length = file.readinto(buf)
-        # write the address of the buffer into the pointer
-        ptrptr = cast(ptrptr, POINTER(c_void_p))
-        ptrptr[0] = buf_p
-        # tell libarchive how much data was written into the buffer
-        return length
-
-    def seek_func(archive_p, context, offset, whence):
-        file.seek(offset, whence)
-        # tell libarchvie the current position
-        return file.tell()
-
-    read_cb = ffi.READ_CALLBACK(read_func)
-    seek_cb = SEEK_CALLBACK(seek_func)
-
-    if new_api:
-        open_cb = ffi.NO_OPEN_CB
-        close_cb = ffi.NO_CLOSE_CB
-    else:
-        open_cb = libarchive.read.OPEN_CALLBACK(ffi.VOID_CB)
-        close_cb = libarchive.read.CLOSE_CALLBACK(ffi.VOID_CB)
-
-    with libarchive.read.new_archive_read(format_name, filter_name) as archive_p:
-        read_set_seek_callback(archive_p, seek_cb)
-        ffi.read_open(archive_p, None, open_cb, read_cb, close_cb)
-        yield libarchive.read.ArchiveRead(archive_p)
-
-
-class LibArchiveFileSystem(AbstractArchiveFileSystem):
-    """Compressed archives as a file-system (read-only)
-
-    Supports the following formats:
-    tar, pax , cpio, ISO9660, zip, mtree, shar, ar, raw, xar, lha/lzh, rar
-    Microsoft CAB, 7-Zip, WARC
-
-    See the libarchive documentation for further restrictions.
-    https://www.libarchive.org/
-
-    Keeps file object open while instance lives. It only works in seekable
-    file-like objects. In case the filesystem does not support this kind of
-    file object, it is recommended to cache locally.
-
-    This class is pickleable, but not necessarily thread-safe (depends on the
-    platform). See libarchive documentation for details.
-    """
-
-    root_marker = ""
-    protocol = "libarchive"
-    cachable = False
-
-    def __init__(
-        self,
-        fo="",
-        mode="r",
-        target_protocol=None,
-        target_options=None,
-        block_size=DEFAULT_BLOCK_SIZE,
-        **kwargs,
-    ):
-        """
-        Parameters
-        ----------
-        fo: str or file-like
-            Contains ZIP, and must exist. If a str, will fetch file using
-            :meth:`~fsspec.open_files`, which must return one file exactly.
-        mode: str
-            Currently, only 'r' accepted
-        target_protocol: str (optional)
-            If ``fo`` is a string, this value can be used to override the
-            FS protocol inferred from a URL
-        target_options: dict (optional)
-            Kwargs passed when instantiating the target FS, if ``fo`` is
-            a string.
-        """
-        super().__init__(self, **kwargs)
-        if mode != "r":
-            raise ValueError("Only read from archive files accepted")
-        if isinstance(fo, str):
-            files = open_files(fo, protocol=target_protocol, **(target_options or {}))
-            if len(files) != 1:
-                raise ValueError(
-                    'Path "{}" did not resolve to exactly'
-                    'one file: "{}"'.format(fo, files)
-                )
-            fo = files[0]
-        self.of = fo
-        self.fo = fo.__enter__()  # the whole instance is a context
-        self.block_size = block_size
-        self.dir_cache = None
-
-    @contextmanager
-    def _open_archive(self):
-        self.fo.seek(0)
-        with custom_reader(self.fo, block_size=self.block_size) as arc:
-            yield arc
-
-    @classmethod
-    def _strip_protocol(cls, path):
-        # file paths are always relative to the archive root
-        return super()._strip_protocol(path).lstrip("/")
-
-    def _get_dirs(self):
-        fields = {
-            "name": "pathname",
-            "size": "size",
-            "created": "ctime",
-            "mode": "mode",
-            "uid": "uid",
-            "gid": "gid",
-            "mtime": "mtime",
-        }
-
-        if self.dir_cache is not None:
-            return
-
-        self.dir_cache = {}
-        list_names = []
-        with self._open_archive() as arc:
-            for entry in arc:
-                if not entry.isdir and not entry.isfile:
-                    # Skip symbolic links, fifo entries, etc.
-                    continue
-                self.dir_cache.update(
-                    {
-                        dirname
-                        + "/": {"name": dirname + "/", "size": 0, "type": "directory"}
-                        for dirname in self._all_dirnames(set(entry.name))
-                    }
-                )
-                f = {key: getattr(entry, fields[key]) for key in fields}
-                f["type"] = "directory" if entry.isdir else "file"
-                list_names.append(entry.name)
-
-                self.dir_cache[f["name"]] = f
-        # libarchive does not seem to return an entry for the directories (at least
-        # not in all formats), so get the directories names from the files names
-        self.dir_cache.update(
-            {
-                dirname + "/": {"name": dirname + "/", "size": 0, "type": "directory"}
-                for dirname in self._all_dirnames(list_names)
-            }
-        )
-
-    def _open(
-        self,
-        path,
-        mode="rb",
-        block_size=None,
-        autocommit=True,
-        cache_options=None,
-        **kwargs,
-    ):
-        path = self._strip_protocol(path)
-        if mode != "rb":
-            raise NotImplementedError
-
-        data = bytes()
-        with self._open_archive() as arc:
-            for entry in arc:
-                if entry.pathname != path:
-                    continue
-
-                if entry.size == 0:
-                    # empty file, so there are no blocks
-                    break
-
-                for block in entry.get_blocks(entry.size):
-                    data = block
-                    break
-                else:
-                    raise ValueError
-        return MemoryFile(fs=self, path=path, data=data)

spaces/DaleChen/AutoGPT/autogpt/commands/times.py

@@ -1,10 +0,0 @@
-from datetime import datetime
-
-
-def get_datetime() -> str:
-    """Return the current date and time
-
-    Returns:
-        str: The current date and time
-    """
-    return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")